检索结果-内蒙古大学图书馆

Generalized word shift graphs: A method for visualizing and explaining pairwise comparisons between texts

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Gallagher, Ryan J. Frank, Morgan R. Mitchell, Lewis Schwartz, Aaron J. Reagan, Andrew J. Danforth, Christopher M. Dodds, Peter Sheridan Network Science Institute Northeastern University BostonMA02115 United States Department of Informatics and Networked Systems University of Pittsburgh PittsburghPA15260 United States Connection Science Massachusetts Institute of Technology CambridgeMA02139 United States Institute for Human-Centered Artifical Intelligence Stanford University StanfordCA94305 United States School of Mathematical Sciences The University of Adelaide SA5005 Australia Computational Story Lab Vermont Complex Systems Center Vermont Advanced Computing Core The University of Vermont BurlingtonVT05401 United States Gund Institute for Environment & Rubenstein School of Environment and Natural Resources The University of Vermont BurlingtonVT05401 United States Department of Ecology and Evolutionary Biology University of Colorado at Boulder BoulderCO80309 United States MassMutual Data Science AmherstMA01002 United States MassMutual Center of Excellence for Complex Systems and Data Science Department of Mathematics and Statistics The University of Vermont BurlingtonVT05401 United States

A common task in computational text analyses is to quantify how two corpora differ according to a measurement like word frequency, sentiment, or information content. However, collapsing the texts' rich stories into a single number is often conceptually perilous, and it is difficult to confidently interpret interesting or unexpected textual patterns without looming concerns about data artifacts or measurement validity. To better capture fine-grained differences between texts, we introduce generalized word shift graphs, visualizations which yield a meaningful and interpretable summary of how individual words contribute to the variation between two texts for any measure that can be formulated as a weighted average. We show that this framework naturally encompasses many of the most commonly used approaches for comparing texts, including relative frequencies, dictionary scores, and entropy-based measures like the Kullback-Leibler and Jensen-Shannon divergences. Through several case studies, we demonstrate how generalized word shift graphs can be flexibly applied across domains for diagnostic investigation, hypothesis generation, and substantive interpretation. By providing a detailed lens into textual shifts between corpora, generalized word shift graphs help computational social scientists, digital humanists, and other text analysis practitioners fashion more robust scientific narratives. Copyright © 2020, The Authors. All rights reserved.

关键词：

The effectiveness of backward contact tracing in networks

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Kojaku, Sadamori Hébert-Dufresne, Laurent Mones, Enys Lehmann, Sune Ahn, Yong-Yeol Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Vermont Complex Systems Center University of Vermont BurlingtonVT05405 United States Department of Computer Science University of Vermont BurlingtonVT05405 United States DTU Compute Technical University of Denmark Kgs. Lyngby 2800 Denmark Center for Social Data Science University of Copenhagen Copenhagen K1353 Denmark Indiana University Network Science Institute Indiana University BloomingtonIN47408 United States

Discovering and isolating infected individuals is a cornerstone of epidemic control1-7. Because many infectious diseases spread through close contacts, contact tracing is a key tool for case discovery and control8-15. However, although contact tracing has been performed widely, the mathematical understanding of contact tracing has not been fully established and it has not been clearly understood what determines the efficacy of contact tracing. Here, we reveal that, compared with "forward" tracing-tracing to whom disease spreads, "backward" tracing-tracing from whom disease spreads-is profoundly more effective. The effectiveness of backward tracing is due to simple but overlooked biases arising from the heterogeneity in contacts. Using simulations on both synthetic and high-resolution empirical contact datasets, we show that even at a small probability of detecting infected individuals, strategically executed contact tracing can prevent a significant fraction of further transmissions. We also show that-in terms of the number of prevented transmissions per isolation-case isolation combined with a small amount of contact tracing is more efficient than case isolation alone. By demonstrating that backward contact tracing is highly effective at discovering super-spreading events, we argue that the potential effectiveness of contact tracing has been underestimated. Therefore, there is a critical need for revisiting current contact tracing strategies so that they leverage all forms of biases. Our results also have important consequences for digital contact tracing because it will be crucial to incorporate the capability for backward and deep tracing while adhering to the privacy-preserving requirements of these new platforms. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

Practical adaptive time-varying output formation tracking for high-order nonlinear multi-agent systems using neural networks

学校读者我要写书评

暂无评论

Practical adaptive time-varying output formation tracking fo...

第三十八届中国控制会议

作者： Jianglong Yu Xiwang Dong Qingdong Li Zhang Ren Ming Ma School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Shananxi Key Laboratory of Integrated and Intelligent Navigation Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Xi'an Research Institute of Navigation Technology

Practical adaptive time-varying output formation tracking problems for high-order nonlinear strict-feedback multiagent systems are investigated in this paper. The time-varying output formation tracking requires the followers' output vectors to track the output vector of the leader while achieving a time-varying formation. The dynamics of the leader and the followers can have uncertain mismatched nonlinearities while the leader's control input is also unknown. To deal with the mismatched nonlinearities, the backstepping techniques are utilized to construct the protocols. Firstly, visual controllers are designed to overcome the effects of the uncertainties while guaranteeing the stability of each layer. The complicated visual controllers are estimated by the neural networks. A full adaptive protocol is constructed with adaptive gains to avoid using the global information of the interaction topology. Then, an algorithm is presented to design the protocol, whose parameters are much less. Besides, the stability of the algorithm is proved by Lyapunov theories. Finally, simulation results are presented to prove the effectiveness.

关键词： Practical time-varying formation tracking Adaptive control High-order nonlinear dynamics Multi-agent systems Neural networks

Assessing the Global Impact of Brain Small Vessel Disease on Cognition: The Multi-Ethnic Study of Atherosclerosis

学校读者我要写书评

暂无评论

Alzheimer's and Dementia 2025年第6期21卷 e70326页

作者： Charisis, Sokratis Rashid, Tanweer Dintica, Christina Gonzales, Mitzi Liu, Hangfan Ware, Jeffrey B. Austin, Thomas R. Jensen, Paul N. Fohner, Alison E. Tanley, Jordan E. Ding, Jingzhong Luchsinger, José A. Sachs, Bonnie Nasrallah, Ilya M. Bryan, R. Nick Hayden, Kathleen M. Wolk, David Rascovsky, Katya Davatzikos, Christos Longstreth, William T. Yaffe, Kristine Seshadri, Sudha Heckbert, Susan R. Hughes, Timothy Habes, Mohamad Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases University of Texas Health Science Center San Antonio San Antonio TX United States Department of Neurology University of Texas Health Science Center at San Antonio San Antonio TX United States Department of Psychiatry and Behavioral Sciences University of California San Francisco San Francisco CA United States Department of Neurology Cedars Sinai Medical Center Los Angeles CA United States Center for Advanced Imaging Research (CAIR) University of Maryland School of Medicine Baltimore MD United States Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Department of Epidemiology University of Washington Seattle WA United States Cardiovascular Health Research Unit University of Washington Seattle WA United States Department of Medicine University of Washington Seattle WA United States Department of Internal Medicine Wake Forest School of Medicine Winston-Salem NC United States Departments of Medicine and Epidemiology Columbia University Medical Center New York NY United States Department of Neurology Wake Forest University School of Medicine Winston-Salem NC United States AI2D Center for AI and Data Science for Integrated Diagnostics and Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA United States Department of Social Sciences and Health Policy Division of Public Health Sciences Wake Forest University School of Medicine Winston-Salem NC United States Alzheimer's Disease Research Center University of Pennsylvania Philadelphia PA United States Department of Neurology University of Pennsylvania Philadelphia PA United States Department of Neurology University of Washington Seattle WA United States

INTRODUCTION: We aimed to examine the global impact of brain small vessel disease (SVD) on cognitive performance. METHODS: In 892 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), we derived perivascular spaces (PVS), white matter hyperintensities (WMH), microbleeds (MB), and white matter fractional anisotropy (FA) and trace (TR). Cognitive function was assessed with a comprehensive neuropsychological battery. RESULTS: A composite SVD measure was constructed as a linear combination of basal ganglia PVS, thalamus PVS, periventricular WMH, subcortical WMH, and white matter FA and TR, and exhibited associations with worse global and domain-specific cognitive performance. Additionally, SVD mediated the effect of age and cardiovascular disease risk on global cognitive function, both directly and through smaller gray matter (GM) volume. DISCUSSION: Integrating multiple individual SVD endophenotypes may more accurately reflect the neurobiology of SVD and capture its global impact on cognition. SVD mediates the effects of age and cardiovascular disease risk on cognition through both atrophy-related and non–atrophy-related pathways. Highlights: Associations between individual magnetic resonance imaging (MRI) markers of brain small vessel disease and cognitive outcomes might not fully capture the global impact of small vessel disease on cognition. We modeled small vessel disease as a latent construct, integrating multiple MRI endophenotypes in strategic brain regions. The small vessel disease construct was associated with worse global and domain-specific cognitive performance. The small vessel disease construct exhibited mediating effects in the relationships of aging and cardiovascular disease risk with cognition through pathways that both involve and are independent of brain atrophy. Integrating information from multiple relevant imaging endophenotypes could open new avenues in small vessel disease research, broadening our understanding of its risk factors

关键词： aging atrophy attention cardiovascular disease risk cerebral small vessel disease cognition cognitive performance deep learning delayed memory diffusion tensor imaging enlarged perivascular spaces executive function factor analysis fractional anisotropy Framingham risk score global cognition gray matter volume immediate memory language latent variable magnetic resonance imaging mediation microbleeds phonemic fluency processing speed semantic fluency structural equation modeling trace visuospatial functioning white matter hyperintensities

GDPR-compliant personal data management: A blockchain-based solution

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Binh Truong, Nguyen Sun, Kai Lee, Gyu Myoung Guo, Yike Data Science Institute Department of Computing Imperial College London LondonSW7 2AZ United Kingdom Department of Computer Science Liverpool John Moores University LiverpoolL3 3AF United Kingdom

The General data Protection Regulation (GDPR) gives control of personal data back to the owners by appointing higher requirements and obligations on service providers who manage and process personal data. As the verification of GDPR-compliance, handled by a supervisory authority, is irregularly conducted;it is challenging to be certified that a service provider has been continuously adhering to the GDPR. Furthermore, it is beyond the data owner’s capability to perceive whether a service provider complies with the GDPR and effectively protects her personal data. This motivates us to envision a design concept for developing a GDPR-compliant personal data management platform leveraging the emerging blockchain and smart contract technologies. The goals of the platform are to provide decentralised mechanisms to both service providers and data owners for processing personal data;meanwhile, empower data provenance and transparency by leveraging advanced features of the blockchain technology. The platform enables data owners to impose data usage consent, ensures only designated parties can process personal data, and logs all data activities in an immutable distributed ledger using smart contract and cryptography techniques. By honestly participating in the platform, a service provider can be endorsed by the blockchain network that it is fully GDPR-compliant;otherwise, any violation is immutably recorded and is easily figured out by associated parties. We then demonstrate the feasibility and efficiency of the proposed design concept by developing a profile management platform implemented on top of the Hyperledger Fabric permissioned blockchain framework, following by valuable analysis and discussion. Copyright © 2019, The Authors. All rights reserved.

关键词： Blockchain

Pandemic drugs at pandemic speed: Infrastructure for accelerating COVID-19 drug discovery with hybrid machine learning- And physics-based simulations on high performance computers

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Bhati, Agastya P. Wan, Shunzhou Alfè, Dario Clyde, Austin R. Bode, Mathis Tan, Li Titov, Mikhail Merzky, Andre Turilli, Matteo Jha, Shantenu Highfield, Roger R. Rocchia, Walter Scafuri, Nicola Succi, Sauro Kranzlmüller, Dieter Mathias, Gerald Wifling, David Donon, Yann Meglio, Alberto Di Vallecorsa, Sofia Ma, Heng Trifan, Anda Ramanathan, Arvind Brettin, Tom Partin, Alexander Xia, Fangfang Duan, Xiaotan Stevens, Rick Coveney, Peter V. Centre for Computational Science University College London Gordon Street LondonWC1H 0AJ United Kingdom Department of Earth Sciences London Centre for Nanotechnology and Thomas Young Centre University College London University College London Gower Street LondonWC1E 6BT United Kingdom Dipartimento di Fisica Ettore Pancini Università di Napoli Federico II Monte Sant’Angelo NapoliI-80126 Italy Department of Computer Science University of Chicago Institute for Combustion Technology RWTH Aachen University Aachen52056 Germany Brookhaven National Laboratory UptonNY11973 United States Rutgers State University of New Jersey PiscatawayNJ08854 United States Science Museum Exhibition Road LondonSW7 2DD United Kingdom Concept Lab Italian Institute of Technology Via Melen Genova Italy Center for Life Nanosciences at La Sapienza Italian Institute of Technology viale Regina Elena Roma Italy of the Bavarian Academy of Sciences and Humanities Boltzmannstraße 1 Garching bei München85748 Germany OpenLab CERN Geneva Switzerland Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Computing Environment and Life Sciences Directorate Argonne National Laboratory LemontIL60439 United States Institute for Informatics Science Park 904 University of Amsterdam Amsterdam1098 XH Netherlands

The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods, in this case developed for linear accelerators, and physics-based methods. The two in silico methods, each have their own advantages and limitations which, interestingly, complement each other. Here, we present an innovative infrastructural development that combines both approaches to accelerate drug discovery. The scale of the potential resulting workflow is such that it is dependent on supercomputing to achieve extremely high throughput. We have demonstrated the viability of this workflow for the study of inhibitors for four COVID-19 target proteins and our ability to perform the required large-scale calculations to identify lead antiviral compounds through repurposing on a variety of supercomputers. © 2021, CC BY.

关键词： Molecular dynamics

Introduction to voice presentation attack detection and recent advances

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Sahidullah, Md Delgado, Héctor Todisco, Massimiliano Kinnunen, Tomi Evans, Nicholas Yamagishi, Junichi Lee, Kong-Aik School of Computing University of Eastern Finland Finland Inria France Department of Digital Security EURECOM France National Institute of Informatics Japan University of Edinburgh United Kingdom Data Science Research Laboratories NEC Corporation Japan

Over the past few years significant progress has been made in the field of presentation attack detection (PAD) for automatic speaker recognition (ASV). This includes the development of new speech corpora, standard evaluation protocols and advancements in front-end feature extraction and back-end classifiers. The use of standard databases and evaluation protocols has enabled for the first time the meaningful benchmarking of different PAD solutions. This chapter summarises the progress, with a focus on studies completed in the last three years. The article presents a summary of findings and lessons learned from two ASVspoof challenges, the first community-led benchmarking efforts. These show that ASV PAD remains an unsolved problem and that further attention is required to develop generalised PAD solutions which have potential to detect diverse and previously unseen spoofing attacks. Copyright © 2019, The Authors. All rights reserved.

关键词： Benchmarking

Planar median graphs and cubesquare-graphs

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Seemann, Carsten R. Moulton, Vincent Stadler, Peter F. Hellmuth, Marc Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics University of Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany School of Computing Sciences University of East Anglia NorwichNR4 7TJ United Kingdom Halle-Jena-Leipzig Competence Center for Scalable Data Services and Solutions Dresden-Leipzig Leipzig Germany Research Center for Civilization Diseases Centre for Biotechnology and Biomedicine Leipzig University Härtelstraße 16-18 LeipzigD-04107 Germany Institute for Theoretical Chemistry University of Vienna Währingerstraße 17 WienA-1090 Austria Facultad de Ciencias Universidad Nacional de Colombia Sede Bogotá Colombia The Santa Fe Institute 1399 Hyde Park Rd. Santa FeNM87501 United States Department of Mathematics Faculty of Science Stockholm University StockholmSE - 106 91 Sweden

Median graphs are connected graphs in which for all three vertices there is a unique vertex that belongs to shortest paths between each pair of these three vertices. In this paper we provide several novel characterizations of planar median graphs. More specifically, we characterize when a planar graph G is a median graph in terms of forbidden subgraphs and the structure of isometric cycles in G, and also in terms of subgraphs of G that are contained inside and outside of 4-cycles with respect to an arbitrary planar embedding of G. These results lead us to a new characterization of planar median graphs in terms of cubesquare-graphs that is, graphs that can be obtained by starting with cubes and square graphs, and iteratively replacing 4-cycle boundaries (relative to some embedding) by cubes or square-graphs. As a corollary we also show that a graph is planar median if and only if it can be obtained from cubes and square-graphs by a sequence of "square-boundary" amalgamations. These considerations also lead to an O(n log n)-time recognition algorithm to compute a decomposition of a planar median graph with n vertices into cubes and square-graphs. Copyright © 2021, The Authors. All rights reserved.

关键词： Graphic methods

RAIL: Risk-Averse Imitation Learning 17

学校读者我要写书评

暂无评论

RAIL: Risk-Averse Imitation Learning

17th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2018

作者： Santara, Anirban Naik, Abhishek Ravindran, Balaraman Das, Dipankar Mudigere, Dheevatsa Avancha, Sasikanth Kaul, Bharat Intel Labs India Indian Institute of Technology Kharagpur United States Department of CSE Robert Bosch Centre for Data Science and AI Indian Institute of Technology Madras United States Parallel Computing Lab - Intel Labs India

ISBN: (纸本)9781510868083

Imitation learning algorithms learn viable policies by imitating an expert's behavior when reward signals are not available. Generative Adversarial Imitation Learning (GALL) is a state-of-the-art algorithm for learning policies when the expert's behavior is available as a fixed set of trajectories. We evaluate in terms of the expert's cost function and observe that the distribution of trajectory-costs is often more heavy-tailed for GAIL-agents than the expert at a number of benchmark continuous-control tasks. Thus, high-cost trajectories, corresponding to tail-end events of catastrophic failure, are more likely to be encountered by the GAIL-agents than the expert. This makes the reliability of GAIL-agents questionable when it comes to deployment in risk-sensitive applications like robotic surgery and autonomous driving. In this work, we aim to minimize the occurrence of tail-end events by minimizing tail risk within the GALL framework. We quantify tail risk by the Conditional-Value-at-Risk (CVaR) of trajectories and develop the Risk-Averse Imitation Learning (RALL) algorithm. We observe that the policies learned with RAIL show lower tail-end risk than those of vanilla GAIL. Thus, the proposed RALL algorithm appears as a potent alternative to GALL for improved reliability in risk-sensitive applications. © 2018 International Foundation for Autonomous Agents and Multiagent Systems (***). All rights reserved.

关键词： Reinforcement learning