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Title: A content-based deep intrusion detection system Abstract: The growing number of Internet users and the prevalence of web applications make it necessary to deal with very complex software and applications in the network. This results in an increasing number of new vulnerabilities in the systems, and leading to an increase in cyber threats and, in particular, zero-day attacks. The cost of generating appropriate signatures for these attacks is a potential motive for using machine learning-based methodologies. Although there are many studies on using learning-based methods for attack detection, they generally use extracted features and overlook raw contents. This approach can lessen the performance of detection systems against content-based attacks like SQL injection, Cross-site Scripting (XSS), and various viruses. In this work, we propose a framework, called deep intrusion detection (DID) system, that uses the pure content of traffic flows in addition to traffic metadata in the learning and detection phases of a passive DNN IDS. To this end, we deploy and evaluate an offline IDS following the framework using LSTM as a deep learning technique. Due to the inherent nature of deep learning, it can process high-dimensional data content and, accordingly, discover the sophisticated relations between the auto extracted features of the traffic. To evaluate the proposed DID system, we use the CIC-IDS2017 and CSE-CIC-IDS2018 datasets. The evaluation metrics, such as precision and recall, reach 0.992 and 0.998 on CIC-IDS2017, and 0.933 and 0.923 on CSE-CIC-IDS2018, respectively, which show the high performance of the proposed DID method.

Title: Strong coloring 2-regular graphs: Cycle restrictions and partial colorings Abstract: Let H $H$ be a graph with Delta ( H ) <= 2 ${\rm{\Delta }}(H)\le 2$, and let G $G$ be obtained from H $H$ by gluing in vertex-disjoint copies of K 4 ${K}_{4}$. We prove that if H $H$ contains at most one odd cycle of length exceeding 3, or if H $H$ contains at most three triangles, then chi ( G ) <= 4 $\chi (G)\le 4$. This proves the Strong Coloring Conjecture for such graphs H $H$. For graphs H $H$ with Delta = 2 ${\rm{\Delta }}=2$ that are not covered by our theorem, we prove an approximation result towards the conjecture.

Title: Everybody’s Talkin’: Let Me Talk as You Want Abstract: We present a method to edit a target portrait footage by taking a sequence of audio as input to synthesize a photo-realistic video. This method is unique because it is highly dynamic. It does not assume a person-specific rendering network yet capable of translating one source audio into one random chosen video output within a set of speech videos. Instead of learning a highly heterogeneous and nonlinear mapping from audio to the video directly, we first factorize each target video frame into orthogonal parameter spaces, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , expression, geometry, and pose, via monocular 3D face reconstruction. Next, a recurrent network is introduced to translate source audio into expression parameters that are primarily related to the audio content. The audio-translated expression parameters are then used to synthesize a photo-realistic human subject in each video frame, with the movement of the mouth regions precisely mapped to the source audio. The geometry and pose parameters of the target human portrait are retained, therefore preserving the context of the original video footage. Finally, we introduce a novel video rendering network and a dynamic programming method to construct a temporally coherent and photo-realistic video. Extensive experiments demonstrate the superiority of our method over existing approaches. Our method is end-to-end learnable and robust to voice variations in the source audio.

Title: Detecting Mixing Services via Mining Bitcoin Transaction Network With Hybrid Motifs Abstract: As the first decentralized peer-to-peer (P2P) cryptocurrency system allowing people to trade with pseudonymous addresses, Bitcoin has become increasingly popular in recent years. However, the P2P and pseudonymous nature of Bitcoin make transactions on this platform very difficult to track, thus triggering the emergence of various illegal activities in the Bitcoin ecosystem. Particularly, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mixing services</i> in Bitcoin, originally designed to enhance transaction anonymity, have been widely employed for money laundering to complicate the process of trailing illicit fund. In this article, we focus on the detection of the addresses belonging to mixing services, which is an important task for anti-money laundering in Bitcoin. Specifically, we provide a feature-based network analysis framework to identify statistical properties of mixing services from three levels, namely, network level, account level, and transaction level. To better characterize the transaction patterns of different types of addresses, we propose the concept of attributed temporal heterogeneous motifs (ATH motifs). Moreover, to deal with the issue of imperfect labeling, we tackle the mixing detection task as a positive and unlabeled learning (PU learning) problem and build a detection model by leveraging the considered features. Experiments on real Bitcoin datasets demonstrate the effectiveness of our detection model and the importance of hybrid motifs including ATH motifs in mixing detection.

Title: Computationally easy, spectrally good multipliers for congruential pseudorandom number generators Abstract: Congruential pseudorandom number generators rely on good multipliers, that is, integers that have good performance with respect to the spectral test. We provide lists of multipliers with a good lattice structure up to dimension eight and up to lag eight for generators with typical power-of-two moduli, analyzing in detail multipliers close to the square root of the modulus, whose product can be computed quickly.

Title: CDGAN: Cyclic Discriminative Generative Adversarial Networks for image-to-image transformation Abstract: Generative Adversarial Networks (GANs) have facilitated a new direction to tackle the image-to-image transformation problem. Different GANs use generator and discriminator networks with different losses in the objective function. Still there is a gap to fill in terms of both the quality of the generated images and close to the ground truth images. In this work, we introduce a new Image-to-Image Transformation network named Cyclic Discriminative Generative Adversarial Networks (CDGAN) that fills the above mentioned gaps. The proposed CDGAN generates high quality and more realistic images by incorporating the additional discriminator networks for cycled images in addition to the original architecture of the CycleGAN. The proposed CDGAN is tested over three image-to-image transformation datasets. The quantitative and qualitative results are analyzed and compared with the state-of-the-art methods. The proposed CDGAN method outperforms the state-of-the-art methods when compared over the three baseline Image-to-Image transformation datasets. The code is available at https://github.com/KishanKancharagunta/CDGAN.

Title: Image Segmentation Using Deep Learning: A Survey Abstract: Image segmentation is a key task in computer vision and image processing with important applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among others, and numerous segmentation algorithms are found in the literature. Against this backdrop, the broad success of deep learning (DL) has prompted the development of new image segmentation approaches leveraging DL models. We provide a comprehensive review of this recent literature, covering the spectrum of pioneering efforts in semantic and instance segmentation, including convolutional pixel-labeling networks, encoder-decoder architectures, multiscale and pyramid-based approaches, recurrent networks, visual attention models, and generative models in adversarial settings. We investigate the relationships, strengths, and challenges of these DL-based segmentation models, examine the widely used datasets, compare performances, and discuss promising research directions.

Title: Turan numbers for hypergraph star forests Abstract: Fix a graph F. We say that a graph is F-free if it does not contain F as a subhypergraph. The Turan number of F, denoted ex(n, F), is the maximum number of edges possible in an n-vertex F-free graph. The study of Turan numbers is a central problem in graph theory. The goal of this paper is to generalize a theorem of Lidicky, Liu and Palmer [Electron. J. of Combin 20 (2016)] that determines ex(n, F) for F a forest of stars. In particular, we consider generalizations of the problem to three different well-studied hypergraph settings and in each case we prove an asymptotic result for all reasonable parameters defining our "star forests ".(c) 2022 Elsevier Ltd. All rights reserved.

Title: A Categorical Framework for Learning Generalised Tree Automata. Abstract: Automata learning is a popular technique used to automatically construct an automaton model from queries. Much research went into devising ad hoc adaptations of algorithms for different types of automata. The CALF project seeks to unify these using category theory in order to ease correctness proofs and guide the design of new algorithms. In this paper, we extend CALF to cover learning of algebraic structures that may not have a coalgebraic presentation. Furthermore, we provide a detailed algorithmic account of an abstract version of the popular L* algorithm, which was missing from CALF. We instantiate the abstract theory to a large class of Set functors, by which we recover for the first time practical tree automata learning algorithms from an abstract framework and at the same time obtain new algorithms to learn algebras of quotiented polynomial functors.

Title: Weight Enumerators and Cardinalities for Number-Theoretic Codes Abstract: The number-theoretic code is a class of codes defined by single or multiple congruences. These codes are mainly used for correcting insertion and deletion errors, and for correcting asymmetric errors. This paper presents a formula for a generalization of the complete weight enumerator for the number-theoretic codes. This formula allows us to derive the weight enumerators and cardinalities for the number-theoretic codes. As a special case, this paper provides the Hamming weight enumerators and cardinalities of the non-binary Tenengolts’ codes, correcting single insertion or deletion. Moreover, we show that the formula deduces the MacWilliams identity for the linear codes over the ring of integers modulo <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> .

Title: Speech emotion recognition based on multi-feature and multi-lingual fusion Abstract: A speech emotion recognition algorithm based on multi-feature and Multi-lingual fusion is proposed in order to resolve low recognition accuracy caused bylack of large speech dataset and low robustness of acoustic features in the recognition of speech emotion. First, handcrafted and deep automatic features are extractedfrom existing data in Chinese and English speech emotions. Then, the various features are fused respectively. Finally, the fused features of different languages are fused again and trained in a classification model. Distinguishing the fused features with the unfused ones, the results manifest that the fused features significantly enhance the accuracy of speech emotion recognition algorithm. The proposedsolution is evaluated on the two Chinese corpus and two English corpus, and isshown to provide more accurate predictions compared to original solution. As a result of this study, the multi-feature and Multi-lingual fusion algorithm can significantly improve the speech emotion recognition accuracy when the dataset is small.

Title: Non-binary universal tree-based networks. Abstract: A tree-based network $N$ on $X$ is called universal if every phylogenetic tree on $X$ is a base tree for $N$. Recently, binary universal tree-based networks have attracted great attention in the literature and their existence has been analyzed in various studies. In this note, we extend the analysis to non-binary networks and show that there exist both a rooted and an unrooted non-binary universal tree-based network with $n$ leaves for all positive integers $n$.

Title: Robust Traffic Control Using a First Order Macroscopic Traffic Flow Model Abstract: Traffic control is at the core of research in transportation engineering because it is one of the hest practices for reducing traffic congestion. It has been shown in recent years that the traffic control problem involving Lighthill-Whitham-Richards (LWR) model can be formulated as a Linear Programming (LP) problem given that the corresponding initial conditions and the model parameters in the fundamental diagram are fixed. However, the initial conditions can be uncertain when studying actual control problems. This paper presents a stochastic programming formulation of the boundary control problem involving chance constraints, to capture the uncertainty in the initial conditions. Different objective functions are explored using this framework, and the proposed model is validated by conducting case studies for both a single highway link and a highway network. In addition, the accuracy of relaxed optimal results is proved using Monte Carlo simulation.

Title: Universal Adversarial Attack on Attention and the Resulting Dataset DAmageNet Abstract: Adversarial attacks on deep neural networks (DNNs) have been found for several years. However, the existing adversarial attacks have high success rates only when the information of the victim DNN is well-known or could be estimated by the structure similarity or massive queries. In this paper, we propose to Attack on Attention (AoA), a semantic property commonly shared by DNNs. Ao...

Title: A scaling limit for the length of the longest cycle in a sparse random digraph Abstract: We discuss the length L -> c,n of the longest directed cycle in the sparse random digraph Dn,p,p=c/n, c constant. We show that for large c there exists a function f ->(c) such that L -> c,n/n -> f ->(c) a.s. The function f ->(c)=1- n-ary sumation k=1 infinity pk(c)e-kc where pk is a polynomial in c. We are only able to explicitly give the values p1,p2, although we could in principle compute any pk.

Title: Harmonic convolutional networks based on discrete cosine transform Abstract: •The harmonic block is designed to learn filter weights in the DCT domain.•Harmonic CNNs are constructed by replacing the convolutional layers.•Parameter learning in frequency domain improves performance.•High-frequency parameter truncation can efficiently compress new or trained CNNs.•The hamonic block can make a CNN invariant to illumination changes.

Title: Quadratic Embedding Constants of Graph Joins Abstract: The quadratic embedding constant (QE constant) of a graph is a new characteristic value of a graph defined through the distance matrix. We derive formulae for the QE constants of the join of two regular graphs, double graphs and certain lexicographic product graphs. Examples include complete bipartite graphs, wheel graphs, friendship graphs, completely split graph, and some graphs associated to strongly regular graphs.

Title: Infinitary action logic with exponentiation Abstract: We introduce infinitary action logic with exponentiation—that is, the multiplicative-additive Lambek calculus extended with Kleene star and with a family of subexponential modalities, which allow some of the structural rules (contraction, weakening, permutation). The logic is presented in the form of an infinitary sequent calculus. We prove cut elimination and, in the case where at least one subexponential allows non-local contraction, establish exact complexity boundaries in two senses. First, we show that the derivability problem for this logic is Π11-complete. Second, we show that the closure ordinal of its derivability operator is ω1CK. In the case where no subexponential allows contraction, we show that complexity is the same as for infinitary action logic itself. Namely, the derivability problem in this case is Π10-complete and the closure ordinal is not greater than ωω.

Title: CWI: A multimodal deep learning approach for named entity recognition from social media using character, word and image features Abstract: Named entity recognition (NER) from social media posts is a challenging task. User-generated content that forms the nature of social media is noisy and contains grammatical and linguistic errors. This noisy content makes tasks such as NER much harder. We propose two novel deep learning approaches utilizing multimodal deep learning and transformers. Both of our approaches use image features from short social media posts to provide better results on the NER task. On the first approach, we extract image features using InceptionV3 and use fusion to combine textual and image features. This approach presents more reliable name entity recognition when the images related to the entities are provided by the user. On the second approach, we use image features combined with text and feed it into a BERT-like transformer. The experimental results using precision, recall, and F1 score metrics show the superiority of our work compared to other state-of-the-art NER solutions.

Title: Distributed State Estimation Over Time-Varying Graphs: Exploiting the Age-of-Information Abstract: We study the problem of designing a distributed observer for an LTI system over a time-varying communication graph. The limited existing work on this topic imposes various restrictions either on the observation model or on the sequence of communication graphs. In contrast, we propose a single-time-scale distributed observer that works under mild assumptions. Specifically, our communication model only requires strong-connectivity to be preserved over nonoverlapping, contiguous intervals that are even allowed to grow unbounded over time. We show that under suitable conditions that bound the growth of such intervals, joint observability is sufficient to track the state of any discrete-time LTI system exponentially fast, at any desired rate. We also develop a variant of our algorithm that is provably robust to worst-case adversarial attacks, provided the sequence of graphs is sufficiently connected over time. The key to our approach is the notion of a “freshness-index” that keeps track of the age-of-information being diffused across the network. Such indices enable nodes to reject stale estimates of the state, and, in turn, contribute to stability of the error dynamics.

Title: A Node-Charge Graph-Based Online Carshare Rebalancing Policy with Capacitated Electric Charging Abstract: Viability of electric car-sharing operations depends on rebalancing algorithms. Earlier methods in the literature suggest a trend toward nonmyopic algorithms using queueing principles. We propose a new rebalancing policy using cost function approximation. The cost function is modeled as a p-median relocation problem with minimum cost flow conservation and path-based charging station capacities on a static node-charge graph structure. The cost function is NP complete, so a heuristic is proposed that ensures feasible solutions that can be solved in an online system. The algorithm is validated in a case study of electric carshare in Brooklyn, New York, with demand data shared from BMW ReachNow operations in September 2017 (262 vehicle fleet, 231 pickups per day, and 303 traffic analysis zones) and charging station location data (18 charging stations with four-port capacities). The proposed nonmyopic rebalancing heuristic reduces the cost increase compared with myopic rebalancing by 38%. Other managerial insights are further discussed.

Title: EdgeNets: Edge Varying Graph Neural Networks Abstract: Driven by the outstanding performance of neural networks in the structured euclidean domain, recent years have seen a surge of interest in developing neural networks for graphs and data supported on graphs. The graph is leveraged at each layer of the neural network as a parameterization to capture detail at the node level with a reduced number of parameters and computational complexity. Following this rationale, this paper puts forth a general framework that unifies state-of-the-art graph neural networks (GNNs) through the concept of EdgeNet. An EdgeNet is a GNN architecture that allows different nodes to use different parameters to weigh the information of different neighbors. By extrapolating this strategy to more iterations between neighboring nodes, the EdgeNet learns edge- and neighbor-dependent weights to capture local detail. This is a general linear and local operation that a node can perform and encompasses under one formulation all existing graph convolutional neural networks (GCNNs) as well as graph attention networks (GATs). In writing different GNN architectures with a common language, EdgeNets highlight specific architecture advantages and limitations, while providing guidelines to improve their capacity without compromising their local implementation. For instance, we show that GCNNs have a parameter sharing structure that induces permutation equivariance. This can be an advantage or a limitation, depending on the application. In cases where it is a limitation, we propose hybrid approaches and provide insights to develop several other solutions that promote parameter sharing without enforcing permutation equivariance. Another interesting conclusion is the unification of GCNNs and GATs —approaches that have been so far perceived as separate. In particular, we show that GATs are GCNNs on a graph that is learned from the features. This particularization opens the doors to develop alternative attention mechanisms for improving discriminatory power.

Title: Distance Preserving Model Order Reduction of Graph-Laplacians and Cluster Analysis Abstract: Graph-Laplacians and their spectral embeddings play an important role in multiple areas of machine learning. This paper is focused on graph-Laplacian dimension reduction for the spectral clustering of data as a primary application, however, it can also be applied in data mining, data manifold learning, etc. Spectral embedding provides a low-dimensional parametrization of the data manifold which makes the subsequent task (e.g., clustering with k-means or any of its approximations) much easier. However, despite reducing the dimensionality of data, the overall computational cost may still be prohibitive for large data sets due to two factors. First, computing the partial eigendecomposition of the graph-Laplacian typically requires a large Krylov subspace. Second, after the spectral embedding is complete, one still has to operate with the same number of data points, which may ruin the efficiency of the approach. For example, clustering of the embedded data is typically performed with various relaxations of k-means which computational cost scales poorly with respect to the size of data set. Also, they become prone to getting stuck in local minima, so their robustness depends on the choice of initial guess. In this work, we switch the focus from the entire data set to a subset of graph vertices (target subset). We develop two novel algorithms for such low-dimensional representation of the original graph that preserves important global distances between the nodes of the target subset. In particular, it allows to ensure that target subset clustering is consistent with the spectral clustering of the full data set if one would perform such. That is achieved by a properly parametrized reduced-order model (ROM) of the graph-Laplacian that approximates accurately the diffusion transfer function of the original graph for inputs and outputs restricted to the target subset. Working with a small target subset reduces greatly the required dimension of Krylov subspace and allows to exploit the conventional algorithms (like approximations of k-means) in the regimes when they are most robust and efficient. This was verified in the numerical clustering experiments with both synthetic and real data. We also note that our ROM approach can be applied in a purely transfer-function-data-driven way, so it becomes the only feasible option for extremely large graphs that are not directly accessible. There are several uses for our algorithms. First, they can be employed on their own for representative subset clustering in cases when handling the full graph is either infeasible or simply not required. Second, they may be used for quality control. Third, as they drastically reduce the problem size, they enable the application of more sophisticated algorithms for the task under consideration (like more powerful approximations of k-means based on semi-definite programming (SDP) instead of the conventional Lloyd's algorithm). Finally, they can be used as building blocks of a multi-level divide-and-conquer type algorithm to handle the full graph. The latter will be reported in a separate article.

Title: Delay and Packet-Drop Tolerant Multistage Distributed Average Tracking in Mean Square Abstract: This article studies the distributed average tracking (DAT) problem pertaining to a discrete-time linear time-invariant multiagent network, which is subject to, concurrently, input delays, random packet drops, and reference noise. The problem amounts to an integrated design of delay and a packet-drop-tolerant algorithm and determining the ultimate upper bound of the tracking error between agents’ states and the average of the reference signals. The investigation is driven by the goal of devising a practically more attainable average tracking algorithm, thereby extending the existing work in the literature, which largely ignored the aforementioned uncertainties. For this purpose, a blend of techniques from Kalman filtering, multistage consensus filtering, and predictive control is employed, which gives rise to a simple yet comepelling DAT algorithm that is robust to the initialization error and allows the tradeoff between communication/computation cost and stationary-state tracking error. Due to the inherent coupling among different control components, convergence analysis is significantly challenging. Nevertheless, it is revealed that the allowable values of the algorithm parameters rely upon the maximal degree of an expected network, while the convergence speed depends upon the second smallest eigenvalue of the same network’s topology. The effectiveness of the theoretical results is verified by a numerical example.

Title: A numerically stable algorithm for integrating Bayesian models using Markov melding Abstract: When statistical analyses consider multiple data sources, Markov melding provides a method for combining the source-specific Bayesian models. Markov melding joins together submodels that have a common quantity. One challenge is that the prior for this quantity can be implicit, and its prior density must be estimated. We show that error in this density estimate makes the two-stage Markov chain Monte Carlo sampler employed by Markov melding unstable and unreliable. We propose a robust two-stage algorithm that estimates the required prior marginal self-density ratios using weighted samples, dramatically improving accuracy in the tails of the distribution. The stabilised version of the algorithm is pragmatic and provides reliable inference. We demonstrate our approach using an evidence synthesis for inferring HIV prevalence, and an evidence synthesis of A/H1N1 influenza.

Title: Loci of 3-periodics in an Elliptic Billiard: Why so many ellipses? Abstract: A triangle center such as the incenter, barycenter, etc., is specified by a function thrice- and cyclically applied on sidelengths and/or angles. Consider the 1d family of 3-periodics in the elliptic billiard, and the loci of its triangle centers. Some will sweep ellipses, and others higher-degree algebraic curves. We propose two rigorous methods to prove if the locus of a given center is an ellipse: one based on computer algebra, and another based on an algebro-geometric method. We also prove that if the triangle center function is rational on sidelengths, the locus is algebraic.

Title: Building high accuracy emulators for scientific simulations with deep neural architecture search Abstract: Computer simulations are invaluable tools for scientific discovery. However, accurate simulations are often slow to execute, which limits their applicability to extensive parameter exploration, large-scale data analysis, and uncertainty quantification. A promising route to accelerate simulations by building fast emulators with machine learning requires large training datasets, which can be prohibitively expensive to obtain with slow simulations. Here we present a method based on neural architecture search to build accurate emulators even with a limited number of training data. The method successfully emulates simulations in 10 scientific cases including astrophysics, climate science, biogeochemistry, high energy density physics, fusion energy, and seismology, using the same super-architecture, algorithm, and hyperparameters. Our approach also inherently provides emulator uncertainty estimation, adding further confidence in their use. We anticipate this work will accelerate research involving expensive simulations, allow more extensive parameters exploration, and enable new, previously unfeasible computational discovery.

Title: Comments are welcome Abstract: Scholars present their new research at seminars and conferences and send drafts to peers in hopes of receiving comments and suggestions that will improve the quality of their work. Using a dataset of projects that were initiated when authors were doing their doctoral studies, this article measures how much peers’ individual and collective comments improve the quality of research. Controlling for the quality of the research idea and author, I find that a one-standard-deviation increase in the number of peers’ individual and collective comments is associated with a 43% increase in the quality of the journal in which the project is published.

Title: Vicinal Vertex Allocation for Matrix Factorization in Networks Abstract: In this article, we present a novel matrix-factorization-based model, labeled here as Vicinal vertex allocated matrix factorization (VVAMo), for uncovering clusters in network data. Different from the past related efforts of network clustering, which consider the edge structure, vertex features, or both in their design, the proposed model includes the additional detail on vertex inclinations with respect to topology and features into the learning. In particular, by taking the latent preferences between vicinal vertices into consideration, VVAMo is then able to uncover network clusters composed of proximal vertices that share analogous inclinations, and correspondingly high structural and feature correlations. To ensure such clusters are effectively uncovered, we propose a unified likelihood function for VVAMo and derive an alternating algorithm for optimizing the proposed function. Subsequently, we provide the theoretical analysis of VVAMo, including the convergence proof and computational complexity analysis. To investigate the effectiveness of the proposed model, a comprehensive empirical study of VVAMo is conducted using extensive commonly used realistic network datasets. The results obtained show that VVAMo attained superior performances over existing classical and state-of-the-art approaches.

Title: Arrangements of Approaching Pseudo-Lines Abstract: We consider arrangements of n pseudo-lines in the Euclidean plane where each pseudo-line l(i) is represented by a bi-infinite connected x-monotone curve f(i)(x), x is an element of R, such that for any two pseudo-lines l(i) and l(j) with i < j, the function x bar right arrow f(j)(x) - f(i)(x) is monotonically decreasing and surjective (i.e., the pseudo-lines approach each other until they cross, and then move away from each other). We show that such arrangements of approaching pseudo-lines, under some aspects, behave similar to arrangements of lines, while for other aspects, they share the freedom of general pseudo-line arrangements. For the former, we prove: There are arrangements of pseudo-lines that are not realizable with approaching pseudo-lines. Every arrangement of approaching pseudo-lines has a dual generalized configuration of points with an underlying arrangement of approaching pseudo-lines. For the latter, we show: There are 2(circle dot(n2)) isomorphism classes of arrangements of approaching pseudo-lines (while there are only 2(circle dot(n log n)) isomorphism classes of line arrangements). It can be decided in polynomial time whether an allowable sequence is realizable by an arrangement of approaching pseudo-lines. Furthermore, arrangements of approaching pseudo-lines can be transformed into each other by flipping triangular cells, i.e., they have a connected flip graph, and every bichromatic arrangement of this type contains a bichromatic triangular cell.

Title: Filter Sketch for Network Pruning Abstract: We propose a novel network pruning approach by information preserving of pretrained network weights (filters). Network pruning with the information preserving is formulated as a matrix sketch problem, which is efficiently solved by the off-the-shelf frequent direction method. Our approach, referred to as FilterSketch, encodes the second-order information of pretrained weights, which enables the representation capacity of pruned networks to be recovered with a simple fine-tuning procedure. FilterSketch requires neither training from scratch nor data-driven iterative optimization, leading to a several-orders-of-magnitude reduction of time cost in the optimization of pruning. Experiments on CIFAR-10 show that FilterSketch reduces 63.3% of floating-point operations (FLOPs) and prunes 59.9% of network parameters with negligible accuracy cost for ResNet-110. On ILSVRC-2012, it reduces 45.5% of FLOPs and removes 43.0% of parameters with only 0.69% accuracy drop for ResNet-50. Our code and pruned models can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/lmbxmu/FilterSketch</uri> .

Title: Toward a Controllable Disentanglement Network Abstract: This article addresses two crucial problems of learning disentangled image representations, namely, controlling the degree of disentanglement during image editing, and balancing the disentanglement strength and the reconstruction quality. To encourage disentanglement, we devise distance covariance-based decorrelation regularization. Further, for the reconstruction step, our model leverages a soft ...

Title: Learning Distributional Programs for Relational Autocompletion. Abstract: Relational autocompletion is the problem of automatically filling out some missing fields in a relational database. We tackle this problem within the probabilistic logic programming framework of Distributional Clauses (DC), which supports both discrete and continuous probability distributions. Within this framework, we introduce Dreaml -- an approach to learn both the structure and the parameters of DC programs from databases that may contain missing information. To realize this, Dreaml integrates statistical modeling, distributional clauses with rule learning. The distinguishing features of Dreaml are that it 1) tackles relational autocompletion, 2) learns distributional clauses extended with statistical models, 3) deals with both discrete and continuous distributions, 4) can exploit background knowledge, and 5) uses an expectation-maximization based algorithm to cope with missing data.

Title: Interpretable End-to-End Urban Autonomous Driving With Latent Deep Reinforcement Learning Abstract: Unlike popular modularized framework, end-to-end autonomous driving seeks to solve the perception, decision and control problems in an integrated way, which can be more adapting to new scenarios and easier to generalize at scale. However, existing end-to-end approaches are often lack of interpretability, and can only deal with simple driving tasks like lane keeping. In this article, we propose an interpretable deep reinforcement learning method for end-to-end autonomous driving, which is able to handle complex urban scenarios. A sequential latent environment model is introduced and learned jointly with the reinforcement learning process. With this latent model, a semantic birdeye mask can be generated, which is enforced to connect with certain intermediate properties in today’s modularized framework for the purpose of explaining the behaviors of learned policy. The latent space also significantly reduces the sample complexity of reinforcement learning. Comparison tests in a realistic driving simulator show that the performance of our method in urban scenarios with crowded surrounding vehicles dominates many baselines including DQN, DDPG, TD3 and SAC. Moreover, through masked outputs, the learned model is able to provide a better explanation of how the car reasons about the driving environment.

Title: Context-aware distribution of fog applications using deep reinforcement learning Abstract: Fog computing is an emerging paradigm that aims to meet the increasing computation demands arising from the billions of devices connected to the Internet. Offloading services of an application from the Cloud to the edge of the network can improve the overall latency of the application since it can process data closer to user devices. Diverse Fog nodes ranging from Wi-Fi routers to mini-clouds with varying resource capabilities makes it challenging to determine which services of an application need to be offloaded. In this paper, a context-aware mechanism for distributing applications across the Cloud and the Fog is proposed. The mechanism dynamically generates (re)deployment plans for the application to maximise the performance efficiency of the application by taking operational conditions, such as hardware utilisation and network state, and running costs into account. The mechanism relies on deep Q-networks to generate a distribution plan without prior knowledge of the available resources on the Fog node, the network condition, and the application. The feasibility of the proposed context-aware distribution mechanism is demonstrated on two use-cases, namely a face detection application and a location-based mobile game. The benefits are increased utility of dynamic distribution by 50% and 20% for the two use-cases respectively when compared to a static distribution approach used in existing research.

Title: Towards High Performance Low Complexity Calibration in Appearance Based Gaze Estimation Abstract: Appearance-based gaze estimation from RGB images provides relatively unconstrained gaze tracking from commonly available hardware. The accuracy of subject-independent models is limited partly by small intra-subject and large inter-subject variations in appearance, and partly by a latent subject-dependent bias. To improve estimation accuracy, we have previously proposed a gaze decomposition method that decomposes the gaze angle into the sum of a subject-independent gaze estimate from the image and a subject-dependent bias. Estimating the bias from images outperforms previously proposed calibration algorithms, unless the amount of calibration data is prohibitively large. This paper extends that work with a more complete characterization of the interplay between the complexity of the calibration dataset and estimation accuracy. In particular, we analyze the effect of the number of gaze targets, the number of images used per gaze target and the number of head positions in calibration data using a new NISLGaze dataset, which is well suited for analyzing these effects as it includes more diversity in head positions and orientations for each subject than other datasets. A better understanding of these factors enables low complexity high performance calibration. Our results indicate that using only a single gaze target and single head position is sufficient to achieve high quality calibration. However, it is useful to include variability in head orientation as the subject is gazing at the target. Our proposed estimator based on these studies (GEDDNet) outperforms state-of-the-art methods by more than <inline-formula><tex-math notation="LaTeX">$6.3\%$</tex-math></inline-formula> . One of the surprising findings of our work is that the same estimator yields the best performance both with and without calibration. This is convenient, as the estimator works well ”straight out of the box,” but can be improved if needed by calibration. However, this seems to violate the conventional wisdom that train and test conditions must be matched. To better understand the reasons, we provide a new theoretical analysis that specifies the conditions under which this can be expected. The dataset is available at <uri>http://nislgaze.ust.hk</uri> . Source code is available at <uri>https://github.com/HKUST-NISL/GEDDnet</uri> .

Title: Tight Regret Bounds for Noisy Optimization of a Brownian Motion Abstract: We consider the problem of Bayesian optimization of a one-dimensional Brownian motion in which the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$T$</tex-math></inline-formula> adaptively chosen observations are corrupted by Gaussian noise. We show that the smallest possible expected cumulative regret and the smallest possible expected simple regret scale as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\Omega (\sigma \sqrt {T / \log (T)}) \cap \mathcal {O}(\sigma \sqrt {T} \cdot \log T)$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\Omega (\sigma / \sqrt {T \log (T)}) \cap \mathcal {O}(\sigma \log T / \sqrt {T})$</tex-math></inline-formula> respectively, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sigma ^2$</tex-math></inline-formula> is the noise variance. Thus, our upper and lower bounds are tight up to a factor of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {O} ((\log T)^{1.5})$</tex-math></inline-formula> . The upper bound uses an algorithm based on confidence bounds and the Markov property of Brownian motion (among other useful properties), and the lower bound is based on a reduction to binary hypothesis testing.

Title: Sentiment and Knowledge Based Algorithmic Trading with Deep Reinforcement Learning Abstract: Algorithmic trading, due to its inherent nature, is a difficult problem to tackle; there are too many variables involved in the real-world which makes it almost impossible to have reliable algorithms for automated stock trading. The lack of reliable labelled data that considers physical and physiological factors that dictate the ups and downs of the market, has hindered the supervised learning attempts for dependable predictions. To learn a good policy for trading, we formulate an approach using reinforcement learning which uses traditional time series stock price data and combines it with news headline sentiments, while leveraging knowledge graphs for exploiting news about implicit relationships.

Title: DUMA: Reading Comprehension With Transposition Thinking Abstract: AbstractMulti-choice Machine Reading Comprehension (MRC) requires models to decide the correct answer from a set of answer options when given a passage and a question. Thus, in addition to a powerful Pre-trained Language Model (PrLM) as an encoder, multi-choice MRC especially relies on a matching network design that is supposed to effectively capture the relationships among the triplet of passage, question, and answers. While the newer and more powerful PrLMs have shown their strengths even without the support from a matching network, we propose a new DUal Multi-head Co-Attention (DUMA) model. It is inspired by the human transposition thinking process solving the multi-choice MRC problem by considering each other’s focus from the standpoint of passage and question. The proposed DUMA has been shown to be effective and is capable of generally promoting PrLMs. Our proposed method is evaluated on two benchmark multi-choice MRC tasks, DREAM, and RACE. Our results show that in terms of powerful PrLMs, DUMA can further boost the models to obtain higher performance.

Title: Unsupervised Disentanglement of Pose, Appearance and Background from Images and Videos Abstract: Unsupervised landmark learning is the task of learning semantic keypoint-like representations without the use of expensive input keypoint annotations. A popular approach is to factorize an image into a pose and appearance data stream, then to reconstruct the image from the factorized components. The pose representation should capture a set of consistent and tightly localized landmarks in order to facilitate reconstruction of the input image. Ultimately, we wish for our learned landmarks to focus on the foreground object of interest. However, the reconstruction task of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">entire</i> image forces the model to allocate landmarks to model the background. Using a motion-based foreground assumption, this work explores the effects of factorizing the reconstruction task into separate foreground and background reconstructions in an unsupervised way, allowing the model to condition only the foreground reconstruction on the unsupervised landmarks. Our experiments demonstrate that the proposed factorization results in landmarks that are focused on the foreground object of interest when measured against ground-truth foreground masks. Furthermore, the rendered background quality is also improved as ill-suited landmarks are no longer forced to model this content. We demonstrate this improvement via improved image fidelity in a video-prediction task. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/NVIDIA/UnsupervisedLandmarkLearning</uri> .

Title: Imperfect ImaGANation: Implications of GANs exacerbating biases on facial data augmentation and snapchat face lenses Abstract: In this paper, we show that popular Generative Adversarial Network (GAN) variants exacerbate biases along the axes of gender and skin tone in the generated data. The use of synthetic data generated by GANs is widely used for a variety of tasks ranging from data augmentation to stylizing images. While practitioners celebrate this method as an economical way to obtain synthetic data to train data-hungry machine learning models or provide new features to users of mobile applications, it is unclear whether they recognize the perils of such techniques when applied to real world datasets biased along latent dimensions. Although one expects GANs to replicate the distribution of the original data, in real-world settings with limited data and finite network capacity, GANs suffer from mode collapse. First, we show readily-accessible GAN variants such as DCGANs ‘imagine’ faces of synthetic engineering professors that have masculine facial features and fair skin tones. When using popular GAN architectures that attempt to address mode-collapse, we observe that these variants either provide a false sense of security or suffer from other inherent limitations due to their design choice. Second, we show that a conditional GAN variant transforms input images of female and nonwhite faces to have more masculine features and lighter skin when asked to generate faces of engineering professors. Worse yet, prevalent filters on Snapchat end up consistently lightening the skin tones in people of color when trying to make face images appear more feminine. Thus, our study is meant to serve as a cautionary tale for practitioners and educate them about the side-effect of bias amplification when applying GAN-based techniques.

Title: Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning Abstract: Deep reinforcement learning (DRL) has numerous applications in the real world, thanks to its ability to achieve high performance in a range of environments with little manual oversight. Despite its great advantages, DRL is susceptible to adversarial attacks, which precludes its use in real-life critical systems and applications (e.g., smart grids, traffic controls, and autonomous vehicles) unless ...

Title: Efficient and Stable Graph Scattering Transforms via Pruning Abstract: Graph convolutional networks (GCNs) have well-documented performance in various graph learning tasks, but their analysis is still at its infancy. Graph scattering transforms (GSTs) offer training-free deep GCN models that extract features from graph data, and are amenable to generalization and stability analyses. The price paid by GSTs is exponential complexity in space and time that increases with the number of layers. This discourages deployment of GSTs when a deep architecture is needed. The present work addresses the complexity limitation of GSTs by introducing an efficient so-termed pruned (p)GST approach. The resultant pruning algorithm is guided by a graph-spectrum-inspired criterion, and retains informative scattering features on-the-fly while bypassing the exponential complexity associated with GSTs. Stability of the novel pGSTs is also established when the input graph data or the network structure are perturbed. Furthermore, the sensitivity of pGST to random and localized signal perturbations is investigated analytically and experimentally. Numerical tests showcase that pGST performs comparably to the baseline GST at considerable computational savings. Furthermore, pGST achieves comparable performance to state-of-the-art GCNs in graph and 3D point cloud classification tasks. Upon analyzing the pGST pruning patterns, it is shown that graph data in different domains call for different network architectures, and that the pruning algorithm may be employed to guide the design choices for contemporary GCNs.

Title: CAHPHF: Context-Aware Hierarchical QoS Prediction With Hybrid Filtering Abstract: With the proliferation of Internet-of-Things and continuous growth in the number of web services at the Internet-scale, the service recommendation is becoming a challenge nowadays. One of the prime aspects influencing the service recommendation is the Quality-of-Service (QoS) parameter, which depicts the performance of a web service. In general, the service provider furnishes the value of the QoS parameters before service deployment. However, in reality, the QoS values of service vary across different users, time, locations, etc. Therefore, estimating the QoS value of service before its execution is an important task, and thus, the QoS prediction has gained significant research attention. Multiple approaches are available in the literature for predicting service QoS. However, these approaches are yet to reach the desired accuracy level. In this article, we study the QoS prediction problem across different users, and propose a novel solution by taking into account the contextual (more specifically, location) information of both services and users. Our proposal includes two key steps: (a) hybrid filtering, and (b) hierarchical prediction mechanism. On the one hand, the hybrid filtering aims to obtain a set of similar users and services, given a target user and a service. On the other hand, the goal of the hierarchical prediction mechanism is to estimate the QoS value accurately by leveraging hierarchical neural-regression. We evaluated our framework on the publicly available WS-DREAM datasets. The experimental results show the outperformance of our framework over the major state-of-the-art approaches.

Title: SemClinBr - a multi-institutional and multi-specialty semantically annotated corpus for Portuguese clinical NLP tasks Abstract: The high volume of research focusing on extracting patient information from electronic health records (EHRs) has led to an increase in the demand for annotated corpora, which are a precious resource for both the development and evaluation of natural language processing (NLP) algorithms. The absence of a multipurpose clinical corpus outside the scope of the English language, especially in Brazilian Portuguese, is glaring and severely impacts scientific progress in the biomedical NLP field. In this study, a semantically annotated corpus was developed using clinical text from multiple medical specialties, document types, and institutions. In addition, we present, (1) a survey listing common aspects, differences, and lessons learned from previous research, (2) a fine-grained annotation schema that can be replicated to guide other annotation initiatives, (3) a web-based annotation tool focusing on an annotation suggestion feature, and (4) both intrinsic and extrinsic evaluation of the annotations. This study resulted in SemClinBr, a corpus that has 1000 clinical notes, labeled with 65,117 entities and 11,263 relations. In addition, both negation cues and medical abbreviation dictionaries were generated from the annotations. The average annotator agreement score varied from 0.71 (applying strict match) to 0.92 (considering a relaxed match) while accepting partial overlaps and hierarchically related semantic types. The extrinsic evaluation, when applying the corpus to two downstream NLP tasks, demonstrated the reliability and usefulness of annotations, with the systems achieving results that were consistent with the agreement scores. The SemClinBr corpus and other resources produced in this work can support clinical NLP studies, providing a common development and evaluation resource for the research community, boosting the utilization of EHRs in both clinical practice and biomedical research. To the best of our knowledge, SemClinBr is the first available Portuguese clinical corpus.

Title: Faster Activity and Data Detection in Massive Random Access: A Multiarmed Bandit Approach Abstract: This article investigates the grant-free random access mechanism for massive Internet of Things (IoT) devices. By embedding the data symbols in the signature sequences, joint device activity detection and data decoding can be achieved, which, however, significantly increases the computational complexity. Coordinate descent algorithms that enjoy a low per-iteration complexity have been employed to solve this detection problem, but previous works typically employ a random coordinate selection policy which leads to slow convergence. In this article, we develop multiarmed bandit (MAB) approaches for more efficient detection via coordinate descent, which achieves a delicate tradeoff between <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">exploration</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">exploitation</i> in coordinate selection. Specifically, we first propose a bandit-based strategy, i.e., Bernoulli sampling, to speed up the convergence rate of coordinate descent, by learning which coordinates will result in more aggressive descent of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nonconvex objective function</i> . To further improve the convergence rate, an inner MAB problem is established to learn the exploration policy of Bernoulli sampling. Both convergence rate analysis and simulation results are provided to show that the proposed bandit-based algorithms enjoy faster convergence rates with a lower time complexity compared with the state-of-the-art algorithm. Furthermore, our proposed algorithms are generally applicable to different scenarios, e.g., massive random access with low-precision analog-to-digital converters (ADCs).

Title: Low-rank matrix denoising for count data using unbiased Kullback-Leibler risk estimation Abstract: Many statistical studies are concerned with the analysis of observations organized in a matrix form whose elements are count data. When these observations are assumed to follow a Poisson or a multinomial distribution, it is of interest to focus on the estimation of either the intensity matrix (Poisson case) or the compositional matrix (multinomial case) when it is assumed to have a low rank structure. In this setting, it is proposed to construct an estimator minimizing the regularized negative log-likelihood by a nuclear norm penalty. Such an approach easily yields a low-rank matrix-valued estimator with positive entries which belongs to the set of row-stochastic matrices in the multinomial case. Then, as a main contribution, a data-driven procedure is constructed to select the regularization parameter in the construction of such estimators by minimizing (approximately) unbiased estimates of the Kullback-Leibler (KL) risk in such models, which generalize Stein's unbiased risk estimation originally proposed for Gaussian data. The evaluation of these quantities is a delicate problem, and novel methods are introduced to obtain accurate numerical approximation of such unbiased estimates. Simulated data are used to validate this way of selecting regularizing parameters for low-rank matrix estimation from count data. For data following a multinomial distribution, the performances of this approach are also compared to K-fold cross-validation. Examples from a survey study and metagenomics also illustrate the benefits of this methodology for real data analysis. (C) 2022 Elsevier B.V. All rights reserved.

Title: Rainbow independent sets on dense graph classes Abstract: Abstract Given a family I of independent sets in a graph, a rainbow independent set is an independent set I such that there is an injection ϕ : I → I where for each v ∈ I , v is contained in ϕ ( v ) . Aharoni et al. (2019) determined for various graph classes C whether C satisfies a property that for every n , there exists N = N ( C , n ) such that every family of N independent sets of size n in a graph in C contains a rainbow independent set of size n . In this paper, we add two dense graph classes satisfying this property, namely, the class of graphs of bounded neighborhood diversity and the class of r -powers of graphs in a bounded expansion class.

Title: UNIFORM ERROR BOUNDS OF TIME-SPLITTING SPECTRAL METHODS FOR THE LONG-TIME DYNAMICS OF THE NONLINEAR KLEIN-GORDON EQUATION WITH WEAK NONLINEARITY Abstract: We establish uniform error bounds of time-splitting Fourier pseudospectral (TSFP) methods for the nonlinear Klein-Gordon equation (NKGE) with weak power-type nonlinearity and O(1) initial data, while the nonlinearity strength is characterized by epsilon(p) with a constant p is an element of N+ and a dimensionless parameter epsilon is an element of (0, 1], for the long-time dynamics up to the time at O(epsilon(-beta)) with 0 <= beta <= p. In fact, when 0 < is an element of << 1, the problem is equivalent to the long-time dynamics of NKGE with small initial data and O(1) nonlinearity strength, while the amplitude of the initial data (and the solution) is at O(epsilon). By reformulating the NKGE into a relativistic nonlinear Schriidinger equation, we adapt the TSFP method to discretize it numerically. By using the method of mathematical induction to bound the numerical solution, we prove uniform error bounds at O(h(m) + epsilon(p)(-beta)tau(2)) of the TSFP method with h mesh size, tau time step and m >= 2 depending on the regularity of the solution. The error bounds are uniformly accurate for the long-time simulation up to the time at O(epsilon(-beta)) and uniformly valid for epsilon is an element of (0, 1]. Especially, the error bounds are uniformly at the second-order rate for the large time step tau = O(epsilon(-(p-beta)/2)) in the parameter regime 0 <= beta < p. Numerical results are reported to confirm our error bounds in the long-time regime. Finally, the TSFP method and its error bounds are extended to a highly oscillatory complex NKGE which propagates waves with wavelength at O(1) in space and O(epsilon(beta)) in time and wave velocity at O(epsilon(-beta)).

Title: TCMI: a non-parametric mutual-dependence estimator for multivariate continuous distributions Abstract: The identification of relevant features, i.e., the driving variables that determine a process or the properties of a system, is an essential part of the analysis of data sets with a large number of variables. A mathematical rigorous approach to quantifying the relevance of these features is mutual information. Mutual information determines the relevance of features in terms of their joint mutual dependence to the property of interest. However, mutual information requires as input probability distributions, which cannot be reliably estimated from continuous distributions such as physical quantities like lengths or energies. Here, we introduce total cumulative mutual information (TCMI), a measure of the relevance of mutual dependences that extends mutual information to random variables of continuous distribution based on cumulative probability distributions. TCMI is a non-parametric, robust, and deterministic measure that facilitates comparisons and rankings between feature sets with different cardinality. The ranking induced by TCMI allows for feature selection, i.e., the identification of variable sets that are nonlinear statistically related to a property of interest, taking into account the number of data samples as well as the cardinality of the set of variables. We evaluate the performance of our measure with simulated data, compare its performance with similar multivariate-dependence measures, and demonstrate the effectiveness of our feature-selection method on a set of standard data sets and a typical scenario in materials science.

Title: On hub location problems in geographically flexible networks Abstract: In this paper, we propose an extension of the uncapacitated hub location problem where the potential positions of the hubs are not fixed in advance. Instead, they are allowed to belong to a region around an initial discrete set of nodes. We give a general framework in which the collection, transportation, and distribution costs are based on norm-based distances and the hub-activation setup costs depend not only on the location of the hub that are opened but also on the size of the region where they are placed. Two alternative mathematical programming formulations are proposed. The first one is a compact formulation while the second one involves a family of constraints of exponential size that we separate efficiently giving rise to a branch-and-cut algorithm. The results of an extensive computational experience are reported showing the advantages of each of the approaches.

Title: Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles Abstract: Academic research in the field of autonomous vehicles has reached high popularity in recent years related to several topics as sensor technologies, V2X communications, safety, security, decision making, control, and even legal and standardization rules. Besides classic control design approaches, Artificial Intelligence and Machine Learning methods are present in almost all of these fields. Another part of research focuses on different layers of Motion Planning, such as strategic decisions, trajectory planning, and control. A wide range of techniques in Machine Learning itself have been developed, and this article describes one of these fields, Deep Reinforcement Learning (DRL). The paper provides insight into the hierarchical motion planning problem and describes the basics of DRL. The main elements of designing such a system are the modeling of the environment, the modeling abstractions, the description of the state and the perception models, the appropriate rewarding, and the realization of the underlying neural network. The paper describes vehicle models, simulation possibilities and computational requirements. Strategic decisions on different layers and the observation models, e.g., continuous and discrete state representations, grid-based, and camera-based solutions are presented. The paper surveys the state-of-art solutions systematized by the different tasks and levels of autonomous driving, such as car-following, lane-keeping, trajectory following, merging, or driving in dense traffic. Finally, open questions and future challenges are discussed.

Title: Optimized feature space learning for generating efficient binary codes for image retrieval Abstract: In this paper, a novel approach for learning a low-dimensional optimized feature space for image retrieval with minimum intra-class variance and maximum inter-class variance is proposed. The classical approach of Linear Discriminant Analysis (LDA) is generally used for generating an optimized low-dimensional feature space for single-labeled images. Since image retrieval involves images with multiple objects, LDA cannot be directly used for dimensionality reduction and feature space optimization. This problem is addressed by utilizing the relationship between LDA and Canonical Correlation Analysis (CCA) eigenvalues to generate an optimized feature space for both single-labeled and multi-labeled images. A CCA-based network architecture which correlates the low-dimensional feature vectors with the image label vectors is proposed. We design a novel loss function such that the correlation coefficients of CCA are maximized. Our experiments prove that we could train the neural network to reach the theoretical lower bound of loss corresponding to the negative sum of the correlation coefficients. Once the optimized feature space is generated, feature vectors are binarized with the Iterative Quantization (ITQ) approach. Finally, we propose an ensemble network to generate binary codes of desired bit length for retrieval. The measurement of mean average precision shows that the proposed approach outperforms the retrieval results of other single-labeled and multi-labeled image retrieval benchmarks at same bit numbers in a considerable number of cases.

Title: Graph convolution machine for context-aware recommender system Abstract: The latest advance in recommendation shows that better user and item representations can be learned via performing graph convolutions on the user-item interaction graph. However, such finding is mostly restricted to the collaborative filtering (CF) scenario, where the interaction contexts are not available. In this work, we extend the advantages of graph convolutions to context-aware recommender system (CARS, which represents a generic type of models that can handle various side information). We propose Graph Convolution Machine (GCM), an end-to-end framework that consists of three components: an encoder, graph convolution (GC) layers, and a decoder. The encoder projects users, items, and contexts into embedding vectors, which are passed to the GC layers that refine user and item embeddings with context-aware graph convolutions on the user-item graph. The decoder digests the refined embeddings to output the prediction score by considering the interactions among user, item, and context embeddings. We conduct experiments on three real-world datasets from Yelp and Amazon, validating the effectiveness of GCM and the benefits of performing graph convolutions for CARS.

Title: Grassmannian Optimization for Online Tensor Completion and Tracking With the t-SVD Abstract: We propose a new fast streaming algorithm for the tensor completion problem of imputing missing entries of a low-tubal-rank tensor using the tensor singular value decomposition (t-SVD) algebraic framework. We show the t-SVD is a specialization of the well-studied block-term decomposition for third-order tensors, and we present an algorithm under this model that can track changing free submodules from incomplete streaming 2-D data. The proposed algorithm uses principles from incremental gradient descent on the Grassmann manifold of subspaces to solve the tensor completion problem with linear complexity and constant memory in the number of time samples. We provide a local expected linear convergence result for our algorithm. Our empirical results are competitive in accuracy but much faster in compute time than state-of-the-art tensor completion algorithms on real applications to recover temporal chemo-sensing and MRI data under limited sampling.

Title: On the Ramsey-Turán Density of Triangles Abstract: One of the oldest results in modern graph theory, due to Mantel, asserts that every triangle-free graph on n vertices has at most ⌊n2/4⌋ edges. About half a century later Andrásfai studied dense triangle-free graphs and proved that the largest triangle-free graphs on n vertices without independent sets of size αn, where 2/5 ≤ α < 1/2, are blow-ups of the pentagon. More than 50 further years have elapsed since Andrásfai’s work. In this article we make the next step towards understanding the structure of dense triangle-free graphs without large independent sets. Notably, we determine the maximum size of triangle-free graphs G on n vertices with α(G) ≥ 3n/8 and state a conjecture on the structure of the densest triangle-free graphs G with α(G) > n/3. We remark that the case α(G) α n/3 behaves differently, but due to the work of Brandt this situation is fairly well understood.

Title: A comparison of vector symbolic architectures Abstract: Vector Symbolic Architectures combine a high-dimensional vector space with a set of carefully designed operators in order to perform symbolic computations with large numerical vectors. Major goals are the exploitation of their representational power and ability to deal with fuzziness and ambiguity. Over the past years, several VSA implementations have been proposed. The available implementations differ in the underlying vector space and the particular implementations of the VSA operators. This paper provides an overview of eleven available VSA implementations and discusses their commonalities and differences in the underlying vector space and operators. We create a taxonomy of available binding operations and show an important ramification for non self-inverse binding operations using an example from analogical reasoning. A main contribution is the experimental comparison of the available implementations in order to evaluate (1) the capacity of bundles, (2) the approximation quality of non-exact unbinding operations, (3) the influence of combining binding and bundling operations on the query answering performance, and (4) the performance on two example applications: visual place- and language-recognition. We expect this comparison and systematization to be relevant for development of VSAs, and to support the selection of an appropriate VSA for a particular task. The implementations are available.

Title: Geometrical Bounds for Variance and Recentered Moments Abstract: We bound the variance and other moments of a random vector based on the range of its realizations, thus generalizing inequalities of Popoviciu and of Bhatia and Davis concerning measures on the line to several dimensions. This is done using convex duality and (infinite-dimensional) linear programming. The following consequence of our bounds exhibits symmetry breaking, provides a new proof of Jung's theorem, and turns out to have applications to the aggregation dynamics modelling attractive-repulsive interactions: among probability measures on R-n whose support has diameter at most root 2, we show that the variance around the mean is maximized precisely by those measures that assign mass 1/(n + 1) to each vertex of a standard simplex. For 1 <= p < infinity, the p th moment-optimally centered-is maximized by the same measures among those satisfying the diameter constraint.

Title: A study of defect-based error estimates for the Krylov approximation of φ-functions Abstract: Prior recent work, devoted to the study of polynomial Krylov techniques for the approximation of the action of the matrix exponential etAv, is extended to the case of associated φ-functions (which occur within the class of exponential integrators). In particular, a posteriori error bounds and estimates, based on the notion of the defect (residual) of the Krylov approximation are considered. Computable error bounds and estimates are discussed and analyzed. This includes a new error bound which favorably compares to existing error bounds in specific cases. The accuracy of various error bounds is characterized in relation to corresponding Ritz values of A. Ritz values yield properties of the spectrum of A (specific properties are known a priori, e.g., for Hermitian or skew-Hermitian matrices) in relation to the actual starting vector v and can be computed. This gives theoretical results together with criteria to quantify the achieved accuracy on the fly. For other existing error estimates, the reliability and performance are studied by similar techniques. Effects of finite precision (floating point arithmetic) are also taken into account.

Title: Optimal Controller Synthesis and Dynamic Quantizer Switching for Linear-Quadratic-Gaussian Systems Abstract: In this article, we consider optimal controller synthesis of a quantized-feedback linear-quadratic-Gaussian (QF-LQG) system, where the measurements are to be quantized before being transmitted to the controller. The system is presented with several choices of quantizers, along with the cost of operating each quantizer. The objective is to jointly select the quantizers and the controller that would...

Title: Implementing A Neural Network Interatomic Model With Performance Portability For Emerging Exascale Architectures Abstract: The two main thrusts of computational science are increasingly accurate predictions and faster calculations; to this end, the zeitgeist in molecular dynamics (MD) simulations is pursuing machine learned and data driven interatomic models, e.g. neural network potentials, and novel hardware architectures, e.g. GPUs. Current implementations of neural network potentials are orders of magnitude slower than traditional interatomic models and while looming exascale computing offers the ability to run large, accurate simulations with these models, achieving portable performance for MD with new and varied exascale hardware requires rethinking traditional algorithms, using novel data structures, and library solutions. We re-implement a neural network interatomic model in CabanaMD, an MD proxy application, built on libraries developed for performance portability. Our implementation shows significantly improved thread scaling in this complex kernel as compared to a current LAMMPS implementation, across both strong and weak scaling. Our single-source solution enables simulations up to 20 million atoms on a single CPU node and 4 million atoms with improved performance on a single GPU. We also explore parallelism and data layout choices (using flexible data structures called AoSoAs) and their effect on performance, seeing up to similar to 50% and similar to 5% improvements in performance on a GPU by choosing the right level of parallelism and data layout respectively.Program summaryProgram title: CabanaMD-NNPCPC Library link to program files: https://doi .org /10 .17632 /x948kyy7jh .1Developer's repository link: https://github.com/ECP-CoPA/CabanaMD, https://github.com/CompPhysVienna/n2p2Licensing provisions: BSD3-Clause, GPL-3.0Programming Language: C++Nature of problem: Developing a performance portable implementation of a neural network potential for exascale architectures.Solution method: CabanaMD-NNP uses algorithms and data-structures from the Kokkos [1] and Cabana [2] libraries to re-implement the computations in Behler-Parrinello neural network potentials [3, 4] for performance portability across hardware. All molecular dynamics data is stored in performance portable data structures, with atomic properties in array-of-structs-of-arrays (Cabana::AoSoAs), and auxiliary values including potential parameters in arrays (Kokkos::Views). All computation is also done in a performance portable way: neural network propagation uses Kokkos parallel kernels (Kokkos::parallel_for), while calculations performed for each atom and neighbor, evaluation of descriptors (symmetry functions) and forces, use Cabana extensions of Kokkos constructs (Cabana::neighbor_parallel_for). These choices provide our implementation with significant speedups both on CPUs and GPUs for large systems, additionally allowing flexibility for parallelism and data layout for further optimizations.Additional comments including restrictions and unusual features: The previously developed n2p2 package [4] contains an interface to LAMMPS [5], which we compare to throughout the paper. We primarily extend the n2p2 library directly (https://github .com /CompPhysVienna /n2p2) and also add an interface to that extension within CabanaMD (https://github .com /ECP-CoPA /CabanaMD), to obtain the main results with an identical LAMMPS input file.

Title: Dynamic Quantum Games Abstract: Quantum games represent the really twenty-first century branch of game theory, tightly linked to the modern development of quantum computing and quantum technologies. The main accent in these developments so far was made on stationary or repeated games. In this paper, we aim at initiating the truly dynamic theory with strategies chosen by players in real time. Since direct continuous observations are known to destroy quantum evolutions (so-called quantum Zeno paradox), the necessary new ingredient for quantum dynamic games must be the theory of non-direct observations and the corresponding quantum filtering. Apart from the technical problems in organizing feedback quantum control in real time, the difficulty in applying this theory for obtaining mathematically amenable control systems is due partially to the fact that it leads usually to rather non-trivial jump-type Markov processes and/or degenerate diffusions on manifolds, for which the corresponding control is very difficult to handle. The starting point for the present research is the remarkable discovery (quite unexpected, at least to the author) that there exists a very natural class of homodyne detections such that the diffusion processes on projective spaces resulting by filtering under such arrangements coincide exactly with the standard Brownian motions (BM) on these spaces. In some cases, one can even reduce the process to the plain BM on Euclidean spaces or tori. The theory of such motions is well studied making it possible to develop a tractable theory of related control and games, which can be at the same time practically implemented on quantum optical devices.

Title: Weighted Words at Degree Two, II: Flat Partitions, Regular Partitions, and Application to Level One Perfect Crystals Abstract: In a recent work, Keith and Xiong gave a refinement of Glaisher's theorem by using a Sylvester-style bijection. In this paper, we introduce two families of colored partitions, flat and regular partitions, and generalize the bijection of Keith and Xiong to these partitions. We then state two results, the first at degree one, where partitions have parts with primary colors, and the second result at degree two for secondary-colored partitions, using the result of the first paper of this series on Siladle's identity. These results allow us to easily retrieve the Frenkel-Kac character formulas of level one standard modules for the type A(2n)((2)), D-n+1((2)) and B-n((1)), and also to make the connection between the result stated in paper one and the representation theory.

Title: Hierarchical Aitchison–Silvey models for incomplete binary sample spaces Abstract: Multivariate sample spaces may be incomplete Cartesian products, when certain combinations of the categories of the variables are not possible. Traditional log-linear models, which generalize independence and conditional independence, do not apply in such cases, as they may associate positive probabilities with the non-existing cells. To describe the association structure in incomplete sample spaces, this paper develops a class of hierarchical multiplicative models which are defined by setting certain non-homogeneous generalized odds ratios equal to one and are named after Aitchison and Silvey who were among the first to consider such ratios. These models are curved exponential families that do not contain an overall effect and, from an algebraic perspective, are non-homogeneous toric ideals. The relationship of this model class with log-linear models and quasi log-linear models is studied in detail in terms of both statistics and algebraic geometry. The existence of maximum likelihood estimates and their properties, as well as the relevant algorithms are also discussed.

Title: An Improved Distributed Nonlinear Observers for Leader-Following Consensus via Differential Geometry Approach Abstract: This article is concerned with the leader-following output consensus problem in the framework of distributed nonlinear observers. Instead of certain hypotheses on the leader system, a group of geometric conditions is put forward to develop a novel distributed observers strategy, thereby definitely improving the applicability of the existing results. To be more specific, the improved distributed observers can precisely handle consensus problems for some nonlinear leader systems, which are invalid for the traditional strategies with a certain assumption, such as elastic shaft single linkage manipulator (ESSLM) systems and most of the first-order nonlinear systems. We prove the sufficient conditions for the exponential stability of our distributed observers’ error dynamic by proposing two pioneered lemmas to show the relationship between the maximum eigenvalues of two matrices appearing in Lyapunov type matrices. Then, a partial feedback linearization method with zero dynamic proposed in differential geometry is employed to design the purely decentralized control law for the affine nonlinear multiagent system. With this advancement, the existing results can be regarded as a specific case owing to that the followers can be chosen as an arbitrary minimum phase affine smooth nonlinear system. At last, the novel distributed observers and the improved purely decentralized control law are applied in the distributed control framework to construct a closed-loop system. We also prove the stability of the closed-loop system to achieve leader-following consensus. Our method is illustrated by the ESSLM system and Van der Pol system as leaders.

Title: A Survey on Knowledge Graphs: Representation, Acquisition, and Applications Abstract: Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction toward cognition and human-level intelligence. In this survey, we provide a comprehensive review of the knowledge graph covering overall research topics about: 1) knowledge graph representation learning; 2) knowledge acquisition and completion; 3) temporal knowledge graph; and 4) knowledge-aware applications and summarize recent breakthroughs and perspective directions to facilitate future research. We propose a full-view categorization and new taxonomies on these topics. Knowledge graph embedding is organized from four aspects of representation space, scoring function, encoding models, and auxiliary information. For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning are reviewed. We further explore several emerging topics, including metarelational learning, commonsense reasoning, and temporal knowledge graphs. To facilitate future research on knowledge graphs, we also provide a curated collection of data sets and open-source libraries on different tasks. In the end, we have a thorough outlook on several promising research directions.

Title: Deep Reinforcement Learning for Autonomous Driving: A Survey Abstract: With the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments. This review summarises deep reinforcement learning (DRL) algorithms and provides a taxonomy of automated driving tasks where (D)RL methods have been employed, while addressing key computational challenges in real world deployment of autonomous driving agents. It also delineates adjacent domains such as behavior cloning, imitation learning, inverse reinforcement learning that are related but are not classical RL algorithms. The role of simulators in training agents, methods to validate, test and robustify existing solutions in RL are discussed.

Title: Excluding a Ladder Abstract: A ladder is a 2 × k grid graph. When does a graph class $${\cal C}$$ exclude some ladder as a minor? We show that this is the case if and only if all graphs G in $${\cal C}$$ admit a proper vertex coloring with a bounded number of colors such that for every 2-connected subgraph H of G, there is a color that appears exactly once in H. This type of vertex coloring is a relaxation of the notion of centered coloring, where for every connected subgraph H of G, there must be a color that appears exactly once in H. The minimum number of colors in a centered coloring of G is the treedepth of G, and it is known that classes of graphs with bounded treedepth are exactly those that exclude a fixed path as a subgraph, or equivalently, as a minor. In this sense, the structure of graphs excluding a fixed ladder as a minor resembles the structure of graphs without long paths. Another similarity is as follows: It is an easy observation that every connected graph with two vertex-disjoint paths of length k has a path of length k+1. We show that every 3-connected graph which contains as a minor a union of sufficiently many vertex-disjoint copies of a 2×k grid has a 2×(k+1) grid minor. Our structural results have applications to poset dimension. We show that posets whose cover graphs exclude a fixed ladder as a minor have bounded dimension. This is a new step towards the goal of understanding which graphs are unavoidable as minors in cover graphs of posets with large dimension.

Title: Wireless Channel Modeling Based on Extreme Value Theory for Ultra-Reliable Communications Abstract: A key building block in the design of ultra-reliable communication systems is a wireless channel model that captures the statistics of rare events occurring due to the significant fading. In this paper, we propose a novel methodology based on extreme value theory (EVT) to statistically model the behavior of extreme events in a wireless channel for ultra-reliable communication. This methodology includes techniques for fitting the lower tail distribution of the received power to the generalized Pareto distribution (GPD), determining the optimum threshold over which the tail statistics are derived, ascertaining the optimum stopping condition on the number of samples required to estimate the tail statistics by using GPD, and finally, assessing the validity of the derived Pareto model. Based on the data collected within the engine compartment of Fiat Linea under various engine vibrations and driving scenarios, we demonstrate that the proposed methodology provides the best fit to the collected data, significantly outperforming the conventional extrapolation-based methods. Moreover, the usage of the EVT in the proposed method decreases the required number of samples for estimating the tail statistics significantly.

Title: A lower bound on the number of inequivalent APN functions Abstract: In this paper, we establish a lower bound on the total number of inequivalent APN functions on the finite field with 22m elements, where m is even. We obtain this result by proving that the APN functions introduced by Pott and the second author [22], which depend on three parameters k, s and α, are pairwise inequivalent for distinct choices of the parameters k and s. Moreover, we determine the automorphism group of these APN functions.

Title: Monogamy constraints on entanglement of four-qubit pure states Abstract: We report a set of monogamy constraints on one-tangle, two-tangles, three-tangles, and four-way correlations of a general four-qubit pure state. It is found that given a two-qubit marginal state $$\rho $$ of a four-qubit pure state $$\left| \Psi _{4}\right\rangle $$ , the non-Hermitian matrix $$\rho {\widetilde{\rho }}$$ where $${\widetilde{\rho }}$$ $$=\left( \sigma _{y} \otimes \sigma _{y}\right) \rho ^{*}\left( \sigma _{y}\otimes \sigma _{y}\right) $$ , contains information not only about the entanglement properties of the two-qubits in state $$\rho $$ but also about three-tangles involving the selected pair as well as four-way correlations of the pair of qubits in $$\left| \Psi _{4}\right\rangle $$ . To extract information about tangles of a four-qubit state $$\left| \Psi _{4}\right\rangle $$ , the coefficients in the characteristic polynomial of matrix $$\rho {\widetilde{\rho }}$$ are analytically expressed in terms of $$2\times 2$$ matrices of state coefficients. Four-tangles distinguish between different types of entangled four-qubit pure states.

Title: Stochastic Geometry to Generalize the Mondrian Process. Abstract: The Mondrian process is a stochastic process that produces a recursive partition of space with random axis-aligned cuts. Random forests and Laplace kernel approximations built from the Mondrian process have led to efficient online learning methods and Bayesian optimization. By viewing the Mondrian process as a special case of the stable under iterated tessellation (STIT) process, we utilize tools from stochastic geometry to resolve three fundamental questions concern generalizability of the Mondrian process in machine learning. First, we show that the Mondrian process with general cut directions can be efficiently simulated, but it is unlikely to give rise to better classification or regression algorithms. Second, we characterize all possible kernels that generalizations of the Mondrian process can approximate. This includes, for instance, various forms of the weighted Laplace kernel and the exponential kernel. Third, we give an explicit formula for the density estimator arising from a Mondrian forest. This allows for precise comparisons between the Mondrian forest, the Mondrian kernel and the Laplace kernel in density estimation. Our paper calls for further developments at the novel intersection of stochastic geometry and machine learning.

Title: When Less Is More: Systematic Analysis of Cascade-Based Community Detection Abstract: AbstractInformation diffusion, spreading of infectious diseases, and spreading of rumors are fundamental processes occurring in real-life networks. In many practical cases, one can observe when nodes become infected, but the underlying network, over which a contagion or information propagates, is hidden. Inferring properties of the underlying network is important since these properties can be used for constraining infections, forecasting, viral marketing, and so on. Moreover, for many applications, it is sufficient to recover only coarse high-level properties of this network rather than all its edges. This article conducts a systematic and extensive analysis of the following problem: Given only the infection times, find communities of highly interconnected nodes. This task significantly differs from the well-studied community detection problem since we do not observe a graph to be clustered. We carry out a thorough comparison between existing and new approaches on several large datasets and cover methodological challenges specific to this problem. One of the main conclusions is that the most stable performance and the most significant improvement on the current state-of-the-art are achieved by our proposed simple heuristic approaches agnostic to a particular graph structure and epidemic model. We also show that some well-known community detection algorithms can be enhanced by including edge weights based on the cascade data.

Title: Disorder detection with costly observations Abstract: We study the Wiener disorder detection problem where each observation is associated with a positive cost. In this setting, a strategy is a pair consisting of a sequence of observation times and a stopping time corresponding to the declaration of disorder. We characterize the minimal cost of the disorder problem with costly observations as the unique fixed point of a certain jump operator, and we determine the optimal strategy.

Title: Multistage Model for Robust Face Alignment Using Deep Neural Networks Abstract: The ability to generalize unconstrained conditions such as severe occlusions and large pose variations remains a challenging goal to achieve in face alignment. In this paper, a multistage model based on deep neural networks is proposed which takes advantage of spatial transformer networks, hourglass networks and exemplar-based shape constraints. First, a spatial transformer-generative adversarial network which consists of convolutional layers and residual units is utilized to solve the initialization issues caused by face detectors, such as rotation and scale variations, to obtain improved face bounding boxes for face alignment. Then, stacked hourglass network is employed to obtain preliminary locations of landmarks as well as their corresponding scores. In addition, an exemplar-based shape dictionary is designed to determine landmarks with low scores based on those with high scores. By incorporating face shape constraints, misaligned landmarks caused by occlusions or cluttered backgrounds can be considerably improved. Extensive experiments based on challenging benchmark datasets are performed to demonstrate the superior performance of the proposed method over other state-of-the-art methods.

Title: Probabilistic State Estimation in Water Networks Abstract: State estimation (SE) in water distribution networks (WDNs), the problem of estimating all unknown network heads and flows given select measurements, is challenging due to the nonconvexity of hydraulic models and significant uncertainty from water demands, network parameters, and measurements. To this end, a probabilistic modeling for SE in WDNs is proposed. After linearizing the nonlinear hydraulic WDN model, the proposed probabilistic SE (PSE) shows that the covariance matrix of unknown system states (unmeasured heads and flows) can be linearly expressed by the covariance matrix of three uncertainty sources (i.e., measurement noise, network parameters, and water demands). Instead of providing deterministic results for unknown states, the proposed PSE approach: 1) regards the system states and uncertainty sources as random variables and yields variances of individual unknown states; 2) considers thorough modeling of various types of valves and measurement scenarios in WDNs; and 3) is also useful for uncertainty quantification, extended period simulations, and confidence limit analysis. The effectiveness and scalability of the proposed approach are tested using several WDN case studies.

Title: Boosting API Recommendation With Implicit Feedback Abstract: Developers often need to use appropriate APIs to program efficiently, but it is usually a difficult task to identify the exact one they need from a vast list of candidates. To ease the burden, a multitude of API recommendation approaches have been proposed. However, most of the currently available API recommenders do not support the effective integration of user feedback into the recommendation loop. In this paper, we propose a framework, BRAID ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</b> oosting <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</b> ecommend <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> tion with <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</b> mplicit Fee <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> back), which leverages learning-to-rank and active learning techniques to boost recommendation performance. By exploiting user feedback information, we train a learning-to-rank model to re-rank the recommendation results. In addition, we speed up the feedback learning process with active learning. Existing query-based API recommendation approaches can be plugged into BRAID. We select three state-of-the-art API recommendation approaches as baselines to demonstrate the performance enhancement of BRAID measured by Hit@k (Top-k), MAP, and MRR. Empirical experiments show that, with acceptable overheads, the recommendation performance improves steadily and substantially with the increasing percentage of feedback data, comparing with the baselines.

Title: Decentralized Observer Design for Virtual Decomposition Control Abstract: In this article, we incorporate velocity observer design into the virtual decomposition control (VDC) strategy of an $n$-degree of freedom ($n$-DoF) open-chain robotic manipulator. Descending from the VDC strategy, the proposed design is based on decomposing...

Title: Dynamic sampling from a discrete probability distribution with a known distribution of rates Abstract: In this paper, we consider several efficient data structures for the problem of sampling from a dynamically changing discrete probability distribution, where some prior information is known on the distribution of the rates, in particular the maximum and minimum rate, and where the number of possible outcomes N is large. We consider three basic data structures, the Acceptance–Rejection method, the Complete Binary Tree and the Alias method. These can be used as building blocks in a multi-level data structure, where at each of the levels, one of the basic data structures can be used, with the top level selecting a group of events, and the bottom level selecting an element from a group. Depending on assumptions on the distribution of the rates of outcomes, different combinations of the basic structures can be used. We prove that for particular data structures the expected time of sampling and update is constant when the rate distribution follows certain conditions. We show that for any distribution, combining a tree structure with the Acceptance–Rejection method, we have an expected time of sampling and update of $$O\left( \log \log {r_{max}}/{r_{min}}\right) $$ is possible, where $$r_{max}$$ is the maximum rate and $$r_{min}$$ the minimum rate. We also discuss an implementation of a Two Levels Acceptance–Rejection data structure, that allows expected constant time for sampling, and amortized constant time for updates, assuming that $$r_{max}$$ and $$r_{min}$$ are known and the number of events is sufficiently large. We also present an experimental verification, highlighting the limits given by the constraints of a real-life setting.

Title: Control of Fork-Join Processing Networks with Multiple Job Types and Parallel Shared Resources Abstract: A fork-join processing network is a queueing network in which tasks associated with a job can be processed simultaneously. Fork-join processing networks are prevalent in computer systems, healthcare, manufacturing, project management, justice system, etc. Unlike the conventional queueing networks, fork-join processing networks have synchronization constraints that arise due to the parallel processing of tasks and can cause significant job delays. We study scheduling control in fork-join processing networks with multiple job types and parallel shared resources. Jobs arriving in the system fork into arbitrary number of tasks, then those tasks are processed in parallel, and then they join and leave the network. There are shared resources processing multiple job types. We study the scheduling problem for those shared resources (that is, which type of job to prioritize at any given time) and propose an asymptotically optimal scheduling policy in diffusion scale.

Title: Gaussian Processes with Input Location Error and Applications to the Composite Parts Assembly Process Abstract: This paper investigates Gaussian process modeling with input location error, where the inputs are corrupted by noise. Here, the best linear unbiased predictor for two cases is considered, according to whether there is noise at the target location or not. We show that the mean squared prediction error converges to a nonzero constant if there is noise at the target location, and we provide an upper bound of the mean squared prediction error if there is no noise at the target location. We investigate the use of stochastic Kriging in the prediction of Gaussian processes with input location error and show that stochastic Kriging is a good approximation when the sample size is large. Several numerical examples are given to illustrate the results, and a case study on the assembly of composite parts is presented. Technical proofs are provided in the appendices.

Title: Uncertainty Quantification for Bayesian Optimization Abstract: Bayesian optimization is a class of global optimization techniques. In Bayesian optimization, the underlying objective function is modeled as a realization of a Gaussian process. Although the Gaussian process assumption implies a random distribution of the Bayesian optimization outputs, quantification of this uncertainty is rarely studied in the literature. In this work, we propose a novel approach to assess the output uncertainty of Bayesian optimization algorithms, which proceeds by constructing confidence regions of the maximum point (or value) of the objective function. These regions can be computed efficiently, and their confidence levels are guaranteed by the uniform error bounds for sequential Gaussian process regression newly developed in the present work. Our theory provides a unified uncertainty quantification framework for all existing sequential sampling policies and stopping criteria.

Title: Quantum Statistical Complexity Measure as a Signaling of Correlation Transitions Abstract: We introduce a quantum version for the statistical complexity measure, in the context of quantum information theory, and use it as a signaling function of quantum order-disorder transitions. We discuss the possibility for such transitions to characterize interesting physical phenomena, as quantum phase transitions, or abrupt variations in correlation distributions. We apply our measure on two exactly solvable Hamiltonian models: the 1D-Quantum Ising Model (in the single-particle reduced state), and on Heisenberg XXZ spin-1/2 chain (in the two-particle reduced state). We analyze its behavior across quantum phase transitions for finite system sizes, as well as in the thermodynamic limit by using Bethe Ansatz technique.

Title: Learning test-time augmentation for content-based image retrieval Abstract: Off-the-shelf convolutional neural network features achieve outstanding results in many image retrieval tasks. However, their invariance to target data is pre-defined by the network architecture and training data. Existing image retrieval approaches require fine-tuning or modification of pre-trained networks to adapt to variations unique to the target data. In contrast, our method enhances the invariance of off-the-shelf features by aggregating features extracted from images augmented at test-time, with augmentations guided by a policy learned through reinforcement learning. The learned policy assigns different magnitudes and weights to the selected transformations, which are selected from a list of image transformations. Policies are evaluated using a metric learning protocol to learn the optimal policy. The model converges quickly and the cost of each policy iteration is minimal as we propose an off-line caching technique to greatly reduce the computational cost of extracting features from augmented images. Experimental results on large trademark retrieval (METU trademark dataset) and landmark retrieval (ROxford5k and RParis6k scene datasets) tasks show that the learned ensemble of transformations is highly effective for improving performance, and is practical, and transferable.

Title: Age of information in a decentralized network of parallel queues with routing and packets losses Abstract: The paper deals with age of information (AoI) in a network of multiple sources and parallel queues with buffering capabilities, preemption in service and losses in served packets. The queues do not communicate between each other and the packets are dispatched through the queues according to a predefined probabilistic routing. By making use of the stochastic hybrid system (SHS) method, we provide a...

Title: CLASS NUMBERS AND REPRESENTATIONS BY TERNARY QUADRATIC FORMS WITH CONGRUENCE CONDITIONS Abstract: In this paper, we are interested in the interplay between integral ternary quadratic forms and class numbers. This is partially motivated by a question of Petersson.

Title: On indicated coloring of lexicographic product of graphs Abstract: Indicated coloring is a graph coloring game in which two players collectively color the vertices of a graph in the following way. In each round the first player (Ann) selects a vertex, and then the second player (Ben) colors it properly, using a fixed set of colors. The goal of Ann is to achieve a proper coloring of the whole graph, while Ben is trying to prevent the realization of this project. The smallest number of colors necessary for Ann to win the game on a graph G (regardless of Ben's strategy) is called the indicated chromatic number of G, denoted by chi i(G). In this paper, we show that for any graphs G and H, G[H] is k-indicated colorable for all k > col(G)col(H). Also, we show that for any graph G and for some classes of graphs H with chi(H) = chi i(H) = l, G[H] is k-indicated colorable if and only if G[Kl] is k-indicated colorable. As a consequence {of this result, we show that if G E G = Chordal graphs, Cographs, Complement of bipartite graphs, {P5, C-4}-free graphs, Connected {P-6, C-5, ((P-5) over bar), K-1,K-3}-free graphs which } { contain an induced C6, Complete multipartite graphs and H E F = Bipartite graphs, Chordal graphs, Cographs, {P-5, K-3}-free graphs, {P-5, paw}-free graphs, Complement of bipartite graphs, {P-5,K-4, kite, bull}-free graphs, Connected {P-6, C-5, P-5, K-1,K-3}-free graphs which contain an induced C-6, {P-5, C-4}-free graphs, K[C-5](m(1), m(2), ... , m(5)), Connected }{P-5, ((P-2 boolean OR P-3) over bar), ((P-5)over bar), dart}-free graphs which contain an induced C-5 , then G[H] is k-indicated colorable for every k > chi(G[H]). This serves as a partial answer to one of the questions {raised by A. Grzesik in Grzesik (2012). In addition, if G E Bipartite graphs, {P5, K3}-free }graphs, {P5, paw}-free graphs and H E F, then we show that chi(i)(G[H])= chi(G[H]). (C) 2021 Elsevier B.V. All rights reserved.

Title: An information-rich sampling technique over spatio-temporal CNN for classification of human actions in videos Abstract: We propose a novel video sampling scheme for human action recognition in videos, using Gaussian Weighing Function. Traditionally in deep learning-based human activity recognition approaches, either a few random frames or every k(th) frame of the video is considered for training the 3D CNN, where k is a small positive integer, like 4, 5, or 6. This kind of sampling reduces the volume of the input data, which speeds-up the training network and also avoids overfitting to some extent, thus enhancing the performance of the 3D CNN model. In the proposed video sampling technique, consecutive k frames of a video are aggregated into a single frame by computing a Gaussian-weighted summation of the k frames. The resulting frame preserves the information in a better way than the conventional approaches and experimentally shown to perform better. In this paper, a 3-Dimensional deep CNN is proposed to extract the spatio-temporal features and follows Long Short-Term Memory (LSTM) to recognize human actions. The proposed 3D CNN architecture is capable of handling the videos where the camera is placed at a distance from the performer. Experiments are performed with KTH, WEIZMANN, and CASIA-B Human Activity and Gait datasets, whereby it is shown to outperform state-of-the-art deep learning based techniques. We achieve 95.78%, 95.27%, and 95.27% over the KTH, WEIZMANN, and CASIA-B human action and gait recognition datasets, respectively.

Title: Fine-Grained Urban Flow Inference Abstract: Spatially fine-grained urban flow data is critical for smart city efforts. Though fine-grained information is desirable for applications, it demands much more resources for the underlying storage system compared to coarse-grained data. To bridge the gap between storage efficiency and data utility, in this paper, we aim to infer fine-grained flows throughout a city from their coarse-grained counter...

Title: Computationally Efficient Algorithm for Eco-Driving Over Long Look-Ahead Horizons Abstract: This paper presents a computationally efficient algorithm for eco-driving along horizons of over 100 km. The eco-driving problem is formulated as a bi-level program, where the bottom level is solved offline, pre-optimising gear as a function of longitudinal velocity (kinetic energy) and acceleration. The top level is solved online, optimising a nonlinear dynamic program with travel time, kinetic energy and acceleration as state variables. To further reduce computational effort, the travel time is adjoined to the objective by applying necessary Pontryagin’s Maximum Principle conditions, and the nonlinear program is solved using real-time iteration sequential quadratic programming scheme in a model predictive control framework. Compared to average driver’s driving cycle, the energy savings of using the proposed algorithm is up to 11.60%.

Title: Conservative Numerical Schemes with Optimal Dispersive Wave Relations: Part II. Numerical Evaluations Abstract: A new energy and enstrophy conserving scheme (EEC) for the shallow water equations is proposed and evaluated using a suite of test cases over the global spherical or bounded domain. The evaluation is organized around a set of pre-defined properties: accuracy of individual operators, accuracy of the whole scheme, conservation of key quantities, control of the divergence variable, representation of the energy and enstrophy spectra, and simulation of nonlinear dynamics. The results confirm that the scheme is between the first and second order accurate, and conserves the total energy and potential enstrophy up to the time truncation errors. The scheme is capable of producing more physically realistic energy and enstrophy spectra, indicating that it can help prevent the unphysical energy cascade towards the finest resolvable scales. With an optimal representation of the dispersive wave relations, the scheme is able to keep the flow close to being non-divergent, and maintain the geostrophically balanced structures with large-scale geophysical flows over long-term simulations.

Title: The threshold bias of the clique-factor game Abstract: Let r≥4 be an integer and consider the following game on the complete graph Kn for n∈rZ: Two players, Maker and Breaker, alternately claim previously unclaimed edges of Kn such that in each turn Maker claims one and Breaker claims b∈N edges. Maker wins if her graph contains a Kr-factor, that is a collection of n/r vertex-disjoint copies of Kr, and Breaker wins otherwise. In other words, we consider a b-biased Kr-factor Maker–Breaker game. We show that the threshold bias for this game is of order n2/(r+2). This makes a step towards determining the threshold bias for making bounded-degree spanning graphs and extends a result of Allen et al. who resolved the case r∈{3,4} up to a logarithmic factor.

Title: Poisson kernel: Avoiding self-smoothing in graph convolutional networks Abstract: •To the best of our knowledge, our work is the first to reveal the self-smoothing phenomenon of graph convolutional kernels in graph convolutional networks, which is a major vulnerability to reduce accuracy, robustness, and adaptability of trained models.•We theoretically study how exactly different graph structures influence the performance of graph convolutional kernels, and several theorems about the properties and effects are given.•By building an eigenvalue mapping of the symmetrically normalized adjacency matrix, we skillfully propose the Poisson kernel to avoid self-smoothing in graph convolutional networks without being sensitive to the parameter and dataset selections.•Our results are evidently superior to state-of-the-art kernels on synthetic datasets with specific structures, where they will lead to severe self-smoothing inevitably. And in most situations, such as on three benchmark datasets (Cora, Citeseer and Pubmed), our Poisson kernel still works well and almost better without training any adaptive kernel.

Title: Machine unlearning: linear filtration for logit-based classifiers Abstract: Recently enacted legislation grants individuals certain rights to decide in what fashion their personal data may be used and in particular a “right to be forgotten”. This poses a challenge to machine learning: how to proceed when an individual retracts permission to use data which has been part of the training process of a model? From this question emerges the field of machine unlearning, which could be broadly described as the investigation of how to “delete training data from models”. Our work complements this direction of research for the specific setting of class-wide deletion requests for classification models (e.g. deep neural networks). As a first step, we propose linear filtration as an intuitive, computationally efficient sanitization method. Our experiments demonstrate benefits in an adversarial setting over naive deletion schemes.

Title: Computational aspects of sturdy and flimsy numbers Abstract: Following Stolarsky, we say that a natural number n is flimsy in base b if some positive multiple of n has smaller digit sum in base b than n does; otherwise it is sturdy. When n is proven flimsy by multiplier k, we say n is k-flimsy. We study computational aspects of sturdy and flimsy numbers.

Title: A polynomial algorithm for maxmin and minmax envy-free rent division on a soft budget Abstract: The current practice of envy-free rent division, led by the fair allocation website Spliddit, is based on quasi-linear preferences. These preferences rule out agents’ well documented financial constraints. To resolve this issue we consider an extension of the quasi-linear domain that admits differences in agents’ marginal disutility of paying rent below and above a given reference, i.e., a soft budget. We construct a polynomial algorithm to calculate a maxmin utility envy-free allocation, and other related solutions, in this domain.

Title: Scalable Vehicle Team Continuum Deformation Coordination With Eigen Decomposition Abstract: The continuum deformation leader–follower cooperative control strategy models vehicles in a multiagent system as particles of a deformable body. A desired continuum deformation is defined based on leaders’ trajectories and acquired by followers in real time through local communication. The existing continuum deformation theory requires followers to be placed inside the convex simplex defined by le...

Title: Semantic Robustness of Models of Source Code Abstract: Deep neural networks are vulnerable to adversarial examples-small input perturbations that result in incorrect predictions. We study this problem for models of source code, where we want the neural network to be robust to source-code modifications that preserve code functionality. To facilitate training robust models, we define a powerful and generic adversary that can employ sequences of parametric, semantics-preserving program transformations. We then explore how, with such an adversary, one can train models that are robust to adversarial program transformations. We conduct a thorough evaluation of our approach and find several surprising facts: we find robust training to beat dataset augmentation in every evaluation we performed; we find that a state-of-the-art architecture (code2seq) for models of code is harder to make robust than a simpler baseline; additionally, we find code2seq to have surprising weaknesses not present in our simpler baseline model; finally, we find that robust models perform better against unseen data from different sources (as one might hope)-however, we also find that robust models are not clearly better in the cross-language transfer task. To the best of our knowledge, we are the first to study the interplay between robustness of models of code and the domain-adaptation and cross-language- transfer tasks.

Title: Index-Based Solutions for Efficient Density Peak Clustering Abstract: Density Peak Clustering (DPC), a popular density-based clustering approach, has received considerable attention from the research community primarily due to its simplicity and fewer-parameter requirement. However, the resultant clusters obtained using DPC are influenced by the sensitive parameter $d_c$<mml:math xmlns:mml=&#34;http://w...

Title: Maximizing the number of independent sets of fixed size in K n-covered graphs Abstract: For some given graph H, a graph G is called H-covered if each vertex in G is contained in a copy of H. In this note, we determine the maximum number of independent sets of size t >= 3 in N-vertex K n-covered graphs and classify the extremal graphs. The result answers a question proposed by Chakraborti and Loh. The proof uses an edge-switching operation on hypergraphs which never increases the number of independent sets.