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686daf99-f788-4700-9002-0289ed0653c3 | automatic-face-reenactment | 1602.02651 | null | http://arxiv.org/abs/1602.02651v1 | http://arxiv.org/pdf/1602.02651v1.pdf | Automatic Face Reenactment | We propose an image-based, facial reenactment system that replaces the face
of an actor in an existing target video with the face of a user from a source
video, while preserving the original target performance. Our system is fully
automatic and does not require a database of source expressions. Instead, it is
able to produce convincing reenactment results from a short source video
captured with an off-the-shelf camera, such as a webcam, where the user
performs arbitrary facial gestures. Our reenactment pipeline is conceived as
part image retrieval and part face transfer: The image retrieval is based on
temporal clustering of target frames and a novel image matching metric that
combines appearance and motion to select candidate frames from the source
video, while the face transfer uses a 2D warping strategy that preserves the
user's identity. Our system excels in simplicity as it does not rely on a 3D
face model, it is robust under head motion and does not require the source and
target performance to be similar. We show convincing reenactment results for
videos that we recorded ourselves and for low-quality footage taken from the
Internet. | ['Thorsten Thormaehlen', 'Pablo Garrido', 'Levi Valgaerts', 'Christian Theobalt', 'Patrick Perez', 'Ole Rehmsen'] | 2016-02-08 | automatic-face-reenactment-1 | http://openaccess.thecvf.com/content_cvpr_2014/html/Garrido_Automatic_Face_Reenactment_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Garrido_Automatic_Face_Reenactment_2014_CVPR_paper.pdf | cvpr-2014-6 | ['face-reenactment', 'face-transfer'] | ['computer-vision', 'computer-vision'] | [ 3.80287230e-01 -1.66393183e-02 7.58156851e-02 -5.91507673e-01
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be172ccc-aec9-4f6d-9acf-616b5221c43e | emotion-cause-pair-extraction-a-new-task-to | 1906.01267 | null | https://arxiv.org/abs/1906.01267v1 | https://arxiv.org/pdf/1906.01267v1.pdf | Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts | Emotion cause extraction (ECE), the task aimed at extracting the potential causes behind certain emotions in text, has gained much attention in recent years due to its wide applications. However, it suffers from two shortcomings: 1) the emotion must be annotated before cause extraction in ECE, which greatly limits its applications in real-world scenarios; 2) the way to first annotate emotion and then extract the cause ignores the fact that they are mutually indicative. In this work, we propose a new task: emotion-cause pair extraction (ECPE), which aims to extract the potential pairs of emotions and corresponding causes in a document. We propose a 2-step approach to address this new ECPE task, which first performs individual emotion extraction and cause extraction via multi-task learning, and then conduct emotion-cause pairing and filtering. The experimental results on a benchmark emotion cause corpus prove the feasibility of the ECPE task as well as the effectiveness of our approach. | ['Zixiang Ding', 'Rui Xia'] | 2019-06-04 | emotion-cause-pair-extraction-a-new-task-to-1 | https://aclanthology.org/P19-1096 | https://aclanthology.org/P19-1096.pdf | acl-2019-7 | ['emotion-cause-pair-extraction', 'emotion-cause-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.90627444e-01 -2.25209996e-01 3.93886724e-03 -3.79849613e-01
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957efe2e-d97d-4976-8eef-4fdb862d62b3 | a-comprehensive-and-large-scale-dataset-for | null | null | https://openreview.net/forum?id=TnX3iwX_6Iu | https://openreview.net/pdf?id=TnX3iwX_6Iu | A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks | Traditionally, a debate usually requires a manual preparation process, including reading plenty of articles, selecting the claims, identifying the stances of the claims, seeking the evidences for the claims, etc. As the AI debate attracts more attention these years, it is worth exploring the methods to automate the tedious process involved in the debating system. In this work, we introduce a comprehensive and large dataset, which can be applied to a series of argument mining tasks, including claim extraction, stance classification, evidence extraction, etc. Our dataset is collected from over 1k articles related to 123 topics. Near 70k sentences in the dataset are fully annotated based on their argument properties (e.g., claims, stances, evidences, etc.). We further propose two new integrated argument mining tasks associated with the debate preparation process: (1) claim extraction with stance classification (CESC) and (2) claim-evidence pair extraction (CEPE). We adopt a pipeline approach and an end-to-end method for each integrated task separately. Promising experimental results are reported to show the values and challenges of our proposed tasks, and motivate future research on argument mining. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['claim-extraction-with-stance-classification', 'claim-evidence-pair-extraction-cepe'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.05443633e-01 3.65969151e-01 -5.91060638e-01 -3.66934776e-01
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a335c7d1-00b9-49e9-a373-66599c762484 | genesis-v2-inferring-unordered-object | 2104.09958 | null | https://arxiv.org/abs/2104.09958v3 | https://arxiv.org/pdf/2104.09958v3.pdf | GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinement | Advances in unsupervised learning of object-representations have culminated in the development of a broad range of methods for unsupervised object segmentation and interpretable object-centric scene generation. These methods, however, are limited to simulated and real-world datasets with limited visual complexity. Moreover, object representations are often inferred using RNNs which do not scale well to large images or iterative refinement which avoids imposing an unnatural ordering on objects in an image but requires the a priori initialisation of a fixed number of object representations. In contrast to established paradigms, this work proposes an embedding-based approach in which embeddings of pixels are clustered in a differentiable fashion using a stochastic stick-breaking process. Similar to iterative refinement, this clustering procedure also leads to randomly ordered object representations, but without the need of initialising a fixed number of clusters a priori. This is used to develop a new model, GENESIS-v2, which can infer a variable number of object representations without using RNNs or iterative refinement. We show that GENESIS-v2 performs strongly in comparison to recent baselines in terms of unsupervised image segmentation and object-centric scene generation on established synthetic datasets as well as more complex real-world datasets. | ['Ingmar Posner', 'Oiwi Parker Jones', 'Martin Engelcke'] | 2021-04-20 | null | http://proceedings.neurips.cc/paper/2021/hash/43ec517d68b6edd3015b3edc9a11367b-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/43ec517d68b6edd3015b3edc9a11367b-Paper.pdf | neurips-2021-12 | ['scene-generation', 'unsupervised-object-segmentation'] | ['computer-vision', 'computer-vision'] | [ 6.29321158e-01 4.57573593e-01 2.65978545e-01 -4.20058459e-01
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c270aa83-3b90-4743-b564-7449376ad340 | image-question-answering-using-convolutional | 1511.05756 | null | http://arxiv.org/abs/1511.05756v1 | http://arxiv.org/pdf/1511.05756v1.pdf | Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction | We tackle image question answering (ImageQA) problem by learning a
convolutional neural network (CNN) with a dynamic parameter layer whose weights
are determined adaptively based on questions. For the adaptive parameter
prediction, we employ a separate parameter prediction network, which consists
of gated recurrent unit (GRU) taking a question as its input and a
fully-connected layer generating a set of candidate weights as its output.
However, it is challenging to construct a parameter prediction network for a
large number of parameters in the fully-connected dynamic parameter layer of
the CNN. We reduce the complexity of this problem by incorporating a hashing
technique, where the candidate weights given by the parameter prediction
network are selected using a predefined hash function to determine individual
weights in the dynamic parameter layer. The proposed network---joint network
with the CNN for ImageQA and the parameter prediction network---is trained
end-to-end through back-propagation, where its weights are initialized using a
pre-trained CNN and GRU. The proposed algorithm illustrates the
state-of-the-art performance on all available public ImageQA benchmarks. | ['Hyeonwoo Noh', 'Bohyung Han', 'Paul Hongsuck Seo'] | 2015-11-18 | image-question-answering-using-convolutional-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Noh_Image_Question_Answering_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Noh_Image_Question_Answering_CVPR_2016_paper.pdf | cvpr-2016-6 | ['multi-modal', 'parameter-prediction'] | ['miscellaneous', 'miscellaneous'] | [ 3.12868923e-01 9.87155586e-02 1.76135883e-01 -6.85266376e-01
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6c933f00-3d3b-4257-a078-125693e2927d | temporal-pattern-mining-for-analysis-of | 2209.04793 | null | https://arxiv.org/abs/2209.04793v1 | https://arxiv.org/pdf/2209.04793v1.pdf | Temporal Pattern Mining for Analysis of Longitudinal Clinical Data: Identifying Risk Factors for Alzheimer's Disease | A novel framework is proposed for handling the complex task of modelling and analysis of longitudinal, multivariate, heterogeneous clinical data. This method uses temporal abstraction to convert the data into a more appropriate form for modelling, temporal pattern mining, to discover patterns in the complex, longitudinal data and machine learning models of survival analysis to select the discovered patterns. The method is applied to a real-world study of Alzheimer's disease (AD), a progressive neurodegenerative disease that has no cure. The patterns discovered were predictive of AD in survival analysis models with a Concordance index of up to 0.8. This is the first work that performs survival analysis of AD data using temporal data collections for AD. A visualisation module also provides a clear picture of the discovered patterns for ease of interpretability. | ['Arcot Sowmya', 'Henry Brodaty', 'Perminder S. Sachdev', 'Gelareh Mohammadi', 'Annette Spooner'] | 2022-09-11 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [ 6.76537603e-02 -7.30621442e-02 -2.49514878e-01 -4.94027793e-01
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11f3bdfe-bea2-4ae1-853b-65f25cdab2bf | learning-compatibility-across-categories-for | 1603.09473 | null | http://arxiv.org/abs/1603.09473v3 | http://arxiv.org/pdf/1603.09473v3.pdf | Learning Compatibility Across Categories for Heterogeneous Item Recommendation | Identifying relationships between items is a key task of an online
recommender system, in order to help users discover items that are functionally
complementary or visually compatible. In domains like clothing recommendation,
this task is particularly challenging since a successful system should be
capable of handling a large corpus of items, a huge amount of relationships
among them, as well as the high-dimensional and semantically complicated
features involved. Furthermore, the human notion of "compatibility" to capture
goes beyond mere similarity: For two items to be compatible---whether jeans and
a t-shirt, or a laptop and a charger---they should be similar in some ways, but
systematically different in others.
In this paper we propose a novel method, Monomer, to learn complicated and
heterogeneous relationships between items in product recommendation settings.
Recently, scalable methods have been developed that address this task by
learning similarity metrics on top of the content of the products involved.
Here our method relaxes the metricity assumption inherent in previous work and
models multiple localized notions of 'relatedness,' so as to uncover ways in
which related items should be systematically similar, and systematically
different. Quantitatively, we show that our system achieves state-of-the-art
performance on large-scale compatibility prediction tasks, especially in cases
where there is substantial heterogeneity between related items. Qualitatively,
we demonstrate that richer notions of compatibility can be learned that go
beyond similarity, and that our model can make effective recommendations of
heterogeneous content. | ['Ruining He', 'Julian McAuley', 'Charles Packer'] | 2016-03-31 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 5.56925870e-02 -4.13977802e-01 -2.49560416e-01 -4.93484855e-01
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e8f81668-b53a-4139-a6de-cc828aa6c0f8 | local-frequency-domain-transformer-networks | 2105.04637 | null | https://arxiv.org/abs/2105.04637v1 | https://arxiv.org/pdf/2105.04637v1.pdf | Local Frequency Domain Transformer Networks for Video Prediction | Video prediction is commonly referred to as forecasting future frames of a video sequence provided several past frames thereof. It remains a challenging domain as visual scenes evolve according to complex underlying dynamics, such as the camera's egocentric motion or the distinct motility per individual object viewed. These are mostly hidden from the observer and manifest as often highly non-linear transformations between consecutive video frames. Therefore, video prediction is of interest not only in anticipating visual changes in the real world but has, above all, emerged as an unsupervised learning rule targeting the formation and dynamics of the observed environment. Many of the deep learning-based state-of-the-art models for video prediction utilize some form of recurrent layers like Long Short-Term Memory (LSTMs) or Gated Recurrent Units (GRUs) at the core of their models. Although these models can predict the future frames, they rely entirely on these recurrent structures to simultaneously perform three distinct tasks: extracting transformations, projecting them into the future, and transforming the current frame. In order to completely interpret the formed internal representations, it is crucial to disentangle these tasks. This paper proposes a fully differentiable building block that can perform all of those tasks separately while maintaining interpretability. We derive the relevant theoretical foundations and showcase results on synthetic as well as real data. We demonstrate that our method is readily extended to perform motion segmentation and account for the scene's composition, and learns to produce reliable predictions in an entirely interpretable manner by only observing unlabeled video data. | ['Sven Behnke', 'Jan Nogga', 'Hafez Farazi'] | 2021-05-10 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [ 4.17283654e-01 1.57707259e-01 -1.46104246e-01 -2.68312156e-01
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b7c93619-8996-49ee-a51e-6b49ac095a8f | person-search-challenges-and-solutions-a | 2105.01605 | null | https://arxiv.org/abs/2105.01605v1 | https://arxiv.org/pdf/2105.01605v1.pdf | Person Search Challenges and Solutions: A Survey | Person search has drawn increasing attention due to its real-world applications and research significance. Person search aims to find a probe person in a gallery of scene images with a wide range of applications, such as criminals search, multicamera tracking, missing person search, etc. Early person search works focused on image-based person search, which uses person image as the search query. Text-based person search is another major person search category that uses free-form natural language as the search query. Person search is challenging, and corresponding solutions are diverse and complex. Therefore, systematic surveys on this topic are essential. This paper surveyed the recent works on image-based and text-based person search from the perspective of challenges and solutions. Specifically, we provide a brief analysis of highly influential person search methods considering the three significant challenges: the discriminative person features, the query-person gap, and the detection-identification inconsistency. We summarise and compare evaluation results. Finally, we discuss open issues and some promising future research directions. | ['Alex Hauptmann', 'Xiaojun Chang', 'Yun Xiao', 'Pengzhen Ren', 'Xiangtan Lin'] | 2021-05-01 | null | null | null | null | ['person-search'] | ['computer-vision'] | [ 4.46449667e-02 -8.04924130e-01 -2.38936961e-01 -2.83753812e-01
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751ae346-93b9-42db-8350-7c4c453888cd | twitter-spam-detection-a-systematic-review | 2011.14754 | null | https://arxiv.org/abs/2011.14754v2 | https://arxiv.org/pdf/2011.14754v2.pdf | Twitter Spam Detection: A Systematic Review | Nowadays, with the rise of Internet access and mobile devices around the globe, more people are using social networks for collaboration and receiving real-time information. Twitter, the microblogging that is becoming a critical source of communication and news propagation, has grabbed the attention of spammers to distract users. So far, researchers have introduced various defense techniques to detect spams and combat spammer activities on Twitter. To overcome this problem, in recent years, many novel techniques have been offered by researchers, which have greatly enhanced the spam detection performance. Therefore, it raises a motivation to conduct a systematic review about different approaches of spam detection on Twitter. This review focuses on comparing the existing research techniques on Twitter spam detection systematically. Literature review analysis reveals that most of the existing methods rely on Machine Learning-based algorithms. Among these Machine Learning algorithms, the major differences are related to various feature selection methods. Hence, we propose a taxonomy based on different feature selection methods and analyses, namely content analysis, user analysis, tweet analysis, network analysis, and hybrid analysis. Then, we present numerical analyses and comparative studies on current approaches, coming up with open challenges that help researchers develop solutions in this topic. | ['Ebrahim Mahdipour', 'Mohammad Akbari', 'Mostafa Haghi Kashani', 'Sepideh Bazzaz Abkenar'] | 2020-11-30 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [-1.48079082e-01 -5.59889734e-01 -5.27167499e-01 1.74505889e-01
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0993a9ac-b4bf-4046-9185-4f494d15c5ca | jentab-meets-semtab-2021-s-new-challenges | null | null | https://www.semanticscholar.org/paper/JenTab-Meets-SemTab-2021's-New-Challenges-Abdelmageed-Schindler/4f492fee6a7ae51d3f2527d9036a1beaf6f1e44b | http://ceur-ws.org/Vol-3103/paper4.pdf | JenTab Meets SemTab 2021's New Challenges | While tables are a rich source of structured information, their automated use is oftentimes prevented by the inherent ambiguity contained within. Issues ranging from mere typos over inconsistent naming conventions to homonymy among values pose substantial barriers to exploiting this source of knowledge. Although the Semantic Web can alleviate many of these issues, the actual annotation process remains challenging. To foster new ideas and the improvement of existing approaches, the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab) since 2019 hosts yearly competitions allowing systems to present their current capabilities. Datasets of different origins and characteristics highlight the various challenges present in this area. In this paper, we report on the evolution of our system, “JenTab”, during SemTab2021. We re-designed the system architecture, optimized individual modules, and developed various pipelines to target specific challenges posed throughout the challenge. JenTab is among the top 5 systems in the first two rounds of SemTab2021. The results demonstrate JenTab’s flexibility and its ability to quickly address new challenges. | ['Sirko Schindler', 'Nora Abdelmageed'] | 2021-10-01 | null | null | null | semtab-iswc-2021-10 | ['graph-matching', 'table-annotation', 'table-annotation', 'column-type-annotation', 'cell-entity-annotation'] | ['graphs', 'knowledge-base', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-1.19772665e-01 2.88502932e-01 -2.95007795e-01 -3.51227820e-01
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dc895f3e-6001-4a6e-968c-a0ee421534d5 | aligntransformer-hierarchical-alignment-of | 2203.10095 | null | https://arxiv.org/abs/2203.10095v1 | https://arxiv.org/pdf/2203.10095v1.pdf | AlignTransformer: Hierarchical Alignment of Visual Regions and Disease Tags for Medical Report Generation | Recently, medical report generation, which aims to automatically generate a long and coherent descriptive paragraph of a given medical image, has received growing research interests. Different from the general image captioning tasks, medical report generation is more challenging for data-driven neural models. This is mainly due to 1) the serious data bias: the normal visual regions dominate the dataset over the abnormal visual regions, and 2) the very long sequence. To alleviate above two problems, we propose an AlignTransformer framework, which includes the Align Hierarchical Attention (AHA) and the Multi-Grained Transformer (MGT) modules: 1) AHA module first predicts the disease tags from the input image and then learns the multi-grained visual features by hierarchically aligning the visual regions and disease tags. The acquired disease-grounded visual features can better represent the abnormal regions of the input image, which could alleviate data bias problem; 2) MGT module effectively uses the multi-grained features and Transformer framework to generate the long medical report. The experiments on the public IU-Xray and MIMIC-CXR datasets show that the AlignTransformer can achieve results competitive with state-of-the-art methods on the two datasets. Moreover, the human evaluation conducted by professional radiologists further proves the effectiveness of our approach. | ['Xian Wu', 'Jing Zhang', 'Xiaoxia Xie', 'Shen Ge', 'Fenglin Liu', 'Di You'] | 2022-03-18 | null | null | null | null | ['medical-report-generation'] | ['medical'] | [ 4.02859569e-01 3.12392086e-01 -1.41482100e-01 -3.35356414e-01
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54123187-8a80-492c-85a2-2a1992ce789a | do-multi-hop-question-answering-systems-know | 2002.09919 | null | https://arxiv.org/abs/2002.09919v2 | https://arxiv.org/pdf/2002.09919v2.pdf | Do Multi-Hop Question Answering Systems Know How to Answer the Single-Hop Sub-Questions? | Multi-hop question answering (QA) requires a model to retrieve and integrate information from different parts of a long text to answer a question. Humans answer this kind of complex questions via a divide-and-conquer approach. In this paper, we investigate whether top-performing models for multi-hop questions understand the underlying sub-questions like humans. We adopt a neural decomposition model to generate sub-questions for a multi-hop complex question, followed by extracting the corresponding sub-answers. We show that multiple state-of-the-art multi-hop QA models fail to correctly answer a large portion of sub-questions, although their corresponding multi-hop questions are correctly answered. This indicates that these models manage to answer the multi-hop questions using some partial clues, instead of truly understanding the reasoning paths. We also propose a new model which significantly improves the performance on answering the sub-questions. Our work takes a step forward towards building a more explainable multi-hop QA system. | ['Hwee Tou Ng', 'Yixuan Tang', 'Anthony K. H. Tung'] | 2020-02-23 | null | https://aclanthology.org/2021.eacl-main.283 | https://aclanthology.org/2021.eacl-main.283.pdf | eacl-2021-2 | ['multi-hop-question-answering'] | ['knowledge-base'] | [-3.11267911e-04 8.96825671e-01 2.16082647e-01 -5.43411016e-01
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82018524-de2d-4c44-be17-9fce7b49e546 | chestx-ray8-hospital-scale-chest-x-ray | 1705.02315 | null | http://arxiv.org/abs/1705.02315v5 | http://arxiv.org/pdf/1705.02315v5.pdf | ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases | The chest X-ray is one of the most commonly accessible radiological
examinations for screening and diagnosis of many lung diseases. A tremendous
number of X-ray imaging studies accompanied by radiological reports are
accumulated and stored in many modern hospitals' Picture Archiving and
Communication Systems (PACS). On the other side, it is still an open question
how this type of hospital-size knowledge database containing invaluable imaging
informatics (i.e., loosely labeled) can be used to facilitate the data-hungry
deep learning paradigms in building truly large-scale high precision
computer-aided diagnosis (CAD) systems.
In this paper, we present a new chest X-ray database, namely "ChestX-ray8",
which comprises 108,948 frontal-view X-ray images of 32,717 unique patients
with the text-mined eight disease image labels (where each image can have
multi-labels), from the associated radiological reports using natural language
processing. Importantly, we demonstrate that these commonly occurring thoracic
diseases can be detected and even spatially-located via a unified
weakly-supervised multi-label image classification and disease localization
framework, which is validated using our proposed dataset. Although the initial
quantitative results are promising as reported, deep convolutional neural
network based "reading chest X-rays" (i.e., recognizing and locating the common
disease patterns trained with only image-level labels) remains a strenuous task
for fully-automated high precision CAD systems. Data download link:
https://nihcc.app.box.com/v/ChestXray-NIHCC | ['Mohammadhadi Bagheri', 'Xiaosong Wang', 'Le Lu', 'Yifan Peng', 'Ronald M. Summers', 'Zhiyong Lu'] | 2017-05-05 | chestx-ray8-hospital-scale-chest-x-ray-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Wang_ChestX-ray8_Hospital-Scale_Chest_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Wang_ChestX-ray8_Hospital-Scale_Chest_CVPR_2017_paper.pdf | cvpr-2017-7 | ['multi-label-image-classification', 'lung-disease-classification'] | ['computer-vision', 'medical'] | [ 1.93995774e-01 -4.47348803e-02 -4.44454670e-01 -5.26028335e-01
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546a01ca-2fbd-4c76-bc2a-bbde5e73cec3 | reinforcement-learning | 2005.14419 | null | https://arxiv.org/abs/2005.14419v2 | https://arxiv.org/pdf/2005.14419v2.pdf | Reinforcement Learning | Reinforcement learning (RL) is a general framework for adaptive control, which has proven to be efficient in many domains, e.g., board games, video games or autonomous vehicles. In such problems, an agent faces a sequential decision-making problem where, at every time step, it observes its state, performs an action, receives a reward and moves to a new state. An RL agent learns by trial and error a good policy (or controller) based on observations and numeric reward feedback on the previously performed action. In this chapter, we present the basic framework of RL and recall the two main families of approaches that have been developed to learn a good policy. The first one, which is value-based, consists in estimating the value of an optimal policy, value from which a policy can be recovered, while the other, called policy search, directly works in a policy space. Actor-critic methods can be seen as a policy search technique where the policy value that is learned guides the policy improvement. Besides, we give an overview of some extensions of the standard RL framework, notably when risk-averse behavior needs to be taken into account or when rewards are not available or not known. | ['Olivier Buffet', 'Paul Weng', 'Olivier Pietquin'] | 2020-05-29 | null | null | null | null | ['board-games'] | ['playing-games'] | [ 1.31315172e-01 3.59738052e-01 -8.11640680e-01 1.23405538e-01
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28a2dae4-c332-47ed-a6d2-dc8ad3207b7a | clickbait-detection-using-word-embeddings | 1710.02861 | null | http://arxiv.org/abs/1710.02861v1 | http://arxiv.org/pdf/1710.02861v1.pdf | Clickbait detection using word embeddings | Clickbait is a pejorative term describing web content that is aimed at
generating online advertising revenue, especially at the expense of quality or
accuracy, relying on sensationalist headlines or eye-catching thumbnail
pictures to attract click-throughs and to encourage forwarding of the material
over online social networks. We use distributed word representations of the
words in the title as features to identify clickbaits in online news media. We
train a machine learning model using linear regression to predict the cickbait
score of a given tweet. Our methods achieve an F1-score of 64.98\% and an MSE
of 0.0791. Compared to other methods, our method is simple, fast to train, does
not require extensive feature engineering and yet moderately effective. | ['Vijayasaradhi Indurthi', 'Subba Reddy Oota'] | 2017-10-08 | null | null | null | null | ['clickbait-detection'] | ['natural-language-processing'] | [-2.99427301e-01 -5.35656102e-02 -8.50564361e-01 -4.09833014e-01
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bcdacc24-0cbf-4617-9b51-183831a7e6ea | native-language-identification-using-stacked | 1703.06541 | null | http://arxiv.org/abs/1703.06541v1 | http://arxiv.org/pdf/1703.06541v1.pdf | Native Language Identification using Stacked Generalization | Ensemble methods using multiple classifiers have proven to be the most
successful approach for the task of Native Language Identification (NLI),
achieving the current state of the art. However, a systematic examination of
ensemble methods for NLI has yet to be conducted. Additionally, deeper ensemble
architectures such as classifier stacking have not been closely evaluated. We
present a set of experiments using three ensemble-based models, testing each
with multiple configurations and algorithms. This includes a rigorous
application of meta-classification models for NLI, achieving state-of-the-art
results on three datasets from different languages. We also present the first
use of statistical significance testing for comparing NLI systems, showing that
our results are significantly better than the previous state of the art. We
make available a collection of test set predictions to facilitate future
statistical tests. | ['Mark Dras', 'Shervin Malmasi'] | 2017-03-19 | null | null | null | null | ['native-language-identification'] | ['natural-language-processing'] | [ 3.27798575e-01 -4.99664038e-01 -2.70263076e-01 -6.10661268e-01
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c4b845b9-0d0b-48be-a4ec-33f4b18817bc | self-supervised-occupancy-grid-learning-from | 1904.00415 | null | https://arxiv.org/abs/1904.00415v2 | https://arxiv.org/pdf/1904.00415v2.pdf | Road Scene Understanding by Occupancy Grid Learning from Sparse Radar Clusters using Semantic Segmentation | Occupancy grid mapping is an important component in road scene understanding for autonomous driving. It encapsulates information of the drivable area, road obstacles and enables safe autonomous driving. Radars are an emerging sensor in autonomous vehicle vision, becoming more widely used due to their long range sensing, low cost, and robustness to severe weather conditions. Despite recent advances in deep learning technology, occupancy grid mapping from radar data is still mostly done using classical filtering approaches.In this work, we propose learning the inverse sensor model used for occupancy grid mapping from clustered radar data. This is done in a data driven approach that leverages computer vision techniques. This task is very challenging due to data sparsity and noise characteristics of the radar sensor. The problem is formulated as a semantic segmentation task and we show how it can be learned using lidar data for generating ground truth. We show both qualitatively and quantitatively that our learned occupancy net outperforms classic methods by a large margin using the recently released NuScenes real-world driving data. | ['Gilad Cohen', 'Shaul Oron', 'Liat Sless', 'Bat El Shlomo'] | 2019-03-31 | null | null | null | null | ['road-scene-understanding'] | ['computer-vision'] | [ 5.42667210e-01 2.15063188e-02 -1.59997240e-01 -6.96778119e-01
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b20bd91a-aea6-46dc-b1bb-63a4b2afb7d0 | graph-fairing-convolutional-networks-for | 2010.10274 | null | https://arxiv.org/abs/2010.10274v1 | https://arxiv.org/pdf/2010.10274v1.pdf | Graph Fairing Convolutional Networks for Anomaly Detection | Graph convolution is a fundamental building block for many deep neural networks on graph-structured data. In this paper, we introduce a simple, yet very effective graph convolutional network with skip connections for semi-supervised anomaly detection. The proposed multi-layer network architecture is theoretically motivated by the concept of implicit fairing in geometry processing, and comprises a graph convolution module for aggregating information from immediate node neighbors and a skip connection module for combining layer-wise neighborhood representations. In addition to capturing information from distant graph nodes through skip connections between the network's layers, our approach exploits both the graph structure and node features for learning discriminative node representations. The effectiveness of our model is demonstrated through extensive experiments on five benchmark datasets, achieving better or comparable anomaly detection results against strong baseline methods. | ['A. Ben Hamza', 'Mahsa Mesgaran'] | 2020-10-20 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [-2.76908487e-01 4.79686797e-01 2.73858034e-03 -5.36183178e-01
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13ebc249-5f1b-4dd6-804d-1e41d6648518 | progressive-hint-prompting-improves-reasoning | 2304.09797 | null | https://arxiv.org/abs/2304.09797v4 | https://arxiv.org/pdf/2304.09797v4.pdf | Progressive-Hint Prompting Improves Reasoning in Large Language Models | The performance of Large Language Models (LLMs) in reasoning tasks depends heavily on prompt design, with Chain-of-Thought (CoT) and self-consistency being critical methods that enhance this ability. However, these methods do not fully exploit the answers generated by the LLM to guide subsequent responses. This paper proposes a new prompting method, named Progressive-Hint Prompting (PHP), that enables automatic multiple interactions between users and LLMs by using previously generated answers as hints to progressively guide toward the correct answers. PHP is orthogonal to CoT and self-consistency, making it easy to combine with state-of-the-art techniques to further improve performance. We conducted extensive and comprehensive experiments on seven benchmarks. The results show that PHP significantly improves accuracy while remaining highly efficient. For instance, with text-davinci-003, we observed a 4.2% improvement on GSM8K with greedy decoding compared to Complex CoT, and a 46.17% reduction in sample paths with self-consistency. With GPT-4 and PHP, we achieve state-of-the-art performances on SVAMP (89.1% -> 91.9%), GSM8K (92% -> 95.5%), AQuA (76.4% -> 79.9%) and MATH (50.3% -> 53.9%). | ['Yu Li', 'Zhenguo Li', 'Enze Xie', 'Zhengying Liu', 'Chuanyang Zheng'] | 2023-04-19 | https-arxiv-org-abs-2304-09797 | https://arxiv.org/abs/2304.09797 | https://arxiv.org/pdf/2304.09797 | null | ['math-word-problem-solving', 'gsm8k', 'arithmetic-reasoning', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'natural-language-processing', 'reasoning', 'reasoning', 'time-series'] | [-3.07857454e-01 7.47094080e-02 -8.69417787e-02 -4.86152828e-01
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8bee9891-7550-4feb-a647-95deac13edd9 | a-study-on-angular-based-embedding-learning | 1908.0399 | null | https://arxiv.org/abs/1908.03990v1 | https://arxiv.org/pdf/1908.03990v1.pdf | A Study on Angular Based Embedding Learning for Text-independent Speaker Verification | Learning a good speaker embedding is important for many automatic speaker recognition tasks, including verification, identification and diarization. The embeddings learned by softmax are not discriminative enough for open-set verification tasks. Angular based embedding learning target can achieve such discriminativeness by optimizing angular distance and adding margin penalty. We apply several different popular angular margin embedding learning strategies in this work and explicitly compare their performance on Voxceleb speaker recognition dataset. Observing the fact that encouraging inter-class separability is important when applying angular based embedding learning, we propose an exclusive inter-class regularization as a complement for angular based loss. We verify the effectiveness of these methods for learning a discriminative embedding space on ASV task with several experiments. These methods together, we manage to achieve an impressive result with 16.5% improvement on equal error rate (EER) and 18.2% improvement on minimum detection cost function comparing with baseline softmax systems. | ['Shugong Xu', 'Zongze Ren', 'Zhiyong Chen'] | 2019-08-12 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [-8.16960260e-02 3.46720368e-01 -3.81481051e-01 -8.32934082e-01
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e57c94a7-05bd-4fab-8767-4690a6694165 | margin-aware-unsupervised-domain-adaptation | null | null | https://aclanthology.org/2020.findings-emnlp.315 | https://aclanthology.org/2020.findings-emnlp.315.pdf | Margin-aware Unsupervised Domain Adaptation for Cross-lingual Text Labeling | Unsupervised domain adaptation addresses the problem of leveraging labeled data in a source domain to learn a well-performing model in a target domain where labels are unavailable. In this paper, we improve upon a recent theoretical work (Zhang et al., 2019b) and adopt the Margin Disparity Discrepancy (MDD) unsupervised domain adaptation algorithm to solve the cross-lingual text labeling problems. Experiments on cross-lingual document classification and NER demonstrate the proposed domain adaptation approach advances the state-of-the-art results by a large margin. Specifically, we improve MDD by efficiently optimizing the margin loss on the source domain via Virtual Adversarial Training (VAT). This bridges the gap between theory and the loss function used in the original work Zhang et al.(2019b), and thereby significantly boosts the performance. Our numerical results also indicate that VAT can remarkably improve the generalization performance of both domains for various domain adaptation approaches. | ['Bing Xiang', 'Kathleen McKeown', 'Cicero Nogueira dos santos', 'Feng Nan', 'Henghui Zhu', 'Ramesh Nallapati', 'Dejiao Zhang'] | 2020-11-01 | null | null | null | findings-of-the-association-for-computational | ['cross-lingual-document-classification'] | ['natural-language-processing'] | [ 1.75053552e-01 2.96432190e-02 -6.19624078e-01 -5.55230141e-01
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3c84c357-ba99-4fa4-be60-2c67b7f95723 | meta-review-generation-with-checklist-guided | 2305.14647 | null | https://arxiv.org/abs/2305.14647v1 | https://arxiv.org/pdf/2305.14647v1.pdf | Meta-review Generation with Checklist-guided Iterative Introspection | Opinions in the scientific domain can be divergent, leading to controversy or consensus among reviewers. However, current opinion summarization datasets mostly focus on product review domains, which do not account for this variability under the assumption that the input opinions are non-controversial. To address this gap, we propose the task of scientific opinion summarization, where research paper reviews are synthesized into meta-reviews. To facilitate this task, we introduce a new ORSUM dataset covering 10,989 paper meta-reviews and 40,903 paper reviews from 39 conferences. Furthermore, we propose the Checklist-guided Iterative Introspection (CGI$^2$) approach, which breaks down the task into several stages and iteratively refines the summary under the guidance of questions from a checklist. We conclude that (1) human-written summaries are not always reliable since many do not follow the guideline, and (2) the combination of task decomposition and iterative self-refinement shows promising discussion involvement ability and can be applied to other complex text generation using black-box LLM. | ['Heng Ji', 'Lu Wang', 'Hou Pong Chan', 'Mankeerat Sidhu', 'Qi Zeng'] | 2023-05-24 | null | null | null | null | ['review-generation'] | ['natural-language-processing'] | [ 4.02337313e-01 6.17888808e-01 -5.73779345e-01 -1.38197139e-01
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b49bc6f3-cf63-4d35-aae6-fc799d0f8bec | deep-markov-spatio-temporal-factorization | 2003.09779 | null | https://arxiv.org/abs/2003.09779v2 | https://arxiv.org/pdf/2003.09779v2.pdf | Deep Markov Spatio-Temporal Factorization | We introduce deep Markov spatio-temporal factorization (DMSTF), a generative model for dynamical analysis of spatio-temporal data. Like other factor analysis methods, DMSTF approximates high dimensional data by a product between time dependent weights and spatially dependent factors. These weights and factors are in turn represented in terms of lower dimensional latents inferred using stochastic variational inference. The innovation in DMSTF is that we parameterize weights in terms of a deep Markovian prior extendable with a discrete latent, which is able to characterize nonlinear multimodal temporal dynamics, and perform multidimensional time series forecasting. DMSTF learns a low dimensional spatial latent to generatively parameterize spatial factors or their functional forms in order to accommodate high spatial dimensionality. We parameterize the corresponding variational distribution using a bidirectional recurrent network in the low-level latent representations. This results in a flexible family of hierarchical deep generative factor analysis models that can be extended to perform time series clustering or perform factor analysis in the presence of a control signal. Our experiments, which include simulated and real-world data, demonstrate that DMSTF outperforms related methodologies in terms of predictive performance for unseen data, reveals meaningful clusters in the data, and performs forecasting in a variety of domains with potentially nonlinear temporal transitions. | ['J. Benjamin Hutchinson', 'Jan-Willem van de Meent', 'Eli Zachary Sennesh', 'Amirreza Farnoosh', 'Jennifer Dy', 'Behnaz Rezaei', 'Ajay Satpute', 'Zulqarnain Khan', 'Sarah Ostadabbas'] | 2020-03-22 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [-3.07550937e-01 -2.12260067e-01 -8.85684192e-02 -1.02433950e-01
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65f20a09-8fbf-41bc-81ab-94bcf9dc19ca | going-beyond-research-datasets-novel-intent | 2305.05474 | null | https://arxiv.org/abs/2305.05474v1 | https://arxiv.org/pdf/2305.05474v1.pdf | Going beyond research datasets: Novel intent discovery in the industry setting | Novel intent discovery automates the process of grouping similar messages (questions) to identify previously unknown intents. However, current research focuses on publicly available datasets which have only the question field and significantly differ from real-life datasets. This paper proposes methods to improve the intent discovery pipeline deployed in a large e-commerce platform. We show the benefit of pre-training language models on in-domain data: both self-supervised and with weak supervision. We also devise the best method to utilize the conversational structure (i.e., question and answer) of real-life datasets during fine-tuning for clustering tasks, which we call Conv. All our methods combined to fully utilize real-life datasets give up to 33pp performance boost over state-of-the-art Constrained Deep Adaptive Clustering (CDAC) model for question only. By comparison CDAC model for the question data only gives only up to 13pp performance boost over the naive baseline. | ['Piotr Rybak', 'Robert Mroczkowski', 'Dariusz Kajtoch', 'Tsimur Hadeliya', 'Aleksandra Chrabrowa'] | 2023-05-09 | null | null | null | null | ['intent-discovery'] | ['natural-language-processing'] | [-7.80233666e-02 2.17483714e-02 4.82829101e-02 -9.23935890e-01
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5eba7a83-f3c5-4494-8e82-018c15dd075b | person-search-via-a-mask-guided-two-stream | 1807.08107 | null | http://arxiv.org/abs/1807.08107v1 | http://arxiv.org/pdf/1807.08107v1.pdf | Person Search via A Mask-Guided Two-Stream CNN Model | In this work, we tackle the problem of person search, which is a challenging
task consisted of pedestrian detection and person re-identification~(re-ID).
Instead of sharing representations in a single joint model, we find that
separating detector and re-ID feature extraction yields better performance. In
order to extract more representative features for each identity, we segment out
the foreground person from the original image patch. We propose a simple yet
effective re-ID method, which models foreground person and original image
patches individually, and obtains enriched representations from two separate
CNN streams. From the experiments on two standard person search benchmarks of
CUHK-SYSU and PRW, we achieve mAP of $83.0\%$ and $32.6\%$ respectively,
surpassing the state of the art by a large margin (more than 5pp). | ['Wanli Ouyang', 'Shanshan Zhang', 'Jian Yang', 'Di Chen', 'Ying Tai'] | 2018-07-21 | person-search-via-a-mask-guided-two-stream-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Di_Chen_Person_Search_via_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Di_Chen_Person_Search_via_ECCV_2018_paper.pdf | eccv-2018-9 | ['person-search'] | ['computer-vision'] | [ 4.93664257e-02 -3.94844800e-01 1.75726175e-01 -3.37800056e-01
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85f5e8d1-93b3-4dba-912b-3a925a184625 | information-based-disentangled-representation | 2103.13283 | null | https://arxiv.org/abs/2103.13283v1 | https://arxiv.org/pdf/2103.13283v1.pdf | Information-based Disentangled Representation Learning for Unsupervised MR Harmonization | Accuracy and consistency are two key factors in computer-assisted magnetic resonance (MR) image analysis. However, contrast variation from site to site caused by lack of standardization in MR acquisition impedes consistent measurements. In recent years, image harmonization approaches have been proposed to compensate for contrast variation in MR images. Current harmonization approaches either require cross-site traveling subjects for supervised training or heavily rely on site-specific harmonization models to encourage harmonization accuracy. These requirements potentially limit the application of current harmonization methods in large-scale multi-site studies. In this work, we propose an unsupervised MR harmonization framework, CALAMITI (Contrast Anatomy Learning and Analysis for MR Intensity Translation and Integration), based on information bottleneck theory. CALAMITI learns a disentangled latent space using a unified structure for multi-site harmonization without the need for traveling subjects. Our model is also able to adapt itself to harmonize MR images from a new site with fine tuning solely on images from the new site. Both qualitative and quantitative results show that the proposed method achieves superior performance compared with other unsupervised harmonization approaches. | ['Jerry L. Prince', 'Peter A. Calabresi', 'Yufan He', 'Yihao Liu', 'Aaron Carass', 'Blake E. Dewey', 'Lianrui Zuo'] | 2021-03-24 | null | null | null | null | ['image-harmonization'] | ['computer-vision'] | [ 2.27010950e-01 4.03362699e-02 -2.32720375e-01 -4.64083880e-01
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8f6af934-d66d-489c-96aa-d8d6686888c1 | one-shot-learning-from-a-demonstration-with-1 | 2203.04806 | null | https://arxiv.org/abs/2203.04806v1 | https://arxiv.org/pdf/2203.04806v1.pdf | One-Shot Learning from a Demonstration with Hierarchical Latent Language | Humans have the capability, aided by the expressive compositionality of their language, to learn quickly by demonstration. They are able to describe unseen task-performing procedures and generalize their execution to other contexts. In this work, we introduce DescribeWorld, an environment designed to test this sort of generalization skill in grounded agents, where tasks are linguistically and procedurally composed of elementary concepts. The agent observes a single task demonstration in a Minecraft-like grid world, and is then asked to carry out the same task in a new map. To enable such a level of generalization, we propose a neural agent infused with hierarchical latent language--both at the level of task inference and subtask planning. Our agent first generates a textual description of the demonstrated unseen task, then leverages this description to replicate it. Through multiple evaluation scenarios and a suite of generalization tests, we find that agents that perform text-based inference are better equipped for the challenge under a random split of tasks. | ['Benjamin Van Durme', 'Harm van Seijen', 'Ida Momennejad', 'Romain Laroche', 'Matthew Hausknecht', 'Marc-Alexandre Côté', 'Xingdi Yuan', 'Nathaniel Weir'] | 2022-03-09 | null | null | null | null | ['one-shot-learning'] | ['methodology'] | [ 2.70478636e-01 3.89717400e-01 2.78468311e-01 -4.08629358e-01
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8ae4381a-354b-42c5-9e2f-ed5460df74b7 | improving-speaker-verification-with-self | 2305.10517 | null | https://arxiv.org/abs/2305.10517v1 | https://arxiv.org/pdf/2305.10517v1.pdf | Improving Speaker Verification with Self-Pretrained Transformer Models | Recently, fine-tuning large pre-trained Transformer models using downstream datasets has received a rising interest. Despite their success, it is still challenging to disentangle the benefits of large-scale datasets and Transformer structures from the limitations of the pre-training. In this paper, we introduce a hierarchical training approach, named self-pretraining, in which Transformer models are pretrained and finetuned on the same dataset. Three pre-trained models including HuBERT, Conformer and WavLM are evaluated on four different speaker verification datasets with varying sizes. Our experiments show that these self-pretrained models achieve competitive performance on downstream speaker verification tasks with only one-third of the data compared to Librispeech pretraining, such as VoxCeleb1 and CNCeleb1. Furthermore, when pre-training only on the VoxCeleb2-dev, the Conformer model outperforms the one pre-trained on 94k hours of data using the same fine-tuning settings. | ['Jan Černocký', 'Lukáš Burget', 'Ladislav Mošner', 'Themos Stafylakis', 'Oldřich Plchot', 'Junyi Peng'] | 2023-05-17 | null | null | null | null | ['speaker-verification'] | ['speech'] | [-6.21139295e-02 7.52702877e-02 1.46081829e-02 -6.79248273e-01
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6d31b5d3-b456-4c65-afb6-44d48f6e17b0 | learning-deep-representations-for-scene | 1706.02493 | null | http://arxiv.org/abs/1706.02493v2 | http://arxiv.org/pdf/1706.02493v2.pdf | Learning Deep Representations for Scene Labeling with Semantic Context Guided Supervision | Scene labeling is a challenging classification problem where each input image
requires a pixel-level prediction map. Recently, deep-learning-based methods
have shown their effectiveness on solving this problem. However, we argue that
the large intra-class variation provides ambiguous training information and
hinders the deep models' ability to learn more discriminative deep feature
representations. Unlike existing methods that mainly utilize semantic context
for regularizing or smoothing the prediction map, we design novel supervisions
from semantic context for learning better deep feature representations. Two
types of semantic context, scene names of images and label map statistics of
image patches, are exploited to create label hierarchies between the original
classes and newly created subclasses as the learning supervisions. Such
subclasses show lower intra-class variation, and help CNN detect more
meaningful visual patterns and learn more effective deep features. Novel
training strategies and network structure that take advantages of such label
hierarchies are introduced. Our proposed method is evaluated extensively on
four popular datasets, Stanford Background (8 classes), SIFTFlow (33 classes),
Barcelona (170 classes) and LM+Sun datasets (232 classes) with 3 different
networks structures, and show state-of-the-art performance. The experiments
show that our proposed method makes deep models learn more discriminative
feature representations without increasing model size or complexity. | ['Zhe Wang', 'Hongsheng Li', 'Wanli Ouyang', 'Xiaogang Wang'] | 2017-06-08 | null | null | null | null | ['scene-labeling'] | ['computer-vision'] | [ 2.28937984e-01 -2.37094373e-01 -4.86495435e-01 -9.94242668e-01
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c2673258-dbb8-4c48-a9b1-422ca3fd9e4b | natgen-generative-pre-training-by | 2206.07585 | null | https://arxiv.org/abs/2206.07585v2 | https://arxiv.org/pdf/2206.07585v2.pdf | NatGen: Generative pre-training by "Naturalizing" source code | Pre-trained Generative Language models (e.g. PLBART, CodeT5, SPT-Code) for source code yielded strong results on several tasks in the past few years, including code generation and translation. These models have adopted varying pre-training objectives to learn statistics of code construction from very large-scale corpora in a self-supervised fashion; the success of pre-trained models largely hinges on these pre-training objectives. This paper proposes a new pre-training objective, "Naturalizing" of source code, exploiting code's bimodal, dual-channel (formal & natural channels) nature. Unlike natural language, code's bimodal, dual-channel nature allows us to generate semantically equivalent code at scale. We introduce six classes of semantic preserving transformations to introduce un-natural forms of code, and then force our model to produce more natural original programs written by developers. Learning to generate equivalent, but more natural code, at scale, over large corpora of open-source code, without explicit manual supervision, helps the model learn to both ingest & generate code. We fine-tune our model in three generative Software Engineering tasks: code generation, code translation, and code refinement with limited human-curated labeled data and achieve state-of-the-art performance rivaling CodeT5. We show that our pre-trained model is especially competitive at zero-shot and few-shot learning, and better at learning code properties (e.g., syntax, data flow). | ['Baishakhi Ray', 'Premkumar Devanbu', 'Yangruibo Ding', 'Toufique Ahmed', 'Saikat Chakraborty'] | 2022-06-15 | null | null | null | null | ['code-translation'] | ['computer-code'] | [ 2.55862564e-01 3.78438354e-01 -1.48392215e-01 -2.13901550e-01
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53418f85-4e7b-42c1-afdd-a2e62064683a | robust-target-localization-in-2d-a-value-at | 2307.00548 | null | https://arxiv.org/abs/2307.00548v2 | https://arxiv.org/pdf/2307.00548v2.pdf | Robust Target Localization in 2D: A Value-at-Risk Approach | This paper consider considers the problem of locating a two dimensional target from range-measurements containing outliers. Assuming that the number of outlier is known, we formulate the problem of minimizing inlier losses while ignoring outliers. This leads to a combinatorial, non-convex, non-smooth problem involving the percentile function. Using the framework of risk analysis from Rockafellar et al., we start by interpreting this formulation as a Value-at-risk (VaR) problem from portfolio optimization. To the best of our knowledge, this is the first time that a localization problem was formulated using risk analysis theory. To study the VaR formulation, we start by designing a majorizer set that contains any solution of a general percentile problem. This set is useful because, when applied to a localization scenario in 2D, it allows to majorize the solution set in terms of singletons, circumferences, ellipses and hyperbolas. Using know parametrization of these curves, we propose a grid method for the original non-convex problem. So we reduce the task of optimizing the VaR objective to that of efficiently sampling the proposed majorizer set. We compare our algorithm with four benchmarks in target localization. Numerical simulations show that our method is fast while, on average, improving the accuracy of the best benchmarks by at least 100m in a 1 Km$^2$ area. | ['João Xavier', 'João Domingos'] | 2023-07-02 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-1.87292382e-01 2.01344609e-01 2.56132901e-01 4.59827147e-02
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ed333c6d-8fd9-4013-8194-d788e1b081d5 | scam-transferring-humans-between-images-with | 2210.04883 | null | https://arxiv.org/abs/2210.04883v1 | https://arxiv.org/pdf/2210.04883v1.pdf | SCAM! Transferring humans between images with Semantic Cross Attention Modulation | A large body of recent work targets semantically conditioned image generation. Most such methods focus on the narrower task of pose transfer and ignore the more challenging task of subject transfer that consists in not only transferring the pose but also the appearance and background. In this work, we introduce SCAM (Semantic Cross Attention Modulation), a system that encodes rich and diverse information in each semantic region of the image (including foreground and background), thus achieving precise generation with emphasis on fine details. This is enabled by the Semantic Attention Transformer Encoder that extracts multiple latent vectors for each semantic region, and the corresponding generator that exploits these multiple latents by using semantic cross attention modulation. It is trained only using a reconstruction setup, while subject transfer is performed at test time. Our analysis shows that our proposed architecture is successful at encoding the diversity of appearance in each semantic region. Extensive experiments on the iDesigner and CelebAMask-HD datasets show that SCAM outperforms SEAN and SPADE; moreover, it sets the new state of the art on subject transfer. | ['Vicky Kalogeiton', 'David Picard', 'Nicolas Dufour'] | 2022-10-10 | null | null | null | null | ['reconstruction', 'pose-transfer'] | ['computer-vision', 'computer-vision'] | [ 6.19308054e-01 3.58312011e-01 2.06330106e-01 -4.86023098e-01
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50b5d7ec-1d4b-4ad0-ba83-a4380a2eec7f | learning-thermodynamically-constrained | 2306.17004 | null | https://arxiv.org/abs/2306.17004v1 | https://arxiv.org/pdf/2306.17004v1.pdf | Learning thermodynamically constrained equations of state with uncertainty | Numerical simulations of high energy-density experiments require equation of state (EOS) models that relate a material's thermodynamic state variables -- specifically pressure, volume/density, energy, and temperature. EOS models are typically constructed using a semi-empirical parametric methodology, which assumes a physics-informed functional form with many tunable parameters calibrated using experimental/simulation data. Since there are inherent uncertainties in the calibration data (parametric uncertainty) and the assumed functional EOS form (model uncertainty), it is essential to perform uncertainty quantification (UQ) to improve confidence in the EOS predictions. Model uncertainty is challenging for UQ studies since it requires exploring the space of all possible physically consistent functional forms. Thus, it is often neglected in favor of parametric uncertainty, which is easier to quantify without violating thermodynamic laws. This work presents a data-driven machine learning approach to constructing EOS models that naturally captures model uncertainty while satisfying the necessary thermodynamic consistency and stability constraints. We propose a novel framework based on physics-informed Gaussian process regression (GPR) that automatically captures total uncertainty in the EOS and can be jointly trained on both simulation and experimental data sources. A GPR model for the shock Hugoniot is derived and its uncertainties are quantified using the proposed framework. We apply the proposed model to learn the EOS for the diamond solid state of carbon, using both density functional theory data and experimental shock Hugoniot data to train the model and show that the prediction uncertainty reduces by considering the thermodynamic constraints. | ['Michael D. Shields', 'Dimitrios Tsapetis', 'Jim A. Gaffney', 'Himanshu Sharma'] | 2023-06-29 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [-1.80523172e-01 -1.15758076e-01 -6.63438961e-02 -3.61685395e-01
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79ee40f5-64a8-4ce2-8e1a-9e6fc8e32cc2 | world-to-words-grounded-open-vocabulary | 2306.08685 | null | https://arxiv.org/abs/2306.08685v1 | https://arxiv.org/pdf/2306.08685v1.pdf | World-to-Words: Grounded Open Vocabulary Acquisition through Fast Mapping in Vision-Language Models | The ability to connect language units to their referents in the physical world, referred to as grounding, is crucial to learning and understanding grounded meanings of words. While humans demonstrate fast mapping in new word learning, it remains unclear whether modern vision-language models can truly represent language with their grounded meanings and how grounding may further bootstrap new word learning. To this end, we introduce Grounded Open Vocabulary Acquisition (GOVA) to examine grounding and bootstrapping in open-world language learning. As an initial attempt, we propose object-oriented BERT (OctoBERT), a novel visually-grounded language model by pre-training on image-text pairs highlighting grounding as an objective. Through extensive experiments and analysis, we demonstrate that OctoBERT is a more coherent and fast grounded word learner, and that the grounding ability acquired during pre-training helps the model to learn unseen words more rapidly and robustly. Our code is available at https://github.com/sled-group/world-to-words | ['Joyce Chai', 'Jiayi Pan', 'Ziqiao Ma'] | 2023-06-14 | null | null | null | null | ['grounded-open-vocabulary-acquisition'] | ['natural-language-processing'] | [ 9.91790444e-02 2.46037871e-01 -2.06236113e-02 -2.02660784e-01
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12d7feff-d131-425a-9645-51d5780a10f2 | data-uncertainty-guided-multi-phase-learning | 2103.16368 | null | https://arxiv.org/abs/2103.16368v1 | https://arxiv.org/pdf/2103.16368v1.pdf | Data-Uncertainty Guided Multi-Phase Learning for Semi-Supervised Object Detection | In this paper, we delve into semi-supervised object detection where unlabeled images are leveraged to break through the upper bound of fully-supervised object detection models. Previous semi-supervised methods based on pseudo labels are severely degenerated by noise and prone to overfit to noisy labels, thus are deficient in learning different unlabeled knowledge well. To address this issue, we propose a data-uncertainty guided multi-phase learning method for semi-supervised object detection. We comprehensively consider divergent types of unlabeled images according to their difficulty levels, utilize them in different phases and ensemble models from different phases together to generate ultimate results. Image uncertainty guided easy data selection and region uncertainty guided RoI Re-weighting are involved in multi-phase learning and enable the detector to concentrate on more certain knowledge. Through extensive experiments on PASCAL VOC and MS COCO, we demonstrate that our method behaves extraordinarily compared to baseline approaches and outperforms them by a large margin, more than 3% on VOC and 2% on COCO. | ['Shengjin Wang', 'Lu Fang', 'Ye Guo', 'YaLi Li', 'Zhenyu Wang'] | 2021-03-29 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Wang_Data-Uncertainty_Guided_Multi-Phase_Learning_for_Semi-Supervised_Object_Detection_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Wang_Data-Uncertainty_Guided_Multi-Phase_Learning_for_Semi-Supervised_Object_Detection_CVPR_2021_paper.pdf | cvpr-2021-1 | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 1.51092723e-01 2.01484114e-01 -4.43283528e-01 -5.68955958e-01
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de415047-aa5c-46ce-b1f1-32a0c6f856a9 | leveraging-deep-learning-techniques-on | 2304.09282 | null | https://arxiv.org/abs/2304.09282v1 | https://arxiv.org/pdf/2304.09282v1.pdf | Leveraging Deep Learning Techniques on Collaborative Filtering Recommender Systems | With the exponentially increasing volume of online data, searching and finding required information have become an extensive and time-consuming task. Recommender Systems as a subclass of information retrieval and decision support systems by providing personalized suggestions helping users access what they need more efficiently. Among the different techniques for building a recommender system, Collaborative Filtering (CF) is the most popular and widespread approach. However, cold start and data sparsity are the fundamental challenges ahead of implementing an effective CF-based recommender. Recent successful developments in enhancing and implementing deep learning architectures motivated many studies to propose deep learning-based solutions for solving the recommenders' weak points. In this research, unlike the past similar works about using deep learning architectures in recommender systems that covered different techniques generally, we specifically provide a comprehensive review of deep learning-based collaborative filtering recommender systems. This in-depth filtering gives a clear overview of the level of popularity, gaps, and ignored areas on leveraging deep learning techniques to build CF-based systems as the most influential recommenders. | ['Javad Mohammadzadeh', 'Ali Fallahi RahmatAbadi'] | 2023-04-18 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [-4.21741158e-01 -4.97451544e-01 -3.83848727e-01 -6.19793415e-01
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bf6c11f3-5bc4-45b1-b01f-f88f9f3194d3 | trade-off-between-communication-and-1 | 2305.04423 | null | https://arxiv.org/abs/2305.04423v1 | https://arxiv.org/pdf/2305.04423v1.pdf | Trade-off Between Communication and Positioning in Millimeter Wave Systems with Bounded and Unbounded Positioning Errors | Millimeter wave has proven to be effective in the integrated positioning and communication (IPAC) system. In this work, we establish a millimeter wave IPAC system by leveraging the inner coupling relationship between estimated data rate and positioning error. Moreover, we formulate robust power allocation problems by minimizing the positioning performance metric, i.e., the Cram\'er-Rao bound (CRB), subject to the data rate and total transmit power constraints to investigate the trade-off between positioning and communication of the IPAC system. Specifically, in the first case, we characterize the positioning error as a bounded ellipsoidal model to formulate the power allocation problem, which can be solved by the ${\cal{S}}$-Procedure and the alternating optimization (AO) algorithm. In the second case, we formulate the optimization problem by characterizing the positioning error as an unbounded Gaussian model, which can be solved by the Bernstein-type inequality and successive convex approximation (SCA) methods. Numerical results verify the robustness against the positioning error of the proposed robust design by comparing with the non-robust design, and analyze the trade-off between communication and positioning of the integrated system. | ['Shiyin Li', 'Ruixin Yang', 'Shuai Ma', 'Junchang Sun'] | 2023-05-08 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [ 1.23750746e-01 4.50149447e-01 2.71451026e-01 1.26733780e-01
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146717c5-dbe0-4a96-933b-a610093e93d5 | dccrn-kws-an-audio-bias-based-model-for-noise | 2305.12331 | null | https://arxiv.org/abs/2305.12331v3 | https://arxiv.org/pdf/2305.12331v3.pdf | DCCRN-KWS: an audio bias based model for noise robust small-footprint keyword spotting | Real-world complex acoustic environments especially the ones with a low signal-to-noise ratio (SNR) will bring tremendous challenges to a keyword spotting (KWS) system. Inspired by the recent advances of neural speech enhancement and context bias in speech recognition, we propose a robust audio context bias based DCCRN-KWS model to address this challenge. We form the whole architecture as a multi-task learning framework for both denosing and keyword spotting, where the DCCRN encoder is connected with the KWS model. Helped with the denoising task, we further introduce an audio context bias module to leverage the real keyword samples and bias the network to better iscriminate keywords in noisy conditions. Feature merge and complex context linear modules are also introduced to strength such discrimination and to effectively leverage contextual information respectively. Experiments on the internal challenging dataset and the HIMIYA public dataset show that our DCCRN-KWS system is superior in performance, while ablation study demonstrates the good design of the whole model. | ['Lei Xie', 'Long Ma', 'Sining Sun', 'Xiong Wang', 'Shubo Lv'] | 2023-05-21 | null | null | null | null | ['small-footprint-keyword-spotting', 'keyword-spotting', 'speech-enhancement'] | ['speech', 'speech', 'speech'] | [ 0.46059433 -0.3345368 0.09412286 -0.47058883 -1.4259113 -0.2109551
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261366e6-1c43-4be0-809f-83c1ffc03e2e | score-based-generative-models-for | 2306.13843 | null | https://arxiv.org/abs/2306.13843v1 | https://arxiv.org/pdf/2306.13843v1.pdf | Score-based Generative Models for Photoacoustic Image Reconstruction with Rotation Consistency Constraints | Photoacoustic tomography (PAT) is a newly emerged imaging modality which enables both high optical contrast and acoustic depth of penetration. Reconstructing images of photoacoustic tomography from limited amount of senser data is among one of the major challenges in photoacoustic imaging. Previous works based on deep learning were trained in supervised fashion, which directly map the input partially known sensor data to the ground truth reconstructed from full field of view. Recently, score-based generative models played an increasingly significant role in generative modeling. Leveraging this probabilistic model, we proposed Rotation Consistency Constrained Score-based Generative Model (RCC-SGM), which recovers the PAT images by iterative sampling between Langevin dynamics and a constraint term utilizing the rotation consistency between the images and the measurements. Our proposed method can generalize to different measurement processes (32.29 PSNR with 16 measurements under random sampling, whereas 28.50 for supervised counterpart), while supervised methods need to train on specific inverse mappings. | ['Fei Gao', 'Jianwen Luo', 'Liming Nie', 'Hengrong Lan', 'Shangqing Tong'] | 2023-06-24 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 6.81303203e-01 -4.44554053e-02 5.58959901e-01 -3.18277955e-01
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d4b3876c-9ed8-4c85-823c-c0e33304e019 | visual-semantic-slam-with-landmarks-for-large | 2001.01028 | null | https://arxiv.org/abs/2001.01028v1 | https://arxiv.org/pdf/2001.01028v1.pdf | Visual Semantic SLAM with Landmarks for Large-Scale Outdoor Environment | Semantic SLAM is an important field in autonomous driving and intelligent agents, which can enable robots to achieve high-level navigation tasks, obtain simple cognition or reasoning ability and achieve language-based human-robot-interaction. In this paper, we built a system to creat a semantic 3D map by combining 3D point cloud from ORB SLAM with semantic segmentation information from Convolutional Neural Network model PSPNet-101 for large-scale environments. Besides, a new dataset for KITTI sequences has been built, which contains the GPS information and labels of landmarks from Google Map in related streets of the sequences. Moreover, we find a way to associate the real-world landmark with point cloud map and built a topological map based on semantic map. | ['Zirui Zhao', 'Yijun Mao', 'Pengju Ren', 'Yan Ding', 'Nanning Zheng'] | 2020-01-04 | null | null | null | null | ['semantic-slam'] | ['computer-vision'] | [-3.62316221e-01 -1.50990576e-01 5.84874339e-02 -8.38017225e-01
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9561c25e-9038-4179-acb6-e26d5ea5a54d | character-n-gram-embeddings-to-improve-rnn | 1906.05506 | null | https://arxiv.org/abs/1906.05506v1 | https://arxiv.org/pdf/1906.05506v1.pdf | Character n-gram Embeddings to Improve RNN Language Models | This paper proposes a novel Recurrent Neural Network (RNN) language model that takes advantage of character information. We focus on character n-grams based on research in the field of word embedding construction (Wieting et al. 2016). Our proposed method constructs word embeddings from character n-gram embeddings and combines them with ordinary word embeddings. We demonstrate that the proposed method achieves the best perplexities on the language modeling datasets: Penn Treebank, WikiText-2, and WikiText-103. Moreover, we conduct experiments on application tasks: machine translation and headline generation. The experimental results indicate that our proposed method also positively affects these tasks. | ['Masaaki Nagata', 'Jun Suzuki', 'Sho Takase'] | 2019-06-13 | null | null | null | null | ['headline-generation'] | ['natural-language-processing'] | [-5.56324646e-02 1.10278524e-01 -7.26443172e-01 2.83728391e-02
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0989d8fb-3fbf-4b90-baeb-2dfce6d79bb3 | ju_cse-a-conditional-random-field-crf-based | null | null | https://aclanthology.org/S14-2063 | https://aclanthology.org/S14-2063.pdf | JU\_CSE: A Conditional Random Field (CRF) Based Approach to Aspect Based Sentiment Analysis | null | ['Sivaji yopadhyay', 'B', 'Soumik al', 'M', 'Dipankar Das', 'Braja Gopal Patra'] | 2014-08-01 | null | null | null | semeval-2014-8 | ['subjectivity-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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c41a549f-645c-455a-9f24-8b5f66731c30 | evaluation-of-latent-space-disentanglement-in | 2110.05587 | null | https://arxiv.org/abs/2110.05587v1 | https://arxiv.org/pdf/2110.05587v1.pdf | Evaluation of Latent Space Disentanglement in the Presence of Interdependent Attributes | Controllable music generation with deep generative models has become increasingly reliant on disentanglement learning techniques. However, current disentanglement metrics, such as mutual information gap (MIG), are often inadequate and misleading when used for evaluating latent representations in the presence of interdependent semantic attributes often encountered in real-world music datasets. In this work, we propose a dependency-aware information metric as a drop-in replacement for MIG that accounts for the inherent relationship between semantic attributes. | ['Alexander Lerch', 'Karn N. Watcharasupat'] | 2021-10-11 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 2.55976081e-01 -1.51843861e-01 -8.62689018e-02 -3.42667818e-01
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664974ee-7393-4c99-bc8b-32d24be6506d | band-biomedical-alert-news-dataset | 2305.1448 | null | https://arxiv.org/abs/2305.14480v1 | https://arxiv.org/pdf/2305.14480v1.pdf | BAND: Biomedical Alert News Dataset | Infectious disease outbreaks continue to pose a significant threat to human health and well-being. To improve disease surveillance and understanding of disease spread, several surveillance systems have been developed to monitor daily news alerts and social media. However, existing systems lack thorough epidemiological analysis in relation to corresponding alerts or news, largely due to the scarcity of well-annotated reports data. To address this gap, we introduce the Biomedical Alert News Dataset (BAND), which includes 1,508 samples from existing reported news articles, open emails, and alerts, as well as 30 epidemiology-related questions. These questions necessitate the model's expert reasoning abilities, thereby offering valuable insights into the outbreak of the disease. The BAND dataset brings new challenges to the NLP world, requiring better disguise capability of the content and the ability to infer important information. We provide several benchmark tasks, including Named Entity Recognition (NER), Question Answering (QA), and Event Extraction (EE), to show how existing models are capable of handling these tasks in the epidemiology domain. To the best of our knowledge, the BAND corpus is the largest corpus of well-annotated biomedical outbreak alert news with elaborately designed questions, making it a valuable resource for epidemiologists and NLP researchers alike. | ['Nigel Collier', 'David Buckeridge', 'Anya Okhmatovskaia', 'Yannan Shen', 'Zaiqiao Meng', 'Meiru Zhang', 'Zihao Fu'] | 2023-05-23 | null | null | null | null | ['epidemiology', 'event-extraction', 'named-entity-recognition-ner'] | ['medical', 'natural-language-processing', 'natural-language-processing'] | [ 1.84879616e-01 1.65180787e-01 -2.46814683e-01 -2.03737170e-01
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e15cf729-cc61-4d88-b6e9-0c8cb5d44a85 | dc-mbr-distributional-cooling-for-minimum | 2212.04205 | null | https://arxiv.org/abs/2212.04205v2 | https://arxiv.org/pdf/2212.04205v2.pdf | DC-MBR: Distributional Cooling for Minimum Bayesian Risk Decoding | Minimum Bayesian Risk Decoding (MBR) emerges as a promising decoding algorithm in Neural Machine Translation. However, MBR performs poorly with label smoothing, which is surprising as label smoothing provides decent improvement with beam search and improves generality in various tasks. In this work, we show that the issue arises from the un-consistency of label smoothing on the token-level and sequence-level distributions. We demonstrate that even though label smoothing only causes a slight change in the token-level, the sequence-level distribution is highly skewed. We coin the issue \emph{autoregressive over-smoothness}. To address this issue, we propose a simple and effective method, Distributional Cooling MBR (DC-MBR), which manipulates the entropy of output distributions by tuning down the Softmax temperature. We theoretically prove the equivalence between pre-tuning label smoothing factor and distributional cooling. Extensive experiments on NMT benchmarks validate that distributional cooling improves MBR in various settings. | ['Yue Zhang', 'Jie zhou', 'Fandong Meng', 'Jin Xu', 'Jianhao Yan'] | 2022-12-08 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 5.95108926e-01 1.87954307e-01 -4.31288034e-01 -6.32345438e-01
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81cb83fa-847f-4616-b2ef-71058cbdf10a | accurate-learning-of-graph-representations-1 | 2102.11533 | null | https://arxiv.org/abs/2102.11533v4 | https://arxiv.org/pdf/2102.11533v4.pdf | Accurate Learning of Graph Representations with Graph Multiset Pooling | Graph neural networks have been widely used on modeling graph data, achieving impressive results on node classification and link prediction tasks. Yet, obtaining an accurate representation for a graph further requires a pooling function that maps a set of node representations into a compact form. A simple sum or average over all node representations considers all node features equally without consideration of their task relevance, and any structural dependencies among them. Recently proposed hierarchical graph pooling methods, on the other hand, may yield the same representation for two different graphs that are distinguished by the Weisfeiler-Lehman test, as they suboptimally preserve information from the node features. To tackle these limitations of existing graph pooling methods, we first formulate the graph pooling problem as a multiset encoding problem with auxiliary information about the graph structure, and propose a Graph Multiset Transformer (GMT) which is a multi-head attention based global pooling layer that captures the interaction between nodes according to their structural dependencies. We show that GMT satisfies both injectiveness and permutation invariance, such that it is at most as powerful as the Weisfeiler-Lehman graph isomorphism test. Moreover, our methods can be easily extended to the previous node clustering approaches for hierarchical graph pooling. Our experimental results show that GMT significantly outperforms state-of-the-art graph pooling methods on graph classification benchmarks with high memory and time efficiency, and obtains even larger performance gain on graph reconstruction and generation tasks. | ['Sung Ju Hwang', 'Minki Kang', 'Jinheon Baek'] | 2021-02-23 | accurate-learning-of-graph-representations | https://openreview.net/forum?id=JHcqXGaqiGn | https://openreview.net/pdf?id=JHcqXGaqiGn | iclr-2021-1 | ['graph-reconstruction'] | ['graphs'] | [ 1.61746770e-01 4.61204916e-01 -3.76901358e-01 -1.89791739e-01
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398bce5c-c699-467b-86ff-a0eb54d2b70a | a-latent-space-model-for-hla-compatibility | 2211.02234 | null | https://arxiv.org/abs/2211.02234v1 | https://arxiv.org/pdf/2211.02234v1.pdf | A Latent Space Model for HLA Compatibility Networks in Kidney Transplantation | Kidney transplantation is the preferred treatment for people suffering from end-stage renal disease. Successful kidney transplants still fail over time, known as graft failure; however, the time to graft failure, or graft survival time, can vary significantly between different recipients. A significant biological factor affecting graft survival times is the compatibility between the human leukocyte antigens (HLAs) of the donor and recipient. We propose to model HLA compatibility using a network, where the nodes denote different HLAs of the donor and recipient, and edge weights denote compatibilities of the HLAs, which can be positive or negative. The network is indirectly observed, as the edge weights are estimated from transplant outcomes rather than directly observed. We propose a latent space model for such indirectly-observed weighted and signed networks. We demonstrate that our latent space model can not only result in more accurate estimates of HLA compatibilities, but can also be incorporated into survival analysis models to improve accuracy for the downstream task of predicting graft survival times. | ['Kevin S. Xu', 'Zhipeng Huang'] | 2022-11-04 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [ 2.93384250e-02 -2.18960002e-01 -6.07280910e-01 -6.42103553e-01
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1ec4983b-3e43-4696-a3da-3baabbd301f2 | user-localization-using-rf-sensing-a | 2205.10321 | null | https://arxiv.org/abs/2205.10321v1 | https://arxiv.org/pdf/2205.10321v1.pdf | User Localization using RF Sensing: A Performance comparison between LIS and mmWave Radars | Since electromagnetic signals are omnipresent, Radio Frequency (RF)-sensing has the potential to become a universal sensing mechanism with applications in localization, smart-home, retail, gesture recognition, intrusion detection, etc. Two emerging technologies in RF-sensing, namely sensing through Large Intelligent Surfaces (LISs) and mmWave Frequency-Modulated Continuous-Wave (FMCW) radars, have been successfully applied to a wide range of applications. In this work, we compare LIS and mmWave radars for localization in real-world and simulated environments. In our experiments, the mmWave radar achieves 0.71 Intersection Over Union (IOU) and 3cm error for bounding boxes, while LIS has 0.56 IOU and 10cm distance error. Although the radar outperforms the LIS in terms of accuracy, LIS features additional applications in communication in addition to sensing scenarios. | ['Stephan Sigg', 'Zheng-Hua Tan', 'Elisabeth de Carvalho', 'Petar Popovski', 'Dariush Salami', 'Cristian J. Vaca-Rubio'] | 2022-05-17 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 5.29904306e-01 -1.88714772e-01 1.76605597e-01 -1.91099346e-01
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f7634584-e041-401d-98a7-99a3dae2fdf9 | explanation-generation-for-multi-modal-multi | 2008.03573 | null | https://arxiv.org/abs/2008.03573v1 | https://arxiv.org/pdf/2008.03573v1.pdf | Explanation Generation for Multi-Modal Multi-Agent Path Finding with Optimal Resource Utilization using Answer Set Programming | The multi-agent path finding (MAPF) problem is a combinatorial search problem that aims at finding paths for multiple agents (e.g., robots) in an environment (e.g., an autonomous warehouse) such that no two agents collide with each other, and subject to some constraints on the lengths of paths. We consider a general version of MAPF, called mMAPF, that involves multi-modal transportation modes (e.g., due to velocity constraints) and consumption of different types of resources (e.g., batteries). The real-world applications of mMAPF require flexibility (e.g., solving variations of mMAPF) as well as explainability. Our earlier studies on mMAPF have focused on the former challenge of flexibility. In this study, we focus on the latter challenge of explainability, and introduce a method for generating explanations for queries regarding the feasibility and optimality of solutions, the nonexistence of solutions, and the observations about solutions. Our method is based on answer set programming. This paper is under consideration for acceptance in TPLP. | ['Esra Erdem', 'Aysu Bogatarkan'] | 2020-08-08 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-2.06446320e-01 4.63732362e-01 -1.58002123e-01 -2.15704471e-01
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1b5bb8af-5579-4be6-8dbe-ccd8dd2db950 | blue-at-memotion-2-0-2022-you-have-my-image | 2202.07543 | null | https://arxiv.org/abs/2202.07543v3 | https://arxiv.org/pdf/2202.07543v3.pdf | BLUE at Memotion 2.0 2022: You have my Image, my Text and my Transformer | Memes are prevalent on the internet and continue to grow and evolve alongside our culture. An automatic understanding of memes propagating on the internet can shed light on the general sentiment and cultural attitudes of people. In this work, we present team BLUE's solution for the second edition of the MEMOTION shared task. We showcase two approaches for meme classification (i.e. sentiment, humour, offensive, sarcasm and motivation levels) using a text-only method using BERT, and a Multi-Modal-Multi-Task transformer network that operates on both the meme image and its caption to output the final scores. In both approaches, we leverage state-of-the-art pretrained models for text (BERT, Sentence Transformer) and image processing (EfficientNetV4, CLIP). Through our efforts, we obtain first place in task A, second place in task B and third place in task C. In addition, our team obtained the highest average score for all three tasks. | ['Ioan-Bogdan Iordache', 'Adrian Cosma', 'Ana-Maria Bucur'] | 2022-02-15 | null | null | null | null | ['meme-classification'] | ['natural-language-processing'] | [-1.62655205e-01 -3.61831844e-01 3.45836997e-01 -1.81856185e-01
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55859447-726b-4225-ac62-1e35a751873a | structure-aware-dropedge-towards-deep-graph | 2306.12091 | null | https://arxiv.org/abs/2306.12091v1 | https://arxiv.org/pdf/2306.12091v1.pdf | Structure-Aware DropEdge Towards Deep Graph Convolutional Networks | It has been discovered that Graph Convolutional Networks (GCNs) encounter a remarkable drop in performance when multiple layers are piled up. The main factor that accounts for why deep GCNs fail lies in over-smoothing, which isolates the network output from the input with the increase of network depth, weakening expressivity and trainability. In this paper, we start by investigating refined measures upon DropEdge -- an existing simple yet effective technique to relieve over-smoothing. We term our method as DropEdge++ for its two structure-aware samplers in contrast to DropEdge: layer-dependent sampler and feature-dependent sampler. Regarding the layer-dependent sampler, we interestingly find that increasingly sampling edges from the bottom layer yields superior performance than the decreasing counterpart as well as DropEdge. We theoretically reveal this phenomenon with Mean-Edge-Number (MEN), a metric closely related to over-smoothing. For the feature-dependent sampler, we associate the edge sampling probability with the feature similarity of node pairs, and prove that it further correlates the convergence subspace of the output layer with the input features. Extensive experiments on several node classification benchmarks, including both full- and semi- supervised tasks, illustrate the efficacy of DropEdge++ and its compatibility with a variety of backbones by achieving generally better performance over DropEdge and the no-drop version. | ['Junzhou Huang', 'Fuchun Sun', 'Tingyang Xu', 'Yu Rong', 'Wenbing Huang', 'Jiaqi Han'] | 2023-06-21 | null | null | null | null | ['node-classification'] | ['graphs'] | [-3.1570829e-02 4.4081894e-01 -8.9243777e-02 -2.3301847e-01
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c1a57908-5c6c-4c64-859f-faf588463968 | dual-path-convolutional-image-text-embedding | 1711.05535 | null | https://arxiv.org/abs/1711.05535v4 | https://arxiv.org/pdf/1711.05535v4.pdf | Dual-Path Convolutional Image-Text Embeddings with Instance Loss | Matching images and sentences demands a fine understanding of both modalities. In this paper, we propose a new system to discriminatively embed the image and text to a shared visual-textual space. In this field, most existing works apply the ranking loss to pull the positive image / text pairs close and push the negative pairs apart from each other. However, directly deploying the ranking loss is hard for network learning, since it starts from the two heterogeneous features to build inter-modal relationship. To address this problem, we propose the instance loss which explicitly considers the intra-modal data distribution. It is based on an unsupervised assumption that each image / text group can be viewed as a class. So the network can learn the fine granularity from every image/text group. The experiment shows that the instance loss offers better weight initialization for the ranking loss, so that more discriminative embeddings can be learned. Besides, existing works usually apply the off-the-shelf features, i.e., word2vec and fixed visual feature. So in a minor contribution, this paper constructs an end-to-end dual-path convolutional network to learn the image and text representations. End-to-end learning allows the system to directly learn from the data and fully utilize the supervision. On two generic retrieval datasets (Flickr30k and MSCOCO), experiments demonstrate that our method yields competitive accuracy compared to state-of-the-art methods. Moreover, in language based person retrieval, we improve the state of the art by a large margin. The code has been made publicly available. | ['Yi-Dong Shen', 'Mingliang Xu', 'Michael Garrett', 'Yi Yang', 'Zhedong Zheng', 'Liang Zheng'] | 2017-11-15 | null | null | null | null | ['person-retrieval', 'nlp-based-person-retrival'] | ['computer-vision', 'computer-vision'] | [ 1.87723543e-02 -2.86362022e-01 -3.85544837e-01 -6.12333536e-01
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e1184058-106f-4cc9-84b6-edff758e8d48 | clarifying-system-1-2-through-the-common | 2305.10654 | null | https://arxiv.org/abs/2305.10654v1 | https://arxiv.org/pdf/2305.10654v1.pdf | Clarifying System 1 & 2 through the Common Model of Cognition | There have been increasing challenges to dual-system descriptions of System-1 and System-2, critiquing them as imprecise and fostering misconceptions. We address these issues here by way of Dennett's appeal to use computational thinking as an analytical tool, specifically we employ the Common Model of Cognition. Results show that the characteristics thought to be distinctive of System-1 and System-2 instead form a spectrum of cognitive properties. By grounding System-1 and System-2 in the Common Model we aim to clarify their underlying mechanisms, persisting misconceptions, and implications for metacognition. | ['Robert L. West', 'Brendan Conway-Smith'] | 2023-05-18 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [ 7.47176632e-03 2.51640171e-01 3.53990614e-01 -3.59853730e-03
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41c62aa4-44f0-40d2-aad4-c8585d2dea96 | learning-from-synthetic-animals | 1912.08265 | null | https://arxiv.org/abs/1912.08265v2 | https://arxiv.org/pdf/1912.08265v2.pdf | Learning from Synthetic Animals | Despite great success in human parsing, progress for parsing other deformable articulated objects, like animals, is still limited by the lack of labeled data. In this paper, we use synthetic images and ground truth generated from CAD animal models to address this challenge. To bridge the domain gap between real and synthetic images, we propose a novel consistency-constrained semi-supervised learning method (CC-SSL). Our method leverages both spatial and temporal consistencies, to bootstrap weak models trained on synthetic data with unlabeled real images. We demonstrate the effectiveness of our method on highly deformable animals, such as horses and tigers. Without using any real image label, our method allows for accurate keypoint prediction on real images. Moreover, we quantitatively show that models using synthetic data achieve better generalization performance than models trained on real images across different domains in the Visual Domain Adaptation Challenge dataset. Our synthetic dataset contains 10+ animals with diverse poses and rich ground truth, which enables us to use the multi-task learning strategy to further boost models' performance. | ['Weichao Qiu', 'Gregory Hager', 'Jiteng Mu', 'Alan Yuille'] | 2019-12-17 | learning-from-synthetic-animals-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Mu_Learning_From_Synthetic_Animals_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Mu_Learning_From_Synthetic_Animals_CVPR_2020_paper.pdf | cvpr-2020-6 | ['human-parsing'] | ['computer-vision'] | [ 3.75153184e-01 2.80012578e-01 -2.15480879e-01 -5.60272157e-01
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51d4d1fe-a02e-4e7b-b2fb-67f432ebba26 | unsupervised-skin-tissue-segmentation-for | null | null | https://www.sciencedirect.com/science/article/abs/pii/S0167865517303860 | https://www.sciencedirect.com/science/article/abs/pii/S0167865517303860 | Unsupervised skin tissue segmentation for remote photoplethysmography | Segmentation is a critical step for many algorithms, especially for remote photoplethysmography (rPPG) applications as only the skin surface provides information. Moreover, it has been shown that the rPPG signal is not distributed homogeneously across the skin. Most of the time, algorithms get input information from face detection provided by a supervised learning of physical appearance and skin pixel selection. However, both methods show several limitations. In this paper, we propose a simple approach to implicitly select skin tissues based on their distinct pulsatility feature. The input video frames are decomposed into several temporal superpixels from which the pulse signals are extracted. A pulsatility measure from each temporal superpixel is then used to merge the pulse traces and estimate the photoplethysmogram signal. Since the most pulsatile signals provide high quality information, areas where the information is predominant are favored. We evaluated our contribution using a new publicly available dataset dedicated to rPPG algorithms comparison. The results of our experiments show that our method outperforms state of the art algorithms, without any critical face or skin detection. | ['Julien Dubois', 'Alamin Mansouri', 'Yannick Benezeth', 'Richard Macwan', 'Serge Bobbia'] | 2019-06-01 | null | null | null | pattern-recognition-letters-2019-6 | ['face-detection'] | ['computer-vision'] | [ 3.97222221e-01 2.28869710e-02 -2.02008516e-01 -2.08971605e-01
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562e678a-4cad-4923-b571-81a055772a46 | decoupled-and-memory-reinforced-networks | 2102.10795 | null | https://arxiv.org/abs/2102.10795v1 | https://arxiv.org/pdf/2102.10795v1.pdf | Decoupled and Memory-Reinforced Networks: Towards Effective Feature Learning for One-Step Person Search | The goal of person search is to localize and match query persons from scene images. For high efficiency, one-step methods have been developed to jointly handle the pedestrian detection and identification sub-tasks using a single network. There are two major challenges in the current one-step approaches. One is the mutual interference between the optimization objectives of multiple sub-tasks. The other is the sub-optimal identification feature learning caused by small batch size when end-to-end training. To overcome these problems, we propose a decoupled and memory-reinforced network (DMRNet). Specifically, to reconcile the conflicts of multiple objectives, we simplify the standard tightly coupled pipelines and establish a deeply decoupled multi-task learning framework. Further, we build a memory-reinforced mechanism to boost the identification feature learning. By queuing the identification features of recently accessed instances into a memory bank, the mechanism augments the similarity pair construction for pairwise metric learning. For better encoding consistency of the stored features, a slow-moving average of the network is applied for extracting these features. In this way, the dual networks reinforce each other and converge to robust solution states. Experimentally, the proposed method obtains 93.2% and 46.9% mAP on CUHK-SYSU and PRW datasets, which exceeds all the existing one-step methods. | ['Yi Yang', 'Nong Sang', 'Changxin Gao', 'Zhedong Zheng', 'Chuchu Han'] | 2021-02-22 | null | null | null | null | ['person-search'] | ['computer-vision'] | [-1.69870928e-01 -5.66457570e-01 1.25568941e-01 -5.58721006e-01
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36057931-3bb2-4fb7-a048-d9daf9eb942d | abess-a-fast-best-subset-selection-library-in | 2110.09697 | null | https://arxiv.org/abs/2110.09697v2 | https://arxiv.org/pdf/2110.09697v2.pdf | abess: A Fast Best Subset Selection Library in Python and R | We introduce a new library named abess that implements a unified framework of best-subset selection for solving diverse machine learning problems, e.g., linear regression, classification, and principal component analysis. Particularly, the abess certifiably gets the optimal solution within polynomial times with high probability under the linear model. Our efficient implementation allows abess to attain the solution of best-subset selection problems as fast as or even 20x faster than existing competing variable (model) selection toolboxes. Furthermore, it supports common variants like best group subset selection and $\ell_2$ regularized best-subset selection. The core of the library is programmed in C++. For ease of use, a Python library is designed for conveniently integrating with scikit-learn, and it can be installed from the Python library Index. In addition, a user-friendly R library is available at the Comprehensive R Archive Network. The source code is available at: https://github.com/abess-team/abess. | ['Xueqin Wang', 'Junxian Zhu', 'Shiyun Lin', 'Yanhang Zhang', 'Kangkang Jiang', 'Junhao Huang', 'Liyuan Hu', 'Jin Zhu'] | 2021-10-19 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [-1.51973844e-01 -4.69400227e-01 -4.62839395e-01 -4.32906449e-01
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6eb21137-e74e-4c5f-971f-5641b3811b1e | simple-unsupervised-summarization-by-1 | 1907.13337 | null | https://arxiv.org/abs/1907.13337v1 | https://arxiv.org/pdf/1907.13337v1.pdf | Simple Unsupervised Summarization by Contextual Matching | We propose an unsupervised method for sentence summarization using only language modeling. The approach employs two language models, one that is generic (i.e. pretrained), and the other that is specific to the target domain. We show that by using a product-of-experts criteria these are enough for maintaining continuous contextual matching while maintaining output fluency. Experiments on both abstractive and extractive sentence summarization data sets show promising results of our method without being exposed to any paired data. | ['Alexander M. Rush', 'Jiawei Zhou'] | 2019-07-31 | simple-unsupervised-summarization-by | https://aclanthology.org/P19-1503 | https://aclanthology.org/P19-1503.pdf | acl-2019-7 | ['abstractive-sentence-summarization', 'unsupervised-sentence-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.98675883e-01 7.01626658e-01 -1.18122727e-01 -4.72381055e-01
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ce625b41-6de1-48af-8cf9-13b676ebf8bb | tell-me-how-to-ask-again-question-data | 2010.01475 | null | https://arxiv.org/abs/2010.01475v1 | https://arxiv.org/pdf/2010.01475v1.pdf | Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space | In this paper, we propose a novel data augmentation method, referred to as Controllable Rewriting based Question Data Augmentation (CRQDA), for machine reading comprehension (MRC), question generation, and question-answering natural language inference tasks. We treat the question data augmentation task as a constrained question rewriting problem to generate context-relevant, high-quality, and diverse question data samples. CRQDA utilizes a Transformer autoencoder to map the original discrete question into a continuous embedding space. It then uses a pre-trained MRC model to revise the question representation iteratively with gradient-based optimization. Finally, the revised question representations are mapped back into the discrete space, which serve as additional question data. Comprehensive experiments on SQuAD 2.0, SQuAD 1.1 question generation, and QNLI tasks demonstrate the effectiveness of CRQDA | ['Ming Zhou', 'Nan Duan', 'Jiancheng Lv', 'Jiusheng Chen', 'Yu Yan', 'Jie Fu', 'Yeyun Gong', 'Dayiheng Liu'] | 2020-10-04 | null | https://aclanthology.org/2020.emnlp-main.467 | https://aclanthology.org/2020.emnlp-main.467.pdf | emnlp-2020-11 | ['question-rewriting'] | ['natural-language-processing'] | [ 5.69194734e-01 4.51230049e-01 2.80785978e-01 -6.32399797e-01
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0cd83a58-fd37-4ac2-898a-231f14e2cda7 | shadow-background-noise-3d-spatial | 2207.03064 | null | https://arxiv.org/abs/2207.03064v2 | https://arxiv.org/pdf/2207.03064v2.pdf | Shadow-Background-Noise 3D Spatial Decomposition Using Sparse Low-Rank Gaussian Properties for Video-SAR Moving Target Shadow Enhancement | Moving target shadows among video synthetic aperture radar (Video-SAR) images are always interfered by low scattering backgrounds and cluttered noises, causing poor detec-tion-tracking accuracy. Thus, a shadow-background-noise 3D spatial decomposition (SBN-3D-SD) model is proposed to enhance shadows for higher detection-tracking accuracy. It leverages the sparse property of shadows, the low-rank property of back-grounds, and the Gaussian property of noises to perform 3D spatial three-decomposition. It separates shadows from back-grounds and noises by the alternating direction method of multi-pliers (ADMM). Results on the Sandia National Laboratories (SNL) data verify its effectiveness. It boosts the shadow saliency from the qualitative and quantitative evaluation. It boosts the shadow detection accuracy of Faster R-CNN, RetinaNet and YOLOv3. It also boosts the shadow tracking accuracy of TransTrack, FairMOT and ByteTrack. | ['Xu Zhan', 'Jun Shi', 'Zhenyu Yang', 'Tianwen Zhang', 'Xiaowo Xu', 'Xiaoling Zhang'] | 2022-07-07 | null | null | null | null | ['shadow-detection'] | ['computer-vision'] | [ 9.27597508e-02 -6.12292945e-01 1.59163490e-01 -2.75942180e-02
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3421c301-759d-4cfe-85d8-c673381a2869 | extractive-summarization-of-legal-decisions | 2210.12437 | null | https://arxiv.org/abs/2210.12437v1 | https://arxiv.org/pdf/2210.12437v1.pdf | Extractive Summarization of Legal Decisions using Multi-task Learning and Maximal Marginal Relevance | Summarizing legal decisions requires the expertise of law practitioners, which is both time- and cost-intensive. This paper presents techniques for extractive summarization of legal decisions in a low-resource setting using limited expert annotated data. We test a set of models that locate relevant content using a sequential model and tackle redundancy by leveraging maximal marginal relevance to compose summaries. We also demonstrate an implicit approach to help train our proposed models generate more informative summaries. Our multi-task learning model variant leverages rhetorical role identification as an auxiliary task to further improve the summarizer. We perform extensive experiments on datasets containing legal decisions from the US Board of Veterans' Appeals and conduct quantitative and expert-ranked evaluations of our models. Our results show that the proposed approaches can achieve ROUGE scores vis-\`a-vis expert extracted summaries that match those achieved by inter-annotator comparison. | ['Matthias Grabmair', 'Shanshan Xu', 'Abhishek Agarwal'] | 2022-10-22 | null | null | null | null | ['extractive-summarization'] | ['natural-language-processing'] | [ 4.40813303e-01 5.29216945e-01 -7.69320130e-01 -4.17784870e-01
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464159bb-cbc0-463f-b610-2724b2f4434d | the-franz-parisi-criterion-and-computational | 2205.09727 | null | https://arxiv.org/abs/2205.09727v2 | https://arxiv.org/pdf/2205.09727v2.pdf | The Franz-Parisi Criterion and Computational Trade-offs in High Dimensional Statistics | Many high-dimensional statistical inference problems are believed to possess inherent computational hardness. Various frameworks have been proposed to give rigorous evidence for such hardness, including lower bounds against restricted models of computation (such as low-degree functions), as well as methods rooted in statistical physics that are based on free energy landscapes. This paper aims to make a rigorous connection between the seemingly different low-degree and free-energy based approaches. We define a free-energy based criterion for hardness and formally connect it to the well-established notion of low-degree hardness for a broad class of statistical problems, namely all Gaussian additive models and certain models with a sparse planted signal. By leveraging these rigorous connections we are able to: establish that for Gaussian additive models the "algebraic" notion of low-degree hardness implies failure of "geometric" local MCMC algorithms, and provide new low-degree lower bounds for sparse linear regression which seem difficult to prove directly. These results provide both conceptual insights into the connections between different notions of hardness, as well as concrete technical tools such as new methods for proving low-degree lower bounds. | ['Ilias Zadik', 'Alexander S. Wein', 'Tselil Schramm', 'Samuel B. Hopkins', 'Ahmed El Alaoui', 'Afonso S. Bandeira'] | 2022-05-19 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 5.61883271e-01 5.58884621e-01 1.76552787e-01 -3.25175852e-01
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4055b9f4-82e8-486c-996d-59a762b788b2 | an-intelligent-decision-support-ensemble | 2210.14906 | null | https://arxiv.org/abs/2210.14906v1 | https://arxiv.org/pdf/2210.14906v1.pdf | An Intelligent Decision Support Ensemble Voting Model for Coronary Artery Disease Prediction in Smart Healthcare Monitoring Environments | Coronary artery disease (CAD) is one of the most common cardiac diseases worldwide and causes disability and economic burden. It is the world's leading and most serious cause of mortality, with approximately 80% of deaths reported in low- and middle-income countries. The preferred and most precise diagnostic tool for CAD is angiography, but it is invasive, expensive, and technically demanding. However, the research community is increasingly interested in the computer-aided diagnosis of CAD via the utilization of machine learning (ML) methods. The purpose of this work is to present an e-diagnosis tool based on ML algorithms that can be used in a smart healthcare monitoring system. We applied the most accurate machine learning methods that have shown superior results in the literature to different medical datasets such as RandomForest, XGboost, MLP, J48, AdaBoost, NaiveBayes, LogitBoost, KNN. Every single classifier can be efficient on a different dataset. Thus, an ensemble model using majority voting was designed to take advantage of the well-performed single classifiers, Ensemble learning aims to combine the forecasts of multiple individual classifiers to achieve higher performance than individual classifiers in terms of precision, specificity, sensitivity, and accuracy; furthermore, we have benchmarked our proposed model with the most efficient and well-known ensemble models, such as Bagging, Stacking methods based on the cross-validation technique, The experimental results confirm that the ensemble majority voting approach based on the top 3 classifiers: MultilayerPerceptron, RandomForest, and AdaBoost, achieves the highest accuracy of 88,12% and outperforms all other classifiers. This study demonstrates that the majority voting ensemble approach proposed above is the most accurate machine learning classification approach for the prediction and detection of coronary artery disease. | ['El Houssine El Mazoudi', 'Noureddine Elalami', 'Jamila Elalami', 'Anas Maach'] | 2022-10-25 | null | null | null | null | ['disease-prediction'] | ['medical'] | [-3.28484744e-01 -3.96321267e-01 -3.10830981e-01 -1.81293726e-01
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fa475c1e-76eb-42ab-9ea6-4b1cc4d57789 | bi-lstm-price-prediction-based-on-attention | 2212.03443 | null | https://arxiv.org/abs/2212.03443v2 | https://arxiv.org/pdf/2212.03443v2.pdf | Bi-LSTM Price Prediction based on Attention Mechanism | With the increasing enrichment and development of the financial derivatives market, the frequency of transactions is also faster and faster. Due to human limitations, algorithms and automatic trading have recently become the focus of discussion. In this paper, we propose a bidirectional LSTM neural network based on an attention mechanism, which is based on two popular assets, gold and bitcoin. In terms of Feature Engineering, on the one hand, we add traditional technical factors, and at the same time, we combine time series models to develop factors. In the selection of model parameters, we finally chose a two-layer deep learning network. According to AUC measurement, the accuracy of bitcoin and gold is 71.94% and 73.03% respectively. Using the forecast results, we achieved a return of 1089.34% in two years. At the same time, we also compare the attention Bi-LSTM model proposed in this paper with the traditional model, and the results show that our model has the best performance in this data set. Finally, we discuss the significance of the model and the experimental results, as well as the possible improvement direction in the future. | ['Ye Li', 'Leyi Cui', 'Jiashu Lou'] | 2022-12-07 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [-9.02952552e-01 -4.33310449e-01 -9.06683579e-02 -8.90154913e-02
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39389e25-ff4e-43b7-b420-741ba9939684 | sisua-semi-supervised-generative-autoencoder | null | null | https://www.biorxiv.org/content/10.1101/631382v1 | https://www.biorxiv.org/content/biorxiv/early/2019/05/08/631382.full-text.pdf | SISUA: Semi-Supervised Generative Autoencoder for Single Cell Data | Single-cell transcriptomics offers a tool to study the diversity of cell phenotypes through snapshots of the abundance of mRNA in individual cells. Often there is additional information available besides the single cell gene expression counts, such as bulk transcriptome data from the same tissue, or quantification of surface protein levels from the same cells. In this study, we propose models based on the Bayesian generative approach, where protein quantification available as CITE-seq counts from the same cells are used to constrain the learning process, thus forming a semi-supervised model. The generative model is based on the deep variational autoencoder (VAE) neural network architecture. | ['Merja Heinäniemi', 'Ville Hautamäki', 'Gerardo González', 'Juha Mehtonen', 'Roger Kramer', 'Trung Ngo Trong'] | 2019-05-08 | null | null | null | icml-workshop-on-computational-biology-2019 | ['single-cell-modeling'] | ['medical'] | [-6.98349718e-03 -2.92278886e-01 1.58896834e-01 -1.40779331e-01
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b6d770b3-0a12-4a9d-9460-0e23fc6084ef | dstcgcn-learning-dynamic-spatial-temporal | 2307.00518 | null | https://arxiv.org/abs/2307.00518v1 | https://arxiv.org/pdf/2307.00518v1.pdf | DSTCGCN: Learning Dynamic Spatial-Temporal Cross Dependencies for Traffic Forecasting | Traffic forecasting is essential to intelligent transportation systems, which is challenging due to the complicated spatial and temporal dependencies within a road network. Existing works usually learn spatial and temporal dependencies separately, ignoring the dependencies crossing spatial and temporal dimensions. In this paper, we propose DSTCGCN, a dynamic spatial-temporal cross graph convolution network to learn dynamic spatial and temporal dependencies jointly via graphs for traffic forecasting. Specifically, we introduce a fast Fourier transform (FFT) based attentive selector to choose relevant time steps for each time step based on time-varying traffic data. Given the selected time steps, we introduce a dynamic cross graph construction module, consisting of the spatial graph construction, temporal connection graph construction, and fusion modules, to learn dynamic spatial-temporal cross dependencies without pre-defined priors. Extensive experiments on six real-world datasets demonstrate that DSTCGCN achieves the state-of-the-art performance. | ['Ling Chen', 'Binqing Wu'] | 2023-07-02 | null | null | null | null | ['graph-construction'] | ['graphs'] | [-1.43842995e-01 -3.61264765e-01 -2.57546842e-01 -7.38382280e-01
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5f776c56-bac5-47ad-8154-e40dfb442827 | learning-enhancement-from-degradation-a | 2303.04603 | null | https://arxiv.org/abs/2303.04603v1 | https://arxiv.org/pdf/2303.04603v1.pdf | Learning Enhancement From Degradation: A Diffusion Model For Fundus Image Enhancement | The quality of a fundus image can be compromised by numerous factors, many of which are challenging to be appropriately and mathematically modeled. In this paper, we introduce a novel diffusion model based framework, named Learning Enhancement from Degradation (LED), for enhancing fundus images. Specifically, we first adopt a data-driven degradation framework to learn degradation mappings from unpaired high-quality to low-quality images. We then apply a conditional diffusion model to learn the inverse enhancement process in a paired manner. The proposed LED is able to output enhancement results that maintain clinically important features with better clarity. Moreover, in the inference phase, LED can be easily and effectively integrated with any existing fundus image enhancement framework. We evaluate the proposed LED on several downstream tasks with respect to various clinically-relevant metrics, successfully demonstrating its superiority over existing state-of-the-art methods both quantitatively and qualitatively. The source code is available at https://github.com/QtacierP/LED. | ['Xiaoying Tang', 'Wenhan Luo', 'Huaqing He', 'Yijin Huang', 'Li Lin', 'Puijin Cheng'] | 2023-03-08 | null | null | null | null | ['image-enhancement'] | ['computer-vision'] | [ 2.70030469e-01 -1.50698513e-01 3.58088762e-02 -3.75992000e-01
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affaa7db-12bf-4421-beb4-2be5d8b34069 | a-deep-face-identification-network-enhanced | 1805.00324 | null | http://arxiv.org/abs/1805.00324v1 | http://arxiv.org/pdf/1805.00324v1.pdf | A Deep Face Identification Network Enhanced by Facial Attributes Prediction | In this paper, we propose a new deep framework which predicts facial
attributes and leverage it as a soft modality to improve face identification
performance. Our model is an end to end framework which consists of a
convolutional neural network (CNN) whose output is fanned out into two separate
branches; the first branch predicts facial attributes while the second branch
identifies face images. Contrary to the existing multi-task methods which only
use a shared CNN feature space to train these two tasks jointly, we fuse the
predicted attributes with the features from the face modality in order to
improve the face identification performance. Experimental results show that our
model brings benefits to both face identification as well as facial attribute
prediction performance, especially in the case of identity facial attributes
such as gender prediction. We tested our model on two standard datasets
annotated by identities and face attributes. Experimental results indicate that
the proposed model outperforms most of the current existing face identification
and attribute prediction methods. | ['Nasser M. Nasrabadi', 'Jeremy Dawson', 'Fariborz Taherkhani'] | 2018-04-20 | null | null | null | null | ['gender-prediction'] | ['computer-vision'] | [ 1.85302824e-01 1.23272650e-01 -2.87813753e-01 -1.09272552e+00
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0aa71286-89de-4a95-b34f-282641e0eeae | towards-enhancing-health-coaching-dialogue-in | null | null | https://aclanthology.org/2022.coling-1.58 | https://aclanthology.org/2022.coling-1.58.pdf | Towards Enhancing Health Coaching Dialogue in Low-Resource Settings | Health coaching helps patients identify and accomplish lifestyle-related goals, effectively improving the control of chronic diseases and mitigating mental health conditions. However, health coaching is cost-prohibitive due to its highly personalized and labor-intensive nature. In this paper, we propose to build a dialogue system that converses with the patients, helps them create and accomplish specific goals, and can address their emotions with empathy. However, building such a system is challenging since real-world health coaching datasets are limited and empathy is subtle. Thus, we propose a modularized health coaching dialogue with simplified NLU and NLG frameworks combined with mechanism-conditioned empathetic response generation. Through automatic and human evaluation, we show that our system generates more empathetic, fluent, and coherent responses and outperforms the state-of-the-art in NLU tasks while requiring less annotation. We view our approach as a key step towards building automated and more accessible health coaching systems. | ['Shweta Yadav', 'Nikolaos Agadakos', 'Ben Gerber', 'Bing Liu', 'Lisa Sharp', 'Brian Ziebart', 'Barbara Di Eugenio', 'Yue Zhou'] | null | null | null | null | coling-2022-10 | ['empathetic-response-generation'] | ['natural-language-processing'] | [ 2.74456572e-02 8.60300124e-01 -4.40217465e-01 -4.92508680e-01
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87fb4865-d8b8-4d56-885b-c4cbf3c87059 | cca-mdd-a-coupled-cross-attention-based | 2111.08191 | null | https://arxiv.org/abs/2111.08191v2 | https://arxiv.org/pdf/2111.08191v2.pdf | CoCA-MDD: A Coupled Cross-Attention based Framework for Streaming Mispronunciation Detection and Diagnosis | Mispronunciation detection and diagnosis (MDD) is a popular research focus in computer-aided pronunciation training (CAPT) systems. End-to-end (e2e) approaches are becoming dominant in MDD. However an e2e MDD model usually requires entire speech utterances as input context, which leads to significant time latency especially for long paragraphs. We propose a streaming e2e MDD model called CoCA-MDD. We utilize conv-transformer structure to encode input speech in a streaming manner. A coupled cross-attention (CoCA) mechanism is proposed to integrate frame-level acoustic features with encoded reference linguistic features. CoCA also enables our model to perform mispronunciation classification with whole utterances. The proposed model allows system fusion between the streaming output and mispronunciation classification output for further performance enhancement. We evaluate CoCA-MDD on publicly available corpora. CoCA-MDD achieves F1 scores of 57.03% and 60.78% for streaming and fusion modes respectively on L2-ARCTIC. For phone-level pronunciation scoring, CoCA-MDD achieves 0.58 Pearson correlation coefficient (PCC) value on SpeechOcean762. | ['Xiao Chen', 'Qun Liu', 'Xin Jiang', 'Yasheng Wang', 'Yuanyuan Guo', 'Baohua Xu', 'Yu Ting Yeung', 'Wenyong Huang', 'Liqun Deng', 'Nianzu Zheng'] | 2021-11-16 | null | null | null | null | ['phone-level-pronunciation-scoring'] | ['speech'] | [ 1.97598599e-02 -7.48424232e-02 1.70394868e-01 -4.82723743e-01
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49ed58cf-e31f-4acf-95fd-d715a60627fe | multilingual-dependency-parsing-for-low-1 | null | null | https://aclanthology.org/2021.iwpt-1.9 | https://aclanthology.org/2021.iwpt-1.9.pdf | Multilingual Dependency Parsing for Low-Resource African Languages: Case Studies on Bambara, Wolof, and Yoruba | This paper describes a methodology for syntactic knowledge transfer between high-resource languages to extremely low-resource languages. The methodology consists in leveraging multilingual BERT self-attention model pretrained on large datasets to develop a multilingual multi-task model that can predict Universal Dependencies annotations for three African low-resource languages. The UD annotations include universal part-of-speech, morphological features, lemmas, and dependency trees. In our experiments, we used multilingual word embeddings and a total of 11 Universal Dependencies treebanks drawn from three high-resource languages (English, French, Norwegian) and three low-resource languages (Bambara, Wolof and Yoruba). We developed various models to test specific language combinations involving contemporary contact languages or genetically related languages. The results of the experiments show that multilingual models that involve high-resource languages and low-resource languages with contemporary contact between each other can provide better results than combinations that only include unrelated languages. As far genetic relationships are concerned, we could not draw any conclusion regarding the impact of language combinations involving the selected low-resource languages, namely Wolof and Yoruba. | ['Cheikh M. Bamba Dione'] | null | null | null | null | acl-iwpt-2021-8 | ['multilingual-word-embeddings'] | ['methodology'] | [-5.45044601e-01 -1.86261218e-02 -2.46166483e-01 -4.02095675e-01
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69348a1a-71be-4049-ac7d-00c2dee6b4ef | heat-hyperedge-attention-networks | 2201.12113 | null | https://arxiv.org/abs/2201.12113v2 | https://arxiv.org/pdf/2201.12113v2.pdf | HEAT: Hyperedge Attention Networks | Learning from structured data is a core machine learning task. Commonly, such data is represented as graphs, which normally only consider (typed) binary relationships between pairs of nodes. This is a substantial limitation for many domains with highly-structured data. One important such domain is source code, where hypergraph-based representations can better capture the semantically rich and structured nature of code. In this work, we present HEAT, a neural model capable of representing typed and qualified hypergraphs, where each hyperedge explicitly qualifies how participating nodes contribute. It can be viewed as a generalization of both message passing neural networks and Transformers. We evaluate HEAT on knowledge base completion and on bug detection and repair using a novel hypergraph representation of programs. In both settings, it outperforms strong baselines, indicating its power and generality. | ['Miltiadis Allamanis', 'Marc Brockschmidt', 'Dobrik Georgiev'] | 2022-01-28 | null | null | null | null | ['knowledge-base-completion', 'knowledge-base-completion'] | ['graphs', 'knowledge-base'] | [ 2.14132648e-02 5.18064260e-01 -7.19837546e-01 -3.36094648e-01
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c4ac3d7b-e809-442f-9a85-1131d53d6dce | a-demographic-attribute-guided-approach-to | 2205.10254 | null | https://arxiv.org/abs/2205.10254v1 | https://arxiv.org/pdf/2205.10254v1.pdf | A Demographic Attribute Guided Approach to Age Estimation | Face-based age estimation has attracted enormous attention due to wide applications to public security surveillance, human-computer interaction, etc. With vigorous development of deep learning, age estimation based on deep neural network has become the mainstream practice. However, seeking a more suitable problem paradigm for age change characteristics, designing the corresponding loss function and designing a more effective feature extraction module still needs to be studied. What is more, change of face age is also related to demographic attributes such as ethnicity and gender, and the dynamics of different age groups is also quite different. This problem has so far not been paid enough attention to. How to use demographic attribute information to improve the performance of age estimation remains to be further explored. In light of these issues, this research makes full use of auxiliary information of face attributes and proposes a new age estimation approach with an attribute guidance module. We first design a multi-scale attention residual convolution unit (MARCU) to extract robust facial features other than simply using other standard feature modules such as VGG and ResNet. Then, after being especially treated through full connection (FC) layers, the facial demographic attributes are weight-summed by 1*1 convolutional layer and eventually merged with the age features by a global FC layer. Lastly, we propose a new error compression ranking (ECR) loss to better converge the age regression value. Experimental results on three public datasets of UTKFace, LAP2016 and Morph show that our proposed approach achieves superior performance compared to other state-of-the-art methods. | ['Heng Zhao', 'Liaojun Pang', 'Kaituo Zhang', 'Zhicheng Cao'] | 2022-05-20 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [-7.43045360e-02 -5.37850931e-02 -7.30310893e-03 -7.86773503e-01
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0633767a-2c00-49f4-8ee7-01607450300d | twitter-sentiment-analysis-via-bi-sense-emoji | 1807.07961 | null | http://arxiv.org/abs/1807.07961v2 | http://arxiv.org/pdf/1807.07961v2.pdf | Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM | Sentiment analysis on large-scale social media data is important to bridge
the gaps between social media contents and real world activities including
political election prediction, individual and public emotional status
monitoring and analysis, and so on. Although textual sentiment analysis has
been well studied based on platforms such as Twitter and Instagram, analysis of
the role of extensive emoji uses in sentiment analysis remains light. In this
paper, we propose a novel scheme for Twitter sentiment analysis with extra
attention on emojis. We first learn bi-sense emoji embeddings under positive
and negative sentimental tweets individually, and then train a sentiment
classifier by attending on these bi-sense emoji embeddings with an
attention-based long short-term memory network (LSTM). Our experiments show
that the bi-sense embedding is effective for extracting sentiment-aware
embeddings of emojis and outperforms the state-of-the-art models. We also
visualize the attentions to show that the bi-sense emoji embedding provides
better guidance on the attention mechanism to obtain a more robust
understanding of the semantics and sentiments. | ['Jianbo Yuan', 'Jiebo Luo', 'Yuxiao Chen', 'Quanzeng You'] | 2018-07-20 | null | null | null | null | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-3.29884619e-01 -2.25878581e-02 -2.21871197e-01 -6.65569901e-01
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6e10369c-8874-4b8c-8998-61412531bcab | armanemo-a-persian-dataset-for-text-based | 2207.11808 | null | https://arxiv.org/abs/2207.11808v1 | https://arxiv.org/pdf/2207.11808v1.pdf | ArmanEmo: A Persian Dataset for Text-based Emotion Detection | With the recent proliferation of open textual data on social media platforms, Emotion Detection (ED) from Text has received more attention over the past years. It has many applications, especially for businesses and online service providers, where emotion detection techniques can help them make informed commercial decisions by analyzing customers/users' feelings towards their products and services. In this study, we introduce ArmanEmo, a human-labeled emotion dataset of more than 7000 Persian sentences labeled for seven categories. The dataset has been collected from different resources, including Twitter, Instagram, and Digikala (an Iranian e-commerce company) comments. Labels are based on Ekman's six basic emotions (Anger, Fear, Happiness, Hatred, Sadness, Wonder) and another category (Other) to consider any other emotion not included in Ekman's model. Along with the dataset, we have provided several baseline models for emotion classification focusing on the state-of-the-art transformer-based language models. Our best model achieves a macro-averaged F1 score of 75.39 percent across our test dataset. Moreover, we also conduct transfer learning experiments to compare our proposed dataset's generalization against other Persian emotion datasets. Results of these experiments suggest that our dataset has superior generalizability among the existing Persian emotion datasets. ArmanEmo is publicly available for non-commercial use at https://github.com/Arman-Rayan-Sharif/arman-text-emotion. | ['Hossein Zeinali', 'Hamid Habibzadeh Moshtaghin', 'Javad Peymanfard', 'Hossein Mirzaee'] | 2022-07-24 | null | null | null | null | ['emotion-classification', 'emotion-classification'] | ['computer-vision', 'natural-language-processing'] | [-5.28145671e-01 -7.09607899e-02 -2.06953615e-01 -6.38195634e-01
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ab3d8bd6-a725-4b28-bc8a-99f27d29b095 | bottom-up-constituency-parsing-and-nested | 2110.05419 | null | https://arxiv.org/abs/2110.05419v2 | https://arxiv.org/pdf/2110.05419v2.pdf | Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networks | Constituency parsing and nested named entity recognition (NER) are similar tasks since they both aim to predict a collection of nested and non-crossing spans. In this work, we cast nested NER to constituency parsing and propose a novel pointing mechanism for bottom-up parsing to tackle both tasks. The key idea is based on the observation that if we traverse a constituency tree in post-order, i.e., visiting a parent after its children, then two consecutively visited spans would share a boundary. Our model tracks the shared boundaries and predicts the next boundary at each step by leveraging a pointer network. As a result, it needs only linear steps to parse and thus is efficient. It also maintains a parsing configuration for structural consistency, i.e., always outputting valid trees. Experimentally, our model achieves the state-of-the-art performance on PTB among all BERT-based models (96.01 F1 score) and competitive performance on CTB7 in constituency parsing; and it also achieves strong performance on three benchmark datasets of nested NER: ACE2004, ACE2005, and GENIA. Our code is publicly available at \url{https://github.com/sustcsonglin/pointer-net-for-nested}. | ['Kewei Tu', 'Songlin Yang'] | 2021-10-11 | null | https://aclanthology.org/2022.acl-long.171 | https://aclanthology.org/2022.acl-long.171.pdf | acl-2022-5 | ['constituency-parsing', 'nested-named-entity-recognition'] | ['natural-language-processing', 'natural-language-processing'] | [-7.44216219e-02 4.01713639e-01 -4.11537588e-01 -5.69003761e-01
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dc0f5d11-93d7-4be9-b5eb-4b286a45e85a | measuring-and-improving-compositional | null | null | https://openreview.net/forum?id=-B3vVVeVyTr | https://openreview.net/pdf?id=-B3vVVeVyTr | Measuring and Improving Compositional Generalization in Text-to-SQL via Component Alignment | Recently, the challenge of compositional generalization in NLP has attracted more and more attention. Specifically, many prior works show that neural networks struggle with compositional generalization where training and testing distributions differ. However, most of these works are based on word-level synthetic data or a specific data split method to generate compositional biases. In this work, we propose a clause-level compositional example generation method, and we focus on text-to-SQL tasks. We start by splitting the sentences in the Spider text-to-SQL dataset into several sub-sentences, annotating each sub-sentence with its corresponding SQL clause, resulting in a new dataset, Spider-SS. Building upon Spider-SS, we further construct a new dataset named Spider-CG, by substituting and appending Spider-SS sub-sentences to test the ability of models to generalize compositionally. Experiments show that previous models suffer significant performance degradation when evaluated on Spider-CG, even though every sub-sentence has been seen during training. To deal with this problem, we modify the RATSQL+GAP model to fit the segmented data of Spider-SS, and results show that this method can improve generalization performance. | ['Anonymous'] | 2021-10-16 | null | null | null | acl-arr-october-2021-10 | ['text-to-sql'] | ['computer-code'] | [ 6.14654422e-01 1.61847249e-01 -1.37379453e-01 -7.85206914e-01
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f03d4101-0439-49fc-88b5-1142d7d3afb7 | iiitt-lt-edi-eacl2021-hope-speech-detection | 2104.09066 | null | https://arxiv.org/abs/2104.09066v1 | https://arxiv.org/pdf/2104.09066v1.pdf | IIITT@LT-EDI-EACL2021-Hope Speech Detection: There is always Hope in Transformers | In a world filled with serious challenges like climate change, religious and political conflicts, global pandemics, terrorism, and racial discrimination, an internet full of hate speech, abusive and offensive content is the last thing we desire for. In this paper, we work to identify and promote positive and supportive content on these platforms. We work with several transformer-based models to classify social media comments as hope speech or not-hope speech in English, Malayalam and Tamil languages. This paper portrays our work for the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion at LT-EDI 2021- EACL 2021. | ['Bharathi Raja Chakravarthi', 'Sajeetha Thavareesan', 'Ruba Priyadharshini', 'Adeep Hande', 'Karthik Puranik'] | 2021-04-19 | iiitt-lt-edi-eacl2021-hope-speech-detection-1 | https://aclanthology.org/2021.ltedi-1.13 | https://aclanthology.org/2021.ltedi-1.13.pdf | null | ['hope-speech-detection'] | ['natural-language-processing'] | [-3.83401334e-01 3.80449623e-01 -6.34102762e-01 9.40471292e-02
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b8286124-ef59-466d-87d8-6b3a50d7e824 | improving-empathetic-response-generation-by | null | null | https://aclanthology.org/2021.findings-emnlp.70 | https://aclanthology.org/2021.findings-emnlp.70.pdf | Improving Empathetic Response Generation by Recognizing Emotion Cause in Conversations | Current approaches to empathetic response generation focus on learning a model to predict an emotion label and generate a response based on this label and have achieved promising results. However, the emotion cause, an essential factor for empathetic responding, is ignored. The emotion cause is a stimulus for human emotions. Recognizing the emotion cause is helpful to better understand human emotions so as to generate more empathetic responses. To this end, we propose a novel framework that improves empathetic response generation by recognizing emotion cause in conversations. Specifically, an emotion reasoner is designed to predict a context emotion label and a sequence of emotion cause-oriented labels, which indicate whether the word is related to the emotion cause. Then we devise both hard and soft gated attention mechanisms to incorporate the emotion cause into response generation. Experiments show that incorporating emotion cause information improves the performance of the model on both emotion recognition and response generation. | ['Ruifeng Xu', 'Jiachen Du', 'Yu Cao', 'Wei Wang', 'Haolin Deng', 'YuHan Liu', 'Jun Gao'] | null | null | null | null | findings-emnlp-2021-11 | ['empathetic-response-generation', 'recognizing-emotion-cause-in-conversations'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.51440397e-01 9.65228453e-02 -2.35288486e-01 -1.01787663e+00
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95f5f8e3-12f9-4c02-9e2a-440fe519d25b | spatio-temporal-tubelet-feature-aggregation | 2004.00451 | null | https://arxiv.org/abs/2004.00451v2 | https://arxiv.org/pdf/2004.00451v2.pdf | Spatio-temporal Tubelet Feature Aggregation and Object Linking in Videos | This paper addresses the problem of how to exploit spatio-temporal information available in videos to improve the object detection precision. We propose a two stage object detector called FANet based on short-term spatio-temporal feature aggregation to give a first detection set, and long-term object linking to refine these detections. Firstly, we generate a set of short tubelet proposals containing the object in $N$ consecutive frames. Then, we aggregate RoI pooled deep features through the tubelet using a temporal pooling operator that summarizes the information with a fixed size output independent of the number of input frames. On top of that, we define a double head implementation that we feed with spatio-temporal aggregated information for spatio-temporal object classification, and with spatial information extracted from the current frame for object localization and spatial classification. Furthermore, we also specialize each head branch architecture to better perform in each task taking into account the input data. Finally, a long-term linking method builds long tubes using the previously calculated short tubelets to overcome detection errors. We have evaluated our model in the widely used ImageNet VID dataset achieving a 80.9% mAP, which is the new state-of-the-art result for single models. Also, in the challenging small object detection dataset USC-GRAD-STDdb, our proposal outperforms the single frame baseline by 5.4% mAP. | ['Víctor M. Brea', 'Manuel Mucientes', 'Daniel Cores'] | 2020-04-01 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 1.85630110e-03 -1.77021567e-02 4.73624319e-02 -3.28249723e-01
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e04e7397-cbc1-49c5-8916-2bdaec09ff48 | overprompt-enhancing-chatgpt-capabilities | 2305.14973 | null | https://arxiv.org/abs/2305.14973v1 | https://arxiv.org/pdf/2305.14973v1.pdf | OverPrompt: Enhancing ChatGPT Capabilities through an Efficient In-Context Learning Approach | The exceptional performance of pre-trained large language models has revolutionised various applications, but their adoption in production environments is hindered by prohibitive costs and inefficiencies, particularly when utilising long prompts. This paper proposes OverPrompt, an in-context learning method aimed at improving LLM efficiency and performance by processing multiple inputs in parallel. Evaluated across diverse datasets, OverPrompt enhances task efficiency and integrates a diverse range of examples for improved performance. Particularly, it amplifies fact-checking and sentiment analysis tasks when supplemented with contextual information. Synthetic data grouping further enhances performance, suggesting a viable approach for data augmentation. | ['Lin Gui', 'Yulan He', 'Runcong Zhao', 'Jiazheng Li'] | 2023-05-24 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [ 3.79700750e-01 -4.80921566e-02 -4.12411541e-01 -5.05904198e-01
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743768d0-9e68-4a7d-8e7f-5f1db0e2dde8 | knowledge-enriched-visual-storytelling | 1912.01496 | null | https://arxiv.org/abs/1912.01496v1 | https://arxiv.org/pdf/1912.01496v1.pdf | Knowledge-Enriched Visual Storytelling | Stories are diverse and highly personalized, resulting in a large possible output space for story generation. Existing end-to-end approaches produce monotonous stories because they are limited to the vocabulary and knowledge in a single training dataset. This paper introduces KG-Story, a three-stage framework that allows the story generation model to take advantage of external Knowledge Graphs to produce interesting stories. KG-Story distills a set of representative words from the input prompts, enriches the word set by using external knowledge graphs, and finally generates stories based on the enriched word set. This distill-enrich-generate framework allows the use of external resources not only for the enrichment phase, but also for the distillation and generation phases. In this paper, we show the superiority of KG-Story for visual storytelling, where the input prompt is a sequence of five photos and the output is a short story. Per the human ranking evaluation, stories generated by KG-Story are on average ranked better than that of the state-of-the-art systems. Our code and output stories are available at https://github.com/zychen423/KE-VIST. | ['Lun-Wei Ku', "Ting-Hao 'Kenneth' Huang", 'Tzu-Yuan Lin', 'Chih-Chia Li', 'Chi-Yang Hsu', 'Zi-Yuan Chen', 'Chao-Chun Hsu'] | 2019-12-03 | null | null | null | null | ['visual-storytelling'] | ['natural-language-processing'] | [ 2.51745284e-02 4.28340107e-01 -1.88209172e-02 -4.31502573e-02
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2ebadc83-f1fb-4298-a495-3ad93c11394f | deep-multi-metric-learning-for-text | 2007.10479 | null | https://arxiv.org/abs/2007.10479v1 | https://arxiv.org/pdf/2007.10479v1.pdf | Deep multi-metric learning for text-independent speaker verification | Text-independent speaker verification is an important artificial intelligence problem that has a wide spectrum of applications, such as criminal investigation, payment certification, and interest-based customer services. The purpose of text-independent speaker verification is to determine whether two given uncontrolled utterances originate from the same speaker or not. Extracting speech features for each speaker using deep neural networks is a promising direction to explore and a straightforward solution is to train the discriminative feature extraction network by using a metric learning loss function. However, a single loss function often has certain limitations. Thus, we use deep multi-metric learning to address the problem and introduce three different losses for this problem, i.e., triplet loss, n-pair loss and angular loss. The three loss functions work in a cooperative way to train a feature extraction network equipped with Residual connections and squeeze-and-excitation attention. We conduct experiments on the large-scale \texttt{VoxCeleb2} dataset, which contains over a million utterances from over $6,000$ speakers, and the proposed deep neural network obtains an equal error rate of $3.48\%$, which is a very competitive result. Codes for both training and testing and pretrained models are available at \url{https://github.com/GreatJiweix/DmmlTiSV}, which is the first publicly available code repository for large-scale text-independent speaker verification with performance on par with the state-of-the-art systems. | ['Wenyu Liu', 'Xinggang Wang', 'Bin Feng', 'Jiwei Xu'] | 2020-07-17 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [-7.60591552e-02 -2.22632840e-01 7.17176497e-02 -9.18032825e-01
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fcc99fa2-900c-4fce-8bbb-b77895014c8e | dynamic-vertex-replacement-grammars | 2303.11553 | null | https://arxiv.org/abs/2303.11553v2 | https://arxiv.org/pdf/2303.11553v2.pdf | Dynamic Vertex Replacement Grammars | Context-free graph grammars have shown a remarkable ability to model structures in real-world relational data. However, graph grammars lack the ability to capture time-changing phenomena since the left-to-right transitions of a production rule do not represent temporal change. In the present work, we describe dynamic vertex-replacement grammars (DyVeRG), which generalize vertex replacement grammars in the time domain by providing a formal framework for updating a learned graph grammar in accordance with modifications to its underlying data. We show that DyVeRG grammars can be learned from, and used to generate, real-world dynamic graphs faithfully while remaining human-interpretable. We also demonstrate their ability to forecast by computing dyvergence scores, a novel graph similarity measurement exposed by this framework. | ['Tim Weninger', 'Grant Boquet', 'Timothy La Fond', 'Justus Isaiah Hibshman', 'Daniel Gonzalez Cedre'] | 2023-03-21 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [ 2.52290457e-01 6.79096341e-01 -1.48190707e-01 -4.26399380e-01
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0f761c39-a855-4259-9032-347ef449ba8c | bottom-up-skill-discovery-from-unsegmented | 2109.13841 | null | https://arxiv.org/abs/2109.13841v2 | https://arxiv.org/pdf/2109.13841v2.pdf | Bottom-Up Skill Discovery from Unsegmented Demonstrations for Long-Horizon Robot Manipulation | We tackle real-world long-horizon robot manipulation tasks through skill discovery. We present a bottom-up approach to learning a library of reusable skills from unsegmented demonstrations and use these skills to synthesize prolonged robot behaviors. Our method starts with constructing a hierarchical task structure from each demonstration through agglomerative clustering. From the task structures of multi-task demonstrations, we identify skills based on the recurring patterns and train goal-conditioned sensorimotor policies with hierarchical imitation learning. Finally, we train a meta controller to compose these skills to solve long-horizon manipulation tasks. The entire model can be trained on a small set of human demonstrations collected within 30 minutes without further annotations, making it amendable to real-world deployment. We systematically evaluated our method in simulation environments and on a real robot. Our method has shown superior performance over state-of-the-art imitation learning methods in multi-stage manipulation tasks. Furthermore, skills discovered from multi-task demonstrations boost the average task success by $8\%$ compared to those discovered from individual tasks. | ['Yuke Zhu', 'Peter Stone', 'Yifeng Zhu'] | 2021-09-28 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 2.67984003e-01 3.47669154e-01 4.18865383e-02 -1.35298461e-01
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8260d72d-dd4e-42aa-ab15-f07239146990 | findings-of-the-tsar-2022-shared-task-on | 2302.02888 | null | https://arxiv.org/abs/2302.02888v1 | https://arxiv.org/pdf/2302.02888v1.pdf | Findings of the TSAR-2022 Shared Task on Multilingual Lexical Simplification | We report findings of the TSAR-2022 shared task on multilingual lexical simplification, organized as part of the Workshop on Text Simplification, Accessibility, and Readability TSAR-2022 held in conjunction with EMNLP 2022. The task called the Natural Language Processing research community to contribute with methods to advance the state of the art in multilingual lexical simplification for English, Portuguese, and Spanish. A total of 14 teams submitted the results of their lexical simplification systems for the provided test data. Results of the shared task indicate new benchmarks in Lexical Simplification with English lexical simplification quantitative results noticeably higher than those obtained for Spanish and (Brazilian) Portuguese. | ['Marcos Zampieri', 'Kai North', 'Matthew Shardlow', 'Kim Cheng SHEANG', 'Daniel Ferrés', 'Sanja Štajner', 'Horacio Saggion'] | 2023-02-06 | null | null | null | null | ['lexical-simplification'] | ['natural-language-processing'] | [-3.38413924e-01 3.85419667e-01 1.74098625e-03 -9.04462188e-02
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a2e60cb4-0d4f-4f87-9bb5-b2f85add512c | improving-the-robustness-of-federated | 2204.13414 | null | https://arxiv.org/abs/2204.13414v1 | https://arxiv.org/pdf/2204.13414v1.pdf | Improving the Robustness of Federated Learning for Severely Imbalanced Datasets | With the ever increasing data deluge and the success of deep neural networks, the research of distributed deep learning has become pronounced. Two common approaches to achieve this distributed learning is synchronous and asynchronous weight update. In this manuscript, we have explored very simplistic synchronous weight update mechanisms. It has been seen that with an increasing number of worker nodes, the performance degrades drastically. This effect has been studied in the context of extreme imbalanced classification (e.g. outlier detection). In practical cases, the assumed conditions of i.i.d. may not be fulfilled. There may also arise global class imbalance situations like that of outlier detection where the local servers receive severely imbalanced data and may not get any samples from the minority class. In that case, the DNNs in the local servers will get completely biased towards the majority class that they receive. This would highly impact the learning at the parameter server (which practically does not see any data). It has been observed that in a parallel setting if one uses the existing federated weight update mechanisms at the parameter server, the performance degrades drastically with the increasing number of worker nodes. This is mainly because, with the increasing number of nodes, there is a high chance that one worker node gets a very small portion of the data, either not enough to train the model without overfitting or having a highly imbalanced class distribution. The chapter, hence, proposes a workaround to this problem by introducing the concept of adaptive cost-sensitive momentum averaging. It is seen that for the proposed system, there was no to minimal degradation in performance while most of the other methods hit their bottom performance before that. | ['Ashish Ghosh', 'Debasrita Chakraborty'] | 2022-04-28 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [-3.30753893e-01 2.83508766e-02 3.47614795e-01 -2.07746252e-01
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d7932d0e-504e-4666-8860-f44148003c8b | emotion-distribution-learning-from-texts | null | null | https://aclanthology.org/D16-1061 | https://aclanthology.org/D16-1061.pdf | Emotion Distribution Learning from Texts | null | ['Xin Geng', 'Yin Zhou', 'Xuan Zhang', 'Quan Zhao', 'Deyu Zhou'] | 2016-11-01 | null | null | null | emnlp-2016-11 | ['product-recommendation'] | ['miscellaneous'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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b7f1c56a-db2f-4dea-b9f1-39ad3c2cbd7d | situation-recognition-visual-semantic-role | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Yatskar_Situation_Recognition_Visual_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Yatskar_Situation_Recognition_Visual_CVPR_2016_paper.pdf | Situation Recognition: Visual Semantic Role Labeling for Image Understanding | This paper introduces situation recognition, the problem of producing a concise summary of the situation an image depicts including: (1) the main activity (e.g., clipping), (2) the participating actors, objects, substances, and locations (e.g., man, shears, sheep, wool, and field) and most importantly (3) the roles these participants play in the activity (e.g., the man is clipping, the shears are his tool, the wool is being clipped from the sheep, and the clipping is in a field). We use FrameNet, a verb and role lexicon developed by linguists, to define a large space of possible situations and collect a large-scale dataset containing over 500 activities, 1,700 roles, 11,000 objects, 125,000 images, and 200,000 unique situations. We also introduce structured prediction baselines and show that, in activity-centric images, situation-driven prediction of objects and activities outperforms independent object and activity recognition. | ['Luke Zettlemoyer', 'Ali Farhadi', 'Mark Yatskar'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['grounded-situation-recognition', 'situation-recognition'] | ['computer-vision', 'computer-vision'] | [ 6.02801263e-01 -9.83706303e-03 -2.22268566e-01 -2.23587275e-01
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b5e03f68-fa12-47f6-b129-bb0d837d9e2b | dudornext-a-hybrid-model-for-dual-domain | 2303.10611 | null | https://arxiv.org/abs/2303.10611v1 | https://arxiv.org/pdf/2303.10611v1.pdf | DuDoRNeXt: A hybrid model for dual-domain undersampled MRI reconstruction | Undersampled MRI reconstruction is crucial for accelerating clinical scanning procedures. Recent deep learning methods for MRI reconstruction adopt CNN or ViT as backbone, which lack in utilizing the complementary properties of CNN and ViT. In this paper, we propose DuDoRNeXt, whose backbone hybridizes CNN and ViT in an domain-specific, intra-stage way. Besides our hybrid vertical layout design, we introduce domain-specific modules for dual-domain reconstruction, namely image-domain parallel local detail enhancement and k-space global initialization. We evaluate different conventions of MRI reconstruction including image-domain, k-space-domain, and dual-domain reconstruction with a reference protocol on the IXI dataset and an in-house multi-contrast dataset. DuDoRNeXt achieves significant improvements over competing deep learning methods. | ['S. Kevin Zhou', 'Ziqi Gao'] | 2023-03-19 | null | null | null | null | ['layout-design', 'mri-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 5.69995120e-02 8.86670500e-02 -6.62828833e-02 -4.62088317e-01
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fd8e6417-71d3-4e7c-b5e7-b64f9b790d66 | one-shot-scene-graph-generation | 2202.10824 | null | https://arxiv.org/abs/2202.10824v2 | https://arxiv.org/pdf/2202.10824v2.pdf | One-shot Scene Graph Generation | As a structured representation of the image content, the visual scene graph (visual relationship) acts as a bridge between computer vision and natural language processing. Existing models on the scene graph generation task notoriously require tens or hundreds of labeled samples. By contrast, human beings can learn visual relationships from a few or even one example. Inspired by this, we design a task named One-Shot Scene Graph Generation, where each relationship triplet (e.g., "dog-has-head") comes from only one labeled example. The key insight is that rather than learning from scratch, one can utilize rich prior knowledge. In this paper, we propose Multiple Structured Knowledge (Relational Knowledge and Commonsense Knowledge) for the one-shot scene graph generation task. Specifically, the Relational Knowledge represents the prior knowledge of relationships between entities extracted from the visual content, e.g., the visual relationships "standing in", "sitting in", and "lying in" may exist between "dog" and "yard", while the Commonsense Knowledge encodes "sense-making" knowledge like "dog can guard yard". By organizing these two kinds of knowledge in a graph structure, Graph Convolution Networks (GCNs) are used to extract knowledge-embedded semantic features of the entities. Besides, instead of extracting isolated visual features from each entity generated by Faster R-CNN, we utilize an Instance Relation Transformer encoder to fully explore their context information. Based on a constructed one-shot dataset, the experimental results show that our method significantly outperforms existing state-of-the-art methods by a large margin. Ablation studies also verify the effectiveness of the Instance Relation Transformer encoder and the Multiple Structured Knowledge. | ['Heng Tao Shen', 'Lianli Gao', 'Jingkuan Song', 'Yuyu Guo'] | 2022-02-22 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 1.54842883e-01 3.66942376e-01 -1.33435667e-01 -5.51668406e-01
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9a6ce385-2923-4a05-8ccd-e4ac9ce9ecf4 | learnability-with-pac-semantics-for-multi | 2306.0549 | null | https://arxiv.org/abs/2306.05490v1 | https://arxiv.org/pdf/2306.05490v1.pdf | Learnability with PAC Semantics for Multi-agent Beliefs | The tension between deduction and induction is perhaps the most fundamental issue in areas such as philosophy, cognition and artificial intelligence. In an influential paper, Valiant recognised that the challenge of learning should be integrated with deduction. In particular, he proposed a semantics to capture the quality possessed by the output of Probably Approximately Correct (PAC) learning algorithms when formulated in a logic. Although weaker than classical entailment, it allows for a powerful model-theoretic framework for answering queries. In this paper, we provide a new technical foundation to demonstrate PAC learning with multi-agent epistemic logics. To circumvent the negative results in the literature on the difficulty of robust learning with the PAC semantics, we consider so-called implicit learning where we are able to incorporate observations to the background theory in service of deciding the entailment of an epistemic query. We prove correctness of the learning procedure and discuss results on the sample complexity, that is how many observations we will need to provably assert that the query is entailed given a user-specified error bound. Finally, we investigate under what circumstances this algorithm can be made efficient. On the last point, given that reasoning in epistemic logics especially in multi-agent epistemic logics is PSPACE-complete, it might seem like there is no hope for this problem. We leverage some recent results on the so-called Representation Theorem explored for single-agent and multi-agent epistemic logics with the only knowing operator to reduce modal reasoning to propositional reasoning. | ['Brendan Juba', 'Vaishak Belle', 'Ionela G. Mocanu'] | 2023-06-08 | null | null | null | null | ['philosophy'] | ['miscellaneous'] | [ 2.46229053e-01 9.70591068e-01 -1.33045629e-01 -2.79103726e-01
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6412cb91-b5b2-43ba-88ac-416292e7e9e0 | pre-trained-language-models-with-domain | null | null | https://www.sciencedirect.com/science/article/pii/S0950705122007328 | https://reader.elsevier.com/reader/sd/pii/S0950705122007328?token=6B0D860FD9A7EA3A7BFE32EF631BCD4F592ADB7606EF0F28451448E7470CE1EF7C7DFE161EB4A1F743EC314FCDD5608C&originRegion=eu-west-1&originCreation=20220724191534 | Pre-trained language models with domain knowledge for biomedical extractive summarization | Biomedical text summarization is a critical task for comprehension of an ever-growing amount of biomedical literature. Pre-trained language models (PLMs) with transformer-based architectures have been shown to greatly improve performance in biomedical text mining tasks. However, existing methods for text summarization generally fine-tune PLMs on the target corpora directly and do not consider how fine-grained domain knowledge, such as PICO elements used in evidence-based medicine, can help to identify the context needed for generating coherent summaries. To fill the gap, we propose KeBioSum, a novel knowledge infusion training framework, and experiment using a number of PLMs as bases, for the task of extractive summarization on biomedical literature. We investigate generative and discriminative training techniques to fuse domain knowledge (i.e., PICO elements) into knowledge adapters and apply adapter fusion to efficiently inject the knowledge adapters into the basic PLMs for fine-tuning the extractive summarization task. Experimental results from the extractive summarization task on three biomedical literature datasets show that existing PLMs (BERT, RoBERTa, BioBERT, and PubMedBERT) are improved by incorporating the KeBioSum knowledge adapters, and our model outperforms the strong baselines. | ['QianqianXie;Jennifer Amy Bishop;PrayagTiwari;Sophia Ananiadoua'] | 2022-07-19 | null | null | null | knowledge-based-systems-2022-7 | ['pico', 'extractive-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.21376073e-01 4.95454967e-01 -4.83701050e-01 -2.01669946e-01
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272b393a-1ad1-4448-9396-0a89ce2214a9 | bipartite-graph-reasoning-gans-for-person | 2008.04381 | null | https://arxiv.org/abs/2008.04381v2 | https://arxiv.org/pdf/2008.04381v2.pdf | Bipartite Graph Reasoning GANs for Person Image Generation | We present a novel Bipartite Graph Reasoning GAN (BiGraphGAN) for the challenging person image generation task. The proposed graph generator mainly consists of two novel blocks that aim to model the pose-to-pose and pose-to-image relations, respectively. Specifically, the proposed Bipartite Graph Reasoning (BGR) block aims to reason the crossing long-range relations between the source pose and the target pose in a bipartite graph, which mitigates some challenges caused by pose deformation. Moreover, we propose a new Interaction-and-Aggregation (IA) block to effectively update and enhance the feature representation capability of both person's shape and appearance in an interactive way. Experiments on two challenging and public datasets, i.e., Market-1501 and DeepFashion, show the effectiveness of the proposed BiGraphGAN in terms of objective quantitative scores and subjective visual realness. The source code and trained models are available at https://github.com/Ha0Tang/BiGraphGAN. | ['Nicu Sebe', 'Hao Tang', 'Philip H. S. Torr', 'Song Bai'] | 2020-08-10 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [-2.03063980e-01 3.26580018e-01 4.97549683e-01 -3.57413620e-01
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7002be68-9ba8-45c1-9908-0d6c4222c9b1 | auxiliary-tasks-in-multi-task-learning | 1805.06334 | null | http://arxiv.org/abs/1805.06334v2 | http://arxiv.org/pdf/1805.06334v2.pdf | Auxiliary Tasks in Multi-task Learning | Multi-task convolutional neural networks (CNNs) have shown impressive results
for certain combinations of tasks, such as single-image depth estimation (SIDE)
and semantic segmentation. This is achieved by pushing the network towards
learning a robust representation that generalizes well to different atomic
tasks. We extend this concept by adding auxiliary tasks, which are of minor
relevance for the application, to the set of learned tasks. As a kind of
additional regularization, they are expected to boost the performance of the
ultimately desired main tasks. To study the proposed approach, we picked
vision-based road scene understanding (RSU) as an exemplary application. Since
multi-task learning requires specialized datasets, particularly when using
extensive sets of tasks, we provide a multi-modal dataset for multi-task RSU,
called synMT. More than 2.5 $\cdot$ 10^5 synthetic images, annotated with 21
different labels, were acquired from the video game Grand Theft Auto V (GTA V).
Our proposed deep multi-task CNN architecture was trained on various
combination of tasks using synMT. The experiments confirmed that auxiliary
tasks can indeed boost network performance, both in terms of final results and
training time. | ['Marco Körner', 'Lukas Liebel'] | 2018-05-16 | null | null | null | null | ['road-scene-understanding'] | ['computer-vision'] | [ 4.37388271e-01 2.01076537e-01 7.48419017e-02 -5.12593091e-01
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49d6fda3-2333-41cc-ba84-297e7a57b07b | prediction-of-bottleneck-points-for | 1911.04676 | null | https://arxiv.org/abs/1911.04676v1 | https://arxiv.org/pdf/1911.04676v1.pdf | Prediction of Bottleneck Points for Manipulation Planning in Cluttered Environment using a 3D Convolutional Neural Network | Latest research in industrial robotics is aimed at making human robot collaboration possible seamlessly. For this purpose, industrial robots are expected to work on the fly in unstructured and cluttered environments and hence the subject of perception driven motion planning plays a vital role. Sampling based motion planners are proven to be the most effective for such high dimensional planning problems with real time constraints. Unluckily random stochastic samplers suffer from the phenomenon of 'narrow passages' or bottleneck regions which need targeted sampling to improve their convergence rate. Also identifying these bottleneck regions in a diverse set of planning problems is a challenge. In this paper an attempt has been made to address these two problems by designing an intelligent 'bottleneck guided' heuristic for a Rapidly Exploring Random Tree Star (RRT*) planner which is based on relevant context extracted from the planning scenario using a 3D Convolutional Neural Network and it is also proven that the proposed technique generalises to unseen problem instances. This paper benchmarks the technique (bottleneck guided RRT*) against a 10% Goal biased RRT star planner, shows significant improvement in planning time and memory requirement and uses ABB 1410 industrial manipulator as a platform for implantation and validation of the results. | ['B. K. Rout', 'Indraneel Patil', 'V. Kalaichelvi'] | 2019-11-12 | null | null | null | null | ['industrial-robots'] | ['robots'] | [ 5.59382737e-01 3.76938045e-01 2.10390121e-01 -9.90416482e-02
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6ce84307-21bd-4afd-921d-0a3f12201ed5 | detection-and-rectification-of-arbitrary | 2103.00785 | null | https://arxiv.org/abs/2103.00785v1 | https://arxiv.org/pdf/2103.00785v1.pdf | Detection and Rectification of Arbitrary Shaped Scene Texts by using Text Keypoints and Links | Detection and recognition of scene texts of arbitrary shapes remain a grand challenge due to the super-rich text shape variation in text line orientations, lengths, curvatures, etc. This paper presents a mask-guided multi-task network that detects and rectifies scene texts of arbitrary shapes reliably. Three types of keypoints are detected which specify the centre line and so the shape of text instances accurately. In addition, four types of keypoint links are detected of which the horizontal links associate the detected keypoints of each text instance and the vertical links predict a pair of landmark points (for each keypoint) along the upper and lower text boundary, respectively. Scene texts can be located and rectified by linking up the associated landmark points (giving localization polygon boxes) and transforming the polygon boxes via thin plate spline, respectively. Extensive experiments over several public datasets show that the use of text keypoints is tolerant to the variation in text orientations, lengths, and curvatures, and it achieves superior scene text detection and rectification performance as compared with state-of-the-art methods. | ['Steven Hoi', 'Shijian Lu', 'Chuhui Xue'] | 2021-03-01 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 1.49776354e-01 -5.72030842e-01 5.73327765e-02 -2.25518540e-01
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a9780e88-9f15-4811-9054-3d99dcfca9c3 | advancing-the-state-of-the-art-in-open-domain | 1812.10757 | null | http://arxiv.org/abs/1812.10757v1 | http://arxiv.org/pdf/1812.10757v1.pdf | Advancing the State of the Art in Open Domain Dialog Systems through the Alexa Prize | Building open domain conversational systems that allow users to have engaging
conversations on topics of their choice is a challenging task. Alexa Prize was
launched in 2016 to tackle the problem of achieving natural, sustained,
coherent and engaging open-domain dialogs. In the second iteration of the
competition in 2018, university teams advanced the state of the art by using
context in dialog models, leveraging knowledge graphs for language
understanding, handling complex utterances, building statistical and
hierarchical dialog managers, and leveraging model-driven signals from user
responses. The 2018 competition also included the provision of a suite of tools
and models to the competitors including the CoBot (conversational bot) toolkit,
topic and dialog act detection models, conversation evaluators, and a sensitive
content detection model so that the competing teams could focus on building
knowledge-rich, coherent and engaging multi-turn dialog systems. This paper
outlines the advances developed by the university teams as well as the Alexa
Prize team to achieve the common goal of advancing the science of
Conversational AI. We address several key open-ended problems such as
conversational speech recognition, open domain natural language understanding,
commonsense reasoning, statistical dialog management, and dialog evaluation.
These collaborative efforts have driven improved experiences by Alexa users to
an average rating of 3.61, the median duration of 2 mins 18 seconds, and
average turns to 14.6, increases of 14%, 92%, 54% respectively since the launch
of the 2018 competition. For conversational speech recognition, we have
improved our relative Word Error Rate by 55% and our relative Entity Error Rate
by 34% since the launch of the Alexa Prize. Socialbots improved in quality
significantly more rapidly in 2018, in part due to the release of the CoBot
toolkit. | ['Dilek Hakkani-Tur', 'Kate Bland', 'Ming Cheng', 'Han Song', 'Rohit Prasad', 'Raefer Gabriel', 'Sanju Pancholi', 'Jeff Nunn', 'Gene Hwang', 'Arindam Mandal', 'Anu Venkatesh', 'Yi Pan', 'Sanjeev Kwatra', 'Qing Liu', 'Nate Michel', 'Lauren Stubel', 'Karthik Gopalakrishnan', 'Behnam Hedayatnia', 'Anna Gottardi', 'Qinglang Chen', 'Eric King', 'Chandra Khatri'] | 2018-12-27 | null | null | null | null | ['open-domain-dialog'] | ['natural-language-processing'] | [-1.97518587e-01 6.06741428e-01 7.71718696e-02 -5.67533195e-01
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