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[ { "docid": "pos:1840613_0", "text": "This article addresses the problem of understanding mathematics described in natural language. Research in this area dates back to early 1960s. Several systems have so far been proposed to involve machines to solve mathematical problems of various domains like algebra, geometry, physics, mechanics, etc. This correspondence provides a state of the art technical review of these systems and approaches proposed by different research groups. A unified architecture that has been used in most of these approaches is identified and differences among the systems are highlighted. Significant achievements of each method are pointed out. Major strengths and weaknesses of the approaches are also discussed. Finally, present efforts and future trends in this research area are presented.", "title": "" }, { "docid": "pos:1840613_1", "text": "Little work from the Natural Language Processing community has targeted the role of quantities in Natural Language Understanding. This paper takes some key steps towards facilitating reasoning about quantities expressed in natural language. We investigate two different tasks of numerical reasoning. First, we consider Quantity Entailment, a new task formulated to understand the role of quantities in general textual inference tasks. Second, we consider the problem of automatically understanding and solving elementary school math word problems. In order to address these quantitative reasoning problems we first develop a computational approach which we show to successfully recognize and normalize textual expressions of quantities. We then use these capabilities to further develop algorithms to assist reasoning in the context of the aforementioned tasks.", "title": "" } ]

[ { "docid": "neg:1840613_0", "text": "We consider the situation in which digital data is to be reliably transmitted over a discrete, memoryless channel (dmc) that is subjected to a wire-tap at the receiver. We assume that the wire-tapper views the channel output via a second dmc). Encoding by the transmitter and decoding by the receiver are permitted. However, the code books used in these operations are assumed to be known by the wire-tapper. The designer attempts to build the encoder-decoder in such a way as to maximize the transmission rate R, and the equivocation d of the data as seen by the wire-tapper. In this paper, we find the trade-off curve between R and d, assuming essentially perfect (“error-free”) transmission. In particular, if d is equal to Hs, the entropy of the data source, then we consider that the transmission is accomplished in perfect secrecy. Our results imply that there exists a Cs > 0, such that reliable transmission at rates up to Cs is possible in approximately perfect secrecy.", "title": "" }, { "docid": "neg:1840613_1", "text": "This paper presents the design and implementation of VibeBin, a low-cost, non-intrusive and easy-to-install waste bin level detection system. Recent popularity of Internet-of-Things (IoT) sensors has brought us unprecedented opportunities to enable a variety of new services for monitoring and controlling smart buildings. Indoor waste management is crucial to a healthy environment in smart buildings. Measuring the waste bin fill-level helps building operators schedule garbage collection more responsively and optimize the quantity and location of waste bins. Existing systems focus on directly and intrusively measuring the physical quantities of the garbage (weight, height, volume, etc.) or its appearance (image), and therefore require careful installation, laborious calibration or labeling, and can be costly. Our system indirectly measures fill-level by sensing the changes in motor-induced vibration characteristics on the outside surface of waste bins. VibeBin exploits the physical nature of vibration resonance of the waste bin and the garbage within, and learns the vibration features of different fill-levels through a few garbage collection (emptying) cycles in a completely unsupervised manner. VibeBin identifies vibration features of different fill-levels by clustering historical vibration samples based on a custom distance metric which measures the dissimilarity between two samples. We deploy our system on eight waste bins of different types and sizes, and show that under normal usage and real waste, it can deliver accurate level measurements after just 3 garbage collection cycles. The average F-score (harmonic mean of precision and recall) of measuring empty, half, and full levels achieves 0.912. A two-week deployment also shows that the false positive and false negative events are satisfactorily rare.", "title": "" }, { "docid": "neg:1840613_2", "text": "We explore several image processing methods to automatically identify the make of a vehicle based focused on the manufacturer’s iconic logo. Our findings reveal that large variations in brightness, vehicle features in the foreground, and specular reflections render the scale-invariant feature transform (SIFT) approach practically useless. Methods such as Fourier shape descriptors and inner structure mean square error analysis are able to achieve more reliable results.", "title": "" }, { "docid": "neg:1840613_3", "text": "Coherent optical OFDM (CO-OFDM) has recently been proposed and the proof-of-concept transmission experiments have shown its extreme robustness against chromatic dispersion and polarization mode dispersion. In this paper, we first review the theoretical fundamentals for CO-OFDM and its channel model in a 2x2 MIMO-OFDM representation. We then present various design choices for CO-OFDM systems and perform the nonlinearity analysis for RF-to-optical up-converter. We also show the receiver-based digital signal processing to mitigate self-phase-modulation (SPM) and Gordon-Mollenauer phase noise, which is equivalent to the midspan phase conjugation.", "title": "" }, { "docid": "neg:1840613_4", "text": "Recent studies witness the success of Bag-of-Features (BoF) frameworks for video based human action recognition. The detection and description of local interest regions are two fundamental problems in BoF framework. In this paper, we propose a motion boundary based sampling strategy and spatialtemporal (3D) co-occurrence descriptors for action video representation and recognition. Our sampling strategy is partly inspired by the recent success of dense trajectory (DT) based features [1] for action recognition. Compared with DT, we densely sample spatial-temporal cuboids along motion boundary which can greatly reduce the number of valid trajectories while preserve the discriminative power. Moreover, we develop a set of 3D co-occurrence descriptors which take account of the spatial-temporal context within local cuboids and deliver rich information for recognition. Furthermore, we decompose each 3D co-occurrence descriptor at pixel level and bin level and integrate the decomposed components with a multi-channel framework, which can improve the performance significantly. To evaluate the proposed methods, we conduct extensive experiments on three benchmarks including KTH, YouTube and HMDB51. The results show that our sampling strategy significantly reduces the computational cost of point tracking without degrading performance. Meanwhile, we achieve superior performance than the state-ofthe-art methods. We report 95.6% on KTH, 87.6% on YouTube and 51.8% on HMDB51.", "title": "" }, { "docid": "neg:1840613_5", "text": "Color transfer between images uses the statistics information of image effectively. We present a novel approach of local color transfer between images based on the simple statistics and locally linear embedding. A sketching interface is proposed for quickly and easily specifying the color correspondences between target and source image. The user can specify the correspondences of local region using scribes, which more accurately transfers the target color to the source image while smoothly preserving the boundaries, and exhibits more natural output results. Our algorithm is not restricted to one-to-one image color transfer and can make use of more than one target images to transfer the color in different regions in the source image. Moreover, our algorithm does not require to choose the same color style and image size between source and target images. We propose the sub-sampling to reduce the computational load. Comparing with other approaches, our algorithm is much better in color blending in the input data. Our approach preserves the other color details in the source image. Various experimental results show that our approach specifies the correspondences of local color region in source and target images. And it expresses the intention of users and generates more actual and natural results of visual effect.", "title": "" }, { "docid": "neg:1840613_6", "text": "workflows, data warehousing, business intelligence Process design and automation technologies are being increasingly used by both traditional and newly-formed, Internet-based enterprises in order to improve the quality and efficiency of their administrative and production processes, to manage e-commerce transactions, and to rapidly and reliably deliver services to businesses and individual customers.", "title": "" }, { "docid": "neg:1840613_7", "text": "This paper presents a predictive current control method and its application to a voltage source inverter. The method uses a discrete-time model of the system to predict the future value of the load current for all possible voltage vectors generated by the inverter. The voltage vector which minimizes a quality function is selected. The quality function used in this work evaluates the current error at the next sampling time. The performance of the proposed predictive control method is compared with hysteresis and pulsewidth modulation control. The results show that the predictive method controls very effectively the load current and performs very well compared with the classical solutions", "title": "" }, { "docid": "neg:1840613_8", "text": "Twenty-two decision tree, nine statistical, and two neural network algorithms are compared on thirty-two datasets in terms of classification accuracy, training time, and (in the case of trees) number of leaves. Classification accuracy is measured by mean error rate and mean rank of error rate. Both criteria place a statistical, spline-based, algorithm called POLYCLSSS at the top, although it is not statistically significantly different from twenty other algorithms. Another statistical algorithm, logistic regression, is second with respect to the two accuracy criteria. The most accurate decision tree algorithm is QUEST with linear splits, which ranks fourth and fifth, respectively. Although spline-based statistical algorithms tend to have good accuracy, they also require relatively long training times. POLYCLASS, for example, is third last in terms of median training time. It often requires hours of training compared to seconds for other algorithms. The QUEST and logistic regression algorithms are substantially faster. Among decision tree algorithms with univariate splits, C4.5, IND-CART, and QUEST have the best combinations of error rate and speed. But C4.5 tends to produce trees with twice as many leaves as those from IND-CART and QUEST.", "title": "" }, { "docid": "neg:1840613_9", "text": "Automatic optimization of spoken dialog management policies that are robust to environmental noise has long been the goal for both academia and industry. Approaches based on reinforcement learning have been proved to be effective. However, the numerical representation of dialog policy is human-incomprehensible and difficult for dialog system designers to verify or modify, which limits its practical application. In this paper we propose a novel framework for optimizing dialog policies specified in domain language using genetic algorithm. The human-interpretable representation of policy makes the method suitable for practical employment. We present learning algorithms using user simulation and real human-machine dialogs respectively. Empirical experimental results are given to show the effectiveness of the proposed approach.", "title": "" }, { "docid": "neg:1840613_10", "text": "We propose a general framework for unsupervised domain adaptation, which allows deep neural networks trained on a source domain to be tested on a different target domain without requiring any training annotations in the target domain. This is achieved by adding extra networks and losses that help regularize the features extracted by the backbone encoder network. To this end we propose the novel use of the recently proposed unpaired image-to-image translation framework to constrain the features extracted by the encoder network. Specifically, we require that the features extracted are able to reconstruct the images in both domains. In addition we require that the distribution of features extracted from images in the two domains are indistinguishable. Many recent works can be seen as specific cases of our general framework. We apply our method for domain adaptation between MNIST, USPS, and SVHN datasets, and Amazon, Webcam and DSLR Office datasets in classification tasks, and also between GTA5 and Cityscapes datasets for a segmentation task. We demonstrate state of the art performance on each of these datasets.", "title": "" }, { "docid": "neg:1840613_11", "text": "In this paper we present DAML-S, a DAML+OIL ontology for describing the properties and capabilities of Web Services. Web Services – Web-accessible programs and devices – are garnering a great deal of interest from industry, and standards are emerging for low-level descriptions of Web Services. DAML-S complements this effort by providing Web Service descriptions at the application layer, describing what a service can do, and not just how it does it. In this paper we describe three aspects of our ontology: the service profile, the process model, and the service grounding. The paper focuses on the grounding, which connects our ontology with low-level XML-based descriptions of Web Services. 1 Services on the Semantic Web The Semantic Web [2] is rapidly becoming a reality through the development of Semantic Web markup languages such as DAML+OIL [9]. These markup languages enable the creation of arbitrary domain ontologies that support the unambiguous description of Web content. Web Services [15] – Web-accessible programs and devices – are among the most important resources on the Web, not only to provide information to a user, but to enable a user to effect change in the world. Web Services are garnering a great deal of interest from industry, and standards are being developed for low-level descriptions of Web Services. Languages such as WSDL (Web Service Description Language) provide a communication level description of the messages and protocols used by a Web Service. To complement this effort, our interest is in developing semantic markup that will sit at the application level above WSDL, and describe what is being sent across the wires and why, not just how it is being sent. We are developing a DAML+OIL ontology for Web Services, called DAML-S [5], with the objective of making Web Services computer-interpretable and hence enabling the following tasks [15]: discovery, i.e. locating Web Services (typically through a registry service) that provide a particular service and that adhere to specified constraints; invocation or activation and execution of an identified service by an agent or other service; interoperation, i.e. breaking down interoperability barriers through semantics, and the automatic insertion of message parameter translations between clients and services [10, 13, 22]; composition of new services through automatic selection, composition and interoperation of existing services [15, 14]; verification of service properties [19]; and execution monitoring, i.e. tracking the execution of complex or composite tasks performed by a service or a set of services, thus identifying failure cases, or providing explanations of different execution traces. To make use of a Web Service, a software agent needs a computer-interpretable description of the service, and the means by which it is accessed. This paper describes a collaborative effort by BBN Technologies, Carnegie Mellon University, Nokia, Stanford University, SRI International, and Yale University, to define the DAML-S Web Services ontology. An earlier version of the DAML-S specification is described in [5]; an updated version of DAML-S is presented at http://www.daml.org/services/daml-s/2001/10/. In this paper we briefly summarize and update this specification, and discuss the important problem of the grounding, i.e. how to translate what is being sent in a message to or from a service into how it is to be sent. In particular, we present the linking of DAML-S to the Web Services Description Language (WSDL). DAML-S complements WSDL, by providing an abstract or application level description lacking in WSDL. 2 An Upper Ontology for Services In DAML+OIL, abstract categories of entities, events, etc. are defined in terms of classes and properties. DAML-S defines a set of classes and properties, specific to the description of services, within DAML+OIL. The class Service is at the top of the DAML-S ontology. Service properties at this level are very general. The upper ontology for services is silent as to what the particular subclasses of Service should be, or even the conceptual basis for structuring this taxonomy, but it is expected that the taxonomy will be structured according to functional and domain differences and market needs. For example, one might imagine a broad subclass, B2C-transaction, which would encompass services for purchasing items from retail Web sites, tracking purchase status, establishing and maintaining accounts with the sites, and so on. The ontology of services provides two essential types of knowledge about a service, characterized by the questions: – What does the service require of agents, and provide for them? This is provided by the profile, a class that describes the capabilities and parameters of the service. We say that the class Service presents a ServiceProfile. – How does it work? The answer to this question is given in the model, a class that describes the workflow and possible execution paths of the service. Thus, the class Service is describedBy a ServiceModel The ServiceProfile provides information about a service that can be used by an agent to determine if the service meets its rough needs, and if it satisfies constraints such as security, locality, affordability, quality-requirements, etc. In contrast, the ServiceModel enables an agent to: (1) perform a more in-depth analysis of whether the service meets its needs; (2) compose service descriptions from multiple services to perform a specific task; (3) coordinate the activities of different agents; and (4) monitor the execution of the service. Generally speaking, the ServiceProfile provides the information needed for an agent to discover a service, whereas the ServiceModel provides enough information for an agent to make use of a service. In the following sections we discuss the service profile and the service model in greater detail, and introduce the service grounding, which describes how agents can communicate with and thus invoke the service.", "title": "" }, { "docid": "neg:1840613_12", "text": "Data mining techniques have been successfully applied in stock, insurance, medicine, banking and retailing domains. In the sport domain, for transforming sport data into actionable knowledge, coaches can use data mining techniques to plan training sessions more effectively, and to reduce the impact of testing activity on athletes. This paper presents one such model, which uses clustering techniques, such as improved K-Means, Expectation-Maximization (EM), DBSCAN, COBWEB and hierarchical clustering approaches to analyze sport physiological data collected during incremental tests. Through analyzing the progress of a test session, the authors assign the tested athlete to a group of athletes and evaluate these groups to support the planning of training sessions.", "title": "" }, { "docid": "neg:1840613_13", "text": "This paper presents a new large scale dataset targeting evaluation of local shape descriptors and 3d object recognition algorithms. The dataset consists of point clouds and triangulated meshes from 292 physical scenes taken from 11 different views, a total of approximately 3204 views. Each of the physical scenes contain 10 occluded objects resulting in a dataset with 32040 unique object poses and 45 different object models. The 45 object models are full 360 degree models which are scanned with a high precision structured light scanner and a turntable. All the included objects belong to different geometric groups, concave, convex, cylindrical and flat 3D object models. The object models have varying amount of local geometric features to challenge existing local shape feature descriptors in terms of descriptiveness and robustness. The dataset is validated in a benchmark which evaluates the matching performance of 7 different state-of-the-art local shape descriptors. Further, we validate the dataset in a 3D object recognition pipeline. Our benchmark shows as expected that local shape feature descriptors without any global point relation across the surface have a poor matching performance with flat and cylindrical objects. It is our objective that this dataset contributes to the future development of next generation of 3D object recognition algorithms. The dataset is public available at http://roboimagedata.compute.dtu.dk/.", "title": "" }, { "docid": "neg:1840613_14", "text": "In real-time systems such as automotives, a distribution system is used to increase the reliability of the system. As the demand and complexity of the distribution system have increased, several automotive communication protocols have been introduced such as LIN, CAN, and FlexRay. Each node of the system chooses the communication protocol that is suitable for the specific purpose. Each node doesn't need to have all of communication protocols because of cost, space, efficiency, and other factors. Therefore, the gateway system was introduced in the automotive system and has became one of the most important components. The gateway makes possible node-to-node communicate over different communication protocols. However, the gateway system has high probability of error because each protocol has different features such as signaling rate, data length, and so on. Moreover, it is difficult to detect the reason and location of errors. If the gateway reports the protocol conversion result when each protocol is converted into another protocol, this report helps developers find the reason and location of errors to debug errors easily. In this paper, we implement the gateway system with a diagnostic function. LIN, CAN, and FlexRay are used as communication protocols.", "title": "" }, { "docid": "neg:1840613_15", "text": "Natural DNA can encode complexity on an enormous scale. Researchers are attempting to achieve the same representational efficiency in computers by implementing developmental encodings, i.e. encodings that map the genotype to the phenotype through a process of growth from a small starting point to a mature form. A major challenge in in this effort is to find the right level of abstraction of biological development to capture its essential properties without introducing unnecessary inefficiencies. In this paper, a novel abstraction of natural development, called Compositional Pattern Producing Networks (CPPNs), is proposed. Unlike currently accepted abstractions such as iterative rewrite systems and cellular growth simulations, CPPNs map to the phenotype without local interaction, that is, each individual component of the phenotype is determined independently of every other component. Results produced with CPPNs through interactive evolution of two-dimensional images show that such an encoding can nevertheless produce structural motifs often attributed to more conventional developmental abstractions, suggesting that local interaction may not be essential to the desirable properties of natural encoding in the way that is usually assumed.", "title": "" }, { "docid": "neg:1840613_16", "text": "Providing access to relevant biomedical literature in a clinical setting has the potential to bridge a critical gap in evidence-based medicine. Here, our goal is specifically to provide relevant articles to clinicians to improve their decision-making in diagnosing, treating, and testing patients. To this end, the TREC 2014 Clinical Decision Support Track evaluated a system’s ability to retrieve relevant articles in one of three categories (Diagnosis, Treatment, Test) using an idealized form of a patient medical record . Over 100 submissions from over 25 participants were evaluated on 30 topics, resulting in over 37k relevance judgments. In this article, we provide an overview of the task, a survey of the information retrieval methods employed by the participants, an analysis of the results, and a discussion on the future directions for this challenging yet important task.", "title": "" }, { "docid": "neg:1840613_17", "text": "Increasing use of the World Wide Web as a B2C commercial tool raises interest in understanding the key issues in building relationships with customers on the Internet. Trust is believed to be the key to these relationships. Given the differences between a virtual and a conventional marketplace, antecedents and consequences of trust merit re-examination. This research identifies a number of key factors related to trust in the B2C context and proposes a framework based on a series of underpinning relationships among these factors. The findings in this research suggest that people are more likely to purchase from the web if they perceive a higher degree of trust in e-commerce and have more experience in using the web. Customer’s trust levels are likely to be influenced by the level of perceived market orientation, site quality, technical trustworthiness, and user’s web experience. People with a higher level of perceived site quality seem to have a higher level of perceived market orientation and trustworthiness towards e-commerce. Furthermore, people with a higher level of trust in e-commerce are more likely to participate in e-commerce. Positive ‘word of mouth’, money back warranty and partnerships with well-known business partners, rank as the top three effective risk reduction tactics. These findings complement the previous findings on e-commerce and shed light on how to establish a trust relationship on the World Wide Web.  2003 Elsevier B.V. All rights reserved.", "title": "" } ]

[ { "docid": "pos:1840614_0", "text": "Artifact rejection is a central issue when dealing with electroencephalogram recordings. Although independent component analysis (ICA) separates data in linearly independent components (IC), the classification of these components as artifact or EEG signal still requires visual inspection by experts. In this paper, we achieve automated artifact elimination using linear discriminant analysis (LDA) for classification of feature vectors extracted from ICA components via image processing algorithms. We compare the performance of this automated classifier to visual classification by experts and identify range filtering as a feature extraction method with great potential for automated IC artifact recognition (accuracy rate 88%). We obtain almost the same level of recognition performance for geometric features and local binary pattern (LBP) features. Compared to the existing automated solutions the proposed method has two main advantages: First, it does not depend on direct recording of artifact signals, which then, e.g. have to be subtracted from the contaminated EEG. Second, it is not limited to a specific number or type of artifact. In summary, the present method is an automatic, reliable, real-time capable and practical tool that reduces the time intensive manual selection of ICs for artifact removal. The results are very promising despite the relatively small channel resolution of 25 electrodes.", "title": "" } ]

[ { "docid": "neg:1840614_0", "text": "Convolutional-deconvolution networks can be adopted to perform end-to-end saliency detection. But, they do not work well with objects of multiple scales. To overcome such a limitation, in this work, we propose a recurrent attentional convolutional-deconvolution network (RACDNN). Using spatial transformer and recurrent network units, RACDNN is able to iteratively attend to selected image sub-regions to perform saliency refinement progressively. Besides tackling the scale problem, RACDNN can also learn context-aware features from past iterations to enhance saliency refinement in future iterations. Experiments on several challenging saliency detection datasets validate the effectiveness of RACDNN, and show that RACDNN outperforms state-of-the-art saliency detection methods.", "title": "" }, { "docid": "neg:1840614_1", "text": "The American Society for Apheresis (ASFA) Journal of Clinical Apheresis (JCA) Special Issue Writing Committee is charged with reviewing, updating, and categorizing indications for the evidence-based use of therapeutic apheresis in human disease. Since the 2007 JCA Special Issue (Fourth Edition), the Committee has incorporated systematic review and evidence-based approaches in the grading and categorization of apheresis indications. This Seventh Edition of the JCA Special Issue continues to maintain this methodology and rigor to make recommendations on the use of apheresis in a wide variety of diseases/conditions. The JCA Seventh Edition, like its predecessor, has consistently applied the category and grading system definitions in the fact sheets. The general layout and concept of a fact sheet that was used since the fourth edition has largely been maintained in this edition. Each fact sheet succinctly summarizes the evidence for the use of therapeutic apheresis in a specific disease entity. The Seventh Edition discusses 87 fact sheets (14 new fact sheets since the Sixth Edition) for therapeutic apheresis diseases and medical conditions, with 179 indications, which are separately graded and categorized within the listed fact sheets. Several diseases that are Category IV which have been described in detail in previous editions and do not have significant new evidence since the last publication are summarized in a separate table. The Seventh Edition of the JCA Special Issue serves as a key resource that guides the utilization of therapeutic apheresis in the treatment of human disease. J. Clin. Apheresis 31:149-162, 2016. © 2016 Wiley Periodicals, Inc.", "title": "" }, { "docid": "neg:1840614_2", "text": "Measurement uncertainty is traditionally represented in the form of expanded uncertainty as defined through the Guide to the Expression of Uncertainty in Measurement (GUM). The International Organization for Standardization GUM represents uncertainty through confidence intervals based on the variances and means derived from probability density functions. A new approach to the evaluation of measurement uncertainty based on the polynomial chaos theory is presented and compared with the traditional GUM method", "title": "" }, { "docid": "neg:1840614_3", "text": "Inverse synthetic aperture radar imaging of moving targets with a stepped frequency waveform presents unique challenges. Intra-step target motion introduces phase discontinuities between frequency bands, which in turn produce degraded range side lobes. Frequency stitching of the stepped-frequency waveform to emulate a contiguous bandwidth can dramatically reduce the effective pulse repetition frequency, which then may impact the maximize target size that can be unambiguously measured and imaged via ISAR. This paper analyzes these effects and validates results via simulated data.", "title": "" }, { "docid": "neg:1840614_4", "text": "This paper proposes a system that can detect and rephrase profanity in Chinese text. Rather than just masking detected profanity, we want to revise the input sentence by using inoffensive words while keeping their original meanings. 29 of such rephrasing rules were invented after observing sentences on real-word social websites. The overall accuracy of the proposed system is 85.56%", "title": "" }, { "docid": "neg:1840614_5", "text": "Mammogram classification is directly related to computer-aided diagnosis of breast cancer. Traditional methods requires great effort to annotate the training data by costly manual labeling and specialized computational models to detect these annotations during test. Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning for labeling a set of instances/patches, we propose end-to-end trained deep multiinstance networks for mass classification based on whole mammogram without the aforementioned costly need to annotate the training data. We explore three different schemes to construct deep multi-instance networks for whole mammogram classification. Experimental results on the INbreast dataset demonstrate the robustness of proposed deep networks compared to previous work using segmentation and detection annotations in the training.", "title": "" }, { "docid": "neg:1840614_6", "text": "Because of the precise temporal resolution of electrophysiological recordings, the event-related potential (ERP) technique has proven particularly valuable for testing theories of perception and attention. Here, I provide a brief tutorial on the ERP technique for consumers of such research and those considering the use of human electrophysiology in their own work. My discussion begins with the basics regarding what brain activity ERPs measure and why they are well suited to reveal critical aspects of perceptual processing, attentional selection, and cognition, which are unobservable with behavioral methods alone. I then review a number of important methodological issues and often-forgotten facts that should be considered when evaluating or planning ERP experiments.", "title": "" }, { "docid": "neg:1840614_7", "text": "There is a growing demand for accurate high-resolution land cover maps in many fields, e.g., in land-use planning and biodiversity conservation. Developing such maps has been performed using Object-Based Image Analysis (OBIA) methods, which usually reach good accuracies, but require a high human supervision and the best configuration for one image can hardly be extrapolated to a different image. Recently, the deep learning Convolutional Neural Networks (CNNs) have shown outstanding results in object recognition in the field of computer vision. However, they have not been fully explored yet in land cover mapping for detecting species of high biodiversity conservation interest. This paper analyzes the potential of CNNs-based methods for plant species detection using free high-resolution Google Earth images and provides an objective comparison with the state-of-the-art OBIA-methods. We consider as case study the detection of Ziziphus lotus shrubs, which are protected as a priority habitat under the European Union Habitats Directive. According to our results, compared to OBIA-based methods, the proposed CNNbased detection model, in combination with data-augmentation, transfer learning and pre-processing, achieves higher performance with less human ∗Both authors have contributed equally to this work 1 ar X iv :1 70 6. 00 91 7v 1 [ cs .C V ] 3 J un 2 01 7 intervention and the knowledge it acquires in the first image can be transferred to other images, which makes the detection process very fast. The provided methodology can be systematically reproduced for other species detection.", "title": "" }, { "docid": "neg:1840614_8", "text": "This paper reports an investigation of some methods for isolating, or segmenting, characters during the reading of machineprinted text by optical character recognition systems. Two new segmentation algorithms using feature extraction techniques are presented; both are intended for use in the recognition of machine-printed lines of lo-, 11and 12-pitch serif-type multifont characters. One of the methods, called quasi-topological segmentation, bases the decision to “section” a character on a combination of featureextraction and character-width measurements. The other method, topological segmentation, involves feature extraction alone. The algorithms have been tested with an evaluation method that is independent of any particular recognition system. Test results are based on application of the algorithm to upper-case alphanumeric characters gathered from print sources that represent the existing world of machine printing. The topological approach demonstrated better performance on the test data than did the quasitopological approach. Introduction When character recognition systems are structured to recognize one character at a time, some means must be provided to divide the incoming data stream into segments that define the beginning and end of each character. Writing about this aspect of pattern recognition in his review article, G. Nagy [l] stated that “object isolation is all too often ignored in laboratory studies. Yet touching characters are responsible for the majority of errors in the automatic reading of both machine-printed and hand-printed text. . . . ” The importance of the touching-character problem in the design of practical character recognition machines motivated the laboratory study reported in this paper. We present two new algorithms for separating upper-case serif characters, develop a general philosophy for evaluating the effectiveness of segmentation algorithms, and evaluate the performance of our algorithms when they are applied to lo-, 11and 12-pitch alphanumeric characters. The segmentation algorithms were developed specifically for potential use with recognition systems that use a raster-type scanner to produce an analog video signal that is digitized before presentation of the data to the recognition logic. The raster is assumed to move from right to left across a line of printed characters and to make approximately 20 vertical scans per character. This approach to recognition technology is the one most commonly used in IBM’s current optical character recognition machines. A paper on the IBM 1975 Optical Page Reader [2] gives one example of how the approach has been implemented. Other approaches to recognition technology may not require that decisions be made to identify the beginning and end of characters. Nevertheless, the performance of any recognition system is affected by the presence of touching characters and the design of recognition algorithms must take the problem into account (see Clayden, Clowes and Parks [3]). Simple character recognition systeMs of the type we are concerned with perform segmentation by requiring that bit patterns of characters be separated by scans containing no “black” bits. However, this method is rarely adequate to separate characters printed in the common business-machine and typewriter fonts. These fonts, after all, were not designed with machine recognition in mind; but they are nevertheless the fonts it is most desirable for a machine to be able to recognize. In the 12-pitch, serif-type fonts examined for the present study, up to 35 percent of the segments occurred not at blank scans, but within touching character pairs. 153 SEGMENTATION ALGORITHMS MARCH 1971", "title": "" }, { "docid": "neg:1840614_9", "text": "This article describes a reliable gateway for in-vehicle networks. Such networks include local interconnect networks, controller area networks, and FlexRay. There is some latency when transferring a message from one node (source) to another node (destination). A high probability of error exists due to different protocol specifications such as baud-rate, and message frame format. Therefore, deploying a reliable gateway is a challenge to the automotive industry. We propose a reliable gateway based on the OSEK/VDX components for in-vehicle networks. We also examine the gateway system developed, and then we evaluate the performance of our proposed system.", "title": "" }, { "docid": "neg:1840614_10", "text": "Dimethylsulfoxide (DMSO) controlled puff induction and repression (or non-induction) in larval polytene chromosomes of Chironomus tentans were studied for the case of the Balbiani rings (BR). A characteristic reaction pattern, involving BR 1, BR 2 and BR 3, all in salivary gland chromosome IV was found. In vivo exposure of 4th instar larvae (not prepupae) to 10% DMSO at 18° C first evokes an over-stimulation of BR 3 while DMSO-stimulation of puffing at BR 1 and BR 2 always follows that of BR 3. After removal of the drug, a rapid uniform collapse of all puffs occurs, thus more or less restoring the banding pattern of all previously decondensed chromosome segments. Recovery proceeds as BR's and other puffs reappear. By observing the restoration, one can locate the site from which a BR (puff) originates. BR 2, which is normally the most active non-ribosomal gene locus in untreated larvae, here serves as an example. As the sizes of BR 3, BR 1 and BR 2 change, so do the quantities of the transcriptional products in these gene loci (and vice versa), as estimated electron-microscopically in ultrathin sections and autoradiographically in squash preparations. In autoradiograms, the DMSO-stimulated BRs exhibit the most dense concentration of silver grains and therefore the highest rate of transcriptional activity. In DMSO-repressed BRs (and other puffs) the transcription of the locus specific genes is not completely shut off. In chromosomes from nuclei with high labelling intensities the repressed BRs (and other puffs) always exhibit a low level of 3H-uridine incorporation in vivo. The absence of cytologically visible BR (puff) formation therefore does not necessarily indicate complete transcriptional inactivity. Typically, before the stage of puff formation the 3H-uridine labelling first appears in the interband-like regions.", "title": "" }, { "docid": "neg:1840614_11", "text": "This paper addresses the problem of simultaneous 3D reconstruction and material recognition and segmentation. Enabling robots to recognise different materials (concrete, metal etc.) in a scene is important for many tasks, e.g. robotic interventions in nuclear decommissioning. Previous work on 3D semantic reconstruction has predominantly focused on recognition of everyday domestic objects (tables, chairs etc.), whereas previous work on material recognition has largely been confined to single 2D images without any 3D reconstruction. Meanwhile, most 3D semantic reconstruction methods rely on computationally expensive post-processing, using Fully-Connected Conditional Random Fields (CRFs), to achieve consistent segmentations. In contrast, we propose a deep learning method which performs 3D reconstruction while simultaneously recognising different types of materials and labeling them at the pixel level. Unlike previous methods, we propose a fully end-to-end approach, which does not require hand-crafted features or CRF post-processing. Instead, we use only learned features, and the CRF segmentation constraints are incorporated inside the fully end-to-end learned system. We present the results of experiments, in which we trained our system to perform real-time 3D semantic reconstruction for 23 different materials in a real-world application. The run-time performance of the system can be boosted to around 10Hz, using a conventional GPU, which is enough to achieve realtime semantic reconstruction using a 30fps RGB-D camera. To the best of our knowledge, this work is the first real-time end-to-end system for simultaneous 3D reconstruction and material recognition.", "title": "" }, { "docid": "neg:1840614_12", "text": "Lacking an operational theory to explain the organization and behaviour of matter in unicellular and multicellular organisms hinders progress in biology. Such a theory should address life cycles from ontogenesis to death. This theory would complement the theory of evolution that addresses phylogenesis, and would posit theoretical extensions to accepted physical principles and default states in order to grasp the living state of matter and define proper biological observables. Thus, we favour adopting the default state implicit in Darwin’s theory, namely, cell proliferation with variation plus motility, and a framing principle, namely, life phenomena manifest themselves as non-identical iterations of morphogenetic processes. From this perspective, organisms become a consequence of the inherent variability generated by proliferation, motility and self-organization. Morphogenesis would then be the result of the default state plus physical constraints, like gravity, and those present in living organisms, like muscular tension.", "title": "" }, { "docid": "neg:1840614_13", "text": "Recent years have observed a significant progress in information retrieval and natural language processing with deep learning technologies being successfully applied into almost all of their major tasks. The key to the success of deep learning is its capability of accurately learning distributed representations (vector representations or structured arrangement of them) of natural language expressions such as sentences, and effectively utilizing the representations in the tasks. This tutorial aims at summarizing and introducing the results of recent research on deep learning for information retrieval, in order to stimulate and foster more significant research and development work on the topic in the future.\n The tutorial mainly consists of three parts. In the first part, we introduce the fundamental techniques of deep learning for natural language processing and information retrieval, such as word embedding, recurrent neural networks, and convolutional neural networks. In the second part, we explain how deep learning, particularly representation learning techniques, can be utilized in fundamental NLP and IR problems, including matching, translation, classification, and structured prediction. In the third part, we describe how deep learning can be used in specific application tasks in details. The tasks are search, question answering (from either documents, database, or knowledge base), and image retrieval.", "title": "" }, { "docid": "neg:1840614_14", "text": "researchers and practitioners doing work in these three related areas. Risk management, fraud detection, and intrusion detection all involve monitoring the behavior of populations of users (or their accounts) to estimate, plan for, avoid, or detect risk. In his paper, Til Schuermann (Oliver, Wyman, and Company) categorizes risk into market risk, credit risk, and operating risk (or fraud). Similarly, Barry Glasgow (Metropolitan Life Insurance Co.) discusses inherent risk versus fraud. This workshop focused primarily on what might loosely be termed “improper behavior,” which includes fraud, intrusion, delinquency, and account defaulting. However, Glasgow does discuss the estimation of “inherent risk,” which is the bread and butter of insurance firms. Problems of predicting, preventing, and detecting improper behavior share characteristics that complicate the application of existing AI and machine-learning technologies. In particular, these problems often have or require more than one of the following that complicate the technical problem of automatically learning predictive models: large volumes of (historical) data, highly skewed distributions (“improper behavior” occurs far less frequently than “proper behavior”), changing distributions (behaviors change over time), widely varying error costs (in certain contexts, false positive errors are far more costly than false negatives), costs that change over time, adaptation of undesirable behavior to detection techniques, changing patterns of legitimate behavior, the trad■ The 1997 AAAI Workshop on AI Approaches to Fraud Detection and Risk Management brought together over 50 researchers and practitioners to discuss problems of fraud detection, computer intrusion detection, and risk scoring. This article presents highlights, including discussions of problematic issues that are common to these application domains, and proposed solutions that apply a variety of AI techniques.", "title": "" }, { "docid": "neg:1840614_15", "text": "The Bitcoin cryptocurrency introduced a novel distributed consensus mechanism relying on economic incentives. While a coalition controlling a majority of computational power may undermine the system, for example by double-spending funds, it is often assumed it would be incentivized not to attack to protect its long-term stake in the health of the currency. We show how an attacker might purchase mining power (perhaps at a cost premium) for a short duration via bribery. Indeed, bribery can even be performed in-band with the system itself enforcing the bribe. A bribing attacker would not have the same concerns about the long-term health of the system, as their majority control is inherently short-lived. New modeling assumptions are needed to explain why such attacks have not been observed in practice. The need for all miners to avoid short-term profits by accepting bribes further suggests a potential tragedy of the commons which has not yet been analyzed.", "title": "" }, { "docid": "neg:1840614_16", "text": "Pattern recognition encompasses two fundamental tasks: description and classification. Given an object to analyze, a pattern recognition system first generates a description of it (i.e., the pattern) and then classifies the object based on that description (i.e., the recognition). Two general approaches for implementing pattern recognition systems, statistical and structural, employ different techniques for description and classification. Statistical approaches to pattern recognition use decision-theoretic concepts to discriminate among objects belonging to different groups based upon their quantitative features. Structural approaches to pattern recognition use syntactic grammars to discriminate among objects belonging to different groups based upon the arrangement of their morphological (i.e., shape-based or structural) features. Hybrid approaches to pattern recognition combine aspects of both statistical and structural pattern recognition. Structural pattern recognition systems are difficult to apply to new domains because implementation of both the description and classification tasks requires domain knowledge. Knowledge acquisition techniques necessary to obtain domain knowledge from experts are tedious and often fail to produce a complete and accurate knowledge base. Consequently, applications of structural pattern recognition have been primarily restricted to domains in which the set of useful morphological features has been established in the literature (e.g., speech recognition and character recognition) and the syntactic grammars can be composed by hand (e.g., electrocardiogram diagnosis). To overcome this limitation, a domain-independent approach to structural pattern recognition is needed that is capable of extracting morphological features and performing classification without relying on domain knowledge. A hybrid system that employs a statistical classification technique to perform discrimination based on structural features is a natural solution. While a statistical classifier is inherently domain independent, the domain knowledge necessary to support the description task can be eliminated with a set of generally-useful morphological features. Such a set of morphological features is suggested as the foundation for the development of a suite of structure detectors to perform generalized feature extraction for structural pattern recognition in time-series data. The ability of the suite of structure detectors to generate features useful for structural pattern recognition is evaluated by comparing the classification accuracies achieved when using the structure detectors versus commonly-used statistical feature extractors. Two real-world databases with markedly different characteristics and established ground truth serve as sources of data for the evaluation. The classification accuracies achieved using the features extracted by the structure detectors were consistently as good as or better than the classification accuracies achieved when using the features generated by the statistical feature extractors, thus demonstrating that the suite of structure detectors effectively performs generalized feature extraction for structural pattern recognition in time-series data.", "title": "" }, { "docid": "neg:1840614_17", "text": "Information asymmetry exists amongst stakeholders in the current food supply chain. Lack of standardization in data format, lack of regulations, and siloed, legacy information systems exasperate the problem. Global agriculture trade is increasing creating a greater need for traceability in the global supply chain. This paper introduces Harvest Network, a theoretical end-to-end, vis a vie “farm-to-fork”, food traceability application integrating the Ethereum blockchain and IoT devices exchanging GS1 message standards. The goal is to create a distributed ledger accessible for all stakeholders in the supply chain. Our design effort creates a basic framework (artefact) for building a prototype or simulation using existing technologies and protocols [1]. The next step is for industry practitioners and researchers to apply AGILE methods for creating working prototypes and advanced projects that bring about greater transparency.", "title": "" }, { "docid": "neg:1840614_18", "text": "A small-size broadband circularly polarized U-slot patch antenna with dual-feed is proposed. By introducing an additional feeding probe near the vertical slot of the conventional singly fed square U-slot patch antenna printed on a high-permittivity substrate, two series resonances in close proximity are excited. The two resonant frequencies are found to be independent of the orientation of U-slot with respect to the patch, and broadband circular polarization is achieved by introducing a nonquadrature phase difference between two feeding ports. Experimental results show that the overlapped bandwidth of VSWR ≤ 1.5 and AR ≤ 3 dB is over 20% with a small overall size of 0.33λ₀ × 0.33λ₀ × 0.068λ₀, where λ₀ is the free-space wavelength at the center frequency within the operating band.", "title": "" }, { "docid": "neg:1840614_19", "text": "Network embedding represents nodes in a continuous vector space and preserves structure information from the Network. Existing methods usually adopt a “one-size-fits-all” approach when concerning multi-scale structure information, such as firstand second-order proximity of nodes, ignoring the fact that different scales play different roles in the embedding learning. In this paper, we propose an Attention-based Adversarial Autoencoder Network Embedding(AAANE) framework, which promotes the collaboration of different scales and lets them vote for robust representations. The proposed AAANE consists of two components: 1) Attention-based autoencoder effectively capture the highly non-linear network structure, which can de-emphasize irrelevant scales during training. 2) An adversarial regularization guides the autoencoder learn robust representations by matching the posterior distribution of the latent embeddings to given prior distribution. This is the first attempt to introduce attention mechanisms to multi-scale network embedding. Experimental results on realworld networks show that our learned attention parameters are different for every network and the proposed approach outperforms existing state-ofthe-art approaches for network embedding.", "title": "" } ]

[ { "docid": "pos:1840615_0", "text": "We show how to learn many layers of features on color images and we use these features to initialize deep autoencoders. We then use the autoencoders to map images to short binary codes. Using semantic hashing [6], 28-bit codes can be used to retrieve images that are similar to a query image in a time that is independent of the size of the database. This extremely fast retrieval makes it possible to search using multiple di erent transformations of the query image. 256-bit binary codes allow much more accurate matching and can be used to prune the set of images found using the 28-bit codes.", "title": "" }, { "docid": "pos:1840615_1", "text": "In this paper, we apply a general deep learning (DL) framework for the answer selection task, which does not depend on manually defined features or linguistic tools. The basic framework is to build the embeddings of questions and answers based on bidirectional long short-term memory (biLSTM) models, and measure their closeness by cosine similarity. We further extend this basic model in two directions. One direction is to define a more composite representation for questions and answers by combining convolutional neural network with the basic framework. The other direction is to utilize a simple but efficient attention mechanism in order to generate the answer representation according to the question context. Several variations of models are provided. The models are examined by two datasets, including TREC-QA and InsuranceQA. Experimental results demonstrate that the proposed models substantially outperform several strong baselines.", "title": "" }, { "docid": "pos:1840615_2", "text": "Learning a similarity function between pairs of objects is at the core of learning to rank approaches. In information retrieval tasks we typically deal with query-document pairs, in question answering -- question-answer pairs. However, before learning can take place, such pairs needs to be mapped from the original space of symbolic words into some feature space encoding various aspects of their relatedness, e.g. lexical, syntactic and semantic. Feature engineering is often a laborious task and may require external knowledge sources that are not always available or difficult to obtain. Recently, deep learning approaches have gained a lot of attention from the research community and industry for their ability to automatically learn optimal feature representation for a given task, while claiming state-of-the-art performance in many tasks in computer vision, speech recognition and natural language processing. In this paper, we present a convolutional neural network architecture for reranking pairs of short texts, where we learn the optimal representation of text pairs and a similarity function to relate them in a supervised way from the available training data. Our network takes only words in the input, thus requiring minimal preprocessing. In particular, we consider the task of reranking short text pairs where elements of the pair are sentences. We test our deep learning system on two popular retrieval tasks from TREC: Question Answering and Microblog Retrieval. Our model demonstrates strong performance on the first task beating previous state-of-the-art systems by about 3\\% absolute points in both MAP and MRR and shows comparable results on tweet reranking, while enjoying the benefits of no manual feature engineering and no additional syntactic parsers.", "title": "" } ]

[ { "docid": "neg:1840615_0", "text": "The aim of this research was to understand what affects people's privacy preferences in smartphone apps. We ran a four-week study in the wild with 34 participants. Participants were asked to answer questions, which were used to gather their personal context and to measure their privacy preferences by varying app name and purpose of data collection. Our results show that participants shared the most when no information about data access or purpose was given, and shared the least when both of these details were specified. When just one of either purpose or the requesting app was shown, participants shared less when just the purpose was specified than when just the app name was given. We found that the purpose for data access was the predominant factor affecting users' choices. In our study the purpose condition vary from being not specified, to vague to be very specific. Participants were more willing to disclose data when no purpose was specified. When a vague purpose was shown, participants became more privacy-aware and were less willing to disclose their information. When specific purposes were shown participants were more willing to disclose when the purpose for requesting the information appeared to be beneficial to them, and shared the least when the purpose for data access was solely beneficial to developers.", "title": "" }, { "docid": "neg:1840615_1", "text": "Type confusion, often combined with use-after-free, is the main attack vector to compromise modern C++ software like browsers or virtual machines. Typecasting is a core principle that enables modularity in C++. For performance, most typecasts are only checked statically, i.e., the check only tests if a cast is allowed for the given type hierarchy, ignoring the actual runtime type of the object. Using an object of an incompatible base type instead of a derived type results in type confusion. Attackers abuse such type confusion issues to attack popular software products including Adobe Flash, PHP, Google Chrome, or Firefox. We propose to make all type checks explicit, replacing static checks with full runtime type checks. To minimize the performance impact of our mechanism HexType, we develop both low-overhead data structures and compiler optimizations. To maximize detection coverage, we handle specific object allocation patterns, e.g., placement new or reinterpret_cast which are not handled by other mechanisms. Our prototype results show that, compared to prior work, HexType has at least 1.1 -- 6.1 times higher coverage on Firefox benchmarks. For SPEC CPU2006 benchmarks with overhead, we show a 2 -- 33.4 times reduction in overhead. In addition, HexType discovered 4 new type confusion bugs in Qt and Apache Xerces-C++.", "title": "" }, { "docid": "neg:1840615_2", "text": "Text simplification aims to rewrite text into simpler versions, and thus make information accessible to a broader audience. Most previous work simplifies sentences using handcrafted rules aimed at splitting long sentences, or substitutes difficult words using a predefined dictionary. This paper presents a datadriven model based on quasi-synchronous grammar, a formalism that can naturally capture structural mismatches and complex rewrite operations. We describe how such a grammar can be induced from Wikipedia and propose an integer linear programming model for selecting the most appropriate simplification from the space of possible rewrites generated by the grammar. We show experimentally that our method creates simplifications that significantly reduce the reading difficulty of the input, while maintaining grammaticality and preserving its meaning.", "title": "" }, { "docid": "neg:1840615_3", "text": "In this paper we present a process called color transfer which can borrow one image's color characteristics from another. Recently Reinhard and his colleagues reported a pioneering work of color transfer. Their technology can produce very believable results, but has to transform pixel values from RGB to lαβ. Inspired by their work, we advise an approach which can directly deal with the color transfer in any 3D space.From the view of statistics, we consider pixel's value as a three-dimension stochastic variable and an image as a set of samples, so the correlations between three components can be measured by covariance. Our method imports covariance between three components of pixel values while calculate the mean along each of the three axes. Then we decompose the covariance matrix using SVD algorithm and get a rotation matrix. Finally we can scale, rotate and shift pixel data of target image to fit data points' cluster of source image in the current color space and get resultant image which takes on source image's look and feel. Besides the global processing, a swatch-based method is introduced in order to manipulate images' color more elaborately. Experimental results confirm the validity and usefulness of our method.", "title": "" }, { "docid": "neg:1840615_4", "text": "The Knowledge Base Population (KBP) track at the Text Analysis Conference 2010 marks the second year of this important information extraction evaluation. This paper describes the design and implementation of LCC’s systems which participated in the tasks of Entity Linking, Slot Filling, and the new task of Surprise Slot Filling. For the entity linking task, our top score was achieved through a robust context modeling approach which incorporates topical evidence. For slot filling, we used the output of the entity linking system together with a combination of different types of relation extractors. For surprise slot filling, our customizable extraction system was extremely useful due to the time sensitive nature of the task.", "title": "" }, { "docid": "neg:1840615_5", "text": "The volume, veracity, variability, and velocity of data produced from the ever increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks, and open problems in the field of neuromemristive circuits for edge computing.", "title": "" }, { "docid": "neg:1840615_6", "text": "─ Cuckoo Search (CS) is a new met heuristic algorithm. It is being used for solving optimization problem. It was developed in 2009 by XinShe Yang and Susah Deb. Uniqueness of this algorithm is the obligatory brood parasitism behavior of some cuckoo species along with the Levy Flight behavior of some birds and fruit flies. Cuckoo Hashing to Modified CS have also been discussed in this paper. CS is also validated using some test functions. After that CS performance is compared with those of GAs and PSO. It has been shown that CS is superior with respect to GAs and PSO. At last, the effect of the experimental results are discussed and proposed for future research. Index terms ─ Cuckoo search, Levy Flight, Obligatory brood parasitism, NP-hard problem, Markov Chain, Hill climbing, Heavy-tailed algorithm.", "title": "" }, { "docid": "neg:1840615_7", "text": "Background: Statistical reviews of the theories of reasoned action (TRA) and planned behavior (TPB) applied to exercise are limited by methodological issues including insufficient sample size and data to examine some moderator associations. Methods: We conducted a meta-analytic review of 111 TRA/TPB and exercise studies and examined the influences of five moderator variables. Results: We found that: a) exercise was most strongly associated with intention and perceived behavioral control; b) intention was most strongly associated with attitude; and c) intention predicted exercise behavior, and attitude and perceived behavioral control predicted intention. Also, the time interval between intention to behavior; scale correspondence; subject age; operationalization of subjective norm, intention, and perceived behavioral control; and publication status moderated the size of the effect. Conclusions: The TRA/TPB effectively explained exercise intention and behavior and moderators of this relationship. Researchers and practitioners are more equipped to design effective interventions by understanding the TRA/TPB constructs.", "title": "" }, { "docid": "neg:1840615_8", "text": "For cold-start recommendation, it is important to rapidly profile new users and generate a good initial set of recommendations through an interview process --- users should be queried adaptively in a sequential fashion, and multiple items should be offered for opinion solicitation at each trial. In this work, we propose a novel algorithm that learns to conduct the interview process guided by a decision tree with multiple questions at each split. The splits, represented as sparse weight vectors, are learned through an L_1-constrained optimization framework. The users are directed to child nodes according to the inner product of their responses and the corresponding weight vector. More importantly, to account for the variety of responses coming to a node, a linear regressor is learned within each node using all the previously obtained answers as input to predict item ratings. A user study, preliminary but first in its kind in cold-start recommendation, is conducted to explore the efficient number and format of questions being asked in a recommendation survey to minimize user cognitive efforts. Quantitative experimental validations also show that the proposed algorithm outperforms state-of-the-art approaches in terms of both the prediction accuracy and user cognitive efforts.", "title": "" }, { "docid": "neg:1840615_9", "text": "Deep generative models have achieved impressive success in recent years. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), as powerful frameworks for deep generative model learning, have largely been considered as two distinct paradigms and received extensive independent studies respectively. This paper aims to establish formal connections between GANs and VAEs through a new formulation of them. We interpret sample generation in GANs as performing posterior inference, and show that GANs and VAEs involve minimizing KL divergences of respective posterior and inference distributions with opposite directions, extending the two learning phases of classic wake-sleep algorithm, respectively. The unified view provides a powerful tool to analyze a diverse set of existing model variants, and enables to transfer techniques across research lines in a principled way. For example, we apply the importance weighting method in VAE literatures for improved GAN learning, and enhance VAEs with an adversarial mechanism that leverages generated samples. Experiments show generality and effectiveness of the transfered techniques.", "title": "" }, { "docid": "neg:1840615_10", "text": "The use of the fast Fourier transform in power spectrum analysis is described. Principal advantages of this method are a reduction in the number of computations and in required core storage, and convenient application in nonstationarity tests. The method involves sectioning the record and averaging modified periodograms of the sections. T INTRODLCTION HIS PAPER outlines a method for the application of the fast Fourier transform algorithm to the estimation of power spectra, which involves sectioning the record, taking modified periodograms of these sections, and averaging these modified periodo-grams. In many instances this method involves fewer computations than other methods. Moreover, it involves the transformation of sequences which are shorter than the whole record which is an advantage when computations are to be performed on a machine with limited core storage. Finally, it directly yields a potential resolution in the time dimension which is useful for testing and measuring nonstationarity. As will be pointed out, it is closely related to the method of complex demodulation described Let X(j), j= 0, N-1 be a sample from a stationary , second-order stochastic sequence. Assume for simplicity that E(X) 0. Let X(j) have spectral density Pcf), I f \\ 5%. We take segments, possibly overlapping, of length L with the starting points of these segments D units apart. Let X,(j),j=O, L 1 be the first such segment. Then Xdj) X($ and finally X&) X(j+ (K 1)D) j 0, ,L-1. We suppose we have K such segments; Xl(j), X,($, and that they cover the entire record, Le., that (K-1)DfL N. This segmenting is illustrated in Fig. 1. The method of estimation is as follows. For each segment of length L we calculate a modified periodo-gram. That is, we select a data window W(j), j= 0, L-1, and form the sequences Xl(j)W(j), X,(j) W(j). We then take the finite Fourier transforms A1(n), AK(~) of these sequences. Here ~k(n) xk(j) w(j)e-z~cijnlL 1 L-1 L j-0 and i= Finally, we obtain the K modified periodograms L U Ik(fn) I Ah(%) k 1, 2, K, where f n 0 , o-,L/2 n \" L and 1 Wyj). L j=o The spectral estimate is the average of these periodo", "title": "" }, { "docid": "neg:1840615_11", "text": "While child and adolescent physicians are familiar with the treatment of attention-deficit/hyperac-tivity disorder (ADHD), many adult physicians have had little experience with the disorder. It is difficult to develop clinical skills in the management of residual adult manifestations of developmental disorders without clinical experience with their presentation in childhood. Adult patients are increasingly seeking treatment for the symptoms of ADHD, and physicians need practice guidelines. Adult ADHD often presents differently from childhood ADHD. Because adult ADHD can be comorbid with other disorders and has symptoms similar to those of other disorders, it is important to understand differential diagnoses. Physicians should work with patients to provide feedback about their symptoms, to educate them about ADHD, and to set treatment goals. Treatment for ADHD in adults should include a medication trial, restructuring of the patient's environment to make it more compatible with the symptoms of ADHD, and ongoing supportive management to address any residual impairment and to facilitate functional and developmental improvements.", "title": "" }, { "docid": "neg:1840615_12", "text": "Our objective is to efficiently and accurately estimate the upper body pose of humans in gesture videos. To this end, we build on the recent successful applications of deep convolutional neural networks (ConvNets). Our novelties are: (i) our method is the first to our knowledge to use ConvNets for estimating human pose in videos; (ii) a new network that exploits temporal information from multiple frames, leading to better performance; (iii) showing that pre-segmenting the foreground of the video improves performance; and (iv) demonstrating that even without foreground segmentations, the network learns to abstract away from the background and can estimate the pose even in the presence of a complex, varying background. We evaluate our method on the BBC TV Signing dataset and show that our pose predictions are significantly better, and an order of magnitude faster to compute, than the state of the art [3].", "title": "" }, { "docid": "neg:1840615_13", "text": "This survey highlights the major issues concerning privacy and security in online social networks. Firstly, we discuss research that aims to protect user data from the various attack vantage points including other users, advertisers, third party application developers, and the online social network provider itself. Next we cover social network inference of user attributes, locating hubs, and link prediction. Because online social networks are so saturated with sensitive information, network inference plays a major privacy role. As a response to the issues brought forth by client-server architectures, distributed social networks are discussed. We then cover the challenges that providers face in maintaining the proper operation of an online social network including minimizing spam messages, and reducing the number of sybil accounts. Finally, we present research in anonymizing social network data. This area is of particular interest in order to continue research in this field both in academia and in industry.", "title": "" }, { "docid": "neg:1840615_14", "text": "Semantic word embeddings represent the meaning of a word via a vector, and are created by diverse methods. Many use nonlinear operations on co-occurrence statistics, and have hand-tuned hyperparameters and reweighting methods. This paper proposes a new generative model, a dynamic version of the log-linear topic model of Mnih and Hinton (2007). The methodological novelty is to use the prior to compute closed form expressions for word statistics. This provides a theoretical justification for nonlinear models like PMI, word2vec, and GloVe, as well as some hyperparameter choices. It also helps explain why low-dimensional semantic embeddings contain linear algebraic structure that allows solution of word analogies, as shown by Mikolov et al. (2013a) and many subsequent papers. Experimental support is provided for the generative model assumptions, the most important of which is that latent word vectors are fairly uniformly dispersed in space.", "title": "" }, { "docid": "neg:1840615_15", "text": "It has become standard for search engines to augment result lists with document summaries. Each document summary consists of a title, abstract, and a URL. In this work, we focus on the task of selecting relevant sentences for inclusion in the abstract. In particular, we investigate how machine learning-based approaches can effectively be applied to the problem. We analyze and evaluate several learning to rank approaches, such as ranking support vector machines (SVMs), support vector regression (SVR), and gradient boosted decision trees (GBDTs). Our work is the first to evaluate SVR and GBDTs for the sentence selection task. Using standard TREC test collections, we rigorously evaluate various aspects of the sentence selection problem. Our results show that the effectiveness of the machine learning approaches varies across collections with different characteristics. Furthermore, the results show that GBDTs provide a robust and powerful framework for the sentence selection task and significantly outperform SVR and ranking SVMs on several data sets.", "title": "" }, { "docid": "neg:1840615_16", "text": "The foundations of functional programming languages are examined from both historical and technical perspectives. Their evolution is traced through several critical periods: early work on lambda calculus and combinatory calculus, Lisp, Iswim, FP, ML, and modern functional languages such as Miranda1 and Haskell. The fundamental premises on which the functional programming methodology stands are critically analyzed with respect to philosophical, theoretical, and pragmatic concerns. Particular attention is paid to the main features that characterize modern functional languages: higher-order functions, lazy evaluation, equations and pattern matching, strong static typing and type inference, and data abstraction. In addition, current research areas—such as parallelism, nondeterminism, input/output, and state-oriented computations—are examined with the goal of predicting the future development and application of functional languages.", "title": "" }, { "docid": "neg:1840615_17", "text": "Hierarchical attention networks have recently achieved remarkable performance for document classification in a given language. However, when multilingual document collections are considered, training such models separately for each language entails linear parameter growth and lack of cross-language transfer. Learning a single multilingual model with fewer parameters is therefore a challenging but potentially beneficial objective. To this end, we propose multilingual hierarchical attention networks for learning document structures, with shared encoders and/or shared attention mechanisms across languages, using multi-task learning and an aligned semantic space as input. We evaluate the proposed models on multilingual document classification with disjoint label sets, on a large dataset which we provide, with 600k news documents in 8 languages, and 5k labels. The multilingual models outperform monolingual ones in low-resource as well as full-resource settings, and use fewer parameters, thus confirming their computational efficiency and the utility of cross-language transfer.", "title": "" }, { "docid": "neg:1840615_18", "text": "This paper presents a simple model for body-shadowing in off-body and body-to-body channels. The model is based on a body shadowing pattern associated with the on-body antenna, represented by a cosine function whose amplitude parameter is calculated from measurements. This parameter, i.e the maximum body-shadowing loss, is found to be linearly dependent on distance. The model was evaluated against a set of off-body channel measurements at 2.45 GHz in an indoor office environment, showing a good fit. The coefficient of determination obtained for the linear model of the maximum body-shadowing loss is greater than 0.6 in all considered scenarios, being higher than 0.8 for the ones with a static user.", "title": "" }, { "docid": "neg:1840615_19", "text": "In this brief, we introduce an architecture for accelerating convolution stages in convolutional neural networks (CNNs) implemented in embedded vision systems. The purpose of the architecture is to exploit the inherent parallelism in CNNs to reduce the required bandwidth, resource usage, and power consumption of highly computationally complex convolution operations as required by real-time embedded applications. We also implement the proposed architecture using fixed-point arithmetic on a ZC706 evaluation board that features a Xilinx Zynq-7000 system on-chip, where the embedded ARM processor with high clocking speed is used as the main controller to increase the flexibility and speed. The proposed architecture runs under a frequency of 150 MHz, which leads to 19.2 Giga multiply accumulation operations per second while consuming less than 10 W in power. This is done using only 391 DSP48 modules, which shows significant utilization improvement compared to the state-of-the-art architectures.", "title": "" } ]

[ { "docid": "pos:1840616_0", "text": "We present an attention-based model for recognizing multiple objects in images. The proposed model is a deep recurrent neural network trained with reinforcement learning to attend to the most relevant regions of the input image. We show that the model learns to both localize and recognize multiple objects despite being given only class labels during training. We evaluate the model on the challenging task of transcribing house number sequences from Google Street View images and show that it is both more accurate than the state-of-the-art convolutional networks and uses fewer parameters and less computation.", "title": "" } ]

[ { "docid": "neg:1840616_0", "text": "Convolutional neural networks (CNNs) have been successfully applied in artificial intelligent systems to perform sensory processing, sequence learning, and image processing. In contrast to conventional computing-centric applications, CNNs are known to be both computationally and memory intensive. The computational and memory resources of CNN applications are mixed together in the network weights. This incurs a significant amount of data movement, especially for high-dimensional convolutions. The emerging Processing-in-Memory (PIM) alleviates this memory bottleneck by integrating both processing elements and memory into a 3D-stacked architecture. Although this architecture can offer fast near-data processing to reduce data movement, memory is still a limiting factor of the entire system. We observe that an unsolved key challenge is how to efficiently allocate convolutions to 3D-stacked PIM to combine the advantages of both neural and computational processing. This paper presents MemoNet, a memory-efficient data allocation strategy for convolutional neural networks on 3D PIM architecture. MemoNet offers fine-grained parallelism that can fully exploit the computational power of PIM architecture. The objective is to capture the characteristics of neural network applications and perfectly match the underlining hardware resources provided by PIM, resulting in a hardware-independent design to transparently allocate data. We formulate the target problem as a dynamic programming model and present an optimal solution. To demonstrate the viability of the proposed MemoNet, we conduct a set of experiments using a variety of realistic convolutional neural network applications. The extensive evaluations show that, MemoNet can significantly improve the performance and the cache utilization compared to representative schemes.", "title": "" }, { "docid": "neg:1840616_1", "text": "Sentence classification, serving as the foundation of the subsequent text-based processing, continues attracting researchers attentions. Recently, with the great success of deep learning, convolutional neural network (CNN), a kind of common architecture of deep learning, has been widely used to this filed and achieved excellent performance. However, most CNN-based studies focus on using complex architectures to extract more effective category information, requiring more time in training models. With the aim to get better performance with less time cost on classification, this paper proposes two simple and effective methods by fully combining information both extracted from statistics and CNN. The first method is S-SFCNN, which combines statistical features and CNN-based probabilistic features of classification to build feature vectors, and then the vectors are used to train the logistic regression classifiers. And the second method is C-SFCNN, which combines CNN-based features and statistics-based probabilistic features of classification to build feature vectors. In the two methods, the Naive Bayes log-count ratios are selected as the text statistical features and the single-layer and single channel CNN is used as our CNN architecture. The testing results executed on 7 tasks show that our methods can achieve better performance than many other complex CNN models with less time cost. In addition, we summarized the main factors influencing the performance of our methods though experiment.", "title": "" }, { "docid": "neg:1840616_2", "text": "An increasing number of people are using dating websites to search for their life partners. This leads to the curiosity of how attractive a specific person is to the opposite gender on an average level. We propose a novel algorithm to evaluate people's objective attractiveness based on their interactions with other users on the dating websites and implement machine learning algorithms to predict their objective attractiveness ratings from their profiles. We validate our method on a large dataset gained from a Japanese dating website and yield convincing results. Our prediction based on users' profiles, which includes image and text contents, is over 80% correlated with the real values of the calculated objective attractiveness for the female and over 50% correlated with the real values of the calculated objective attractiveness for the male.", "title": "" }, { "docid": "neg:1840616_3", "text": "The information security community has come to realize that the weakest link in a cybersecurity chain is human behavior. To develop effective cybersecurity training programs for employees in the workplace, it is necessary to identify factors that contribute to employees’ cybersecurity behaviors and then build a theoretical model to understand how these factors affect employees’ self-reported security behavior in the workplace. Supported by a grant from the National Science Foundation (NSF), we developed a model for studying employees’ self-reported cybersecurity behaviors, and conducted a survey study to investigate the cybersecurity behavior and beliefs of employees. Five-hundred-seventy-nine employees from various U.S. organizations and companies completed an online survey with 87 items carefully designed by six experts in cybersecurity, information technology, psychology, and decision science. The results from statistical analysis of the cybersecurity behavior survey questionnaire will be presented in this TREO Talk. Some of the key findings include:  Prior Experience was correlated with self-reported cyber security behavior. However, it was not identified as a unique predictor in our regression analysis. This suggests that the prior training may indirectly affect cybersecurity behavior through other variables.  Peer Behavior was not a unique predictor of self-reported cybersecurity behavior. Perceptions of peer behavior may reflect people’s own self-efficacy with cybersecurity and their perceptions of the benefits from cybersecurity behaviors.  The regression model revealed four unique predictors of self-reported cybersecurity behavior: Computer Skill, Perceived Benefits, Perceived Barriers, and Security Self-efficacy. These variables should be assessed to identify employees who are at risk of cyber attacks and could be the target of interventions.  There are statistically significant gender-wise differences in terms of computer skills, prior experience, cues-to-action, security self-efficacy and self-reported cybersecurity behaviors. Since women’s self-efficacy is significantly lower than men, women’s self-efficacy may be a target for intervention.", "title": "" }, { "docid": "neg:1840616_4", "text": "A review on applications of metal-based inkjet inks for printed electronics with a particular focus on inks containing metal nanoparticles, complexes and metallo-organic compounds. The review describes the preparation of such inks and obtaining conductive patterns by using various sintering methods: thermal, photonic, microwave, plasma, electrical, and chemically triggered. Various applications of metal-based inkjet inks (metallization of solar cell, RFID antennas, OLEDs, thin film transistors, electroluminescence devices) are reviewed.", "title": "" }, { "docid": "neg:1840616_5", "text": "Phosphorylation of the transcription factor CREB is thought to be important in processes underlying long-term memory. It is unclear whether CREB phosphorylation can carry information about the sign of changes in synaptic strength, whether CREB pathways are equally activated in neurons receiving or providing synaptic input, or how synapse-to-nucleus communication is mediated. We found that Ca(2+)-dependent nuclear CREB phosphorylation was rapidly evoked by synaptic stimuli including, but not limited to, those that induced potentiation and depression of synaptic strength. In striking contrast, high frequency action potential firing alone failed to trigger CREB phosphorylation. Activation of a submembranous Ca2+ sensor, just beneath sites of Ca2+ entry, appears critical for triggering nuclear CREB phosphorylation via calmodulin and a Ca2+/calmodulin-dependent protein kinase.", "title": "" }, { "docid": "neg:1840616_6", "text": "Speech recognition of inflectional and morphologically rich languages like Czech is currently quite a challenging task, because simple n-gram techniques are unable to capture important regularities in the data. Several possible solutions were proposed, namely class based models, factored models, decision trees and neural networks. This paper describes improvements obtained in recognition of spoken Czech lectures using language models based on neural networks. Relative reductions in word error rate are more than 15% over baseline obtained with adapted 4-gram backoff language model using modified Kneser-Ney smoothing.", "title": "" }, { "docid": "neg:1840616_7", "text": "The morality of transformational leadership has been sharply questioned, particularly by libertarians, “grass roots” theorists, and organizational development consultants. This paper argues that to be truly transformational, leadership must be grounded in moral foundations. The four components of authentic transformational leadership (idealized influence, inspirational motivation, intellectual stimulation, and individualized consideration) are contrasted with their counterfeits in dissembling pseudo-transformational leadership on the basis of (1) the moral character of the leaders and their concerns for self and others; (2) the ethical values embedded in the leaders’ vision, articulation, and program, which followers can embrace or reject; and (3) the morality of the processes of social ethical choices and action in which the leaders and followers engage and collectively pursue. The literature on transformational leadership is linked to the long-standing literature on virtue and moral character, as exemplified by Socratic and Confucian typologies. It is related as well to the major themes of the modern Western ethical agenda: liberty, utility, and distributive justice Deception, sophistry, and pretense are examined alongside issues of transcendence, agency, trust, striving for congruence in values, cooperative action, power, persuasion, and corporate governance to establish the strategic and moral foundations of authentic transformational leadership.", "title": "" }, { "docid": "neg:1840616_8", "text": "Network Security is playing a vital role in all types of networks. Nowadays the network is implemented in all places like offices, schools, banks etc. and almost all the individuals are taking part in social network media. Even though many types of network security systems are in use, the vulnerable activities are taking place now and then. This paper presents a survey about various types of network attacks mainly web attacks, and different Intrusion Detection Systems(IDS) which are in use. This may pave a path to design a new type of IDS which may protect the network system from various types of network attacks.", "title": "" }, { "docid": "neg:1840616_9", "text": "Since smartphones have stored diverse sensitive privacy information, including credit card and so on, a great deal of malware are desired to tamper them. As one of the most prevalent platforms, Android contains sensitive resources that can only be accessed via corresponding APIs, and the APIs can be invoked only when user has authorized permissions in the Android permission model. However, a novel threat called privilege escalation attack may bypass this watchdog. It's presented as that an application with less permissions can access sensitive resources through public interfaces of a more privileged application, which is especially useful for malware to hide sensitive functions by dispersing them into multiple programs. We explore privilege-escalation malware evolution techniques on samples from Android Malware Genome Project. And they have showed great effectiveness against a set of powerful antivirus tools provided by VirusTotal. The detection ratios present different and distinguished reduction, compared to an average 61% detection ratio before transformation. In order to conquer this threat model, we have developed a tool called DroidAlarm to conduct a full-spectrum analysis for identifying potential capability leaks and present concrete capability leak paths by static analysis on Android applications. And we can still alarm all these cases by exposing capability leak paths in them.", "title": "" }, { "docid": "neg:1840616_10", "text": "We train a set of state of the art neural networks, the Maxout networks (Goodfellow et al., 2013a), on three benchmark datasets: the MNIST, CIFAR10 and SVHN, with three distinct storing formats: floating point, fixed point and dynamic fixed point. For each of those datasets and for each of those formats, we assess the impact of the precision of the storage on the final error of the training. We find that very low precision storage is sufficient not just for running trained networks but also for training them. For example, Maxout networks state-of-the-art results are nearly maintained with 10 bits for storing activations and gradients, and 12 bits for storing parameters.", "title": "" }, { "docid": "neg:1840616_11", "text": "Memory units have been widely used to enrich the capabilities of deep networks on capturing long-term dependencies in reasoning and prediction tasks, but little investigation exists on deep generative models (DGMs) which are good at inferring high-level invariant representations from unlabeled data. This paper presents a deep generative model with a possibly large external memory and an attention mechanism to capture the local detail information that is often lost in the bottom-up abstraction process in representation learning. By adopting a smooth attention model, the whole network is trained end-to-end by optimizing a variational bound of data likelihood via auto-encoding variational Bayesian methods, where an asymmetric recognition network is learnt jointly to infer high-level invariant representations. The asymmetric architecture can reduce the competition between bottom-up invariant feature extraction and top-down generation of instance details. Our experiments on several datasets demonstrate that memory can significantly boost the performance of DGMs on various tasks, including density estimation, image generation, and missing value imputation, and DGMs with memory can achieve state-ofthe-art quantitative results.", "title": "" }, { "docid": "neg:1840616_12", "text": "This paper proposes a novel approach and a new benchmark for video summarization. Thereby we focus on user videos, which are raw videos containing a set of interesting events. Our method starts by segmenting the video by using a novel “superframe” segmentation, tailored to raw videos. Then, we estimate visual interestingness per superframe using a set of low-, midand high-level features. Based on this scoring, we select an optimal subset of superframes to create an informative and interesting summary. The introduced benchmark comes with multiple human created summaries, which were acquired in a controlled psychological experiment. This data paves the way to evaluate summarization methods objectively and to get new insights in video summarization. When evaluating our method, we find that it generates high-quality results, comparable to manual, human-created summaries.", "title": "" }, { "docid": "neg:1840616_13", "text": "In this paper, the problem of switching stabilization for a class of switched nonlinear systems is studied by using average dwell time (ADT) switching, where the subsystems are possibly all unstable. First, a new concept of ADT is given, which is different from the traditional definition of ADT. Based on the new proposed switching signals, a sufficient condition of stabilization for switched nonlinear systems with unstable subsystems is derived. Then, the T-S fuzzy modeling approach is applied to represent the underlying nonlinear system to make the obtained condition easily verified. A novel multiple quadratic Lyapunov function approach is also proposed, by which some conditions are provided in terms of a set of linear matrix inequalities to guarantee the derived T-S fuzzy system to be asymptotically stable. Finally, a numerical example is given to demonstrate the effectiveness of our developed results.", "title": "" }, { "docid": "neg:1840616_14", "text": "The convenience of cell-phone cameras has made them one of the most common ways by which people document their lives, whether it is everyday pleasures or celebrations. With thousands of images, it might prove to be a daunting task to organize them by hand. When applying automated algorithms to help us, we would like to have both images that are dear to us but are also of good quality. In this paper we explore the performance of the MobileNet CNN architecture, and the different design (inputs size, and layer depth) choices, in their ability in solving various aesthetic inference task: binary classification, regression, image cropping. We show that the baseline MobileNet architecture achieves near state-of-the-art results for binary classification on the AVA dataset while being more than 10 times smaller and compute efficient. We further show that these models, when trained for fine-grained aesthetics inference, achieve better cropping performance than other aestheticsbased croppers.", "title": "" }, { "docid": "neg:1840616_15", "text": "The introduction of crowdsourcing offers numerous business opportunities. In recent years, manifold forms of crowdsourcing have emerged on the market -- also in logistics. Thereby, the ubiquitous availability and sensor-supported assistance functions of mobile devices support crowdsourcing applications, which promotes contextual interactions between users at the right place at the right time. This paper presents the results of an in-depth-analysis on crowdsourcing in logistics in the course of ongoing research in the field of location-based crowdsourcing (LBCS). This paper analyzes LBCS for both, 'classic' logistics as well as 'information' logistics. Real-world examples of crowdsourcing applications are used to underpin the two evaluated types of logistics using crowdsourcing. Potential advantages and challenges of logistics with the crowd ('crowd-logistics') are discussed. Accordingly, this paper aims to provide the necessary basis for a novel interdisciplinary research field.", "title": "" }, { "docid": "neg:1840616_16", "text": "Numerous complains have been made by Android users who severely suffer from the sluggish response when interacting with their devices. However, very few studies have been conducted to understand the user-perceived latency or mitigate the UI-lagging problem. In this paper, we conduct the first systematic measurement study to quantify the user-perceived latency using typical interaction-intensive Android apps in running with and without background workloads. We reveal the insufficiency of Android system in ensuring the performance of foreground apps and therefore design a new system to address the insufficiency accordingly. We develop a lightweight tracker to accurately identify all delay-critical threads that contribute to the slow response of user interactions. We then build a resource manager that can efficiently schedule various system resources including CPU, I/O, and GPU, for optimizing the performance of these threads. We implement the proposed system on commercial smartphones and conduct comprehensive experiments to evaluate our implementation. Evaluation results show that our system is able to significantly reduce the user-perceived latency of foreground apps in running with aggressive background workloads, up to 10x, while incurring negligible system overhead of less than 3.1 percent CPU and 7 MB memory.", "title": "" }, { "docid": "neg:1840616_17", "text": "Bayesian filters have now become the standard for spam filtering; unfortunately most Bayesian filters seem to reach a plateau of accuracy at 99.9 percent. We experimentally compare the training methods TEFT, TOE, and TUNE, as well as pure Bayesian, token-bag, tokensequence, SBPH, and Markovian ddiscriminators. The results deomonstrate that TUNE is indeed best for training, but computationally exorbitant, and that Markovian discrimination is considerably more accurate than Bayesian, but not sufficient to reach four-nines accuracy, and that other techniques such as inoculation are needed. MIT Spam Conference 2004 This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Copyright c © Mitsubishi Electric Research Laboratories, Inc., 2004 201 Broadway, Cambridge, Massachusetts 02139 The Spam-Filtering Accuracy Plateau at 99.9% Accuracy and How to Get Past It. William S. Yerazunis, PhD* Presented at the 2004 MIT Spam Conference January 18, 2004 MIT, Cambridge, Massachusetts Abstract: Bayesian filters have now become the standard for spam filtering; unfortunately most Bayesian filters seem to reach a plateau of accuracy at 99.9%. We experimentally compare the training methods TEFT, TOE, and TUNE, as well as pure Bayesian, token-bag, token-sequence, SBPH, and Markovian discriminators. The results demonstrate that TUNE is indeed best for training, but computationally exorbitant, and that Markovian discrimination is considerably more accurate than Bayesian, but not sufficient to reach four-nines accuracy, and that other techniques such as inoculation are needed. Bayesian filters have now become the standard for spam filtering; unfortunately most Bayesian filters seem to reach a plateau of accuracy at 99.9%. We experimentally compare the training methods TEFT, TOE, and TUNE, as well as pure Bayesian, token-bag, token-sequence, SBPH, and Markovian discriminators. The results demonstrate that TUNE is indeed best for training, but computationally exorbitant, and that Markovian discrimination is considerably more accurate than Bayesian, but not sufficient to reach four-nines accuracy, and that other techniques such as inoculation are needed.", "title": "" }, { "docid": "neg:1840616_18", "text": "Keyword extraction problem is one of the most significant tasks in information retrieval. High-quality keyword extraction sufficiently influences the progress in the following subtasks of information retrieval: classification and clustering, data mining, knowledge extraction and representation, etc. The research environment has specified a layout for keyphrase extraction. However, some of the possible decisions remain uninvolved in the paradigm. In the paper the authors observe the scope of interdisciplinary methods applicable to automatic stop list feeding. The chosen method belongs to the class of experiential models. The research procedure based on this method allows to improve the quality of keyphrase extraction on the stage of candidate keyphrase building. Several ways to automatic feeding of the stop lists are proposed in the paper as well. One of them is based on provisions of lexical statistics and the results of its application to the discussed task point out the non-gaussian nature of text corpora. The second way based on usage of the Inspec train collection to the feeding of stop lists improves the quality considerably.", "title": "" }, { "docid": "neg:1840616_19", "text": "In order to get real time image processing for mobile robot vision, we propose to use a discrete time cellular neural network implementation by a convolutional structure on Altora FPGA using VHDL language. We obtain at least 9 times faster processing than other emulations for the same problem.", "title": "" } ]

[ { "docid": "pos:1840617_0", "text": "Faceted search is becoming a popular method to allow users to interactively search and navigate complex information spaces. A faceted search system presents users with key-value metadata that is used for query refinement. While popular in e-commerce and digital libraries, not much research has been conducted on which metadata to present to a user in order to improve the search experience. Nor are there repeatable benchmarks for evaluating a faceted search engine. This paper proposes the use of collaborative filtering and personalization to customize the search interface to each user's behavior. This paper also proposes a utility based framework to evaluate the faceted interface. In order to demonstrate these ideas and better understand personalized faceted search, several faceted search algorithms are proposed and evaluated using the novel evaluation methodology.", "title": "" } ]

[ { "docid": "neg:1840617_0", "text": "A high-voltage high-speed gate driver to enable synchronous rectifiers with zero-voltage-switching (ZVS) operation is presented in this paper. A capacitive-coupled level-shifter (CCLS) is developed to achieve negligible propagation delay and static current consumption. With only 1 off-chip capacitor, the proposed gate driver possesses strong driving capability and requires no external floating supply for the high-side driving. A dynamic timing control is also proposed not only to enable ZVS operation in the converter for minimizing the capacitive switching loss, but also to eliminate the converter short-circuit power loss. Implemented in a 0.5μm HV CMOS process, the proposed CCLS of the gate driver can shift up a 5V signal to the 100V DC rail with sub-nanosecond delay, improving the FoM by at least 29 times compared with that of state-of-the-art counterparts. The dynamic dead-time control properly enables ZVS operation in a synchronous buck converter under different input voltages (30V to 100V). The power losses of the high-voltage buck converter are thus greatly reduced under different load currents, achieving a maximum power efficiency improvement of 11.5%.", "title": "" }, { "docid": "neg:1840617_1", "text": "Conventional feedback control methods can solve various types of robot control problems very efficiently by capturing the structure with explicit models, such as rigid body equations of motion. However, many control problems in modern manufacturing deal with contacts and friction, which are difficult to capture with first-order physical modeling. Hence, applying control design methodologies to these kinds of problems often results in brittle and inaccurate controllers, which have to be manually tuned for deployment. Reinforcement learning (RL) methods have been demonstrated to be capable of learning continuous robot controllers from interactions with the environment, even for problems that include friction and contacts. In this paper, we study how we can solve difficult control problems in the real world by decomposing them into a part that is solved efficiently by conventional feedback control methods, and the residual which is solved with RL. The final control policy is a superposition of both control signals. We demonstrate our approach by training an agent to successfully perform a real-world block assembly task involving contacts and unstable objects.", "title": "" }, { "docid": "neg:1840617_2", "text": "Robust object detection is a critical skill for robotic applications in complex environments like homes and offices. In this paper we propose a method for using multiple cameras to simultaneously view an object from multiple angles and at high resolutions. We show that our probabilistic method for combining the camera views, which can be used with many choices of single-image object detector, can significantly improve accuracy for detecting objects from many viewpoints. We also present our own single-image object detection method that uses large synthetic datasets for training. Using a distributed, parallel learning algorithm, we train from very large datasets (up to 100 million image patches). The resulting object detector achieves high performance on its own, but also benefits substantially from using multiple camera views. Our experimental results validate our system in realistic conditions and demonstrates significant performance gains over using standard single-image classifiers, raising accuracy from 0.86 area-under-curve to 0.97.", "title": "" }, { "docid": "neg:1840617_3", "text": "This paper considers the state of art real-time detection network single-shot multi-box detector (SSD) for multi-targets detection. It is built on top of a base network VGG16 that ends with some convolution layers. Its base network VGG16, designed for 1000 categories in Imagenet dataset, is obviously over-parametered, when used for 21 categories classification in VOC dataset. In this paper, we visualize the base network VGG16 in SSD network by deconvolution method. We analyze the discriminative feature learned by last layer conv5_3 of VGG16 network due to its semantic property. Redundancy intra-channel can be seen in the form of deconvolution image. Accordingly, we propose a pruning method to obtain a compressed network with high accuracy. Experiments illustrate the efficiency of our method by comparing different fine-tune methods. A reduced SSD network is obtained with even higher mAP than the original one by 2 percent. When only 4% of the original kernels in conv5_3 is remained, mAP is still as high as that of the original network.", "title": "" }, { "docid": "neg:1840617_4", "text": "In this paper, we present a method for estimating articulated human poses in videos. We cast this as an optimization problem defined on body parts with spatio-temporal links between them. The resulting formulation is unfortunately intractable and previous approaches only provide approximate solutions. Although such methods perform well on certain body parts, e.g., head, their performance on lower arms, i.e., elbows and wrists, remains poor. We present a new approximate scheme with two steps dedicated to pose estimation. First, our approach takes into account temporal links with subsequent frames for the less-certain parts, namely elbows and wrists. Second, our method decomposes poses into limbs, generates limb sequences across time, and recomposes poses by mixing these body part sequences. We introduce a new dataset \"Poses in the Wild\", which is more challenging than the existing ones, with sequences containing background clutter, occlusions, and severe camera motion. We experimentally compare our method with recent approaches on this new dataset as well as on two other benchmark datasets, and show significant improvement.", "title": "" }, { "docid": "neg:1840617_5", "text": "Software fault isolation (SFI) is an effective mechanism to confine untrusted modules inside isolated domains to protect their host applications. Since its debut, researchers have proposed different SFI systems for many purposes such as safe execution of untrusted native browser plugins. However, most of these systems focus on the x86 architecture. Inrecent years, ARM has become the dominant architecture for mobile devices and gains in popularity in data centers.Hence there is a compellingneed for an efficient SFI system for the ARM architecture. Unfortunately, existing systems either have prohibitively high performance overhead or place various limitations on the memory layout and instructions of untrusted modules.\n In this paper, we propose ARMlock, a hardware-based fault isolation for ARM. It uniquely leverages the memory domain support in ARM processors to create multiple sandboxes. Memory accesses by the untrusted module (including read, write, and execution) are strictly confined by the hardware,and instructions running inside the sandbox execute at the same speed as those outside it. ARMlock imposes virtually no structural constraints on untrusted modules. For example, they can use self-modifying code, receive exceptions, and make system calls. Moreover, system calls can be interposed by ARMlock to enforce the policies set by the host. We have implemented a prototype of ARMlock for Linux that supports the popular ARMv6 and ARMv7 sub-architecture. Our security assessment and performance measurement show that ARMlock is practical, effective, and efficient.", "title": "" }, { "docid": "neg:1840617_6", "text": "Integrating smart temperature sensors into digital platforms facilitates information to be processed and transmitted, and open up new applications. Furthermore, temperature sensors are crucial components in computing platforms to manage power-efficiency trade-offs reliably under a thermal budget. This paper presents a holistic perspective about smart temperature sensor design from system- to device-level including manufacturing concerns. Through smart sensor design evolutions, we identify some scaling paths and circuit techniques to surmount analog/mixed-signal design challenges in 32-nm and beyond. We close with opportunities to design smarter temperature sensors.", "title": "" }, { "docid": "neg:1840617_7", "text": "Debugging data processing logic in Data-Intensive Scalable Computing (DISC) systems is a difficult and time consuming effort. Today's DISC systems offer very little tooling for debugging programs, and as a result programmers spend countless hours collecting evidence (e.g., from log files) and performing trial and error debugging. To aid this effort, we built Titian, a library that enables data provenance-tracking data through transformations-in Apache Spark. Data scientists using the Titian Spark extension will be able to quickly identify the input data at the root cause of a potential bug or outlier result. Titian is built directly into the Spark platform and offers data provenance support at interactive speeds-orders-of-magnitude faster than alternative solutions-while minimally impacting Spark job performance; observed overheads for capturing data lineage rarely exceed 30% above the baseline job execution time.", "title": "" }, { "docid": "neg:1840617_8", "text": "The predominantly anaerobic microbiota of the distal ileum and colon contain an extraordinarily complex variety of metabolically active bacteria and fungi that intimately interact with the host's epithelial cells and mucosal immune system. Crohn's disease, ulcerative colitis, and pouchitis are the result of continuous microbial antigenic stimulation of pathogenic immune responses as a consequence of host genetic defects in mucosal barrier function, innate bacterial killing, or immunoregulation. Altered microbial composition and function in inflammatory bowel diseases result in increased immune stimulation, epithelial dysfunction, or enhanced mucosal permeability. Although traditional pathogens probably are not responsible for these disorders, increased virulence of commensal bacterial species, particularly Escherichia coli, enhance their mucosal attachment, invasion, and intracellular persistence, thereby stimulating pathogenic immune responses. Host genetic polymorphisms most likely interact with functional bacterial changes to stimulate aggressive immune responses that lead to chronic tissue injury. Identification of these host and microbial alterations in individual patients should lead to selective targeted interventions that correct underlying abnormalities and induce sustained and predictable therapeutic responses.", "title": "" }, { "docid": "neg:1840617_9", "text": "Neuroblastoma (NBL) and medulloblastoma (MBL) are tumors of the neuroectoderm that occur in children. NBL and MBL express Trk family tyrosine kinase receptors, which regulate growth, differentiation, and cell death. CEP-751 (KT-6587), an indolocarbazole derivative, is an inhibitor of Trk family tyrosine kinases at nanomolar concentrations. This study was designed to determine the effect of CEP-751 on the growth of NBL and MBL cell lines as xenografts. In vivo studies were conducted on four NBL cell lines (IMR-5, CHP-134, NBL-S, and SY5Y) and three MBL cell lines (D283, D341, and DAOY) using two treatment schedules: (a) treatment was started after the tumors were measurable (therapeutic study); or (b) 4-6 days after inoculation, before tumors were palpable (prevention study). CEP-751 was given at 21 mg/kg/dose administered twice a day, 7 days a week; the carrier vehicle was used as a control. In therapeutic studies, a significant difference in tumor size was seen between treated and control animals with IMR-5 on day 8 (P = 0.01), NBL-S on day 17 (P = 0.016), and CHP-134 on day 15 (P = 0.034). CEP-751 also had a significant growth-inhibitory effect on the MBL line D283 (on day 39, P = 0.031). Inhibition of tumor growth of D341 did not reach statistical significance, and no inhibition was apparent with DAOY. In prevention studies, CEP-751 showed a modest growth-inhibitory effect on IMR5 (P = 0.062) and CHP-134 (P = 0.049). Furthermore, inhibition of growth was greater in the SY5Y cell line transfected with TrkB compared with the untransfected parent cell line expressing no detectable TrkB. Terminal deoxynucleotidyl transferase-mediated nick end labeling studies showed CEP-751 induced apoptosis in the treated CHP-134 tumors, whereas no evidence of apoptosis was seen in the control tumors. Finally, there was no apparent toxicity identified in any of the treated mice. These results suggest that CEP-751 may be a useful therapeutic agent for NBL or MBL.", "title": "" }, { "docid": "neg:1840617_10", "text": "New updated! The latest book from a very famous author finally comes out. Book of the tower of hanoi myths and maths, as an amazing reference becomes what you need to get. What's for is this book? Are you still thinking for what the book is? Well, this is what you probably will get. You should have made proper choices for your better life. Book, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with.", "title": "" }, { "docid": "neg:1840617_11", "text": "Geographically annotated social media is extremely valuable for modern information retrieval. However, when researchers can only access publicly-visible data, one quickly finds that social media users rarely publish location information. In this work, we provide a method which can geolocate the overwhelming majority of active Twitter users, independent of their location sharing preferences, using only publicly-visible Twitter data. Our method infers an unknown user's location by examining their friend's locations. We frame the geotagging problem as an optimization over a social network with a total variation-based objective and provide a scalable and distributed algorithm for its solution. Furthermore, we show how a robust estimate of the geographic dispersion of each user's ego network can be used as a per-user accuracy measure which is effective at removing outlying errors. Leave-many-out evaluation shows that our method is able to infer location for 101, 846, 236 Twitter users at a median error of 6.38 km, allowing us to geotag over 80% of public tweets.", "title": "" }, { "docid": "neg:1840617_12", "text": "We consider 27 population and community terms used frequently by parasitologists when describing the ecology of parasites. We provide suggestions for various terms in an attempt to foster consistent use and to make terms used in parasite ecology easier to interpret for those who study free-living organisms. We suggest strongly that authors, whether they agree or disagree with us, provide complete and unambiguous definitions for all parameters of their studies.", "title": "" }, { "docid": "neg:1840617_13", "text": "n The polarization of electromagnetic signals is an important feature in the design of modern radar and telecommunications. Standard electromagnetic theory readily shows that a linearly polarized plane wave propagating in free space consists of two equal but counter-rotating components of circular polarization. In magnetized media, these circular modes can be arranged to produce the nonreciprocal propagation effects that are the basic properties of isolator and circulator devices. Independent phase control of right-hand (+) and left-hand (–) circular waves is accomplished by splitting their propagation velocities through differences in the e ± m ± parameter. A phenomenological analysis of the permeability m and permittivity e in dispersive media serves to introduce the corresponding magneticand electric-dipole mechanisms of interaction length with the propagating signal. As an example of permeability dispersion, a Lincoln Laboratory quasi-optical Faradayrotation isolator circulator at 35 GHz (l ~ 1 cm) with a garnet-ferrite rotator element is described. At infrared wavelengths (l = 1.55 mm), where fiber-optic laser sources also require protection by passive isolation of the Faraday-rotation principle, e rather than m provides the dispersion, and the frequency is limited to the quantum energies of the electric-dipole atomic transitions peculiar to the molecular structure of the magnetic garnet. For optimum performance, bismuth additions to the garnet chemical formula are usually necessary. Spectroscopic and molecular theory models developed at Lincoln Laboratory to explain the bismuth effects are reviewed. In a concluding section, proposed advances in present technology are discussed in the context of future radar and telecommunications challenges.", "title": "" }, { "docid": "neg:1840617_14", "text": "The placenta is a complex organ, playing multiple roles during fetal development. Very little is known about the association between placental morphological abnormalities and fetal physiology. In this work, we present an open sourced, computationally tractable deep learning pipeline to analyse placenta histology at the level of the cell. By utilising two deep convolutional neural network architectures and transfer learning, we can robustly localise and classify placental cells within five classes with an accuracy of 89%. Furthermore, we learn deep embeddings encoding phenotypic knowledge that is capable of both stratifying five distinct cell populations and learn intraclass phenotypic variance. We envisage that the automation of this pipeline to population scale studies of placenta histology has the potential to improve our understanding of basic cellular placental biology and its variations, particularly its role in predicting adverse birth outcomes.", "title": "" }, { "docid": "neg:1840617_15", "text": "The progressive ability of a six-strains L. monocytogenes cocktail to form biofilm on stainless steel (SS), under fish-processing simulated conditions, was investigated, together with the biocide tolerance of the developed sessile communities. To do this, the pathogenic bacteria were left to form biofilms on SS coupons incubated at 15°C, for up to 240h, in periodically renewable model fish juice substrate, prepared by aquatic extraction of sea bream flesh, under both mono-species and mixed-culture conditions. In the latter case, L. monocytogenes cells were left to produce biofilms together with either a five-strains cocktail of four Pseudomonas species (fragi, savastanoi, putida and fluorescens), or whole fish indigenous microflora. The biofilm populations of L. monocytogenes, Pseudomonas spp., Enterobacteriaceae, H2S producing and aerobic plate count (APC) bacteria, both before and after disinfection, were enumerated by selective agar plating, following their removal from surfaces through bead vortexing. Scanning electron microscopy was also applied to monitor biofilm formation dynamics and anti-biofilm biocidal actions. Results revealed the clear dominance of Pseudomonas spp. bacteria in all the mixed-culture sessile communities throughout the whole incubation period, with the in parallel sole presence of L. monocytogenes cells to further increase (ca. 10-fold) their sessile growth. With respect to L. monocytogenes and under mono-species conditions, its maximum biofilm population (ca. 6logCFU/cm2) was reached at 192h of incubation, whereas when solely Pseudomonas spp. cells were also present, its biofilm formation was either slightly hindered or favored, depending on the incubation day. However, when all the fish indigenous microflora was present, biofilm formation by the pathogen was greatly hampered and never exceeded 3logCFU/cm2, while under the same conditions, APC biofilm counts had already surpassed 7logCFU/cm2 by the end of the first 96h of incubation. All here tested disinfection treatments, composed of two common food industry biocides gradually applied for 15 to 30min, were insufficient against L. monocytogenes mono-species biofilm communities, with the resistance of the latter to significantly increase from the 3rd to 7th day of incubation. However, all these treatments resulted in no detectable L. monocytogenes cells upon their application against the mixed-culture sessile communities also containing the fish indigenous microflora, something probably associated with the low attached population level of these pathogenic cells before disinfection (<102CFU/cm2) under such mixed-culture conditions. Taken together, all these results expand our knowledge on both the population dynamics and resistance of L. monocytogenes biofilm cells under conditions resembling those encountered within the seafood industry and should be considered upon designing and applying effective anti-biofilm strategies.", "title": "" }, { "docid": "neg:1840617_16", "text": "We demonstrate a MEMS-based display system with a very wide projection angle of up to 120deg. The system utilizes a gimbal-less two-axis micromirror scanner for high-speed laser beam-steering in both axes. The optical scan angle of the micromirrors is up to 16deg on each axis. A custom-designed fisheye lens is utilized to magnify scan angles. The system can display a variety of vector graphics as well as multiframe animations at arbitrary refresh rates, up to the overall bandwidth limit of the MEMS device. It is also possible to operate the scanners in point-to-point scanning, resonant and/or rastering modes. The system is highly adaptable for projection on a variety of surfaces including projection on specially coated transparent surfaces (Fig. 3.) The size of the displayed area, refresh rate, display mode (vector graphic or image raster,) and many other parameters are all adjustable by the user. The small size of the MEMS devices and lens as well as the ultra-low power consumption of the MEMS devices, in the milliwatt range, makes the overall system highly portable and miniaturizable.", "title": "" }, { "docid": "neg:1840617_17", "text": "Purpose – The paper’s aim is to explore the factors that affect the online game addiction and the role that online game addiction plays in the relationship between online satisfaction and loyalty. Design/methodology/approach – A web survey of online game players was conducted, with 1,186 valid responses collected. Structure equation modeling – specifically partial least squares – was used to assess the relationships in the proposed research framework. Findings – The results indicate that perceived playfulness and descriptive norms influence online game addiction. Furthermore, descriptive norms indirectly affect online game addiction through perceived playfulness. Addiction also directly contributes to loyalty and attenuates the relationship between satisfaction and loyalty. This finding partially explains why people remain loyal to an online game despite being dissatisfied. Practical implications – Online gaming vendors should strive to create amusing game content and to maintain their online game communities in order to enhance players’ perceptions of playfulness and the effects of social influences. Also, because satisfaction is the most significant indicator of loyalty, vendors can enhance loyalty by providing better services, such as fraud prevention and the detection of cheating behaviors. Originality/value – The value of this study is that it reveals the moderating influences of addiction on the satisfaction-loyalty relationship and factors that contribute to the online game addiction. Moreover, while many past studies focused on addiction’s negative effects and on groups considered particularly vulnerable to Internet addiction, this paper extends previous work by investigating the relationship of addiction to other marketing variables and by using a more general population, mostly young adults, as research subjects.", "title": "" }, { "docid": "neg:1840617_18", "text": "In this paper we present a Model Predictive Control (MPC) approach for combined braking and steering systems in autonomous vehicles. We start from the result presented in (Borrelli et al. (2005)) and (Falcone et al. (2007a)), where a Model Predictive Controller (MPC) for autonomous steering systems has been presented. As in (Borrelli et al. (2005)) and (Falcone et al. (2007a)) we formulate an MPC control problem in order to stabilize a vehicle along a desired path. In the present paper, the control objective is to best follow a given path by controlling the front steering angle and the brakes at the four wheels independently, while fulfilling various physical and design constraints.", "title": "" } ]

[ { "docid": "pos:1840618_0", "text": "and others at both organizations for their support and valuable input. Special thanks to Grey Advertising's Ben Arno who suggested the term brand resonance. Additional thanks to workshop participants at Duke University and Dartmouth College. MSI was established in 1961 as a not-for profit institute with the goal of bringing together business leaders and academics to create knowledge that will improve business performance. The primary mission was to provide intellectual leadership in marketing and its allied fields. Over the years, MSI's global network of scholars from leading graduate schools of management and thought leaders from sponsoring corporations has expanded to encompass multiple business functions and disciplines. Issues of key importance to business performance are identified by the Board of Trustees, which represents MSI corporations and the academic community. MSI supports studies by academics on these issues and disseminates the results through conferences and workshops, as well as through its publications series. This report, prepared with the support of MSI, is being sent to you for your information and review. It is not to be reproduced or published, in any form or by any means, electronic or mechanical, without written permission from the Institute and the author. Building a strong brand has been shown to provide numerous financial rewards to firms, and has become a top priority for many organizations. In this report, author Keller outlines the Customer-Based Brand Equity (CBBE) model to assist management in their brand-building efforts. According to the model, building a strong brand involves four steps: (1) establishing the proper brand identity, that is, establishing breadth and depth of brand awareness, (2) creating the appropriate brand meaning through strong, favorable, and unique brand associations, (3) eliciting positive, accessible brand responses, and (4) forging brand relationships with customers that are characterized by intense, active loyalty. Achieving these four steps, in turn, involves establishing six brand-building blocks—brand salience, brand performance, brand imagery, brand judgments, brand feelings, and brand resonance. The most valuable brand-building block, brand resonance, occurs when all the other brand-building blocks are established. With true brand resonance, customers express a high degree of loyalty to the brand such that they actively seek means to interact with the brand and share their experiences with others. Firms that are able to achieve brand resonance should reap a host of benefits, for example, greater price premiums and more efficient and effective marketing programs. The CBBE model provides a yardstick by …", "title": "" } ]

[ { "docid": "neg:1840618_0", "text": "An analytical model of an ultrawideband range gating radar is developed. The model is used for the system design of a radar for breath activity monitoring having sub-millimeter movement resolution and fulfilling the requirements of the Federal Communications Commission in terms of effective isotropic radiated power. The system study has allowed to define the requirements of the various radar subsystems that have been designed and realized by means of a low cost hybrid technology. The radar has been assembled and some performance factors, such as range and movement resolution, and the receiver conversion factor have been experimentally evaluated and compared with the model predictions. Finally, the radar has been tested for remote breath activity monitoring, showing recorded respiratory signals in very good agreement with those obtained by means of a conventional technique employing a piezoelectric belt.", "title": "" }, { "docid": "neg:1840618_1", "text": "Software is a complex entity composed in various modules with varied range of defect occurrence possibility. Efficient and timely prediction of defect occurrence in software allows software project managers to effectively utilize people, cost, time for better quality assurance. The presence of defects in a software leads to a poor quality software and also responsible for the failure of a software project. Sometime it is not possible to identify the defects and fixing them at the time of development and it is required to handle such defects any time whenever they are noticed by the team members. So it is important to predict defect-prone software modules prior to deployment of software project in order to plan better maintenance strategy. Early knowledge of defect prone software module can also help to make efficient process improvement plan within justified period of time and cost. This can further lead to better software release as well as high customer satisfaction subsequently. Accurate measurement and prediction of defect is a crucial issue in any software because it is an indirect measurement and is based on several metrics. Therefore, instead of considering all the metrics, it would be more appropriate to find out a suitable set of metrics which are relevant and significant for prediction of defects in any software modules. This paper proposes a feature selection based Linear Twin Support Vector Machine (LSTSVM) model to predict defect prone software modules. F-score, a feature selection technique, is used to determine the significant metrics set which are prominently affecting the defect prediction in a software modules. The efficiency of predictive model could be enhanced with reduced metrics set obtained after feature selection and further used to identify defective modules in a given set of inputs. This paper evaluates the performance of proposed model and compares it against other existing machine learning models. The experiment has been performed on four PROMISE software engineering repository datasets. The experimental results indicate the effectiveness of the proposed feature selection based LSTSVM predictive model on the basis standard performance evaluation parameters.", "title": "" }, { "docid": "neg:1840618_2", "text": "While many recent hand pose estimation methods critically rely on a training set of labelled frames, the creation of such a dataset is a challenging task that has been overlooked so far. As a result, existing datasets are limited to a few sequences and individuals, with limited accuracy, and this prevents these methods from delivering their full potential. We propose a semi-automated method for efficiently and accurately labeling each frame of a hand depth video with the corresponding 3D locations of the joints: The user is asked to provide only an estimate of the 2D reprojections of the visible joints in some reference frames, which are automatically selected to minimize the labeling work by efficiently optimizing a sub-modular loss function. We then exploit spatial, temporal, and appearance constraints to retrieve the full 3D poses of the hand over the complete sequence. We show that this data can be used to train a recent state-of-the-art hand pose estimation method, leading to increased accuracy.", "title": "" }, { "docid": "neg:1840618_3", "text": "Experts in sport benefit from some cognitive mechanisms and strategies which enables them to reduce response times and increase response accuracy.Reaction time is mediated by different factors including type of sport that athlete is participating in and expertise status. The present study aimed to investigate the relationship between CRTs and expertise level in collegiate athletes, as well as evaluating the role of sport and gender differences.44 male and female athletesrecruited from team and individual sports at elite and non-elite levels. The Lafayette multi-choice reaction time was used to collect data.All subjectsperformed a choice reaction time task that required response to visual and auditory stimuli. Results demonstrated a significant overall choice reaction time advantage for maleathletes, as well as faster responses to stimuli in elite participants.Athletes of team sportsdid not showmore accurate performance on the choice reaction time tasks than athletes of individual sports. These findings suggest that there is a relation between choice reaction time and expertise in athletes and this relationship can be mediated by gender differences. Overall, athletes with intrinsic perceptualmotor advantages such as faster reaction times are potentially more equipped for participation in high levels of sport.", "title": "" }, { "docid": "neg:1840618_4", "text": "We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization. Spectral Inference Networks generalize Slow Feature Analysis to generic symmetric operators, and are closely related to Variational Monte Carlo methods from computational physics. As such, they can be a powerful tool for unsupervised representation learning from video or pairs of data. We derive a training algorithm for Spectral Inference Networks that addresses the bias in the gradients due to finite batch size and allows for online learning of multiple eigenfunctions. We show results of training Spectral Inference Networks on problems in quantum mechanics and feature learning for videos on synthetic datasets as well as the Arcade Learning Environment. Our results demonstrate that Spectral Inference Networks accurately recover eigenfunctions of linear operators, can discover interpretable representations from video and find meaningful subgoals in reinforcement learning environments.", "title": "" }, { "docid": "neg:1840618_5", "text": "Network traffic prediction aims at predicting the subsequent network traffic by using the previous network traffic data. This can serve as a proactive approach for network management and planning tasks. The family of recurrent neural network (RNN) approaches is known for time series data modeling which aims to predict the future time series based on the past information with long time lags of unrevealed size. RNN contains different network architectures like simple RNN, long short term memory (LSTM), gated recurrent unit (GRU), identity recurrent unit (IRNN) which is capable to learn the temporal patterns and long range dependencies in large sequences of arbitrary length. To leverage the efficacy of RNN approaches towards traffic matrix estimation in large networks, we use various RNN networks. The performance of various RNN networks is evaluated on the real data from GÉANT backbone networks. To identify the optimal network parameters and network structure of RNN, various experiments are done. All experiments are run up to 200 epochs with learning rate in the range [0.01-0.5]. LSTM has performed well in comparison to the other RNN and classical methods. Moreover, the performance of various RNN methods is comparable to LSTM.", "title": "" }, { "docid": "neg:1840618_6", "text": "Mullerian duct anomalies (MDAs) are rare, affecting approximately 1% of all women and about 3% of women with poor reproductive outcomes. These congenital anomalies usually result from one of the following categories of abnormalities of the mullerian ducts: failure of formation (no development or underdevelopment) or failure of fusion of the mullerian ducts. The American Fertility Society (AFS) classification of uterine anomalies is widely accepted and includes seven distinct categories. MR imaging has consolidated its role as the imaging modality of choice in the evaluation of MDA. MRI is capable of demonstrating the anatomy of the female genital tract remarkably well and is able to provide detailed images of the intra-uterine zonal anatomy, delineate the external fundal contour of the uterus, and comprehensively image the entire female pelvis in multiple imaging planes in a single examination. The purpose of this pictorial essay is to show the value of MRI in the diagnosis of MDA and to review the key imaging features of anomalies of formation and fusion, emphasizing the relevance of accurate diagnosis before therapeutic intervention.", "title": "" }, { "docid": "neg:1840618_7", "text": "This paper presents a method of implementing impedance control (with inertia, damping, and stiffness terms) on a dual-arm system by using the relative Jacobian technique. The proposed method significantly simplifies the control implementation because the dual arm is treated as a single manipulator, whose end-effector motion is defined by the relative motion between the two end effectors. As a result, task description becomes simpler and more intuitive when specifying the desired impedance and the desired trajectories. This is the basis for the relative impedance control. In addition, the use of time-delay estimation enhances ease of implementation of our proposed method to a physical system, which would have been otherwise a very tedious and complicated process.", "title": "" }, { "docid": "neg:1840618_8", "text": "We address the computational problem of novel human pose synthesis. Given an image of a person and a desired pose, we produce a depiction of that person in that pose, retaining the appearance of both the person and background. We present a modular generative neural network that synthesizes unseen poses using training pairs of images and poses taken from human action videos. Our network separates a scene into different body part and background layers, moves body parts to new locations and refines their appearances, and composites the new foreground with a hole-filled background. These subtasks, implemented with separate modules, are trained jointly using only a single target image as a supervised label. We use an adversarial discriminator to force our network to synthesize realistic details conditioned on pose. We demonstrate image synthesis results on three action classes: golf, yoga/workouts and tennis, and show that our method produces accurate results within action classes as well as across action classes. Given a sequence of desired poses, we also produce coherent videos of actions.", "title": "" }, { "docid": "neg:1840618_9", "text": "Affect Control Theory is a mathematical representation of the interactions between two persons, in which it is posited that people behave in a way so as to minimize the amount of deflection between their cultural emotional sentiments and the transient emotional sentiments that are created by each situation. Affect Control Theory presents a maximum likelihood solution in which optimal behaviours or identities can be predicted based on past interactions. Here, we formulate a probabilistic and decision theoretic model of the same underlying principles, and show this to be a generalisation of the basic theory. The model is more expressive than the original theory, as it can maintain multiple hypotheses about behaviours and identities simultaneously as a probability distribution. This allows the model to generate affectively believable interactions with people by learning about their identity and predicting their behaviours. We demonstrate this generalisation with a set of simulations. We then show how our model can be used as an emotional \"plug-in\" for systems that interact with humans. We demonstrate human-interactive capability by building a simple intelligent tutoring application and pilot-testing it in an experiment with 20 participants.", "title": "" }, { "docid": "neg:1840618_10", "text": "More recently, remote sensing image classification has been moving from pixel-level interpretation to scene-level semantic understanding, which aims to label each scene image with a specific semantic class. While significant efforts have been made in developing various methods for remote sensing image scene classification, most of them rely on handcrafted features. In this letter, we propose a novel feature representation method for scene classification, named bag of convolutional features (BoCF). Different from the traditional bag of visual words-based methods in which the visual words are usually obtained by using handcrafted feature descriptors, the proposed BoCF generates visual words from deep convolutional features using off-the-shelf convolutional neural networks. Extensive evaluations on a publicly available remote sensing image scene classification benchmark and comparison with the state-of-the-art methods demonstrate the effectiveness of the proposed BoCF method for remote sensing image scene classification.", "title": "" }, { "docid": "neg:1840618_11", "text": "In-database analytics is of great practical importance as it avoids the costly repeated loop data scientists have to deal with on a daily basis: select features, export the data, convert data format, train models using an external tool, reimport the parameters. It is also a fertile ground of theoretically fundamental and challenging problems at the intersection of relational and statistical data models. This paper introduces a unified framework for training and evaluating a class of statistical learning models inside a relational database. This class includes ridge linear regression, polynomial regression, factorization machines, and principal component analysis. We show that, by synergizing key tools from relational database theory such as schema information, query structure, recent advances in query evaluation algorithms, and from linear algebra such as various tensor and matrix operations, one can formulate in-database learning problems and design efficient algorithms to solve them. The algorithms and models proposed in the paper have already been implemented and deployed in retail-planning and forecasting applications, with significant performance benefits over out-of-database solutions that require the costly data-export loop.", "title": "" }, { "docid": "neg:1840618_12", "text": "At extreme scale, irregularities in the structure of scale-free graphs such as social network graphs limit our ability to analyze these important and growing datasets. A key challenge is the presence of high-degree vertices (hubs), that leads to parallel workload and storage imbalances. The imbalances occur because existing partitioning techniques are not able to effectively partition high-degree vertices.\n We present techniques to distribute storage, computation, and communication of hubs for extreme scale graphs in distributed memory supercomputers. To balance the hub processing workload, we distribute hub data structures and related computation among a set of delegates. The delegates coordinate using highly optimized, yet portable, asynchronous broadcast and reduction operations. We demonstrate scalability of our new algorithmic technique using Breadth-First Search (BFS), Single Source Shortest Path (SSSP), K-Core Decomposition, and PageRank on synthetically generated scale-free graphs. Our results show excellent scalability on large scale-free graphs up to 131K cores of the IBM BG/P, and outperform the best known Graph500 performance on BG/P Intrepid by 15%.", "title": "" }, { "docid": "neg:1840618_13", "text": "INTRODUCTION\nThe majority of osteoporotic, spinal cord compressive, vertebral fractures occurs at the thoracolumbar junction level. When responsible for neurological impairment, these rare lesions require a decompression procedure. We present the results of a new option to treat these lesions: an open balloon kyphoplasty associated with a short-segment posterior internal fixation.\n\n\nMATERIALS AND METHODS\nTwelve patients, aged a mean 72.3 years, were included in this prospective series; all of them presented osteoporotic burst fractures located between T11 and L2 associated with neurological impairment. The surgical procedure first consisted of a laminectomy, for decompression, followed by an open balloon kyphoplasty. A short-segment posterior internal fixation was subsequently put into place when the local kyphosis was considered severe. A CAT scan study evaluated local vertebral body's height restoration using two pre- and postoperative radiological indices.\n\n\nRESULTS\nAll of the patients in the series were followed up for a mean 14 months. Local kyphosis improved a mean 10.8 (p<0.001). Vertebral body height was also substantially restored, with a mean gain of 26% according to the anterior height/adjacent height ratio and 28% according to the Beck Index (p<0.001). Two cases of cement leakage were recorded, with no adverse clinical side effect. Complete neurological recovery was observed in 10 patients; two retained a minimal neurological deficit but kept a walking capacity.\n\n\nDISCUSSION\nThe results presented in this study confirm the data reported in the literature in terms of local kyphosis correction and vertebral body height restoration. The combination of this technique with laminectomy plus osteosynthesis allowed us to effectively treat burst fractures of the thoracolumbar junction and led to stable results 1 year after surgery. This can be advantageous in a population often carrying multiple co-morbidities. With a single operation, we can achieve neurological decompression and spinal column stability in a minimally invasive way; this avoids more substantial surgery in these fragile patients.\n\n\nLEVEL OF EVIDENCE\nLevel IV. Therapeutic prospective study.", "title": "" }, { "docid": "neg:1840618_14", "text": "K-means is a popular algorithm in document clustering, which is fast and efficient. The disadvantages of K-means are that it requires one to set the number of clusters first and select the initial clustering centers randomly. Latent Dirichlet Allocation (LDA) is a mature probabilistic topic model, which aids in document dimensionality reduction, semantic mining and information retrieval. We present a document clustering method based on LDA and K-means (LDA_K-means). In order to improve document clustering effect with K-means, we discover the initial clustering centers by finding the typical latent topics extracted by LDA. The effectiveness of LDA_K-means is evaluated on the 20 Newsgroups data sets. We show that LDA_K-means can significantly improve the clustering effect in contrast to clustering based on random initialization of K-means and LDA (LDA_KMR).", "title": "" }, { "docid": "neg:1840618_15", "text": "The task of event trigger labeling is typically addressed in the standard supervised setting: triggers for each target event type are annotated as training data, based on annotation guidelines. We propose an alternative approach, which takes the example trigger terms mentioned in the guidelines as seeds, and then applies an eventindependent similarity-based classifier for trigger labeling. This way we can skip manual annotation for new event types, while requiring only minimal annotated training data for few example events at system setup. Our method is evaluated on the ACE-2005 dataset, achieving 5.7% F1 improvement over a state-of-the-art supervised system which uses the full training data.", "title": "" }, { "docid": "neg:1840618_16", "text": "With the shortage of the energy and ever increasing of the oil price, research on the renewable and green energy sources, especially the solar arrays and the fuel cells, becomes more and more important. How to achieve high step-up and high efficiency DC/DC converters is the major consideration in the renewable grid-connected power applications due to the low voltage of PV arrays and fuel cells. The topology study with high step-up conversion is concentrated and most topologies recently proposed in these applications are covered and classified. The advantages and disadvantages of these converters are discussed and the major challenges of high step-up converters in renewable energy applications are summarized. This paper would like to make a clear picture on the general law and framework for the next generation non-isolated high step-up DC/DC converters.", "title": "" }, { "docid": "neg:1840618_17", "text": "Questions and their corresponding answers within a community based question answering (CQA) site are frequently presented as top search results forWeb search queries and viewed by millions of searchers daily. The number of answers for CQA questions ranges from a handful to dozens, and a searcher would be typically interested in the different suggestions presented in various answers for a question. Yet, especially when many answers are provided, the viewer may not want to sift through all answers but to read only the top ones. Prior work on answer ranking in CQA considered the qualitative notion of each answer separately, mainly whether it should be marked as best answer. We propose to promote CQA answers not only by their relevance to the question but also by the diversification and novelty qualities they hold compared to other answers. Specifically, we aim at ranking answers by the amount of new aspects they introduce with respect to higher ranked answers (novelty), on top of their relevance estimation. This approach is common in Web search and information retrieval, yet it was not addressed within the CQA settings before, which is quite different from classic document retrieval. We propose a novel answer ranking algorithm that borrows ideas from aspect ranking and multi-document summarization, but adapts them to our scenario. Answers are ranked in a greedy manner, taking into account their relevance to the question as well as their novelty compared to higher ranked answers and their coverage of important aspects. An experiment over a collection of Health questions, using a manually annotated gold-standard dataset, shows that considering novelty for answer ranking improves the quality of the ranked answer list.", "title": "" }, { "docid": "neg:1840618_18", "text": "In the recent past, several sampling-based algorithms have been proposed to compute trajectories that are collision-free and dynamically-feasible. However, the outputs of such algorithms are notoriously jagged. In this paper, by focusing on robots with car-like dynamics, we present a fast and simple heuristic algorithm, named Convex Elastic Smoothing (CES) algorithm, for trajectory smoothing and speed optimization. The CES algorithm is inspired by earlier work on elastic band planning and iteratively performs shape and speed optimization. The key feature of the algorithm is that both optimization problems can be solved via convex programming, making CES particularly fast. A range of numerical experiments show that the CES algorithm returns high-quality solutions in a matter of a few hundreds of milliseconds and hence appears amenable to a real-time implementation.", "title": "" }, { "docid": "neg:1840618_19", "text": "Automatic Dependent Surveillance-Broadcast (ADS-B) is one of the key technologies for future “e-Enabled” aircrafts. ADS-B uses avionics in the e-Enabled aircrafts to broadcast essential flight data such as call sign, altitude, heading, and other extra positioning information. On the one hand, ADS-B brings significant benefits to the aviation industry, but, on the other hand, it could pose security concerns as channels between ground controllers and aircrafts for the ADS-B communication are not secured, and ADS-B messages could be captured by random individuals who own ADS-B receivers. In certain situations, ADS-B messages contain sensitive information, particularly when communications occur among mission-critical civil airplanes. These messages need to be protected from any interruption and eavesdropping. The challenge here is to construct an encryption scheme that is fast enough for very frequent encryption and that is flexible enough for effective key management. In this paper, we propose a Staged Identity-Based Encryption (SIBE) scheme, which modifies Boneh and Franklin's original IBE scheme to address those challenges, that is, to construct an efficient and functional encryption scheme for ADS-B system. Based on the proposed SIBE scheme, we provide a confidentiality framework for future e-Enabled aircraft with ADS-B capability.", "title": "" } ]

[ { "docid": "pos:1840619_0", "text": "We present a systematic comparison of machine learning methods applied to the problem of fully automatic recognition of facial expressions. We report results on a series of experiments comparing recognition engines, including AdaBoost, support vector machines, linear discriminant analysis. We also explored feature selection techniques, including the use of AdaBoost for feature selection prior to classification by SVM or LDA. Best results were obtained by selecting a subset of Gabor filters using AdaBoost followed by classification with support vector machines. The system operates in real-time, and obtained 93% correct generalization to novel subjects for a 7-way forced choice on the Cohn-Kanade expression dataset. The outputs of the classifiers change smoothly as a function of time and thus can be used to measure facial expression dynamics. We applied the system to to fully automated recognition of facial actions (FACS). The present system classifies 17 action units, whether they occur singly or in combination with other actions, with a mean accuracy of 94.8%. We present preliminary results for applying this system to spontaneous facial expressions.", "title": "" } ]

[ { "docid": "neg:1840619_0", "text": "Hypertext documents, such as web pages and academic papers, are of great importance in delivering information in our daily life. Although being effective on plain documents, conventional text embedding methods suffer from information loss if directly adapted to hyper-documents. In this paper, we propose a general embedding approach for hyper-documents, namely, hyperdoc2vec, along with four criteria characterizing necessary information that hyper-document embedding models should preserve. Systematic comparisons are conducted between hyperdoc2vec and several competitors on two tasks, i.e., paper classification and citation recommendation, in the academic paper domain. Analyses and experiments both validate the superiority of hyperdoc2vec to other models w.r.t. the four criteria.", "title": "" }, { "docid": "neg:1840619_1", "text": " In many countries, especially in under developed and developing countries proper health care service is a major concern. The health centers are far and even the medical personnel are deficient when compared to the requirement of the people. For this reason, health services for people who are unhealthy and need health monitoring on regular basis is like impossible. This makes the health monitoring of healthy people left far more behind. In order for citizens not to be deprived of the primary care it is always desirable to implement some system to solve this issue. The application of Internet of Things (IoT) is wide and has been implemented in various areas like security, intelligent transport system, smart cities, smart factories and health. This paper focuses on the application of IoT in health care system and proposes a novel architecture of making use of an IoT concept under fog computing. The proposed architecture can be used to acknowledge the underlying problem of deficient clinic-centric health system and change it to smart patientcentric health system.", "title": "" }, { "docid": "neg:1840619_2", "text": "Monitoring is the act of collecting information concerning the characteristics and status of resources of interest. Monitoring grid resources is a lively research area given the challenges and manifold applications. The aim of this paper is to advance the understanding of grid monitoring by introducing the involved concepts, requirements, phases, and related standardisation activities, including Global Grid Forum’s Grid Monitoring Architecture. Based on a refinement of the latter, the paper proposes a taxonomy of grid monitoring systems, which is employed to classify a wide range of projects and frameworks. The value of the offered taxonomy lies in that it captures a given system’s scope, scalability, generality and flexibility. The paper concludes with, among others, a discussion of the considered systems, as well as directions for future research. © 2004 Elsevier B.V. All rights reserved.", "title": "" }, { "docid": "neg:1840619_3", "text": "Recently the world of the web has become more social and more real-time. Facebook and Twitter are perhaps the exemplars of a new generation of social, real-time web services and we believe these types of service provide a fertile ground for recommender systems research. In this paper we focus on one of the key features of the social web, namely the creation of relationships between users. Like recent research, we view this as an important recommendation problem -- for a given user, UT which other users might be recommended as followers/followees -- but unlike other researchers we attempt to harness the real-time web as the basis for profiling and recommendation. To this end we evaluate a range of different profiling and recommendation strategies, based on a large dataset of Twitter users and their tweets, to demonstrate the potential for effective and efficient followee recommendation.", "title": "" }, { "docid": "neg:1840619_4", "text": "We propose a complete probabilistic discriminative framework for performing sentencelevel discourse analysis. Our framework comprises a discourse segmenter, based on a binary classifier, and a discourse parser, which applies an optimal CKY-like parsing algorithm to probabilities inferred from a Dynamic Conditional Random Field. We show on two corpora that our approach outperforms the state-of-the-art, often by a wide margin.", "title": "" }, { "docid": "neg:1840619_5", "text": "As an ubiquitous method in natural language processing, word embeddings are extensively employed to map semantic properties of words into a dense vector representation. They capture semantic and syntactic relations among words but the vector corresponding to the words are only meaningful relative to each other. Neither the vector nor its dimensions have any absolute, interpretable meaning. We introduce an additive modification to the objective function of the embedding learning algorithm that encourages the embedding vectors of words that are semantically related a predefined concept to take larger values along a specified dimension, while leaving the original semantic learning mechanism mostly unaffected. In other words, we align words that are already determined to be related, along predefined concepts. Therefore, we impart interpretability to the word embedding by assigning meaning to its vector dimensions. The predefined concepts are derived from an external lexical resource, which in this paper is chosen as Roget’s Thesaurus. We observe that alignment along the chosen concepts is not limited to words in the Thesaurus and extends to other related words as well. We quantify the extent of interpretability and assignment of meaning from our experimental results. We also demonstrate the preservation of semantic coherence of the resulting vector space by using word-analogy and word-similarity tests. These tests show that the interpretability-imparted word embeddings that are obtained by the proposed framework do not sacrifice performances in common benchmark tests.", "title": "" }, { "docid": "neg:1840619_6", "text": "Machine learning requires access to all the data used for training. Recently, Google Research proposed Federated Learning as an alternative, where the training data is distributed over a federation of clients that each only access their own training data; the partially trained model is updated in a distributed fashion to maintain a situation where the data from all participating clients remains unknown. In this research we construct different distributions of the DMOZ dataset over the clients in the network and compare the resulting performance of Federated Averaging when learning a classifier. We find that the difference in spread of topics for each client has a strong correlation with the performance of the Federated Averaging algorithm.", "title": "" }, { "docid": "neg:1840619_7", "text": "In recent years, spatial applications have become more and more important in both scientific research and industry. Spatial query processing is the fundamental functioning component to support spatial applications. However, the state-of-the-art techniques of spatial query processing are facing significant challenges as the data expand and user accesses increase. In this paper we propose and implement a novel scheme (named VegaGiStore) to provide efficient spatial query processing over big spatial data and numerous concurrent user queries. Firstly, a geography-aware approach is proposed to organize spatial data in terms of geographic proximity, and this approach can achieve high aggregate I/O throughput. Secondly, in order to improve data retrieval efficiency, we design a two-tier distributed spatial index for efficient pruning of the search space. Thirdly, we propose an \"indexing + MapReduce'' data processing architecture to improve the computation capability of spatial query. Performance evaluations of the real-deployed VegaGiStore system confirm its effectiveness.", "title": "" }, { "docid": "neg:1840619_8", "text": "1 Ekaterina Prasolova-Forland, IDI, NTNU, Sem Salandsv 7-9, N-7491 Trondheim, Norway ekaterip@idi.ntnu.no Abstract  This paper discusses awareness support in educational context, focusing on the support offered by collaborative virtual environments. Awareness plays an important role in everyday educational activities, especially in engineering courses where projects and group work is an integral part of the curriculum. In this paper we will provide a general overview of awareness in computer supported cooperative work and then focus on the awareness mechanisms offered by CVEs. We will also discuss the role and importance of these mechanisms in educational context and make some comparisons between awareness support in CVEs and in more traditional tools.", "title": "" }, { "docid": "neg:1840619_9", "text": "An audience effect arises when a person's behaviour changes because they believe someone else is watching them. Though these effects have been known about for over 110 years, the cognitive mechanisms of the audience effect and how it might vary across different populations and cultures remains unclear. In this review, we examine the hypothesis that the audience effect draws on implicit mentalising abilities. Behavioural and neuroimaging data from a number of tasks are consistent with this hypothesis. We further review data suggest that how people respond to audiences may vary over development, personality factors, cultural background and clinical diagnosis including autism and anxiety disorder. Overall, understanding and exploring the audience effect may contribute to our models of social interaction, including reputation management and mentalising.", "title": "" }, { "docid": "neg:1840619_10", "text": "Darknet markets are online services behind Tor where cybercriminals trade illegal goods and stolen datasets. In recent years, security analysts and law enforcement start to investigate the darknet markets to study the cybercriminal networks and predict future incidents. However, vendors in these markets often create multiple accounts (\\em i.e., Sybils), making it challenging to infer the relationships between cybercriminals and identify coordinated crimes. In this paper, we present a novel approach to link the multiple accounts of the same darknet vendors through photo analytics. The core idea is that darknet vendors often have to take their own product photos to prove the possession of the illegal goods, which can reveal their distinct photography styles. To fingerprint vendors, we construct a series deep neural networks to model the photography styles. We apply transfer learning to the model training, which allows us to accurately fingerprint vendors with a limited number of photos. We evaluate the system using real-world datasets from 3 large darknet markets (7,641 vendors and 197,682 product photos). A ground-truth evaluation shows that the system achieves an accuracy of 97.5%, outperforming existing stylometry-based methods in both accuracy and coverage. In addition, our system identifies previously unknown Sybil accounts within the same markets (23) and across different markets (715 pairs). Further case studies reveal new insights into the coordinated Sybil activities such as price manipulation, buyer scam, and product stocking and reselling.", "title": "" }, { "docid": "neg:1840619_11", "text": "We argue in this article that many common adverbial phrases generally taken to signal a discourse relation between syntactically connected units within discourse structure instead work anaphorically to contribute relational meaning, with only indirect dependence on discourse structure. This allows a simpler discourse structure to provide scaffolding for compositional semantics and reveals multiple ways in which the relational meaning conveyed by adverbial connectives can interact with that associated with discourse structure. We conclude by sketching out a lexicalized grammar for discourse that facilitates discourse interpretation as a product of compositional rules, anaphor resolution, and inference.", "title": "" }, { "docid": "neg:1840619_12", "text": "The recent advances in deep neural networks have convincingly demonstrated high capability in learning vision models on large datasets. Nevertheless, collecting expert labeled datasets especially with pixel-level annotations is an extremely expensive process. An appealing alternative is to render synthetic data (e.g., computer games) and generate ground truth automatically. However, simply applying the models learnt on synthetic images may lead to high generalization error on real images due to domain shift. In this paper, we facilitate this issue from the perspectives of both visual appearance-level and representation-level domain adaptation. The former adapts source-domain images to appear as if drawn from the \"style\" in the target domain and the latter attempts to learn domain-invariant representations. Specifically, we present Fully Convolutional Adaptation Networks (FCAN), a novel deep architecture for semantic segmentation which combines Appearance Adaptation Networks (AAN) and Representation Adaptation Networks (RAN). AAN learns a transformation from one domain to the other in the pixel space and RAN is optimized in an adversarial learning manner to maximally fool the domain discriminator with the learnt source and target representations. Extensive experiments are conducted on the transfer from GTA5 (game videos) to Cityscapes (urban street scenes) on semantic segmentation and our proposal achieves superior results when comparing to state-of-the-art unsupervised adaptation techniques. More remarkably, we obtain a new record: mIoU of 47.5% on BDDS (drive-cam videos) in an unsupervised setting.", "title": "" }, { "docid": "neg:1840619_13", "text": "In recent times, Bitcoin has gained special attention both from industry and academia. The underlying technology that enables Bitcoin (or more generally crypto-currency) is called blockchain. At the core of the blockchain technology is a data structure that keeps record of the transactions in the network. The special feature that distinguishes it from existing technology is its immutability of the stored records. To achieve immutability, it uses consensus and cryptographic mechanisms. As the data is stored in distributed nodes this technology is also termed as \"Distributed Ledger Technology (DLT)\". As many researchers and practitioners are joining the hype of blockchain, some of them are raising the question about the fundamental difference between blockchain and traditional database and its real value or potential. In this paper, we present a critical analysis of both technologies based on a survey of the research literature where blockchain solutions are applied to various scenarios. Based on this analysis, we further develop a decision tree diagram that will help both practitioners and researchers to choose the appropriate technology for their use cases. Using our proposed decision tree we evaluate a sample of the existing works to see to what extent the blockchain solutions have been used appropriately in the relevant problem domains.", "title": "" }, { "docid": "neg:1840619_14", "text": "OBJECTIVE\nThe relative short-term efficacy and long-term benefits of pharmacologic versus psychotherapeutic interventions have not been studied for posttraumatic stress disorder (PTSD). This study compared the efficacy of a selective serotonin reup-take inhibitor (SSRI), fluoxetine, with a psychotherapeutic treatment, eye movement desensitization and reprocessing (EMDR), and pill placebo and measured maintenance of treatment gains at 6-month follow-up.\n\n\nMETHOD\nEighty-eight PTSD subjects diagnosed according to DSM-IV criteria were randomly assigned to EMDR, fluoxetine, or pill placebo. They received 8 weeks of treatment and were assessed by blind raters posttreatment and at 6-month follow-up. The primary outcome measure was the Clinician-Administered PTSD Scale, DSM-IV version, and the secondary outcome measure was the Beck Depression Inventory-II. The study ran from July 2000 through July 2003.\n\n\nRESULTS\nThe psychotherapy intervention was more successful than pharmacotherapy in achieving sustained reductions in PTSD and depression symptoms, but this benefit accrued primarily for adult-onset trauma survivors. At 6-month follow-up, 75.0% of adult-onset versus 33.3% of child-onset trauma subjects receiving EMDR achieved asymptomatic end-state functioning compared with none in the fluoxetine group. For most childhood-onset trauma patients, neither treatment produced complete symptom remission.\n\n\nCONCLUSIONS\nThis study supports the efficacy of brief EMDR treatment to produce substantial and sustained reduction of PTSD and depression in most victims of adult-onset trauma. It suggests a role for SSRIs as a reliable first-line intervention to achieve moderate symptom relief for adult victims of childhood-onset trauma. Future research should assess the impact of lengthier intervention, combination treatments, and treatment sequencing on the resolution of PTSD in adults with childhood-onset trauma.", "title": "" }, { "docid": "neg:1840619_15", "text": "In this paper, a methodology for the automated detection and classification of transient events in electroencephalographic (EEG) recordings is presented. It is based on association rule mining and classifies transient events into four categories: epileptic spikes, muscle activity, eye blinking activity, and sharp alpha activity. The methodology involves four stages: 1) transient event detection; 2) clustering of transient events and feature extraction; 3) feature discretization and feature subset selection; and 4) association rule mining and classification of transient events. The methodology is evaluated using 25 EEG recordings, and the best obtained accuracy was 87.38%. The proposed approach combines high accuracy with the ability to provide interpretation for the decisions made, since it is based on a set of association rules", "title": "" }, { "docid": "neg:1840619_16", "text": "The first micrometer-sized graphene flakes extracted from graphite demonstrated outstanding electrical, mechanical and chemical properties, but they were too small for practical applications. However, the recent advances in graphene synthesis and transfer techniques have enabled various macroscopic applications such as transparent electrodes for touch screens and light-emitting diodes (LEDs) and thin-film transistors for flexible electronics in particular. With such exciting potential, a great deal of effort has been put towards producing larger size graphene in the hopes of industrializing graphene production. Little less than a decade after the first discovery, graphene now can be synthesized up to 30 inches in its diagonal size using chemical vapour deposition methods. In making this possible, it was not only the advances in the synthesis techniques but also the transfer methods that deliver graphene onto target substrates without significant mechanical damage. In this article, the recent advancements in transferring graphene to arbitrary substrates will be extensively reviewed. The methods are categorized into mechanical exfoliation, polymer-assisted transfer, continuous transfer by roll-to-roll process, and transfer-free techniques including direct synthesis on insulating substrates.", "title": "" }, { "docid": "neg:1840619_17", "text": "Since debt is typically riskier in recessions, transfers from equity holders to debt holders associated with each investment also tend to concentrate in recessions. Such systematic risk exposure of debt overhang has important implications for the investment and financing decisions of firms and on the ex ante costs of debt overhang. Using a calibrated dynamic capital structure/real option model, we show that the costs of debt overhang become significantly higher in the presence of macroeconomic risk. We also provide several new predictions that relate the cyclicality of a firm’s assets in place and growth options to its investment and capital structure decisions. We are grateful to Santiago Bazdresch, Bob Goldstein, David Mauer (WFA discussant), Erwan Morellec, Stew Myers, Chris Parsons, Michael Roberts, Antoinette Schoar, Neng Wang, Ivo Welch, and seminar participants at MIT, Federal Reserve Bank of Boston, Boston University, Dartmouth, University of Lausanne, University of Minnesota, the Third Risk Management Conference at Mont Tremblant, the Minnesota Corporate Finance Conference, and the WFA for their comments. MIT Sloan School of Management and NBER. Email: huichen@mit.edu. Tel. 617-324-3896. MIT Sloan School of Management. Email: manso@mit.edu. Tel. 617-253-7218.", "title": "" }, { "docid": "neg:1840619_18", "text": "One of the major challenges with electric shipboard power systems (SPS) is preserving the survivability of the system under fault situations. Some minor faults in SPS can result in catastrophic consequences. Therefore, it is essential to investigate available fault management techniques for SPS applications that can enhance SPS robustness and reliability. Many recent studies in this area take different approaches to address fault tolerance in SPSs. This paper provides an overview of the concepts and methodologies that are utilized to deal with faults in the electric SPS. First, a taxonomy of the types of faults and their sources in SPS is presented; then, the methods that are used to detect, identify, isolate, and manage faults are reviewed. Furthermore, common techniques for designing a fault management system in SPS are analyzed and compared. This paper also highlights several possible future research directions.", "title": "" }, { "docid": "neg:1840619_19", "text": "Deep neural networks (DNNs) have become the dominant technique for acoustic-phonetic modeling due to their markedly improved performance over other models. Despite this, little is understood about the computation they implement in creating phonemic categories from highly variable acoustic signals. In this paper, we analyzed a DNN trained for phoneme recognition and characterized its representational properties, both at the single node and population level in each layer. At the single node level, we found strong selectivity to distinct phonetic features in all layers. Node selectivity to specific manners and places of articulation appeared from the first hidden layer and became more explicit in deeper layers. Furthermore, we found that nodes with similar phonetic feature selectivity were differentially activated to different exemplars of these features. Thus, each node becomes tuned to a particular acoustic manifestation of the same feature, providing an effective representational basis for the formation of invariant phonemic categories. This study reveals that phonetic features organize the activations in different layers of a DNN, a result that mirrors the recent findings of feature encoding in the human auditory system. These insights may provide better understanding of the limitations of current models, leading to new strategies to improve their performance.", "title": "" } ]

[ { "docid": "pos:1840620_0", "text": "We survey recent developments in the design of large-capacity content-addressable memory (CAM). A CAM is a memory that implements the lookup-table function in a single clock cycle using dedicated comparison circuitry. CAMs are especially popular in network routers for packet forwarding and packet classification, but they are also beneficial in a variety of other applications that require high-speed table lookup. The main CAM-design challenge is to reduce power consumption associated with the large amount of parallel active circuitry, without sacrificing speed or memory density. In this paper, we review CAM-design techniques at the circuit level and at the architectural level. At the circuit level, we review low-power matchline sensing techniques and searchline driving approaches. At the architectural level we review three methods for reducing power consumption.", "title": "" } ]

[ { "docid": "neg:1840620_0", "text": "In this paper, we take the view that any formalization of commitments has to come together with a formalization of time, events/actions and change. We enrich a suitable formalism for reasoning about time, event/action and change in order to represent and reason about commitments. We employ a three-valued based temporal first-order non-monotonic logic (TFONL) that allows an explicit representation of time and events/action. TFONL subsumes the action languages presented in the literature and takes into consideration the frame, qualification and ramification problems, and incorporates to a domain description the set of rules governing change. It can handle protocols for the different types of dialogues such as information seeking, inquiry and negotiation. We incorporate commitments into TFONL to obtain Com-TFONL. Com-TFONL allows an agent to reason about its commitments and about other agents’ behaviour during a dialogue. Thus, agents can employ social commitments to act on, argue with and reason about during interactions with other agents. Agents may use their reasoning and argumentative capabilities in order to determine the appropriate communicative acts during conversations. Furthermore, Com-TFONL allows for an integration of commitments and arguments which helps in capturing the public aspects of a conversation and the reasoning aspects required in coherent conversations.", "title": "" }, { "docid": "neg:1840620_1", "text": "A 1.8-kV 100-ps rise-time pulsed-power generator operating at a repetition frequency of 50 kHz is presented. The generator consists of three compression stages. In the first stage, a power MOSFET produces high voltage by breaking an inductor current. In the second stage, a 3-kV drift-step-recovery diode cuts the reverse current rapidly to create a 1-ns rise-time pulse. In the last stage, a silicon-avalanche shaper is used as a fast 100-ps closing switch. Experimental investigation showed that, by optimizing the generator operating point, the shot-to-shot jitter can be reduced to less than 13 ps. The theoretical model of the pulse-forming circuit is presented.", "title": "" }, { "docid": "neg:1840620_2", "text": "BACKGROUND\nA modified version of the Berg Balance Scale (mBBS) was developed for individuals with intellectual and visual disabilities (IVD). However, the concurrent and predictive validity has not yet been determined.\n\n\nAIM\nThe purpose of the current study was to evaluate the concurrent and predictive validity of the mBBS for individuals with IVD.\n\n\nMETHOD\nFifty-four individuals with IVD and Gross Motor Functioning Classification System (GMFCS) Levels I and II participated in this study. The mBBS, the Centre of Gravity (COG), the Comfortable Walking Speed (CWS), and the Barthel Index (BI) were assessed during one session in order to determine the concurrent validity. The percentage of explained variance was determined by analyzing the squared multiple correlation between the mBBS and the BI, COG, CWS, GMFCS, and age, gender, level of intellectual disability, presence of epilepsy, level of visual impairment, and presence of hearing impairment. Furthermore, an overview of the degree of dependence between the mBBS, BI, CWS, and COG was obtained by graphic modelling. Predictive validity of mBBS was determined with respect to the number of falling incidents during 26 weeks and evaluated with Zero-inflated regression models using the explanatory variables of mBBS, BI, COG, CWS, and GMFCS.\n\n\nRESULTS\nThe results demonstrated that two significant explanatory variables, the GMFCS Level and the BI, and one non-significant variable, the CWS, explained approximately 60% of the mBBS variance. Graphical modelling revealed that BI was the most important explanatory variable for mBBS moreso than COG and CWS. Zero-inflated regression on the frequency of falling incidents demonstrated that the mBBS was not predictive, however, COG and CWS were.\n\n\nCONCLUSIONS\nThe results indicated that the concurrent validity as well as the predictive validity of mBBS were low for persons with IVD.", "title": "" }, { "docid": "neg:1840620_3", "text": "In this paper, an advanced design of an Autonomous Underwater Vehicle (AUV) is presented. The design is driven only by four water pumps. The different power combinations of the four motors provides the force and moment for propulsion and maneuvering. No control surfaces are needed in this design, which make the manufacturing cost of such a vehicle minimal and more reliable. Based on the propulsion method of the vehicle, a nonlinear AUV dynamic model is studied. This nonlinear model is linearized at the operation point. A control strategy of the AUV is proposed including attitude control and auto-pilot design. Simulation results for the attitude control loop are presented to validate this approach.", "title": "" }, { "docid": "neg:1840620_4", "text": "A compact multiple-input-multiple-output (MIMO) antenna is presented for ultrawideband (UWB) applications. The antenna consists of two open L-shaped slot (LS) antenna elements and a narrow slot on the ground plane. The antenna elements are placed perpendicularly to each other to obtain high isolation, and the narrow slot is added to reduce the mutual coupling of antenna elements in the low frequency band (3-4.5 GHz). The proposed MIMO antenna has a compact size of 32 ×32 mm2, and the antenna prototype is fabricated and measured. The measured results show that the proposed antenna design achieves an impedance bandwidth of larger than 3.1-10.6 GHz, low mutual coupling of less than 15 dB, and a low envelope correlation coefficient of better than 0.02 across the frequency band, which are suitable for portable UWB applications.", "title": "" }, { "docid": "neg:1840620_5", "text": "The lightning impulse withstand voltage for an oil-immersed power transformer is determined by the value of the lightning surge overvoltage generated at the transformer terminal. This overvoltage value has been conventionally obtained through lightning surge analysis using the electromagnetic transients program (EMTP), where the transformer is often simulated by a single lumped capacitance. However, since high frequency surge overvoltages ranging from several kHz to several MHz are generated in an actual system, a transformer circuit model capable of simulating the range up to this high frequency must be developed for further accurate analysis. In this paper, a high frequency circuit model for an oil-immersed transformer was developed and its validity was verified through comparison with the measurement results on the model winding actually produced. Consequently, it emerged that a high frequency model with three serially connected LC parallel circuits could adequately simulate the impedance characteristics of the winding up to a high frequency range of several MHz. Following lightning surge analysis for a 500 kV substation using this high frequency model, the peak value of the waveform was evaluated as lower than that simulated by conventional lumped capacitance even though the front rising was steeper. This phenomenon can be explained by the charging process of the capacitance circuit inside the transformer. Furthermore, the waveform analyzed by each model was converted into an equivalent standard lightning impulse waveform and the respective peak values were compared. As a result, the peak value obtained by the lumped capacitance simulation was evaluated as relatively higher under the present analysis conditions.", "title": "" }, { "docid": "neg:1840620_6", "text": "8 Research on speech and emotion is moving from a period of exploratory research into one where there is a prospect 9 of substantial applications, notably in human–computer interaction. Progress in the area relies heavily on the devel10 opment of appropriate databases. This paper addresses four main issues that need to be considered in developing 11 databases of emotional speech: scope, naturalness, context and descriptors. The state of the art is reviewed. A good deal 12 has been done to address the key issues, but there is still a long way to go. The paper shows how the challenge of 13 developing appropriate databases is being addressed in three major recent projects––the Reading–Leeds project, the 14 Belfast project and the CREST–ESP project. From these and other studies the paper draws together the tools and 15 methods that have been developed, addresses the problems that arise and indicates the future directions for the de16 velopment of emotional speech databases. 2002 Published by Elsevier Science B.V.", "title": "" }, { "docid": "neg:1840620_7", "text": "Priapism of the clitoris is a rare entity. A case of painful priapism is reported in a patient who had previously suffered a radical cystectomy for bladder carcinoma pT3-GIII, followed by local recurrence in the pelvis. From a symptomatic point of view she showed a good response to conservative treatment (analgesics and anxiolytics), as she refused surgical treatment. She survived 6 months from the recurrence, and died with lung metastases. The priapism did not recur. The physiopathological mechanisms involved in the process are discussed and the literature reviewed.", "title": "" }, { "docid": "neg:1840620_8", "text": "The perspective directions in evaluating network security are simulating possible malefactor’s actions, building the representation of these actions as attack graphs (trees, nets), the subsequent checking of various properties of these graphs, and determining security metrics which can explain possible ways to increase security level. The paper suggests a new approach to security evaluation based on comprehensive simulation of malefactor’s actions, construction of attack graphs and computation of different security metrics. The approach is intended for using both at design and exploitation stages of computer networks. The implemented software system is described, and the examples of experiments for analysis of network security level are considered.", "title": "" }, { "docid": "neg:1840620_9", "text": "This paper presents a physical description of two specific aspects in drain-extended MOS transistors, i.e., quasi-saturation and impact-ionization effects. The 2-D device simulator Medici provides the physical insights, and both the unique features are originally attributed to the Kirk effect. The transistor dc model is derived from regional analysis of carrier transport in the intrinsic MOS and the drift region. The substrate-current equations, considering extra impact-ionization factors in the drift region, are also rigorously derived. The proposed model is primarily validated by MATLAB program and exhibits excellent scalability for various transistor dimensions, drift-region doping concentration, and voltage-handling capability.", "title": "" }, { "docid": "neg:1840620_10", "text": "The Internet of Things (IoT) is converting the agriculture industry and solving the immense problems or the major challenges faced by the farmers todays in the field. India is one of the 13th countries in the world having scarcity of water resources. Due to ever increasing of world population, we are facing difficulties in the shortage of water resources, limited availability of land, difficult to manage the costs while meeting the demands of increasing consumption needs of a global population that is expected to grow by 70% by the year 2050. The influence of population growth on agriculture leads to a miserable impact on the farmers livelihood. To overcome the problems we design a low cost system for monitoring the agriculture farm which continuously measure the level of soil moisture of the plants and alert the farmers if the moisture content of particular plants is low via sms or an email. This system uses an esp8266 microcontroller and a moisture sensor using Losant platform. Losant is a simple and most powerful IoT cloud platform for the development of coming generation. It offers the real time data visualization of sensors data which can be operate from any part of the world irrespective of the position of field.", "title": "" }, { "docid": "neg:1840620_11", "text": "Chronic inflammation plays a multifaceted role in carcinogenesis. Mounting evidence from preclinical and clinical studies suggests that persistent inflammation functions as a driving force in the journey to cancer. The possible mechanisms by which inflammation can contribute to carcinogenesis include induction of genomic instability, alterations in epigenetic events and subsequent inappropriate gene expression, enhanced proliferation of initiated cells, resistance to apoptosis, aggressive tumor neovascularization, invasion through tumor-associated basement membrane and metastasis, etc. Inflammation-induced reactive oxygen and nitrogen species cause damage to important cellular components (e.g., DNA, proteins and lipids), which can directly or indirectly contribute to malignant cell transformation. Overexpression, elevated secretion, or abnormal activation of proinflammatory mediators, such as cytokines, chemokines, cyclooxygenase-2, prostaglandins, inducible nitric oxide synthase, and nitric oxide, and a distinct network of intracellular signaling molecules including upstream kinases and transcription factors facilitate tumor promotion and progression. While inflammation promotes development of cancer, components of the tumor microenvironment, such as tumor cells, stromal cells in surrounding tissue and infiltrated inflammatory/immune cells generate an intratumoral inflammatory state by aberrant expression or activation of some proinflammatory molecules. Many of proinflammatory mediators, especially cytokines, chemokines and prostaglandins, turn on the angiogenic switches mainly controlled by vascular endothelial growth factor, thereby inducing inflammatory angiogenesis and tumor cell-stroma communication. This will end up with tumor angiogenesis, metastasis and invasion. Moreover, cellular microRNAs are emerging as a potential link between inflammation and cancer. The present article highlights the role of various proinflammatory mediators in carcinogenesis and their promise as potential targets for chemoprevention of inflammation-associated carcinogenesis.", "title": "" }, { "docid": "neg:1840620_12", "text": "It is traditional wisdom that one should start from the goals when generating a plan in order to focus the plan generation process on potentially relevant actions. The graphplan system, however, which is the most eecient planning system nowadays, builds a \\planning graph\" in a forward-chaining manner. Although this strategy seems to work well, it may possibly lead to problems if the planning task description contains irrelevant information. Although some irrelevant information can be ltered out by graphplan, most cases of irrelevance are not noticed. In this paper, we analyze the eeects arising from \\irrelevant\" information to planning task descriptions for diierent types of planners. Based on that, we propose a family of heuristics that select relevant information by minimizing the number of initial facts that are used when approximating a plan by backchaining from the goals ignoring any connicts. These heuristics, although not solution-preserving, turn out to be very useful for guiding the planning process, as shown by applying the heuristics to a large number of examples from the literature.", "title": "" }, { "docid": "neg:1840620_13", "text": "Situated in the western Sierra Nevada foothills of California, CA-MRP-402 exhibits 103 rock art panels. By combining archaeological field research and excavation, this paper explores the ancient activities that took place at MRP-402. These efforts reveal that ancient Native Americans intentionally altered the landscape to create an astronomical observation area and generate consistent equinoctial solar and shadow alignments.", "title": "" }, { "docid": "neg:1840620_14", "text": "As we observe a trend towards the recentralisation of the Internet, this paper raises the question of guaranteeing an everlasting decentralisation. We introduce the properties of strong and soft uncentralisability in order to describe systems in which all authorities can be untrusted at any time without affecting the system. We link the soft uncentralisability to another property called perfect forkability. Using that knowledge, we introduce a new cryptographic primitive called uncentralisable ledger and study its properties. We use those properties to analyse what an uncentralisable ledger may offer to classic electronic voting systems and how it opens up the realm of possibilities for completely new voting mechanisms. We review a list of selected projects that implement voting systems using blockchain technology. We then conclude that the true revolutionary feature enabled by uncentralisable ledgers is a self-sovereign and distributed identity provider.", "title": "" }, { "docid": "neg:1840620_15", "text": "Penumbras, or soft shadows, are an important means to enhance the realistic ap pearance of computer generated images. We present a fast method based on Minkowski operators to reduce t he run ime for penumbra calculation with stochastic ray tracing. Detailed run time analysis on some examples shows that the new method is significantly faster than the conventional approach. Moreover, it adapts to the environment so that small penumbras are calculated faster than larger ones. The algorithm needs at most twice as much memory as the underlying ray tracing algorithm.", "title": "" }, { "docid": "neg:1840620_16", "text": "OBJECTIVE\nTo assess the bioequivalence of an ezetimibe/simvastatin (EZE/SIMVA) combination tablet compared to the coadministration of ezetimibe and simvastatin as separate tablets (EZE + SIMVA).\n\n\nMETHODS\nIn this open-label, randomized, 2-part, 2-period crossover study, 96 healthy subjects were randomly assigned to participate in each part of the study (Part I or II), with each part consisting of 2 single-dose treatment periods separated by a 14-day washout. Part I consisted of Treatments A (EZE 10 mg + SIMVA 10 mg) and B (EZE/SIMVA 10/10 mg/mg) and Part II consisted of Treatments C (EZE 10 mg + SIMVA 80 mg) and D (EZE/SIMVA 10/80 mg/mg). Blood samples were collected up to 96 hours post-dose for determination of ezetimibe, total ezetimibe (ezetimibe + ezetimibe glucuronide), simvastatin and simvastatin acid (the most prevalent active metabolite of simvastatin) concentrations. Ezetimibe and simvastatin acid AUC(0-last) were predefined as primary endpoints and ezetimibe and simvastatin acid Cmax were secondary endpoints. Bioequivalence was achieved if 90% confidence intervals (CI) for the geometric mean ratios (GMR) (single tablet/coadministration) of AUC(0-last) and Cmax fell within prespecified bounds of (0.80, 1.25).\n\n\nRESULTS\nThe GMRs of the AUC(0-last) and Cmax for ezetimibe and simvastatin acid fell within the bioequivalence limits (0.80, 1.25). EZE/ SIMVA and EZE + SIMVA were generally well tolerated.\n\n\nCONCLUSIONS\nThe lowest and highest dosage strengths of EZE/SIMVA tablet were bioequivalent to the individual drug components administered together. Given the exact weight multiples of the EZE/SIMVA tablet and linear pharmacokinetics of simvastatin across the marketed dose range, bioequivalence of the intermediate tablet strengths (EZE/SIMVA 10/20 mg/mg and EZE/SIMVA 10/40 mg/mg) was inferred, although these dosages were not tested directly. These results indicate that the safety and efficacy profile of EZE + SIMVA coadministration therapy can be applied to treatment with the EZE/SIMVA tablet across the clinical dose range.", "title": "" }, { "docid": "neg:1840620_17", "text": "3.1. URINARY TRACT INFECTION Urinary tract infection is one of the important causes of morbidity and mortality in Indian population, affecting all age groups across the life span. Anatomically, urinary tract is divided into an upper portion composed of kidneys, renal pelvis, and ureters and a lower portion made up of urinary bladder and urethra. UTI is an inflammatory response of the urothelium to bacterial invasion that is usually associated with bacteriuria and pyuria. UTI may involve only the lower urinary tract or both the upper and lower tract [19].", "title": "" }, { "docid": "neg:1840620_18", "text": "OBJECTIVES\nFew low income countries have emergency medical services to provide prehospital medical care and transport to road traffic crash casualties. In Ghana most roadway casualties receive care and transport to the hospital from taxi, bus, or truck drivers. This study reports the methods used to devise a model for prehospital trauma training for commercial drivers in Ghana.\n\n\nMETHODS\nOver 300 commercial drivers attended a first aid and rescue course designed specifically for roadway trauma and geared to a low education level. The training programme has been evaluated twice at one and two year intervals by interviewing both trained and untrained drivers with regard to their experiences with injured persons. In conjunction with a review of prehospital care literature, lessons learnt from the evaluations were used in the revision of the training model.\n\n\nRESULTS\nControl of external haemorrhage was quickly learnt and used appropriately by the drivers. Areas identified needing emphasis in future trainings included consistent use of universal precautions and protection of airways in unconscious persons using the recovery position.\n\n\nCONCLUSION\nIn low income countries, prehospital trauma care for roadway casualties can be improved by training laypersons already involved in prehospital transport and care. Training should be locally devised, evidence based, educationally appropriate, and focus on practical demonstrations.", "title": "" }, { "docid": "neg:1840620_19", "text": "Silver-Russell syndrome (SRS) and Beckwith-Wiedemann syndrome (BWS) are 2 clinically opposite growth-affecting disorders belonging to the group of congenital imprinting disorders. The expression of both syndromes usually depends on the parental origin of the chromosome in which the imprinted genes reside. SRS is characterized by severe intrauterine and postnatal growth retardation with various additional clinical features such as hemihypertrophy, relative macrocephaly, fifth finger clinodactyly, and triangular facies. BWS is an overgrowth syndrome with many additional clinical features such as macroglossia, organomegaly, and an increased risk of childhood tumors. Both SRS and BWS are clinically and genetically heterogeneous, and for clinical diagnosis, different diagnostic scoring systems have been developed. Six diagnostic scoring systems for SRS and 4 for BWS have been previously published. However, neither syndrome has common consensus diagnostic criteria yet. Most cases of SRS and BWS are associated with opposite epigenetic or genetic abnormalities in the 11p15 chromosomal region leading to opposite imbalances in the expression of imprinted genes. SRS is also caused by maternal uniparental disomy 7, which is usually identified in 5-10% of the cases, and is therefore the first imprinting disorder that affects 2 different chromosomes. In this review, we describe in detail the clinical diagnostic criteria and scoring systems as well as molecular causes in both SRS and BWS.", "title": "" } ]

[ { "docid": "pos:1840621_0", "text": "Clustering is central to many image processing and remote sensing applications. ISODATA is one of the most popular and widely used clustering methods in geoscience applications, but it can run slowly, particularly with large data sets. We present a more efficient approach to ISODATA clustering, which achieves better running times by storing the points in a kd-tree and through a modification of the way in which the algorithm estimates the dispersion of each cluster. We also present an approximate version of the algorithm which allows the user to further improve the running time, at the expense of lower fidelity in computing the nearest cluster center to each point. We provide both theoretical and empirical justification that our modified approach produces clusterings that are very similar to those produced by the standard ISODATA approach. We also provide empirical studies on both synthetic data and remotely sensed Landsat and MODIS images that show that our approach has significantly lower running times. *A preliminary version of this paper appeared in Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS'O3), Toulouse, France, 2003, Vol. 111, 2057-2059. +NASA Goddard Space Flight Center, Architecture and Automation Branch, Greenbelt, MD 20771 and Department of Computer Science, University of Maryland, College Park, Maryland, 20742. Email: nargess@cs.umd.edu. $ ~ e ~ a r t m e n t of Computer Science, University df Maryland, College Park, Maryland, 20742. The work of this author was supported by the Science Foundation under grant CCR-0098151. Email: mount@cs.umd.edu. l ~ e ~ a r t m e n t of Computer Science, Bar-Ilan University, Ramat-Gan 52900, Israel, and Center for Automation Research, University of Maryland, College Park, Maryland, 20742. Email: nathanOcs.biu.ac.il. NASA Goddard Space Flight Center, previously Applied Information Sciences Branch, currently Advanced Architectures and Automation Branch, Greenbelt, MD 20771. Email: Jacqueline.LeMoigne@nasa.gov. https://ntrs.nasa.gov/search.jsp?R=20070038185 2017-12-21T21:41:30+00:00Z", "title": "" }, { "docid": "pos:1840621_1", "text": "This paper presents ROC curve, lift chart and calibration plot, three well known graphical techniques that are useful for evaluating the quality of classification models used in data mining and machine learning. Each technique, normally used and studied separately, defines its own measure of classification quality and its visualization. Here, we give a brief survey of the methods and establish a common mathematical framework which adds some new aspects, explanations and interrelations between these techniques. We conclude with an empirical evaluation and a few examples on how to use the presented techniques to boost classification accuracy.", "title": "" }, { "docid": "pos:1840621_2", "text": "As the widespread use of computers and the high-speed development of the Internet, E-Commerce has already penetrated as a part of our daily life. For a popular product, there are a large number of reviews. This makes it difficult for a potential customer to make an informed decision on purchasing the product, as well as for the manufacturer of the product to keep track and to manage customer opinions. In this paper, we pay attention to online hotel reviews, and propose a supervised machine learning approach using unigram feature with two types of information (frequency and TF-IDF) to realize polarity classification of documents. As shown in our experimental results, the information of TF-IDF is more effective than frequency.", "title": "" } ]

[ { "docid": "neg:1840621_0", "text": "Recently, with the advent of location-based social networking services (LBSNs), travel planning and location-aware information recommendation based on LBSNs have attracted much research attention. In this paper, we study the impact of social relations hidden in LBSNs, i.e., The social influence of friends. We propose a new social influence-based user recommender framework (SIR) to discover the potential value from reliable users (i.e., Close friends and travel experts). Explicitly, our SIR framework is able to infer influential users from an LBSN. We claim to capture the interactions among virtual communities, physical mobility activities and time effects to infer the social influence between user pairs. Furthermore, we intend to model the propagation of influence using diffusion-based mechanism. Moreover, we have designed a dynamic fusion framework to integrate the features mined into a united follow probability score. Finally, our SIR framework provides personalized top-k user recommendations for individuals. To evaluate the recommendation results, we have conducted extensive experiments on real datasets (i.e., The Go Walla dataset). The experimental results show that the performance of our SIR framework is better than the state-of the-art user recommendation mechanisms in terms of accuracy and reliability.", "title": "" }, { "docid": "neg:1840621_1", "text": "This is an innovative work for the field of web usage mining. The main feature of our work a complete framework and findings in mining Web usage patterns from Web log files of a real Web site that has all the difficult aspects of real-life Web usage mining, including developing user profiles and external data describing an ontology of the Web content. We are presenting a method for discovering and tracking evolving user profiles. Profiles are also enriched with other domain-specific information facets that give a panoramic view of the discovered mass usage modes. An objective validation plan is also used to assess the quality of the mined profiles, in particular their adaptability in the face of evolving user behaviour. Keywords— Web mining, Cookies, Session.", "title": "" }, { "docid": "neg:1840621_2", "text": "In this paper, we study the problem of recovering a tensor with missing data. We propose a new model combining the total variation regularization and low-rank matrix factorization. A block coordinate decent (BCD) algorithm is developed to efficiently solve the proposed optimization model. We theoretically show that under some mild conditions, the algorithm converges to the coordinatewise minimizers. Experimental results are reported to demonstrate the effectiveness of the proposed model and the efficiency of the numerical scheme. © 2015 Elsevier Inc. All rights reserved.", "title": "" }, { "docid": "neg:1840621_3", "text": "Social media is becoming a major and popular technological platform that allows users to express personal opinions toward the subjects with shared interests, opinion are good for decision making to People would want to know others' opinion before taking a decision, while corporate would like to monitor pulse of people in a social media about their products and services and take appropriate actions. This paper reviewed about world are realizing that e-commerce is not just buying and selling over Internet, rather it is improve the efficiency to compete with other giants in the market. Their opinions on specific topic are inevitably dependent on many social effects such as user preference on topics, peer influence, user profile information.", "title": "" }, { "docid": "neg:1840621_4", "text": "The burst fracture of the spine was first described by Holdsworth in 1963 and redefined by Denis in 1983 as being a fracture of the anterior and middle columns of the spine with or without an associated posterior column fracture. This injury has received much attention in the literature as regards its radiological diagnosis and also its clinical managment. The purpose of this article is to review the way that imaging has been used both to diagnose the injury and to guide management. Current concepts of the stability of this fracture are presented and our experience in the use of magnetic resonance imaging in deciding treatment options is discussed.", "title": "" }, { "docid": "neg:1840621_5", "text": "To address this shortcoming, we propose a method for training binary neural networks with a mixture of bits, yielding effectively fractional bitwidths. We demonstrate that our method is not only effective in allowing finer tuning of the speed to accuracy trade-off, but also has inherent representational advantages. Middle-Out Algorithm Heterogeneous Bitwidth Binarization in Convolutional Neural Networks", "title": "" }, { "docid": "neg:1840621_6", "text": "TAI's multi-sensor fusion technology is accelerating the development of accurate MEMS sensor-based inertial navigation in situations where GPS does not operate reliably (GPS-denied environments). TAI has demonstrated that one inertial device per axis is not sufficient to produce low drift errors for long term accuracy needed for GPS-denied applications. TAI's technology uses arrays of off-the-shelf MEMS inertial sensors to create an inertial measurement unit (IMU) suitable for inertial navigation systems (INS) that require only occasional GPS updates. Compared to fiber optics gyros, properly combined MEMS gyro arrays are lower cost, fit into smaller volume, use less power and have equal or better performance. The patents TAI holds address this development for both gyro and accelerometer arrays. Existing inertial measurement units based on such array combinations, the backbone of TAI's inertial navigation system (INS) design, have demonstrated approximately 100 times lower sensor drift error to support very accurate angular rates, very accurate position measurements, and very low angle error for long durations. TAI's newest, fourth generation, product occupies small volume, has low weight, and consumes little power. The complete assembly can be potted in a protective sheath to form a rugged standalone product. An external exoskeleton case protects the electronic assembly for munitions and UAV applications. TAI's IMU/INS will provide the user with accurate real-time navigation information in difficult situations where GPS is not reliable. The key to such accurate performance is to achieve low sensor drift errors. The INS responds to quick movements without introducing delays while sharply reducing sensor drift errors that result in significant navigation errors. Discussed in the paper are physical characteristics of the IMU, an overview of the system design, TAI's systematic approach to drift reduction and some early results of applying a sigma point Kalman filter to sustain low gyro drift.", "title": "" }, { "docid": "neg:1840621_7", "text": "Human action recognition from well-segmented 3D skeleton data has been intensively studied and attracting an increasing attention. Online action detection goes one step further and is more challenging, which identifies the action type and localizes the action positions on the fly from the untrimmed stream. In this paper, we study the problem of online action detection from the streaming skeleton data. We propose a multi-task end-to-end Joint Classification-Regression Recurrent Neural Network to better explore the action type and temporal localization information. By employing a joint classification and regression optimization objective, this network is capable of automatically localizing the start and end points of actions more accurately. Specifically, by leveraging the merits of the deep Long Short-Term Memory (LSTM) subnetwork, the proposed model automatically captures the complex long-range temporal dynamics, which naturally avoids the typical sliding window design and thus ensures high computational efficiency. Furthermore, the subtask of regression optimization provides the ability to forecast the action prior to its occurrence. To evaluate our proposed model, we build a large streaming video dataset with annotations. Experimental results on our dataset and the public G3D dataset both demonstrate very promising performance of our scheme.", "title": "" }, { "docid": "neg:1840621_8", "text": "--------------------------------------------------------ABSTRACT-----------------------------------------------------------Classification and regression as data mining techniques for predicting the diseases outbreak has been permitted in the health institutions which have relative opportunities for conducting the treatment of diseases. But there is a need to develop a strong model for predicting disease outbreak in datasets based in various countries by filling the existing data mining technique gaps where the majority of models are relaying on single data mining techniques which their accuracies in prediction are not maximized for achieving expected results and also prediction are still few. This paper presents a survey and analysis for existing techniques on both classification and regression models techniques that have been applied for diseases outbreak prediction in datasets.", "title": "" }, { "docid": "neg:1840621_9", "text": "We present a device demonstrating a lithographically patterned transmon integrated with a micromachined cavity resonator. Our two-cavity, one-qubit device is a multilayer microwave-integrated quantum circuit (MMIQC), comprising a basic unit capable of performing circuit-QED operations. We describe the qubit-cavity coupling mechanism of a specialized geometry using an electric-field picture and a circuit model, and obtain specific system parameters using simulations. Fabrication of the MMIQC includes lithography, etching, and metallic bonding of silicon wafers. Superconducting wafer bonding is a critical capability that is demonstrated by a micromachined storage-cavity lifetime of 34.3 μs, corresponding to a quality factor of 2 × 10 at single-photon energies. The transmon coherence times are T1 1⁄4 6.4 μs, and Techo 2 1⁄4 11.7 μs. We measure qubit-cavity dispersive coupling with a rate χqμ=2π 1⁄4 −1.17 MHz, constituting a Jaynes-Cummings system with an interaction strength g=2π 1⁄4 49 MHz. With these parameters we are able to demonstrate circuit-QED operations in the strong dispersive regime with ease. Finally, we highlight several improvements and anticipated extensions of the technology to complex MMIQCs.", "title": "" }, { "docid": "neg:1840621_10", "text": "Railways is the major means of transport in most of the countries. Rails are the backbone of the track structure and should be protected from defects. Surface defects are irregularities in the rails caused due to the shear stresses between the rails and wheels of the trains. This type of defects should be detected to avoid rail fractures. The objective of this paper is to propose an innovative technique to detect the surface defect on rail heads. In order to identify the defects, it is essential to extract the rails from the background and further enhance the image for thresholding. The proposed method uses Binary Image Based Rail Extraction (BIBRE) algorithm to extract the rails from the background. The extracted rails are enhanced to achieve uniform background with the help of direct enhancement method. The direct enhancement method enhance the image by enhancing the brightness difference between objects and their backgrounds. The enhanced rail image uses Gabor filters to identify the defects from the rails. The Gabor filters maximizes the energy difference between defect and defect less surface. Thresholding is done based on the energy of the defects. From the thresholded image the defects are identified and a message box is generated when there is a presence of defects.", "title": "" }, { "docid": "neg:1840621_11", "text": "This study aims to develop a comprehensive review on the issue of poor school performance for professionals in both health and education areas. It discusses current aspects of education, learning and the main conditions involved in underachievement. It also presents updated data on key aspects of neurobiology, epidemiology, etiology, clinical presentation, comorbidities and diagnosis, early intervention and treatment of the major pathologies comprised. It is a comprehensive, non-systematic literature review on learning, school performance, learning disorders (dyslexia, dyscalculia and dysgraphia), attention deficit / hyperactivity disorder (ADHD) and developmental coordination disorder (DCD). Poor school performance is a frequent problem faced by our children, causing serious emotional, social and economic issues. An updated view of the subject facilitates clinical reasoning, accurate diagnosis and appropriate treatment.", "title": "" }, { "docid": "neg:1840621_12", "text": "Research at the intersection of machine learning, programming languages, and software engineering has recently taken important steps in proposing learnable probabilistic models of source code that exploit the abundance of patterns of code. In this article, we survey this work. We contrast programming languages against natural languages and discuss how these similarities and differences drive the design of probabilistic models. We present a taxonomy based on the underlying design principles of each model and use it to navigate the literature. Then, we review how researchers have adapted these models to application areas and discuss cross-cutting and application-specific challenges and opportunities.", "title": "" }, { "docid": "neg:1840621_13", "text": "Modern medical devices and equipment have become very complex and sophisticated and are expected to operate under stringent environments. Hospitals must ensure that their critical medical devices are safe, accurate, reliable and operating at the required level of performance. Even though the importance, the application of all inspection, maintenance and optimization models to medical devices is fairly new. In Canada, most, if not all healthcare organizations include all their medical equipment in their maintenance program and just follow manufacturers’ recommendations for preventative maintenance. Then, current maintenance strategies employed in hospitals and healthcare organizations have difficulty in identifying specific risks and applying optimal risk reduction activities. This paper addresses these gaps found in literature for medical equipment inspection and maintenance and reviews various important aspects including current policies applied in hospitals. Finally we suggest future research which will be the starting point to develop tools and policies for better medical devices management in the future.", "title": "" }, { "docid": "neg:1840621_14", "text": "Almost all of today’s knowledge is stored in databases and thus can only be accessed with the help of domain specific query languages, strongly limiting the number of people which can access the data. In this work, we demonstrate an end-to-end trainable question answering (QA) system that allows a user to query an external NoSQL database by using natural language. A major challenge of such a system is the non-differentiability of database operations which we overcome by applying policy-based reinforcement learning. We evaluate our approach on Facebook’s bAbI Movie Dialog dataset and achieve a competitive score of 84.2% compared to several benchmark models. We conclude that our approach excels with regard to real-world scenarios where knowledge resides in external databases and intermediate labels are too costly to gather for non-end-to-end trainable QA systems.", "title": "" }, { "docid": "neg:1840621_15", "text": "Email classification is still a mostly manual task. Consequently, most Web mail users never define a single folder. Recently however, automatic classification offering the same categories to all users has started to appear in some Web mail clients, such as AOL or Gmail. We adopt this approach, rather than previous (unsuccessful) personalized approaches because of the change in the nature of consumer email traffic, which is now dominated by (non-spam) machine-generated email. We propose here a novel approach for (1) automatically distinguishing between personal and machine-generated email and (2) classifying messages into latent categories, without requiring users to have defined any folder. We report how we have discovered that a set of 6 \"latent\" categories (one for human- and the others for machine-generated messages) can explain a significant portion of email traffic. We describe in details the steps involved in building a Web-scale email categorization system, from the collection of ground-truth labels, the selection of features to the training of models. Experimental evaluation was performed on more than 500 billion messages received during a period of six months by users of Yahoo mail service, who elected to be part of such research studies. Our system achieved precision and recall rates close to 90% and the latent categories we discovered were shown to cover 70% of both email traffic and email search queries. We believe that these results pave the way for a change of approach in the Web mail industry, and could support the invention of new large-scale email discovery paradigms that had not been possible before.", "title": "" }, { "docid": "neg:1840621_16", "text": "This article aims to contribute to a critical research agenda for investigating the democratic implications of citizen journalism and social news. The article calls for a broad conception of ‘citizen journalism’ which is (1) not an exclusively online phenomenon, (2) not confined to explicitly ‘alternative’ news sources, and (3) includes ‘metajournalism’ as well as the practices of journalism itself. A case is made for seeing democratic implications not simply in the horizontal or ‘peer-to-peer’ public sphere of citizen journalism networks, but also in the possibility of a more ‘reflexive’ culture of news consumption through citizen participation. The article calls for a research agenda that investigates new forms of gatekeeping and agendasetting power within social news and citizen journalism networks and, drawing on the example of three sites, highlights the importance of both formal and informal status differentials and of the software ‘code’ structuring these new modes of news", "title": "" }, { "docid": "neg:1840621_17", "text": "With recent advances in computer vision and graphics, it is now possible to generate videos with extremely realistic synthetic faces, even in real time. Countless applications are possible, some of which raise a legitimate alarm, calling for reliable detectors of fake videos. In fact, distinguishing between original and manipulated video can be a challenge for humans and computers alike, especially when the videos are compressed or have low resolution, as it often happens on social networks. Research on the detection of face manipulations has been seriously hampered by the lack of adequate datasets. To this end, we introduce a novel face manipulation dataset of about half a million edited images (from over 1000 videos). The manipulations have been generated with a state-of-the-art face editing approach. It exceeds all existing video manipulation datasets by at least an order of magnitude. Using our new dataset, we introduce benchmarks for classical image forensic tasks, including classification and segmentation, considering videos compressed at various quality levels. In addition, we introduce a benchmark evaluation for creating indistinguishable forgeries with known ground truth; for instance with generative refinement models.", "title": "" }, { "docid": "neg:1840621_18", "text": "The reliable and effective localization system is the basis of Automatic Guided Vehicle (AGV) to complete given tasks automatically in warehouse environment. However, there are no obvious features that can be used for localization of AGV to be extracted in warehouse environment and it dose make it difficult to realize the localization of AGV. So in this paper, we concentrate on the problem of optimal landmarks placement in warehouse so as to improve the reliability of localization. Firstly, we take the practical warehouse environment into consideration and transform the problem of landmarks placement into an optimization problem which aims at maximizing the difference degree between each basic unit of localization. Then Genetic Algorithm (GA) is used to solve the optimization problem. Then we match the observed landmarks with the already known ones stored in the map and the Triangulation method is used to estimate the position of AGV after the matching has been done. Finally, experiments in a real warehouse environment validate the effectiveness and reliability of our method.", "title": "" }, { "docid": "neg:1840621_19", "text": "We provide a new understanding of the fundamental nature of adversarially robust classifiers and how they differ from standard models. In particular, we show that there provably exists a trade-off between the standard accuracy of a model and its robustness to adversarial perturbations. We demonstrate an intriguing phenomenon at the root of this tension: a certain dichotomy between “robust” and “non-robust” features. We show that while robustness comes at a price, it also has some surprising benefits. Robust models turn out to have interpretable gradients and feature representations that align unusually well with salient data characteristics. In fact, they yield striking feature interpolations that have thus far been possible to obtain only using generative models such as GANs.", "title": "" } ]

[ { "docid": "pos:1840622_0", "text": "The paper describes the engineering and design of a doubly fed induction generator (DFIG), using back-to-back PWM voltage-source converters in the rotor circuit. A vector-control scheme for the supply-side PWM converter results in independent control of active and reactive power drawn from the supply, while ensuring sinusoidal supply currents. Vector control of the rotor-connected converter provides for wide speed-range operation; the vector scheme is embedded in control loops which enable optimal speed tracking for maximum energy capture from the wind. An experimental rig, which represents a 1.5 kW variable speed wind-energy generation system is described, and experimental results are given that illustrate the excellent performance characteristics of the system. The paper considers a grid-connected system; a further paper will describe a stand-alone system.", "title": "" } ]

[ { "docid": "neg:1840622_0", "text": "We describe an estimation technique which, given a measurement of the depth of a target from a wide-fieldof-view (WFOV) stereo camera pair, produces a minimax risk fixed-size confidence interval estimate for the target depth. This work constitutes the first application to the computer vision domain of optimal fixed-size confidenceinterval decision theory. The approach is evaluated in terms of theoretical capture probability and empirical cap ture frequency during actual experiments with a target on an optical bench. The method is compared to several other procedures including the Kalman Filter. The minimax approach is found to dominate all the other methods in performance. In particular, for the minimax approach, a very close agreement is achieved between theoreticalcapture probability andempiricalcapture frequency. This allows performance to be accurately predicted, greatly facilitating the system design, and delineating the tasks that may be performed with a given system.", "title": "" }, { "docid": "neg:1840622_1", "text": "In this paper, we describe a system that applies maximum entropy (ME) models to the task of named entity recognition (NER). Starting with an annotated corpus and a set of features which are easily obtainable for almost any language, we first build a baseline NE recognizer which is then used to extract the named entities and their context information from additional nonannotated data. In turn, these lists are incorporated into the final recognizer to further improve the recognition accuracy.", "title": "" }, { "docid": "neg:1840622_2", "text": "In any collaborative system, there are both symmetries and asymmetries present in the design of the technology and in the ways that technology is appropriated. Yet media space research tends to focus more on supporting and fostering the symmetries than the asymmetries. Throughout more than 20 years of media space research, the pursuit of increased symmetry, whether achieved through technical or social means, has been a recurrent theme. The research literature on the use of contemporary awareness systems, in contrast, displays little if any of this emphasis on symmetrical use; indeed, this body of research occasionally highlights the perceived value of asymmetry. In this paper, we unpack the different forms of asymmetry present in both media spaces and contemporary awareness systems. We argue that just as asymmetry has been demonstrated to have value in contemporary awareness systems, so might asymmetry have value in media spaces and in other CSCW systems, more generally. To illustrate, we present a media space that emphasizes and embodies multiple forms of asymmetry and does so in response to the needs of a particular work context.", "title": "" }, { "docid": "neg:1840622_3", "text": "This paper presents QurAna: a large corpus created from the original Quranic text, where personal pronouns are tagged with their antecedence. These antecedents are maintained as an ontological list of concepts, which has proved helpful for information retrieval tasks. QurAna is characterized by: (a) comparatively large number of pronouns tagged with antecedent information (over 24,500 pronouns), and (b) maintenance of an ontological concept list out of these antecedents. We have shown useful applications of this corpus. This corpus is the first of its kind covering Classical Arabic text, and could be used for interesting applications for Modern Standard Arabic as well. This corpus will enable researchers to obtain empirical patterns and rules to build new anaphora resolution approaches. Also, this corpus can be used to train, optimize and evaluate existing approaches.", "title": "" }, { "docid": "neg:1840622_4", "text": "Received Feb 14, 2017 Revised Apr 14, 2017 Accepted Apr 28, 2017 This paper proposes maximum boost control for 7-level z-source cascaded h-bridge inverter and their affiliation between voltage boost gain and modulation index. Z-source network avoids the usage of external dc-dc boost converter and improves output voltage with minimised harmonic content. Z-source network utilises distinctive LC impedance combination with 7-level cascaded inverter and it conquers the conventional voltage source inverter. The maximum boost controller furnishes voltage boost and maintain constant voltage stress across power switches, which provides better output voltage with variation of duty cycles. Single phase 7-level z-source cascaded inverter simulated using matlab/simulink. Keyword:", "title": "" }, { "docid": "neg:1840622_5", "text": "In Ethernet-based time-triggered networks, like TTEthernet, a global communication scheme, for which the schedule synthesis is known to be an NP-complete problem, establishes contention-free windows for the exchange of messages with guaranteed low latency and minimal jitter. However, in order to achieve end-to-end determinism at the application level, software tasks running on the end-system nodes need to obey a similar execution scheme with tight dependencies towards the network domain. In this paper we address the simultaneous co-synthesis of network as well as application schedules for preemptive time-triggered tasks communicating in a switched multi-speed time-triggered network. We use Satisfiability Modulo Theories (SMT) to formulate the scheduling constraints and solve the resulting problem using a state-of-the-art SMT solver. Furthermore, we introduce a novel incremental scheduling approach, based on the demand bound test for asynchronous constrained-deadline periodic tasks, which significantly improves scalability for the average case without sacrificing schedulability. We demonstrate the performance of our approach using synthetic network topologies and system configurations.", "title": "" }, { "docid": "neg:1840622_6", "text": "This paper discusses dexterous, within-hand manipulation with differential-type underactuated hands. We discuss the fact that not only can this class of hands, which to date have been considered almost exclusively for adaptive grasping, be utilized for precision manipulation, but also that the reduction of the number of actuators and constraints can make within-hand manipulation easier to implement and control. Next, we introduce an analytical framework for evaluating the dexterous workspace of objects held within the fingertips in a precision grasp. A set of design principles for underactuated fingers are developed that enable fingertip grasping and manipulation. Finally, we apply this framework to analyze the workspace of stable object configurations for an object held within a pinch grasp of a two-fingered underactuated planar hand, demonstrating a large and useful workspace despite only one actuator per finger. The in-hand manipulation workspace for the iRobot–Harvard–Yale Hand is experimentally measured and presented.", "title": "" }, { "docid": "neg:1840622_7", "text": "Being a leading location-based social network (LBSN), Foursquare’s Swarm app allows users to conduct checkins at a specified location and share their real-time locations with friends. This app records a massive set of spatio-temporal information of users around the world. In this paper, we track the evolution of user density of the Swarm app in New York City (NYC) for one entire week. We study the temporal patterns of different venue categories, and investigate how the function of venue categories affects the temporal behavior of visitors. Moreover, by applying time-series analysis, we validate that the temporal patterns can be effectively decomposed into regular parts which represent the regular human behavior and stochastic parts which represent the randomness of human behavior. Finally, we build a model to predict the evolution of the user density, and our results demonstrate an accurate prediction.", "title": "" }, { "docid": "neg:1840622_8", "text": "A mathematical study is performed to assess how the arterial pressure-volume (P-V) relationship, blood pressure pulse amplitude and shape affect the results of non-invasive oscillometric finger mean blood pressure estimation by the maximum oscillation criterion (MOC). The exponential models for a relaxed finger artery and for a partly contracted artery are studied. A new modification of the error equation is suggested. This equation and the results of simulation demonstrate that the value of pressure estimated by the MOC does not exactly agree with the value of the true mean blood pressure (the latter being defined as pressure corresponding to maximum arterial compliance). The error depends on the arterial pressure pulse amplitude, as well as on the difference between the arterial pressure pulse shape index and the arterial P-V curve shape index. In the case of contracted finger arteries, the MOC can give an overestimation of up to 19 mmHg, the pressure pulse shape index being 0.21 and the pulse amplitude 60 mmHg. In the case of relaxed arteries, the error is less evident.", "title": "" }, { "docid": "neg:1840622_9", "text": "In this paper it is shown that bifilar of a Quadrifilar Helix Antenna (QHA) when designed in side-fed configuration at a given diameter and length of helical arm, effectively becomes equivalent to combination of a loop and a dipole antenna. The vertical and horizontal electric fields caused by these equivalent antennas can be made to vary by changing the turn angle of the bifilar. It is shown how the variation in horizontal and vertical electric field dominance is seen until perfect circular polarization is achieved when two fields are equal at a certain turn angle where area of the loop equals product of pitch of helix and radian length i.e. equivalent dipole length. The antenna is low profile and does not require ground plane and thus can be used in high speed aerodynamic and platform bodies made of composite material where metallic ground is unavailable. Additionally not requiring ground plane increases the isolation between the antennas with stable radiation pattern and hence can be used in MIMO systems.", "title": "" }, { "docid": "neg:1840622_10", "text": "An ongoing, annual survey of publications in systems and software engineering identifies the top 15 scholars and institutions in the field over a 5-year period. Each ranking is based on the weighted scores of the number of papers published in TSE, TOSEM, JSS, SPE, EMSE, IST, and Software of the corresponding period. This report summarizes the results for 2003–2007 and 2004–2008. The top-ranked institution is Korea Advanced Institute of Science and Technology, Korea for 2003–2007, and Simula Research Laboratory, Norway for 2004–2008, while Magne Jørgensen is the top-ranked scholar for both periods.", "title": "" }, { "docid": "neg:1840622_11", "text": "Supercomputing centers are seeing increasing demand for user-defined software stacks (UDSS), instead of or in addition to the stack provided by the center. These UDSS support user needs such as complex dependencies or build requirements, externally required configurations, portability, and consistency. The challenge for centers is to provide these services in a usable manner while minimizing the risks: security, support burden, missing functionality, and performance. We present Charliecloud, which uses the Linux user and mount namespaces to run industry-standard Docker containers with no privileged operations or daemons on center resources. Our simple approach avoids most security risks while maintaining access to the performance and functionality already on offer, doing so in just 800 lines of code. Charliecloud promises to bring an industry-standard UDSS user workflow to existing, minimally altered HPC resources.", "title": "" }, { "docid": "neg:1840622_12", "text": "The design of a Web search evaluation metric is closely related with how the user's interaction process is modeled. Each behavioral model results in a different metric used to evaluate search performance. In these models and the user behavior assumptions behind them, when a user ends a search session is one of the prime concerns because it is highly related to both benefit and cost estimation. Existing metric design usually adopts some simplified criteria to decide the stopping time point: (1) upper limit for benefit (e.g. RR, AP); (2) upper limit for cost (e.g. Precision@N, DCG@N). However, in many practical search sessions (e.g. exploratory search), the stopping criterion is more complex than the simplified case. Analyzing benefit and cost of actual users' search sessions, we find that the stopping criteria vary with search tasks and are usually combination effects of both benefit and cost factors. Inspired by a popular computer game named Bejeweled, we propose a Bejeweled Player Model (BPM) to simulate users' search interaction processes and evaluate their search performances. In the BPM, a user stops when he/she either has found sufficient useful information or has no more patience to continue. Given this assumption, a new evaluation framework based on upper limits (either fixed or changeable as search proceeds) for both benefit and cost is proposed. We show how to derive a new metric from the framework and demonstrate that it can be adopted to revise traditional metrics like Discounted Cumulative Gain (DCG), Expected Reciprocal Rank (ERR) and Average Precision (AP). To show effectiveness of the proposed framework, we compare it with a number of existing metrics in terms of correlation between user satisfaction and the metrics based on a dataset that collects users' explicit satisfaction feedbacks and assessors' relevance judgements. Experiment results show that the framework is better correlated with user satisfaction feedbacks.", "title": "" }, { "docid": "neg:1840622_13", "text": "Interpenetrating network (IPN) hydrogel membranes of sodium alginate (SA) and poly(vinyl alcohol) (PVA) were prepared by solvent casting method for transdermal delivery of an anti-hypertensive drug, prazosin hydrochloride. The prepared membranes were thin, flexible and smooth. The X-ray diffraction studies indicated the amorphous dispersion of drug in the membranes. Differential scanning calorimetric analysis confirmed the IPN formation and suggests that the membrane stiffness increases with increased concentration of glutaraldehyde (GA) in the membranes. All the membranes were permeable to water vapors depending upon the extent of cross-linking. The in vitro drug release study was performed through excised rat abdominal skin; drug release depends on the concentrations of GA in membranes. The IPN membranes extended drug release up to 24 h, while SA and PVA membranes discharged the drug quickly. The primary skin irritation and skin histopathology study indicated that the prepared IPN membranes were less irritant and safe for skin application.", "title": "" }, { "docid": "neg:1840622_14", "text": "Endotoxin, a constituent of Gram-negative bacteria, stimulates macrophages to release large quantities of tumor necrosis factor (TNF) and interleukin-1 (IL-1), which can precipitate tissue injury and lethal shock (endotoxemia). Antagonists of TNF and IL-1 have shown limited efficacy in clinical trials, possibly because these cytokines are early mediators in pathogenesis. Here a potential late mediator of lethality is identified and characterized in a mouse model. High mobility group-1 (HMG-1) protein was found to be released by cultured macrophages more than 8 hours after stimulation with endotoxin, TNF, or IL-1. Mice showed increased serum levels of HMG-1 from 8 to 32 hours after endotoxin exposure. Delayed administration of antibodies to HMG-1 attenuated endotoxin lethality in mice, and administration of HMG-1 itself was lethal. Septic patients who succumbed to infection had increased serum HMG-1 levels, suggesting that this protein warrants investigation as a therapeutic target.", "title": "" }, { "docid": "neg:1840622_15", "text": "The focus of this paper is on presentation attack detection for the iris biometrics, which measures the pattern within the colored concentric circle of the subjects' eyes, to authenticate an individual to a generic user verification system. Unlike previous deep learning methods that use single convolutional neural network architectures, this paper develops a framework built upon triplet convolutional networks that takes as input two real iris patches and a fake patch or two fake patches and a genuine patch. The aim is to increase the number of training samples and to generate a representation that separates the real from the fake iris patches. The smaller architecture provides a way to do early stopping based on the liveness of single patches rather than the whole image. The matching is performed by computing the distance with respect to a reference set of real and fake examples. The proposed approach allows for real-time processing using a smaller network and provides equal or better than state-of-the-art performance on three benchmark datasets of photo-based and contact lens presentation attacks.", "title": "" }, { "docid": "neg:1840622_16", "text": "BACKGROUND\nΔ(9)-Tetrahydrocannabinol (THC), 11-nor-9-carboxy-THC (THCCOOH), and cannabinol (CBN) were measured in breath following controlled cannabis smoking to characterize the time course and windows of detection of breath cannabinoids.\n\n\nMETHODS\nExhaled breath was collected from chronic (≥4 times per week) and occasional (<twice per week) smokers before and after smoking a 6.8% THC cigarette. Sample analysis included methanol extraction from breath pads, solid-phase extraction, and liquid chromatography-tandem mass spectrometry quantification.\n\n\nRESULTS\nTHC was the major cannabinoid in breath; no sample contained THCCOOH and only 1 contained CBN. Among chronic smokers (n = 13), all breath samples were positive for THC at 0.89 h, 76.9% at 1.38 h, and 53.8% at 2.38 h, and only 1 sample was positive at 4.2 h after smoking. Among occasional smokers (n = 11), 90.9% of breath samples were THC-positive at 0.95 h and 63.6% at 1.49 h. One occasional smoker had no detectable THC. Analyte recovery from breath pads by methanolic extraction was 84.2%-97.4%. Limits of quantification were 50 pg/pad for THC and CBN and 100 pg/pad for THCCOOH. Solid-phase extraction efficiency was 46.6%-52.1% (THC) and 76.3%-83.8% (THCCOOH, CBN). Matrix effects were -34.6% to 12.3%. Cannabinoids fortified onto breath pads were stable (≤18.2% concentration change) for 8 h at room temperature and -20°C storage for 6 months.\n\n\nCONCLUSIONS\nBreath may offer an alternative matrix for identifying recent driving under the influence of cannabis, but currently sensitivity is limited to a short detection window (0.5-2 h).", "title": "" }, { "docid": "neg:1840622_17", "text": "Question classification is very important for question answering. This paper present our research work on question classification through machine learning approach. In order to train the learning model, we designed a rich set of features that are predictive of question categories. An important component of question answering systems is question classification. The task of question classification is to predict the entity type of the answer of a natural language question. Question classification is typically done using machine learning techniques. Different lexical, syntactical and semantic features can be extracted from a question. In this work we combined lexical, syntactic and semantic features which improve the accuracy of classification. Furthermore, we adopted three different classifiers: Nearest Neighbors (NN), Naïve Bayes (NB), and Support Vector Machines (SVM) using two kinds of features: bag-of-words and bag-of n grams. Furthermore, we discovered that when we take SVM classifier and combine the semantic, syntactic, lexical feature we found that it will improve the accuracy of classification. We tested our proposed approaches on the well-known UIUC dataset and succeeded to achieve a new record on the accuracy of classification on this dataset.", "title": "" }, { "docid": "neg:1840622_18", "text": "This paper highlights the role of humans in the next generation of driver assistance and intelligent vehicles. Understanding, modeling, and predicting human agents are discussed in three domains where humans and highly automated or self-driving vehicles interact: 1) inside the vehicle cabin, 2) around the vehicle, and 3) inside surrounding vehicles. Efforts within each domain, integrative frameworks across domains, and scientific tools required for future developments are discussed to provide a human-centered perspective on research in intelligent vehicles.", "title": "" } ]

[ { "docid": "pos:1840623_0", "text": "Today, visual recognition systems are still rarely employed in robotics applications. Perhaps one of the main reasons for this is the lack of demanding benchmarks that mimic such scenarios. In this paper, we take advantage of our autonomous driving platform to develop novel challenging benchmarks for the tasks of stereo, optical flow, visual odometry/SLAM and 3D object detection. Our recording platform is equipped with four high resolution video cameras, a Velodyne laser scanner and a state-of-the-art localization system. Our benchmarks comprise 389 stereo and optical flow image pairs, stereo visual odometry sequences of 39.2 km length, and more than 200k 3D object annotations captured in cluttered scenarios (up to 15 cars and 30 pedestrians are visible per image). Results from state-of-the-art algorithms reveal that methods ranking high on established datasets such as Middlebury perform below average when being moved outside the laboratory to the real world. Our goal is to reduce this bias by providing challenging benchmarks with novel difficulties to the computer vision community. Our benchmarks are available online at: www.cvlibs.net/datasets/kitti.", "title": "" } ]

[ { "docid": "neg:1840623_0", "text": "We study the design of local algorithms for massive graphs. A local graph algorithm is one that finds a solution containing or near a given vertex without looking at the whole graph. We present a local clustering algorithm. Our algorithm finds a good cluster—a subset of vertices whose internal connections are significantly richer than its external connections—near a given vertex. The running time of our algorithm, when it finds a nonempty local cluster, is nearly linear in the size of the cluster it outputs. The running time of our algorithm also depends polylogarithmically on the size of the graph and polynomially on the conductance of the cluster it produces. Our clustering algorithm could be a useful primitive for handling massive graphs, such as social networks and webgraphs. As an application of this clustering algorithm, we present a partitioning algorithm that finds an approximate sparsest cut with nearly optimal balance. Our algorithm takes time nearly linear in the number edges of the graph. Using the partitioning algorithm of this paper, we have designed a nearly linear time algorithm for constructing spectral sparsifiers of graphs, which we in turn use in a nearly linear time algorithm for solving linear systems in symmetric, diagonally dominant matrices. The linear system solver also leads to a nearly linear time algorithm for approximating the secondsmallest eigenvalue and corresponding eigenvector of the Laplacian matrix of a graph. These other results are presented in two companion papers.", "title": "" }, { "docid": "neg:1840623_1", "text": "Centrality indices are an essential concept in network analysis. For those based on shortest-path distances the computation is at least quadratic in the number of nodes, since it usually involves solving the single-source shortest-paths (SSSP) problem from every node. Therefore, exact computation is infeasible for many large networks of interest today. Centrality scores can be estimated, however, from a limited number of SSSP computations. We present results from an experimental study of the quality of such estimates under various selection strategies for the source vertices. ∗Research supported in part by DFG under grant Br 2158/2-3", "title": "" }, { "docid": "neg:1840623_2", "text": "We present a new deep learning approach to pose-guided resynthesis of human photographs. At the heart of the new approach is the estimation of the complete body surface texture based on a single photograph. Since the input photograph always observes only a part of the surface, we suggest a new inpainting method that completes the texture of the human body. Rather than working directly with colors of texture elements, the inpainting network estimates an appropriate source location in the input image for each element of the body surface. This correspondence field between the input image and the texture is then further warped into the target image coordinate frame based on the desired pose, effectively establishing the correspondence between the source and the target view even when the pose change is drastic. The final convolutional network then uses the established correspondence and all other available information to synthesize the output image using a fully-convolutional architecture with deformable convolutions. We show stateof-the-art result for pose-guided image synthesis. Additionally, we demonstrate the performance of our system for garment transfer and pose-guided face resynthesis.", "title": "" }, { "docid": "neg:1840623_3", "text": "Advanced driver assistance systems are the newest addition to vehicular technology. Such systems use a wide array of sensors to provide a superior driving experience. Vehicle safety and driver alert are important parts of these system. This paper proposes a driver alert system to prevent and mitigate adjacent vehicle collisions by proving warning information of on-road vehicles and possible collisions. A dynamic Bayesian network (DBN) is utilized to fuse multiple sensors to provide driver awareness. It detects oncoming adjacent vehicles and gathers ego vehicle motion characteristics using an on-board camera and inertial measurement unit (IMU). A histogram of oriented gradient feature based classifier is used to detect any adjacent vehicles. Vehicles front-rear end and side faces were considered in training the classifier. Ego vehicles heading, speed and acceleration are captured from the IMU and feed into the DBN. The network parameters were learned from data via expectation maximization(EM) algorithm. The DBN is designed to provide two type of warning to the driver, a cautionary warning and a brake alert for possible collision with other vehicles. Experiments were completed on multiple public databases, demonstrating successful warnings and brake alerts in most situations.", "title": "" }, { "docid": "neg:1840623_4", "text": "Recent rapid advances in ICTs, specifically in Internet and mobile technologies, have highlighted the rising importance of the Business Model (BM) in Information Systems (IS). Despite agreement on its importance to an organization’s success, the concept is still fuzzy and vague, and there is no consensus regarding its definition. Furthermore, understanding the BM domain by identifying its meaning, fundamental pillars, and its relevance to other business concepts is by no means complete. In this paper we aim to provide further clarification by first presenting a classification of definitions found in the IS literature; second, proposing guidelines on which to develop a more comprehensive definition in order to reach consensus; and third, identifying the four main business model concepts and values and their interaction, and thus place the business model within the world of digital business. Based on this discussion, we propose a new definition for the business model that we argue is more appropriate to this new world.", "title": "" }, { "docid": "neg:1840623_5", "text": "This document and any map included herein are without prejudice to the status of or sovereignty over any territory, to the delimitation of international frontiers and boundaries and to the name of any territory, city or area. In recent years, the cost of delivering health care in developed and developing countries has been rising exponentially. Governments around the world are searching for alternative mechanisms to reduce costs while increasing the capacity of social programmes with significant investments in infrastructure. A number of jurisdictions have begun to utilise public-private partnerships (PPPs) as a means of achieving these objectives. The use of PPPs in the Canadian health system is a relatively new phenomenon. Generally, the success of PPP projects is evaluated on the basis of the qualitative outcomes of the project, most commonly in a value-for-money analysis. In this article, we explore whether quantitative elements are sufficient to measure PPPs in politically sensitive areas of public policy, such as health care. We propose that the best way to evaluate the outcomes of PPPs in public health system projects requires both quantitative and qualitative criteria. We use a framework developed from neo-institutional economics that contextualises outcomes through a balance of quantitative and qualitative assessment criteria. We apply this evaluation framework to a specific Canadian case study in order to determine key success factors for future PPP health infrastructure projects. The analysis concludes that, given the complex and politically sensitive nature of health care, particular attention must be paid to communications and public relations and to design and post-construction planning in order to deliver a successful PPP. 2 PPP relationships differ in a fundamental way from conventional procurement contracting. In conventional procurement, risks are assumed to be contained in a contract focused on a short-term infrastructure deliverable, such as construction of a road, airport, water and sewer facility, or hospital. In PPPs, developing risk-sharing mechanisms is key to enhancing the returns to both the public and private sector. PPPs are based upon a stewardship model in which the private sector takes a more aggressive role in aspects of the project from which it had previously been excluded in the conventional procurement approach, such as design, financing, operations and maintenance. The hypothesis is that when the private sector assumes greater responsibility in the project, there will be incentives to ensure a steady stream of revenue for the private sector over the life of the project. …", "title": "" }, { "docid": "neg:1840623_6", "text": "This paper presents the design and construction of a microstrip Yagi array antenna operating at 5.3 GHz, to be used with an avalanche sensor in avalanche measurement. The advantage of the antenna is it can achieve a high gain of 15.2 dB with bandwidth of 8% in compact size. The gain enhancement is achieved by using a compact microstrip Yagi antenna as the array element; separating the feed network from the main radiating elements; and increasing the antenna height by installing the feed layer at the back of the patch layer, sharing the same ground plane. In order to ensure the power is transferred smoothly from the main input port to the radiating elements, the corporate feed is also design and tested. The fabricated antenna shows an agreeable performance with the simulated version.", "title": "" }, { "docid": "neg:1840623_7", "text": "In this essay a new theory of stress and linguistic rhythm will be elaborated, based on the proposals of Liberman (1975).' It will be argued that certain features of prosodic systems like that of English, in particular the phenomenon of \"stress subordination\", are not to be referred primarily to the properties of individual segments (or syllables), but rather reflect a hierarchical rhythmic structuring that organizes the syllables, words, and syntactic phrases of a sentence. The character of this structuring, properly understood, will give fresh insight into phenomena that have been apprehended in terms of the phonological cycle, the stress-subordination convention, the theory of disjunctive ordering, and the use of crucial variables in phonological rules. Our theory will employ two basic ideas about the representation of traditional prosodic concepts: first, we represent the notion relative prominence in terms of a relation defined on constituent structure; and second, we represent certain aspects of the notion linguistic rhythm in terms of the alignment of linguistic material with a \"metrical grid\". The perceived \"stressing\" of an utterance, we think, reflects the combined influence of a constituent-structure pattern and its grid alignment. This pattern-grid combination is reminiscent of the traditional picture of verse scansion, so that the theory as a whole deserves the name \"metrical\". We will also use the expression \"'metrical theory\" as a convenient term for that portion of the theory which deals with the assignment of relative prominence in terms of a relation defined on constituent structure. Section 1 will apply the metrical theory of stress-pattern assignment to the system of English phrasal stress, arguing this theory's value in rationalizing otherwise arbitrary characteristics of stress features and stress rules. Section 2 will extend this treatment to the domain of English word stress, adopting a somewhat traditional view of the assignment of the feature [+stress], but explaining the generation of word-level * We would like to thank", "title": "" }, { "docid": "neg:1840623_8", "text": "Lack of trust is one of the most frequently cited reasons for consumers not purchasing from Internet vendors. During the last four years a number of empirical studies have investigated the role of trust in the specific context of e-commerce, focusing on different aspects of this multi-dimensional construct. However, empirical research in this area is beset by conflicting conceptualizations of the trust construct, inadequate understanding of the relationships between trust, its antecedents and consequents, and the frequent use of trust scales that are neither theoretically derived nor rigorously validated. The major objective of this paper is to provide an integrative review of the empirical literature on trust in e-commerce in order to allow cumulative analysis of results. The interpretation and comparison of different empirical studies on on-line trust first requires conceptual clarification. A set of trust constructs is proposed that reflects both institutional phenomena (system trust) and personal and interpersonal forms of trust (dispositional trust, trusting beliefs, trusting intentions and trust-related behaviours), thus facilitating a multi-level and multi-dimensional analysis of research problems related to trust in e-commerce. r 2003 Elsevier Science Ltd. All rights reserved.", "title": "" }, { "docid": "neg:1840623_9", "text": "The use of information technology (IT) as a competitive weapon has become a popular cliché; but there is still a marked lack of understanding of the issues that determine the influence of information technology on a particular organization and the processes that will allow a smooth coordination of technology and corporate strategy. This article surveys the major efforts to arrive at a relevant framework and attempts to integrate them in a more comprehensive viewpoint. The focus then turns to the major research issues in understanding the impact of information technology on competitive strategy. Copyright © 1986 Yannis Bakos and Michael Treacy", "title": "" }, { "docid": "neg:1840623_10", "text": "Mechanical valves used for aortic valve replacement (AVR) continue to be associated with bleeding risks because of anticoagulation therapy, while bioprosthetic valves are at risk of structural valve deterioration requiring reoperation. This risk/benefit ratio of mechanical and bioprosthetic valves has led American and European guidelines on valvular heart disease to be consistent in recommending the use of mechanical prostheses in patients younger than 60 years of age. Despite these recommendations, the use of bioprosthetic valves has significantly increased over the last decades in all age groups. A systematic review of manuscripts applying propensity-matching or multivariable analysis to compare the usage of mechanical vs. bioprosthetic valves found either similar outcomes between the two types of valves or favourable outcomes with mechanical prostheses, particularly in younger patients. The risk/benefit ratio and choice of valves will be impacted by developments in valve designs, anticoagulation therapy, reducing the required international normalized ratio, and transcatheter and minimally invasive procedures. However, there is currently no evidence to support lowering the age threshold for implanting a bioprosthesis. Physicians in the Heart Team and patients should be cautious in pursuing more bioprosthetic valve use until its benefit is clearly proven in middle-aged patients.", "title": "" }, { "docid": "neg:1840623_11", "text": "With the continuous increase in data velocity and volume nowadays, preserving system and data security is particularly affected. In order to handle the huge amount of data and to discover security incidents in real-time, analyses of log data streams are required. However, most of the log anomaly detection techniques fall short in considering continuous data processing. Thus, this paper aligns an anomaly detection technique for data stream processing. It thereby provides a conceptual basis for future adaption of other techniques and further delivers proof of concept by prototype implementation.", "title": "" }, { "docid": "neg:1840623_12", "text": "We describe CACTI-IO, an extension to CACTI [4] that includes power, area and timing models for the IO and PHY of the off-chip memory interface for various server and mobile configurations. CACTI-IO enables design space exploration of the off-chip IO along with the DRAM and cache parameters. We describe the models added and three case studies that use CACTI-IO to study the tradeoffs between memory capacity, bandwidth and power.\n The case studies show that CACTI-IO helps (i) provide IO power numbers that can be fed into a system simulator for accurate power calculations, (ii) optimize off-chip configurations including the bus width, number of ranks, memory data width and off-chip bus frequency, especially for novel buffer-based topologies, and (iii) enable architects to quickly explore new interconnect technologies, including 3-D interconnect. We find that buffers on board and 3-D technologies offer an attractive design space involving power, bandwidth and capacity when appropriate interconnect parameters are deployed.", "title": "" }, { "docid": "neg:1840623_13", "text": "The function of brown adipose tissue is to transfer energy from food into heat; physiologically, both the heat produced and the resulting decrease in metabolic efficiency can be of significance. Both the acute activity of the tissue, i.e., the heat production, and the recruitment process in the tissue (that results in a higher thermogenic capacity) are under the control of norepinephrine released from sympathetic nerves. In thermoregulatory thermogenesis, brown adipose tissue is essential for classical nonshivering thermogenesis (this phenomenon does not exist in the absence of functional brown adipose tissue), as well as for the cold acclimation-recruited norepinephrine-induced thermogenesis. Heat production from brown adipose tissue is activated whenever the organism is in need of extra heat, e.g., postnatally, during entry into a febrile state, and during arousal from hibernation, and the rate of thermogenesis is centrally controlled via a pathway initiated in the hypothalamus. Feeding as such also results in activation of brown adipose tissue; a series of diets, apparently all characterized by being low in protein, result in a leptin-dependent recruitment of the tissue; this metaboloregulatory thermogenesis is also under hypothalamic control. When the tissue is active, high amounts of lipids and glucose are combusted in the tissue. The development of brown adipose tissue with its characteristic protein, uncoupling protein-1 (UCP1), was probably determinative for the evolutionary success of mammals, as its thermogenesis enhances neonatal survival and allows for active life even in cold surroundings.", "title": "" }, { "docid": "neg:1840623_14", "text": "We introduce a method to stabilize Generative Adversarial Networks (GANs) by defining the generator objective with respect to an unrolled optimization of the discriminator. This allows training to be adjusted between using the optimal discriminator in the generator’s objective, which is ideal but infeasible in practice, and using the current value of the discriminator, which is often unstable and leads to poor solutions. We show how this technique solves the common problem of mode collapse, stabilizes training of GANs with complex recurrent generators, and increases diversity and coverage of the data distribution by the generator.", "title": "" }, { "docid": "neg:1840623_15", "text": "A low-power low-voltage OTA with rail-to-rail output is introduced. The proposed topology is based on the common current mirror OTA topology and provide gain enhancement without extra power consumption. Implemented in a standard 0.25/spl mu/m CMOS technology, the proposed OTA achieves 50 dB DC gain in 0.8 V supply voltage. The GBW is 1.2MHz and the static power consumption is 8/spl mu/W while driving 18pF load. The class AB operation increases the slew rate and still maintains low static biasing current. This topology is suitable for low-power low-voltage switched-capacitor application.", "title": "" }, { "docid": "neg:1840623_16", "text": "YAGO is a large knowledge base that is built automatically from Wikipedia, WordNet and GeoNames. The project combines information from Wikipedias in 10 di erent languages, thus giving the knowledge a multilingual dimension. It also attaches spatial and temporal information to many facts, and thus allows the user to query the data over space and time. YAGO focuses on extraction quality and achieves a manually evaluated precision of 95%. In this paper, we explain from a general perspective how YAGO is built from its sources, how its quality is evaluated, how a user can access it, and how other projects utilize it.", "title": "" }, { "docid": "neg:1840623_17", "text": "In training deep neural networks for semantic segmentation, the main limiting factor is the low amount of ground truth annotation data that is available in currently existing datasets. The limited availability of such data is due to the time cost and human effort required to accurately and consistently label real images on a pixel level. Modern sandbox video game engines provide open world environments where traffic and pedestrians behave in a pseudo-realistic manner. This caters well to the collection of a believable road-scene dataset. Utilizing open-source tools and resources found in single-player modding communities, we provide a method for persistent, ground truth, asset annotation of a game world. By collecting a synthetic dataset containing upwards of 1, 000, 000 images, we demonstrate realtime, on-demand, ground truth data annotation capability of our method. Supplementing this synthetic data to Cityscapes dataset, we show that our data generation method provides qualitative as well as quantitative improvements—for training networks—over previous methods that use video games as surrogate.", "title": "" }, { "docid": "neg:1840623_18", "text": "The objective of the current study was to compare the effects of sensory-integration therapy (SIT) and a behavioral intervention on rates of challenging behavior (including self-injurious behavior) in four children diagnosed with Autism Spectrum Disorder. For each of the participants a functional assessment was conducted to identify the variables maintaining challenging behavior. Results of these assessments were used to design function-based behavioral interventions for each participant. Recommendations for the sensory-integration treatment were designed by an Occupational Therapist, trained in the use of sensory-integration theory and techniques. The sensory-integration techniques were not dependent on the results of the functional assessments. The study was conducted within an alternating treatments design, with initial baseline and final best treatment phase. For each participant, results demonstrated that the behavioral intervention was more effective than the sensory integration therapy in the treatment of challenging behavior. In the best treatment phase, the behavioral intervention alone was implemented and further reduction was observed in the rate of challenging behavior. Analysis of saliva samples revealed relatively low levels of cortisol and very little stress-responsivity across the SIT condition and the behavioral intervention condition, which may be related to the participants' capacity to perceive stress in terms of its social significance.", "title": "" }, { "docid": "neg:1840623_19", "text": "PURPOSE\nTo evaluate the problems encountered on revising a multiply operated nose and the methods used in correcting such problems.\n\n\nPATIENTS AND METHODS\nThe study included 50 cases presenting for revision rhinoplasty after having had 2 or more previous rhinoplasties. An external rhinoplasty approach was used in all cases. Simultaneous septal surgery was done whenever indicated. All cases were followed for a mean period of 32 months (range, 1.5-8 years). Evaluation of the surgical result depended on clinical examination, comparison of pre- and postoperative photographs, and degree of patients' satisfaction with their aesthetic and functional outcome.\n\n\nRESULTS\nFunctionally, 68% suffered nasal obstruction that was mainly caused by septal deviations and nasal valve problems. Aesthetically, the most common deformities of the upper two thirds of the nose included pollybeak (64%), dorsal irregularities (54%), dorsal saddle (44%), and open roof deformity (42%), whereas the deformities of lower third included depressed tip (68%), tip contour irregularities (60%), and overrotated tip (42%). Nasal grafting was necessary in all cases; usually more than 1 type of graft was used in each case. Postoperatively, 79% of the patients, with preoperative nasal obstruction, reported improved breathing; 84% were satisfied with their aesthetic result; and only 8 cases (16%) requested further revision to correct minor deformities.\n\n\nCONCLUSION\nRevision of a multiply operated nose is a complex and technically demanding task, yet, in a good percentage of cases, aesthetic as well as functional improvement are still possible.", "title": "" } ]

[ { "docid": "pos:1840624_0", "text": "Previous recent research on human wayfinding has focused primarily on mental representations rather than processes of wayfinding. This paper presents a formal model of some aspects of the process of wayfinding, where appropriate elements of human perception and cognition are formally realized using image schemata and affordances. The goal-driven reasoning chain that leads to action begins with incomplete and imprecise knowledge derived from imperfect observations of space. Actions result in further observations, derived knowledge and, recursively, further actions, until the goal is achieved or the wayfinder gives up. This paper gives a formalization of this process, using a modal extension to classical propositional logic to represent incomplete knowledge. Both knowledge and action are represented through a wayfinding graph. A special case of wayfinding in a building, that is finding one’s way through an airport, is used to demonstrate the formal model.", "title": "" }, { "docid": "pos:1840624_1", "text": "Autonomous robots must be able to learn and maintain models of their environments. Research on mobile robot navigation has produced two major paradigms for mapping indoor environments: grid-based and topological. While grid-based methods produce accurate metric maps, their complexity often prohibits efficient planning and problem solving in large-scale indoor environments. Topological maps, on the other hand, can be used much more efficiently, yet accurate and consistent topological maps are often difficult to learn and maintain in large-scale environments, particularly if momentary sensor data is highly ambiguous. This paper describes an approach that integrates both paradigms: grid-based and topoIogica1. Grid-based maps are learned using artificial neural networks and naive Bayesian integration. Topological maps are generated on top of the grid-based maps, by partitioning the latter into coherent regions. By combining both paradigms, the approach presented here gains advantages from both worlds: accuracy/consistency and efficiency. The paper gives results for autonomous exploration, mapping and operation of a mobile robot in populated multi-room environments. @ 1998 Elsevier Science B.V.", "title": "" } ]

[ { "docid": "neg:1840624_0", "text": "MOTIVATION\nProtein interaction networks provide an important system-level view of biological processes. One of the fundamental problems in biological network analysis is the global alignment of a pair of networks, which puts the proteins of one network into correspondence with the proteins of another network in a manner that conserves their interactions while respecting other evidence of their homology. By providing a mapping between the networks of different species, alignments can be used to inform hypotheses about the functions of unannotated proteins, the existence of unobserved interactions, the evolutionary divergence between the two species and the evolution of complexes and pathways.\n\n\nRESULTS\nWe introduce GHOST, a global pairwise network aligner that uses a novel spectral signature to measure topological similarity between subnetworks. It combines a seed-and-extend global alignment phase with a local search procedure and exceeds state-of-the-art performance on several network alignment tasks. We show that the spectral signature used by GHOST is highly discriminative, whereas the alignments it produces are also robust to experimental noise. When compared with other recent approaches, we find that GHOST is able to recover larger and more biologically significant, shared subnetworks between species.\n\n\nAVAILABILITY\nAn efficient and parallelized implementation of GHOST, released under the Apache 2.0 license, is available at http://cbcb.umd.edu/kingsford_group/ghost\n\n\nCONTACT\nrob@cs.umd.edu.", "title": "" }, { "docid": "neg:1840624_1", "text": "This paper describes recent work on the “Crosswatch” project, which is a computer vision-based smartphone system developed for providing guidance to blind and visually impaired travelers at traffic intersections. A key function of Crosswatch is self-localization - the estimation of the user's location relative to the crosswalks in the current traffic intersection. Such information may be vital to users with low or no vision to ensure that they know which crosswalk they are about to enter, and are properly aligned and positioned relative to the crosswalk. However, while computer vision-based methods have been used for finding crosswalks and helping blind travelers align themselves to them, these methods assume that the entire crosswalk pattern can be imaged in a single frame of video, which poses a significant challenge for a user who lacks enough vision to know where to point the camera so as to properly frame the crosswalk. In this paper we describe work in progress that tackles the problem of crosswalk detection and self-localization, building on recent work describing techniques enabling blind and visually impaired users to acquire 360° image panoramas while turning in place on a sidewalk. The image panorama is converted to an aerial (overhead) view of the nearby intersection, centered on the location that the user is standing at, so as to facilitate matching with a template of the intersection obtained from Google Maps satellite imagery. The matching process allows crosswalk features to be detected and permits the estimation of the user's precise location relative to the crosswalk of interest. We demonstrate our approach on intersection imagery acquired by blind users, thereby establishing the feasibility of the approach.", "title": "" }, { "docid": "neg:1840624_2", "text": "The recognition of personal emotional state or sentiment conveyed through text is the main task we address in our research. The communication of emotions through text messaging and posts of personal blogs poses the ‘informal style of writing’ challenge for researchers expecting grammatically correct input. Our Affect Analysis Model was designed to handle the informal messages written in an abbreviated or expressive manner. While constructing our rule-based approach to affect recognition from text, we followed the compositionality principle. Our method is capable of processing sentences of different complexity, including simple, compound, complex (with complement and relative clauses), and complex-compound sentences. The evaluation of the Affect Analysis Model algorithm showed promising results regarding its capability to accurately recognize affective information in text from an existing corpus of personal blog posts.", "title": "" }, { "docid": "neg:1840624_3", "text": "Friction effects are particularly critical for industrial robots, since they can induce large positioning errors, stick-slip motions, and limit cycles. This paper offers a reasoned overview of the main friction compensation techniques that have been developed in the last years, regrouping them according to the adopted kind of control strategy. Some experimental results are reported, to show how the control performances can be affected not only by the chosen method, but also by the characteristics of the available robotic architecture and of the executed task.", "title": "" }, { "docid": "neg:1840624_4", "text": "We have used a modified technique in five patients to correct winging of the scapula caused by injury to the brachial plexus or the long thoracic nerve during transaxillary resection of the first rib. The procedure stabilises the scapulothoracic articulation by using strips of autogenous fascia lata wrapped around the 4th, 6th and 7th ribs at least two, and preferably three, times. The mean age of the patients at the time of operation was 38 years (26 to 47) and the mean follow-up six years and four months (three years and three months to 11 years). Satisfactory stability was achieved in all patients with considerable improvement in shoulder function. There were no complications.", "title": "" }, { "docid": "neg:1840624_5", "text": "Micro-expressions are brief involuntary facial expressions that reveal genuine emotions and, thus, help detect lies. Because of their many promising applications, they have attracted the attention of researchers from various fields. Recent research reveals that two perceptual color spaces (CIELab and CIELuv) provide useful information for expression recognition. This paper is an extended version of our International Conference on Pattern Recognition paper, in which we propose a novel color space model, tensor independent color space (TICS), to help recognize micro-expressions. In this paper, we further show that CIELab and CIELuv are also helpful in recognizing micro-expressions, and we indicate why these three color spaces achieve better performance. A micro-expression color video clip is treated as a fourth-order tensor, i.e., a four-dimension array. The first two dimensions are the spatial information, the third is the temporal information, and the fourth is the color information. We transform the fourth dimension from RGB into TICS, in which the color components are as independent as possible. The combination of dynamic texture and independent color components achieves a higher accuracy than does that of RGB. In addition, we define a set of regions of interests (ROIs) based on the facial action coding system and calculated the dynamic texture histograms for each ROI. Experiments are conducted on two micro-expression databases, CASME and CASME 2, and the results show that the performances for TICS, CIELab, and CIELuv are better than those for RGB or gray.", "title": "" }, { "docid": "neg:1840624_6", "text": "We propose a method of generating teaching policies for use in intelligent tutoring systems (ITS) for concept learning tasks <xref ref-type=\"bibr\" rid=\"ref1\">[1]</xref> , e.g., teaching students the meanings of words by showing images that exemplify their meanings à la Rosetta Stone <xref ref-type=\"bibr\" rid=\"ref2\">[2]</xref> and Duo Lingo <xref ref-type=\"bibr\" rid=\"ref3\">[3]</xref> . The approach is grounded in control theory and capitalizes on recent work by <xref ref-type=\"bibr\" rid=\"ref4\">[4] </xref> , <xref ref-type=\"bibr\" rid=\"ref5\">[5]</xref> that frames the “teaching” problem as that of finding approximately optimal teaching policies for approximately optimal learners (AOTAOL). Our work expands on <xref ref-type=\"bibr\" rid=\"ref4\">[4]</xref> , <xref ref-type=\"bibr\" rid=\"ref5\">[5]</xref> in several ways: (1) We develop a novel student model in which the teacher's actions can <italic>partially </italic> eliminate hypotheses about the curriculum. (2) With our student model, inference can be conducted <italic> analytically</italic> rather than numerically, thus allowing computationally efficient planning to optimize learning. (3) We develop a reinforcement learning-based hierarchical control technique that allows the teaching policy to search through <italic>deeper</italic> learning trajectories. We demonstrate our approach in a novel ITS for foreign language learning similar to Rosetta Stone and show that the automatically generated AOTAOL teaching policy performs favorably compared to two hand-crafted teaching policies.", "title": "" }, { "docid": "neg:1840624_7", "text": "In this paper, a novel fabric detect detection scheme based on HOG and SVM is proposed. Firstly, each block-based feature of the image is encoded using the histograms of orientated gradients (HOG), which are insensitive to various lightings and noises. Then, a powerful feature selection algorithm, AdaBoost, is performed to automatically select a small set of discriminative HOG features in order to achieve robust detection results. In the end, support vector machine (SVM) is used to classify the fabric defects. Experimental results demonstrate the efficiency of our proposed algorithm.", "title": "" }, { "docid": "neg:1840624_8", "text": "Taipei Metro adopted the diode-grounded scheme for stray current collection in construction of its cross rail network. During operation of the network, a high rail-to-earth potential (V/sub rail/) has been observed at the east end of the Blue Line (i.e., stations BL13-BL16). To find effective countermeasures, a series of field tests in a step-by-step development nature was conducted from 1999-2000, which led to the decision of disconnecting the impedance bond at G11 of the tie line so that the negative return current of the Blue Line cannot flow to the rails of the Red-Green Line, and vice versa (detailed in Sections III-A and V-C). This decision was implemented through contract-out work in 2003. Since then, the V/sub rail/ has been lowered by almost half before disconnection. To gain the insight characteristic before the contract-out, numerical simulations were also conducted by simulating the multi-train and multisection features of the cross transportation network. The simulation results (for V/sub rail/ and stray current, or I/sub stray/) were consistent with the field-test results. This paper presents the design of these field tests and their test results in comparison with the simulation results, based on which the countermeasures for reducing V/sub rail/ and the present status after V/sub rail/ reduction at Taipei Metro are presented.", "title": "" }, { "docid": "neg:1840624_9", "text": "Novice qualitative researchers are often unsure regarding the analysis of their data and, where grounded theory is chosen, they may be uncertain regarding the differences that now exist between the approaches of Glaser and Strauss, who together first described the method. These two approaches are compared in relation to roots and divergences, role of induction, deduction and verification, ways in which data are coded and the format of generated theory. Personal experience of developing as a ground theorist is used to illustrate some of the key differences. A conclusion is drawn that, rather than debate relative merits of the two approaches, suggests that novice researchers need to select the method that best suits their cognitive style and develop analytic skills through doing research.", "title": "" }, { "docid": "neg:1840624_10", "text": "Neural network based sequence-to-sequence models in an encoder-decoder framework have been successfully applied to solve Question Answering (QA) problems, predicting answers from statements and questions. However, almost all previous models have failed to consider detailed context information and unknown states under which systems do not have enough information to answer given questions. These scenarios with incomplete or ambiguous information are very common in the setting of Interactive Question Answering (IQA). To address this challenge, we develop a novel model, employing context-dependent word-level attention for more accurate statement representations and question-guided sentence-level attention for better context modeling. We also generate unique IQA datasets to test our model, which will be made publicly available. Employing these attention mechanisms, our model accurately understands when it can output an answer or when it requires generating a supplementary question for additional input depending on different contexts. When available, user's feedback is encoded and directly applied to update sentence-level attention to infer an answer. Extensive experiments on QA and IQA datasets quantitatively demonstrate the effectiveness of our model with significant improvement over state-of-the-art conventional QA models.", "title": "" }, { "docid": "neg:1840624_11", "text": "As the bring your own device (BYOD) to work trend grows, so do the network security risks. This fast-growing trend has huge benefits for both employees and employers. With malware, spyware and other malicious downloads, tricking their way onto personal devices, organizations need to consider their information security policies. Malicious programs can download onto a personal device without a user even knowing. This can have disastrous results for both an organization and the personal device. When this happens, it risks BYODs making unauthorized changes to policies and leaking sensitive information into the public domain. A privacy breach can cause a domino effect with huge financial and legal implications, and loss of productivity for organizations. This is a difficult challenge. Organizations need to consider user privacy and rights together with protecting networks from attacks. This paper evaluates a new architectural framework to control the risks that challenge organizations and the use of BYODs. After analysis of large volumes of research, the previous studies addressed single issues. We integrated parts of these single solutions into a new framework to develop a complete solution for access control. With too many organizations failing to implement and enforce adequate security policies, the process needs to be simpler. This framework reduces system restrictions while enforcing access control policies for BYOD and cloud environments using an independent platform. Primary results of the study are positive with the framework reducing access control issues. Keywords—Bring your own device; access control; policy; security", "title": "" }, { "docid": "neg:1840624_12", "text": "The generation of multimode orbital angular momentum (OAM) carrying beams has attracted more and more attention. A broadband dual-polarized dual-OAM-mode uniform circular array is proposed in this letter. The proposed antenna array, which consists of a broadband dual-polarized bow-tie dipole array and a broadband phase-shifting feeding network, can be used to generate OAM mode −1 and OAM mode 1 beams from 2.1 to 2.7 GHz (a bandwidth of 25%) for each of two polarizations. Four orthogonal channels can be provided by the proposed antenna array. A 2.5-m broadband OAM link is built. The measured crosstalk between the mode matched channels and the mode mismatched channels is less than −12 dB at 2.1, 2.4, and 2.7 GHz. Four different data streams are transmitted simultaneously by the proposed array with a bit error rate less than 4.2×10-3 at 2.1, 2.4, and 2.7 GHz.", "title": "" }, { "docid": "neg:1840624_13", "text": "Breast cancer is one of the leading causes of cancer death among women worldwide. The proposed approach comprises three steps as follows. Firstly, the image is preprocessed to remove speckle noise while preserving important features of the image. Three methods are investigated, i.e., Frost Filter, Detail Preserving Anisotropic Diffusion, and Probabilistic Patch-Based Filter. Secondly, Normalized Cut or Quick Shift is used to provide an initial segmentation map for breast lesions. Thirdly, a postprocessing step is proposed to select the correct region from a set of candidate regions. This approach is implemented on a dataset containing 20 B-mode ultrasound images, acquired from UDIAT Diagnostic Center of Sabadell, Spain. The overall system performance is determined against the ground truth images. The best system performance is achieved through the following combinations: Frost Filter with Quick Shift, Detail Preserving Anisotropic Diffusion with Normalized Cut and Probabilistic Patch-Based with Normalized Cut.", "title": "" }, { "docid": "neg:1840624_14", "text": "Neural networks are becoming central in several areas of computer vision and image processing and different architectures have been proposed to solve specific problems. The impact of the loss layer of neural networks, however, has not received much attention in the context of image processing: the default and virtually only choice is $\\ell _2$. In this paper, we bring attention to alternative choices for image restoration. In particular, we show the importance of perceptually-motivated losses when the resulting image is to be evaluated by a human observer. We compare the performance of several losses, and propose a novel, differentiable error function. We show that the quality of the results improves significantly with better loss functions, even when the network architecture is left unchanged.", "title": "" }, { "docid": "neg:1840624_15", "text": "The filter bank methods have been a popular non-parametric way of computing the complex amplitude spectrum. So far, the length of the filters in these filter banks has been set to some constant value independently of the data. In this paper, we take the first step towards considering the filter length as an unknown parameter. Specifically, we derive a very simple and approximate way of determining the optimal filter length in a data-adaptive way. Based on this analysis, we also derive a model averaged version of the forward and the forward-backward amplitude spectral Capon estimators. Through simulations, we show that these estimators significantly improve the estimation accuracy compared to the traditional Capon estimators.", "title": "" }, { "docid": "neg:1840624_16", "text": "A new CMOS buffer without short-circuit power consumption is proposed. The gatedriving signal of the output pull-up (pull-down) transistor is fed back to the output pull-down (pull-up) transistor to get tri-state output momentarily, eliminating the short-circuit power consumption. The HSPICE simulation results verified the operation of the proposed buffer and showed the power-delay product is about 15% smaller than conventional tapered CMOS buffer.", "title": "" }, { "docid": "neg:1840624_17", "text": "This paper compares advantages and disadvantages of several commonly used current sensing methods such as dedicated sense resistor sensing, MOSFET Rds(on) current sensing, and inductor DC resistance (DCR) current sensing. Among these current sense methods, inductor DCR current sense that shows more advantages over other current sensing methods is chosen for analysis. The time constants mismatch issue between the time constant made by the current sensing RC network and the one formed with output inductor and its DC resistance is addressed in this paper. And an unified small signal modeling of a buck converter using inductor DCR current sensing with matched and mismatched time constants is presented, and the modeling has been verified experimentally.", "title": "" }, { "docid": "neg:1840624_18", "text": "We propose an approach to cross-lingual named entity recognition model transfer without the use of parallel corpora. In addition to global de-lexicalized features, we introduce multilingual gazetteers that are generated using graph propagation, and cross-lingual word representation mappings without the use of parallel data. We target the e-commerce domain, which is challenging due to its unstructured and noisy nature. The experiments have shown that our approaches beat the strong MT baseline, where the English model is transferred to two languages: Spanish and Chinese.", "title": "" }, { "docid": "neg:1840624_19", "text": "Multiple treebanks annotated under heterogeneous standards give rise to the research question of best utilizing multiple resources for improving statistical models. Prior research has focused on discrete models, leveraging stacking and multi-view learning to address the problem. In this paper, we empirically investigate heterogeneous annotations using neural network models, building a neural network counterpart to discrete stacking and multiview learning, respectively, finding that neural models have their unique advantages thanks to the freedom from manual feature engineering. Neural model achieves not only better accuracy improvements, but also an order of magnitude faster speed compared to its discrete baseline, adding little time cost compared to a neural model trained on a single treebank.", "title": "" } ]

[ { "docid": "pos:1840625_0", "text": "We propose a new learning-based method for estimating 2D human pose from a single image, using Dual-Source Deep Convolutional Neural Networks (DS-CNN). Recently, many methods have been developed to estimate human pose by using pose priors that are estimated from physiologically inspired graphical models or learned from a holistic perspective. In this paper, we propose to integrate both the local (body) part appearance and the holistic view of each local part for more accurate human pose estimation. Specifically, the proposed DS-CNN takes a set of image patches (category-independent object proposals for training and multi-scale sliding windows for testing) as the input and then learns the appearance of each local part by considering their holistic views in the full body. Using DS-CNN, we achieve both joint detection, which determines whether an image patch contains a body joint, and joint localization, which finds the exact location of the joint in the image patch. Finally, we develop an algorithm to combine these joint detection/localization results from all the image patches for estimating the human pose. The experimental results show the effectiveness of the proposed method by comparing to the state-of-the-art human-pose estimation methods based on pose priors that are estimated from physiologically inspired graphical models or learned from a holistic perspective.", "title": "" }, { "docid": "pos:1840625_1", "text": "This paper offers the first variational approach to the problem of dense 3D reconstruction of non-rigid surfaces from a monocular video sequence. We formulate non-rigid structure from motion (nrsfm) as a global variational energy minimization problem to estimate dense low-rank smooth 3D shapes for every frame along with the camera motion matrices, given dense 2D correspondences. Unlike traditional factorization based approaches to nrsfm, which model the low-rank non-rigid shape using a fixed number of basis shapes and corresponding coefficients, we minimize the rank of the matrix of time-varying shapes directly via trace norm minimization. In conjunction with this low-rank constraint, we use an edge preserving total-variation regularization term to obtain spatially smooth shapes for every frame. Thanks to proximal splitting techniques the optimization problem can be decomposed into many point-wise sub-problems and simple linear systems which can be easily solved on GPU hardware. We show results on real sequences of different objects (face, torso, beating heart) where, despite challenges in tracking, illumination changes and occlusions, our method reconstructs highly deforming smooth surfaces densely and accurately directly from video, without the need for any prior models or shape templates.", "title": "" }, { "docid": "pos:1840625_2", "text": "This paper presents a data-driven matching cost for stereo matching. A novel deep visual correspondence embedding model is trained via Convolutional Neural Network on a large set of stereo images with ground truth disparities. This deep embedding model leverages appearance data to learn visual similarity relationships between corresponding image patches, and explicitly maps intensity values into an embedding feature space to measure pixel dissimilarities. Experimental results on KITTI and Middlebury data sets demonstrate the effectiveness of our model. First, we prove that the new measure of pixel dissimilarity outperforms traditional matching costs. Furthermore, when integrated with a global stereo framework, our method ranks top 3 among all two-frame algorithms on the KITTI benchmark. Finally, cross-validation results show that our model is able to make correct predictions for unseen data which are outside of its labeled training set.", "title": "" } ]

[ { "docid": "neg:1840625_0", "text": "In computer vision problems such as pair matching, only binary information ‘same’ or ‘different’ label for pairs of images is given during training. This is in contrast to classification problems, where the category labels of training images are provided. We propose a unified discriminative dictionary learning approach for both pair matching and multiclass classification tasks. More specifically, we introduce a new discriminative term called ‘pairwise sparse code error’ for the discriminativeness in sparse representation of pairs of signals, and then combine it with the classification error for discriminativeness in classifier construction to form a unified objective function. The solution to the new objective function is achieved by employing the efficient feature-sign search algorithm. The learned dictionary encourages feature points from a similar pair (or the same class) to have similar sparse codes. We validate the effectiveness of our approach through a series of experiments on face verification and recognition problems.", "title": "" }, { "docid": "neg:1840625_1", "text": "The anatomic structures in the female that prevent incontinence and genital organ prolapse on increases in abdominal pressure during daily activities include sphincteric and supportive systems. In the urethra, the action of the vesical neck and urethral sphincteric mechanisms maintains urethral closure pressure above bladder pressure. Decreases in the number of striated muscle fibers of the sphincter occur with age and parity. A supportive hammock under the urethra and vesical neck provides a firm backstop against which the urethra is compressed during increases in abdominal pressure to maintain urethral closure pressures above the rapidly increasing bladder pressure. This supporting layer consists of the anterior vaginal wall and the connective tissue that attaches it to the pelvic bones through the pubovaginal portion of the levator ani muscle, and the uterosacral and cardinal ligaments comprising the tendinous arch of the pelvic fascia. At rest the levator ani maintains closure of the urogenital hiatus. They are additionally recruited to maintain hiatal closure in the face of inertial loads related to visceral accelerations as well as abdominal pressurization in daily activities involving recruitment of the abdominal wall musculature and diaphragm. Vaginal birth is associated with an increased risk of levator ani defects, as well as genital organ prolapse and urinary incontinence. Computer models indicate that vaginal birth places the levator ani under tissue stretch ratios of up to 3.3 and the pudendal nerve under strains of up to 33%, respectively. Research is needed to better identify the pathomechanics of these conditions.", "title": "" }, { "docid": "neg:1840625_2", "text": "The purpose of the present text is to present the theory and techniques behind the Gray Level Coocurrence Matrix (GLCM) method, and the stateof-the-art of the field, as applied to two dimensional images. It does not present a survey of practical results. 1 Gray Level Coocurrence Matrices In statistical texture analysis, texture features are computed from the statistical distribution of observed combinations of intensities at specified positions relative to each other in the image. According to the number of intensity points (pixels) in each combination, statistics are classified into first-order, second-order and higher-order statistics. The Gray Level Coocurrence Matrix (GLCM) method is a way of extracting second order statistical texture features. The approach has been used in a number of applications, e.g. [5],[6],[14],[5],[7],[12],[2],[8],[10],[1]. A GLCM is a matrix where the number of rows and colums is equal to the number of gray levels, G, in the image. The matrix element P (i, j | ∆x, ∆y) is the relative frequency with which two pixels, separated by a pixel distance (∆x, ∆y), occur within a given neighborhood, one with intensity i and the other with intensity j. One may also say that the matrix element P (i, j | d, θ) contains the second order 1 Albregtsen : Texture Measures Computed from GLCM-Matrices 2 statistical probability values for changes between gray levels i and j at a particular displacement distance d and at a particular angle (θ). Given an M ×N neighborhood of an input image containing G gray levels from 0 to G − 1, let f(m, n) be the intensity at sample m, line n of the neighborhood. Then P (i, j | ∆x, ∆y) = WQ(i, j | ∆x, ∆y) (1) where W = 1 (M − ∆x)(N − ∆y) Q(i, j | ∆x, ∆y) = N−∆y ∑", "title": "" }, { "docid": "neg:1840625_3", "text": "A systematic review of the literature related to effective occupational therapy interventions in rehabilitation of individuals with work-related low back injuries and illnesses was carried out as part of the Evidence-Based Literature Review Project of the American Occupational Therapy Association. This review evaluated research on a broad range of occupational therapy-related intervention procedures and approaches. Findings from the review indicate that the evidence is insufficient to support or refute the effectiveness of exercise therapy and other conservative treatments for subacute and chronic low back injuries. The research reviewed strongly suggests that for interventions to be effective, occupational therapy practitioners should use a holistic, client-centered approach. The research supports the need for occupational therapy practitioners to consider multiple strategies for addressing clients' needs. Specifically, interventions for individuals with low back injuries and illnesses should incorporate a biopsychosocial, client-centered approach that includes actively involving the client in the rehabilitation process at the beginning of the intervention process and addressing the client's psychosocial needs in addition to his or her physical impairments. The implications for occupational therapy practice, research, and education are also discussed.", "title": "" }, { "docid": "neg:1840625_4", "text": "Domain Name System (DNS) is ubiquitous in any network. DNS tunnelling is a technique to transfer data, convey messages or conduct TCP activities over DNS protocol that is typically not blocked or watched by security enforcement such as firewalls. As a technique, it can be utilized in many malicious ways which can compromise the security of a network by the activities of data exfiltration, cyber-espionage, and command and control. On the other side, it can also be used by legitimate users. The traditional methods may not be able to distinguish between legitimate and malicious uses even if they can detect the DNS tunnelling activities. We propose a behaviour analysis based method that can not only detect the DNS tunnelling, but also classify the activities in order to catch and block the malicious tunnelling traffic. The proposed method can achieve the scale of real-time detection on fast and large DNS data with the use of big data technologies in offline training and online detection systems.", "title": "" }, { "docid": "neg:1840625_5", "text": "With the explosive growth of Internet of Things devices and massive data produced at the edge of the network, the traditional centralized cloud computing model has come to a bottleneck due to the bandwidth limitation and resources constraint. Therefore, edge computing, which enables storing and processing data at the edge of the network, has emerged as a promising technology in recent years. However, the unique features of edge computing, such as content perception, real-time computing, and parallel processing, has also introduced several new challenges in the field of data security and privacy-preserving, which are also the key concerns of the other prevailing computing paradigms, such as cloud computing, mobile cloud computing, and fog computing. Despites its importance, there still lacks a survey on the recent research advance of data security and privacy-preserving in the field of edge computing. In this paper, we present a comprehensive analysis of the data security and privacy threats, protection technologies, and countermeasures inherent in edge computing. Specifically, we first make an overview of edge computing, including forming factors, definition, architecture, and several essential applications. Next, a detailed analysis of data security and privacy requirements, challenges, and mechanisms in edge computing are presented. Then, the cryptography-based technologies for solving data security and privacy issues are summarized. The state-of-the-art data security and privacy solutions in edge-related paradigms are also surveyed. Finally, we propose several open research directions of data security in the field of edge computing.", "title": "" }, { "docid": "neg:1840625_6", "text": "Placing the DRAM in the same package as a processor enables several times higher memory bandwidth than conventional off-package DRAM. Yet, the latency of in-package DRAM is not appreciably lower than that of off-package DRAM. A promising use of in-package DRAM is as a large cache. Unfortunately, most previous DRAM cache designs optimize mainly for cache hit latency and do not consider bandwidth efficiency as a first-class design constraint. Hence, as we show in this paper, these designs are suboptimal for use with in-package DRAM.\n We propose a new DRAM cache design, Banshee, that optimizes for both in-package and off-package DRAM bandwidth efficiency without degrading access latency. Banshee is based on two key ideas. First, it eliminates the tag lookup overhead by tracking the contents of the DRAM cache using TLBs and page table entries, which is efficiently enabled by a new lightweight TLB coherence protocol we introduce. Second, it reduces unnecessary DRAM cache replacement traffic with a new bandwidth-aware frequency-based replacement policy. Our evaluations show that Banshee significantly improves performance (15% on average) and reduces DRAM traffic (35.8% on average) over the best-previous latency-optimized DRAM cache design.", "title": "" }, { "docid": "neg:1840625_7", "text": "We study the problem of named entity recognition (NER) from electronic medical records, which is one of the most fundamental and critical problems for medical text mining. Medical records which are written by clinicians from different specialties usually contain quite different terminologies and writing styles. The difference of specialties and the cost of human annotation makes it particularly difficult to train a universal medical NER system. In this paper, we propose a labelaware double transfer learning framework (LaDTL) for cross-specialty NER, so that a medical NER system designed for one specialty could be conveniently applied to another one with minimal annotation efforts. The transferability is guaranteed by two components: (i) we propose label-aware MMD for feature representation transfer, and (ii) we perform parameter transfer with a theoretical upper bound which is also label aware. We conduct extensive experiments on 12 cross-specialty NER tasks. The experimental results demonstrate that La-DTL provides consistent accuracy improvement over strong baselines. Besides, the promising experimental results on non-medical NER scenarios indicate that LaDTL is potential to be seamlessly adapted to a wide range of NER tasks.", "title": "" }, { "docid": "neg:1840625_8", "text": "Techniques for partitioning objects into optimally homogeneous groups on the basis of empirical measures of similarity among those objects have received increasing attention in several different fields. This paper develops a useful correspondence between any hierarchical system of such clusters, and a particular type of distance measure. The correspondence gives rise to two methods of clustering that are computationally rapid and invariant under monotonic transformations of the data. In an explicitly defined sense, one method forms clusters that are optimally \"connected,\" while the other forms clusters that are optimally \"compact.\"", "title": "" }, { "docid": "neg:1840625_9", "text": "We present a low-cost IoT based system able to monitor acoustic, olfactory, visual and thermal comfort levels. The system is provided with different ambient sensors, computing, control and connectivity features. The integration of the device with a smartwatch makes it possible the analysis of the personal comfort parameters.", "title": "" }, { "docid": "neg:1840625_10", "text": "The parietal lobe plays a major role in sensorimotor integration and action. Recent neuroimaging studies have revealed more than 40 retinotopic areas distributed across five visual streams in the human brain, two of which enter the parietal lobe. A series of retinotopic areas occupy the length of the intraparietal sulcus and continue into the postcentral sulcus. On themedial wall, retinotopy extends across the parieto-occipital sulcus into the precuneus and reaches the cingulate sulcus. Full-body tactile stimulation revealed a multisensory homunculus lying along the postcentral sulcus just posterior to primary somatosensory cortical areas and overlapping with the anteriormost retinotopic maps. These topologically organized higher-level maps lay the foundation for actions in peripersonal space (e.g., reaching and grasping) aswell as navigation through space. A preliminary yet comprehensive multilayer functional atlas was constructed to specify the relative locations of cortical unisensory, multisensory, and action representations. We expect that those areal and functional definitions will be refined by future studies using more sophisticated stimuli and tasks tailored to regions with different specificity. The long-term goal is to construct an online surface-based atlas containing layered maps of multiple modalities that can be used as a reference to understand the functions and disorders of the parietal lobe.", "title": "" }, { "docid": "neg:1840625_11", "text": "Purpose – The purpose of this paper is to introduce the concept of strategic integration of knowledge management (KM ) and customer relationship management (CRM). The integration is a strategic issue that has strong ramifications in the long-term competitiveness of organizations. It is not limited to CRM; the concept can also be applied to supply chain management (SCM), product development management (PDM), eterprise resource planning (ERP) and retail network management (RNM) that offer different perspectives into knowledge management adoption. Design/methodology/approach – Through literature review and establishing new perspectives with examples, the components of knowledge management, customer relationship management, and strategic planning are amalgamated. Findings – Findings include crucial details in the various components of knowledge management, customer relationship management, and strategic planning, i.e. strategic planning process, value formula, intellectual capital measure, different levels of CRM and their core competencies. Practical implications – Although the strategic integration of knowledge management and customer relationship management is highly conceptual, a case example has been provided where the concept is applied. The same concept could also be applied to other industries that focus on customer service. Originality/value – The concept of strategic integration of knowledge management and customer relationship management is new. There are other areas, yet to be explored in terms of additional integration such as SCM, PDM, ERP, and RNM. The concept of integration would be useful for future research as well as for KM and CRM practitioners.", "title": "" }, { "docid": "neg:1840625_12", "text": "The prevalence of diet-related chronic diseases strongly impacts global health and health services. Currently, it takes training and strong personal involvement to manage or treat these diseases. One way to assist with dietary assessment is through computer vision systems that can recognize foods and their portion sizes from images and output the corresponding nutritional information. When multiple food items may exist, a food segmentation stage should also be applied before recognition. In this study, we propose a method to detect and segment the food of already detected dishes in an image. The method combines region growing/merging techniques with a deep CNN-based food border detection. A semi-automatic version of the method is also presented that improves the result with minimal user input. The proposed methods are trained and tested on non-overlapping subsets of a food image database including 821 images, taken under challenging conditions and annotated manually. The automatic and semi-automatic dish segmentation methods reached average accuracies of 88% and 92%, respectively, in roughly 0.5 seconds per image.", "title": "" }, { "docid": "neg:1840625_13", "text": "This article presents an up-to-date review of the several extraction methods commonly used to determine the value of the threshold voltage of MOSFETs. It includes the different methods that extract this quantity from the drain current versus gate voltage transfer characteristics measured under linear operation conditions for crystalline and non-crystalline MOSFETs. The various methods presented for the linear region are adapted to the saturation region and tested as a function of drain voltage whenever possible. The implementation of the extraction methods is discussed and tested by applying them to real state-ofthe-art devices in order to compare their performance. The validity of the different methods with respect to the presence of parasitic series resistance is also evaluated using 2-D simulations. 2012 Elsevier Ltd. All rights reserved.", "title": "" }, { "docid": "neg:1840625_14", "text": "In this paper we propose a human interface device that converts the mechanism of hand sign language into alphanumerical characters. This device is in the form of a portable right hand glove. We propose this device in concurrence with assistive engineering to help the underprivileged. Our main goal is to identify 26 alphabets and 10 numbers of American Sign Language and display it on the LCD. Once the text is obtained on the LCD, text to speech conversion operation is carried out and a voice output is obtained. Further, the text obtained can also be viewed on a PC or any portable hand held device. People with hearing disability find it difficult to communicate with others using their Universal Sign Language, as a normal person doesn't understand these sign languages. Our main objective is to set an interface between the Deaf/Dumb and normal person to improve the communication capabilities so that they can communicate easily with others. We mount dual axis accelerometers on the glove and propose and efficient methodology to convert these sign languages.", "title": "" }, { "docid": "neg:1840625_15", "text": "More than 60 percent of the population in the India, agriculture as the primary sector occupation. In recent years, due increase in labor shortage interest has grown for the development of the autonomous vehicles like robots in the agriculture. An robot called agribot have been designed for agricultural purposes. It is designed to minimize the labor of farmers in addition to increasing the speed and accuracy of the work. It performs the elementary functions involved in farming i.e. spraying of pesticide, sowing of seeds, and so on. Spraying pesticides especially important for the workers in the area of potentially harmful for the safety and health of the workers. This is especially important for the workers in the area of potentially harmful for the safety and health of the workers. The Proposed system aims at designing multipurpose autonomous agricultural robotic vehicle which can be controlled through IoT for seeding and spraying of pesticides. These robots are used to reduce human intervention, ensuring high yield and efficient utilization of resources. KeywordsIoT, Agribot, Sprayer, Pesticides", "title": "" }, { "docid": "neg:1840625_16", "text": "Cannabis sativa L. is an important herbaceous species originating from Central Asia, which has been used in folk medicine and as a source of textile fiber since the dawn of times. This fast-growing plant has recently seen a resurgence of interest because of its multi-purpose applications: it is indeed a treasure trove of phytochemicals and a rich source of both cellulosic and woody fibers. Equally highly interested in this plant are the pharmaceutical and construction sectors, since its metabolites show potent bioactivities on human health and its outer and inner stem tissues can be used to make bioplastics and concrete-like material, respectively. In this review, the rich spectrum of hemp phytochemicals is discussed by putting a special emphasis on molecules of industrial interest, including cannabinoids, terpenes and phenolic compounds, and their biosynthetic routes. Cannabinoids represent the most studied group of compounds, mainly due to their wide range of pharmaceutical effects in humans, including psychotropic activities. The therapeutic and commercial interests of some terpenes and phenolic compounds, and in particular stilbenoids and lignans, are also highlighted in view of the most recent literature data. Biotechnological avenues to enhance the production and bioactivity of hemp secondary metabolites are proposed by discussing the power of plant genetic engineering and tissue culture. In particular two systems are reviewed, i.e., cell suspension and hairy root cultures. Additionally, an entire section is devoted to hemp trichomes, in the light of their importance as phytochemical factories. Ultimately, prospects on the benefits linked to the use of the -omics technologies, such as metabolomics and transcriptomics to speed up the identification and the large-scale production of lead agents from bioengineered Cannabis cell culture, are presented.", "title": "" }, { "docid": "neg:1840625_17", "text": "The Great East Japan Earthquake and Tsunami drastically changed Japanese society, and the requirements for ICT was completely redefined. After the disaster, it was impossible for disaster victims to utilize their communication devices, such as cellular phones, tablet computers, or laptop computers, to notify their families and friends of their safety and confirm the safety of their loved ones since the communication infrastructures were physically damaged or lacked the energy necessary to operate. Due to this drastic event, we have come to realize the importance of device-to-device communications. With the recent increase in popularity of D2D communications, many research works are focusing their attention on a centralized network operated by network operators and neglect the importance of decentralized infrastructureless multihop communication, which is essential for disaster relief applications. In this article, we propose the concept of multihop D2D communication network systems that are applicable to many different wireless technologies, and clarify requirements along with introducing open issues in such systems. The first generation prototype of relay by smartphone can deliver messages using only users' mobile devices, allowing us to send out emergency messages from disconnected areas as well as information sharing among people gathered in evacuation centers. The success of field experiments demonstrates steady advancement toward realizing user-driven networking powered by communication devices independent of operator networks.", "title": "" }, { "docid": "neg:1840625_18", "text": "This article examined the relationships and outcomes of behaviors falling at the interface of general and sexual forms of interpersonal mistreatment in the workplace. Data were collected with surveys of two different female populations (Ns = 833 and 1,425) working within a large public-sector organization. Findings revealed that general incivility and sexual harassment were related constructs, with gender harassment bridging the two. Moreover, these behaviors tended to co-occur in organizations, and employee well-being declined with the addition of each type of mistreatment to the workplace experience. This behavior type (or behavior combination) effect remained significant even after controlling for behavior frequency. The findings are interpreted from perspectives on sexual aggression, social power, and multiple victimization.", "title": "" }, { "docid": "neg:1840625_19", "text": "0141-9331/$ see front matter 2011 Elsevier B.V. A doi:10.1016/j.micpro.2011.06.002 ⇑ Corresponding author. E-mail address: jmanikandan.nitt@gmail.com (J. M In this paper, Texas Instruments TMS320C6713 DSP based real-time speech recognition system using Modified One Against All Support Vector Machine (SVM) classifier is proposed. The major contributions of this paper are: the study and evaluation of the performance of the classifier using three feature extraction techniques and proposal for minimizing the computation time for the classifier. From this study, it is found that the recognition accuracies of 93.33%, 98.67% and 96.67% are achieved for the classifier using Mel Frequency Cepstral Coefficients (MFCC) features, zerocrossing (ZC) and zerocrossing with peak amplitude (ZCPA) features respectively. To reduce the computation time required for the systems, two techniques – one using optimum threshold technique for the SVM classifier and another using linear assembly are proposed. The ZC based system requires the least computation time and the above techniques reduce the execution time by a factor of 6.56 and 5.95 respectively. For the purpose of comparison, the speech recognition system is also implemented using Altera Cyclone II FPGA with Nios II soft processor and custom instructions. Of the two approaches, the DSP approach requires 87.40% less number of clock cycles. Custom design of the recognition system on the FPGA without using the soft-core processor would have resulted in less computational complexity. The proposed classifier is also found to reduce the number of support vectors by a factor of 1.12–3.73 when applied to speaker identification and isolated letter recognition problems. The techniques proposed here can be adapted for various other SVM based pattern recognition systems. 2011 Elsevier B.V. All rights reserved.", "title": "" } ]

[ { "docid": "pos:1840626_0", "text": "A method is presented for the representation of (pictures of) faces. Within a specified framework the representation is ideal. This results in the characterization of a face, to within an error bound, by a relatively low-dimensional vector. The method is illustrated in detail by the use of an ensemble of pictures taken for this purpose.", "title": "" } ]

[ { "docid": "neg:1840626_0", "text": "Enhancing the performance of emotional speaker recognition process has witnessed an increasing interest in the last years. This paper highlights a methodology for speaker recognition under different emotional states based on the multiclass Support Vector Machine (SVM) classifier. We compare two feature extraction methods which are used to represent emotional speech utterances in order to obtain best accuracies. The first method known as traditional Mel-Frequency Cepstral Coefficients (MFCC) and the second one is MFCC combined with Shifted-Delta-Cepstra (MFCC-SDC). Experimentations are conducted on IEMOCAP database using two multiclass SVM approaches: One-Against-One (OAO) and One Against-All (OAA). Obtained results show that MFCC-SDC features outperform the conventional MFCC. Keywords—Emotion; Speaker recognition; Mel Frequency Cepstral Coefficients (MFCC); Shifted-Delta-Cepstral (SDC); SVM", "title": "" }, { "docid": "neg:1840626_1", "text": "Radar sensors operating in the 76–81 GHz range are considered key for Advanced Driver Assistance Systems (ADAS) like adaptive cruise control (ACC), collision mitigation and avoidance systems (CMS) or lane change assist (LCA). These applications are the next wave in automotive safety systems and have thus generated increased interest in lower-cost solutions especially for the mm-wave front-end (FE) section. Today, most of the radar sensors in this frequency range use GaAs based FEs. These multi-chip GaAs FEs are a main cost driver in current radar sensors due to their low integration level. The step towards monolithic microwave integrated circuits (MMIC) based on a 200 GHz ft silicon-germanium (SiGe) technology integrating all needed RF building blocks (mixers, VCOs, dividers, buffers, PAs) on an single die does not only lead to cost reductions but also benefits the testability of these MMICs. This is especially important in the light of upcoming functional safety standards like ASIL-D and ISO26262.", "title": "" }, { "docid": "neg:1840626_2", "text": "We consider the problem of building high-level, class-specific feature detectors from only unlabeled data. For example, is it possible to learn a face detector using only unlabeled images? To answer this, we train a deep sparse autoencoder on a large dataset of images (the model has 1 billion connections, the dataset has 10 million 200×200 pixel images downloaded from the Internet). We train this network using model parallelism and asynchronous SGD on a cluster with 1,000 machines (16,000 cores) for three days. Contrary to what appears to be a widely-held intuition, our experimental results reveal that it is possible to train a face detector without having to label images as containing a face or not. Control experiments show that this feature detector is robust not only to translation but also to scaling and out-of-plane rotation. We also find that the same network is sensitive to other high-level concepts such as cat faces and human bodies. Starting from these learned features, we trained our network to recognize 22,000 object categories from ImageNet and achieve a leap of 70% relative improvement over the previous state-of-the-art.", "title": "" }, { "docid": "neg:1840626_3", "text": "Several researchers, present authors included, envision personal mobile robot agents that can assist humans in their daily tasks. Despite many advances in robotics, such mobile robot agents still face many limitations in their perception, cognition, and action capabilities. In this work, we propose a symbiotic interaction between robot agents and humans to overcome the robot limitations while allowing robots to also help humans. We introduce a visitor’s companion robot agent, as a natural task for such symbiotic interaction. The visitor lacks knowledge of the environment but can easily open a door or read a door label, while the mobile robot with no arms cannot open a door and may be confused about its exact location, but can plan paths well through the building and can provide useful relevant information to the visitor. We present this visitor companion task in detail with an enumeration and formalization of the actions of the robot agent in its interaction with the human. We briefly describe the wifi-based robot localization algorithm and show results of the different levels of human help to the robot during its navigation. We then test the value of robot help to the visitor during the task to understand the relationship tradeoffs. Our work has been fully implemented in a mobile robot agent, CoBot, which has successfully navigated for several hours and continues to navigate in our indoor environment.", "title": "" }, { "docid": "neg:1840626_4", "text": "Multilabel learning deals with data associated with multiple labels simultaneously. Like other data mining and machine learning tasks, multilabel learning also suffers from the curse of dimensionality. Dimensionality reduction has been studied for many years, however, multilabel dimensionality reduction remains almost untouched. In this article, we propose a multilabel dimensionality reduction method, MDDM, with two kinds of projection strategies, attempting to project the original data into a lower-dimensional feature space maximizing the dependence between the original feature description and the associated class labels. Based on the Hilbert-Schmidt Independence Criterion, we derive a eigen-decomposition problem which enables the dimensionality reduction process to be efficient. Experiments validate the performance of MDDM.", "title": "" }, { "docid": "neg:1840626_5", "text": "Nature uses a limited, conservative set of amino acids to synthesize proteins. The ability to genetically encode an expanded set of building blocks with new chemical and physical properties is transforming the study, manipulation and evolution of proteins, and is enabling diverse applications, including approaches to probe, image and control protein function, and to precisely engineer therapeutics. Underpinning this transformation are strategies to engineer and rewire translation. Emerging strategies aim to reprogram the genetic code so that noncanonical biopolymers can be synthesized and evolved, and to test the limits of our ability to engineer the translational machinery and systematically recode genomes.", "title": "" }, { "docid": "neg:1840626_6", "text": "Coilgun electromagnetic launchers have capability for low and high speed applications. Through the development of four guns having projectiles ranging from 10 g to 5 kg and speeds up to 1 km/s, Sandia National Laboratories has succeeded in coilgun design and operations, validating the computational codes and basis for gun system control. Coilguns developed at Sandia consist of many coils stacked end-to-end forming a barrel, with each coil energized in sequence to create a traveling magnetic wave that accelerates a projectile. Active tracking of the projectile location during launch provides precise feedback to control when the coils arc triggered to create this wave. However, optimum performance depends also on selection of coil parameters. This paper discusses issues related to coilgun design and control such as tradeoffs in geometry and circuit parameters to achieve the necessary current risetime to establish the energy in the coils. The impact of switch jitter on gun performance is also assessed for high-speed applications.", "title": "" }, { "docid": "neg:1840626_7", "text": "Social network sites provide the opportunity for bu ilding and maintaining online social network groups around a specific interest. Despite the increasing use of social networks in higher education, little previous research has studied their impacts on stud en ’s engagement and on their perceived educational outcomes. This research investigates the impact of instructors’ self-disclosure and use of humor via course-based social networks as well as their credi bility, and the moderating impact of time spent in hese course-based social networks, on the students’ enga g ment in course-based social networks. The researc h provides a theoretical viewpoint, supported by empi rical evidence, on the impact of students’ engageme nt in course-based social networks on their perceived educational outcomes. The findings suggest that instructors who create course-based online social n etworks to communicate with their students can increase their engagement, motivation, and satisfac on. We conclude the paper by suggesting the theoretical implications for the study and by provi ding strategies for instructors to adjust their act ivities in order to succeed in improving their students’ engag ement and educational outcomes.", "title": "" }, { "docid": "neg:1840626_8", "text": "Design and developmental procedure of strip-line based 1.5 MW, 30-96 MHz, ultra-wideband high power 3 dB hybrid coupler has been presented and its applicability in ion cyclotron resonance heating (ICRH) in tokamak is discussed. For the high power handling capability, spacing between conductors and ground need to very high. Hence other structural parameters like strip-width, strip thickness coupling gap, and junction also become large which can be gone upto optimum limit where various constrains like fabrication tolerance, discontinuities, and excitation of higher TE and TM modes become prominent and significantly deteriorates the desired parameters of the coupled lines system. In designed hybrid coupler, two 8.34 dB coupled lines are connected in tandem to get desired coupling of 3 dB and air is used as dielectric. The spacing between ground and conductors are taken as 0.164 m for 1.5 MW power handling capability. To have the desired spacing, each of 8.34 dB segments are designed with inner dimension of 3.6 × 1.0 × 40 cm where constraints have been significantly realized, compensated, and applied in designing of 1.5 MW hybrid coupler and presented in paper.", "title": "" }, { "docid": "neg:1840626_9", "text": "OBJECTIVE\nThe purpose of this study was to examine the relationship between two forms of helping behavior among older adults--informal caregiving and formal volunteer activity.\n\n\nMETHODS\nTo evaluate our hypotheses, we employed Tobit regression models to analyze panel data from the first two waves of the Americans' Changing Lives survey.\n\n\nRESULTS\nWe found that older adult caregivers were more likely to be volunteers than noncaregivers. Caregivers who provided a relatively high number of caregiving hours annually reported a greater number of volunteer hours than did noncaregivers. Caregivers who provided care to nonrelatives were more likely than noncaregivers to be a volunteer and to volunteer more hours. Finally, caregivers were more likely than noncaregivers to be asked to volunteer.\n\n\nDISCUSSION\nOur results provide support for the hypothesis that caregivers are embedded in networks that provide them with more opportunities for volunteering. Additional research on the motivations for volunteering and greater attention to the context and hierarchy of caregiving and volunteering are needed.", "title": "" }, { "docid": "neg:1840626_10", "text": "Technological Pedagogical Content Knowledge (TPACK) has been introduced as a conceptual framework for the knowledge base teachers need to effectively teach with technology. The framework stems from the notion that technology integration in a specific educational context benefits from a careful alignment of content, pedagogy and the potential of technology, and that teachers who want to integrate technology in their teaching practice therefore need to be competent in all three domains. This study is a systematic literature review about TPACK of 55 peer-reviewed journal articles (and one book chapter), published between 2005 and 2011. The purpose of the review was to investigate the theoretical basis and the practical use of TPACK. Findings showed different understandings of TPACK and of technological knowledge. Implications of these different views impacted the way TPACK was measured. Notions about TPACK in subject domains were hardly found in the studies selected for this review. Teacher knowledge (TPACK) and beliefs about pedagogy and technology are intertwined. Both determine whether a teacher decides to teach with technology. Active involvement in (re)design and enactment of technology-enhanced lessons was found as a promising strategy for the development of TPACK in (student-)teachers. Future directions for research are discussed.", "title": "" }, { "docid": "neg:1840626_11", "text": "Along with the burst of open source projects, software theft (or plagiarism) has become a very serious threat to the healthiness of software industry. Software birthmark, which represents the unique characteristics of a program, can be used for software theft detection. We propose a system call dependence graph based software birthmark called SCDG birthmark, and examine how well it reflects unique behavioral characteristics of a program. To our knowledge, our detection system based on SCDG birthmark is the first one that is capable of detecting software component theft where only partial code is stolen. We demonstrate the strength of our birthmark against various evasion techniques, including those based on different compilers and different compiler optimization levels as well as two state-of-the-art obfuscation tools. Unlike the existing work that were evaluated through small or toy software, we also evaluate our birthmark on a set of large software. Our results show that SCDG birthmark is very practical and effective in detecting software theft that even adopts advanced evasion techniques.", "title": "" }, { "docid": "neg:1840626_12", "text": "Microblogging services such as Twitter are said to have the potential for increasing political participation. Given the feature of 'retweeting' as a simple yet powerful mechanism for information diffusion, Twitter is an ideal platform for users to spread not only information in general but also political opinions through their networks as Twitter may also be used to publicly agree with, as well as to reinforce, someone's political opinions or thoughts. Besides their content and intended use, Twitter messages ('tweets') also often convey pertinent information about their author's sentiment. In this paper, we seek to examine whether sentiment occurring in politically relevant tweets has an effect on their retweetability (i.e., how often these tweets will be retweeted). Based on a data set of 64,431 political tweets, we find a positive relationship between the quantity of words indicating affective dimensions, including positive and negative emotions associated with certain political parties or politicians, in a tweet and its retweet rate. Furthermore, we investigate how political discussions take place in the Twitter network during periods of political elections with a focus on the most active and most influential users. Finally, we conclude by discussing the implications of our results.", "title": "" }, { "docid": "neg:1840626_13", "text": "Nearly 40% of mortality in the United States is linked to social and behavioral factors such as smoking, diet and sedentary lifestyle. Autonomous self-regulation of health-related behaviors is thus an important aspect of human behavior to assess. In 1997, the Behavior Change Consortium (BCC) was formed. Within the BCC, seven health behaviors, 18 theoretical models, five intervention settings and 26 mediating variables were studied across diverse populations. One of the measures included across settings and health behaviors was the Treatment Self-Regulation Questionnaire (TSRQ). The purpose of the present study was to examine the validity of the TSRQ across settings and health behaviors (tobacco, diet and exercise). The TSRQ is composed of subscales assessing different forms of motivation: amotivation, external, introjection, identification and integration. Data were obtained from four different sites and a total of 2731 participants completed the TSRQ. Invariance analyses support the validity of the TSRQ across all four sites and all three health behaviors. Overall, the internal consistency of each subscale was acceptable (most alpha values >0.73). The present study provides further evidence of the validity of the TSRQ and its usefulness as an assessment tool across various settings and for different health behaviors.", "title": "" }, { "docid": "neg:1840626_14", "text": "The arrival of a multinational corporation often looks like a death sentence to local companies in an emerging market. After all, how can they compete in the face of the vast financial and technological resources, the seasoned management, and the powerful brands of, say, a Compaq or a Johnson & Johnson? But local companies often have more options than they might think, say the authors. Those options vary, depending on the strength of globalization pressures in an industry and the nature of a company's competitive assets. In the worst case, when globalization pressures are strong and a company has no competitive assets that it can transfer to other countries, it needs to retreat to a locally oriented link within the value chain. But if globalization pressures are weak, the company may be able to defend its market share by leveraging the advantages it enjoys in its home market. Many companies in emerging markets have assets that can work well in other countries. Those that operate in industries where the pressures to globalize are weak may be able to extend their success to a limited number of other markets that are similar to their home base. And those operating in global markets may be able to contend head-on with multinational rivals. By better understanding the relationship between their company's assets and the industry they operate in, executives from emerging markets can gain a clearer picture of the options they really have when multinationals come to stay.", "title": "" }, { "docid": "neg:1840626_15", "text": "Recently, the fusion of hyperspectral and light detection and ranging (LiDAR) data has obtained a great attention in the remote sensing community. In this paper, we propose a new feature fusion framework using deep neural network (DNN). The proposed framework employs a novel 3D convolutional neural network (CNN) to extract the spectral-spatial features of hyperspectral data, a deep 2D CNN to extract the elevation features of LiDAR data, and then a fully connected deep neural network to fuse the extracted features in the previous CNNs. Through the aforementioned three deep networks, one can extract the discriminant and invariant features of hyperspectral and LiDAR data. At last, logistic regression is used to produce the final classification results. The experimental results reveal that the proposed deep fusion model provides competitive results. Furthermore, the proposed deep fusion idea opens a new window for future research.", "title": "" }, { "docid": "neg:1840626_16", "text": "Network Function Virtualization (NFV) is emerging as one of the most innovative concepts in the networking landscape. By migrating network functions from dedicated mid-dleboxes to general purpose computing platforms, NFV can effectively reduce the cost to deploy and to operate large networks. However, in order to achieve its full potential, NFV needs to encompass also the radio access network allowing Mobile Virtual Network Operators to deploy custom resource allocation solutions within their virtual radio nodes. Such requirement raises several challenges in terms of performance isolation and resource provisioning. In this work we formalize the Virtual Network Function (VNF) placement problem for radio access networks as an integer linear programming problem and we propose a VNF placement heuristic. Moreover, we also present a proof-of-concept implementation of an NFV management and orchestration framework for Enterprise WLANs. The proposed architecture builds upon a programmable network fabric where pure forwarding nodes are mixed with radio and packet processing nodes leveraging on general computing platforms.", "title": "" }, { "docid": "neg:1840626_17", "text": "Evolutionary algorithms often have to solve optimization problems in the presence of a wide range of uncertainties. Generally, uncertainties in evolutionary computation can be divided into the following four categories. First, the fitness function is noisy. Second, the design variables and/or the environmental parameters may change after optimization, and the quality of the obtained optimal solution should be robust against environmental changes or deviations from the optimal point. Third, the fitness function is approximated, which means that the fitness function suffers from approximation errors. Fourth, the optimum of the problem to be solved changes over time and, thus, the optimizer should be able to track the optimum continuously. In all these cases, additional measures must be taken so that evolutionary algorithms are still able to work satisfactorily. This paper attempts to provide a comprehensive overview of the related work within a unified framework, which has been scattered in a variety of research areas. Existing approaches to addressing different uncertainties are presented and discussed, and the relationship between the different categories of uncertainties are investigated. Finally, topics for future research are suggested.", "title": "" }, { "docid": "neg:1840626_18", "text": "This paper presents original research on prevalence, user characteristics and effect profile of N,N-dimethyltryptamine (DMT), a potent hallucinogenic which acts primarily through the serotonergic system. Data were obtained from the Global Drug Survey (an anonymous online survey of people, many of whom have used drugs) conducted between November and December 2012 with 22,289 responses. Lifetime prevalence of DMT use was 8.9% (n=1980) and past year prevalence use was 5.0% (n=1123). We explored the effect profile of DMT in 472 participants who identified DMT as the last new drug they had tried for the first time and compared it with ratings provided by other respondents on psilocybin (magic mushrooms), LSD and ketamine. DMT was most often smoked and offered a strong, intense, short-lived psychedelic high with relatively few negative effects or \"come down\". It had a larger proportion of new users compared with the other substances (24%), suggesting its popularity may increase. Overall, DMT seems to have a very desirable effect profile indicating a high abuse liability that maybe offset by a low urge to use more.", "title": "" }, { "docid": "neg:1840626_19", "text": "With the increasing adoption of Web Services and service-oriented computing paradigm, matchmaking of web services with the request has become a significant task. This warrants the need to establish an effective and reliable Web Service discovery. Here reducing the service discovery time and increasing the quality of discovery are key issues. This paper proposes a new semantic Web Service discovery scheme where the similarity between the query and service is decided using the WSDL specification and ontology, and the improved Hungarian algorithm is applied to quickly find the maximum match. The proposed approach utilizes the structure of datatype and operation, and natural language description used for information retrieval. Computer simulation reveals that the proposed scheme substantially increases the quality of service discovery compared to the existing schemes in terms of precision, recall rate, and F-measure. Moreover, the proposed scheme allows consistently smaller discovery time, while the improvement gets more significant as the number of compared parameters increases.", "title": "" } ]

[ { "docid": "pos:1840627_0", "text": "Recurrent Neural Networks are showing much promise in many sub-areas of natural language processing, ranging from document classification to machine translation to automatic question answering. Despite their promise, many recurrent models have to read the whole text word by word, making it slow to handle long documents. For example, it is difficult to use a recurrent network to read a book and answer questions about it. In this paper, we present an approach of reading text while skipping irrelevant information if needed. The underlying model is a recurrent network that learns how far to jump after reading a few words of the input text. We employ a standard policy gradient method to train the model to make discrete jumping decisions. In our benchmarks on four different tasks, including number prediction, sentiment analysis, news article classification and automatic Q&A, our proposed model, a modified LSTM with jumping, is up to 6 times faster than the standard sequential LSTM, while maintaining the same or even better accuracy.", "title": "" }, { "docid": "pos:1840627_1", "text": "This paper proposes a deep learning architecture based on Residual Network that dynamically adjusts the number of executed layers for the regions of the image. This architecture is end-to-end trainable, deterministic and problem-agnostic. It is therefore applicable without any modifications to a wide range of computer vision problems such as image classification, object detection and image segmentation. We present experimental results showing that this model improves the computational efficiency of Residual Networks on the challenging ImageNet classification and COCO object detection datasets. Additionally, we evaluate the computation time maps on the visual saliency dataset cat2000 and find that they correlate surprisingly well with human eye fixation positions.", "title": "" }, { "docid": "pos:1840627_2", "text": "Text Categorization (TC), also known as Text Classification, is the task of automatically classifying a set of text documents into different categories from a predefined set. If a document belongs to exactly one of the categories, it is a single-label classification task; otherwise, it is a multi-label classification task. TC uses several tools from Information Retrieval (IR) and Machine Learning (ML) and has received much attention in the last years from both researchers in the academia and industry developers. In this paper, we first categorize the documents using KNN based machine learning approach and then return the most relevant documents.", "title": "" } ]

[ { "docid": "neg:1840627_0", "text": "The stochastic approximation method is behind the solution to many important, actively-studied problems in machine learning. Despite its farreaching application, there is almost no work on applying stochastic approximation to learning problems with general constraints. The reason for this, we hypothesize, is that no robust, widely-applicable stochastic approximation method exists for handling such problems. We propose that interior-point methods are a natural solution. We establish the stability of a stochastic interior-point approximation method both analytically and empirically, and demonstrate its utility by deriving an on-line learning algorithm that also performs feature selection via L1 regularization.", "title": "" }, { "docid": "neg:1840627_1", "text": "A prime requirement for autonomous driving is a fast and reliable estimation of the motion state of dynamic objects in the ego-vehicle's surroundings. An instantaneous approach for extended objects based on two Doppler radar sensors has recently been proposed. In this paper, that approach is augmented by prior knowledge of the object's heading angle and rotation center. These properties can be determined reliably by state-of-the-art methods based on sensors such as LIDAR or cameras. The information fusion is performed utilizing an appropriate measurement model, which directly maps the motion state in the Doppler velocity space. This model integrates the geometric properties. It is used to estimate the object's motion state using a linear regression. Additionally, the model allows a straightforward calculation of the corresponding variances. The resulting method shows a promising accuracy increase of up to eight times greater than the original approach.", "title": "" }, { "docid": "neg:1840627_2", "text": "Suppose your netmail is being erratically censored by Captain Yossarian. Whenever you send a message, he censors each bit of the message with probability 1/2, replacing each censored bit by some reserved character. Well versed in such concepts as redundancy, this is no real problem to you. The question is, can it actually be turned around and used to your advantage? We answer this question strongly in the affirmative. We show that this protocol, more commonly known as oblivious transfer, can be used to simulate a more sophisticated protocol, known as oblivious circuit evaluation([Y]). We also show that with such a communication channel, one can have completely noninteractive zero-knowledge proofs of statements in NP. These results do not use any complexity-theoretic assumptions. We can show that they have applications to a variety of models in which oblivious transfer can be done.", "title": "" }, { "docid": "neg:1840627_3", "text": "Neural networks have recently had a lot of success for many tasks. However, neural network architectures that perform well are still typically designed manually by experts in a cumbersome trial-and-error process. We propose a new method to automatically search for well-performing CNN architectures based on a simple hill climbing procedure whose operators apply network morphisms, followed by short optimization runs by cosine annealing. Surprisingly, this simple method yields competitive results, despite only requiring resources in the same order of magnitude as training a single network. E.g., on CIFAR-10, our method designs and trains networks with an error rate below 6% in only 12 hours on a single GPU; training for one day reduces this error further, to almost 5%.", "title": "" }, { "docid": "neg:1840627_4", "text": "OBJECTIVES\nThe role of social media as a source of timely and massive information has become more apparent since the era of Web 2.0.Multiple studies illustrated the use of information in social media to discover biomedical and health-related knowledge.Most methods proposed in the literature employ traditional document classification techniques that represent a document as a bag of words.These techniques work well when documents are rich in text and conform to standard English; however, they are not optimal for social media data where sparsity and noise are norms.This paper aims to address the limitations posed by the traditional bag-of-word based methods and propose to use heterogeneous features in combination with ensemble machine learning techniques to discover health-related information, which could prove to be useful to multiple biomedical applications, especially those needing to discover health-related knowledge in large scale social media data.Furthermore, the proposed methodology could be generalized to discover different types of information in various kinds of textual data.\n\n\nMETHODOLOGY\nSocial media data is characterized by an abundance of short social-oriented messages that do not conform to standard languages, both grammatically and syntactically.The problem of discovering health-related knowledge in social media data streams is then transformed into a text classification problem, where a text is identified as positive if it is health-related and negative otherwise.We first identify the limitations of the traditional methods which train machines with N-gram word features, then propose to overcome such limitations by utilizing the collaboration of machine learning based classifiers, each of which is trained to learn a semantically different aspect of the data.The parameter analysis for tuning each classifier is also reported.\n\n\nDATA SETS\nThree data sets are used in this research.The first data set comprises of approximately 5000 hand-labeled tweets, and is used for cross validation of the classification models in the small scale experiment, and for training the classifiers in the real-world large scale experiment.The second data set is a random sample of real-world Twitter data in the US.The third data set is a random sample of real-world Facebook Timeline posts.\n\n\nEVALUATIONS\nTwo sets of evaluations are conducted to investigate the proposed model's ability to discover health-related information in the social media domain: small scale and large scale evaluations.The small scale evaluation employs 10-fold cross validation on the labeled data, and aims to tune parameters of the proposed models, and to compare with the stage-of-the-art method.The large scale evaluation tests the trained classification models on the native, real-world data sets, and is needed to verify the ability of the proposed model to handle the massive heterogeneity in real-world social media.\n\n\nFINDINGS\nThe small scale experiment reveals that the proposed method is able to mitigate the limitations in the well established techniques existing in the literature, resulting in performance improvement of 18.61% (F-measure).The large scale experiment further reveals that the baseline fails to perform well on larger data with higher degrees of heterogeneity, while the proposed method is able to yield reasonably good performance and outperform the baseline by 46.62% (F-Measure) on average.", "title": "" }, { "docid": "neg:1840627_5", "text": "Recurrent neural network language models (RNNLMs) have recently become increasingly popular for many applications i ncluding speech recognition. In previous research RNNLMs have normally been trained on well-matched in-domain data. The adaptation of RNNLMs remains an open research area to be explored. In this paper, genre and topic based RNNLM adaptation techniques are investigated for a multi-genre broad cast transcription task. A number of techniques including Proba bilistic Latent Semantic Analysis, Latent Dirichlet Alloc ation and Hierarchical Dirichlet Processes are used to extract sh ow level topic information. These were then used as additional input to the RNNLM during training, which can facilitate unsupervised test time adaptation. Experiments using a state-o f-theart LVCSR system trained on 1000 hours of speech and more than 1 billion words of text showed adaptation could yield pe rplexity reductions of 8% relatively over the baseline RNNLM and small but consistent word error rate reductions.", "title": "" }, { "docid": "neg:1840627_6", "text": "The AMBA-AHB Multilayer Bus matrix Self-Motivated Arbitration scheme proposed three methods for data transmiting from master to slave for on chip communication. Multilayer advanced high-performance bus (ML-AHB) busmatrix employs slave-side arbitration. Slave-side arbitration is different from master-side arbitration in terms of request and grant signals since, in the former, the master merely starts a burst transaction and waits for the slave response to proceed to the next transfer. Therefore, in the former, the unit of arbitration can be a transaction or a transfer. However, the ML-AHB busmatrix of ARM offers only transferbased fixed-pri-ority and round-robin arbitration schemes. In this paper, we propose the design and implementation of a flexible arbiter for the ML-AHB busmatrix to support three priority policies fixed priority, round robin, and dynamic priority and three data multiplexing modes transfer, transaction, and desired transfer length. In total, there are nine possible arbitration schemes. The proposed arbiter, which is self-motivated (SM), selects one of the nine possible arbitration schemes based upon the priority-level notifications and the desired transfer length from the masters so that arbitration leads to the maximum performance. Experimental results show that, although the area overhead of the proposed SM arbitration scheme is 9%–25% larger than those of the other arbitration schemes, our arbiter improves the throughput by 14%–62% compared to other schemes.", "title": "" }, { "docid": "neg:1840627_7", "text": "Given a finite set of points P ⊆ R, we would like to find a small subset S ⊆ P such that the convex hull of S approximately contains P . More formally, every point in P is within distance from the convex hull of S. Such a subset S is called an -hull. Computing an -hull is an important problem in computational geometry, machine learning, and approximation algorithms. In many applications, the set P is too large to fit in memory. We consider the streaming model where the algorithm receives the points of P sequentially and strives to use a minimal amount of memory. Existing streaming algorithms for computing an -hull require O( (1−d)/2) space, which is optimal for a worst-case input. However, this ignores the structure of the data. The minimal size of an -hull of P , which we denote by OPT, can be much smaller. A natural question is whether a streaming algorithm can compute an -hull using only O(OPT) space. We begin with lower bounds that show, under a reasonable streaming model, that it is not possible to have a single-pass streaming algorithm that computes an -hull with O(OPT) space. We instead propose three relaxations of the problem for which we can compute -hulls using space near-linear to the optimal size. Our first algorithm for points in R2 that arrive in random-order uses O(logn ·OPT) space. Our second algorithm for points in R2 makes O(log( −1)) passes before outputting the -hull and requires O(OPT) space. Our third algorithm, for points in R for any fixed dimension d, outputs, with high probability, an -hull for all but δ-fraction of directions and requires O(OPT · log OPT) space. 1 This work was supported in part by the National Science Foundation under grant CCF-1525971. Work was done while the author was at Carnegie Mellon University. 2 This material is based upon work supported in part by the National Science Foundation under Grants No. 1447639, 1650041 and 1652257, Cisco faculty award, and by the ONR Award N00014-18-1-2364. 3 Now at DeepMind. 4 This research was supported by the Franco-American Fulbright Commission and supported in part by National Science Foundation under Grant No. 1447639, 1650041 and 1652257. The author thanks INRIA (l’Institut national de recherche en informatique et en automatique) for hosting him during the writing of this paper. 5 This material is based upon work supported in part by National Science Foundation under Grant No. 1447639, 1650041 and 1652257. Work was done while the author was at Johns Hopkins University. EA T C S © Avrim Blum, Vladimir Braverman, Ananya Kumar, Harry Lang, and Lin F. Yang; licensed under Creative Commons License CC-BY 45th International Colloquium on Automata, Languages, and Programming (ICALP 2018). Editors: Ioannis Chatzigiannakis, Christos Kaklamanis, Dániel Marx, and Donald Sannella; Article No. 21; pp. 21:1–21:13 Leibniz International Proceedings in Informatics Schloss Dagstuhl – Leibniz-Zentrum für Informatik, Dagstuhl Publishing, Germany 21:2 Approximate Convex Hull of Data Streams 2012 ACM Subject Classification Theory of computation → Computational geometry, Theory of computation → Sketching and sampling, Theory of computation → Streaming models", "title": "" }, { "docid": "neg:1840627_8", "text": "Human-Following robots are being actively researched for their immense potential to carry out mundane tasks like load carrying and monitoring of target individual through interaction and collaboration. The recent advancements in vision and sensor technologies have helped in creating more user-friendly robots that are able to coexist with humans by leveraging the sensors for human detection, human movement estimation, collision avoidance, and obstacle avoidance. But most of these sensors are suitable only for Line of Sight following of human. In the case of loss of sight of the target, most of them fail to re-acquire their target. In this paper, we are proposing a novel method to develop a human following robot using Bluetooth and Inertial Measurement Unit (IMU) on Smartphones which can work under high interference environment and can reacquire the target when lost. The proposed method leverages IMU sensors on the smartphone to estimate the direction of human movement while estimating the distance traveled from the RSSI of the Bluetooth. Thus, the Follow Me robot which estimates the position of target human and direction of heading and effectively track the person was implemented using Smartphone on a differential drive robot.", "title": "" }, { "docid": "neg:1840627_9", "text": "Real-time ETL and data warehouse multidimensional modeling (DMM) of business operational data has become an important research issue in the area of real-time data warehousing (RTDW). In this study, some of the recently proposed real-time ETL technologies from the perspectives of data volumes, frequency, latency, and mode have been discussed. In addition, we highlight several advantages of using semi-structured DMM (i.e. XML) in RTDW instead of traditional structured DMM (i.e., relational). We compare the two DMMs on the basis of four characteristics: heterogeneous data integration, types of measures supported, aggregate query processing, and incremental maintenance. We implemented the RTDW framework for an example telecommunication organization. Our experimental analysis shows that if the delay comes from the incremental maintenance of DMM, no ETL technology (full-reloading or incremental-loading) can help in real-time business intelligence.", "title": "" }, { "docid": "neg:1840627_10", "text": "In continuous action domains, standard deep reinforcement learning algorithms like DDPG suffer from inefficient exploration when facing sparse or deceptive reward problems. Conversely, evolutionary and developmental methods focusing on exploration like Novelty Search, QualityDiversity or Goal Exploration Processes explore more robustly but are less efficient at fine-tuning policies using gradient-descent. In this paper, we present the GEP-PG approach, taking the best of both worlds by sequentially combining a Goal Exploration Process and two variants of DDPG. We study the learning performance of these components and their combination on a low dimensional deceptive reward problem and on the larger Half-Cheetah benchmark. We show that DDPG fails on the former and that GEP-PG improves over the best DDPG variant in both environments. Supplementary videos and discussion can be found at frama.link/gep_pg, the code at github.com/flowersteam/geppg.", "title": "" }, { "docid": "neg:1840627_11", "text": "A survey was conducted from May to Oct of 2011 of the parasitoid community of the imported cabbageworm, Pieris rapae (Lepidoptera: Pieridae), in cole crops in part of the eastern United States and southeastern Canada. The findings of our survey indicate that Cotesia rubecula (Hymenoptera: Braconidae) now occurs as far west as North Dakota and has become the dominant parasitoid of P. rapae in the northeastern and north central United States and adjacent parts of southeastern Canada, where it has displaced the previously common parasitoid Cotesia glomerata (Hymenoptera: Braconidae). Cotesia glomerata remains the dominant parasitoid in the mid-Atlantic states, from Virginia to North Carolina and westward to southern Illinois, below latitude N 38° 48’. This pattern suggests that the released populations of C. rubecula presently have a lower latitudinal limit south of which they are not adapted.", "title": "" }, { "docid": "neg:1840627_12", "text": "DMA-capable interconnects, providing ultra-low latency and high bandwidth, are increasingly being used in the context of distributed storage and data processing systems. However, the deployment of such systems in virtualized data centers is currently inhibited by the lack of a flexible and high-performance virtualization solution for RDMA network interfaces.\n In this work, we present a hybrid virtualization architecture which builds upon the concept of separation of paths for control and data operations available in RDMA. With hybrid virtualization, RDMA control operations are virtualized using hypervisor involvement, while data operations are set up to bypass the hypervisor completely. We describe HyV (Hybrid Virtualization), a virtualization framework for RDMA devices implementing such a hybrid architecture. In the paper, we provide a detailed evaluation of HyV for different RDMA technologies and operations. We further demonstrate the advantages of HyV in the context of a real distributed system by running RAMCloud on a set of HyV-enabled virtual machines deployed across a 6-node RDMA cluster. All of the performance results we obtained illustrate that hybrid virtualization enables bare-metal RDMA performance inside virtual machines while retaining the flexibility typically associated with paravirtualization.", "title": "" }, { "docid": "neg:1840627_13", "text": "This paper presents the implementation of the interval type-2 to control the process of production of High-strength low-alloy (HSLA) steel in a secondary metallurgy process in a simply way. The proposal evaluate fuzzy techniques to ensure the accuracy of the model, the most important advantage is that the systems do not need pretreatment of the historical data, it is used as it is. The system is a multiple input single output (MISO) and the main goal of this paper is the proposal of a system that optimizes the resources: computational, time, among others.", "title": "" }, { "docid": "neg:1840627_14", "text": "Psilocybin, an indoleamine hallucinogen, produces a psychosis-like syndrome in humans that resembles first episodes of schizophrenia. In healthy human volunteers, the psychotomimetic effects of psilocybin were blocked dose-dependently by the serotonin-2A antagonist ketanserin or the atypical antipsychotic risperidone, but were increased by the dopamine antagonist and typical antipsychotic haloperidol. These data are consistent with animal studies and provide the first evidence in humans that psilocybin-induced psychosis is due to serotonin-2A receptor activation, independently of dopamine stimulation. Thus, serotonin-2A overactivity may be involved in the pathophysiology of schizophrenia and serotonin-2A antagonism may contribute to therapeutic effects of antipsychotics.", "title": "" }, { "docid": "neg:1840627_15", "text": "Physical trauma to the brain has always been known to affect brain functions and subsequent neurobiological development. Research primarily since the early 1990s has shown that psychological trauma can have detrimental effects on brain function that are not only lasting but that may alter patterns of subsequent neurodevelopment, particularly in children although developmental effects may be seen in adults as well. Childhood trauma produces a diverse range of symptoms and defining the brain's response to trauma and the factors that mediate the body's stress response systems is at the forefront of scientific investigation. This paper reviews the current evidence relating psychological trauma to anatomical and functional changes in the brain and discusses the need for accurate diagnosis and treatment to minimize such effects and to recognize their existence in developing treatment programs.", "title": "" }, { "docid": "neg:1840627_16", "text": "Gesture recognition remains a very challenging task in the field of computer vision and human computer interaction (HCI). A decade ago the task seemed to be almost unsolvable with the data provided by a single RGB camera. Due to recent advances in sensing technologies, such as time-of-flight and structured light cameras, there are new data sources available, which make hand gesture recognition more feasible. In this work, we propose a highly precise method to recognize static gestures from a depth data, provided from one of the above mentioned devices. The depth images are used to derive rotation-, translation- and scale-invariant features. A multi-layered random forest (MLRF) is then trained to classify the feature vectors, which yields to the recognition of the hand signs. The training time and memory required by MLRF are much smaller, compared to a simple random forest with equivalent precision. This allows to repeat the training procedure of MLRF without significant effort. To show the advantages of our technique, we evaluate our algorithm on synthetic data, on publicly available dataset, containing 24 signs from American Sign Language(ASL) and on a new dataset, collected using recently appeared Intel Creative Gesture Camera.", "title": "" }, { "docid": "neg:1840627_17", "text": "The UK's recent move to polymer banknotes has seen some of the currently used fingermark enhancement techniques for currency potentially become redundant, due to the surface characteristics of the polymer substrates. Possessing a non-porous surface with some semi-porous properties, alternate processes are required for polymer banknotes. This preliminary investigation explored the recovery of fingermarks from polymer notes via vacuum metal deposition using elemental copper. The study successfully demonstrated that fresh latent fingermarks, from an individual donor, could be clearly developed and imaged in the near infrared. By varying the deposition thickness of the copper, the contrast between the fingermark minutiae and the substrate could be readily optimised. Where the deposition thickness was thin enough to be visually indistinguishable, forensic gelatin lifters could be used to lift the fingermarks. These lifts could then be treated with rubeanic acid to produce a visually distinguishable mark. The technique has shown enough promise that it could be effectively utilised on other semi- and non-porous substrates.", "title": "" }, { "docid": "neg:1840627_18", "text": "Quad-robot type (QRT) unmanned aerial vehicles (UAVs) have been developed for quick detection and observation of the circumstances under calamity environment such as indoor fire spots. The UAV is equipped with four propellers driven by each electric motor, an embedded controller, an Inertial Navigation System (INS) using three rate gyros and accelerometers, a CCD (Charge Coupled Device) camera with wireless communication transmitter for observation, and an ultrasonic range sensor for height control. Accurate modeling and robust flight control of QRT UAVs are mainly discussed in this work. Rigorous dynamic model of a QRT UAV is obtained both in the reference and body frame coordinate systems. A disturbance observer (DOB) based controller using the derived dynamic models is also proposed for robust hovering control. The control input induced by DOB is helpful to use simple equations of motion satisfying accurately derived dynamics. The developed hovering robot shows stable flying performances under the adoption of DOB and the vision based localization method. Although a model is incorrect, DOB method can design a controller by regarding the inaccurate part of the model J. Kim Department of Mechanical Engineering, Seoul National University of Technology, Seoul, South Korea e-mail: jinhyun@snut.ac.kr M.-S. Kang Department of Mechatronics Engineering, Hanyang University, Ansan, South Korea e-mail: wowmecha@gmail.com S. Park (B) Division of Applied Robot Technology, Korea Institute of Industrial Technology, Ansan, South Korea e-mail: sdpark@kitech.re.kr 10 J Intell Robot Syst (2010) 57:9–26 and sensor noises as disturbances. The UAV can also avoid obstacles using eight IR (Infrared) and four ultrasonic range sensors. This kind of micro UAV can be widely used in various calamity observation fields without danger of human beings under harmful environment. The experimental results show the performance of the proposed control algorithm.", "title": "" } ]

[ { "docid": "pos:1840628_0", "text": "The Bitcoin scheme is a rare example of a large scale global payment system in which all the transactions are publicly accessible (but in an anonymous way). We downloaded the full history of this scheme, and analyzed many statistical properties of its associated transaction graph. In this paper we answer for the first time a variety of interesting questions about the typical behavior of users, how they acquire and how they spend their bitcoins, the balance of bitcoins they keep in their accounts, and how they move bitcoins between their various accounts in order to better protect their privacy. In addition, we isolated all the large transactions in the system, and discovered that almost all of them are closely related to a single large transaction that took place in November 2010, even though the associated users apparently tried to hide this fact with many strange looking long chains and fork-merge structures in the transaction graph.", "title": "" }, { "docid": "pos:1840628_1", "text": "At the current stratospheric value of Bitcoin, miners with access to significant computational horsepower are literally printing money. For example, the first operator of a USD $1,500 custom ASIC mining platform claims to have recouped his investment in less than three weeks in early February 2013, and the value of a bitcoin has more than tripled since then. Not surprisingly, cybercriminals have also been drawn to this potentially lucrative endeavor, but instead are leveraging the resources available to them: stolen CPU hours in the form of botnets. We conduct the first comprehensive study of Bitcoin mining malware, and describe the infrastructure and mechanism deployed by several major players. By carefully reconstructing the Bitcoin transaction records, we are able to deduce the amount of money a number of mining botnets have made.", "title": "" } ]

[ { "docid": "neg:1840628_0", "text": "We present ORB-SLAM2, a complete simultaneous localization and mapping (SLAM) system for monocular, stereo and RGB-D cameras, including map reuse, loop closing, and relocalization capabilities. The system works in real time on standard central processing units in a wide variety of environments from small hand-held indoors sequences, to drones flying in industrial environments and cars driving around a city. Our back-end, based on bundle adjustment with monocular and stereo observations, allows for accurate trajectory estimation with metric scale. Our system includes a lightweight localization mode that leverages visual odometry tracks for unmapped regions and matches with map points that allow for zero-drift localization. The evaluation on 29 popular public sequences shows that our method achieves state-of-the-art accuracy, being in most cases the most accurate SLAM solution. We publish the source code, not only for the benefit of the SLAM community, but with the aim of being an out-of-the-box SLAM solution for researchers in other fields.", "title": "" }, { "docid": "neg:1840628_1", "text": "Near Field Communication (NFC) is an emerging wireless short-range communication technology that is based on existing standards of the Radio Frequency Identification (RFID) infrastructure. In combination with NFC-capable smartphones it enables intuitive application scenarios for contactless transactions, in particular services for mobile payment and over-theair ticketing. The intention of this paper is to describe basic characteristics and benefits of the underlaying technology, to classify modes of operation and to present various use cases. Both existing NFC applications and possible future scenarios will be analyzed in this context. Furthermore, security concerns, challenges and present conflicts will be discussed eventually.", "title": "" }, { "docid": "neg:1840628_2", "text": "This paper studies how to incorporate the external word correlation knowledge to improve the coherence of topic modeling. Existing topic models assume words are generated independently and lack the mechanism to utilize the rich similarity relationships among words to learn coherent topics. To solve this problem, we build a Markov Random Field (MRF) regularized Latent Dirichlet Allocation (LDA) model, which defines a MRF on the latent topic layer of LDA to encourage words labeled as similar to share the same topic label. Under our model, the topic assignment of each word is not independent, but rather affected by the topic labels of its correlated words. Similar words have better chance to be put into the same topic due to the regularization of MRF, hence the coherence of topics can be boosted. In addition, our model can accommodate the subtlety that whether two words are similar depends on which topic they appear in, which allows word with multiple senses to be put into different topics properly. We derive a variational inference method to infer the posterior probabilities and learn model parameters and present techniques to deal with the hardto-compute partition function in MRF. Experiments on two datasets demonstrate the effectiveness of our model.", "title": "" }, { "docid": "neg:1840628_3", "text": "Recently, there has been considerable debate concerning key sizes for publ i c key based cry p t o graphic methods. Included in the debate have been considerations about equivalent key sizes for diffe rent methods and considerations about the minimum re q u i red key size for diffe rent methods. In this paper we propose a method of a n a lyzing key sizes based upon the value of the data being protected and the cost of b reaking ke y s . I . I n t ro d u c t i o n A . W H Y I S K E Y S I Z E I M P O R T A N T ? In order to keep transactions based upon public key cryptography secure, one must ensure that the underlying keys are sufficiently large as to render the best possible attack infeasible. However, this really just begs the question as one is now left with the task of defining ‘infeasible’. Does this mean infeasible given access to (say) most of the Internet to do the computations? Does it mean infeasible to a large adversary with a large (but unspecified) budget to buy the hardware for an attack? Does it mean infeasible with what hardware might be obtained in practice by utilizing the Internet? Is it reasonable to assume that if utilizing the entire Internet in a key breaking effort makes a key vulnerable that such an attack might actually be conducted? If a public effort involving a substantial fraction of the Internet breaks a single key, does this mean that similar sized keys are unsafe? Does one need to be concerned about such public efforts or does one only need to be concerned about possible private, sur reptitious efforts? After all, if a public attack is known on a particular key, it is easy to change that key. We shall attempt to address these issues within this paper. number 13 Apr i l 2000 B u l l e t i n News and A dv i c e f rom RSA La bo rat o r i e s I . I n t ro d u c t i o n I I . M et ho ds o f At tac k I I I . H i s tor i ca l R es u l t s and t he R S A Ch a l le nge I V. Se cu r i t y E st i m ate s", "title": "" }, { "docid": "neg:1840628_4", "text": "This paper presents a new neural network system called the Evolving Tree. This network resembles the Self-Organizing map, but deviates from it in several aspects, which are desirable in many analysis tasks. First of all the Evolving Tree grows automatically, so the user does not have to decide the network’s size before training. Secondly the network has a hierarchical structure, which makes network training and use computationally very efficient. Test results with both synthetic and actual data show that the Evolving Tree works quite well.", "title": "" }, { "docid": "neg:1840628_5", "text": "Recent work has suggested enhancing Bloom filters by using a pre-filter, based on applying machine learning to model the data set the Bloom filter is meant to represent. Here we model such learned Bloom filters, clarifying what guarantees can and cannot be associated with such a structure.", "title": "" }, { "docid": "neg:1840628_6", "text": "We present a deep learning framework for probabilistic pixel-wise semantic segmentation, which we term Bayesian SegNet. Semantic segmentation is an important tool for visual scene understanding and a meaningful measure of uncertainty is essential for decision making. Our contribution is a practical system which is able to predict pixelwise class labels with a measure of model uncertainty using Bayesian deep learning. We achieve this by Monte Carlo sampling with dropout at test time to generate a posterior distribution of pixel class labels. In addition, we show that modelling uncertainty improves segmentation performance by 2-3% across a number of datasets and architectures such as SegNet, FCN, Dilation Network and DenseNet.", "title": "" }, { "docid": "neg:1840628_7", "text": "Neurotoxins and fillers continue to remain in high demand, comprising a large part of the growing business of cosmetic minimally invasive procedures. Multiple Food and Drug Administration-approved safe yet different products exist within each category, and the role of each product continues to expand. The authors review the literature to provide an overview of the use of neurotoxins and fillers and their future directions.", "title": "" }, { "docid": "neg:1840628_8", "text": "Although the uncanny exists, the inherent, unavoidable dip (or valley) may be an illusion. Extremely abstract robots can be uncanny if the aesthetic is off, as can cosmetically atypical humans. Thus, the uncanny occupies a continuum ranging from the abstract to the real, although norms of acceptability may narrow as one approaches human likeness. However, if the aesthetic is right, any level of realism or abstraction can be appealing. If so, then avoiding or creating an uncanny effect just depends on the quality of the aesthetic design, regardless of the level of realism. The author’s preliminary experiments on human reaction to near-realistic androids appear to support this hypothesis.", "title": "" }, { "docid": "neg:1840628_9", "text": "52 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis", "title": "" }, { "docid": "neg:1840628_10", "text": "A novel concept called gesture-changeable under-actuated (GCUA) function is proposed to improve the dexterities of traditional under-actuated hands and reduce the control difficulties of dexterous hands. Based on the GCUA function, a new humanoid robot hand, GCUA Hand is designed and manufactured. The GCUA Hand can grasp different objects self-adaptively and change its initial gesture dexterously before contacting objects. The hand has 5 fingers and 15 DOFs, each finger is based on screw-nut transmission, flexible drawstring constraint and belt-pulley under-actuated mechanism to realize GCUA function. The analyses on grasping static forces and grasping stabilities are put forward. The analyses and Experimental results show that the GCUA function is very nice and valid. The hands with the GCUA function can meet the requirements of grasping and operating with lower control and cost, which is the middle road between traditional under-actuated hands and dexterous hands.", "title": "" }, { "docid": "neg:1840628_11", "text": "Identity management through biometrics offer potential advantages over knowledge and possession based methods. A wide variety of biometric modalities have been tested so far but several factors paralyse the accuracy of mono modal biometric systems. Usually, the analysis of multiple modalities offers better accuracy. An extensive review of biometric technology is presented here. Besides the mono modal systems, the article also discusses multi modal biometric systems along with their architecture and information fusion levels. The paper along with the exemplary evidences highlights the potential for biometric technology, market value and prospects. Keywords— Biometrics, Fingerprint, Face, Iris, Retina, Behavioral biometrics, Gait, Voice, Soft biometrics, Multi-modal biometrics.", "title": "" }, { "docid": "neg:1840628_12", "text": "360° videos and Head-Mounted Displays (HMDs) are geŠing increasingly popular. However, streaming 360° videos to HMDs is challenging. Œis is because only video content in viewers’ Fieldof-Views (FoVs) is rendered, and thus sending complete 360° videos wastes resources, including network bandwidth, storage space, and processing power. Optimizing the 360° video streaming to HMDs is, however, highly data and viewer dependent, and thus dictates real datasets. However, to our best knowledge, such datasets are not available in the literature. In this paper, we present our datasets of both content data (such as image saliency maps and motion maps derived from 360° videos) and sensor data (such as viewer head positions and orientations derived from HMD sensors). We put extra e‚orts to align the content and sensor data using the timestamps in the raw log €les. Œe resulting datasets can be used by researchers, engineers, and hobbyists to either optimize existing 360° video streaming applications (like rate-distortion optimization) and novel applications (like crowd-driven cameramovements). We believe that our dataset will stimulate more research activities along this exciting new research direction. ACM Reference format: Wen-Chih Lo, Ching-Ling Fan, Jean Lee, Chun-Ying Huang, Kuan-Ta Chen, and Cheng-Hsin Hsu. 2017. 360° Video Viewing Dataset in Head-Mounted Virtual Reality. In Proceedings ofMMSys’17, Taipei, Taiwan, June 20-23, 2017, 6 pages. DOI: hŠp://dx.doi.org/10.1145/3083187.3083219 CCS Concept • Information systems→Multimedia streaming", "title": "" }, { "docid": "neg:1840628_13", "text": "A critical application of metabolomics is the evaluation of tissues, which are often the primary sites of metabolic dysregulation in disease. Laboratory rodents have been widely used for metabolomics studies involving tissues due to their facile handing, genetic manipulability and similarity to most aspects of human metabolism. However, the necessary step of administration of anesthesia in preparation for tissue sampling is not often given careful consideration, in spite of its potential for causing alterations in the metabolome. We examined, for the first time using untargeted and targeted metabolomics, the effect of several commonly used methods of anesthesia and euthanasia for collection of skeletal muscle, liver, heart, adipose and serum of C57BL/6J mice. The data revealed dramatic, tissue-specific impacts of tissue collection strategy. Among many differences observed, post-euthanasia samples showed elevated levels of glucose 6-phosphate and other glycolytic intermediates in skeletal muscle. In heart and liver, multiple nucleotide and purine degradation metabolites accumulated in tissues of euthanized compared to anesthetized animals. Adipose tissue was comparatively less affected by collection strategy, although accumulation of lactate and succinate in euthanized animals was observed in all tissues. Among methods of tissue collection performed pre-euthanasia, ketamine showed more variability compared to isoflurane and pentobarbital. Isoflurane induced elevated liver aspartate but allowed more rapid initiation of tissue collection. Based on these findings, we present a more optimal collection strategy mammalian tissues and recommend that rodent tissues intended for metabolomics studies be collected under anesthesia rather than post-euthanasia.", "title": "" }, { "docid": "neg:1840628_14", "text": "To enhance the security of mobile cloud users, a few proposals have been presented recently. However we argue that most of them are not suitable for mobile cloud where mobile users might join or leave the mobile networks arbitrarily. In this paper, we design a secure mobile user-based data service mechanism (SDSM) to provide confidentiality and fine-grained access control for data stored in the cloud. This mechanism enables the mobile users to enjoy a secure outsourced data services at a minimized security management overhead. The core idea of SDSM is that SDSM outsources not only the data but also the security management to the mobile cloud in a trust way. Our analysis shows that the proposed mechanism has many advantages over the existing traditional methods such as lower overhead and convenient update, which could better cater the requirements in mobile cloud computing scenarios.", "title": "" }, { "docid": "neg:1840628_15", "text": "OBJECTIVES\nThe aim of this study was to assess the long-term safety and efficacy of the CYPHER (Cordis, Johnson and Johnson, Bridgewater, New Jersey) sirolimus-eluting coronary stent (SES) in percutaneous coronary intervention (PCI) for ST-segment elevation myocardial infarction (STEMI).\n\n\nBACKGROUND\nConcern over the safety of drug-eluting stents implanted during PCI for STEMI remains, and long-term follow-up from randomized trials are necessary. TYPHOON (Trial to assess the use of the cYPHer sirolimus-eluting stent in acute myocardial infarction treated with ballOON angioplasty) randomized 712 patients with STEMI treated by primary PCI to receive either SES (n = 355) or bare-metal stents (BMS) (n = 357). The primary end point, target vessel failure at 1 year, was significantly lower in the SES group than in the BMS group (7.3% vs. 14.3%, p = 0.004) with no increase in adverse events.\n\n\nMETHODS\nA 4-year follow-up was performed. Complete data were available in 501 patients (70%), and the survival status is known in 580 patients (81%).\n\n\nRESULTS\nFreedom from target lesion revascularization (TLR) at 4 years was significantly better in the SES group (92.4% vs. 85.1%; p = 0.002); there were no significant differences in freedom from cardiac death (97.6% and 95.9%; p = 0.37) or freedom from repeat myocardial infarction (94.8% and 95.6%; p = 0.85) between the SES and BMS groups. No difference in definite/probable stent thrombosis was noted at 4 years (SES: 4.4%, BMS: 4.8%, p = 0.83). In the 580 patients with known survival status at 4 years, the all-cause death rate was 5.8% in the SES and 7.0% in the BMS group (p = 0.61).\n\n\nCONCLUSIONS\nIn the 70% of patients with complete follow-up at 4 years, SES demonstrated sustained efficacy to reduce TLR with no difference in death, repeat myocardial infarction or stent thrombosis. (The Study to Assess AMI Treated With Balloon Angioplasty [TYPHOON]; NCT00232830).", "title": "" }, { "docid": "neg:1840628_16", "text": "The advent of cost-effectiveness and easy-operation depth cameras has facilitated a variety of visual recognition tasks including human activity recognition. This paper presents a novel framework for recognizing human activities from video sequences captured by depth cameras. We extend the surface normal to polynormal by assembling local neighboring hypersurface normals from a depth sequence to jointly characterize local motion and shape information. We then propose a general scheme of super normal vector (SNV) to aggregate the low-level polynormals into a discriminative representation, which can be viewed as a simplified version of the Fisher kernel representation. In order to globally capture the spatial layout and temporal order, an adaptive spatio-temporal pyramid is introduced to subdivide a depth video into a set of space-time cells. In the extensive experiments, the proposed approach achieves superior performance to the state-of-the-art methods on the four public benchmark datasets, i.e., MSRAction3D, MSRDailyActivity3D, MSRGesture3D, and MSRActionPairs3D.", "title": "" }, { "docid": "neg:1840628_17", "text": "SHARE is a unique panel database of micro data on health, socio-economic status and social and family networks covering most of the European Union and Israel. To date, SHARE has collected three panel waves (2004, 2006, 2010) of current living circumstances and retrospective life histories (2008, SHARELIFE); 6 additional waves are planned until 2024. The more than 150 000 interviews give a broad picture of life after the age of 50 years, measuring physical and mental health, economic and non-economic activities, income and wealth, transfers of time and money within and outside the family as well as life satisfaction and well-being. The data are available to the scientific community free of charge at www.share-project.org after registration. SHARE is harmonized with the US Health and Retirement Study (HRS) and the English Longitudinal Study of Ageing (ELSA) and has become a role model for several ageing surveys worldwide. SHARE's scientific power is based on its panel design that grasps the dynamic character of the ageing process, its multidisciplinary approach that delivers the full picture of individual and societal ageing, and its cross-nationally ex-ante harmonized design that permits international comparisons of health, economic and social outcomes in Europe and the USA.", "title": "" }, { "docid": "neg:1840628_18", "text": "Real-time traffic sign detection and recognition has been receiving increasingly more attention in recent years due to the popularity of driver-assistance systems and autonomous vehicles. This paper proposes an accurate and efficient traffic sign detection technique by exploring AdaBoost and support vector regression (SVR) for discriminative detector learning. Different from the reported traffic sign detection techniques, a novel saliency estimation approach is first proposed, where a new saliency model is built based on the traffic sign-specific color, shape, and spatial information. By incorporating the saliency information, enhanced feature pyramids are built to learn an AdaBoost model that detects a set of traffic sign candidates from images. A novel iterative codeword selection algorithm is then designed to generate a discriminative codebook for the representation of sign candidates, as detected by the AdaBoost, and an SVR model is learned to identify the real traffic signs from the detected sign candidates. Experiments on three public data sets show that the proposed traffic sign detection technique is robust and obtains superior accuracy and efficiency.", "title": "" }, { "docid": "neg:1840628_19", "text": "Due to limitations of chemical analysis procedures, small concentrations cannot be precisely measured. These concentrations are said to be below the limit of detection (LOD). In statistical analyses, these values are often censored and substituted with a constant value, such as half the LOD, the LOD divided by the square root of 2, or zero. These methods for handling below-detection values results in two distributions, a uniform distribution for those values below the LOD, and the true distribution. As a result, this can produce questionable descriptive statistics depending upon the percentage of values below the LOD. An alternative method uses the characteristics of the distribution of the values above the LOD to estimate the values below the LOD. This can be done with an extrapolation technique or maximum likelihood estimation. An example program using the same data is presented calculating the mean, standard deviation, t-test, and relative difference in the means for various methods and compares the results. The extrapolation and maximum likelihood estimate techniques have smaller error rates than all the standard replacement techniques. Although more computational, these methods produce more reliable descriptive statistics.", "title": "" } ]

[ { "docid": "pos:1840629_0", "text": "Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.", "title": "" }, { "docid": "pos:1840629_1", "text": "The independent component analysis (ICA) of a random vector consists of searching for a linear transformation that minimizes the statistical dependence between its components. In order to define suitable search criteria, the expansion of mutual information is utilized as a function of cumulants of increasing orders. An efficient algorithm is proposed, which allows the computation of the ICA of a data matrix within a polynomial time. The concept of lCA may actually be seen as an extension of the principal component analysis (PCA), which can only impose independence up to the second order and, consequently, defines directions that are orthogonal. Potential applications of ICA include data analysis and compression, Bayesian detection, localization of sources, and blind identification and deconvolution. Zusammenfassung Die Analyse unabhfingiger Komponenten (ICA) eines Vektors beruht auf der Suche nach einer linearen Transformation, die die statistische Abh~ingigkeit zwischen den Komponenten minimiert. Zur Definition geeigneter Such-Kriterien wird die Entwicklung gemeinsamer Information als Funktion von Kumulanten steigender Ordnung genutzt. Es wird ein effizienter Algorithmus vorgeschlagen, der die Berechnung der ICA ffir Datenmatrizen innerhalb einer polynomischen Zeit erlaubt. Das Konzept der ICA kann eigentlich als Erweiterung der 'Principal Component Analysis' (PCA) betrachtet werden, die nur die Unabh~ingigkeit bis zur zweiten Ordnung erzwingen kann und deshalb Richtungen definiert, die orthogonal sind. Potentielle Anwendungen der ICA beinhalten Daten-Analyse und Kompression, Bayes-Detektion, Quellenlokalisierung und blinde Identifikation und Entfaltung.", "title": "" }, { "docid": "pos:1840629_2", "text": "The application of boosting technique to regression problems has received relatively little attention in contrast to research aimed at classification problems. This letter describes a new boosting algorithm, AdaBoost.RT, for regression problems. Its idea is in filtering out the examples with the relative estimation error that is higher than the preset threshold value, and then following the AdaBoost procedure. Thus, it requires selecting the suboptimal value of the error threshold to demarcate examples as poorly or well predicted. Some experimental results using the M5 model tree as a weak learning machine for several benchmark data sets are reported. The results are compared to other boosting methods, bagging, artificial neural networks, and a single M5 model tree. The preliminary empirical comparisons show higher performance of AdaBoost.RT for most of the considered data sets.", "title": "" } ]

[ { "docid": "neg:1840629_0", "text": "A long-standing goal in the field of artificial intelligence is to develop agents that can perceive and understand the rich visual world around us and who can communicate with us about it in natural language. Significant strides have been made towards this goal over the last few years due to simultaneous advances in computing infrastructure, data gathering and algorithms. The progress has been especially rapid in visual recognition, where computers can now classify images into categories with a performance that rivals that of humans, or even surpasses it in some cases such as classifying breeds of dogs. However, despite much encouraging progress, most of the advances in visual recognition still take place in the context of assigning one or a few discrete labels to an image (e.g. person, boat, keyboard, etc.). In this dissertation we develop models and techniques that allow us to connect the domain of visual data and the domain of natural language utterances, enabling translation between elements of the two domains. In particular, first we introduce a model that embeds both images and sentences into a common multi-modal embedding space. This space then allows us to identify images that depict an arbitrary sentence description and conversely, we can identify sentences that describe any image. Second, we develop an image captioning model that takes an image and directly generates a sentence description without being constrained a finite collection of human-written sentences to choose from. Lastly, we describe a model that can take an image and both localize and describe all if its salient parts. We demonstrate that this model can also be used backwards to take any arbitrary description (e.g. white tennis shoes) and e ciently localize the described concept in a large collection of images. We argue that these models, the techniques they take advantage of internally and the interactions they enable are a stepping stone towards artificial intelligence and that connecting images and natural language o↵ers many practical benefits and immediate valuable applications. From the modeling perspective, instead of designing and staging explicit algorithms to process images and sentences in complex processing pipelines, our contribution lies in the design of hybrid convolutional and recurrent neural network architectures that connect visual data and natural language utterances with a single network. Therefore, the computational processing of images,", "title": "" }, { "docid": "neg:1840629_1", "text": "–Malicious programs have been the main actors in complex, sophisticated attacks against nations, governments, diplomatic agencies, private institutions and people. Knowledge about malicious program behavior forms the basis for constructing more secure information systems. In this article, we introduce MBO, a Malicious Behavior Ontology that represents complex behaviors of suspicious executions, and through inference rules calculates their associated threat level for analytical proposals. We evaluate MBO using over two thousand unique known malware and 385 unique known benign software. Results highlight the representativeness of the MBO for expressing typical malicious activities. Security ontologyMalware behaviorThreat analysis", "title": "" }, { "docid": "neg:1840629_2", "text": "This paper analyzes customer product-choice behavior based on the recency and frequency of each customer’s page views on e-commerce sites. Recently, we devised an optimization model for estimating product-choice probabilities that satisfy monotonicity, convexity, and concavity constraints with respect to recency and frequency. This shape-restricted model delivered high predictive performance even when there were few training samples. However, typical e-commerce sites deal in many different varieties of products, so the predictive performance of the model can be further improved by integration of such product heterogeneity. For this purpose, we develop a novel latent-class shape-restricted model for estimating product-choice probabilities for each latent class of products. We also give a tailored expectation-maximization algorithm for parameter estimation. Computational results demonstrate that higher predictive performance is achieved with our latent-class model than with the previous shape-restricted model and common latent-class logistic regression.", "title": "" }, { "docid": "neg:1840629_3", "text": "In this paper, we introduce ReTSO, a reliable and efficient design for transactional support in large-scale storage systems. ReTSO uses a centralized scheme and implements snapshot isolation, a property that guarantees that read operations of a transaction read a consistent snapshot of the data stored. The centralized scheme of ReTSO enables a lock-free commit algorithm that prevents unre-leased locks of a failed transaction from blocking others. We analyze the bottlenecks in a single-server implementation of transactional logic and propose solutions for each. The experimental results show that our implementation can service up to 72K transaction per second (TPS), which is an order of magnitude larger than the maximum achieved traffic in similar data storage systems. Consequently, we do not expect ReTSO to be a bottleneck even for current large distributed storage systems.", "title": "" }, { "docid": "neg:1840629_4", "text": "Despite years of research yielding systems and guidelines to aid visualization design, practitioners still face the challenge of identifying the best visualization for a given dataset and task. One promising approach to circumvent this problem is to leverage perceptual laws to quantitatively evaluate the effectiveness of a visualization design. Following previously established methodologies, we conduct a large scale (n = 1687) crowdsourced experiment to investigate whether the perception of correlation in nine commonly used visualizations can be modeled using Weber's law. The results of this experiment contribute to our understanding of information visualization by establishing that: (1) for all tested visualizations, the precision of correlation judgment could be modeled by Weber's law, (2) correlation judgment precision showed striking variation between negatively and positively correlated data, and (3) Weber models provide a concise means to quantify, compare, and rank the perceptual precision afforded by a visualization.", "title": "" }, { "docid": "neg:1840629_5", "text": "Mobile broadband demand keeps growing at an overwhelming pace. Though emerging wireless technologies will provide more bandwidth, the increase in demand may easily consume the extra bandwidth. To alleviate this problem, we propose using the content available on individual devices as caches. Particularly, when a user reaches areas with dense clusters of mobile devices, \"data spots\", the operator can instruct the user to connect with other users sharing similar interests and serve the requests locally. This paper presents feasibility study as well as prototype implementation of this idea.", "title": "" }, { "docid": "neg:1840629_6", "text": "State-of-the-art methods for zero-shot visual recognition formulate learning as a joint embedding problem of images and side information. In these formulations the current best complement to visual features are attributes: manually-encoded vectors describing shared characteristics among categories. Despite good performance, attributes have limitations: (1) finer-grained recognition requires commensurately more attributes, and (2) attributes do not provide a natural language interface. We propose to overcome these limitations by training neural language models from scratch, i.e. without pre-training and only consuming words and characters. Our proposed models train end-to-end to align with the fine-grained and category-specific content of images. Natural language provides a flexible and compact way of encoding only the salient visual aspects for distinguishing categories. By training on raw text, our model can do inference on raw text as well, providing humans a familiar mode both for annotation and retrieval. Our model achieves strong performance on zero-shot text-based image retrieval and significantly outperforms the attribute-based state-of-the-art for zero-shot classification on the Caltech-UCSD Birds 200-2011 dataset.", "title": "" }, { "docid": "neg:1840629_7", "text": "Methods for learning to search for structured prediction typically imitate a reference policy, with existing theoretical guarantees demonstrating low regret compared to that reference. This is unsatisfactory in many applications where the reference policy is suboptimal and the goal of learning is to improve upon it. Can learning to search work even when the reference is poor? We provide a new learning to search algorithm, LOLS, which does well relative to the reference policy, but additionally guarantees low regret compared to deviations from the learned policy: a local-optimality guarantee. Consequently, LOLS can improve upon the reference policy, unlike previous algorithms. This enables us to develop structured contextual bandits, a partial information structured prediction setting with many potential applications.", "title": "" }, { "docid": "neg:1840629_8", "text": "Steganography is a method of hiding secret messages in a cover object while communication takes place between sender and receiver. Security of confidential information has always been a major issue from the past times to the present time. It has always been the interested topic for researchers to develop secure techniques to send data without revealing it to anyone other than the receiver. Therefore from time to time researchers have developed many techniques to fulfill secure transfer of data and steganography is one of them. In this paper we have proposed a new technique of image steganography i.e. Hash-LSB with RSA algorithm for providing more security to data as well as our data hiding method. The proposed technique uses a hash function to generate a pattern for hiding data bits into LSB of RGB pixel values of the cover image. This technique makes sure that the message has been encrypted before hiding it into a cover image. If in any case the cipher text got revealed from the cover image, the intermediate person other than receiver can't access the message as it is in encrypted form.", "title": "" }, { "docid": "neg:1840629_9", "text": "In this paper, we propose a new method to estimate synthetic aperture radar interferometry (InSAR) interferometric phase in the presence of large coregistration errors. The method takes advantage of the coherence information of neighboring pixel pairs to automatically coregister the SAR images and employs the projection of the joint signal subspace onto the corresponding joint noise subspace to estimate the terrain interferometric phase. The method can automatically coregister the SAR images and reduce the interferometric phase noise simultaneously. Theoretical analysis and computer simulation results show that the method can provide accurate estimate of the terrain interferometric phase (interferogram) as the coregistration error reaches one pixel. The effectiveness of the method is also verified with the real data from the Spaceborne Imaging Radar-C/X Band SAR and the European Remote Sensing 1 and 2 satellites.", "title": "" }, { "docid": "neg:1840629_10", "text": "The design of the Smart Grid requires solving a complex problem of combined sensing, communications and control and, thus, the problem of choosing a networking technology cannot be addressed without also taking into consideration requirements related to sensor networking and distributed control. These requirements are today still somewhat undefined so that it is not possible yet to give quantitative guidelines on how to choose one communication technology over the other. In this paper, we make a first qualitative attempt to better understand the role that Power Line Communications (PLCs) can have in the Smart Grid. Furthermore, we here report recent results on the electrical and topological properties of the power distribution network. The topological characterization of the power grid is not only important because it allows us to model the grid as an information source, but also because the grid becomes the actual physical information delivery infrastructure when PLCs are used.", "title": "" }, { "docid": "neg:1840629_11", "text": "Singular Spectrum Transform (SST) is a fundamental subspace analysis technique which has been widely adopted for solving change-point detection (CPD) problems in information security applications. However, the performance of a SST based CPD algorithm is limited to the lack of robustness to corrupted observations with large noises in practice. Based on the observation that large noises in practical time series are generally sparse, in this paper, we study a combination of Robust Principal Component Analysis (RPCA) and SST to obtain a robust CPD algorithm dealing with sparse large noises. The sparse large noises are to be eliminated from observation trajectory matrices by performing a low-rank matrix recovery procedure of RPCA. The noise-eliminated matrices are then used to extract SST subspaces for CPD. The effectiveness of the proposed method is demonstrated through experiments based on both synthetic and real-world datasets. Experimental results show that the proposed method outperforms the competing state-of-the-arts in terms of detection accuracy for time series with sparse large noises.", "title": "" }, { "docid": "neg:1840629_12", "text": "The immune system protects from infections primarily by detecting and eliminating the invading pathogens; however, the host organism can also protect itself from infectious diseases by reducing the negative impact of infections on host fitness. This ability to tolerate a pathogen's presence is a distinct host defense strategy, which has been largely overlooked in animal and human studies. Introduction of the notion of \"disease tolerance\" into the conceptual tool kit of immunology will expand our understanding of infectious diseases and host pathogen interactions. Analysis of disease tolerance mechanisms should provide new approaches for the treatment of infections and other diseases.", "title": "" }, { "docid": "neg:1840629_13", "text": "A hierarchical scheme for clustering data is presented which applies to spaces with a high number of dimensions ( 3 D N > ). The data set is first reduced to a smaller set of partitions (multi-dimensional bins). Multiple clustering techniques are used, including spectral clustering; however, new techniques are also introduced based on the path length between partitions that are connected to one another. A Line-of-Sight algorithm is also developed for clustering. A test bank of 12 data sets with varying properties is used to expose the strengths and weaknesses of each technique. Finally, a robust clustering technique is discussed based on reaching a consensus among the multiple approaches, overcoming the weaknesses found individually.", "title": "" }, { "docid": "neg:1840629_14", "text": "This paper surveys the state of the art on multimodal gesture recognition and introduces the JMLR special topic on gesture recognition 2011-2015. We began right at the start of the KinectT Mrevolution when inexpensive infrared cameras providing image depth recordings became available. We published papers using this technology and other more conventional methods, including regular video cameras, to record data, thus providing a good overview of uses of machine learning and computer vision using multimodal data in this area of application. Notably, we organized a series of challenges and made available several datasets we recorded for that purpose, including tens of thousands of videos, which are available to conduct further research. We also overview recent state of the art works on gesture recognition based on a proposed taxonomy for gesture recognition, discussing challenges and future lines of research.", "title": "" }, { "docid": "neg:1840629_15", "text": "To date no AIS1 neck injury mechanism has been established, thus no neck injury criterion has been validated against such mechanism. Validation methods not related to an injury mechanism may be used. The aim of this paper was to validate different proposed neck injury criteria with reconstructed reallife crashes with recorded crash pulses and with known injury outcomes. A car fleet of more than 40,000 cars fitted with crash pulse recorders have been monitored in Sweden since 1996. All crashes with these cars, irrespective of repair cost and injury outcome have been reported. With the inclusion criteria of the three most represented car models, single rear-end crashes with a recorded crash pulse, and front seat occupants with no previous long-term AIS1 neck injury, 79 crashes with 110 front seat occupants remained to be analysed in this study. Madymo models of a BioRID II dummy in the three different car seats were exposed to the recorded crash pulses. The dummy readings were correlated to the real-life injury outcome, divided into duration of AIS1 neck injury symptoms. Effectiveness to predict neck injury was assessed for the criteria NIC, Nkm, NDC and lower neck moment, aimed at predicting AIS1 neck injury. Also risk curves were assessed for the effective criteria as well as for impact severity. It was found that NICmax and Nkm are applicable to predict risk of AIS1 neck injury when using a BioRID dummy. It is suggested that both BioRID NICmax and Nkm should be considered in rear-impact test evaluation. Furthermore, lower neck moment was found to be less applicable. Using the BioRID dummy NDC was also found less applicable.", "title": "" }, { "docid": "neg:1840629_16", "text": "Goal-oriented dialogue has been paid attention for its numerous applications in artificial intelligence. To solve this task, deep learning and reinforcement learning have recently been applied. However, these approaches struggle to find a competent recurrent neural questioner, owing to the complexity of learning a series of sentences. Motivated by theory of mind, we propose “Answerer in Questioner’s Mind” (AQM), a novel algorithm for goal-oriented dialogue. With AQM, a questioner asks and infers based on an approximated probabilistic model of the answerer. The questioner figures out the answerer’s intent via selecting a plausible question by explicitly calculating the information gain of the candidate intentions and possible answers to each question. We test our framework on two goal-oriented visual dialogue tasks: “MNIST Counting Dialog” and “GuessWhat?!.” In our experiments, AQM outperforms comparative algorithms and makes human-like dialogue. We further use AQM as a tool for analyzing the mechanism of deep reinforcement learning approach and discuss the future direction of practical goal-oriented neural dialogue systems.", "title": "" }, { "docid": "neg:1840629_17", "text": "This article presents a robotic dataset collected from the largest underground copper mine in the world. The sensor measurements from a 3D scanning lidar, a 2D radar, and stereo cameras were recorded from an approximately two kilometer traverse of a production-active tunnel. The equipment used and the data collection process is discussed in detail, along with the format of the data. This dataset is suitable for research in robotic navigation, as well as simultaneous localization and mapping. The download instructions are available at the following website http://dataset.amtc.cl.", "title": "" }, { "docid": "neg:1840629_18", "text": "Research and development (R&D) project selection is an important task for organizations with R&D project management. It is a complicated multi-stage decision-making process, which involves groups of decision makers. Current research on R&D project selection mainly focuses on mathematical decision models and their applications, but ignores the organizational aspect of the decision-making process. This paper proposes an organizational decision support system (ODSS) for R&D project selection. Object-oriented method is used to design the architecture of the ODSS. An organizational decision support system has also been developed and used to facilitate the selection of project proposals in the National Natural Science Foundation of China (NSFC). The proposed system supports the R&D project selection process at the organizational level. It provides useful information for decision-making tasks in the R&D project selection process. D 2004 Elsevier B.V. All rights reserved.", "title": "" }, { "docid": "neg:1840629_19", "text": "Manipulator collision avoidance using genetic algorithms is presented. Control gains in the collision avoidance control model are selected based on genetic algorithms. A repulsive force is artificially created using the distances between the robot links and obstacles, which are generated by a distance computation algorithm. Real-time manipulator collision avoidance control has achieved. A repulsive force gain is introduced through the approaches for definition of link coordinate frames and kinematics computations. The safety distance between objects is affected by the repulsive force gain. This makes the safety zone adjustable and provides greater intelligence for robotic tasks under the ever-changing environment.", "title": "" } ]

[ { "docid": "pos:1840630_0", "text": "We describe our experience in developing a discourse-annotated corpus for community-wide use. Working in the framework of Rhetorical Structure Theory, we were able to create a large annotated resource with very high consistency, using a well-defined methodology and protocol. This resource is made publicly available through the Linguistic Data Consortium to enable researchers to develop empirically grounded, discourse-specific applications.", "title": "" } ]

[ { "docid": "neg:1840630_0", "text": "Communication primitives such as coding and multiple antenna processing have provided significant benefits for traditional wireless systems. Existing designs, however, consume significant power and computational resources, and hence cannot be run on low complexity, power constrained backscatter devices. This paper makes two main contributions: (1) we introduce the first multi-antenna cancellation design that operates on backscatter devices while retaining a small form factor and power footprint, (2) we introduce a novel coding mechanism that enables long range communication as well as concurrent transmissions and can be decoded on backscatter devices. We build hardware prototypes of the above designs that can be powered solely using harvested energy from TV and solar sources. The results show that our designs provide benefits for both RFID and ambient backscatter systems: they enable RFID tags to communicate directly with each other at distances of tens of meters and through multiple walls. They also increase the communication rate and range achieved by ambient backscatter systems by 100X and 40X respectively. We believe that this paper represents a substantial leap in the capabilities of backscatter communication.", "title": "" }, { "docid": "neg:1840630_1", "text": "While mobile advertisement is the dominant source of revenue for mobile apps, the usage patterns of mobile users, and thus their engagement and exposure times, may be in conflict with the effectiveness of current ads. Users engagement with apps can range from a few seconds to several minutes, depending on a number of factors such as users' locations, concurrent activities and goals. Despite the wide-range of engagement times, the current format of ad auctions dictates that ads are priced, sold and configured prior to actual viewing, that is regardless of the actual ad exposure time.\n We argue that the wealth of easy-to-gather contextual information on mobile devices is sufficient to allow advertisers to make better choices by effectively predicting exposure time. We analyze mobile device usage patters with a detailed two-week long user study of 37 users in the US and South Korea. After characterizing application session times, we use factor analysis to derive a simple predictive model and show that is able to offer improved accuracy compared to mean session time over 90% of the time. We make the case for including predicted ad exposure duration in the price of mobile advertisements and posit that such information could significantly impact the effectiveness of mobile ads by giving publishers the ability to tune campaigns for engagement length, and enable a more efficient market for ad impressions while lowering network utilization and device power consumption.", "title": "" }, { "docid": "neg:1840630_2", "text": "We propose a reactive controller framework for robust quadrupedal locomotion, designed to cope with terrain irregularities, trajectory tracking errors and poor state estimation. The framework comprises two main modules: One related to the generation of elliptic trajectories for the feet and the other for control of the stability of the whole robot. We propose a task space CPG-based trajectory generation that can be modulated according to terrain irregularities and the posture of the robot trunk. To improve the robot's stability, we implemented a null space based attitude control for the trunk and a push recovery algorithm based on the concept of capture points. Simulations and experimental results on the hydraulically actuated quadruped robot HyQ will be presented to demonstrate the effectiveness of our framework.", "title": "" }, { "docid": "neg:1840630_3", "text": "Most important way of communication among humans is language and primary medium used for the said is speech. The speech recognizers make use of a parametric form of a signal to obtain the most important distinguishable features of speech signal for recognition purpose. In this paper, Linear Prediction Cepstral Coefficient (LPCC), Mel Frequency Cepstral Coefficient (MFCC) and Bark frequency Cepstral coefficient (BFCC) feature extraction techniques for recognition of Hindi Isolated, Paired and Hybrid words have been studied and the corresponding recognition rates are compared. Artifical Neural Network is used as back end processor. The experimental results show that the better recognition rate is obtained for MFCC as compared to LPCC and BFCC for all the three types of words.", "title": "" }, { "docid": "neg:1840630_4", "text": "This study investigated long-term effects of training on postural control using the model of deficits in activation of transversus abdominis (TrA) in people with recurrent low back pain (LBP). Nine volunteers with LBP attended four sessions for assessment and/or training (initial, two weeks, four weeks and six months). Training of repeated isolated voluntary TrA contractions were performed at the initial and two-week session with feedback from real-time ultrasound imaging. Home program involved training twice daily for four weeks. Electromyographic activity (EMG) of trunk and deltoid muscles was recorded with surface and fine-wire electrodes. Rapid arm movement and walking were performed at each session, and immediately after training on the first two sessions. Onset of trunk muscle activation relative to prime mover deltoid during arm movements, and the coefficient of variation (CV) of EMG during averaged gait cycle were calculated. Over four weeks of training, onset of TrA EMG was earlier during arm movements and CV of TrA EMG was reduced (consistent with more sustained EMG activity). Changes were retained at six months follow-up (p<0.05). These results show persistence of motor control changes following training and demonstrate that this training approach leads to motor learning of automatic postural control strategies.", "title": "" }, { "docid": "neg:1840630_5", "text": "In radar imaging it is well known that relative motion or deformation of parts of illuminated objects induce additional features in the Doppler frequency spectra. These features are called micro-Doppler effect and appear as sidebands around the central Doppler frequency. They can provide valuable information about the structure of the moving parts and may be used for identification purposes [1].", "title": "" }, { "docid": "neg:1840630_6", "text": "Active learning is an area of machine learning examining strategies for allocation of finite resources, particularly human labeling efforts and to an extent feature extraction, in situations where available data exceeds available resources. In this open problem paper, we motivate the necessity of active learning in the security domain, identify problems caused by the application of present active learning techniques in adversarial settings, and propose a framework for experimentation and implementation of active learning systems in adversarial contexts. More than other contexts, adversarial contexts particularly need active learning as ongoing attempts to evade and confuse classifiers necessitate constant generation of labels for new content to keep pace with adversarial activity. Just as traditional machine learning algorithms are vulnerable to adversarial manipulation, we discuss assumptions specific to active learning that introduce additional vulnerabilities, as well as present vulnerabilities that are amplified in the active learning setting. Lastly, we present a software architecture, Security-oriented Active Learning Testbed (SALT), for the research and implementation of active learning applications in adversarial contexts.", "title": "" }, { "docid": "neg:1840630_7", "text": "Cython is a Python language extension that allows explicit type declarations and is compiled directly to C. As such, it addresses Python's large overhead for numerical loops and the difficulty of efficiently using existing C and Fortran code, which Cython can interact with natively.", "title": "" }, { "docid": "neg:1840630_8", "text": "Several methods exist for a computer to generate music based on data including Markov chains, recurrent neural networks, recombinancy, and grammars. We explore the use of unit selection and concatenation as a means of generating music using a procedure based on ranking, where, we consider a unit to be a variable length number of measures of music. We first examine whether a unit selection method, that is restricted to a finite size unit library, can be sufficient for encompassing a wide spectrum of music. This is done by developing a deep autoencoder that encodes a musical input and reconstructs the input by selecting from the library. We then describe a generative model that combines a deep structured semantic model (DSSM) with an LSTM to predict the next unit, where units consist of four, two, and one measures of music. We evaluate the generative model using objective metrics including mean rank and accuracy and with a subjective listening test in which expert musicians are asked to complete a forcedchoiced ranking task. Our system is compared to a note-level generative baseline model that consists of a stacked LSTM trained to predict forward by one note.", "title": "" }, { "docid": "neg:1840630_9", "text": "The lowand mid-frequency model of the transformer with resistive load is analysed for different values of coupling coefficients. The model comprising of coupling-dependent inductances is used to derive the following characteristics: voltage gain, current gain, bandwidth, input impedance, and transformer efficiency. It is shown that in the lowand mid-frequency range, the turns ratio between the windings is a strong function of the coupling coefficient, i.e., if the coupling coefficient decreases, then the effective turns ratio reduces. A practical transformer was designed, simulated, and tested. It was observed that the magnitudes of the voltage transfer function and current transfer function exhibit a maximum value each at a different value of coupling coefficient. In addition, as the coupling coefficient decreases, the transformer bandwidth also decreases. Furthermore, analytical expressions for the transformer efficiency for resistive loads are derived and its variation with respect to frequency at different coupling coefficients is investigated. It is shown that the transformer efficiency is maximum at any coupling coefficient if the input resistance is equal to the load resistance. Experimental validation of the theoretical results was performed using a practical transformer set-up. The theoretical predictions were found to be in good agreement with the experimental results.", "title": "" }, { "docid": "neg:1840630_10", "text": "This paper proposes a Modified Particle Swarm Optimization with Time Varying Acceleration Coefficients (MPSO-TVAC) for solving economic load dispatch (ELD) problem. Due to prohibited operating zones (POZ) and ramp rate limits of the practical generators, the ELD problems become nonlinear and nonconvex optimization problem. Furthermore, the ELD problem may be more complicated if transmission losses are considered. Particle swarm optimization (PSO) is one of the famous heuristic methods for solving nonconvex problems. However, this method may suffer to trap at local minima especially for multimodal problem. To improve the solution quality and robustness of PSO algorithm, a new best neighbour particle called ‘rbest’ is proposed. The rbest provides extra information for each particle that is randomly selected from other best particles in order to diversify the movement of particle and avoid premature convergence. The effectiveness of MPSO-TVAC algorithm is tested on different power systems with POZ, ramp-rate limits and transmission loss constraints. To validate the performances of the proposed algorithm, comparative studies have been carried out in terms of convergence characteristic, solution quality, computation time and robustness. Simulation results found that the proposed MPSO-TVAC algorithm has good solution quality and more robust than other methods reported in previous work.", "title": "" }, { "docid": "neg:1840630_11", "text": "BACKGROUND\nSocial media are dynamic and interactive computer-mediated communication tools that have high penetration rates in the general population in high-income and middle-income countries. However, in medicine and health care, a large number of stakeholders (eg, clinicians, administrators, professional colleges, academic institutions, ministries of health, among others) are unaware of social media's relevance, potential applications in their day-to-day activities, as well as the inherent risks and how these may be attenuated and mitigated.\n\n\nOBJECTIVE\nWe conducted a narrative review with the aim to present case studies that illustrate how, where, and why social media are being used in the medical and health care sectors.\n\n\nMETHODS\nUsing a critical-interpretivist framework, we used qualitative methods to synthesize the impact and illustrate, explain, and provide contextual knowledge of the applications and potential implementations of social media in medicine and health care. Both traditional (eg, peer-reviewed) and nontraditional (eg, policies, case studies, and social media content) sources were used, in addition to an environmental scan (using Google and Bing Web searches) of resources.\n\n\nRESULTS\nWe reviewed, evaluated, and synthesized 76 articles, 44 websites, and 11 policies/reports. Results and case studies are presented according to 10 different categories of social media: (1) blogs (eg, WordPress), (2) microblogs (eg, Twitter), (3) social networking sites (eg, Facebook), (4) professional networking sites (eg, LinkedIn, Sermo), (5) thematic networking sites (eg, 23andMe), (6) wikis (eg, Wikipedia), (7) mashups (eg, HealthMap), (8) collaborative filtering sites (eg, Digg), (9) media sharing sites (eg, YouTube, Slideshare), and others (eg, SecondLife). Four recommendations are provided and explained for stakeholders wishing to engage with social media while attenuating risk: (1) maintain professionalism at all times, (2) be authentic, have fun, and do not be afraid, (3) ask for help, and (4) focus, grab attention, and engage.\n\n\nCONCLUSIONS\nThe role of social media in the medical and health care sectors is far reaching, and many questions in terms of governance, ethics, professionalism, privacy, confidentiality, and information quality remain unanswered. By following the guidelines presented, professionals have a starting point to engage with social media in a safe and ethical manner. Future research will be required to understand the synergies between social media and evidence-based practice, as well as develop institutional policies that benefit patients, clinicians, public health practitioners, and industry alike.", "title": "" }, { "docid": "neg:1840630_12", "text": "Widespread use of biometric architectures implies the need to secure highly sensitive data to respect the privacy rights of the users. In this paper, we discuss the following question: To what extent can biometric designs be characterized as Privacy Enhancing Technologies? The terms of privacy and security for biometric schemes are defined, while current regulations for the protection of biometric information are presented. Additionally, we analyze and compare cryptographic techniques for secure biometric designs. Finally, we introduce a privacy-preserving approach for biometric authentication in mobile electronic financial applications. Our model utilizes the mechanism of pseudonymous biometric identities for secure user registration and authentication. We discuss how the privacy requirements for the processing of biometric data can be met in our scenario. This work attempts to contribute to the development of privacy-by-design biometric technologies.", "title": "" }, { "docid": "neg:1840630_13", "text": "In this paper, a miniaturized planar antenna with enhanced bandwidth is designed for the ISM 433 MHz applications. The antenna is realized by cascading two resonant structures with meander lines, thus introducing two different radiating branches to realize two neighboring resonant frequencies. The techniques of shorting pin and novel ground plane are adopted for bandwidth enhancement. Combined with these structures, a novel antenna with a total size of 23 mm × 49.5 mm for the ISM band application is developed and fabricated. Measured results show that the proposed antenna has good performance with the -10 dB impedance bandwidth is about 12.5 MHz and the maximum gain is about -2.8 dBi.", "title": "" }, { "docid": "neg:1840630_14", "text": "BACKGROUND\nThere is no evidence from randomized trials to support a strategy of lowering systolic blood pressure below 135 to 140 mm Hg in persons with type 2 diabetes mellitus. We investigated whether therapy targeting normal systolic pressure (i.e., <120 mm Hg) reduces major cardiovascular events in participants with type 2 diabetes at high risk for cardiovascular events.\n\n\nMETHODS\nA total of 4733 participants with type 2 diabetes were randomly assigned to intensive therapy, targeting a systolic pressure of less than 120 mm Hg, or standard therapy, targeting a systolic pressure of less than 140 mm Hg. The primary composite outcome was nonfatal myocardial infarction, nonfatal stroke, or death from cardiovascular causes. The mean follow-up was 4.7 years.\n\n\nRESULTS\nAfter 1 year, the mean systolic blood pressure was 119.3 mm Hg in the intensive-therapy group and 133.5 mm Hg in the standard-therapy group. The annual rate of the primary outcome was 1.87% in the intensive-therapy group and 2.09% in the standard-therapy group (hazard ratio with intensive therapy, 0.88; 95% confidence interval [CI], 0.73 to 1.06; P=0.20). The annual rates of death from any cause were 1.28% and 1.19% in the two groups, respectively (hazard ratio, 1.07; 95% CI, 0.85 to 1.35; P=0.55). The annual rates of stroke, a prespecified secondary outcome, were 0.32% and 0.53% in the two groups, respectively (hazard ratio, 0.59; 95% CI, 0.39 to 0.89; P=0.01). Serious adverse events attributed to antihypertensive treatment occurred in 77 of the 2362 participants in the intensive-therapy group (3.3%) and 30 of the 2371 participants in the standard-therapy group (1.3%) (P<0.001).\n\n\nCONCLUSIONS\nIn patients with type 2 diabetes at high risk for cardiovascular events, targeting a systolic blood pressure of less than 120 mm Hg, as compared with less than 140 mm Hg, did not reduce the rate of a composite outcome of fatal and nonfatal major cardiovascular events. (ClinicalTrials.gov number, NCT00000620.)", "title": "" }, { "docid": "neg:1840630_15", "text": "The advent of crowdsourcing has created a variety of new opportunities for improving upon traditional methods of data collection and annotation. This in turn has created intriguing new opportunities for data-driven machine learning (ML). Convenient access to crowd workers for simple data collection has further generalized to leveraging more arbitrary crowd-based human computation (von Ahn 2005) to supplement automated ML. While new potential applications of crowdsourcing continue to emerge, a variety of practical and sometimes unexpected obstacles have already limited the degree to which its promised potential can be actually realized in practice. This paper considers two particular aspects of crowdsourcing and their interplay, data quality control (QC) and ML, reflecting on where we have been, where we are, and where we might go from here.", "title": "" }, { "docid": "neg:1840630_16", "text": "In brief: Diagnosis of skier's thumb-a common sports injury-is based on physical examination and history of the injury. The most important findings from the physical exam are point tenderness over the ulnar collateral ligament and instability, which is tested with the thumb at 0° and at 20° to 30° of flexion. Grade 1 and 2 injuries, which involve torn fibers but no loss of integrity, can be treated with casting and/or splinting and physical therapy. Grade 3 injuries involve complete disruption of the ligament and usually require surgical repair. Results from treatment are generally excellent, and with appropriate rehabilitation, athletes recover pinch and grip strength and return to sports.", "title": "" }, { "docid": "neg:1840630_17", "text": "BACKGROUND\nMyocardium irreversibly injured by ischemic stress must be efficiently repaired to maintain tissue integrity and contractile performance. Macrophages play critical roles in this process. These cells transform across a spectrum of phenotypes to accomplish diverse functions ranging from mediating the initial inflammatory responses that clear damaged tissue to subsequent reparative functions that help rebuild replacement tissue. Although macrophage transformation is crucial to myocardial repair, events governing this transformation are poorly understood.\n\n\nMETHODS\nHere, we set out to determine whether innate immune responses triggered by cytoplasmic DNA play a role.\n\n\nRESULTS\nWe report that ischemic myocardial injury, along with the resulting release of nucleic acids, activates the recently described cyclic GMP-AMP synthase-stimulator of interferon genes pathway. Animals lacking cyclic GMP-AMP synthase display significantly improved early survival after myocardial infarction and diminished pathological remodeling, including ventricular rupture, enhanced angiogenesis, and preserved ventricular contractile function. Furthermore, cyclic GMP-AMP synthase loss of function abolishes the induction of key inflammatory programs such as inducible nitric oxide synthase and promotes the transformation of macrophages to a reparative phenotype, which results in enhanced repair and improved hemodynamic performance.\n\n\nCONCLUSIONS\nThese results reveal, for the first time, that the cytosolic DNA receptor cyclic GMP-AMP synthase functions during cardiac ischemia as a pattern recognition receptor in the sterile immune response. Furthermore, we report that this pathway governs macrophage transformation, thereby regulating postinjury cardiac repair. Because modulators of this pathway are currently in clinical use, our findings raise the prospect of new treatment options to combat ischemic heart disease and its progression to heart failure.", "title": "" }, { "docid": "neg:1840630_18", "text": "Tunneling-field-effect-transistor (TFET) has emerged as an alternative for conventional CMOS by enabling the supply voltage (VDD) scaling in ultra-low power, energy efficient computing, due to its sub-60 mV/ decade sub-threshold slope (SS). Given its unique device characteristics such as the asymmetrical source/drain design induced uni-directional conduction, enhanced on-state Miller capacitance effect and steep switching at low voltages, TFET based circuit design requires strong interactions between the device-level and the circuit-level to explore the performance benefits, with certain modifications of the conventional CMOS circuits to achieve the functionality and optimal energy efficiency. Because TFET operates at low supply voltage range (VDD < 0:5 V) to outperform CMOS, reliability issues can have profound impact on the circuit design from the practical application perspective. In this review paper, we present recent development on Tunnel FET device design, and modeling technique for circuit implementation and performance benchmarking. We focus on the reliability issues such as soft-error, electrical noise and process variation, and their impact on TFET based circuit performance compared to sub-threshold CMOS. Analytical models of electrical noise and process variation are also discussed for circuit-level", "title": "" }, { "docid": "neg:1840630_19", "text": "Alphabetical ciphers are being used since centuries for inducing confusion in messages, but there are some drawbacks that are associated with Classical alphabetic techniques like concealment of key and plaintext. Here in this paper we will suggest an encryption technique that is a blend of both classical encryption as well as modern technique, this hybrid technique will be superior in terms of security than average Classical ciphers.", "title": "" } ]

[ { "docid": "pos:1840631_0", "text": "Language modeling approaches to information retrieval are attractive and promising because they connect the problem of retrieval with that of language model estimation, which has been studied extensively in other application areas such as speech recognition. The basic idea of these approaches is to estimate a language model for each document, and then rank documents by the likelihood of the query according to the estimated language model. A core problem in language model estimation is smoothing, which adjusts the maximum likelihood estimator so as to correct the inaccuracy due to data sparseness. In this paper, we study the problem of language model smoothing and its influence on retrieval performance. We examine the sensitivity of retrieval performance to the smoothing parameters and compare several popular smoothing methods on different test collection.", "title": "" } ]

[ { "docid": "neg:1840631_0", "text": "We built a highly compliant, underactuated, robust and at the same time dexterous anthropomorphic hand. We evaluate its dexterous grasping capabilities by implementing the comprehensive Feix taxonomy of human grasps and by assessing the dexterity of its opposable thumb using the Kapandji test. We also illustrate the hand’s payload limits and demonstrate its grasping capabilities in real-world grasping experiments. To support our claim that compliant structures are beneficial for dexterous grasping, we compare the dimensionality of control necessary to implement the diverse grasp postures with the dimensionality of the grasp postures themselves. We find that actuation space is smaller than posture space and explain the difference with the mechanic interaction between hand and grasped object. Additional desirable properties are derived from using soft robotics technology: the hand is robust to impact and blunt collisions, inherently safe, and not affected by dirt, dust, or liquids. Furthermore, the hand is simple and inexpensive to manufacture.", "title": "" }, { "docid": "neg:1840631_1", "text": "In the Western world, aging is a growing problem of the society and computer assisted treatments can facilitate the telemedicine for old people or it can help in rehabilitations of patients after sport accidents in far locations. Physical exercises play an important role in physiotherapy and RGB-D devices can be utilized to recognize them in order to make interactive computer healthcare applications in the future. A practical model definition is introduced in this paper to recognize different exercises with Asus Xtion camera. One of the contributions is the extendable recognition models to detect other human activities with noisy sensors, but avoiding heavy data collection. The experiments show satisfactory detection performance without any false positives which is unique in the field to the best of the author knowledge. The computational costs are negligible thus the developed models can be suitable for embedded systems.", "title": "" }, { "docid": "neg:1840631_2", "text": "Crowd evacuation of a building has been studied over the last decades. In this paper, seven methodological approaches for crowd evacuation have been identified. These approaches include cellular automata models, lattice gas models, social force models, fluid-dynamic models, agent-based models, game theoretic models, and approaches based on experiments with animals. According to available literatures, we discuss the advantages and disadvantages of these approaches, and conclude that a variety of different kinds of approaches should be combined to study crowd evacuation. Psychological and physiological elements affecting individual and collective behaviors should be also incorporated into the evacuation models. & 2008 Elsevier Ltd. All rights reserved.", "title": "" }, { "docid": "neg:1840631_3", "text": "I propose a common framework that combines three different paradigms in machine learning: generative, discriminative and imitative learning. A generative probabilistic distribution is a principled way to model many machine learning and machine perception problems. Therein, one provides domain specific knowledge in terms of structure and parameter priors over the joint space of variables. Bayesian networks and Bayesian statistics provide a rich and flexible language for specifying this knowledge and subsequently refining it with data and observations. The final result is a distribution that is a good generator of novel exemplars. Conversely, discriminative algorithms adjust a possibly non-distributional model to data optimizing for a specific task, such as classification or prediction. This typically leads to superior performance yet compromises the flexibility of generative modeling. I present Maximum Entropy Discrimination (MED) as a framework to combine both discriminative estimation and generative probability densities. Calculations involve distributions over parameters, margins, and priors and are provably and uniquely solvable for the exponential family. Extensions include regression, feature selection, and transduction. SVMs are also naturally subsumed and can be augmented with, for example, feature selection, to obtain substantial improvements. To extend to mixtures of exponential families, I derive a discriminative variant of the ExpectationMaximization (EM) algorithm for latent discriminative learning (or latent MED). While EM and Jensen lower bound log-likelihood, a dual upper bound is made possible via a novel reverse-Jensen inequality. The variational upper bound on latent log-likelihood has the same form as EM bounds, is computable efficiently and is globally guaranteed. It permits powerful discriminative learning with the wide range of contemporary probabilistic mixture models (mixtures of Gaussians, mixtures of multinomials and hidden Markov models). We provide empirical results on standardized data sets that demonstrate the viability of the hybrid discriminative-generative approaches of MED and reverse-Jensen bounds over state of the art discriminative techniques or generative approaches. Subsequently, imitative learning is presented as another variation on generative modeling which also learns from exemplars from an observed data source. However, the distinction is that the generative model is an agent that is interacting in a much more complex surrounding external world. It is not efficient to model the aggregate space in a generative setting. I demonstrate that imitative learning (under appropriate conditions) can be adequately addressed as a discriminative prediction task which outperforms the usual generative approach. This discriminative-imitative learning approach is applied with a generative perceptual system to synthesize a real-time agent that learns to engage in social interactive behavior. Thesis Supervisor: Alex Pentland Title: Toshiba Professor of Media Arts and Sciences, MIT Media Lab Discriminative, Generative and Imitative Learning", "title": "" }, { "docid": "neg:1840631_4", "text": "As the nature of many materials handling tasks have begun to change from lifting to pushing and pulling, it is important that one understands the biomechanical nature of the risk to which the lumbar spine is exposed. Most previous assessments of push-pull tasks have employed models that may not be sensitive enough to consider the effects of the antagonistic cocontraction occurring during complex pushing and pulling motions in understanding the risk to the spine and the few that have considered the impact of cocontraction only consider spine load at one lumbar level. This study used an electromyography-assisted biomechanical model sensitive to complex motions to assess spine loadings throughout the lumbar spine as 10 males and 10 females pushed and pulled loads at three different handle heights and of three different load magnitudes. Pulling induced greater spine compressive loads than pushing, whereas the reverse was true for shear loads at the different lumbar levels. The results indicate that, under these conditions, anterior-posterior (A/P) shear loads were of sufficient magnitude to be of concern especially at the upper lumbar levels. Pushing and pulling loads equivalent to 20% of body weight appeared to be the limit of acceptable exertions, while pulling at low and medium handle heights (50% and 65% of stature) minimised A/P shear. These findings provide insight to the nature of spine loads and their potential risk to the low back during modern exertions.", "title": "" }, { "docid": "neg:1840631_5", "text": "We apply new bilevel and trilevel optimization models to make critical infrastructure more resilient against terrorist attacks. Each model features an intelligent attacker (terrorists) and a defender (us), information transparency, and sequential actions by attacker and defender. We illustrate with examples of the US Strategic Petroleum Reserve, the US Border Patrol at Yuma, Arizona, and an electrical transmission system. We conclude by reporting insights gained from the modeling experience and many “red-team” exercises. Each exercise gathers open-source data on a real-world infrastructure system, develops an appropriate bilevel or trilevel model, and uses these to identify vulnerabilities in the system or to plan an optimal defense.", "title": "" }, { "docid": "neg:1840631_6", "text": "An estimated 24 million people worldwide have dementia, the majority of whom are thought to have Alzheimer's disease. Thus, Alzheimer's disease represents a major public health concern and has been identified as a research priority. Although there are licensed treatments that can alleviate symptoms of Alzheimer's disease, there is a pressing need to improve our understanding of pathogenesis to enable development of disease-modifying treatments. Methods for improving diagnosis are also moving forward, but a better consensus is needed for development of a panel of biological and neuroimaging biomarkers that support clinical diagnosis. There is now strong evidence of potential risk and protective factors for Alzheimer's disease, dementia, and cognitive decline, but further work is needed to understand these better and to establish whether interventions can substantially lower these risks. In this Seminar, we provide an overview of recent evidence regarding the epidemiology, pathogenesis, diagnosis, and treatment of Alzheimer's disease, and discuss potential ways to reduce the risk of developing the disease.", "title": "" }, { "docid": "neg:1840631_7", "text": "Time series data are an ubiquitous and important data source in many domains. Most companies and organizations rely on this data for critical tasks like decision-making, planning, and analytics in general. Usually, all these tasks focus on actual data representing organization and business processes. In order to assess the robustness of current systems and methods, it is also desirable to focus on time-series scenarios which represent specific time-series features. This work presents a generally applicable and easy-to-use method for the feature-driven generation of time series data. Our approach extracts descriptive features of a data set and allows the construction of a specific version by means of the modification of these features.", "title": "" }, { "docid": "neg:1840631_8", "text": "Since the invention of word2vec, the skip-gram model has significantly advanced the research of network embedding, such as the recent emergence of the DeepWalk, LINE, PTE, and node2vec approaches. In this work, we show that all of the aforementioned models with negative sampling can be unified into the matrix factorization framework with closed forms. Our analysis and proofs reveal that: (1) DeepWalk empirically produces a low-rank transformation of a network's normalized Laplacian matrix; (2) LINE, in theory, is a special case of DeepWalk when the size of vertices' context is set to one; (3) As an extension of LINE, PTE can be viewed as the joint factorization of multiple networks» Laplacians; (4) node2vec is factorizing a matrix related to the stationary distribution and transition probability tensor of a 2nd-order random walk. We further provide the theoretical connections between skip-gram based network embedding algorithms and the theory of graph Laplacian. Finally, we present the NetMF method as well as its approximation algorithm for computing network embedding. Our method offers significant improvements over DeepWalk and LINE for conventional network mining tasks. This work lays the theoretical foundation for skip-gram based network embedding methods, leading to a better understanding of latent network representation learning.", "title": "" }, { "docid": "neg:1840631_9", "text": "Web usage mining is the application of data mining techniques to discover usage patterns from Web data, in order to understand and better serve the needs of Web-based applications. Web usage mining consists of three phases, namely preprocessing, pattern discovery, and pattern analysis. This paper describes each of these phases in detail. Given its application potential, Web usage mining has seen a rapid increase in interest, from both the research and practice communities. This paper provides a detailed taxonomy of the work in this area, including research efforts as well as commercial offerings. An up-to-date survey of the existing work is also provided. Finally, a brief overview of the WebSIFT system as an example of a prototypical Web usage mining system is given.", "title": "" }, { "docid": "neg:1840631_10", "text": "Traditional keyboard and mouse based presentation prevents lecturers from interacting with the audiences freely and closely. In this paper, we propose a gesture-aware presentation tool named SlideShow to liberate lecturers from physical space constraints and make human-computer interaction more natural and convenient. In our system, gesture data is obtained by a handle controller with 3-axis accelerometer and gyro and transmitted to host-side through bluetooth, then we use Bayesian change point detection to segment continuous gesture series and HMM to recognize the gesture. In consequence Slideshow could carry out the corresponding operations on PowerPoint(PPT) to make a presentation, and operation states can be switched automatically and intelligently during the presentation. Both the experimental and testing results show our approach is practical, useful and convenient.", "title": "" }, { "docid": "neg:1840631_11", "text": "Library of Congress Cataloging in Publication Data EB. Boston studies in the philosophy of science.The concept of autopoiesis is due to Maturana and Varela 8, 9. The aim of this article is to revisit the concepts of autopoiesis and cognition in the hope of.Amazon.com: Autopoiesis and Cognition: The Realization of the Living Boston Studies in the Philosophy of Science, Vol. 42 9789027710161: H.R. Maturana.Autopoiesis, The Santiago School of Cognition, and. In their early work together Maturana and Varela developed the idea of autopoiesis.Autopoiesis and Cognition: The Realization of the Living Dordecht.", "title": "" }, { "docid": "neg:1840631_12", "text": "Research into the values motivating unsustainable behavior has generated unique insight into how NGOs and environmental campaigns contribute toward successfully fostering significant and long-term behavior change, yet thus far this research has not been applied to the domain of sustainable HCI. We explore the implications of this research as it relates to the potential limitations of current approaches to persuasive technology, and what it means for designing higher impact interventions. As a means of communicating these implications to be readily understandable and implementable, we develop a set of antipatterns to describe persuasive technology approaches that values research suggests are unlikely to yield significant sustainability wins, and a complementary set of patterns to describe new guidelines for what may become persuasive technology best practice.", "title": "" }, { "docid": "neg:1840631_13", "text": "There have been a number of techniques developed in recent years for the efficient analysis of probabilistic inference problems, represented as Bayes' networks or influence diagrams (Lauritzen and Spiegelhalter [9], Pearl [12], Shachter [14]). To varying degrees these methods exploit the conditional independence assumed and revealed in the problem structure to analyze problems in polynomial time, essentially polynomial in the number of variables and the size of the largest state space encountered during the evaluation. Unfortunately, there are many problems of interest for which the variables of interest are continuous rather than discrete, so the relevant state spaces become infinite and the polynomial complexity is of little help.", "title": "" }, { "docid": "neg:1840631_14", "text": "De novo assembly of RNA-seq data enables researchers to study transcriptomes without the need for a genome sequence; this approach can be usefully applied, for instance, in research on 'non-model organisms' of ecological and evolutionary importance, cancer samples or the microbiome. In this protocol we describe the use of the Trinity platform for de novo transcriptome assembly from RNA-seq data in non-model organisms. We also present Trinity-supported companion utilities for downstream applications, including RSEM for transcript abundance estimation, R/Bioconductor packages for identifying differentially expressed transcripts across samples and approaches to identify protein-coding genes. In the procedure, we provide a workflow for genome-independent transcriptome analysis leveraging the Trinity platform. The software, documentation and demonstrations are freely available from http://trinityrnaseq.sourceforge.net. The run time of this protocol is highly dependent on the size and complexity of data to be analyzed. The example data set analyzed in the procedure detailed herein can be processed in less than 5 h.", "title": "" }, { "docid": "neg:1840631_15", "text": "Computer-generated texts, whether from Natural Language Generation (NLG) or Machine Translation (MT) systems, are often post-edited by humans before being released to users. The frequency and type of post-edits is a measure of how well the system works, and can be used for evaluation. We describe how we have used post-edit data to evaluate SUMTIME-MOUSAM, an NLG system that produces weather forecasts.", "title": "" }, { "docid": "neg:1840631_16", "text": "Social Networks (SN) Sites are becoming very popular and the number of users is increasing rapidly. However, with that increase there is also an increase in the security threats which affect the users’ privacy, identity and confidentiality. Different research groups highlighted the security threats in SN and attempted to offer some solutions to these issues. In this paper we survey several examples of this research and highlight the approaches. All the models we surveyed were focusing on protecting users’ information yet they failed to cover other important issues. For example, none of the mechanisms provided the users with control over what others can reveal about them; and encryption of images is still not achieved properly. Generally having higher security measures will affect the system’s performance in terms of speed and response time. However, this trade-off was not discussed or addressed in any of the models we surveyed.", "title": "" }, { "docid": "neg:1840631_17", "text": "A tunable broadband inverted microstrip line phase shifter filled with Liquid Crystals (LCs) is investigated between 1.125 GHz and 35 GHz at room temperature. The effective dielectric anisotropy is tuned by a DC-voltage of up to 30 V. In addition to standard LCs like K15 (5CB), a novel highly anisotropic LC mixture is characterized by a resonator method at 8.5 GHz, showing a very high dielectric anisotropy /spl Delta/n of 0.32 for the novel mixture compared to 0.13 for K15. These LCs are filled into two inverted microstrip line phase shifter devices with different polyimide films and heights. With a physical length of 50 mm, the insertion losses are about 4 dB for the novel mixture compared to 6 dB for K15 at 24 GHz. A differential phase shift of 360/spl deg/ can be achieved at 30 GHz with the novel mixture. The figure-of-merit of the phase shifter exceeds 110/spl deg//dB for the novel mixture compared to 21/spl deg//dB for K15 at 24 GHz. To our knowledge, this is the best value above 20 GHz at room temperature demonstrated for a tunable phase shifter based on nonlinear dielectrics up to now. This substantial progress opens up totally new low-cost LC applications beyond optics.", "title": "" }, { "docid": "neg:1840631_18", "text": "Parkinson's disease (PD) is a neurodegenerative disorder with symptoms that progressively worsen with age. Pathologically, PD is characterized by the aggregation of α-synuclein in cells of the substantia nigra in the brain and loss of dopaminergic neurons. This pathology is associated with impaired movement and reduced cognitive function. The etiology of PD can be attributed to a combination of environmental and genetic factors. A popular animal model, the nematode roundworm Caenorhabditis elegans, has been frequently used to study the role of genetic and environmental factors in the molecular pathology and behavioral phenotypes associated with PD. The current review summarizes cellular markers and behavioral phenotypes in transgenic and toxin-induced PD models of C. elegans.", "title": "" } ]

[ { "docid": "pos:1840632_0", "text": "Code-switching is a very common phenomenon in multilingual communities. In this paper, we investigate language modeling for conversational Mandarin-English code-switching (CS) speech recognition. First, we investigate the prediction of code switches based on textual features with focus on Part-of-Speech (POS) tags and trigger words. Second, we propose a structure of recurrent neural networks to predict code-switches. We extend the networks by adding POS information to the input layer and by factorizing the output layer into languages. The resulting models are applied to our task of code-switching language modeling. The final performance shows 10.8% relative improvement in perplexity on the SEAME development set which transforms into a 2% relative improvement in terms of Mixed Error Rate and a relative improvement of 16.9% in perplexity on the evaluation set which leads to a 2.7% relative improvement of MER.", "title": "" }, { "docid": "pos:1840632_1", "text": "We present freely available open-source toolkit for training recurrent neural network based language models. I t can be easily used to improve existing speech recognition and ma chine translation systems. Also, it can be used as a baseline for fu ture research of advanced language modeling techniques. In the p a er, we discuss optimal parameter selection and different modes of functionality. The toolkit, example scripts and basic setups are freely available at http://rnnlm.sourceforge.net/. I. I NTRODUCTION, MOTIVATION AND GOALS Statistical language modeling attracts a lot of attention, as models of natural languages are important part of many practical systems today. Moreover, it can be estimated that with further research progress, language models will becom e closer to human understanding [1] [2], and completely new applications will become practically realizable. Immedia tely, any significant progress in language modeling can be utilize d in the esisting speech recognition and statistical machine translation systems. However, the whole research field struggled for decades to overcome very simple, but also effective models based on ngram frequencies [3] [4]. Many techniques were developed to beat n-grams, but the improvements came at the cost of computational complexity. Moreover, the improvements wer e often reported on very basic systems, and after application to state-of-the-art setups and comparison to n-gram models trained on large amounts of data, improvements provided by many techniques vanished. This has lead to scepticism among speech recognition researchers. In our previous work, we have compared many major advanced language modeling techniques, and found that neur al network based language models (NNLM) perform the best on several standard setups [5]. Models of this type were introduced by Bengio in [6], about ten years ago. Their main weaknesses were huge computational complexity, and nontrivial implementation. Successful training of neural net works require well chosen hyper-parameters, such as learning rat e and size of hidden layer. To help overcome these basic obstacles, we have decided to release our toolkit for training recurrent neural network b ased language models (RNNLM). We have shown that the recurrent architecture outperforms the feedforward one on several se tup in [7]. Moreover, the implemenation is simple and easy to understand. The most importantly, recurrent neural networ ks are very interesting from the research point of view, as they allow effective processing of sequences and patterns with arbitraty length these models can learn to store informati on in the hidden layer. Recurrent neural networks can have memory , and are thus important step forward to overcome the most painful and often criticized drawback of n-gram models dependence on previous two or three words only. In this paper we present an open source and freely available toolkit for training statistical language models base d or recurrent neural networks. It includes techniques for redu cing computational complexity (classes in the output layer and direct connections between input and output layer). Our too lkit has been designed to provide comparable results to the popul ar toolkit for training n-gram models, SRILM [8]. The main goals for the RNNLM toolkit are these: • promotion of research of advanced language modeling techniques • easy usage • simple portable code without any dependencies • computational efficiency In the paper, we describe how to easily make RNNLM part of almost any speech recognition or machine translation syste m that produces lattices. II. RECURRENTNEURAL NETWORK The recurrent neural network architecture used in the toolk it is shown at Figure 1 (usually called Elman network, or simple RNN). The input layer uses the 1-of-N representation of the previous wordw(t) concatenated with previous state of the hidden layers(t − 1). The neurons in the hidden layer s(t) use sigmoid activation function. The output layer (t) has the same dimensionality as w(t), and after the network is trained, it represents probability distribution of the next word giv en the previous word and state of the hidden layer in the previous time step [9]. The class layer c(t) can be optionally used to reduce computational complexity of the model, at a small cost of accuracy [7]. Training is performed by the standard stochastic gradient descent algorithm, and the matrix W that", "title": "" }, { "docid": "pos:1840632_2", "text": "The aim of this paper is to investigate the rules and constraints of code-switching (CS) in Hindi-English mixed language data. In this paper, we’ll discuss how we collected the mixed language corpus. This corpus is primarily made up of student interview speech. The speech was manually transcribed and verified by bilingual speakers of Hindi and English. The code-switching cases in the corpus are discussed and the reasons for code-switching are explained.", "title": "" } ]

[ { "docid": "neg:1840632_0", "text": "State-of-the-art 3D shape classification and retrieval algorithms, hereinafter referred to as shape analysis, are often based on comparing signatures or descriptors that capture the main geometric and topological properties of 3D objects. None of the existing descriptors, however, achieve best performance on all shape classes. In this article, we explore, for the first time, the usage of covariance matrices of descriptors, instead of the descriptors themselves, in 3D shape analysis. Unlike histogram -based techniques, covariance-based 3D shape analysis enables the fusion and encoding of different types of features and modalities into a compact representation. Covariance matrices, however, are elements of the non-linear manifold of symmetric positive definite (SPD) matrices and thus \\BBL2 metrics are not suitable for their comparison and clustering. In this article, we study geodesic distances on the Riemannian manifold of SPD matrices and use them as metrics for 3D shape matching and recognition. We then: (1) introduce the concepts of bag of covariance (BoC) matrices and spatially-sensitive BoC as a generalization to the Riemannian manifold of SPD matrices of the traditional bag of features framework, and (2) generalize the standard kernel methods for supervised classification of 3D shapes to the space of covariance matrices. We evaluate the performance of the proposed BoC matrices framework and covariance -based kernel methods and demonstrate their superiority compared to their descriptor-based counterparts in various 3D shape matching, retrieval, and classification setups.", "title": "" }, { "docid": "neg:1840632_1", "text": "Shape from shading is known to be an ill-posed problem. We show in this paper that if we model the problem in a different way than it is usually done, more precisely by taking into account the 1/r/sup 2/ attenuation term of the illumination, shape from shading becomes completely well-posed. Thus the shading allows to recover (almost) any surface from only one image (of this surface) without any additional data (in particular, without the knowledge of the heights of the solution at the local intensity \"minima\", contrary to [P. Dupuis et al. (1994), E. Prados et al. (2004), B. Horn (1986), E. Rouy et al. (1992), R. Kimmel et al. (2001)]) and without regularity assumptions (contrary to [J. Oliensis et al. (1993), R. Kimmel et al. (1995)], for example). More precisely, we formulate the problem as that of solving a new partial differential equation (PDE), we develop a complete mathematical study of this equation and we design a new provably convergent numerical method. Finally, we present results of our new shape from shading method on various synthetic and real images.", "title": "" }, { "docid": "neg:1840632_2", "text": "The vector representation of Bengali words using word2vec model (Mikolov et al. (2013)) plays an important role in Bengali sentiment classification. It is observed that the words that are from same context stay closer in the vector space of word2vec model and they are more similar than other words. In this article, a new approach of sentiment classification of Bengali comments with word2vec and Sentiment extraction of words are presented. Combining the results of word2vec word co-occurrence score with the sentiment polarity score of the words, the accuracy obtained is 75.5%.", "title": "" }, { "docid": "neg:1840632_3", "text": "This paper proposes a new system for offline writer identification and writer verification. The proposed method uses GMM supervectors to encode the feature distribution of individual writers. Each supervector originates from an individual GMM which has been adapted from a background model via a maximum-a-posteriori step followed by mixing the new statistics with the background model. We show that this approach improves the TOP-1 accuracy of the current best ranked methods evaluated at the ICDAR-2013 competition dataset from 95.1% [13] to 97.1%, and from 97.9% [11] to 99.2% at the CVL dataset, respectively. Additionally, we compare the GMM supervector encoding with other encoding schemes, namely Fisher vectors and Vectors of Locally Aggregated Descriptors.", "title": "" }, { "docid": "neg:1840632_4", "text": "When the landmark patient Phineas Gage died in 1861, no autopsy was performed, but his skull was later recovered. The brain lesion that caused the profound personality changes for which his case became famous has been presumed to have involved the left frontal region, but questions have been raised about the involvement of other regions and about the exact placement of the lesion within the vast frontal territory. Measurements from Gage's skull and modern neuroimaging techniques were used to reconstitute the accident and determine the probable location of the lesion. The damage involved both left and right prefrontal cortices in a pattern that, as confirmed by Gage's modern counterparts, causes a defect in rational decision making and the processing of emotion.", "title": "" }, { "docid": "neg:1840632_5", "text": "State of the art methods for image and object retrieval exploit both appearance (via visual words) and local geometry (spatial extent, relative pose). In large scale problems, memory becomes a limiting factor - local geometry is stored for each feature detected in each image and requires storage larger than the inverted file and term frequency and inverted document frequency weights together. We propose a novel method for learning discretized local geometry representation based on minimization of average reprojection error in the space of ellipses. The representation requires only 24 bits per feature without drop in performance. Additionally, we show that if the gravity vector assumption is used consistently from the feature description to spatial verification, it improves retrieval performance and decreases the memory footprint. The proposed method outperforms state of the art retrieval algorithms in a standard image retrieval benchmark.", "title": "" }, { "docid": "neg:1840632_6", "text": "Cognitive behavioral therapy (CBT) is one of the most effective psychotherapy modalities used to treat depression and anxiety disorders. Homework is an integral component of CBT, but homework compliance in CBT remains problematic in real-life practice. The popularization of the mobile phone with app capabilities (smartphone) presents a unique opportunity to enhance CBT homework compliance; however, there are no guidelines for designing mobile phone apps created for this purpose. Existing literature suggests 6 essential features of an optimal mobile app for maximizing CBT homework compliance: (1) therapy congruency, (2) fostering learning, (3) guiding therapy, (4) connection building, (5) emphasis on completion, and (6) population specificity. We expect that a well-designed mobile app incorporating these features should result in improved homework compliance and better outcomes for its users.", "title": "" }, { "docid": "neg:1840632_7", "text": "Orofacial analysis has been used by dentists for many years. The process involves applying mathematical rules, geometric principles, and straight lines to create either parallel or perpendicular references based on the true horizon and/or natural head position. These reference lines guide treatment planning and smile design for restorative treatments to achieve harmony between the new smile and the face. The goal is to obtain harmony and not symmetry. Faces are asymmetrical entities and because of that cannot be analyzed using purely straight lines. In this article, a more natural, organic, and dynamic process of evaluation is presented to minimize errors and generate harmoniously balanced smiles instead of perfect, mathematical smiles.", "title": "" }, { "docid": "neg:1840632_8", "text": "Localization is one of the problems that often appears in the world of robotics. Monte Carlo Localization (MCL) are the one of the popular algorithms in localization because easy to implement on issues Global Localization. This algorithm using particles to represent the robot position. MCL can simulated by Robot Operating System (ROS) using robot type is Pioneer3-dx. In this paper we will discuss about this algorithm on ROS, by analyzing the influence of the number particle that are used for localization of the actual robot position.", "title": "" }, { "docid": "neg:1840632_9", "text": "The TRAM flap, DIEP flap, and gluteal free flaps are routinely used for breast reconstruction. However, these have seldom been described for reconstruction of buttock deformities. We present three cases of free flaps used to restore significant buttock contour deformities. They introduce vascularised bulky tissue and provide adequate cushioning for future sitting, as well as correction of the aesthetic defect.", "title": "" }, { "docid": "neg:1840632_10", "text": "Online freelancing marketplaces have grown quickly in recent years. In theory, these sites offer workers the ability to earn money without the obligations and potential social biases associated with traditional employment frameworks. In this paper, we study whether two prominent online freelance marketplaces - TaskRabbit and Fiverr - are impacted by racial and gender bias. From these two platforms, we collect 13,500 worker profiles and gather information about workers' gender, race, customer reviews, ratings, and positions in search rankings. In both marketplaces, we find evidence of bias: we find that gender and race are significantly correlated with worker evaluations, which could harm the employment opportunities afforded to the workers. We hope that our study fuels more research on the presence and implications of discrimination in online environments.", "title": "" }, { "docid": "neg:1840632_11", "text": "BACKGROUND\nMandatory labeling of products with top allergens has improved food safety for consumers. Precautionary allergen labeling (PAL), such as \"may contain\" or \"manufactured on shared equipment,\" are voluntarily placed by the food industry.\n\n\nOBJECTIVE\nTo establish knowledge of PAL and its impact on purchasing habits by food-allergic consumers in North America.\n\n\nMETHODS\nFood Allergy Research & Education and Food Allergy Canada surveyed consumers in the United States and Canada on purchasing habits of food products featuring different types of PAL. Associations between respondents' purchasing behaviors and individual characteristics were estimated using multiple logistic regression.\n\n\nRESULTS\nOf 6684 participants, 84.3% (n = 5634) were caregivers of a food-allergic child and 22.4% had food allergy themselves. Seventy-one percent reported a history of experiencing a severe allergic reaction. Buying practices varied on the basis of PAL wording; 11% of respondents purchased food with \"may contain\" labeling, whereas 40% purchased food that used \"manufactured in a facility that also processes.\" Twenty-nine percent of respondents were unaware that the law requires labeling of priority food allergens. Forty-six percent were either unsure or incorrectly believed that PAL is required by law. Thirty-seven percent of respondents thought PAL was based on the amount of allergen present. History of a severe allergic reaction decreased the odds of purchasing foods with PAL.\n\n\nCONCLUSIONS\nAlmost half of consumers falsely believed that PAL was required by law. Up to 40% surveyed consumers purchased products with PAL. Understanding of PAL is poor, and improved awareness and guidelines are needed to help food-allergic consumers purchase food safely.", "title": "" }, { "docid": "neg:1840632_12", "text": "Mobile phones and carriers trust the traditional base stations which serve as the interface between the mobile devices and the fixed-line communication network. Femtocells, miniature cellular base stations installed in homes and businesses, are equally trusted yet are placed in possibly untrustworthy hands. By making several modifications to a commercially available femtocell, we evaluate the impact of attacks originating from a compromised device. We show that such a rogue device can violate all the important aspects of security for mobile subscribers, including tracking phones, intercepting communication and even modifying and impersonating traffic. The specification also enables femtocells to directly communicate with other femtocells over a VPN and the carrier we examined had no filtering on such communication, enabling a single rogue femtocell to directly communicate with (and thus potentially attack) all other femtocells within the carrier’s network.", "title": "" }, { "docid": "neg:1840632_13", "text": "There is increasing evidence for the involvement of glutamate-mediated neurotoxicity in the pathogenesis of Alzheimer's disease (AD). We suggest that glutamate receptors of the N-methyl-D-aspartate (NMDA) type are overactivated in a tonic rather than a phasic manner in this disorder. This continuous mild activation may lead to neuronal damage and impairment of synaptic plasticity (learning). It is likely that under such conditions Mg(2+) ions, which block NMDA receptors under normal resting conditions, can no longer do so. We found that overactivation of NMDA receptors using a direct agonist or a decrease in Mg(2+) concentration produced deficits in synaptic plasticity (in vivo: passive avoidance test and/or in vitro: LTP in the CA1 region). In both cases, memantine-an uncompetitive NMDA receptor antagonists with features of an 'improved' Mg(2+) (voltage-dependency, kinetics, affinity)-attenuated this deficit. Synaptic plasticity was restored by therapeutically-relevant concentrations of memantine (1 microM). Moreover, doses leading to similar brain/serum levels provided neuroprotection in animal models relevant for neurodegeneration in AD such as neurotoxicity produced by inflammation in the NBM or beta-amyloid injection to the hippocampus. As such, if overactivation of NMDA receptors is present in AD, memantine would be expected to improve both symptoms (cognition) and to slow down disease progression because it takes over the physiological function of magnesium.", "title": "" }, { "docid": "neg:1840632_14", "text": "Low-cost hardware platforms for biomedical engineering are becoming increasingly available, which empower the research community in the development of new projects in a wide range of areas related with physiological data acquisition. Building upon previous work by our group, this work compares the quality of the data acquired by means of two different versions of the multimodal physiological computing platform BITalino, with a device that can be considered a reference. We acquired data from 5 sensors, namely Accelerometry (ACC), Electrocardiography (ECG), Electroencephalography (EEG), Electrodermal Activity (EDA) and Electromyography (EMG). Experimental evaluation shows that ACC, ECG and EDA data are highly correlated with the reference in what concerns the raw waveforms. When compared by means of their commonly used features, EEG and EMG data are also quite similar across the different devices.", "title": "" }, { "docid": "neg:1840632_15", "text": "Extracting temporal information from raw text is fundamental for deep language understanding, and key to many applications like question answering, information extraction, and document summarization. Our long-term goal is to build complete temporal structure of documents and use the temporal structure in other applications like textual entailment, question answering, visualization, or others. In this paper, we present a first step, a system for extracting events, event features, main events, temporal expressions and their normalized values from raw text. Our system is a combination of deep semantic parsing with extraction rules, Markov Logic Network classifiers and Conditional Random Field classifiers. To compare with existing systems, we evaluated our system on the TempEval 1 and TempEval 2 corpus. Our system outperforms or performs competitively with existing systems that evaluate on the TimeBank, TempEval 1 and TempEval 2 corpus and our performance is very close to inter-annotator agreement of the TimeBank annotators.", "title": "" }, { "docid": "neg:1840632_16", "text": "In this paper, we design a fast MapReduce algorithm for Monte Carlo approximation of personalized PageRank vectors of all the nodes in a graph. The basic idea is very efficiently doing single random walks of a given length starting at each node in the graph. More precisely, we design a MapReduce algorithm, which given a graph G and a length », outputs a single random walk of length » starting at each node in G. We will show that the number of MapReduce iterations used by our algorithm is optimal among a broad family of algorithms for the problem, and its I/O efficiency is much better than the existing candidates. We will then show how we can use this algorithm to very efficiently approximate all the personalized PageRank vectors. Our empirical evaluation on real-life graph data and in production MapReduce environment shows that our algorithm is significantly more efficient than all the existing algorithms in the MapReduce setting.", "title": "" }, { "docid": "neg:1840632_17", "text": "Object recognition is challenging especially when the objects from different categories are visually similar to each other. In this paper, we present a novel joint dictionary learning (JDL) algorithm to exploit the visual correlation within a group of visually similar object categories for dictionary learning where a commonly shared dictionary and multiple category-specific dictionaries are accordingly modeled. To enhance the discrimination of the dictionaries, the dictionary learning problem is formulated as a joint optimization by adding a discriminative term on the principle of the Fisher discrimination criterion. As well as presenting the JDL model, a classification scheme is developed to better take advantage of the multiple dictionaries that have been trained. The effectiveness of the proposed algorithm has been evaluated on popular visual benchmarks.", "title": "" }, { "docid": "neg:1840632_18", "text": "As automotive electronics have increased, models for predicting the transmission characteristics of wiring harnesses, suitable for the automotive EMC tests, are needed. In this paper, the repetitive structures of the cross-sectional shape of the twisted pair cable is focused on. By taking account of RLGC parameters, a theoretical analysis modeling for whole cables, based on multi-conductor transmission line theory, is proposed. Furthermore, the theoretical values are compared with measured values and a full-wave simulator. In case that a twisted pitch, a length of the cable, and a height of reference ground plane are changed, the validity of the proposed model is confirmed.", "title": "" } ]

[ { "docid": "pos:1840633_0", "text": "In this paper, we present a representation for three-dimensional geometric animation sequences. Different from standard key-frame techniques, this approach is based on the determination of principal animation components and decouples the animation from the underlying geometry. The new representation supports progressive animation compression with spatial, as well as temporal, level-of-detail and high compression ratios. The distinction of animation and geometry allows for mapping animations onto other objects.", "title": "" } ]

[ { "docid": "neg:1840633_0", "text": "Internet business models have been widely discussed in literature and applied within the last decade. Nevertheless, a clear understanding of some e-commerce concepts does not exist yet. The classification of business models in e-commerce is one of these areas. The current research tries to fill this gap through a conceptual and qualitative study. Nine main e-commerce business model types are selected from literature and analyzed to define the criteria and their sub-criteria (characteristics). As a result three different classifications for business models are determined. This study can be used to improve the understanding of essential functions, relations and mechanisms of existing e-commerce business models.", "title": "" }, { "docid": "neg:1840633_1", "text": "By combining linear graph theory with the principle of virtual work, a dynamic formulation is obtained that extends graph-theoretic modelling methods to the analysis of exible multibody systems. The system is represented by a linear graph, in which nodes represent reference frames on rigid and exible bodies, and edges represent components that connect these frames. By selecting a spanning tree for the graph, the analyst can choose the set of coordinates appearing in the nal system of equations. This set can include absolute, joint, or elastic coordinates, or some combination thereof. If desired, all non-working constraint forces and torques can be automatically eliminated from the dynamic equations by exploiting the properties of virtual work. The formulation has been implemented in a computer program, DynaFlex, that generates the equations of motion in symbolic form. Three examples are presented to demonstrate the application of the formulation, and to validate the symbolic computer implementation.", "title": "" }, { "docid": "neg:1840633_2", "text": "We have witnessed the Fixed Internet emerging with virtually every computer being connected today; we are currently witnessing the emergence of the Mobile Internet with the exponential explosion of smart phones, tablets and net-books. However, both will be dwarfed by the anticipated emergence of the Internet of Things (IoT), in which everyday objects are able to connect to the Internet, tweet or be queried. Whilst the impact onto economies and societies around the world is undisputed, the technologies facilitating such a ubiquitous connectivity have struggled so far and only recently commenced to take shape. To this end, this paper introduces in a timely manner and for the first time the wireless communications stack the industry believes to meet the important criteria of power-efficiency, reliability and Internet connectivity. Industrial applications have been the early adopters of this stack, which has become the de-facto standard, thereby bootstrapping early IoT developments with already thousands of wireless nodes deployed. Corroborated throughout this paper and by emerging industry alliances, we believe that a standardized approach, using latest developments in the IEEE 802.15.4 and IETF working groups, is the only way forward. We introduce and relate key embodiments of the power-efficient IEEE 802.15.4-2006 PHY layer, the power-saving and reliable IEEE 802.15.4e MAC layer, the IETF 6LoWPAN adaptation layer enabling universal Internet connectivity, the IETF ROLL routing protocol enabling availability, and finally the IETF CoAP enabling seamless transport and support of Internet applications. The protocol stack proposed in the present work converges towards the standardized notations of the ISO/OSI and TCP/IP stacks. What thus seemed impossible some years back, i.e., building a clearly defined, standards-compliant and Internet-compliant stack given the extreme restrictions of IoT networks, is commencing to become reality.", "title": "" }, { "docid": "neg:1840633_3", "text": "Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at http://www.jstor.org/journals/aea.html. Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission.", "title": "" }, { "docid": "neg:1840633_4", "text": "Recent results suggest that it is possible to grasp a variety of singulated objects with high precision using Convolutional Neural Networks (CNNs) trained on synthetic data. This paper considers the task of bin picking, where multiple objects are randomly arranged in a heap and the objective is to sequentially grasp and transport each into a packing box. We model bin picking with a discrete-time Partially Observable Markov Decision Process that specifies states of the heap, point cloud observations, and rewards. We collect synthetic demonstrations of bin picking from an algorithmic supervisor uses full state information to optimize for the most robust collision-free grasp in a forward simulator based on pybullet to model dynamic object-object interactions and robust wrench space analysis from the Dexterity Network (Dex-Net) to model quasi-static contact between the gripper and object. We learn a policy by fine-tuning a Grasp Quality CNN on Dex-Net 2.1 to classify the supervisor’s actions from a dataset of 10,000 rollouts of the supervisor in the simulator with noise injection. In 2,192 physical trials of bin picking with an ABB YuMi on a dataset of 50 novel objects, we find that the resulting policies can achieve 94% success rate and 96% average precision (very few false positives) on heaps of 5-10 objects and can clear heaps of 10 objects in under three minutes. Datasets, experiments, and supplemental material are available at http://berkeleyautomation.github.io/dex-net.", "title": "" }, { "docid": "neg:1840633_5", "text": "An Intrusion Detection System (IDS) is a software that monitors a single or a network of computers for malicious activities (attacks) that are aimed at stealing or censoring information or corrupting network protocols. Most techniques used in today’s IDS are not able to deal with the dynamic and complex nature of cyber attacks on computer networks. Hence, efficient adaptive methods like various techniques of machine learning can result in higher detection rates, lower false alarm rates and reasonable computation and communication costs. In this paper, we study several such schemes and compare their performance. We divide the schemes into methods based on classical artificial intelligence (AI) and methods based on computational intelligence (CI). We explain how various characteristics of CI techniques can be used to build efficient IDS.", "title": "" }, { "docid": "neg:1840633_6", "text": "Memory leaks are major problems in all kinds of applications, depleting their performance, even if they run on platforms with automatic memory management, such as Java Virtual Machine. In addition, memory leaks contribute to software aging, increasing the complexity of software maintenance. So far memory leak detection was considered to be a part of development process, rather than part of software maintenance. To detect slow memory leaks as a part of quality assurance process or in production environments statistical approach for memory leak detection was implemented and deployed in a commercial tool called Plumbr. It showed promising results in terms of leak detection precision and recall, however, even better detection quality was desired. To achieve this improvement goal, classification algorithms were applied to the statistical data, which was gathered from customer environments where Plumbr was deployed. This paper presents the challenges which had to be solved, method that was used to generate features for supervised learning and the results of the corresponding experiments.", "title": "" }, { "docid": "neg:1840633_7", "text": "ISSN 2277 5080 | © 2012 Bonfring Abstract--This paper discusses DSP based implementation of Gaussian Minimum Shift Keying (GMSK) demodulator using Polarity type Costas loop. The demodulator consists of a Polarity type Costas loop for carrier recovery, data recovery, and phase detection. Carrier has been recovered using a loop of center-frequency locking scheme as in M-ary Phase Shift Keying (MPSK) Polarity type Costas-loop. Phase unwrapping and Bit-Reconstruction is presented in detail. All the modules are first modeled in MATLAB (Simulink) and Systemview. After bit true simulation, the design is coded in VHDL and code simulation is done using QuestaSim 6.3c. The design is targeted to Virtex-4 XC4VSX35-10FF668 Xilinx FPGA (Field programmable gate array) for real time testing, which is carried out on Xtreme DSP development platform.", "title": "" }, { "docid": "neg:1840633_8", "text": "Ego-motion estimation based on images from a stereo camera has become a common function for autonomous mobile systems and is gaining increasing importance in the automotive sector. Unlike general robotic platforms, vehicles have a suspension adding degrees of freedom and thus complexity to their dynamics model. Some parameters of the model, such as the vehicle mass, are non-static as they depend on e.g. the specific load conditions and thus need to be estimated online to guarantee a concise and safe autonomous maneuvering of the vehicle. In this paper, a novel visual odometry based approach to simultaneously estimate ego-motion and selected vehicle parameters using a dual Ensemble Kalman Filter and a non-linear single-track model with pitch dynamics is presented. The algorithm has been validated using simulated data and showed a good performance for both the estimation of the ego-motion and of the relevant vehicle parameters.", "title": "" }, { "docid": "neg:1840633_9", "text": "We compare two technological approaches to augmented reality for 3-D medical visualization: optical and video see-through devices. We provide a context to discuss the technology by reviewing several medical applications of augmented-reality re search efforts driven by real needs in the medical field, both in the United States and in Europe. We then discuss the issues for each approach, optical versus video, from both a technology and human-factor point of view. Finally, we point to potentially promising future developments of such devices including eye tracking and multifocus planes capabilities, as well as hybrid optical/video technology.", "title": "" }, { "docid": "neg:1840633_10", "text": "This paper presents a 50-d.o.f. humanoid robot, Computational Brain (CB). CB is a humanoid robot created for exploring the underlying processing of the human brain while dealing with the real world. We place our investigations within real—world contexts, as humans do. In so doing, we focus on utilizing a system that is closer to humans—in sensing, kinematics configuration and performance. We present the real-time network-based architecture for the control of all 50 d.o.f. The controller provides full position/velocity/force sensing and control at 1 kHz, allowing us the flexibility in deriving various forms of control. A dynamic simulator is also presented; the simulator acts as a realistic testbed for our controllers and acts as a common interface to our humanoid robots. A contact model developed to allow better validation of our controllers prior to final testing on the physical robot is also presented. Three aspects of the system are highlighted in this paper: (i) physical power for walking, (ii) full-body compliant control—physical interactions and (iii) perception and control—visual ocular-motor responses.", "title": "" }, { "docid": "neg:1840633_11", "text": "lmost a decade has passed since we started advocating a process of usability design [20-22]. This article is a status report about the value of this process and, mainly, a description of new ideas for enhancing the use of the process. We first note that, when followed , the process leads to usable, useful, likeable computer systems and applications. Nevertheless, experience and observational evidence show that (because of the way development work is organized and carried out) the process is often not followed, despite designers' enthusiasm and motivation to do so. To get around these organizational and technical obstacles, we propose a) greater reliance on existing methodologies for establishing test-able usability and productivity-enhancing goals; b) a new method for identifying and focuging attention on long-term, trends about the effects that computer applications have on end-user productivity; and c) a new approach, now under way, to application development, particularly the development of user interfaces. The process consists of four activities [18, 20-22]. Early Focus On Users. Designers should have direct contact with intended or actual users-via interviews , observations, surveys, partic-ipatory design. The aim is to understand users' cognitive, behav-ioral, attitudinal, and anthropomet-ric characteristics-and the characteristics of the jobs they will be doing. Integrated Design. All aspects of usability (e.g., user interface, help system, training plan, documentation) should evolve in parallel, rather than be defined sequentially, and should be under one management. Early~And Continual~User Testing. The only presently feasible approach to successful design is an empirical one, requiring observation and measurement of user behavior , careful evaluation of feedback , insightful solutions to existing problems, and strong motivation to make design changes. Iterative Design. A system under development must be modified based upon the results of behav-ioral tests of functions, user interface , help system, documentation, training approach. This process of implementation, testing, feedback, evaluation, and change must be repeated to iteratively improve the system. We, and others proposing similar ideas (see below), have worked hard at spreading this process of usabil-ity design. We have used numerous channels to accomplish this: frequent talks, workshops, seminars, publications, consulting, addressing arguments used against it [22], conducting a direct case study of the process [20], and identifying methods for people not fully trained as human factors professionals to use in carrying out this process [18]. The Process Works. Several lines of evidence indicate that this usabil-ity design process leads to systems, applications, and products …", "title": "" }, { "docid": "neg:1840633_12", "text": "Origami describes rules for creating folded structures from patterns on a flat sheet, but does not prescribe how patterns can be designed to fit target shapes. Here, starting from the simplest periodic origami pattern that yields one-degree-of-freedom collapsible structures-we show that scale-independent elementary geometric constructions and constrained optimization algorithms can be used to determine spatially modulated patterns that yield approximations to given surfaces of constant or varying curvature. Paper models confirm the feasibility of our calculations. We also assess the difficulty of realizing these geometric structures by quantifying the energetic barrier that separates the metastable flat and folded states. Moreover, we characterize the trade-off between the accuracy to which the pattern conforms to the target surface, and the effort associated with creating finer folds. Our approach enables the tailoring of origami patterns to drape complex surfaces independent of absolute scale, as well as the quantification of the energetic and material cost of doing so.", "title": "" }, { "docid": "neg:1840633_13", "text": "Use and users have an important and acknowledged role to most designers of interactive systems. Nevertheless any touch of user hands does not in itself secure development of meaningful artifacts. In this article we stress the need for a professional PD practice in order to yield the full potentiality of user involvement. We suggest two constituting elements of such a professional PD practice. The existence of a shared 'where-to' and 'why' artifact and an ongoing reflection and off-loop reflection among practitioners in the PD process.", "title": "" }, { "docid": "neg:1840633_14", "text": "Accurate and reliable inventory forecasting can save an organization from overstock, under-stock and no stock/stock-out situation of inventory. Overstocking leads to high cost of storage and its maintenance, whereas under-stocking leads to failure to meet the demand and losing profit and customers, similarly stock-out leads to complete halt of production or sale activities. Inventory transactions generate data, which is a time-series data having characteristic volume, speed, range and regularity. The inventory level of an item depends on many factors namely, current stock, stock-on-order, lead-time, annual/monthly target. In this paper, we present a perspective of treating Inventory management as a problem of Genetic Programming based on inventory transactions data. A Genetic Programming — Symbolic Regression (GP-SR) based mathematical model is developed and subsequently used to make forecasts using Holt-Winters Exponential Smoothing method for time-series modeling. The GP-SR model evolves based on RMSE as the fitness function. The performance of the model is measured in terms of RMSE and MAE. The estimated values of item demand from the GP-SR model is finally used to simulate a time-series and forecasts are generated for inventory required on a monthly time horizon.", "title": "" }, { "docid": "neg:1840633_15", "text": "In this paper, we describe a statistical approach to both an articulatory-to-acoustic mapping and an acoustic-to-articulatory inversion mapping without using phonetic information. The joint probability density of an articulatory parameter and an acoustic parameter is modeled using a Gaussian mixture model (GMM) based on a parallel acoustic-articulatory speech database. We apply the GMM-based mapping using the minimum mean-square error (MMSE) criterion, which has been proposed for voice conversion, to the two mappings. Moreover, to improve the mapping performance, we apply maximum likelihood estimation (MLE) to the GMM-based mapping method. The determination of a target parameter trajectory having appropriate static and dynamic properties is obtained by imposing an explicit relationship between static and dynamic features in the MLE-based mapping. Experimental results demonstrate that the MLE-based mapping with dynamic features can significantly improve the mapping performance compared with the MMSE-based mapping in both the articulatory-to-acoustic mapping and the inversion mapping.", "title": "" }, { "docid": "neg:1840633_16", "text": "The rise of blockchain-based cryptocurrencies has led to an explosion of services using distributed ledgers as their underlying infrastructure. However, due to inherently single-service oriented blockchain protocols, such services can bloat the existing ledgers, fail to provide sufficient security, or completely forego the property of trustless auditability. Security concerns, trust restrictions, and scalability limits regarding the resource requirements of users hamper the sustainable development of loosely-coupled services on blockchains. This paper introduces Aspen, a sharded blockchain protocol designed to securely scale with increasing number of services. Aspen shares the same trust model as Bitcoin in a peer-to-peer network that is prone to extreme churn containing Byzantine participants. It enables introduction of new services without compromising the security, leveraging the trust assumptions, or flooding users with irrelevant messages.", "title": "" }, { "docid": "neg:1840633_17", "text": "Smartphones providing proprietary encryption schemes, albeit offering a novel paradigm to privacy, are becoming a bone of contention for certain sovereignties. These sovereignties have raised concerns about their security agencies not having any control on the encrypted data leaving their jurisdiction and the ensuing possibility of it being misused by people with malicious intents. Such smartphones have typically two types of customers, independent users who use it to access public mail servers and corporates/enterprises whose employees use it to access corporate emails in an encrypted form. The threat issues raised by security agencies concern mainly the enterprise servers where the encrypted data leaves the jurisdiction of the respective sovereignty while on its way to the global smartphone router. In this paper, we have analyzed such email message transfer mechanisms in smartphones and proposed some feasible solutions, which, if accepted and implemented by entities involved, can lead to a possible win-win situation for both the parties, viz., the smartphone provider who does not want to lose the customers and these sovereignties who can avoid the worry of encrypted data leaving their jurisdiction.", "title": "" }, { "docid": "neg:1840633_18", "text": "As social robots become more widely used as educational tutoring agents, it is important to study how children interact with these systems, and how effective they are as assessed by learning gains, sustained engagement, and perceptions of the robot tutoring system as a whole. In this paper, we summarize our prior work involving a long-term child-robot interaction study and outline important lessons learned regarding individual differences in children. We then discuss how these lessons inform future research in child-robot interaction.", "title": "" }, { "docid": "neg:1840633_19", "text": "As interest in adopting Cloud computing for various applications is rapidly growing, it is important to understand how these applications and systems will perform when deployed on Clouds. Due to the scale and complexity of shared resources, it is often hard to analyze the performance of new scheduling and provisioning algorithms on actual Cloud test beds. Therefore, simulation tools are becoming more and more important in the evaluation of the Cloud computing model. Simulation tools allow researchers to rapidly evaluate the efficiency, performance and reliability of their new algorithms on a large heterogeneous Cloud infrastructure. However, current solutions lack either advanced application models such as message passing applications and workflows or scalable network model of data center. To fill this gap, we have extended a popular Cloud simulator (CloudSim) with a scalable network and generalized application model, which allows more accurate evaluation of scheduling and resource provisioning policies to optimize the performance of a Cloud infrastructure.", "title": "" } ]

[ { "docid": "pos:1840634_0", "text": "We developed a pneumatically powered orthosis for the human ankle joint. The orthosis consisted of a carbon fiber shell, hinge joint, and two artificial pneumatic muscles. One artificial pneumatic muscle provided plantar flexion torque and the second one provided dorsiflexion torque. Computer software adjusted air pressure in each artificial muscle independently so that artificial muscle force was proportional to rectified low-pass-filtered electromyography (EMG) amplitude (i.e., proportional myoelectric control). Tibialis anterior EMG activated the artificial dorsiflexor and soleus EMG activated the artificial plantar flexor. We collected joint kinematic and artificial muscle force data as one healthy participant walked on a treadmill with the orthosis. Peak plantar flexor torque provided by the orthosis was 70 Nm, and peak dorsiflexor torque provided by the orthosis was 38 Nm. The orthosis could be useful for basic science studies on human locomotion or possibly for gait rehabilitation after neurological injury.", "title": "" }, { "docid": "pos:1840634_1", "text": "Sarcos Research Corporation, and the Center for Engineering Design at the University of Utah, have long been interested in both the fundamental and the applied aspects of robots and other computationally driven machines. We have produced substantial numbers of systems that function as products for commercial applications, and as advanced research tools specifically designed for experimental", "title": "" } ]

[ { "docid": "neg:1840634_0", "text": "Back Side Illumination (BSI) CMOS image sensors with two-layer photo detectors (2LPDs) have been fabricated and evaluated. The test pixel array has green pixels (2.2um x 2.2um) and a magenta pixel (2.2um x 4.4um). The green pixel has a single-layer photo detector (1LPD). The magenta pixel has a 2LPD and a vertical charge transfer (VCT) path to contact a back side photo detector. The 2LPD and the VCT were implemented by high-energy ion implantation from the circuit side. Measured spectral response curves from the 2LPDs fitted well with those estimated based on lightabsorption theory for Silicon detectors. Our measurement results show that the keys to realize the 2LPD in BSI are; (1) the reduction of crosstalk to the VCT from adjacent pixels and (2) controlling the backside photo detector thickness variance to reduce color signal variations.", "title": "" }, { "docid": "neg:1840634_1", "text": "When collecting patient-level resource use data for statistical analysis, for some patients and in some categories of resource use, the required count will not be observed. Although this problem must arise in most reported economic evaluations containing patient-level data, it is rare for authors to detail how the problem was overcome. Statistical packages may default to handling missing data through a so-called 'complete case analysis', while some recent cost-analyses have appeared to favour an 'available case' approach. Both of these methods are problematic: complete case analysis is inefficient and is likely to be biased; available case analysis, by employing different numbers of observations for each resource use item, generates severe problems for standard statistical inference. Instead we explore imputation methods for generating 'replacement' values for missing data that will permit complete case analysis using the whole data set and we illustrate these methods using two data sets that had incomplete resource use information.", "title": "" }, { "docid": "neg:1840634_2", "text": "This paper presents the kinematic calibration of a novel 7-degree-of-freedom (DOF) cable-driven robotic arm (CDRA), aimed at improving its absolute positioning accuracy. This CDRA consists of three 'self-calibrated' cable-driven parallel mechanism (CDPM) modules. In order to account for any kinematic errors that might arise when assembling the individual CDPMs, a calibration model is formulated based on the local product-of-exponential formula and the measurement residues in the tool-tip frame poses. An iterative least-squares algorithm is employed to identify the errors in the fixed transformation frames of the sequentially assembled 'self- calibrated' CDPM modules. Both computer simulations and experimental studies were carried out to verify the robustness and effectiveness of the proposed calibration algorithm. From the experimental studies, errors in the fixed kinematic transformation frames were precisely recovered after a minimum of 15 pose measurements.", "title": "" }, { "docid": "neg:1840634_3", "text": "The paper investigates potential bias in awards of player disciplinary sanctions, in the form of cautions (yellow cards) and dismissals (red cards) by referees in the English Premier League and the German Bundesliga. Previous studies of behaviour of soccer referees have not adequately incorporated within-game information.Descriptive statistics from our samples clearly show that home teams receive fewer yellow and red cards than away teams. These differences may be wrongly interpreted as evidence of bias where the modeller has failed to include withingame events such as goals scored and recent cards issued.What appears as referee favouritism may actually be excessive and illegal aggressive behaviour by players in teams that are behind in score. We deal with these issues by using a minute-by-minute bivariate probit analysis of yellow and red cards issued in games over six seasons in the two leagues. The significance of a variable to denote the difference in score at the time of sanction suggests that foul play that is induced by a losing position is an important influence on the award of yellow and red cards. Controlling for various pre-game and within-game variables, we find evidence that is indicative of home team favouritism induced by crowd pressure: in Germany home teams with running tracks in their stadia attract more yellow and red cards than teams playing in stadia with less distance between the crowd and the pitch. Separating the competing teams in matches by favourite and underdog status, as perceived by the betting market, yields further evidence, this time for both leagues, that the source of home teams receiving fewer cards is not just that they are disproportionately often the favoured team and disproportionately ahead in score.Thus there is evidence that is consistent with pure referee bias in relative treatments of home and away teams.", "title": "" }, { "docid": "neg:1840634_4", "text": "The rapid growth in the population density in urban cities and the advancement in technology demands real-time provision of services and infrastructure. Citizens, especially travelers, want to be reached within time to the destination. Consequently, they require to be facilitated with smart and real-time traffic information depending on the current traffic scenario. Therefore, in this paper, we proposed a graph-oriented mechanism to achieve the smart transportation system in the city. We proposed to deploy road sensors to get the overall traffic information as well as the vehicular network to obtain location and speed information of the individual vehicle. These Internet of Things (IoT) based networks generate enormous volume of data, termed as Big Data, depicting the traffic information of the city. To process incoming Big Data from IoT devices, then generating big graphs from the data, and processing them, we proposed an efficient architecture that uses the Giraph tool with parallel processing servers to achieve real-time efficiency. Later, various graph algorithms are used to achieve smart transportation by making real-time intelligent decisions to facilitate the citizens as well as the metropolitan authorities. Vehicular Datasets from various reliable resources representing the real city traffic are used for analysis and evaluation purpose. The system is implemented using Giraph and Spark tool at the top of the Hadoop parallel nodes to generate and process graphs with near real-time. Moreover, the system is evaluated in terms of efficiency by considering the system throughput and processing time. The results show that the proposed system is more scalable and efficient.", "title": "" }, { "docid": "neg:1840634_5", "text": "Building Information Modeling (BIM) has rapidly grown from merely being a three-dimensional (3D) model of a facility to serving as “a shared knowledge resource for information about a facility, forming a reliable basis for decisions during its life cycle from inception onward” [1]. BIM with three primary spatial dimensions (width, height, and depth) becomes 4D BIM when time (construction scheduling information) is added, and 5D BIM when cost information is added to it. Although the sixth dimension of the 6D BIM is often attributed to asset information useful for Facility Management (FM) processes, there is no agreement in the research literature on what each dimension represents beyond the fifth dimension [2]. BIM ultimately seeks to digitize the different stages of a building lifecycle such as planning, design, construction, and operation such that consistent digital information of a building project can be used by stakeholders throughout the building life-cycle [3]. The United States National Building Information Model Standard (NBIMS) initially characterized BIMs as digital representations of physical and functional aspects of a facility. But, in the most recent version released in July 2015, the NBIMS’ definition of BIM includes three separate but linked functions, namely business process, digital representation, and organization and control [4]. A number of national-level initiatives are underway in various countries to formally encourage the adoption of BIM technologies in the Architecture, Engineering, and Construction (AEC) and FM industries. Building SMART, with 18 chapters across the globe, including USA, UK, Australasia, etc., was established in 1995 with the aim of developing and driving the active use of open internationally-recognized standards to support the wider adoption of BIM across the building and infrastructure sectors [5]. The UK BIM Task Group, with experts from industry, government, public sector, institutes, and academia, is committed to facilitate the implementation of ‘collaborative 3D BIM’, a UK Government Construction Strategy initiative [6]. Similarly, the EUBIM Task Group was started with a vision to foster the common use of BIM in public works and produce a handbook containing the common BIM principles, guidance and practices for public contracting entities and policy makers [7].", "title": "" }, { "docid": "neg:1840634_6", "text": "Some of the more difficult to define aspects of the therapeutic process (empathy, compassion, presence) remain some of the most important. Teaching them presents a challenge for therapist trainees and educators alike. In this study, we examine our beginning practicum students' experience of learning mindfulness meditation as a way to help them develop therapeutic presence. Through thematic analysis of their journal entries a variety of themes emerged, including the effects of meditation practice, the ability to be present, balancing being and doing modes in therapy, and the development of acceptance and compassion for themselves and for their clients. Our findings suggest that mindfulness meditation may be a useful addition to clinical training.", "title": "" }, { "docid": "neg:1840634_7", "text": "In this paper, a novel broadband dual-polarized (slant ±45°) base station antenna element operating at 790–960 MHz is proposed. The antenna element consists of two pairs of symmetrical dipoles, four couples of baluns, a cricoid pedestal and two kinds of plastic fasteners. Specific shape metal reflector is also designed to achieve stable radiation pattern and high front-to-back ratio (FBR). All the simulated and measured results show that the proposed antenna element has wide impedance bandwidth (about 19.4%), low voltage standing wave ratio (VSWR < 1.4) and high port to port isolation (S21 < −25 dB) at the whole operating frequency band. Stable horizontal half-power beam width (HPBW) with 65°±4.83° and high gain (> 9.66 dBi) are also achieved. The proposed antenna element fabricated by integrated metal casting technology has great mechanical properties such as compact structure, low profile, good stability, light weight and easy to fabricate. Due to its good electrical and mechanical characteristics, the antenna element is suitable for European Digital Dividend, CDMA800 and GSM900 bands in base station antenna of modern mobile communication.", "title": "" }, { "docid": "neg:1840634_8", "text": "BACKGROUND\nAcne is a common condition seen in up to 80% of people between 11 and 30 years of age and in up to 5% of older adults. In some patients, it can result in permanent scars that are surprisingly difficult to treat. A relatively new treatment, termed skin needling (needle dermabrasion), seems to be appropriate for the treatment of rolling scars in acne.\n\n\nAIM\nTo confirm the usefulness of skin needling in acne scarring treatment.\n\n\nMETHODS\nThe present study was conducted from September 2007 to March 2008 at the Department of Systemic Pathology, University of Naples Federico II and the UOC Dermatology Unit, University of Rome La Sapienza. In total, 32 patients (20 female, 12 male patients; age range 17-45) with acne rolling scars were enrolled. Each patient was treated with a specific tool in two sessions. Using digital cameras, photos of all patients were taken to evaluate scar depth and, in five patients, silicone rubber was used to make a microrelief impression of the scars. The photographic data were analysed by using the sign test statistic (alpha < 0.05) and the data from the cutaneous casts were analysed by fast Fourier transformation (FFT).\n\n\nRESULTS\nAnalysis of the patient photographs, supported by the sign test and of the degree of irregularity of the surface microrelief, supported by FFT, showed that, after only two sessions, the severity grade of rolling scars in all patients was greatly reduced and there was an overall aesthetic improvement. No patient showed any visible signs of the procedure or hyperpigmentation.\n\n\nCONCLUSION\nThe present study confirms that skin needling has an immediate effect in improving acne rolling scars and has advantages over other procedures.", "title": "" }, { "docid": "neg:1840634_9", "text": "Visual perceptual learning (VPL) is defined as a long-term improvement in performance on a visual task. In recent years, the idea that conscious effort is necessary for VPL to occur has been challenged by research suggesting the involvement of more implicit processing mechanisms, such as reinforcement-driven processing and consolidation. In addition, we have learnt much about the neural substrates of VPL and it has become evident that changes in visual areas and regions beyond the visual cortex can take place during VPL.", "title": "" }, { "docid": "neg:1840634_10", "text": "The semi-supervised learning usually only predict labels for unlabeled data appearing in training data, and cannot effectively predict labels for testing data never appearing in training set. To handle this outof-sample problem, many inductive methods make a constraint such that the predicted label matrix should be exactly equal to a linear model. In practice, this constraint is too rigid to capture the manifold structure of data. Motivated by this deficiency, we relax the rigid linear embedding constraint and propose to use an elastic embedding constraint on the predicted label matrix such that the manifold structure can be better explored. To solve our new objective and also a more general optimization problem, we study a novel adaptive loss with efficient optimization algorithm. Our new adaptive loss minimization method takes the advantages of both L1 norm and L2 norm, and is robust to the data outlier under Laplacian distribution and can efficiently learn the normal data under Gaussian distribution. Experiments have been performed on image classification tasks and our approach outperforms other state-of-the-art methods.", "title": "" }, { "docid": "neg:1840634_11", "text": "Cerebral organoids recapitulate human brain development at a considerable level of detail, even in the absence of externally added signaling factors. The patterning events driving this self-organization are currently unknown. Here, we examine the developmental and differentiative capacity of cerebral organoids. Focusing on forebrain regions, we demonstrate the presence of a variety of discrete ventral and dorsal regions. Clearing and subsequent 3D reconstruction of entire organoids reveal that many of these regions are interconnected, suggesting that the entire range of dorso-ventral identities can be generated within continuous neuroepithelia. Consistent with this, we demonstrate the presence of forebrain organizing centers that express secreted growth factors, which may be involved in dorso-ventral patterning within organoids. Furthermore, we demonstrate the timed generation of neurons with mature morphologies, as well as the subsequent generation of astrocytes and oligodendrocytes. Our work provides the methodology and quality criteria for phenotypic analysis of brain organoids and shows that the spatial and temporal patterning events governing human brain development can be recapitulated in vitro.", "title": "" }, { "docid": "neg:1840634_12", "text": "Human goal-directed action emerges from the interaction between stimulus-driven sensorimotor online systems and slower-working control systems that relate highly processed perceptual information to the construction of goal-related action plans. This distribution of labor requires the acquisition of enduring action representations; that is, of memory traces which capture the main characteristics of successful actions and their consequences. It is argued here that these traces provide the building blocks for off-line prospective action planning, which renders the search through stored action representations an essential part of action control. Hence, action planning requires cognitive search (through possible options) and might have led to the evolution of cognitive search routines that humans have learned to employ for other purposes as well, such as searching for perceptual events and through memory. Thus, what is commonly considered to represent different types of search operations may all have evolved from action planning and share the same characteristics. Evidence is discussed which suggests that all types of cognitive search—be it in searching for perceptual events, for suitable actions, or through memory—share the characteristic of following a fi xed sequence of cognitive operations: divergent search followed by convergent search.", "title": "" }, { "docid": "neg:1840634_13", "text": "A dual-band 0.92/2.45 GHz circularly-polarized (CP) unidirectional antenna using the wideband dual-feed network, two orthogonally positioned asymmetric H-shape slots, and two stacked concentric annular-ring patches is proposed for RF identification (RFID) applications. The measurement result shows that the antenna achieves the impedance bandwidths of 15.4% and 41.9%, the 3-dB axial-ratio (AR) bandwidths of 4.3% and 21.5%, and peak gains of 7.2 dBic and 8.2 dBic at 0.92 and 2.45 GHz bands, respectively. Moreover, the antenna provides stable symmetrical radiation patterns and wide-angle 3-dB AR beamwidths in both lower and higher bands for unidirectional wide-coverage RFID reader applications. Above all, the dual-band CP unidirectional patch antenna presented is beneficial to dual-band RFID system on configuration, implementation, as well as cost reduction.", "title": "" }, { "docid": "neg:1840634_14", "text": "Submission instructions: These questions require thought but do not require long answers. Please be as concise as possible. You should submit your answers as a writeup in PDF format via GradeScope and code via the Snap submission site. Submitting writeup: Prepare answers to the homework questions into a single PDF file and submit it via http://gradescope.com. Make sure that the answer to each question is on a separate page. On top of each page write the number of the question you are answering. Please find the cover sheet and the recommended templates located here: Not including the cover sheet in your submission will result in a 2-point penalty. It is also important to tag your answers correctly on Gradescope. We will deduct 5/N points for each incorrectly tagged subproblem (where N is the number of subproblems). This means you can lose up to 5 points for incorrect tagging. Put all the code for a single question into a single file and upload it. Consider a user-item bipartite graph where each edge in the graph between user U to item I, indicates that user U likes item I. We also represent the ratings matrix for this set of users and items as R, where each row in R corresponds to a user and each column corresponds to an item. If user i likes item j, then R i,j = 1, otherwise R i,j = 0. Also assume we have m users and n items, so matrix R is m × n.", "title": "" }, { "docid": "neg:1840634_15", "text": "Osteochondrosis dissecans (OCD) is a form of osteochondrosis limited to the articular epiphysis. The most commonly affected areas include, in decreasing order of frequency, the femoral condyles, talar dome and capitellum of the humerus. OCD rarely occurs in the shoulder joint, where it involves either the humeral head or the glenoid. The purpose of this report is to present a case with glenoid cavity osteochondritis dissecans and clinical and radiological outcome after arthroscopic debridement. The patient underwent arthroscopy to remove the loose body and to microfracture the cavity. The patient was followed-up for 4 years and she is pain-free with full range of motion and a stable shoulder joint.", "title": "" }, { "docid": "neg:1840634_16", "text": "This article is an extremely rapid survey of the modern theory of partial differential equations (PDEs). Sources of PDEs are legion: mathematical physics, geometry, probability theory, continuum mechanics, optimization theory, etc. Indeed, most of the fundamental laws of the physical sciences are partial differential equations and most papers published in applied math concern PDEs. The following discussion is consequently very broad, but also very shallow, and will certainly be inadequate for any given PDE the reader may care about. The goal is rather to highlight some of the many key insights and unifying principles across the entire subject.", "title": "" }, { "docid": "neg:1840634_17", "text": "In this paper, a MATLAB based simulation of Grid connected PV system is presented. The main components of this simulation are PV solar panel, Boost converter; Maximum Power Point Tracking System (MPPT) and Grid Connected PV inverter with closed loop control system is designed and simulated. A simulation studies is carried out in different solar radiation level.", "title": "" }, { "docid": "neg:1840634_18", "text": "Rynes, Colbert, and Brown (2002) presented the following statement to 959 members of the Society for Human Resource Management (SHRM): “Surveys that directly ask employees how important pay is to them are likely to overestimate pay’s true importance in actual decisions” (p. 158). If our interpretation (and that of Rynes et al.) of the research literature is accurate, then the correct true-false answer to the above statement is “false.” In other words, people are more likely to underreport than to overreport the importance of pay as a motivational factor in most situations. Put another way, research suggests that pay is much more important in people’s actual choices and behaviors than it is in their self-reports of what motivates them, much like the cartoon viewers mentioned in the quote above. Yet, only 35% of the respondents in the Rynes et al. study answered in a way consistent with research findings (i.e., chose “false”). Our objective in this article is to show that employee surveys regarding the importance of various factors in motivation generally produce results that are inconsistent with studies of actual employee behavior. In particular, we focus on well-documented findings that employees tend to say that pay THE IMPORTANCE OF PAY IN EMPLOYEE MOTIVATION: DISCREPANCIES BETWEEN WHAT PEOPLE SAY AND WHAT THEY DO", "title": "" }, { "docid": "neg:1840634_19", "text": "Cross-domain recommendations are currently available in closed, proprietary social networking ecosystems such as Facebook, Twitter and Google+. I propose an open framework as an alternative, which enables cross-domain recommendations with domain-agnostic user profiles modeled as semantic interest graphs. This novel framework covers all parts of a recommender system. It includes an architecture for privacy-enabled profile exchange, a distributed and domain-agnostic user model and a cross-domain recommendation algorithm. This enables users to receive recommendations for a target domain (e.g. food) based on any kind of previous interests.", "title": "" } ]

[ { "docid": "pos:1840635_0", "text": "This paper presents a new class of thin, dexterous continuum robots, which we call active cannulas due to their potential medical applications. An active cannula is composed of telescoping, concentric, precurved superelastic tubes that can be axially translated and rotated at the base relative to one another. Active cannulas derive bending not from tendon wires or other external mechanisms but from elastic tube interaction in the backbone itself, permitting high dexterity and small size, and dexterity improves with miniaturization. They are designed to traverse narrow and winding environments without relying on ldquoguidingrdquo environmental reaction forces. These features seem ideal for a variety of applications where a very thin robot with tentacle-like dexterity is needed. In this paper, we apply beam mechanics to obtain a kinematic model of active cannula shape and describe design tools that result from the modeling process. After deriving general equations, we apply them to a simple three-link active cannula. Experimental results illustrate the importance of including torsional effects and the ability of our model to predict energy bifurcation and active cannula shape.", "title": "" }, { "docid": "pos:1840635_1", "text": "This paper describes the results of field trials and associated testing of the OctArm series of multi-section continuous backbone \"continuum\" robots. This novel series of manipulators has recently (Spring 2005) undergone a series of trials including open-air and in-water field tests. Outcomes of the trials, in which the manipulators demonstrated the ability for adaptive and novel manipulation in challenging environments, are described. Implications for the deployment of continuum robots in a variety of applications are discussed", "title": "" }, { "docid": "pos:1840635_2", "text": "Continuum robotics has rapidly become a rich and diverse area of research, with many designs and applications demonstrated. Despite this diversity in form and purpose, there exists remarkable similarity in the fundamental simplified kinematic models that have been applied to continuum robots. However, this can easily be obscured, especially to a newcomer to the field, by the different applications, coordinate frame choices, and analytical formalisms employed. In this paper we review several modeling approaches in a common frame and notational convention, illustrating that for piecewise constant curvature, they produce identical results. This discussion elucidates what has been articulated in different ways by a number of researchers in the past several years, namely that constant-curvature kinematics can be considered as consisting of two separate submappings: one that is general and applies to all continuum robots, and another that is robot-specific. These mappings are then developed both for the singlesection and for the multi-section case. Similarly, we discuss the decomposition of differential kinematics (the robot’s Jacobian) into robot-specific and robot-independent portions. The paper concludes with a perspective on several of the themes of current research that are shaping the future of continuum robotics.", "title": "" } ]

[ { "docid": "neg:1840635_0", "text": "Intrusion detection for computer network systems becomes one of the most critical tasks for network administrators today. It has an important role for organizations, governments and our society due to its valuable resources on computer networks. Traditional misuse detection strategies are unable to detect new and unknown intrusion. Besides , anomaly detection in network security is aim to distinguish between illegal or malicious events and normal behavior of network systems. Anomaly detection can be considered as a classification problem where it builds models of normal network behavior, which it uses to detect new patterns that significantly deviate from the model. Most of the current research on anomaly detection is based on the learning of normally and anomaly behaviors. They do not take into account the previous, recent events to detect the new incoming one. In this paper, we propose a real time collective anomaly detection model based on neural network learning and feature operating. Normally a Long Short-Term Memory Recurrent Neural Network (LSTM RNN) is trained only on normal data and it is capable of predicting several time steps ahead of an input. In our approach, a LSTM RNN is trained with normal time series data before performing a live prediction for each time step. Instead of considering each time step separately, the observation of prediction errors from a certain number of time steps is now proposed as a new idea for detecting collective anomalies. The prediction errors from a number of the latest time steps above a threshold will indicate a collective anomaly. The model is built on a time series version of the KDD 1999 dataset. The experiments demonstrate that it is possible to offer reliable and efficient for collective anomaly detection.", "title": "" }, { "docid": "neg:1840635_1", "text": "Permissioned blockchains are arising as a solution to federate companies prompting accountable interactions. A variety of consensus algorithms for such blockchains have been proposed, each of which has different benefits and drawbacks. Proof-of-Authority (PoA) is a new family of Byzantine fault-tolerant (BFT) consensus algorithms largely used in practice to ensure better performance than traditional Practical Byzantine Fault Tolerance (PBFT). However, the lack of adequate analysis of PoA hinders any cautious evaluation of their effectiveness in real-world permissioned blockchains deployed over the Internet, hence on an eventually synchronous network experimenting Byzantine nodes. In this paper, we analyse two of the main PoA algorithms, named Aura and Clique, both in terms of provided guarantees and performances. First, we derive their functioning including how messages are exchanged, then we weight, by relying on the CAP theorem, consistency, availability and partition tolerance guarantees. We also report a qualitative latency analysis based on message rounds. The analysis advocates that PoA for permissioned blockchains, deployed over the Internet with Byzantine nodes, do not provide adequate consistency guarantees for scenarios where data integrity is essential. We claim that PBFT can fit better such scenarios, despite a limited loss in terms of performance.", "title": "" }, { "docid": "neg:1840635_2", "text": "A novel multi-level AC six-phase motor drive is developed in this paper. The scheme is based on three conventional 2-level three-phase voltage source inverters (VSIs) supplying the open-end windings of a dual three-phase motor (six-phase induction machine). The proposed inverter is capable of supply the machine with multi-level voltage waveforms. The developed system is compared with the conventional solution and it is demonstrated that the drive system permits to reduce the harmonic distortion of the machine currents, to reduce the total semiconductor losses and to decrease the power processed by converter switches. The system model and the Pulse-Width Modulation (PWM) strategy are presented. The experimental verification was obtained by using IGBTs with dedicated drives and a digital signal processor (DSP) with plug-in boards and sensors.", "title": "" }, { "docid": "neg:1840635_3", "text": "Predicting the outcome of National Basketball Association (NBA) matches poses a challenging problem of interest to the research community as well as the general public. In this article, we formalize the problem of predicting NBA game results as a classification problem and apply the principle of Maximum Entropy to construct an NBA Maximum Entropy (NBAME) model that fits to discrete statistics for NBA games, and then predict the outcomes of NBA playoffs using the model. Our results reveal that the model is able to predict the winning team with 74.4% accuracy, outperforming other classical machine learning algorithms that could only afford a maximum prediction accuracy of 70.6% in the experiments that we performed.", "title": "" }, { "docid": "neg:1840635_4", "text": "Autonomous vehicles platooning has received considerable attention in recent years, due to its potential to significantly benefit road transportation, improving traffic efficiency, enhancing road safety and reducing fuel consumption. The Vehicular ad hoc Networks and the de facto vehicular networking standard IEEE 802.11p communication protocol are key tools for the deployment of platooning applications, since the cooperation among vehicles is based on a reliable communication structure. However, vehicular networks can suffer different security threats. Indeed, in collaborative driving applications, the sudden appearance of a malicious attack can mainly compromise: (i) the correctness of data traffic flow on the vehicular network by sending malicious messages that alter the platoon formation and its coordinated motion; (ii) the safety of platooning application by altering vehicular network communication capability. In view of the fact that cyber attacks can lead to dangerous implications for the security of autonomous driving systems, it is fundamental to consider their effects on the behavior of the interconnected vehicles, and to try to limit them from the control design stage. To this aim, in this work we focus on some relevant types of malicious threats that affect the platoon safety, i.e. application layer attacks (Spoofing and Message Falsification) and network layer attacks (Denial of Service and Burst Transmission), and we propose a novel collaborative control strategy for enhancing the protection level of autonomous platoons. The control protocol is designed and validated in both analytically and experimental way, for the appraised malicious attack scenarios and for different communication topology structures. The effectiveness of the proposed strategy is shown by using PLEXE, a state of the art inter-vehicular communications and mobility simulator that includes basic building blocks for platooning. A detailed experimental analysis discloses the robustness of the proposed approach and its capabilities in reacting to the malicious attack effects.", "title": "" }, { "docid": "neg:1840635_5", "text": "The notion of creativity, as opposed to related concepts such as beauty or interestingness, has not been studied from the perspective of automatic analysis of multimedia content. Meanwhile, short online videos shared on social media platforms, or micro-videos, have arisen as a new medium for creative expression. In this paper we study creative micro-videos in an effort to understand the features that make a video creative, and to address the problem of automatic detection of creative content. Defining creative videos as those that are novel and have aesthetic value, we conduct a crowdsourcing experiment to create a dataset of over 3, 800 micro-videos labelled as creative and non-creative. We propose a set of computational features that we map to the components of our definition of creativity, and conduct an analysis to determine which of these features correlate most with creative video. Finally, we evaluate a supervised approach to automatically detect creative video, with promising results, showing that it is necessary to model both aesthetic value and novelty to achieve optimal classification accuracy.", "title": "" }, { "docid": "neg:1840635_6", "text": "BACKGROUND AND PURPOSE\nNeuromyelitis optica (NMO) or Devic's disease is a rare inflammatory and demyelinating autoimmune disorder of the central nervous system (CNS) characterized by recurrent attacks of optic neuritis (ON) and longitudinally extensive transverse myelitis (LETM), which is distinct from multiple sclerosis (MS). The guidelines are designed to provide guidance for best clinical practice based on the current state of clinical and scientific knowledge.\n\n\nSEARCH STRATEGY\nEvidence for this guideline was collected by searches for original articles, case reports and meta-analyses in the MEDLINE and Cochrane databases. In addition, clinical practice guidelines of professional neurological and rheumatological organizations were studied.\n\n\nRESULTS\nDifferent diagnostic criteria for NMO diagnosis [Wingerchuk et al. Revised NMO criteria, 2006 and Miller et al. National Multiple Sclerosis Society (NMSS) task force criteria, 2008] and features potentially indicative of NMO facilitate the diagnosis. In addition, guidance for the work-up and diagnosis of spatially limited NMO spectrum disorders is provided by the task force. Due to lack of studies fulfilling requirement for the highest levels of evidence, the task force suggests concepts for treatment of acute exacerbations and attack prevention based on expert opinion.\n\n\nCONCLUSIONS\nStudies on diagnosis and management of NMO fulfilling requirements for the highest levels of evidence (class I-III rating) are limited, and diagnostic and therapeutic concepts based on expert opinion and consensus of the task force members were assembled for this guideline.", "title": "" }, { "docid": "neg:1840635_7", "text": "We investigate a family of poisoning attacks against Support Vector Machines (SVM). Such attacks inject specially crafted training data that increases the SVM’s test error. Central to the motivation for these attacks is the fact that most learning algorithms assume that their training data comes from a natural or well-behaved distribution. However, this assumption does not generally hold in security-sensitive settings. As we demonstrate, an intelligent adversary can, to some extent, predict the change of the SVM’s decision function due to malicious input and use this ability to construct malicious data. The proposed attack uses a gradient ascent strategy in which the gradient is computed based on properties of the SVM’s optimal solution. This method can be kernelized and enables the attack to be constructed in the input space even for non-linear kernels. We experimentally demonstrate that our gradient ascent procedure reliably identifies good local maxima of the non-convex validation error surface, which significantly increases the classifier’s test error.", "title": "" }, { "docid": "neg:1840635_8", "text": "PROBLEM\nThe current nursing workforce is composed of multigenerational staff members creating challenges and at times conflict for managers.\n\n\nMETHODS\nGenerational cohorts are defined and two multigenerational scenarios are presented and discussed using the ACORN imperatives and Hahn's Five Managerial Strategies for effectively managing a multigenerational staff.\n\n\nFINDINGS\nCommunication and respect are the underlying key strategies to understanding and bridging the generational gap in the workplace.\n\n\nCONCLUSION\nEmbracing and respecting generational differences can bring strength and cohesiveness to nursing teams on the managerial or unit level.", "title": "" }, { "docid": "neg:1840635_9", "text": "Compensation for geometrical spreading along a raypath is one of the key steps in AVO amplitude-variation-with-offset analysis, in particular, for wide-azimuth surveys. Here, we propose an efficient methodology to correct long-spread, wide-azimuth reflection data for geometrical spreading in stratified azimuthally anisotropic media. The P-wave geometrical-spreading factor is expressed through the reflection traveltime described by a nonhyperbolic moveout equation that has the same form as in VTI transversely isotropic with a vertical symmetry axis media. The adapted VTI equation is parameterized by the normal-moveout NMO ellipse and the azimuthally varying anellipticity parameter . To estimate the moveout parameters, we apply a 3D nonhyperbolic semblance algorithm of Vasconcelos and Tsvankin that operates simultaneously with traces at all offsets and", "title": "" }, { "docid": "neg:1840635_10", "text": "This article provides a review of the traditional clinical concepts for the design and fabrication of removable partial dentures (RPDs). Although classic theories and rules for RPD designs have been presented and should be followed, excellent clinical care for partially edentulous patients may also be achieved with computer-aided design/computer-aided manufacturing technology and unique blended designs. These nontraditional RPD designs and fabrication methods provide for improved fit, function, and esthetics by using computer-aided design software, composite resin for contours and morphology of abutment teeth, metal support structures for long edentulous spans and collapsed occlusal vertical dimensions, and flexible, nylon thermoplastic material for metal-supported clasp assemblies.", "title": "" }, { "docid": "neg:1840635_11", "text": "This demo presents the features of the Proactive Insights (PI) engine, which uses machine learning and artificial intelligence capabilities to automatically identify weaknesses in business processes, to reveal their root causes, and to give intelligent advice on how to improve process inefficiencies. We demonstrate the four PI elements covering Conformance, Machine Learning, Social, and Companion. The new insights are especially valuable for process managers and academics interested in BPM and process mining.", "title": "" }, { "docid": "neg:1840635_12", "text": "Training deep neural networks with Stochastic Gradient Descent, or its variants, requires careful choice of both learning rate and batch size. While smaller batch sizes generally converge in fewer training epochs, larger batch sizes offer more parallelism and hence better computational efficiency. We have developed a new training approach that, rather than statically choosing a single batch size for all epochs, adaptively increases the batch size during the training process. Our method delivers the convergence rate of small batch sizes while achieving performance similar to large batch sizes. We analyse our approach using the standard AlexNet, ResNet, and VGG networks operating on the popular CIFAR10, CIFAR-100, and ImageNet datasets. Our results demonstrate that learning with adaptive batch sizes can improve performance by factors of up to 6.25 on 4 NVIDIA Tesla P100 GPUs while changing accuracy by less than 1% relative to training with fixed batch sizes.", "title": "" }, { "docid": "neg:1840635_13", "text": "Three basic switching structures are defined: one is formed by two capacitors and three diodes; the other two are formed by two inductors and two diodes. They are inserted in either a Cuk converter, or a Sepic, or a Zeta converter. The SC/SL structures are built in such a way as when the active switch of the converter is on, the two inductors are charged in series or the two capacitors are discharged in parallel. When the active switch is off, the two inductors are discharged in parallel or the two capacitors are charged in series. As a result, the line voltage is reduced more times than in classical Cuk/Sepic/Zeta converters. The steady-state analysis of the new converters, a comparison of the DC voltage gain and of the voltage and current stresses of the new hybrid converters with those of the available quadratic converters, and experimental results are given", "title": "" }, { "docid": "neg:1840635_14", "text": "Invariance to nuisance transformations is one of the desirable properties of effective representations. We consider transformations that form a group and propose an approach based on kernel methods to derive local group invariant representations. Locality is achieved by defining a suitable probability distribution over the group which in turn induces distributions in the input feature space. We learn a decision function over these distributions by appealing to the powerful framework of kernel methods and generate local invariant random feature maps via kernel approximations. We show uniform convergence bounds for kernel approximation and provide generalization bounds for learning with these features. We evaluate our method on three real datasets, including Rotated MNIST and CIFAR-10, and observe that it outperforms competing kernel based approaches. The proposed method also outperforms deep CNN on RotatedMNIST and performs comparably to the recently proposed group-equivariant CNN.", "title": "" }, { "docid": "neg:1840635_15", "text": "In this paper, we rigorously study tractable models for provably recovering low-rank tensors. Unlike their matrix-based predecessors, current convex approaches for recovering low-rank tensors based on incomplete (tensor completion) and/or grossly corrupted (tensor robust principal analysis) observations still suffer from the lack of theoretical guarantees, although they have been used in various recent applications and have exhibited promising empirical performance. In this work, we attempt to fill this gap. Specifically, we propose a class of convex recovery models (including strongly convex programs) that can be proved to guarantee exact recovery under certain conditions. All parameters in our formulations can be determined beforehand based on the measurement data and thus there is no parameter tuning involved.", "title": "" }, { "docid": "neg:1840635_16", "text": "Current tools for exploratory data analysis (EDA) require users to manually select data attributes, statistical computations and visual encodings. This can be daunting for large-scale, complex data. We introduce Foresight, a visualization recommender system that helps the user rapidly explore large high-dimensional datasets through “guideposts.” A guidepost is a visualization corresponding to a pronounced instance of a statistical descriptor of the underlying data, such as a strong linear correlation between two attributes, high skewness or concentration about the mean of a single attribute, or a strong clustering of values. For each descriptor, Foresight initially presents visualizations of the “strongest” instances, based on an appropriate ranking metric. Given these initial guideposts, the user can then look at “nearby” guideposts by issuing “guidepost queries” containing constraints on metric type, metric strength, data attributes, and data values. Thus, the user can directly explore the network of guideposts, rather than the overwhelming space of data attributes and visual encodings. Foresight also provides for each descriptor a global visualization of ranking-metric values to both help orient the user and ensure a thorough exploration process. Foresight facilitates interactive exploration of large datasets using fast, approximate sketching to compute ranking metrics. We also contribute insights on EDA practices of data scientists, summarizing results from an interview study we conducted to inform the design of Foresight.", "title": "" }, { "docid": "neg:1840635_17", "text": "Understanding the 3D structure of a scene is of vital importance, when it comes to developing fully autonomous robots. To this end, we present a novel deep learning based framework that estimates depth, surface normals and surface curvature by only using a single RGB image. To the best of our knowledge this is the first work to estimate surface curvature from colour using a machine learning approach. Additionally, we demonstrate that by tuning the network to infer well designed features, such as surface curvature, we can achieve improved performance at estimating depth and normals. This indicates that network guidance is still a useful aspect of designing and training a neural network. We run extensive experiments where the network is trained to infer different tasks while the model capacity is kept constant resulting in different feature maps based on the tasks at hand. We outperform the previous state-of-the-art benchmarks which jointly estimate depths and surface normals while predicting surface curvature in parallel.", "title": "" }, { "docid": "neg:1840635_18", "text": "We present SWARM, a wearable affective technology designed to help a user to reflect on their own emotional state, modify their affect, and interpret the emotional states of others. SWARM aims for a universal design (inclusive of people with various disabilities), with a focus on modular actuation components to accommodate users' sensory capabilities and preferences, and a scarf form-factor meant to reduce the stigma of accessible technologies through a fashionable embodiment. Using an iterative, user-centered approach, we present SWARM's design. Additionally, we contribute findings for communicating emotions through technology actuations, wearable design techniques (including a modular soft circuit design technique that fuses conductive fabric with actuation components), and universal design considerations for wearable technology.", "title": "" } ]

[ { "docid": "pos:1840636_0", "text": "The research described in this paper was supported in part by the National Science Foundation under Grants IST-g0-12418 and IST-82-10564. and in part by the Office of Naval Research under Grant N00014-80-C-0197. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission. © 1983 ACM 0001-0782/83/1100.0832 75¢", "title": "" } ]

[ { "docid": "neg:1840636_0", "text": "The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that: • a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders. Please consult the full DRO policy for further details.", "title": "" }, { "docid": "neg:1840636_1", "text": "We propose a method for inferring the existence of a latent common cause (“confounder”) of two observed random variables. The method assumes that the two effects of the confounder are (possibly nonlinear) functions of the confounder plus independent, additive noise. We discuss under which conditions the model is identifiable (up to an arbitrary reparameterization of the confounder) from the joint distribution of the effects. We state and prove a theoretical result that provides evidence for the conjecture that the model is generically identifiable under suitable technical conditions. In addition, we propose a practical method to estimate the confounder from a finite i.i.d. sample of the effects and illustrate that the method works well on both simulated and real-world data.", "title": "" }, { "docid": "neg:1840636_2", "text": "The automatic evaluation of text-based assessment items, such as short answers or essays, is an open and important research challenge. In this paper, we compare several features for the classification of short open-ended responses to questions related to a large first-year health sciences course. These features include a) traditional n-gram models; b) entity URIs (Uniform Resource Identifier) and c) entity mentions extracted using a semantic annotation API; d) entity mention embeddings based on GloVe, and e) entity URI embeddings extracted from Wikipedia. These features are used in combination with classification algorithms to discriminate correct answers from incorrect ones. Our results show that, on average, n-gram features performed the best in terms of precision and entity mentions in terms of f1-score. Similarly, in terms of accuracy, entity mentions and n-gram features performed the best. Finally, features based on dense vector representations such as entity embeddings and mention embeddings obtained the best f1-score for predicting correct answers.", "title": "" }, { "docid": "neg:1840636_3", "text": "Monte Carlo Tree Search (MCTS) is an AI technique that has been successfully applied to many deterministic games of perfect information, leading to large advances in a number of domains, such as Go and General Game Playing. Imperfect information games are less well studied in the field of AI despite being popular and of significant commercial interest, for example in the case of computer and mobile adaptations of turn based board and card games. This is largely because hidden information and uncertainty leads to a large increase in complexity compared to perfect information games. In this thesis MCTS is extended to games with hidden information and uncertainty through the introduction of the Information Set MCTS (ISMCTS) family of algorithms. It is demonstrated that ISMCTS can handle hidden information and uncertainty in a variety of complex board and card games. This is achieved whilst preserving the general applicability of MCTS and using computational budgets appropriate for use in a commercial game. The ISMCTS algorithm is shown to outperform the existing approach of Perfect Information Monte Carlo (PIMC) search. Additionally it is shown that ISMCTS can be used to solve two known issues with PIMC search, namely strategy fusion and non-locality. ISMCTS has been integrated into a commercial game, Spades by AI Factory, with over 2.5 million downloads. The Information Capture And ReUSe (ICARUS) framework is also introduced in this thesis. The ICARUS framework generalises MCTS enhancements in terms of information capture (from MCTS simulations) and reuse (to improve MCTS tree and simulation policies). The ICARUS framework is used to express existing enhancements, to provide a tool to design new ones, and to rigorously define how MCTS enhancements can be combined. The ICARUS framework is tested across a wide variety of games.", "title": "" }, { "docid": "neg:1840636_4", "text": "Phosphorus is one of the most abundant elements preserved in earth, and it comprises a fraction of ∼0.1% of the earth crust. In general, phosphorus has several allotropes, and the two most commonly seen allotropes, i.e. white and red phosphorus, are widely used in explosives and safety matches. In addition, black phosphorus, though rarely mentioned, is a layered semiconductor and has great potential in optical and electronic applications. Remarkably, this layered material can be reduced to one single atomic layer in the vertical direction owing to the van der Waals structure, and is known as phosphorene, in which the physical properties can be tremendously different from its bulk counterpart. In this review article, we trace back to the research history on black phosphorus of over 100 years from the synthesis to material properties, and extend the topic from black phosphorus to phosphorene. The physical and transport properties are highlighted for further applications in electronic and optoelectronics devices.", "title": "" }, { "docid": "neg:1840636_5", "text": "Calcite is a new Eclipse plugin that helps address the difficulty of understanding and correctly using an API. Calcite finds the most popular ways to instantiate a given class or interface by using code examples. To allow the users to easily add these object instantiations to their code, Calcite adds items to the popup completion menu that will insert the appropriate code into the user’s program. Calcite also uses crowd sourcing to add to the menu instructions in the form of comments that help the user perform functions that people have identified as missing from the API. In a user study, Calcite improved users’ success rate by 40%.", "title": "" }, { "docid": "neg:1840636_6", "text": "RGB-D data is getting ever more interest from the research community as both cheap cameras appear in the market and the applications of this type of data become more common. A current trend in processing image data is the use of convolutional neural networks (CNNs) that have consistently beat competition in most benchmark data sets. In this paper we investigate the possibility of transferring knowledge between CNNs when processing RGB-D data with the goal of both improving accuracy and reducing training time. We present experiments that show that our proposed approach can achieve both these goals.", "title": "" }, { "docid": "neg:1840636_7", "text": "Surgical tasks are complex multi-step sequences of smaller subtasks (often called surgemes) and it is useful to segment task demonstrations into meaningful subsequences for:(a) extracting finite-state machines for automation, (b) surgical training and skill assessment, and (c) task classification. Existing supervised methods for task segmentation use segment labels from a dictionary of motions to build classifiers. However, as the datasets become voluminous, the labeling becomes arduous and further, this method doesnt́ generalize to new tasks that dont́ use the same dictionary. We propose an unsupervised semantic task segmentation framework by learning “milestones”, ellipsoidal regions of the position and feature states at which a task transitions between motion regimes modeled as locally linear. Milestone learning uses a hierarchy of Dirichlet Process Mixture Models, learned through Expectation-Maximization, to cluster the transition points and optimize the number of clusters. It leverages transition information from kinematic state as well as environment state such as visual features. We also introduce a compaction step which removes repetitive segments that correspond to a mid-demonstration failure recovery by retrying an action. We evaluate Milestones Learning on three surgical subtasks: pattern cutting, suturing, and needle passing. Initial results suggest that our milestones qualitatively match manually annotated segmentation. While one-to-one correspondence of milestones with annotated data is not meaningful, the milestones recovered from our method have exactly one annotated surgeme transition in 74% (needle passing) and 66% (suturing) of total milestones, indicating a semantic match.", "title": "" }, { "docid": "neg:1840636_8", "text": "Despite the noticeable progress in perceptual tasks like detection, instance segmentation and human parsing, computers still perform unsatisfactorily on visually understanding humans in crowded scenes, such as group behavior analysis, person re-identification and autonomous driving, etc. To this end, models need to comprehensively perceive the semantic information and the differences between instances in a multi-human image, which is recently defined as the multi-human parsing task. In this paper, we present a new large-scale database “Multi-Human Parsing (MHP)” for algorithm development and evaluation, and advances the state-of-the-art in understanding humans in crowded scenes. MHP contains 25,403 elaborately annotated images with 58 fine-grained semantic category labels, involving 2-26 persons per image and captured in real-world scenes from various viewpoints, poses, occlusion, interactions and background. We further propose a novel deep Nested Adversarial Network (NAN) model for multi-human parsing. NAN consists of three Generative Adversarial Network (GAN)-like sub-nets, respectively performing semantic saliency prediction, instance-agnostic parsing and instance-aware clustering. These sub-nets form a nested structure and are carefully designed to learn jointly in an end-to-end way. NAN consistently outperforms existing state-of-the-art solutions on our MHP and several other datasets, and serves as a strong baseline to drive the future research for multi-human parsing.", "title": "" }, { "docid": "neg:1840636_9", "text": "Objective This study examined the associations between household food security (access to sufficient, safe, and nutritious food) during infancy and attachment and mental proficiency in toddlerhood. Methods Data from a longitudinal nationally representative sample of infants and toddlers (n = 8944) from the Early Childhood Longitudinal Study—9-month (2001–2002) and 24-month (2003–2004) surveys were used. Structural equation modeling was used to examine the direct and indirect associations between food insecurity at 9 months, and attachment and mental proficiency at 24 months. Results Food insecurity worked indirectly through depression and parenting practices to influence security of attachment and mental proficiency in toddlerhood. Conclusions Social policies that address the adequacy and predictability of food supplies in families with infants have the potential to affect parental depression and parenting behavior, and thereby attachment and cognitive development at very early ages.", "title": "" }, { "docid": "neg:1840636_10", "text": "Due to the broadcast nature of WiFi communication anyone with suitable hardware is able to monitor surrounding traffic. However, a WiFi device is able to listen to only one channel at any given time. The simple solution for capturing traffic across multiple channels involves channel hopping, which as a side effect reduces dwell time per channel. Hence monitoring with channel hopping does not produce a comprehensive view of the traffic across all channels at a given time.\n In this paper we present an inexpensive multi-channel WiFi capturing system (dubbed the wireless shark\") and evaluate its performance in terms of traffic cap- turing efficiency. Our results confirm and quantify the intuition that the performance is directly related to the number of WiFi adapters being used for listening. As a second contribution of the paper we use the wireless shark to observe the behavior of 14 different mobile devices, both in controlled and normal office environments. In our measurements, we focus on the probe traffic that the devices send when they attempt to discover available WiFi networks. Our results expose some distinct characteristics in various mobile devices' probing behavior.", "title": "" }, { "docid": "neg:1840636_11", "text": "PURPOSE OF REVIEW\nTo review the current practice in the field of auricular reconstruction and to highlight the recent advances reported in the medical literature.\n\n\nRECENT FINDINGS\nThe majority of surgeons who perform auricular reconstruction continue to employ the well-established techniques developed by Brent and Nagata. Surgery takes between two and four stages, with the initial stage being construction of a framework of autogenous rib cartilage which is implanted into a subcutaneous pocket. Several modifications of these techniques have been reported. More recently, synthetic frameworks have been employed instead of autogenous rib cartilage. For this procedure, the implant is generally covered with a temporoparietal flap and a skin graft at the first stage of surgery. Tissue engineering is a rapidly developing field, and there have been several articles related to the field of auricular reconstruction. These show great potential to offer a solution to the challenge associated with construction of a viable autogenous cartilage framework, whilst avoiding donor-site morbidity.\n\n\nSUMMARY\nThis article gives an overview of the current practice in the field of auricular reconstruction and summarizes the recent surgical developments and relevant tissue engineering research.", "title": "" }, { "docid": "neg:1840636_12", "text": "The FIRE 2016 Microblog track focused on retrieval of microblogs (tweets posted on Twitter) during disaster events. A collection of about 50,000 microblogs posted during a recent disaster event was made available to the participants, along with a set of seven practical information needs during a disaster situation. The task was to retrieve microblogs relevant to these needs. 10 teams participated in the task, submitting a total of 15 runs. The task resulted in comparison among performances of various microblog retrieval strategies over a benchmark collection, and brought out the challenges in microblog retrieval.", "title": "" }, { "docid": "neg:1840636_13", "text": "In this paper, we present the suite of tools of the FOMCON (“Fractional-order Modeling and Control”) toolbox for MATLAB that are used to carry out fractional-order PID controller design and hardware realization. An overview of the toolbox, its structure and particular modules, is presented with appropriate comments. We use a laboratory object designed to conduct temperature control experiments to illustrate the methods employed in FOMCON to derive suitable parameters for the controller and arrive at a digital implementation thereof on an 8-bit AVR microprocessor. The laboratory object is working under a real-time simulation platform with Simulink, Real-Time Windows Target toolbox and necessary drivers as its software backbone. Experimental results are provided which support the effectiveness of the proposed software solution.", "title": "" }, { "docid": "neg:1840636_14", "text": "Named entity recognition (NER) is a subtask of information extraction that seeks to locate and classify atomic elements in text into predefined categories such as the names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc. We use the JavaNLP repository(http://nlp.stanford.edu/javanlp/ ) for its implementation of a Conditional Random Field(CRF) and a Conditional Markov Model(CMM), also called a Maximum Entropy Markov Model. We have obtained results on majority voting with different labeling schemes, with backward and forward parsing of the CMM, and also some results when we trained a decision tree to take a decision based on the outputs of the different labeling schemes. We have also tried to solve the problem of label inconsistency issue by attempting the naive approach of enforcing hard label-consistency by choosing the majority entity for a sequence of tokens, in the specific test document, as well as the whole test corpus, and managed to get reasonable gains. We also attempted soft label consistency in the following way. We use a portion of the training data to train a CRF to make predictions on the rest of the train data and on the test data. We then train a second CRF with the majority label predictions as additional input features.", "title": "" }, { "docid": "neg:1840636_15", "text": "Learning phonetic categories is one of the first steps to learning a language, yet is hard to do using only distributional phonetic information. Semantics could potentially be useful, since words with different meanings have distinct phonetics, but it is unclear how many word meanings are known to infants learning phonetic categories. We show that attending to a weaker source of semantics, in the form of a distribution over topics in the current context, can lead to improvements in phonetic category learning. In our model, an extension of a previous model of joint word-form and phonetic category inference, the probability of word-forms is topic-dependent, enabling the model to find significantly better phonetic vowel categories and word-forms than a model with no semantic knowledge.", "title": "" }, { "docid": "neg:1840636_16", "text": "Humans learn to play video games significantly faster than state-of-the-art reinforcement learning (RL) algorithms. Inspired by this, we introduce strategic object oriented reinforcement learning (SOORL) to learn simple dynamics model through automatic model selection and perform efficient planning with strategic exploration. We compare different exploration strategies in a model-based setting in which exact planning is impossible. Additionally, we test our approach on perhaps the hardest Atari game Pitfall! and achieve significantly improved exploration and performance over prior methods.", "title": "" }, { "docid": "neg:1840636_17", "text": "Users routinely access cloud services through third-party apps on smartphones by giving apps login credentials (i.e., a username and password). Unfortunately, users have no assurance that their apps will properly handle this sensitive information. In this paper, we describe the design and implementation of ScreenPass, which significantly improves the security of passwords on touchscreen devices. ScreenPass secures passwords by ensuring that they are entered securely, and uses taint-tracking to monitor where apps send password data. The primary technical challenge addressed by ScreenPass is guaranteeing that trusted code is always aware of when a user is entering a password. ScreenPass provides this guarantee through two techniques. First, ScreenPass includes a trusted software keyboard that encourages users to specify their passwords' domains as they are entered (i.e., to tag their passwords). Second, ScreenPass performs optical character recognition (OCR) on a device's screenbuffer to ensure that passwords are entered only through the trusted software keyboard. We have evaluated ScreenPass through experiments with a prototype implementation, two in-situ user studies, and a small app study. Our prototype detected a wide range of dynamic and static keyboard-spoofing attacks and generated zero false positives. As long as a screen is off, not updated, or not tapped, our prototype consumes zero additional energy; in the worst case, when a highly interactive app rapidly updates the screen, our prototype under a typical configuration introduces only 12% energy overhead. Participants in our user studies tagged their passwords at a high rate and reported that tagging imposed no additional burden. Finally, a study of malicious and non-malicious apps running under ScreenPass revealed several cases of password mishandling.", "title": "" }, { "docid": "neg:1840636_18", "text": "In the imitation learning paradigm algorithms learn from expert demonstrations in order to become able to accomplish a particular task. Daumé III et al. (2009) framed structured prediction in this paradigm and developed the search-based structured prediction algorithm (Searn) which has been applied successfully to various natural language processing tasks with state-of-the-art performance. Recently, Ross et al. (2011) proposed the dataset aggregation algorithm (DAgger) and compared it with Searn in sequential prediction tasks. In this paper, we compare these two algorithms in the context of a more complex structured prediction task, namely biomedical event extraction. We demonstrate that DAgger has more stable performance and faster learning than Searn, and that these advantages are more pronounced in the parameter-free versions of the algorithms.", "title": "" } ]

[ { "docid": "pos:1840637_0", "text": "We study the problem of clustering data objects whose locations are uncertain. A data object is represented by an uncertainty region over which a probability density function (pdf) is defined. One method to cluster uncertain objects of this sort is to apply the UK-means algorithm, which is based on the traditional K-means algorithm. In UK-means, an object is assigned to the cluster whose representative has the smallest expected distance to the object. For arbitrary pdf, calculating the expected distance between an object and a cluster representative requires expensive integration computation. We study various pruning methods to avoid such expensive expected distance calculation.", "title": "" }, { "docid": "pos:1840637_1", "text": "This paper introduces U-relations, a succinct and purely relational representation system for uncertain databases. U-relations support attribute-level uncertainty using vertical partitioning. If we consider positive relational algebra extended by an operation for computing possible answers, a query on the logical level can be translated into, and evaluated as, a single relational algebra query on the U-relational representation. The translation scheme essentially preserves the size of the query in terms of number of operations and, in particular, number of joins. Standard techniques employed in off-the-shelf relational database management systems are effective for optimizing and processing queries on U-relations. In our experiments we show that query evaluation on U-relations scales to large amounts of data with high degrees of uncertainty.", "title": "" } ]

[ { "docid": "neg:1840637_0", "text": "Clustering image pixels is an important image segmentation technique. While a large amount of clustering algorithms have been published and some of them generate impressive clustering results, their performance often depends heavily on user-specified parameters. This may be a problem in the practical tasks of data clustering and image segmentation. In order to remove the dependence of clustering results on user-specified parameters, we investigate the characteristics of existing clustering algorithms and present a parameter-free algorithm based on the DSets (dominant sets) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithms. First, we apply histogram equalization to the pairwise similarity matrix of input data and make DSets clustering results independent of user-specified parameters. Then, we extend the clusters from DSets with DBSCAN, where the input parameters are determined based on the clusters from DSets automatically. By merging the merits of DSets and DBSCAN, our algorithm is able to generate the clusters of arbitrary shapes without any parameter input. In both the data clustering and image segmentation experiments, our parameter-free algorithm performs better than or comparably with other algorithms with careful parameter tuning.", "title": "" }, { "docid": "neg:1840637_1", "text": "W -algebras of finite type are certain finitely generated associative algebras closely related to universal enveloping algebras of semisimple Lie algebras. In this paper we prove a conjecture of Premet that gives an almost complete classification of finite dimensional irreducible modules for W -algebras. Also we get some partial results towards a conjecture by Ginzburg on their finite dimensional bimodules.", "title": "" }, { "docid": "neg:1840637_2", "text": "Owing to inevitable thermal/moisture instability for organic–inorganic hybrid perovskites, pure inorganic perovskite cesium lead halides with both inherent stability and prominent photovoltaic performance have become research hotspots as a promising candidate for commercial perovskite solar cells. However, it is still a serious challenge to synthesize desired cubic cesium lead iodides (CsPbI3) with superior photovoltaic performance for its thermodynamically metastable characteristics. Herein, polymer poly-vinylpyrrolidone (PVP)-induced surface passivation engineering is reported to synthesize extra-long-term stable cubic CsPbI3. It is revealed that acylamino groups of PVP induce electron cloud density enhancement on the surface of CsPbI3, thus lowering surface energy, conducive to stabilize cubic CsPbI3 even in micrometer scale. The cubic-CsPbI3 PSCs exhibit extra-long carrier diffusion length (over 1.5 μm), highest power conversion efficiency of 10.74% and excellent thermal/moisture stability. This result provides important progress towards understanding of phase stability in realization of large-scale preparations of efficient and stable inorganic PSCs. Inorganic cesium lead iodide perovskite is inherently more stable than the hybrid perovskites but it undergoes phase transition that degrades the solar cell performance. Here Li et al. stabilize it with poly-vinylpyrrolidone and obtain high efficiency of 10.74% with excellent thermal and moisture stability.", "title": "" }, { "docid": "neg:1840637_3", "text": "Glomus tumors of the penis are extremely rare. A patient with multiple regional glomus tumors involving the penis is reported. A 16-year-old boy presented with the complaint of painless penile masses and resection of the lesions was performed. The pathologic diagnosis was glomus tumor of the penis. This is the ninth case of glomus tumor of the penis to be reported in the literature.", "title": "" }, { "docid": "neg:1840637_4", "text": "The global gold market has recently attracted a lot of attention and the price of gold is relatively higher than its historical trend. For mining companies to mitigate risk and uncertainty in gold price fluctuations, make hedging, future investment and evaluation decisions, depend on forecasting future price trends. The first section of this paper reviews the world gold market and the historical trend of gold prices from January 1968 to December 2008. This is followed by an investigation into the relationship between gold price and other key influencing variables, such as oil price and global inflation over the last 40 years. The second section applies a modified econometric version of the longterm trend reverting jump and dip diffusion model for forecasting natural-resource commodity prices. This method addresses the deficiencies of previous models, such as jumps and dips as parameters and unit root test for long-term trends. The model proposes that historical data of mineral commodities have three terms to demonstrate fluctuation of prices: a long-term trend reversion component, a diffusion component and a jump or dip component. The model calculates each term individually to estimate future prices of mineral commodities. The study validates the model and estimates the gold price for the next 10 years, based on monthly historical data of nominal gold price. & 2010 Elsevier Ltd. All rights reserved.", "title": "" }, { "docid": "neg:1840637_5", "text": "The aim of this study was to evaluate the effects of calisthenic exercises on psychological status in patients with ankylosing spondylitis (AS) and multiple sclerosis (MS). This study comprised 40 patients diagnosed with AS randomized into two exercise groups (group 1 = hospital-based, group 2 = home-based) and 40 patients diagnosed with MS randomized into two exercise groups (group 1 = hospital-based, group 2 = home-based). The exercise programme was completed by 73 participants (hospital-based = 34, home-based = 39). Mean age was 33.75 ± 5.77 years. After the 8-week exercise programme in the AS group, the home-based exercise group showed significant improvements in erythrocyte sedimentation rates (ESR). The hospital-based exercise group showed significant improvements in terms of the Bath AS Metrology Index (BASMI) and Hospital Anxiety and Depression Scale-Anxiety (HADS-A) scores. After the 8-week exercise programme in the MS group, the home-based and hospital-based exercise groups showed significant improvements in terms of the 10-m walking test, Berg Balance Scale (BBS), HADS-A, and MS international Quality of Life (MusiQoL) scores. There was a significant improvement in the hospital-based and a significant deterioration in the home-based MS patients according to HADS-Depression (HADS-D) score. The positive effects of exercises on neurologic and rheumatic chronic inflammatory processes associated with disability should not be underestimated. Ziel der vorliegenden Studie war die Untersuchung der Wirkungen von gymnastischen Übungen auf die psychische Verfassung von Patienten mit Spondylitis ankylosans (AS) und multipler Sklerose (MS). Die Studie umfasste 40 Patienten mit der Diagnose AS, die randomisiert in 2 Übungsgruppen aufgeteilt wurden (Gruppe 1: stationär, Gruppe 2: ambulant), und 40 Patienten mit der Diagnose MS, die ebenfalls randomisiert in 2 Übungsgruppen aufgeteilt wurden (Gruppe 1: stationär, Gruppe 2: ambulant). Vollständig absolviert wurde das Übungsprogramm von 73 Patienten (stationär: 34, ambulant: 39). Das Durchschnittsalter betrug 33,75 ± 5,77 Jahre. Nach dem 8-wöchigen Übungsprogramm in der AS-Gruppe zeigten sich bei der ambulanten Übungsgruppe signifikante Verbesserungen bei der Blutsenkungsgeschwindigkeit (BSG). Die stationäre Übungsgruppe wies signifikante Verbesserungen in Bezug auf den BASMI-Score (Bath AS Metrology Index) und den HADS-A-Score (Hospital Anxiety and Depression Scale-Anxiety) auf. Nach dem 8-wöchigen Übungsprogramm in der MS-Gruppe zeigten sich sowohl in der ambulanten als auch in der stationären Übungsgruppe signifikante Verbesserungen hinsichtlich des 10-m-Gehtests, des BBS-Ergebnisses (Berg Balance Scale), des HADS-A- sowie des MusiQoL-Scores (MS international Quality of Life). Beim HADS-D-Score (HADS-Depression) bestand eine signifikante Verbesserung bei den stationären und eine signifikante Verschlechterung bei den ambulanten MS-Patienten. Die positiven Wirkungen von gymnastischen Übungen auf neurologische und rheumatische chronisch entzündliche Prozesse mit Behinderung sollten nicht unterschätzt werden.", "title": "" }, { "docid": "neg:1840637_6", "text": "Fault analysis in solar photovoltaic (PV) arrays is a fundamental task to protect PV modules from damage and to eliminate risks of safety hazards. This paper focuses on line–line faults in PV arrays that may be caused by short-circuit faults or double ground faults. The effect on fault current from a maximum-power-point tracking of a PV inverter is discussed and shown to, at times, prevent overcurrent protection devices (OCPDs) to operate properly. Furthermore, fault behavior of PV arrays is highly related to the fault location, fault impedance, irradiance level, and use of blocking diodes. Particularly, this paper examines the challenges to OCPD in a PV array brought by unique faults: One is a fault that occurs under low-irradiance conditions, and the other is a fault that occurs at night and evolves during “night-to-day” transition. In both circumstances, the faults might remain hidden in the PV system, no matter how irradiance changes afterward. These unique faults may subsequently lead to unexpected safety hazards, reduced system efficiency, and reduced reliability. A small-scale experimental PV system has been developed to further validate the conclusions.", "title": "" }, { "docid": "neg:1840637_7", "text": "In many sequential decision-making problems one is interested in minimizing an expected cumulative cost while taking into account risk, i.e., increased awareness of events of small probability and high consequences. Accordingly, the objective of this paper is to present efficient reinforcement learning algorithms for risk-constrained Markov decision processes (MDPs), where risk is represented via a chance constraint or a constraint on the conditional value-at-risk (CVaR) of the cumulative cost. We collectively refer to such problems as percentile risk-constrained MDPs. Specifically, we first derive a formula for computing the gradient of the Lagrangian function for percentile riskconstrained MDPs. Then, we devise policy gradient and actor-critic algorithms that (1) estimate such gradient, (2) update the policy in the descent direction, and (3) update the Lagrange multiplier in the ascent direction. For these algorithms we prove convergence to locally optimal policies. Finally, we demonstrate the effectiveness of our algorithms in an optimal stopping problem and an online marketing application.", "title": "" }, { "docid": "neg:1840637_8", "text": "We propose a mechanism to reconstruct part annotated 3D point clouds of objects given just a single input image. We demonstrate that jointly training for both reconstruction and segmentation leads to improved performance in both the tasks, when compared to training for each task individually. The key idea is to propagate information from each task so as to aid the other during the training procedure. Towards this end, we introduce a location-aware segmentation loss in the training regime. We empirically show the effectiveness of the proposed loss in generating more faithful part reconstructions while also improving segmentation accuracy. We thoroughly evaluate the proposed approach on different object categories from the ShapeNet dataset to obtain improved results in reconstruction as well as segmentation. Codes are available at https://github.com/val-iisc/3d-psrnet.", "title": "" }, { "docid": "neg:1840637_9", "text": "An ego-motion estimation method based on the spatial and Doppler information obtained by an automotive radar is proposed. The estimation of the motion state vector is performed in a density-based framework. Compared to standard vehicle odometry the approach is capable to estimate the full two dimensional motion state with three degrees of freedom. The measurement of a Doppler radar sensor is represented as a mixture of Gaussians. This mixture is matched with the mixture of a previous measurement by applying the appropriate egomotion transformation. The parameters of the transformation are found by the optimization of a suitable join metric. Due to the Doppler information the method is very robust against disturbances by moving objects and clutter. It provides excellent results for highly nonlinear movements. Real world results of the proposed method are presented. The measurements are obtained by a 77GHz radar sensor mounted on a test vehicle. A comparison using a high-precision inertial measurement unit with differential GPS support is made. The results show a high accuracy in velocity and yaw-rate estimation.", "title": "" }, { "docid": "neg:1840637_10", "text": "Numerous studies have established that aggregating judgments or predictions across individuals can be surprisingly accurate in a variety of domains, including prediction markets, political polls, game shows, and forecasting (see Surowiecki, 2004). Under Galton’s (1907) conditions of individuals having largely unbiased and independent judgments, the aggregated judgment of a group of individuals is uncontroversially better, on average, than the individual judgments themselves (e.g., Armstrong, 2001; Clemen, 1989; Galton, 1907; Surowiecki, 2004; Winkler, 1971). The boundary conditions of crowd wisdom, however, are not as well-understood. For example, when group members are allowed access to other members’ predictions, as opposed to making them independently, their predictions become more positively correlated and the crowd’s performance can diminish (Lorenz, Rauhut, Schweitzer, & Helbing, 2011). In the context of handicapping sports results, individuals have been found to make systematically biased predictions, so that their aggregated judgments may not be wise (Simmons, Nelson, Galak, & Frederick, 2011). How robust is crowd wisdom to factors such as non-independence and bias of crowd members’ judgments? If the conditions for crowd wisdom are less than ideal, is it better to aggregate judgments or, for instance, rely on a skilled individual judge? Would it be better to add a highly skilled crowd member or a less skilled one who makes systematically different predictions than other members, increasing diversity? We provide a simple, precise definition of the wisdom-of-the-crowd effect and a systematic way to examine its boundary conditions. We define a crowd as wise if a linear aggregate of its members’ judgments of a criterion value has less expected squared error than the judgments of an individual sampled randomly, but not necessarily uniformly, from the crowd. Previous definitions of the wisdom of the crowd effect have largely focused on comparing the crowd’s accuracy to that of the average individual member (Larrick, Mannes, & Soll, 2012). Our definition generalizes prior approaches in a couple of ways. We consider crowds created by any linear aggregate, not just simple averaging. Second, our definition allows the comparison of the crowd to an individual selected according to a distribution that could reflect past individual performance, e.g., their skill, or other attributes. On the basis of our definition, we develop a framework for analyzing crowd wisdom that includes various aggregation and sampling rules. These rules include both weighting the aggregate and sampling the individual according to skill, where skill is operationalized as predictive validity, i.e., the correlation between a judge’s prediction and the criterion. Although the amount of the crowd’s wisdom the expected difference between individual error and crowd error is non-linear in the amount of bias and non-independence of the judgments, our results yield simple and general rules specifying when a simple average will be wise. While a simple average of the crowd is not always wise if individuals are not sampled uniformly at random, we show that there always exists some a priori aggregation rule that makes the crowd wise.", "title": "" }, { "docid": "neg:1840637_11", "text": "Driving simulators play an important role in the development of new vehicles and advanced driver assistance devices. In fact, on the one hand, having a human driver on a driving simulator allows automotive OEMs to bridge the gap between virtual prototyping and on-road testing during the vehicle development phase. On the other hand, novel driver assistance systems (such as advanced accident avoidance systems) can be safely tested by having the driver operating the vehicle in a virtual, highly realistic environment, while being exposed to hazardous situations. In both applications, it is crucial to faithfully reproduce in the simulator the drivers perception of forces acting on the vehicle and its acceleration. The strategy used to operate the simulator platform within its limited working space to provide the driver with the most realistic perception goes under the name of motion cueing. In this paper we describe a novel approach to motion cueing design that is based on Model Predictive Control techniques. Two features characterize the algorithm, namely, the use of a detailed model of the human vestibular system and a predictive strategy based on the availability of a virtual driver. Differently from classical schemes based on washout filters, such features allows a better implementation of tilt coordination and to handle more efficiently the platform limits.", "title": "" }, { "docid": "neg:1840637_12", "text": "“You make what you measure” is a familiar mantra at datadriven companies. Accordingly, companies must be careful to choose North Star metrics that create a better product. Metrics fall into two general categories: direct count metrics such as total revenue and monthly active users, and nuanced quality metrics regarding value or other aspects of the user experience. Count metrics, when used exclusively as the North Star, might inform product decisions that harm user experience. Therefore, quality metrics play an important role in product development. We present a five-step framework for developing quality metrics using a combination of machine learning and product intuition. Machine learning ensures that the metric accurately captures user experience. Product intuition makes the metric interpretable and actionable. Through a case study of the Endorsements product at LinkedIn, we illustrate the danger of optimizing exclusively for count metrics, and showcase the successful application of our framework toward developing a quality metric. We show how the new quality metric has driven significant improvements toward creating a valuable, user-first product.", "title": "" }, { "docid": "neg:1840637_13", "text": "We utilise smart eyeglasses for dietary monitoring, in particular to sense food chewing. Our approach is based on a 3D-printed regular eyeglasses design that could accommodate processing electronics and Electromyography (EMG) electrodes. Electrode positioning was analysed and an optimal electrode placement at the temples was identified. We further compared gel and dry fabric electrodes. For the subsequent analysis, fabric electrodes were attached to the eyeglasses frame. The eyeglasses were used in a data recording study with eight participants eating different foods. Two chewing cycle detection methods and two food classification algorithms were compared. Detection rates for individual chewing cycles reached a precision and recall of 80%. For five foods, classification accuracy for individual chewing cycles varied between 43% and 71%. Majority voting across intake sequences improved accuracy, ranging between 63% and 84%. We concluded that EMG-based chewing analysis using smart eyeglasses can contribute essential chewing structure information to dietary monitoring systems, while the eyeglasses remain inconspicuous and thus could be continuously used.", "title": "" }, { "docid": "neg:1840637_14", "text": "Many real-world applications involve multilabel classification, in which the labels are organized in the form of a tree or directed acyclic graph (DAG). However, current research efforts typically ignore the label dependencies or can only exploit the dependencies in tree-structured hierarchies. In this paper, we present a novel hierarchical multilabel classification algorithm which can be used on both treeand DAG-structured hierarchies. The key idea is to formulate the search for the optimal consistent multi-label as the finding of the best subgraph in a tree/DAG. Using a simple greedy strategy, the proposed algorithm is computationally efficient, easy to implement, does not suffer from the problem of insufficient/skewed training data in classifier training, and can be readily used on large hierarchies. Theoretical results guarantee the optimality of the obtained solution. Experiments are performed on a large number of functional genomics data sets. The proposed method consistently outperforms the state-of-the-art method on both treeand DAG-structured hierarchies.", "title": "" }, { "docid": "neg:1840637_15", "text": "Last decade witnessed a lot of research in the field of sentiment analysis. Understanding the attitude and the emotions that people express in written text proved to be really important and helpful in sociology, political science, psychology, market research, and, of course, artificial intelligence. This paper demonstrates a rule-based approach to clause-level sentiment analysis of reviews in Ukrainian. The general architecture of the implemented sentiment analysis system is presented, the current stage of research is described and further work is explained. The main emphasis is made on the design of rules for computing sentiments.", "title": "" }, { "docid": "neg:1840637_16", "text": "The problem of classifying subjects into disease categories is of common occurrence in medical research. Machine learning tools such as Artificial Neural Network (ANN), Support Vector Machine (SVM) and Logistic Regression (LR) and Fisher’s Linear Discriminant Analysis (LDA) are widely used in the areas of prediction and classification. The main objective of these competing classification strategies is to predict a dichotomous outcome (e.g. disease/healthy) based on several features.", "title": "" }, { "docid": "neg:1840637_17", "text": "The use of computed tomography (CT) in clinical practice has been increasing rapidly, with the number of CT examinations performed in adults and children rising by 10% per year in England. Because the radiology community strives to reduce the radiation dose associated with pediatric examinations, external factors, including guidelines for pediatric head injury, are raising expectations for use of cranial CT in the pediatric population. Thus, radiologists are increasingly likely to encounter pediatric head CT examinations in daily practice. The variable appearance of cranial sutures at different ages can be confusing for inexperienced readers of radiologic images. The evolution of multidetector CT with thin-section acquisition increases the clarity of some of these sutures, which may be misinterpreted as fractures. Familiarity with the normal anatomy of the pediatric skull, how it changes with age, and normal variants can assist in translating the increased resolution of multidetector CT into more accurate detection of fractures and confident determination of normality, thereby reducing prolonged hospitalization of children with normal developmental structures that have been misinterpreted as fractures. More important, the potential morbidity and mortality related to false-negative interpretation of fractures as normal sutures may be avoided. The authors describe the normal anatomy of all standard pediatric sutures, common variants, and sutural mimics, thereby providing an accurate and safe framework for CT evaluation of skull trauma in pediatric patients.", "title": "" }, { "docid": "neg:1840637_18", "text": "One of the major restrictions on the performance of videobased person re-id is partial noise caused by occlusion, blur and illumination. Since different spatial regions of a single frame have various quality, and the quality of the same region also varies across frames in a tracklet, a good way to address the problem is to effectively aggregate complementary information from all frames in a sequence, using better regions from other frames to compensate the influence of an image region with poor quality. To achieve this, we propose a novel Region-based Quality Estimation Network (RQEN), in which an ingenious training mechanism enables the effective learning to extract the complementary region-based information between different frames. Compared with other feature extraction methods, we achieved comparable results of 92.4%, 76.1% and 77.83% on the PRID 2011, iLIDS-VID and MARS, respectively. In addition, to alleviate the lack of clean large-scale person re-id datasets for the community, this paper also contributes a new high-quality dataset, named “Labeled Pedestrian in the Wild (LPW)” which contains 7,694 tracklets with over 590,000 images. Despite its relatively large scale, the annotations also possess high cleanliness. Moreover, it’s more challenging in the following aspects: the age of characters varies from childhood to elderhood; the postures of people are diverse, including running and cycling in addition to the normal walking state.", "title": "" }, { "docid": "neg:1840637_19", "text": "Building curious machines that can answer as well as ask questions is an important challenge for AI. The two tasks of question answering and question generation are usually tackled separately in the NLP literature. At the same time, both require significant amounts of supervised data which is hard to obtain in many domains. To alleviate these issues, we propose a self-training method for jointly learning to ask as well as answer questions, leveraging unlabeled text along with labeled question answer pairs for learning. We evaluate our approach on four benchmark datasets: SQUAD, MS MARCO, WikiQA and TrecQA, and show significant improvements over a number of established baselines on both question answering and question generation tasks. We also achieved new state-of-the-art results on two competitive answer sentence selection tasks: WikiQA and TrecQA.", "title": "" } ]

[ { "docid": "pos:1840638_0", "text": "Perception of universal facial beauty has long been debated amongst psychologists and anthropologists. In this paper, we perform experiments to evaluate the extent of universal beauty by surveying a number of diverse human referees to grade a collection of female facial images. Results obtained show that there exists a strong central tendency in the human grades, thus exhibiting agreement on beauty assessment. We then trained an automated classifier using the average human grades as the ground truth and used it to classify an independent test set of facial images. The high accuracy achieved proves that this classifier can be used as a general, automated tool for objective classification of female facial beauty. Potential applications exist in the entertainment industry, cosmetic industry, virtual media, and plastic surgery.", "title": "" }, { "docid": "pos:1840638_1", "text": "Online dating sites have become popular platforms for people to look for potential romantic partners. Many online dating sites provide recommendations on compatible partners based on their proprietary matching algorithms. It is important that not only the recommended dates match the user’s preference or criteria, but also the recommended users are interested in the user and likely to reciprocate when contacted. The goal of this paper is to predict whether an initial contact message from a user will be replied to by the receiver. The study is based on a large scale real-world dataset obtained from a major dating site in China with more than sixty million registered users. We formulate our reply prediction as a link prediction problem of social networks and approach it using a machine learning framework. The availability of a large amount of user profile information and the bipartite nature of the dating network present unique opportunities and challenges to the reply prediction problem. We extract user-based features from user profiles and graph-based features from the bipartite dating network, apply them in a variety of classification algorithms, and compare the utility of the features and performance of the classifiers. Our results show that the user-based and graph-based features result in similar performance, and can be used to effectively predict the reciprocal links. Only a small performance gain is achieved when both feature sets are used. Among the five classifiers we considered, random forests method outperforms the other four algorithms (naive Bayes, logistic regression, KNN, and SVM). Our methods and results can provide valuable guidelines to the design and performance of recommendation engine for online dating sites.", "title": "" } ]

[ { "docid": "neg:1840638_0", "text": "The synapse is a crucial element in biological neural networks, but a simple electronic equivalent has been absent. This complicates the development of hardware that imitates biological architectures in the nervous system. Now, the recent progress in the experimental realization of memristive devices has renewed interest in artificial neural networks. The resistance of a memristive system depends on its past states and exactly this functionality can be used to mimic the synaptic connections in a (human) brain. After a short introduction to memristors, we present and explain the relevant mechanisms in a biological neural network, such as long-term potentiation and spike time-dependent plasticity, and determine the minimal requirements for an artificial neural network. We review the implementations of these processes using basic electric circuits and more complex mechanisms that either imitate biological systems or could act as a model system for them. (Some figures may appear in colour only in the online journal)", "title": "" }, { "docid": "neg:1840638_1", "text": "In this review we examine recent research in the area of motivation in mathematics education and discuss findings from research perspectives in this domain. We note consistencies across research perspectives that suggest a set of generalizable conclusions about the contextual factors, cognitive processes, and benefits of interventions that affect students’ and teachers’ motivational attitudes. Criticisms are leveled concerning the lack of theoretical guidance driving the conduct and interpretation of the majority of studies in the field. Few researchers have attempted to extend current theories of motivation in ways that are consistent with the current research on learning and classroom discourse. In particular, researchers interested in studying motivation in the content domain of school mathematics need to examine the relationship that exists between mathematics as a socially constructed field and students’ desire to achieve.", "title": "" }, { "docid": "neg:1840638_2", "text": "Finger veins have been proved to be an effective biometric for personal identification in the recent years. However, finger vein images are easily affected by influences such as image translation, orientation, scale, scattering, finger structure, complicated background, uneven illumination, and collection posture. All these factors may contribute to inaccurate region of interest (ROI) definition, and so degrade the performance of finger vein identification system. To improve this problem, in this paper, we propose a finger vein ROI localization method that has high effectiveness and robustness against the above factors. The proposed method consists of a set of steps to localize ROIs accurately, namely segmentation, orientation correction, and ROI detection. Accurate finger region segmentation and correct calculated orientation can support each other to produce higher accuracy in localizing ROIs. Extensive experiments have been performed on the finger vein image database, MMCBNU_6000, to verify the robustness of the proposed method. The proposed method shows the segmentation accuracy of 100%. Furthermore, the average processing time of the proposed method is 22 ms for an acquired image, which satisfies the criterion of a real-time finger vein identification system.", "title": "" }, { "docid": "neg:1840638_3", "text": "We propose a new CNN-CRF end-to-end learning framework, which is based on joint stochastic optimization with respect to both Convolutional Neural Network (CNN) and Conditional Random Field (CRF) parameters. While stochastic gradient descent is a standard technique for CNN training, it was not used for joint models so far. We show that our learning method is (i) general, i.e. it applies to arbitrary CNN and CRF architectures and potential functions; (ii) scalable, i.e. it has a low memory footprint and straightforwardly parallelizes on GPUs; (iii) easy in implementation. Additionally, the unified CNN-CRF optimization approach simplifies a potential hardware implementation. We empirically evaluate our method on the task of semantic labeling of body parts in depth images and show that it compares favorably to competing techniques.", "title": "" }, { "docid": "neg:1840638_4", "text": "This report explores the relationship between narcissism and unethical conduct in an organization by answering two questions: (1) In what ways does narcissism affect an organization?, and (2) What is the relationship between narcissism and the financial industry? Research suggests the overall conclusion that narcissistic individuals directly influence the identity of an organization and how it behaves. Ways to address these issues are shown using Enron as a case study example.", "title": "" }, { "docid": "neg:1840638_5", "text": "This paper investigates the effectiveness of state-of-the-art classification algorithms to categorise road vehicles for an urban traffic monitoring system using a multi-shape descriptor. The analysis is applied to monocular video acquired from a static pole-mounted road side CCTV camera on a busy street. Manual vehicle segmentation was used to acquire a large (>2000 sample) database of labelled vehicles from which a set of measurement-based features (MBF) in combination with a pyramid of HOG (histogram of orientation gradients, both edge and intensity based) features. These are used to classify the objects into four main vehicle categories: car, van, bus and motorcycle. Results are presented for a number of experiments that were conducted to compare support vector machines (SVM) and random forests (RF) classifiers. 10-fold cross validation has been used to evaluate the performance of the classification methods. The results demonstrate that all methods achieve a recognition rate above 95% on the dataset, with SVM consistently outperforming RF. A combination of MBF and IPHOG features gave the best performance of 99.78%.", "title": "" }, { "docid": "neg:1840638_6", "text": "Learning-based methods for appearance-based gaze estimation achieve state-of-the-art performance in challenging real-world settings but require large amounts of labelled training data. Learningby-synthesis was proposed as a promising solution to this problem but current methods are limited with respect to speed, the appearance variability as well as the head pose and gaze angle distribution they can synthesize. We present UnityEyes, a novel method to rapidly synthesize large amounts of variable eye region images as training data. Our method combines a novel generative 3D model of the human eye region with a real-time rendering framework. The model is based on high-resolution 3D face scans and uses realtime approximations for complex eyeball materials and structures as well as novel anatomically inspired procedural geometry methods for eyelid animation. We show that these synthesized images can be used to estimate gaze in difficult in-the-wild scenarios, even for extreme gaze angles or in cases in which the pupil is fully occluded. We also demonstrate competitive gaze estimation results on a benchmark in-the-wild dataset, despite only using a light-weight nearest-neighbor algorithm. We are making our UnityEyes synthesis framework freely available online for the benefit of the research community.", "title": "" }, { "docid": "neg:1840638_7", "text": "The stiff man syndrome (SMS) and its variants, focal SMS, stiff limb (or leg) syndrome (SLS), jerking SMS, and progressive encephalomyelitis with rigidity and myoclonus (PERM), appear to occur more frequently than hitherto thought. A characteristic ensemble of symptoms and signs allows a tentative clinical diagnosis. Supportive ancillary findings include (1) the demonstration of continuous muscle activity in trunk and proximal limb muscles despite attempted relaxation, (2) enhanced exteroceptive reflexes, and (3) antibodies to glutamic acid decarboxylase (GAD) in both serum and spinal fluid. Antibodies to GAD are not diagnostic or specific for SMS and the role of these autoantibodies in the pathogenesis of SMS/SLS/PERM is the subject of debate and difficult to reconcile on the basis of our present knowledge. Nevertheless, evidence is emerging to suggest that SMS/SLS/PERM are manifestations of an immune-mediated chronic encephalomyelitis and immunomodulation is an effective therapeutic approach.", "title": "" }, { "docid": "neg:1840638_8", "text": "We study the impact of regulation on competition between brand-names and generics and pharmaceutical expenditures using a unique policy experiment in Norway, where reference pricing (RP) replaced price cap regulation in 2003 for a sub-sample of o¤-patent products. First, we construct a vertical di¤erentiation model to analyze the impact of regulation on prices and market shares of brand-names and generics. Then, we exploit a detailed panel data set at product level covering several o¤-patent molecules before and after the policy reform. O¤-patent drugs not subject to RP serve as our control group. We …nd that RP signi…cantly reduces both brand-name and generic prices, and results in signi…cantly lower brand-name market shares. Finally, we show that RP has a strong negative e¤ect on average molecule prices, suggesting signi…cant cost-savings, and that patients’ copayments decrease despite the extra surcharges under RP. Key words: Pharmaceuticals; Regulation; Generic Competition JEL Classi…cations: I11; I18; L13; L65 We thank David Bardey, Øivind Anti Nilsen, Frode Steen, and two anonymous referees for valuable comments and suggestions. We also thank the Norwegian Research Council, Health Economics Bergen (HEB) for …nancial support. Corresponding author. Department of Economics and Health Economics Bergen, Norwegian School of Economics and Business Administration, Helleveien 30, N-5045 Bergen, Norway. E-mail: kurt.brekke@nhh.no. Uni Rokkan Centre, Health Economics Bergen, Nygårdsgaten 5, N-5015 Bergen, Norway. E-mail: tor.holmas@uni.no. Department of Economics/NIPE, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal; and University of Bergen (Economics), Norway. E-mail: o.r.straume@eeg.uminho.pt.", "title": "" }, { "docid": "neg:1840638_9", "text": "Touchless hand gesture recognition systems are becoming important in automotive user interfaces as they improve safety and comfort. Various computer vision algorithms have employed color and depth cameras for hand gesture recognition, but robust classification of gestures from different subjects performed under widely varying lighting conditions is still challenging. We propose an algorithm for drivers’ hand gesture recognition from challenging depth and intensity data using 3D convolutional neural networks. Our solution combines information from multiple spatial scales for the final prediction. It also employs spatiotemporal data augmentation for more effective training and to reduce potential overfitting. Our method achieves a correct classification rate of 77.5% on the VIVA challenge dataset.", "title": "" }, { "docid": "neg:1840638_10", "text": "This paper describes a unified model for role-based access control (RBAC). RBAC is a proven technology for large-scale authorization. However, lack of a standard model results in uncertainty and confusion about its utility and meaning. The NIST model seeks to resolve this situation by unifying ideas from prior RBAC models, commercial products and research prototypes. It is intended to serve as a foundation for developing future standards. RBAC is a rich and open-ended technology which is evolving as users, researchers and vendors gain experience with it. The NIST model focuses on those aspects of RBAC for which consensus is available. It is organized into four levels of increasing functional capabilities called flat RBAC, hierarchical RBAC, constrained RBAC and symmetric RBAC. These levels are cumulative and each adds exactly one new requirement. An alternate approach comprising flat and hierarchical RBAC in an ordered sequence and two unordered features—constraints and symmetry—is also presented. The paper furthermore identifies important attributes of RBAC not included in the NIST model. Some are not suitable for inclusion in a consensus document. Others require further work and agreement before standardization is feasible.", "title": "" }, { "docid": "neg:1840638_11", "text": "A chronic alcoholic who had also been submitted to partial gastrectomy developed a syndrome of continuous motor unit activity responsive to phenytoin therapy. There were signs of minimal distal sensorimotor polyneuropathy. Symptoms of the syndrome of continuous motor unit activity were fasciculation, muscle stiffness, myokymia, impaired muscular relaxation and percussion myotonia. Electromyography at rest showed fasciculation, doublets, triplets, multiplets, trains of repetitive discharges and myotonic discharges. Trousseau's and Chvostek's signs were absent. No abnormality of serum potassium, calcium, magnesium, creatine kinase, alkaline phosphatase, arterial blood gases and pH were demonstrated, but the serum Vitamin B12 level was reduced. The electrophysiological findings and muscle biopsy were compatible with a mixed sensorimotor polyneuropathy. Tests of neuromuscular transmission showed a significant decrement in the amplitude of the evoked muscle action potential in the abductor digiti minimi on repetitive nerve stimulation. These findings suggest that hyperexcitability and hyperactivity of the peripheral motor axons underlie the syndrome of continuous motor unit activity in the present case. Ein chronischer Alkoholiker, mit subtotaler Gastrectomie, litt an einem Syndrom dauernder Muskelfaseraktivität, das mit Diphenylhydantoin behandelt wurde. Der Patient wies minimale Störungen im Sinne einer distalen sensori-motorischen Polyneuropathie auf. Die Symptome dieses Syndroms bestehen in: Fazikulationen, Muskelsteife, Myokymien, eine gestörte Erschlaffung nach der Willküraktivität und eine Myotonie nach Beklopfen des Muskels. Das Elektromyogramm in Ruhe zeigt: Faszikulationen, Doublets, Triplets, Multiplets, Trains repetitiver Potentiale und myotonische Entladungen. Trousseau- und Chvostek-Zeichen waren nicht nachweisbar. Gleichzeitig lagen die Kalium-, Calcium-, Magnesium-, Kreatinkinase- und Alkalinphosphatase-Werte im Serumspiegel sowie O2, CO2 und pH des arteriellen Blutes im Normbereich. Aber das Niveau des Vitamin B12 im Serumspiegel war deutlich herabgesetzt. Die muskelbioptische und elektrophysiologische Veränderungen weisen auf eine gemischte sensori-motorische Polyneuropathie hin. Die Abnahme der Amplitude der evozierten Potentiale, vom M. abductor digiti minimi abgeleitet, bei repetitiver Reizung des N. ulnaris, stellten eine Störung der neuromuskulären Überleitung dar. Aufgrund unserer klinischen und elektrophysiologischen Befunde könnten wir die Hypererregbarkeit und Hyperaktivität der peripheren motorischen Axonen als Hauptmechanismus des Syndroms dauernder motorischer Einheitsaktivität betrachten.", "title": "" }, { "docid": "neg:1840638_12", "text": "The most common question asked by patients with inflammatory bowel disease (IBD) is, \"Doctor, what should I eat?\" Findings from epidemiology studies have indicated that diets high in animal fat and low in fruits and vegetables are the most common pattern associated with an increased risk of IBD. Low levels of vitamin D also appear to be a risk factor for IBD. In murine models, diets high in fat, especially saturated animal fats, also increase inflammation, whereas supplementation with omega 3 long-chain fatty acids protect against intestinal inflammation. Unfortunately, omega 3 supplements have not been shown to decrease the risk of relapse in patients with Crohn's disease. Dietary intervention studies have shown that enteral therapy, with defined formula diets, helps children with Crohn's disease and reduces inflammation and dysbiosis. Although fiber supplements have not been shown definitively to benefit patients with IBD, soluble fiber is the best way to generate short-chain fatty acids such as butyrate, which has anti-inflammatory effects. Addition of vitamin D and curcumin has been shown to increase the efficacy of IBD therapy. There is compelling evidence from animal models that emulsifiers in processed foods increase risk for IBD. We discuss current knowledge about popular diets, including the specific carbohydrate diet and diet low in fermentable oligo-, di-, and monosaccharides and polyols. We present findings from clinical and basic science studies to help gastroenterologists navigate diet as it relates to the management of IBD.", "title": "" }, { "docid": "neg:1840638_13", "text": "Real-Time Line and Disk Light Shading\n Eric Heitz and Stephen Hill\n At SIGGRAPH 2016, we presented a new real-time area lighting technique for polygonal sources. In this talk, we will show how the underlying framework, based on Linearly Transformed Cosines (LTCs), can be extended to support line and disk lights. We will discuss the theory behind these approaches as well as practical implementation tips and tricks concerning numerical precision and performance.\n Physically Based Shading at DreamWorks Animation\n Feng Xie and Jon Lanz\n PDI/DreamWorks was one of the first animation studios to adopt global illumination in production rendering. Concurrently, we have also been developing and applying physically based shading principles to improve the consistency and realism of our material models, while balancing the need for intuitive artistic control required for feature animations.\n In this talk, we will start by presenting the evolution of physically based shading in our films. Then we will present some fundamental principles with respect to importance sampling and energy conservation in our BSDF framework with a pragmatic and efficient approach to transimssion fresnel modeling. Finally, we will present our new set of physically plausible production shaders for our new path tracer, which includes our new hard surface shader, our approach to material layering and some new developments in fabric and glitter shading.\n Volumetric Skin and Fabric Shading at Framestore\n Nathan Walster\n Recent advances in shading have led to the use of free-path sampling to better solve complex light transport within volumetric materials. In this talk, we describe how we have implemented these ideas and techniques within a production environment, their application on recent shows---such as Guardians of the Galaxy Vol. 2 and Alien: Covenant---and the effect this has had on artists' workflow within our studio.\n Practical Multilayered Materials in Call of Duty: Infinite Warfare\n Michał Drobot\n This talk presents a practical approach to multilayer, physically based surface rendering, specifically optimized for Forward+ rendering pipelines. The presented pipeline allows for the creation of complex surface by decomposing them into different mediums, each represented by a simple BRDF/BSSRDF and set of simple, physical macro properties, such as thickness, scattering and absorption. The described model is explained via practical examples of common multilayer materials such as car paint, lacquered wood, ice, and semi-translucent plastics. Finally, the talk describes intrinsic implementation details for achieving a low performance budget for 60 Hz titles as well as supporting multiple rendering modes: opaque, alpha blend, and refractive blend.\n Pixar's Foundation for Materials: PxrSurface and PxrMarschnerHair\n Christophe Hery and Junyi Ling\n Pixar's Foundation Materials, PxrSurface and PxrMarschnerHair, began shipping with RenderMan 21.\n PxrSurface is the standard surface shader developed in the studio for Finding Dory and used more recently for Cars 3 and Coco. This shader contains nine lobes that cover the entire gamut of surface materials for these two films: diffuse, three specular, iridescence, fuzz, subsurface, single scatter and a glass lobe. Each of these BxDF lobes is energy conserving, but conservation is not enforced between lobes on the surface level. We use parameter layering methods to feed a PxrSurface with pre-layered material descriptions. This simultaneously allows us the flexibility of a multilayered shading pipeline together with efficient and consistent rendering behavior.\n We also implemented our individual BxDFs with the latest state-of-the-art techniques. For example, our three specular lobes can be switched between Beckmann and GGX modes. Many compound materials have multiple layers of specular; these lobes interact with each other modulated by the Fresnel effect of the clearcoat layer. We also leverage LEADR mapping to recreate sub-displacement micro features such as metal flakes and clearcoat scratches.\n Another example is that PxrSurface ships with Jensen, d'Eon and Burley diffusion profiles. Additionally, we implemented a novel subsurface model using path-traced volumetric scattering, which represents a significant advancement. It captures zero and single scattering events of subsurface scattering implicit to the path-tracing algorithm. The user can adjust the phase-function of the scattering events and change the extinction profiles, and it also comes with standardized color inversion features for intuitive albedo input. To the best of our knowledge, this is the first commercially available rendering system to model these features and the rendering cost is comparable to classic diffusion subsurface scattering models.\n PxrMarschnerHair implements Marschner's seminal hair illumination model with importance sampling. We also account for the residual energy left after the R, TT, TRT and glint lobes, through a fifth diffuse lobe. We show that this hair surface shader can reproduce dark and blonde hair effectively in a path-traced production context. Volumetric scattering from fiber to fiber changes the perceived hue and saturation of a groom, so we also provide a color inversion scheme to invert input albedos, such that the artistic inputs are straightforward and intuitive.\n Revisiting Physically Based Shading at Imageworks\n Christopher Kulla and Alejandro Conty\n Two years ago, the rendering and shading groups at Sony Imageworks embarked on a project to review the structure of our physically based shaders in an effort to simplify their implementation, improve quality and pave the way to take advantage of future improvements in light transport algorithms.\n We started from classic microfacet BRDF building blocks and investigated energy conservation and artist friendly parametrizations. We continued by unifying volume rendering and subsurface scattering algorithms and put in place a system for medium tracking to improve the setup of nested media. Finally, from all these building blocks, we rebuilt our artist-facing shaders with a simplified interface and a more flexible layering approach through parameter blending.\n Our talk will discuss the details of our various building blocks, what worked and what didn't, as well as some future research directions we are still interested in exploring.", "title": "" }, { "docid": "neg:1840638_14", "text": "The growth of the software game development industry is enormous and is gaining importance day by day. This growth imposes severe pressure and a number of issues and challenges on the game development community. Game development is a complex process, and one important game development choice is to consider the developer’s perspective to produce good-quality software games by improving the game development process. The objective of this study is to provide a better understanding of the developer’s dimension as a factor in software game success. It focuses mainly on an empirical investigation of the effect of key developer’s factors on the software game development process and eventually on the quality of the resulting game. A quantitative survey was developed and conducted to identify key developer’s factors for an enhanced game development process. For this study, the developed survey was used to test the research model and hypotheses. The results provide evidence that game development organizations must deal with multiple key factors to remain competitive and to handle high pressure in the software game industry. The main contribution of this paper is to investigate empirically the influence of key developer’s factors on the game development process.", "title": "" }, { "docid": "neg:1840638_15", "text": "Schelling (1969, 1971a,b, 1978) considered a simple proximity model of segregation where individual agents only care about the types of people living in their own local geographical neighborhood, the spatial structure being represented by oneor two-dimensional lattices. In this paper, we argue that segregation might occur not only in the geographical space, but also in social environments. Furthermore, recent empirical studies have documented that social interaction structures are well-described by small-world networks. We generalize Schelling’s model by allowing agents to interact in small-world networks instead of regular lattices. We study two alternative dynamic models where agents can decide to move either arbitrarily far away (global model) or are bound to choose an alternative location in their social neighborhood (local model). Our main result is that the system attains levels of segregation that are in line with those reached in the lattice-based spatial proximity model. Thus, Schelling’s original results seem to be robust to the structural properties of the network.", "title": "" }, { "docid": "neg:1840638_16", "text": "We develop several predictive models linking legislative sentiment to legislative text. Our models, which draw on ideas from ideal point estimation and topic models, predict voting patterns based on the contents of bills and infer the political leanings of legislators. With supervised topics, we provide an exploratory window into how the language of the law is correlated with political support. We also derive approximate posterior inference algorithms based on variational methods. Across 12 years of legislative data, we predict specific voting patterns with high accuracy.", "title": "" }, { "docid": "neg:1840638_17", "text": "Most machine learning methods are known to capture and exploit biases of the training data. While some biases are beneficial for learning, others are harmful. Specifically, image captioning models tend to exaggerate biases present in training data (e.g., if a word is present in 60% of training sentences, it might be predicted in 70% of sentences at test time). This can lead to incorrect captions in domains where unbiased captions are desired, or required, due to over-reliance on the learned prior and image context. In this work we investigate generation of gender-specific caption words (e.g. man, woman) based on the person’s appearance or the image context. We introduce a new Equalizer model that encourages equal gender probability when gender evidence is occluded in a scene and confident predictions when gender evidence is present. The resulting model is forced to look at a person rather than use contextual cues to make a gender-specific prediction. The losses that comprise our model, the Appearance Confusion Loss and the Confident Loss, are general, and can be added to any description model in order to mitigate impacts of unwanted bias in a description dataset. Our proposed model has lower error than prior work when describing images with people and mentioning their gender and more closely matches the ground truth ratio of sentences including women to sentences including men. Finally, we show that our model more often looks at people when predicting their gender. 1", "title": "" }, { "docid": "neg:1840638_18", "text": "Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into high or low risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. A variety of these techniques, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs) and Decision Trees (DTs) have been widely applied in cancer research for the development of predictive models, resulting in effective and accurate decision making. Even though it is evident that the use of ML methods can improve our understanding of cancer progression, an appropriate level of validation is needed in order for these methods to be considered in the everyday clinical practice. In this work, we present a review of recent ML approaches employed in the modeling of cancer progression. The predictive models discussed here are based on various supervised ML techniques as well as on different input features and data samples. Given the growing trend on the application of ML methods in cancer research, we present here the most recent publications that employ these techniques as an aim to model cancer risk or patient outcomes.", "title": "" }, { "docid": "neg:1840638_19", "text": "Nowadays, a great effort is done to find new alternative renewable energy sources to replace part of nuclear energy production. In this context, this paper presents a new axial counter-rotating turbine for small-hydro applications which is developed to recover the energy lost in release valves of water supply. The design of the two PM-generators, their mechanical integration in a bulb placed into the water conduit and the AC-DC Vienna converter developed for these turbines are presented. The sensorless regulation of the two generators is also briefly discussed. Finally, measurements done on the 2-kW prototype are analyzed and compared with the simulation.", "title": "" } ]

[ { "docid": "pos:1840639_0", "text": "In this study I investigated the relation between normal heterosexual attraction and autogynephilia (a man's propensity to be sexually aroused by the thought or image of himself as a woman). The subjects were 427 adult male outpatients who reported histories of dressing in women's garments, of feeling like women, or both. The data were questionnaire measures of autogynephilia, heterosexual interest, and other psychosexual variables. As predicted, the highest levels of autogynephilia were observed at intermediate rather than high levels of heterosexual interest; that is, the function relating these variables took the form of an inverted U. This finding supports the hypothesis that autogynephilia is a misdirected type of heterosexual impulse, which arises in association with normal heterosexuality but also competes with it.", "title": "" } ]

[ { "docid": "neg:1840639_0", "text": "BACKGROUND\nPerforator-based flaps have been explored across almost all of the lower leg except in the Achilles tendon area. This paper introduced a perforator flap sourced from this area with regard to its anatomic basis and clinical applications.\n\n\nMETHODS\nTwenty-four adult cadaver legs were dissected to investigate the perforators emerging along the lateral edge of the Achilles tendon in terms of number and location relative to the tip of the lateral malleolus, and distribution. Based on the anatomic findings, perforator flaps, based on the perforator(s) of the lateral calcaneal artery (LCA) alone or in concert with the perforator of the peroneal artery (PA), were used for reconstruction of lower-posterior heel defects in eight cases. Postoperatively, subjective assessment and Semmes-Weinstein filament test were performed to evaluate the sensibility of the sural nerve-innerved area.\n\n\nRESULTS\nThe PA ended into the anterior perforating branch and LCA at the level of 6.0 ± 1.4 cm (range 3.3-9.4 cm) above the tip of the lateral malleolus. Both PA and LCA, especially the LCA, gave rise to perforators to contribute to the integument overlying the Achilles tendon. Of eight flaps, six were based on perforator(s) of the LCA and two were on perforators of the PA and LCA. Follow-up lasted for 6-28 months (mean 13.8 months), during which total flap loss and nerve injury were not found. Functional and esthetic outcomes were good in all patients.\n\n\nCONCLUSION\nThe integument overlying the Achilles tendon gets its blood supply through the perforators of the LCA primarily and that of through the PA secondarily. The LCA perforator(s)-based and the LCA plus PA perforators-based stepladder flap is a reliable, sensate flap, and should be thought of as a valuable procedure of choice for coverage of lower-posterior heel defects in selected patients.", "title": "" }, { "docid": "neg:1840639_1", "text": "Public health thrives on high-quality evidence, yet acquiring meaningful data on a population remains a central challenge of public health research and practice. Social monitoring, the analysis of social media and other user-generated web data, has brought advances in the way we leverage population data to understand health. Social media offers advantages over traditional data sources, including real-time data availability, ease of access, and reduced cost. Social media allows us to ask, and answer, questions we never thought possible. This book presents an overview of the progress on uses of social monitoring to study public health over the past decade. We explain available data sources, common methods, and survey research on social monitoring in a wide range of public health areas. Our examples come from topics such as disease surveillance, behavioral medicine, and mental health, among others. We explore the limitations and concerns of these methods. Our survey of this exciting new field of data-driven research lays out future research directions.", "title": "" }, { "docid": "neg:1840639_2", "text": "“The distinctive faculties of Man are visibly expressed in his elevated cranial domeda feature which, though much debased in certain savage races, essentially characterises the human species. But, considering that the Neanderthal skull is eminently simial, both in its general and particular characters, I feel myself constrained to believe that the thoughts and desires which once dwelt within it never soared beyond those of a brute. The Andamaner, it is indisputable, possesses but the dimmest conceptions of the existence of the Creator of the Universe: his ideas on this subject, and on his own moral obligations, place him very little above animals of marked sagacity; nevertheless, viewed in connection with the strictly human conformation of his cranium, they are such as to specifically identify him with Homo sapiens. Psychical endowments of a lower grade than those characterising the Andamaner cannot be conceived to exist: they stand next to brute benightedness. (.) Applying the above argument to the Neanderthal skull, and considering . that it more closely conforms to the brain-case of the Chimpanzee, . there seems no reason to believe otherwise than that similar darkness characterised the being to which the fossil belonged” (King, 1864; pp. 96).", "title": "" }, { "docid": "neg:1840639_3", "text": "Dependency networks approximate a joint probability distribution over multiple random variables as a product of conditional distributions. Relational Dependency Networks (RDNs) are graphical models that extend dependency networks to relational domains. This higher expressivity, however, comes at the expense of a more complex model-selection problem: an unbounded number of relational abstraction levels might need to be explored. Whereas current learning approaches for RDNs learn a single probability tree per random variable, we propose to turn the problem into a series of relational function-approximation problems using gradient-based boosting. In doing so, one can easily induce highly complex features over several iterations and in turn estimate quickly a very expressive model. Our experimental results in several different data sets show that this boosting method results in efficient learning of RDNs when compared to state-of-the-art statistical relational learning approaches.", "title": "" }, { "docid": "neg:1840639_4", "text": "A novel PFC (Power Factor Corrected) Converter using Zeta DC-DC converter feeding a BLDC (Brush Less DC) motor drive using a single voltage sensor is proposed for fan applications. A single phase supply followed by an uncontrolled bridge rectifier and a Zeta DC-DC converter is used to control the voltage of a DC link capacitor which is lying between the Zeta converter and a VSI (Voltage Source Inverter). Voltage of a DC link capacitor of Zeta converter is controlled to achieve the speed control of BLDC motor. The Zeta converter is working as a front end converter operating in DICM (Discontinuous Inductor Current Mode) and thus using a voltage follower approach. The DC link capacitor of the Zeta converter is followed by a VSI which is feeding a BLDC motor. A sensorless control of BLDC motor is used to eliminate the requirement of Hall Effect position sensors. A MATLAB/Simulink environment is used to simulate the developed model to achieve a wide range of speed control with high PF (power Factor) and improved PQ (Power Quality) at the supply.", "title": "" }, { "docid": "neg:1840639_5", "text": "Video sensors become particularly important in traffic applications mainly due to their fast response, easy installation, operation and maintenance, and their ability to monitor wide areas. Research in several fields of traffic applications has resulted in a wealth of video processing and analysis methods. Two of the most demanding and widely studied applications relate to traffic monitoring and automatic vehicle guidance. In general, systems developed for these areas must integrate, amongst their other tasks, the analysis of their static environment (automatic lane finding) and the detection of static or moving obstacles (object detection) within their space of interest. In this paper we present an overview of image processing and analysis tools used in these applications and we relate these tools with complete systems developed for specific traffic applications. More specifically, we categorize processing methods based on the intrinsic organization of their input data (feature-driven, area-driven, or model-based) and the domain of processing (spatial/frame or temporal/video). Furthermore, we discriminate between the cases of static and mobile camera. Based on this categorization of processing tools, we present representative systems that have been deployed for operation. Thus, the purpose of the paper is threefold. First, to classify image-processing methods used in traffic applications. Second, to provide the advantages and disadvantages of these algorithms. Third, from this integrated consideration, to attempt an evaluation of shortcomings and general needs in this field of active research. q 2003 Elsevier Science B.V. All rights reserved.", "title": "" }, { "docid": "neg:1840639_6", "text": "We present in this work an economic analysis of ransomware, with relevant data from Cryptolocker, CryptoWall, TeslaCrypt and other major strands. We include a detailed study of the impact that different price discrimination strategies can have on the success of a ransomware family, examining uniform pricing, optimal price discrimination and bargaining strategies and analysing their advantages and limitations. In addition, we present results of a preliminary survey that can helps in estimating an optimal ransom value. We discuss at each stage whether the different schemes we analyse have been encountered already in existing malware, and the likelihood of them being implemented and becoming successful. We hope this work will help to gain some useful insights for predicting how ransomware may evolve in the future and be better prepared to counter its current and future threat.", "title": "" }, { "docid": "neg:1840639_7", "text": "The underrepresentation of women at the top of math-intensive fields is controversial, with competing claims of biological and sociocultural causation. The authors develop a framework to delineate possible causal pathways and evaluate evidence for each. Biological evidence is contradictory and inconclusive. Although cross-cultural and cross-cohort differences suggest a powerful effect of sociocultural context, evidence for specific factors is inconsistent and contradictory. Factors unique to underrepresentation in math-intensive fields include the following: (a) Math-proficient women disproportionately prefer careers in non-math-intensive fields and are more likely to leave math-intensive careers as they advance; (b) more men than women score in the extreme math-proficient range on gatekeeper tests, such as the SAT Mathematics and the Graduate Record Examinations Quantitative Reasoning sections; (c) women with high math competence are disproportionately more likely to have high verbal competence, allowing greater choice of professions; and (d) in some math-intensive fields, women with children are penalized in promotion rates. The evidence indicates that women's preferences, potentially representing both free and constrained choices, constitute the most powerful explanatory factor; a secondary factor is performance on gatekeeper tests, most likely resulting from sociocultural rather than biological causes.", "title": "" }, { "docid": "neg:1840639_8", "text": "0 7 4 0 7 4 5 9 / 0 2 / $ 1 7 . 0 0 © 2 0 0 2 I E E E McDonald’s develop product lines. But software product lines are a relatively new concept. They are rapidly emerging as a practical and important software development paradigm. A product line succeeds because companies can exploit their software products’ commonalities to achieve economies of production. The Software Engineering Institute’s (SEI) work has confirmed the benefits of pursuing this approach; it also found that doing so is both a technical and business decision. To succeed with software product lines, an organization must alter its technical practices, management practices, organizational structure and personnel, and business approach.", "title": "" }, { "docid": "neg:1840639_9", "text": "This paper presents a novel manipulator for human-robot interaction that has low mass and inertia without losing stiffness and payload performance. A lightweight tension amplifying mechanism that increases the joint stiffness in quadratic order is proposed. High stiffness is essential for precise and rapid manipulation, and low mass and inertia are important factors for safety due to low stored kinetic energy. The proposed tension amplifying mechanism was applied to a 1-DOF elbow joint and then extended to a 3-DOF wrist joint. The developed manipulator was analyzed in terms of inertia, stiffness, and strength properties. Its moving part weighs 3.37 kg, and its inertia is 0.57 kg·m2, which is similar to that of a human arm. The stiffness of the developed elbow joint is 1440Nm/rad, which is comparable to that of the joints with rigid components in industrial manipulators. A detailed description of the design is provided, and thorough analysis verifies the performance of the proposed mechanism.", "title": "" }, { "docid": "neg:1840639_10", "text": "OBJECTIVE\nTo encourage treatment of depression and prevention of suicide in physicians by calling for a shift in professional attitudes and institutional policies to support physicians seeking help.\n\n\nPARTICIPANTS\nAn American Foundation for Suicide Prevention planning group invited 15 experts on the subject to evaluate the state of knowledge about physician depression and suicide and barriers to treatment. The group assembled for a workshop held October 6-7, 2002, in Philadelphia, Pa.\n\n\nEVIDENCE\nThe planning group worked with each participant on a preworkshop literature review in an assigned area. Abstracts of presentations and key publications were distributed to participants before the workshop. After workshop presentations, participants were assigned to 1 of 2 breakout groups: (1) physicians in their role as patients and (2) medical institutions and professional organizations. The groups identified areas that required further research, barriers to treatment, and recommendations for reform.\n\n\nCONSENSUS PROCESS\nThis consensus statement emerged from a plenary session during which each work group presented its recommendations. The consensus statement was circulated to and approved by all participants.\n\n\nCONCLUSIONS\nThe culture of medicine accords low priority to physician mental health despite evidence of untreated mood disorders and an increased burden of suicide. Barriers to physicians' seeking help are often punitive, including discrimination in medical licensing, hospital privileges, and professional advancement. This consensus statement recommends transforming professional attitudes and changing institutional policies to encourage physicians to seek help. As barriers are removed and physicians confront depression and suicidality in their peers, they are more likely to recognize and treat these conditions in patients, including colleagues and medical students.", "title": "" }, { "docid": "neg:1840639_11", "text": "Intelligent transport systems are the rising technology in the near future to build cooperative vehicular networks in which a variety of different ITS applications are expected to communicate with a variety of different units. Therefore, the demand for highly customized communication channel for each or sets of similar ITS applications is increased. This article explores the capabilities of available wireless communication technologies in order to produce a win-win situation while selecting suitable carrier( s) for a single application or a profile of similar applications. Communication requirements for future ITS applications are described to select the best available communication interface for the target application(s).", "title": "" }, { "docid": "neg:1840639_12", "text": "Based on the sense definition of words available in the Bengali WordNet, an attempt is made to classify the Bengali sentences automatically into different groups in accordance with their underlying senses. The input sentences are collected from 50 different categories of the Bengali text corpus developed in the TDIL project of the Govt. of India, while information about the different senses of particular ambiguous lexical item is collected from Bengali WordNet. In an experimental basis we have used Naive Bayes probabilistic model as a useful classifier of sentences. We have applied the algorithm over 1747 sentences that contain a particular Bengali lexical item which, because of its ambiguous nature, is able to trigger different senses that render sentences in different meanings. In our experiment we have achieved around 84% accurate result on the sense classification over the total input sentences. We have analyzed those residual sentences that did not comply with our experiment and did affect the results to note that in many cases, wrong syntactic structures and less semantic information are the main hurdles in semantic classification of sentences. The applicational relevance of this study is attested in automatic text classification, machine learning, information extraction, and word sense disambiguation.", "title": "" }, { "docid": "neg:1840639_13", "text": "The calculation of a transformer's parasitics, such as its self capacitance, is fundamental for predicting the frequency behavior of the device, reducing this capacitance value and moreover for more advanced aims of capacitance integration and cancellation. This paper presents a comprehensive procedure for calculating all contributions to the self-capacitance of high-voltage transformers and provides a detailed analysis of the problem, based on a physical approach. The advantages of the analytical formulation of the problem rather than a finite element method analysis are discussed. The approach and formulas presented in this paper can also be used for other wound components rather than just step-up transformers. Finally, analytical and experimental results are presented for three different high-voltage transformer architectures.", "title": "" }, { "docid": "neg:1840639_14", "text": "If, as many psychologists seem to believe, im­ mediate memory represents a distinct system or set of processes from long-term memory (L TM), then what might· it be for? This fundamental, functional question was surprisingly unanswer­ able in the 1970s, given the volume of research that had explored short-term memory (STM), and given the ostensible role that STM was thought to play in cognitive control (Atkinson & Shiffrin, 1971 ). Indeed, failed attempts to link STM to complex cognitive· functions, such as reading comprehension, loomed large in Crow­ der's (1982) obituary for the concept. Baddeley and Hitch ( 197 4) tried to validate immediate memory's functions by testing sub­ jects in reasoning, comprehension, and list­ learning tasks at the same time their memory was occupied by irrelevant material. Generally, small memory loads (i.e., three or fewer items) were retained with virtually no effect on the primary tasks, whereas memory loads of six items consistently impaired reasoning, compre­ hension, and learning. Baddeley and Hitch therefore argued that \"working memory\" (WM)", "title": "" }, { "docid": "neg:1840639_15", "text": "This special issue of the proceedings of the IEEE presents a systematical and complete tutorial on digital television (DTV), produced by a team of DTV experts worldwide. This introductory paper puts the current DTV systems into perspective and explains the historical background and different evolution paths that each system took. The main focus is on terrestrial DTV systems, but satellite and cable DTV are also covered,as well as several other emerging services.", "title": "" }, { "docid": "neg:1840639_16", "text": "This paper proposes a new method for fabric defect classification by incorporating the design of a wavelet frames based feature extractor with the design of a Euclidean distance based classifier. Channel variances at the outputs of the wavelet frame decomposition are used to characterize each nonoverlapping window of the fabric image. A feature extractor using linear transformation matrix is further employed to extract the classification-oriented features. With a Euclidean distance based classifier, each nonoverlapping window of the fabric image is then assigned to its corresponding category. Minimization of the classification error is achieved by incorporating the design of the feature extractor with the design of the classifier based on minimum classification error (MCE) training method. The proposed method has been evaluated on the classification of 329 defect samples containing nine classes of fabric defects, and 328 nondefect samples, where 93.1% classification accuracy has been achieved.", "title": "" }, { "docid": "neg:1840639_17", "text": "Data mining is the extraction of useful, prognostic, interesting, and unknown information from massive transaction databases and other repositories. Data mining tools predict potential trends and actions, allowing various fields to make proactive, knowledge-driven decisions. Recently, with the rapid growth of information technology, the amount of data has exponentially increased in various fields. Big data mostly comes from people’s day-to-day activities and Internet-based companies. Mining frequent itemsets and association rule mining (ARM) are well-analysed techniques for revealing attractive correlations among variables in huge datasets. The Apriori algorithm is one of the most broadly used algorithms in ARM, and it collects the itemsets that frequently occur in order to discover association rules in massive datasets. The original Apriori algorithm is for sequential (single node or computer) environments. This Apriori algorithm has many drawbacks for processing huge datasets, such as that a single machine’s memory, CPU and storage capacity are insufficient. Parallel and distributed computing is the better solution to overcome the above problems. Many researchers have parallelized the Apriori algorithm. This study performs a survey on several well-enhanced and revised techniques for the parallel Apriori algorithm in the HadoopMapReduce environment. The Hadoop-MapReduce framework is a programming model that efficiently and effectively processes enormous databases in parallel. It can handle large clusters of commodity hardware in a reliable and fault-tolerant manner. This survey will provide an overall view of the parallel Apriori algorithm implementation in the Hadoop-MapReduce environment and briefly discuss the challenges and open issues of big data in the cloud and Hadoop-MapReduce. Moreover, this survey will not only give overall existing improved Apriori algorithm methods on Hadoop-MapReduce but also provide future research direction for upcoming researchers.", "title": "" }, { "docid": "neg:1840639_18", "text": "Material recognition is an important subtask in computer vision. In this paper, we aim for the identification of material categories from a single image captured under unknown illumination and view conditions. Therefore, we use several features which cover various aspects of material appearance and perform supervised classification using Support Vector Machines. We demonstrate the feasibility of our approach by testing on the challenging Flickr Material Database. Based on this dataset, we also carry out a comparison to a previously published work [Liu et al., ”Exploring Features in a Bayesian Framework for Material Recognition”, CVPR 2010] which uses Bayesian inference and reaches a recognition rate of 44.6% on this dataset and represents the current state-of the-art. With our SVM approach we obtain 53.1% and hence, significantly outperform this approach.", "title": "" }, { "docid": "neg:1840639_19", "text": "Major security issues for banking and financial institutions are Phishing. Phishing is a webpage attack, it pretends a customer web services using tactics and mimics from unauthorized persons or organization. It is an illegitimate act to steals user personal information such as bank details, social security numbers and credit card details, by showcasing itself as a truthful object, in the public network. When users provide confidential information, they are not aware of the fact that the websites they are using are phishing websites. This paper presents a technique for detecting phishing website attacks and also spotting phishing websites by combines source code and URL in the webpage. Keywords—Phishing, Website attacks, Source Code, URL.", "title": "" } ]

[ { "docid": "pos:1840640_0", "text": "Multi-Agent Path Finding (MAPF) is well studied in both AI and robotics. Given a discretized environment and agents with assigned start and goal locations, MAPF solvers from AI find collision-free paths for hundreds of agents with userprovided sub-optimality guarantees. However, they ignore that actual robots are subject to kinematic constraints (such as finite maximum velocity limits) and suffer from imperfect plan-execution capabilities. We therefore introduce MAPFPOST, a novel approach that makes use of a simple temporal network to postprocess the output of a MAPF solver in polynomial time to create a plan-execution schedule that can be executed on robots. This schedule works on non-holonomic robots, takes their maximum translational and rotational velocities into account, provides a guaranteed safety distance between them, and exploits slack to absorb imperfect plan executions and avoid time-intensive replanning in many cases. We evaluate MAPF-POST in simulation and on differentialdrive robots, showcasing the practicality of our approach.", "title": "" } ]

[ { "docid": "neg:1840640_0", "text": "An algorithm with linear filters and morphological operations has been proposed for automatic fabric defect detection. The algorithm is applied off-line and real-time to denim fabric samples for five types of defects. All defect types have been detected successfully and the defective regions are labeled. The defective fabric samples are then classified by using feed forward neural network method. Both defect detection and classification application performances are evaluated statistically. Defect detection performance of real time and off-line applications are obtained as 88% and 83% respectively. The defective images are classified with an average accuracy rate of 96.3%.", "title": "" }, { "docid": "neg:1840640_1", "text": "Studying a software project by mining data from a single repository has been a very active research field in software engineering during the last years. However, few efforts have been devoted to perform studies by integrating data from various repositories, with different kinds of information, which would, for instance, track the different activities of developers. One of the main problems of these multi-repository studies is the different identities that developers use when they interact with different tools in different contexts. This makes them appear as different entities when data is mined from different repositories (and in some cases, even from a single one). In this paper we propose an approach, based on the application of heuristics, to identify the many identities of developers in such cases, and a data structure for allowing both the anonymized distribution of information, and the tracking of identities for verification purposes. The methodology will be presented in general, and applied to the GNOME project as a case example. Privacy issues and partial merging with new data sources will also be considered and discussed.", "title": "" }, { "docid": "neg:1840640_2", "text": "A reversible gate has the equal number of inputs and outputs and one-to-one mappings between input vectors and output vectors; so that, the input vector states can be always uniquely reconstructed from the output vector states. This correspondence introduces a reversible full-adder circuit that requires only three reversible gates and produces least number of \"garbage outputs \", that is two. After that, a theorem has been proposed that proves the optimality of the propounded circuit in terms of number of garbage outputs. An efficient algorithm is also introduced in this paper that leads to construct a reversible circuit.", "title": "" }, { "docid": "neg:1840640_3", "text": "Cite this article Romager JA, Hughes K, Trimble JE. Personality traits as predictors of leadership style preferences: Investigating the relationship between social dominance orientation and attitudes towards authentic leaders. Soc Behav Res Pract Open J. 2017; 3(1): 1-9. doi: 10.17140/SBRPOJ-3-110 Personality Traits as Predictors of Leadership Style Preferences: Investigating the Relationship Between Social Dominance Orientation and Attitudes Towards Authentic Leaders Original Research", "title": "" }, { "docid": "neg:1840640_4", "text": "Dynamic magnetic resonance imaging (MRI) scans can be accelerated by utilizing compressed sensing (CS) reconstruction methods that allow for diagnostic quality images to be generated from undersampled data. Unfortunately, CS reconstruction is time-consuming, requiring hours between a dynamic MRI scan and image availability for diagnosis. In this work, we train a convolutional neural network (CNN) to perform fast reconstruction of severely undersampled dynamic cardiac MRI data, and we explore the utility of CNNs for further accelerating dynamic MRI scan times. Compared to state-of-the-art CS reconstruction techniques, our CNN achieves reconstruction speeds that are 150x faster without significant loss of image quality. Additionally, preliminary results suggest that CNNs may allow scan times that are 2x faster than those allowed by CS.", "title": "" }, { "docid": "neg:1840640_5", "text": "The local-dimming backlight has recently been presented for use in LCD TVs. However, the image resolution is low, particularly at weak edges. In this work, a local-dimming backlight is developed to improve the image contrast and reduce power dissipation. The algorithm enhances low-level edge information to improve the perceived image resolution. Based on the algorithm, a 42-in backlight module with white light-emitting diode (LED) devices was driven by a local dimming control core. The block-wise register approach substantially reduced the number of required line-buffers and shortened the latency time. The measurements made in the laboratory indicate that the backlight system reduces power dissipation by an average of 48% and exhibits no visible distortion compared relative to the fixed backlighting system. The system was successfully demonstrated in a 42-in LCD TV, and the contrast ratio was greatly improved by a factor of 100.", "title": "" }, { "docid": "neg:1840640_6", "text": "Cortical circuits work through the generation of coordinated, large-scale activity patterns. In sensory systems, the onset of a discrete stimulus usually evokes a temporally organized packet of population activity lasting ∼50–200 ms. The structure of these packets is partially stereotypical, and variation in the exact timing and number of spikes within a packet conveys information about the identity of the stimulus. Similar packets also occur during ongoing stimuli and spontaneously. We suggest that such packets constitute the basic building blocks of cortical coding.", "title": "" }, { "docid": "neg:1840640_7", "text": "This literature review focuses on aesthetics of interaction design with further goal of outlining a study towards prediction model of aesthetic value. The review covers three main issues, tightly related to aesthetics of interaction design: evaluation of aesthetics, relations between aesthetics and interaction qualities and implementation of aesthetics in interaction design. Analysis of previous models is carried out according to definition of interaction aesthetics: holistic approach to aesthetic perception considering its' action- and appearance-related components. As a result the empirical study is proposed for investigating the relations between attributes of interaction and users' aesthetic experience.", "title": "" }, { "docid": "neg:1840640_8", "text": "Unlike conventional anomaly detection research that focuses on point anomalies, our goal is to detect anomalous collections of individual data points. In particular, we perform group anomaly detection (GAD) with an emphasis on irregular group distributions (e.g. irregular mixtures of image pixels). GAD is an important task in detecting unusual and anomalous phenomena in real-world applications such as high energy particle physics, social media and medical imaging. In this paper, we take a generative approach by proposing deep generative models: Adversarial autoencoder (AAE) and variational autoencoder (VAE) for group anomaly detection. Both AAE and VAE detect group anomalies using point-wise input data where group memberships are known a priori. We conduct extensive experiments to evaluate our models on real world datasets. The empirical results demonstrate that our approach is effective and robust in detecting group anomalies.", "title": "" }, { "docid": "neg:1840640_9", "text": "Steganography and steganalysis received a great deal of attention from media and law enforcement. Many powerful and robust methods of steganography and steganalysis have been developed. In this paper we are considering the methods of steganalysis that are to be used for this processes. Paper giving some idea about the steganalysis and its method. Keywords— Include at least 5 keywords or phrases", "title": "" }, { "docid": "neg:1840640_10", "text": "A brief introduction is given to the actual mechanics of simulated annealing, and a simple example from an IC layout is used to illustrate how these ideas can be applied. The complexities and tradeoffs involved in attacking a realistically complex design problem are illustrated by dissecting two very different annealing algorithms for VLSI chip floorplanning. Several current research problems aimed at determining more precisely how and why annealing algorithms work are examined. Some philosophical issues raised by the introduction of annealing are discussed.<<ETX>>", "title": "" }, { "docid": "neg:1840640_11", "text": "The proposed social media crisis mapping platform for natural disasters uses locations from gazetteer, street map, and volunteered geographic information (VGI) sources for areas at risk of disaster and matches them to geoparsed real-time tweet data streams. The authors use statistical analysis to generate real-time crisis maps. Geoparsing results are benchmarked against existing published work and evaluated across multilingual datasets. Two case studies compare five-day tweet crisis maps to official post-event impact assessment from the US National Geospatial Agency (NGA), compiled from verified satellite and aerial imagery sources.", "title": "" }, { "docid": "neg:1840640_12", "text": "The barrier function of the intestine is essential for maintaining the normal homeostasis of the gut and mucosal immune system. Abnormalities in intestinal barrier function expressed by increased intestinal permeability have long been observed in various gastrointestinal disorders such as Crohn's disease (CD), ulcerative colitis (UC), celiac disease, and irritable bowel syndrome (IBS). Imbalance of metabolizing junction proteins and mucosal inflammation contributes to intestinal hyperpermeability. Emerging studies exploring in vitro and in vivo model system demonstrate that Rho-associated coiled-coil containing protein kinase- (ROCK-) and myosin light chain kinase- (MLCK-) mediated pathways are involved in the regulation of intestinal permeability. With this perspective, we aim to summarize the current state of knowledge regarding the role of inflammation and ROCK-/MLCK-mediated pathways leading to intestinal hyperpermeability in gastrointestinal disorders. In the near future, it may be possible to specifically target these specific pathways to develop novel therapies for gastrointestinal disorders associated with increased gut permeability.", "title": "" }, { "docid": "neg:1840640_13", "text": "Smart Home technology is the future of residential related technology which is designed to deliver and distribute number of services inside and outside the house via networked devices in which all the different applications & the intelligence behind them are integrated and interconnected. These smart devices have the potential to share information with each other given the permanent availability to access the broadband internet connection. Hence, Smart Home Technology has become part of IoT (Internet of Things). In this work, a home model is analyzed to demonstrate an energy efficient IoT based smart home. Several Multiphysics simulations were carried out focusing on the kitchen of the home model. A motion sensor with a surveillance camera was used as part of the home security system. Coupled with the home light and HVAC control systems, the smart system can remotely control the lighting and heating or cooling when an occupant enters or leaves the kitchen.", "title": "" }, { "docid": "neg:1840640_14", "text": "In this survey we review the image processing literature on the various approaches and models investigators have used for texture. These include statistical approaches of autocorrelation function, optical transforms, digital transforms, textural edgeness, structural element, gray tone cooccurrence, run lengths, and autoregressive models. We discuss and generalize some structural approaches to texture based on more complex primitives than gray tone. We conclude with some structural-statistical generalizations which apply the statistical techniques to the structural primitives.", "title": "" }, { "docid": "neg:1840640_15", "text": "Correspondence Lars Ruthotto, Department of Mathematics and Computer Science, Emory University, 400 Dowman Dr, Atlanta, GA 30322, USA. Email: lruthotto@emory.edu Summary Image registration is a central problem in a variety of areas involving imaging techniques and is known to be challenging and ill-posed. Regularization functionals based on hyperelasticity provide a powerful mechanism for limiting the ill-posedness. A key feature of hyperelastic image registration approaches is their ability to model large deformations while guaranteeing their invertibility, which is crucial in many applications. To ensure that numerical solutions satisfy this requirement, we discretize the variational problem using piecewise linear finite elements, and then solve the discrete optimization problem using the Gauss–Newton method. In this work, we focus on computational challenges arising in approximately solving the Hessian system. We show that the Hessian is a discretization of a strongly coupled system of partial differential equations whose coefficients can be severely inhomogeneous. Motivated by a local Fourier analysis, we stabilize the system by thresholding the coefficients. We propose a Galerkin-multigrid scheme with a collective pointwise smoother. We demonstrate the accuracy and effectiveness of the proposed scheme, first on a two-dimensional problem of a moderate size and then on a large-scale real-world application with almost 9 million degrees of freedom.", "title": "" }, { "docid": "neg:1840640_16", "text": "Uncontrolled wind turbine configuration, such as stall-regulation captures, energy relative to the amount of wind speed. This configuration requires constant turbine speed because the generator that is being directly coupled is also connected to a fixed-frequency utility grid. In extremely strong wind conditions, only a fraction of available energy is captured. Plants designed with such a configuration are economically unfeasible to run in these circumstances. Thus, wind turbines operating at variable speed are better alternatives. This paper focuses on a controller design methodology applied to a variable-speed, horizontal axis wind turbine. A simple but rigid wind turbine model was used and linearised to some operating points to meet the desired objectives. By using blade pitch control, the deviation of the actual rotor speed from a reference value is minimised. The performances of PI and PID controllers were compared relative to a step wind disturbance. Results show comparative responses between these two controllers. The paper also concludes that with the present methodology, despite the erratic wind data, the wind turbine still manages to operate most of the time at 88% in the stable region.", "title": "" }, { "docid": "neg:1840640_17", "text": "The detection of symmetry axes through the optimization of a given symmetry measure, computed as a function of the mean-square error between the original and reflected images, is investigated in this paper. A genetic algorithm and an optimization scheme derived from the self-organizing maps theory are presented. The notion of symmetry map is then introduced. This transform allows us to map an object into a symmetry space where its symmetry properties can be analyzed. The locations of the different axes that globally and locally maximize the symmetry value can be obtained. The input data are assumed to be vector-valued, which allow to focus on either shape. color or texture information. Finally, the application to skin cancer diagnosis is illustrated and discussed.", "title": "" }, { "docid": "neg:1840640_18", "text": "Whenever a document containing sensitive information needs to be made public, privacy-preserving measures should be implemented. Document sanitization aims at detecting sensitive pieces of information in text, which are removed or hidden prior publication. Even though methods detecting sensitive structured information like e-mails, dates or social security numbers, or domain specific data like disease names have been developed, the sanitization of raw textual data has been scarcely addressed. In this paper, we present a general-purpose method to automatically detect sensitive information from textual documents in a domain-independent way. Relying on the Information Theory and a corpus as large as the Web, it assess the degree of sensitiveness of terms according to the amount of information they provide. Preliminary results show that our method significantly improves the detection recall in comparison with approaches based on trained classifiers.", "title": "" } ]

[ { "docid": "pos:1840641_0", "text": "Recently, Rao-Blackwellized particle filters (RBPF) have been introduced as an effective means to solve the simultaneous localization and mapping problem. This approach uses a particle filter in which each particle carries an individual map of the environment. Accordingly, a key question is how to reduce the number of particles. In this paper, we present adaptive techniques for reducing this number in a RBPF for learning grid maps. We propose an approach to compute an accurate proposal distribution, taking into account not only the movement of the robot, but also the most recent observation. This drastically decreases the uncertainty about the robot's pose in the prediction step of the filter. Furthermore, we present an approach to selectively carry out resampling operations, which seriously reduces the problem of particle depletion. Experimental results carried out with real mobile robots in large-scale indoor, as well as outdoor, environments illustrate the advantages of our methods over previous approaches", "title": "" } ]

[ { "docid": "neg:1840641_0", "text": "In recent years, multiple-line acquisition (MLA) has been introduced to increase frame rate in cardiac ultrasound medical imaging. However, this method induces blocklike artifacts in the image. One approach suggested, synthetic transmit beamforming (STB), involves overlapping transmit beams which are then interpolated to remove the MLA blocking artifacts. Independently, the application of minimum variance (MV) beamforming has been suggested in the context of MLA. We demonstrate here that each approach is only a partial solution and that combining them provides a better result than applying either approach separately. This is demonstrated by using both simulated and real phantom data, as well as cardiac data. We also show that the STB-compensated MV beamfomer outperforms single-line acquisition (SLA) delay- and-sum in terms of lateral resolution.", "title": "" }, { "docid": "neg:1840641_1", "text": "In this paper, shadow detection and compensation are treated as image enhancement tasks. The principal components analysis (PCA) and luminance based multi-scale Retinex (LMSR) algorithm are explored to detect and compensate shadow in high resolution satellite image. PCA provides orthogonally channels, thus allow the color to remain stable despite the modification of luminance. Firstly, the PCA transform is used to obtain the luminance channel, which enables us to detect shadow regions using histogram threshold technique. After detection, the LMSR technique is used to enhance the image only in luminance channel to compensate for shadows. Then the enhanced image is obtained by inverse transform of PCA. The final shadow compensation image is obtained by comparison of the original image, the enhanced image and the shadow detection image. Experiment results show the effectiveness of the proposed method.", "title": "" }, { "docid": "neg:1840641_2", "text": "Abstract. In this paper we use the contraction mapping theorem to obtain asymptotic stability results of the zero solution of a nonlinear neutral Volterra integro-differential equation with variable delays. Some conditions which allow the coefficient functions to change sign and do not ask the boundedness of delays are given. An asymptotic stability theorem with a necessary and sufficient condition is proved, which improve and extend the results in the literature. Two examples are also given to illustrate this work.", "title": "" }, { "docid": "neg:1840641_3", "text": "In this paper, we propose a new concept - the \"Reciprocal Velocity Obstacle\"- for real-time multi-agent navigation. We consider the case in which each agent navigates independently without explicit communication with other agents. Our formulation is an extension of the Velocity Obstacle concept [3], which was introduced for navigation among (passively) moving obstacles. Our approach takes into account the reactive behavior of the other agents by implicitly assuming that the other agents make a similar collision-avoidance reasoning. We show that this method guarantees safe and oscillation- free motions for each of the agents. We apply our concept to navigation of hundreds of agents in densely populated environments containing both static and moving obstacles, and we show that real-time and scalable performance is achieved in such challenging scenarios.", "title": "" }, { "docid": "neg:1840641_4", "text": "We compiled details of over 8000 assessments of protected area management effectiveness across the world and developed a method for analyzing results across diverse assessment methodologies and indicators. Data was compiled and analyzed for over 4000 of these sites. Management of these protected areas varied from weak to effective, with about 40% showing major deficiencies. About 14% of the surveyed areas showed significant deficiencies across many management effectiveness indicators and hence lacked basic requirements to operate effectively. Strongest management factors recorded on average related to establishment of protected areas (legal establishment, design, legislation and boundary marking) and to effectiveness of governance; while the weakest aspects of management included community benefit programs, resourcing (funding reliability and adequacy, staff numbers and facility and equipment maintenance) and management effectiveness evaluation. Estimations of management outcomes, including both environmental values conservation and impact on communities, were positive. We conclude that in spite of inadequate funding and management process, there are indications that protected areas are contributing to biodiversity conservation and community well-being.", "title": "" }, { "docid": "neg:1840641_5", "text": "The group Steiner tree problem is a generalization of the Steiner tree problem where we are given several subsets (groups) of vertices in a weighted graph, and the goal is to find a minimum-weight connected subgraph containing at least one vertex from each group. The problem was introduced by Reich and Widmayer and linds applications in VLSI design. The group Steiner tree problem generalizes the set covering problem, and is therefore at least as hard. We give a randomized O(log3 n log k)-approximation algorithm for the group Steiner tree problem on an n-node graph, where k is the number of groups. The best previous performance guarantee was (1 + ?)a (Bateman, Helvig, Robins and Zelikovsky). Noting that the group Steiner problem also models the network design problems with location-theoretic constraints studied by Marathe, Bavi and Sundaram, our results also improve their bicriteria approximation results. Similarly, we improve previous results by Slavik on a tour version, called the errand scheduling problem. We use the result of Bartal on probabilistic approximation of finite metric spaces by tree metrics problem to one in a tree metric. To find a solution on a tree, we use a generalization of randomized rounding. Our approximation guarantees improve to O(log’ nlog k) in the case of graphs that exclude small minors by using a better alternative to Bartal’s result on probabilistic approximations of metrics induced by such graphs (Konjevod, Ravi and Salman) this improvement is valid for the group Steiner problem on planar graphs as well as on a set of points in the 2D-Euclidean case. -", "title": "" }, { "docid": "neg:1840641_6", "text": "This paper presents a novel mobility metric for mobile ad hoc networks (MANET) that is based on the ratio between the received power levels of successive transmissions measured at any node from all its neighboring nodes. This mobility metric is subsequently used as a basis for cluster formation which can be used for improving the scalability of services such as routing in such networks. We propose a distributed clustering algorithm, MOBIC, based on the use of this mobility metric for selection of clusterheads, and demonstrate that it leads to more stable cluster formation than the Lowest-ID clustering algorithm ( “least clusterhead change” [3]) which is a well known clustering algorithms for MANETs. We show reduction of as much as 33% in the number of clusterhead changes owing to the use of the proposed technique. In a MANET that uses scalable cluster-based services, the network performance metrics such as throughput and delay are tightly coupled with the frequency of cluster reorganization. Therefore, we believe that since using MOBIC results in a more stable configuration, it will directly lead to improvement of performance.", "title": "" }, { "docid": "neg:1840641_7", "text": "We treated the mandibular retrusion of a 20-year-old man by distraction osteogenesis. Our aim was to avoid any visible discontinuities in the soft tissue profile that may result from conventional \"one-step\" genioplasty. The result was excellent. In addition to a good aesthetic outcome, there was increased bone formation not only between the two surfaces of the osteotomy but also adjacent to the distraction zone, resulting in improved coverage of the roots of the lower incisors. Only a few patients have been treated so far, but the method seems to hold promise for the treatment of extreme retrognathism, as these patients often have insufficient buccal bone coverage.", "title": "" }, { "docid": "neg:1840641_8", "text": "Forecasting is an integral part of any organization for their decision-making process so that they can predict their targets and modify their strategy in order to improve their sales or productivity in the coming future. This paper evaluates and compares various machine learning models, namely, ARIMA, Auto Regressive Neural Network(ARNN), XGBoost, SVM, Hy-brid Models like Hybrid ARIMA-ARNN, Hybrid ARIMA-XGBoost, Hybrid ARIMA-SVM and STL Decomposition (using ARIMA, Snaive, XGBoost) to forecast sales of a drug store company called Rossmann. Training data set contains past sales and supplemental information about drug stores. Accuracy of these models is measured by metrics such as MAE and RMSE. Initially, linear model such as ARIMA has been applied to forecast sales. ARIMA was not able to capture nonlinear patterns precisely, hence nonlinear models such as Neural Network, XGBoost and SVM were used. Nonlinear models performed better than ARIMA and gave low RMSE. Then, to further optimize the performance, composite models were designed using hybrid technique and decomposition technique. Hybrid ARIMA-ARNN, Hybrid ARIMA-XGBoost, Hybrid ARIMA-SVM were used and all of them performed better than their respective individual models. Then, the composite model was designed using STL Decomposition where the decomposed components namely seasonal, trend and remainder components were forecasted by Snaive, ARIMA and XGBoost. STL gave better results than individual and hybrid models. This paper evaluates and analyzes why composite models give better results than an individual model and state that decomposition technique is better than the hybrid technique for this application.", "title": "" }, { "docid": "neg:1840641_9", "text": "A level designer typically creates the levels of a game to cater for a certain set of objectives, or mission. But in procedural content generation, it is common to treat the creation of missions and the generation of levels as two separate concerns. This often leads to generic levels that allow for various missions. However, this also creates a generic impression for the player, because the potential for synergy between the objectives and the level is not utilised. Following up on the mission-space generation concept, as described by Dormans [5], we explore the possibilities of procedurally generating a level from a designer-made mission. We use a generative grammar to transform a mission into a level in a mixed-initiative design setting. We provide two case studies, dungeon levels for a rogue-like game, and platformer levels for a metroidvania game. The generators differ in the way they use the mission to generate the space, but are created with the same tool for content generation based on model transformations. We discuss the differences between the two generation processes and compare it with a parameterized approach.", "title": "" }, { "docid": "neg:1840641_10", "text": "The concept of “truth,” as a public good is the production of a collective understanding, which emerges from a complex network of social interactions. The recent impact of social networks on shaping the perception of truth in political arena shows how such perception is corroborated and established by the online users, collectively. However, investigative journalism for discovering truth is a costly option, given the vast spectrum of online information. In some cases, both journalist and online users choose not to investigate the authenticity of the news they receive, because they assume other actors of the network had carried the cost of validation. Therefore, the new phenomenon of “fake news” has emerged within the context of social networks. The online social networks, similarly to System of Systems, cause emergent properties, which makes authentication processes difficult, given availability of multiple sources. In this study, we show how this conflict can be modeled as a volunteer's dilemma. We also show how the public contribution through news subscription (shared rewards) can impact the dominance of truth over fake news in the network.", "title": "" }, { "docid": "neg:1840641_11", "text": "This research covers an endeavor by the author on the usage of automated vision and navigation framework; the research is conducted by utilizing a Kinect sensor requiring minimal effort framework for exploration purposes in the zone of robot route. For this framework, GMapping (a highly efficient Rao-Blackwellized particle filer to learn grid maps from laser range data) parameters have been optimized to improve the accuracy of the map generation and the laser scan. With the use of Robot Operating System (ROS), the open source GMapping bundle was utilized as a premise for a map era and Simultaneous Localization and Mapping (SLAM). Out of the many different map generation techniques, the tele-operation used is interactive marker, which controls the TurtleBot 2 movements via RVIZ (3D visualization tool for ROS). Test results completed with the multipurpose robot in a counterfeit and regular environment represents the preferences of the proposed strategy. From experiments, it is found that Kinect sensor produces a more accurate map compared to non-filtered laser range finder data, which is excellent since the price of a Kinect sensor is much cheaper than a laser range finder. An expansion of experimental results was likewise done to test the performance of the portable robot frontier exploring in an obscure environment while performing SLAM alongside the proposed technique.", "title": "" }, { "docid": "neg:1840641_12", "text": "This article described the feature extraction methods of crop disease based on computer image processing technology in detail. Based on color, texture and shape feature extraction method in three aspects features and their respective problems were introduced start from the perspective of lesion leaves. Application research of image feature extraction in the filed of crop disease was reviewed in recent years. The results were analyzed that about feature extraction methods, and then the application of image feature extraction techniques in the future detection of crop diseases in the field of intelligent was prospected.", "title": "" }, { "docid": "neg:1840641_13", "text": "This paper introduces an extension of collocational analysis that takes into account grammatical structure and is specifically geared to investigating the interaction of lexemes and the grammatical constructions associated with them. The method is framed in a construction-based approach to language, i.e. it assumes that grammar consists of signs (form-meaning pairs) and is thus not fundamentally different from the lexicon. The method is applied to linguistic expressions at various levels of abstraction (words, semi-fixed phrases, argument structures, tense, aspect and mood). The method has two main applications: first, to increase the adequacy of grammatical description by providing an objective way of identifying the meaning of a grammatical construction and determining the degree to which particular slots in it prefer or are restricted to a particular set of lexemes; second, to provide data for linguistic theory-building.", "title": "" }, { "docid": "neg:1840641_14", "text": "This paper provides an overview of CMOS-based sensor technology with specific attention placed on devices made through micromachining of CMOS substrates and thin films. Microstructures may be formed using either pre-CMOS, intra-CMOS and post-CMOS fabrication approaches. To illustrate and motivate monolithic integration, a handful of microsystem examples, including inertial sensors, gravimetric chemical sensors, microphones, and a bone implantable sensor will be highlighted. Design constraints and challenges for CMOS-MEMS devices will be covered", "title": "" }, { "docid": "neg:1840641_15", "text": "Test-driven development is a discipline that helps professional software developers ship clean, flexible code that works, on time. In this article, the author discusses how test-driven development can help software developers achieve a higher degree of professionalism", "title": "" }, { "docid": "neg:1840641_16", "text": "The field of spondyloarthritis (SpA) has experienced major progress in the last decade, especially with regard to new treatments, earlier diagnosis, imaging technology and a better definition of outcome parameters for clinical trials. In the present work, the Assessment in SpondyloArthritis international Society (ASAS) provides a comprehensive handbook on the most relevant aspects for the assessments of spondyloarthritis, covering classification criteria, MRI and x rays for sacroiliac joints and the spine, a complete set of all measurements relevant for clinical trials and international recommendations for the management of SpA. The handbook focuses at this time on axial SpA, with ankylosing spondylitis (AS) being the prototype disease, for which recent progress has been faster than in peripheral SpA. The target audience includes rheumatologists, trial methodologists and any doctor and/or medical student interested in SpA. The focus of this handbook is on practicality, with many examples of MRI and x ray images, which will help to standardise not only patient care but also the design of clinical studies.", "title": "" }, { "docid": "neg:1840641_17", "text": "A 1 Mbit MRAM, a nonvolatile memory that uses magnetic tunnel junction (MJT) storage elements, has been characterized for total ionizing dose (TID) and single event latchup (SEL). Our results indicate that these devices show no single event latchup up to an effective LET of 84 MeV-cm2/mg (where our testing ended) and no bit failures to a TID of 75 krad (Si).", "title": "" }, { "docid": "neg:1840641_18", "text": "This paper presents a single-pole eight-throw switch, based on an eight-way power divider, using substrate integrate waveguide(SIW) technology. Eight sectorial-lines are formed by inserting radial slot-lines on the top plate of SIW power divider. Each sectorial-line can be controlled independently with high level of isolation. The switching is accomplished by altering the capacitance of the varactor on the line, which causes different input impedances to be seen at a central probe to each sectorial line. The proposed structure works as a switching circuit and an eight-way power divider depending on the bias condition. The change in resonant frequency and input impedance are estimated by adapting a tapered transmission line model. The detailed design, fabrication, and measurement are discussed.", "title": "" }, { "docid": "neg:1840641_19", "text": "Light detection and ranging (lidar) is becoming an increasingly popular technology among scientists for the development of predictive models of forest biophysical variables. However, before this technology can be adopted with confidence for long-term monitoring applications in Canada, robust models must be developed that can be applied and validated over large and complex forested areas. This will require “scaling-up” from current models developed from high-density lidar data to low-density data collected at higher altitudes. This paper investigates the effect of lowering the average point spacing of discrete lidar returns on models of forest biophysical variables. Validation of results revealed that high-density models are well correlated with mean dominant height, basal area, crown closure, and average aboveground biomass (R2 = 0.84, 0.89, 0.60, and 0.91, respectively). Low-density models could not accurately predict crown closure (R2 = 0.36). However, they did provide slightly improved estimates for mean dominant height, basal area, and average aboveground biomass (R2 = 0.90, 0.91, and 0.92, respectively). Maps were generated and validated for the entire study area from the low-density models. The ability of low-density models to accurately map key biophysical variables is a positive indicator for the utility of lidar data for monitoring large forested areas. Résumé : Le lidar est en voie de devenir une technique de plus en plus populaire parmi les chercheurs pour le développement de modèles de prédiction des variables biophysiques de la forêt. Cependant, avant que cette technologie puisse être adoptée avec confiance pour le suivi à long terme au Canada, des modèles robustes pouvant être appliqués et validés pour des superficies de forêt vastes et complexes doivent être développés. Cela va exiger de passer des modèles courants développés à partir d’une forte densité de données lidar à une plus faible densité de données collectées à plus haute altitude. Cet article se penche sur l’effet de la diminution de l’espacement ponctuel moyen des échos individuels du lidar sur les modèles de variables biophysiques de la forêt. La validation des résultats a montré que les modèles à forte densité sont bien corrélés avec la hauteur dominante moyenne, la surface terrière, la fermeture du couvert et la biomasse aérienne moyenne (R2 = 0,84, 0,89, 0,60 et 0,91 respectivement). Les modèles à faible densité ne pouvaient pas correctement (R2 = 0,36) prédire la fermeture du couvert. Cependant, ils ont fourni des estimations légèrement meilleures pour la hauteur dominante moyenne, la surface terrière et la biomasse aérienne moyenne (R2 = 0,90, 0,91 et 0,92 respectivement). Des cartes ont été générées et validées pour toute la zone d’étude à partir de modèles à faible densité. La capacité des modèles à faible densité à cartographier correctement les variables biophysiques importantes est une indication positive de l’utilité des données lidar pour le suivi de vastes zones de forêt. [Traduit par la Rédaction] Thomas et al. 47", "title": "" } ]