pwc-archive/papers-with-abstracts huggingface dataset:
Predictive PER: Balancing Priority and Diversity towards Stable Deep Reinforcement Learning
Authors: Sanghwa Lee, Jaeyoung Lee, Ichiro Hasuo
Date: 2020-11-26 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, Diversity, reinforcement-learning, Reinforcement Learning (RL)

Prioritized experience replay (PER) samples important transitions, rather than uniformly, to improve the performance of a deep reinforcement learning agent. We claim that such prioritization has to be balanced with sample diversity for making the DQN stabilized and preventing forgetting. Our proposed improvement over PER, called Predictive PER (PPER), takes three countermeasures (TDInit, TDClip, TDPred) to (i) eliminate priority outliers and explosions and (ii) improve the sample diversity and distributions, weighted by priorities, both leading to stabilizing the DQN. The most notable among the three is the introduction of the second DNN called TDPred to generalize the in-distribution priorities. Ablation study and full experiments with Atari games show that each countermeasure by its own way and PPER contribute to successfully enhancing stability and thus performance over PER.
Patterns of retractions from 1981-2020 : Does a fraud lead to another fraud?
Authors: Kiran Sharma
Date: 2020-11-26 00:00:00

Misconduct accounts for the majority of retracted scientific publications and this database reveals the disturbing trend in science~\citep{fang2012misconduct, brainard2018massive}. The objective of the study is to find the association among the authors' collaboration, the number of retracted papers, the number of retracted citations, journal impact factor, and research areas. We present a detailed analysis of 12231 research papers indexed by Web of Science (WoS) as retracted publications from 1981-2020. The study demonstrates the collaboration patterns of retracted publications where 61.5% of authors have only one and 24.6% have two retracted papers; however, 2% of authors have more than 10 retracted papers. To study the impact of citing retracted papers, we investigated the retracted papers with citations. The study reveals that 55.2% of retracted papers have been cited at least once, where 25.4% of papers are such papers where at least one citation turned out to be a retraction. This shows the impact of scientific misconduct or fraud on new research. The number of retractions is independent of the journal impact factor and as compared to high impact papers, low impact papers are attracting more citations. We also investigate the citations received by retracted papers published in higher as well as lower impact factor journals. 1/4th of the papers are retracted citations that cited the retracted papers; however, there is no significant relationship exists between the higher impact or lower impact journals with retractions or citations. Finally, how the average team size and average retracted citations vary among different research areas are studied. The study provides an insight that how a fraud leads to another fraud in the scientific world. Also, the rising trend of citations of retracted papers is a serious concern.
The Evolution of Concept-Acquisition based on Developmental Psychology
Authors: Hui Wei
Date: 2020-11-26 00:00:00

A conceptual system with rich connotation is key to improving the performance of knowledge-based artificial intelligence systems. While a conceptual system, which has abundant concepts and rich semantic relationships, and is developable, evolvable, and adaptable to multi-task environments, its actual construction is not only one of the major challenges of knowledge engineering, but also the fundamental goal of research on knowledge and conceptualization. Finding a new method to represent concepts and construct a conceptual system will therefore greatly improve the performance of many intelligent systems. Fortunately the core of human cognition is a system with relatively complete concepts and a mechanism that ensures the establishment and development of the system. The human conceptual system can not be achieved immediately, but rather must develop gradually. Developmental psychology carefully observes the process of concept acquisition in humans at the behavioral level, and along with cognitive psychology has proposed some rough explanations of those observations. However, due to the lack of research in aspects such as representation, systematic models, algorithm details and realization, many of the results of developmental psychology have not been applied directly to the building of artificial conceptual systems. For example, Karmiloff-Smith's Representation Redescription (RR) supposition reflects a concept-acquisition process that re-describes a lower level representation of a concept to a higher one. This paper is inspired by this developmental psychology viewpoint. We use an object-oriented approach to re-explain and materialize RR supposition from the formal semantic perspective, because the OO paradigm is a natural way to describe the outside world, and it also has strict grammar regulations.
Text Analytics for Resilience-Enabled Extreme Events Reconnaissance
Authors: Alicia Y. Tsai, Selim Gunay, Minjune Hwang, Pengyuan Zhai, Chenglong Li, Laurent El Ghaoui, Khalid M. Mosalam
Date: 2020-11-26 00:00:00

Post-hazard reconnaissance for natural disasters (e.g., earthquakes) is important for understanding the performance of the built environment, speeding up the recovery, enhancing resilience and making informed decisions related to current and future hazards. Natural language processing (NLP) is used in this study for the purposes of increasing the accuracy and efficiency of natural hazard reconnaissance through automation. The study particularly focuses on (1) automated data (news and social media) collection hosted by the Pacific Earthquake Engineering Research (PEER) Center server, (2) automatic generation of reconnaissance reports, and (3) use of social media to extract post-hazard information such as the recovery time. Obtained results are encouraging for further development and wider usage of various NLP methods in natural hazard reconnaissance.
Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach
Authors: Minji Yoon, Bryan Hooi, Kijung Shin, Christos Faloutsos
Date: 2020-11-26 00:00:00
Tasks: Anomaly Detection

Given a dynamic graph stream, how can we detect the sudden appearance of anomalous patterns, such as link spam, follower boosting, or denial of service attacks? Additionally, can we categorize the types of anomalies that occur in practice, and theoretically analyze the anomalous signs arising from each type? In this work, we propose AnomRank, an online algorithm for anomaly detection in dynamic graphs. AnomRank uses a two-pronged approach defining two novel metrics for anomalousness. Each metric tracks the derivatives of its own version of a 'node score' (or node importance) function. This allows us to detect sudden changes in the importance of any node. We show theoretically and experimentally that the two-pronged approach successfully detects two common types of anomalies: sudden weight changes along an edge, and sudden structural changes to the graph. AnomRank is (a) Fast and Accurate: up to 49.5x faster or 35% more accurate than state-of-the-art methods, (b) Scalable: linear in the number of edges in the input graph, processing millions of edges within 2 seconds on a stock laptop/desktop, and (c) Theoretically Sound: providing theoretical guarantees of the two-pronged approach.
Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes
Authors: Zhengqi Li, Simon Niklaus, Noah Snavely, Oliver Wang
Date: 2020-11-26 00:00:00
Proceeding: CVPR 2021 1

We present a method to perform novel view and time synthesis of dynamic scenes, requiring only a monocular video with known camera poses as input. To do this, we introduce Neural Scene Flow Fields, a new representation that models the dynamic scene as a time-variant continuous function of appearance, geometry, and 3D scene motion. Our representation is optimized through a neural network to fit the observed input views. We show that our representation can be used for complex dynamic scenes, including thin structures, view-dependent effects, and natural degrees of motion. We conduct a number of experiments that demonstrate our approach significantly outperforms recent monocular view synthesis methods, and show qualitative results of space-time view synthesis on a variety of real-world videos.
Photoacoustic Reconstruction Using Sparsity in Curvelet Frame: Image versus Data Domain
Authors: Bolin Pan, Simon R. Arridge, Felix Lucka, Ben T. Cox, Nam Huynh, Paul C. Beard, Edward Z. Zhang, Marta M. Betcke
Date: 2020-11-26 00:00:00
Tasks: compressed sensing, Image Reconstruction

Curvelet frame is of special significance for photoacoustic tomography (PAT) due to its sparsifying and microlocalisation properties. We derive a one-to-one map between wavefront directions in image and data spaces in PAT which suggests near equivalence between the recovery of the initial pressure and PAT data from compressed/subsampled measurements when assuming sparsity in Curvelet frame. As the latter is computationally more tractable, investigation to which extent this equivalence holds conducted in this paper is of immediate practical significance. To this end we formulate and compare DR, a two step approach based on the recovery of the complete volume of the photoacoustic data from the subsampled data followed by the acoustic inversion, and p0R, a one step approach where the photoacoustic image (the initial pressure, p0) is directly recovered from the subsampled data. Effective representation of the photoacoustic data requires basis defined on the range of the photoacoustic forward operator. To this end we propose a novel wedge-restriction of Curvelet transform which enables us to construct such basis. Both recovery problems are formulated in a variational framework. As the Curvelet frame is heavily overdetermined, we use reweighted l1 norm penalties to enhance the sparsity of the solution. The data reconstruction problem DR is a standard compressed sensing recovery problem, which we solve using an ADMMtype algorithm, SALSA. Subsequently, the initial pressure is recovered using time reversal as implemented in the k-Wave Toolbox. The p0 reconstruction problem, p0R, aims to recover the photoacoustic image directly via FISTA, or ADMM when in addition including a non-negativity constraint. We compare and discuss the relative merits of the two approaches and illustrate them on 2D simulated and 3D real data in a fair and rigorous manner.
Functional Time Series Forecasting: Functional Singular Spectrum Analysis Approaches
Authors: Jordan Trinka, Hossein Haghbin, Mehdi Maadooliat
Date: 2020-11-26 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting

In this paper, we propose two nonparametric methods used in the forecasting of functional time-dependent data, namely functional singular spectrum analysis recurrent forecasting and vector forecasting. Both algorithms utilize the results of functional singular spectrum analysis and past observations in order to predict future data points where recurrent forecasting predicts one function at a time and the vector forecasting makes predictions using functional vectors. We compare our forecasting methods to a gold standard algorithm used in the prediction of functional, time-dependent data by way of simulation and real data and we find our techniques do better for periodic stochastic processes.
Non-Rigid Puzzles
Authors: Or Litany, Emanuele Rodolà, Alex Bronstein, Michael Bronstein, Daniel Cremers
Date: 2020-11-26 00:00:00

Shape correspondence is a fundamental problem in computer graphics and vision, with applications in various problems including animation, texture mapping, robotic vision, medical imaging, archaeology and many more. In settings where the shapes are allowed to undergo non-rigid deformations and only partial views are available, the problem becomes very challenging. To this end, we present a non-rigid multi-part shape matching algorithm. We assume to be given a reference shape and its multiple parts undergoing a non-rigid deformation. Each of these query parts can be additionally contaminated by clutter, may overlap with other parts, and there might be missing parts or redundant ones. Our method simultaneously solves for the segmentation of the reference model, and for a dense correspondence to (subsets of) the parts. Experimental results on synthetic as well as real scans demonstrate the effectiveness of our method in dealing with this challenging matching scenario.
Energy Drain of the Object Detection Processing Pipeline for Mobile Devices: Analysis and Implications
Authors: Haoxin Wang, BaekGyu Kim, Jiang Xie, Zhu Han
Date: 2020-11-26 00:00:00
Tasks: Object, object-detection, Object Detection

Applying deep learning to object detection provides the capability to accurately detect and classify complex objects in the real world. However, currently, few mobile applications use deep learning because such technology is computation-intensive and energy-consuming. This paper, to the best of our knowledge, presents the first detailed experimental study of a mobile augmented reality (AR) client's energy consumption and the detection latency of executing Convolutional Neural Networks (CNN) based object detection, either locally on the smartphone or remotely on an edge server. In order to accurately measure the energy consumption on the smartphone and obtain the breakdown of energy consumed by each phase of the object detection processing pipeline, we propose a new measurement strategy. Our detailed measurements refine the energy analysis of mobile AR clients and reveal several interesting perspectives regarding the energy consumption of executing CNN-based object detection. Furthermore, several insights and research opportunities are proposed based on our experimental results. These findings from our experimental study will guide the design of energy-efficient processing pipeline of CNN-based object detection.
Hawkes Processes Modeling, Inference and Control: An Overview
Authors: Rafael Lima
Date: 2020-11-26 00:00:00
Tasks: BIG-bench Machine Learning

Hawkes Processes are a type of point process which models self-excitement among time events. It has been used in a myriad of applications, ranging from finance and earthquakes to crime rates and social network activity analysis.Recently, a surge of different tools and algorithms have showed their way up to top-tier Machine Learning conferences. This work aims to give a broad view of the recent advances on the Hawkes Processes modeling and inference to a newcomer to the field.
Early Life Cycle Software Defect Prediction. Why? How?
Authors: N. C. Shrikanth, Suvodeep Majumder, Tim Menzies
Date: 2020-11-26 00:00:00
Tasks: Prediction, Transfer Learning

Many researchers assume that, for software analytics, "more data is better." We write to show that, at least for learning defect predictors, this may not be true. To demonstrate this, we analyzed hundreds of popular GitHub projects. These projects ran for 84 months and contained 3,728 commits (median values). Across these projects, most of the defects occur very early in their life cycle. Hence, defect predictors learned from the first 150 commits and four months perform just as well as anything else. This means that, at least for the projects studied here, after the first few months, we need not continually update our defect prediction models. We hope these results inspire other researchers to adopt a "simplicity-first" approach to their work. Some domains require a complex and data-hungry analysis. But before assuming complexity, it is prudent to check the raw data looking for "short cuts" that can simplify the analysis.
USCL: Pretraining Deep Ultrasound Image Diagnosis Model through Video Contrastive Representation Learning
Authors: Yixiong Chen, Chunhui Zhang, Li Liu, Cheng Feng, Changfeng Dong, Yongfang Luo, Xiang Wan
Date: 2020-11-25 00:00:00
Tasks: Contrastive Learning, Medical Image Analysis, Representation Learning

Most deep neural networks (DNNs) based ultrasound (US) medical image analysis models use pretrained backbones (e.g., ImageNet) for better model generalization. However, the domain gap between natural and medical images causes an inevitable performance bottleneck. To alleviate this problem, an US dataset named US-4 is constructed for direct pretraining on the same domain. It contains over 23,000 images from four US video sub-datasets. To learn robust features from US-4, we propose an US semi-supervised contrastive learning method, named USCL, for pretraining. In order to avoid high similarities between negative pairs as well as mine abundant visual features from limited US videos, USCL adopts a sample pair generation method to enrich the feature involved in a single step of contrastive optimization. Extensive experiments on several downstream tasks show the superiority of USCL pretraining against ImageNet pretraining and other state-of-the-art (SOTA) pretraining approaches. In particular, USCL pretrained backbone achieves fine-tuning accuracy of over 94% on POCUS dataset, which is 10% higher than 84% of the ImageNet pretrained model. The source codes of this work are available at https://github.com/983632847/USCL.
Can GAN originate new electronic dance music genres? -- Generating novel rhythm patterns using GAN with Genre Ambiguity Loss
Authors: Nao Tokui
Date: 2020-11-25 00:00:00
Tasks: Deep Learning, Music Generation, Rhythm

Since the introduction of deep learning, researchers have proposed content generation systems using deep learning and proved that they are competent to generate convincing content and artistic output, including music. However, one can argue that these deep learning-based systems imitate and reproduce the patterns inherent within what humans have created, instead of generating something new and creative. This paper focuses on music generation, especially rhythm patterns of electronic dance music, and discusses if we can use deep learning to generate novel rhythms, interesting patterns not found in the training dataset. We extend the framework of Generative Adversarial Networks(GAN) and encourage it to diverge from the dataset's inherent distributions by adding additional classifiers to the framework. The paper shows that our proposed GAN can generate rhythm patterns that sound like music rhythms but do not belong to any genres in the training dataset. The source code, generated rhythm patterns, and a supplementary plugin software for a popular Digital Audio Workstation software are available on our website.
Rethinking conditional GAN training: An approach using geometrically structured latent manifolds
Authors: Sameera Ramasinghe, Moshiur Farazi, Salman Khan, Nick Barnes, Stephen Gould
Date: 2020-11-25 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Diversity, Image-to-Image Translation, Translation

Conditional GANs (cGAN), in their rudimentary form, suffer from critical drawbacks such as the lack of diversity in generated outputs and distortion between the latent and output manifolds. Although efforts have been made to improve results, they can suffer from unpleasant side-effects such as the topology mismatch between latent and output spaces. In contrast, we tackle this problem from a geometrical perspective and propose a novel training mechanism that increases both the diversity and the visual quality of a vanilla cGAN, by systematically encouraging a bi-lipschitz mapping between the latent and the output manifolds. We validate the efficacy of our solution on a baseline cGAN (i.e., Pix2Pix) which lacks diversity, and show that by only modifying its training mechanism (i.e., with our proposed Pix2Pix-Geo), one can achieve more diverse and realistic outputs on a broad set of image-to-image translation tasks. Codes are available at https://github.com/samgregoost/Rethinking-CGANs.
Squared $\ell_2$ Norm as Consistency Loss for Leveraging Augmented Data to Learn Robust and Invariant Representations
Authors: Haohan Wang, Zeyi Huang, Xindi Wu, Eric P. Xing
Date: 2020-11-25 00:00:00
Tasks: Data Augmentation

Data augmentation is one of the most popular techniques for improving the robustness of neural networks. In addition to directly training the model with original samples and augmented samples, a torrent of methods regularizing the distance between embeddings/representations of the original samples and their augmented counterparts have been introduced. In this paper, we explore these various regularization choices, seeking to provide a general understanding of how we should regularize the embeddings. Our analysis suggests the ideal choices of regularization correspond to various assumptions. With an invariance test, we argue that regularization is important if the model is to be used in a broader context than the accuracy-driven setting because non-regularized approaches are limited in learning the concept of invariance, despite equally high accuracy. Finally, we also show that the generic approach we identified (squared $\ell_2$ norm regularized augmentation) outperforms several recent methods, which are each specially designed for one task and significantly more complicated than ours, over three different tasks.
Can Temporal Information Help with Contrastive Self-Supervised Learning?
Authors: Yutong Bai, Haoqi Fan, Ishan Misra, Ganesh Venkatesh, Yongyi Lu, Yuyin Zhou, Qihang Yu, Vikas Chandra, Alan Yuille
Date: 2020-11-25 00:00:00
Tasks: Data Augmentation, Representation Learning, Self-Supervised Learning, Video Understanding

Leveraging temporal information has been regarded as essential for developing video understanding models. However, how to properly incorporate temporal information into the recent successful instance discrimination based contrastive self-supervised learning (CSL) framework remains unclear. As an intuitive solution, we find that directly applying temporal augmentations does not help, or even impair video CSL in general. This counter-intuitive observation motivates us to re-design existing video CSL frameworks, for better integration of temporal knowledge. To this end, we present Temporal-aware Contrastive self-supervised learningTaCo, as a general paradigm to enhance video CSL. Specifically, TaCo selects a set of temporal transformations not only as strong data augmentation but also to constitute extra self-supervision for video understanding. By jointly contrasting instances with enriched temporal transformations and learning these transformations as self-supervised signals, TaCo can significantly enhance unsupervised video representation learning. For instance, TaCo demonstrates consistent improvement in downstream classification tasks over a list of backbones and CSL approaches. Our best model achieves 85.1% (UCF-101) and 51.6% (HMDB-51) top-1 accuracy, which is a 3% and 2.4% relative improvement over the previous state-of-the-art.