Datasets:

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ubowang

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cite-llm-multi-cite-eval

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[ "<|reference_start|> Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning: A significant amount of the world's knowledge is stored in relational databases. However, the ability for users to retrieve facts from a database is limited due to a lack of understanding of query languages such as SQL. We propose Seq2SQL, a deep neural network for translating natural language questions to corresponding SQL queries. Our model leverages the structure of SQL queries to significantly reduce the output space of generated queries. Moreover, we use rewards from in-the-loop query execution over the database to learn a policy to generate unordered parts of the query, which we show are less suitable for optimization via cross entropy loss. In addition, we will publish WikiSQL, a dataset of 80654 hand-annotated examples of questions and SQL queries distributed across 24241 tables from Wikipedia. This dataset is required to train our model and is an order of magnitude larger than comparable datasets. By applying policy-based reinforcement learning with a query execution environment to WikiSQL, our model Seq2SQL outperforms attentional sequence to sequence models, improving execution accuracy from 35.9% to 59.4% and logical form accuracy from 23.4% to 48.3%. <|reference_end|>", "<|reference_start|> EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records: We present a new text-to-SQL dataset for electronic health records (EHRs). The utterances were collected from 222 hospital staff members, including physicians, nurses, and insurance review and health records teams. To construct the QA dataset on structured EHR data, we conducted a poll at a university hospital and used the responses to create seed questions. We then manually linked these questions to two open-source EHR databases, MIMIC-III and eICU, and included various time expressions and held-out unanswerable questions in the dataset, which were also collected from the poll. Our dataset poses a unique set of challenges: the model needs to 1) generate SQL queries that reflect a wide range of needs in the hospital, including simple retrieval and complex operations such as calculating survival rate, 2) understand various time expressions to answer time-sensitive questions in healthcare, and 3) distinguish whether a given question is answerable or unanswerable. We believe our dataset, EHRSQL, can serve as a practical benchmark for developing and assessing QA models on structured EHR data and take a step further towards bridging the gap between text-to-SQL research and its real-life deployment in healthcare. EHRSQL is available at https://github.com/glee4810/EHRSQL. <|reference_end|>", "<|reference_start|> RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers: When translating natural language questions into SQL queries to answer questions from a database, contemporary semantic parsing models struggle to generalize to unseen database schemas. The generalization challenge lies in (a) encoding the database relations in an accessible way for the semantic parser, and (b) modeling alignment between database columns and their mentions in a given query. We present a unified framework, based on the relation-aware self-attention mechanism, to address schema encoding, schema linking, and feature representation within a text-to-SQL encoder. On the challenging Spider dataset this framework boosts the exact match accuracy to 57.2%, surpassing its best counterparts by 8.7% absolute improvement. Further augmented with BERT, it achieves the new state-of-the-art performance of 65.6% on the Spider leaderboard. In addition, we observe qualitative improvements in the model’s understanding of schema linking and alignment. Our implementation will be open-sourced at https://github.com/Microsoft/rat-sql. <|reference_end|>", "<|reference_start|> {{STAR: 焦虑测验研究协会(Society for Test Anxiety Research)于1987年6月25—27日在挪威的卑尔根大学召开了第八届国际大会。本届大会的主席由卑尔根大学心理系教授KnutA.Hagtvet 担任。在开幕式上,STAR 的主席、美国加利福尼亚大学(伯克利)心理学教授Martin V.Covington 与卑尔根大学校长Magne Lerheim 博士致了开幕词。大会共分 <|reference_end|>" ]

[ 0, 4, 5, 6 ]

[ "<|reference_start|> Mastering the game of Go with deep neural networks and tree search: <|reference_end|>", "<|reference_start|> Magnetic control of tokamak plasmas through deep reinforcement learning: <|reference_end|>", "<|reference_start|> Terminal Adaptive Guidance for Autonomous Hypersonic Strike Weapons via Reinforcement Learning: An adaptive guidance system suitable for the terminal phase trajectory of a hypersonic strike weapon is optimized using reinforcement meta learning. The guidance system maps observations directly to commanded bank angle, angle of attack, and sideslip angle rates. Importantly, the observations are directly measurable from radar seeker outputs with minimal processing. The optimization framework implements a shaping reward that minimizes the line of sight rotation rate, with a terminal reward given if the agent satisfies path constraints and meets terminal accuracy and speed criteria. We show that the guidance system can adapt to off-nominal flight conditions including perturbation of aerodynamic coefficient parameters, actuator failure scenarios, sensor scale factor errors, and actuator lag, while satisfying heating rate, dynamic pressure, and load path constraints, as well as a minimum impact speed constraint. We demonstrate precision strike capability against a maneuvering ground target and the ability to divert to a new target, the latter being important to maximize strike effectiveness for a group of hypersonic strike weapons. Moreover, we demonstrate a threat evasion strategy against interceptors with limited midcourse correction capability, where the hypersonic strike weapon implements multiple diverts to alternate targets, with the last divert to the actual target. Finally, we include preliminary results for an integrated guidance and control system in a six degrees-of-freedom environment. <|reference_end|>", "<|reference_start|> Exploring Network Structure, Dynamics, and Function using NetworkX: NetworkX is a Python language package for exploration and analysis of networks and network algorithms. The core package provides data structures for representing many types of networks, or graphs, including simple graphs, directed graphs, and graphs with parallel edges and self-loops. The nodes in NetworkX graphs can be any (hashable) Python object and edges can contain arbitrary data; this flexibility makes NetworkX ideal for representing networks found in many different scientific fields. In addition to the basic data structures many graph algorithms are implemented for calculating network properties and structure measures: shortest paths, betweenness centrality, clustering, and degree distribution and many more. NetworkX can read and write various graph formats for easy exchange with existing data, and provides generators for many classic graphs and popular graph models, such as the Erdos-Renyi, Small World, and Barabasi-Albert models. The ease-of-use and flexibility of the Python programming language together with connection to the SciPy tools make NetworkX a powerful tool for scientific computations. We discuss some of our recent work studying synchronization of coupled oscillators to demonstrate how NetworkX enables research in the field of computational networks. <|reference_end|>" ]

[ 0, 3, 4, 6 ]

[ "<|reference_start|> The Search for a Finite Projective Plane of Order 10: When I was a graduate student looking for a thesis topic, Herbert Ryser advised me not to work on the projective plane of order 10. Even though he was extremely interested in this subject, he believed that it was too difficult and that I might get nowhere with it. I took his advice and chose another problem. Somehow, this problem has a beauty that fascinates me as well as many other mathematicians. Finally in 1980, I succumbed to the temptation and started working on it with some of my colleagues. We eventually managed to get somewhere, but unfortunately, Dr. Ryser is no longer with us to hear of the final result. This is an expository article describing the evolution of the problem and how computers were used to solve it. <|reference_end|>", "<|reference_start|> DRAT-trim: Efficient Checking and Trimming Using Expressive Clausal Proofs: <|reference_end|>", "<|reference_start|> Solving Very Hard Problems: Cube-and-Conquer, a Hybrid SAT Solving Method: A recent success of SAT solving has been the solution of the boolean Pythagorean Triples problem [Heule et al., 2016], delivering the largest proof yet, of 200 terabytes in size. We present this and the underlying paradigm Cube-and-Conquer, a powerful general method to solve big SAT problems, based on integrating the “old” and “new” methods of SAT solving. <|reference_end|>", "<|reference_start|> The nonexistence of code words of weight 16 in a projective plane of order 10: <|reference_end|>" ]

[ 0, 2, 6, 10 ]

[ "<|reference_start|> {SAR: SAR영상의 가장 큰 문제점은 경계선 부근에서 스패클(Speckle)잡음을 어떻게 줄이느냐 하는 것이다. 본 논문에서는 제안한 방법을 이용하여 경계선을 보존할 수 있는 효과적인 필터를 개발하고자 한다. 스패클 잡음을 줄이면서 에지 영역에 대한 블러링 없는 영상을 추출하기 위하여 웨이브렛 기반의 sigma 필터를 적용하였다. 실험 결과 에지정보에 대한 블러링을 줄인 출력 영상을 구성하였다. 제안한 방법을 미디언 필터와 비교한 결과, 스패클 잡음을 효과적으로 제거한 우수한 영상을 얻을 수 있었다. 【Any classification process using SAR images presupposes the reduction of multiplicative speckle noise, since the variations caused by speckle make it extremely difficult to distinguish between neighboring classes within the feature space. This paper focus an argument of effective filter for preserving the weak boundaries by using the proposed method. To reduce speckle noise without blurring the edges of reconstructed image use wavelet-based sigma filter. As a result, the edge information of reconstructed image reduce blurring. Simulation results show that proposed method gives a better subjective quality than conventional methods for the speckle noise.】 <|reference_end|>", "<|reference_start|> Image Reconstruction from Undersampled Fourier Data Using the Polynomial Annihilation Transform: <|reference_end|>", "<|reference_start|> Improving tissue segmentation of human brain MRI through preprocessing by the Gegenbauer reconstruction method: <|reference_end|>", "<|reference_start|> {Statistical and Computational Inverse Problems: Classification Without Interaction”), and 13 (“Two-Way Crossed Classification With Interaction”). Every chapter contains two or more numerical example with the exception of Chapters 14 (“Three-Way and Higher-Order Crossed Classifications”) and 17 (“General r-Way Nested Classification”), which only contain one example each. Examples appear in the estimation, confidence interval, and hypothesis testing sections. Distribution of estimators is only discussed for the models in Chapters 11 and 15 (“Two-Way Nested Classification”). Chapters 11, 13, 15, and 16 (“Three-Way Nested Classification”) contain information on design considerations involving unbalanced experiments. The appendixes contain basic theoretical and methodological results useful in the development of unbalanced random models as well as information on the capabilities of widely available software. Packages discussed are SAS, SPSS, BMDP, S–PLUS, GENSTAT, and BUGS. The book is well organized and focused. It contains extensive coverage on crossed and nested unbalanced models. Because of the number of topics, the depth of coverage is occasionally limited. This is only a minor issue, since there are always a substantial number of references given. The organization of the book and the presentation of the material make difficult subject matter easier to follow. The main drawback to the book is that it deals only with completely random univariate models. Given the volume of information in the book, however, this is understandable. The authors point out this shortcoming in the Preface and suggest that a future work covering these topics may be forthcoming. For the application-oriented practitioner, a small disadvantage is that a number of the estimation approaches discussed, while interesting, cannot be found in the more commonly used statistical software packages. Regardless, the book makes an excellent resource for anyone working with unbalanced random models. <|reference_end|>" ]

[ 1, 4, 8, 11 ]

[ "<|reference_start|> Fast embedding of sparse music similarity graphs: This paper applies fast sparse multidimensional scaling (MDS) to a large graph of music similarity, with 267K vertices that represent artists, albums, and tracks; and 3.22M edges that represent similarity between those entities. Once vertices are assigned locations in a Euclidean space, the locations can be used to browse music and to generate playlists. \n \nMDS on very large sparse graphs can be effectively performed by a family of algorithms called Rectangular Dijsktra (RD) MDS algorithms. These RD algorithms operate on a dense rectangular slice of the distance matrix, created by calling Dijsktra a constant number of times. Two RD algorithms are compared: Landmark MDS, which uses the Nystrom approximation to perform MDS; and a new algorithm called Fast Sparse Embedding, which uses FastMap. These algorithms compare favorably to Laplacian Eigenmaps, both in terms of speed and embedding quality. <|reference_end|>", "<|reference_start|> Active landmark sampling for manifold learning based spectral unmixing: Nonlinear manifold learning based spectral unmixing provides an alternative to direct nonlinear unmixing methods for accommodating nonlinearities inherent in hyperspectral data. Although manifolds can effectively capture nonlinear features in the dimensionality reduction stage of unmixing, the computational overhead is excessive for large remotely sensed data sets. Manifold approximation using a set of distinguishing points is commonly utilized to mitigate the computational burden, but selection of these landmark points is important for adequately representing the topology of the manifold. This study proposes an active landmark sampling framework for manifold learning based spectral unmixing using a small initial landmark set and a computationally efficient backbone-based strategy for constructing the manifold. The active landmark sampling strategy selects the best additional landmarks to develop a more representative manifold and to increase unmixing accuracy. <|reference_end|>", "<|reference_start|> Selecting landmark points for sparse manifold learning: There has been a surge of interest in learning non-linear manifold models to approximate high-dimensional data. Both for computational complexity reasons and for generalization capability, sparsity is a desired feature in such models. This usually means dimensionality reduction, which naturally implies estimating the intrinsic dimension, but it can also mean selecting a subset of the data to use as landmarks, which is especially important because many existing algorithms have quadratic complexity in the number of observations. This paper presents an algorithm for selecting landmarks, based on LASSO regression, which is well known to favor sparse approximations because it uses regularization with an l1 norm. As an added benefit, a continuous manifold parameterization, based on the landmarks, is also found. Experimental results with synthetic and real data illustrate the algorithm. <|reference_end|>", "<|reference_start|> An incremental dimensionality reduction method on discriminant information for pattern classification: <|reference_end|>" ]

[ 25, 29, 33, 38 ]

[ "<|reference_start|> Large-Scale Adversarial Training for Vision-and-Language Representation Learning: We present VILLA, the first known effort on large-scale adversarial training for vision-and-language (V+L) representation learning. VILLA consists of two training stages: (i) task-agnostic adversarial pre-training; followed by (ii) task-specific adversarial finetuning. Instead of adding adversarial perturbations on image pixels and textual tokens, we propose to perform adversarial training in the embedding space of each modality. To enable large-scale training, we adopt the \"free\" adversarial training strategy, and combine it with KL-divergence-based regularization to promote higher invariance in the embedding space. We apply VILLA to current best-performing V+L models, and achieve new state of the art on a wide range of tasks, including Visual Question Answering, Visual Commonsense Reasoning, Image-Text Retrieval, Referring Expression Comprehension, Visual Entailment, and NLVR2. <|reference_end|>", "<|reference_start|> Learning Transferable Visual Models From Natural Language Supervision: State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP. <|reference_end|>", "<|reference_start|> Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss: Employing large-scale pre-trained model CLIP to conduct video-text retrieval task (VTR) has become a new trend, which exceeds previous VTR methods. Though, due to the heterogeneity of structures and contents between video and text, previous CLIP-based models are prone to overfitting in the training phase, resulting in relatively poor retrieval performance. In this paper, we propose a multi-stream Corpus Alignment network with single gate Mixture-of-Experts (CAMoE) and a novel Dual Softmax Loss (DSL) to solve the two heterogeneity. The CAMoE employs Mixture-of-Experts (MoE) to extract multi-perspective video representations, including action, entity, scene, etc., then align them with the corresponding part of the text. In this stage, we conduct massive explorations towards the feature extraction module and feature alignment module. DSL is proposed to avoid the one-way optimum-match which occurs in previous contrastive methods. Introducing the intrinsic prior of each pair in a batch, DSL serves as a reviser to correct the similarity matrix and achieves the dual optimal match. DSL is easy to implement with only one-line code but improves significantly. The results show that the proposed CAMoE and DSL are of strong efficiency, and each of them is capable of achieving State-of-The-Art (SOTA) individually on various benchmarks such as MSR-VTT, MSVD, and LSMDC. Further, with both of them, the performance is advanced to a big extend, surpassing the previous SOTA methods for around 4.6\\% R@1 in MSR-VTT. <|reference_end|>", "<|reference_start|> ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision: Vision-and-Language Pre-training (VLP) has improved performance on various joint vision-and-language downstream tasks. Current approaches to VLP heavily rely on image feature extraction processes, most of which involve region supervision (e.g., object detection) and the convolutional architecture (e.g., ResNet). Although disregarded in the literature, we find it problematic in terms of both (1) efficiency/speed, that simply extracting input features requires much more computation than the multimodal interaction steps; and (2) expressive power, as it is upper bounded to the expressive power of the visual embedder and its predefined visual vocabulary. In this paper, we present a minimal VLP model, Vision-and-Language Transformer (ViLT), monolithic in the sense that the processing of visual inputs is drastically simplified to just the same convolution-free manner that we process textual inputs. We show that ViLT is up to tens of times faster than previous VLP models, yet with competitive or better downstream task performance. Our code and pre-trained weights are available at https://github.com/dandelin/vilt. <|reference_end|>" ]

[ 4, 20, 23, 28 ]

[ "<|reference_start|> Degrees of Freedom of Time Correlated MISO Broadcast Channel with Delayed CSIT: We consider the time correlated multiple-input single-output (MISO) broadcast channel where the transmitter has imperfect knowledge on the current channel state, in addition to delayed channel state information. By representing the quality of the current channel state information as P^-{\\alpha} for the signal-to-noise ratio P and some constant {\\alpha} \\geq 0, we characterize the optimal degree of freedom region for this more general two-user MISO broadcast correlated channel. The essential ingredients of the proposed scheme lie in the quantization and multicasting of the overheard interferences, while broadcasting new private messages. Our proposed scheme smoothly bridges between the scheme recently proposed by Maddah-Ali and Tse with no current state information and a simple zero-forcing beamforming with perfect current state information. <|reference_end|>", "<|reference_start|> Degrees-of-Freedom Region of the MISO Broadcast Channel with General Mixed-CSIT: In the setting of the two-user broadcast channel, recent work by Maddah-Ali and Tse has shown that knowledge of prior channel state information at the transmitter (CSIT) can be useful, even in the absence of any knowledge of current CSIT. Very recent work by Kobayashi et al., Yang et al., and Gou and Jafar, extended this to the case where, instead of no current CSIT knowledge, the transmitter has partial knowledge, and where under a symmetry assumption, the quality of this knowledge is identical for the different users' channels. Motivated by the fact that in multiuser settings, the quality of CSIT feedback may vary across different links, we here generalize the above results to the natural setting where the current CSIT quality varies for different users' channels. For this setting we derive the optimal degrees-of-freedom (DoF) region, and provide novel multi-phase broadcast schemes that achieve this optimal region. Finally this generalization incorporates and generalizes the corresponding result in Maleki et al. which considered the broadcast channel with one user having perfect CSIT and the other only having prior CSIT. <|reference_end|>", "<|reference_start|> On the synergistic benefits of alternating csit for the miso broadcast channel: The degrees of freedom (DoFs) of the two-user multiple-input single-output (MISO) broadcast channel (BC) are studied under the assumption that the form, <i>Ii</i>, <i>i</i>=1, 2, of the channel state information at the transmitter (CSIT) for each user's channel can be either perfect (<i>P</i>), delayed (<i>D</i>), or not available (<i>N</i>), i.e., <i>I</i>₁,<i>I</i>₂ ∈ {<i>P</i>,<i>N</i>,<i>D</i>} , and therefore, the overall CSIT can alternate between the nine resulting states <i>I</i>₁<i>I</i>₂. The fraction of time associated with CSIT state <i>I</i>₁<i>I</i>₂ is denoted by the parameter λ<i>I</i>₁<i>I</i>₂ and it is assumed throughout that λ<i>I</i>₁<i>I</i>₂ = λ<i>I</i>₂<i>I</i>₁, i.e., λ<i>PN</i> = λ<i>NP</i>, λ<i>PD</i>=λ<i>DP</i>, λ<i>DN</i>=λ<i>ND</i> . Under this assumption of symmetry, the main contribution of this paper is a complete characterization of the DoF region of the two-user MISO BC with alternating CSIT. Surprisingly, the DoF region is found to depend only on the marginal probabilities (λ<i>P</i>, λ<i>D</i>,λ<i>N</i>) = (Σ<i>I</i>₂ λ<i>PI</i>₂, Σ<i>I</i>₂ λ<i>DI</i>₂, Σ<i>I</i>₂ λ<i>NI</i>₂), <i>I</i>₂ ∈ {<i>P</i>, <i>D</i>, <i>N</i>}, which represent the fraction of time that any given user (e.g., user 1) is associated with perfect, delayed, or no CSIT, respectively. As a consequence, the DoF region with all nine CSIT states, <i>D</i>(λ<i>I</i>₁<i>I</i>₂:<i>I</i>₁,<i>I</i>₂ ∈ {<i>P</i>,<i>D</i>,<i>N</i>}) , is the same as the DoF region with only three CSIT states <i>D</i>(λ<i>PP</i>, λ<i>DD</i>, λ<i>NN</i>), under the same marginal distribution of CSIT states, i.e., (λ<i>PP</i>, λ<i>DD</i>,λ<i>NN</i>)=(λ<i>P</i>,λ<i>D</i>,λ<i>N</i>). The sum-DoF value can be expressed as DoF=min([(4+2λ<i>P</i>)/3], 1+λ<i>P</i>+λ<i>D</i>), from which one can uniquely identify the minimum required marginal CSIT fractions to achieve any target DoF value as (λ<i>P</i>,λ<i>D</i>)_min=([3/2] DoF-2,1- [1/2] DoF) when DoF ∈ [[4/3],2] and (λ<i>P</i>,λ<i>D</i>)_min=(0,(DoF-1)⁺) when DoF ∈ [0, [4/3]). The results highlight the synergistic benefits of alternating CSIT and the tradeoffs between various forms of CSIT for any given DoF value. Partial results are also presented for the multiuser MISO BC with <i>M</i> transmit antennas and <i>K</i> single antenna users. For this problem, the minimum amount of perfect CSIT required per user to achieve the maximum DoFs of min(<i>M</i>,<i>K</i>) is characterized. By the minimum amount of CSIT per user, we refer to the minimum fraction of time that the transmitter has access to perfect and instantaneous CSIT from a user. Through a novel converse proof and an achievable scheme, it is shown that the minimum fraction of time perfect CSIT is required per user in order to achieve the DoF of min(<i>M</i>,<i>K</i>) is given by min(<i>M</i>,<i>K</i>)/<i>K</i>. <|reference_end|>", "<|reference_start|> On the synergistic benefits of alternating csit for the miso broadcast channel: The degrees of freedom (DoFs) of the two-user multiple-input single-output (MISO) broadcast channel (BC) are studied under the assumption that the form, <i>Ii</i>, <i>i</i>=1, 2, of the channel state information at the transmitter (CSIT) for each user's channel can be either perfect (<i>P</i>), delayed (<i>D</i>), or not available (<i>N</i>), i.e., <i>I</i>₁,<i>I</i>₂ ∈ {<i>P</i>,<i>N</i>,<i>D</i>} , and therefore, the overall CSIT can alternate between the nine resulting states <i>I</i>₁<i>I</i>₂. The fraction of time associated with CSIT state <i>I</i>₁<i>I</i>₂ is denoted by the parameter λ<i>I</i>₁<i>I</i>₂ and it is assumed throughout that λ<i>I</i>₁<i>I</i>₂ = λ<i>I</i>₂<i>I</i>₁, i.e., λ<i>PN</i> = λ<i>NP</i>, λ<i>PD</i>=λ<i>DP</i>, λ<i>DN</i>=λ<i>ND</i> . Under this assumption of symmetry, the main contribution of this paper is a complete characterization of the DoF region of the two-user MISO BC with alternating CSIT. Surprisingly, the DoF region is found to depend only on the marginal probabilities (λ<i>P</i>, λ<i>D</i>,λ<i>N</i>) = (Σ<i>I</i>₂ λ<i>PI</i>₂, Σ<i>I</i>₂ λ<i>DI</i>₂, Σ<i>I</i>₂ λ<i>NI</i>₂), <i>I</i>₂ ∈ {<i>P</i>, <i>D</i>, <i>N</i>}, which represent the fraction of time that any given user (e.g., user 1) is associated with perfect, delayed, or no CSIT, respectively. As a consequence, the DoF region with all nine CSIT states, <i>D</i>(λ<i>I</i>₁<i>I</i>₂:<i>I</i>₁,<i>I</i>₂ ∈ {<i>P</i>,<i>D</i>,<i>N</i>}) , is the same as the DoF region with only three CSIT states <i>D</i>(λ<i>PP</i>, λ<i>DD</i>, λ<i>NN</i>), under the same marginal distribution of CSIT states, i.e., (λ<i>PP</i>, λ<i>DD</i>,λ<i>NN</i>)=(λ<i>P</i>,λ<i>D</i>,λ<i>N</i>). The sum-DoF value can be expressed as DoF=min([(4+2λ<i>P</i>)/3], 1+λ<i>P</i>+λ<i>D</i>), from which one can uniquely identify the minimum required marginal CSIT fractions to achieve any target DoF value as (λ<i>P</i>,λ<i>D</i>)_min=([3/2] DoF-2,1- [1/2] DoF) when DoF ∈ [[4/3],2] and (λ<i>P</i>,λ<i>D</i>)_min=(0,(DoF-1)⁺) when DoF ∈ [0, [4/3]). The results highlight the synergistic benefits of alternating CSIT and the tradeoffs between various forms of CSIT for any given DoF value. Partial results are also presented for the multiuser MISO BC with <i>M</i> transmit antennas and <i>K</i> single antenna users. For this problem, the minimum amount of perfect CSIT required per user to achieve the maximum DoFs of min(<i>M</i>,<i>K</i>) is characterized. By the minimum amount of CSIT per user, we refer to the minimum fraction of time that the transmitter has access to perfect and instantaneous CSIT from a user. Through a novel converse proof and an achievable scheme, it is shown that the minimum fraction of time perfect CSIT is required per user in order to achieve the DoF of min(<i>M</i>,<i>K</i>) is given by min(<i>M</i>,<i>K</i>)/<i>K</i>. <|reference_end|>" ]

[ 1, 3, 7, 8 ]

[ "<|reference_start|> Supervised Learning of Universal Sentence Representations from Natural Language Inference Data: Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features. Efforts to obtain embeddings for larger chunks of text, such as sentences, have however not been so successful. Several attempts at learning unsupervised representations of sentences have not reached satisfactory enough performance to be widely adopted. In this paper, we show how universal sentence representations trained using the supervised data of the Stanford Natural Language Inference datasets can consistently outperform unsupervised methods like SkipThought vectors on a wide range of transfer tasks. Much like how computer vision uses ImageNet to obtain features, which can then be transferred to other tasks, our work tends to indicate the suitability of natural language inference for transfer learning to other NLP tasks. Our encoder is publicly available. <|reference_end|>", "<|reference_start|> Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning: A lot of the recent success in natural language processing (NLP) has been driven by distributed vector representations of words trained on large amounts of text in an unsupervised manner. These representations are typically used as general purpose features for words across a range of NLP problems. However, extending this success to learning representations of sequences of words, such as sentences, remains an open problem. Recent work has explored unsupervised as well as supervised learning techniques with different training objectives to learn general purpose fixed-length sentence representations. In this work, we present a simple, effective multi-task learning framework for sentence representations that combines the inductive biases of diverse training objectives in a single model. We train this model on several data sources with multiple training objectives on over 100 million sentences. Extensive experiments demonstrate that sharing a single recurrent sentence encoder across weakly related tasks leads to consistent improvements over previous methods. We present substantial improvements in the context of transfer learning and low-resource settings using our learned general-purpose representations. <|reference_end|>", "<|reference_start|> Predicting Entry-Level Categories: <|reference_end|>", "<|reference_start|> Diverse concept-level features for multi-object classification: We consider the problem of image classification with semantic features that are built from a set of base classifier outputs, each reflecting visual concepts. However, existing approaches consider visual concepts independently from each other whereas they are often linked together. When those relations are considered, existing models strongly rely on image low-level features, yielding in irrelevant relations when the low-level representation fails. On the contrary, the approach we propose, uses existing human knowledge, the application context itself and the human categorization mechanism to reflect complex relations between concepts. By nesting this human knowledge and the application context in the concept detection and selection processes, our final semantic feature captures the most useful information for an effective categorization. Thus, it enables to give good representation, even if some important concepts failed to be recognized. Experimental validation is conducted on three publicly available benchmarks of multi-class object classification and leads to results that outperforms comparable approaches. <|reference_end|>" ]

[ 3, 5, 9, 10 ]

[ "<|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|> Policy gradient methods for reinforcement learning with function\napproximation: Function approximation is essential to reinforcement learning, but the standard approach of approximating a value function and determining a policy from it has so far proven theoretically intractable. In this paper we explore an alternative approach in which the policy is explicitly represented by its own function approximator, independent of the value function, and is updated according to the gradient of expected reward with respect to the policy parameters. Williams's REINFORCE method and actor-critic methods are examples of this approach. Our main new result is to show that the gradient can be written in a form suitable for estimation from experience aided by an approximate action-value or advantage function. Using this result, we prove for the first time that a version of policy iteration with arbitrary differentiable function approximation is convergent to a locally optimal policy. <|reference_end|>", "<|reference_start|> Maximum a Posteriori Policy Optimisation: We introduce a new algorithm for reinforcement learning called Maximum aposteriori Policy Optimisation (MPO) based on coordinate ascent on a relative entropy objective. We show that several existing methods can directly be related to our derivation. We develop two off-policy algorithms and demonstrate that they are competitive with the state-of-the-art in deep reinforcement learning. In particular, for continuous control, our method outperforms existing methods with respect to sample efficiency, premature convergence and robustness to hyperparameter settings while achieving similar or better final performance. <|reference_end|>", "<|reference_start|> Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation: In this work, we propose to apply trust region optimization to deep reinforcement learning using a recently proposed Kronecker-factored approximation to the curvature. We extend the framework of natural policy gradient and propose to optimize both the actor and the critic using Kronecker-factored approximate curvature (K-FAC) with trust region; hence we call our method Actor Critic using Kronecker-Factored Trust Region (ACKTR). To the best of our knowledge, this is the first scalable trust region natural gradient method for actor-critic methods. It is also a method that learns non-trivial tasks in continuous control as well as discrete control policies directly from raw pixel inputs. We tested our approach across discrete domains in Atari games as well as continuous domains in the MuJoCo environment. With the proposed methods, we are able to achieve higher rewards and a 2- to 3-fold improvement in sample efficiency on average, compared to previous state-of-the-art on-policy actor-critic methods. Code is available at https://github.com/openai/baselines <|reference_end|>" ]

[ 7, 8, 12, 23 ]

[ "<|reference_start|> Text Data Augmentation for Deep Learning: <|reference_end|>", "<|reference_start|> Conditional BERT Contextual Augmentation: We propose a novel data augmentation method for labeled sentences called conditional BERT contextual augmentation. Data augmentation methods are often applied to prevent overfitting and improve generalization of deep neural network models. Recently proposed contextual augmentation augments labeled sentences by randomly replacing words with more varied substitutions predicted by language model. BERT demonstrates that a deep bidirectional language model is more powerful than either an unidirectional language model or the shallow concatenation of a forward and backward model. We retrofit BERT to conditional BERT by introducing a new conditional masked language model\\footnote{The term \"conditional masked language model\" appeared once in original BERT paper, which indicates context-conditional, is equivalent to term \"masked language model\". In our paper, \"conditional masked language model\" indicates we apply extra label-conditional constraint to the \"masked language model\".} task. The well trained conditional BERT can be applied to enhance contextual augmentation. Experiments on six various different text classification tasks show that our method can be easily applied to both convolutional or recurrent neural networks classifier to obtain obvious improvement. <|reference_end|>", "<|reference_start|> {{BART: 旧金山市湾区捷运(BART)系统技术标准高，具备较高的安全可靠度；容量大，储备充分，服务功能完善，舒适度高，具有可持续发展性；四通八达，与其它交通方式有效衔接系统充分体现了可持续发展，安全可靠，畅达、快捷、高效，客运交通一体化的先进理念，非常值得我国轨道交通建设和发展借鉴。 <|reference_end|>", "<|reference_start|> Distributed Representations of Words and Phrases and their Compositionality: The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several extensions that improve both the quality of the vectors and the training speed. By subsampling of the frequent words we obtain significant speedup and also learn more regular word representations. We also describe a simple alternative to the hierarchical softmax called negative sampling. An inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of \"Canada\" and \"Air\" cannot be easily combined to obtain \"Air Canada\". Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible. <|reference_end|>" ]

[ 0, 6, 12, 13 ]

[ "<|reference_start|> Graph Learning From Data Under Laplacian and Structural Constraints: Graphs are fundamental mathematical structures used in various fields to represent data, signals, and processes. In this paper, we propose a novel framework for learning/estimating graphs from data. The proposed framework includes (i) formulation of various graph learning problems, (ii) their probabilistic interpretations, and (iii) associated algorithms. Specifically, graph learning problems are posed as the estimation of graph Laplacian matrices from some observed data under given structural constraints (e.g., graph connectivity and sparsity level). From a probabilistic perspective, the problems of interest correspond to maximum a posteriori parameter estimation of Gaussian–Markov random field models, whose precision (inverse covariance) is a graph Laplacian matrix. For the proposed graph learning problems, specialized algorithms are developed by incorporating the graph Laplacian and structural constraints. The experimental results demonstrate that the proposed algorithms outperform the current state-of-the-art methods in terms of accuracy and computational efficiency. <|reference_end|>", "<|reference_start|> Signal Processing on Graphs: Causal Modeling of Unstructured Data: Many applications collect a large number of time series, for example, the financial data of companies quoted in a stock exchange, the health care data of all patients that visit the emergency room of a hospital, or the temperature sequences continuously measured by weather stations across the US. These data are often referred to as unstructured. A first task in its analytics is to derive a low dimensional representation, a graph or discrete manifold, that describes well the interrelations among the time series and their intrarelations across time. This paper presents a computationally tractable algorithm for estimating this graph that structures the data. The resulting graph is directed and weighted, possibly capturing causal relations, not just reciprocal correlations as in many existing approaches in the literature. A convergence analysis is carried out. The algorithm is demonstrated on random graph datasets and real network time series datasets, and its performance is compared to that of related methods. The adjacency matrices estimated with the new method are close to the true graph in the simulated data and consistent with prior physical knowledge in the real dataset tested. <|reference_end|>", "<|reference_start|> Network Inference via the Time-Varying Graphical Lasso: Many important problems can be modeled as a system of interconnected entities, where each entity is recording time-dependent observations or measurements. In order to spot trends, detect anomalies, and interpret the temporal dynamics of such data, it is essential to understand the relationships between the different entities and how these relationships evolve over time. In this paper, we introduce the time-varying graphical lasso (TVGL), a method of inferring time-varying networks from raw time series data. We cast the problem in terms of estimating a sparse time-varying inverse covariance matrix, which reveals a dynamic network of interdependencies between the entities. Since dynamic network inference is a computationally expensive task, we derive a scalable message-passing algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve this problem in an efficient way. We also discuss several extensions, including a streaming algorithm to update the model and incorporate new observations in real time. Finally, we evaluate our TVGL algorithm on both real and synthetic datasets, obtaining interpretable results and outperforming state-of-the-art baselines in terms of both accuracy and scalability. <|reference_end|>", "<|reference_start|> Connecting the Dots: Identifying Network Structure\nvia Graph Signal Processing: Network topology inference is a significant problem in network science. Most graph signal processing (GSP) efforts to date assume that the underlying network is known and then analyze how the graph?s algebraic and spectral characteristics impact the properties of the graph signals of interest. Such an assumption is often untenable beyond applications dealing with, e.g., directly observable social and infrastructure networks; and typically adopted graph construction schemes are largely informal, distinctly lacking an element of validation. This article offers an overview of graph-learning methods developed to bridge the aforementioned gap, by using information available from graph signals to infer the underlying graph topology. Fairly mature statistical approaches are surveyed first, where correlation analysis takes center stage along with its connections to covariance selection and high-dimensional regression for learning Gaussian graphical models. Recent GSP-based network inference frameworks are also described, which postulate that the network exists as a latent underlying structure and that observations are generated as a result of a network process defined in such a graph. A number of arguably more nascent topics are also briefly outlined, including inference of dynamic networks and nonlinear models of pairwise interaction, as well as extensions to directed (di) graphs and their relation to causal inference. All in all, this article introduces readers to challenges and opportunities for SP research in emerging topic areas at the crossroads of modeling, prediction, and control of complex behavior arising in networked systems that evolve over time. <|reference_end|>" ]

[ 17, 20, 26, 37 ]

[ "<|reference_start|> Online dictionary learning for sparse coding: Sparse coding---that is, modelling data vectors as sparse linear combinations of basis elements---is widely used in machine learning, neuroscience, signal processing, and statistics. This paper focuses on learning the basis set, also called dictionary, to adapt it to specific data, an approach that has recently proven to be very effective for signal reconstruction and classification in the audio and image processing domains. This paper proposes a new online optimization algorithm for dictionary learning, based on stochastic approximations, which scales up gracefully to large datasets with millions of training samples. A proof of convergence is presented, along with experiments with natural images demonstrating that it leads to faster performance and better dictionaries than classical batch algorithms for both small and large datasets. <|reference_end|>", "<|reference_start|> Online Learning for Matrix Factorization and Sparse Coding: Sparse coding--that is, modelling data vectors as sparse linear combinations of basis elements--is widely used in machine learning, neuroscience, signal processing, and statistics. This paper focuses on the large-scale matrix factorization problem that consists of learning the basis set, adapting it to specific data. Variations of this problem include dictionary learning in signal processing, non-negative matrix factorization and sparse principal component analysis. In this paper, we propose to address these tasks with a new online optimization algorithm, based on stochastic approximations, which scales up gracefully to large datasets with millions of training samples, and extends naturally to various matrix factorization formulations, making it suitable for a wide range of learning problems. A proof of convergence is presented, along with experiments with natural images and genomic data demonstrating that it leads to state-of-the-art performance in terms of speed and optimization for both small and large datasets. <|reference_end|>", "<|reference_start|> {Regularization paths for generalized linear models via coordinate descent: We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multinomial regression problems while the penalties include ℓ(1) (the lasso), ℓ(2) (ridge regression) and mixtures of the two (the elastic net). The algorithms use cyclical coordinate descent, computed along a regularization path. The methods can handle large problems and can also deal efficiently with sparse features. In comparative timings we find that the new algorithms are considerably faster than competing methods. <|reference_end|>", "<|reference_start|> Sparse representation for signal classification: In this paper, application of sparse representation (factorization) of signals over an overcomplete basis (dictionary) for signal classification is discussed. Searching for the sparse representation of a signal over an overcomplete dictionary is achieved by optimizing an objective function that includes two terms: one that measures the signal reconstruction error and another that measures the sparsity. This objective function works well in applications where signals need to be reconstructed, like coding and denoising. On the other hand, discriminative methods, such as linear discriminative analysis (LDA), are better suited for classification tasks. However, discriminative methods are usually sensitive to corruption in signals due to lacking crucial properties for signal reconstruction. In this paper, we present a theoretical framework for signal classification with sparse representation. The approach combines the discrimination power of the discriminative methods with the reconstruction property and the sparsity of the sparse representation that enables one to deal with signal corruptions: noise, missing data and outliers. The proposed approach is therefore capable of robust classification with a sparse representation of signals. The theoretical results are demonstrated with signal classification tasks, showing that the proposed approach outperforms the standard discriminative methods and the standard sparse representation in the case of corrupted signals. <|reference_end|>" ]

[ 1, 2, 3, 4 ]

[ "<|reference_start|> EmoDiff: Intensity Controllable Emotional Text-to-Speech with Soft-Label Guidance: Although current neural text-to-speech (TTS) models are able to generate high-quality speech, intensity controllable emotional TTS is still a challenging task. Most existing methods need external optimizations for intensity calculation, leading to suboptimal results or degraded quality. In this paper, we propose EmoDiff, a diffusion-based TTS model where emotion intensity can be manipulated by a proposed soft-label guidance technique derived from classifier guidance. Specifically, instead of being guided with a one-hot vector for the specified emotion, EmoDiff is guided with a soft label where the value of the specified emotion and \\textit{Neutral} is set to $\\alpha$ and $1-\\alpha$ respectively. The $\\alpha$ here represents the emotion intensity and can be chosen from 0 to 1. Our experiments show that EmoDiff can precisely control the emotion intensity while maintaining high voice quality. Moreover, diverse speech with specified emotion intensity can be generated by sampling in the reverse denoising process. <|reference_end|>", "<|reference_start|> Self-supervised Context-aware Style Representation for Expressive Speech Synthesis: Expressive speech synthesis, like audiobook synthesis, is still challenging for style representation learning and prediction. Deriving from reference audio or predicting style tags from text requires a huge amount of labeled data, which is costly to acquire and difficult to define and annotate accurately. In this paper, we propose a novel framework for learning style representation from abundant plain text in a self-supervised manner. It leverages an emotion lexicon and uses contrastive learning and deep clustering. We further integrate the style representation as a conditioned embedding in a multi-style Transformer TTS. Comparing with multi-style TTS by predicting style tags trained on the same dataset but with human annotations, our method achieves improved results according to subjective evaluations on both in-domain and out-of-domain test sets in audiobook speech. Moreover, with implicit context-aware style representation, the emotion transition of synthesized audio in a long paragraph appears more natural. The audio samples are available on the demo web. <|reference_end|>", "<|reference_start|> This is the post-print version of Matamala , Anna ( 2019 ) \" Voice-over : practice , research and future prospects \": <|reference_end|>", "<|reference_start|> Voice for Performance: Training the Actor's Voice: <|reference_end|>" ]

[ 5, 9, 14, 15 ]

[ "<|reference_start|> New Analysis and Algorithm for Learning with Drifting Distributions: We present a new analysis of the problem of learning with drifting distributions in the batch setting using the notion of discrepancy. We prove learning bounds based on the Rademacher complexity of the hypothesis set and the discrepancy of distributions both for a drifting PAC scenario and a tracking scenario. Our bounds are always tighter and in some cases substantially improve upon previous ones based on the $L_1$ distance. We also present a generalization of the standard on-line to batch conversion to the drifting scenario in terms of the discrepancy and arbitrary convex combinations of hypotheses. We introduce a new algorithm exploiting these learning guarantees, which we show can be formulated as a simple QP. Finally, we report the results of preliminary experiments demonstrating the benefits of this algorithm. <|reference_end|>", "<|reference_start|> Adaptation based on generalized discrepancy: We present a new algorithm for domain adaptation improving upon a discrepancy minimization algorithm, (DM), previously shown to outperform a number of algorithms for this problem. Unlike many previously proposed solutions for domain adaptation, our algorithm does not consist of a fixed reweighting of the losses over the training sample. Instead, the reweighting depends on the hypothesis sought. The algorithm is derived from a less conservative notion of discrepancy than the DM algorithm called generalized discrepancy. We present a detailed description of our algorithm and show that it can be formulated as a convex optimization problem. We also give a detailed theoretical analysis of its learning guarantees which helps us select its parameters. Finally, we report the results of experiments demonstrating that it improves upon discrepancy minimization in several tasks. <|reference_end|>", "<|reference_start|> Lipschitz Density-Ratios, Structured Data, and Data-driven Tuning: Density-ratio estimation (i.e. estimating f = fQ/fP for two unknown distributions Q and P ) has proved useful in many Machine Learning tasks, e.g., risk-calibration in transfer-learning, two-sample tests, and also useful in common techniques such importance sampling and bias correction. While there are many important analyses of this estimation problem, the present paper derives convergence rates in other practical settings that are less understood, namely, extensions of traditional Lipschitz smoothness conditions, and common high-dimensional settings with structured data (e.g. manifold data, sparse data). Various interesting facts, which hold in earlier settings, are shown to extend to these settings. Namely, (1) optimal rates depend only on the smoothness of the ratio f , and not on the densities fQ, fP , supporting the belief that plugging in estimates for fQ, fP is suboptimal; (2) optimal rates depend only on the intrinsic dimension of data, i.e. this problem – unlike density estimation – escapes the curse of dimension. We further show that near-optimal rates are attainable by estimators tuned from data alone, i.e. with no prior distributional information. This last fact is of special interest in unsupervised settings such as this one, where only oracle rates seem to be known, i.e., rates which assume critical distributional information usually unavailable in practice. <|reference_end|>", "<|reference_start|> On the Hardness of Domain Adaptation and the Utility of Unlabeled Target Samples: <|reference_end|>" ]

[ 4, 5, 8, 12 ]

[ "<|reference_start|> Understanding Human Hands in Contact at Internet Scale: Hands are the central means by which humans manipulate their world and being able to reliably extract hand state information from Internet videos of humans engaged in their hands has the potential to pave the way to systems that can learn from petabytes of video data. This paper proposes steps towards this by inferring a rich representation of hands engaged in interaction method that includes: hand location, side, contact state, and a box around the object in contact. To support this effort, we gather a large-scale dataset of hands in contact with objects consisting of 131 days of footage as well as a 100K annotated hand-contact video frame dataset. The learned model on this dataset can serve as a foundation for hand-contact understanding in videos. We quantitatively evaluate it both on its own and in service of predicting and learning from 3D meshes of human hands. <|reference_end|>", "<|reference_start|> HICO: A Benchmark for Recognizing Human-Object Interactions in Images: We introduce a new benchmark \"Humans Interacting with Common Objects\" (HICO) for recognizing human-object interactions (HOI). We demonstrate the key features of HICO: a diverse set of interactions with common object categories, a list of well-defined, sense-based HOI categories, and an exhaustive labeling of co-occurring interactions with an object category in each image. We perform an in-depth analysis of representative current approaches and show that DNNs enjoy a significant edge. In addition, we show that semantic knowledge can significantly improve HOI recognition, especially for uncommon categories. <|reference_end|>", "<|reference_start|> Learning to Detect Human-Object Interactions: We study the problem of detecting human-object interactions (HOI) in static images, defined as predicting a human and an object bounding box with an interaction class label that connects them. HOI detection is a fundamental problem in computer vision as it provides semantic information about the interactions among the detected objects. We introduce HICO-DET, a new large benchmark for HOI detection, by augmenting the current HICO classification benchmark with instance annotations. To solve the task, we propose Human-Object Region-based Convolutional Neural Networks (HO-RCNN). At the core of our HO-RCNN is the Interaction Pattern, a novel DNN input that characterizes the spatial relations between two bounding boxes. Experiments on HICO-DET demonstrate that our HO-RCNN, by exploiting human-object spatial relations through Interaction Patterns, significantly improves the performance of HOI detection over baseline approaches. <|reference_end|>", "<|reference_start|> ImageNet Large Scale Visual Recognition Challenge: The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the five years of the challenge, and propose future directions and improvements. <|reference_end|>" ]

[ 10, 17, 18, 45 ]

[ "<|reference_start|> {A fast quantum mechanical algorithm for database search: were proposed in the early 1980’s [Benioff80] and shown to be at least as powerful as classical computers an important but not surprising result, since classical computers, at the deepest level, ultimately follow the laws of quantum mechanics. The description of quantum mechanical computers was formalized in the late 80’s and early 90’s [Deutsch85][BB92] [BV93] [Yao93] and they were shown to be more powerful than classical computers on various specialized problems. In early 1994, [Shor94] demonstrated that a quantum mechanical computer could efficiently solve a well-known problem for which there was no known efficient algorithm using classical computers. This is the problem of integer factorization, i.e. testing whether or not a given integer, N, is prime, in a time which is a finite power of o (logN) . ---------------------------------------------- <|reference_end|>", "<|reference_start|> Quantum and classical tradeoffs: <|reference_end|>", "<|reference_start|> Quantum search algorithms: We review some of quantum algorithms for search problems: Grover's search algorithm, its generalization to amplitude amplification, the applications of amplitude amplification to various problems and the recent quantum algorithms based on quantum walks. <|reference_end|>", "<|reference_start|> Strengths and Weaknesses of Quantum Computing: Recently a great deal of attention has been focused on quantum computation following a sequence of results [Bernstein and Vazirani, in Proc. 25th Annual ACM Symposium Theory Comput., 1993, pp. 11--20, SIAM J. Comput., 26 (1997), pp. 1277--1339], [Simon, in Proc. 35th Annual IEEE Symposium Foundations Comput. Sci., 1994, pp. 116--123, SIAM J. Comput., 26 (1997), pp. 1340--1349], [Shor, in Proc. 35th Annual IEEE Symposium Foundations Comput. Sci., 1994, pp. 124--134] suggesting that quantum computers are more powerful than classical probabilistic computers. Following Shor's result that factoring and the extraction of discrete logarithms are both solvable in quantum polynomial time, it is natural to ask whether all of $\\NP$ can be efficiently solved in quantum polynomial time. In this paper, we address this question by proving that relative to an oracle chosen uniformly at random with probability 1 the class $\\NP$ cannot be solved on a quantum Turing machine (QTM) in time $o(2^{n/2})$. We also show that relative to a permutation oracle chosen uniformly at random with probability 1 the class $\\NP \\cap \\coNP$ cannot be solved on a QTM in time $o(2^{n/3})$. The former bound is tight since recent work of Grover [in {\\it Proc.\\ $28$th Annual ACM Symposium Theory Comput.}, 1996] shows how to accept the class $\\NP$ relative to any oracle on a quantum computer in time $O(2^{n/2})$. <|reference_end|>" ]

[ 0, 1, 2, 3 ]

[ "<|reference_start|> UnifiedQA: Crossing Format Boundaries With a Single QA System: Question answering (QA) tasks have been posed using a variety of formats, such as extractive span selection, multiple choice, etc. This has led to format-specialized models, and even to an implicit division in the QA community. We argue that such boundaries are artificial and perhaps unnecessary, given the reasoning abilities we seek to teach are not governed by the format. As evidence, we use the latest advances in language modeling to build a single pre-trained QA model, UnifiedQA, that performs surprisingly well across 17 QA datasets spanning 4 diverse formats. UnifiedQA performs on par with 9 different models that were trained on individual datasets themselves. Even when faced with 12 unseen datasets of observed formats, UnifiedQA performs surprisingly well, showing strong generalization from its out-of-format training data. Finally, simply fine-tuning this pre-trained QA model into specialized models results in a new state of the art on 6 datasets, establishing UnifiedQA as a strong starting point for building QA systems. <|reference_end|>", "<|reference_start|> Language Models as Knowledge Bases?: Recent progress in pretraining language models on large textual corpora led to a surge of improvements for downstream NLP tasks. Whilst learning linguistic knowledge, these models may also be storing relational knowledge present in the training data, and may be able to answer queries structured as \"fill-in-the-blank\" cloze statements. Language models have many advantages over structured knowledge bases: they require no schema engineering, allow practitioners to query about an open class of relations, are easy to extend to more data, and require no human supervision to train. We present an in-depth analysis of the relational knowledge already present (without fine-tuning) in a wide range of state-of-the-art pretrained language models. We find that (i) without fine-tuning, BERT contains relational knowledge competitive with traditional NLP methods that have some access to oracle knowledge, (ii) BERT also does remarkably well on open-domain question answering against a supervised baseline, and (iii) certain types of factual knowledge are learned much more readily than others by standard language model pretraining approaches. The surprisingly strong ability of these models to recall factual knowledge without any fine-tuning demonstrates their potential as unsupervised open-domain QA systems. The code to reproduce our analysis is available at https://github.com/facebookresearch/LAMA. <|reference_end|>", "<|reference_start|> X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Models: Language models (LMs) have proven surprisingly successful at capturing factual knowledge by completing cloze-style fill-in-the-blank questions such as \"Punta Cana is located in _.\" However, while knowledge is both written and queried in many languages, studies on LMs' factual representation ability have almost invariably been performed on English. To assess factual knowledge retrieval in LMs in different languages, we create a multilingual benchmark of cloze-style probes for 23 typologically diverse languages. To properly handle language variations, we expand probing methods from single- to multi-word entities, and develop several decoding algorithms to generate multi-token predictions. Extensive experimental results provide insights about how well (or poorly) current state-of-the-art LMs perform at this task in languages with more or fewer available resources. We further propose a code-switching-based method to improve the ability of multilingual LMs to access knowledge, and verify its effectiveness on several benchmark languages. Benchmark data and code have been released at https://x-factr.github.io. <|reference_end|>", "<|reference_start|> How Can We Know What Language Models Know?: Recent work has presented intriguing results examining the knowledge contained in language models (LM) by having the LM fill in the blanks of prompts such as \"Obama is a _ by profession\". These prompts are usually manually created, and quite possibly sub-optimal; another prompt such as \"Obama worked as a _\" may result in more accurately predicting the correct profession. Because of this, given an inappropriate prompt, we might fail to retrieve facts that the LM does know, and thus any given prompt only provides a lower bound estimate of the knowledge contained in an LM. In this paper, we attempt to more accurately estimate the knowledge contained in LMs by automatically discovering better prompts to use in this querying process. Specifically, we propose mining-based and paraphrasing-based methods to automatically generate high-quality and diverse prompts, as well as ensemble methods to combine answers from different prompts. Extensive experiments on the LAMA benchmark for extracting relational knowledge from LMs demonstrate that our methods can improve accuracy from 31.1% to 39.6%, providing a tighter lower bound on what LMs know. We have released the code and the resulting LM Prompt And Query Archive (LPAQA) at https://github.com/jzbjyb/LPAQA. <|reference_end|>" ]

[ 14, 20, 22, 23 ]

[ "<|reference_start|> LESS: Selecting Influential Data for Targeted Instruction Tuning: Instruction tuning has unlocked powerful capabilities in large language models (LLMs), effectively using combined datasets to develop generalpurpose chatbots. However, real-world applications often require a specialized suite of skills (e.g., reasoning). The challenge lies in identifying the most relevant data from these extensive datasets to effectively develop specific capabilities, a setting we frame as targeted instruction tuning. We propose LESS, an optimizer-aware and practically efficient algorithm to effectively estimate data influences and perform Low-rank gradiEnt Similarity Search for instruction data selection. Crucially, LESS adapts existing influence formulations to work with the Adam optimizer and variable-length instruction data. LESS first constructs a highly reusable and transferable gradient datastore with low-dimensional gradient features and then selects examples based on their similarity to few-shot examples embodying a specific capability. Experiments show that training on a LESS-selected 5% of the data can often outperform training on the full dataset across diverse downstream tasks. Furthermore, the selected data is highly transferable: smaller models can be leveraged to select useful data for larger models and models from different families. Our qualitative analysis shows that our method goes beyond surface form cues to identify data that exemplifies the necessary reasoning skills for the intended downstream application. <|reference_end|>", "<|reference_start|> IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models: In-context learning is a promising paradigm that utilizes in-context examples as prompts for the predictions of large language models. These prompts are crucial for achieving strong performance. However, since the prompts need to be sampled from a large volume of annotated examples, finding the right prompt may result in high annotation costs. To address this challenge, this paper introduces an influence-driven selective annotation method that aims to minimize annotation costs while improving the quality of in-context examples. The essence of our method is to select a pivotal subset from a large-scale unlabeled data pool to annotate for the subsequent sampling of prompts. Specifically, a directed graph is first constructed to represent unlabeled data. Afterward, the influence of candidate unlabeled subsets is quantified with a diffusion process. A simple yet effective greedy algorithm for unlabeled data selection is lastly introduced. It iteratively selects the data if it provides a maximum marginal gain with respect to quantified influence. Compared with previous efforts on selective annotations, our influence-driven method works in an end-to-end manner, avoids an intractable explicit balance between data diversity and representativeness, and enjoys theoretical support. Experiments confirm the superiority of the proposed method on various benchmarks, achieving better performance under lower time consumption during subset selection. The project page is available at https://skzhang1.github.io/IDEAL/. <|reference_end|>", "<|reference_start|> In-Context Learning Demonstration Selection via Influence Analysis: Large Language Models (LLMs) have showcased their In-Context Learning (ICL) capabilities, enabling few-shot learning without the need for gradient updates. Despite its advantages, the effectiveness of ICL heavily depends on the choice of demonstrations. Selecting the most effective demonstrations for ICL remains a significant research challenge. To tackle this issue, we propose a demonstration selection method named InfICL, which utilizes influence functions to analyze impacts of training samples. By identifying the most influential training samples as demonstrations, InfICL aims to enhance the ICL generalization performance. To keep InfICL cost-effective, we only use the LLM to generate sample input embeddings, avoiding expensive fine-tuning. Through empirical studies on various real-world datasets, we demonstrate advantages of InfICL compared to state-of-the-art baselines. <|reference_end|>", "<|reference_start|> In-Context Learning Demonstration Selection via Influence Analysis: Large Language Models (LLMs) have showcased their In-Context Learning (ICL) capabilities, enabling few-shot learning without the need for gradient updates. Despite its advantages, the effectiveness of ICL heavily depends on the choice of demonstrations. Selecting the most effective demonstrations for ICL remains a significant research challenge. To tackle this issue, we propose a demonstration selection method named InfICL, which utilizes influence functions to analyze impacts of training samples. By identifying the most influential training samples as demonstrations, InfICL aims to enhance the ICL generalization performance. To keep InfICL cost-effective, we only use the LLM to generate sample input embeddings, avoiding expensive fine-tuning. Through empirical studies on various real-world datasets, we demonstrate advantages of InfICL compared to state-of-the-art baselines. <|reference_end|>" ]

[ 2, 3, 4, 5 ]

[ "<|reference_start|> Grammar as a Foreign Language: Syntactic constituency parsing is a fundamental problem in natural language processing and has been the subject of intensive research and engineering for decades. As a result, the most accurate parsers are domain specific, complex, and inefficient. In this paper we show that the domain agnostic attention-enhanced sequence-to-sequence model achieves state-of-the-art results on the most widely used syntactic constituency parsing dataset, when trained on a large synthetic corpus that was annotated using existing parsers. It also matches the performance of standard parsers when trained only on a small human-annotated dataset, which shows that this model is highly data-efficient, in contrast to sequence-to-sequence models without the attention mechanism. Our parser is also fast, processing over a hundred sentences per second with an unoptimized CPU implementation. <|reference_end|>", "<|reference_start|> An Investigation of the Interactions Between Pre-Trained Word Embeddings, Character Models and POS Tags in Dependency Parsing: We provide a comprehensive analysis of the interactions between pre-trained word embeddings, character models and POS tags in a transition-based dependency parser. While previous studies have shown POS information to be less important in the presence of character models, we show that in fact there are complex interactions between all three techniques. In isolation each produces large improvements over a baseline system using randomly initialised word embeddings only, but combining them quickly leads to diminishing returns. We categorise words by frequency, POS tag and language in order to systematically investigate how each of the techniques affects parsing quality. For many word categories, applying any two of the three techniques is almost as good as the full combined system. Character models tend to be more important for low-frequency open-class words, especially in morphologically rich languages, while POS tags can help disambiguate high-frequency function words. We also show that large character embedding sizes help even for languages with small character sets, especially in morphologically rich languages. <|reference_end|>", "<|reference_start|> Multiplanarity -- a model for dependency structures in treebanks: The number of treebanks available for different languages is growing steadily. A considerable portion of the recent treebanks use annotation schemes that are based on dependency syntax. In this paper, we give a model for linguistically adequate classes of dependency structures in treebanks. Our model is tested using the Danish Dependency Treebank [13]. The modern dependency syntax was pioneered by Tesniere [27]. His core concepts, binary dependencies and unique heads, are mostly shared in the recent dependency syntactic theories [6, 9, 18, 24, 26]. These theories stress the functional structure, while paying much less attention to linear word order. Lecerf’s projectivity hypothesis [15, 17] assumes a constraint on linear wordorder in dependency analyses. It says that if a word A depends directly on a word B and some word C intervenes between them in linear order, then C depends directly on A or on B or some other intervening word[23]. The projectivity constraint has been a popular simplification in many computational dependency grammars since 1960’s [4]. It does not only make parsing algorithms simple and efficient [16], but also equips us with neat ways to visualize analyses, with an equivalent, constituent based representation for dependency trees [4, 7], and with a criterion for stylistically marked analyses [27, 22] and for abnormal information structure [10]. The tendency that sentences admit projective analyses has been observed in many languages, including French [15], Swedish [20], Finnish [1], and Turkish [21]. Unfortunately, projectivity does not lend itself to adequate treatment of certain non-local syntactic phenomena which are extensively studied in the literature of constituent-based theories such as TG, GB, GPSG, TAG, and LFG. Among these <|reference_end|>", "<|reference_start|> Squibs: Restricted Non-Projectivity: Coverage vs. Efficiency: In the last decade, various restricted classes of non-projective dependency trees have been proposed with the goal of achieving a good tradeoff between parsing efficiency and coverage of the syntactic structures found in natural languages. We perform an extensive study measuring the coverage of a wide range of such classes on corpora of 30 languages under two different syntactic annotation criteria. The results show that, among the currently known relaxations of projectivity, the best tradeoff between coverage and computational complexity of exact parsing is achieved by either 1-endpoint-crossing trees or MHk trees, depending on the level of coverage desired. We also present some properties of the relation of MHk trees to other relevant classes of trees. <|reference_end|>" ]

[ 0, 8, 12, 13 ]

[ "<|reference_start|> Integrated Genomic Analyses of Ovarian Carcinoma: <|reference_end|>", "<|reference_start|> The Tandem Duplication Distance is NP-hard: In computational biology, tandem duplication is an important biological phenomenon which can occur either at the genome or at the DNA level. A tandem duplication takes a copy of a genome segment and inserts it right after the segment - this can be represented as the string operation $AXB \\Rightarrow AXXB$. For example, Tandem exon duplications have been found in many species such as human, fly or worm, and have been largely studied in computational biology. The Tandem Duplication (TD) distance problem we investigate in this paper is defined as follows: given two strings $S$ and $T$ over the same alphabet, compute the smallest sequence of tandem duplications required to convert $S$ to $T$. The natural question of whether the TD distance can be computed in polynomial time was posed in 2004 by Leupold et al. and had remained open, despite the fact that tandem duplications have received much attention ever since. In this paper, we prove that this problem is NP-hard. We further show that this hardness holds even if all characters of $S$ are distinct. This is known as the exemplar TD distance, which is of special relevance in bioinformatics. One of the tools we develop for the reduction is a new problem called the Cost-Effective Subgraph, for which we obtain W[1]-hardness results that might be of independent interest. We finally show that computing the exemplar TD distance between $S$ and $T$ is fixed-parameter tractable. Our results open the door to many other questions, and we conclude with several open problems. <|reference_end|>", "<|reference_start|> Beyond the Longest Letter-duplicated Subsequence Problem: Given a sequence $S$ of length $n$, a letter-duplicated subsequence is a subsequence of $S$ in the form of $x_1^{d_1}x_2^{d_2}\\cdots x_k^{d_k}$ with $x_i\\in\\Sigma$, $x_j\\neq x_{j+1}$ and $d_i\\geq 2$ for all $i$ in $[k]$ and $j$ in $[k-1]$. A linear time algorithm for computing the longest letter-duplicated subsequence (LLDS) of $S$ can be easily obtained. In this paper, we focus on two variants of this problem. We first consider the constrained version when $\\Sigma$ is unbounded, each letter appears in $S$ at least 6 times and all the letters in $\\Sigma$ must appear in the solution. We show that the problem is NP-hard (a further twist indicates that the problem does not admit any polynomial time approximation). The reduction is from possibly the simplest version of SAT that is NP-complete, $(\\leq 2,1,\\leq 3)$-SAT, where each variable appears at most twice positively and exact once negatively, and each clause contains at most three literals and some clauses must contain exactly two literals. (We hope that this technique will serve as a general tool to help us proving the NP-hardness for some more tricky sequence problems involving only one sequence -- much harder than with at least two input sequences, which we apply successfully at the end of the paper on some extra variations of the LLDS problem.) We then show that when each letter appears in $S$ at most 3 times, then the problem admits a factor $1.5-O(\\frac{1}{n})$ approximation. Finally, we consider the weighted version, where the weight of a block $x_i^{d_i} (d_i\\geq 2)$ could be any positive function which might not grow with $d_i$. We give a non-trivial $O(n^2)$ time dynamic programming algorithm for this version, i.e., computing an LD-subsequence of $S$ whose weight is maximized. <|reference_end|>", "<|reference_start|> Using the longest run subsequence problem within homology-based scaffolding: <|reference_end|>" ]

[ 6, 11, 16, 18 ]

[ "<|reference_start|> Playing Atari with Deep Reinforcement Learning: We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. We apply our method to seven Atari 2600 games from the Arcade Learning Environment, with no adjustment of the architecture or learning algorithm. We find that it outperforms all previous approaches on six of the games and surpasses a human expert on three of them. <|reference_end|>", "<|reference_start|> DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic Navigation: We present a traffic simulation named DeepTraffic where the planning systems for a subset of the vehicles are handled by a neural network as part of a model-free, off-policy reinforcement learning process. The primary goal of DeepTraffic is to make the hands-on study of deep reinforcement learning accessible to thousands of students, educators, and researchers in order to inspire and fuel the exploration and evaluation of deep Q-learning network variants and hyperparameter configurations through large-scale, open competition. This paper investigates the crowd-sourced hyperparameter tuning of the policy network that resulted from the first iteration of the DeepTraffic competition where thousands of participants actively searched through the hyperparameter space. <|reference_end|>", "<|reference_start|> Cooperative Multi-agent Control Using Deep Reinforcement Learning: <|reference_end|>", "<|reference_start|> Proximal Policy Optimization Algorithms: We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a \"surrogate\" objective function using stochastic gradient ascent. Whereas standard policy gradient methods perform one gradient update per data sample, we propose a novel objective function that enables multiple epochs of minibatch updates. The new methods, which we call proximal policy optimization (PPO), have some of the benefits of trust region policy optimization (TRPO), but they are much simpler to implement, more general, and have better sample complexity (empirically). Our experiments test PPO on a collection of benchmark tasks, including simulated robotic locomotion and Atari game playing, and we show that PPO outperforms other online policy gradient methods, and overall strikes a favorable balance between sample complexity, simplicity, and wall-time. <|reference_end|>" ]

[ 0, 7, 12, 17 ]

[ "<|reference_start|> {Model Order Reduction: 報告番号: ; 学位授与年月日: 2012-03-22 ; 学位の種別: 修士 ; 学位の種類: 修士(環境学) ; 学位記番号: 修創域第4421号 ; 研究科・専攻: 新領域創成科学研究科環境学研究系人間環境学専攻 <|reference_end|>", "<|reference_start|> Non-intrusive PODI-ROM for patient-specific aortic blood flow in presence of a LVAD device: Left ventricular assist devices (LVADs) are used to provide haemodynamic support to patients with critical cardiac failure. Severe complications can occur because of the modifications of the blood flow in the aortic region. In this work, the effect of a continuous flow LVAD device on the aortic flow is investigated by means of a non-intrusive reduced order model (ROM) built using the proper orthogonal decomposition with interpolation (PODI) method. The full order model (FOM) is represented by the incompressible Navier-Stokes equations discretized by using a Finite Volume (FV) technique, coupled with three-element Windkessel models to enforce outlet boundary conditions in a multi-scale approach. A patient-specific framework is proposed: a personalized geometry reconstructed from Computed Tomography (CT) images is used and the individualization of the coefficients of the three-element Windkessel models is based on experimental data provided by the Right Heart Catheterization (RCH) and Echocardiography (ECHO) tests. Pre-surgery configuration is also considered at FOM level in order to further validate the model. A parametric study with respect to the LVAD flow rate is considered. The accuracy of the reduced order model is assessed against results obtained with the full order model. <|reference_end|>", "<|reference_start|> Myocardial infarction after left ventricular assist device implantation: clinical course, role of aortic root thrombus, and outcomes.: <|reference_end|>", "<|reference_start|> Aortic Valve Thrombus in a Patient With an Extracorporeal Left Ventricular Assist Device: The Dilemma of Management: PATIENTS with severely compromised left ventricular output undergo left ventricular assist device (LVAD) placement for a variety of reasons. Intracorporeal (totally implantable) devices are placed either as bridge therapy to cardiac transplantation or as destination therapy in patients ineligible to undergo transplantation. Most modern devices are small and rely on impellers to produce axial, nonpulsatile flow in series with the left ventricle (LV). An example of such a device is the HeartMate II (Thoratec Corporation), which is approved by the FDA for both bridge-to-transplantation and destination therapy. In the setting of cardiogenic shock such as acute myocardial infarction (AMI) or significant insult to the LV during cardiopulmonary bypass (CPB), temporary extracorporeal devices may be used for circulatory support. In a patient with a sternotomy, typical cannulation sites are the left atrium and ascending aorta, resulting in flow that is in parallel to native LV flow. These devices are meant to be used as a bridge to recovery of myocardial function or as a bridge to a more definitive decision (such as intracorporeal LVAD or termination of care). An example of a device used in this setting is the Centrimags (Thoratecs Corporation) VAD, which also can be used for biventricular support if indicated. High flow through both types of devices can overpower and completely bypass the flow through the native left ventricular outflow tract. As a result, stasis of flow in the ascending aorta can occur, with reduced AV cusp excursion. This can contribute to perivalvular thrombus formation. Possible sequelae may include compromised coronary blood flow, heart failure, and embolic events such as AMI or cerebrovascular accident (CVA). The guidelines released in 2013 for mechanical circulatory support recommend warfarin with the optional addition of aspirin to prevent thrombotic complications in LVAD patients. On the flip side, mechanical shear stress from ventricular assist devices leads to an acquired von Willebrand factor deficiency and platelet dysfunction. This, along with pharmacologic anticoagulation, can place these patients at an increased risk for bleeding complications. Karimi et al have suggested that antiplatelet therapy titration with the utilization of thromboelastography may reduce bleeding risk in this population. <|reference_end|>" ]

[ 0, 7, 12, 13 ]

[ "<|reference_start|> Unpaired Image Captioning with Semantic-Constrained Self-Learning: Image captioning has been an emerging and fast-developing research topic. Nevertheless, most existing works heavily rely on large amounts of image-sentence pairs and therefore hinder the practical applications of captioning in the wild. In this paper, we present a novel Semantic-Constrained Self-learning (SCS) framework that explores an iterative self-learning strategy to learn an image captioner with only unpaired image and text data. Technically, SCS consists of two stages, i.e., pseudo pair generation and captioner re-training, iteratively producing \"pseudo\" image-sentence pairs via a pre-trained captioner and re-training the captioner with the pseudo pairs, respectively. Particularly, both stages are guided by the recognized objects in the image, that act as semantic constraint to strengthen the semantic alignment between the input image and the output sentence. We leverage a semantic-constrained beam search for pseudo pair generation to regularize the decoding process with the recognized objects via forcing the inclusion/exclusion of the recognized/irrelevant objects in output sentence. For captioner re-training, a self-supervised triplet loss is utilized to preserve the relative semantic similarity ordering among generated sentences with regard to the input image triplets. Moreover, an object inclusion reward and an adversarial reward are adopted to encourage the inclusion of the predicted objects in the output sentence and pursue the generation of more realistic sentences during self-critical training, respectively. Experiments conducted on both dependent and independent unpaired data validate the superiority of SCS. More remarkably, we obtain the best published CIDEr score to-date of 74.7\\% on COCO Karpathy test split for unpaired image captioning. <|reference_end|>", "<|reference_start|> Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing: This paper surveys and organizes research works in a new paradigm in natural language processing, which we dub \"prompt-based learning\". Unlike traditional supervised learning, which trains a model to take in an input x and predict an output y as P(y|x), prompt-based learning is based on language models that model the probability of text directly. To use these models to perform prediction tasks, the original input x is modified using a template into a textual string prompt x' that has some unfilled slots, and then the language model is used to probabilistically fill the unfilled information to obtain a final string x, from which the final output y can be derived. This framework is powerful and attractive for a number of reasons: it allows the language model to be pre-trained on massive amounts of raw text, and by defining a new prompting function the model is able to perform few-shot or even zero-shot learning, adapting to new scenarios with few or no labeled data. In this paper we introduce the basics of this promising paradigm, describe a unified set of mathematical notations that can cover a wide variety of existing work, and organize existing work along several dimensions, e.g.the choice of pre-trained models, prompts, and tuning strategies. To make the field more accessible to interested beginners, we not only make a systematic review of existing works and a highly structured typology of prompt-based concepts, but also release other resources, e.g., a website http://pretrain.nlpedia.ai/ including constantly-updated survey, and paperlist. <|reference_end|>", "<|reference_start|> Multi-Level Policy and Reward-Based Deep Reinforcement Learning Framework\nfor Image Captioning: Image captioning is one of the most challenging tasks in AI because it requires an understanding of both complex visuals and natural language. Because image captioning is essentially a sequential prediction task, recent advances in image captioning have used reinforcement learning (RL) to better explore the dynamics of word-by-word generation. However, the existing RL-based image captioning methods rely primarily on a single policy network and reward function—an approach that is not well matched to the multi-level (word and sentence) and multi-modal (vision and language) nature of the task. To solve this problem, we propose a novel multi-level policy and reward RL framework for image captioning that can be easily integrated with RNN-based captioning models, language metrics, or visual-semantic functions for optimization. Specifically, the proposed framework includes two modules: 1) a multi-level policy network that jointly updates the word- and sentence-level policies for word generation; and 2) a multi-level reward function that collaboratively leverages both a vision-language reward and a language-language reward to guide the policy. Furthermore, we propose a guidance term to bridge the policy and the reward for RL optimization. The extensive experiments on the MSCOCO and Flickr30k datasets and the analyses show that the proposed framework achieves competitive performances on a variety of evaluation metrics. In addition, we conduct ablation studies on multiple variants of the proposed framework and explore several representative image captioning models and metrics for the word-level policy network and the language-language reward function to evaluate the generalization ability of the proposed framework. <|reference_end|>", "<|reference_start|> A Comprehensive Survey of Deep Learning for Image Captioning: Generating a description of an image is called image captioning. Image captioning requires to recognize the important objects, their attributes and their relationships in an image. It also needs to generate syntactically and semantically correct sentences. Deep learning-based techniques are capable of handling the complexities and challenges of image captioning. In this survey paper, we aim to present a comprehensive review of existing deep learning-based image captioning techniques. We discuss the foundation of the techniques to analyze their performances, strengths and limitations. We also discuss the datasets and the evaluation metrics popularly used in deep learning based automatic image captioning. <|reference_end|>" ]

[ 13, 27, 34, 35 ]

[ "<|reference_start|> Smooth finite element methods: Convergence, accuracy and properties: A stabilized conforming nodal integration finite element method based on strain smoothing stabilization is presented. The integration of the stiffness matrix is performed on the boundaries of the finite elements. A rigorous variational framework based on the Hu–Washizu assumed strain variational form is developed. <|reference_end|>", "<|reference_start|> Addressing volumetric locking and instabilities by selective integration in smoothed finite elements: This paper promotes the development of a novel family of finite elements with smoothed strains, offering remarkable properties. In the smoothed finite element method (FEM), elements are divided into subcells. The strain at a point is defined as a weighted average of the standard strain field over a representative domain. \n \n \n \nThis yields superconvergent stresses, both in regular and singular settings, as well as increased accuracy, with slightly lower computational cost than the standard FEM. \n \n \n \nThe one-subcell version that does not exhibit volumetric locking yields more accurate stresses but less accurate displacements and is equivalent to a quasi-equilibrium FEM. It is also subject to instabilities. In the limit where the number of subcells goes to infinity, the standard FEM is recovered, which yields more accurate displacements and less accurate stresses. \n \n \n \nThe specific contribution of this paper is to show that expressing the volumetric part of the strain field using a one-subcell formulation is sufficient to get rid of volumetric locking and increase the displacement accuracy compared with the standard FEM when the single subcell version is used to express both the volumetric and deviatoric parts of the strain. Selective integration also alleviates instabilities associated with the single subcell element, which are due to rank deficiency. \n \n \n \nNumerical examples on various compressible and incompressible linear elastic test cases show that high accuracy is retained compared with the standard FEM without increasing computational cost. Copyright © 2008 John Wiley & Sons, Ltd. <|reference_end|>", "<|reference_start|> Addressing volumetric locking and instabilities by selective integration in smoothed finite elements: This paper promotes the development of a novel family of finite elements with smoothed strains, offering remarkable properties. In the smoothed finite element method (FEM), elements are divided into subcells. The strain at a point is defined as a weighted average of the standard strain field over a representative domain. \n \n \n \nThis yields superconvergent stresses, both in regular and singular settings, as well as increased accuracy, with slightly lower computational cost than the standard FEM. \n \n \n \nThe one-subcell version that does not exhibit volumetric locking yields more accurate stresses but less accurate displacements and is equivalent to a quasi-equilibrium FEM. It is also subject to instabilities. In the limit where the number of subcells goes to infinity, the standard FEM is recovered, which yields more accurate displacements and less accurate stresses. \n \n \n \nThe specific contribution of this paper is to show that expressing the volumetric part of the strain field using a one-subcell formulation is sufficient to get rid of volumetric locking and increase the displacement accuracy compared with the standard FEM when the single subcell version is used to express both the volumetric and deviatoric parts of the strain. Selective integration also alleviates instabilities associated with the single subcell element, which are due to rank deficiency. \n \n \n \nNumerical examples on various compressible and incompressible linear elastic test cases show that high accuracy is retained compared with the standard FEM without increasing computational cost. Copyright © 2008 John Wiley & Sons, Ltd. <|reference_end|>", "<|reference_start|> Addressing volumetric locking and instabilities by selective integration in smoothed finite elements: This paper promotes the development of a novel family of finite elements with smoothed strains, offering remarkable properties. In the smoothed finite element method (FEM), elements are divided into subcells. The strain at a point is defined as a weighted average of the standard strain field over a representative domain. \n \n \n \nThis yields superconvergent stresses, both in regular and singular settings, as well as increased accuracy, with slightly lower computational cost than the standard FEM. \n \n \n \nThe one-subcell version that does not exhibit volumetric locking yields more accurate stresses but less accurate displacements and is equivalent to a quasi-equilibrium FEM. It is also subject to instabilities. In the limit where the number of subcells goes to infinity, the standard FEM is recovered, which yields more accurate displacements and less accurate stresses. \n \n \n \nThe specific contribution of this paper is to show that expressing the volumetric part of the strain field using a one-subcell formulation is sufficient to get rid of volumetric locking and increase the displacement accuracy compared with the standard FEM when the single subcell version is used to express both the volumetric and deviatoric parts of the strain. Selective integration also alleviates instabilities associated with the single subcell element, which are due to rank deficiency. \n \n \n \nNumerical examples on various compressible and incompressible linear elastic test cases show that high accuracy is retained compared with the standard FEM without increasing computational cost. Copyright © 2008 John Wiley & Sons, Ltd. <|reference_end|>" ]

[ 3, 6, 9, 14 ]

[ "<|reference_start|> FLAME: A probabilistic model combining aspect based opinion mining and collaborative filtering: Aspect-based opinion mining from online reviews has attracted a lot of attention recently. Given a set of reviews, the main task of aspect-based opinion mining is to extract major aspects of the items and to infer the latent aspect ratings from each review. However, users may have different preferences which might lead to different opinions on the same aspect of an item. Even if fine-grained aspect rating analysis is provided for each review, it is still difficult for a user to judge whether a specific aspect of an item meets his own expectation. In this paper, we study the problem of estimating personalized sentiment polarities on different aspects of the items. We propose a unified probabilistic model called Factorized Latent Aspect ModEl (FLAME), which combines the advantages of collaborative filtering and aspect based opinion mining. FLAME learns users' personalized preferences on different aspects from their past reviews, and predicts users' aspect ratings on new items by collective intelligence. Experiments on two online review datasets show that FLAME outperforms state-of-the-art methods on the tasks of aspect identification and aspect rating prediction. <|reference_end|>", "<|reference_start|> Crowd-based personalized natural language explanations for recommendations: Explanations are important for users to make decisions on whether to take recommendations. However, algorithm generated explanations can be overly simplistic and unconvincing. We believe that humans can overcome these limitations. Inspired by how people explain word-of-mouth recommendations, we designed a process, combining crowdsourcing and computation, that generates personalized natural language explanations. We modeled key topical aspects of movies, asked crowdworkers to write explanations based on quotes from online movie reviews, and personalized the explanations presented to users based on their rating history. We evaluated the explanations by surveying 220 MovieLens users, finding that compared to personalized tag-based explanations, natural language explanations: 1) contain a more appropriate amount of information, 2) earn more trust from users, and 3) make users more satisfied. This paper contributes to the research literature by describing a scalable process for generating high quality and personalized natural language explanations, improving on state-of-the-art content-based explanations, and showing the feasibility and advantages of approaches that combine human wisdom with algorithmic processes. <|reference_end|>", "<|reference_start|> Reinforcement Knowledge Graph Reasoning for Explainable Recommendation: Recent advances in personalized recommendation have sparked great interest in the exploitation of rich structured information provided by knowledge graphs. Unlike most existing approaches that only focus on leveraging knowledge graphs for more accurate recommendation, we perform explicit reasoning with knowledge for decision making so that the recommendations are generated and supported by an interpretable causal inference procedure. To this end, we propose a method called Policy-Guided Path Reasoning (PGPR), which couples recommendation and interpretability by providing actual paths in a knowledge graph. Our contributions include four aspects. We first highlight the significance of incorporating knowledge graphs into recommendation to formally define and interpret the reasoning process. Second, we propose a reinforcement learning (RL) approach featuring an innovative soft reward strategy, user-conditional action pruning and a multi-hop scoring function. Third, we design a policy-guided graph search algorithm to efficiently and effectively sample reasoning paths for recommendation. Finally, we extensively evaluate our method on several large-scale real-world benchmark datasets, obtaining favorable results compared with state-of-the-art methods. <|reference_end|>", "<|reference_start|> Novel online recommendation algorithm for massive open online courses (NoR-MOOCs): Massive Open Online Courses (MOOCs) have gained in popularity over the last few years. The space of online learning resources has been increasing exponentially and has created a problem of information overload. To overcome this problem, recommender systems that can recommend learning resources to users according to their interests have been proposed. MOOCs contain a huge amount of data with the quantity of data increasing as new learners register. Traditional recommendation techniques suffer from scalability, sparsity and cold start problems resulting in poor quality recommendations. Furthermore, they cannot accommodate the incremental update of the model with the arrival of new data making them unsuitable for MOOCs dynamic environment. From this line of research, we propose a novel online recommender system, namely NoR-MOOCs, that is accurate, scales well with the data and moreover overcomes previously recorded problems with recommender systems. Through extensive experiments conducted over the COCO data-set, we have shown empirically that NoR-MOOCs significantly outperforms traditional KMeans and Collaborative Filtering algorithms in terms of predictive and classification accuracy metrics. <|reference_end|>" ]

[ 5, 10, 11, 23 ]

[ "<|reference_start|> PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation: Despite their success, policy gradient methods suffer from high variance of the gradient estimate, which can result in unsatisfactory sample complexity. Recently, numerous variance-reduced extensions of policy gradient methods with provably better sample complexity and competitive numerical performance have been proposed. After a compact survey on some of the main variance-reduced REINFORCE-type methods, we propose ProbAbilistic Gradient Estimation for Policy Gradient (PAGE-PG), a novel loopless variance-reduced policy gradient method based on a probabilistic switch between two types of updates. Our method is inspired by the PAGE estimator for supervised learning and leverages importance sampling to obtain an unbiased gradient estimator. We show that PAGE-PG enjoys a $\\mathcal{O}\\left( \\epsilon^{-3} \\right)$ average sample complexity to reach an $\\epsilon$-stationary solution, which matches the sample complexity of its most competitive counterparts under the same setting. A numerical evaluation confirms the competitive performance of our method on classical control tasks. <|reference_end|>", "<|reference_start|> Infinite-Horizon Policy-Gradient Estimation: Gradient-based approaches to direct policy search in reinforcement learning have received much recent attention as a means to solve problems of partial observability and to avoid some of the problems associated with policy degradation in value-function methods. In this paper we introduce GPOMDP, a simulation-based algorithm for generating a {\\em biased} estimate of the gradient of the {\\em average reward} in Partially Observable Markov Decision Processes (POMDPs) controlled by parameterized stochastic policies. A similar algorithm was proposed by Kimura, Yamamura, and Kobayashi (1995). The algorithm's chief advantages are that it requires storage of only twice the number of policy parameters, uses one free parameter $\\beta\\in [0,1)$ (which has a natural interpretation in terms of bias-variance trade-off), and requires no knowledge of the underlying state. We prove convergence of GPOMDP, and show how the correct choice of the parameter $\\beta$ is related to the {\\em mixing time} of the controlled POMDP. We briefly describe extensions of GPOMDP to controlled Markov chains, continuous state, observation and control spaces, multiple-agents, higher-order derivatives, and a version for training stochastic policies with internal states. In a companion paper (Baxter, Bartlett, & Weaver, 2001) we show how the gradient estimates generated by GPOMDP can be used in both a traditional stochastic gradient algorithm and a conjugate-gradient procedure to find local optima of the average reward <|reference_end|>", "<|reference_start|> Policy gradient methods for reinforcement learning with function\napproximation: Function approximation is essential to reinforcement learning, but the standard approach of approximating a value function and determining a policy from it has so far proven theoretically intractable. In this paper we explore an alternative approach in which the policy is explicitly represented by its own function approximator, independent of the value function, and is updated according to the gradient of expected reward with respect to the policy parameters. Williams's REINFORCE method and actor-critic methods are examples of this approach. Our main new result is to show that the gradient can be written in a form suitable for estimation from experience aided by an approximate action-value or advantage function. Using this result, we prove for the first time that a version of policy iteration with arbitrary differentiable function approximation is convergent to a locally optimal policy. <|reference_end|>", "<|reference_start|> Policy gradient in Lipschitz Markov Decision Processes: <|reference_end|>" ]

[ 37, 46, 47, 48 ]

[ "<|reference_start|> Precoding and power optimization in cell-free massive mimo systems: Cell-free Massive multiple-input multiple-output (MIMO) comprises a large number of distributed low-cost low-power single antenna access points (APs) connected to a network controller. The number of AP antennas is significantly larger than the number of users. The system is not partitioned into cells and each user is served by all APs simultaneously. The simplest linear precoding schemes are conjugate beamforming and zero-forcing. Max–min power control provides equal throughput to all users and is considered in this paper. Surprisingly, under max–min power control, most APs are found to transmit at less than full power. The zero-forcing precoder significantly outperforms conjugate beamforming. For zero-forcing, a near-optimal power control algorithm is developed that is considerably simpler than exact max–min power control. An alternative to cell-free systems is small-cell operation in which each user is served by only one AP for which power optimization algorithms are also developed. Cell-free Massive MIMO is shown to provide five- to ten-fold improvement in 95%-likely per-user throughput over small-cell operation. <|reference_end|>", "<|reference_start|> Cell-Free Massive MIMO versus Small Cells: A Cell-Free Massive MIMO (multiple-input multiple-output) system comprises a very large number of distributed access points (APs)which simultaneously serve a much smaller number of users over the same time/frequency resources based on directly measured channel characteristics. The APs and users have only one antenna each. The APs acquire channel state information through time-division duplex operation and the reception of uplink pilot signals transmitted by the users. The APs perform multiplexing/de-multiplexing through conjugate beamforming on the downlink and matched filtering on the uplink. Closed-form expressions for individual user uplink and downlink throughputs lead to max-min power control algorithms. Max-min power control ensures uniformly good service throughout the area of coverage. A pilot assignment algorithm helps to mitigate the effects of pilot contamination, but power control is far more important in that regard. Cell-Free Massive MIMO has considerably improved performance with respect to a conventional small-cell scheme, whereby each user is served by a dedicated AP, in terms of both 95%-likely per-user throughput and immunity to shadow fading spatial correlation. Under uncorrelated shadow fading conditions, the cell-free scheme provides nearly 5-fold improvement in 95%-likely per-user throughput over the small-cell scheme, and 10-fold improvement when shadow fading is correlated. <|reference_end|>", "<|reference_start|> Channel Estimation for RIS-Empowered Multi-User MISO Wireless Communications: Reconfigurable Intelligent Surfaces (RISs) have been recently considered as an energy-efficient solution for future wireless networks due to their fast and low-power configuration, which has increased potential in enabling massive connectivity and low-latency communications. Accurate and low-overhead channel estimation in RIS-based systems is one of the most critical challenges due to the usually large number of RIS unit elements and their distinctive hardware constraints. In this paper, we focus on the uplink of a RIS-empowered multi-user Multiple Input Single Output (MISO) uplink communication systems and propose a channel estimation framework based on the parallel factor decomposition to unfold the resulting cascaded channel model. We present two iterative estimation algorithms for the channels between the base station and RIS, as well as the channels between RIS and users. One is based on alternating least squares (ALS), while the other uses vector approximate message passing to iteratively reconstruct two unknown channels from the estimated vectors. To theoretically assess the performance of the ALS-based algorithm, we derived its estimation Cram\\'er-Rao Bound (CRB). We also discuss the downlink achievable sum rate computation with estimated channels and different precoding schemes for the base station. Our extensive simulation results show that our algorithms outperform benchmark schemes and that the ALS technique achieves the CRB. It is also demonstrated that the sum rate using the estimated channels always reach that of perfect channels under various settings, thus, verifying the effectiveness and robustness of the proposed estimation algorithms. <|reference_end|>", "<|reference_start|> Weighted Sum-Rate of Intelligent Reflecting Surface Aided Multiuser Downlink Transmission with Statistical {CSI: Intelligent reflecting surface (IRS) is a newly emerged technology that can increase the energy and spectral efficiency of wireless communication systems. This paper considers an IRS-aided multi-user multiple-input single-output (MISO) communication system, and presents a detailed analysis and optimization framework for the weighted sum-rate (WSR) of the downlink transmission over Rician fading channels. Unlike most of the prior works where the active beamformer at the base station (BS) and passive beamformer at the IRS are jointly designed based on the instantaneous channel state information (CSI), this paper proposes a low-complexity transmission protocol where the IRS passive beamforming and BS power allocation coefficient vector are optimized in the large timescale based on the statistical CSI, and the BS transmit beamforming is designed in the small timescale based on only the instantaneous CSI of the effective BS-user channels. Therefore, the channel training overhead in each channel coherence interval under our proposed protocol is independent of the number of IRS reflecting elements, which is in sharp contrast to most of the prior works. By considering maximum-ratio transmit beamforming at the BS, we derive a lower bound of the ergodic WSR in closed-form. Then, we propose an efficient algorithm to jointly optimize the IRS passive beamforming and BS power allocation coefficient vector for maximizing the ergodic WSR lower bound. Numerical results validate the tightness of our derived WSR bound and show that the proposed scheme outperforms various existing schemes in terms of complexity or capacity performance. <|reference_end|>" ]

[ 0, 1, 26, 28 ]

[ "<|reference_start|> Stack Overflow Considered Harmful? The Impact of Copy&Paste on Android Application Security: Online programming discussion platforms such as Stack Overflow serve as a rich source of information for software developers. Available information include vibrant discussions and oftentimes ready-to-use code snippets. Anecdotes report that software developers copy and paste code snippets from those information sources for convenience reasons. Such behavior results in a constant flow of community-provided code snippets into production software. To date, the impact of this behaviour on code security is unknown. We answer this highly important question by quantifying the proliferation of security-related code snippets from Stack Overflow in Android applications available on Google Play. Access to the rich source of information available on Stack Overflow including ready-to-use code snippets provides huge benefits for software developers. However, when it comes to code security there are some caveats to bear in mind: Due to the complex nature of code security, it is very difficult to provide ready-to-use and secure solutions for every problem. Hence, integrating a security-related code snippet from Stack Overflow into production software requires caution and expertise. Unsurprisingly, we observed insecure code snippets being copied into Android applications millions of users install from Google Play every day. To quantitatively evaluate the extent of this observation, we scanned Stack Overflow for code snippets and evaluated their security score using a stochastic gradient descent classifier. In order to identify code reuse in Android applications, we applied state-of-the-art static analysis. Our results are alarming: 15.4% of the 1.3 million Android applications we analyzed, contained security-related code snippets from Stack Overflow. Out of these 97.9% contain at least one insecure code snippet. <|reference_end|>", "<|reference_start|> Cybersecurity curricular guidelines: The Cybersecurity Curricular Guidelines, a joint effort of the ACM, IEEE Computer Society, AIS SIGSAC, and IFIP WG 11.8, were created to provide developers of cybersecurity curricula with guidelines for material to include. The curricular guidelines have eight knowledge areas, broken down into knowledge units and topics. Underlying cross-cutting concepts provide linkages among the knowledge areas. Disciplinary lenses enable the developer to emphasize the knowledge units appropriate to the goals of the developed curricula. Each knowledge area also includes a list of essential concepts that all curricula should cover to an appropriate depth. The guidelines can be linked to workforce frameworks and certification criteria as well as academic curricula. <|reference_end|>", "<|reference_start|> Exploring CyBOK with Topic Modeling Techniques: <|reference_end|>", "<|reference_start|> Open Source and Commercial Capture The Flag Cyber Security Learning Platforms-A Case Study: The use of gamified learning platforms as a method of introducing cyber security education, training and awareness has risen greatly. With this rise, the availability of platforms to create, host or otherwise provide the challenges that make up the foundation of this education has also increased. In order to identify the best of these platforms, we need a method to compare their feature sets. In this paper, we compare related work on identifying the best platforms for a gamified cyber security learning platform as well as contemporary literature that describes the most needed feature sets for an ideal platform. We then use this to develop a metric for comparing these platforms, before then applying this metric to popular current platforms. <|reference_end|>" ]

[ 10, 14, 17, 25 ]

[ "<|reference_start|> A survey of 5G technologies: regulatory, standardization and industrial perspectives: <|reference_end|>", "<|reference_start|> Systematic resource allocation in cloud RAN with caching as a service under two timescales: Recently, cloud radio access network (C-RAN) with caching as a service (CaaS) was proposed to merge the functionalities of communication, computing, and caching (CC&C) together. In this paper, we dissect the interactions of CC&C in C-RAN with CaaS from two dimensions: physical resource dimension and time dimension. In the physical resource dimension, we identify how to segment the baseband unit (BBU) pool resources (i.e., computation and storage) into different types of virtual machines (VMs). In the time dimension, we address how the long-term resource segmentation in the BBU pool impacts on the short-term transmit beamforming at the remote radio heads. We formulate the problem as a stochastic mixed-integer nonlinear programming (SMINLP) to minimize the system cost, including the server cost, VM cost and wireless transmission cost. After a series of approximation, including sample average approximation, successive convex approximation, and semidefinite relaxation, the SMINLP is approximated as a global consensus problem. The alternating direction method of multipliers (ADMM) is utilized to obtain the solution in a parallel fashion. Simulation results verify the convergence of our proposed algorithm, and also confirm that the proposed scheme is more cost-saving than that without considering the integration of CC&C. <|reference_end|>", "<|reference_start|> CoMP transmission in downlink NOMA-based heterogeneous cloud radio access networks: In this paper, we investigate the integration between the coordinated multipoint (CoMP) transmission and the non-orthogonal multiple access (NOMA) in downlink heterogeneous cloud radio access networks (H-CRANs). In H-CRAN, low-power high-density small remote radio heads (SRRHs) are underlaid by high-power low-density macro RRH (MRRH). However, co-channel deployment of the different RRHs gives rise to the problem of inter-cell interference that significantly affects system performance especially the cell-edge users. Thus, the users are first categorized into Non-CoMP users and CoMP users based on the relation between the useful signal to the dominant interference signal. The Non-CoMP user is the user equipment (UEs) having high signal-to-interference-plus-noise-ratio ( $\\mathtt {SINR}$ ) and hence associates with only one RRH. On the other hand, the CoMP user, cell-edge user, is the UE that experiences less distinctive received power with the best two RRHs. In the proposed CoMP-NOMA framework, each RRH schedules CoMP-UE and non-CoMP-UE over the same transmission channel using NOMA. We first design an analytical framework based on tools from the stochastic geometry to evaluate the performance of the proposed framework (CoMP-NOMA) which is based on H-CRAN in terms of the average achievable data rate for each NOMA UE. We then examine the spectral efficiency of the proposed CoMP-NOMA based H-CRAN. Simulation results are provided to validate the accuracy of the analytical models and to reveal the superiority of the proposed CoMP-NOMA framework compared with conventional CoMP orthogonal multiple access (CoMP-OMA) techniques. By reaping the benefits of both JT-CoMP and NOMA, we prove that the proposed framework can successfully deal with the inter-cell interference by using CoMP and improve the network’s spectral efficiency through NOMA technique. We also show that, with an appropriate power allocation coefficient setting at the Non-CoMP-UEs, a fairness performance can be achieved between the CoMP-UEs and the Non-CoMP-UEs. <|reference_end|>", "<|reference_start|> Systematic resource allocation in cloud RAN with caching as a service under two timescales: Recently, cloud radio access network (C-RAN) with caching as a service (CaaS) was proposed to merge the functionalities of communication, computing, and caching (CC&C) together. In this paper, we dissect the interactions of CC&C in C-RAN with CaaS from two dimensions: physical resource dimension and time dimension. In the physical resource dimension, we identify how to segment the baseband unit (BBU) pool resources (i.e., computation and storage) into different types of virtual machines (VMs). In the time dimension, we address how the long-term resource segmentation in the BBU pool impacts on the short-term transmit beamforming at the remote radio heads. We formulate the problem as a stochastic mixed-integer nonlinear programming (SMINLP) to minimize the system cost, including the server cost, VM cost and wireless transmission cost. After a series of approximation, including sample average approximation, successive convex approximation, and semidefinite relaxation, the SMINLP is approximated as a global consensus problem. The alternating direction method of multipliers (ADMM) is utilized to obtain the solution in a parallel fashion. Simulation results verify the convergence of our proposed algorithm, and also confirm that the proposed scheme is more cost-saving than that without considering the integration of CC&C. <|reference_end|>" ]

[ 0, 32, 34, 41 ]

[ "<|reference_start|> Visualizing structure and transitions in high-dimensional biological data: <|reference_end|>", "<|reference_start|> {Introduction to manifold learning: A popular research area today in statistics and machine learning is that of manifold learning, which is related to the algorithmic techniques of dimensionality reduction. Manifold learning can be divided into linear and nonlinear methods. Linear methods, which have long been part of the statistician's toolbox for analyzing multivariate data, include principal component analysis (PCA) and multidimensional scaling (MDS). Recently, there has been a flurry of research activity on nonlinear manifold learning, which includes Isomap, local linear embedding, Laplacian eigenmaps, Hessian eigenmaps, and diffusion maps. Some of these techniques are nonlinear generalizations of the linear methods. The algorithmic process of most of these techniques consists of three steps: a nearest‐neighbor search, a definition of distances or affinities between points (a key ingredient for the success of these methods), and an eigenproblem for embedding high‐dimensional points into a lower dimensional space. This article gives us a brief survey of these new methods and indicates their strengths and weaknesses. WIREs Comput Stat 2012 doi: 10.1002/wics.1222 <|reference_end|>", "<|reference_start|> Visualizing structure and transitions in high-dimensional biological data: <|reference_end|>", "<|reference_start|> Tree-Sliced Variants of Wasserstein Distances: Optimal transport (\\OT) theory defines a powerful set of tools to compare probability distributions. \\OT~suffers however from a few drawbacks, computational and statistical, which have encouraged the proposal of several regularized variants of OT in the recent literature, one of the most notable being the \\textit{sliced} formulation, which exploits the closed-form formula between univariate distributions by projecting high-dimensional measures onto random lines. We consider in this work a more general family of ground metrics, namely \\textit{tree metrics}, which also yield fast closed-form computations and negative definite, and of which the sliced-Wasserstein distance is a particular case (the tree is a chain). We propose the tree-sliced Wasserstein distance, computed by averaging the Wasserstein distance between these measures using random tree metrics, built adaptively in either low or high-dimensional spaces. Exploiting the negative definiteness of that distance, we also propose a positive definite kernel, and test it against other baselines on a few benchmark tasks. <|reference_end|>" ]

[ 7, 14, 18, 34 ]

[ "<|reference_start|> DifFace: Blind Face Restoration with Diffused Error Contraction: While deep learning-based methods for blind face restoration have achieved unprecedented success, they still suffer from two major limitations. First, most of them deteriorate when facing complex degradations out of their training data. Second, these methods require multiple constraints, e.g., fidelity, perceptual, and adversarial losses, which require laborious hyper-parameter tuning to stabilize and balance their influences. In this work, we propose a novel method named DifFace that is capable of coping with unseen and complex degradations more gracefully without complicated loss designs. The key of our method is to establish a posterior distribution from the observed low-quality (LQ) image to its high-quality (HQ) counterpart. In particular, we design a transition distribution from the LQ image to the intermediate state of a pre-trained diffusion model and then gradually transmit from this intermediate state to the HQ target by recursively applying a pre-trained diffusion model. The transition distribution only relies on a restoration backbone that is trained with $L_2$ loss on some synthetic data, which favorably avoids the cumbersome training process in existing methods. Moreover, the transition distribution can contract the error of the restoration backbone and thus makes our method more robust to unknown degradations. Comprehensive experiments show that DifFace is superior to current state-of-the-art methods, especially in cases with severe degradations. Code and model are available at https://github.com/zsyOAOA/DifFace. <|reference_end|>", "<|reference_start|> Improved Denoising Diffusion Probabilistic Models: Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples. We show that with a few simple modifications, DDPMs can also achieve competitive log-likelihoods while maintaining high sample quality. Additionally, we find that learning variances of the reverse diffusion process allows sampling with an order of magnitude fewer forward passes with a negligible difference in sample quality, which is important for the practical deployment of these models. We additionally use precision and recall to compare how well DDPMs and GANs cover the target distribution. Finally, we show that the sample quality and likelihood of these models scale smoothly with model capacity and training compute, making them easily scalable. We release our code at https://github.com/openai/improved-diffusion <|reference_end|>", "<|reference_start|> Image Restoration with Mean-Reverting Stochastic Differential Equations: This paper presents a stochastic differential equation (SDE) approach for general-purpose image restoration. The key construction consists in a mean-reverting SDE that transforms a high-quality image into a degraded counterpart as a mean state with fixed Gaussian noise. Then, by simulating the corresponding reverse-time SDE, we are able to restore the origin of the low-quality image without relying on any task-specific prior knowledge. Crucially, the proposed mean-reverting SDE has a closed-form solution, allowing us to compute the ground truth time-dependent score and learn it with a neural network. Moreover, we propose a maximum likelihood objective to learn an optimal reverse trajectory that stabilizes the training and improves the restoration results. The experiments show that our proposed method achieves highly competitive performance in quantitative comparisons on image deraining, deblurring, and denoising, setting a new state-of-the-art on two deraining datasets. Finally, the general applicability of our approach is further demonstrated via qualitative results on image super-resolution, inpainting, and dehazing. Code is available at https://github.com/Algolzw/image-restoration-sde. <|reference_end|>", "<|reference_start|> Image Restoration with Mean-Reverting Stochastic Differential Equations: This paper presents a stochastic differential equation (SDE) approach for general-purpose image restoration. The key construction consists in a mean-reverting SDE that transforms a high-quality image into a degraded counterpart as a mean state with fixed Gaussian noise. Then, by simulating the corresponding reverse-time SDE, we are able to restore the origin of the low-quality image without relying on any task-specific prior knowledge. Crucially, the proposed mean-reverting SDE has a closed-form solution, allowing us to compute the ground truth time-dependent score and learn it with a neural network. Moreover, we propose a maximum likelihood objective to learn an optimal reverse trajectory that stabilizes the training and improves the restoration results. The experiments show that our proposed method achieves highly competitive performance in quantitative comparisons on image deraining, deblurring, and denoising, setting a new state-of-the-art on two deraining datasets. Finally, the general applicability of our approach is further demonstrated via qualitative results on image super-resolution, inpainting, and dehazing. Code is available at https://github.com/Algolzw/image-restoration-sde. <|reference_end|>" ]

[ 0, 1, 5, 7 ]

[ "<|reference_start|> Efficient Estimation of Word Representations in Vector Space: We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Furthermore, we show that these vectors provide state-of-the-art performance on our test set for measuring syntactic and semantic word similarities. <|reference_end|>", "<|reference_start|> Efficient Estimation of Word Representations in Vector Space: We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Furthermore, we show that these vectors provide state-of-the-art performance on our test set for measuring syntactic and semantic word similarities. <|reference_end|>", "<|reference_start|> GloVe: Global Vectors for word representation: Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed for such regularities to emerge in word vectors. The result is a new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods. Our model efficiently leverages statistical information by training only on the nonzero elements in a word-word cooccurrence matrix, rather than on the entire sparse matrix or on individual context windows in a large corpus. The model produces a vector space with meaningful substructure, as evidenced by its performance of 75% on a recent word analogy task. It also outperforms related models on similarity tasks and named entity recognition. <|reference_end|>", "<|reference_start|> Neural Word Embedding As Implicit Matrix Factorization: We analyze skip-gram with negative-sampling (SGNS), a word embedding method introduced by Mikolov et al., and show that it is implicitly factorizing a word-context matrix, whose cells are the pointwise mutual information (PMI) of the respective word and context pairs, shifted by a global constant. We find that another embedding method, NCE, is implicitly factorizing a similar matrix, where each cell is the (shifted) log conditional probability of a word given its context. We show that using a sparse Shifted Positive PMI word-context matrix to represent words improves results on two word similarity tasks and one of two analogy tasks. When dense low-dimensional vectors are preferred, exact factorization with SVD can achieve solutions that are at least as good as SGNS's solutions for word similarity tasks. On analogy questions SGNS remains superior to SVD. We conjecture that this stems from the weighted nature of SGNS's factorization. <|reference_end|>" ]

[ 0, 2, 3, 9 ]

[ "<|reference_start|> {Distributed Control Design for Spatially Interconnected Systems: This paper deals with analysis, synthesis, and implementation of distributed controllers, designed for spatially interconnected systems. We develop a state space framework for posing problems of this type, and focus on systems whose model is spatially discrete. In this paper, analysis and synthesis results are developed for this class of systems using the l/sub 2/-induced norm as the performance criterion. The results are stated in terms of linear matrix inequalities and are thus readily amenable to computation. A special implementation of the resulting controllers is presented, which is particularly attractive for distributed operation of the controller. Several examples are provided to further illustrate the application of the results. <|reference_end|>", "<|reference_start|> Optimal control of spatially distributed systems: In this paper, we study the structural properties of optimal control of spatially distributed systems. Such systems consist of an infinite collection of possibly heterogeneous linear control systems that are spatially interconnected via certain distant-dependent coupling functions over arbitrary graphs. We study the structural properties of optimal control problems with infinite-horizon linear quadratic criteria, by analyzing the spatial structure of the solution to the corresponding operator Lyapunov and Riccati equations. The key idea of the paper is the introduction of a special class of operators called spatially decaying (SD). These operators are a generalization of translation invariant operators used in the study of spatially invariant systems. We prove that given a control system with a state-space representation consisting of SD operators, the solution of operator Lyapunov and Riccati equations are SD. Furthermore, we show that the kernel of the optimal state feedback for each subsystem decays in the spatial domain, with the type of decay (e.g., exponential, polynomial or logarithmic) depending on the type of coupling between subsystems. <|reference_end|>", "<|reference_start|> Optimal estimation in spatially distributed systems: how far to share measurements from?: We consider the centralized optimal estimation problem in spatially distributed systems. We use the setting of spatially invariant systems as an idealization for which concrete and detailed results are given. Such estimators are known to have a degree of spatial localization in the sense that the estimator gains decay in space, with the spatial decay rates serving as a proxy for how far measurements need to be shared in an optimal distributed estimator. In particular, we examine the dependence of spatial decay rates on problem specifications such as system dynamics, measurement and process noise variances, as well as their spatial autocorrelations. We propose non-dimensional parameters that characterize the decay rates as a function of problem specifications. In particular, we find an interesting matching condition between the characteristic lengthscale of the dynamics and the measurement noise correlation lengthscale for which the optimal centralized estimator is completely decentralized. A new technique - termed the branch point locus - is introduced to quantify spatial decay rates in terms of analyticity regions in the complex spatial frequency plane. Our results are illustrated through two case studies of systems with dynamics modeled by diffusion and the Swift-Hohenberg equation, respectively. <|reference_end|>", "<|reference_start|> {A unified transform method for solving linear and certain nonlinear PDEs: A new transform method for solving initial boundary value problems for linear and for integrable nonlinear PDEs in two independent variables is introduced. This unified method is based on the fact that linear and integrable nonlinear equations have the distinguished property that they possess a Lax pair formulation. The implementation of this method involves performing a simultaneous spectral analysis of both parts of the Lax pair and solving a Riemann–Hilbert problem. In addition to a unification in the method of solution, there also exists a unification in the representation of the solution. The sine–Gordon equation in light–cone coordinates, the nonlinear Schrödinger equation and their linearized versions are used as illustrative examples. It is also shown that appropriate deformations of the Lax pairs of linear equations can be used to construct Lax pairs for integrable nonlinear equations. As an example, a new Lax pair of the nonlinear Schrödinger equation is derived. <|reference_end|>" ]

[ 1, 2, 6, 7 ]

[ "<|reference_start|> CosFace: Large Margin Cosine Loss for Deep Face Recognition: Face recognition has made extraordinary progress owing to the advancement of deep convolutional neural networks (CNNs). The central task of face recognition, including face verification and identification, involves face feature discrimination. However, the traditional softmax loss of deep CNNs usually lacks the power of discrimination. To address this problem, recently several loss functions such as center loss, large margin softmax loss, and angular softmax loss have been proposed. All these improved losses share the same idea: maximizing inter-class variance and minimizing intra-class variance. In this paper, we propose a novel loss function, namely large margin cosine loss (LMCL), to realize this idea from a different perspective. More specifically, we reformulate the softmax loss as a cosine loss by $L_2$ normalizing both features and weight vectors to remove radial variations, based on which a cosine margin term is introduced to further maximize the decision margin in the angular space. As a result, minimum intra-class variance and maximum inter-class variance are achieved by virtue of normalization and cosine decision margin maximization. We refer to our model trained with LMCL as CosFace. Extensive experimental evaluations are conducted on the most popular public-domain face recognition datasets such as MegaFace Challenge, Youtube Faces (YTF) and Labeled Face in the Wild (LFW). We achieve the state-of-the-art performance on these benchmarks, which confirms the effectiveness of our proposed approach. <|reference_end|>", "<|reference_start|> ArcFace: Additive Angular Margin Loss for Deep Face Recognition: Recently, a popular line of research in face recognition is adopting margins in the well-established softmax loss function to maximize class separability. In this paper, we first introduce an Additive Angular Margin Loss (ArcFace), which not only has a clear geometric interpretation but also significantly enhances the discriminative power. Since ArcFace is susceptible to the massive label noise, we further propose sub-center ArcFace, in which each class contains $K$ sub-centers and training samples only need to be close to any of the $K$ positive sub-centers. Sub-center ArcFace encourages one dominant sub-class that contains the majority of clean faces and non-dominant sub-classes that include hard or noisy faces. Based on this self-propelled isolation, we boost the performance through automatically purifying raw web faces under massive real-world noise. Besides discriminative feature embedding, we also explore the inverse problem, mapping feature vectors to face images. Without training any additional generator or discriminator, the pre-trained ArcFace model can generate identity-preserved face images for both subjects inside and outside the training data only by using the network gradient and Batch Normalization (BN) priors. Extensive experiments demonstrate that ArcFace can enhance the discriminative feature embedding as well as strengthen the generative face synthesis. <|reference_end|>", "<|reference_start|> CosFace: Large Margin Cosine Loss for Deep Face Recognition: Face recognition has made extraordinary progress owing to the advancement of deep convolutional neural networks (CNNs). The central task of face recognition, including face verification and identification, involves face feature discrimination. However, the traditional softmax loss of deep CNNs usually lacks the power of discrimination. To address this problem, recently several loss functions such as center loss, large margin softmax loss, and angular softmax loss have been proposed. All these improved losses share the same idea: maximizing inter-class variance and minimizing intra-class variance. In this paper, we propose a novel loss function, namely large margin cosine loss (LMCL), to realize this idea from a different perspective. More specifically, we reformulate the softmax loss as a cosine loss by $L_2$ normalizing both features and weight vectors to remove radial variations, based on which a cosine margin term is introduced to further maximize the decision margin in the angular space. As a result, minimum intra-class variance and maximum inter-class variance are achieved by virtue of normalization and cosine decision margin maximization. We refer to our model trained with LMCL as CosFace. Extensive experimental evaluations are conducted on the most popular public-domain face recognition datasets such as MegaFace Challenge, Youtube Faces (YTF) and Labeled Face in the Wild (LFW). We achieve the state-of-the-art performance on these benchmarks, which confirms the effectiveness of our proposed approach. <|reference_end|>", "<|reference_start|> ArcFace: Additive Angular Margin Loss for Deep Face Recognition: Recently, a popular line of research in face recognition is adopting margins in the well-established softmax loss function to maximize class separability. In this paper, we first introduce an Additive Angular Margin Loss (ArcFace), which not only has a clear geometric interpretation but also significantly enhances the discriminative power. Since ArcFace is susceptible to the massive label noise, we further propose sub-center ArcFace, in which each class contains $K$ sub-centers and training samples only need to be close to any of the $K$ positive sub-centers. Sub-center ArcFace encourages one dominant sub-class that contains the majority of clean faces and non-dominant sub-classes that include hard or noisy faces. Based on this self-propelled isolation, we boost the performance through automatically purifying raw web faces under massive real-world noise. Besides discriminative feature embedding, we also explore the inverse problem, mapping feature vectors to face images. Without training any additional generator or discriminator, the pre-trained ArcFace model can generate identity-preserved face images for both subjects inside and outside the training data only by using the network gradient and Batch Normalization (BN) priors. Extensive experiments demonstrate that ArcFace can enhance the discriminative feature embedding as well as strengthen the generative face synthesis. <|reference_end|>" ]

[ 2, 3, 5, 6 ]

[ "<|reference_start|> Disaggregation of home energy display data using probabilistic approach: Home energy displays are emerging home energy management devices. However, their energy savings potential is limited, because most display whole-home electricity consumption data. We propose a new approach to disaggregation electricity consumption by individual appliances and/or end uses that would enhance the effectiveness of home energy displays. <|reference_end|>", "<|reference_start|> An unsupervised training method for non-intrusive appliance load monitoring: <|reference_end|>", "<|reference_start|> Improving Multiobjective Particle Swarm Optimization Method: <|reference_end|>", "<|reference_start|> A Genetic Algorithm for the Multidimensional Knapsack Problem: <|reference_end|>" ]

[ 6, 11, 15, 21 ]

[ "<|reference_start|> Goal-conditioned Imitation Learning: Designing rewards for Reinforcement Learning (RL) is challenging because it needs to convey the desired task, be efficient to optimize, and be easy to compute. The latter is particularly problematic when applying RL to robotics, where detecting whether the desired configuration is reached might require considerable supervision and instrumentation. Furthermore, we are often interested in being able to reach a wide range of configurations, hence setting up a different reward every time might be unpractical. Methods like Hindsight Experience Replay (HER) have recently shown promise to learn policies able to reach many goals, without the need of a reward. Unfortunately, without tricks like resetting to points along the trajectory, HER might require many samples to discover how to reach certain areas of the state-space. In this work we investigate different approaches to incorporate demonstrations to drastically speed up the convergence to a policy able to reach any goal, also surpassing the performance of an agent trained with other Imitation Learning algorithms. Furthermore, we show our method can also be used when the available expert trajectories do not contain the actions, which can leverage kinesthetic or third person demonstration. The code is available at https://sites.google.com/view/goalconditioned-il/. <|reference_end|>", "<|reference_start|> Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization: Reinforcement learning can acquire complex behaviors from high-level specifications. However, defining a cost function that can be optimized effectively and encodes the correct task is challenging in practice. We explore how inverse optimal control (IOC) can be used to learn behaviors from demonstrations, with applications to torque control of high-dimensional robotic systems. Our method addresses two key challenges in inverse optimal control: first, the need for informative features and effective regularization to impose structure on the cost, and second, the difficulty of learning the cost function under unknown dynamics for high-dimensional continuous systems. To address the former challenge, we present an algorithm capable of learning arbitrary nonlinear cost functions, such as neural networks, without meticulous feature engineering. To address the latter challenge, we formulate an efficient sample-based approximation for MaxEnt IOC. We evaluate our method on a series of simulated tasks and real-world robotic manipulation problems, demonstrating substantial improvement over prior methods both in terms of task complexity and sample efficiency. <|reference_end|>", "<|reference_start|> End-to-end Driving via Conditional Imitation Learning: Deep networks trained on demonstrations of human driving have learned to follow roads and avoid obstacles. However, driving policies trained via imitation learning cannot be controlled at test time. A vehicle trained end-to-end to imitate an expert cannot be guided to take a specific turn at an upcoming intersection. This limits the utility of such systems. We propose to condition imitation learning on high-level command input. At test time, the learned driving policy functions as a chauffeur that handles sensorimotor coordination but continues to respond to navigational commands. We evaluate different architectures for conditional imitation learning in vision-based driving. We conduct experiments in realistic three-dimensional simulations of urban driving and on a 1/5 scale robotic truck that is trained to drive in a residential area. Both systems drive based on visual input yet remain responsive to high-level navigational commands. The supplementary video can be viewed at https://youtu.be/cFtnflNe5fM <|reference_end|>", "<|reference_start|> Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation: Simulation is a crucial tool for accelerating the development of autonomous vehicles. Making simulation realistic requires models of the human road users who interact with such cars. Such models can be obtained by applying learning from demonstration (LfD) to trajectories observed by cars already on the road. However, existing LfD methods are typically insufficient, yielding policies that frequently collide or drive off the road. To address this problem, we propose Symphony, which greatly improves realism by combining conventional policies with a parallel beam search. The beam search refines these policies on the fly by pruning branches that are unfavourably evaluated by a discriminator. However, it can also harm diversity, i.e., how well the agents cover the entire distribution of realistic behaviour, as pruning can encourage mode collapse. Symphony addresses this issue with a hierarchical approach, factoring agent behaviour into goal generation and goal conditioning. The use of such goals ensures that agent diversity neither disappears during adversarial training nor is pruned away by the beam search. Experiments on both proprietary and open Waymo datasets confirm that Symphony agents learn more realistic and diverse behaviour than several baselines. <|reference_end|>" ]

[ 10, 18, 32, 46 ]

[ "<|reference_start|> Space-Time Representation of People Based on 3D Skeletal Data: A Review: Spatiotemporal human representation based on 3D visual perception data is a rapidly growing research area. Based on the information sources, these representations can be broadly categorized into two groups based on RGB-D information or 3D skeleton data. Recently, skeleton-based human representations have been intensively studied and kept attracting an increasing attention, due to their robustness to variations of viewpoint, human body scale and motion speed as well as the realtime, online performance. This paper presents a comprehensive survey of existing space-time representations of people based on 3D skeletal data, and provides an informative categorization and analysis of these methods from the perspectives, including information modality, representation encoding, structure and transition, and feature engineering. We also provide a brief overview of skeleton acquisition devices and construction methods, enlist a number of public benchmark datasets with skeleton data, and discuss potential future research directions. <|reference_end|>", "<|reference_start|> 3D skeleton-based human action classification: A survey: <|reference_end|>", "<|reference_start|> Space-Time Representation of People Based on 3D Skeletal Data: A Review: Spatiotemporal human representation based on 3D visual perception data is a rapidly growing research area. Based on the information sources, these representations can be broadly categorized into two groups based on RGB-D information or 3D skeleton data. Recently, skeleton-based human representations have been intensively studied and kept attracting an increasing attention, due to their robustness to variations of viewpoint, human body scale and motion speed as well as the realtime, online performance. This paper presents a comprehensive survey of existing space-time representations of people based on 3D skeletal data, and provides an informative categorization and analysis of these methods from the perspectives, including information modality, representation encoding, structure and transition, and feature engineering. We also provide a brief overview of skeleton acquisition devices and construction methods, enlist a number of public benchmark datasets with skeleton data, and discuss potential future research directions. <|reference_end|>", "<|reference_start|> A survey of human motion analysis using depth imagery: <|reference_end|>" ]

[ 16, 23, 24, 31 ]

[ "<|reference_start|> Personalization in context: Does context matter when building personalized customer models?: The idea that context is important when predicting customer behavior has been maintained by scholars in marketing and data mining. However, no systematic study measuring how much the contextual information really matters in building customer models in personalization applications have been done before. In this paper, we address this problem. To this aim, we collected data containing rich contextual information by developing a special-purpose browser to help users to navigate a well- known e-commerce retail portal and purchase products on its site. The experimental results show that context does matter for the case of modeling behavior of individual customers. The granularity of contextual information also matters, and the effect of contextual information gets diluted during the process of aggregating customers' data. <|reference_end|>", "<|reference_start|> Incorporating contextual information in recommender systems using a multidimensional approach: The article presents a multidimensional (MD) approach to recommender systems that can provide recommendations based on additional contextual information besides the typical information on users and items used in most of the current recommender systems. This approach supports multiple dimensions, profiling information, and hierarchical aggregation of recommendations. The article also presents a multidimensional rating estimation method capable of selecting two-dimensional segments of ratings pertinent to the recommendation context and applying standard collaborative filtering or other traditional two-dimensional rating estimation techniques to these segments. A comparison of the multidimensional and two-dimensional rating estimation approaches is made, and the tradeoffs between the two are studied. Moreover, the article introduces a combined rating estimation method, which identifies the situations where the MD approach outperforms the standard two-dimensional approach and uses the MD approach in those situations and the standard two-dimensional approach elsewhere. Finally, the article presents a pilot empirical study of the combined approach, using a multidimensional movie recommender system that was developed for implementing this approach and testing its performance. <|reference_end|>", "<|reference_start|> Using context to improve predictive modeling of customers in personalization applications: The idea that context is important when predicting customer behavior has been maintained by scholars in marketing and data mining. However, no systematic study measuring how much the contextual information really matters in building customer models in personalization applications has been done before. In this paper, we study how important the contextual information is when predicting customer behavior and how to use it when building customer models. It is done by conducting an empirical study across a wide range of experimental conditions. The experimental results show that context does matter when modeling the behavior of individual customers and that it is possible to infer the context from the existing data with reasonable accuracy in certain cases. It is also shown that significant performance improvements can be achieved if the context is \"cleverly\" modeled, as described in this paper. These findings have significant implications for data miners and marketers. They show that contextual information does matter in personalization and companies have different opportunities to both make context valuable for improving predictive performance of customers' behavior and decreasing the costs of gathering contextual information. <|reference_end|>", "<|reference_start|> Using context to improve predictive modeling of customers in personalization applications: The idea that context is important when predicting customer behavior has been maintained by scholars in marketing and data mining. However, no systematic study measuring how much the contextual information really matters in building customer models in personalization applications has been done before. In this paper, we study how important the contextual information is when predicting customer behavior and how to use it when building customer models. It is done by conducting an empirical study across a wide range of experimental conditions. The experimental results show that context does matter when modeling the behavior of individual customers and that it is possible to infer the context from the existing data with reasonable accuracy in certain cases. It is also shown that significant performance improvements can be achieved if the context is \"cleverly\" modeled, as described in this paper. These findings have significant implications for data miners and marketers. They show that contextual information does matter in personalization and companies have different opportunities to both make context valuable for improving predictive performance of customers' behavior and decreasing the costs of gathering contextual information. <|reference_end|>" ]

[ 2, 3, 6, 8 ]

[ "<|reference_start|> Linear Associative Information Retrieval: Abstract : The recognition and exploitation of term associations for the retrieval of documents is discussed. A general theory of association and associative retrieval is presented; it is based on the use of linear transformations, both for establishing associations among terms and for discriminating among documents. The design and behavior of a simple experimental device which realizes the theory is discussed. <|reference_end|>", "<|reference_start|> Conceptual Query Expansion Model for Web Information Retrieval: <|reference_end|>", "<|reference_start|> Exploring the Web of Spatial Data with Facete: The majority of data (including data published on the Web as Linked Open Data) has a spatial dimension. However, the efficient, user friendly exploration of spatial data remains a major challenge. We present Facete, a web-based exploration and visualization application enabling the spatial-faceted browsing of data with a spatial dimension. Facete implements a novel spatial data exploration paradigm based on the following three key components: First, a domain independent faceted filtering module, which operates directly on SPARQL and supports nested facets. Second, an algorithm that efficiently detects spatial information related to those resources that satisfy the facet selection. The detected relations are used for automatically presenting data on a map. And third, a workflow for making the map display interact with data sources that contain large amounts of geometric information. We demonstrate Facete in large-scale, real world application scenarios. <|reference_end|>", "<|reference_start|> Composite match autocompletion (COMMA): a semantic result-oriented autocompletion technique for e-marketplaces: Autocompletion systems support users in the formulation of queries in different situations, from development environments to the web. In this paper we describe Composite Match Autocompletion COMMA, a lightweight approach to the introduction of semantics in the realization of a semi-structured data autocompletion matching algorithm. The approach is formally described, then it is applied and evaluated with specific reference to the e-commerce context. The semantic extension to the matching algorithm exploits available information about product categories and distinguishing features of products to enhance the elaboration of exploratory queries. COMMA supports a seamless management of both targeted/precise queries and exploratory/vague ones, combining different filtering and scoring techniques. The algorithm is evaluated with respect both to effectiveness and efficiency in a real-world scenario: the achieved improvement is significant and it is not associated to a sensible increase of computational costs. <|reference_end|>" ]

[ 0, 5, 9, 14 ]

[ "<|reference_start|> Machine learning based channel estimation: A computational approach for universal channel conditions: Recently, machine learning has been introduced in communications to deal with channel estimation. Under non-linear system models, the superiority of machine learning based estimation has been demonstrated by simulation expriments, but the theoretical analysis is not sufficient, since the performance of machine learning, especially deep learning, is hard to analyze. This paper focuses on some theoretical problems in machine learning based channel estimation. As a data-driven method, certain amount of training data is the prerequisite of a workable machine learning based estimation, and it is analyzed qualitively in a statistic view in this paper. To deduce the exact sample size, we build a statistic model ignoring the exact structure of the learning module and then the relationship between sample size and learning performance is derived. To testify our analysis, we employ machine learning based channel estimation in OFDM system and apply two typical neural networks as the learning module: single layer or linear structure and three layer structure. The simulation results show that the analysis sample size is correct when input dimension and complexity of learning module are low, but the true required sample size will be larger the analysis result otherwise, since the influence of the two factors is not considered in the analysis of sample size. Also, we simulate the performance of machine learning based channel estimation under quasi-stationary channel condition, where the explicit form of MMSE estimation is hard to obtain, and the simulation results exhibit the effectiveness and convenience of machine learning based channel estimation under complex channel models. <|reference_end|>", "<|reference_start|> An Introduction to Deep Learning for the Physical Layer: We present and discuss several novel applications of deep learning for the physical layer. By interpreting a communications system as an autoencoder, we develop a fundamental new way to think about communications system design as an end-to-end reconstruction task that seeks to jointly optimize transmitter and receiver components in a single process. We show how this idea can be extended to networks of multiple transmitters and receivers and present the concept of radio transformer networks as a means to incorporate expert domain knowledge in the machine learning model. Lastly, we demonstrate the application of convolutional neural networks on raw IQ samples for modulation classification which achieves competitive accuracy with respect to traditional schemes relying on expert features. The paper is concluded with a discussion of open challenges and areas for future investigation. <|reference_end|>", "<|reference_start|> Power Control in Cellular Massive MIMO with Varying User Activity: A Deep Learning Solution: This paper considers the sum spectral efficiency (SE) optimization problem in multi-cell Massive MIMO systems with a varying number of active users. This is formulated as a joint pilot and data power control problem. Since the problem is non-convex, we first derive a novel iterative algorithm that obtains a stationary point in polynomial time. To enable real-time implementation, we also develop a deep learning solution. The proposed neural network, PowerNet, only uses the large-scale fading information to predict both the pilot and data powers. The main novelty is that we exploit the problem structure to design a single neural network that can handle a dynamically varying number of active users; hence, PowerNet is simultaneously approximating many different power control functions with varying number inputs and outputs. This is not the case in prior works and thus makes PowerNet an important step towards a practically useful solution. Numerical results demonstrate that PowerNet only loses $2\\%$ in sum SE, compared to the iterative algorithm, in a nine-cell system with up to $90$ active users per in each coherence interval, and the runtime was only $0.03$ ms on a graphics processing unit (GPU). When good data labels are selected for the training phase, PowerNet can yield better sum SE than by solving the optimization problem with one initial point. <|reference_end|>", "<|reference_start|> Deep-learning-based channel estimation for wireless energy transfer: We propose a deep-learning-based channel estimation technique for wireless energy transfer. Specifically, we develop a channel learning scheme using the deep autoencoder, which learns the channel state information (CSI) at the energy transmitter based on the harvested energy feedback from the energy receiver, in the sense of minimizing the mean square error (mse) of the channel estimation. Numerical results demonstrate that the proposed scheme learns the CSI very well and significantly outperforms the conventional scheme in terms of the channel estimation mse as well as the harvested energy. <|reference_end|>" ]

[ 4, 8, 10, 13 ]

[ "<|reference_start|> Method of Optimal Directions for frame design: A frame design technique for use with vector selection algorithms, for example matching pursuits (MP), is presented. The design algorithm is iterative and requires a training set of signal vectors. The algorithm, called method of optimal directions (MOD), is an improvement of the algorithm presented by Engan, Aase and Husoy see (Proc. ICASSP '98, Seattle, USA, p.1817-20, 1998). The MOD is applied to speech and electrocardiogram (ECG) signals, and the designed frames are tested on signals outside the training sets. Experiments demonstrate that the approximation capabilities, in terms of mean squared error (MSE), of the optimized frames are significantly better than those obtained using frames designed by the algorithm of Engan et. al. Experiments show typical reduction in MSE by 20-50%. <|reference_end|>", "<|reference_start|> A Practical Algorithm for Topic Modeling with Provable Guarantees: Topic models provide a useful method for dimensionality reduction and exploratory data analysis in large text corpora. Most approaches to topic model inference have been based on a maximum likelihood objective. Efficient algorithms exist that approximate this objective, but they have no provable guarantees. Recently, algorithms have been introduced that provide provable bounds, but these algorithms are not practical because they are inefficient and not robust to violations of model assumptions. In this paper we present an algorithm for topic model inference that is both provable and practical. The algorithm produces results comparable to the best MCMC implementations while running orders of magnitude faster. <|reference_end|>", "<|reference_start|> Local stability and robustness of sparse dictionary learning in the presence of noise: A popular approach within the signal processing and machine learning communities consists in modelling signals as sparse linear combinations of atoms selected from a learned dictionary. While this paradigm has led to numerous empirical successes in various fields ranging from image to audio processing, there have only been a few theoretical arguments supporting these evidences. In particular, sparse coding, or sparse dictionary learning, relies on a non-convex procedure whose local minima have not been fully analyzed yet. In this paper, we consider a probabilistic model of sparse signals, and show that, with high probability, sparse coding admits a local minimum around the reference dictionary generating the signals. Our study takes into account the case of over-complete dictionaries and noisy signals, thus extending previous work limited to noiseless settings and/or under-complete dictionaries. The analysis we conduct is non-asymptotic and makes it possible to understand how the key quantities of the problem, such as the coherence or the level of noise, can scale with respect to the dimension of the signals, the number of atoms, the sparsity and the number of observations. <|reference_end|>", "<|reference_start|> Exact Recovery of Sparsely Used Overcomplete Dictionaries: We consider the problem of learning overcomplete dictionaries in the context of sparse coding, where each sample selects a sparse subset of dictionary elements. Our method consists of two stages, viz., initial estimation of the dictionary, and a clean-up phase involving estimation of the coefficient matrix, and re-estimation of the dictionary. We prove that our method exactly recovers both the dictionary and the coefficient matrix under a set of sufficient conditions. <|reference_end|>" ]

[ 8, 17, 22, 34 ]

[ "<|reference_start|> Graph Clustering based on Structural/Attribute Similarities: The goal of graph clustering is to partition vertices in a large graph into different clusters based on various criteria such as vertex connectivity or neighborhood similarity. Graph clustering techniques are very useful for detecting densely connected groups in a large graph. Many existing graph clustering methods mainly focus on the topological structure for clustering, but largely ignore the vertex properties which are often heterogenous. In this paper, we propose a novel graph clustering algorithm, SA-Cluster, based on both structural and attribute similarities through a unified distance measure. Our method partitions a large graph associated with attributes into k clusters so that each cluster contains a densely connected subgraph with homogeneous attribute values. An effective method is proposed to automatically learn the degree of contributions of structural similarity and attribute similarity. Theoretical analysis is provided to show that SA-Cluster is converging. Extensive experimental results demonstrate the effectiveness of SA-Cluster through comparison with the state-of-the-art graph clustering and summarization methods. <|reference_end|>", "<|reference_start|> Joint cluster analysis of attribute data and relationship data: the connected k-center problem: Attribute data and relationship data are two principle types of data, representing the intrinsic and extrinsic properties of entities. While attribute data has been the main source of data for cluster analysis, relationship data such as social networks or metabolic networks are becoming increasingly available. It is also common to observe both data types carry orthogonal information such as in market segmentation and community identification, which calls for a joint cluster analysis of both data types so as to achieve more accurate results. For this purpose, we introduce the novel Connected kCenter problem, taking into account attribute data as well as relationship data. We analyze the complexity of this problem and prove its NP-completeness. We also present a constant factor approximation algorithm, based on which we further design NetScan, a heuristic algorithm that is efficient for large, real databases. Our experimental evaluation demonstrates the meaningfulness and accuracy of the NetScan results. <|reference_end|>", "<|reference_start|> Community detection in incomplete information networks: With the recent advances in information networks, the problem of community detection has attracted much attention in the last decade. While network community detection has been ubiquitous, the task of collecting complete network data remains challenging in many real-world applications. Usually the collected network is incomplete with most of the edges missing. Commonly, in such networks, all nodes with attributes are available while only the edges within a few local regions of the network can be observed. In this paper, we study the problem of detecting communities in incomplete information networks with missing edges. We first learn a distance metric to reproduce the link-based distance between nodes from the observed edges in the local information regions. We then use the learned distance metric to estimate the distance between any pair of nodes in the network. A hierarchical clustering approach is proposed to detect communities within the incomplete information networks. Empirical studies on real-world information networks demonstrate that our proposed method can effectively detect community structures within incomplete information networks. <|reference_end|>", "<|reference_start|> Community detection in incomplete information networks: With the recent advances in information networks, the problem of community detection has attracted much attention in the last decade. While network community detection has been ubiquitous, the task of collecting complete network data remains challenging in many real-world applications. Usually the collected network is incomplete with most of the edges missing. Commonly, in such networks, all nodes with attributes are available while only the edges within a few local regions of the network can be observed. In this paper, we study the problem of detecting communities in incomplete information networks with missing edges. We first learn a distance metric to reproduce the link-based distance between nodes from the observed edges in the local information regions. We then use the learned distance metric to estimate the distance between any pair of nodes in the network. A hierarchical clustering approach is proposed to detect communities within the incomplete information networks. Empirical studies on real-world information networks demonstrate that our proposed method can effectively detect community structures within incomplete information networks. <|reference_end|>" ]

[ 4, 6, 10, 11 ]

[ "<|reference_start|> Multicasting in WDM networks: Wavelength-division multiplexing (WDM) networks are believed to be a promising candidate to meet the explosive increase of bandwidth demand in the Internet. In this article, we survey the problems of and approaches to multicasting in WDM networks. In particular, we address the issues in the context of three types of WDM networks: broadcast-and-select, wavelength-routed, and optical burst-switched (OBS) WDM networks. Broadcast-and-select WDM networks are typically for WDM LANs¿MANs, and can be either single-hop or multihop. Various multicast scheduling algorithms (MSAs) are discussed for single-hop networks. For multihop networks, we discuss how channel sharing can be employed to effectively support multicast. In a wavelength-routed WDM network, supporting multicast leads to the multicast routing and wavelength assignment (MC-RWA) problem, which has been discussed for different scenarios, including sparse-splitting networks. We also discuss the problem of efficiently supporting multicast in optical burst-switched (OBS) networks, where the overheads due to control packets and guard bands need to considered. <|reference_end|>", "<|reference_start|> Cost-effective Implementation of Multicasting in Wavelength-Routed Networks: Multicasting in the optical domain has been recently shown to provide substantial savings in terms of the network-wide average packet hop distance and the total number of transceivers in the network. Current proposed multicasting architectures [e.g., splitter-and-delivery (SaD)] employ power splitting mechanisms which have the side effect of high fabrication cost due to the large number of splitters and the need for optical amplifiers. We propose a low-cost novel architecture called tap-and-continue (TaC) for realizing multicasting. This architecture provides a natural evolution from current unicast cross-connects and is based on tapping devices. We prove that any multicasting session can be feasibly realized in networks employing only TaC cross-connects, and the problem of finding the optimal multiple-destination minimum cost trail in such networks is NP-complete. Therefore, we develop a 4-approximation algorithm for multiple-destination routing. Simulation results demonstrate that the TaC cross-connect provides a realistic, cost-effective approach for implementing multicasting with negligible blocking degradation especially in multifiber networks. <|reference_end|>", "<|reference_start|> Constrained multicast routing in wdm networks with sparse light splitting: As WDM technology matures and multicast applications become increasingly popular, supporting multicast at the WDM layer becomes an important and yet challenging topic. In this paper, we study constrained multicast routing in WDM networks with sparse light splitting, i.e., where some switches are incapable of splitting light (or copying data in the optical domain). Specifically, we propose four WDM multicast routing algorithms, namely, Re-route-to Source, Re-route-to-Any, Member-First, and Member-Only. Given the network topology, multicast membership information, and light splitting capability of the switches, these algorithms construct a source-based multicast light-forest (consisting one or more multicast trees) for each multicast session. The performance of these algorithms are compared in terms of the average number of wavelengths used per forest (or multicast session), average number of branches involved (bandwidth) per forest as well as average number of hops encountered (delay) from a multicast source to a multicast member. <|reference_end|>", "<|reference_start|> Avoidance of multicast incapable branching nodes for multicast routing in WDM networks: In this articlewestudy themulticast routing problem in all-opticalWDMnetworks under the spare light splitting constraint. To implement a multicast session, several light-trees may have to be used due to the limited fanouts of network nodes. Although many multicast routing algorithms have been proposed in order to reduce the total number of wavelength channels used (total cost) for a multicast session, the maximum number of wavelengths required in one fiber link (link stress) and the end-to-end delay are two parameters which are not always taken into consideration. It is known that the shortest path tree (SPT) results in the optimal end-to-end delay, but it can not be employed directly for multicast routing in sparse light splitting WDM networks. Hence, we propose a novel wavelength routing algorithm which tries to avoid the multicast incapable branching nodes (MIBs, branching nodes without splitting capability) in the shortest-path-based multicast tree to diminish the link stress. Good parts of the shortest-path-tree are retained by the algorithm to reduce the end-to-end delay. The algorithm consists of tree steps: (1) aDijkstraPro algorithmwith priority assignment and node adoption is introduced to produce a SPT with up to 38% fewer MIB nodes in the NSF topology and 46% fewerMIB nodes in the USA Longhaul topology, (2) critical articulation and deepest branch heuristics are used to process the MIB nodes, (3) a distance-based light-tree reconnection algorithm is proposed to create the multicast light-trees. Extensive simulations demonstrate the algorithm's efficiency in terms of link stress and end-to-end delay. <|reference_end|>" ]

[ 1, 7, 10, 12 ]

[ "<|reference_start|> Coordination of Transmission, Distribution and Communication Systems for Prompt Power System Recovery after Disasters:Report – Grid and Communication Interdependency Review and Characterization of Typical Communication Systems: <|reference_end|>", "<|reference_start|> 2019 20th International Conference on Intelligent System Application to Power Systems (ISAP): Zero Steady State Error on Voltage Source Inverters <|reference_end|>", "<|reference_start|> Generating statistically correct random topologies for testing smart grid communication and control networks: In order to design an efficient communication scheme and examine the efficiency of any networked control architecture in smart grid applications, we need to characterize statistically its information source, namely the power grid itself. Investigating the statistical properties of power grids has the immediate benefit of providing a natural simulation platform, producing a large number of power grid test cases with realistic topologies, with scalable network size, and with realistic electrical parameter settings. The second benefit is that one can start analyzing the performance of decentralized control algorithms over information networks whose topology matches that of the underlying power network and use network scientific approaches to determine analytically if these architectures would scale well. With these motivations, in this paper we study both the topological and electrical characteristics of power grid networks based on a number of synthetic and real-world power systems. The most interesting discoveries include: the power grid is sparsely connected with obvious small-world properties; its nodal degree distribution can be well fitted by a mixture distribution coming from the sum of a truncated geometric random variable and an irregular discrete random variable; the power grid has very distinctive graph spectral density and its algebraic connectivity scales as a power function of the network size; the line impedance has a heavy-tailed distribution, which can be captured quite accurately by a clipped double Pareto lognormal distribution. Based on the discoveries mentioned above, we propose an algorithm that generates random topology power grids featuring the same topology and electrical characteristics found from the real data. <|reference_end|>", "<|reference_start|> On Realizability of a Set of Integers as Degrees of the Vertices of a Linear Graph. I: This paper is mainly concerned with the realizability of a set of n integers as the degrees of vertices of an n-vertex linear graph. Other related problems, such as when a set of integers is realizable as a connected graph, connected graph without “parallel” elements, separable graph, and nonseparable graph, are considered. The relationship between this problem and the problem of isomers in the organic chemistry is described. A similar problem in weighted graphs is also studied. <|reference_end|>" ]

[ 1, 6, 9, 12 ]

[ "<|reference_start|> The Impact of Non-stationarity on Generalisation in Deep Reinforcement Learning: Non-stationarity arises in Reinforcement Learning (RL) even in stationary environments. Most RL algorithms collect new data throughout training, using a non-stationary behaviour policy. Furthermore, training targets in RL can change even with a fixed state distribution when the policy, critic, or bootstrap values are updated. We study these types of non-stationarity in supervised learning settings as well as in RL, finding that they can lead to worse generalisation performance when using deep neural network function approximators. Consequently, to improve generalisation of deep RL agents, we propose Iterated Relearning (ITER). ITER augments standard RL training by repeated knowledge transfer of the current policy into a freshly initialised network, which thereby experiences less non-stationarity during training. Experimentally, we show that ITER improves performance on the challenging generalisation benchmarks ProcGen and Multiroom. <|reference_end|>", "<|reference_start|> A Geometric Perspective on Optimal Representations for Reinforcement Learning: We propose a new perspective on representation learning in reinforcement learning based on geometric properties of the space of value functions. We leverage this perspective to provide formal evidence regarding the usefulness of value functions as auxiliary tasks. Our formulation considers adapting the representation to minimize the (linear) approximation of the value function of all stationary policies for a given environment. We show that this optimization reduces to making accurate predictions regarding a special class of value functions which we call adversarial value functions (AVFs). We demonstrate that using value functions as auxiliary tasks corresponds to an expected-error relaxation of our formulation, with AVFs a natural candidate, and identify a close relationship with proto-value functions (Mahadevan, 2005). We highlight characteristics of AVFs and their usefulness as auxiliary tasks in a series of experiments on the four-room domain. <|reference_end|>", "<|reference_start|> What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study: In recent years, on-policy reinforcement learning (RL) has been successfully applied to many different continuous control tasks. While RL algorithms are often conceptually simple, their state-of-the-art implementations take numerous low- and high-level design decisions that strongly affect the performance of the resulting agents. Those choices are usually not extensively discussed in the literature, leading to discrepancy between published descriptions of algorithms and their implementations. This makes it hard to attribute progress in RL and slows down overall progress [Engstrom'20]. As a step towards filling that gap, we implement >50 such ``choices'' in a unified on-policy RL framework, allowing us to investigate their impact in a large-scale empirical study. We train over 250'000 agents in five continuous control environments of different complexity and provide insights and practical recommendations for on-policy training of RL agents. <|reference_end|>", "<|reference_start|> Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning: In this paper, we aim to develop a simple and scalable reinforcement learning algorithm that uses standard supervised learning methods as subroutines. Our goal is an algorithm that utilizes only simple and convergent maximum likelihood loss functions, while also being able to leverage off-policy data. Our proposed approach, which we refer to as advantage-weighted regression (AWR), consists of two standard supervised learning steps: one to regress onto target values for a value function, and another to regress onto weighted target actions for the policy. The method is simple and general, can accommodate continuous and discrete actions, and can be implemented in just a few lines of code on top of standard supervised learning methods. We provide a theoretical motivation for AWR and analyze its properties when incorporating off-policy data from experience replay. We evaluate AWR on a suite of standard OpenAI Gym benchmark tasks, and show that it achieves competitive performance compared to a number of well-established state-of-the-art RL algorithms. AWR is also able to acquire more effective policies than most off-policy algorithms when learning from purely static datasets with no additional environmental interactions. Furthermore, we demonstrate our algorithm on challenging continuous control tasks with highly complex simulated characters. <|reference_end|>" ]

[ 11, 12, 19, 20 ]

[ "<|reference_start|> Representation Learning with Contrastive Predictive Coding: While supervised learning has enabled great progress in many applications, unsupervised learning has not seen such widespread adoption, and remains an important and challenging endeavor for artificial intelligence. In this work, we propose a universal unsupervised learning approach to extract useful representations from high-dimensional data, which we call Contrastive Predictive Coding. The key insight of our model is to learn such representations by predicting the future in latent space by using powerful autoregressive models. We use a probabilistic contrastive loss which induces the latent space to capture information that is maximally useful to predict future samples. It also makes the model tractable by using negative sampling. While most prior work has focused on evaluating representations for a particular modality, we demonstrate that our approach is able to learn useful representations achieving strong performance on four distinct domains: speech, images, text and reinforcement learning in 3D environments. <|reference_end|>", "<|reference_start|> Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoders: We present Mockingjay as a new speech representation learning approach, where bidirectional Transformer encoders are pre-trained on a large amount of unlabeled speech. Previous speech representation methods learn through conditioning on past frames and predicting information about future frames. Whereas Mockingjay is designed to predict the current frame through jointly conditioning on both past and future contexts. The Mockingjay representation improves performance for a wide range of downstream tasks, including phoneme classification, speaker recognition, and sentiment classification on spoken content, while outperforming other approaches. Mockingjay is empirically powerful and can be fine-tuned with downstream models, with only 2 epochs we further improve performance dramatically. In a low resource setting with only 0.1% of labeled data, we outperform the result of Mel-features that uses all 100% labeled data. <|reference_end|>", "<|reference_start|> {TERA:: Tera is the Hausa and English name for the Nyimatli [nimaáli] people as they call themselves, and their language. Their communities lie principally in the north and east of present-day Gombe State and in the adjoining area of Borno State in north-eastern Nigeria. There are approximately 100,000 people who speak the language as their mother tongue (Gordon & Grimes 2005: 175), many of whom also use Hausa as the local lingua franca; increasing numbers are trilingual as the result of the growing importance of English in commerce and education. <|reference_end|>", "<|reference_start|> A Simple Framework for Contrastive Learning of Visual Representations: This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5% top-1 accuracy, which is a 7% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1% of the labels, we achieve 85.8% top-5 accuracy, outperforming AlexNet with 100X fewer labels. <|reference_end|>" ]

[ 13, 24, 25, 31 ]

[ "<|reference_start|> The influence of practical factors on the benefits of condition-based maintenance over time-based maintenance: <|reference_end|>", "<|reference_start|> Predictive maintenance in the Industry 4.0: A systematic literature review: <|reference_end|>", "<|reference_start|> A Practical Guide to Multi-Objective Reinforcement Learning and Planning: Real-world decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcement learning and decision-theoretic planning either assumes only a single objective, or that multiple objectives can be adequately handled via a simple linear combination. Such approaches may oversimplify the underlying problem and hence produce suboptimal results. This paper serves as a guide to the application of multi-objective methods to difficult problems, and is aimed at researchers who are already familiar with single-objective reinforcement learning and planning methods who wish to adopt a multi-objective perspective on their research, as well as practitioners who encounter multi-objective decision problems in practice. It identifies the factors that may influence the nature of the desired solution, and illustrates by example how these influence the design of multi-objective decision-making systems for complex problems. <|reference_end|>", "<|reference_start|> Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey: The majority of multi-agent system (MAS) implementations aim to optimise agents' policies with respect to a single objective, despite the fact that many real-world problem domains are inherently multi-objective in nature. Multi-objective multi-agent systems (MOMAS) explicitly consider the possible trade-offs between conflicting objective functions. We argue that, in MOMAS, such compromises should be analysed on the basis of the utility that these compromises have for the users of a system. As is standard in multi-objective optimisation, we model the user utility using utility functions that map value or return vectors to scalar values. This approach naturally leads to two different optimisation criteria: expected scalarised returns (ESR) and scalarised expected returns (SER). We develop a new taxonomy which classifies multi-objective multi-agent decision making settings, on the basis of the reward structures, and which and how utility functions are applied. This allows us to offer a structured view of the field, to clearly delineate the current state-of-the-art in multi-objective multi-agent decision making approaches and to identify promising directions for future research. Starting from the execution phase, in which the selected policies are applied and the utility for the users is attained, we analyse which solution concepts apply to the different settings in our taxonomy. Furthermore, we define and discuss these solution concepts under both ESR and SER optimisation criteria. We conclude with a summary of our main findings and a discussion of many promising future research directions in multi-objective multi-agent systems. <|reference_end|>" ]

[ 3, 4, 20, 36 ]

[ "<|reference_start|> {From Labeled to Unlabeled Data – On the Data\nChallenge in Automatic Drum Transcription: Automatic Drum Transcription (ADT), like many other music information retrieval tasks, has made progress in the past years through the integration of machine learning and audio signal processing techniques. However, with the increasing popularity of data-hungry approaches such as deep learning, the insufficient amount of data becomes more and more a challenge that concerns the generality of the resulting models and the validity of the evaluation. To address this challenge in ADT, this paper first examines the existing labeled datasets and how representative they are of the research problem. Next, possibilities of using unlabeled data to improve general ADT systems are explored. Specifically, two paradigms that harness information from unlabeled data, namely feature learning and student-teacher learning, are applied to two major types of ADT systems. All systems are evaluated on four different drum datasets. The results highlight the necessity of more and larger annotated datasets and indicate the feasibility of exploiting unlabeled data for improving ADT systems. <|reference_end|>", "<|reference_start|> Real-Time Transcription and Separation of Drum Recordings Based on NMF Decomposition.: This paper proposes a real-time capable method for transcribing and separating occurrences of single drum instruments in poly-phonic drum recordings. Both the detection and the decomposition are based on Non-Negative Matrix Factorization and can be implemented with very small systemic delay. We propose a simple modification to the update rules that allows to capture time-dynamic spectral characteristics of the involved drum sounds. The method can be applied in music production and music education software. Performance results with respect to drum transcription are presented and discussed. The evaluation data-set consisting of annotated drum recordings is published for use in further studies in the field. <|reference_end|>", "<|reference_start|> Drumkit transcription via convolutive NMF: Audio to midi software exists for transcribing the output of a multi-mic’ed drumkit. Such software requires that the drummer uses multiple microphones to capture a single stream of audio for each kit piece. This paper explores the first steps towards a system for transcribing a drum score based upon the input of a single mono microphone. Non-negative Matrix Factorisation is a widely re-searched source separation technique. We describe a system for transcribing drums using this technique presenting an improved gains update method. A good level of accuracy is achieved on on complex loops and there are indications the mis-transcriptions are for perceptually less important parts of the score. <|reference_end|>", "<|reference_start|> Automatic Drum Transcription for Polyphonic Recordings using Soft Attention Mechanisms and Convolutional Neural Networks: Automatic drum transcription is the process of generating symbolic notation for percussion instruments within audio recordings. To date, recurrent neural network (RNN) systems have achieved the highest evaluation accuracies for \nboth drum solo and polyphonic recordings, however the accuracies within a polyphonic context still remain relatively low. To improve accuracy for polyphonic recordings, we present two approaches to the ADT problem: First, to capture the dynamism of features in multiple time-step hidden \nlayers, we propose the use of soft attention mechanisms (SA) and an alternative RNN configuration containing additional peripheral connections (PC). Second, to capture these same trends at the input level, we propose the use of a convolutional neural network (CNN), which uses a larger set of time-step features. In addition, we propose the use of a bidirectional recurrent neural network (BRNN) in the peak-picking stage. The proposed systems are evaluated along with two state-of-the-art ADT systems in five \nevaluation scenarios, including a newly-proposed evaluation methodology designed to assess the generalisability of ADT systems. The results indicate that all of the newly proposed systems achieve higher accuracies than the stateof- the-art RNN systems for polyphonic recordings and that \nthe additional BRNN peak-picking stage offers slight improvement in certain contexts. <|reference_end|>" ]

[ 13, 18, 19, 24 ]

[ "<|reference_start|> mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality: Large language models (LLMs) have demonstrated impressive zero-shot abilities on a variety of open-ended tasks, while recent research has also explored the use of LLMs for multi-modal generation. In this study, we introduce mPLUG-Owl, a novel training paradigm that equips LLMs with multi-modal abilities through modularized learning of foundation LLM, a visual knowledge module, and a visual abstractor module. This approach can support multiple modalities and facilitate diverse unimodal and multimodal abilities through modality collaboration. The training paradigm of mPLUG-Owl involves a two-stage method for aligning image and text, which learns visual knowledge with the assistance of LLM while maintaining and even improving the generation abilities of LLM. In the first stage, the visual knowledge module and abstractor module are trained with a frozen LLM module to align the image and text. In the second stage, language-only and multi-modal supervised datasets are used to jointly fine-tune a low-rank adaption (LoRA) module on LLM and the abstractor module by freezing the visual knowledge module. We carefully build a visually-related instruction evaluation set OwlEval. Experimental results show that our model outperforms existing multi-modal models, demonstrating mPLUG-Owl's impressive instruction and visual understanding ability, multi-turn conversation ability, and knowledge reasoning ability. Besides, we observe some unexpected and exciting abilities such as multi-image correlation and scene text understanding, which makes it possible to leverage it for harder real scenarios, such as vision-only document comprehension. Our code, pre-trained model, instruction-tuned models, and evaluation set are available at https://github.com/X-PLUG/mPLUG-Owl. The online demo is available at https://www.modelscope.cn/studios/damo/mPLUG-Owl. <|reference_end|>", "<|reference_start|> ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision: Vision-and-Language Pre-training (VLP) has improved performance on various joint vision-and-language downstream tasks. Current approaches to VLP heavily rely on image feature extraction processes, most of which involve region supervision (e.g., object detection) and the convolutional architecture (e.g., ResNet). Although disregarded in the literature, we find it problematic in terms of both (1) efficiency/speed, that simply extracting input features requires much more computation than the multimodal interaction steps; and (2) expressive power, as it is upper bounded to the expressive power of the visual embedder and its predefined visual vocabulary. In this paper, we present a minimal VLP model, Vision-and-Language Transformer (ViLT), monolithic in the sense that the processing of visual inputs is drastically simplified to just the same convolution-free manner that we process textual inputs. We show that ViLT is up to tens of times faster than previous VLP models, yet with competitive or better downstream task performance. Our code and pre-trained weights are available at https://github.com/dandelin/vilt. <|reference_end|>", "<|reference_start|> ViperGPT: Visual Inference via Python Execution for Reasoning: Answering visual queries is a complex task that requires both visual processing and reasoning. End-to-end models, the dominant approach for this task, do not explicitly differentiate between the two, limiting interpretability and generalization. Learning modular programs presents a promising alternative, but has proven challenging due to the difficulty of learning both the programs and modules simultaneously. We introduce ViperGPT, a framework that leverages code-generation models to compose vision-and-language models into subroutines to produce a result for any query. ViperGPT utilizes a provided API to access the available modules, and composes them by generating Python code that is later executed. This simple approach requires no further training, and achieves state-of-the-art results across various complex visual tasks. <|reference_end|>", "<|reference_start|> VideoChat: Chat-Centric Video Understanding: In this paper, we initiate an attempt of developing an end-to-end chat-centric video understanding system, coined as VideoChat. It integrates video foundation models and large language models via a learnable neural interface, excelling in spatiotemporal reasoning, event localization, and causal relationship inference. To instructively tune this system, we build a video-centric instruction dataset, composed of thousands of videos associated with detailed descriptions and conversations. This dataset emphasizes spatiotemporal reasoning and captures causal relationships, providing a valuable asset for training our chat-centric video understanding system. Preliminary qualitative experiments demonstrate the potential of our system across a broad spectrum of video applications, which could serve as a simple prototype system for future research on chat-centric video understanding. Access our code and data at https://github.com/OpenGVLab/Ask-Anything <|reference_end|>" ]

[ 4, 20, 31, 38 ]

[ "<|reference_start|> Shape-Aware Matching of Implicit Surfaces Based on Thin Shell Energies: <|reference_end|>", "<|reference_start|> Variational Methods in Image Matching and Motion Extraction: <|reference_end|>", "<|reference_start|> A variational model dedicated to joint segmentation, registration, and atlas generation for shape analysis: In medical image analysis, constructing an atlas, i.e. a mean representative of an ensemble of images, is a critical task for practitioners to estimate variability of shapes inside a population, and to characterise and understand how structural shape changes have an impact on health. This involves identifying significant shape constituents of a set of images, a process called segmentation, and mapping this group of images to an unknown mean image, a task called registration, making a statistical analysis of the image population possible. To achieve this goal, we propose treating these operations jointly to leverage their positive mutual influence, in a hyperelasticity setting, by viewing the shapes to be matched as Ogden materials. \nThe approach is complemented by novel hard constraints on the $L^\\infty$ norm of both the Jacobian and its inverse, ensuring that the deformation is a bi-Lipschitz homeomorphism. Segmentation is based on the Potts model, which allows for a partition into more than two regions, i.e. more than one shape. The connection to the registration problem is ensured by the dissimilarity measure that aims to align the segmented shapes. A representation of the deformation field in a linear space equipped with a scalar product is then computed in order to perform a geometry-driven Principal Component Analysis (PCA) and to extract the main modes of variations inside the image population. Theoretical results emphasizing the mathematical soundness of the model are provided, among which existence of minimisers, analysis of a numerical method of resolution, asymptotic results and a PCA analysis, as well as numerical simulations demonstrating the ability of the modeling to produce an atlas exhibiting sharp edges, high contrast and a consistent shape. <|reference_end|>", "<|reference_start|> Image comparison and scaling via nonlinear elasticity: A nonlinear elasticity model for comparing images is formulated and analyzed, in which optimal transformations between images are sought as minimizers of an integral functional. The existence of minimizers in a suitable class of homeomorphisms between image domains is established under natural hypotheses. We investigate whether for linearly related images the minimization algorithm delivers the linear transformation as the unique minimizer. <|reference_end|>" ]

[ 7, 14, 17, 21 ]

[ "<|reference_start|> The Expression of a Tensor or a Polyadic as a Sum of Products: <|reference_end|>", "<|reference_start|> {Tensor completion for estimating missing values in visual data: In this paper, we propose an algorithm to estimate missing values in tensors of visual data. The values can be missing due to problems in the acquisition process or because the user manually identified unwanted outliers. Our algorithm works even with a small amount of samples and it can propagate structure to fill larger missing regions. Our methodology is built on recent studies about matrix completion using the matrix trace norm. The contribution of our paper is to extend the matrix case to the tensor case by proposing the first definition of the trace norm for tensors and then by building a working algorithm. First, we propose a definition for the tensor trace norm that generalizes the established definition of the matrix trace norm. Second, similarly to matrix completion, the tensor completion is formulated as a convex optimization problem. Unfortunately, the straightforward problem extension is significantly harder to solve than the matrix case because of the dependency among multiple constraints. To tackle this problem, we developed three algorithms: simple low rank tensor completion (SiLRTC), fast low rank tensor completion (FaLRTC), and high accuracy low rank tensor completion (HaLRTC). The SiLRTC algorithm is simple to implement and employs a relaxation technique to separate the dependant relationships and uses the block coordinate descent (BCD) method to achieve a globally optimal solution; the FaLRTC algorithm utilizes a smoothing scheme to transform the original nonsmooth problem into a smooth one and can be used to solve a general tensor trace norm minimization problem; the HaLRTC algorithm applies the alternating direction method of multipliers (ADMMs) to our problem. Our experiments show potential applications of our algorithms and the quantitative evaluation indicates that our methods are more accurate and robust than heuristic approaches. The efficiency comparison indicates that FaLTRC and HaLRTC are more efficient than SiLRTC and between FaLRTC and HaLRTC the former is more efficient to obtain a low accuracy solution and the latter is preferred if a high-accuracy solution is desired. <|reference_end|>", "<|reference_start|> Link prediction in heterogeneous data via generalized coupled tensor factorization: <|reference_end|>", "<|reference_start|> Matrix Completion With Noise: On the heels of compressed sensing, a remarkable new field has very recently emerged. This field addresses a broad range of problems of significant practical interest, namely, the recovery of a data matrix from what appears to be incomplete, and perhaps even corrupted, information. In its simplest form, the problem is to recover a matrix from a small sample of its entries, and comes up in many areas of science and engineering including collaborative filtering, machine learning, control, remote sensing, and computer vision to name a few. This paper surveys the novel literature on matrix completion, which shows that under some suitable conditions, one can recover an unknown low-rank matrix from a nearly minimal set of entries by solving a simple convex optimization problem, namely, nuclear-norm minimization subject to data constraints. Further, this paper introduces novel results showing that matrix completion is provably accurate even when the few observed entries are corrupted with a small amount of noise. A typical result is that one can recover an unknown n x n matrix of low rank r from just about nr log^2 n noisy samples with an error which is proportional to the noise level. We present numerical results which complement our quantitative analysis and show that, in practice, nuclear norm minimization accurately fills in the many missing entries of large low-rank matrices from just a few noisy samples. Some analogies between matrix completion and compressed sensing are discussed throughout. <|reference_end|>" ]

[ 0, 4, 9, 13 ]

[ "<|reference_start|> Deep Learning in Neural Networks: An Overview: In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relevant work, much of it from the previous millennium. Shallow and deep learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks. <|reference_end|>", "<|reference_start|> gradient-based learning applied to document recognition: Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation recognition, and language modeling. A new learning paradigm, called graph transformer networks (GTN), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of graph transformer networks. A graph transformer network for reading a bank cheque is also described. It uses convolutional neural network character recognizers combined with global training techniques to provide record accuracy on business and personal cheques. It is deployed commercially and reads several million cheques per day. <|reference_end|>", "<|reference_start|> Manifold Learning Theory and Applications: Trained to extract actionable information from large volumes of high-dimensional data, engineers and scientists often have trouble isolating meaningful low-dimensional structures hidden in their high-dimensional observations. Manifold learning, a groundbreaking technique designed to tackle these issues of dimensionality reduction, finds widespread application in machine learning, neural networks, pattern recognition, image processing, and computer vision. Filling a void in the literature, Manifold Learning Theory and Applications incorporates state-of-the-art techniques in manifold learning with a solid theoretical and practical treatment of the subject. Comprehensive in its coverage, this pioneering work explores this novel modality from algorithm creation to successful implementationoffering examples of applications in medical, biometrics, multimedia, and computer vision. Emphasizing implementation, it highlights the various permutations of manifold learning in industry including manifold optimization, large scale manifold learning, semidefinite programming for embedding, manifold models for signal acquisition, compression and processing, and multi scale manifold. Beginning with an introduction to manifold learning theories and applications, the book includes discussions on the relevance to nonlinear dimensionality reduction, clustering, graph-based subspace learning, spectral learning and embedding, extensions, and multi-manifold modeling. It synergizes cross-domain knowledge for interdisciplinary instructions, offers a rich set of specialized topics contributed by expert professionals and researchers from a variety of fields. Finally, the book discusses specific algorithms and methodologies using case studies to apply manifold learning for real-world problems. <|reference_end|>", "<|reference_start|> Embeddings of Riemannian manifolds with finite eigenvector fields of connection Laplacian: <|reference_end|>" ]

[ 0, 1, 5, 10 ]

[ "<|reference_start|> Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem: <|reference_end|>", "<|reference_start|> Continual Learning Through Synaptic Intelligence: While deep learning has led to remarkable advances across diverse applications, it struggles in domains where the data distribution changes over the course of learning. In stark contrast, biological neural networks continually adapt to changing domains, possibly by leveraging complex molecular machinery to solve many tasks simultaneously. In this study, we introduce intelligent synapses that bring some of this biological complexity into artificial neural networks. Each synapse accumulates task relevant information over time, and exploits this information to rapidly store new memories without forgetting old ones. We evaluate our approach on continual learning of classification tasks, and show that it dramatically reduces forgetting while maintaining computational efficiency. <|reference_end|>", "<|reference_start|> Selective Experience Replay for Lifelong Learning: Deep reinforcement learning has emerged as a powerful tool for a variety of learning tasks, however deep nets typically exhibit forgetting when learning multiple tasks in sequence. To mitigate forgetting, we propose an experience replay process that augments the standard FIFO buffer and selectively stores experiences in a long-term memory. We explore four strategies for selecting which experiences will be stored: favoring surprise, favoring reward, matching the global training distribution, and maximizing coverage of the state space. We show that distribution matching successfully prevents catastrophic forgetting, and is consistently the best approach on all domains tested. While distribution matching has better and more consistent performance, we identify one case in which coverage maximization is beneficial - when tasks that receive less trained are more important. Overall, our results show that selective experience replay, when suitable selection algorithms are employed, can prevent catastrophic forgetting. <|reference_end|>", "<|reference_start|> Stochastic gradient descent on Riemannian manifolds: Stochastic gradient descent is a simple approach to find the local minima of a cost function whose evaluations are corrupted by noise. In this paper, we develop a procedure extending stochastic gradient descent algorithms to the case where the function is defined on a Riemannian manifold. We prove that, as in the Euclidian case, the gradient descent algorithm converges to a critical point of the cost function. The algorithm has numerous potential applications, and is illustrated here by four examples. In particular a novel gossip algorithm on the set of covariance matrices is derived and tested numerically. <|reference_end|>" ]

[ 0, 2, 10, 14 ]

[ "<|reference_start|> Learning Transferable Visual Models From Natural Language Supervision: State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP. <|reference_end|>", "<|reference_start|> Hierarchical Text-Conditional Image Generation with CLIP Latents: Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style. To leverage these representations for image generation, we propose a two-stage model: a prior that generates a CLIP image embedding given a text caption, and a decoder that generates an image conditioned on the image embedding. We show that explicitly generating image representations improves image diversity with minimal loss in photorealism and caption similarity. Our decoders conditioned on image representations can also produce variations of an image that preserve both its semantics and style, while varying the non-essential details absent from the image representation. Moreover, the joint embedding space of CLIP enables language-guided image manipulations in a zero-shot fashion. We use diffusion models for the decoder and experiment with both autoregressive and diffusion models for the prior, finding that the latter are computationally more efficient and produce higher-quality samples. <|reference_end|>", "<|reference_start|> Microsoft COCO Captions: Data Collection and Evaluation Server: In this paper we describe the Microsoft COCO Caption dataset and evaluation server. When completed, the dataset will contain over one and a half million captions describing over 330,000 images. For the training and validation images, five independent human generated captions will be provided. To ensure consistency in evaluation of automatic caption generation algorithms, an evaluation server is used. The evaluation server receives candidate captions and scores them using several popular metrics, including BLEU, METEOR, ROUGE and CIDEr. Instructions for using the evaluation server are provided. <|reference_end|>", "<|reference_start|> Fluency-Guided Cross-Lingual Image Captioning: Image captioning has so far been explored mostly in English, as most available datasets are in this language. However, the application of image captioning should not be restricted by language. Only few studies have been conducted for image captioning in a cross-lingual setting. Different from these works that manually build a dataset for a target language, we aim to learn a cross-lingual captioning model fully from machine-translated sentences. To conquer the lack of fluency in the translated sentences, we propose in this paper a fluency-guided learning framework. The framework comprises a module to automatically estimate the fluency of the sentences and another module to utilize the estimated fluency scores to effectively train an image captioning model for the target language. As experiments on two bilingual (English-Chinese) datasets show, our approach improves both fluency and relevance of the generated captions in Chinese, but without using any manually written sentences from the target language. <|reference_end|>" ]

[ 0, 8, 17, 19 ]

[ "<|reference_start|> Memory-Constrained Algorithms for Shortest Path Problem: We present an algorithm computing a shortest path between to vertices in a square grid graph with edge weights that uses memory less than linear in the number of vertices (apart from that for storing in the input). For any e > 0, our algorithm uses a work space of <|reference_end|>", "<|reference_start|> Planarity, determinants, permanents, and (unique) matchings: Viewing the computation of the determinant and the permanent of integer matrices as combinatorial problems on associated graphs, we explore the restrictiveness of planarity on their complexities and show that both problems remain as hard as in the general case, that is, GapL- and P- complete. On the other hand, both bipartite planarity and bimodal planarity bring the complexity of permanents down (but no further) to that of determinants. The permanent or the determinant modulo 2 is complete for ⊕L, and we show that parity of paths in a layered grid graph (which is bimodal planar) is also complete for this class. We also relate the complexity of grid graph reachability to that of testing existence/uniqueness of a perfect matching in a planar bipartite graph. <|reference_end|>", "<|reference_start|> Algorithm design: The quest for efficiency in computational methods yields not only fast algorithms, but also insights that lead to elegant, simple, and general problem-solving methods. <|reference_end|>", "<|reference_start|> The complexity of restricted spanning tree problems: The complexity of the foUowmg class of problems Is investigated: Given a distance matrix, fred the shortest spanning tree that is isomorphic to a given prototype. Several classical combinatorial problems, both easy and hard, fall into this category for an appropriate choice of the family of prototypes, for example, taking the family to be the set of all paths gives the traveling salesman problem or taking the family to be the set of all 2-stars gives the weighted matching problem It is shown that the complexity of these problems depends explicitly on the rate of growth of a sLmple parameter of the family of prototypes. <|reference_end|>" ]

[ 8, 15, 17, 19 ]

[ "<|reference_start|> Stratified Transfer Learning for Cross-domain Activity Recognition: In activity recognition, it is often expensive and time-consuming to acquire sufficient activity labels. To solve this problem, transfer learning leverages the labeled samples from the source domain to annotate the target domain which has few or none labels. Existing approaches typically consider learning a global domain shift while ignoring the intra-affinity between classes, which will hinder the performance of the algorithms. In this paper, we propose a novel and general cross-domain learning framework that can exploit the intra-affinity of classes to perform intra-class knowledge transfer. The proposed framework, referred to as Stratified Transfer Learning (STL), can dramatically improve the classification accuracy for cross-domain activity recognition. Specifically, STL first obtains pseudo labels for the target domain via majority voting technique. Then, it performs intra-class knowledge transfer iteratively to transform both domains into the same subspaces. Finally, the labels of target domain are obtained via the second annotation. To evaluate the performance of STL, we conduct comprehensive experiments on three large public activity recognition datasets~(i.e. OPPORTUNITY, PAMAP2, and UCI DSADS), which demonstrates that STL significantly outperforms other state-of-the-art methods w.r.t. classification accuracy (improvement of 7.68%). Furthermore, we extensively investigate the performance of STL across different degrees of similarities and activity levels between domains. And we also discuss the potential of STL in other pervasive computing applications to provide empirical experience for future research. <|reference_end|>", "<|reference_start|> A systematic study of unsupervised domain adaptation for robust human-activity recognition: Wearable sensors are increasingly becoming the primary interface for monitoring human activities. However, in order to scale human activity recognition (HAR) using wearable sensors to million of users and devices, it is imperative that HAR computational models are robust against real-world heterogeneity in inertial sensor data. In this paper, we study the problem of wearing diversity which pertains to the placement of the wearable sensor on the human body, and demonstrate that even state-of-the-art deep learning models are not robust against these factors. The core contribution of the paper lies in presenting a first-of-its-kind in-depth study of unsupervised domain adaptation (UDA) algorithms in the context of wearing diversity -- we develop and evaluate three adaptation techniques on four HAR datasets to evaluate their relative performance towards addressing the issue of wearing diversity. More importantly, we also do a careful analysis to learn the downsides of each UDA algorithm and uncover several implicit data-related assumptions without which these algorithms suffer a major degradation in accuracy. Taken together, our experimental findings caution against using UDA as a silver bullet for adapting HAR models to new domains, and serve as practical guidelines for HAR practitioners as well as pave the way for future research on domain adaptation in HAR. <|reference_end|>", "<|reference_start|> Unsupervised Meta-Learning For Few-Shot Image Classification: Few-shot or one-shot learning of classifiers requires a significant inductive bias towards the type of task to be learned. One way to acquire this is by meta-learning on tasks similar to the target task. In this paper, we propose UMTRA, an algorithm that performs unsupervised, model-agnostic meta-learning for classification tasks. The meta-learning step of UMTRA is performed on a flat collection of unlabeled images. While we assume that these images can be grouped into a diverse set of classes and are relevant to the target task, no explicit information about the classes or any labels are needed. UMTRA uses random sampling and augmentation to create synthetic training tasks for meta-learning phase. Labels are only needed at the final target task learning step, and they can be as little as one sample per class. On the Omniglot and Mini-Imagenet few-shot learning benchmarks, UMTRA outperforms every tested approach based on unsupervised learning of representations, while alternating for the best performance with the recent CACTUs algorithm. Compared to supervised model-agnostic meta-learning approaches, UMTRA trades off some classification accuracy for a reduction in the required labels of several orders of magnitude. <|reference_end|>", "<|reference_start|> Unsupervised Meta-Learning through Latent-Space Interpolation in Generative Models: Unsupervised meta-learning approaches rely on synthetic meta-tasks that are created using techniques such as random selection, clustering and/or augmentation. Unfortunately, clustering and augmentation are domain-dependent, and thus they require either manual tweaking or expensive learning. In this work, we describe an approach that generates meta-tasks using generative models. A critical component is a novel approach of sampling from the latent space that generates objects grouped into synthetic classes forming the training and validation data of a meta-task. We find that the proposed approach, LAtent Space Interpolation Unsupervised Meta-learning (LASIUM), outperforms or is competitive with current unsupervised learning baselines on few-shot classification tasks on the most widely used benchmark datasets. In addition, the approach promises to be applicable without manual tweaking over a wider range of domains than previous approaches. <|reference_end|>" ]

[ 3, 6, 10, 14 ]

[ "<|reference_start|> Concentration of Measure Inequalities in Information Theory, Communications, and Coding: Concentration inequalities have been the subject of exciting developments during the last two decades, and have been intensively studied and used as a powerful tool in various areas. These include convex geometry, functional analysis, statistical physics, mathematical statistics, pure and applied probability theory, information theory, theoretical computer science, learning theory, and dynamical systems. Concentration of Measure Inequalities in Information Theory, Communications, and Coding focuses on some of the key modern mathematical tools that are used for the derivation of concentration inequalities, on their links to information theory, and on their various applications to communications and coding. In addition to being a survey, this monograph also includes various new recent results derived by the authors. This third edition of the bestselling book introduces the reader to the martingale method and the Efron-Stein-Steele inequalities in completely new sections. A new application of lossless source coding with side information is described in detail. Finally, the references have been updated and ones included that have been published since the original publication. Concentration of Measure Inequalities in Information Theory, Communications, and Coding is essential reading for all researchers and scientists in information theory and coding. <|reference_end|>", "<|reference_start|> PAC-Bayesian Inequalities for Martingales: We present a set of high-probability inequalities that control the concentration of weighted averages of multiple (possibly uncountably many) simultaneously evolving and interdependent martingales. Our results extend the PAC-Bayesian analysis in learning theory from the i.i.d. setting to martingales opening the way for its application to importance weighted sampling, reinforcement learning, and other interactive learning domains, as well as many other domains in probability theory and statistics, where martingales are encountered. We also present a comparison inequality that bounds the expectation of a convex function of a martingale difference sequence shifted to the [0,1] interval by the expectation of the same function of independent Bernoulli variables. This inequality is applied to derive a tighter analog of Hoeffding-Azuma's inequality. <|reference_end|>", "<|reference_start|> Hoeffding's lemma for Markov Chains and its applications to statistical learning: We extend Hoeffding's lemma to general-state-space and not necessarily reversible Markov chains. Let $\\{X_i\\}_{i \\ge 1}$ be a stationary Markov chain with invariant measure $\\pi$ and absolute spectral gap $1-\\lambda$, where $\\lambda$ is defined as the operator norm of the transition kernel acting on mean zero and square-integrable functions with respect to $\\pi$. Then, for any bounded functions $f_i: x \\mapsto [a_i,b_i]$, the sum of $f_i(X_i)$ is sub-Gaussian with variance proxy $\\frac{1+\\lambda}{1-\\lambda} \\cdot \\sum_i \\frac{(b_i-a_i)^2}{4}$. This result differs from the classical Hoeffding's lemma by a multiplicative coefficient of $(1+\\lambda)/(1-\\lambda)$, and simplifies to the latter when $\\lambda = 0$. The counterpart of Hoeffding's inequality for Markov chains immediately follows. Our results assume none of countable state space, reversibility and time-homogeneity of Markov chains and cover time-dependent functions with various ranges. We illustrate the utility of these results by applying them to six problems in statistics and machine learning. <|reference_end|>", "<|reference_start|> {Online and Non-Parametric Drift Detection Methods Based on Hoeffding's\nBounds: Incremental and online learning algorithms are more relevant in the data mining context because of the increasing necessity to process data streams. In this context, the target function may change overtime, an inherent problem of online learning (known as concept drift). In order to handle concept drift regardless of the learning model, we propose new methods to monitor the performance metrics measured during the learning process, to trigger drift signals when a significant variation has been detected. To monitor this performance, we apply some probability inequalities that assume only independent, univariate and bounded random variables to obtain theoretical guarantees for the detection of such distributional changes. Some common restrictions for the online change detection as well as relevant types of change (abrupt and gradual) are considered. Two main approaches are proposed, the first one involves moving averages and is more suitable to detect abrupt changes. The second one follows a widespread intuitive idea to deal with gradual changes using weighted moving averages. The simplicity of the proposed methods, together with the computational efficiency make them very advantageous. We use a Naive Bayes classifier and a Perceptron to evaluate the performance of the methods over synthetic and real data. <|reference_end|>" ]

[ 7, 8, 9, 11 ]

[ "<|reference_start|> Human-like motion planning model for driving in signalized intersections: <|reference_end|>", "<|reference_start|> End-to-end Learning of Driving Models from Large-scale Video Datasets: Robust perception-action models should be learned from training data with diverse visual appearances and realistic behaviors, yet current approaches to deep visuomotor policy learning have been generally limited to in-situ models learned from a single vehicle or a simulation environment. We advocate learning a generic vehicle motion model from large scale crowd-sourced video data, and develop an end-to-end trainable architecture for learning to predict a distribution over future vehicle egomotion from instantaneous monocular camera observations and previous vehicle state. Our model incorporates a novel FCN-LSTM architecture, which can be learned from large-scale crowd-sourced vehicle action data, and leverages available scene segmentation side tasks to improve performance under a privileged learning paradigm. <|reference_end|>", "<|reference_start|> Failure Prediction for Autonomous Driving: The primary focus of autonomous driving research is to improve driving accuracy. While great progress has been made, state-of-the-art algorithms still fail at times. Such failures may have catastrophic consequences. It therefore is important that automated cars foresee problems ahead as early as possible. This is also of paramount importance if the driver will be asked to take over. We conjecture that failures do not occur randomly. For instance, driving models may fail more likely at places with heavy traffic, at complex intersections, and/or under adverse weather/illumination conditions. This work presents a method to learn to predict the occurrence of these failures, i.e. to assess how difficult a scene is to a given driving model and to possibly give the human driver an early headsup. A camera-based driving model is developed and trained over real driving datasets. The discrepancies between the model's predictions and the human `ground-truth' maneuvers were then recorded, to yield the `failure' scores. Experimental results show that the failure score can indeed be learned and predicted. Thus, our prediction method is able to improve the overall safety of an automated driving model by alerting the human driver timely, leading to better human-vehicle collaborative driving. <|reference_end|>", "<|reference_start|> Generative Adversarial Imitation Learning: Consider learning a policy from example expert behavior, without interaction with the expert or access to reinforcement signal. One approach is to recover the expert's cost function with inverse reinforcement learning, then extract a policy from that cost function with reinforcement learning. This approach is indirect and can be slow. We propose a new general framework for directly extracting a policy from data, as if it were obtained by reinforcement learning following inverse reinforcement learning. We show that a certain instantiation of our framework draws an analogy between imitation learning and generative adversarial networks, from which we derive a model-free imitation learning algorithm that obtains significant performance gains over existing model-free methods in imitating complex behaviors in large, high-dimensional environments. <|reference_end|>" ]

[ 15, 28, 35, 67 ]

[ "<|reference_start|> Will the rise of sci-hub pave the road for the subscription-based access to publishing databases?: Sci-hub has become the new phenomenon on the academic publishing market. While its popularity is growing worldwide, large academic publishers are losing millions of dollars on paid article downloads. Perhaps the time has come to re-think the rules of the game of the publishing market and to look for novel solutions for monetizing scientific output. Subscription-based access to publishing databases, similar to the model used in online music streaming, might be one of these solutions that could secure the status quo and stabilize the market. <|reference_end|>", "<|reference_start|> Sci-hub provides access to nearly all scholarly literature.: The website Sci-Hub enables users to download PDF versions of scholarly articles, including many articles that are paywalled at their journal’s site. Sci-Hub has grown rapidly since its creation in 2011, but the extent of its coverage has been unclear. Here we report that, as of March 2017, Sci-Hub’s database contains 68.9% of the 81.6 million scholarly articles registered with Crossref and 85.1% of articles published in toll access journals. We find that coverage varies by discipline and publisher, and that Sci-Hub preferentially covers popular, paywalled content. For toll access articles, we find that Sci-Hub provides greater coverage than the University of Pennsylvania, a major research university in the United States. Green open access to toll access articles via licit services, on the other hand, remains quite limited. Our interactive browser at https://greenelab.github.io/scihub allows users to explore these findings in more detail. For the first time, nearly all scholarly literature is available gratis to anyone with an Internet connection, suggesting the toll access business model may become unsustainable. <|reference_end|>", "<|reference_start|> Sci-hub provides access to nearly all scholarly literature.: The website Sci-Hub enables users to download PDF versions of scholarly articles, including many articles that are paywalled at their journal’s site. Sci-Hub has grown rapidly since its creation in 2011, but the extent of its coverage has been unclear. Here we report that, as of March 2017, Sci-Hub’s database contains 68.9% of the 81.6 million scholarly articles registered with Crossref and 85.1% of articles published in toll access journals. We find that coverage varies by discipline and publisher, and that Sci-Hub preferentially covers popular, paywalled content. For toll access articles, we find that Sci-Hub provides greater coverage than the University of Pennsylvania, a major research university in the United States. Green open access to toll access articles via licit services, on the other hand, remains quite limited. Our interactive browser at https://greenelab.github.io/scihub allows users to explore these findings in more detail. For the first time, nearly all scholarly literature is available gratis to anyone with an Internet connection, suggesting the toll access business model may become unsustainable. <|reference_end|>", "<|reference_start|> Inventor team size as a predictor of the future citation impact of patents: <|reference_end|>" ]

[ 6, 7, 10, 20 ]

[ "<|reference_start|> Multirotor Aerial Vehicles: Modeling Estimation and Control of Quadrotor: This article provides a tutorial introduction to modeling, estimation, and control for multirotor aerial vehicles that includes the common four-rotor or quadrotor case. <|reference_end|>", "<|reference_start|> Hardware and Software Architecture for Nonlinear Control of Multirotor Helicopters: This paper presents the design and implementation of a nonlinear control scheme for multirotor helicopters that takes first-order drag effects into account explicitly. A dynamic model including the blade flapping and induced drag forces is provided and a hierarchical nonlinear controller is presented. This controller is designed for both high-precision flights as well as robustness against model uncertainties and external disturbances. This is achieved by using saturated integrators with fast desaturation properties. The implementation of the controller on the flybox hexacopter platform is described. The hardware and software architecture of this UAV is discussed, and useful hints and insights gained during its design process are presented. Finally, experimental results and videos are reported to demonstrate the successful implementation and the performance of the overall system. <|reference_end|>", "<|reference_start|> Fault/damage tolerant control of a quadrotor helicopter UAV using model reference adaptive control and gain-scheduled PID: In this paper, two useful approaches to Fault Tolerant Control (FTC) for a quadrotor helicopter Unmanned Aerial Vehicle (UAV) in the presence of fault(s) in one or more actuators during flight have been investigated and experimentally tested based on a Model Reference Adaptive Control (MRAC) and a Gain-Scheduled Proportional-IntegralDerivative (GS-PID) control. A Linear Quadratic Regulator (LQR) controller is used in cooperation with the MRAC and the GS-PID to control the pitch and roll attitudes of the helicopter. Unlike the MRAC, the GS-PID is used only to control the helicopter in height control mode. MRAC is used to control the helicopter in both height control as well as trajectory control. For damage tolerant control the MRAC is evaluated based on partial damage of one of propellers during flight. Finally, the experimental flight testing results of both controllers are presented for the fault tolerant control performance comparison in the presence of actuator faults in the quadrotor UAV. <|reference_end|>", "<|reference_start|> Development of an Active Fault-Tolerant Flight Control Strategy: This paper discusses the design of an active fault-tolerant flight control strategy for improvement of the operational control capability of the aircraft system. The research work draws expertise from actions undertaken within the European Flight Mechanics Action Group [FM-AG(16)] on fault-tolerant control, which develops a collaborative effort in Europe to create new fault-tolerant control technologies that significantly advance the goals of the aviation safety. The methodology is applied to a trimmable horizontal stabilizer runaway fault occurring in a large transport aircraft. The goal is to provide a self-repairing capability to enable the pilot to land the aircraft safely. The fault-tolerant control strategy works in such a way that once the fault is detected by the fault detection and isolation unit, a compensation loop is activated for safe recovery. A key feature of the proposed strategy is that the design of the fault-tolerant control loop is done independently of the nominal autopilot and the nominal flight control system in place. Nonlinear simulation results demonstrate the effectiveness of the proposed fault-tolerant control scheme. <|reference_end|>" ]

[ 0, 1, 3, 6 ]

[ "<|reference_start|> SPARCs for Unsourced Random Access: Unsourced random-access (U-RA) is a type of grant-free random access with a virtually unlimited number of users, of which only a certain number $K_a$ are active on the same time slot. Users employ exactly the same codebook, and the task of the receiver is to decode the list of transmitted messages. We present a concatenated coding construction for U-RA on the AWGN channel, in which a sparse regression code (SPARC) is used as an inner code to create an effective outer OR-channel. Then an outer code is used to resolve the multiple-access interference in the OR-MAC. We propose a modified version of the approximate message passing (AMP) algorithm as an inner decoder and give a precise asymptotic analysis of the error probabilities of the AMP decoder and of a hypothetical optimal inner MAP decoder. This analysis shows that the concatenated construction can achieve a vanishing per-user error probability in the limit of large blocklength and a large number of active users at sum-rates up to the symmetric Shannon capacity, i.e. as long as $K_aR < 0.5\\log_2(1+K_a\\SNR)$. This extends previous point-to-point optimality results about SPARCs to the unsourced multiuser scenario. Furthermore, we give an optimization algorithm to find the power allocation for the inner SPARC code that minimizes the required $\\SNR$. <|reference_end|>", "<|reference_start|> Unsourced Random Access With Coded Compressed Sensing: Integrating AMP and Belief Propagation: Sparse regression codes with approximate message passing (AMP) decoding have gained much attention in recent times. The concepts underlying this coding scheme extend to unsourced random access with coded compressed sensing (CCS), as first demonstrated by Fengler, Jung, and Caire. Specifically, their approach employs a concatenated coding framework with an inner AMP decoder followed by an outer tree decoder. In their original implementation, these two components work independently of each other, with the tree decoder acting on the static output of the AMP decoder. This article introduces a novel framework where the inner AMP decoder and the outer decoder operate in tandem, dynamically passing information back and forth to take full advantage of the underlying CCS structure. This scheme necessitates the redesign of the outer code as to enable belief propagation in a computationally tractable manner. The enhanced architecture exhibits significant performance benefits over a range of system parameters. The error performance of the proposed scheme can be accurately predicted through a set of equations known as state evolution of AMP. These findings are supported both analytically and through numerical methods. <|reference_end|>", "<|reference_start|> {Two-step random access in 5G new radio: Channel structure design and performance: A common design of the random access procedure on the physical random access channel (PRACH) is required for the diverse usage scenarios in the fifth generation new radio (5G NR) mobile networks. Based on the latest 3GPP specifications and evaluation assumptions agreed for Release 16, the 2 step-RACH (2SR) enhancement, composed of the denoted MsgA and MsgB, not only reduces the latency but also the control-signalling overhead due to the reduced number of messages transmitted. The channel structure of MsgA comprises RACH preamble and data in the physical uplink shared channel (PUSCH) while MsgB combines the random access response and the contention resolution. This procedure should operate in local area (LA), medium range (MR) and wide area (WA) cells despite the lack of time alignment (TA) in the PUSCH part of MsgA. The demodulation performance degradation observed without time offset compensation at the base station (gNB), specially for MR or WA cells, highlight that practical gNB implementations relying in MAC control element-based TA command for PUSCH time alignment are not conceivable for 2SR. Furthermore, in the case that all preambles from multiple users (UEs) trying to perform the initial access are mapped to the same PUSCH physical resources, the associated data parts overlap and may result in unsuccessful decoding. There is therefore a trade-off between the collision probability of the PUSCH part of MsgA and the resource overhead for 2SR. This paper addresses the channel structure design of this procedure for the preamble and data parts of MsgA together with the receiver processing framework. The performance results suggest that using lower payload sizes provide higher resource utilization and allow more UEs to be multiplexed within the same PUSCH occasion. In addition, using different DMRS ports for UEs sharing same physical resources decrease the probability of failure in the decoding of the data part of MsgA while reduces the resource overhead for 2SR. <|reference_end|>", "<|reference_start|> Joint Message Detection and Channel Estimation for Unsourced Random Access in Cell-Free User-Centric Wireless Networks: We consider unsourced random access (uRA) in a cell-free (CF) user-centric wireless network, where a large number of potential users compete for a random access slot, while only a finite subset is active. The random access users transmit codewords of length $L$ symbols from a shared codebook, which are received by $B$ geographically distributed radio units (RUs) equipped with $M$ antennas each. Our goal is to devise and analyze a \\emph{centralized} decoder to detect the transmitted messages (without prior knowledge of the active users) and estimate the corresponding channel state information. A specific challenge lies in the fact that, due to the geographically distributed nature of the CF network, there is no fixed correspondence between codewords and large-scale fading coefficients (LSFCs). To overcome this problem, we propose a scheme where the access codebook is partitioned in \"location-based\" subcodes, such that users in a particular location make use of the corresponding subcode. The joint message detection and channel estimation is obtained via a novel {\\em Approximated Message Passing} (AMP) algorithm to estimate the linear superposition of matrix-valued \"sources\" corrupted by Gaussian noise. The matrices to be estimated exhibit zero rows for inactive messages and Gaussian-distributed rows corresponding to the active messages. The asymmetry in the LSFCs and message activity probabilities leads to \\emph{different statistics} for the matrix sources, which distinguishes the AMP formulation from previous cases. In the regime where the codebook size scales linearly with $L$, while $B$ and $M$ are fixed, we present a rigorous high-dimensional analysis of the proposed AMP algorithm. Then, exploiting the fundamental decoupling principle of AMP, we provide a comprehensive analysis of Neyman-Pearson message detection, along with the subsequent channel estimation. <|reference_end|>" ]

[ 1, 3, 4, 6 ]

[ "<|reference_start|> Home networking with IEEE 802.15.4: a developing standard for low-rate wireless personal area networks: This article presents the IEEE 802.15.4 draft standard and its home networking applications. The main features of the standard are network flexibility, low cost, and low power consumption; the standard is suitable for many applications in the home requiring low-data-rate communications in an ad hoc self-organizing network. <|reference_end|>", "<|reference_start|> Subset selection in Regression: OBJECTIVES Prediction, Explanation, Elimination or What? How Many Variables in the Prediction Formula? Alternatives to Using Subsets 'Black Box' Use of Best-Subsets Techniques LEAST-SQUARES COMPUTATIONS Using Sums of Squares and Products Matrices Orthogonal Reduction Methods Gauss-Jordan v. Orthogonal Reduction Methods Interpretation of Projections Appendix A: Operation Counts for All-Subsets Regression FINDING SUBSETS WHICH FIT WELL Objectives and Limitations of this Chapter Forward Selection Efroymson's Algorithm Backward Elimination Sequential Replacement Algorithm Replacing Two Variables at a Time Generating All Subsets Using Branch-and-Bound Techniques Grouping Variables Ridge Regression and Other Alternatives The Non-Negative Garrote and the Lasso Some Examples Conclusions and Recommendations HYPOTHESIS TESTING Is There any Information in the Remaining Variables? Is One Subset Better than Another? Appendix A: Spjftvoll's Method - Detailed Description WHEN TO STOP? What Criterion Should We Use? Prediction Criteria Cross-Validation and the PRESS Statistic Bootstrapping Likelihood and Information-Based Stopping Rules Appendix A. Approximate Equivalence of Stopping Rules ESTIMATION OF REGRESSION COEFFICIENTS Selection Bias Choice Between Two Variables Selection Bias in the General Case, and its Reduction Conditional Likelihood Estimation Estimation of Population Means Estimating Least-Squares Projections Appendix A: Changing Projections to Equate Sums of Squares BAYESIAN METHODS Bayesian Introduction 'Spike and Slab' Prior Normal prior for Regression Coefficients Model Averaging Picking the Best Model CONCLUSIONS AND SOME RECOMMENDATIONS REFERENCES INDEX <|reference_end|>", "<|reference_start|> Fast acquisition scheme and implementation of PRACH in WCDMA system: The performance and implementation of PRACH (physical random access channel) acquisition in WCDMA system is investigated. The analysis shows that the conventional methods are not satisfying. Thus we proposed the quasi-matched filter acquisition scheme of PRACH preamble which based on fast Hadamard transform. We implement this method by hardware in the practical WCDMA field trial system. The simulation and test results show that the proposed scheme achieves the following performance: the detection probability with E/sub b//N/sub 0/ =7 dB is not less than 95%, and the mean acquisition time is less than 1.33 ms. <|reference_end|>", "<|reference_start|> Orthogonal least squares methods and their application to non-linear system identification: Abstract Identification algorithms based on the well-known linear least squares methods of gaussian elimination, Cholesky decomposition, classical Gram-Schmidt, modified Gram-Schmidt, Householder transformation, Givens method, and singular value decomposition are reviewed. The classical Gram-Schmidt, modified Gram-Schmidt, and Householder transformation algorithms are then extended to combine structure determination, or which terms to include in the model, and parameter estimation in a very simple and efficient manner for a class of multivariate discrete-time non-linear stochastic systems which are linear in the parameters. <|reference_end|>" ]

[ 0, 17, 21, 25 ]

[ "<|reference_start|> Interpolants in Nonlinear Theories Over the Reals: <|reference_end|>", "<|reference_start|> Craig Interpolation in the Presence of Non-linear Constraints: <|reference_end|>", "<|reference_start|> Interpolants in Nonlinear Theories Over the Reals: <|reference_end|>", "<|reference_start|> Interpolants in Nonlinear Theories Over the Reals: <|reference_end|>" ]

[ 22, 24, 33, 40 ]

[ "<|reference_start|> {Video Captioning with Attention-based LSTM and Semantic Consistency: Recent progress in using long short-term memory (LSTM) for image captioning has motivated the exploration of their applications for video captioning. By taking a video as a sequence of features, an LSTM model is trained on video-sentence pairs and learns to associate a video to a sentence. However, most existing methods compress an entire video shot or frame into a static representation, without considering attention mechanism which allows for selecting salient features. Furthermore, existing approaches usually model the translating error, but ignore the correlations between sentence semantics and visual content. To tackle these issues, we propose a novel end-to-end framework named aLSTMs, an attention-based LSTM model with semantic consistency, to transfer videos to natural sentences. This framework integrates attention mechanism with LSTM to capture salient structures of video, and explores the correlation between multimodal representations (i.e., words and visual content) for generating sentences with rich semantic content. Specifically, we first propose an attention mechanism that uses the dynamic weighted sum of local two-dimensional convolutional neural network representations. Then, an LSTM decoder takes these visual features at time <inline-formula><tex-math notation=\"LaTeX\">$t$</tex-math></inline-formula> and the word-embedding feature at time <inline-formula><tex-math notation=\"LaTeX\">$t$</tex-math></inline-formula><inline-formula><tex-math notation=\"LaTeX\"> $-$</tex-math></inline-formula>1 to generate important words. Finally, we use multimodal embedding to map the visual and sentence features into a joint space to guarantee the semantic consistence of the sentence description and the video visual content. Experiments on the benchmark datasets demonstrate that our method using single feature can achieve competitive or even better results than the state-of-the-art baselines for video captioning in both BLEU and METEOR. <|reference_end|>", "<|reference_start|> Object Relational Graph with Teacher-Recommended Learning for Video Captioning: Taking full advantage of the information from both vision and language is critical for the video captioning task. Existing models lack adequate visual representation due to the neglect of interaction between object, and sufficient training for content-related words due to long-tailed problems. In this paper, we propose a complete video captioning system including both a novel model and an effective training strategy. Specifically, we propose an object relational graph (ORG) based encoder, which captures more detailed interaction features to enrich visual representation. Meanwhile, we design a teacher-recommended learning (TRL) method to make full use of the successful external language model (ELM) to integrate the abundant linguistic knowledge into the caption model. The ELM generates more semantically similar word proposals which extend the ground-truth words used for training to deal with the long-tailed problem. Experimental evaluations on three benchmarks: MSVD, MSR-VTT and VATEX show the proposed ORG-TRL system achieves state-of-the-art performance. Extensive ablation studies and visualizations illustrate the effectiveness of our system. <|reference_end|>", "<|reference_start|> {MSR-VTT: A large video description dataset for bridging video and language: While there has been increasing interest in the task of describing video with natural language, current computer vision algorithms are still severely limited in terms of the variability and complexity of the videos and their associated language that they can recognize. This is in part due to the simplicity of current benchmarks, which mostly focus on specific fine-grained domains with limited videos and simple descriptions. While researchers have provided several benchmark datasets for image captioning, we are not aware of any large-scale video description dataset with comprehensive categories yet diverse video content. In this paper we present MSR-VTT (standing for \"MSRVideo to Text\") which is a new large-scale video benchmark for video understanding, especially the emerging task of translating video to text. This is achieved by collecting 257 popular queries from a commercial video search engine, with 118 videos for each query. In its current version, MSR-VTT provides 10K web video clips with 41.2 hours and 200K clip-sentence pairs in total, covering the most comprehensive categories and diverse visual content, and representing the largest dataset in terms of sentence and vocabulary. Each clip is annotated with about 20 natural sentences by 1,327 AMT workers. We present a detailed analysis of MSR-VTT in comparison to a complete set of existing datasets, together with a summarization of different state-of-the-art video-to-text approaches. We also provide an extensive evaluation of these approaches on this dataset, showing that the hybrid Recurrent Neural Networkbased approach, which combines single-frame and motion representations with soft-attention pooling strategy, yields the best generalization capability on MSR-VTT. <|reference_end|>", "<|reference_start|> Normalized and Geometry-Aware Self-Attention Network for Image Captioning: Self-attention (SA) network has shown profound value in image captioning. In this paper, we improve SA from two aspects to promote the performance of image captioning. First, we propose Normalized Self-Attention (NSA), a reparameterization of SA that brings the benefits of normalization inside SA. While normalization is previously only applied outside SA, we introduce a novel normalization method and demonstrate that it is both possible and beneficial to perform it on the hidden activations inside SA. Second, to compensate for the major limit of Transformer that it fails to model the geometry structure of the input objects, we propose a class of Geometry-aware Self-Attention (GSA) that extends SA to explicitly and efficiently consider the relative geometry relations between the objects in the image. To construct our image captioning model, we combine the two modules and apply it to the vanilla self-attention network. We extensively evaluate our proposals on MS-COCO image captioning dataset and superior results are achieved when comparing to state-of-the-art approaches. Further experiments on three challenging tasks, i.e. video captioning, machine translation, and visual question answering, show the generality of our methods. <|reference_end|>" ]

[ 13, 28, 35, 45 ]

[ "<|reference_start|> Forensic Camera Model Identification: <|reference_end|>", "<|reference_start|> Digital camera identification from sensor pattern noise: In this paper, we propose a new method for the problem of digital camera identification from its images based on the sensor's pattern noise. For each camera under investigation, we first determine its reference pattern noise, which serves as a unique identification fingerprint. This is achieved by averaging the noise obtained from multiple images using a denoising filter. To identify the camera from a given image, we consider the reference pattern noise as a spread-spectrum watermark, whose presence in the image is established by using a correlation detector. Experiments on approximately 320 images taken with nine consumer digital cameras are used to estimate false alarm rates and false rejection rates. Additionally, we study how the error rates change with common image processing, such as JPEG compression or gamma correction. <|reference_end|>", "<|reference_start|> Image Origin Classification Based on Social Network Provenance: Recognizing information about the origin of a digital image has been individuated as a crucial task to be tackled by the image forensic scientific community. Understanding something on the previous history of an image could be strategic to address any successive assessment to be made on it: knowing the kind of device used for acquisition or, better, the model of the camera could focus investigations in a specific direction. Sometimes just revealing that a determined post-processing, such as an interpolation or a filtering, has been performed on an image could be of fundamental importance to go back to its provenance. This paper locates in such a context and proposes an innovative method to inquire if an image derives from a social network and, in particular, try to distinguish from, which one has been downloaded. The technique is based on the assumption that each social network applies a peculiar and mostly unknown manipulation that, however, leaves some distinctive traces on the image; such traces can be extracted to feature every platform. By resorting at trained classifiers, the presented methodology is satisfactorily able to discern different social network origins. Experimental results carried out on diverse image datasets and in various operative conditions witness that such a distinction is possible. In addition, the proposed method is also able to go back to the original JPEG quality factor the image had before being uploaded on a social network. <|reference_end|>", "<|reference_start|> Forensic Camera Model Identification: <|reference_end|>" ]

[ 25, 28, 43, 53 ]

[ "<|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|> VoxCeleb: a large-scale speaker identification dataset: Most existing datasets for speaker identification contain samples obtained under quite constrained conditions, and are usually hand-annotated, hence limited in size. The goal of this paper is to generate a large scale text-independent speaker identification dataset collected 'in the wild'. We make two contributions. First, we propose a fully automated pipeline based on computer vision techniques to create the dataset from open-source media. Our pipeline involves obtaining videos from YouTube; performing active speaker verification using a two-stream synchronization Convolutional Neural Network (CNN), and confirming the identity of the speaker using CNN based facial recognition. We use this pipeline to curate VoxCeleb which contains hundreds of thousands of 'real world' utterances for over 1,000 celebrities. Our second contribution is to apply and compare various state of the art speaker identification techniques on our dataset to establish baseline performance. We show that a CNN based architecture obtains the best performance for both identification and verification. <|reference_end|>", "<|reference_start|> VoxCeleb2: Deep Speaker Recognition: The objective of this paper is speaker recognition under noisy and unconstrained conditions. We make two key contributions. First, we introduce a very large-scale audio-visual speaker recognition dataset collected from open-source media. Using a fully automated pipeline, we curate VoxCeleb2 which contains over a million utterances from over 6,000 speakers. This is several times larger than any publicly available speaker recognition dataset. Second, we develop and compare Convolutional Neural Network (CNN) models and training strategies that can effectively recognise identities from voice under various conditions. The models trained on the VoxCeleb2 dataset surpass the performance of previous works on a benchmark dataset by a significant margin. <|reference_end|>" ]

[ 0, 1, 3, 4 ]

[ "<|reference_start|> {A fast quantum mechanical algorithm for database search: were proposed in the early 1980’s [Benioff80] and shown to be at least as powerful as classical computers an important but not surprising result, since classical computers, at the deepest level, ultimately follow the laws of quantum mechanics. The description of quantum mechanical computers was formalized in the late 80’s and early 90’s [Deutsch85][BB92] [BV93] [Yao93] and they were shown to be more powerful than classical computers on various specialized problems. In early 1994, [Shor94] demonstrated that a quantum mechanical computer could efficiently solve a well-known problem for which there was no known efficient algorithm using classical computers. This is the problem of integer factorization, i.e. testing whether or not a given integer, N, is prime, in a time which is a finite power of o (logN) . ---------------------------------------------- <|reference_end|>", "<|reference_start|> stating the issues and recommending solutions.: <|reference_end|>", "<|reference_start|> Polylithic modeling and solution approaches using algebraic modeling systems: <|reference_end|>", "<|reference_start|> The decomposition algorithm for linear programs: A procedure is presented for the efficient computational solution of linear programs having a certain structural property characteristic of a large class of problems of practical interest. The property makes possible the decomposition of the problem into a sequence of small linear programs whose iterated solutions solve the given problem through a generalization of the simplex method for linear programming. 1. THE DECOMPOSED LINEAR PROGRAM MANY LINEAR programming problems of practical interest have the property that they may be described, in part, as composed of separate linear programming problems tied together by a number of constraints considerably smaller than the total number imposed on the problem. When the matrix of coefficients of such a problem, suitably ordered, is displayed in the usual way, a pattern emerges like that shown in Figure 1. In this figure the constraint matrix has been partitioned into nonzero blocks A1 and By, the right-hand side column of constants correspondingly into b, bl,..., bn; and the \"costs,\" <|reference_end|>" ]

[ 5, 18, 24, 26 ]

[ "<|reference_start|> Prognostic value of automatically extracted nuclear morphometric features in whole slide images of male breast cancer: <|reference_end|>", "<|reference_start|> X-net with different loss functions for cell image segmentation: Convolutional neural network is valid for object segmentation. In recent years, it has been applied to the fields of medicine and cell biology. Each class has a different number of pixels in an image. Therefore, the accuracy of semantic segmentation varies drastically between objects with a large number of pixels and objects with a small number of pixels. In this paper, we propose X-Net that integrates two encoders and decoders to solve this problem. This has the advantage of extracting rich features from two encoders and using two decoders to complement the location information and small objects. By using different loss functions for each decoder, we can use the ensemble of two decoders with different viewpoints. We evaluated our method on the Arabidopsis cell images and Drosophila cell images. Experimental results show that our method achieved better accuracy than the conventional methods. <|reference_end|>", "<|reference_start|> Panoptic Feature Fusion Net: A Novel Instance Segmentation Paradigm for Biomedical and Biological Images: Instance segmentation is an important task for biomedical and biological image analysis. Due to the complicated background components, the high variability of object appearances, numerous overlapping objects, and ambiguous object boundaries, this task still remains challenging. Recently, deep learning based methods have been widely employed to solve these problems and can be categorized into proposal-free and proposal-based methods. However, both proposal-free and proposal-based methods suffer from information loss, as they focus on either global-level semantic or local-level instance features. To tackle this issue, we present a Panoptic Feature Fusion Net (PFFNet) that unifies the semantic and instance features in this work. Specifically, our proposed PFFNet contains a residual attention feature fusion mechanism to incorporate the instance prediction with the semantic features, in order to facilitate the semantic contextual information learning in the instance branch. Then, a mask quality sub-branch is designed to align the confidence score of each object with the quality of the mask prediction. Furthermore, a consistency regularization mechanism is designed between the semantic segmentation tasks in the semantic and instance branches, for the robust learning of both tasks. Extensive experiments demonstrate the effectiveness of our proposed PFFNet, which outperforms several state-of-the-art methods on various biomedical and biological datasets. <|reference_end|>", "<|reference_start|> A review of domain adaptation without target labels: Domain adaptation has become a prominent problem setting in machine learning and related fields. This review asks the question: how can a classifier learn from a source domain and generalize to a target domain? We present a categorization of approaches, divided into, what we refer to as, sample-based, feature-based and inference-based methods. Sample-based methods focus on weighting individual observations during training based on their importance to the target domain. Feature-based methods revolve around on mapping, projecting and representing features such that a source classifier performs well on the target domain and inference-based methods incorporate adaptation into the parameter estimation procedure, for instance through constraints on the optimization procedure. Additionally, we review a number of conditions that allow for formulating bounds on the cross-domain generalization error. Our categorization highlights recurring ideas and raises questions important to further research. <|reference_end|>" ]

[ 2, 6, 7, 13 ]

[ "<|reference_start|> Frustum PointNets for 3D Object Detection from RGB-D Data: In this work, we study 3D object detection from RGB-D data in both indoor and outdoor scenes. While previous methods focus on images or 3D voxels, often obscuring natural 3D patterns and invariances of 3D data, we directly operate on raw point clouds by popping up RGB-D scans. However, a key challenge of this approach is how to efficiently localize objects in point clouds of large-scale scenes (region proposal). Instead of solely relying on 3D proposals, our method leverages both mature 2D object detectors and advanced 3D deep learning for object localization, achieving efficiency as well as high recall for even small objects. Benefited from learning directly in raw point clouds, our method is also able to precisely estimate 3D bounding boxes even under strong occlusion or with very sparse points. Evaluated on KITTI and SUN RGB-D 3D detection benchmarks, our method outperforms the state of the art by remarkable margins while having real-time capability. <|reference_end|>", "<|reference_start|> Multi-View 3D Object Detection Network for Autonomous Driving: This paper aims at high-accuracy 3D object detection in autonomous driving scenario. We propose Multi-View 3D networks (MV3D), a sensory-fusion framework that takes both LIDAR point cloud and RGB images as input and predicts oriented 3D bounding boxes. We encode the sparse 3D point cloud with a compact multi-view representation. The network is composed of two subnetworks: one for 3D object proposal generation and another for multi-view feature fusion. The proposal network generates 3D candidate boxes efficiently from the bird's eye view representation of 3D point cloud. We design a deep fusion scheme to combine region-wise features from multiple views and enable interactions between intermediate layers of different paths. Experiments on the challenging KITTI benchmark show that our approach outperforms the state-of-the-art by around 25% and 30% AP on the tasks of 3D localization and 3D detection. In addition, for 2D detection, our approach obtains 10.3% higher AP than the state-of-the-art on the hard data among the LIDAR-based methods. <|reference_end|>", "<|reference_start|> Joint 3D Proposal Generation and Object Detection from View Aggregation: We present AVOD, an Aggregate View Object Detection network for autonomous driving scenarios. The proposed neural network architecture uses LIDAR point clouds and RGB images to generate features that are shared by two subnetworks: a region proposal network (RPN) and a second stage detector network. The proposed RPN uses a novel architecture capable of performing multimodal feature fusion on high resolution feature maps to generate reliable 3D object proposals for multiple object classes in road scenes. Using these proposals, the second stage detection network performs accurate oriented 3D bounding box regression and category classification to predict the extents, orientation, and classification of objects in 3D space. Our proposed architecture is shown to produce state of the art results on the KITTI 3D object detection benchmark while running in real time with a low memory footprint, making it a suitable candidate for deployment on autonomous vehicles. Code is at: https://github.com/kujason/avod <|reference_end|>", "<|reference_start|> Deep Continuous Fusion for Multi-Sensor 3D Object Detection: In this paper, we propose a novel 3D object detector that can exploit both LIDAR as well as cameras to perform very accurate localization. Towards this goal, we design an end-to-end learnable architecture that exploits continuous convolutions to fuse image and LIDAR feature maps at different levels of resolution. Our proposed continuous fusion layer encode both discrete-state image features as well as continuous geometric information. This enables us to design a novel, reliable and efficient end-to-end learnable 3D object detector based on multiple sensors. Our experimental evaluation on both KITTI as well as a large scale 3D object detection benchmark shows significant improvements over the state of the art. <|reference_end|>" ]

[ 3, 6, 7, 9 ]

[ "<|reference_start|> XQuery Streaming by Forest Transducers: Streaming of XML transformations is a challenging task and only very few systems support streaming. Research approaches generally define custom fragments of XQuery and XPath that are amenable to streaming, and then design custom algorithms for each fragment. These languages have several shortcomings. Here we take a more principles approach to the problem of streaming XQuery-based transformations. We start with an elegant transducer model for which many static analysis problems are well-understood: the Macro Forest Transducer (MFT). We show that a large fragment of XQuery can be translated into MFTs --- indeed, a fragment of XQuery, that can express important features that are missing from other XQuery stream engines, such as GCX: our fragment of XQuery supports XPath predicates and let-statements. We then rely on a streaming execution engine for MFTs, one which uses a well-founded set of optimizations from functional programming, such as strictness analysis and deforestation. Our prototype achieves time and memory efficiency comparable to the fastest known engine for XQuery streaming, GCX. This is surprising because our engine relies on the OCaml built in garbage collector and does not use any specialized buffer management, while GCX's efficiency is due to clever and explicit buffer management. <|reference_end|>", "<|reference_start|> The unsolvability of the Equivalence Problem for Λ-Free nondeterministic generalized machines: It is shown that the equivalence problem for A-free nondeterministic generalized machines is unsolvable, and it is observed that this result implies the unsolvability of the equality problem for c-finite languages. <|reference_end|>", "<|reference_start|> On Synthesis of Resynchronizers for Transducers: We study two formalisms that allow to compare transducers over words under origin semantics: rational and regular resynchronizers, and show that the former are captured by the latter. We then consider some instances of the following synthesis problem: given transducers T1, T2, construct a rational (resp. regular) resynchronizer R, if it exists, such that T1 is contained in R(T2) under the origin semantics. We show that synthesis of rational resynchronizers is decidable for functional, and even finite-valued, one-way transducers, and undecidable for relational one-way transducers. In the two-way setting, synthesis of regular resynchronizers is shown to be decidable for unambiguous two-way transducers. For larger classes of two-way transducers, the decidability status is open. <|reference_end|>", "<|reference_start|> Foundations of Software Science and Computational Structures: <|reference_end|>" ]

[ 3, 10, 26, 30 ]

[ "<|reference_start|> Fast R-CNN: This paper proposes a Fast Region-based Convolutional Network method (Fast R-CNN) for object detection. Fast R-CNN builds on previous work to efficiently classify object proposals using deep convolutional networks. Compared to previous work, Fast R-CNN employs several innovations to improve training and testing speed while also increasing detection accuracy. Fast R-CNN trains the very deep VGG16 network 9x faster than R-CNN, is 213x faster at test-time, and achieves a higher mAP on PASCAL VOC 2012. Compared to SPPnet, Fast R-CNN trains VGG16 3x faster, tests 10x faster, and is more accurate. Fast R-CNN is implemented in Python and C++ (using Caffe) and is available under the open-source MIT License at https://github.com/rbgirshick/fast-rcnn. <|reference_end|>", "<|reference_start|> Contextual Priming and Feedback for Faster R-CNN: <|reference_end|>", "<|reference_start|> Gated Bi-directional CNN for Object Detection: <|reference_end|>", "<|reference_start|> You Only Look Once: Unified, Real-Time Object Detection: We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. Our unified architecture is extremely fast. Our base YOLO model processes images in real-time at 45 frames per second. A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the mAP of other real-time detectors. Compared to state-of-the-art detection systems, YOLO makes more localization errors but is far less likely to predict false detections where nothing exists. Finally, YOLO learns very general representations of objects. It outperforms all other detection methods, including DPM and R-CNN, by a wide margin when generalizing from natural images to artwork on both the Picasso Dataset and the People-Art Dataset. <|reference_end|>" ]

[ 14, 25, 26, 29 ]

[ "<|reference_start|> Sever: A Robust Meta-Algorithm for Stochastic Optimization: In high dimensions, most machine learning methods are brittle to even a small fraction of structured outliers. To address this, we introduce a new meta-algorithm that can take in a base learner such as least squares or stochastic gradient descent, and harden the learner to be resistant to outliers. Our method, Sever, possesses strong theoretical guarantees yet is also highly scalable -- beyond running the base learner itself, it only requires computing the top singular vector of a certain $n \\times d$ matrix. We apply Sever on a drug design dataset and a spam classification dataset, and find that in both cases it has substantially greater robustness than several baselines. On the spam dataset, with $1\\%$ corruptions, we achieved $7.4\\%$ test error, compared to $13.4\\%-20.5\\%$ for the baselines, and $3\\%$ error on the uncorrupted dataset. Similarly, on the drug design dataset, with $10\\%$ corruptions, we achieved $1.42$ mean-squared error test error, compared to $1.51$-$2.33$ for the baselines, and $1.23$ error on the uncorrupted dataset. <|reference_end|>", "<|reference_start|> A natural robustification of the ordinary instrumental variables estimator: Instrumental variables estimators are designed to provide consistent parameter estimates for linear regression models when some covariates are correlated with the error term. We propose a new robust instrumental variables estimator (RIV) which is a natural robustification of the ordinary instrumental variables estimator (OIV). Specifically, we construct RIV using a robust multivariate location and scatter S‐estimator to robustify the solution of the estimating equations that define OIV. RIV is computationally inexpensive and readily available for applications through the R‐library riv. It has attractive robustness and asymptotic properties, including high resilience to outliers, bounded influence function, consistency under weak distributional assumptions, asymptotic normality under mild regularity conditions, and equivariance. We further endow RIV with an iterative algorithm which allows for the estimation of models with endogenous continuous covariates and exogenous dummy covariates. We study the performance of RIV when the data contains outliers using an extensive Monte Carlo simulation study and by applying it to a limited‐access dataset from the Framingham Heart Study‐Cohort to estimate the effect of long‐term systolic blood pressure on left atrial size. <|reference_end|>", "<|reference_start|> Robust inference with GMM estimators: <|reference_end|>", "<|reference_start|> Robust inference with GMM estimators: <|reference_end|>" ]

[ 11, 15, 17, 20 ]

[ "<|reference_start|> Audio-visual feature selection and reduction for emotion classification: Recognition of expressed emotion from speech and facial gestures was investigated in experiments on an audio-visual emotional database. A total of 106 audio and 240 visual features were extracted and then features were selected with Plus l-Take Away r algorithm based on Bhattacharyya distance criterion. In the second step, linear transformation methods, principal component analysis (PCA) and linear discriminant analysis (LDA), were applied to the selected features and Gaussian classifiers were used for classification of emotions. The performance was higher for LDA features compared to PCA features. The visual features performed better than audio features, for both PCA and LDA. Across a range of fusion schemes, the audio-visual feature results were close to that of visual features. A highest recognition rate of 53% was achieved with audio features, 98% with visual features, and 98% with audio-visual features selected by Bhattacharyya distance and transformed by LDA. <|reference_end|>", "<|reference_start|> Emotion Analysis of Songs Based on Lyrical and Audio Features: In this paper, a method is proposed to detect the emotion of a song based on its lyrical and audio features. Lyrical features are generated by segmentation of lyrics during the process of data extraction. ANEW and WordNet knowledge is then incorporated to compute Valence and Arousal values. In addition to this, linguistic association rules are applied to ensure that the issue of ambiguity is properly addressed. Audio features are used to supplement the lyrical ones and include attributes like energy, tempo, and danceability. These features are extracted from The Echo Nest, a widely used music intelligence platform. Construction of training and test sets is done on the basis of social tags extracted from the last.fm website. The classification is done by applying feature weighting and stepwise threshold reduction on the k-Nearest Neighbors algorithm to provide fuzziness in the classification. <|reference_end|>", "<|reference_start|> Communicating emotion: The role of prosodic features.: <|reference_end|>", "<|reference_start|> Emotional Musical Prosody for the Enhancement of Trust in Robotic Arm Communication: As robotic arms become prevalent in industry it is crucial to improve levels of trust from human collaborators. Low levels of trust in human-robot interaction can reduce overall performance and prevent full robot utilization. We investigated the potential benefits of using emotional musical prosody to allow the robot to respond emotionally to the user's actions. We tested participants' responses to interacting with a virtual robot arm that acted as a decision agent, helping participants select the next number in a sequence. We compared results from three versions of the application in a between-group experiment, where the robot had different emotional reactions to the user's input depending on whether the user agreed with the robot and whether the user's choice was correct. In all versions, the robot reacted with emotional gestures. One version used prosody-based emotional audio phrases selected from our dataset of singer improvisations, the second version used audio consisting of a single pitch randomly assigned to each emotion, and the final version used no audio, only gestures. Our results showed no significant difference for the percentage of times users from each group agreed with the robot, and no difference between user's agreement with the robot after it made a mistake. However, participants also took a trust survey following the interaction, and we found that the reported trust ratings of the musical prosody group were significantly higher than both the single-pitch and no audio groups. <|reference_end|>" ]

[ 8, 10, 13, 14 ]

[ "<|reference_start|> A randomized algorithm for closest-point queries: An algorithm for closest-point queries is given. The problem is this: given a set S of n points in d-dimensional space, build a data structure so that given an arbitrary query point p, a closest point in S to p can be found quickly. The measure of distance is the Euclidean norm. This is sometimes called the post-office problem. The new data structure will be termed an RPO tree, from Randomized Post Office. The expected time required to build an RPO tree is $O(n^{\\lceil {{d / 2}} \\rceil (1 + \\epsilon )} )$, for any fixed $\\epsilon > 0$, and a query can be answered in $O(\\log n)$ worst-case time. An RPO tree requires $O(n^{\\lceil {{d / 2}} \\rceil (1 + \\epsilon )} )$ space in the worst case. The constant factors in these bounds depend on d and $\\epsilon $. The bounds are average-case due to the randomization employed by the algorithm, and hold for any set of input points. This result approaches the $\\Omega (n^{\\lceil {{d / 2}} \\rceil } )$ worst-case time required for any algorithm that constructs the Voronoi... <|reference_end|>", "<|reference_start|> Weighted capacitated, priority, and geometric set cover via improved\nquasi-uniform sampling: The minimum-weight set cover problem is widely known to be O(log n)-approximable, with no improvement possible in the general case. We take the approach of exploiting problem structure to achieve better results, by providing a geometry-inspired algorithm whose approximation guarantee depends solely on an instance-specific combinatorial property known as shallow cell complexity (SCC). Roughly speaking, a set cover instance has low SCC if any column-induced submatrix of the corresponding element-set incidence matrix has few distinct rows. By adapting and improving Varadarajan's recent quasi-uniform random sampling method for weighted geometric covering problems, we obtain strong approximation algorithms for a structurally rich class of weighted covering problems with low SCC. We also show how to derandomize our algorithm. \n \nOur main result has several immediate consequences. Among them, we settle an open question of Chakrabarty et al. [8] by showing that weighted instances of the capacitated covering problem with underlying network structure have O(1)-approximations. Additionally, our improvements to Varadarajan's sampling framework yield several new results for weighted geometric set cover, hitting set, and dominating set problems. In particular, for weighted covering problems exhibiting linear (or near-linear) union complexity, we obtain approximability results agreeing with those known for the unweighted case. For example, we obtain a constant approximation for the weighted disk cover problem, improving upon the 2O(log* n)-approximation known prior to our work and matching the O(1)-approximation known for the unweighted variant. <|reference_end|>", "<|reference_start|> Epsilon nets and union complexity: We consider the following combinatorial problem: given a set of n objects (for example, disks in the plane, triangles), and an integer L ≥ 1, what is the size of the smallest subset of these n objects that covers all points that are in at least L of the objects? This is the classic question about the size of an L/n-net for these objects. It is well known that for fairly general classes of geometric objects the size of an L/n-net is O(n/L log n/L). There are some instances where this general bound can be improved, and this improvement is usually due to bounds on the combinatorial complexity (size) of the boundary of the union of these objects. Thus, the boundary of the union of m disks has size O(m), and this translates to an O(n/L) bound on the size of an L/n-net for disks. For m fat triangles, the size of the union boundary is O(m log log m), and this yields L/n-nets of size O(n/L log log n/L). Improved nets directly translate into an upper bound on the ratio between the optimal integral solution and the optimal fractional solution for the corresponding geometric set cover problem. Thus, for covering k points by disks, this ratio is O(1); and for covering k points by fat triangles, this ratio is O(log log k). This connection to approximation algorithms for geometric set cover is a major motivation for attempting to improve bounds on nets. Our main result is an argument that in some cases yields nets that are smaller than those previously obtained from the size of the union boundary. Thus for fat triangles, for instance, we obtain nets of size O(n/L log log log n). We use this to obtain a randomized polynomial time algorithm that gives an O(log log log k)-approximation for the problem of covering k points by the smallest subset of a given set of triangles. <|reference_end|>", "<|reference_start|> Computing Optimal Epsilon-Nets Is as Easy as Finding an Unhit Set: Given a set system (X,R) with VC-dimension d, the celebrated result of Haussler and Welzl (1987) showed that there exists an -net for (X,R) of size O ( d log 1 ) . Furthermore, the algorithm is simple: just take a uniform random sample from X! However, for many geometric set systems this bound is sub-optimal and since then, there has been much work presenting improved bounds and algorithms tailored to specific geometric set systems. In this paper, we consider the following natural algorithm to compute an -net: start with an initial random sample N . Iteratively, as long as N is not an -net for R, pick any unhit set S ∈ R (say, given by an Oracle), and add O(1) randomly chosen points from S to N . We prove that the above algorithm computes, in expectation, -nets of asymptotically optimal size for all known cases of geometric set systems. Furthermore, it makes O ( 1 ) calls to the Oracle. In particular, this implies that computing optimal-sized -nets are as easy as computing an unhit set in the given set system. 2012 ACM Subject Classification Theory of computation → Sketching and sampling <|reference_end|>" ]

[ 10, 16, 17, 25 ]

[ "<|reference_start|> A New Kind of Science: Book Review for:\"A New Kind of Science\", by Stephen Wolfram (Wolfram Media, Inc. Champaign IL 2002). <|reference_end|>", "<|reference_start|> It's Hard for Neural Networks To Learn the Game of Life: Efforts to improve the learning abilities of neural networks have focused mostly on the role of optimization methods rather than on weight initializations. Recent findings, however, suggest that neural networks rely on lucky random initial weights of subnetworks called \"lottery tickets\" that converge quickly to a solution. To investigate how weight initializations affect performance, we examine small convolutional networks that are trained to predict n steps of the two-dimensional cellular automaton Conway's Game of Life, the update rules of which can be implemented efficiently in a 2n+1 layer convolutional network. We find that networks of this architecture trained on this task rarely converge. Rather, networks require substantially more parameters to consistently converge. In addition, near-minimal architectures are sensitive to tiny changes in parameters: changing the sign of a single weight can cause the network to fail to learn. Finally, we observe a critical value d_0 such that training minimal networks with examples in which cells are alive with probability d_0 dramatically increases the chance of convergence to a solution. We conclude that training convolutional neural networks to learn the input/output function represented by n steps of Game of Life exhibits many characteristics predicted by the lottery ticket hypothesis, namely, that the size of the networks required to learn this function are often significantly larger than the minimal network required to implement the function. <|reference_end|>", "<|reference_start|> The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks: Neural network pruning techniques can reduce the parameter counts of trained networks by over 90%, decreasing storage requirements and improving computational performance of inference without compromising accuracy. However, contemporary experience is that the sparse architectures produced by pruning are difficult to train from the start, which would similarly improve training performance. We find that a standard pruning technique naturally uncovers subnetworks whose initializations made them capable of training effectively. Based on these results, we articulate the \"lottery ticket hypothesis:\" dense, randomly-initialized, feed-forward networks contain subnetworks (\"winning tickets\") that - when trained in isolation - reach test accuracy comparable to the original network in a similar number of iterations. The winning tickets we find have won the initialization lottery: their connections have initial weights that make training particularly effective. We present an algorithm to identify winning tickets and a series of experiments that support the lottery ticket hypothesis and the importance of these fortuitous initializations. We consistently find winning tickets that are less than 10-20% of the size of several fully-connected and convolutional feed-forward architectures for MNIST and CIFAR10. Above this size, the winning tickets that we find learn faster than the original network and reach higher test accuracy. <|reference_end|>", "<|reference_start|> Learning Without Loss: We explore a new approach for training neural networks where all loss functions are replaced by hard constraints. The same approach is very successful in phase retrieval, where signals are reconstructed from magnitude constraints and general characteristics (sparsity, support, etc.). Instead of taking gradient steps, the optimizer in the constraint based approach, called relaxed-reflect-reflect (RRR), derives its steps from projections to local constraints. In neural networks one such projection makes the minimal modification to the inputs $x$, the associated weights $w$, and the pre-activation value $y$ at each neuron, to satisfy the equation $x\\cdot w=y$. These projections, along with a host of other local projections (constraining pre- and post-activations, etc.) can be partitioned into two sets such that all the projections in each set can be applied concurrently, across the network and across all data in the training batch. This partitioning into two sets is analogous to the situation in phase retrieval and the setting for which the general purpose RRR optimizer was designed. Owing to the novelty of the method, this paper also serves as a self-contained tutorial. Starting with a single-layer network that performs non-negative matrix factorization, and concluding with a generative model comprising an autoencoder and classifier, all applications and their implementations by projections are described in complete detail. Although the new approach has the potential to extend the scope of neural networks (e.g. by defining activation not through functions but constraint sets), most of the featured models are standard to allow comparison with stochastic gradient descent. <|reference_end|>" ]

[ 0, 1, 2, 3 ]

[ "<|reference_start|> Microservices: yesterday, today, and tomorrow: Microservices is an architectural style inspired by service-oriented computing that has recently started gaining popularity. Before presenting the current state-of-the-art in the field, this chapter reviews the history of software architecture, the reasons that led to the diffusion of objects and services first, and microservices later. Finally, open problems and future challenges are introduced. This survey primarily addresses newcomers to the discipline, while offering an academic viewpoint on the topic. In addition, we investigate some practical issues and point out some potential solutions. <|reference_end|>", "<|reference_start|> Sliceable Monolith: Monolith First, Microservices Later: We propose Sliceable Monolith, a new methodology for developing microservice architectures and perform their integration testing by leveraging most of the simplicity of a monolith: a single codebase and a local execution environment that simulates distribution. Then, a tool compiles a codebase for each microservice and a cloud deployment configuration. The key enabler of our approach is the technology-agnostic service definition language offered by Jolie. <|reference_end|>", "<|reference_start|> Service-Oriented Programming with Jolie: <|reference_end|>", "<|reference_start|> Service-Oriented Programming with Jolie: <|reference_end|>" ]

[ 0, 1, 2, 3 ]

[ "<|reference_start|> 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2021.: The American Diabetes Association (ADA) \"Standards of Medical Care in Diabetes\" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc21-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc21-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC. <|reference_end|>", "<|reference_start|> {Attain: Attention-based time-aware LSTM networks for disease progression modeling: Modeling patient disease progression using Electronic Health Records (EHRs) is critical to assist clinical decision making. Long-Short Term Memory (LSTM) is an effective model to handle sequential data, such as EHRs, but it encounters two major limitations when applied to EHRs: it is unable to interpret the prediction results and it ignores the irregular time intervals between consecutive events. To tackle these limitations, we propose an attention-based time-aware LSTM Networks (ATTAIN), to improve the interpretability of LSTM and to identify the critical previous events for current diagnosis by modeling the inherent time irregularity. We validate ATTAIN on modeling the progression of an extremely challenging disease, septic shock, by using real-world EHRs. Our results demonstrate that the proposed framework outperforms the state-of-the-art models such as RETAIN and T-LSTM. Also, the generated interpretative time-aware attention weights shed some lights on the progression behaviors of septic shock. <|reference_end|>", "<|reference_start|> Predicting battery life from usage trajectory patterns: This paper addresses the task of predicting the battery capacity degradation ratio for a given usage pattern. This is an interesting pattern recognition task, where each usage pattern is represented as a trajectory in a feature space, and the prediction model captures the previous usage trajectory patterns. The main technical challenge here is how to build a good model from a limited number of training samples. To tackle this, we introduce a new smoothing technique in the trajectory space. The trajectory smoothing technique is shown to be equivalent of a novel regularization scheme for linear regression. Using real Li-ion battery data, we show that our approach outperforms existing methods. <|reference_end|>", "<|reference_start|> Trajectory topic modelling to characterize driving behaviors with GPS-based trajectory data: The rapid accumulation of large-scale driving data represents an opportunity to improve our understanding of driving behavior patterns and driver traveling intentions. However, limited efforts have been devoted to understanding these patterns and the travel intentions behind them. This study proposes a new trajectory topic model (TTM) to explore latent driving patterns from driving trajectory data and to qualitatively analyze drivers’ main traveling intentions. These trajectory data were collected from more than 150,000 commercial vehicles in Fujian Province, China. After data preprocessing, the TTM was then established to decompose trajectory data into various topics with corresponding probabilities, which were correlated to drivers’ preferences. Several experiments conducted in Fuzhou City were performed to evaluate the feasibility and efficiency of the TTM using a real trajectory dataset. The results show that the TTM could effectively mine users’ driving behavior patterns with topic probability. The model would enable us to understand the context in which drivers travel and learn their individual preferences. It is also beneficial in that it can predict drivers’ behaviors, analyze traffic patterns in an entire city, and even help autonomous vehicles to learn from drivers. <|reference_end|>" ]

[ 4, 8, 9, 10 ]

[ "<|reference_start|> Adapting to Context-Aware Knowledge in Natural Conversation for Multi-Turn Response Selection: Virtual assistants aim to build a human-like conversational agent. However, current human-machine conversations still cannot make users feel intelligent enough to build a continued dialog over time. Some responses from agents are usually inconsistent, uninformative, less-engaging and even memoryless. In recent years, most researchers have tried to employ conversation context and external knowledge, e.g. wiki pages and knowledge graphs, into the model which only focuses on solving some special conversation problems in local perspectives. Few researchers are dedicated to the whole capability of the conversational agent which is endowed with abilities of not only passively reacting the conversation but also proactively leading the conversation. In this paper, we first explore the essence of conversations among humans by analyzing real dialog records. We find that some conversations revolve around the same context and topic, and some require additional information or even move on to a new topic. Base on that, we conclude three conversation modes shown in Figure 1 and try to solve how to adapt to them for a continuous conversation. To this end, we define “Adaptive Knowledge-Grounded Conversations” (AKGCs) where the knowledge is to ground the conversation within a multi-turn context by adapting to three modes. To achieve AKGC, a model called MNDB is proposed to model natural dialog behaviors for multi-turn response selection. To ensure a consistent response, MNDB constructs a multi-turn context flow. Then, to mimic user behaviors of incorporating knowledge in natural conversations, we design a ternary-grounding network along with the context flow. In this network, to gain the ability to adapt to diversified conversation modes, we exploit multi-view semantical relations among response candidates, context and knowledge. Thus, three adaptive matching signals are extracted for final response selection. Evaluation results on two benchmarks indicate that MNDB can significantly outperform state-of-the-art models. <|reference_end|>", "<|reference_start|> Towards Knowledge-Based Recommender Dialog System: In this paper, we propose a novel end-to-end framework called KBRD, which stands for Knowledge-Based Recommender Dialog System. It integrates the recommender system and the dialog generation system. The dialog system can enhance the performance of the recommendation system by introducing knowledge-grounded information about users' preferences, and the recommender system can improve that of the dialog generation system by providing recommendation-aware vocabulary bias. Experimental results demonstrate that our proposed model has significant advantages over the baselines in both the evaluation of dialog generation and recommendation. A series of analyses show that the two systems can bring mutual benefits to each other, and the introduced knowledge contributes to both their performances. <|reference_end|>", "<|reference_start|> Towards Deep Conversational Recommendations: There has been growing interest in using neural networks and deep learning techniques to create dialogue systems. Conversational recommendation is an interesting setting for the scientific exploration of dialogue with natural language as the associated discourse involves goal-driven dialogue that often transforms naturally into more free-form chat. This paper provides two contributions. First, until now there has been no publicly available large-scale dataset consisting of real-world dialogues centered around recommendations. To address this issue and to facilitate our exploration here, we have collected ReDial, a dataset consisting of over 10,000 conversations centered around the theme of providing movie recommendations. We make this data available to the community for further research. Second, we use this dataset to explore multiple facets of conversational recommendations. In particular we explore new neural architectures, mechanisms, and methods suitable for composing conversational recommendation systems. Our dataset allows us to systematically probe model sub-components addressing different parts of the overall problem domain ranging from: sentiment analysis and cold-start recommendation generation to detailed aspects of how natural language is used in this setting in the real world. We combine such sub-components into a full-blown dialogue system and examine its behavior. <|reference_end|>", "<|reference_start|> Interactive Path Reasoning on Graph for Conversational Recommendation: Traditional recommendation systems estimate user preference on items from past interaction history, thus suffering from the limitations of obtaining fine-grained and dynamic user preference. Conversational recommendation system (CRS) brings revolutions to those limitations by enabling the system to directly ask users about their preferred attributes on items. However, existing CRS methods do not make full use of such advantage -- they only use the attribute feedback in rather implicit ways such as updating the latent user representation. In this paper, we propose Conversational Path Reasoning (CPR), a generic framework that models conversational recommendation as an interactive path reasoning problem on a graph. It walks through the attribute vertices by following user feedback, utilizing the user preferred attributes in an explicit way. By leveraging on the graph structure, CPR is able to prune off many irrelevant candidate attributes, leading to better chance of hitting user preferred attributes. To demonstrate how CPR works, we propose a simple yet effective instantiation named SCPR (Simple CPR). We perform empirical studies on the multi-round conversational recommendation scenario, the most realistic CRS setting so far that considers multiple rounds of asking attributes and recommending items. Through extensive experiments on two datasets Yelp and LastFM, we validate the effectiveness of our SCPR, which significantly outperforms the state-of-the-art CRS methods EAR (arXiv:2002.09102) and CRM (arXiv:1806.03277). In particular, we find that the more attributes there are, the more advantages our method can achieve. <|reference_end|>" ]

[ 0, 1, 2, 3 ]

[ "<|reference_start|> Accelerated Methods for Non-Convex Optimization: We present an accelerated gradient method for non-convex optimization problems with Lipschitz continuous first and second derivatives. The method requires time $O(\\epsilon^{-7/4} \\log(1/ \\epsilon) )$ to find an $\\epsilon$-stationary point, meaning a point $x$ such that $\\|\\nabla f(x)\\| \\le \\epsilon$. The method improves upon the $O(\\epsilon^{-2} )$ complexity of gradient descent and provides the additional second-order guarantee that $\\nabla^2 f(x) \\succeq -O(\\epsilon^{1/2})I$ for the computed $x$. Furthermore, our method is Hessian free, i.e. it only requires gradient computations, and is therefore suitable for large scale applications. <|reference_end|>", "<|reference_start|> Fast low-rank estimation by projected gradient descent: General statistical and algorithmic guarantees: Optimization problems with rank constraints arise in many applications, including matrix regression, structured PCA, matrix completion and matrix decomposition problems. An attractive heuristic for solving such problems is to factorize the low-rank matrix, and to run projected gradient descent on the nonconvex factorized optimization problem. The goal of this problem is to provide a general theoretical framework for understanding when such methods work well, and to characterize the nature of the resulting fixed point. We provide a simple set of conditions under which projected gradient descent, when given a suitable initialization, converges geometrically to a statistically useful solution. Our results are applicable even when the initial solution is outside any region of local convexity, and even when the problem is globally concave. Working in a non-asymptotic framework, we show that our conditions are satisfied for a wide range of concrete models, including matrix regression, structured PCA, matrix completion with real and quantized observations, matrix decomposition, and graph clustering problems. Simulation results show excellent agreement with the theoretical predictions. <|reference_end|>", "<|reference_start|> Dropping Convexity for Faster Semi-definite Optimization: We study the minimization of a convex function $f(X)$ over the set of $n\\times n$ positive semi-definite matrices, but when the problem is recast as $\\min_U g(U) := f(UU^\\top)$, with $U \\in \\mathbb{R}^{n \\times r}$ and $r \\leq n$. We study the performance of gradient descent on $g$---which we refer to as Factored Gradient Descent (FGD)---under standard assumptions on the original function $f$. We provide a rule for selecting the step size and, with this choice, show that the local convergence rate of FGD mirrors that of standard gradient descent on the original $f$: i.e., after $k$ steps, the error is $O(1/k)$ for smooth $f$, and exponentially small in $k$ when $f$ is (restricted) strongly convex. In addition, we provide a procedure to initialize FGD for (restricted) strongly convex objectives and when one only has access to $f$ via a first-order oracle; for several problem instances, such proper initialization leads to global convergence guarantees. FGD and similar procedures are widely used in practice for problems that can be posed as matrix factorization. To the best of our knowledge, this is the first paper to provide precise convergence rate guarantees for general convex functions under standard convex assumptions. <|reference_end|>", "<|reference_start|> Basis learning as an algorithmic primitive: A number of important problems in theoretical computer science and machine learning can be interpreted as recovering a certain basis. These include symmetric matrix eigendecomposition, certain tensor decompositions, Independent Component Analysis (ICA), spectral clustering and Gaussian mixture learning. Each of these problems reduces to an instance of our general model, which we call a \"Basis Encoding Function\" (BEF). We show that learning a basis within this model can then be provably and efficiently achieved using a first order iteration algorithm (gradient iteration). Our algorithm goes beyond tensor methods while generalizing a number of existing algorithms---e.g., the power method for symmetric matrices, the tensor power iteration for orthogonal decomposable tensors, and cumulant-based FastICA---all within a broader function-based dynamical systems framework. Our framework also unifies the unusual phenomenon observed in these domains that they can be solved using efficient non-convex optimization. Specifically, we describe a class of BEFs such that their local maxima on the unit sphere are in one-to-one correspondence with the basis elements. This description relies on a certain \"hidden convexity\" property of these functions. \nWe provide a complete theoretical analysis of the gradient iteration even when the BEF is perturbed. We show convergence and complexity bounds polynomial in dimension and other relevant parameters, such as perturbation size. Our perturbation results can be considered as a non-linear version of the classical Davis-Kahan theorem for perturbations of eigenvectors of symmetric matrices. In addition we show that our algorithm exhibits fast (superlinear) convergence and relate the speed of convergence to the properties of the BEF. Moreover, the gradient iteration algorithm can be easily and efficiently implemented in practice. <|reference_end|>" ]

[ 8, 32, 43, 44 ]

[ "<|reference_start|> Modern Cryptography, Probabilistic Proofs and Pseudorandomness: <|reference_end|>", "<|reference_start|> Modern Cryptography, Probabilistic Proofs and Pseudorandomness: <|reference_end|>", "<|reference_start|> Randomized distributed edge coloring via an extension of the Chernoff-Hoeffding bounds: Certain types of routing, scheduling, and resource-allocation problems in a distributed setting can be modeled as edge-coloring problems. We present fast and simple randomized algorithms for edge coloring a graph in the synchronous distributed point-to-point model of computation. Our algorithms compute an edge coloring of a graph G with n nodes and maximum degree ∆ with at most 1.6∆ +O(log n) colors with high probability (arbitrarily close to 1) for any fixed δ > 0; they run in polylogarithmic time. The upper bound on the number of colors improves upon the (2∆ − 1)-coloring achievable by a simple reduction to vertex coloring. To analyze the performance of our algorithms, we introduce new techniques for proving upper bounds on the tail probabilities of certain random variables. The Chernoff–Hoeffding bounds are fundamental tools that are used very frequently in estimating tail probabilities. However, they assume stochastic independence among certain random variables, which may not always hold. Our results extend the Chernoff–Hoeffding bounds to certain types of random variables which are not stochastically independent. We believe that these results are of independent interest and merit further study. <|reference_end|>", "<|reference_start|> Constructive Proofs of Concentration Bounds: <|reference_end|>" ]

[ 1, 2, 3, 4 ]

[ "<|reference_start|> Pumma: Parallel universal matrix multiplication algorithms on distributed memory concurrent computers: This paper describes the Parallel Universal Matrix Multiplication Algorithms (PUMMA) on distributed memory concurrent computers. The PUMMA package includes not only the non-transposed matrix multiplication routine C = A{center_dot}B, but also transposed multiplication routines C = A{sup T}{center_dot}B, C = A{center_dot}B{sup T}, and C = A{sup T}{center_dot}B{sup T}, for a block scattered data distribution. The routines perform efficiently for a wide range of processor configurations and block sizes. The PUMMA together provide the same functionality as the Level 3 BLAS routine xGEMM. Details of the parallel implementation of the routines are given, and results are presented for runs on the Intel Touchstone Delta computer. <|reference_end|>", "<|reference_start|> Gradient coding: Avoiding stragglers in distributed learning: We propose a novel coding theoretic framework for mitigating stragglers in distributed learning. We show how carefully replicating data blocks and coding across gradients can provide tolerance to failures and stragglers for synchronous Gradient Descent. We implement our schemes in python (using MPI) to run on Amazon EC2, and show how we compare against baseline approaches in running time and generalization error. <|reference_end|>", "<|reference_start|> Hierarchical Coded Computation: Coded computation is a method to mitigate \"stragglers\" in distributed computing systems through the use of error correction coding that has lately received significant attention. First used in vector-matrix multiplication, the range of application was later extended to include matrix-matrix multiplication, heterogeneous networks, convolution, and approximate computing. A drawback to previous results is they completely ignore work completed by stragglers. While stragglers are slower compute nodes, in many settings the amount of work completed by stragglers can be non-negligible. Thus, in this work, we propose a hierarchical coded computation method that exploits the work completed by all compute nodes. We partition each node's computation into layers of sub-computations such that each layer can be treated as (distinct) erasure channel. We then design different erasure codes for each layer so that all layers have the same failure exponent. We propose design guidelines to optimize parameters of such codes. Numerical results show the proposed scheme has an improvement of a factor of 1.5 in the expected finishing time compared to previous work. <|reference_end|>", "<|reference_start|> Speeding Up Distributed Gradient Descent by Utilizing Non-persistent Stragglers: Distributed gradient descent (DGD) is an efficient way of implementing gradient descent (GD), especially for large data sets, by dividing the computation tasks into smaller subtasks and assigning to different computing servers (CSs) to be executed in parallel. In standard parallel execution, per-iteration waiting time is limited by the execution time of the straggling servers. Coded DGD techniques have been introduced recently, which can tolerate straggling servers via assigning redundant computation tasks to the CSs. In most of the existing DGD schemes, either with coded computation or coded communication, the non-straggling CSs transmit one message per iteration once they complete all their assigned computation tasks. However, although the straggling servers cannot complete all their assigned tasks, they are often able to complete a certain portion of them. In this paper, we allow multiple transmissions from each CS at each iteration in order to make sure a maximum number of completed computations can be reported to the aggregating server (AS), including the straggling servers. We numerically show that the average completion time per iteration can be reduced significantly by slightly increasing the communication load per server. <|reference_end|>" ]

[ 1, 9, 21, 24 ]

[ "<|reference_start|> The algorithmic foundations of Differential Privacy: The problem of privacy-preserving data analysis has a long history spanning multiple disciplines. As electronic data about individuals becomes increasingly detailed, and as technology enables ever more powerful collection and curation of these data, the need increases for a robust, meaningful, and mathematically rigorous definition of privacy, together with a computationally rich class of algorithms that satisfy this definition. Differential Privacy is such a definition.After motivating and discussing the meaning of differential privacy, the preponderance of this monograph is devoted to fundamental techniques for achieving differential privacy, and application of these techniques in creative combinations, using the query-release problem as an ongoing example. A key point is that, by rethinking the computational goal, one can often obtain far better results than would be achieved by methodically replacing each step of a non-private computation with a differentially private implementation. Despite some astonishingly powerful computational results, there are still fundamental limitations — not just on what can be achieved with differential privacy but on what can be achieved with any method that protects against a complete breakdown in privacy. Virtually all the algorithms discussed herein maintain differential privacy against adversaries of arbitrary computational power. Certain algorithms are computationally intensive, others are efficient. Computational complexity for the adversary and the algorithm are both discussed.We then turn from fundamentals to applications other than queryrelease, discussing differentially private methods for mechanism design and machine learning. The vast majority of the literature on differentially private algorithms considers a single, static, database that is subject to many analyses. Differential privacy in other models, including distributed databases and computations on data streams is discussed.Finally, we note that this work is meant as a thorough introduction to the problems and techniques of differential privacy, but is not intended to be an exhaustive survey — there is by now a vast amount of work in differential privacy, and we can cover only a small portion of it. <|reference_end|>", "<|reference_start|> On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis: Bayesian inference has great promise for the privacy-preserving analysis of sensitive data, as posterior sampling automatically preserves differential privacy, an algorithmic notion of data privacy, under certain conditions (Dimitrakakis et al., 2014; Wang et al., 2015). While this one posterior sample (OPS) approach elegantly provides privacy \"for free,\" it is data inefficient in the sense of asymptotic relative efficiency (ARE). We show that a simple alternative based on the Laplace mechanism, the workhorse of differential privacy, is as asymptotically efficient as non-private posterior inference, under general assumptions. This technique also has practical advantages including efficient use of the privacy budget for MCMC. We demonstrate the practicality of our approach on a time-series analysis of sensitive military records from the Afghanistan and Iraq wars disclosed by the Wikileaks organization. <|reference_end|>", "<|reference_start|> Exact Inference with Approximate Computation for Differentially Private Data via Perturbations: This paper discusses how two classes of approximate computation algorithms can be adapted, in a modular fashion, to achieve exact statistical inference from differentially private data products. Considered are approximate Bayesian computation for Bayesian inference, and Monte Carlo Expectation-Maximization for likelihood inference. Up to Monte Carlo error, inference from these algorithms is exact with respect to the joint specification of both the analyst's original data model, and the curator's differential privacy mechanism. Highlighted is a duality between approximate computation on exact data, and exact computation on approximate data, which can be leveraged by a well-designed computational procedure for statistical inference. <|reference_end|>", "<|reference_start|> Data augmentation mcmc for bayesian inference from privatized data: Differentially private mechanisms protect privacy by introducing additional randomness into the data. Restricting access to only the privatized data makes it challenging to perform valid statistical inference on parameters underlying the confidential data. Specifically, the likelihood function of the privatized data requires integrating over the large space of confidential databases and is typically intractable. For Bayesian analysis, this results in a posterior distribution that is doubly intractable, rendering traditional MCMC techniques inapplicable. We propose an MCMC framework to perform Bayesian inference from the privatized data, which is applicable to a wide range of statistical models and privacy mechanisms. Our MCMC algorithm augments the model parameters with the unobserved confidential data, and alternately updates each one conditional on the other. For the potentially challenging step of updating the confidential data, we propose a generic approach that exploits the privacy guarantee of the mechanism to ensure efficiency. We give results on the computational complexity, acceptance rate, and mixing properties of our MCMC. We illustrate the efficacy and applicability of our methods on a na\\\"ive-Bayes log-linear model as well as on a linear regression model. <|reference_end|>" ]

[ 1, 7, 12, 19 ]

[ "<|reference_start|> Feedback Capacity of Stationary Gaussian Channels: The feedback capacity of additive stationary Gaussian noise channels is characterized as the solution to a variational problem. Toward this end, it is proved that the optimal feedback coding scheme is stationary. When specialized to the first-order autoregressive moving average noise spectrum, this variational characterization yields a closed-form expression for the feedback capacity. In particular, this result shows that the celebrated Schalkwijk-Kailath coding scheme achieves the feedback capacity for the first-order autoregressive moving average Gaussian channel, positively answering a long-standing open problem studied by Butman, Schalkwijk-Tiernan, Wolfowitz, Ozarow, Ordentlich, Yang-Kavcic-Tatikonda, and others. More generally, it is shown that a k-dimensional generalization of the Schalkwijk-Kailath coding scheme achieves the feedback capacity for any autoregressive moving average noise spectrum of order k. Simply put, the optimal transmitter iteratively refines the receiver's knowledge of the intended message. <|reference_end|>", "<|reference_start|> Capacity Region of the Finite-State Multiple Access Channel with and without Feedback: The capacity region of the Finite-State Multiple Access Channel (FS-MAC) with feedback that may be an arbitrary time-invariant function of the channel output samples is considered. We characterize both an inner and an outer bound for this region, using Masseys's directed information. These bounds are shown to coincide, and hence yield the capacity region, of FS-MACs where the state process is stationary and ergodic and not affected by the inputs. Though `multi-letter' in general, our results yield explicit conclusions when applied to specific scenarios of interest. E.g., our results allow us to: - Identify a large class of FS-MACs, that includes the additive mod-2 noise MAC where the noise may have memory, for which feedback does not enlarge the capacity region. - Deduce that, for a general FS-MAC with states that are not affected by the input, if the capacity (region) without feedback is zero, then so is the capacity (region) with feedback. - Deduce that the capacity region of a MAC that can be decomposed into a `multiplexer' concatenated by a point-to-point channel (with, without, or with partial feedback), the capacity region is given by $\\sum_{m} R_m \\leq C$, where C is the capacity of the point to point channel and m indexes the encoders. Moreover, we show that for this family of channels source-channel coding separation holds. <|reference_end|>", "<|reference_start|> Source coding with feed-forward: Rate-distortion theorems and error exponents for a general source: In this work, we consider a source coding model with feed-forward. We analyze a system with a noiseless, feed-forward link where the decoder has knowledge of all previous source samples while reconstructing the present sample. The rate-distortion function for an arbitrary source with feed-forward is derived in terms of directed information, a variant of mutual information. We further investigate the nature of the rate-distortion function with feed-forward for two common types of sources- discrete memory- less sources and Gaussian sources. We then characterize the error exponent for a general source with feed-forward. The results are then extended to feed-forward with an arbitrary delay larger than the block length. <|reference_end|>", "<|reference_start|> On the calculation of Mutual Information: Abstract : Calculating the amount of information about a random function contained in another random function has important uses in communication theory. An expression for the mutual information for continuous time random processes has been given by Gelfand and Yaglom, Chiang, and Perez by generalizing Shannon's result in a natural way. Under a condition of absolute continuity of measures the continuous time expression has the same form as Shannon's result. For two Gaussian processes Gelfand and Yaglom express the mutual information in terms of a mean square estimation error. We generalize this result to diffusion processes and express the solution in a different form which is more naturally related to a corresponding filtering problem. We also use these results to calculate some information rates. <|reference_end|>" ]

[ 7, 11, 14, 18 ]

[ "<|reference_start|> Review of Unmanned Aerial System (UAS) applications in the built environment: Towards automated building inspection procedures using drones: <|reference_end|>", "<|reference_start|> Model predictive local motion planning with boundary state constrained primitives: Motion primitives are frequently used to find valid local trajectories for mobile robots, especially in cases where fast replanning is required, but the onboard computational power is limited. In this letter, we present a practical framework for constructing motion primitives from boundary state constraints, and then using them for online planning. The primitives are offline constructed with either a boundary value problem solver or a controller. They are then approximated with a neural network for fast evaluation during online optimization. The references and nominal inputs are generated in a receding horizon fashion by solving a model predictive control problem in the continuous domain with either gradient-based or gradient-free techniques. The proposed approach is computationally efficient and has been tested on quadrotors in real flight experiments, including sensor-based navigation, flying through a complex three-dimensional environment, dynamic obstacle avoidance, and tracking moving references. <|reference_end|>", "<|reference_start|> A UAV-based explore-then-exploit system for autonomous indoor facility inspection and scene reconstruction: <|reference_end|>", "<|reference_start|> Fly-crash-recover: A sensor-based reactive framework for online collision recovery of uavs: Unmanned Aerial Vehicles (UAVs) are becoming increasingly popular thanks to the multiplicity of operations in which they can be deployed such as surveillance, search and rescue, mapping, transportation, hobby and recreational activities. Although sensors like LIDARs and cameras are often present on such systems for motion planning to avoid obstacles, collisions can still occur in very dense and unstructured environments, especially if disturbances are present. In this work, we research techniques to recover UAVs after a collision has occurred. We note that the on-board sensors, especially the inertial sensor used to stabilize the UAV, run at a high frequencies obtaining hundreds of data points every second. At run-time, this can be leveraged at the moment of a collision to quickly detect and recover the system. Our approach considers knowledge of UAV system dynamics to predict the expected behavior of the vehicle under safe flight conditions and leverage such expectations together with inertial data to detect collisions rapidly (on the order of milliseconds). We also propose a potential field-based approach to map the collision and create the correct reactive maneuver to avoid the collided object and bring the system back to a stable and safe configuration. Experiments are executed using ROS on two micro-quadrotor UAV platforms having different dynamics and performances, while colliding with poles and walls positioned in different configurations. In our results, we are able to show that the UAVs are successfully able to detect and avoid a collision, while also providing a rigorous analysis of the conditions in which the system can recover from imminent collisions. <|reference_end|>" ]

[ 0, 11, 16, 21 ]

[ "<|reference_start|> Uncovering the Temporal Dynamics of Diffusion Networks: Time plays an essential role in the diffusion of information, influence and disease over networks. In many cases we only observe when a node copies information, makes a decision or becomes infected -- but the connectivity, transmission rates between nodes and transmission sources are unknown. Inferring the underlying dynamics is of outstanding interest since it enables forecasting, influencing and retarding infections, broadly construed. To this end, we model diffusion processes as discrete networks of continuous temporal processes occurring at different rates. Given cascade data -- observed infection times of nodes -- we infer the edges of the global diffusion network and estimate the transmission rates of each edge that best explain the observed data. The optimization problem is convex. The model naturally (without heuristics) imposes sparse solutions and requires no parameter tuning. The problem decouples into a collection of independent smaller problems, thus scaling easily to networks on the order of hundreds of thousands of nodes. Experiments on real and synthetic data show that our algorithm both recovers the edges of diffusion networks and accurately estimates their transmission rates from cascade data. <|reference_end|>", "<|reference_start|> A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators: <|reference_end|>", "<|reference_start|> Identifiability of sparse structural equation models for directed and cyclic networks: Structural equation models (SEMs) provide a statistical description of directed networks. The networks modeled by SEMs may have signed edge weights, a property that is pertinent to represent the activating and inhibitory interactions characteristic of biological systems, as well as the collaborative and antagonist behaviors found in social networks, among other applications. They may also have cyclic paths, accommodating the presence of protein stabilizing loops, or the feedback in decision making processes. Starting from the mathematical description of a linear SEM, this paper aims to identify the topology, edge directions, and edge weights of the underlying network. It is established that perturbation data is essential for this purpose, otherwise directional ambiguities cannot be resolved. It is also proved that the required amount of data is significantly reduced when the network topology is assumed to be sparse; that is, when the number of incoming edges per node is much smaller than the network size. Identifying a dynamic network with step changes across time is also considered, but it is left as an open problem to be addressed in an extended version of this paper. <|reference_end|>", "<|reference_start|> A proximal decomposition method for solving convex variational inverse problems: A broad range of inverse problems can be abstracted into the problem of minimizing the sum of several convex functions in a Hilbert space. We propose a proximal decomposition algorithm for solving this problem with an arbitrary number of nonsmooth functions and establish its weak convergence. The algorithm fully decomposes the problem in that it involves each function individually via its own proximity operator. A significant improvement over the methods currently in use in the area of inverse problems is that it is not limited to two nonsmooth functions. Numerical applications to signal and image processing problems are demonstrated. <|reference_end|>" ]

[ 4, 14, 20, 29 ]

[ "<|reference_start|> A Primal-Dual Online Deterministic Algorithm for Matching with Delays: In the Min-cost Perfect Matching with Delays (MPMD) problem, 2 m requests arrive over time at points of a metric space. An online algorithm has to connect these requests in pairs, but a decision to match may be postponed till a more suitable matching pair is found. The goal is to minimize the joint cost of connection and the total waiting time of all requests. We present an O(m)-competitive deterministic algorithm for this problem, improving on an existing bound of O(m^(log(5.5))) = O(m^2.46). Our algorithm also solves (with the same competitive ratio) a bipartite variant of MPMD, where requests are either positive or negative and only requests with different polarities may be matched with each other. Unlike the existing randomized solutions, our approach does not depend on the size of the metric space and does not have to know it in advance. <|reference_end|>", "<|reference_start|> Deterministic Min-Cost Matching with Delays: We consider the online Minimum-Cost Perfect Matching with Delays (MPMD) problem introduced by Emek et al. (STOC 2016), in which a general metric space is given, and requests are submitted in different times in this space by an adversary. The goal is to match requests, while minimizing the sum of distances between matched pairs in addition to the time intervals passed from the moment each request appeared until it is matched. In the online Minimum-Cost Bipartite Perfect Matching with Delays (MBPMD) problem introduced by Ashlagi et al. (APPROX/RANDOM 2017), each request is also associated with one of two classes, and requests can only be matched with requests of the other class. Previous algorithms for the problems mentioned above, include randomized $O\\left(\\log n\\right)$-competitive algorithms for known and finite metric spaces, $n$ being the size of the metric space, and a deterministic $O\\left(m\\right)$-competitive algorithm, $m$ being the number of requests. We introduce $O\\left(m^{\\log\\left(\\frac{3}{2}+\\epsilon\\right)}\\right)$-competitive deterministic algorithms for both problems and for any fixed $\\epsilon > 0$. In particular, for a small enough $\\epsilon$ the competitive ratio becomes $O\\left(m^{0.59}\\right)$. These are the first deterministic algorithms for the mentioned online matching problems, achieving a sub-linear competitive ratio. Our algorithms do not need to know the metric space in advance. <|reference_end|>", "<|reference_start|> The Price of Clustering in Bin-Packing with Applications to Bin-Packingwith Delays: One of the most significant algorithmic challenges in the \"big data era\" is handling instances that are too large to be processed by a single machine. The common practice in this regard is to partition the massive problem instance into smaller ones and process each one of them separately. In some cases, the solutions for the smaller instances are later on assembled into a solution for the whole instance, but in many cases this last stage cannot be pursued (e.g., because it is too costly, because of locality issues, or due to privacy considerations). Motivated by this phenomenon, we consider the following natural combinatorial question: Given a bin-packing instance (namely, a set of items with sizes in (0, 1] that should be packed into unit capacity bins) I and a partition Ii \\ i of I into clusters, how large is the ratio ∑i Øpt(Ii) / Øpt(I), where Øpt(J) denotes the optimal number of bins into which the items in J can be packed? In this paper, we investigate the supremum of this ratio over all instances I and partitions Ii \\ i, referred to as the bin-packing price of clustering (¶oC ). It is trivial to observe that if each cluster contains only one tiny item (and hence, Øpt(Ii) = 1), then the ¶oC is unbounded. On the other hand, a relatively straightforward argument shows that under the constraint that Øpt(Ii) ≥ 2, the ¶oC is 2. Our main challenge was to determine whether the ¶oC drops below 2 when Øpt(Ii) > 2. In addition, one may hope that łimk -> ∞ ¶oC(k) = 1, where ¶oC(k) denotes the ¶oC under the restriction to clusters Ii with Øpt(Ii) ≥ k. We resolve the former question affirmatively and the latter one negatively: Our main results are that ¶oC(k) łeq 1.951 for any k ≥ 3 and łimk -> ∞ ¶oC(k) = 1.691... Moreover, the former bound cannot be significantly improved as ¶oC(3) > 1.933. In addition to the immediate contribution of this combinatorial result to \"big data\" kind of applications, it turns out that it is useful also for an interesting online problem called bin-packing with delays. <|reference_end|>", "<|reference_start|> Online k-Way Matching with Delays and the H-Metric: In this paper, we study $k$-Way Min-cost Perfect Matching with Delays - the $k$-MPMD problem. This problem considers a metric space with $n$ nodes. Requests arrive at these nodes in an online fashion. The task is to match these requests into sets of exactly $k$, such that the space and time cost of all matched requests are minimized. The notion of the space cost requires a definition of an underlying metric space that gives distances of subsets of $k$ elements. For $k>2$, the task of finding a suitable metric space is at the core of our problem: We show that for some known generalizations to $k=3$ points, such as the $2$-metric and the $D$-metric, there exists no competitive randomized algorithm for the $3$-MPMD problem. The $G$-metrics are defined for 3 points and allows for a competitive algorithm for the $3$-MPMD problem. For $k>3$ points, there exist two generalizations of the $G$-metrics known as $n$- and $K$-metrics. We show that neither the $n$-metrics nor the $K$-metrics can be used for the $k$-MPMD problem. On the positive side, we introduce the $H$-metrics, the first metrics to allow for a solution of the $k$-MPMD problem for all $k$. In order to devise an online algorithm for the $k$-MPMD problem on the $H$-metrics, we embed the $H$-metric into trees with an $O(\\log n)$ distortion. Based on this embedding result, we extend the algorithm proposed by Azar et al. (2017) and achieve a competitive ratio of $O(\\log n)$ for the $k$-MPMD problem. <|reference_end|>" ]

[ 5, 15, 36, 41 ]

[ "<|reference_start|> An Introduction to Optimization on Smooth Manifolds: Optimization on Riemannian manifolds-the result of smooth geometry and optimization merging into one elegant modern framework-spans many areas of science and engineering, including machine learning, computer vision, signal processing, dynamical systems and scientific computing. This text introduces the differential geometry and Riemannian geometry concepts that will help students and researchers in applied mathematics, computer science and engineering gain a firm mathematical grounding to use these tools confidently in their research. Its charts-last approach will prove more intuitive from an optimizer's viewpoint, and all definitions and theorems are motivated to build time-tested optimization algorithms. Starting from first principles, the text goes on to cover current research on topics including worst-case complexity and geodesic convexity. Readers will appreciate the tricks of the trade for conducting research and for numerical implementations sprinkled throughout the book. <|reference_end|>", "<|reference_start|> Optimization algorithms on matrix manifolds: Many problems in the sciences and engineering can be rephrased as optimization problems on matrix search spaces endowed with a so-called manifold structure. This book shows how to exploit the special structure of such problems to develop efficient numerical algorithms. It places careful emphasis on both the numerical formulation of the algorithm and its differential geometric abstraction--illustrating how good algorithms draw equally from the insights of differential geometry, optimization, and numerical analysis. Two more theoretical chapters provide readers with the background in differential geometry necessary to algorithmic development. In the other chapters, several well-known optimization methods such as steepest descent and conjugate gradients are generalized to abstract manifolds. The book provides a generic development of each of these methods, building upon the material of the geometric chapters. It then guides readers through the calculations that turn these geometrically formulated methods into concrete numerical algorithms. The state-of-the-art algorithms given as examples are competitive with the best existing algorithms for a selection of eigenspace problems in numerical linear algebra. Optimization Algorithms on Matrix Manifolds offers techniques with broad applications in linear algebra, signal processing, data mining, computer vision, and statistical analysis. It can serve as a graduate-level textbook and will be of interest to applied mathematicians, engineers, and computer scientists. <|reference_end|>", "<|reference_start|> A Grassmann Manifold Handbook: Basic Geometry and Computational Aspects: The Grassmann manifold of linear subspaces is important for the mathematical modelling of a multitude of applications, ranging from problems in machine learning, computer vision and image processing to low-rank matrix optimization problems, dynamic low-rank decompositions and model reduction. With this mostly expository work, we aim to provide a collection of the essential facts and formulae on the geometry of the Grassmann manifold in a fashion that is fit for tackling the aforementioned problems with matrix-based algorithms. Moreover, we expose the Grassmann geometry both from the approach of representing subspaces with orthogonal projectors and when viewed as a quotient space of the orthogonal group, where subspaces are identified as equivalence classes of (orthogonal) bases. This bridges the associated research tracks and allows for an easy transition between these two approaches. Original contributions include a modified algorithm for computing the Riemannian logarithm map on the Grassmannian that is advantageous numerically but also allows for a more elementary, yet more complete description of the cut locus and the conjugate points. We also derive a formula for parallel transport along geodesics in the orthogonal projector perspective, formulae for the derivative of the exponential map, as well as a formula for Jacobi fields vanishing at one point. <|reference_end|>", "<|reference_start|> Improved Performance Guarantees for Orthogonal Group Synchronization via Generalized Power Method: Given the noisy pairwise measurements among a set of unknown group elements, how to recover them efficiently and robustly? This problem, known as group synchronization, has drawn tremendous attention in the scientific community. In this work, we focus on orthogonal group synchronization that has found many applications, including computer vision, robotics, and cryo-electron microscopy. One commonly used approach is the least squares estimation that requires solving a highly nonconvex optimization program. The past few years have witnessed considerable advances in tackling this challenging problem by convex relaxation and efficient first-order methods. However, one fundamental theoretical question remains to be answered: how does the recovery performance depend on the noise strength? To answer this question, we study a benchmark model: recovering orthogonal group elements from their pairwise measurements corrupted by Gaussian noise. We investigate the performance of convex relaxation and the generalized power method (GPM). By applying the novel~\\emph{leave-one-out} technique, we prove that the GPM with spectral initialization enjoys linear convergence to the global optima to the convex relaxation that also matches the maximum likelihood estimator. Our result achieves a near-optimal performance bound on the convergence of the GPM and improves the state-of-the-art theoretical guarantees on the tightness of convex relaxation by a large margin. <|reference_end|>" ]

[ 47, 49, 63, 76 ]

[ "<|reference_start|> Generative Adversarial Imitation Learning: Consider learning a policy from example expert behavior, without interaction with the expert or access to reinforcement signal. One approach is to recover the expert's cost function with inverse reinforcement learning, then extract a policy from that cost function with reinforcement learning. This approach is indirect and can be slow. We propose a new general framework for directly extracting a policy from data, as if it were obtained by reinforcement learning following inverse reinforcement learning. We show that a certain instantiation of our framework draws an analogy between imitation learning and generative adversarial networks, from which we derive a model-free imitation learning algorithm that obtains significant performance gains over existing model-free methods in imitating complex behaviors in large, high-dimensional environments. <|reference_end|>", "<|reference_start|> Improved Robustness and Safety for Autonomous Vehicle Control with Adversarial Reinforcement Learning: To improve efficiency and reduce failures in autonomous vehicles, research has focused on developing robust and safe learning methods that take into account disturbances in the environment. Existing literature in robust reinforcement learning poses the learning problem as a two player game between the autonomous system and disturbances. This paper examines two different algorithms to solve the game, Robust Adversarial Reinforcement Learning and Neural Fictitious Self Play, and compares performance on an autonomous driving scenario. We extend the game formulation to a semi-competitive setting and demonstrate that the resulting adversary better captures meaningful disturbances that lead to better overall performance. The resulting robust policy exhibits improved driving efficiency while effectively reducing collision rates compared to baseline control policies produced by traditional reinforcement learning methods. <|reference_end|>", "<|reference_start|> Toward the Fundamental Limits of Imitation Learning: Imitation learning (IL) aims to mimic the behavior of an expert policy in a sequential decision-making problem given only demonstrations. In this paper, we focus on understanding the minimax statistical limits of IL in episodic Markov Decision Processes (MDPs). We first consider the setting where the learner is provided a dataset of $N$ expert trajectories ahead of time, and cannot interact with the MDP. Here, we show that the policy which mimics the expert whenever possible is in expectation $\\lesssim \\frac{|\\mathcal{S}| H^2 \\log (N)}{N}$ suboptimal compared to the value of the expert, even when the expert follows an arbitrary stochastic policy. Here $\\mathcal{S}$ is the state space, and $H$ is the length of the episode. Furthermore, we establish a suboptimality lower bound of $\\gtrsim |\\mathcal{S}| H^2 / N$ which applies even if the expert is constrained to be deterministic, or if the learner is allowed to actively query the expert at visited states while interacting with the MDP for $N$ episodes. To our knowledge, this is the first algorithm with suboptimality having no dependence on the number of actions, under no additional assumptions. We then propose a novel algorithm based on minimum-distance functionals in the setting where the transition model is given and the expert is deterministic. The algorithm is suboptimal by $\\lesssim \\min \\{ H \\sqrt{|\\mathcal{S}| / N} ,\\ |\\mathcal{S}| H^{3/2} / N \\}$, showing that knowledge of transition improves the minimax rate by at least a $\\sqrt{H}$ factor. <|reference_end|>", "<|reference_start|> {Disagreement-regularized Imitation Learning: We present a simple and effective algorithm designed to address the covariate shift problem in imitation learning. It operates by training an ensemble of policies on the expert demonstration data, and using the variance of their predictions as a cost which is minimized with RL together with a supervised behavioral cloning cost. Unlike adversarial imitation methods, it uses a fixed reward function which is easy to optimize. We prove a regret bound for the algorithm in the tabular setting which is linear in the time horizon multiplied by a coefficient which we show to be low for certain problems in which behavioral cloning fails. We evaluate our algorithm empirically across multiple pixel-based Atari environments and continuous control tasks, and show that it matches or significantly outperforms behavioral cloning and generative adversarial imitation learning. <|reference_end|>" ]

[ 5, 14, 18, 20 ]

[ "<|reference_start|> Massively parallel cosmological simulations with ChaNGa: Cosmological simulators are an important component in the study of the formation of galaxies and large scale structures, and can help answer many important questions about the Universe. Despite their utility, existing parallel simulators do not scale effectively on modern machines containing thousands of processors. In this paper we present ChaNGa, a production simulator based on the CHARM++ infrastructure. To achieve scalable performance, ChaNGa employs various optimizations that maximize the overlap between computation and communication. We present experimental results of ChaNGa simulations on machines with thousands of processors, including the IBM Blue Gene/L and the Cray XT3. The paper goes on to highlight efforts toward even more efficient and scalable cosmological simulations. In particular, novel load balancing schemes that base decisions on certain characteristics of tree-based particle codes are discussed. Further, the multistepping capabilities of ChaNGa are presented, as are solutions to the load imbalance that such multiphase simulations face. We outline key requirements for an effective practical implementation and conclude by discussing preliminary results from simulations run with our multiphase load balancer. <|reference_end|>", "<|reference_start|> Parallel Dual Tree Traversal on Multi-core and Many-core Architectures for Astrophysical N-body Simulations: <|reference_end|>", "<|reference_start|> Improving the scalability of parallel N-body applications with an event-driven constraint-based execution model: The scalability and efficiency of graph applications are significantly constrained by conventional systems and their supporting programming models. Technology trends such as multicore, manycore, and heterogeneous system architectures are introducing further challenges and possibilities for emerging application domains such as graph applications. This paper explores the parallel execution of graphs that are generated using the Barnes–Hut algorithm to exemplify dynamic workloads. The workloads are expressed using the semantics of an exascale computing execution model called ParalleX. For comparison, results using conventional execution model semantics are also presented. We find improved load balancing during runtime and automatic parallelism discovery by using the advanced semantics for exascale computing. <|reference_end|>", "<|reference_start|> A massively parallel adaptive fast multipole method on heterogeneous architectures: We describe a parallel fast multipole method (FMM) for highly nonuniform distributions of particles. We employ both distributed memory parallelism (via MPI) and shared memory parallelism (via OpenMP and GPU acceleration) to rapidly evaluate two-body nonoscillatory potentials in three dimensions on heterogeneous high performance computing architectures. We have performed scalability tests with up to 30 billion particles on 196,608 cores on the AMD/CRAY-based Jaguar system at ORNL. On a GPU-enabled system (NSF's Keeneland at Georgia Tech/ORNL), we observed 30× speedup over a single core CPU and 7× speedup over a multicore CPU implementation. By combining GPUs with MPI, we achieve less than 10 ns/particle and six digits of accuracy for a run with 48 million nonuniformly distributed particles on 192 GPUs. <|reference_end|>" ]

[ 2, 4, 5, 9 ]

[ "<|reference_start|> Persuading voters: In a symmetric information voting model, an individual (politician) can influence voters’ choices by strategically designing a policy experiment (public signal). We characterize the politician’s optimal experiment. With a non-unanimous voting rule, she exploits voters’ heterogeneity by designing an experiment with realizations targeting different winning coalitions. Consequently, under a simple-majority rule, a majority of voters might be strictly worse off due to the politician’s influence. We characterize voters’ preferences over electoral rules and provide conditions for a majority of voters to prefer a supermajority (or unanimity) voting rule, in order to induce the politician to supply a more informative experiment. <|reference_end|>", "<|reference_start|> Bayesian Persuasion in Sequential Decision-Making: We study a dynamic model of Bayesian persuasion in sequential decision-making settings. An informed principal observes an external parameter of the world and advises an uninformed agent about actions to take over time. The agent takes actions in each time step based on the current state, the principal's advice/signal, and beliefs about the external parameter. The action of the agent updates the state according to a stochastic process. The model arises naturally in many applications, e.g., an app (the principal) can advice the user (the agent) on possible choices between actions based on additional real-time information the app has. We study the problem of designing a signaling strategy from the principal's point of view. We show that the principal has an optimal strategy against a myopic agent, who only optimizes their rewards locally, and the optimal strategy can be computed in polynomial time. In contrast, it is NP-hard to approximate an optimal policy against a far-sighted agent. Further, if the principal has the power to threaten the agent by not providing future signals, then we can efficiently compute a threat-based strategy. This strategy guarantees the principal's payoff as if playing against an agent who is far-sighted but myopic to future signals. <|reference_end|>", "<|reference_start|> Safe Learning in Tree-Form Sequential Decision Making: Handling Hard\nand Soft Constraints: We study decision making problems in which an agent sequentially interacts with a stochastic environment defined by means of a tree structure . The agent repeatedly faces the environment over time, and, after each round, it perceives a utility and a cost , which are both stochastic. The goal of the agent is to learn an optimal strategy in an online fashion, while keeping costs below a given safety threshold at the same time. Our model naturally fits many real-world scenarios, such as, e.g. , opponent exploitation in games and web link selection. We study the hard-threshold problem of achieving sublinear regret while guaranteeing that the threshold constraint is satisfied at every iteration with high probability. First, we show that, in general, any algorithm with such a guarantee incurs in a linear regret. This motivates the introduction of a relaxed problem, called the soft-threshold problem, in which we only require that the cumulative violation of the threshold constraint grows sublin-early, and, thus, we can provide an algorithm with sublinear regret. Next, in the hard-threshold problem, we show how a sublinear regret algorithm can be designed under the additional assumption that there exists a known strategy strictly satisfying the threshold constraint. We also show that our regret bounds are tight. Finally, we cast the opponent exploitation problem to our model, and we experimentally evaluate our algorithms on a standard testbed of sequential games. <|reference_end|>", "<|reference_start|> Multi-Receiver Online Bayesian Persuasion: Bayesian persuasion studies how an informed sender should partially disclose information to influence the behavior of a self-interested receiver. Classical models make the stringent assumption that the sender knows the receiver's utility. This can be relaxed by considering an online learning framework in which the sender repeatedly faces a receiver of an unknown, adversarially selected type. We study, for the first time, an online Bayesian persuasion setting with multiple receivers. We focus on the case with no externalities and binary actions, as customary in offline models. Our goal is to design no-regret algorithms for the sender with polynomial per-iteration running time. First, we prove a negative result: for any $0 < \\alpha \\leq 1$, there is no polynomial-time no-$\\alpha$-regret algorithm when the sender's utility function is supermodular or anonymous. Then, we focus on the case of submodular sender's utility functions and we show that, in this case, it is possible to design a polynomial-time no-$(1 - \\frac{1}{e})$-regret algorithm. To do so, we introduce a general online gradient descent scheme to handle online learning problems with a finite number of possible loss functions. This requires the existence of an approximate projection oracle. We show that, in our setting, there exists one such projection oracle which can be implemented in polynomial time. <|reference_end|>" ]

[ 2, 18, 22, 25 ]

[ "<|reference_start|> {Probabilistic Latent Semantic Indexing: Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a training corpus of text documents by a generalization of the Expectation Maximization algorithm, the utilized model is able to deal with domain{specific synonymy as well as with polysemous words. In contrast to standard Latent Semantic Indexing (LSI) by Singular Value Decomposition, the probabilistic variant has a solid statistical foundation and defines a proper generative data model. Retrieval experiments on a number of test collections indicate substantial performance gains over direct term matching methods as well as over LSI. In particular, the combination of models with different dimensionalities has proven to be advantageous. <|reference_end|>", "<|reference_start|> Combining concept hierarchies and statistical topic models: Statistical topic models provide a general data-driven framework for automated discovery of high-level knowledge from large collections of text documents. While topic models can potentially discover a broad range of themes in a data set, the interpretability of the learned topics is not always ideal. Human-defined concepts, on the other hand, tend to be semantically richer due to careful selection of words to define concepts but they tend not to cover the themes in a data set exhaustively. In this paper, we propose a probabilistic framework to combine a hierarchy of human-defined semantic concepts with statistical topic models to seek the best of both worlds. Experimental results using two different sources of concept hierarchies and two collections of text documents indicate that this combination leads to systematic improvements in the quality of the associated language models as well as enabling new techniques for inferring and visualizing the semantics of a document. <|reference_end|>", "<|reference_start|> Gaussian LDA for Topic Models with Word Embeddings: Continuous space word embeddings learned from large, unstructured corpora have been shown to be effective at capturing semantic regularities in language. In this paper we replace LDA’s parameterization of “topics” as categorical distributions over opaque word types with multivariate Gaussian distributions on the embedding space. This encourages the model to group words that are a priori known to be semantically related into topics. To perform inference, we introduce a fast collapsed Gibbs sampling algorithm based on Cholesky decompositions of covariance matrices of the posterior predictive distributions. We further derive a scalable algorithm that draws samples from stale posterior predictive distributions and corrects them with a Metropolis–Hastings step. Using vectors learned from a domain-general corpus (English Wikipedia), we report results on two document collections (20-newsgroups and NIPS). Qualitatively, Gaussian LDA infers different (but still very sensible) topics relative to standard LDA. Quantitatively, our technique outperforms existing models at dealing with OOV words in held-out documents. <|reference_end|>", "<|reference_start|> {Hierarchical attention networks for document classification: We propose a hierarchical attention network for document classification. Our model has two distinctive characteristics: (i) it has a hierarchical structure that mirrors the hierarchical structure of documents; (ii) it has two levels of attention mechanisms applied at the wordand sentence-level, enabling it to attend differentially to more and less important content when constructing the document representation. Experiments conducted on six large scale text classification tasks demonstrate that the proposed architecture outperform previous methods by a substantial margin. Visualization of the attention layers illustrates that the model selects qualitatively informative words and sentences. <|reference_end|>" ]

[ 12, 17, 30, 38 ]

[ "<|reference_start|> Planning for tomorrow: global cancer incidence and the role of prevention 2020–2070: <|reference_end|>", "<|reference_start|> Aligning artificial intelligence with climate change mitigation: <|reference_end|>", "<|reference_start|> Multitask Learning: <|reference_end|>", "<|reference_start|> Semi-Parametric Neural Image Synthesis: Novel architectures have recently improved generative image synthesis leading to excellent visual quality in various tasks. Much of this success is due to the scalability of these architectures and hence caused by a dramatic increase in model complexity and in the computational resources invested in training these models. Our work questions the underlying paradigm of compressing large training data into ever growing parametric representations. We rather present an orthogonal, semi-parametric approach. We complement comparably small diffusion or autoregressive models with a separate image database and a retrieval strategy. During training we retrieve a set of nearest neighbors from this external database for each training instance and condition the generative model on these informative samples. While the retrieval approach is providing the (local) content, the model is focusing on learning the composition of scenes based on this content. As demonstrated by our experiments, simply swapping the database for one with different contents transfers a trained model post-hoc to a novel domain. The evaluation shows competitive performance on tasks which the generative model has not been trained on, such as class-conditional synthesis, zero-shot stylization or text-to-image synthesis without requiring paired text-image data. With negligible memory and computational overhead for the external database and retrieval we can significantly reduce the parameter count of the generative model and still outperform the state-of-the-art. <|reference_end|>" ]

[ 3, 12, 16, 34 ]

[ "<|reference_start|> Neural Auto-Curricula in Two-Player Zero-Sum Games: <|reference_end|>", "<|reference_start|> A general reinforcement learning algorithm that\nmasters chess, shogi, and Go through self-play: One program to rule them all Computers can beat humans at increasingly complex games, including chess and Go. However, these programs are typically constructed for a particular game, exploiting its properties, such as the symmetries of the board on which it is played. Silver et al. developed a program called AlphaZero, which taught itself to play Go, chess, and shogi (a Japanese version of chess) (see the Editorial, and the Perspective by Campbell). AlphaZero managed to beat state-of-the-art programs specializing in these three games. The ability of AlphaZero to adapt to various game rules is a notable step toward achieving a general game-playing system. Science, this issue p. 1140; see also pp. 1087 and 1118 AlphaZero teaches itself to play three different board games and beats state-of-the-art programs in each. The game of chess is the longest-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. By contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go by reinforcement learning from self-play. In this paper, we generalize this approach into a single AlphaZero algorithm that can achieve superhuman performance in many challenging games. Starting from random play and given no domain knowledge except the game rules, AlphaZero convincingly defeated a world champion program in the games of chess and shogi (Japanese chess), as well as Go. <|reference_end|>", "<|reference_start|> Emergent Tool Use From Multi-Agent Autocurricula: Through multi-agent competition, the simple objective of hide-and-seek, and standard reinforcement learning algorithms at scale, we find that agents create a self-supervised autocurriculum inducing multiple distinct rounds of emergent strategy, many of which require sophisticated tool use and coordination. We find clear evidence of six emergent phases in agent strategy in our environment, each of which creates a new pressure for the opposing team to adapt; for instance, agents learn to build multi-object shelters using moveable boxes which in turn leads to agents discovering that they can overcome obstacles using ramps. We further provide evidence that multi-agent competition may scale better with increasing environment complexity and leads to behavior that centers around far more human-relevant skills than other self-supervised reinforcement learning methods such as intrinsic motivation. Finally, we propose transfer and fine-tuning as a way to quantitatively evaluate targeted capabilities, and we compare hide-and-seek agents to both intrinsic motivation and random initialization baselines in a suite of domain-specific intelligence tests. <|reference_end|>", "<|reference_start|> {Human-level performance in 3D multiplayer games with population-based reinforcement learning: Artificial teamwork Artificially intelligent agents are getting better and better at two-player games, but most real-world endeavors require teamwork. Jaderberg et al. designed a computer program that excels at playing the video game Quake III Arena in Capture the Flag mode, where two multiplayer teams compete in capturing the flags of the opposing team. The agents were trained by playing thousands of games, gradually learning successful strategies not unlike those favored by their human counterparts. Computer agents competed successfully against humans even when their reaction times were slowed to match those of humans. Science, this issue p. 859 Teams of artificial agents compete successfully against humans in the video game Quake III Arena in Capture the Flag mode. Reinforcement learning (RL) has shown great success in increasingly complex single-agent environments and two-player turn-based games. However, the real world contains multiple agents, each learning and acting independently to cooperate and compete with other agents. We used a tournament-style evaluation to demonstrate that an agent can achieve human-level performance in a three-dimensional multiplayer first-person video game, Quake III Arena in Capture the Flag mode, using only pixels and game points scored as input. We used a two-tier optimization process in which a population of independent RL agents are trained concurrently from thousands of parallel matches on randomly generated environments. Each agent learns its own internal reward signal and rich representation of the world. These results indicate the great potential of multiagent reinforcement learning for artificial intelligence research. <|reference_end|>" ]

[ 13, 16, 45, 46 ]

[ "<|reference_start|> Beyond Accuracy: Assessing Software Documentation Quality: Good software documentation encourages good software engineering, but the meaning of \"good\" documentation is vaguely defined in the software engineering literature. To clarify this ambiguity, we draw on work from the data and information quality community to propose a framework that decomposes documentation quality into ten dimensions of structure, content, and style. To demonstrate its application, we recruited technical editors to apply the framework when evaluating examples from several genres of software documentation. We summarise their assessments -- for example, reference documentation and README files excel in quality whereas blog articles have more problems -- and we describe our vision for reasoning about software documentation quality and for the expansion and potential of a unified quality framework. <|reference_end|>", "<|reference_start|> Software documentation: The practitioners' perspective: In theory, (good) documentation is an invaluable asset to any software project, as it helps stakeholders to use, understand, maintain, and evolve a system. In practice, however, documentation is generally affected by numerous shortcomings and issues, such as insufficient and inadequate content and obsolete, ambiguous information. To counter this, researchers are investigating the development of advanced recommender systems that automatically suggest high-quality documentation, useful for a given task. A crucial first step is to understand what quality means for practitioners and what information is actually needed for specific tasks. We present two surveys performed with 146 practitioners to investigate (i) the documentation issues they perceive as more relevant together with solutions they apply when these issues arise; and (ii) the types of documentation considered as important in different tasks. Our findings can help researchers in designing the next generation of documentation recommender systems. <|reference_end|>", "<|reference_start|> Extractive Summarization via ChatGPT for Faithful Summary Generation: Extractive summarization is a crucial task in natural language processing that aims to condense long documents into shorter versions by directly extracting sentences. The recent introduction of large language models has attracted significant interest in the NLP community due to its remarkable performance on a wide range of downstream tasks. This paper first presents a thorough evaluation of ChatGPT's performance on extractive summarization and compares it with traditional fine-tuning methods on various benchmark datasets. Our experimental analysis reveals that ChatGPT exhibits inferior extractive summarization performance in terms of ROUGE scores compared to existing supervised systems, while achieving higher performance based on LLM-based evaluation metrics. In addition, we explore the effectiveness of in-context learning and chain-of-thought reasoning for enhancing its performance. Furthermore, we find that applying an extract-then-generate pipeline with ChatGPT yields significant performance improvements over abstractive baselines in terms of summary faithfulness. These observations highlight potential directions for enhancing ChatGPT's capabilities in faithful summarization using two-stage approaches. <|reference_end|>", "<|reference_start|> On Faithfulness and Factuality in Abstractive Summarization: It is well known that the standard likelihood training and approximate decoding objectives in neural text generation models lead to less human-like responses for open-ended tasks such as language modeling and story generation. In this paper we have analyzed limitations of these models for abstractive document summarization and found that these models are highly prone to hallucinate content that is unfaithful to the input document. We conducted a large scale human evaluation of several neural abstractive summarization systems to better understand the types of hallucinations they produce. Our human annotators found substantial amounts of hallucinated content in all model generated summaries. However, our analysis does show that pretrained models are better summarizers not only in terms of raw metrics, i.e., ROUGE, but also in generating faithful and factual summaries as evaluated by humans. Furthermore, we show that textual entailment measures better correlate with faithfulness than standard metrics, potentially leading the way to automatic evaluation metrics as well as training and decoding criteria. <|reference_end|>" ]

[ 8, 24, 27, 32 ]

[ "<|reference_start|> Feature-based dynamic pricing: We consider the problem faced by a firm that receives highly differentiated products in an online fashion and needs to price them in order to sell them to its customer base. Products are described by vectors of features and the market value of each product is linear in the values of the features. The firm does not initially know the values of the different features, but it can learn the values of the features based on whether products were sold at the posted prices in the past. This model is motivated by a question in online advertising, where impressions arrive over time and can be described by vectors of features. We first consider a multi-dimensional version of binary search over polyhedral sets, and show that it has exponential worst-case regret. We then propose a modification of the prior algorithm where uncertainty sets are replaced by their Lowner-John ellipsoids. We show that this algorithm has a worst-case regret that is quadratic in the dimensionality of the feature space and logarithmic in the time horizon. <|reference_end|>", "<|reference_start|> Semi-parametric dynamic contextual pricing: Motivated by the application of real-time pricing in e-commerce platforms, we consider the problem of revenue-maximization in a setting where the seller can leverage contextual information describing the customer's history and the product's type to predict her valuation of the product. However, her true valuation is unobservable to the seller, only binary outcome in the form of success-failure of a transaction is observed. Unlike in usual contextual bandit settings, the optimal price/arm given a covariate in our setting is sensitive to the detailed characteristics of the residual uncertainty distribution. We develop a semi-parametric model in which the residual distribution is non-parametric and provide the first algorithm which learns both regression parameters and residual distribution with $\\tilde O(\\sqrt{n})$ regret. We empirically test a scalable implementation of our algorithm and observe good performance. <|reference_end|>", "<|reference_start|> Tracking the best linear predictor: In most on-line learning research the total on-line loss of the algorithm is compared to the total loss of the best o¬-line predictor u from a comparison class of predictors. We call such bounds static bounds. The interesting feature of these bounds is that they hold for an arbitrary sequence of examples. Recently some work has been done where the predictor ut at each trial t is allowed to change with time, and the total on-line loss of the algorithm is compared to the sum of the losses of ut at each trial plus the total \\cost\" for shifting to successive predictors. This is to model situations in which the examples change over time, and di¬erent predictors from the comparison class are best for di¬erent segments of the sequence of examples. We call such bounds shifting bounds. They hold for arbitrary sequences of examples and arbitrary sequences of predictors. Naturally shifting bounds are much harder to prove. The only known bounds are for the case when the comparison class consists of a sequences of experts or boolean disjunctions. In this paper we develop the methodology for lifting known static bounds to the shifting case. In particular we obtain bounds when the comparison class consists of linear neurons (linear combinations of experts). Our essential technique is to project the hypothesis of the static algorithm at the end of each trial into a suitably chosen convex region. This keeps the hypothesis of the algorithm well-behaved and the static bounds can be converted to shifting bounds. <|reference_end|>", "<|reference_start|> Optimal dynamic mechanism design and the virtual-pivot mechanism: We consider the problem of designing optimal mechanisms for settings where agents have dynamic private information. We present the virtual-pivot mechanism, which is optimal in a large class of environments that satisfy a separability condition. The mechanism satisfies a rather strong equilibrium notion (it is periodic ex post incentive compatible and individually rational). We provide both necessary and sufficient conditions for immediate incentive compatibility for mechanisms that satisfy periodic ex post incentive compatibility in future periods. The result also yields a strikingly simple mechanism for selling a sequence of items to a single buyer. We also show that the allocation rule of the virtual-pivot mechanism has a very simple structure (a virtual index) in multiarmed bandit settings. Finally, we show through examples that the relaxation technique we use does not produce optimal dynamic mechanisms in general nonseparable environments. <|reference_end|>" ]

[ 4, 9, 14, 20 ]

[ "<|reference_start|> Learn to chill: Intelligent chiller scheduling using meta-learning and deep reinforcement learning: Centralized chiller plants with multiple chillers are typically over-provisioned. Therefore, intelligent scheduling is required for the supply (operating chillers) to efficiently meet the demand (actual cooling load of buildings). Traditional cooling-load based control (CLC) may result in poor part-loaded efficiency. Recent data-driven approaches to chiller control either unrealistically assume perfect knowledge of individual chiller power at various leaving chilled water temperatures (LWTs) or control all chillers with same LWT. We complement existing work with iChill, an end-to-end learning-based intelligent chiller power prediction and scheduling strategy. First, given a dataset of chillers of varying capacities, each of which operates at a fixed LWT and varying loads, iChill meta-learns a model for power prediction. Specifically, for an unseen target chiller, the meta-learned model is re-trained with known LWT to predict power at unseen LWT. Second, given the configuration of a chiller plant and a cooling load profile, iChill learns to schedule individual chillers by jointly deciding the ON/OFF status and LWT; using deep reinforcement learning (DRL). We train and evaluate iChill in a simulated environment with real-world data from a chiller plant of 22 chillers. Specifically, we compare iChill's (1) meta-learned power model with regular transfer learning; and (2) DRL scheduling with multiple baselines including CLC and an oracle model-based predictive control (MPC) strategy with perfect knowledge. We find that iChill's (1) meta-learning improves over transfer learning by up to 15.5%; and (2) DRL scheduling saves 11.5% energy over CLC and is comparable with oracle MPC (12% over CLC). Finally, off-line pre-training of iChill's DRL on the meta-learned chiller models reduces the need for real-world training experimentation by 11x from 3 years to 96 days. <|reference_end|>", "<|reference_start|> Model-free optimal chiller loading method based on q-learning: Chillers consume considerable energy in building HVAC systems, and the optimal operation of chillers is essential for energy conservation in buildings. This article proposes a model-free optimal chiller loading (OCL) method for optimizing chiller operation. Unlike model-based OCL methods, the proposed method does not require accurate chiller performance models as a priori knowledge. The proposed method is based on the Q-learning method, a classical reinforcement learning method. With the comprehensive coefficient of performance (COP) of chillers as the environmental feedback, the model-free loading controller can learn autonomously and optimize the chiller loading by adjusting the set points of the chilled water outlet temperature. A central chiller plant in an office building located in Shanghai is selected as a case system to investigate the energy conservation performance of the proposed method through simulations. The simulation results suggest that the proposed method can save 4.36% of chiller energy during the first cooling season compared to the baseline control, which is slightly inferior to the value for the model-based loading method (4.95%). Owing to its acceptable energy-saving capability, the proposed method can be applied to central chiller plants that lack a system model and historical data. <|reference_end|>", "<|reference_start|> Learn to chill: Intelligent chiller scheduling using meta-learning and deep reinforcement learning: Centralized chiller plants with multiple chillers are typically over-provisioned. Therefore, intelligent scheduling is required for the supply (operating chillers) to efficiently meet the demand (actual cooling load of buildings). Traditional cooling-load based control (CLC) may result in poor part-loaded efficiency. Recent data-driven approaches to chiller control either unrealistically assume perfect knowledge of individual chiller power at various leaving chilled water temperatures (LWTs) or control all chillers with same LWT. We complement existing work with iChill, an end-to-end learning-based intelligent chiller power prediction and scheduling strategy. First, given a dataset of chillers of varying capacities, each of which operates at a fixed LWT and varying loads, iChill meta-learns a model for power prediction. Specifically, for an unseen target chiller, the meta-learned model is re-trained with known LWT to predict power at unseen LWT. Second, given the configuration of a chiller plant and a cooling load profile, iChill learns to schedule individual chillers by jointly deciding the ON/OFF status and LWT; using deep reinforcement learning (DRL). We train and evaluate iChill in a simulated environment with real-world data from a chiller plant of 22 chillers. Specifically, we compare iChill's (1) meta-learned power model with regular transfer learning; and (2) DRL scheduling with multiple baselines including CLC and an oracle model-based predictive control (MPC) strategy with perfect knowledge. We find that iChill's (1) meta-learning improves over transfer learning by up to 15.5%; and (2) DRL scheduling saves 11.5% energy over CLC and is comparable with oracle MPC (12% over CLC). Finally, off-line pre-training of iChill's DRL on the meta-learned chiller models reduces the need for real-world training experimentation by 11x from 3 years to 96 days. <|reference_end|>", "<|reference_start|> Soft Actor-Critic Deep Reinforcement Learning with Hybrid Mixed-Integer Actions for Demand Responsive Scheduling of Energy Systems: <|reference_end|>" ]

[ 50, 51, 55, 58 ]

[ "<|reference_start|> lofi hip hop radio - beats to relax/study to: “lofi hip-hop radio: beats to relax/study to” by Justin Wang is both a synopsis for and an analysis of a subgenre of hiphop which has only recently risen to popularity. In his analysis, Wang discusses some of lo-fi music's greatest influences, mainly anime and jazz. Using an analysis of lofi channel titles, comments, and graphics on YouTube, Wang theorizes the connections between the recent and rapid ascent of the subgenre, young Americans’ mental health, and recent events in the public sphere, including the election of President Donald Trump. <|reference_end|>", "<|reference_start|> Personalizing Ambience and Illusionary Presence: How People Use “Study with me” Videos to Create Effective Studying Environments: “Study with me” videos contain footage of people studying for hours, in which social components like conversations or informational content like instructions are absent. Recently, they became increasingly popular on video-sharing platforms. This paper provides the first broad look into what “study with me” videos are and how people use them. We analyzed 30 “study with me” videos and conducted 12 interviews with their viewers to understand their motivation and viewing practices. We identified a three-factor model that explains the mechanism for shaping a satisfactory studying experience in general. One of the factors, a well-suited ambience, was difficult to achieve because of two common challenges: external conditions that prevent studying in study-friendly places and extra cost needed to create a personally desired ambience. We found that the viewers used “study with me” videos to create a personalized ambience at a lower cost, to find controllable peer pressure, and to get emotional support. These findings suggest that the viewers self-regulate their learning through watching “study with me” videos to improve efficiency even when studying alone at home. <|reference_end|>", "<|reference_start|> Sharing the Studio: How Creative Livestreaming Can Inspire, Educate, and Engage: Many artists livestream their creative process, allowing viewers to learn and be inspired from the decisions -- and mistakes -- they make along the way. This paper presents the first broad look at the range of creative activities people stream. Through content analysis of livestream archives, interviews with 8 streamers, and online surveys with 165 viewers, we study current practices and challenges in creative livestream communities and compare them with prior observations of livestreaming in other domains. We observed four common types of creative livestreams: teaching, making, socializing, and performing. We identify three open questions for the research community around how to better support the goals of creative streamers and viewers: how to support richer audience interactions at scale, how to support all parts of the creative process, and how to support watching livestream archives. <|reference_end|>", "<|reference_start|> \"I feel it is my responsibility to stream\": Streaming and Engaging with Intangible Cultural Heritage through Livestreaming: Globalization has led to the destruction of many cultural practices, expressions, and knowledge found within local communities. These practices, defined by UNESCO as Intangible Cultural Heritage (ICH), have been identified, promoted, and safeguarded by nations, academia, organizations and local communities to varying degrees. Despite such efforts, many practices are still in danger of being lost or forgotten forever. With the increased popularity of livestreaming in China, some streamers have begun to use livestreaming to showcase and promote ICH activities. To better understand the practices, opportunities, and challenges inherent in sharing and safeguarding ICH through livestreaming, we interviewed 10 streamers and 8 viewers from China. Through our qualitative investigation, we found that ICH streamers had altruistic motivations and engaged with viewers using multiple modalities beyond livestreams. We also found that livestreaming encouraged real-time interaction and sociality, while non-live curated videos attracted attention from a broader audience and assisted in the archiving of knowledge. <|reference_end|>" ]

[ 0, 1, 2, 3 ]

[ "<|reference_start|> Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction: We present dynamic neural radiance fields for modeling the appearance and dynamics of a human face. Digitally modeling and reconstructing a talking human is a key building-block for a variety of applications. Especially, for telepresence applications in AR or VR, a faithful reproduction of the appearance including novel viewpoints or head-poses is required. In contrast to state-of-the-art approaches that model the geometry and material properties explicitly, or are purely image-based, we introduce an implicit representation of the head based on scene representation networks. To handle the dynamics of the face, we combine our scene representation network with a low-dimensional morphable model which provides explicit control over pose and expressions. We use volumetric rendering to generate images from this hybrid representation and demonstrate that such a dynamic neural scene representation can be learned from monocular input data only, without the need of a specialized capture setup. In our experiments, we show that this learned volumetric representation allows for photo-realistic image generation that surpasses the quality of state-of-the-art video-based reenactment methods. <|reference_end|>", "<|reference_start|> 3D Gaussian Splatting for Real-Time Radiance Field Rendering: Radiance Field methods have recently revolutionized novel-view synthesis of scenes captured with multiple photos or videos. However, achieving high visual quality still requires neural networks that are costly to train and render, while recent faster methods inevitably trade off speed for quality. For unbounded and complete scenes (rather than isolated objects) and 1080p resolution rendering, no current method can achieve real-time display rates. We introduce three key elements that allow us to achieve state-of-the-art visual quality while maintaining competitive training times and importantly allow high-quality real-time (>= 30 fps) novel-view synthesis at 1080p resolution. First, starting from sparse points produced during camera calibration, we represent the scene with 3D Gaussians that preserve desirable properties of continuous volumetric radiance fields for scene optimization while avoiding unnecessary computation in empty space; Second, we perform interleaved optimization/density control of the 3D Gaussians, notably optimizing anisotropic covariance to achieve an accurate representation of the scene; Third, we develop a fast visibility-aware rendering algorithm that supports anisotropic splatting and both accelerates training and allows realtime rendering. We demonstrate state-of-the-art visual quality and real-time rendering on several established datasets. <|reference_end|>", "<|reference_start|> {A morphable model for the synthesis of 3D faces: In this paper, a new technique for modeling textured 3D faces is introduced. 3D faces can either be generated automatically from one or more photographs, or modeled directly through an intuitive user interface. Users are assisted in two key problems of computer aided face modeling. First, new face images or new 3D face models can be registered automatically by computing dense one-to-one correspondence to an internal face model. Second, the approach regulates the naturalness of modeled faces avoiding faces with an ''unlikely'' appearance Starting from an example set of 3D face models, we derive a morphable face model by transforming the shape and texture of the examples into a vector space representation. New faces and expressions can be modeled by forming linear combinations of the prototypes. Shape and texture constraints derived from the statistics of our example faces are used to guide manual modeling or automated matching algorithms We show 3D face reconstructions from single images and their applications for photo-realistic image manipulations. We also demonstrate face manipulations according to complex parameters such as gender, fullness of a face or its distinctiveness. <|reference_end|>", "<|reference_start|> Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Video: We present Non-Rigid Neural Radiance Fields (NR-NeRF), a reconstruction and novel view synthesis approach for general non-rigid dynamic scenes. Our approach takes RGB images of a dynamic scene as input (e.g., from a monocular video recording), and creates a high-quality space-time geometry and appearance representation. We show that a single handheld consumer-grade camera is sufficient to synthesize sophisticated renderings of a dynamic scene from novel virtual camera views, e.g. a `bullet-time' video effect. NR-NeRF disentangles the dynamic scene into a canonical volume and its deformation. Scene deformation is implemented as ray bending, where straight rays are deformed non-rigidly. We also propose a novel rigidity network to better constrain rigid regions of the scene, leading to more stable results. The ray bending and rigidity network are trained without explicit supervision. Our formulation enables dense correspondence estimation across views and time, and compelling video editing applications such as motion exaggeration. Our code will be open sourced. <|reference_end|>" ]

[ 4, 16, 18, 50 ]

[ "<|reference_start|> Predicting the severity of a reported bug: The severity of a reported bug is a critical factor in deciding how soon it needs to be fixed. Unfortunately, while clear guidelines exist on how to assign the severity of a bug, it remains an inherent manual process left to the person reporting the bug. In this paper we investigate whether we can accurately predict the severity of a reported bug by analyzing its textual description using text mining algorithms. Based on three cases drawn from the open-source community (Mozilla, Eclipse and GNOME), we conclude that given a training set of sufficient size (approximately 500 reports per severity), it is possible to predict the severity with a reasonable accuracy (both precision and recall vary between 0.65–0.75 with Mozilla and Eclipse; 0.70–0.85 in the case of GNOME). <|reference_end|>", "<|reference_start|> Predicting Software Defect Severity Level using Sentence Embedding and Ensemble Learning: Bug tracking is one of the prominent activities during the maintenance phase of software development. The severity of the bug acts as a key indicator of its criticality and impact towards planning evolution and maintenance of various types of software products. This indicator measures how negatively the bug may affect the system functionality. This helps in determining how quickly the development teams need to address the bug for successful execution of the software system. Due to a large number of bugs reported every day, the developers find it really difficult to assign the severity level to bugs accurately. Assigning incorrect severity level results in delaying the bug resolution process. Thus automated systems were developed which will assign a severity level using various machine learning techniques. In this work, five different types of sentence embedding techniques have been applied on bugs description to convert the description comments to an n-dimensional vector. These computed vectors are used as an input of the software defect severity level prediction models and ensemble techniques like Bagging, Random Forest classifier, Extra Trees classifier, AdaBoost and Gradient Boosting have been used to train these models. We have also considered different variants of the Synthetic Minority Oversampling Technique (SMOTE) to handle the class imbalance problem as the considered datasets are not evenly distributed. The experimental results on six projects highlight that the usage of sentence embedding, ensemble techniques, and different variants of SMOTE techniques helps in improving the predictive ability of defect severity level prediction models. <|reference_end|>", "<|reference_start|> Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach: Fine-tuned pre-trained language models (LMs) have achieved enormous success in many natural language processing (NLP) tasks, but they still require excessive labeled data in the fine-tuning stage. We study the problem of fine-tuning pre-trained LMs using only weak supervision, without any labeled data. This problem is challenging because the high capacity of LMs makes them prone to overfitting the noisy labels generated by weak supervision. To address this problem, we develop a contrastive self-training framework, COSINE, to enable fine-tuning LMs with weak supervision. Underpinned by contrastive regularization and confidence-based reweighting, this contrastive self-training framework can gradually improve model fitting while effectively suppressing error propagation. Experiments on sequence, token, and sentence pair classification tasks show that our model outperforms the strongest baseline by large margins on 7 benchmarks in 6 tasks, and achieves competitive performance with fully-supervised fine-tuning methods. <|reference_end|>", "<|reference_start|> Evaluating defect prediction approaches: a benchmark and an extensive comparison: <|reference_end|>" ]

[ 7, 9, 15, 17 ]

[ "<|reference_start|> Marine Wireless Big Data: Efficient Transmission, Related Applications, and Challenges: The vast volume of marine wireless sampling data and its continuously explosive growth herald the coming of the era of marine wireless big data. Two challenges imposed by these data are how to fast, reliably, and sustainably deliver them in extremely hostile marine environments and how to apply them after collection. In this article, we first propose an architecture of heterogeneous marine networks that flexibly exploits the existing underwater wireless techniques as a potential solution for fast data transmission. We then investigate the possibilities of and develop the schemes for energy-efficient and reliable undersea transmission without or slightly with data rate reduction. After discussing the data transmission, we summarize the possible applications of the collected big data and particularly focus on the problems of applying these data in sea-surface object detection and marine object recognition. Open issues and challenges that need to be further explored regarding transmission and detection/recognition are also discussed in the article. <|reference_end|>", "<|reference_start|> Low observable targets detection by joint fractal properties of sea clutter: An experimental study of IPIX OHGR datasets: We exploit the joint fractal properties of sea clutter extracted from detrended fluctuation analysis (DFA) for targets detection. We find that two specific fractal statistics, i.e., the intercept at the crucial scale and the Hurst exponent of optimal scales provide valuable information for targets detection. The first statistic measures the discrepancy between sea clutter and low observable targets at the crucial fractal scale, and the second one evaluates the average fractal difference within the optimal multi-scales. A target detection method integrating these two statistics is proposed, which is validated by real-life IPIX radar datasets. We find that this joint fractal detection approach achieves more accurate results for low observable targets detection. <|reference_end|>", "<|reference_start|> Tri-feature-based detection of floating small targets in sea clutter: It is always a challenging problem for marine surface surveillance radar to detect sea-surface floating small targets. Conventional detectors using incoherent integration and adaptive clutter suppression have low detection probabilities for such targets with weak returns and unobservable Doppler shifts. In this paper, three features of a received vector at a resolution cell-the relative amplitude, relative Doppler peak height, and relative entropy of the Doppler amplitude spectrum-are exploited to give returns with targets from sea clutter. Real datasets show that each feature alone has some discriminability, and the three features jointly exhibit strong discriminability. Due to diversity of targets in practice, it is impossible to get features of returns with all kinds of targets. We recast detection of sea-surface floating small targets as a one-class anomaly detection problem in the 3D feature space. A fast convexhull learning algorithm is proposed to learn the decision region of the clutter pattern from feature vectors of clutter-only observations. As a result, a tri-feature-based detector is developed. The experiment results for the IPIX datasets show that the proposed detector at an observation time of several seconds attains better detection performance than several existing detectors. <|reference_end|>", "<|reference_start|> A modified CFAR algorithm based on object proposals for ship target detection in SAR images: Target detection for synthetic aperture radar (SAR) images has great influence on the successive discrimination based on the target regions. However, as a pixel-based method, the traditional constant false alarm rate (CFAR) detection could not work well for the ship target detection problem of multiple ship targets with different sizes in a SAR image, which is referred to as the multiscale situation. Moreover, it needs to use the clustering method on the pixel-level detection results to obtain the accurate target regions, which may merge two or more different targets into a target region. In this letter, a modified CFAR based on object proposals is proposed. We use the object proposal generator to generate a small set of object proposals with different sizes, and then use the proposal-based CFAR detector, where the extracted object proposals are regarded as the guard windows instead of setting fixed guard window, to detect the true positive object proposals. By introducing the object proposals as the variable guard windows in the CFAR detector, the proposed algorithm could gain good detection performance in the multiscale situation, since the missed detection resulting from the big differences between the sizes of the fixed guard window and ship targets can be avoided. Meanwhile, the proposed method can directly obtain the accurate target regions. The effectiveness of the proposed algorithm is verified using the measured SAR data. <|reference_end|>" ]

[ 0, 5, 6, 11 ]