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ubowang

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cite-llm-multi-cite-eval

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[ "<|reference_start|> Facebook graph search: Το χαρακτηριστικό του Facebook Graph Search, eίναι πeρίπου όπως τη μηχανή αναζήτησης της Google, ... <|reference_end|>", "<|reference_start|> Neighborhood Based Fast Graph Search in Large Networks: Complex social and information network search becomes important with a variety of applications. In the core of these applications, lies a common and critical problem: Given a labeled network and a query graph, how to efficiently search the query graph in the target network. The presence of noise and the incomplete knowledge about the structure and content of the target network make it unrealistic to find an exact match. Rather, it is more appealing to find the top-k approximate matches.\n In this paper, we propose a neighborhood-based similarity measure that could avoid costly graph isomorphism and edit distance computation. Under this new measure, we prove that subgraph similarity search is NP hard, while graph similarity match is polynomial. By studying the principles behind this measure, we found an information propagation model that is able to convert a large network into a set of multidimensional vectors, where sophisticated indexing and similarity search algorithms are available. The proposed method, called Ness (Neighborhood Based Similarity Search), is appropriate for graphs with low automorphism and high noise, which are common in many social and information networks. Ness is not only efficient, but also robust against structural noise and information loss. Empirical results show that it can quickly and accurately find high-quality matches in large networks, with negligible cost. <|reference_end|>", "<|reference_start|> NeMa: Fast graph search with label similarity: It is increasingly common to find real-life data represented as networks of labeled, heterogeneous entities. To query these networks, one often needs to identify the matches of a given query graph in a (typically large) network modeled as a target graph. Due to noise and the lack of fixed schema in the target graph, the query graph can substantially differ from its matches in the target graph in both structure and node labels, thus bringing challenges to the graph querying tasks. In this paper, we propose NeMa (Network Match), a neighborhood-based subgraph matching technique for querying real-life networks. (1) To measure the quality of the match, we propose a novel subgraph matching cost metric that aggregates the costs of matching individual nodes, and unifies both structure and node label similarities. (2) Based on the metric, we formulate the minimum cost subgraph matching problem. Given a query graph and a target graph, the problem is to identify the (top-k) matches of the query graph with minimum costs in the target graph. We show that the problem is NP-hard, and also hard to approximate. (3) We propose a heuristic algorithm for solving the problem based on an inference model. In addition, we propose optimization techniques to improve the efficiency of our method. (4) We empirically verify that NeMa is both effective and efficient compared to the keyword search and various state-of-the-art graph querying techniques. <|reference_end|>", "<|reference_start|> DELTACON: A Principled Massive-Graph Similarity Function: How much did a network change since yesterday? How different is the wiring between Bob's brain (a left-handed male) and Alice's brain (a right-handed female)? Graph similarity with known node correspondence, i.e. the detection of changes in the connectivity of graphs, arises in numerous settings. In this work, we formally state the axioms and desired properties of the graph similarity functions, and evaluate when state-of-the-art methods fail to detect crucial connectivity changes in graphs. We propose DeltaCon, a principled, intuitive, and scalable algorithm that assesses the similarity between two graphs on the same nodes (e.g. employees of a company, customers of a mobile carrier). Experiments on various synthetic and real graphs showcase the advantages of our method over existing similarity measures. Finally, we employ DeltaCon to real applications: (a) we classify people to groups of high and low creativity based on their brain connectivity graphs, and (b) do temporal anomaly detection in the who-emails-whom Enron graph. <|reference_end|>" ]

[ 5, 14, 15, 39 ]

[ "<|reference_start|> Uncertainty-DTW for Time Series and Sequences: Dynamic Time Warping (DTW) is used for matching pairs of sequences and celebrated in applications such as forecasting the evolution of time series, clustering time series or even matching sequence pairs in few-shot action recognition. The transportation plan of DTW contains a set of paths; each path matches frames between two sequences under a varying degree of time warping, to account for varying temporal intra-class dynamics of actions. However, as DTW is the smallest distance among all paths, it may be affected by the feature uncertainty which varies across time steps/frames. Thus, in this paper, we propose to model the so-called aleatoric uncertainty of a differentiable (soft) version of DTW. To this end, we model the heteroscedastic aleatoric uncertainty of each path by the product of likelihoods from Normal distributions, each capturing variance of pair of frames. (The path distance is the sum of base distances between features of pairs of frames of the path.) The Maximum Likelihood Estimation (MLE) applied to a path yields two terms: (i) a sum of Euclidean distances weighted by the variance inverse, and (ii) a sum of log-variance regularization terms. Thus, our uncertainty-DTW is the smallest weighted path distance among all paths, and the regularization term (penalty for the high uncertainty) is the aggregate of log-variances along the path. The distance and the regularization term can be used in various objectives. We showcase forecasting the evolution of time series, estimating the Fr\\'echet mean of time series, and supervised/unsupervised few-shot action recognition of the articulated human 3D body joints. <|reference_end|>", "<|reference_start|> Few-Shot Object Detection by Second-Order Pooling: <|reference_end|>", "<|reference_start|> Decoupling GCN with DropGraph Module for Skeleton-Based Action Recognition: <|reference_end|>", "<|reference_start|> Hypergraph Neural Network for Skeleton-Based Action Recognition: Recently, skeleton-based human action recognition has attracted a lot of research attention in the field of computer vision. Graph convolutional networks (GCNs), which model the human body skeletons as spatial-temporal graphs, have shown excellent results. However, the existing methods only focus on the local physical connection between the joints, and ignore the non-physical dependencies among joints. To address this issue, we propose a hypergraph neural network (Hyper-GNN) to capture both spatial-temporal information and high-order dependencies for skeleton-based action recognition. In particular, to overcome the influence of noise caused by unrelated joints, we design the Hyper-GNN to extract the local and global structure information via the hyperedge (i.e., non-physical connection) constructions. In addition, the hypergraph attention mechanism and improved residual module are induced to further obtain the discriminative feature representations. Finally, a three-stream Hyper-GNN fusion architecture is adopted in the whole framework for action recognition. The experimental results performed on two benchmark datasets demonstrate that our proposed method can achieve the best performance when compared with the state-of-the-art skeleton-based methods. <|reference_end|>" ]

[ 9, 38, 49, 59 ]

[ "<|reference_start|> Top-down Neural Attention by Excitation Backprop: We aim to model the top-down attention of a Convolutional Neural Network (CNN) classifier for generating task-specific attention maps. Inspired by a top-down human visual attention model, we propose a new backpropagation scheme, called Excitation Backprop, to pass along top-down signals downwards in the network hierarchy via a probabilistic Winner-Take-All process. Furthermore, we introduce the concept of contrastive attention to make the top-down attention maps more discriminative. In experiments, we demonstrate the accuracy and generalizability of our method in weakly supervised localization tasks on the MS COCO, PASCAL VOC07 and ImageNet datasets. The usefulness of our method is further validated in the text-to-region association task. On the Flickr30k Entities dataset, we achieve promising performance in phrase localization by leveraging the top-down attention of a CNN model that has been trained on weakly labeled web images. <|reference_end|>", "<|reference_start|> RISE: Randomized Input Sampling for Explanation of Black-box Models: Deep neural networks are being used increasingly to automate data analysis and decision making, yet their decision-making process is largely unclear and is difficult to explain to the end users. In this paper, we address the problem of Explainable AI for deep neural networks that take images as input and output a class probability. We propose an approach called RISE that generates an importance map indicating how salient each pixel is for the model's prediction. In contrast to white-box approaches that estimate pixel importance using gradients or other internal network state, RISE works on black-box models. It estimates importance empirically by probing the model with randomly masked versions of the input image and obtaining the corresponding outputs. We compare our approach to state-of-the-art importance extraction methods using both an automatic deletion/insertion metric and a pointing metric based on human-annotated object segments. Extensive experiments on several benchmark datasets show that our approach matches or exceeds the performance of other methods, including white-box approaches. Project page: http://cs-people.bu.edu/vpetsiuk/rise/ <|reference_end|>", "<|reference_start|> Axiomatic Attribution for Deep Networks: We study the problem of attributing the prediction of a deep network to its input features, a problem previously studied by several other works. We identify two fundamental axioms---Sensitivity and Implementation Invariance that attribution methods ought to satisfy. We show that they are not satisfied by most known attribution methods, which we consider to be a fundamental weakness of those methods. We use the axioms to guide the design of a new attribution method called Integrated Gradients. Our method requires no modification to the original network and is extremely simple to implement; it just needs a few calls to the standard gradient operator. We apply this method to a couple of image models, a couple of text models and a chemistry model, demonstrating its ability to debug networks, to extract rules from a network, and to enable users to engage with models better. <|reference_end|>", "<|reference_start|> Smooth Grad-CAM++: An Enhanced Inference Level Visualization Technique for Deep Convolutional Neural Network Models: Gaining insight into how deep convolutional neural network models perform image classification and how to explain their outputs have been a concern to computer vision researchers and decision makers. These deep models are often referred to as black box due to low comprehension of their internal workings. As an effort to developing explainable deep learning models, several methods have been proposed such as finding gradients of class output with respect to input image (sensitivity maps), class activation map (CAM), and Gradient based Class Activation Maps (Grad-CAM). These methods under perform when localizing multiple occurrences of the same class and do not work for all CNNs. In addition, Grad-CAM does not capture the entire object in completeness when used on single object images, this affect performance on recognition tasks. With the intention to create an enhanced visual explanation in terms of visual sharpness, object localization and explaining multiple occurrences of objects in a single image, we present Smooth Grad-CAM++ \\footnote{Simple demo: http://35.238.22.135:5000/}, a technique that combines methods from two other recent techniques---SMOOTHGRAD and Grad-CAM++. Our Smooth Grad-CAM++ technique provides the capability of either visualizing a layer, subset of feature maps, or subset of neurons within a feature map at each instance at the inference level (model prediction process). After experimenting with few images, Smooth Grad-CAM++ produced more visually sharp maps with better localization of objects in the given input images when compared with other methods. <|reference_end|>" ]

[ 14, 25, 29, 45 ]

[ "<|reference_start|> On the Finite Volume Element Method for General Self-Adjoint Elliptic Problems: The finite volume element method (FVE) is a discretization technique for partial differential equations. This paper develops discretization energy error estimates for general self-adjoint elliptic boundary value problems with FVE based on triangulations, on which there exist linear finite element spaces, and a very general type of control volumes (covolumes). \nThe energy error estimates of this paper are also optimal but the restriction conditions for the covolumes given in [R. E. Bank and D. J. Rose, SIAM J. Numer. Anal., 24 (1987), pp. 777--787], [Z. Q. Cai, Numer. Math., 58 (1991), pp. 713--735] are removed. The authors finally provide a counterexample to show that an expected L2-error estimate does not exist in the usual sense. It is conjectured that the optimal order of $\\|u-u_h\\|_{0,\\Omega}$ should be O(h) for the general case. <|reference_end|>", "<|reference_start|> Optimal Biquadratic Finite Volume Element Methods on Quadrilateral Meshes: In this paper, an optimal biquadratic finite volume element scheme is established and analyzed for elliptic equations on quadrilateral meshes. It is proved that the new scheme has optimal $O(h^2)$ convergence rate in $H^1$ norm and $O(h^3)$ convergence rate in $L^2$ norm with the assumption that each element is an $h^2$-parallelogram. <|reference_end|>", "<|reference_start|> Superconvergence of Finite Element Approximations for the Stokes Problem by Projection Methods: This paper derives a general superconvergence result for finite element approximations of the Stokes problem by using projection methods proposed and analyzed recently by Wang [J. Math. Study, 33 (2000), pp. 229--243] for the standard Galerkin method. The superconvergence result is based on some regularity assumption for the Stokes problem and is applicable to any finite element method with regular but nonuniform partitions. The method is proved to give a convergent scheme for certain finite element spaces which fail to satisfy the well-known uniform inf-sup condition of Brezzi and Babuska. <|reference_end|>", "<|reference_start|> Asymptotically exact a posteriori error estimators, part I: Grids with superconvergence: In Part I of this work, we develop superconvergence estimates for piecewise linear finite element approximations on quasi-uniform triangular meshes where most pairs of triangles sharing a common edge form approximate parallelograms. In particular, we first show a superconvergence of the gradient of the finite element solution uh and to the gradient of the interpolant uI. We then analyze a postprocessing gradient recovery scheme, showing that $Q_h\\nabla u_h$ is a superconvergent approximation to $\\nabla u$. Here Qh is the global L2 projection. In Part II, we analyze a superconvergent gradient recovery scheme for general unstructured, shape regular triangulations. This is the foundation for an a posteriori error estimate and local error indicators. <|reference_end|>" ]

[ 19, 22, 33, 48 ]

[ "<|reference_start|> Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference on: <|reference_end|>", "<|reference_start|> Towards Facial Expression Robustness in Multi-scale Wild Environments: <|reference_end|>", "<|reference_start|> Feature Extraction and Selection for Emotion Recognition from EEG: Emotion recognition from EEG signals allows the direct assessment of the “inner” state of a user, which is considered an important factor in human-machine-interaction. Many methods for feature extraction have been studied and the selection of both appropriate features and electrode locations is usually based on neuro-scientific findings. Their suitability for emotion recognition, however, has been tested using a small amount of distinct feature sets and on different, usually small data sets. A major limitation is that no systematic comparison of features exists. Therefore, we review feature extraction methods for emotion recognition from EEG based on 33 studies. An experiment is conducted comparing these features using machine learning techniques for feature selection on a self recorded data set. Results are presented with respect to performance of different feature selection methods, usage of selected feature types, and selection of electrode locations. Features selected by multivariate methods slightly outperform univariate methods. Advanced feature extraction techniques are found to have advantages over commonly used spectral power bands. Results also suggest preference to locations over parietal and centro-parietal lobes. <|reference_end|>", "<|reference_start|> Multichannel EEG-Based Emotion Recognition via Group Sparse Canonical Correlation Analysis: In this paper, a novel group sparse canonical correlation analysis (GSCCA) method is proposed for simultaneous electroencephalogram (EEG) channel selection and emotion recognition. GSCCA is a group sparse extension of the conventional CCA method to model the linear correlationship between emotional EEG class label vectors and the corresponding EEG feature vectors. In contrast to conventional CCA method or previous GSCCA methods, a major advantage of our GSCCA method is the ability of handling the group feature selection problem from raw EEG features, which makes it very suitable for simultaneously coping with both EEG emotion recognition and automatic channel selection issues where each EEG channel is associated with a group of raw EEG features. To deal with EEG emotion recognition problem, we adopt the popularly used frequency feature to describe the EEG signal by dividing the full EEG frequency band into five parts, i.e., <inline-formula> <tex-math notation=\"LaTeX\">$\\boldsymbol {\\delta }$ </tex-math></inline-formula>, <inline-formula> <tex-math notation=\"LaTeX\">$\\boldsymbol {\\theta }$ </tex-math></inline-formula>, <inline-formula> <tex-math notation=\"LaTeX\">$\\boldsymbol {\\alpha }$ </tex-math></inline-formula>, <inline-formula> <tex-math notation=\"LaTeX\">$\\boldsymbol {\\beta }$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation=\"LaTeX\">$\\boldsymbol {\\gamma }$ </tex-math></inline-formula> frequency bands, and then extract the frequency band features from each band for GSCCA model learning and emotion recognition. Finally, we conduct extensive experiments on EEG-based emotion recognition based on the SJTU emotion EEG dataset and experimental results demonstrate that the proposed GSCCA method would outperform the state-of-the-art EEG-based emotion recognition approaches. <|reference_end|>" ]

[ 3, 4, 6, 7 ]

[ "<|reference_start|> evaluating cloud workload characteristics: <|reference_end|>", "<|reference_start|> Gaining Insights into Multicore Cache Partitioning: Bridging the Gap\nbetween Simulation and Real Systems: Cache partitioning and sharing is critical to the effective utilization of multicore processors. However, almost all existing studies have been evaluated by simulation that often has several limitations, such as excessive simulation time, absence of OS activities and proneness to simulation inaccuracy. To address these issues, we have taken an efficient software approach to supporting both static and dynamic cache partitioning in OS through memory address mapping. We have comprehensively evaluated several representative cache partitioning schemes with different optimization objectives, including performance, fairness, and quality of service (QoS). Our software approach makes it possible to run the SPEC CPU2006 benchmark suite to completion. Besides confirming important conclusions from previous work, we are able to gain several insights from whole-program executions, which are infeasible from simulation. For example, giving up some cache space in one program to help another one may improve the performance of both programs for certain workloads due to reduced contention for memory bandwidth. Our evaluation of previously proposed fairness metrics is also significantly different from a simulation-based study. The contributions of this study are threefold. (1) To the best of our knowledge, this is a highly comprehensive execution- and measurement-based study on multicore cache partitioning. This paper not only confirms important conclusions from simulation-based studies, but also provides new insights into dynamic behaviors and interaction effects. (2) Our approach provides a unique and efficient option for evaluating multicore cache partitioning. The implemented software layer can be used as a tool in multicore performance evaluation and hardware design. (3) The proposed schemes can be further refined for OS kernels to improve performance. <|reference_end|>", "<|reference_start|> Sharing incentives and fair division for multiprocessors: The trend in datacenter computing is toward large, shared hardware platforms, which poses two challenges to architects: sharing fairly and sharing multiple resources. Drawing on economic game theory, the authors rethink fairness in computer architecture and propose Resource Elasticity Fairness to find fair allocations that ensure sharing incentives, envy-freeness, Pareto efficiency, and strategy proofness in large systems. <|reference_end|>", "<|reference_start|> Auctions and bidding: A guide for computer scientists: There is a veritable menagerie of auctions—single-dimensional, multi-dimensional, single-sided, double-sided, first-price, second-price, English, Dutch, Japanese, sealed-bid—and these have been extensively discussed and analyzed in the economics literature. The main purpose of this article is to survey this literature from a computer science perspective, primarily from the viewpoint of computer scientists who are interested in learning about auction theory, and to provide pointers into the economics literature for those who want a deeper technical understanding. In addition, since auctions are an increasingly important topic in computer science, we also look at work on auctions from the computer science literature. Overall, our aim is to identifying what both these bodies of work these tell us about creating electronic auctions. <|reference_end|>" ]

[ 10, 14, 40, 45 ]

[ "<|reference_start|> Spatial Pyramid-Enhanced NetVLAD With Weighted Triplet Loss for Place Recognition: We propose an end-to-end place recognition model based on a novel deep neural network. First, we propose to exploit the spatial pyramid structure of the images to enhance the vector of locally aggregated descriptors (VLAD) such that the enhanced VLAD features can reflect the structural information of the images. To encode this feature extraction into the deep learning method, we build a spatial pyramid-enhanced VLAD (SPE-VLAD) layer. Next, we impose weight constraints on the terms of the traditional triplet loss (T-loss) function such that the weighted T-loss (WT-loss) function avoids the suboptimal convergence of the learning process. The loss function can work well under weakly supervised scenarios in that it determines the semantically positive and negative samples of each query through not only the GPS tags but also the Euclidean distance between the image representations. The SPE-VLAD layer and the WT-loss layer are integrated with the VGG-16 network or ResNet-18 network to form a novel end-to-end deep neural network that can be easily trained via the standard backpropagation method. We conduct experiments on three benchmark data sets, and the results demonstrate that the proposed model defeats the state-of-the-art deep learning approaches applied to place recognition. <|reference_end|>", "<|reference_start|> Robust visual semi-semantic loop closure detection by a covisibility graph and CNN features: <|reference_end|>", "<|reference_start|> Real-Time Visual Place Recognition Based on Analyzing Distribution of Multi-scale CNN Landmarks: <|reference_end|>", "<|reference_start|> A Hybrid Compact Neural Architecture for Visual Place Recognition: State-of-the-art algorithms for visual place recognition, and related visual navigation systems, can be broadly split into two categories: computer-science-oriented models including deep learning or image retrieval-based techniques with minimal biological plausibility, and neuroscience-oriented dynamical networks that model temporal properties underlying spatial navigation in the brain. In this letter, we propose a new compact and high-performing place recognition model that bridges this divide for the first time. Our approach comprises two key neural models of these categories: (1) FlyNet, a compact, sparse two-layer neural network inspired by brain architectures of fruit flies, Drosophila melanogaster, and (2) a one-dimensional continuous attractor neural network (CANN). The resulting FlyNet+CANN network incorporates the compact pattern recognition capabilities of our FlyNet model with the powerful temporal filtering capabilities of an equally compact CANN, replicating entirely in a hybrid neural implementation the functionality that yields high performance in algorithmic localization approaches like SeqSLAM. We evaluate our model, and compare it to three state-of-the-art methods, on two benchmark real-world datasets with small viewpoint variations and extreme environmental changes - achieving 87% AUC results under day to night transitions compared to 60% for Multi-Process Fusion, 46% for LoST-X and 1% for SeqSLAM, while being 6.5, 310, and 1.5 times faster, respectively. <|reference_end|>" ]

[ 4, 6, 10, 11 ]

[ "<|reference_start|> {Process Variation-Aware Nonuniform Cache Management in a 3D Die-Stacked Multicore Processor: Process variations in integrated circuits have significant impact on their performance, leakage, and stability. This is particularly evident in large, regular, and dense structures such as DRAMs. D... <|reference_end|>", "<|reference_start|> {Hardware and Software Techniques for Controlling DRAM Power Modes: The anticipated explosive growth of pervasive and mobile computing devices that are typically constrained by energy has brought hardware and software techniques for energy conservation into the spotlight. While there have been several studies and proposals for energy conservation for CPUs and peripherals, energy optimization techniques for selective operating mode control of DRAMs have not been fully explored. It has been shown that, for some systems, as much as 90 percent of overall system energy (excluding I/O) is consumed by the DRAM modules, thus, they serve as a good candidate for energy optimizations. Further, DRAM technology has also matured to provide several low energy operating modes (power modes), making it an opportunistic moment to conduct studies exploring the potential benefits of mode control techniques. This paper conducts an in-depth investigation of software and hardware techniques to take advantage of the DRAM mode control capabilities at a module granularity for energy savings. Using a memory system architecture capturing five different energy modes and corresponding resynchronization times, this paper presents several novel compilation techniques to both cluster the data across memory banks as well as to detect module idleness and perform energy mode transitions. In addition, hardware-assisted approaches (called self-monitoring) based on predictions of module interaccess times are proposed. These techniques are extensively evaluated using a set of a dozen benchmarks. It is shown that we get an average of 61 percent savings in DRAM energy using compiler-directed mode control. One of the self-monitored approaches gives as much as 89 percent savings (72 percent on the average), coming as close as 8.8 percent to the optimal energy savings that one can expect with DRAM module mode control. The optimization techniques are demonstrated to be invaluable for energy savings as memory technologies continue to evolve. <|reference_end|>", "<|reference_start|> 20th International Symposium on Quality Electronic Design, ISQED 2019, Santa Clara, CA, USA, March 6-7, 2019: <|reference_end|>", "<|reference_start|> 2017 IEEE International Symposium on High Performance Computer Architecture, HPCA 2017, Austin, TX, USA, February 4-8, 2017: <|reference_end|>" ]

[ 3, 7, 25, 44 ]

[ "<|reference_start|> Simultaneous localization and mapping (SLAM): part II: This paper discusses the recursive Bayesian formulation of the simultaneous localization and mapping (SLAM) problem in which probability distributions or estimates of absolute or relative locations of landmarks and vehicle pose are obtained. The paper focuses on three key areas: computational complexity; data association; and environment representation <|reference_end|>", "<|reference_start|> Closed form solutions to the multiple-platform simultaneous localization and map building (SLAM) problem: This paper presents a closed form solution to the multiple platform simultaneous localization and map building (SLAM) problem. Closed form solutions are presented in both state space and information based forms. A key conclusion of this paper is that the information-state based form offers many advantages over the state space formulation in allowing the SLAM algorithm to be decentralized across multiple platforms. <|reference_end|>", "<|reference_start|> A Review of Sensor Technologies for Perception in Automated Driving: After more than 20 years of research, ADAS are common in modern vehicles available in the market. Automated Driving systems, still in research phase and limited in their capabilities, are starting early commercial tests in public roads. These systems rely on the information provided by on-board sensors, which allow to describe the state of the vehicle, its environment and other actors. Selection and arrangement of sensors represent a key factor in the design of the system. This survey reviews existing, novel and upcoming sensor technologies, applied to common perception tasks for ADAS and Automated Driving. They are put in context making a historical review of the most relevant demonstrations on Automated Driving, focused on their sensing setup. Finally, the article presents a snapshot of the future challenges for sensing technologies and perception, finishing with an overview of the commercial initiatives and manufacturers alliances that will show the intention of the market in sensors technologies for Automated Vehicles. <|reference_end|>", "<|reference_start|> ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual--Inertial, and Multimap SLAM: This article presents ORB-SLAM3, the first system able to perform visual, visual-inertial and multimap SLAM with monocular, stereo and RGB-D cameras, using pin-hole and fisheye lens models. The first main novelty is a tightly integrated visual-inertial SLAM system that fully relies on maximum a posteriori (MAP) estimation, even during IMU initialization, resulting in real-time robust operation in small and large, indoor and outdoor environments, being two to ten times more accurate than previous approaches. The second main novelty is a multiple map system relying on a new place recognition method with improved recall that lets ORB-SLAM3 survive to long periods of poor visual information: when it gets lost, it starts a new map that will be seamlessly merged with previous maps when revisiting them. Compared with visual odometry systems that only use information from the last few seconds, ORB-SLAM3 is the first system able to reuse in all the algorithm stages all previous information from high parallax co-visible keyframes, even if they are widely separated in time or come from previous mapping sessions, boosting accuracy. Our experiments show that, in all sensor configurations, ORB-SLAM3 is as robust as the best systems available in the literature and significantly more accurate. Notably, our stereo-inertial SLAM achieves an average accuracy of 3.5 cm in the EuRoC drone and 9 mm under quick hand-held motions in the room of TUM-VI dataset, representative of AR/VR scenarios. For the benefit of the community we make public the source code. <|reference_end|>" ]

[ 0, 1, 2, 3 ]

[ "<|reference_start|> Efficient graphlet kernels for large graph comparison: State-of-the-art graph kernels do not scale to large graphs with hundreds of nodes and thousands of edges. In this article we propose to compare graphs by counting graphlets, i.e., subgraphs with k nodes where k ∈ {3, 4, 5}. Exhaustive enumeration of all graphlets being prohibitively expensive, we introduce two theoretically grounded speedup schemes, one based on sampling and the second one specifically designed for bounded degree graphs. In our experimental evaluation, our novel kernels allow us to efficiently compare large graphs that cannot be tackled by existing graph kernels. <|reference_end|>", "<|reference_start|> NetLSD: Hearing the Shape of a Graph: Comparison among graphs is ubiquitous in graph analytics. However, it is a hard task in terms of the expressiveness of the employed similarity measure and the efficiency of its computation. Ideally, graph comparison should be invariant to the order of nodes and the sizes of compared graphs, adaptive to the scale of graph patterns, and scalable. Unfortunately, these properties have not been addressed together. Graph comparisons still rely on direct approaches, graph kernels, or representation-based methods, which are all inefficient and impractical for large graph collections. In this paper, we propose the Network Laplacian Spectral Descriptor (NetLSD): the first, to our knowledge, permutation- and size-invariant, scale-adaptive, and efficiently computable graph representation method that allows for straightforward comparisons of large graphs. NetLSD extracts a compact signature that inherits the formal properties of the Laplacian spectrum, specifically its heat or wave kernel; thus, it hears the shape of a graph. Our evaluation on a variety of real-world graphs demonstrates that it outperforms previous works in both expressiveness and efficiency. <|reference_end|>", "<|reference_start|> {Shortest-Path Kernels on Graphs: Data mining algorithms are facing the challenge to deal with an increasing number of complex objects. For graph data, a whole toolbox of data mining algorithms becomes available by defining a kernel function on instances of graphs. Graph kernels based on walks, subtrees and cycles in graphs have been proposed so far. As a general problem, these kernels are either computationally expensive or limited in their expressiveness. We try to overcome this problem by defining expressive graph kernels which are based on paths. As the computation of all paths and longest paths in a graph is NP-hard, we propose graph kernels based on shortest paths. These kernels are computable in polynomial time, retain expressivity and are still positive definite. In experiments on classification of graph models of proteins, our shortest-path kernels show significantly higher classification accuracy than walk-based kernels. <|reference_end|>", "<|reference_start|> Network similarity via multiple social theories: Given a set of k networks, possibly with different sizes and no overlaps in nodes or links, how can we quickly assess similarity between them? Analogously, are there a set of social theories which, when represented by a small number of descriptive, numerical features, effectively serve as a “signature” for the network? Having such signatures will enable a wealth of graph mining and social network analysis tasks, including clustering, outlier detection, visualization, etc. We propose a novel, effective, and scalable method, called NetSimile, for solving the above problem. Our approach has the following desirable properties: (a) It is supported by a set of social theories. (b) It gives similarity scores that are size-invariant. (c) It is scalable, being linear on the number of links for graph signature extraction. In extensive experiments on numerous synthetic and real networks from disparate domains, NetSimile outperforms baseline competitors. We also demonstrate how our approach enables several mining tasks such as clustering, visualization, discontinuity detection, network transfer learning, and re-identification across networks. <|reference_end|>" ]

[ 25, 29, 36, 50 ]

[ "<|reference_start|> Patient-specific computational flow modelling for assessing hemodynamic changes following fenestrated endovascular aneurysm repair: <|reference_end|>", "<|reference_start|> CRIMSON: An open-source software framework for cardiovascular integrated modelling and simulation: In this work, we describe the CRIMSON (CardiovasculaR Integrated Modelling and SimulatiON) software environment. CRIMSON provides a powerful, customizable and user-friendly system for performing three-dimensional and reduced-order computational haemodynamics studies via a pipeline which involves: 1) segmenting vascular structures from medical images; 2) constructing analytic arterial and venous geometric models; 3) performing finite element mesh generation; 4) designing, and 5) applying boundary conditions; 6) running incompressible Navier-Stokes simulations of blood flow with fluid-structure interaction capabilities; and 7) post-processing and visualizing the results, including velocity, pressure and wall shear stress fields. A key aim of CRIMSON is to create a software environment that makes powerful computational haemodynamics tools accessible to a wide audience, including clinicians and students, both within our research laboratories and throughout the community. The overall philosophy is to leverage best-in-class open source standards for medical image processing, parallel flow computation, geometric solid modelling, data assimilation, and mesh generation. It is actively used by researchers in Europe, North and South America, Asia, and Australia. It has been applied to numerous clinical problems; we illustrate applications of CRIMSON to real-world problems using examples ranging from pre-operative surgical planning to medical device design optimization. CRIMSON binaries for Microsoft Windows 10, documentation and example input files are freely available for download from www.crimson.software, and the source code with compilation instructions is available on GitHub https://github.com/carthurs/CRIMSONFlowsolver (CRIMSON Flowsolver) under the GPL v3.0 license, and https://github.com/carthurs/CRIMSONGUI (CRIMSON GUI), under the AGPL v3.0 license. Support is available on the CRIMSON Google Groups forum, located at https://groups.google.com/forum/#!forum/crimson-users. <|reference_end|>", "<|reference_start|> NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis: We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. Our algorithm represents a scene using a fully-connected (non-convolutional) deep network, whose input is a single continuous 5D coordinate (spatial location $(x,y,z)$ and viewing direction $(\\theta, \\phi)$) and whose output is the volume density and view-dependent emitted radiance at that spatial location. We synthesize views by querying 5D coordinates along camera rays and use classic volume rendering techniques to project the output colors and densities into an image. Because volume rendering is naturally differentiable, the only input required to optimize our representation is a set of images with known camera poses. We describe how to effectively optimize neural radiance fields to render photorealistic novel views of scenes with complicated geometry and appearance, and demonstrate results that outperform prior work on neural rendering and view synthesis. View synthesis results are best viewed as videos, so we urge readers to view our supplementary video for convincing comparisons. <|reference_end|>", "<|reference_start|> Implicit Neural Representations with Periodic Activation Functions: Implicitly defined, continuous, differentiable signal representations parameterized by neural networks have emerged as a powerful paradigm, offering many possible benefits over conventional representations. However, current network architectures for such implicit neural representations are incapable of modeling signals with fine detail, and fail to represent a signal's spatial and temporal derivatives, despite the fact that these are essential to many physical signals defined implicitly as the solution to partial differential equations. We propose to leverage periodic activation functions for implicit neural representations and demonstrate that these networks, dubbed sinusoidal representation networks or Sirens, are ideally suited for representing complex natural signals and their derivatives. We analyze Siren activation statistics to propose a principled initialization scheme and demonstrate the representation of images, wavefields, video, sound, and their derivatives. Further, we show how Sirens can be leveraged to solve challenging boundary value problems, such as particular Eikonal equations (yielding signed distance functions), the Poisson equation, and the Helmholtz and wave equations. Lastly, we combine Sirens with hypernetworks to learn priors over the space of Siren functions. <|reference_end|>" ]

[ 1, 4, 15, 16 ]

[ "<|reference_start|> Neuronal dynamics: From single neurons to networks and\nmodels of cognition: What happens in our brain when we make a decision? What triggers a neuron to send out a signal? What is the neural code? This textbook for advanced undergraduate and beginning graduate students provides a thorough and up-to-date introduction to the fields of computational and theoretical neuroscience. It covers classical topics, including the Hodgkin-Huxley equations and Hopfield model, as well as modern developments in the field such as Generalized Linear Models and decision theory. Concepts are introduced using clear step-by-step explanations suitable for readers with only a basic knowledge of differential equations and probabilities, and are richly illustrated by figures and worked-out examples. End-of-chapter summaries and classroom-tested exercises make the book ideal for courses or for self-study. The authors also give pointers to the literature and an extensive bibliography, which will prove invaluable to readers interested in further study. <|reference_end|>", "<|reference_start|> {Compiling Machine Learning Programs via High-Level Tracing: We describe JAX, a domain-specific tracing JIT compiler for gen-erating high-performance accelerator code from pure Python and Numpy machine learning programs. JAX uses the XLA compiler infrastructure to generate optimized code for the program subroutines that are most favorable for acceleration, and these optimized subroutines can be called and orchestrated by arbitrary Python. Because the system is fully compatible with Autograd, it allows forward- and reverse-mode automatic differentiation of Python functions to arbitrary order. Because JAX supports structured control flow, it can generate code for sophisticated machine learning algorithms while maintaining high performance. We show that by combining JAX with Autograd and Numpy we get an easily pro-grammable and highly performant ML system that targets CPUs, GPUs, and TPUs, capable of scaling to multi-core Cloud TPUs. <|reference_end|>", "<|reference_start|> BindsNET: A machine learning-oriented spiking neural networks library in Python: The development of spiking neural network simulation software is a critical component enabling the modeling of neural systems and the development of biologically inspired algorithms. Existing software frameworks support a wide range of neural functionality, software abstraction levels, and hardware devices, yet are typically not suitable for rapid prototyping or application to problems in the domain of machine learning. In this paper, we describe a new Python package for the simulation of spiking neural networks, specifically geared towards machine learning and reinforcement learning. Our software, called BindsNET, enables rapid building and simulation of spiking networks and features user-friendly, concise syntax. BindsNET is built on top of the PyTorch deep neural networks library, enabling fast CPU and GPU computation for large spiking networks. The BindsNET framework can be adjusted to meet the needs of other existing computing and hardware environments, e.g., TensorFlow. We also provide an interface into the OpenAI gym library, allowing for training and evaluation of spiking networks on reinforcement learning problems. We argue that this package facilitates the use of spiking networks for large-scale machine learning experimentation, and show some simple examples of how we envision BindsNET can be used in practice. BindsNET code is available at https://github.com/Hananel-Hazan/bindsnet <|reference_end|>", "<|reference_start|> TensorFlow: A system for large-scale machine learning: TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general-purpose GPUs, and custom designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer: whereas in previous \"parameter server\" designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with particularly strong support for training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model in contrast to existing systems, and demonstrate the compelling performance that TensorFlow achieves for several real-world applications. <|reference_end|>" ]

[ 0, 11, 23, 28 ]

[ "<|reference_start|> {{GPT-4: Большие языковые модели (LLM) продемонстрировали замечательные возможности в понимании и генерации естественного языка в различных областях, включая медицину. В статье представлена оценка GPT-4 на основе двух точек зрения на проблему применения этой языковой модели: разработчиков из OpenAI, Microsoft и пользователей-медиков из двух европейских проектов. За последние несколько лет LLM, обученные на массивных междисциплинарных корпусах, стали мощными строительными блоками при создании систем, ориентированных на решение конкретных задач. В статье рассматривается три задачи: медицинское образование, работоспособность ChatGPT-4 в клинике (консультации, записи стенограмм беседы врача и пациента), и конкретные уровни точности диагностики (разные области медицины). Ответ на поставленный вопрос о необходимости медицинского GPT есть в мире, -он положительный. <|reference_end|>", "<|reference_start|> On Measuring Faithfulness or Self-consistency of Natural Language Explanations: Large language models (LLMs) can explain their predictions through post-hoc or Chain-of-Thought (CoT) explanations. But an LLM could make up reasonably sounding explanations that are unfaithful to its underlying reasoning. Recent work has designed tests that aim to judge the faithfulness of post-hoc or CoT explanations. In this work we argue that these faithfulness tests do not measure faithfulness to the models' inner workings -- but rather their self-consistency at output level. Our contributions are three-fold: i) We clarify the status of faithfulness tests in view of model explainability, characterising them as self-consistency tests instead. This assessment we underline by ii) constructing a Comparative Consistency Bank for self-consistency tests that for the first time compares existing tests on a common suite of 11 open LLMs and 5 tasks -- including iii) our new self-consistency measure CC-SHAP. CC-SHAP is a fine-grained measure (not a test) of LLM self-consistency. It compares how a model's input contributes to the predicted answer and to generating the explanation. Our fine-grained CC-SHAP metric allows us iii) to compare LLM behaviour when making predictions and to analyse the effect of other consistency tests at a deeper level, which takes us one step further towards measuring faithfulness by bringing us closer to the internals of the model than strictly surface output-oriented tests. Our code is available at \\url{https://github.com/Heidelberg-NLP/CC-SHAP} <|reference_end|>", "<|reference_start|> Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting: Large Language Models (LLMs) can achieve strong performance on many tasks by producing step-by-step reasoning before giving a final output, often referred to as chain-of-thought reasoning (CoT). It is tempting to interpret these CoT explanations as the LLM's process for solving a task. This level of transparency into LLMs' predictions would yield significant safety benefits. However, we find that CoT explanations can systematically misrepresent the true reason for a model's prediction. We demonstrate that CoT explanations can be heavily influenced by adding biasing features to model inputs--e.g., by reordering the multiple-choice options in a few-shot prompt to make the answer always \"(A)\"--which models systematically fail to mention in their explanations. When we bias models toward incorrect answers, they frequently generate CoT explanations rationalizing those answers. This causes accuracy to drop by as much as 36% on a suite of 13 tasks from BIG-Bench Hard, when testing with GPT-3.5 from OpenAI and Claude 1.0 from Anthropic. On a social-bias task, model explanations justify giving answers in line with stereotypes without mentioning the influence of these social biases. Our findings indicate that CoT explanations can be plausible yet misleading, which risks increasing our trust in LLMs without guaranteeing their safety. Building more transparent and explainable systems will require either improving CoT faithfulness through targeted efforts or abandoning CoT in favor of alternative methods. <|reference_end|>", "<|reference_start|> Faithful Chain-of-Thought Reasoning: While Chain-of-Thought (CoT) prompting boosts Language Models' (LM) performance on a gamut of complex reasoning tasks, the generated reasoning chain does not necessarily reflect how the model arrives at the answer (aka. faithfulness). We propose Faithful CoT, a reasoning framework involving two stages: Translation (Natural Language query $\\rightarrow$ symbolic reasoning chain) and Problem Solving (reasoning chain $\\rightarrow$ answer), using an LM and a deterministic solver respectively. This guarantees that the reasoning chain provides a faithful explanation of the final answer. Aside from interpretability, Faithful CoT also improves empirical performance: it outperforms standard CoT on 9 of 10 benchmarks from 4 diverse domains, with a relative accuracy gain of 6.3% on Math Word Problems (MWP), 3.4% on Planning, 5.5% on Multi-hop Question Answering (QA), and 21.4% on Relational Inference. Furthermore, with GPT-4 and Codex, it sets the new state-of-the-art few-shot performance on 7 datasets (with 95.0+ accuracy on 6 of them), showing a strong synergy between faithfulness and accuracy. <|reference_end|>" ]

[ 2, 8, 17, 19 ]

[ "<|reference_start|> A survey of adaptive sorting algorithms: The design and analysis of adaptive sorting algorithms has made important contributions to both theory and practice. The main contributions from the theoretical point of view are: the description of the complexity of a sorting algorithm not only in terms of the size of a problem instance but also in terms of the disorder of the given problem instance; the establishment of new relationships among measures of disorder; the introduction of new sorting algorithms that take advantage of the existing order in the input sequence; and, the proofs that several of the new sorting algorithms achieve maximal (optimal) adaptivity with respect to several measures of disorder. The main contributions from the practical point of view are: the demonstration that several algorithms currently in use are adaptive; and, the development of new algorithms, similar to currently used algorithms that perform competitively on random sequences and are significantly faster on nearly sorted sequences. In this survey, we present the basic notions and concepts of adaptive sorting and the state of the art of adaptive sorting algorithms. <|reference_end|>", "<|reference_start|> A Survey on Priority Queues: <|reference_end|>", "<|reference_start|> A Framework for Adaptive Sorting: <|reference_end|>", "<|reference_start|> A survey of adaptive sorting algorithms: The design and analysis of adaptive sorting algorithms has made important contributions to both theory and practice. The main contributions from the theoretical point of view are: the description of the complexity of a sorting algorithm not only in terms of the size of a problem instance but also in terms of the disorder of the given problem instance; the establishment of new relationships among measures of disorder; the introduction of new sorting algorithms that take advantage of the existing order in the input sequence; and, the proofs that several of the new sorting algorithms achieve maximal (optimal) adaptivity with respect to several measures of disorder. The main contributions from the practical point of view are: the demonstration that several algorithms currently in use are adaptive; and, the development of new algorithms, similar to currently used algorithms that perform competitively on random sequences and are significantly faster on nearly sorted sequences. In this survey, we present the basic notions and concepts of adaptive sorting and the state of the art of adaptive sorting algorithms. <|reference_end|>" ]

[ 4, 5, 7, 9 ]

[ "<|reference_start|> A survey on coverage path planning for robotics: <|reference_end|>", "<|reference_start|> A Distributed Control Framework for a Team of Unmanned Aerial Vehicles for Dynamic Wildfire Tracking: Wildland fire fighting is a very dangerous job, and the lack of information of the fire front is one of main reasons that causes many accidents. Using unmanned aerial vehicle (UAV) to cover wildfire is promising because it can replace human in hazardous fire tracking and save operation costs significantly. In this paper we propose a distributed control framework designed for a team of UAVs that can closely monitor a wildfire in open space, and precisely track its development. The UAV team, designed for flexible deployment, can effectively avoid in-flight collision as well as cooperate well with other neighbors. Experimental results are conducted to demonstrate the capabilites of the UAV team in covering a spreading wildfire. <|reference_end|>", "<|reference_start|> Deep reinforcement learning robot for search and rescue applications: Exploration in unknown cluttered environments: Rescue robots can be used in urban search and rescue (USAR) applications to perform the important task of exploring unknown cluttered environments. Due to the unpredictable nature of these environments, deep learning techniques can be used to perform these tasks. In this letter, we present the first use of deep learning to address the robot exploration task in USAR applications. In particular, we uniquely combine the traditional approach of frontier-based exploration with deep reinforcement learning to allow a robot to autonomously explore unknown cluttered environments. Experiments conducted with a mobile robot in unknown cluttered environments of varying sizes and layouts showed that the proposed exploration approach can effectively determine appropriate frontier locations to navigate to, while being robust to different environment layouts and sizes. Furthermore, a comparison study with other frontier exploration approaches showed that our learning-based frontier exploration technique was able to explore more of an environment earlier on, allowing for potential identification of a larger number of victims at the beginning of the time-critical exploration task. <|reference_end|>", "<|reference_start|> A survey on multi-robot coverage path planning for model reconstruction and mapping: <|reference_end|>" ]

[ 1, 3, 6, 7 ]

[ "<|reference_start|> Bidirectional translation between uhd-hdr and hd-sdr videos: With the popularization of ultra high definition (UHD) high dynamic range (HDR) displays, recent works focus on upgrading high definition (HD) standard dynamic range (SDR) videos to UHD-HDR versions, aiming to provides richer details and higher contrasts on advanced modern displays. However, joint considering the upgrading & downgrading translations between two types of videos, which is practical in real applications, is generally neglected. On the one hand, downgrading translation is the key to showing UHD-HDR videos on HD-SDR displays. On the other hand, considering both translations enables joint optimization and results in high quality translation. To this end, we propose the bidirectional translation network (BiT-Net), which jointly considers two translations in one network for the first time. In brief, BiT-Net is elaborately designed in an invertible fashion that can be efficiently inferred along forward and backward directions for downgrading and upgrading tasks, respectively. Based on this framework, we divide each direction into three sub-tasks, i.e., decomposition, structure-guided translation, and synthesis, to effectively translate the dynamic range and the high-frequency details. Benefiting from the dedicated architecture, our BiT-Net can work on 1) downgrading UHD-HDR videos, 2) upgrading existing HD-SDR videos, and 3) synthesizing UHD-HDR versions from the downgraded HD-SDR videos. Experiments show that the proposed method achieves state-of-the-art performances on all these three tasks. <|reference_end|>", "<|reference_start|> SR-ITM-GAN: Learning 4K UHD HDR with a generative adversarial network: Currently, high dynamic range (HDR) videos with high resolution (HR) have become popular due to the display and the rendered technological advancements. However, making ultra-high definition (UHD) with HDR videos is expensive. The legacy low-resolution (LR) standard dynamic range (SDR) format is still largely used in practice. It is necessary to search for a solution to transform LR SDR videos into UHD HDR format. In this paper, we consider joint super resolution and learning inverse tone mapping an issue of high-frequency reconstruction and local contrast enhancement, and we propose an architecture based on a generative adversarial network to apply joint SR-ITM learning. Specifically, we include the residual ResNeXt block (RRXB) as a basic module to better capture high-frequency textures and adopt YUV interpolation to achieve local contrast enhancement. By adopting a generative adversarial network as a pivotal training mechanism, our designs show advantages in both integration and performance. Our code is now available on GitHub: SR-ITM-GAN. <|reference_end|>", "<|reference_start|> Towards real-world HDRTV reconstruction: A data synthesis-based approach: Existing deep learning based HDRTV reconstruction methods assume one kind of tone mapping operators (TMOs) as the degradation procedure to synthesize SDRTV-HDRTV pairs for supervised training. In this paper, we argue that, although traditional TMOs exploit efficient dynamic range compression priors, they have several drawbacks on modeling the realistic degradation: information over-preservation, color bias and possible artifacts, making the trained reconstruction networks hard to generalize well to real-world cases. To solve this problem, we propose a learning-based data synthesis approach to learn the properties of real-world SDRTVs by integrating several tone mapping priors into both network structures and loss functions. In specific, we design a conditioned two-stream network with prior tone mapping results as a guidance to synthesize SDRTVs by both global and local transformations. To train the data synthesis network, we form a novel self-supervised content loss to constraint different aspects of the synthesized SDRTVs at regions with different brightness distributions and an adversarial loss to emphasize the details to be more realistic. To validate the effectiveness of our approach, we synthesize SDRTV-HDRTV pairs with our method and use them to train several HDRTV reconstruction networks. Then we collect two inference datasets containing both labeled and unlabeled real-world SDRTVs, respectively. Experimental results demonstrate that, the networks trained with our synthesized data generalize significantly better to these two real-world datasets than existing solutions. <|reference_end|>", "<|reference_start|> Towards real-world HDRTV reconstruction: A data synthesis-based approach: Existing deep learning based HDRTV reconstruction methods assume one kind of tone mapping operators (TMOs) as the degradation procedure to synthesize SDRTV-HDRTV pairs for supervised training. In this paper, we argue that, although traditional TMOs exploit efficient dynamic range compression priors, they have several drawbacks on modeling the realistic degradation: information over-preservation, color bias and possible artifacts, making the trained reconstruction networks hard to generalize well to real-world cases. To solve this problem, we propose a learning-based data synthesis approach to learn the properties of real-world SDRTVs by integrating several tone mapping priors into both network structures and loss functions. In specific, we design a conditioned two-stream network with prior tone mapping results as a guidance to synthesize SDRTVs by both global and local transformations. To train the data synthesis network, we form a novel self-supervised content loss to constraint different aspects of the synthesized SDRTVs at regions with different brightness distributions and an adversarial loss to emphasize the details to be more realistic. To validate the effectiveness of our approach, we synthesize SDRTV-HDRTV pairs with our method and use them to train several HDRTV reconstruction networks. Then we collect two inference datasets containing both labeled and unlabeled real-world SDRTVs, respectively. Experimental results demonstrate that, the networks trained with our synthesized data generalize significantly better to these two real-world datasets than existing solutions. <|reference_end|>" ]

[ 1, 6, 10, 14 ]

[ "<|reference_start|> Software Engineering and Formal Methods : 14th International Conference, SEFM 2016, Held as Part of STAF 2016, Vienna, Austria, July 4-8, 2016, Proceedings: This book constitutes the proceedings of the 14th International Conference on Software Engineering and Formal Methods, SEFM 2016, held as part of STAF 2016, in Vienna, Austria, in July 2016. The 20 full and 5 short papers presented in this volume were carefully reviewed and selected from 88 submissions. They were organized in topical sections named: concurrency and non-interference; program analysis; model checking; verification; interaction and adaptation; and development methods <|reference_end|>", "<|reference_start|> 2016 Formal Methods in Computer-Aided Design, FMCAD 2016, Mountain View, CA, USA, October 3-6, 2016: <|reference_end|>", "<|reference_start|> Proceedings of the 24th ACM Conference on Economics and Computation: <|reference_end|>", "<|reference_start|> Computer aided verification : 27th international conference, CAV 2015, San Francisco, CA, USA, July 18-24, 2015 : proceedings: A Trusted Mechanised Specification of JavaScript: One Year On.- Model Checking and Refinements.- On Automation of CTL* Verification for Infinite-State Systems.- Algorithms for Model Checking HyperLTL and HyperCTL .- Fairness Modulo Theory: A New Approach to LTL Software Model Checking.- Model Checking Parameterized Asynchronous Shared-Memory Systems.- SMT and POR Beat Counter Abstraction: Parameterized Model Checking of Threshold-Based Distributed Algorithms.- Skipping Refinement.- Quantitative Reasoning.- Percentile Queries in Multi-dimensional Markov Decision Processes.- Faster Algorithms for Quantitative Verification in Constant Treewidth Graphs.- Counterexample Explanation by Learning Small Strategies in Markov Decision Processes.- Symbolic Polytopes for Quantitative Interpolation and Verification.- Adaptive Aggregation of Markov Chains: Quantitative Analysis of Chemical Reaction Networks.- PROPhESY: A PRObabilistic ParamEter SYnthesis Tool.- Software Analysis.- Effective Search-Space Pruning for Solvers of String Equations, Regular Expressions and Length Constraints.- Automata-Based Model Counting for String Constraints.- OpenJDK's Java.utils.Collection.sort() Is Broken: The Good, the Bad and the Worst Case.- Tree Buffers.- Learning Commutativity.- Specifications.- Angelic Verification: Precise Verification Modulo Unknowns.- The SeaHorn Verification Framework.- Automatic Rootcausing for Program Equivalence Failures in Binaries.- Fine-Grained Caching of Verification Results.- Predicting a Correct Program in Programming by Example.- Abstract Interpretation with Higher-Dimensional Ellipsoids and Conic Extrapolation.- Lightning Talks.- ADAM: Causality-Based Synthesis of Distributed Systems.- Alchemist: Learning Guarded Affine Functions.- OptiMathSAT: A Tool for Optimization Modulo Theories.- Systematic Asynchrony Bug Exploration for Android Apps.- Norn: An SMT Solver for String Constraints.- PVSio-web 2.0: Joining PVS to HCI.- The Hanoi Omega-Automata Format.- The Open-Source LearnLib: A Framework for Active Automata Learning.- BBS: A Phase-Bounded Model Checker for Asynchronous Programs.- Time-Aware Abstractions in HybridSal.- A Type-Directed Approach to Program Repair.- Formal Design and Safety Analysis of AIR6110 Wheel Brake System.- Meeting a Powertrain Verification Challenge.- Synthesising Executable Gene Regulatory Networks from Single-Cell Gene Expression Data.- Empirical Software Metrics for Benchmarking of Verification Tools.- Interpolation, IC3/PDR, and Invariants Property-Directed Inference of Universal Invariants or Proving Their Absence.- Efficient Anytime Techniques for Model-Based Safety Analysis.- Boosting k-induction with Continuously-Refined Invariants.- Fast Interpolating BMC.- Counterexample-Guided Polynomial Loop Invariant Generation by Lagrange Interpolation. <|reference_end|>" ]

[ 1, 4, 10, 13 ]

[ "<|reference_start|> Going Deeper with Convolutions: We propose a deep convolutional neural network architecture codenamed \"Inception\", which was responsible for setting the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC 2014). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. This was achieved by a carefully crafted design that allows for increasing the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC 2014 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection. <|reference_end|>", "<|reference_start|> Deep Residual Learning for Image Recognition: Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation. <|reference_end|>", "<|reference_start|> Learning Not to Learn: Training Deep Neural Networks with Biased Data: We propose a novel regularization algorithm to train deep neural networks, in which data at training time is severely biased. Since a neural network efficiently learns data distribution, a network is likely to learn the bias information to categorize input data. It leads to poor performance at test time, if the bias is, in fact, irrelevant to the categorization. In this paper, we formulate a regularization loss based on mutual information between feature embedding and bias. Based on the idea of minimizing this mutual information, we propose an iterative algorithm to unlearn the bias information. We employ an additional network to predict the bias distribution and train the network adversarially against the feature embedding network. At the end of learning, the bias prediction network is not able to predict the bias not because it is poorly trained, but because the feature embedding network successfully unlearns the bias information. We also demonstrate quantitative and qualitative experimental results which show that our algorithm effectively removes the bias information from feature embedding. <|reference_end|>", "<|reference_start|> Learning De-biased Representations with Biased Representations: Many machine learning algorithms are trained and evaluated by splitting data from a single source into training and test sets. While such focus on in-distribution learning scenarios has led to interesting advancement, it has not been able to tell if models are relying on dataset biases as shortcuts for successful prediction (e.g., using snow cues for recognising snowmobiles), resulting in biased models that fail to generalise when the bias shifts to a different class. The cross-bias generalisation problem has been addressed by de-biasing training data through augmentation or re-sampling, which are often prohibitive due to the data collection cost (e.g., collecting images of a snowmobile on a desert) and the difficulty of quantifying or expressing biases in the first place. In this work, we propose a novel framework to train a de-biased representation by encouraging it to be different from a set of representations that are biased by design. This tactic is feasible in many scenarios where it is much easier to define a set of biased representations than to define and quantify bias. We demonstrate the efficacy of our method across a variety of synthetic and real-world biases; our experiments show that the method discourages models from taking bias shortcuts, resulting in improved generalisation. Source code is available at https://github.com/clovaai/rebias. <|reference_end|>" ]

[ 0, 1, 5, 7 ]

[ "<|reference_start|> Usage and perceptions of Scrum and and large-scale scrum (LeSS) in academic settings: <|reference_end|>", "<|reference_start|> Issues in the Adoption of the Scaled Agile Framework: Agile methods were originally introduced for small sized, colocated teams. Their successful products immediately brought up the issue of adapting the methods also for large and distributed organizations engaged in projects to build major, complex products. Currently the most popular multi-teams agile method is the Scaled Agile Framework (SAFe) which, however, is subject to criticism: it appears to be quite demanding and expensive in terms of human resource and project management practices. Moreover, SAFe allegedly goes against some of the principles of agility. This research attempts to gather a deeper understanding of the matter first reviewing and analysing the studies published on this topic via a multivocal literature review and then with an extended empirical investigation on the matters that appear most controversial via the direct analysis of the work of 25 respondents from 17 different companies located in eight countries. Thus, the originality of this research is in the systemic assessment of the “level of flexibility” of SAFe, highlighting the challenges of adopting this framework as it relates to decision making, structure, and the technical and managerial competencies of the company. The results show that SAFe can be an effective and adequate approach if the company is ready to invest a significant effort and resources into it both in the form of providing time for SAFe to be properly absorbed and specific training for individuals. <|reference_end|>", "<|reference_start|> Scaled Agile Framework: Presentation and real world example: This case focuses on the applicability of the Scaled Agile Framework (SAFe) founded by Dean Leffingwell. Modern organizations often work with agile software engineering teams using traditional single team-level methods like Scrum, but multiple teams and the program or portfolio level are not part of methods like Scrum. SAFe tries to apply agile methodologies to the whole organisation. The real world example focuses on a key element of SAFe, the Program Increment (PI) planning meeting and how it can improve multiple team collaboration. <|reference_end|>", "<|reference_start|> Challenges and success factors for large-scale agile transformations: A systematic literature review: <|reference_end|>" ]

[ 0, 1, 2, 3 ]

[ "<|reference_start|> Learning Joint Representations of Videos and Sentences with Web Image Search: Our objective is video retrieval based on natural language queries. In addition, we consider the analogous problem of retrieving sentences or generating descriptions given an input video. Recent work has addressed the problem by embedding visual and textual inputs into a common space where semantic similarities correlate to distances. We also adopt the embedding approach, and make the following contributions: First, we utilize web image search in sentence embedding process to disambiguate fine-grained visual concepts. Second, we propose embedding models for sentence, image, and video inputs whose parameters are learned simultaneously. Finally, we show how the proposed model can be applied to description generation. Overall, we observe a clear improvement over the state-of-the-art methods in the video and sentence retrieval tasks. In description generation, the performance level is comparable to the current state-of-the-art, although our embeddings were trained for the retrieval tasks. <|reference_end|>", "<|reference_start|> Multiple feature hashing for real-time large scale near-duplicate video retrieval: Near-duplicate video retrieval (NDVR) has recently attracted lots of research attention due to the exponential growth of online videos. It helps in many areas, such as copyright protection, video tagging, online video usage monitoring, etc. Most of existing approaches use only a single feature to represent a video for NDVR. However, a single feature is often insufficient to characterize the video content. Besides, while the accuracy is the main concern in previous literatures, the scalability of NDVR algorithms for large scale video datasets has been rarely addressed. In this paper, we present a novel approach - Multiple Feature Hashing (MFH) to tackle both the accuracy and the scalability issues of NDVR. MFH preserves the local structure information of each individual feature and also globally consider the local structures for all the features to learn a group of hash functions which map the video keyframes into the Hamming space and generate a series of binary codes to represent the video dataset. We evaluate our approach on a public video dataset and a large scale video dataset consisting of 132,647 videos, which was collected from YouTube by ourselves. The experiment results show that the proposed method outperforms the state-of-the-art techniques in both accuracy and efficiency. <|reference_end|>", "<|reference_start|> Multimodal Speech Summarization Through Semantic Concept Learning.: We propose a cascaded multimodal abstractive speech summarization model that generates semantic concepts as an intermediate step towards summarization. We describe a method to leverage existing multimodal dataset annotations to curate groundtruth labels for such intermediate concept modeling. In addition to cascaded training, the concept labels also provide an interpretable intermediate output level that helps improve performance on the downstream summarization task. On the open-domain How2 data, we conduct utterance-level and video-level experiments for two granularities of concepts: Specific and Abstract. We compare various multimodal fusion models for concept generation based on the respective input modalities. We observe consistent improvements in concept modeling by using multimodal adaptation models over unimodal models. Using the cascaded multimodal speech summarization model, we see a significant improvement of 7.5 METEOR points and 5.1 ROUGE-L points compared to previous methods of speech summarization. Finally, we show the benefits of scalability of the proposed approaches on 2000h of video data. <|reference_end|>", "<|reference_start|> Flamingo: a Visual Language Model for Few-Shot Learning: Building models that can be rapidly adapted to novel tasks using only a handful of annotated examples is an open challenge for multimodal machine learning research. We introduce Flamingo, a family of Visual Language Models (VLM) with this ability. We propose key architectural innovations to: (i) bridge powerful pretrained vision-only and language-only models, (ii) handle sequences of arbitrarily interleaved visual and textual data, and (iii) seamlessly ingest images or videos as inputs. Thanks to their flexibility, Flamingo models can be trained on large-scale multimodal web corpora containing arbitrarily interleaved text and images, which is key to endow them with in-context few-shot learning capabilities. We perform a thorough evaluation of our models, exploring and measuring their ability to rapidly adapt to a variety of image and video tasks. These include open-ended tasks such as visual question-answering, where the model is prompted with a question which it has to answer; captioning tasks, which evaluate the ability to describe a scene or an event; and close-ended tasks such as multiple-choice visual question-answering. For tasks lying anywhere on this spectrum, a single Flamingo model can achieve a new state of the art with few-shot learning, simply by prompting the model with task-specific examples. On numerous benchmarks, Flamingo outperforms models fine-tuned on thousands of times more task-specific data. <|reference_end|>" ]

[ 3, 4, 6, 8 ]

[ "<|reference_start|> Power allocation for an energy harvesting transmitter with hybrid energy sources: In this work, we consider a point-to-point communication link where the transmitter has a hybrid supply of energy. Specifically, the hybrid energy is supplied by a constant energy source and an energy harvester, which harvests energy from its surrounding environment and stores it in a battery which suffers from energy leakage. Our goal is to minimize the power consumed by the constant energy source for transmission of a given amount of data in a given number of time intervals. Two scenarios are considered for packet arrival. In the first scenario, we assume that all data packets have arrived before transmission begins, whereas in the second scenario, we assume that data packets are arriving during the course of data transmission. For both scenarios, we propose an optimal offline transmit power allocation scheme which provides insight into how to efficiently consume the energy supplied by the constant energy source and the energy harvester. For offline power allocation, we assume that causal and non-causal information regarding the channel and the amount of harvested energy is available a priori. For optimal online power allocation, we adopt a stochastic dynamic programming (DP) approach for both considered scenarios. For online power allocation, only causal information regarding the channel and the amount of harvested energy is assumed available. Due to the inherent high complexity of DP, we propose suboptimal online algorithms which are appealing because of their low complexity. Simulation results reveal that the offline scheme performs best among all considered schemes and the suboptimal online scheme provides a good performance-complexity tradeoff. <|reference_end|>", "<|reference_start|> Energy-Aware Traffic Offloading for Green Heterogeneous Networks: With small cell base stations (SBSs) densely deployed in addition to conventional macro base stations (MBSs), the heterogeneous cellular network (HCN) architecture can effectively boost network capacity. To support the huge power demand of HCNs, renewable energy harvesting technologies can be leveraged. In this paper, we aim to make efficient use of the harvested energy for on-grid power saving while satisfying the quality of service (QoS) requirement. To this end, energy-aware traffic offloading schemes are proposed, whereby user associations, ON-OFF states of SBSs, and power control are jointly optimized according to the statistical information of energy arrival and traffic load. Specifically, for the single SBS case, the power saving gain achieved by activating the SBS is derived in closed form, based on which the SBS activation condition and optimal traffic offloading amount are obtained. Furthermore, a two-stage energy-aware traffic offloading (TEATO) scheme is proposed for the multiple-SBS case, considering various operating characteristics of SBSs with different power sources. Simulation results demonstrate that the proposed scheme can achieve more than 50% power saving gain for typical daily traffic and solar energy profiles, compared with the conventional traffic offloading schemes. <|reference_end|>", "<|reference_start|> Cocycles and Lyapunov Exponents: <|reference_end|>", "<|reference_start|> Distributed Energy Management for Multiuser Mobile-Edge Computing Systems With Energy Harvesting Devices and QoS Constraints: Mobile-edge computing (MEC) has evolved as a promising technology to alleviate the computing pressure of mobile devices by offloading computation tasks to MEC server. Energy management is challenging since the unpredictability of the energy harvesting (EH) and the quality of service (QoS). In this paper, we investigate the problem of power consumption in a multiuser MEC system with EH devices. The system power consumption, which includes the local execution power and the offloading transmission power, is designated as the main system performance index. First, we formulate the power consumption minimization problem with the battery queue stability and QoS constraints as a stochastic optimization programming, which is difficult to solve due to the time-coupling constraints. Then, we adopt the Lyapunov optimization approach to tackle the problem by reformulating it into a problem with relaxed queue stability constraints. We design an online algorithm based on the Lyapunov optimization method, which only uses current states of the mobile users and does not depend on the system statistic information. Furthermore, we propose a distributed algorithm based on the alternating direction method of multipliers to reduce the system computational complexity. We prove the optimality of the online algorithm and the distributed algorithm using rigorous theoretical analysis. Finally, we perform extensive trace-simulations to verify the theoretical results and evaluate the effectiveness of the proposed algorithms. <|reference_end|>" ]

[ 5, 9, 15, 19 ]

[ "<|reference_start|> Solving Rubik's Cube with a Robot Hand: We demonstrate that models trained only in simulation can be used to solve a manipulation problem of unprecedented complexity on a real robot. This is made possible by two key components: a novel algorithm, which we call automatic domain randomization (ADR) and a robot platform built for machine learning. ADR automatically generates a distribution over randomized environments of ever-increasing difficulty. Control policies and vision state estimators trained with ADR exhibit vastly improved sim2real transfer. For control policies, memory-augmented models trained on an ADR-generated distribution of environments show clear signs of emergent meta-learning at test time. The combination of ADR with our custom robot platform allows us to solve a Rubik's cube with a humanoid robot hand, which involves both control and state estimation problems. Videos summarizing our results are available: https://openai.com/blog/solving-rubiks-cube/ <|reference_end|>", "<|reference_start|> Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model: Constructing agents with planning capabilities has long been one of the main challenges in the pursuit of artificial intelligence. Tree-based planning methods have enjoyed huge success in challenging domains, such as chess and Go, where a perfect simulator is available. However, in real-world problems the dynamics governing the environment are often complex and unknown. In this work we present the MuZero algorithm which, by combining a tree-based search with a learned model, achieves superhuman performance in a range of challenging and visually complex domains, without any knowledge of their underlying dynamics. MuZero learns a model that, when applied iteratively, predicts the quantities most directly relevant to planning: the reward, the action-selection policy, and the value function. When evaluated on 57 different Atari games - the canonical video game environment for testing AI techniques, in which model-based planning approaches have historically struggled - our new algorithm achieved a new state of the art. When evaluated on Go, chess and shogi, without any knowledge of the game rules, MuZero matched the superhuman performance of the AlphaZero algorithm that was supplied with the game rules. <|reference_end|>", "<|reference_start|> Trust Region Policy Optimization: We describe an iterative procedure for optimizing policies, with guaranteed monotonic improvement. By making several approximations to the theoretically-justified procedure, we develop a practical algorithm, called Trust Region Policy Optimization (TRPO). This algorithm is similar to natural policy gradient methods and is effective for optimizing large nonlinear policies such as neural networks. Our experiments demonstrate its robust performance on a wide variety of tasks: learning simulated robotic swimming, hopping, and walking gaits; and playing Atari games using images of the screen as input. Despite its approximations that deviate from the theory, TRPO tends to give monotonic improvement, with little tuning of hyperparameters. <|reference_end|>", "<|reference_start|> Separated Trust Regions Policy Optimization Method: In this work, we propose a moderate policy update method for reinforcement learning, which encourages the agent to explore more boldly in early episodes but updates the policy more cautious. Based on the maximum entropy framework, we propose a softer objective with more conservative constraints and build the separated trust regions for optimization. To reduce the variance of expected entropy return, a calculated state policy entropy of Gaussian distribution is preferred instead of collecting log probability by sampling. This new method, which we call separated trust region for policy mean and variance (STRMV), can be view as an extension to proximal policy optimization (PPO) but it is gentler for policy update and more lively for exploration. We test our approach on a wide variety of continuous control benchmark tasks in the MuJoCo environment. The experiments demonstrate that STRMV outperforms the previous state of art on-policy methods, not only achieving higher rewards but also improving the sample efficiency. <|reference_end|>" ]

[ 0, 2, 3, 7 ]

[ "<|reference_start|> Diverse Data Augmentation with Diffusions for Effective Test-time Prompt Tuning: Benefiting from prompt tuning, recent years have witnessed the promising performance of pre-trained vision-language models, e.g., CLIP, on versatile downstream tasks. In this paper, we focus on a particular setting of learning adaptive prompts on the fly for each test sample from an unseen new domain, which is known as test-time prompt tuning (TPT). Existing TPT methods typically rely on data augmentation and confidence selection. However, conventional data augmentation techniques, e.g., random resized crops, suffers from the lack of data diversity, while entropy-based confidence selection alone is not sufficient to guarantee prediction fidelity. To address these issues, we propose a novel TPT method, named DiffTPT, which leverages pre-trained diffusion models to generate diverse and informative new data. Specifically, we incorporate augmented data by both conventional method and pre-trained stable diffusion to exploit their respective merits, improving the models ability to adapt to unknown new test data. Moreover, to ensure the prediction fidelity of generated data, we introduce a cosine similarity-based filtration technique to select the generated data with higher similarity to the single test sample. Our experiments on test datasets with distribution shifts and unseen categories demonstrate that DiffTPT improves the zero-shot accuracy by an average of 5.13\\% compared to the state-of-the-art TPT method. Our code and models will be publicly released. <|reference_end|>", "<|reference_start|> LoRA: Low-Rank Adaptation of Large Language Models: An important paradigm of natural language processing consists of large-scale pre-training on general domain data and adaptation to particular tasks or domains. As we pre-train larger models, full fine-tuning, which retrains all model parameters, becomes less feasible. Using GPT-3 175B as an example -- deploying independent instances of fine-tuned models, each with 175B parameters, is prohibitively expensive. We propose Low-Rank Adaptation, or LoRA, which freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture, greatly reducing the number of trainable parameters for downstream tasks. Compared to GPT-3 175B fine-tuned with Adam, LoRA can reduce the number of trainable parameters by 10,000 times and the GPU memory requirement by 3 times. LoRA performs on-par or better than fine-tuning in model quality on RoBERTa, DeBERTa, GPT-2, and GPT-3, despite having fewer trainable parameters, a higher training throughput, and, unlike adapters, no additional inference latency. We also provide an empirical investigation into rank-deficiency in language model adaptation, which sheds light on the efficacy of LoRA. We release a package that facilitates the integration of LoRA with PyTorch models and provide our implementations and model checkpoints for RoBERTa, DeBERTa, and GPT-2 at https://github.com/microsoft/LoRA. <|reference_end|>", "<|reference_start|> In Advances in Neural Information Processing Systems: <|reference_end|>", "<|reference_start|> CALIP: Zero-Shot Enhancement of CLIP with Parameter-free Attention: Contrastive Language-Image Pre-training (CLIP) has been shown to learn visual representations with great transferability, which achieves promising accuracy for zero-shot classification. To further improve its downstream performance, existing works propose additional learnable modules upon CLIP and fine-tune them by few-shot training sets. However, the resulting extra training cost and data requirement severely hinder the efficiency for model deployment and knowledge transfer. In this paper, we introduce a free-lunch enhancement method, CALIP, to boost CLIP's zero-shot performance via a parameter-free Attention module. Specifically, we guide visual and textual representations to interact with each other and explore cross-modal informative features via attention. As the pre-training has largely reduced the embedding distances between two modalities, we discard all learnable parameters in the attention and bidirectionally update the multi-modal features, enabling the whole process to be parameter-free and training-free. In this way, the images are blended with textual-aware signals and the text representations become visual-guided for better adaptive zero-shot alignment. We evaluate CALIP on various benchmarks of 14 datasets for both 2D image and 3D point cloud few-shot classification, showing consistent zero-shot performance improvement over CLIP. Based on that, we further insert a small number of linear layers in CALIP's attention module and verify our robustness under the few-shot settings, which also achieves leading performance compared to existing methods. Those extensive experiments demonstrate the superiority of our approach for efficient enhancement of CLIP. <|reference_end|>" ]

[ 13, 15, 25, 36 ]

[ "<|reference_start|> Anchor Diffusion for Unsupervised Video Object Segmentation: Unsupervised video object segmentation has often been tackled by methods based on recurrent neural networks and optical flow. Despite their complexity, these kinds of approaches tend to favour short-term temporal dependencies and are thus prone to accumulating inaccuracies, which cause drift over time. Moreover, simple (static) image segmentation models, alone, can perform competitively against these methods, which further suggests that the way temporal dependencies are modelled should be reconsidered. Motivated by these observations, in this paper we explore simple yet effective strategies to model long-term temporal dependencies. Inspired by the non-local operators of [70], we introduce a technique to establish dense correspondences between pixel embeddings of a reference \"anchor\" frame and the current one. This allows the learning of pairwise dependencies at arbitrarily long distances without conditioning on intermediate frames. Without online supervision, our approach can suppress the background and precisely segment the foreground object even in challenging scenarios, while maintaining consistent performance over time. With a mean IoU of $81.7\\%$, our method ranks first on the DAVIS-2016 leaderboard of unsupervised methods, while still being competitive against state-of-the-art online semi-supervised approaches. We further evaluate our method on the FBMS dataset and the ViSal video saliency dataset, showing results competitive with the state of the art. <|reference_end|>", "<|reference_start|> Making a Case for 3D Convolutions for Object Segmentation in Videos: The task of object segmentation in videos is usually accomplished by processing appearance and motion information separately using standard 2D convolutional networks, followed by a learned fusion of the two sources of information. On the other hand, 3D convolutional networks have been successfully applied for video classification tasks, but have not been leveraged as effectively to problems involving dense per-pixel interpretation of videos compared to their 2D convolutional counterparts and lag behind the aforementioned networks in terms of performance. In this work, we show that 3D CNNs can be effectively applied to dense video prediction tasks such as salient object segmentation. We propose a simple yet effective encoder-decoder network architecture consisting entirely of 3D convolutions that can be trained end-to-end using a standard cross-entropy loss. To this end, we leverage an efficient 3D encoder, and propose a 3D decoder architecture, that comprises novel 3D Global Convolution layers and 3D Refinement modules. Our approach outperforms existing state-of-the-arts by a large margin on the DAVIS'16 Unsupervised, FBMS and ViSal dataset benchmarks in addition to being faster, thus showing that our architecture can efficiently learn expressive spatio-temporal features and produce high quality video segmentation masks. We have made our code and trained models publicly available at https://github.com/sabarim/3DC-Seg. <|reference_end|>", "<|reference_start|> A Closer Look at Spatiotemporal Convolutions for Action Recognition: In this paper we discuss several forms of spatiotemporal convolutions for video analysis and study their effects on action recognition. Our motivation stems from the observation that 2D CNNs applied to individual frames of the video have remained solid performers in action recognition. In this work we empirically demonstrate the accuracy advantages of 3D CNNs over 2D CNNs within the framework of residual learning. Furthermore, we show that factorizing the 3D convolutional filters into separate spatial and temporal components yields significantly advantages in accuracy. Our empirical study leads to the design of a new spatiotemporal convolutional block \"R(2+1)D\" which gives rise to CNNs that achieve results comparable or superior to the state-of-the-art on Sports-1M, Kinetics, UCF101 and HMDB51. <|reference_end|>", "<|reference_start|> Making a Case for 3D Convolutions for Object Segmentation in Videos: The task of object segmentation in videos is usually accomplished by processing appearance and motion information separately using standard 2D convolutional networks, followed by a learned fusion of the two sources of information. On the other hand, 3D convolutional networks have been successfully applied for video classification tasks, but have not been leveraged as effectively to problems involving dense per-pixel interpretation of videos compared to their 2D convolutional counterparts and lag behind the aforementioned networks in terms of performance. In this work, we show that 3D CNNs can be effectively applied to dense video prediction tasks such as salient object segmentation. We propose a simple yet effective encoder-decoder network architecture consisting entirely of 3D convolutions that can be trained end-to-end using a standard cross-entropy loss. To this end, we leverage an efficient 3D encoder, and propose a 3D decoder architecture, that comprises novel 3D Global Convolution layers and 3D Refinement modules. Our approach outperforms existing state-of-the-arts by a large margin on the DAVIS'16 Unsupervised, FBMS and ViSal dataset benchmarks in addition to being faster, thus showing that our architecture can efficiently learn expressive spatio-temporal features and produce high quality video segmentation masks. We have made our code and trained models publicly available at https://github.com/sabarim/3DC-Seg. <|reference_end|>" ]

[ 7, 20, 54, 55 ]

[ "<|reference_start|> Sequential and Diverse Recommendation with Long Tail.: Sequential recommendation is a task that learns a temporal dynamic of a user behavior in sequential data and predicts items that a user would like afterward. However, diversity has been rarely emphasized in the context of sequential recommendation. Sequential and diverse recommendation must learn temporal preference on diverse items as well as on general items. Thus, we propose a sequential and diverse recommendation model that predicts a ranked list containing general items and also diverse items without compromising significant accuracy.To learn temporal preference on diverse items as well as on general items, we cluster and relocate consumed long tail items to make a pseudo ground truth for diverse items and learn the preference on long tail using recurrent neural network, which enables us to directly learn a ranking function. Extensive online and offline experiments deployed on a commercial platform demonstrate that our models significantly increase diversity while preserving accuracy compared to the state-of-the-art sequential recommendation model, and consequently our models improve user satisfaction. <|reference_end|>", "<|reference_start|> E4SRec: An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential Recommendation: The recent advancements in Large Language Models (LLMs) have sparked interest in harnessing their potential within recommender systems. Since LLMs are designed for natural language tasks, existing recommendation approaches have predominantly transformed recommendation tasks into open-domain natural language generation tasks. However, this approach necessitates items to possess rich semantic information, often generates out-of-range results, and suffers from notably low efficiency and limited extensibility. Furthermore, practical ID-based recommendation strategies, reliant on a huge number of unique identities (IDs) to represent users and items, have gained prominence in real-world recommender systems due to their effectiveness and efficiency. Nevertheless, the incapacity of LLMs to model IDs presents a formidable challenge when seeking to leverage LLMs for personalized recommendations. In this paper, we introduce an Elegant Effective Efficient Extensible solution for large language models for Sequential Recommendation (E4SRec), which seamlessly integrates LLMs with traditional recommender systems that exclusively utilize IDs to represent items. Specifically, E4SRec takes ID sequences as inputs, ensuring that the generated outputs fall within the candidate lists. Furthermore, E4SRec possesses the capability to generate the entire ranking list in a single forward process, and demands only a minimal set of pluggable parameters, which are trained for each dataset while keeping the entire LLM frozen. We substantiate the effectiveness, efficiency, and extensibility of our proposed E4SRec through comprehensive experiments conducted on four widely-used real-world datasets. The implementation code is accessible at https://github.com/HestiaSky/E4SRec/. <|reference_end|>", "<|reference_start|> Simplified State Space Layers for Sequence Modeling: Models using structured state space sequence (S4) layers have achieved state-of-the-art performance on long-range sequence modeling tasks. An S4 layer combines linear state space models (SSMs), the HiPPO framework, and deep learning to achieve high performance. We build on the design of the S4 layer and introduce a new state space layer, the S5 layer. Whereas an S4 layer uses many independent single-input, single-output SSMs, the S5 layer uses one multi-input, multi-output SSM. We establish a connection between S5 and S4, and use this to develop the initialization and parameterization used by the S5 model. The result is a state space layer that can leverage efficient and widely implemented parallel scans, allowing S5 to match the computational efficiency of S4, while also achieving state-of-the-art performance on several long-range sequence modeling tasks. S5 averages 87.4% on the long range arena benchmark, and 98.5% on the most difficult Path-X task. <|reference_end|>", "<|reference_start|> State-space models: Slow convergence is observed in the EM algorithm for linear state-space models. We propose to circumvent the problem by applying any off-the-shelf quasi-Newton-type optimizer, which operates on the... <|reference_end|>" ]

[ 18, 31, 40, 41 ]

[ "<|reference_start|> {{SWAG: The purpose of this study is to delve into the identity of the swag style which has diversified into various forms by exploring the phenomenon, formative characteristics and the internal values of the swag style in modern fashioin. This study discusses the concept and the socio-cultural meanings of swag from the perspective of Jean Baudrillard\"s hyper-reality, and a form of existence. The classifies the swag fashion styles into parody, hip hop and collage-type mix-and-match. Expressive characteristics of the swag look in modern fashion are as follows. First, the swag look utilizes the parody technique. In the mid-2000s, the look parodied brand logos as a form of self-mocking and active self-derision toward cheap imitations. Second, the swag look borrows from the expressive factors of the hip-hop style. Born as a sub-culture based on music, hip-hop has become a way of life, as its nature became multi-cultural and trans-cultural while its fashion style gained popularity globally after the 1980s. Third, the swag look barrows from the pop-type collage form as it mixes-and-matches costume items based on the expressive characteristics of hip hop, and this can be seen through items being used in new, non-formative and free styles. Comic aesthetics is revealed in parodied expression, hip-hop factors and collage-style mix-and-match. Swag as a hyper-reality manifests itself in various natures: humorous nature, negative nature and deconstructive nature through reflection and re-enactment of reality, transmutation and distortion of reality, and absence of reality respectively. However, it does not have a binding nature, which is the norm for subcultures. This characteristic, in combination with it having internal lightness, strong meaning of communication, and a sharing of self-contentment, distinguishes itself from the general meanings of existing parody fashion, hip-hop fashion and collage fashion. <|reference_end|>", "<|reference_start|> Understanding Deep Learning Performance through an Examination of Test Set Difficulty: A Psychometric Case Study: Interpreting the performance of deep learning models beyond test set accuracy is challenging. Characteristics of individual data points are often not considered during evaluation, and each data point is treated equally. We examine the impact of a test set question's difficulty to determine if there is a relationship between difficulty and performance. We model difficulty using well-studied psychometric methods on human response patterns. Experiments on Natural Language Inference (NLI) and Sentiment Analysis (SA) show that the likelihood of answering a question correctly is impacted by the question's difficulty. As DNNs are trained with more data, easy examples are learned more quickly than hard examples. <|reference_end|>", "<|reference_start|> Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds: Incorporating Item Response Theory (IRT) into NLP tasks can provide valuable information about model performance and behavior. Traditionally, IRT models are learned using human response pattern (RP) data, presenting a significant bottleneck for large data sets like those required for training deep neural networks (DNNs). In this work we propose learning IRT models using RPs generated from artificial crowds of DNN models. We demonstrate the effectiveness of learning IRT models using DNN-generated data through quantitative and qualitative analyses for two NLP tasks. Parameters learned from human and machine RPs for natural language inference and sentiment analysis exhibit medium to large positive correlations. We demonstrate a use-case for latent difficulty item parameters, namely training set filtering, and show that using difficulty to sample training data outperforms baseline methods. Finally, we highlight cases where human expectation about item difficulty does not match difficulty as estimated from the machine RPs. <|reference_end|>", "<|reference_start|> Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling: Natural language understanding has recently seen a surge of progress with the use of sentence encoders like ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2019) which are pretrained on variants of language modeling. We conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks both as alternatives and complements to language modeling. Our primary results support the use language modeling, especially when combined with pretraining on additional labeled-data tasks. However, our results are mixed across pretraining tasks and show some concerning trends: In ELMo's pretrain-then-freeze paradigm, random baselines are worryingly strong and results vary strikingly across target tasks. In addition, fine-tuning BERT on an intermediate task often negatively impacts downstream transfer. In a more positive trend, we see modest gains from multitask training, suggesting the development of more sophisticated multitask and transfer learning techniques as an avenue for further research. <|reference_end|>" ]

[ 3, 11, 12, 18 ]

[ "<|reference_start|> Low-Rank Subspaces in GANs: The latent space of a Generative Adversarial Network (GAN) has been shown to encode rich semantics within some subspaces. To identify these subspaces, researchers typically analyze the statistical information from a collection of synthesized data, and the identified subspaces tend to control image attributes globally (i.e., manipulating an attribute causes the change of an entire image). By contrast, this work introduces low-rank subspaces that enable more precise control of GAN generation. Concretely, given an arbitrary image and a region of interest (e.g., eyes of face images), we manage to relate the latent space to the image region with the Jacobian matrix and then use low-rank factorization to discover steerable latent subspaces. There are three distinguishable strengths of our approach that can be aptly called LowRankGAN. First, compared to analytic algorithms in prior work, our low-rank factorization of Jacobians is able to find the low-dimensional representation of attribute manifold, making image editing more precise and controllable. Second, low-rank factorization naturally yields a null space of attributes such that moving the latent code within it only affects the outer region of interest. Therefore, local image editing can be simply achieved by projecting an attribute vector into the null space without relying on a spatial mask as existing methods do. Third, our method can robustly work with a local region from one image for analysis yet well generalize to other images, making it much easy to use in practice. Extensive experiments on state-of-the-art GAN models (including StyleGAN2 and BigGAN) trained on various datasets demonstrate the effectiveness of our LowRankGAN. <|reference_end|>", "<|reference_start|> Orthogonal Jacobian Regularization for Unsupervised Disentanglement in Image Generation: Unsupervised disentanglement learning is a crucial issue for understanding and exploiting deep generative models. Recently, SeFa tries to find latent disentangled directions by performing SVD on the first projection of a pre-trained GAN. However, it is only applied to the first layer and works in a post-processing way. Hessian Penalty minimizes the off-diagonal entries of the output's Hessian matrix to facilitate disentanglement, and can be applied to multi-layers.However, it constrains each entry of output independently, making it not sufficient in disentangling the latent directions (e.g., shape, size, rotation, etc.) of spatially correlated variations. In this paper, we propose a simple Orthogonal Jacobian Regularization (OroJaR) to encourage deep generative model to learn disentangled representations. It simply encourages the variation of output caused by perturbations on different latent dimensions to be orthogonal, and the Jacobian with respect to the input is calculated to represent this variation. We show that our OroJaR also encourages the output's Hessian matrix to be diagonal in an indirect manner. In contrast to the Hessian Penalty, our OroJaR constrains the output in a holistic way, making it very effective in disentangling latent dimensions corresponding to spatially correlated variations. Quantitative and qualitative experimental results show that our method is effective in disentangled and controllable image generation, and performs favorably against the state-of-the-art methods. Our code is available at https://github.com/csyxwei/OroJaR <|reference_end|>", "<|reference_start|> Is Generator Conditioning Causally Related to GAN Performance?: Recent work (Pennington et al, 2017) suggests that controlling the entire distribution of Jacobian singular values is an important design consideration in deep learning. Motivated by this, we study the distribution of singular values of the Jacobian of the generator in Generative Adversarial Networks (GANs). We find that this Jacobian generally becomes ill-conditioned at the beginning of training. Moreover, we find that the average (with z from p(z)) conditioning of the generator is highly predictive of two other ad-hoc metrics for measuring the 'quality' of trained GANs: the Inception Score and the Frechet Inception Distance (FID). We test the hypothesis that this relationship is causal by proposing a 'regularization' technique (called Jacobian Clamping) that softly penalizes the condition number of the generator Jacobian. Jacobian Clamping improves the mean Inception Score and the mean FID for GANs trained on several datasets. It also greatly reduces inter-run variance of the aforementioned scores, addressing (at least partially) one of the main criticisms of GANs. <|reference_end|>", "<|reference_start|> {{GAN: مركبات الجاليوم نترايد ( GaN ) هى إحدى فصائل أشباه الموصلات تعتبر من أهم الوصلات الالكترونية وذلك لتميزها التطبيقي وخاصة فى المجالات الكهروضوئية ( الليزر ) والمجالات التطبيقية الالكترونية الاخرى ( High power Devices ) بسبب سعة فجوة الطاقة حيث أن هذه السعة تتراوح ما بين 1.9 ev حتى 6.28 ونتيجة لذلك نستطيع الحصول على مصادر متعددة للطاقة ومثال على ذلك ثنائيات اﻹنبعاث الضوئي (LEDs ) ومصادر ثنائية أ خرى ( LDs ) ونظرا لأهمية هذا النوع من التقنية حاليا فان هناك توجها عالميا كبيرآ جدآ على مستوى مراكز الأبحاث سواء كان ذلك فى الشركات المتخصصة أو مراكزالأبحاث فى الجامعات والمعاهد لدراسة خصائص هذه المواد من النواحي الفيزيائية والتطبيقية سواء عن طريق التصنيع أو القياسات الكهربية والضوئية والمغنا طيسية وبناء على ماسبق فقد قمنا بدراسات عدة للدخول فى هذا المجال من أجل دراسة خصائص هذه المواد الكهربية وتم الحصول على العلاقة مابين التيار والجهد ودرجة الحرارة وكذلك دراسة السعة الكهربية لها وقد توصلنا الى نتائج جيدة جدا لهذه الوصلات الكهربية حيث اننا استخدمنا معدن الذهب وكذلك معدن الألمنيوم من أجل التوصيلات الكهربية وتمكنا بعد ذلك من الحصول على نتائج مطابقة بشكل كبير لماهو موجود نظريا كما هو موضح فى الجزء الخاص بالنتائج العملية فى هذا البحث . <|reference_end|>" ]

[ 0, 6, 12, 22 ]

[ "<|reference_start|> The 1,000-km challenge: Insights and quantitative and qualitative results: On 18 November 2014, a team of four autonomous CoBot robots reached 1,000-km of overall autonomous navigation, as a result of a 1,000-km challenge that the authors had set three years earlier. The authors are frequently asked for the lessons learned, as well as the performance results. In this article, they introduce the challenge and contribute a detailed presentation of technical insights as well as quantitative and qualitative results. They have previously presented the algorithms for the individual technical contributions, namely robot localization, symbiotic robot autonomy, and robot task scheduling. In this article, they present the data collected over the 1,000-km challenge and analyze it to evaluate the accuracy and robustness of the localization algorithms on the CoBots. Furthermore, they present technical insights into the algorithms, which they believe are responsible for the robots' continuous robust performance. <|reference_end|>", "<|reference_start|> The STRANDS Project: Long-Term Autonomy in Everyday Environments: Thanks to the efforts of the robotics and autonomous systems community, robots are becoming ever more capable. There is also an increasing demand from end-users for autonomous service robots that can operate in real environments for extended periods. In the STRANDS project we are tackling this demand head-on by integrating state-of-the-art artificial intelligence and robotics research into mobile service robots, and deploying these systems for long-term installations in security and care environments. Over four deployments, our robots have been operational for a combined duration of 104 days autonomously performing end-user defined tasks, covering 116km in the process. In this article we describe the approach we have used to enable long-term autonomous operation in everyday environments, and how our robots are able to use their long run times to improve their own performance. <|reference_end|>", "<|reference_start|> RoboEarth: • A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. <|reference_end|>", "<|reference_start|> Robot web tools: Efficient messaging for cloud robotics: Since its official introduction in 2012, the Robot Web Tools project has grown tremendously as an open-source community, enabling new levels of interoperability and portability across heterogeneous robot systems, devices, and front-end user interfaces. At the heart of Robot Web Tools is the rosbridge protocol as a general means for messaging ROS topics in a client-server paradigm suitable for wide area networks, and human-robot interaction at a global scale through modern web browsers. Building from rosbridge, this paper describes our efforts with Robot Web Tools to advance: 1) human-robot interaction through usable client and visualization libraries for more efficient development of front-end human-robot interfaces, and 2) cloud robotics through more efficient methods of transporting high-bandwidth topics (e.g., kinematic transforms, image streams, and point clouds). We further discuss the significant impact of Robot Web Tools through a diverse set of use cases that showcase the importance of a generic messaging protocol and front-end development systems for human-robot interaction. <|reference_end|>" ]

[ 0, 1, 4, 9 ]

[ "<|reference_start|> Highly Efficient Test Architecture for Low-Power AI Accelerators: Low-power artificial intelligence (AI) accelerators are being developed to support the battery-operated edge devices at a minimum expense of classification error. However, the testing of such large AI accelerators with traditional techniques is inefficient in achieving the required certifications for Autonomous Driving Assistant Systems (ISO 26262). ISO 26262 sets very stringent requirements on the testing time and fault coverage during the diagnosability of faults leading to system-level failures during in-field testing. This article proposes a test architecture for low-power AI accelerators by reusing the existing data paths for large AI accelerator arrays. As compared to the full scan-DFT, the proposed test architecture reduces the test time and peak test power, which enhances the reliability of the test responses. The proposed technique reduces 1) the switching power by 87%; 2) testing times by 72% on average for cases up to $32\\times 32$ ; and 3) the peak power by 59%. Further, there is an average reduction in the area by 10% for the accelerator. <|reference_end|>", "<|reference_start|> Adaptive Extreme Edge Computing for Wearable Devices: Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networks, power consumption, processing speed, and system adaptation are vital in future smart wearable devices. The visioning and forecasting of how to bring computation to the edge in smart sensors have already begun, with an aspiration to provide adaptive extreme edge computing. Here, we provide a holistic view of hardware and theoretical solutions towards smart wearable devices that can provide guidance to research in this pervasive computing era. We propose various solutions for biologically plausible models for continual learning in neuromorphic computing technologies for wearable sensors. To envision this concept, we provide a systematic outline in which prospective low power and low latency scenarios of wearable sensors in neuromorphic platforms are expected. We successively describe vital potential landscapes of neuromorphic processors exploiting complementary metal-oxide semiconductors (CMOS) and emerging memory technologies (e.g. memristive devices). Furthermore, we evaluate the requirements for edge computing within wearable devices in terms of footprint, power consumption, latency, and data size. We additionally investigate the challenges beyond neuromorphic computing hardware, algorithms and devices that could impede enhancement of adaptive edge computing in smart wearable devices. <|reference_end|>", "<|reference_start|> SpiNNaker: A Spiking Neural Network Architecture: <|reference_end|>", "<|reference_start|> A Scalable Multicore Architecture With Heterogeneous Memory Structures for Dynamic Neuromorphic Asynchronous Processors ({DYNAPs: Neuromorphic computing systems comprise networks of neurons that use asynchronous events for both computation and communication. This type of representation offers several advantages in terms of bandwidth and power consumption in neuromorphic electronic systems. However, managing the traffic of asynchronous events in large scale systems is a daunting task, both in terms of circuit complexity and memory requirements. Here, we present a novel routing methodology that employs both hierarchical and mesh routing strategies and combines heterogeneous memory structures for minimizing both memory requirements and latency, while maximizing programming flexibility to support a wide range of event-based neural network architectures, through parameter configuration. We validated the proposed scheme in a prototype multicore neuromorphic processor chip that employs hybrid analog/digital circuits for emulating synapse and neuron dynamics together with asynchronous digital circuits for managing the address-event traffic. We present a theoretical analysis of the proposed connectivity scheme, describe the methods and circuits used to implement such scheme, and characterize the prototype chip. Finally, we demonstrate the use of the neuromorphic processor with a convolutional neural network for the real-time classification of visual symbols being flashed to a dynamic vision sensor (DVS) at high speed. <|reference_end|>" ]

[ 0, 4, 8, 9 ]

[ "<|reference_start|> Incorporating BERT into Neural Machine Translation: The recently proposed BERT has shown great power on a variety of natural language understanding tasks, such as text classification, reading comprehension, etc. However, how to effectively apply BERT to neural machine translation (NMT) lacks enough exploration. While BERT is more commonly used as fine-tuning instead of contextual embedding for downstream language understanding tasks, in NMT, our preliminary exploration of using BERT as contextual embedding is better than using for fine-tuning. This motivates us to think how to better leverage BERT for NMT along this direction. We propose a new algorithm named BERT-fused model, in which we first use BERT to extract representations for an input sequence, and then the representations are fused with each layer of the encoder and decoder of the NMT model through attention mechanisms. We conduct experiments on supervised (including sentence-level and document-level translations), semi-supervised and unsupervised machine translation, and achieve state-of-the-art results on seven benchmark datasets. Our code is available at \\url{https://github.com/bert-nmt/bert-nmt}. <|reference_end|>", "<|reference_start|> UNITER: UNiversal Image-TExt Representation Learning: Joint image-text embedding is the bedrock for most Vision-and-Language (V+L) tasks, where multimodality inputs are simultaneously processed for joint visual and textual understanding. In this paper, we introduce UNITER, a UNiversal Image-TExt Representation, learned through large-scale pre-training over four image-text datasets (COCO, Visual Genome, Conceptual Captions, and SBU Captions), which can power heterogeneous downstream V+L tasks with joint multimodal embeddings. We design four pre-training tasks: Masked Language Modeling (MLM), Masked Region Modeling (MRM, with three variants), Image-Text Matching (ITM), and Word-Region Alignment (WRA). Different from previous work that applies joint random masking to both modalities, we use conditional masking on pre-training tasks (i.e., masked language/region modeling is conditioned on full observation of image/text). In addition to ITM for global image-text alignment, we also propose WRA via the use of Optimal Transport (OT) to explicitly encourage fine-grained alignment between words and image regions during pre-training. Comprehensive analysis shows that both conditional masking and OT-based WRA contribute to better pre-training. We also conduct a thorough ablation study to find an optimal combination of pre-training tasks. Extensive experiments show that UNITER achieves new state of the art across six V+L tasks (over nine datasets), including Visual Question Answering, Image-Text Retrieval, Referring Expression Comprehension, Visual Commonsense Reasoning, Visual Entailment, and NLVR$^2$. Code is available at https://github.com/ChenRocks/UNITER. <|reference_end|>", "<|reference_start|> Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks: Large-scale pre-training methods of learning cross-modal representations on image-text pairs are becoming popular for vision-language tasks. While existing methods simply concatenate image region features and text features as input to the model to be pre-trained and use self-attention to learn image-text semantic alignments in a brute force manner, in this paper, we propose a new learning method Oscar (Object-Semantics Aligned Pre-training), which uses object tags detected in images as anchor points to significantly ease the learning of alignments. Our method is motivated by the observation that the salient objects in an image can be accurately detected, and are often mentioned in the paired text. We pre-train an Oscar model on the public corpus of 6.5 million text-image pairs, and fine-tune it on downstream tasks, creating new state-of-the-arts on six well-established vision-language understanding and generation tasks. <|reference_end|>", "<|reference_start|> ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks: We present ViLBERT (short for Vision-and-Language BERT), a model for learning task-agnostic joint representations of image content and natural language. We extend the popular BERT architecture to a multi-modal two-stream model, pro-cessing both visual and textual inputs in separate streams that interact through co-attentional transformer layers. We pretrain our model through two proxy tasks on the large, automatically collected Conceptual Captions dataset and then transfer it to multiple established vision-and-language tasks -- visual question answering, visual commonsense reasoning, referring expressions, and caption-based image retrieval -- by making only minor additions to the base architecture. We observe significant improvements across tasks compared to existing task-specific models -- achieving state-of-the-art on all four tasks. Our work represents a shift away from learning groundings between vision and language only as part of task training and towards treating visual grounding as a pretrainable and transferable capability. <|reference_end|>" ]

[ 13, 28, 29, 30 ]

[ "<|reference_start|> Spherical Coverage Characterization of 5G Millimeter Wave User Equipment With 3GPP Specifications: Millimeter-wave (mmWave) frequency bands are promising candidate spectrum for the fifth-generation (5G) mobile communication system, but it requires high directional antenna systems to be applied to the base station and the user equipment (UE) for compensating the high path loss. Due to the randomness of mobile wireless channels, antenna systems in a mobile UE must own a large spherical coverage, which raises new challenges for the performance characterization of 5G mmWave UEs. In the latest specification of the Third-Generation Partnership Project (3GPP), the requirement on UE’s spherical coverage in mmWave frequencies is defined, which is evaluated with the cumulative distribution function of the effective isotropic radiated power. In this paper, the spherical coverage of mmWave UEs is characterized based on the specification of 3GPP, where the impact of device integration, antenna topologies, and user body blockage on the spherical coverage of UE will be analyzed with simulation and measurement results. <|reference_end|>", "<|reference_start|> Suboptimal Beam Search Algorithm and Codebook Design for Millimeter-Wave Communications: <|reference_end|>", "<|reference_start|> Hierarchical Codebook Design for Beamforming Training in Millimeter-Wave Communication: In millimeter-wave communication, large antenna arrays are required to achieve high power gain by steering towards each other with narrow beams, which poses the problem to efficiently search the best beam direction in the angle domain at both Tx and Rx sides. As the exhaustive search is time consuming, hierarchical search has been widely accepted to reduce the complexity, and its performance is highly dependent on the codebook design. In this paper, we propose two basic criteria for the hierarchical codebook design, and devise an efficient hierarchical codebook by jointly exploiting sub-array and deactivation (turning-off) antenna processing techniques, where closed-form expressions are provided to generate the codebook. Performance evaluations are conducted under different system and channel models. Results show superiority of the proposed codebook over the existing alternatives. <|reference_end|>", "<|reference_start|> Modular and High-Resolution Channel State Information and Beam Management for 5G New Radio: This article provides an overview of key features pertaining to CSI reporting and beam management for the 5G New Radio (NR) currently being standardized in 3GPP. For CSI reporting, the modular design framework and high-resolution spatial information feedback offer not only flexibility in a host of use cases and deployment scenarios, but also improved average user throughput over state-of-the-art 4G LTE. To accommodate cellular communications in the milimeter-wave regime where a combination of analog and digital beamforming is typically used at both a base station and user equipment, beam management procedures such as measurement, reporting, and recovery are introduced. The utility and joint usage of these two features are demonstrated along with some potential upgrades for the next phase of 5G NR. Introduction <|reference_end|>" ]

[ 3, 5, 6, 7 ]

[ "<|reference_start|> Quantitative signal detection using spontaneous ADR reporting: Quantitative methods are increasingly used to analyse spontaneous reports. We describe the core concepts behind the most common methods, the proportional reporting ratio (PRR), reporting odds ratio (ROR), information component (IC) and empirical Bayes geometric mean (EBGM). We discuss the role of Bayesian shrinkage in screening spontaneous reports, the importance of changes over time in screening the properties of the measures. Additionally we discuss three major areas of controversy and ongoing research: stratification, method evaluation and implementation. Finally we give some suggestions as to where emerging research is likely to lead. Copyright © 2009 John Wiley & Sons, Ltd. <|reference_end|>", "<|reference_start|> Perspectives on the Use of Data Mining in Pharmacovigilance: <|reference_end|>", "<|reference_start|> Disproportionality methods for pharmacovigilance in longitudinal observational databases: Data mining disproportionality methods (PRR, ROR, EBGM, IC, etc.) are commonly used to identify drug safety signals in spontaneous report system (SRS) databases. Newer data sources such as longitudinal observational databases (LOD) provide time-stamped patient-level information and overcome some of the SRS limitations such as an absence of the denominator, total number of patients who consume a drug, and limited temporal information. Application of the disproportionality methods to LODs has not been widely explored. The scale of the LOD data provides an interesting computational challenge. Larger health claims databases contain information on more than 50 million patients and each patient has records for up to 10 years. In this article we systematically explore the application of commonly used disproportionality methods to simulated and real LOD data. <|reference_end|>", "<|reference_start|> Temporal pattern discovery in longitudinal electronic patient records: <|reference_end|>" ]

[ 0, 4, 9, 11 ]

[ "<|reference_start|> We Are Anonymous: Inside the Hacker World of LulzSec, Anonymous, and the Global Cyber Insurgency: A thrilling, exclusive expose of the hacker collectives Anonymous and LulzSec. WE ARE ANONYMOUS is the first full account of how a loosely assembled group of hackers scattered across the globe formed a new kind of insurgency, seized headlines, and tortured the feds-and the ultimate betrayal that would eventually bring them down. Parmy Olson goes behind the headlines and into the world of Anonymous and LulzSec with unprecedented access, drawing upon hundreds of conversations with the hackers themselves, including exclusive interviews with all six core members of LulzSec. In late 2010, thousands of hacktivists joined a mass digital assault on the websites of VISA, MasterCard, and PayPal to protest their treatment of WikiLeaks. Other targets were wide ranging-the websites of corporations from Sony Entertainment and Fox to the Vatican and the Church of Scientology were hacked, defaced, and embarrassed-and the message was that no one was safe. Thousands of user accounts from pornography websites were released, exposing government employees and military personnel.Although some attacks were perpetrated by masses of users who were rallied on the message boards of 4Chan, many others were masterminded by a small, tight-knit group of hackers who formed a splinter group of Anonymous called LulzSec. The legend of Anonymous and LulzSec grew in the wake of each ambitious hack. But how were they penetrating intricate corporate security systems? Were they anarchists or activists? Teams or lone wolves? A cabal of skilled hackers or a disorganized bunch of kids?WE ARE ANONYMOUS delves deep into the internet's underbelly to tell the incredible full story of the global cyber insurgency movement, and its implications for the future of computer security. <|reference_end|>", "<|reference_start|> We Are Anonymous: Inside the Hacker World of LulzSec, Anonymous, and the Global Cyber Insurgency: A thrilling, exclusive expose of the hacker collectives Anonymous and LulzSec. WE ARE ANONYMOUS is the first full account of how a loosely assembled group of hackers scattered across the globe formed a new kind of insurgency, seized headlines, and tortured the feds-and the ultimate betrayal that would eventually bring them down. Parmy Olson goes behind the headlines and into the world of Anonymous and LulzSec with unprecedented access, drawing upon hundreds of conversations with the hackers themselves, including exclusive interviews with all six core members of LulzSec. In late 2010, thousands of hacktivists joined a mass digital assault on the websites of VISA, MasterCard, and PayPal to protest their treatment of WikiLeaks. Other targets were wide ranging-the websites of corporations from Sony Entertainment and Fox to the Vatican and the Church of Scientology were hacked, defaced, and embarrassed-and the message was that no one was safe. Thousands of user accounts from pornography websites were released, exposing government employees and military personnel.Although some attacks were perpetrated by masses of users who were rallied on the message boards of 4Chan, many others were masterminded by a small, tight-knit group of hackers who formed a splinter group of Anonymous called LulzSec. The legend of Anonymous and LulzSec grew in the wake of each ambitious hack. But how were they penetrating intricate corporate security systems? Were they anarchists or activists? Teams or lone wolves? A cabal of skilled hackers or a disorganized bunch of kids?WE ARE ANONYMOUS delves deep into the internet's underbelly to tell the incredible full story of the global cyber insurgency movement, and its implications for the future of computer security. <|reference_end|>", "<|reference_start|> We Are Anonymous: Inside the Hacker World of LulzSec, Anonymous, and the Global Cyber Insurgency: A thrilling, exclusive expose of the hacker collectives Anonymous and LulzSec. WE ARE ANONYMOUS is the first full account of how a loosely assembled group of hackers scattered across the globe formed a new kind of insurgency, seized headlines, and tortured the feds-and the ultimate betrayal that would eventually bring them down. Parmy Olson goes behind the headlines and into the world of Anonymous and LulzSec with unprecedented access, drawing upon hundreds of conversations with the hackers themselves, including exclusive interviews with all six core members of LulzSec. In late 2010, thousands of hacktivists joined a mass digital assault on the websites of VISA, MasterCard, and PayPal to protest their treatment of WikiLeaks. Other targets were wide ranging-the websites of corporations from Sony Entertainment and Fox to the Vatican and the Church of Scientology were hacked, defaced, and embarrassed-and the message was that no one was safe. Thousands of user accounts from pornography websites were released, exposing government employees and military personnel.Although some attacks were perpetrated by masses of users who were rallied on the message boards of 4Chan, many others were masterminded by a small, tight-knit group of hackers who formed a splinter group of Anonymous called LulzSec. The legend of Anonymous and LulzSec grew in the wake of each ambitious hack. But how were they penetrating intricate corporate security systems? Were they anarchists or activists? Teams or lone wolves? A cabal of skilled hackers or a disorganized bunch of kids?WE ARE ANONYMOUS delves deep into the internet's underbelly to tell the incredible full story of the global cyber insurgency movement, and its implications for the future of computer security. <|reference_end|>", "<|reference_start|> `{Anonymous: Not available. <|reference_end|>" ]

[ 10, 14, 20, 28 ]

[ "<|reference_start|> Misa: Modality-invariant and-specific representations for multimodal sentiment analysis: Multimodal Sentiment Analysis is an active area of research that leverages multimodal signals for affective understanding of user-generated videos. The predominant approach, addressing this task, has been to develop sophisticated fusion techniques. However, the heterogeneous nature of the signals creates distributional modality gaps that pose significant challenges. In this paper, we aim to learn effective modality representations to aid the process of fusion. We propose a novel framework, MISA, which projects each modality to two distinct subspaces. The first subspace is modality-invariant, where the representations across modalities learn their commonalities and reduce the modality gap. The second subspace is modality-specific, which is private to each modality and captures their characteristic features. These representations provide a holistic view of the multimodal data, which is used for fusion that leads to task predictions. Our experiments on popular sentiment analysis benchmarks, MOSI and MOSEI, demonstrate significant gains over state-of-the-art models. We also consider the task of Multimodal Humor Detection and experiment on the recently proposed UR_FUNNY dataset. Here too, our model fares better than strong baselines, establishing MISA as a useful multimodal framework. <|reference_end|>", "<|reference_start|> Modality translation-based multimodal sentiment analysis under uncertain missing modalities: <|reference_end|>", "<|reference_start|> A Unified Self-Distillation Framework for Multimodal Sentiment Analysis with Uncertain Missing Modalities: Multimodal Sentiment Analysis (MSA) has attracted widespread research attention recently. Most MSA studies are based on the assumption of modality completeness. However, many inevitable factors in real-world scenarios lead to uncertain missing modalities, which invalidate the fixed multimodal fusion approaches. To this end, we propose a Unified multimodal Missing modality self-Distillation Framework (UMDF) to handle the problem of uncertain missing modalities in MSA. Specifically, a unified self-distillation mechanism in UMDF drives a single network to automatically learn robust inherent representations from the consistent distribution of multimodal data. Moreover, we present a multi-grained crossmodal interaction module to deeply mine the complementary semantics among modalities through coarse- and fine-grained crossmodal attention. Eventually, a dynamic feature integration module is introduced to enhance the beneficial semantics in incomplete modalities while filtering the redundant information therein to obtain a refined and robust multimodal representation. Comprehensive experiments on three datasets demonstrate that our framework significantly improves MSA performance under both uncertain missing-modality and complete-modality testing conditions. <|reference_end|>", "<|reference_start|> Context De-confounded Emotion Recognition: Context-Aware Emotion Recognition (CAER) is a crucial and challenging task that aims to perceive the emotional states of the target person with contextual information. Recent approaches invariably focus on designing sophisticated architectures or mechanisms to extract seemingly meaningful representations from subjects and contexts. However, a long-overlooked issue is that a context bias in existing datasets leads to a significantly unbalanced distribution of emotional states among different context scenarios. Concretely, the harmful bias is a confounder that misleads existing models to learn spurious correlations based on conventional likelihood estimation, significantly limiting the models' performance. To tackle the issue, this paper provides a causality-based perspective to disentangle the models from the impact of such bias, and formulate the causalities among variables in the CAER task via a tailored causal graph. Then, we propose a Contextual Causal Intervention Module (CCIM) based on the backdoor adjustment to de-confound the confounder and exploit the true causal effect for model training. CCIM is plug-in and model-agnostic, which improves diverse state-of-the-art approaches by considerable margins. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our CCIM and the significance of causal insight. <|reference_end|>" ]

[ 11, 29, 31, 38 ]

[ "<|reference_start|> Pyramidal Attention for Saliency Detection: Salient object detection (SOD) extracts meaningful contents from an input image. RGB-based SOD methods lack the complementary depth clues; hence, providing limited performance for complex scenarios. Similarly, RGB-D models process RGB and depth inputs, but the depth data availability during testing may hinder the model's practical applicability. This paper exploits only RGB images, estimates depth from RGB, and leverages the intermediate depth features. We employ a pyramidal attention structure to extract multi-level convolutional-transformer features to process initial stage representations and further enhance the subsequent ones. At each stage, the backbone transformer model produces global receptive fields and computing in parallel to attain fine-grained global predictions refined by our residual convolutional attention decoder for optimal saliency prediction. We report significantly improved performance against 21 and 40 state-of-the-art SOD methods on eight RGB and RGB-D datasets, respectively. Consequently, we present a new SOD perspective of generating RGB-D SOD without acquiring depth data during training and testing and assist RGB methods with depth clues for improved performance. The code and trained models are available at https://github.com/tanveer-hussain/EfficientSOD2 <|reference_end|>", "<|reference_start|> Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer: Attention plays a critical role in human visual experience. Furthermore, it has recently been demonstrated that attention can also play an important role in the context of applying artificial neural networks to a variety of tasks from fields such as computer vision and NLP. In this work we show that, by properly defining attention for convolutional neural networks, we can actually use this type of information in order to significantly improve the performance of a student CNN network by forcing it to mimic the attention maps of a powerful teacher network. To that end, we propose several novel methods of transferring attention, showing consistent improvement across a variety of datasets and convolutional neural network architectures. Code and models for our experiments are available at https://github.com/szagoruyko/attention-transfer <|reference_end|>", "<|reference_start|> Mimicking very efficient network for object detection: Current CNN based object detectors need initialization from pre-trained ImageNet classification models, which are usually time-consuming. In this paper, we present a fully convolutional feature mimic framework to train very efficient CNN based detectors, which do not need ImageNet pre-training and achieve competitive performance as the large and slow models. We add supervision from high-level features of the large networks in training to help the small network better learn object representation. More specifically, we conduct a mimic method for the features sampled from the entire feature map and use a transform layer to map features from the small network onto the same dimension of the large network. In training the small network, we optimize the similarity between features sampled from the same region on the feature maps of both networks. Extensive experiments are conducted on pedestrian and common object detection tasks using VGG, Inception and ResNet. On both Caltech and Pascal VOC, we show that the modified 2.5&#xd7; accelerated Inception network achieves competitive performance as the full Inception Network. Our faster model runs at 80 FPS for a 1000&#xd7;1500 large input with only a minor degradation of performance on Caltech. <|reference_end|>", "<|reference_start|> KD-SCFNet: Towards More Accurate and Efficient Salient Object Detection via Knowledge Distillation: Most existing salient object detection (SOD) models are difficult to apply due to the complex and huge model structures. Although some lightweight models are proposed, the accuracy is barely satisfactory. In this paper, we design a novel semantics-guided contextual fusion network (SCFNet) that focuses on the interactive fusion of multi-level features for accurate and efficient salient object detection. Furthermore, we apply knowledge distillation to SOD task and provide a sizeable dataset KD-SOD80K. In detail, we transfer the rich knowledge from a seasoned teacher to the untrained SCFNet through unlabeled images, enabling SCFNet to learn a strong generalization ability to detect salient objects more accurately. The knowledge distillation based SCFNet (KDSCFNet) achieves comparable accuracy to the state-of-the-art heavyweight methods with less than 1M parameters and 174 FPS real-time detection speed. Extensive experiments demonstrate the robustness and effectiveness of the proposed distillation method and SOD framework. Code and data: https://github.com/zhangjinCV/KD-SCFNet. <|reference_end|>" ]

[ 3, 9, 10, 13 ]

[ "<|reference_start|> Global-local fusion network for face super-resolution: <|reference_end|>", "<|reference_start|> Towards Real-World Blind Face Restoration with Generative Facial Prior: Blind face restoration usually relies on facial priors, such as facial geometry prior or reference prior, to restore realistic and faithful details. However, very low-quality inputs cannot offer accurate geometric prior while high-quality references are inaccessible, limiting the applicability in real-world scenarios. In this work, we propose GFP-GAN that leverages rich and diverse priors encapsulated in a pretrained face GAN for blind face restoration. This Generative Facial Prior (GFP) is incorporated into the face restoration process via novel channel-split spatial feature transform layers, which allow our method to achieve a good balance of realness and fidelity. Thanks to the powerful generative facial prior and delicate designs, our GFP-GAN could jointly restore facial details and enhance colors with just a single forward pass, while GAN inversion methods require expensive image-specific optimization at inference. Extensive experiments show that our method achieves superior performance to prior art on both synthetic and real-world datasets. <|reference_end|>", "<|reference_start|> GAN Prior Embedded Network for Blind Face Restoration in the Wild: Blind face restoration (BFR) from severely degraded face images in the wild is a very challenging problem. Due to the high illness of the problem and the complex unknown degradation, directly training a deep neural network (DNN) usually cannot lead to acceptable results. Existing generative adversarial network (GAN) based methods can produce better results but tend to generate over-smoothed restorations. In this work, we propose a new method by first learning a GAN for high-quality face image generation and embedding it into a U-shaped DNN as a prior decoder, then fine-tuning the GAN prior embedded DNN with a set of synthesized low-quality face images. The GAN blocks are designed to ensure that the latent code and noise input to the GAN can be respectively generated from the deep and shallow features of the DNN, controlling the global face structure, local face details and background of the reconstructed image. The proposed GAN prior embedded network (GPEN) is easy-to-implement, and it can generate visually photo-realistic results. Our experiments demonstrated that the proposed GPEN achieves significantly superior results to state-of-the-art BFR methods both quantitatively and qualitatively, especially for the restoration of severely degraded face images in the wild. The source code and models can be found at https://github.com/yangxy/GPEN. <|reference_end|>", "<|reference_start|> Unpaired Image Super-Resolution using Pseudo-Supervision: In most studies on learning-based image super-resolution (SR), the paired training dataset is created by downscaling high-resolution (HR) images with a predetermined operation (e.g., bicubic). However, these methods fail to super-resolve real-world low-resolution (LR) images, for which the degradation process is much more complicated and unknown. In this paper, we propose an unpaired SR method using a generative adversarial network that does not require a paired/aligned training dataset. Our network consists of an unpaired kernel/noise correction network and a pseudo-paired SR network. The correction network removes noise and adjusts the kernel of the inputted LR image; then, the corrected clean LR image is upscaled by the SR network. In the training phase, the correction network also produces a pseudo-clean LR image from the inputted HR image, and then a mapping from the pseudo-clean LR image to the inputted HR image is learned by the SR network in a paired manner. Because our SR network is independent of the correction network, well-studied existing network architectures and pixel-wise loss functions can be integrated with the proposed framework. Experiments on diverse datasets show that the proposed method is superior to existing solutions to the unpaired SR problem. <|reference_end|>" ]

[ 2, 23, 24, 35 ]

[ "<|reference_start|> Computational models of emotion inference in Theory of Mind: A review and roadmap: Abstract Research on social cognition has fruitfully applied computational modeling approaches to explain how observers understand and reason about others’ mental states. By contrast, there has been less work on modeling observers’ understanding of emotional states. We propose an intuitive theory framework to studying affective cognition—how humans reason about emotions—and derive a taxonomy of inferences within affective cognition. Using this taxonomy, we review formal computational modeling work on such inferences, including causal reasoning about how others react to events, reasoning about unseen causes of emotions, reasoning with multiple cues, as well as reasoning from emotions to other mental states. In addition, we provide a roadmap for future research by charting out inferences—such as hypothetical and counterfactual reasoning about emotions—that are ripe for future computational modeling work. This framework proposes unifying these various types of reasoning as Bayesian inference within a common “intuitive Theory of Emotion.” Finally, we end with a discussion of important theoretical and methodological challenges that lie ahead in modeling affective cognition. <|reference_end|>", "<|reference_start|> The James-Lange theory of emotions: a critical examination and an alternative theory. By Walter B. Cannon, 1927.: <|reference_end|>", "<|reference_start|> Interaction between physiological and cognitive determinants of emotions: Experimental studies on Schachter's theory of emotions: <|reference_end|>", "<|reference_start|> Toward an attribution therapy: the reduction of fear through induced cognitive-emotional misattribution.: <|reference_end|>" ]

[ 6, 14, 28, 30 ]

[ "<|reference_start|> Soft-decision decoding of Reed-Muller codes: recursive lists: Recursive list decoding is considered for Reed-Muller (RM) codes. The algorithm repeatedly relegates itself to the shorter RM codes by recalculating the posterior probabilities of their symbols. Intermediate decodings are only performed when these recalculations reach the trivial RM codes. In turn, the updated lists of most plausible codewords are used in subsequent decodings. The algorithm is further improved by using permutation techniques on code positions and by eliminating the most error-prone information bits. Simulation results show that for all RM codes of length 256 and many subcodes of length 512, these algorithms approach maximum-likelihood (ML) performance within a margin of 0.1 dB. As a result, we present tight experimental bounds on ML performance for these codes <|reference_end|>", "<|reference_start|> Channel polarization: A method for constructing capacity-achieving codes for symmetric binary-input memoryless channels: A method is proposed, called channel polarization, to construct code sequences that achieve the symmetric capacity $I(W)$ of any given binary-input discrete memoryless channel (B-DMC) $W$. The symmetric capacity is the highest rate achievable subject to using the input letters of the channel with equal probability. Channel polarization refers to the fact that it is possible to synthesize, out of $N$ independent copies of a given B-DMC $W$, a second set of $N$ binary-input channels $\\{W_N^{(i)}:1\\le i\\le N\\}$ such that, as $N$ becomes large, the fraction of indices $i$ for which $I(W_N^{(i)})$ is near 1 approaches $I(W)$ and the fraction for which $I(W_N^{(i)})$ is near 0 approaches $1-I(W)$. The polarized channels $\\{W_N^{(i)}\\}$ are well-conditioned for channel coding: one need only send data at rate 1 through those with capacity near 1 and at rate 0 through the remaining. Codes constructed on the basis of this idea are called polar codes. The paper proves that, given any B-DMC $W$ with $I(W)>0$ and any target rate $R < I(W)$, there exists a sequence of polar codes $\\{{\\mathscr C}_n;n\\ge 1\\}$ such that ${\\mathscr C}_n$ has block-length $N=2^n$, rate $\\ge R$, and probability of block error under successive cancellation decoding bounded as $P_{e}(N,R) \\le \\bigoh(N^{-\\frac14})$ independently of the code rate. This performance is achievable by encoders and decoders with complexity $O(N\\log N)$ for each. <|reference_end|>", "<|reference_start|> Decoding Reed-Muller and Polar Codes by Successive Factor Graph Permutations: Reed-Muller (RM) and polar codes are a class of capacity-achieving channel coding schemes with the same factor graph representation. Low-complexity decoding algorithms fall short in providing a good error-correction performance for RM and polar codes. Using the symmetric group of RM and polar codes, the specific decoding algorithm can be carried out on multiple permutations of the factor graph to boost the error-correction performance. However, this approach results in high decoding complexity. In this paper, we first derive the total number of factor graph permutations on which the decoding can be performed. We further propose a successive permutation (SP) scheme which finds the permutations on the fly, thus the decoding always progresses on a single factor graph permutation. We show that SP can be used to improve the error-correction performance of RM and polar codes under successive-cancellation (SC) and SC list (SCL) decoding, while keeping the memory requirements of the decoders unaltered. Our results for RM and polar codes of length $128$ and rate $0.5$ show that when SP is used and at a target frame error rate of $10^{-4}$, up to $0.5$ dB and $0.1$ dB improvement can be achieved for RM and polar codes respectively. <|reference_end|>", "<|reference_start|> {Reed-Muller: 针对或-符合代数系统中缺失对称变量检测的有效方法等问题,提出了该代数系统基于或-符合运算Reed-Muller展开系数的十二类变量对称性检测算法.该算法通过分析逻辑函数关于变量χi、χj展开的子函数系数矩阵和或-符合运算Reed-Muller展开系数按变量Xi、χi组合分解系数矩阵的对应关系,揭示了任意两变量间各类对称性所满足的分解系数矩阵的约束条件,提出了各类逻辑变量的对称性检测步骤.应用结果表明,与传统方法相比,免去了从逻辑函数的CRM展开式变换为最小项展开式或RM展开式的变换域转换过程,也解决了在该域中图形方法检测的完备性问题,具有简单、直观、完备及适合计算机编程等优点. <|reference_end|>" ]

[ 4, 10, 18, 19 ]

[ "<|reference_start|> The State of Sparsity in Deep Neural Networks: We rigorously evaluate three state-of-the-art techniques for inducing sparsity in deep neural networks on two large-scale learning tasks: Transformer trained on WMT 2014 English-to-German, and ResNet-50 trained on ImageNet. Across thousands of experiments, we demonstrate that complex techniques (Molchanov et al., 2017; Louizos et al., 2017b) shown to yield high compression rates on smaller datasets perform inconsistently, and that simple magnitude pruning approaches achieve comparable or better results. Additionally, we replicate the experiments performed by (Frankle & Carbin, 2018) and (Liu et al., 2018) at scale and show that unstructured sparse architectures learned through pruning cannot be trained from scratch to the same test set performance as a model trained with joint sparsification and optimization. Together, these results highlight the need for large-scale benchmarks in the field of model compression. We open-source our code, top performing model checkpoints, and results of all hyperparameter configurations to establish rigorous baselines for future work on compression and sparsification. <|reference_end|>", "<|reference_start|> Exploring the granularity of sparsity in convolutional neural networks: Sparsity helps reducing the computation complexity of DNNs by skipping the multiplication with zeros. The granularity of sparsity affects the efficiency of hardware architecture and the prediction accuracy. In this paper we quantitatively measure the accuracy-sparsity relationship with different granularity. Coarse-grained sparsity brings more regular sparsity pattern, making it easier for hardware acceleration, and our experimental results show that coarsegrained sparsity have very small impact on the sparsity ratio given no loss of accuracy. Moreover, due to the index saving effect, coarse-grained sparsity is able to obtain similar or even better compression rates than fine-grained sparsity at the same accuracy threshold. Our analysis, which is based on the framework of a recent sparse convolutional neural network (SCNN) accelerator, further demonstrates that it saves 30% – 35% of memory references compared with fine-grained sparsity. <|reference_end|>", "<|reference_start|> NISP: Pruning Networks using Neuron Importance Score Propagation: To reduce the significant redundancy in deep Convolutional Neural Networks (CNNs), most existing methods prune neurons by only considering statistics of an individual layer or two consecutive layers (e.g., prune one layer to minimize the reconstruction error of the next layer), ignoring the effect of error propagation in deep networks. In contrast, we argue that it is essential to prune neurons in the entire neuron network jointly based on a unified goal: minimizing the reconstruction error of important responses in the \"final response layer\" (FRL), which is the second-to-last layer before classification, for a pruned network to retrain its predictive power. Specifically, we apply feature ranking techniques to measure the importance of each neuron in the FRL, and formulate network pruning as a binary integer optimization problem and derive a closed-form solution to it for pruning neurons in earlier layers. Based on our theoretical analysis, we propose the Neuron Importance Score Propagation (NISP) algorithm to propagate the importance scores of final responses to every neuron in the network. The CNN is pruned by removing neurons with least importance, and then fine-tuned to retain its predictive power. NISP is evaluated on several datasets with multiple CNN models and demonstrated to achieve significant acceleration and compression with negligible accuracy loss. <|reference_end|>", "<|reference_start|> Picking Winning Tickets Before Training by Preserving Gradient Flow: Overparameterization has been shown to benefit both the optimization and generalization of neural networks, but large networks are resource hungry at both training and test time. Network pruning can reduce test-time resource requirements, but is typically applied to trained networks and therefore cannot avoid the expensive training process. We aim to prune networks at initialization, thereby saving resources at training time as well. Specifically, we argue that efficient training requires preserving the gradient flow through the network. This leads to a simple but effective pruning criterion we term Gradient Signal Preservation (GraSP). We empirically investigate the effectiveness of the proposed method with extensive experiments on CIFAR-10, CIFAR-100, Tiny-ImageNet and ImageNet, using VGGNet and ResNet architectures. Our method can prune 80% of the weights of a VGG-16 network on ImageNet at initialization, with only a 1.6% drop in top-1 accuracy. Moreover, our method achieves significantly better performance than the baseline at extreme sparsity levels. <|reference_end|>" ]

[ 3, 19, 32, 37 ]

[ "<|reference_start|> Particle flow SMC-PHD filter for audio-visualmulti-speaker tracking. Proc. 13th International Conference on Latent Variable Analysis and Signal Separation(LVA/ICA 2017), Grenoble, France, February 21-23, 2017.: Sequential Monte Carlo probability hypothesis density (SMC- \nPHD) filtering has been recently exploited for audio-visual (AV) based \ntracking of multiple speakers, where audio data are used to inform the \nparticle distribution and propagation in the visual SMC-PHD filter. However, the performance of the AV-SMC-PHD filter can be affected by the \nmismatch between the proposal and the posterior distribution. In this paper, we present a new method to improve the particle distribution where \naudio information (i.e. DOA angles derived from microphone array measurements) is used to detect new born particles and visual information \n(i.e. histograms) is used to modify the particles with particle \nflow (PF). \nUsing particle \nflow has the benefit of migrating particles smoothly from \nthe prior to the posterior distribution. We compare the proposed algorithm with the baseline AV-SMC-PHD algorithm using experiments on \nthe AV16.3 dataset with multi-speaker sequences. <|reference_end|>", "<|reference_start|> An epileptic seizures diagnosis system using feature selection, fuzzy temporal naive Bayes and T-CNN: <|reference_end|>", "<|reference_start|> Multichannel signal processing with deep neural networks for automatic speech recognition: Multichannel automatic speech recognition (ASR) systems commonly separate speech enhancement, including localization, beamforming, and postfiltering, from acoustic modeling. In this paper, we perform multichannel enhancement jointly with acoustic modeling in a deep neural network framework. Inspired by beamforming, which leverages differences in the fine time structure of the signal at different microphones to filter energy arriving from different directions, we explore modeling the raw time-domain waveform directly. We introduce a neural network architecture, which performs multichannel filtering in the first layer of the network, and show that this network learns to be robust to varying target speaker direction of arrival, performing as well as a model that is given oracle knowledge of the true target speaker direction. Next, we show how performance can be improved by factoring the first layer to separate the multichannel spatial filtering operation from a single channel filterbank which computes a frequency decomposition. We also introduce an adaptive variant, which updates the spatial filter coefficients at each time frame based on the previous inputs. Finally, we demonstrate that these approaches can be implemented more efficiently in the frequency domain. Overall, we find that such multichannel neural networks give a relative word error rate improvement of more than 5% compared to a traditional beamforming-based multichannel ASR system and more than 10% compared to a single channel waveform model. <|reference_end|>", "<|reference_start|> Microphone array speech enhancement based on optimized IMCRA: Microphone array speech enhancement algorithm uses temporal and spatial informa- tion to improve the performance of speech noise reduction significantly. By combining noise estimation algorithm with microphone array speech enhancement, the accuracy of noise estimation is improved, and\n the computation is reduced. In traditional noise es- timation algorithms, the noise power spectrum is not updated in the presence of speech, which leads to the delay and deviation of noise spectrum estimation. An optimized im- proved minimum controlled recursion average speech enhancement\n algorithm, based on a microphone matrix is proposed in this paper. It consists of three parts. The first part is the preprocessing, divided into two branches: the upper branch enhances the speech signal, and the lower branch gets the noise. The second part is the optimized improved minimum\n controlled recursive averaging. The noise power spectrum is updated not only in the non-speech segments but also in the speech segments. Fi- nally, according to the estimated noise power spectrum, the minimum mean-square error log-spectral amplitude algorithm is used to enhance speech. Testing\n data are from TIMIT and Noisex-92 databases. Short-time objective intelligibility and seg- mental signal-to-noise ratio are chosen as evaluation metrics. Experimental results show that the proposed speech enhancement algorithm can improve the segmental signal-to-noise ratio and short-time\n objective intelligibility for various noise types at different signal-to-noise ratio levels. <|reference_end|>" ]

[ 1, 8, 13, 19 ]

[ "<|reference_start|> BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models: Existing neural information retrieval (IR) models have often been studied in homogeneous and narrow settings, which has considerably limited insights into their out-of-distribution (OOD) generalization capabilities. To address this, and to facilitate researchers to broadly evaluate the effectiveness of their models, we introduce Benchmarking-IR (BEIR), a robust and heterogeneous evaluation benchmark for information retrieval. We leverage a careful selection of 18 publicly available datasets from diverse text retrieval tasks and domains and evaluate 10 state-of-the-art retrieval systems including lexical, sparse, dense, late-interaction and re-ranking architectures on the BEIR benchmark. Our results show BM25 is a robust baseline and re-ranking and late-interaction-based models on average achieve the best zero-shot performances, however, at high computational costs. In contrast, dense and sparse-retrieval models are computationally more efficient but often underperform other approaches, highlighting the considerable room for improvement in their generalization capabilities. We hope this framework allows us to better evaluate and understand existing retrieval systems, and contributes to accelerating progress towards better robust and generalizable systems in the future. BEIR is publicly available at https://github.com/UKPLab/beir. <|reference_end|>", "<|reference_start|> Semi-Siamese Bi-encoder Neural Ranking Model Using Lightweight Fine-Tuning: A BERT-based Neural Ranking Model (NRM) can be either a crossencoder or a bi-encoder. Between the two, bi-encoder is highly efficient because all the documents can be pre-processed before the actual query time. In this work, we show two approaches for improving the performance of BERT-based bi-encoders. The first approach is to replace the full fine-tuning step with a lightweight fine-tuning. We examine lightweight fine-tuning methods that are adapter-based, prompt-based, and hybrid of the two. The second approach is to develop semi-Siamese models where queries and documents are handled with a limited amount of difference. The limited difference is realized by learning two lightweight fine-tuning modules, where the main language model of BERT is kept common for both query and document. We provide extensive experiment results for monoBERT, TwinBERT, and ColBERT where three performance metrics are evaluated over Robust04, ClueWeb09b, and MS-MARCO datasets. The results confirm that both lightweight fine-tuning and semi-Siamese are considerably helpful for improving BERT-based bi-encoders. In fact, lightweight fine-tuning is helpful for crossencoder, too <|reference_end|>", "<|reference_start|> {TwinBERT: Distilling Knowledge to Twin-Structured Compressed BERT Models for Large-Scale Retrieval: Pre-trained language models have achieved great success in a wide variety of natural language processing (NLP) tasks, while the superior performance comes with high demand in computational resources, which hinders the application in low-latency information retrieval (IR) systems. To address the problem, we present TwinBERT model, which has two improvements: 1) represent query and document separately using twin-structured encoders and 2) each encoder is a highly compressed BERT-like model with less than one third of the parameters. The former allows document embeddings to be pre-computed offline and cached in memory, which is different from BERT, where the two input sentences are concatenated and encoded together. The change saves large amount of computation time, however, it is still not sufficient for real-time retrieval considering the complexity of BERT model itself. To further reduce computational cost, a compressed multi-layer transformer encoder is proposed with special training strategies as a substitution of the original complex BERT encoder. Lastly, two versions of TwinBERT are developed to combine the query and keyword embeddings for retrieval and relevance tasks correspondingly. Both of them have met the real-time latency requirement and achieve close or on-par performance to BERT-Base model. The models were trained following the teacher-student framework and evaluated with data from one of the major search engines. Experimental results showed that the inference time was significantly reduced and was for the first time controlled within 20ms on CPUs while at the same time the performance gain from fine-tuned BERT-Base model was mostly retained. Integration of the models in production systems also demonstrated remarkable improvements on relevance metrics with negligible influence on latency. The models were released in 2019 with significant production impacts. <|reference_end|>", "<|reference_start|> Dense Passage Retrieval for Open-Domain Question Answering: Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dual-encoder framework. When evaluated on a wide range of open-domain QA datasets, our dense retriever outperforms a strong Lucene-BM25 system largely by 9%-19% absolute in terms of top-20 passage retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA benchmarks. <|reference_end|>" ]

[ 8, 23, 30, 34 ]

[ "<|reference_start|> Very Deep Convolutional Networks for Large-Scale Image Recognition: In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision. <|reference_end|>", "<|reference_start|> Identity Mappings in Deep Residual Networks: Deep residual networks have emerged as a family of extremely deep architectures showing compelling accuracy and nice convergence behaviors. In this paper, we analyze the propagation formulations behind the residual building blocks, which suggest that the forward and backward signals can be directly propagated from one block to any other block, when using identity mappings as the skip connections and after-addition activation. A series of ablation experiments support the importance of these identity mappings. This motivates us to propose a new residual unit, which makes training easier and improves generalization. We report improved results using a 1001-layer ResNet on CIFAR-10 (4.62% error) and CIFAR-100, and a 200-layer ResNet on ImageNet. Code is available at: https://github.com/KaimingHe/resnet-1k-layers <|reference_end|>", "<|reference_start|> DESTINY: A Comprehensive Tool with 3D and Multi-Level Cell Memory Modeling Capability: To enable the design of large capacity memory structures, novel memory technologies such as non-volatile memory (NVM) and novel fabrication approaches, e.g., 3D stacking and multi-level cell (MLC) design have been explored. The existing modeling tools, however, cover only few memory technologies, technology nodes and fabrication approaches. We present DESTINY, a tool for modeling 2D/3D memories designed using SRAM, resistive RAM (ReRAM), spin transfer torque RAM (STT-RAM), phase change RAM (PCM) and embedded DRAM (eDRAM) and 2D memories designed using spin orbit torque RAM (SOT-RAM), domain wall memory (DWM) and Flash memory. In addition to single-level cell (SLC) designs for all these memories, DESTINY also supports modeling MLC designs for NVMs. We have extensively validated DESTINY against commercial and research prototypes of these memories. DESTINY is very useful for performing design-space exploration across several dimensions, such as optimizing for a target (e.g. latency, area or energy-delay product) for a given memory technology, choosing the suitable memory technology or fabrication method (i.e. 2D v/s 3D) for a given optimization target, etc. We believe that DESTINY will boost studies of next-generation memory architectures used in systems ranging from mobile devices to extreme-scale supercomputers. <|reference_end|>", "<|reference_start|> Not All Ops Are Created Equal!: Efficient and compact neural network models are essential for enabling the deployment on mobile and embedded devices. In this work, we point out that typical design metrics for gauging the efficiency of neural network architectures -- total number of operations and parameters -- are not sufficient. These metrics may not accurately correlate with the actual deployment metrics such as energy and memory footprint. We show that throughput and energy varies by up to 5X across different neural network operation types on an off-the-shelf Arm Cortex-M7 microcontroller. Furthermore, we show that the memory required for activation data also need to be considered, apart from the model parameters, for network architecture exploration studies. <|reference_end|>" ]

[ 1, 5, 13, 14 ]

[ "<|reference_start|> Faculty Opinions recommendation of Familial hypercholesterolaemia is underdiagnosed and undertreated in the general population: guidance for clinicians to prevent coronary heart disease: consensus statement of the European Atherosclerosis Society.: <|reference_end|>", "<|reference_start|> Global perspective of familial hypercholesterolaemia: a cross-sectional study from the EAS Familial Hypercholesterolaemia Studies Collaboration (FHSC): Background The European <|reference_end|>", "<|reference_start|> Clinical Genetic Testing for Familial Hypercholesterolemia: JACC Scientific Expert Panel.: <|reference_end|>", "<|reference_start|> A Machine Learning Model to Aid Detection of Familial Hypercholesterolemia: <|reference_end|>" ]

[ 4, 5, 6, 26 ]

[ "<|reference_start|> Hierarchical retinal blood vessel segmentation based on feature and ensemble learning: <|reference_end|>", "<|reference_start|> Retinal vessel segmentation using multi-scale textons derived from keypoints: <|reference_end|>", "<|reference_start|> Obtaining optic disc center and pixel region by automatic thresholding methods on morphologically processed fundus images: <|reference_end|>", "<|reference_start|> Automated Detection of Diabetic Retinopathy using Deep Learning: Diabetic retinopathy is a leading cause of blindness among working-age adults. Early detection of this condition is critical for good prognosis. In this paper, we demonstrate the use of convolutional neural networks (CNNs) on color fundus images for the recognition task of diabetic retinopathy staging. Our network models achieved test metric performance comparable to baseline literature results, with validation sensitivity of 95%. We additionally explored multinomial classification models, and demonstrate that errors primarily occur in the misclassification of mild disease as normal due to the CNNs inability to detect subtle disease features. We discovered that preprocessing with contrast limited adaptive histogram equalization and ensuring dataset fidelity by expert verification of class labels improves recognition of subtle features. Transfer learning on pretrained GoogLeNet and AlexNet models from ImageNet improved peak test set accuracies to 74.5%, 68.8%, and 57.2% on 2-ary, 3-ary, and 4-ary classification models, respectively. <|reference_end|>" ]

[ 0, 1, 7, 11 ]

[ "<|reference_start|> Accountability in Algorithmic Decision Making: Every fiscal quarter automated writing algorithms churn out thousands of corporate earnings articles for the AP (Associated Press) based on little more than structured data. Companies such as Automated Insights, which produces the articles for AP, and Narrative Science can now write straight news articles in almost any domain that has clean and well-structured data: finance, sure, but also sports, weather, and education, among others. The articles aren’t cardboard either; they have variability, tone, and style, and in some cases readers even have difficulty distinguishing the machine-produced articles from human-written ones. <|reference_end|>", "<|reference_start|> It’s Time to Do Something: Mitigating the Negative Impacts of Computing Through a Change to the Peer Review Process: The computing research community needs to work much harder to address the downsides of our innovations. Between the erosion of privacy, threats to democracy, and automation's effect on employment (among many other issues), we can no longer simply assume that our research will have a net positive impact on the world. While bending the arc of computing innovation towards societal benefit may at first seem intractable, we believe we can achieve substantial progress with a straightforward step: making a small change to the peer review process. As we explain below, we hypothesize that our recommended change will force computing researchers to more deeply consider the negative impacts of their work. We also expect that this change will incentivize research and policy that alleviates computing's negative impacts. <|reference_end|>", "<|reference_start|> Principles of {{Biomedical Ethics: In this presentation, I will discuss the principles of biomedical and Islamic medical ethics and an interfaith perspective on end-of-life issues. I will also discuss three cases to exemplify some of the conflicts in ethical decision-making. <|reference_end|>", "<|reference_start|> Science, Policy, and the Value-Free Ideal: The role of science in policymaking has gained unprecedented stature in the United States, raising questions about the place of science and scientific expertise in the democratic process. Some scientists have been given considerable epistemic authority in shaping policy on issues of great moral and cultural significance, and the politicizing of these issues has become highly contentious. Since World War II, most philosophers of science have purported the concept that science should be 'value-free'. In \"Science, Policy and the Value-Free Ideal\", Heather E. Douglas argues that such an ideal is neither adequate nor desirable for science. She contends that the moral responsibilities of scientists require the consideration of values even at the heart of science. She lobbies for a new ideal in which values serve an essential function throughout scientific inquiry, but where the role values play is constrained at key points, thus protecting the integrity and objectivity of science. In this vein, Douglas outlines a system for the application of values to guide scientists through points of uncertainty fraught with moral valence. Following a philosophical analysis of the historical background of science advising and the value-free ideal, Douglas defines how values should - and should not - function in science. She discusses the distinctive direct and indirect roles for values in reasoning, and outlines seven senses of objectivity, showing how each can be employed to determine the reliability of scientific claims. Douglas then uses these philosophical insights to clarify the distinction between junk science and sound science to be used in policymaking. In conclusion, she calls for greater openness on the values utilized in policymaking, and more public participation in the policymaking process, by suggesting various models for effective use of both the public and experts in key risk assessments. <|reference_end|>" ]

[ 10, 11, 21, 24 ]

[ "<|reference_start|> Smaller extended formulations for spanning tree polytopes in minor-closed classes and beyond: Let $G$ be a connected $n$-vertex graph in a proper minor-closed class $\\mathcal G$. We prove that the extension complexity of the spanning tree polytope of $G$ is $O(n^{3/2})$. This improves on the $O(n^2)$ bounds following from the work of Wong (1980) and Martin (1991). It also extends a result of Fiorini, Huynh, Joret, and Pashkovich (2017), who obtained a $O(n^{3/2})$ bound for graphs embedded in a fixed surface. Our proof works more generally for all graph classes admitting strongly sublinear balanced separators: We prove that for every constant $\\beta$ with $0<\\beta<1$, if $\\mathcal G$ is a graph class closed under induced subgraphs such that all $n$-vertex graphs in $\\mathcal G$ have balanced separators of size $O(n^\\beta)$, then the extension complexity of the spanning tree polytope of every connected $n$-vertex graph in $\\mathcal{G}$ is $O(n^{1+\\beta})$. We in fact give two proofs of this result, one is a direct construction of the extended formulation, the other is via communication protocols. Using the latter approach we also give a short proof of the $O(n)$ bound for planar graphs due to Williams (2002). <|reference_end|>", "<|reference_start|> Exponential Lower Bounds for Polytopes in Combinatorial Optimization: We solve a 20-year old problem posed by Yannakakis and prove that there exists no polynomial-size linear program (LP) whose associated polytope projects to the traveling salesman polytope, even if the LP is not required to be symmetric. Moreover, we prove that this holds also for the cut polytope and the stable set polytope. These results were discovered through a new connection that we make between one-way quantum communication protocols and semidefinite programming reformulations of LPs. <|reference_end|>", "<|reference_start|> The matching polytope has exponential extension complexity: A popular method in combinatorial optimization is to express polytopes P, which may potentially have exponentially many facets, as solutions of linear programs that use few extra variables to reduce the number of constraints down to a polynomial. After two decades of standstill, recent years have brought amazing progress in showing lower bounds for the so called extension complexity, which for a polytope P denotes the smallest number of inequalities necessary to describe a higher dimensional polytope Q that can be linearly projected on P. However, the central question in this field remained wide open: can the perfect matching polytope be written as an LP with polynomially many constraints? We answer this question negatively. In fact, the extension complexity of the perfect matching polytope in a complete n-node graph is 2^Omega(n). By a known reduction this also improves the lower bound on the extension complexity for the TSP polytope from 2^Omega(n^1/2) to 2^Omega(n). <|reference_end|>", "<|reference_start|> Extended formulations, nonnegative factorizations, and randomized communication protocols: <|reference_end|>" ]

[ 9, 11, 12, 22 ]

[ "<|reference_start|> Sur la topologie des écoulements stationnaires des fluides parfaits: <|reference_end|>", "<|reference_start|> Sharp uniform in time error estimate on a stochastic structure-preserving Lagrangian method and computation of effective diffusivity in 3D chaotic flows: In this paper, we study the problem of computing the effective diffusivity for a particle moving in chaotic flows. Instead of solving a convection-diffusion type cell problem in the Eulerian formulation (arising from homogenization theory for the Fokker-Planck equation), we compute the motion of particles in the Lagrangian formulation, which is modeled by stochastic differential equations (SDEs). A robust numerical integrator based on a splitting method was proposed to solve the SDEs and a rigorous error analysis for the numerical integrator was provided using the backward error analysis (BEA) technique [29]. However, the upper bound in the error estimate is not sharp. In this paper, we propose a completely new and sharp error analysis for the numerical integrator that allows us to get rid of the exponential growth factor in our previous error estimate. Our new error analysis is based on a probabilistic approach, which interprets the solution process generated by our numerical integrator as a Markov process. By exploring the ergodicity of the solution process, we prove the convergence analysis of our method in computing the effective diffusivity over infinite time. We present numerical results to demonstrate the accuracy and efficiency of the proposed method in computing effective diffusivity for several chaotic flows, especially the Arnold-Beltrami-Childress (ABC) flow and the Kolmogorov flow in three-dimensional space. <|reference_end|>", "<|reference_start|> Semi-Lagrangian Schemes for Hamilton-Jacobi Equations, Discrete Representation Formulae and Godunov Methods: We study a class of semi-Lagrangian schemes which can be interpreted as a discrete version of the Hopf-Lax-Oleinik representation formula for the exact viscosity solution of first order evolutive Hamilton-Jacobi equations. That interpretation shows that the scheme is potentially accurate to any prescribed order. We discuss how the method can be implemented for convex and coercive Hamiltonians with a particular structure and how this method can be coupled with a discrete Legendre trasform. We also show that in one dimension, the first-order semi-Lagrangian scheme coincides with the integration of the Godunov scheme for the corresponding conservation laws. Several test illustrate the main features of semi-Lagrangian schemes for evolutive Hamilton-Jacobi equations. <|reference_end|>", "<|reference_start|> High order weighted essentially nonoscillatory schemes for\nconvection dominated problems: High order accurate weighted essentially nonoscillatory (WENO) schemes are relatively new but have gained rapid popularity in numerical solutions of hyperbolic partial differential equations (PDEs) and other convection dominated problems. The main advantage of such schemes is their capability to achieve arbitrarily high order formal accuracy in smooth regions while maintaining stable, nonoscillatory, and sharp discontinuity transitions. The schemes are thus especially suitable for problems containing both strong discontinuities and complex smooth solution features. WENO schemes are robust and do not require the user to tune parameters. At the heart of the WENO schemes is actually an approximation procedure not directly related to PDEs, hence the WENO procedure can also be used in many non-PDE applications. In this paper we review the history and basic formulation of WENO schemes, outline the main ideas in using WENO schemes to solve various hyperbolic PDEs and other convection dominated problems, and present a collection of applications in areas including computational fluid dynamics, computational astronomy and astrophysics, semiconductor device simulation, traffic flow models, computational biology, and some non-PDE applications. Finally, we mention a few topics concerning WENO schemes that are currently under investigation. <|reference_end|>" ]

[ 5, 14, 24, 29 ]

[ "<|reference_start|> On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation: Modern recommender systems operate in a fully server-based fashion. To cater to millions of users, the frequent model maintaining and the high-speed processing for concurrent user requests are required, which comes at the cost of a huge carbon footprint. Meanwhile, users need to upload their behavior data even including the immediate environmental context to the server, raising the public concern about privacy. On-device recommender systems circumvent these two issues with cost-conscious settings and local inference. However, due to the limited memory and computing resources, on-device recommender systems are confronted with two fundamental challenges: (1) how to reduce the size of regular models to fit edge devices? (2) how to retain the original capacity? Previous research mostly adopts tensor decomposition techniques to compress the regular recommendation model with limited compression ratio so as to avoid drastic performance degradation. In this paper, we explore ultra-compact models for next-item recommendation, by loosing the constraint of dimensionality consistency in tensor decomposition. Meanwhile, to compensate for the capacity loss caused by compression, we develop a self-supervised knowledge distillation framework which enables the compressed model (student) to distill the essential information lying in the raw data, and improves the long-tail item recommendation through an embedding-recombination strategy with the original model (teacher). The extensive experiments on two benchmarks demonstrate that, with 30x model size reduction, the compressed model almost comes with no accuracy loss, and even outperforms its uncompressed counterpart in most cases. <|reference_end|>", "<|reference_start|> On the Difficulty of Evaluating Baselines: A Study on Recommender Systems: Numerical evaluations with comparisons to baselines play a central role when judging research in recommender systems. In this paper, we show that running baselines properly is difficult. We demonstrate this issue on two extensively studied datasets. First, we show that results for baselines that have been used in numerous publications over the past five years for the Movielens 10M benchmark are suboptimal. With a careful setup of a vanilla matrix factorization baseline, we are not only able to improve upon the reported results for this baseline but even outperform the reported results of any newly proposed method. Secondly, we recap the tremendous effort that was required by the community to obtain high quality results for simple methods on the Netflix Prize. Our results indicate that empirical findings in research papers are questionable unless they were obtained on standardized benchmarks where baselines have been tuned extensively by the research community. <|reference_end|>", "<|reference_start|> On-Device Recommender Systems: A Comprehensive Survey: Recommender systems have been widely deployed in various real-world applications to help users identify content of interest from massive amounts of information. Traditional recommender systems work by collecting user-item interaction data in a cloud-based data center and training a centralized model to perform the recommendation service. However, such cloud-based recommender systems (CloudRSs) inevitably suffer from excessive resource consumption, response latency, as well as privacy and security risks concerning both data and models. Recently, driven by the advances in storage, communication, and computation capabilities of edge devices, there has been a shift of focus from CloudRSs to on-device recommender systems (DeviceRSs), which leverage the capabilities of edge devices to minimize centralized data storage requirements, reduce the response latency caused by communication overheads, and enhance user privacy and security by localizing data processing and model training. Despite the rapid rise of DeviceRSs, there is a clear absence of timely literature reviews that systematically introduce, categorize and contrast these methods. To bridge this gap, we aim to provide a comprehensive survey of DeviceRSs, covering three main aspects: (1) the deployment and inference of DeviceRSs (2) the training and update of DeviceRSs (3) the security and privacy of DeviceRSs. Furthermore, we provide a fine-grained and systematic taxonomy of the methods involved in each aspect, followed by a discussion regarding challenges and future research directions. This is the first comprehensive survey on DeviceRSs that covers a spectrum of tasks to fit various needs. We believe this survey will help readers effectively grasp the current research status in this field, equip them with relevant technical foundations, and stimulate new research ideas for developing DeviceRSs. <|reference_end|>", "<|reference_start|> Experimental Analysis of Large-scale Learnable Vector Storage Compression: Learnable embedding vector is one of the most important applications in machine learning, and is widely used in various database-related domains. However, the high dimensionality of sparse data in recommendation tasks and the huge volume of corpus in retrieval-related tasks lead to a large memory consumption of the embedding table, which poses a great challenge to the training and deployment of models. Recent research has proposed various methods to compress the embeddings at the cost of a slight decrease in model quality or the introduction of other overheads. Nevertheless, the relative performance of these methods remains unclear. Existing experimental comparisons only cover a subset of these methods and focus on limited metrics. In this paper, we perform a comprehensive comparative analysis and experimental evaluation of embedding compression. We introduce a new taxonomy that categorizes these techniques based on their characteristics and methodologies, and further develop a modular benchmarking framework that integrates 14 representative methods. Under a uniform test environment, our benchmark fairly evaluates each approach, presents their strengths and weaknesses under different memory budgets, and recommends the best method based on the use case. In addition to providing useful guidelines, our study also uncovers the limitations of current methods and suggests potential directions for future research. <|reference_end|>" ]

[ 7, 26, 35, 36 ]

[ "<|reference_start|> Subgradient Methods: 3 Convergence proof 4 3.1 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 3.2 Some basic inequalities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 3.3 A bound on the suboptimality bound . . . . . . . . . . . . . . . . . . . . . . 7 3.4 A stopping criterion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.5 Numerical example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 <|reference_end|>", "<|reference_start|> Subgradient Methods: 3 Convergence proof 4 3.1 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 3.2 Some basic inequalities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 3.3 A bound on the suboptimality bound . . . . . . . . . . . . . . . . . . . . . . 7 3.4 A stopping criterion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.5 Numerical example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 <|reference_end|>", "<|reference_start|> Network Topology and Communication-Computation Tradeoffs in Decentralized Optimization: In decentralized optimization, nodes cooperate to minimize an overall objective function that is the sum (or average) of per-node private objective functions. Algorithms interleave local computations with communication among all or a subset of the nodes. Motivated by a variety of applications---distributed estimation in sensor networks, fitting models to massive data sets, and distributed control of multi-robot systems, to name a few---significant advances have been made towards the development of robust, practical algorithms with theoretical performance guarantees. This paper presents an overview of recent work in this area. In general, rates of convergence depend not only on the number of nodes involved and the desired level of accuracy, but also on the structure and nature of the network over which nodes communicate (e.g., whether links are directed or undirected, static or time-varying). We survey the state-of-the-art algorithms and their analyses tailored to these different scenarios, highlighting the role of the network topology. <|reference_end|>", "<|reference_start|> Optimal Algorithms for Non-Smooth Distributed Optimization in Networks: In this work, we consider the distributed optimization of non-smooth convex functions using a network of computing units. We investigate this problem under two regularity assumptions: (1) the Lipschitz continuity of the global objective function, and (2) the Lipschitz continuity of local individual functions. Under the local regularity assumption, we provide the first optimal first-order decentralized algorithm called multi-step primal-dual (MSPD) and its corresponding optimal convergence rate. A notable aspect of this result is that, for non-smooth functions, while the dominant term of the error is in $O(1/\\sqrt{t})$, the structure of the communication network only impacts a second-order term in $O(1/t)$, where $t$ is time. In other words, the error due to limits in communication resources decreases at a fast rate even in the case of non-strongly-convex objective functions. Under the global regularity assumption, we provide a simple yet efficient algorithm called distributed randomized smoothing (DRS) based on a local smoothing of the objective function, and show that DRS is within a $d^{1/4}$ multiplicative factor of the optimal convergence rate, where $d$ is the underlying dimension. <|reference_end|>" ]

[ 5, 6, 11, 14 ]

[ "<|reference_start|> Optimal control of execution costs: <|reference_end|>", "<|reference_start|> Reinforcement learning for optimized trade execution: We present the first large-scale empirical application of reinforcement learning to the important problem of optimized trade execution in modern financial markets. Our experiments are based on 1.5 years of millisecond time-scale limit order data from NASDAQ, and demonstrate the promise of reinforcement learning methods to market microstructure problems. Our learning algorithm introduces and exploits a natural \"low-impact\" factorization of the state space. <|reference_end|>", "<|reference_start|> An End-to-End Optimal Trade Execution Framework based on Proximal Policy Optimization: In this article, we propose an end-to-end adaptive framework for optimal trade execution based on Proximal Policy Optimization (PPO). We use two methods to account for the time dependencies in the market data based on two different neural network architecture: 1) Long short-term memory (LSTM) networks, 2) Fully-connected networks (FCN) by stacking the most recent limit orderbook (LOB) information as model inputs. The proposed framework can make trade execution decisions based on level-2 limit order book (LOB) information such as bid/ask prices and volumes directly without manually designed attributes as in previous research. Furthermore, we use a sparse reward function, which gives the agent reward signals at the end of each episode as an indicator of its relative performances against the baseline model, rather than implementation shortfall (IS) or a shaped reward function. The experimental results have demonstrated advantages over IS and the shaped reward function in terms of performance and simplicity. The proposed framework has outperformed the industry commonly used baseline models such as TWAP, VWAP, and AC as well as several Deep Reinforcement Learning (DRL) models on most of the 14 US equities in our experiments. <|reference_end|>", "<|reference_start|> Data-Efficient Hierarchical Reinforcement Learning: Hierarchical reinforcement learning (HRL) is a promising approach to extend traditional reinforcement learning (RL) methods to solve more complex tasks. Yet, the majority of current HRL methods require careful task-specific design and on-policy training, making them difficult to apply in real-world scenarios. In this paper, we study how we can develop HRL algorithms that are general, in that they do not make onerous additional assumptions beyond standard RL algorithms, and efficient, in the sense that they can be used with modest numbers of interaction samples, making them suitable for real-world problems such as robotic control. For generality, we develop a scheme where lower-level controllers are supervised with goals that are learned and proposed automatically by the higher-level controllers. To address efficiency, we propose to use off-policy experience for both higher and lower-level training. This poses a considerable challenge, since changes to the lower-level behaviors change the action space for the higher-level policy, and we introduce an off-policy correction to remedy this challenge. This allows us to take advantage of recent advances in off-policy model-free RL to learn both higher- and lower-level policies using substantially fewer environment interactions than on-policy algorithms. We term the resulting HRL agent HIRO and find that it is generally applicable and highly sample-efficient. Our experiments show that HIRO can be used to learn highly complex behaviors for simulated robots, such as pushing objects and utilizing them to reach target locations, learning from only a few million samples, equivalent to a few days of real-time interaction. In comparisons with a number of prior HRL methods, we find that our approach substantially outperforms previous state-of-the-art techniques. <|reference_end|>" ]

[ 16, 24, 35, 50 ]

[ "<|reference_start|> {Approximate Low-Weight Check Codes and Circuit Lower Bounds for Noisy Ground States: The No Low-Energy Trivial States (NLTS) conjecture of Freedman and Hastings (Quantum Information and Computation 2014), which asserts the existence of local Hamiltonians whose low energy states cannot be generated by constant depth quantum circuits, identifies a fundamental obstacle to resolving the quantum PCP conjecture. Progress towards the NLTS conjecture was made by Eldar and Harrow (Foundations of Computer Science 2017), who proved a closely related theorem called No Low-Error Trivial States (NLETS). In this paper, we give a much simpler proof of the NLETS theorem, and use the same technique to establish superpolynomial circuit size lower bounds for noisy ground states of local Hamiltonians (assuming $\\mathsf{QCMA} \\neq \\mathsf{QMA}$), resolving an open question of Eldar and Harrow. We discuss the new light our results cast on the relationship between NLTS and NLETS. \nFinally, our techniques imply the existence of $\\textit{approximate quantum low-weight check (qLWC) codes}$ with linear rate, linear distance, and constant weight checks. These codes are similar to quantum LDPC codes except (1) each particle may participate in a large number of checks, and (2) errors only need to be corrected up to fidelity $1 - 1/\\mathsf{poly}(n)$. This stands in contrast to the best-known stabilizer LDPC codes due to Freedman, Meyer, and Luo which achieve a distance of $O(\\sqrt{n \\log n})$. \nThe principal technique used in our results is to leverage the Feynman-Kitaev clock construction to approximately embed a subspace of states defined by a circuit as the ground space of a local Hamiltonian. <|reference_end|>", "<|reference_start|> Quantum LDPC Codes with Almost Linear Minimum Distance: We give a construction of quantum LDPC codes of dimension $\\Theta(\\log N)$ and distance $\\Theta(N/\\log N)$ as the code length $N\\to\\infty$. Using a product of chain complexes this construction also provides a family of quantum LDPC codes of distance $\\Omega(N^{1-\\alpha/2}/\\log N)$ and dimension $\\Omega(N^\\alpha \\log N)$, where $0 \\le \\alpha < 1$. We also introduce and study a new operation called lifted product, which naturally generalizes the product operations for quantum codes and chain complexes. Moreover, as a simple byproduct of our results on quantum codes, we obtain a new result on classical codes. We show that for any fixed $R < 1$ there exists an asymptotically good family of classical quasi-cyclic LDPC codes of rate at least $R$ with, in some sense, optimal circulant size $\\Omega(N/\\log N)$ as the code length $N\\to\\infty$. <|reference_end|>", "<|reference_start|> Tradeoffs for reliable quantum information storage in 2d systems: We ask whether there are fundamental limits on storing quantum information reliably in a bounded volume of space. To investigate this question, we study quantum error correcting codes specified by geometrically local commuting constraints on a 2D lattice of finite-dimensional quantum particles. For these 2D systems, we derive a tradeoff between the number of encoded qubits k, the distance of the code d, and the number of particles n. It is shown that kd{2}=O(n) where the coefficient in O(n) depends only on the locality of the constraints and dimension of the Hilbert spaces describing individual particles. The analogous tradeoff for the classical information storage is k sqrt[d]=O(n). <|reference_end|>", "<|reference_start|> Towards local testability for quantum coding: We introduce the hemicubic codes, a family of quantum codes obtained by associating qubits with the $p$-faces of the $n$-cube (for $n>p$) and stabilizer constraints with faces of dimension $(p\\pm1)$. The quantum code obtained by identifying antipodal faces of the resulting complex encodes one logical qubit into $N = 2^{n-p-1} \\tbinom{n}{p}$ physical qubits and displays local testability with a soundness of $\\Omega(1/\\log(N))$ beating the current state-of-the-art of $1/\\log^{2}(N)$ due to Hastings. We exploit this local testability to devise an efficient decoding algorithm that corrects arbitrary errors of size less than the minimum distance, up to polylog factors. We then extend this code family by considering the quotient of the $n$-cube by arbitrary linear classical codes of length $n$. We establish the parameters of these generalized hemicubic codes. Interestingly, if the soundness of the hemicubic code could be shown to be constant, similarly to the ordinary $n$-cube, then the generalized hemicubic codes could yield quantum locally testable codes of length not exceeding an exponential or even polynomial function of the code dimension. <|reference_end|>" ]

[ 3, 14, 18, 37 ]

[ "<|reference_start|> You Only Learn One Representation: Unified Network for Multiple Tasks: People ``understand'' the world via vision, hearing, tactile, and also the past experience. Human experience can be learned through normal learning (we call it explicit knowledge), or subconsciously (we call it implicit knowledge). These experiences learned through normal learning or subconsciously will be encoded and stored in the brain. Using these abundant experience as a huge database, human beings can effectively process data, even they were unseen beforehand. In this paper, we propose a unified network to encode implicit knowledge and explicit knowledge together, just like the human brain can learn knowledge from normal learning as well as subconsciousness learning. The unified network can generate a unified representation to simultaneously serve various tasks. We can perform kernel space alignment, prediction refinement, and multi-task learning in a convolutional neural network. The results demonstrate that when implicit knowledge is introduced into the neural network, it benefits the performance of all tasks. We further analyze the implicit representation learnt from the proposed unified network, and it shows great capability on catching the physical meaning of different tasks. The source code of this work is at : https://github.com/WongKinYiu/yolor. <|reference_end|>", "<|reference_start|> RoBERTa: A Robustly Optimized BERT Pretraining Approach: Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code. <|reference_end|>", "<|reference_start|> Microsoft COCO: Common Objects in Context: We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of 2.5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation. We present a detailed statistical analysis of the dataset in comparison to PASCAL, ImageNet, and SUN. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model. <|reference_end|>", "<|reference_start|> Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering: Problems at the intersection of vision and language are of significant importance both as challenging research questions and for the rich set of applications they enable. However, inherent structure in our world and bias in our language tend to be a simpler signal for learning than visual modalities, resulting in models that ignore visual information, leading to an inflated sense of their capability. We propose to counter these language priors for the task of Visual Question Answering (VQA) and make vision (the V in VQA) matter! Specifically, we balance the popular VQA dataset by collecting complementary images such that every question in our balanced dataset is associated with not just a single image, but rather a pair of similar images that result in two different answers to the question. Our dataset is by construction more balanced than the original VQA dataset and has approximately twice the number of image-question pairs. Our complete balanced dataset is publicly available at www.visualqa.org as part of the 2nd iteration of the Visual Question Answering Dataset and Challenge (VQA v2.0). We further benchmark a number of state-of-art VQA models on our balanced dataset. All models perform significantly worse on our balanced dataset, suggesting that these models have indeed learned to exploit language priors. This finding provides the first concrete empirical evidence for what seems to be a qualitative sense among practitioners. Finally, our data collection protocol for identifying complementary images enables us to develop a novel interpretable model, which in addition to providing an answer to the given (image, question) pair, also provides a counter-example based explanation. Specifically, it identifies an image that is similar to the original image, but it believes has a different answer to the same question. This can help in building trust for machines among their users. <|reference_end|>" ]

[ 1, 2, 3, 5 ]

[ "<|reference_start|> A Robust Class of Context-Sensitive Languages: We define a new class of languages defined by multi-stack automata that forms a robust subclass of context-sensitive languages, with decidable emptiness and closure under boolean operations. This class, called multi-stack visibly pushdown languages (MVPLs), is defined using multi-stack pushdown automata with two restrictions: (a) the pushdown automaton is visible, i.e. the input letter determines the operation on the stacks, and (b) any computation of the machine can be split into k stages, where in each stage, there is at most one stack that is popped. MVPLs are an extension of visibly pushdown languages that captures noncontext free behaviors, and has applications in analyzing abstractions of multithreaded recursive programs, signifi- cantly enlarging the search space that can be explored for them. We show that MVPLs are closed under boolean operations, and problems such as emptiness and inclusion are decidable. We characterize MVPLs using monadic second-order logic over appropriate structures, and exhibit a Parikh theorem for them. <|reference_end|>", "<|reference_start|> Context-Bounded Analysis For Concurrent Programs With Dynamic Creation of Threads: Context-bounded analysis has been shown to be both efficient and effective at finding bugs in concurrent programs. According to its original definition, context-bounded analysis explores all behaviors of a concurrent program up to some fixed number of context switches between threads. This definition is inadequate for programs that create threads dynamically because bounding the number of context switches in a computation also bounds the number of threads involved in the computation. In this paper, we propose a more general definition of context-bounded analysis useful for programs with dynamic thread creation. The idea is to bound the number of context switches for each thread instead of bounding the number of switches of all threads. We consider several variants based on this new definition, and we establish decidability and complexity results for the analysis induced by them. <|reference_end|>", "<|reference_start|> The mathematical theory of context free languages: <|reference_end|>", "<|reference_start|> A Note on Semilinear Sets and Bounded-Reversal Multihead Pushdown Automata: <|reference_end|>" ]

[ 4, 9, 10, 19 ]

[ "<|reference_start|> LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking: Self-supervised pre-training techniques have achieved remarkable progress in Document AI. Most multimodal pre-trained models use a masked language modeling objective to learn bidirectional representations on the text modality, but they differ in pre-training objectives for the image modality. This discrepancy adds difficulty to multimodal representation learning. In this paper, we propose \\textbf{LayoutLMv3} to pre-train multimodal Transformers for Document AI with unified text and image masking. Additionally, LayoutLMv3 is pre-trained with a word-patch alignment objective to learn cross-modal alignment by predicting whether the corresponding image patch of a text word is masked. The simple unified architecture and training objectives make LayoutLMv3 a general-purpose pre-trained model for both text-centric and image-centric Document AI tasks. Experimental results show that LayoutLMv3 achieves state-of-the-art performance not only in text-centric tasks, including form understanding, receipt understanding, and document visual question answering, but also in image-centric tasks such as document image classification and document layout analysis. The code and models are publicly available at \\url{https://aka.ms/layoutlmv3}. <|reference_end|>", "<|reference_start|> Towards End-to-End Unified Scene Text Detection and Layout Analysis: Scene text detection and document layout analysis have long been treated as two separate tasks in different image domains. In this paper, we bring them together and introduce the task of unified scene text detection and layout analysis. The first hierarchical scene text dataset is introduced to enable this novel research task. We also propose a novel method that is able to simultaneously detect scene text and form text clusters in a unified way. Comprehensive experiments show that our unified model achieves better performance than multiple well-designed baseline methods. Additionally, this model achieves state-of-the-art results on multiple scene text detection datasets without the need of complex post-processing. Dataset and code: https://github.com/google-research-datasets/hiertext and https://github.com/tensorflow/models/tree/master/official/projects/unified_detector. <|reference_end|>", "<|reference_start|> Segment Anything: We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation. Using our efficient model in a data collection loop, we built the largest segmentation dataset to date (by far), with over 1 billion masks on 11M licensed and privacy respecting images. The model is designed and trained to be promptable, so it can transfer zero-shot to new image distributions and tasks. We evaluate its capabilities on numerous tasks and find that its zero-shot performance is impressive -- often competitive with or even superior to prior fully supervised results. We are releasing the Segment Anything Model (SAM) and corresponding dataset (SA-1B) of 1B masks and 11M images at https://segment-anything.com to foster research into foundation models for computer vision. <|reference_end|>", "<|reference_start|> Matte Anything: Interactive Natural Image Matting with Segment Anything Models: Natural image matting algorithms aim to predict the transparency map (alpha-matte) with the trimap guidance. However, the production of trimap often requires significant labor, which limits the widespread application of matting algorithms on a large scale. To address the issue, we propose Matte Anything (MatAny), an interactive natural image matting model that could produce high-quality alpha-matte with various simple hints. The key insight of MatAny is to generate pseudo trimap automatically with contour and transparency prediction. In our work, we leverage vision foundation models to enhance the performance of natural image matting. Specifically, we use the segment anything model to predict high-quality contour with user interaction and an open-vocabulary detector to predict the transparency of any object. Subsequently, a pre-trained image matting model generates alpha mattes with pseudo trimaps. MatAny is the interactive matting algorithm with the most supported interaction methods and the best performance to date. It consists of orthogonal vision models without any additional training. We evaluate the performance of MatAny against several current image matting algorithms. MatAny has 58.3% improvement on MSE and 40.6% improvement on SAD compared to the previous image matting methods with simple guidance, achieving new state-of-the-art (SOTA) performance. The source codes and pre-trained models are available at https://github.com/hustvl/Matte-Anything. <|reference_end|>" ]

[ 1, 3, 13, 14 ]

[ "<|reference_start|> Adding Concurrency to Smart Contracts: Modern cryptocurrency systems, such as Ethereum, permit complex financial transactions through scripts called smart contracts. These smart contracts are executed many, many times, always without real concurrency. First, all smart contracts are serially executed by miners before appending them to the blockchain. Later, those contracts are serially re-executed by validators to verify that the smart contracts were executed correctly by miners. Serial execution limits system throughput and fails to exploit today's concurrent multicore and cluster architectures. Nevertheless, serial execution appears to be required: contracts share state, and contract programming languages have a serial semantics. This paper presents a novel way to permit miners and validators to execute smart contracts in parallel, based on techniques adapted from software transactional memory. Miners execute smart contracts speculatively in parallel, allowing non-conflicting contracts to proceed concurrently, and \"discovering\" a serializable concurrent schedule for a block's transactions, This schedule is captured and encoded as a deterministic fork-join program used by validators to re-execute the miner's parallel schedule deterministically but concurrently. Smart contract benchmarks run on a JVM with ScalaSTM show that a speedup of of 1.33x can be obtained for miners and 1.69x for validators with just three concurrent threads. <|reference_end|>", "<|reference_start|> Optimal Routing for Constant Function Market Makers: We consider the problem of optimally executing an order involving multiple crypto-assets, sometimes called tokens, on a network of multiple constant function market makers (CFMMs). When we ignore the fixed cost associated with executing an order on a CFMM, this optimal routing problem can be cast as a convex optimization problem, which is computationally tractable. When we include the fixed costs, the optimal routing problem is a mixed-integer convex problem, which can be solved using (sometimes slow) global optimization methods, or approximately solved using various heuristics based on convex optimization. The optimal routing problem includes as a special case the problem of identifying an arbitrage present in a network of CFMMs, or certifying that none exists. <|reference_end|>", "<|reference_start|> Routing MEV in Constant Function Market Makers: <|reference_end|>", "<|reference_start|> Sui Lutris: A Blockchain Combining Broadcast and Consensus: Sui Lutris is the first smart-contract platform to sustainably achieve sub-second finality. It achieves this significant decrease by employing consensusless agreement not only for simple payments but for a large variety of transactions. Unlike prior work, Sui Lutris neither compromises expressiveness nor throughput and can run perpetually without restarts. Sui Lutris achieves this by safely integrating consensuless agreement with a high-throughput consensus protocol that is invoked out of the critical finality path but ensures that when a transaction is at risk of inconsistent concurrent accesses, its settlement is delayed until the total ordering is resolved. Building such a hybrid architecture is especially delicate during reconfiguration events, where the system needs to preserve the safety of the consensusless path without compromising the long-term liveness of potentially misconfigured clients. We thus develop a novel reconfiguration protocol, the first to provably show the safe and efficient reconfiguration of a consensusless blockchain. Sui Lutris is currently running in production and underpins the Sui smart-contract platform. Combined with the use of Objects instead of accounts it enables the safe execution of smart contracts that expose objects as a first-class resource. In our experiments Sui Lutris achieves latency lower than 0.5 seconds for throughput up to 5,000 certificates per second (150k ops/s with transaction blocks), compared to the state-of-the-art real-world consensus latencies of 3 seconds. Furthermore, it gracefully handles validators crash-recovery and does not suffer visible performance degradation during reconfiguration. <|reference_end|>" ]

[ 10, 18, 20, 48 ]

[ "<|reference_start|> Design, fabrication and control of soft robots: <|reference_end|>", "<|reference_start|> IEEE/RSJ International Conference on Intelligent Robots and Systems: The 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022) will be held on October 23–27, 2022 in The Kyoto International Conference Center, Kyoto, Japan. The IROS is one of the largest and most impacting robotics research conferences worldwide. It provides an international forum for the international robotics research community to explore the frontier of science and technology in intelligent robots and smart machines. The theme of IROS 2022 is “Embodied AI for a Symbiotic Society’’. In addition to technical sessions and multi-media presentations, IROS conferences also hold panel discussions, forums, workshops, tutorials, exhibits, and technical tours to enrich the fruitful discussions among conference attendees. <|reference_end|>", "<|reference_start|> DIW 3D printing of hybrid magnetorheological materials for application in soft robotic grippers: <|reference_end|>", "<|reference_start|> 2019 2nd IEEE International Conference on Soft Robotics (RoboSoft): <|reference_end|>" ]

[ 28, 29, 42, 52 ]

[ "<|reference_start|> Climate Impacts on Agriculture: Implications for Crop Production: Changes in temperature, CO 2 , and precipitation under the scenarios of climate change for the next 30 yr present a challenge to crop production. This review focuses on the impact of temperature, CO 2 , and ozone on agronomic crops and the implications for crop production. Understanding these implications for agricultural crops is critical for developing cropping systems resilient to stresses induced by climate change. There is variation among crops in their response to CO 2 , temperature, and precipitation changes and, with the regional differences in predicted climate, a situation is created in which the responses will be further complicated. For example, the temperature effects on soybean [Glycine max (L.) Merr.] could potentially cause yield reductions of 2.4% in the South but an increase of 1.7% in the Midwest. The frequency of years when temperatures exceed thresholds for damage during critical growth stages is likely to increase for some crops and regions. The increase in CO 2 contributes significantly to enhanced plant growth and improved water use efficiency (WUE); however, there may be a downscaling of these positive impacts due to higher temperatures plants will experience during their growth cycle. A challenge is to understand the interactions of the changing climatic parameters because of the interactions among temperature, CO 2 , and precipitation on plant growth and development and also on the biotic stresses of weeds, insects, and diseases. Agronomists will have to consider the variations in temperature and precipitation as part of the production system if they are to ensure the food security required by an ever increasing population. <|reference_end|>", "<|reference_start|> Classification of soil and crop suggestion using machine learning techniques: Agriculture is the major source for living for the people of India. Agriculture research is the major source of economy for the country. Soil is an important key factor for agriculture .There are several soil varieties in India. In order to predict the type of crop that can be cultivated in that particular soil type we need to understand the features and characteristics of the soil type. Machine learning techniques provides a flexible way in this case. Classifying the soil according to the soil nutrients is much beneficial or the famers to predict which crop can be cultivated in a particular soil type. Data mining and machine learning is still an emerging technique in the field of agriculture and horticulture. In this paper we have proposed a method for classifying the soil according to the macro nutrients and micro nutrients and predicting the type of crop that can be cultivate in that particular soil type. Several type of machine learning algorithms are used such as K-Nearest Neighbour (k-NN), Bagged tree, Support vector machine(SVM) and logistic regression. KeywordsMachine learning, agriculture, soil, classification, nutrients, chemical feature, accuracy. <|reference_end|>", "<|reference_start|> Forecasting yield by integrating agrarian factors and machine learning models: A survey: <|reference_end|>", "<|reference_start|> An IoT-based cognitive monitoring system for early plant disease forecast: <|reference_end|>" ]

[ 2, 5, 6, 7 ]

[ "<|reference_start|> {History Driven Program Repair: Effective automated program repair techniques have great potential to reduce the costs of debugging and maintenance. Previously proposed automated program repair (APR) techniques often follow a generate-and-validate and test-case-driven procedure: They first randomly generate a large pool of fix candidates and then exhaustively validate the quality of the candidates by testing them against existing or provided test suites. Unfortunately, many real-world bugs cannot be repaired by existing techniques even after more than 12 hours of computation in a multi-core cloud environment. More work is needed to advance the capabilities of modern APR techniques. We propose a new technique that utilizes the wealth of bug fixesacross projects in their development history to effectively guide and drive a programrepair process. Our main insight is that recurring bug fixes are common inreal-world applications, and that previously-appearing fix patterns canprovide useful guidance to an automated repair technique. Based on this insight, our technique first automaticallymines bug fix patterns from the history of many projects. We then employ existingmutation operators to generate fix candidates for a given buggy program. Candidates that match frequently occurring historical bug fixes are consideredmore likely to be relevant, and we thus give them priority inthe random search process. Finally, candidates thatpass all the previously failed test cases are recommended as likely fixes. We compare our technique against existinggenerate-and-validate and test-driven APR approaches using 90 bugs from 5 Javaprograms. The experiment results show that our technique can producegood-quality fixes for many more bugs as compared to the baselines, while beingreasonably computationally efficient: it takes less than 20minutes, on average, to correctly fix a bug. <|reference_end|>", "<|reference_start|> {Staged Program Repair with Condition Synthesis: We present SPR, a new program repair system that combines staged program repair and condition synthesis. These techniques enable SPR to work productively with a set of parameterized transformation schemas to generate and efficiently search a rich space of program repairs. Together these techniques enable SPR to generate correct repairs for over five times as many defects as previous systems evaluated on the same benchmark set. <|reference_end|>", "<|reference_start|> Neural Program Repair with Execution-based Backpropagation: Neural machine translation (NMT) architectures have achieved promising results for automatic program repair. Yet, they have the limitation of generating low-quality patches (e.g., not compilable patches). This is because the existing works only optimize a purely syntactic loss function based on characters and tokens without incorporating program-specific information during neural network weight optimization. In this paper, we propose a novel program repair model called RewardRepair. The core novelty of RewardRepair is to improve NMT-based program repair with a loss function based on program compilation and test execution information, rewarding the network to produce patches that compile and that do not overfit. We conduct several experiments to evaluate RewardRepair showing that it is feasible and effective to use compilation and test execution results to optimize the underlying neural repair model. RewardRepair correctly repairs 207 bugs over four benchmarks. we report on repair success for 121 bugs that are fixed for the first time in the literature. Also, RewardRepair produces up to 45.3% of compilable patches, an improvement over the 39% by the state-of-the-art. <|reference_end|>", "<|reference_start|> FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow: Most sequence-to-sequence (seq2seq) models are autoregressive; they generate each token by conditioning on previously generated tokens. In contrast, non-autoregressive seq2seq models generate all tokens in one pass, which leads to increased efficiency through parallel processing on hardware such as GPUs. However, directly modeling the joint distribution of all tokens simultaneously is challenging, and even with increasingly complex model structures accuracy lags significantly behind autoregressive models. In this paper, we propose a simple, efficient, and effective model for non-autoregressive sequence generation using latent variable models. Specifically, we turn to generative flow, an elegant technique to model complex distributions using neural networks, and design several layers of flow tailored for modeling the conditional density of sequential latent variables. We evaluate this model on three neural machine translation (NMT) benchmark datasets, achieving comparable performance with state-of-the-art non-autoregressive NMT models and almost constant decoding time w.r.t the sequence length. <|reference_end|>" ]

[ 7, 9, 15, 22 ]

[ "<|reference_start|> Human Identification and Localization by Robots in Collaborative Environments: <|reference_end|>", "<|reference_start|> A methodology for sound source localization and tracking: Development of 3d microphone array for near-field and far-field applications: Acoustic source localization and tracking using microphone arrays has become a focus of interest in room acoustics, teleconference systems and tracking of sound producing objects. The current methods to estimate the source localization depend on conventional time-delay estimation techniques between microphone pairs, however, ignoring the ambient noise, reflections from surrounding and reverberation in the closed space. In this study, an acoustic source localizer and tracker (ASLT) based on 3D microphone array is designed and developed for real time source detection and localization. Two practical approaches were examined and evaluated, based on direction of arrival (DOA) estimation techniques and steered power response (SPR) of array algorithms in order to improve the accuracy for tracking multiple sources in full 3D coordinates. Among time delay estimation techniques, generalized cross correlation (GCC) is employed in frequency domain using multiple combinations of microphone pairs of the array with Phase transform (PHAT) weighting function for optimum detection of sources in the presence of reverberant environments. PHAT gives a good performance in the presence of ambience, even when the signal to noise ratio (SNR) is low. For SPR, minimum variance distortion less response (MVDR) beamformer weights are evaluated and purposed for accurate source tracking applications. A microphone array is designed using six transducers in spherical configurations and used for the evaluated for proposed methodology. The measurements are carried out in a reverberant chamber under different noise conditions to validate the practicality of the algorithmic chain and finally, the results are obtained and presented to demonstrate the efficiency of the proposed microphone array design and localization technique. <|reference_end|>", "<|reference_start|> High-order Diffraction and Diffuse Reflections for Interactive Sound Propagation in Large Environments: We present novel algorithms for modeling interactive diffuse reflections and higher-order diffraction in large-scale virtual environments. Our formulation is based on ray-based sound propagation and is directly applicable to complex geometric datasets. We use an incremental approach that combines radiosity and path tracing techniques to iteratively compute diffuse reflections. We also present algorithms for wavelength-dependent simplification and visibility graph computation to accelerate higher-order diffraction at runtime. The overall system can generate plausible sound effects at interactive rates in large, dynamic scenes that have multiple sound sources. We highlight the performance in complex indoor and outdoor environments and observe an order of magnitude performance improvement over previous methods. <|reference_end|>", "<|reference_start|> New higher-order boundary element methods for wave diffraction/radiation: <|reference_end|>" ]

[ 1, 2, 18, 20 ]

[ "<|reference_start|> Diversified Texture Synthesis with Feed-forward Networks: Recent progresses on deep discriminative and generative modeling have shown promising results on texture synthesis. However, existing feed-forward based methods trade off generality for efficiency, which suffer from many issues, such as shortage of generality (i.e., build one network per texture), lack of diversity (i.e., always produce visually identical output) and suboptimality (i.e., generate less satisfying visual effects). In this work, we focus on solving these issues for improved texture synthesis. We propose a deep generative feed-forward network which enables efficient synthesis of multiple textures within one single network and meaningful interpolation between them. Meanwhile, a suite of important techniques are introduced to achieve better convergence and diversity. With extensive experiments, we demonstrate the effectiveness of the proposed model and techniques for synthesizing a large number of textures and show its applications with the stylization. <|reference_end|>", "<|reference_start|> Sparse Coding for Third-order Super-symmetric Tensor Descriptors with Application to Texture Recognition: Super-symmetric tensors - a higher-order extension of scatter matrices - are becoming increasingly popular in machine learning and computer vision for modeling data statistics, co-occurrences, or even as visual descriptors. They were shown recently to outperform second-order approaches, however, the size of these tensors are exponential in the data dimensionality, which is a significant concern. In this paper, we study third-order supersymmetric tensor descriptors in the context of dictionary learning and sparse coding. For this purpose, we propose a novel non-linear third-order texture descriptor. Our goal is to approximate these tensors as sparse conic combinations of atoms from a learned dictionary. Apart from the significant benefits to tensor compression that this framework offers, our experiments demonstrate that the sparse coefficients produced by this scheme lead to better aggregation of high-dimensional data and showcase superior performance on two common computer vision tasks compared to the state of the art. <|reference_end|>", "<|reference_start|> Controlling Perceptual Factors in Neural Style Transfer: Neural Style Transfer has shown very exciting results enabling new forms of image manipulation. Here we extend the existing method to introduce control over spatial location, colour information and across spatial scale. We demonstrate how this enhances the method by allowing high-resolution controlled stylisation and helps to alleviate common failure cases such as applying ground textures to sky regions. Furthermore, by decomposing style into these perceptual factors we enable the combination of style information from multiple sources to generate new, perceptually appealing styles from existing ones. We also describe how these methods can be used to more efficiently produce large size, high-quality stylisation. Finally we show how the introduced control measures can be applied in recent methods for Fast Neural Style Transfer. <|reference_end|>", "<|reference_start|> A study on recognizing non-artistic face sketches: Face sketches are being used in eyewitness testimonies for about a century. These sketches are crucial in finding suspects when no photo is available, but a mental image in the eyewitness's mind. However, research shows that current procedures used for eyewitness testimonies have two main problems. First, they can significantly disturb the memories of the eyewitness. Second, in many cases, these procedures result in face images far from their target faces. These two problems are related to the plasticity of the human visual system and the differences between face perception in humans (holistic) and current methods of sketch production (piecemeal). In this paper, we present some insights for more realistic sketch to photo matching. We describe how to retrieve identity specific information from crude sketches, directly drawn by the non-artistic eyewitnesses. The sketches we used merely contain facial component outlines and facial marks (e.g. wrinkles and moles). We compare results of automatically matching two types sketches (trace-over and user-provided, 25 each) to four types of faces (original, locally exaggerated, configurally exaggerated, and globally exaggerated, 249 each), using two methods (PDM distance comparison and PCA classification). Based on our results, we argue that for automatic non-artistic sketch to photo matching, the algorithms should compare the user-provided sketches with globally exaggerated faces, with a soft constraint on facial marks, to achieve the best matching rates. This is because the user-provided sketch from the user's mental image, seems to be caricatured both locally and configurally. <|reference_end|>" ]

[ 9, 15, 20, 46 ]

[ "<|reference_start|> The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables: The reparameterization trick enables optimizing large scale stochastic computation graphs via gradient descent. The essence of the trick is to refactor each stochastic node into a differentiable function of its parameters and a random variable with fixed distribution. After refactoring, the gradients of the loss propagated by the chain rule through the graph are low variance unbiased estimators of the gradients of the expected loss. While many continuous random variables have such reparameterizations, discrete random variables lack useful reparameterizations due to the discontinuous nature of discrete states. In this work we introduce Concrete random variables---continuous relaxations of discrete random variables. The Concrete distribution is a new family of distributions with closed form densities and a simple reparameterization. Whenever a discrete stochastic node of a computation graph can be refactored into a one-hot bit representation that is treated continuously, Concrete stochastic nodes can be used with automatic differentiation to produce low-variance biased gradients of objectives (including objectives that depend on the log-probability of latent stochastic nodes) on the corresponding discrete graph. We demonstrate the effectiveness of Concrete relaxations on density estimation and structured prediction tasks using neural networks. <|reference_end|>", "<|reference_start|> Network information criterion-determining the number of hidden units for an artificial neural network model: The problem of model selection, or determination of the number of hidden units, can be approached statistically, by generalizing Akaike's information criterion (AIC) to be applicable to unfaithful (i.e., unrealizable) models with general loss criteria including regularization terms. The relation between the training error and the generalization error is studied in terms of the number of the training examples and the complexity of a network which reduces to the number of parameters in the ordinary statistical theory of AIC. This relation leads to a new network information criterion which is useful for selecting the optimal network model based on a given training set. <|reference_end|>", "<|reference_start|> Normal likelihood functions: <|reference_end|>", "<|reference_start|> The selection of prior distributions by formal rules: Abstract Subjectivism has become the dominant philosophical foundation for Bayesian inference. Yet in practice, most Bayesian analyses are performed with so-called “noninformative” priors, that is, priors constructed by some formal rule. We review the plethora of techniques for constructing such priors and discuss some of the practical and philosophical issues that arise when they are used. We give special emphasis to Jeffreys's rules and discuss the evolution of his viewpoint about the interpretation of priors, away from unique representation of ignorance toward the notion that they should be chosen by convention. We conclude that the problems raised by the research on priors chosen by formal rules are serious and may not be dismissed lightly: When sample sizes are small (relative to the number of parameters being estimated), it is dangerous to put faith in any “default” solution; but when asymptotics take over, Jeffreys's rules and their variants remain reasonable choices. We also provide an annotated b... <|reference_end|>" ]

[ 16, 20, 43, 50 ]

[ "<|reference_start|> Magnetically Driven Manipulation of Nonmagnetic Liquid Marbles: Billiards with Liquid Marbles: Liquid marbles are droplets encapsulated by a layer of hydrophobic nanoparticles and have been extensively employed in digital microfluidics and lab-on-a-chip systems in recent years. In this study, magnetic liquid marbles were used to manipulate nonmagnetic liquid marbles. To achieve this purpose, a ferrofluid liquid marble (FLM) was employed and attracted toward an electromagnet, resulting in an impulse to a water liquid marble (WLM) on its way to the electromagnet. It was observed that the manipulation of the WLM by the FLM was similar to the collision of billiard balls except that the liquid marbles exhibited an inelastic collision. Taking the FLM as the projectile ball and the WLM as the other target balls, one can adjust the displacement and direction of the WLM precisely, similar to an expert billiard player. Firstly, the WLM displacement can be adjusted by altering the liquid marble volumes, the initial distances from the electromagnet, and the coil current. Secondly, the WLM direction can be adjusted by changing the position of the WLM relative to the connecting line between the FLM center and the electromagnet. Results show that when the FLM or WLM volume increases by five times, the WLM shooting distance approximately increases by 200% and decreases by 75%, respectively. <|reference_end|>", "<|reference_start|> Liquid marbles, elastic nonstick droplets: From minireactors to self-propulsion: Liquid marbles are nonstick droplets wrapped by micro- or nanometrically scaled colloidal particles, representing a platform for a variety of chemical, biological, and microfluidics applications. Liquid marbles demonstrate elastic properties and do not coalesce when bounced or pressed. The effective surface tension and Young modulus of liquid marbles are discussed. Physical sources of the elasticity of liquid marbles are considered. Liquids and powders used for the fabrication of liquid marbles are surveyed. This feature article reviews properties and applications of liquid marbles. Liquid marbles demonstrate potential as microreactors, microcontainers for growing micro-organisms and cells, and microfluidics devices. The Marangoni-flow-driven self-propulsion of marbles supported by liquids is addressed. <|reference_end|>", "<|reference_start|> On the mechanism of floating and sliding of liquid marbles: The mechanisms of floating and sliding of liquid marbles are studied. Liquid marbles containing CaCl(2) and marbles containing NaOH water solutions float on water containing Na(2)CO(3) and an alcoholic solution of phenolphthalein with no chemical reaction. Sliding of liquid marbles, consisting of NaOH water solutions, on polymer substrates coated with phenolphthalein is studied as well. No chemical reaction is observed. These observations supply direct experimental evidence for the suggestion that interfaces are separated by an air layer when marbles roll on solid substrates. It is concluded that a liquid marble rests on hydrophobic particles coating the liquid. In contrast, drops containing an NaOH water solution sliding on superhydrophobic surfaces coated with phenolphthalein leave a colored trace. The mechanism of low-friction sliding of drops deposited on superhydrophobic surfaces and liquid marbles turns out to be quite different: there is no direct contact between liquid and solid in the case of marbles' motion. <|reference_end|>", "<|reference_start|> On the nature of the friction between nonstick droplets and solid substrates: The lateral rolling of nonstick water and glycerol droplets deposited on solid substrates inclined at the sliding angles was studied. Droplets deposited on lotuslike water-repellent surfaces and liquid marbles (powder-coated droplets) deposited on solid substrates demonstrated similar behavior. The motion of both droplets and marbles featured friction that is far from Amonton type. The sliding angles of nonstick drops are sensitive to the \"history\" of a drop on the substrate. The friction of glycerol marbles is governed by viscous dissipation, whereas the rolling of water marbles is dictated by adhesion forces acting within the contact area. <|reference_end|>" ]

[ 8, 11, 13, 14 ]

[ "<|reference_start|> Evaluating nlp models via contrast sets: Standard test sets for supervised learning evaluate in-distribution generalization. Unfortunately, when a dataset has systematic gaps (e.g., annotation artifacts), these evaluations are misleading: a model can learn simple decision rules that perform well on the test set but do not capture a dataset's intended capabilities. We propose a new annotation paradigm for NLP that helps to close systematic gaps in the test data. In particular, after a dataset is constructed, we recommend that the dataset authors manually perturb the test instances in small but meaningful ways that (typically) change the gold label, creating contrast sets. Contrast sets provide a local view of a model's decision boundary, which can be used to more accurately evaluate a model's true linguistic capabilities. We demonstrate the efficacy of contrast sets by creating them for 10 diverse NLP datasets (e.g., DROP reading comprehension, UD parsing, IMDb sentiment analysis). Although our contrast sets are not explicitly adversarial, model performance is significantly lower on them than on the original test sets---up to 25\\% in some cases. We release our contrast sets as new evaluation benchmarks and encourage future dataset construction efforts to follow similar annotation processes. <|reference_end|>", "<|reference_start|> Heuristic and analytic processes in reasoning: A general two-stage theory of human inference is proposed. A distinction is drawn between heuristic processes which select items of task information as ‘relevant’, and analytic processes which operate on the selected items to generate inferences or judgements. \n \nThese two stages are illustrated in a selective review of work on both deductive and statistical reasoning. Factors identified as contributing to heuristic selection include perceptual salience, linguistic suppositions and semantic associations. Analytic processes are considered to be context dependent: people reason from experience, not from inference rules. The paper includes discussion of the theory in comparison with other contemporary theories of human inference, and in relation to the current debate about human rationality. <|reference_end|>", "<|reference_start|> Neural Module Networks for Reasoning over Text: Answering compositional questions that require multiple steps of reasoning against text is challenging, especially when they involve discrete, symbolic operations. Neural module networks (NMNs) learn to parse such questions as executable programs composed of learnable modules, performing well on synthetic visual QA domains. However, we find that it is challenging to learn these models for non-synthetic questions on open-domain text, where a model needs to deal with the diversity of natural language and perform a broader range of reasoning. We extend NMNs by: (a) introducing modules that reason over a paragraph of text, performing symbolic reasoning (such as arithmetic, sorting, counting) over numbers and dates in a probabilistic and differentiable manner; and (b) proposing an unsupervised auxiliary loss to help extract arguments associated with the events in text. Additionally, we show that a limited amount of heuristically-obtained question program and intermediate module output supervision provides sufficient inductive bias for accurate learning. Our proposed model significantly outperforms state-of-the-art models on a subset of the DROP dataset that poses a variety of reasoning challenges that are covered by our modules. <|reference_end|>", "<|reference_start|> Reasoning Over Paragraph Effects in Situations: A key component of successfully reading a passage of text is the ability to apply knowledge gained from the passage to a new situation. In order to facilitate progress on this kind of reading, we present ROPES, a challenging benchmark for reading comprehension targeting Reasoning Over Paragraph Effects in Situations. We target expository language describing causes and effects (e.g., \"animal pollinators increase efficiency of fertilization in flowers\"), as they have clear implications for new situations. A system is presented a background passage containing at least one of these relations, a novel situation that uses this background, and questions that require reasoning about effects of the relationships in the background passage in the context of the situation. We collect background passages from science textbooks and Wikipedia that contain such phenomena, and ask crowd workers to author situations, questions, and answers, resulting in a 14,322 question dataset. We analyze the challenges of this task and evaluate the performance of state-of-the-art reading comprehension models. The best model performs only slightly better than randomly guessing an answer of the correct type, at 61.6% F1, well below the human performance of 89.0%. <|reference_end|>" ]

[ 8, 9, 17, 19 ]

[ "<|reference_start|> Throughput Analysis for Full-Duplex Wireless Networks with Imperfect Self-interference Cancellation: This paper investigates the throughput for wireless network with full-duplex radios using stochastic geometry. Full-duplex (FD) radios can exchange data simultaneously with each other. On the other hand, the downside of FD transmission is that it will inevitably cause extra interference to the network compared to half-duplex (HD) transmission. Moreover, the residual self-interference has negative effects on the network throughput. In this paper, we focus on a wireless network of nodes with both HD and FD capabilities and derive and optimize the throughput in such a network. Our analytical result shows that if the network is adapting an ALOHA protocol, the maximal throughput is achieved by scheduling all concurrently transmitting nodes to work in either FD mode or HD mode depending on one simple condition. Moreover, the effects of imperfect self-interference cancellation on the signal-to-interference ratio (SIR) loss and throughput are also analyzed based on our mathematical model. We rigorously quantify the impact of imperfect self-interference cancellation on the throughput gain, transmission range, and other metrics, and we establish the minimum amount of self-interference suppression needed for FD to be beneficial. <|reference_end|>", "<|reference_start|> On the Capacity of the Two-Hop Half-Duplex Relay Channel: Although extensively investigated, the capacity of the two-hop half-duplex (HD) relay channel is not fully understood. In particular, a capacity expression which can be easily evaluated is not available and an explicit coding scheme which achieves the capacity is not known either. In this paper, we derive a new expression for the capacity of the two-hop HD relay channel by simplifying previously derived converse expressions. Compared to previous results, the new capacity expression can be easily evaluated. Moreover, we propose an explicit coding scheme which achieves the capacity. To achieve the capacity, the relay does not only send information to the destination by transmitting information-carrying symbols but also with the zero symbols resulting from the relay's silence during reception. As examples, we compute the capacities of the two-hop HD relay channel for the cases when the source-relay and relay-destination links are both binary-symmetric channels (BSCs) and additive white Gaussian noise (AWGN) channels, respectively, and numerically compare the capacities with the rates achieved by conventional relaying where the relay receives and transmits in a codeword-by-codeword fashion and switches between reception and transmission in a strictly alternating manner. Our numerical results show that the capacities of the two-hop HD relay channel for BSC and AWGN links are significantly larger than the rates achieved with conventional relaying. <|reference_end|>", "<|reference_start|> Capacity theorems for the relay channel: A relay channel consists of an input x_{l} , a relay output y_{1} , a channel output y , and a relay sender x_{2} (whose transmission is allowed to depend on the past symbols y_{1} . The dependence of the received symbols upon the inputs is given by p(y,y_{1}|x_{1},x_{2}) . The channel is assumed to be memoryless. In this paper the following capacity theorems are proved. 1)If y is a degraded form of y_{1} , then C \\: = \\: \\max \\!_{p(x_{1},x_{2})} \\min \\,{I(X_{1},X_{2};Y), I(X_{1}; Y_{1}|X_{2})} . 2)If y_{1} is a degraded form of y , then C \\: = \\: \\max \\!_{p(x_{1})} \\max_{x_{2}} I(X_{1};Y|x_{2}) . 3)If p(y,y_{1}|x_{1},x_{2}) is an arbitrary relay channel with feedback from (y,y_{1}) to both x_{1} \\and x_{2} , then C\\: = \\: \\max_{p(x_{1},x_{2})} \\min \\,{I(X_{1},X_{2};Y),I \\,(X_{1};Y,Y_{1}|X_{2})} . 4)For a general relay channel, C \\: \\leq \\: \\max_{p(x_{1},x_{2})} \\min \\,{I \\,(X_{1}, X_{2};Y),I(X_{1};Y,Y_{1}|X_{2}) . Superposition block Markov encoding is used to show achievability of C , and converses are established. The capacities of the Gaussian relay channel and certain discrete relay channels are evaluated. Finally, an achievable lower bound to the capacity of the general relay channel is established. <|reference_end|>", "<|reference_start|> Capacity theorems for the relay channel: A relay channel consists of an input x_{l} , a relay output y_{1} , a channel output y , and a relay sender x_{2} (whose transmission is allowed to depend on the past symbols y_{1} . The dependence of the received symbols upon the inputs is given by p(y,y_{1}|x_{1},x_{2}) . The channel is assumed to be memoryless. In this paper the following capacity theorems are proved. 1)If y is a degraded form of y_{1} , then C \\: = \\: \\max \\!_{p(x_{1},x_{2})} \\min \\,{I(X_{1},X_{2};Y), I(X_{1}; Y_{1}|X_{2})} . 2)If y_{1} is a degraded form of y , then C \\: = \\: \\max \\!_{p(x_{1})} \\max_{x_{2}} I(X_{1};Y|x_{2}) . 3)If p(y,y_{1}|x_{1},x_{2}) is an arbitrary relay channel with feedback from (y,y_{1}) to both x_{1} \\and x_{2} , then C\\: = \\: \\max_{p(x_{1},x_{2})} \\min \\,{I(X_{1},X_{2};Y),I \\,(X_{1};Y,Y_{1}|X_{2})} . 4)For a general relay channel, C \\: \\leq \\: \\max_{p(x_{1},x_{2})} \\min \\,{I \\,(X_{1}, X_{2};Y),I(X_{1};Y,Y_{1}|X_{2}) . Superposition block Markov encoding is used to show achievability of C , and converses are established. The capacities of the Gaussian relay channel and certain discrete relay channels are evaluated. Finally, an achievable lower bound to the capacity of the general relay channel is established. <|reference_end|>" ]

[ 1, 6, 15, 16 ]

[ "<|reference_start|> Sparse Networks from Scratch: Faster Training without Losing Performance: We demonstrate the possibility of what we call sparse learning: accelerated training of deep neural networks that maintain sparse weights throughout training while achieving dense performance levels. We accomplish this by developing sparse momentum, an algorithm which uses exponentially smoothed gradients (momentum) to identify layers and weights which reduce the error efficiently. Sparse momentum redistributes pruned weights across layers according to the mean momentum magnitude of each layer. Within a layer, sparse momentum grows weights according to the momentum magnitude of zero-valued weights. We demonstrate state-of-the-art sparse performance on MNIST, CIFAR-10, and ImageNet, decreasing the mean error by a relative 8%, 15%, and 6% compared to other sparse algorithms. Furthermore, we show that sparse momentum reliably reproduces dense performance levels while providing up to 5.61x faster training. In our analysis, ablations show that the benefits of momentum redistribution and growth increase with the depth and size of the network. Additionally, we find that sparse momentum is insensitive to the choice of its hyperparameters suggesting that sparse momentum is robust and easy to use. <|reference_end|>", "<|reference_start|> Picking Winning Tickets Before Training by Preserving Gradient Flow: Overparameterization has been shown to benefit both the optimization and generalization of neural networks, but large networks are resource hungry at both training and test time. Network pruning can reduce test-time resource requirements, but is typically applied to trained networks and therefore cannot avoid the expensive training process. We aim to prune networks at initialization, thereby saving resources at training time as well. Specifically, we argue that efficient training requires preserving the gradient flow through the network. This leads to a simple but effective pruning criterion we term Gradient Signal Preservation (GraSP). We empirically investigate the effectiveness of the proposed method with extensive experiments on CIFAR-10, CIFAR-100, Tiny-ImageNet and ImageNet, using VGGNet and ResNet architectures. Our method can prune 80% of the weights of a VGG-16 network on ImageNet at initialization, with only a 1.6% drop in top-1 accuracy. Moreover, our method achieves significantly better performance than the baseline at extreme sparsity levels. <|reference_end|>", "<|reference_start|> Pruning Neural Networks at Initialization: Why are We Missing the Mark?: Recent work has explored the possibility of pruning neural networks at initialization. We assess proposals for doing so: SNIP (Lee et al., 2019), GraSP (Wang et al., 2020), SynFlow (Tanaka et al., 2020), and magnitude pruning. Although these methods surpass the trivial baseline of random pruning, they remain below the accuracy of magnitude pruning after training, and we endeavor to understand why. We show that, unlike pruning after training, randomly shuffling the weights these methods prune within each layer or sampling new initial values preserves or improves accuracy. As such, the per-weight pruning decisions made by these methods can be replaced by a per-layer choice of the fraction of weights to prune. This property suggests broader challenges with the underlying pruning heuristics, the desire to prune at initialization, or both. <|reference_end|>", "<|reference_start|> The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training: Random pruning is arguably the most naive way to attain sparsity in neural networks, but has been deemed uncompetitive by either post-training pruning or sparse training. In this paper, we focus on sparse training and highlight a perhaps counter-intuitive finding, that random pruning at initialization can be quite powerful for the sparse training of modern neural networks. Without any delicate pruning criteria or carefully pursued sparsity structures, we empirically demonstrate that sparsely training a randomly pruned network from scratch can match the performance of its dense equivalent. There are two key factors that contribute to this revival: (i) the network sizes matter: as the original dense networks grow wider and deeper, the performance of training a randomly pruned sparse network will quickly grow to matching that of its dense equivalent, even at high sparsity ratios; (ii) appropriate layer-wise sparsity ratios can be pre-chosen for sparse training, which shows to be another important performance booster. Simple as it looks, a randomly pruned subnetwork of Wide ResNet-50 can be sparsely trained to outperforming a dense Wide ResNet-50, on ImageNet. We also observed such randomly pruned networks outperform dense counterparts in other favorable aspects, such as out-of-distribution detection, uncertainty estimation, and adversarial robustness. Overall, our results strongly suggest there is larger-than-expected room for sparse training at scale, and the benefits of sparsity might be more universal beyond carefully designed pruning. Our source code can be found at https://github.com/VITA-Group/Random_Pruning. <|reference_end|>" ]

[ 3, 6, 14, 15 ]

[ "<|reference_start|> On Specification Transparency: Toward A Formal Framework for Designer Comprehensibility of Discrete-Event Control Specifications in Finite Automata: In control of discrete-event systems (DESs), specifying control requirements in automata is not a trivial task. For many DES applications, designers are often confronted with the long-standing problem of uncertainty in specification, namely, how do we know that a specification automaton does indeed model the intended control requirement? Toward a formal framework that helps mitigate this uncertainty for designer comprehensibility, in this paper, we introduce and develop a new specification concept of automaton transparency and investigate the problem of maximizing the transparency of specification automata for DESs. In a transparent specification automaton, events that are irrelevant to the specification but can occur in the system are “hidden” in self-loops. Different automata of the same specification on a DES can be associated with different sets of such irrelevant events, and any such automaton is said to be the most transparent if it has an irrelevant event set of maximal cardinality. The transparency maximization problem is theoretically formulated, and a provably correct solution algorithm is obtained. Given a specification automaton for a DES, the transparent specification automaton produced by the algorithm is a more comprehensible structure, essentially showing the precedence ordering among events from a minimal cardinality set that is relevant in modeling some requirement for the DES, and should aid designers in clarifying if the requirement prescribed is the one intended. <|reference_end|>", "<|reference_start|> Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction: Off-policy reinforcement learning aims to leverage experience collected from prior policies for sample-efficient learning. However, in practice, commonly used off-policy approximate dynamic programming methods based on Q-learning and actor-critic methods are highly sensitive to the data distribution, and can make only limited progress without collecting additional on-policy data. As a step towards more robust off-policy algorithms, we study the setting where the off-policy experience is fixed and there is no further interaction with the environment. We identify bootstrapping error as a key source of instability in current methods. Bootstrapping error is due to bootstrapping from actions that lie outside of the training data distribution, and it accumulates via the Bellman backup operator. We theoretically analyze bootstrapping error, and demonstrate how carefully constraining action selection in the backup can mitigate it. Based on our analysis, we propose a practical algorithm, bootstrapping error accumulation reduction (BEAR). We demonstrate that BEAR is able to learn robustly from different off-policy distributions, including random and suboptimal demonstrations, on a range of continuous control tasks. <|reference_end|>", "<|reference_start|> Offline Reinforcement Learning: We study a novel setting in offline reinforcement learning (RL) where a number of distributed machines jointly cooperate to solve the problem but only one single round of communication is allowed and there is a budget constraint on the total number of information (in terms of bits) that each machine can send out. For value function prediction in contextual bandits, and both episodic and non-episodic MDPs, we establish information-theoretic lower bounds on the minimax risk for distributed statistical estimators; this reveals the minimum amount of communication required by any offline RL algorithms. Specifically, for contextual bandits, we show that the number of bits must scale at least as Ω ( 𝐴𝐶 ) to match the centralised minimax optimal rate, where 𝐴 is the number of actions and 𝐶 is the context dimension; meanwhile, we reach similar results in the MDP settings. Furthermore, we develop learning algorithms based on least-squares estimates and Monte-Carlo return estimates and provide a sharp analysis showing that they can achieve optimal risk up to logarithmic factors. Additionally, we also show that temporal difference is unable to efficiently utilise information from all available devices under the single-round communication setting due to the initial bias of this method. To our best knowledge, this paper presents the first minimax lower bounds for distributed offline RL problems. <|reference_end|>", "<|reference_start|> Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble: Offline reinforcement learning (offline RL), which aims to find an optimal policy from a previously collected static dataset, bears algorithmic difficulties due to function approximation errors from out-of-distribution (OOD) data points. To this end, offline RL algorithms adopt either a constraint or a penalty term that explicitly guides the policy to stay close to the given dataset. However, prior methods typically require accurate estimation of the behavior policy or sampling from OOD data points, which themselves can be a non-trivial problem. Moreover, these methods under-utilize the generalization ability of deep neural networks and often fall into suboptimal solutions too close to the given dataset. In this work, we propose an uncertainty-based offline RL method that takes into account the confidence of the Q-value prediction and does not require any estimation or sampling of the data distribution. We show that the clipped Q-learning, a technique widely used in online RL, can be leveraged to successfully penalize OOD data points with high prediction uncertainties. Surprisingly, we find that it is possible to substantially outperform existing offline RL methods on various tasks by simply increasing the number of Q-networks along with the clipped Q-learning. Based on this observation, we propose an ensemble-diversified actor-critic algorithm that reduces the number of required ensemble networks down to a tenth compared to the naive ensemble while achieving state-of-the-art performance on most of the D4RL benchmarks considered. <|reference_end|>" ]

[ 26, 30, 35, 39 ]

[ "<|reference_start|> Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning: Offline Reinforcement Learning (RL) aims to learn policies from previously collected datasets without exploring the environment. Directly applying off-policy algorithms to offline RL usually fails due to the extrapolation error caused by the out-of-distribution (OOD) actions. Previous methods tackle such problem by penalizing the Q-values of OOD actions or constraining the trained policy to be close to the behavior policy. Nevertheless, such methods typically prevent the generalization of value functions beyond the offline data and also lack precise characterization of OOD data. In this paper, we propose Pessimistic Bootstrapping for offline RL (PBRL), a purely uncertainty-driven offline algorithm without explicit policy constraints. Specifically, PBRL conducts uncertainty quantification via the disagreement of bootstrapped Q-functions, and performs pessimistic updates by penalizing the value function based on the estimated uncertainty. To tackle the extrapolating error, we further propose a novel OOD sampling method. We show that such OOD sampling and pessimistic bootstrapping yields provable uncertainty quantifier in linear MDPs, thus providing the theoretical underpinning for PBRL. Extensive experiments on D4RL benchmark show that PBRL has better performance compared to the state-of-the-art algorithms. <|reference_end|>", "<|reference_start|> Approximately Optimal Approximate Reinforcement Learning: <|reference_end|>", "<|reference_start|> Unsupervised Domain Adaptation with Dynamics-Aware Rewards in Reinforcement Learning: Unsupervised reinforcement learning aims to acquire skills without prior goal representations, where an agent automatically explores an open-ended environment to represent goals and learn the goal-conditioned policy. However, this procedure is often time-consuming, limiting the rollout in some potentially expensive target environments. The intuitive approach of training in another interaction-rich environment disrupts the reproducibility of trained skills in the target environment due to the dynamics shifts and thus inhibits direct transferring. Assuming free access to a source environment, we propose an unsupervised domain adaptation method to identify and acquire skills across dynamics. Particularly, we introduce a KL regularized objective to encourage emergence of skills, rewarding the agent for both discovering skills and aligning its behaviors respecting dynamics shifts. This suggests that both dynamics (source and target) shape the reward to facilitate the learning of adaptive skills. We also conduct empirical experiments to demonstrate that our method can effectively learn skills that can be smoothly deployed in target. <|reference_end|>", "<|reference_start|> Independent skill transfer for deep reinforcement learning: Recently, diverse primitive skills have been learned by adopting the entropy as intrinsic reward, which further shows that new practical skills can be produced by combining a variety of primitive skills. This is essentially skill transfer, very useful for learning high-level skills but quite challenging due to the low efficiency of transferring primitive skills. In this paper, we propose a novel efficient skill transfer method, where we learn independent skills and only independent components of skills are transferred instead of the whole set of skills. More concretely, independent components of skills are obtained through independent component analysis (ICA), which always have a smaller amount (or lower dimension) compared with their mixtures. With a lower dimension, independent skill transfer (IST) exhibits a higher efficiency on learning a given task. Extensive experiments including three robotic tasks demonstrate the effectiveness and high efficiency of our proposed IST method in comparison to direct primitive-skill transfer and conventional reinforcement learning. <|reference_end|>" ]

[ 33, 37, 44, 46 ]

[ "<|reference_start|> Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress: <|reference_end|>", "<|reference_start|> Super-resolution reconstruction of hyperspectral images: Hyperspectral imagery is used for a wide variety of applications, including target detection, tacking, agricultural monitoring and natural resources exploration. The main reason for using hyperspectral imagery is that these images reveal spectral information about the scene that are not available in a single band. Unfortunately, many factors such as sensor noise and atmospheric scattering degrade the spatial quality of these images. Recently, many algorithms are introduced in the literature to improve the resolution of hyperspectral images [7]. In this paper, we propose a new method to produce high resolution bands from low resolution bands that are strongly correlated to the corresponding high resolution panchromatic image. The proposed method is based on using the local correlation instead of using the global correlation to improve the estimated interpolation in order to construct the high resolution image. The utilization of local correlation significantly improved the resolution of high resolution images when compared to the corresponding results obtained using the traditional algorithms. The local correlation is implemented by using predefined small windows across the low resolution image. In addition, numerous experiments are conducted to investigate the effect of the chosen window size in the image quality. Experiments results obtained using real life hyperspectral imagery is presented to verify the effectiveness of the proposed algorithm. <|reference_end|>", "<|reference_start|> RGB-guided hyperspectral image upsampling: Hyperspectral imaging usually lack of spatial resolution due to limitations of hardware design of imaging sensors. On the contrary, latest imaging sensors capture a RGB image with resolution of multiple times larger than a hyperspectral image. In this paper, we present an algorithm to enhance and upsample the resolution of hyperspectral images. Our algorithm consists of two stages: spatial upsampling stage and spectrum substitution stage. The spatial upsampling stage is guided by a high resolution RGB image of the same scene, and the spectrum substitution stage utilizes sparse coding to locally refine the upsampled hyperspectral image through dictionary substitution. Experiments show that our algorithm is highly effective and has outperformed state-of-the-art matrix factorization based approaches. <|reference_end|>", "<|reference_start|> Learning a Deep Convolutional Network for Image Super-Resolution: <|reference_end|>" ]

[ 3, 7, 10, 19 ]

[ "<|reference_start|> City-scale Localization for Cameras with Known Vertical Direction: We consider the problem of localizing a novel image in a large 3D model, given that the gravitational vector is known. In principle, this is just an instance of camera pose estimation, but the scale of the problem introduces some interesting challenges. Most importantly, it makes the correspondence problem very difficult so there will often be a significant number of outliers to handle. To tackle this problem, we use recent theoretical as well as technical advances. Many modern cameras and phones have gravitational sensors that allow us to reduce the search space. Further, there are new techniques to efficiently and reliably deal with extreme rates of outliers. We extend these methods to camera pose estimation by using accurate approximations and fast polynomial solvers. Experimental results are given demonstrating that it is possible to reliably estimate the camera pose despite cases with more than 99 percent outlier correspondences in city-scale models with several millions of 3D points. <|reference_end|>", "<|reference_start|> {Parallel Tracking and Mapping for Small AR Workspaces: This paper presents a method of estimating camera pose in an unknown scene. While this has previously been attempted by adapting SLAM algorithms developed for robotic exploration, we propose a system specifically designed to track a hand-held camera in a small AR workspace. We propose to split tracking and mapping into two separate tasks, processed in parallel threads on a dual-core computer: one thread deals with the task of robustly tracking erratic hand-held motion, while the other produces a 3D map of point features from previously observed video frames. This allows the use of computationally expensive batch optimisation techniques not usually associated with real-time operation: The result is a system that produces detailed maps with thousands of landmarks which can be tracked at frame-rate, with an accuracy and robustness rivalling that of state-of-the-art model-based systems. <|reference_end|>", "<|reference_start|> Image-based localization using LSTMs for structured feature correlation: In this work we propose a new CNN+LSTM architecture for camera pose regression for indoor and outdoor scenes. CNNs allow us to learn suitable feature representations for localization that are robust against motion blur and illumination changes. We make use of LSTM units on the CNN output, which play the role of a structured dimensionality reduction on the feature vector, leading to drastic improvements in localization performance. We provide extensive quantitative comparison of CNN-based and SIFT-based localization methods, showing the weaknesses and strengths of each. Furthermore, we present a new large-scale indoor dataset with accurate ground truth from a laser scanner. Experimental results on both indoor and outdoor public datasets show our method outperforms existing deep architectures, and can localize images in hard conditions, e.g., in the presence of mostly textureless surfaces, where classic SIFT-based methods fail. <|reference_end|>", "<|reference_start|> Deep Auxiliary Learning for Visual Localization and Odometry: Localization is an indispensable component of a robot's autonomy stack that enables it to determine where it is in the environment, essentially making it a precursor for any action execution or planning. Although convolutional neural networks have shown promising results for visual localization, they are still grossly outperformed by state-of-the-art local feature-based techniques. In this work, we propose VLocNet, a new convolutional neural network architecture for 6-DoF global pose regression and odometry estimation from consecutive monocular images. Our multitask model incorporates hard parameter sharing, thus being compact and enabling real-time inference, in addition to being end-to-end trainable. We propose a novel loss function that utilizes auxiliary learning to leverage relative pose information during training, thereby constraining the search space to obtain consistent pose estimates. We evaluate our proposed VLocNet on indoor as well as outdoor datasets and show that even our single task model exceeds the performance of state-of-the-art deep architectures for global localization, while achieving competitive performance for visual odometry estimation. Furthermore, we present extensive experimental evaluations utilizing our proposed Geometric Consistency Loss that show the effectiveness of multitask learning and demonstrate that our model is the first deep learning technique to be on par with, and in some cases outperforms state-of-the-art SIFT-based approaches. <|reference_end|>" ]

[ 9, 24, 67, 68 ]

[ "<|reference_start|> 22nd Annual Computer Security Applications Conference (ACSAC 2006), 11-15 December 2006, Miami Beach, Florida, USA: <|reference_end|>", "<|reference_start|> Towards scalable security analysis using multi-layered security models: <|reference_end|>", "<|reference_start|> Knowledge-based Decision Making for Simulating Cyber Attack Behaviors: Knowledge-based Decision Making for Simulating Cyber Attack Behaviors Stephen Frank Moskal Supervising Professor: Dr. Shanchieh Jay Yang Computer networks are becoming more complex as the reliance on these network increases in this era of exponential technological growth. This makes the potential gains for criminal activity on these networks extremely serious and can not only devastate organizations or enterprises but also the general population. As complexity of the network increases so does the difficulty to protect the networks as more potential vulnerabilities are introduced. Despite best efforts, traditional defenses like Intrusion Detection Systems and penetration tests are rendered ineffective to even amateur cyber adversaries. Networks now need to be analyzed at all times to preemptively detect weaknesses which harbored a new research field called Cyber Threat Analytics. However, current techniques for cyber threat analytics typically perform static analysis on the network and system vulnerabilities but few address the most variable and most critical piece of the puzzle – the attacker themselves. This work focuses on defining a baseline framework for modeling a wide variety of cyber attack behaviors which can be used in conjunction with a cyber attack simulator to analyze the effects of individual or multiple attackers on a network. To model a cyber attacker’s behaviors with reasonable accuracy and flexibility, the model must be based on aspects of an attacker that are used in real scenarios. Real cyber attackers base their decisions on what they know and learn about the network, vulnerabilities, and targets. This attacker behavior model introduces the aspect of knowledge-based decision making to cyber attack behavior modeling with the goal of providing user configurable options. This behavior model employs Cyber Attack Kill Chain R ©along with an ensemble of the attacker <|reference_end|>", "<|reference_start|> Penetration testing== pomdp solving?: Penetration Testing is a methodology for assessing network security, by generating and executing possible attacks. Doing so automatically allows for regular and systematic testing without a prohibitive amount of human labor. A key question then is how to generate the attacks. This is naturally formulated as a planning problem. Previous work (Lucangeli et al. 2010) used classical planning and hence ignores all the incomplete knowledge that characterizes hacking. More recent work (Sarraute et al. 2011) makes strong independence assumptions for the sake of scaling, and lacks a clear formal concept of what the attack planning problem actually is. Herein, we model that problem in terms of partially observable Markov decision processes (POMDP). This grounds penetration testing in a well-researched formalism, highlighting important aspects of this problem's nature. POMDPs allow to model information gathering as an integral part of the problem, thus providing for the first time a means to intelligently mix scanning actions with actual exploits. <|reference_end|>" ]

[ 3, 7, 11, 39 ]

[ "<|reference_start|> Algebraic algorithms for linear matroid parity problems: We present fast and simple algebraic algorithms for the linear matroid parity problem and its applications. For the linear matroid parity problem, we obtain a simple randomized algorithm with running time <i>O</i>(<i>mr</i>^ω-1), where <i>m</i> and <i>r</i> are the number of columns and the number of rows, respectively, and ω ≈ 2.3727 is the matrix multiplication exponent. This improves the <i>O</i>(<i>mr</i>^ω)-time algorithm by Gabow and Stallmann and matches the running time of the algebraic algorithm for linear matroid intersection, answering a question of Harvey. We also present a very simple alternative algorithm with running time <i>O</i>(<i>mr</i>²), which does not need fast matrix multiplication.\n We further improve the algebraic algorithms for some specific graph problems of interest. For the Mader’s disjoint <i>S</i>-path problem, we present an <i>O</i>(<i>n</i>^ω)-time randomized algorithm where <i>n</i> is the number of vertices. This improves the running time of the existing results considerably and matches the running time of the algebraic algorithms for graph matching. For the graphic matroid parity problem, we give an <i>O</i>(<i>n</i>⁴)-time randomized algorithm where <i>n</i> is the number of vertices, and an <i>O</i>(<i>n</i>³)-time randomized algorithm for a special case useful in designing approximation algorithms. These algorithms are optimal in terms of <i>n</i> as the input size could be Ω (<i>n</i>⁴) and Ω (<i>n</i>³), respectively.\n The techniques are based on the algebraic algorithmic framework developed by Mucha and Sankowski, Harvey, and Sankowski. While linear matroid parity and Mader’s disjoint <i>S</i>-path are challenging generalizations for the design of combinatorial algorithms, our results show that both the algebraic algorithms for linear matroid intersection and graph matching can be extended nicely to more general settings. All algorithms are still faster than the existing algorithms even if fast matrix multiplication is not used. These provide simple algorithms that can be easily implemented in practice. <|reference_end|>", "<|reference_start|> Efficient algorithms for independent assignment on graphic and linear matroids: Efficient algorithms are presented for the matroid intersection problem and generalizations. The algorithm for weighted intersection works by scaling the weights. The cardinality algorithm is a special case that takes advantage of greater structure. Efficiency of the algorithms is illustrated by several implementations. On graphic matroids the algorithms run close to the best bounds for trivial matroids (i.e. ordinary bipartite graph matching): O( square root nm log n) for cardinality intersection and O( square root nm log/sup 2/n log(nN)) for weighted intersection (n, m, and N denote the number of vertices, edges, and largest edge weight, respectively; weights are assumed integral). Efficient algorithms are also given for linear matroids. These include both algorithms that are practical and algorithms exploiting fast matrix multiplication.<<ETX>> <|reference_end|>", "<|reference_start|> Exact and approximation algorithms for weighted matroid intersection: <|reference_end|>", "<|reference_start|> Query lower bounds for matroid intersection (combinatorial optimization and discrete algorithms): We consider the number of queries needed to solve the matroid intersection problem, a question raised by Welsh (1976). Given two matroids of rank r on n elements, it is known that O(nr 1:5 ) independence queries suffice. Unfortunately, very little is known about lower bounds for this problem. This paper describes three lower bounds which, to our knowledge, are the best known: 2n − 2 queries are needed for rank 1 matroids, n queries are needed for rank n − 1 matroids, and (log 2 3)n − o(n) queries are needed for matroids of rank n=2. The first two results are elementary, and the last uses methods from communication complexity and group representation theory. <|reference_end|>" ]

[ 5, 6, 8, 11 ]

[ "<|reference_start|> Statistical verification of hyperproperties for cyber-physical systems: Many important properties of cyber-physical systems (CPS) are defined upon the relationship between multiple executions simultaneously in continuous time. Examples include probabilistic fairness and sensitivity to modeling errors (i.e., parameters changes) for real-valued signals. These requirements can only be specified by hyperproperties. In this article, we focus on verifying probabilistic hyperproperties for CPS. To cover a wide range of modeling formalisms, we first propose a general model of probabilistic uncertain systems (PUSs) that unify commonly studied CPS models such as continuous-time Markov chains (CTMCs) and probabilistically parametrized Hybrid I/O Automata (P2HIOA). To formally specify hyperproperties, we propose a new temporal logic, hyper probabilistic signal temporal logic (HyperPSTL) that serves as a hyper and probabilistic version of the conventional signal temporal logic (STL). Considering the complexity of real-world systems that can be captured as PUSs, we adopt a statistical model checking (SMC) approach for their verification. We develop a new SMC technique based on the direct computation of significance levels of statistical assertions for HyperPSTL specifications, which requires no a priori knowledge on the indifference margin. Then, we introduce SMC algorithms for HyperPSTL specifications on the joint probabilistic distribution of multiple paths, as well as specifications with nested probabilistic operators quantifying different paths, which cannot be handled by existing SMC algorithms. Finally, we show the effectiveness of our SMC algorithms on CPS benchmarks with varying levels of complexity, including the Toyota Powertrain Control System. <|reference_end|>", "<|reference_start|> Monitoring Hyperproperties: Hyperproperties, such as non-interference and observational determinism, relate multiple system executions to each other. They are not expressible in standard temporal logics, like LTL, CTL, and CTL*, and thus cannot be monitored with standard runtime verification techniques. HyperLTL extends linear-time temporal logic (LTL) with explicit quantification over traces in order to express Hyperproperties. We investigate the runtime verification problem of HyperLTL formulas for three different input models: (1) The parallel model, where a fixed number of system executions is processed in parallel. (2) The unbounded sequential model, where system executions are processed sequentially, one execution at a time. In this model, the number of incoming executions may grow forever. (3) The bounded sequential model where the traces are processed sequentially and the number of incoming executions is bounded. We show that deciding monitorability of HyperLTL formulas is PSPACE-complete for input models (1) and (3). Deciding monitorability is PSPACE-complete for alternation-free HyperLTL formulas in input model (2). For every input model, we provide practical monitoring algorithms. We also present various optimization techniques. By recognizing properties of specifications such as reflexivity, symmetry, and transitivity, we reduce the number of comparisons between traces. For the sequential models, we present a technique that minimized the number of traces that need to be stored. Finally, we provide an optimization that succinctly represents the stored traces by sharing common prefixes. We evaluate our optimizations, showing that this leads to much more scalable monitoring, in particular, significantly lower memory consumption. <|reference_end|>", "<|reference_start|> EAHyper: Satisfiability, Implication, and Equivalence Checking of Hyperproperties: <|reference_end|>", "<|reference_start|> RVHyper: A Runtime Verification Tool for Temporal Hyperproperties: We present RVHyper, a runtime verification tool for hyperproperties. Hyperproperties, such as non-interference and observational determinism, relate multiple computation traces with each other. Specifications are given as formulas in the temporal logic HyperLTL, which extends linear-time temporal logic (LTL) with trace quantifiers and trace variables. RVHyper processes execution traces sequentially until a violation of the specification is detected. In this case, a counter example, in the form of a set of traces, is returned. As an example application, we show how RVHyper can be used to detect spurious dependencies in hardware designs. <|reference_end|>" ]

[ 39, 50, 57, 59 ]

[ "<|reference_start|> ImageNet classification with deep convolutional neural\nnetworks: We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called \"dropout\" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry. <|reference_end|>", "<|reference_start|> DeepPose: Human Pose Estimation via Deep Neural Networks: We propose a method for human pose estimation based on Deep Neural Networks (DNNs). The pose estimation is formulated as a DNN-based regression problem towards body joints. We present a cascade of such DNN regressors which results in high precision pose estimates. The approach has the advantage of reasoning about pose in a holistic fashion and has a simple but yet powerful formulation which capitalizes on recent advances in Deep Learning. We present a detailed empirical analysis with state-of-art or better performance on four academic benchmarks of diverse real-world images. <|reference_end|>", "<|reference_start|> Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation: We present techniques for speeding up the test-time evaluation of large convolutional networks, designed for object recognition tasks. These models deliver impressive accuracy but each image evaluation requires millions of floating point operations, making their deployment on smartphones and Internet-scale clusters problematic. The computation is dominated by the convolution operations in the lower layers of the model. We exploit the linear structure present within the convolutional filters to derive approximations that significantly reduce the required computation. Using large state-of-the-art models, we demonstrate we demonstrate speedups of convolutional layers on both CPU and GPU by a factor of 2x, while keeping the accuracy within 1% of the original model. <|reference_end|>", "<|reference_start|> DenseNet: Implementing Efficient ConvNet Descriptor Pyramids: Convolutional Neural Networks (CNNs) can provide accurate object classification. They can be extended to perform object detection by iterating over dense or selected proposed object regions. However, the runtime of such detectors scales as the total number and/or area of regions to examine per image, and training such detectors may be prohibitively slow. However, for some CNN classifier topologies, it is possible to share significant work among overlapping regions to be classified. This paper presents DenseNet, an open source system that computes dense, multiscale features from the convolutional layers of a CNN based object classifier. Future work will involve training efficient object detectors with DenseNet feature descriptors. <|reference_end|>" ]

[ 0, 2, 13, 21 ]

[ "<|reference_start|> A Fast Matrix Decoding Algorithm for Rank-Error-Correcting Codes: <|reference_end|>", "<|reference_start|> Decoding Interleaved Gabidulin Codes and Multisequence Linearized Shift-Register Synthesis: An interleaved Gabidulin code is the direct sum of ℓ Gabidulin codes. We propose an efficient decoding algorithm that corrects with high probability errors of rank up to ℓ over ℓ+1(d−1), where d is the rank distance of the interleaved code. The probability of decoding failure is estimated. The proposed decoding is based on a multisequence linearized shift-register synthesis algorithm, given in the paper. The time complexity of the decoding algorithm is O(ℓd2). <|reference_end|>", "<|reference_start|> Bounds on List Decoding of Rank-Metric Codes: So far, there is no polynomial-time list decoding algorithm (beyond half the minimum distance) for Gabidulin codes. These codes can be seen as the rank-metric equivalent of Reed--Solomon codes. In this paper, we provide bounds on the list size of rank-metric codes in order to understand whether polynomial-time list decoding is possible or whether it works only with exponential time complexity. Three bounds on the list size are proven. The first one is a lower exponential bound for Gabidulin codes and shows that for these codes no polynomial-time list decoding beyond the Johnson radius exists. Second, an exponential upper bound is derived, which holds for any rank-metric code of length $n$ and minimum rank distance $d$. The third bound proves that there exists a rank-metric code over $\\Fqm$ of length $n \\leq m$ such that the list size is exponential in the length for any radius greater than half the minimum rank distance. This implies that there cannot exist a polynomial upper bound depending only on $n$ and $d$ similar to the Johnson bound in Hamming metric. All three rank-metric bounds reveal significant differences to bounds for codes in Hamming metric. <|reference_end|>", "<|reference_start|> A Welch-Berlekamp Like Algorithm for Decoding Gabidulin Codes: <|reference_end|>" ]

[ 6, 9, 18, 31 ]

[ "<|reference_start|> The Semantic Reader Project: Augmenting Scholarly Documents through AI-Powered Interactive Reading Interfaces: Scholarly publications are key to the transfer of knowledge from scholars to others. However, research papers are information-dense, and as the volume of the scientific literature grows, the need for new technology to support the reading process grows. In contrast to the process of finding papers, which has been transformed by Internet technology, the experience of reading research papers has changed little in decades. The PDF format for sharing research papers is widely used due to its portability, but it has significant downsides including: static content, poor accessibility for low-vision readers, and difficulty reading on mobile devices. This paper explores the question \"Can recent advances in AI and HCI power intelligent, interactive, and accessible reading interfaces -- even for legacy PDFs?\" We describe the Semantic Reader Project, a collaborative effort across multiple institutions to explore automatic creation of dynamic reading interfaces for research papers. Through this project, we've developed ten research prototype interfaces and conducted usability studies with more than 300 participants and real-world users showing improved reading experiences for scholars. We've also released a production reading interface for research papers that will incorporate the best features as they mature. We structure this paper around challenges scholars and the public face when reading research papers -- Discovery, Efficiency, Comprehension, Synthesis, and Accessibility -- and present an overview of our progress and remaining open challenges. <|reference_end|>", "<|reference_start|> Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models: Large Language Models (LLMs), with their remarkable task-handling capabilities and innovative outputs, have catalyzed significant advancements across a spectrum of fields. However, their proficiency within specialized domains such as biomolecular studies remains limited. To address this challenge, we introduce Mol-Instructions, a comprehensive instruction dataset designed for the biomolecular domain. Mol-Instructions encompasses three key components: molecule-oriented instructions, protein-oriented instructions, and biomolecular text instructions. Each component aims to improve the understanding and prediction capabilities of LLMs concerning biomolecular features and behaviors. Through extensive instruction tuning experiments on LLMs, we demonstrate the effectiveness of Mol-Instructions in enhancing large models' performance in the intricate realm of biomolecular studies, thus fostering progress in the biomolecular research community. Mol-Instructions is publicly available for ongoing research and will undergo regular updates to enhance its applicability. <|reference_end|>", "<|reference_start|> Self-Alignment with Instruction Backtranslation: We present a scalable method to build a high quality instruction following language model by automatically labelling human-written text with corresponding instructions. Our approach, named instruction backtranslation, starts with a language model finetuned on a small amount of seed data, and a given web corpus. The seed model is used to construct training examples by generating instruction prompts for web documents (self-augmentation), and then selecting high quality examples from among these candidates (self-curation). This data is then used to finetune a stronger model. Finetuning LLaMa on two iterations of our approach yields a model that outperforms all other LLaMa-based models on the Alpaca leaderboard not relying on distillation data, demonstrating highly effective self-alignment. <|reference_end|>", "<|reference_start|> PMC-LLaMA: toward building open-source language models for medicine: OBJECTIVE\nRecently, large language models (LLMs) have showcased remarkable capabilities in natural language understanding. While demonstrating proficiency in everyday conversations and question-answering (QA) situations, these models frequently struggle in domains that require precision, such as medical applications, due to their lack of domain-specific knowledge. In this article, we describe the procedure for building a powerful, open-source language model specifically designed for medicine applications, termed as PMC-LLaMA.\n\n\nMATERIALS AND METHODS\nWe adapt a general-purpose LLM toward the medical domain, involving data-centric knowledge injection through the integration of 4.8M biomedical academic papers and 30K medical textbooks, as well as comprehensive domain-specific instruction fine-tuning, encompassing medical QA, rationale for reasoning, and conversational dialogues with 202M tokens.\n\n\nRESULTS\nWhile evaluating various public medical QA benchmarks and manual rating, our lightweight PMC-LLaMA, which consists of only 13B parameters, exhibits superior performance, even surpassing ChatGPT. All models, codes, and datasets for instruction tuning will be released to the research community.\n\n\nDISCUSSION\nOur contributions are 3-fold: (1) we build up an open-source LLM toward the medical domain. We believe the proposed PMC-LLaMA model can promote further development of foundation models in medicine, serving as a medical trainable basic generative language backbone; (2) we conduct thorough ablation studies to demonstrate the effectiveness of each proposed component, demonstrating how different training data and model scales affect medical LLMs; (3) we contribute a large-scale, comprehensive dataset for instruction tuning.\n\n\nCONCLUSION\nIn this article, we systematically investigate the process of building up an open-source medical-specific LLM, PMC-LLaMA. <|reference_end|>" ]

[ 1, 4, 22, 32 ]

[ "<|reference_start|> Trans-dimensional Random Fields for Language Modeling: Language modeling (LM) involves determining the joint probability of words in a sentence. The conditional approach is dominant, representing the joint probability in terms of conditionals. Examples include n-gram LMs and neural network LMs. An alternative approach, called the random field (RF) approach, is used in whole-sentence maximum entropy (WSME) LMs. Although the RF approach has potential benefits, the empirical results of previous WSME models are not satisfactory. In this paper, we revisit the RF approach for language modeling, with a number of innovations. We propose a trans-dimensional RF (TDRF) model and develop a training algorithm using joint stochastic approximation and trans-dimensional mixture sampling. We perform speech recognition experiments on Wall Street Journal data, and find that our TDRF models lead to performances as good as the recurrent neural network LMs but are computationally more efficient in computing sentence probability. <|reference_end|>", "<|reference_start|> Language modeling with Neural trans-dimensional random fields: Trans-dimensional random field language models (TRF LMs) have recently been introduced, where sentences are modeled as a collection of random fields. The TRF approach has been shown to have the advantages of being computationally more efficient in inference than LSTM LMs with close performance and being able to flexibly integrating rich features. In this paper we propose neural TRFs, beyond of the previous discrete TRFs that only use linear potentials with discrete features. The idea is to use nonlinear potentials with continuous features, implemented by neural networks (NNs), in the TRF framework. Neural TRFs combine the advantages of both NNs and TRFs. The benefits of word embedding, nonlinear feature learning and larger context modeling are inherited from the use of NNs. At the same time, the strength of efficient inference by avoiding expensive softmax is preserved. A number of technical contributions, including employing deep convolutional neural networks (CNNs) to define the potentials and incorporating the joint stochastic approximation (JSA) strategy in the training algorithm, are developed in this work, which enable us to successfully train neural TRF LMs. Various LMs are evaluated in terms of speech recognition WERs by rescoring the 1000-best lists of WSJ'92 test data. The results show that neural TRF LMs not only improve over discrete TRF LMs, but also perform slightly better than LSTM LMs with only one fifth of parameters and 16x faster inference efficiency. <|reference_end|>", "<|reference_start|> Learning neural trans-dimensional random field language models with noise-contrastive estimation: Trans-dimensional random field language models (TRF LMs) where sentences are modeled as a collection of random fields, have shown close performance with LSTM LMs in speech recognition and are computationally more efficient in inference. However, the training efficiency of neural TRF LMs is not satisfactory, which limits the scalability of TRF LMs on large training corpus. In this paper, several techniques on both model formulation and parameter estimation are proposed to improve the training efficiency and the performance of neural TRF LMs. First, TRFs are reformulated in the form of exponential tilting of a reference distribution. Second, noise-contrastive estimation (NCE) is introduced to jointly estimate the model parameters and normalization constants. Third, we extend the neural TRF LMs by marrying the deep convolutional neural network (CNN) and the bidirectional LSTM into the potential function to extract the deep hierarchical features and bidirectionally sequential features. Utilizing all the above techniques enables the successful and efficient training of neural TRF LMs on a 40x larger training set with only 1/3 training time and further reduces the WER with relative reduction of 4.7% on top of a strong LSTM LM baseline. <|reference_end|>", "<|reference_start|> Language modeling with Neural trans-dimensional random fields: Trans-dimensional random field language models (TRF LMs) have recently been introduced, where sentences are modeled as a collection of random fields. The TRF approach has been shown to have the advantages of being computationally more efficient in inference than LSTM LMs with close performance and being able to flexibly integrating rich features. In this paper we propose neural TRFs, beyond of the previous discrete TRFs that only use linear potentials with discrete features. The idea is to use nonlinear potentials with continuous features, implemented by neural networks (NNs), in the TRF framework. Neural TRFs combine the advantages of both NNs and TRFs. The benefits of word embedding, nonlinear feature learning and larger context modeling are inherited from the use of NNs. At the same time, the strength of efficient inference by avoiding expensive softmax is preserved. A number of technical contributions, including employing deep convolutional neural networks (CNNs) to define the potentials and incorporating the joint stochastic approximation (JSA) strategy in the training algorithm, are developed in this work, which enable us to successfully train neural TRF LMs. Various LMs are evaluated in terms of speech recognition WERs by rescoring the 1000-best lists of WSJ'92 test data. The results show that neural TRF LMs not only improve over discrete TRF LMs, but also perform slightly better than LSTM LMs with only one fifth of parameters and 16x faster inference efficiency. <|reference_end|>" ]

[ 2, 4, 13, 23 ]

[ "<|reference_start|> Cooperative strategies and capacity theorems for relay networks: Coding strategies that exploit node cooperation are developed for relay networks. Two basic schemes are studied: the relays decode-and-forward the source message to the destination, or they compress-and-forward their channel outputs to the destination. The decode-and-forward scheme is a variant of multihopping, but in addition to having the relays successively decode the message, the transmitters cooperate and each receiver uses several or all of its past channel output blocks to decode. For the compress-and-forward scheme, the relays take advantage of the statistical dependence between their channel outputs and the destination's channel output. The strategies are applied to wireless channels, and it is shown that decode-and-forward achieves the ergodic capacity with phase fading if phase information is available only locally, and if the relays are near the source node. The ergodic capacity coincides with the rate of a distributed antenna array with full cooperation even though the transmitting antennas are not colocated. The capacity results generalize broadly, including to multiantenna transmission with Rayleigh fading, single-bounce fading, certain quasi-static fading problems, cases where partial channel knowledge is available at the transmitters, and cases where local user cooperation is permitted. The results further extend to multisource and multidestination networks such as multiaccess and broadcast relay channels. <|reference_end|>", "<|reference_start|> On the Achievable Diversity-Multiplexing Tradeoff in Half-Duplex Cooperative Channels: We propose novel cooperative transmission protocols for delay-limited coherent fading channels consisting of N (half-duplex and single-antenna) partners and one cell site. In our work, we differentiate between the relay, cooperative broadcast (down-link), and cooperative multiple-access (CMA) (up-link) channels. The proposed protocols are evaluated using Zheng-Tse diversity-multiplexing tradeoff. For the relay channel, we investigate two classes of cooperation schemes; namely, amplify and forward (AF) protocols and decode and forward (DF) protocols. For the first class, we establish an upper bound on the achievable diversity-multiplexing tradeoff with a single relay. We then construct a new AF protocol that achieves this upper bound. The proposed algorithm is then extended to the general case with (N-1) relays where it is shown to outperform the space-time coded protocol of Laneman and Wornell without requiring decoding/encoding at the relays. For the class of DF protocols, we develop a dynamic decode and forward (DDF) protocol that achieves the optimal tradeoff for multiplexing gains 0lesrles1/N. Furthermore, with a single relay, the DDF protocol is shown to dominate the class of AF protocols for all multiplexing gains. The superiority of the DDF protocol is shown to be more significant in the cooperative broadcast channel. The situation is reversed in the CMA channel where we propose a new AF protocol that achieves the optimal tradeoff for all multiplexing gains. A distinguishing feature of the proposed protocols in the three scenarios is that they do not rely on orthogonal subspaces, allowing for a more efficient use of resources. In fact, using our results one can argue that the suboptimality of previously proposed protocols stems from their use of orthogonal subspaces rather than the half-duplex constraint. <|reference_end|>", "<|reference_start|> Distributed rotation recovers spatial diversity: In relay networks, a conventional way to exploit spatial diversity is to introduce distributed space-time processing at the relays. In our work, we show that even simple time-varying distributed rotation can recover spatial diversity. The main idea is to convert the inherent spatial diversity to time diversity by creating an artificial fast fading channel. It turns out that the proposed framework is both tractable from the theoretical point of view and simple from the practical point of view. Furthermore, the framework is quite general and can be applied to a wide range of linear/nonlinear relaying strategies. As applications, we first propose a linear relaying scheme called rotate-and-forward for multiple-antenna two-hop layered networks. It is shown that, in some non-trivial setting, this scheme outperforms existing schemes and achieves the optimal diversity-multiplexing tradeoff. The second application is a decode-and-forward scheme based on the same idea in the single-antenna multiple-relay channel. It is shown to achieve the maximum diversity with low signaling complexity. <|reference_end|>", "<|reference_start|> Distributed rotation recovers spatial diversity: In relay networks, a conventional way to exploit spatial diversity is to introduce distributed space-time processing at the relays. In our work, we show that even simple time-varying distributed rotation can recover spatial diversity. The main idea is to convert the inherent spatial diversity to time diversity by creating an artificial fast fading channel. It turns out that the proposed framework is both tractable from the theoretical point of view and simple from the practical point of view. Furthermore, the framework is quite general and can be applied to a wide range of linear/nonlinear relaying strategies. As applications, we first propose a linear relaying scheme called rotate-and-forward for multiple-antenna two-hop layered networks. It is shown that, in some non-trivial setting, this scheme outperforms existing schemes and achieves the optimal diversity-multiplexing tradeoff. The second application is a decode-and-forward scheme based on the same idea in the single-antenna multiple-relay channel. It is shown to achieve the maximum diversity with low signaling complexity. <|reference_end|>" ]

[ 0, 1, 2, 3 ]

[ "<|reference_start|> 2020 International Joint Conference on Neural Networks, IJCNN 2020, Glasgow, United Kingdom, July 19-24, 2020: <|reference_end|>", "<|reference_start|> A survey on vision-based fall detection: Falls are a major cause of fatal injury for the elderly population. To improve the quality of living for seniors, a wide range of monitoring systems with fall detection functionality have been proposed over recent years. This article is a survey of systems and algorithms which aim at automatically detecting cases where a human falls and may have been injured. Existing fall detection methods can be categorized as using sensors, or being exclusively vision-based. This literature review focuses on vision-based methods. <|reference_end|>", "<|reference_start|> 2021 IEEE 4th International Conference on Electronics Technology (ICET): <|reference_end|>", "<|reference_start|> 学界情報 国際会議レポート：The 32nd International Symposium on Industrial Electronics (ISIE) June 19-21, 2023, Helsinki-Espoo, Finland: <|reference_end|>" ]

[ 23, 29, 34, 37 ]

[ "<|reference_start|> Dual-Path Modeling for Long Recording Speech Separation in Meetings: The continuous speech separation (CSS) is a task to separate the speech sources from a long, partially overlapped recording, which involves a varying number of speakers. A straightforward extension of conventional utterance-level speech separation to the CSS task is to segment the long recording with a size-fixed window and process each window separately. Though effective, this extension fails to model the long dependency in speech and thus leads to sub-optimum performance. The recent proposed dual-path modeling could be a remedy to this problem, thanks to its capability in jointly modeling the cross-window dependency and the local-window processing. In this work, we further extend the dual-path modeling framework for CSS task. A transformer-based dual-path system is proposed, which integrates transform layers for global modeling. The proposed models are applied to LibriCSS, a real recorded multi-talk dataset, and consistent WER reduction can be observed in the ASR evaluation for separated speech. Also, a dual-path transformer equipped with convolutional layers is proposed. It significantly reduces the computation amount by 30% with better WER evaluation. Furthermore, the online processing dual-path models are investigated, which shows 10% relative WER reduction compared to the baseline. <|reference_end|>", "<|reference_start|> BinaryConnect: Training Deep Neural Networks with binary weights during propagations: Deep Neural Networks (DNN) have achieved state-of-the-art results in a wide range of tasks, with the best results obtained with large training sets and large models. In the past, GPUs enabled these breakthroughs because of their greater computational speed. In the future, faster computation at both training and test time is likely to be crucial for further progress and for consumer applications on low-power devices. As a result, there is much interest in research and development of dedicated hardware for Deep Learning (DL). Binary weights, i.e., weights which are constrained to only two possible values (e.g. -1 or 1), would bring great benefits to specialized DL hardware by replacing many multiply-accumulate operations by simple accumulations, as multipliers are the most space and power-hungry components of the digital implementation of neural networks. We introduce BinaryConnect, a method which consists in training a DNN with binary weights during the forward and backward propagations, while retaining precision of the stored weights in which gradients are accumulated. Like other dropout schemes, we show that BinaryConnect acts as regularizer and we obtain near state-of-the-art results with BinaryConnect on the permutation-invariant MNIST, CIFAR-10 and SVHN. <|reference_end|>", "<|reference_start|> HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks: Quantization is an effective method for reducing memory footprint and inference time of Neural Networks, e.g., for efficient inference in the cloud, especially at the edge. However, ultra low precision quantization could lead to significant degradation in model generalization. A promising method to address this is to perform mixed-precision quantization, where more sensitive layers are kept at higher precision. However, the search space for a mixed-precision quantization is exponential in the number of layers. Recent work has proposed HAWQ, a novel Hessian based framework, with the aim of reducing this exponential search space by using second-order information. While promising, this prior work has three major limitations: (i) HAWQV1 only uses the top Hessian eigenvalue as a measure of sensitivity and do not consider the rest of the Hessian spectrum; (ii) HAWQV1 approach only provides relative sensitivity of different layers and therefore requires a manual selection of the mixed-precision setting; and (iii) HAWQV1 does not consider mixed-precision activation quantization. Here, we present HAWQV2 which addresses these shortcomings. For (i), we perform a theoretical analysis showing that a better sensitivity metric is to compute the average of all of the Hessian eigenvalues. For (ii), we develop a Pareto frontier based method for selecting the exact bit precision of different layers without any manual selection. For (iii), we extend the Hessian analysis to mixed-precision activation quantization. We have found this to be very beneficial for object detection. We show that HAWQV2 achieves new state-of-the-art results for a wide range of tasks. <|reference_end|>", "<|reference_start|> Mixed precision quantization of transformer language models for speech recognition: State-of-the-art neural language models represented by Transformers are becoming increasingly complex and expensive for practical applications. Low-bit deep neural network quantization techniques provides a powerful solution to dramatically reduce their model size. Current low-bit quantization methods are based on uniform precision and fail to account for the varying performance sensitivity at different parts of the system to quantization errors. To this end, novel mixed precision DNN quantization methods are proposed in this paper. The optimal local precision settings are automatically learned using two techniques. The first is based on a quantization sensitivity metric in the form of Hessian trace weighted quantization perturbation. The second is based on mixed precision Transformer architecture search. Alternating direction methods of multipliers (ADMM) are used to efficiently train mixed precision quantized DNN systems. Experiments conducted on Penn Treebank (PTB) and a Switchboard corpus trained LF-MMI TDNN system suggest the proposed mixed precision Transformer quantization techniques achieved model size compression ratios of up to 16 times over the full precision baseline with no recognition performance degradation. When being used to compress a larger full precision Transformer LM with more layers, overall word error rate (WER) reductions up to 1.7% absolute (18% relative) were obtained. <|reference_end|>" ]

[ 8, 14, 18, 22 ]

[ "<|reference_start|> Bringing AI To Edge: From Deep Learning's Perspective: <|reference_end|>", "<|reference_start|> An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping their overall structure in place. We show that this reliance on CNNs is not necessary and a pure transformer applied directly to sequences of image patches can perform very well on image classification tasks. When pre-trained on large amounts of data and transferred to multiple mid-sized or small image recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.), Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train. <|reference_end|>", "<|reference_start|> SPAWC 2023 Cover Page: <|reference_end|>", "<|reference_start|> IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021, Montreal, QC, Canada, October 11-17, 2021: <|reference_end|>" ]

[ 0, 1, 2, 4 ]

[ "<|reference_start|> Incremental Sampling-based Algorithm for Minimum-violation Motion Planning: This paper studies the problem of control strategy synthesis for dynamical systems with differential constraints to fulfill a given reachability goal while satisfying a set of safety rules. Particular attention is devoted to goals that become feasible only if a subset of the safety rules are violated. The proposed algorithm computes a control law, that minimizes the level of unsafety while the desired goal is guaranteed to be reached. This problem is motivated by an autonomous car navigating an urban environment while following rules of the road such as \"always travel in right lane'' and \"do not change lanes frequently''. Ideas behind sampling based motion-planning algorithms, such as Probabilistic Road Maps (PRMs) and Rapidly-exploring Random Trees (RRTs), are employed to incrementally construct a finite concretization of the dynamics as a durational Kripke structure. In conjunction with this, a weighted finite automaton that captures the safety rules is used in order to find an optimal trajectory that minimizes the violation of safety rules. We prove that the proposed algorithm guarantees asymptotic optimality, i.e., almost-sure convergence to optimal solutions. We present results of simulation experiments and an implementation on an autonomous urban mobility-on-demand system. <|reference_end|>", "<|reference_start|> Principles of robot motion: theory, algorithms, and implementations: Robot motion planning has become a major focus of robotics. Research findings can be applied not only to robotics but to planning routes on circuit boards, directing digital actors in computer graphics, robot-assisted surgery and medicine, and in novel areas such as drug design and protein folding. This text reflects the great advances that have taken place in the last ten years, including sensor-based planning, probabalistic planning, localization and mapping, and motion planning for dynamic and nonholonomic systems. Its presentation makes the mathematical underpinnings of robot motion accessible to students of computer science and engineering, rleating low-level implementation details to high-level algorithmic concepts. <|reference_end|>", "<|reference_start|> Predictive control for agile semi-autonomous ground vehicles using motion primitives: This paper presents a hierarchical control framework for the obstacle avoidance of autonomous and semi-autonomous ground vehicles. The high-level planner is based on motion primitives created from a four-wheel nonlinear dynamic model. Parameterized clothoids and drifting maneuvers are used to improve vehicle agility. The low-level tracks the planned trajectory with a nonlinear Model Predictive Controller. The first part of the paper describes the proposed control architecture and methodology. The second part presents simulative and experimental results with an autonomous and semi-autonomous ground vehicle traveling at high speed on an icy surface. <|reference_end|>", "<|reference_start|> Lp string stability of cascaded systems: Application to vehicle platooning: Nowadays, throughput has become a limiting factor in road transport. An effective means to increase the road throughput is to employ a small intervehicle time gap using automatic vehicle-following control systems. String stability, i.e., the disturbance attenuation along the vehicle string, is considered an essential requirement for the design of those systems. However, the formal notion of string stability is not unambiguous in literature, since both stability and performance interpretations exist. Therefore, a novel definition for string stability of nonlinear cascaded systems is proposed, using input-output properties. This definition is shown to result in well-known string stability conditions for linear cascaded systems. The theoretical results are experimentally validated using a platoon of six passenger vehicles equipped with cooperative adaptive cruise control. <|reference_end|>" ]

[ 1, 4, 8, 28 ]

[ "<|reference_start|> Image reconstruction and restoration: overview of common estimation structures and problems: Developments in the theory of image reconstruction and restoration over the past 20 or 30 years are outlined. Particular attention is paid to common estimation structures and to practical problems not properly solved yet. The problem of image reconstruction and restoration is first formulated. Some of the current regularization approaches used to solve the problem are then described. The concepts of a priori information and compound criterion are introduced. A Bayesian interpretation of the regularization techniques is given which clarifies the role of the tuning parameters and indicates how they could be estimated. The practical aspects of computing the solution, first when the hyperparameters are known and second when they must be estimated, are then considered. Conclusions are drawn, and points that still need to be investigated are outlined. > <|reference_end|>", "<|reference_start|> Digital image restoration: The article introduces digital image restoration to the reader who is just beginning in this field, and provides a review and analysis for the reader who may already be well-versed in image restoration. The perspective on the topic is one that comes primarily from work done in the field of signal processing. Thus, many of the techniques and works cited relate to classical signal processing approaches to estimation theory, filtering, and numerical analysis. In particular, the emphasis is placed primarily on digital image restoration algorithms that grow out of an area known as \"regularized least squares\" methods. It should be noted, however, that digital image restoration is a very broad field, as we discuss, and thus contains many other successful approaches that have been developed from different perspectives, such as optics, astronomy, and medical imaging, just to name a few. In the process of reviewing this topic, we address a number of very important issues in this field that are not typically discussed in the technical literature. <|reference_end|>", "<|reference_start|> Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems: This paper studies gradient-based schemes for image denoising and deblurring problems based on the discretized total variation (TV) minimization model with constraints. We derive a fast algorithm for the constrained TV-based image deburring problem. To achieve this task, we combine an acceleration of the well known dual approach to the denoising problem with a novel monotone version of a fast iterative shrinkage/thresholding algorithm (FISTA) we have recently introduced. The resulting gradient-based algorithm shares a remarkable simplicity together with a proven global rate of convergence which is significantly better than currently known gradient projections-based methods. Our results are applicable to both the anisotropic and isotropic discretized TV functionals. Initial numerical results demonstrate the viability and efficiency of the proposed algorithms on image deblurring problems with box constraints. <|reference_end|>", "<|reference_start|> Total Variation Minimization and a Class of Binary MRF Models: <|reference_end|>" ]

[ 1, 3, 7, 9 ]

[ "<|reference_start|> Piecewise Affine Neural Networks and Nonlinear Control: <|reference_end|>", "<|reference_start|> Deep neural networks algorithms for stochastic control problems on finite horizon: Convergence analysis: This paper develops algorithms for high-dimensional stochastic control problems based on deep learning and dynamic programming. Unlike classical approximate dynamic programming approaches, we first approximate the optimal policy by means of neural networks in the spirit of deep reinforcement learning, and then the value function by Monte Carlo regression. This is achieved in the dynamic programming recursion by performance or hybrid iteration, and regress now methods from numerical probabilities. We provide a theoretical justification of these algorithms. Consistency and rate of convergence for the control and value function estimates are analyzed and expressed in terms of the universal approximation error of the neural networks, and of the statistical error when estimating network function, leaving aside the optimization error. Numerical results on various applications are presented in a companion paper (arxiv.org/abs/1812.05916) and illustrate the performance of the proposed algorithms. <|reference_end|>", "<|reference_start|> Convergence of a robust deep FBSDE method for stochastic control: In this paper, we propose a deep learning based numerical scheme for strongly coupled FBSDEs, stemming from stochastic control. It is a modification of the deep BSDE method in which the initial value to the backward equation is not a free parameter, and with a new loss function being the weighted sum of the cost of the control problem, and a variance term which coincides with the mean squared error in the terminal condition. We show by a numerical example that a direct extension of the classical deep BSDE method to FBSDEs, fails for a simple linear-quadratic control problem, and motivate why the new method works. Under regularity and boundedness assumptions on the exact controls of time continuous and time discrete control problems, we provide an error analysis for our method. We show empirically that the method converges for three different problems, one being the one that failed for a direct extension of the deep BSDE method. <|reference_end|>", "<|reference_start|> Solving stochastic optimal control problem via stochastic maximum principle with deep learning method: In this paper, we aim to solve the high dimensional stochastic optimal control problem from the view of the stochastic maximum principle via deep learning. By introducing the extended Hamiltonian system which is essentially an FBSDE with a maximum condition, we reformulate the original control problem as a new one. Three algorithms are proposed to solve the new control problem. Numerical results for different examples demonstrate the effectiveness of our proposed algorithms, especially in high dimensional cases. And an important application of this method is to calculate the sub-linear expectations, which correspond to a kind of fully nonlinear PDEs. <|reference_end|>" ]

[ 9, 13, 30, 32 ]

[ "<|reference_start|> Learning to match transient sound events using attentional similarity for few-shot sound recognition: In this paper, we introduce a novel attentional similarity module for the problem of few-shot sound recognition. Given a few examples of an unseen sound event, a classifier must be quickly adapted to recognize the new sound event without much fine-tuning. The proposed attentional similarity module can be plugged into any metric-based learning method for few-shot learning, allowing the resulting model to especially match related short sound events. Extensive experiments on two datasets shows that the proposed module consistently improves the performance of five different metric-based learning methods for few-shot sound recognition. The relative improvement ranges from +4.1% to +7.7% for 5-shot 5-way accuracy for the ESC-50 dataset, and from +2.1% to +6.5% for noiseESC-50. Qualitative results demonstrate that our method contributes in particular to the recognition of transient sound events. <|reference_end|>", "<|reference_start|> {Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces: Large multi-label datasets contain labels that occur thousands of times (frequent group), those that occur only a few times (few-shot group), and labels that never appear in the training dataset (zero-shot group). Multi-label few- and zero-shot label prediction is mostly unexplored on datasets with large label spaces, especially for text classification. In this paper, we perform a fine-grained evaluation to understand how state-of-the-art methods perform on infrequent labels. Furthermore, we develop few- and zero-shot methods for multi-label text classification when there is a known structure over the label space, and evaluate them on two publicly available medical text datasets: MIMIC II and MIMIC III. For few-shot labels we achieve improvements of 6.2% and 4.8% in R@10 for MIMIC II and MIMIC III, respectively, over prior efforts; the corresponding R@10 improvements for zero-shot labels are 17.3% and 19%. <|reference_end|>", "<|reference_start|> Fine-Grained Generalized Zero-Shot Learning via Dense Attribute-Based Attention: We address the problem of fine-grained generalized zero-shot recognition of visually similar classes without training images for some classes. We propose a dense attribute-based attention mechanism that for each attribute focuses on the most relevant image regions, obtaining attribute-based features. Instead of aligning a global feature vector of an image with its associated class semantic vector, we propose an attribute embedding technique that aligns each attribute-based feature with its attribute semantic vector. Hence, we compute a vector of attribute scores, for the presence of each attribute in an image, whose similarity with the true class semantic vector is maximized. Moreover, we adjust each attribute score using an attention mechanism over attributes to better capture the discriminative power of different attributes. To tackle the challenge of bias towards seen classes during testing, we propose a new self-calibration loss that adjusts the probability of unseen classes to account for the training bias. We conduct experiments on three popular datasets of CUB, SUN and AWA2 as well as the large-scale DeepFashion dataset, showing that our model significantly improves the state of the art. <|reference_end|>", "<|reference_start|> Contrastive Embedding for Generalized Zero-Shot Learning: Generalized zero-shot learning (GZSL) aims to recognize objects from both seen and unseen classes, when only the labeled examples from seen classes are provided. Recent feature generation methods learn a generative model that can synthesize the missing visual features of unseen classes to mitigate the data-imbalance problem in GZSL. However, the original visual feature space is suboptimal for GZSL classification since it lacks discriminative information. To tackle this issue, we propose to integrate the generation model with the embedding model, yielding a hybrid GZSL framework. The hybrid GZSL approach maps both the real and the synthetic samples produced by the generation model into an embedding space, where we perform the final GZSL classification. Specifically, we propose a contrastive embedding (CE) for our hybrid GZSL framework. The proposed contrastive embedding can leverage not only the class-wise supervision but also the instance-wise supervision, where the latter is usually neglected by existing GZSL researches. We evaluate our proposed hybrid GZSL framework with contrastive embedding, named CE-GZSL, on five benchmark datasets. The results show that our CEGZSL method can outperform the state-of-the-arts by a significant margin on three datasets. Our codes are available on https://github.com/Hanzy1996/CE-GZSL. <|reference_end|>" ]

[ 8, 14, 16, 18 ]

[ "<|reference_start|> Practical Large-Scale Linear Programming using Primal-Dual Hybrid\nGradient: We present PDLP, a practical first-order method for linear programming (LP) that can solve to the high levels of accuracy that are expected in traditional LP applications. In addition, it can scale to very large problems because its core operation is matrix-vector multiplications. PDLP is derived by applying the primal-dual hybrid gradient (PDHG) method, popularized by Chambolle and Pock (2011), to a saddle-point formulation of LP. PDLP enhances PDHG for LP by combining several new techniques with older tricks from the literature; the enhancements include diagonal preconditioning, presolving, adaptive step sizes, and adaptive restarting. PDLP improves the state of the art for first-order methods applied to LP. We compare PDLP with SCS, an ADMM-based solver, on a set of 383 LP instances derived from MIPLIB 2017. With a target of $10^{-8}$ relative accuracy and 1 hour time limit, PDLP achieves a 6.3x reduction in the geometric mean of solve times and a 4.6x reduction in the number of instances unsolved (from 227 to 49). Furthermore, we highlight standard benchmark instances and a large-scale application (PageRank) where our open-source prototype of PDLP, written in Julia, outperforms a commercial LP solver. <|reference_end|>", "<|reference_start|> Faster first-order primal-dual methods for linear programming using restarts and sharpness: <|reference_end|>", "<|reference_start|> The extragradient method for finding saddle points and other problems: <|reference_end|>", "<|reference_start|> A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach: In this paper we consider solving saddle point problems using two variants of Gradient Descent-Ascent algorithms, Extra-gradient (EG) and Optimistic Gradient Descent Ascent (OGDA) methods. We show that both of these algorithms admit a unified analysis as approximations of the classical proximal point method for solving saddle point problems. This viewpoint enables us to develop a new framework for analyzing EG and OGDA for bilinear and strongly convex-strongly concave settings. Moreover, we use the proximal point approximation interpretation to generalize the results for OGDA for a wide range of parameters. <|reference_end|>" ]

[ 5, 21, 22, 45 ]

[ "<|reference_start|> Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise: Stochastic first-order methods such as Stochastic Extragradient (SEG) or Stochastic Gradient Descent-Ascent (SGDA) for solving smooth minimax problems and, more generally, variational inequality problems (VIP) have been gaining a lot of attention in recent years due to the growing popularity of adversarial formulations in machine learning. However, while high-probability convergence bounds are known to reflect the actual behavior of stochastic methods more accurately, most convergence results are provided in expectation. Moreover, the only known high-probability complexity results have been derived under restrictive sub-Gaussian (light-tailed) noise and bounded domain assumption [Juditsky et al., 2011]. In this work, we prove the first high-probability complexity results with logarithmic dependence on the confidence level for stochastic methods for solving monotone and structured non-monotone VIPs with non-sub-Gaussian (heavy-tailed) noise and unbounded domains. In the monotone case, our results match the best-known ones in the light-tails case [Juditsky et al., 2011], and are novel for structured non-monotone problems such as negative comonotone, quasi-strongly monotone, and/or star-cocoercive ones. We achieve these results by studying SEG and SGDA with clipping. In addition, we numerically validate that the gradient noise of many practical GAN formulations is heavy-tailed and show that clipping improves the performance of SEG/SGDA. <|reference_end|>", "<|reference_start|> High probability convergence of clipped-sgd under heavy-tailed noise: While the convergence behaviors of stochastic gradient methods are well understood \\emph{in expectation}, there still exist many gaps in the understanding of their convergence with \\emph{high probability}, where the convergence rate has a logarithmic dependency on the desired success probability parameter. In the \\emph{heavy-tailed noise} setting, where the stochastic gradient noise only has bounded $p$-th moments for some $p\\in(1,2]$, existing works could only show bounds \\emph{in expectation} for a variant of stochastic gradient descent (SGD) with clipped gradients, or high probability bounds in special cases (such as $p=2$) or with extra assumptions (such as the stochastic gradients having bounded non-central moments). In this work, using a novel analysis framework, we present new and time-optimal (up to logarithmic factors) \\emph{high probability} convergence bounds for SGD with clipping under heavy-tailed noise for both convex and non-convex smooth objectives using only minimal assumptions. <|reference_end|>", "<|reference_start|> From low probability to high confidence in stochastic convex optimization: Standard results in stochastic convex optimization bound the number of samples that an algorithm needs to generate a point with small function value in expectation. More nuanced high probability guarantees are rare, and typically either rely on \"light-tail\" noise assumptions or exhibit worse sample complexity. In this work, we show that a wide class of stochastic optimization algorithms for strongly convex problems can be augmented with high confidence bounds at an overhead cost that is only logarithmic in the confidence level and polylogarithmic in the condition number. The procedure we propose, called proxBoost, is elementary and builds on two well-known ingredients: robust distance estimation and the proximal point method. We discuss consequences for both streaming (online) algorithms and offline algorithms based on empirical risk minimization. <|reference_end|>", "<|reference_start|> Improved convergence in high probability of clipped gradient methods with heavy tails: In this work, we study the convergence \\emph{in high probability} of clipped gradient methods when the noise distribution has heavy tails, ie., with bounded $p$th moments, for some $1<p\\le2$. Prior works in this setting follow the same recipe of using concentration inequalities and an inductive argument with union bound to bound the iterates across all iterations. This method results in an increase in the failure probability by a factor of $T$, where $T$ is the number of iterations. We instead propose a new analysis approach based on bounding the moment generating function of a well chosen supermartingale sequence. We improve the dependency on $T$ in the convergence guarantee for a wide range of algorithms with clipped gradients, including stochastic (accelerated) mirror descent for convex objectives and stochastic gradient descent for nonconvex objectives. This approach naturally allows the algorithms to use time-varying step sizes and clipping parameters when the time horizon is unknown, which appears impossible in prior works. We show that in the case of clipped stochastic mirror descent, problem constants, including the initial distance to the optimum, are not required when setting step sizes and clipping parameters. <|reference_end|>" ]

[ 2, 4, 7, 8 ]

[ "<|reference_start|> Directional MAC protocol for millimeter wave based wireless personal area networks: Recently, up to 7 GHz license-free spectrum around 60 GHz has been allocated worldwide for high data rate wireless communications. This enables the deployment of WPANs at 60 GHz for short-range multimedia applications up to gigabits per second. In this paper we propose a new scheme to increase the efficiency of MAC layer protocol for WPANs at 60 GHz when directional antennas are used. Our scheme is based on an adaptation of the current IEEE 15.3 standard MAC protocol for WPANs on two aspects. Firstly, we propose a rate-adaptation based scheme to coordinate the directional and omni-directional transmissions in WPANs. Secondly, we propose a novel channel time allocation algorithm, which enables spatial reuse TDMA. The analytical results reveal that our algorithm significantly increases the system capacity. <|reference_end|>", "<|reference_start|> Millimeter Wave Cellular Wireless Networks: Potentials and Challenges: Millimeter wave (mmW) frequencies between 30 and 300 GHz are a new frontier for cellular communication that offers the promise of orders of magnitude greater bandwidths combined with further gains via beamforming and spatial multiplexing from multi-element antenna arrays. This paper surveys measurements and capacity studies to assess this technology with a focus on small cell deployments in urban environments. The conclusions are extremely encouraging; measurements in New York City at 28 and 73 GHz demonstrate that, even in an urban canyon environment, significant non-line-of-sight (NLOS) outdoor, street-level coverage is possible up to approximately 200 m from a potential low power micro- or picocell base station. In addition, based on statistical channel models from these measurements, it is shown that mmW systems can offer more than an order of magnitude increase in capacity over current state-of-the-art 4G cellular networks at current cell densities. Cellular systems, however, will need to be significantly redesigned to fully achieve these gains. Specifically, the requirement of highly directional and adaptive transmissions, directional isolation between links and significant possibilities of outage have strong implications on multiple access, channel structure, synchronization and receiver design. To address these challenges, the paper discusses how various technologies including adaptive beamforming, multihop relaying, heterogeneous network architectures and carrier aggregation can be leveraged in the mmW context. <|reference_end|>", "<|reference_start|> On the Efficient Beam-Forming Training for 60GHz Wireless Personal Area Networks: In this article, we suggest an efficient beam switching technique for the emerging 60GHz wireless personal area networks. Given the pre-specified beam codebooks, the beam switching process, aiming to identify the best beam-pair for data transmissions, is formulated as a global optimization problem in a two-dimension plane that is formed by the potential beam pattern index. As the analytical gradient information of the objective reward function is practically unavailable, Rosenbrock numerical algorithm is properly adopted to implement beam searching, by implicitly approaching and exploiting the gradient descent direction through the numerical pattern-search mechanic. In order to enhance search performance, furthermore, a novel initialization process is presented to provide the feasible initial solution for Rosenbrock search. Inspired by the appealing conception of small-region dividing and conquering, this pre-search algorithm can efficiently reduce the search scope and hence improve the success probability. The developed beam switching technique, i.e. an initialization process followed by Rosenbrock search, exhibits a much lower complexity than the current state-of-the-art strategies. It is demonstrated from both theoretical analysis and numerical experiments that, compared with the existing popular methods, the required protocol overhead of the new beam-training procedure can be significantly reduced, accompanying the power consumption of 60GHz devices. <|reference_end|>", "<|reference_start|> STDMA-based Scheduling Algorithm for Concurrent Transmissions in Directional Millimeter Wave Networks: In this paper, a concurrent transmission scheduling algorithm is proposed to enhance the resource utilization efficiency for multi-Gbps millimeter-wave (mmWave) networks. Specifically, we exploit spatial-time division multiple access (STDMA) to improve the system throughput by allowing both non-interfering and interfering links to transmit concurrently, considering the high propagation loss at mmWave band and the utilization of directional antenna. Concurrent transmission scheduling in mmWave networks is formulated as an optimization model to maximize the number of flows scheduled in the network such that the quality of service (QoS) requirement of each flow is satisfied. We further decompose the optimization problem and propose a flip-based heuristic scheduling algorithm with low computational complexity to solve the problem. Extensive simulations demonstrate that the proposed algorithm can significantly improve the network performance in terms of network throughput and the number of supported flows. <|reference_end|>" ]

[ 1, 5, 6, 8 ]

[ "<|reference_start|> Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications: In this paper we extend our previous work on the stochastic block model, a commonly used generative model for social and biological networks, and the problem of inferring functional groups or communities from the topology of the network. We use the cavity method of statistical physics to obtain an asymptotically exact analysis of the phase diagram. We describe in detail properties of the detectability/undetectability phase transition and the easy/hard phase transition for the community detection problem. Our analysis translates naturally into a belief propagation algorithm for inferring the group memberships of the nodes in an optimal way, i.e., that maximizes the overlap with the underlying group memberships, and learning the underlying parameters of the block model. Finally, we apply the algorithm to two examples of real-world networks and discuss its performance. <|reference_end|>", "<|reference_start|> Heuristics for Semirandom Graph Problems: We consider semirandom graph models for finding large independent sets, colorings, and bisections in graphs. These models generate problem instances by blending random and adversarial decisions. To generate semirandom independent set problems, an independent set S of ?n vertices is randomly chosen. Each edge connecting S with S is chosen with probability p, and an adversary is then allowed to add new edges arbitrarily, provided that S remains an independent set. The smaller p is, the greater the control the adversary has over the semirandom graph. We give a heuristic that with high probability recovers an independent set of size ?n whenever p> (1+?)lnn/?n, for any constant ?>0. We show that when p<(1??)lnn /?n, an independent set of size |S| cannot be recovered, unless NP?BPP. We use our result for maximum independent sets to obtain greatly improved heuristics for the model of k-colorable semirandom graphs introduced by Blum and Spencer. For constant k, our results are optimal up to constant factors in the edge probabilities. In the semirandom model for graph bisection, a random bisection (S, S) of the vertices is chosen. Each edge (u, v)?S×S is independently chosen with probability q and each edge (u, v)?S×S is independently chosen with probability pq. The adversary may then arbitrarily remove edges in S×S and add edges not in S×S. Extending the work of Boppana, we give a heuristic that recovers this bisection with high probability when p?q?cplogn/n, for c a sufficiently large constant. <|reference_end|>", "<|reference_start|> Robust recovery for stochastic block models: We develop an efficient algorithm for weak recovery in a robust version of the stochastic block model. The algorithm matches the statistical guarantees of the best known algorithms for the vanilla version of the stochastic block model. In this sense, our results show that there is no price of robustness in the stochastic block model. Our work is heavily inspired by recent work of Banks, Mohanty, and Raghavendra (SODA 2021) that provided an efficient algorithm for the corresponding distinguishing problem. Our algorithm and its analysis significantly depart from previous ones for robust recovery. A key challenge is the peculiar optimization landscape underlying our algorithm: The planted partition may be far from optimal in the sense that completely unrelated solutions could achieve the same objective value. This phenomenon is related to the push-out effect at the BBP phase transition for PCA. To the best of our knowledge, our algorithm is the first to achieve robust recovery in the presence of such a push-out effect in a non-asymptotic setting. Our algorithm is an instantiation of a framework based on convex optimization (related to but distinct from sum-of-squares), which may be useful for other robust matrix estimation problems. A by-product of our analysis is a general technique that boosts the probability of success (over the randomness of the input) of an arbitrary robust weak-recovery algorithm from constant (or slowly vanishing) probability to exponentially high probability. <|reference_end|>", "<|reference_start|> Reaching Kesten-Stigum Threshold in the Stochastic Block Model under Node Corruptions: We study robust community detection in the context of node-corrupted stochastic block model, where an adversary can arbitrarily modify all the edges incident to a fraction of the $n$ vertices. We present the first polynomial-time algorithm that achieves weak recovery at the Kesten-Stigum threshold even in the presence of a small constant fraction of corrupted nodes. Prior to this work, even state-of-the-art robust algorithms were known to break under such node corruption adversaries, when close to the Kesten-Stigum threshold. We further extend our techniques to the $Z_2$ synchronization problem, where our algorithm reaches the optimal recovery threshold in the presence of similar strong adversarial perturbations. The key ingredient of our algorithm is a novel identifiability proof that leverages the push-out effect of the Grothendieck norm of principal submatrices. <|reference_end|>" ]

[ 36, 41, 44, 45 ]

[ "<|reference_start|> Finding Intensional Knowledge of Distance-Based Outliers: <|reference_end|>", "<|reference_start|> Dolphin: an efficient algorithm for mining distance-based outliers in very large datasets: In this work a novel distance-based outlier detection algorithm, named DOLPHIN, working on disk-resident datasets and whose I/O cost corresponds to the cost of sequentially reading the input dataset file twice, is presented.\n It is both theoretically and empirically shown that the main memory usage of DOLPHIN amounts to a small fraction of the dataset and that DOLPHIN has linear time performance with respect to the dataset size. DOLPHIN gains efficiency by naturally merging together in a unified schema three strategies, namely the selection policy of objects to be maintained in main memory, usage of pruning rules, and similarity search techniques. Importantly, similarity search is accomplished by the algorithm without the need of preliminarily indexing the whole dataset, as other methods do.\n The algorithm is simple to implement and it can be used with any type of data, belonging to either metric or nonmetric spaces. Moreover, a modification to the basic method allows DOLPHIN to deal with the scenario in which the available buffer of main memory is smaller than its standard requirements. DOLPHIN has been compared with state-of-the-art distance-based outlier detection algorithms, showing that it is much more efficient. <|reference_end|>", "<|reference_start|> Estimating the Support of a High-dimensional Distribution: Suppose you are given some data set drawn from an underlying probability distribution P and you want to estimate a simple subset S of input space such that the probability that a test point drawn from P lies outside of S equals some a priori specified value between 0 and 1. We propose a method to approach this problem by trying to estimate a function f that is positive on S and negative on the complement. The functional form of f is given by a kernel expansion in terms of a potentially small subset of the training data; it is regularized by controlling the length of the weight vector in an associated feature space. The expansion coefficients are found by solving a quadratic programming problem, which we do by carrying out sequential optimization over pairs of input patterns. We also provide a theoretical analysis of the statistical performance of our algorithm. The algorithm is a natural extension of the support vector algorithm to the case of unlabeled data. <|reference_end|>", "<|reference_start|> Subsampling for efficient and effective unsupervised outlier detection ensembles: Outlier detection and ensemble learning are well established research directions in data mining yet the application of ensemble techniques to outlier detection has been rarely studied. Here, we propose and study subsampling as a technique to induce diversity among individual outlier detectors. We show analytically and experimentally that an outlier detector based on a subsample per se, besides inducing diversity, can, under certain conditions, already improve upon the results of the same outlier detector on the complete dataset. Building an ensemble on top of several subsamples is further improving the results. While in the literature so far the intuition that ensembles improve over single outlier detectors has just been transferred from the classification literature, here we also justify analytically why ensembles are also expected to work in the unsupervised area of outlier detection. As a side effect, running an ensemble of several outlier detectors on subsamples of the dataset is more efficient than ensembles based on other means of introducing diversity and, depending on the sample rate and the size of the ensemble, can be even more efficient than just the single outlier detector on the complete data. <|reference_end|>" ]

[ 16, 25, 27, 40 ]

[ "<|reference_start|> An Exponential Separation Between Randomized and Deterministic Complexity in the LOCAL Model: Over the past 30 years numerous algorithms have been designed for symmetry breaking problems in the LOCAL model, such as maximal matching, MIS, vertex coloring, and edge-coloring. For most problems the best randomized algorithm is at least exponentially faster than the best deterministic algorithm. In this paper we prove that these exponential gaps are necessary and establish connections between the deterministic and randomized complexities in the LOCAL model. Each result has a very compelling take-away message: 1. Fast $\\Delta$-coloring of trees requires random bits: Building on the recent lower bounds of Brandt et al., we prove that the randomized complexity of $\\Delta$-coloring a tree with maximum degree $\\Delta\\ge 55$ is $\\Theta(\\log_\\Delta\\log n)$, whereas its deterministic complexity is $\\Theta(\\log_\\Delta n)$ for any $\\Delta\\ge 3$. This also establishes a large separation between the deterministic complexity of $\\Delta$-coloring and $(\\Delta+1)$-coloring trees. 2. Randomized lower bounds imply deterministic lower bounds: We prove that any deterministic algorithm for a natural class of problems that runs in $O(1)+o(\\log_\\Delta n)$ rounds can be transformed to run in $O(\\log^*n-\\log^*\\Delta+1)$ rounds. If the transformed algorithm violates a lower bound (even allowing randomization), then one can conclude that the problem requires $\\Omega(\\log_\\Delta n)$ time deterministically. 3. Deterministic lower bounds imply randomized lower bounds: We prove that the randomized complexity of any natural problem on instances of size $n$ is at least its deterministic complexity on instances of size $\\sqrt{\\log n}$. This shows that a deterministic $\\Omega(\\log_\\Delta n)$ lower bound for any problem implies a randomized $\\Omega(\\log_\\Delta\\log n)$ lower bound. It also illustrates that the graph shattering technique is absolutely essential to the LOCAL model. <|reference_end|>", "<|reference_start|> Deterministic Coin Tossing with Applications to Optimal Parallel List Ranking: <|reference_end|>", "<|reference_start|> Lower bounds for maximal matchings and maximal independent sets: There are distributed graph algorithms for finding maximal matchings and maximal independent sets in $O(\\Delta + \\log^* n)$ communication rounds; here $n$ is the number of nodes and $\\Delta$ is the maximum degree. The lower bound by Linial (1987, 1992) shows that the dependency on $n$ is optimal: these problems cannot be solved in $o(\\log^* n)$ rounds even if $\\Delta = 2$. However, the dependency on $\\Delta$ is a long-standing open question, and there is currently an exponential gap between the upper and lower bounds. We prove that the upper bounds are tight. We show that any algorithm that finds a maximal matching or maximal independent set with probability at least $1-1/n$ requires $\\Omega(\\min\\{\\Delta,\\log \\log n / \\log \\log \\log n\\})$ rounds in the LOCAL model of distributed computing. As a corollary, it follows that any deterministic algorithm that finds a maximal matching or maximal independent set requires $\\Omega(\\min\\{\\Delta, \\log n / \\log \\log n\\})$ rounds; this is an improvement over prior lower bounds also as a function of $n$. <|reference_end|>", "<|reference_start|> Brief Announcement: Round eliminator: a tool for automatic speedup simulation: In the last years, the round elimination technique has been successfully used to prove many lower bounds for the LOCAL model of distributed computing. In 2019, Brandt proved that this technique can be theoretically automated: given a locally checkable problem Π that can be solved in T rounds of communication, it is possible to mechanically define a problem Π′ that requires T − 1 rounds, and by repeating this procedure many times one can obtain interesting lower and upper bounds. In this work, we show that this technique can be automated also in practice: round eliminator is a computer program where we can feed our favorite locally checkable problem and obtain lower (and sometimes upper) bounds for it, automatically. <|reference_end|>" ]

[ 5, 8, 9, 11 ]

[ "<|reference_start|> Global Explainability of GNNs via Logic Combination of Learned Concepts: While instance-level explanation of GNN is a well-studied problem with plenty of approaches being developed, providing a global explanation for the behaviour of a GNN is much less explored, despite its potential in interpretability and debugging. Existing solutions either simply list local explanations for a given class, or generate a synthetic prototypical graph with maximal score for a given class, completely missing any combinatorial aspect that the GNN could have learned. In this work, we propose GLGExplainer (Global Logic-based GNN Explainer), the first Global Explainer capable of generating explanations as arbitrary Boolean combinations of learned graphical concepts. GLGExplainer is a fully differentiable architecture that takes local explanations as inputs and combines them into a logic formula over graphical concepts, represented as clusters of local explanations. Contrary to existing solutions, GLGExplainer provides accurate and human-interpretable global explanations that are perfectly aligned with ground-truth explanations (on synthetic data) or match existing domain knowledge (on real-world data). Extracted formulas are faithful to the model predictions, to the point of providing insights into some occasionally incorrect rules learned by the model, making GLGExplainer a promising diagnostic tool for learned GNNs. <|reference_end|>", "<|reference_start|> Extracting Tree-Structured Representations of Trained Networks: A significant limitation of neural networks is that the representations they learn are usually incomprehensible to humans. We present a novel algorithm, TREPAN, for extracting comprehensible, symbolic representations from trained neural networks. Our algorithm uses queries to induce a decision tree that approximates the concept represented by a given network. Our experiments demonstrate that TREPAN is able to produce decision trees that maintain a high level of fidelity to their respective networks while being comprehensible and accurate. Unlike previous work in this area, our algorithm is general in its applicability and scales well to large networks and problems with high-dimensional input spaces. <|reference_end|>", "<|reference_start|> Beyond Sparsity: Tree Regularization of Deep Models for Interpretability: The lack of interpretability remains a key barrier to the adoption of deep models in many applications. In this work, we explicitly regularize deep models so human users might step through the process behind their predictions in little time. Specifically, we train deep time-series models so their class-probability predictions have high accuracy while being closely modeled by decision trees with few nodes. Using intuitive toy examples as well as medical tasks for treating sepsis and HIV, we demonstrate that this new tree regularization yields models that are easier for humans to simulate than simpler L1 or L2 penalties without sacrificing predictive power. <|reference_end|>", "<|reference_start|> The Descriptive Complexity of Graph Neural Networks: We analyse the power of graph neural networks (GNNs) in terms of Boolean circuit complexity and descriptive complexity. We prove that the graph queries that can be computed by a polynomial-size bounded-depth family of GNNs are exactly those definable in the guarded fragment GFO+C of first-order logic with counting and with built-in relations. This puts GNNs in the circuit complexity class (non-uniform) TC^0. Remarkably, the GNN families may use arbitrary real weights and a wide class of activation functions that includes the standard ReLU, logistic \"sigmod\", and hyperbolic tangent functions. If the GNNs are allowed to use random initialisation and global readout (both standard features of GNNs widely used in practice), they can compute exactly the same queries as bounded depth Boolean circuits with threshold gates, that is, exactly the queries in TC^0. Moreover, we show that queries computable by a single GNN with piecewise linear activations and rational weights are definable in GFO+C without built-in relations. Therefore, they are contained in uniform TC^0. <|reference_end|>" ]

[ 7, 8, 13, 17 ]

[ "<|reference_start|> Large Scale Distributed Deep Networks: Recent work in unsupervised feature learning and deep learning has shown that being able to train large models can dramatically improve performance. In this paper, we consider the problem of training a deep network with billions of parameters using tens of thousands of CPU cores. We have developed a software framework called DistBelief that can utilize computing clusters with thousands of machines to train large models. Within this framework, we have developed two algorithms for large-scale distributed training: (i) Downpour SGD, an asynchronous stochastic gradient descent procedure supporting a large number of model replicas, and (ii) Sandblaster, a framework that supports a variety of distributed batch optimization procedures, including a distributed implementation of L-BFGS. Downpour SGD and Sandblaster L-BFGS both increase the scale and speed of deep network training. We have successfully used our system to train a deep network 30x larger than previously reported in the literature, and achieves state-of-the-art performance on ImageNet, a visual object recognition task with 16 million images and 21k categories. We show that these same techniques dramatically accelerate the training of a more modestly- sized deep network for a commercial speech recognition service. Although we focus on and report performance of these methods as applied to training large neural networks, the underlying algorithms are applicable to any gradient-based machine learning algorithm. <|reference_end|>", "<|reference_start|> Deep learning with Elastic Averaging SGD: We study the problem of stochastic optimization for deep learning in the parallel computing environment under communication constraints. A new algorithm is proposed in this setting where the communication and coordination of work among concurrent processes (local workers), is based on an elastic force which links the parameters they compute with a center variable stored by the parameter server (master). The algorithm enables the local workers to perform more exploration, i.e. the algorithm allows the local variables to fluctuate further from the center variable by reducing the amount of communication between local workers and the master. We empirically demonstrate that in the deep learning setting, due to the existence of many local optima, allowing more exploration can lead to the improved performance. We propose synchronous and asynchronous variants of the new algorithm. We provide the stability analysis of the asynchronous variant in the round-robin scheme and compare it with the more common parallelized method ADMM. We show that the stability of EASGD is guaranteed when a simple stability condition is satisfied, which is not the case for ADMM. We additionally propose the momentum-based version of our algorithm that can be applied in both synchronous and asynchronous settings. Asynchronous variant of the algorithm is applied to train convolutional neural networks for image classification on the CIFAR and ImageNet datasets. Experiments demonstrate that the new algorithm accelerates the training of deep architectures compared to DOWNPOUR and other common baseline approaches and furthermore is very communication efficient. <|reference_end|>", "<|reference_start|> {1-Bit Stochastic Gradient Descent and Its Application to Data-Parallel Distributed Training of Speech DNNs: We show empirically that in SGD training of deep neural networks, one can, at no or nearly no loss of accuracy, quantize the gradients aggressively—to but one bit per value—if the quantization error is carried forward across minibatches (error feedback). This size reduction makes it feasible to parallelize SGD through data-parallelism with fast processors like recent GPUs. We implement data-parallel deterministically distributed SGD by combining this finding with AdaGrad, automatic minibatch-size selection, double buffering, and model parallelism. Unexpectedly, quantization benefits AdaGrad, giving a small accuracy gain. For a typical Switchboard DNN with 46M parameters, we reach computation speeds of 27k frames per second (kfps) when using 2880 samples per minibatch, and 51kfps with 16k, on a server with 8 K20X GPUs. This corresponds to speed-ups over a single GPU of 3.6 and 6.3, respectively. 7 training passes over 309h of data complete in under 7h. A 160M-parameter model training processes 3300h of data in under 16h on 20 dual-GPU servers—a 10 times speed-up—albeit at a small accuracy loss. <|reference_end|>", "<|reference_start|> LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks: Although weight and activation quantization is an effective approach for Deep Neural Network (DNN) compression and has a lot of potentials to increase inference speed leveraging bit-operations, there is still a noticeable gap in terms of prediction accuracy between the quantized model and the full-precision model. To address this gap, we propose to jointly train a quantized, bit-operation-compatible DNN and its associated quantizers, as opposed to using fixed, handcrafted quantization schemes such as uniform or logarithmic quantization. Our method for learning the quantizers applies to both network weights and activations with arbitrary-bit precision, and our quantizers are easy to train. The comprehensive experiments on CIFAR-10 and ImageNet datasets show that our method works consistently well for various network structures such as AlexNet, VGG-Net, GoogLeNet, ResNet, and DenseNet, surpassing previous quantization methods in terms of accuracy by an appreciable margin. Code available at https://github.com/Microsoft/LQ-Nets <|reference_end|>" ]

[ 2, 8, 10, 24 ]

[ "<|reference_start|> TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion: Inferring missing facts in temporal knowledge graphs (TKGs) is a fundamental and challenging task. Previous works have approached this problem by augmenting methods for static knowledge graphs to leverage time-dependent representations. However, these methods do not explicitly leverage multi-hop structural information and temporal facts from recent time steps to enhance their predictions. Additionally, prior work does not explicitly address the temporal sparsity and variability of entity distributions in TKGs. We propose the Temporal Message Passing (TeMP) framework to address these challenges by combining graph neural networks, temporal dynamics models, data imputation and frequency-based gating techniques. Experiments on standard TKG tasks show that our approach provides substantial gains compared to the previous state of the art, achieving a 10.7% average relative improvement in Hits@10 across three standard benchmarks. Our analysis also reveals important sources of variability both within and across TKG datasets, and we introduce several simple but strong baselines that outperform the prior state of the art in certain settings. <|reference_end|>", "<|reference_start|> Attention Is All You Need: The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data. <|reference_end|>", "<|reference_start|> TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion: Inferring missing facts in temporal knowledge graphs (TKGs) is a fundamental and challenging task. Previous works have approached this problem by augmenting methods for static knowledge graphs to leverage time-dependent representations. However, these methods do not explicitly leverage multi-hop structural information and temporal facts from recent time steps to enhance their predictions. Additionally, prior work does not explicitly address the temporal sparsity and variability of entity distributions in TKGs. We propose the Temporal Message Passing (TeMP) framework to address these challenges by combining graph neural networks, temporal dynamics models, data imputation and frequency-based gating techniques. Experiments on standard TKG tasks show that our approach provides substantial gains compared to the previous state of the art, achieving a 10.7% average relative improvement in Hits@10 across three standard benchmarks. Our analysis also reveals important sources of variability both within and across TKG datasets, and we introduce several simple but strong baselines that outperform the prior state of the art in certain settings. <|reference_end|>", "<|reference_start|> Learning to Walk across Time for Interpretable Temporal Knowledge\nGraph Completion: Static knowledge graphs (KGs), despite their wide usage in relational reasoning and downstream tasks, fall short of realistic modeling of knowledge and facts that are only temporarily valid. Compared to static knowledge graphs, temporal knowledge graphs (TKGs) inherently reflect the transient nature of real-world knowledge. Naturally, automatic TKG completion has drawn much research interests for a more realistic modeling of relational reasoning. However, most of the existing models for TKG completion extend static KG embeddings that do not fully exploit TKG structure, thus lacking in 1) accounting for temporally relevant events already residing in the local neighborhood of a query, and 2) path-based inference that facilitates multi-hop reasoning and better interpretability. In this paper, we propose T-GAP, a novel model for TKG completion that maximally utilizes both temporal information and graph structure in its encoder and decoder. T-GAP encodes query-specific substructure of TKG by focusing on the temporal displacement between each event and the query timestamp, and performs path-based inference by propagating attention through the graph. Our empirical experiments demonstrate that T-GAP not only achieves superior performance against state-of-the-art baselines, but also competently generalizes to queries with unseen timestamps. Through extensive qualitative analyses, we also show that T-GAP enjoys transparent interpretability, and follows human intuition in its reasoning process. <|reference_end|>" ]

[ 10, 12, 13, 14 ]

[ "<|reference_start|> Tensor decompositions and data fusion in epileptic electroencephalography and functional magnetic resonance imaging data: Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) record a mixture of ongoing neural processes, physiological and nonphysiological noise. The pattern of interest, such as epileptic activity, is often hidden within this noisy mixture. Therefore, blind source separation (BSS) techniques, which can retrieve the activity pattern of each underlying source, are very useful. Tensor decomposition techniques are very well suited to solve the BSS problem, as they provide a unique solution under mild constraints. Uniqueness is crucial for an unambiguous interpretation of the components, matching them to true neural processes and characterizing them using the component signatures. Moreover, tensors provide a natural representation of the inherently multidimensional EEG and fMRI, and preserve the structural information defined by the interdependencies among the various modes such as channels, time, patients, etc. Despite the well‐developed theoretical framework, tensor‐based analysis of real, large‐scale clinical datasets is still scarce. Indeed, the application of tensor methods is not straightforward. Finding an appropriate tensor representation, suitable tensor model, and interpretation are application dependent choices, which require expertise both in neuroscience and in multilinear algebra. The aim of this paper is to provide a general guideline for these choices and illustrate them through successful applications in epilepsy. WIREs Data Mining Knowl Discov 2017, 7:e1197. doi: 10.1002/widm.1197 <|reference_end|>", "<|reference_start|> Unraveling Diagnostic Biomarkers of Schizophrenia through Structure-Revealing Fusion of Multi-Modal Neuroimaging Data: Fusing complementary information from different modalities can lead to the discovery of more accurate diagnostic biomarkers for psychiatric disorders. However, biomarker discovery through data fusion is challenging since it requires extracting interpretable and reproducible patterns from data sets, consisting of shared/unshared patterns and of different orders. For example, multi-channel electroencephalography (EEG) signals from multiple subjects can be represented as a third-order tensor with modes: subject, time, and channel, while functional magnetic resonance imaging (fMRI) data may be in the form of subject by voxel matrices. Traditional data fusion methods rearrange higher-order tensors, such as EEG, as matrices to use matrix factorization-based approaches. In contrast, fusion methods based on coupled matrix and tensor factorizations (CMTF) exploit the potential multi-way structure of higher-order tensors. The CMTF approach has been shown to capture underlying patterns more accurately without imposing strong constraints on the latent neural patterns, i.e., biomarkers. In this paper, EEG, fMRI and structural MRI (sMRI) data collected during an auditory oddball task (AOD) from a group of subjects consisting of patients with schizophrenia and healthy controls, are arranged as matrices and higher-order tensors coupled along the subject mode, and jointly analyzed using structure-revealing CMTF methods (also known as advanced CMTF (ACMTF)) focusing on unique identification of underlying patterns in the presence of shared/unshared patterns. We demonstrate that joint analysis of the EEG tensor and fMRI matrix using ACMTF reveals significant and biologically meaningful components in terms of differentiating between patients with schizophrenia and healthy controls while also providing spatial patterns with high resolution and improving the clustering performance compared to the analysis of only the EEG tensor. We also show that these patterns are reproducible, and study reproducibility for different model parameters. In comparison to the joint independent component analysis (jICA) data fusion approach, ACMTF provides easier interpretation of EEG data by revealing a single summary map of the topography for each component. Furthermore, fusion of sMRI data with EEG and fMRI through an ACMTF model provides structural patterns; however, we also show that when fusing data sets from multiple modalities, hence of very different nature, preprocessing plays a crucial role. <|reference_end|>", "<|reference_start|> Fusion of electroencephalography and functional magnetic resonance imaging to explore epileptic network activity: Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are two complementary modalities capturing a mixture of various underlying neural sources. The fusion of these modalities promises the best of both worlds, i.e. a better resolution in time and space, respectively. Assuming that EEG and fMRI observations are generated by the same mixing system in both modalities, their fusion can be achieved by joint blind source separation (BSS). We solve the joint BSS problem using different variants of joint independent component analysis (jointICA) and coupled matrix-tensor factorization (CMTF). We demonstrate that EEG-fMRI fusion provides a detailed spatio-temporal characterization of an EEG-fMRI dataset recorded in epilepsy patients, leading to new insights in epileptic network behaviour. <|reference_end|>", "<|reference_start|> Concurrent EEG/fMRI analysis by multiway Partial Least Squares: <|reference_end|>" ]

[ 15, 17, 18, 21 ]

[ "<|reference_start|> {System R: Relational Approach to Database Management: System R is a database management system which provides a high level relational data interface. The systems provides a high level of data independence by isolating the end user as much as possible from underlying storage structures. The system permits definition of a variety of relational views on common underlying data. Data control features are provided, including authorization, integrity assertions, triggered transactions, a logging and recovery subsystem, and facilities for maintaining data consistency in a shared-update environment.\nThis paper contains a description of the overall architecture and design of the system. At the present time the system is being implemented and the design evaluated. We emphasize that System R is a vehicle for research in database architecture, and is not planned as a product. <|reference_end|>", "<|reference_start|> Bigtable: A Distributed Storage System For Structured Data: Bigtable is a distributed storage system for managing structured data that is designed to scale to a very large size: petabytes of data across thousands of commodity servers. Many projects at Google store data in Bigtable, including web indexing, Google Earth, and Google Finance. These applications place very different demands on Bigtable, both in terms of data size (from URLs to web pages to satellite imagery) and latency requirements (from backend bulk processing to real-time data serving). Despite these varied demands, Bigtable has successfully provided a flexible, high-performance solution for all of these Google products. In this paper we describe the simple data model provided by Bigtable, which gives clients dynamic control over data layout and format, and we describe the design and implementation of Bigtable. <|reference_end|>", "<|reference_start|> Cassandra: A Decentralized Structured Storage System: Cassandra is a distributed storage system for managing very large amounts of structured data spread out across many commodity servers, while providing highly available service with no single point of failure. Cassandra aims to run on top of an infrastructure of hundreds of nodes (possibly spread across different data centers). At this scale, small and large components fail continuously. The way Cassandra manages the persistent state in the face of these failures drives the reliability and scalability of the software systems relying on this service. While in many ways Cassandra resembles a database and shares many design and implementation strategies therewith, Cassandra does not support a full relational data model; instead, it provides clients with a simple data model that supports dynamic control over data layout and format. Cassandra system was designed to run on cheap commodity hardware and handle high write throughput while not sacrificing read efficiency. <|reference_end|>", "<|reference_start|> XBRL: У статті досліджено сучасний стан впровадження IT у систему бухгалтерського обліку, а са-ме: у процес формування та подання фінансової звітності. Цифрова фінансова звітність змінить практику ведення бухгалтерського обліку найближчими роками, а зміни полягатимуть у технологіях представлення да-них. У 2021 році усі підприємства в Україні, що подають фінансову звітність за МСФЗ, повинні подавати фінансову звітність у форматі таксономії XBRL. У статті розкрито технологію подання фінансової звіт-ності у форматі іXBRL, яка є сучасним та зрозумілим стандартом обміну фінансовою інформацією між різ-ними зацікавленими користувачами з будь-якій країні світу. Розглянуто сучасний стан, проблеми та перспек-тиви впровадження нового цифрового стандарту подання фінансової звітності – iXBRL в Україні. Про-аналізовано сучасне програмне забезпечення щодо формування та подання фінансової звітності у форматі iXBRL на портал FRS. Визначено основні переваги та недоліки використання різних способів формування та подання фінансової звітності у форматі iXBRL. Надано рекомендації щодо процесу впровадження системи подання фінансової звітності у форматі iXBRL. Зроблено висновок, що на сучасному етапі розвитку інфор-маційних технологій в Україні кожна компанія може обирати свій шлях переходу на XBRL формат подання фінансової звітності. Проте, перед тим, як прийняти рішення щодо переходу на звітування в XBRL, необхідно: провести аналіз та перевірку власних ІТ-систем на предмет можливості формувати фінансову звітність у форматі XBRL; провести мапінг або зіставлення звітної інформації з власної ІТ-системи та розширеної так-сономії UA XBRL МСФЗ; провести валідацію XBRL, використавши уже існуюче програмне забезпечення (до прикладу, Arelle) чи на власному внутрішньому програмному забезпеченні компанії. <|reference_end|>" ]

[ 1, 3, 4, 7 ]

[ "<|reference_start|> Co-evolving parasites improve simulated evolution as an optimization procedure: <|reference_end|>", "<|reference_start|> Coevolutionary computation: This article proposes a general framework for the use of coevolution to boost the performance of genetic search. It combines coevolution with yet another biologically inspired technique, called lifetime fitness evaluation (LTFE). Two unrelated problems—neural net learning and constraint satisfaction—are used to illustrate the approach. Both problems use predator-prey interactions to boost the search. In contrast with traditional single population genetic algorithms (GAs), two populations constantly interact and coevolve. However, the same algorithm can also be used with different types of coevolutionary interactions. As an example, the symbiotic coevolution of solutions and genetic representations is shown to provide an elegant solution to the problem of finding a suitable genetic representation. The approach presented here greatly profits from the partial and continuous nature of LTFE. Noise tolerance is one advantage. Even more important, LTFE is ideally suited to deal with coupled fitness landscapes typical for coevolution. <|reference_end|>", "<|reference_start|> Solution concepts in coevolutionary algorithms: Inspired by the principle of natural selection, coevolutionary algorithms are search methods in which processes of mutual adaptation occur amongst agents that interact strategically. The outcomes of interaction reveal a reward structure that guides evolution towards the discovery of increasingly adaptive behaviors. Thus, coevolutionary algorithms are often used to search for optimal agent behaviors in domains of strategic interaction. \nCoevolutionary algorithms require little a priori knowledge about the domain. We assume the learning task necessitates the algorithm to (1) discover agent behaviors, (2) learn the domain's reward structure, and (3) approximate an optimal solution. Despite the many successes of coevolutionary optimization, the practitioner frequently observes a gap between the properties that actually confer agent adaptivity and those expected (or desired) to yield adaptivity, or optimality. This gap is manifested by a variety of well-known pathologies, such as cyclic dynamics, loss of fitness gradient, and evolutionary forgetting. \nThis dissertation examines the divergence between expectation and actuality in co-evolutionary algorithms—why selection pressures fail to conform to our beliefs about adaptiveness, or why our beliefs are evidently erroneous. When we confront the pathologies of coevolutionary algorithms as a collection, we find that they are essentially epiphenomena of a single fundamental problem, namely a lack of rigor in our solution concepts . \nA solution concept is a formalism with which to describe and understand the incentive structures of agents that interact strategically. All coevolutionary algorithms implement some solution concept, whether by design or by accident, and optimize according to it. Failures to obtain the desiderata of “complexity” or “optimality” often indicate a dissonance between the implemented solution concept and that required by our envisaged goal. \nWe make the following contributions: (1) We show that solution concepts are the critical link between our expectations of coevolution and the outcomes actually delivered by algorithm operation, and are therefore crucial to explicating the divergence between the two, (2) We provide analytic results that show how solution concepts bring our expectations in line with algorithmic reality, and (3) We show how solution concepts empower us to construct algorithms that operate more in line with our goals. <|reference_end|>", "<|reference_start|> Revisiting the edge of chaos: evolving cellular automata to perform computations: Author(s): Mitchell, Melanie; Hraber, Peter; Crutchfield, James P | Abstract: We present results from an experiment similar to one performed by Packard (1988), in which a genetic algorithm is used to evolve cellular automata (CA) to perform a particular computational task. Packard examined the frequency of evolved CA rules as a function of Langton's lambda parameter (Langton, 1990), and interpreted the results of his experiment as giving evidence for the following two hypotheses: (1) CA rules able to perform complex computations are most likely to be found near ``critical'' lambda values, which have been claimed to correlate with a phase transition between ordered and chaotic behavioral regimes for CA; (2) When CA rules are evolved to perform a complex computation, evolution will tend to select rules with lambda values close to the critical values. Our experiment produced very different results, and we suggest that the interpretation of the original results is not correct. We also review and discuss issues related to lambda, dynamical-behavior classes, and computation in CA. The main constructive results of our study are identifying the emergence and competition of computational strategies and analyzing the central role of symmetries in an evolutionary system. In particular, we demonstrate how symmetry breaking can impede the evolution toward higher computational capability. <|reference_end|>" ]

[ 0, 3, 7, 12 ]

[ "<|reference_start|> Training language models to follow instructions with human feedback: Making language models bigger does not inherently make them better at following a user's intent. For example, large language models can generate outputs that are untruthful, toxic, or simply not helpful to the user. In other words, these models are not aligned with their users. In this paper, we show an avenue for aligning language models with user intent on a wide range of tasks by fine-tuning with human feedback. Starting with a set of labeler-written prompts and prompts submitted through the OpenAI API, we collect a dataset of labeler demonstrations of the desired model behavior, which we use to fine-tune GPT-3 using supervised learning. We then collect a dataset of rankings of model outputs, which we use to further fine-tune this supervised model using reinforcement learning from human feedback. We call the resulting models InstructGPT. In human evaluations on our prompt distribution, outputs from the 1.3B parameter InstructGPT model are preferred to outputs from the 175B GPT-3, despite having 100x fewer parameters. Moreover, InstructGPT models show improvements in truthfulness and reductions in toxic output generation while having minimal performance regressions on public NLP datasets. Even though InstructGPT still makes simple mistakes, our results show that fine-tuning with human feedback is a promising direction for aligning language models with human intent. <|reference_end|>", "<|reference_start|> Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5): For a long time, different recommendation tasks require designing task-specific architectures and training objectives. As a result, it is hard to transfer the knowledge and representations from one task to another, thus restricting the generalization ability of existing recommendation approaches. To deal with such issues, considering that language can describe almost anything and language grounding is a powerful medium to represent various problems or tasks, we present a flexible and unified text-to-text paradigm called “Pretrain, Personalized Prompt, and Predict Paradigm” (P5) for recommendation, which unifies various recommendation tasks in a shared framework. In P5, all data such as user-item interactions, user descriptions, item metadata, and user reviews are converted to a common format — natural language sequences. The rich information from natural language assists P5 to capture deeper semantics for personalization and recommendation. Specifically, P5 learns different tasks with the same language modeling objective during pretraining. Thus, it serves as the foundation model for various downstream recommendation tasks, allows easy integration with other modalities, and enables instruction-based recommendation. P5 advances recommender systems from shallow model to deep model to big model, and will revolutionize the technical form of recommender systems towards universal recommendation engine. With adaptive personalized prompt for different users, P5 is able to make predictions in a zero-shot or few-shot manner and largely reduces the necessity for extensive fine-tuning. On several benchmarks, we conduct experiments to show the effectiveness of P5. To help advance future research on Recommendation as Language Processing (RLP), Personalized Foundation Models (PFM), and Universal Recommendation Engine (URE), we release the source code, dataset, prompts, and pretrained P5 model at https://github.com/jeykigung/P5. <|reference_end|>", "<|reference_start|> M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems: Industrial recommender systems have been growing increasingly complex, may involve \\emph{diverse domains} such as e-commerce products and user-generated contents, and can comprise \\emph{a myriad of tasks} such as retrieval, ranking, explanation generation, and even AI-assisted content production. The mainstream approach so far is to develop individual algorithms for each domain and each task. In this paper, we explore the possibility of developing a unified foundation model to support \\emph{open-ended domains and tasks} in an industrial recommender system, which may reduce the demand on downstream settings' data and can minimize the carbon footprint by avoiding training a separate model from scratch for every task. Deriving a unified foundation is challenging due to (i) the potentially unlimited set of downstream domains and tasks, and (ii) the real-world systems' emphasis on computational efficiency. We thus build our foundation upon M6, an existing large-scale industrial pretrained language model similar to GPT-3 and T5, and leverage M6's pretrained ability for sample-efficient downstream adaptation, by representing user behavior data as plain texts and converting the tasks to either language understanding or generation. To deal with a tight hardware budget, we propose an improved version of prompt tuning that outperforms fine-tuning with negligible 1\\% task-specific parameters, and employ techniques such as late interaction, early exiting, parameter sharing, and pruning to further reduce the inference time and the model size. We demonstrate the foundation model's versatility on a wide range of tasks such as retrieval, ranking, zero-shot recommendation, explanation generation, personalized content creation, and conversational recommendation, and manage to deploy it on both cloud servers and mobile devices. <|reference_end|>", "<|reference_start|> GPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretation: Recent advancements in Natural Language Processing (NLP) have led to the development of NLP-based recommender systems that have shown superior performance. However, current models commonly treat items as mere IDs and adopt discriminative modeling, resulting in limitations of (1) fully leveraging the content information of items and the language modeling capabilities of NLP models; (2) interpreting user interests to improve relevance and diversity; and (3) adapting practical circumstances such as growing item inventories. To address these limitations, we present GPT4Rec, a novel and flexible generative framework inspired by search engines. It first generates hypothetical \"search queries\" given item titles in a user's history, and then retrieves items for recommendation by searching these queries. The framework overcomes previous limitations by learning both user and item embeddings in the language space. To well-capture user interests with different aspects and granularity for improving relevance and diversity, we propose a multi-query generation technique with beam search. The generated queries naturally serve as interpretable representations of user interests and can be searched to recommend cold-start items. With GPT-2 language model and BM25 search engine, our framework outperforms state-of-the-art methods by $75.7\\%$ and $22.2\\%$ in Recall@K on two public datasets. Experiments further revealed that multi-query generation with beam search improves both the diversity of retrieved items and the coverage of a user's multi-interests. The adaptiveness and interpretability of generated queries are discussed with qualitative case studies. <|reference_end|>" ]

[ 0, 5, 6, 7 ]

[ "<|reference_start|> FVC: A New Framework towards Deep Video Compression in Feature Space: Learning based video compression attracts increasing attention in the past few years. The previous hybrid coding approaches rely on pixel space operations to reduce spatial and temporal redundancy, which may suffer from inaccurate motion estimation or less effective motion compensation. In this work, we propose a feature-space video coding network (FVC) by performing all major operations (i.e., motion estimation, motion compression, motion compensation and residual compression) in the feature space. Specifically, in the proposed deformable compensation module, we first apply motion estimation in the feature space to produce motion information (i.e., the offset maps), which will be compressed by using the auto-encoder style network. Then we perform motion compensation by using deformable convolution and generate the predicted feature. After that, we compress the residual feature between the feature from the current frame and the predicted feature from our deformable compensation module. For better frame reconstruction, the reference features from multiple previous reconstructed frames are also fused by using the non-local attention mechanism in the multi-frame feature fusion module. Comprehensive experimental results demonstrate that the proposed framework achieves the state-of-the-art performance on four benchmark datasets including HEVC, UVG, VTL and MCL-JCV. <|reference_end|>", "<|reference_start|> ELF-VC: Efficient Learned Flexible-Rate Video Coding: While learned video codecs have demonstrated great promise, they have yet to achieve sufficient efficiency for practical deployment. In this work, we propose several novel ideas for learned video compression which allow for improved performance for the low-latency mode (I- and P-frames only) along with a considerable increase in computational efficiency. In this setting, for natural videos our approach compares favorably across the entire R-D curve under metrics PSNR, MS-SSIM and VMAF against all mainstream video standards (H.264, H.265, AV1) and all ML codecs. At the same time, our approach runs at least 5x faster and has fewer parameters than all ML codecs which report these figures. Our contributions include a flexible-rate framework allowing a single model to cover a large and dense range of bitrates, at a negligible increase in computation and parameter count; an efficient backbone optimized for ML-based codecs; and a novel in-loop flow prediction scheme which leverages prior information towards more efficient compression. We benchmark our method, which we call ELF-VC (Efficient, Learned and Flexible Video Coding) on popular video test sets UVG and MCL-JCV under metrics PSNR, MS-SSIM and VMAF. For example, on UVG under PSNR, it reduces the BD-rate by 44% against H.264, 26% against H.265, 15% against AV1, and 35% against the current best ML codec. At the same time, on an NVIDIA Titan V GPU our approach encodes/decodes VGA at 49/91 FPS, HD 720 at 19/35 FPS, and HD 1080 at 10/18 FPS. <|reference_end|>", "<|reference_start|> Deep Contextual Video Compression: Most of the existing neural video compression methods adopt the predictive coding framework, which first generates the predicted frame and then encodes its residue with the current frame. However, as for compression ratio, predictive coding is only a sub-optimal solution as it uses simple subtraction operation to remove the redundancy across frames. In this paper, we propose a deep contextual video compression framework to enable a paradigm shift from predictive coding to conditional coding. In particular, we try to answer the following questions: how to define, use, and learn condition under a deep video compression framework. To tap the potential of conditional coding, we propose using feature domain context as condition. This enables us to leverage the high dimension context to carry rich information to both the encoder and the decoder, which helps reconstruct the high-frequency contents for higher video quality. Our framework is also extensible, in which the condition can be flexibly designed. Experiments show that our method can significantly outperform the previous state-of-the-art (SOTA) deep video compression methods. When compared with x265 using veryslow preset, we can achieve 26.0% bitrate saving for 1080P standard test videos. <|reference_end|>", "<|reference_start|> A deeply modulated scheme for variable-rate video compression: Rate adaption is one of the decisive factors for the applications of video compression. Previous deep video compression methods are usually optimized for a single fixed rate-distortion (R-D) tradeoff. While they can achieve multiple bitrates by training multiple independent models, the achievable bitrates are limited to several discrete points on the R-D curve and the storage cost increases proportionally to the number of models. We propose a variable-rate scheme for deep video compression, which can achieve continuously variable rate by a single model, i.e., reaching any point on the R-D curve. In our scheme, two deep auto-encoders are used to compress the residual and the motion vector field respectively, which directly generate the final bitstream. The basic rate adaptation can be achieved by using the R-D tradeoff parameter to deeply modulate all the internal feature maps of the auto-encoders. In addition, other modules in our scheme, notably motion estimation and motion compensation, also affect the final bitrate indirectly. We further use the R-D tradeoff parameter to modulate them via a conditional map, thereby effectively improving the compression efficiency. We use a multi-rate-distortion loss function together with a step-by-step training strategy to optimize the entire scheme. The experimental results show the proposed scheme achieves continuously variable rate by a single model with almost the same compression efficiency as multiple fixed-rate models. The additional parameters and computation of our model are negligible when compared with a single fixed-rate model. <|reference_end|>" ]

[ 3, 4, 6, 8 ]

[ "<|reference_start|> Modeling Relational Data with Graph Convolutional Networks: Knowledge graphs enable a wide variety of applications, including question answering and information retrieval. Despite the great effort invested in their creation and maintenance, even the largest (e.g., Yago, DBPedia or Wikidata) remain incomplete. We introduce Relational Graph Convolutional Networks (R-GCNs) and apply them to two standard knowledge base completion tasks: Link prediction (recovery of missing facts, i.e. subject-predicate-object triples) and entity classification (recovery of missing entity attributes). R-GCNs are related to a recent class of neural networks operating on graphs, and are developed specifically to deal with the highly multi-relational data characteristic of realistic knowledge bases. We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification. We further show that factorization models for link prediction such as DistMult can be significantly improved by enriching them with an encoder model to accumulate evidence over multiple inference steps in the relational graph, demonstrating a large improvement of 29.8% on FB15k-237 over a decoder-only baseline. <|reference_end|>", "<|reference_start|> A Collection of Benchmark Datasets for Systematic Evaluations of Machine Learning on the Semantic Web: <|reference_end|>", "<|reference_start|> GraphZoom: A multi-level spectral approach for accurate and scalable graph embedding: Graph embedding techniques have been increasingly deployed in a multitude of different applications that involve learning on non-Euclidean data. However, existing graph embedding models either fail to incorporate node attribute information during training or suffer from node attribute noise, which compromises the accuracy. Moreover, very few of them scale to large graphs due to their high computational complexity and memory usage. In this paper we propose GraphZoom, a multi-level framework for improving both accuracy and scalability of unsupervised graph embedding algorithms. GraphZoom first performs graph fusion to generate a new graph that effectively encodes the topology of the original graph and the node attribute information. This fused graph is then repeatedly coarsened into much smaller graphs by merging nodes with high spectral similarities. GraphZoom allows any existing embedding methods to be applied to the coarsened graph, before it progressively refine the embeddings obtained at the coarsest level to increasingly finer graphs. We have evaluated our approach on a number of popular graph datasets for both transductive and inductive tasks. Our experiments show that GraphZoom can substantially increase the classification accuracy and significantly accelerate the entire graph embedding process by up to 40.8x, when compared to the state-of-the-art unsupervised embedding methods. <|reference_end|>", "<|reference_start|> GraphZoom: A multi-level spectral approach for accurate and scalable graph embedding: Graph embedding techniques have been increasingly deployed in a multitude of different applications that involve learning on non-Euclidean data. However, existing graph embedding models either fail to incorporate node attribute information during training or suffer from node attribute noise, which compromises the accuracy. Moreover, very few of them scale to large graphs due to their high computational complexity and memory usage. In this paper we propose GraphZoom, a multi-level framework for improving both accuracy and scalability of unsupervised graph embedding algorithms. GraphZoom first performs graph fusion to generate a new graph that effectively encodes the topology of the original graph and the node attribute information. This fused graph is then repeatedly coarsened into much smaller graphs by merging nodes with high spectral similarities. GraphZoom allows any existing embedding methods to be applied to the coarsened graph, before it progressively refine the embeddings obtained at the coarsest level to increasingly finer graphs. We have evaluated our approach on a number of popular graph datasets for both transductive and inductive tasks. Our experiments show that GraphZoom can substantially increase the classification accuracy and significantly accelerate the entire graph embedding process by up to 40.8x, when compared to the state-of-the-art unsupervised embedding methods. <|reference_end|>" ]

[ 2, 3, 6, 7 ]

[ "<|reference_start|> Compressive sensing by random convolution: This paper demonstrates that convolution with random waveform followed by random time-domain subsampling is a universally efficient compressive sensing strategy. We show that an $n$-dimensional signal which is $S$-sparse in any fixed orthonormal representation can be recovered from $m\\gtrsim S\\log n$ samples from its convolution with a pulse whose Fourier transform has unit magnitude and random phase at all frequencies. The time-domain subsampling can be done in one of two ways: in the first, we simply observe $m$ samples of the random convolution; in the second, we break the random convolution into $m$ blocks and summarize each with a single randomized sum. We also discuss several imaging applications where convolution with a random pulse allows us to superresolve fine-scale features, allowing us to recover high-resolution signals from low-resolution measurements. <|reference_end|>", "<|reference_start|> Convolutional Compressed Sensing Using Deterministic Sequences: In this paper, a new class of circulant matrices built from deterministic sequences is proposed for convolution-based compressed sensing (CS). In contrast to random convolution, the coefficients of the underlying filter are given by the discrete Fourier transform of a deterministic sequence with good autocorrelation. Both uniform recovery and non-uniform recovery of sparse signals are investigated, based on the coherence parameter of the proposed sensing matrices. Many examples of the sequences are investigated, particularly the Frank-Zadoff-Chu (FZC) sequence, the \\textit{m}-sequence and the Golay sequence. A salient feature of the proposed sensing matrices is that they can not only handle sparse signals in the time domain, but also those in the frequency and/or or discrete-cosine transform (DCT) domain. <|reference_end|>", "<|reference_start|> Deterministic construction of compressed sensing matrices via algebraic curves: Compressed sensing is a sampling technique which provides a fundamentally new approach to data acquisition. Comparing with traditional methods, compressed sensing makes full use of sparsity so that a sparse signal can be reconstructed from very few measurements. A central problem in compressed sensing is the construction of sensing matrices. While random sensing matrices have been studied intensively, only a few deterministic constructions are known. Inspired by algebraic geometry codes, we introduce a new deterministic construction via algebraic curves over finite fields, which is a natural generalization of DeVore's construction using polynomials over finite fields. The diversity of algebraic curves provides numerous choices for sensing matrices. By choosing appropriate curves, we are able to construct binary sensing matrices which are superior to Devore's ones. We hope this connection between algebraic geometry and compressed sensing will provide a new point of view and stimulate further research in both areas. <|reference_end|>", "<|reference_start|> Quantization of compressive samples with stable and robust recovery: In this paper we study the quantization stage that is implicit in any compressed sensing signal acquisition paradigm. We propose using Sigma-Delta quantization and a subsequent reconstruction scheme based on convex optimization. We prove that the reconstruction error due to quantization decays polynomially in the number of measurements. Our results apply to arbitrary signals, including compressible ones, and account for measurement noise. Additionally, they hold for sub-Gaussian (including Gaussian and Bernoulli) random compressed sensing measurements, as well as for both high bit-depth and coarse quantizers, and they extend to 1-bit quantization. In the noise-free case, when the signal is strictly sparse we prove that by optimizing the order of the quantization scheme one can obtain root-exponential decay in the reconstruction error due to quantization. <|reference_end|>" ]

[ 2, 3, 5, 9 ]