检索结果-内蒙古大学图书馆

arXiv 2017年

作者： Zhang, Wenwen Wang, Kunfeng Qu, Hua Zhao, Jihong Wang, Fei-Yue School of Software Engineering Xi’an Jiaotong University Xi’an710049 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266000 China School of Software Engineering Xi’an Jiaotong University Xi’an710049 China Department of Communication and Information Engineering Xi’an Jiaotong University Xi’an710049 China Research Center for Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha410073 China

As a special type of object detection, pedestrian detection in generic scenes has made a significant progress trained with large amounts of labeled training data manually. While the models trained with generic dataset work bad when they are directly used in specific scenes. With special viewpoints, flow light and backgrounds, datasets from specific scenes are much different from the datasets from generic scenes. In order to make the generic scene pedestrian detectors work well in specific scenes, the labeled data from specific scenes are needed to adapt the models to the specific scenes. While labeling the data manually spends much time and money, especially for specific scenes, each time with a new specific scene, large amounts of images must be labeled. What’s more, the labeling information is not so accurate in the pixels manually and different people make different labeling information. In this paper, we propose an ACP-based method, with augmented reality’s help, we build the virtual world of specific scenes, and make people walking in the virtual scenes where it is possible for them to appear to solve this problem of lacking labeled data and the results show that data from virtual world is helpful to adapt generic pedestrian detectors to specific scenes. Copyright © 2017, The Authors. All rights reserved.

关键词： Virtual reality

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Training and testing object detectors with virtual images

arXiv

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arXiv 2017年

作者： Tian, Yonglin Li, Xuan Wang, Kunfeng Wang, Fei-Yue Department of Automation University of Science and Technology of China Hefei230027 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Automation Beijing Institute of Technology Beijing100081 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266000 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Research Center for Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha410073 China

In the area of computer vision, deep learning has produced a variety of state-of-the-art models that rely on massive labeled data. However, collecting and annotating images from the real world has a great demand for labor and money investments and is usually too passive to build datasets with specific characteristics, such as small area of objects and high occlusion level. Under the framework of parallel Vision, this paper presents a purposeful way to design artificial scenes and automatically generate virtual images with precise annotations. A virtual dataset named parallelEye is built, which can be used for several computer vision tasks. Then, by training the DPM (Deformable Parts Model) and Faster R-CNN detectors, we prove that the performance of models can be significantly improved by combining parallelEye with publicly available real-world datasets during the training phase. In addition, we investigate the potential of testing the trained models from a specific aspect using intentionally designed virtual datasets, in order to discover the flaws of trained models. From the experimental results, we conclude that our virtual dataset is viable to train and test the object detectors. Copyright © 2017, The Authors. All rights reserved.

关键词： Object detection

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Mutual clustering on comparative texts via heterogeneous information networks

arXiv

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arXiv 2019年

作者： Cao, Jianping Wang, Senzhang Wen, Danyan Peng, Zhaohui Yu, Philip S. Wang, Fei-yue Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha410073 China Ministry of Industry and Information Technology Nanjing211106 China School of Economics & Management Nanjing University of Science and Technology Nanjing210094 China School of Computer Science and Technology Shandong University Qingdao China Department of Computer Science University of Illinois at Chicago Chicago60607 United States Institute for Data Science Tsinghua University Beijing100084 China Beijing China Qingdao Academy of Intelligent Industries Qingdao266000 China Institute of Systems Engineering Macau University of Science and Technology Macau999078 China

Currently, many intelligence systems contain the texts from multi-sources, e.g., bulletin board system (BBS) posts, tweets and news. These texts can be "comparative" since they may be semantically correlated and thus provide us with different perspectives toward the same topics or events. To better organize the multi-sourced texts and obtain more comprehensive knowledge, we propose to study the novel problem of Mutual Clustering on Comparative Texts (MCCT), which aims to cluster the comparative texts simultaneously and collaboratively. The MCCT problem is difficult to address because 1) comparative texts usually present different data formats and structures and thus they are hard to organize, and 2) there lacks an effective method to connect the semantically correlated comparative texts to facilitate clustering them in an unified way. To this aim, in this paper we propose a Heterogeneous Information Network-based Text clustering framework HINT. HINT first models multi-sourced texts (e.g. news and tweets) as heterogeneous information networks by introducing the shared "anchor texts" to connect the comparative texts. Next, two similarity matrices based on HINT as well as a transition matrix for cross-text-source knowledge transfer are constructed. Comparative texts clustering are then conducted by utilizing the constructed matrices. Finally, a mutual clustering algorithm is also proposed to further unify the separate clustering results of the comparative texts by introducing a clustering consistency constraint. We conduct extensive experimental on three tweets-news datasets, and the results demonstrate the effectiveness and robustness of the proposed method in addressing the MCCT problem. Copyright © 2019, The Authors. All rights reserved.

关键词： Clustering algorithms

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Decision-Making in Driver-Automation Shared Control: A Review and Perspectives

arXiv

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arXiv 2020年

作者： Wang, Wenshuo Na, Xiaoxiang Cao, Dongpu Gong, Jianwei Xi, Junqiang Xing, Yang Wang, Fei-Yue CA94720 United States Department of Engineering University of Cambridge Trumpington Street CambridgeCB2 1PZ United Kingdom Mechanical and Mechatronics Engineering University of Waterloo 200 University Ave. West Waterloo ONN2L3G1 Canada Department of Mechanical Engineering Beijing Institute of Technology Beijing100081 China School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore639798 Singapore State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha410073 China

Shared control schemes allow a human driver to work with an automated driving agent in driver-vehicle systems while retaining the driver’s abilities to control. The human driver, as an essential agent in the driver-vehicle shared control systems, should be precisely modeled regarding their cognitive processes, control strategies, and decision-making processes. The interactive strategy design between drivers and automated driving agents brings an excellent challenge for human-centric driver assistance systems due to the inherent characteristics of humans. Many open-ended questions arise, such as what proper role of human drivers should act in a shared control scheme? How to make an intelligent decision capable of balancing the benefits of agents in shared control systems? Due to the advent of these attentions and questions, it is desirable to present a survey on the decision-making between human drivers and highly automated vehicles, to understand their architectures, human driver modeling, and interaction strategies under the driver-vehicle shared schemes. Finally, we give a further discussion on the key future challenges and opportunities. They are likely to shape new potential research directions. Copyright © 2020, The Authors. All rights reserved.

关键词： Decision making

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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CP2K: An electronic structure and molecular dynamics software package quickstep: Efficient and accurate electronic structure calculations

arXiv

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arXiv 2020年

作者： Kühne, Thomas D. Iannuzzi, Marcella Ben, Mauro Del Rybkin, Vladimir V. Seewald, Patrick Stein, Frederick Laino, Teodoro Khaliullin, Rustam Z. Schütt, Ole Schiffmann, Florian Golze, Dorothea Wilhelm, Jan Chulkov, Sergey Bani-Hashemian, Mohammad Hossein Weber, Valéry Borštnik, Urban Taillefumier, Mathieu Jakobovits, Alice Shoshana Lazzaro, Alfio Pabst, Hans Müller, Tiziano Schade, Robert Guidon, Manuel Andermatt, Samuel Holmberg, Nico Schenter, Gregory K. Hehn, Anna Bussy, Augustin Belleflamme, Fabian Tabacchi, Gloria Glöß, Andreas Lass, Michael Bethune, Iain Mundy, Christopher J. Plessl, Christian Watkins, Matt VandeVondele, Joost Krack, Matthias Hutter, Jürg Dynamics of Condensed Matter and Center for Sustainable Systems Design Chair of Theoretical Chemistry Paderborn University Warburger Str. 100 PaderbornD-33098 Germany Department of Chemistry University of Zurich Winterthurerstrasse 190 ZürichCH-8057 Switzerland Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States IBM Research - Zurich Säumerstrasse 4 RüschlikonCH-8803 Switzerland Department of Chemistry McGill University CH-801 Sherbrooke St. West MontrealQCH3A 0B8 Canada Department of Materials ETH Zürich ZürichCH-8092 Switzerland Centre of Policy Studies Victoria University Melbourne Australia Department of Applied Physics Aalto University Otakaari 1 EspooFI-02150 Finland Institute of Theoretical Physics University of Regensburg Universitätsstraße 31 RegensburgD-93053 Germany School of Mathematics and Physics University of Lincoln Brayford Pool Lincoln United Kingdom Integrated Systems Laboratory ETH Zürich ZürichCH-8092 Switzerland Scientific IT Services ETH Zürich Zürich Switzerland ETH Zürich HPE Switzerland GmbH Basel Switzerland Intel Extreme Computing Software and Systems Zürich Switzerland Department of Computer Science and Paderborn Center for Parallel Computing Paderborn University Warburger Str. 100 PaderbornD-33098 Germany Department of Chemistry and Materials Science Aalto University P.O. Box 16100 Aalto00076 Finland Physical Science Division Pacific Northwest National Laboratory P. O. Box 999 RichlandWA99352 United States Department of Science and High Technology University of Insubria INSTM via Valleggio 9 ComoI-22100 Italy BASF SE Carl-Bosch-Straße 38 Ludwigshafen am RheinD-67056 Germany Hartree Centre Science and Technology Facilities Council United Kingdom Laboratory for Scientific Computing and Modelling Paul Scherrer Institute Villigen PSICH-5232 Switzerland

CP2K is an open source electronic structure and molecular dynamics software package to perform atomistic simulations of solid-state, liquid, molecular and biological systems. It is especially aimed at massively-parallel and linear-scaling electronic structure methods and state-of-the-art ab-initio molecular dynamics simulations. Excellent performance for electronic structure calculations is achieved using novel algorithms implemented for modern high-performance computing systems. This review revisits the main capabilities of CP2K to perform efficient and accurate electronic structure simulations. The emphasis is put on density functional theory and multiple post-Hartree-Fock methods using the Gaussian and plane wave approach and its augmented all-electron extension. Copyright © 2020, The Authors. All rights reserved.

关键词： Electronic structure

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Proteomic Progress: Bangalore Institute Becomes a Major center of Biotech research

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CHEMISTRY & BIOLOGY 2010年第3期17卷 207-208页

作者： Shekhar, Chandra Main Text In the U.S. or Europe India is often associated with information technology or service outsourcing not with scientific research. Nonetheless largely unnoticed by western media India has been making strides toward becoming a research powerhouse in the life sciences. Enjoying unique manpower and cost advantages this country now has several major universities and scientific institutions with active programs in this area. Spurred by supportive government agencies these institutions are producing an increasing number of publications patents and industry spin-offs. Consider Bangalore-based Institute of Bioinformatics (IOB) a relatively small 40 member outfit that has nonetheless become a major global player in proteomics research. In less than a decade of existence the institute has produced what is arguably the world's best curated protein database and is well on its way to replicating this success in cancer biomarkers proteogenomics and signaling pathways. “We undertake projects that most other labs would find extremely hard” says IOB's founder and director Akhilesh Pandey M.D. Ph.D. who is also a researcher at Johns Hopkins University School of Medicine. “And we accomplish them in a time frame that would be virtually unthinkable elsewhere.” Proteogenomics the use of proteomics to annotate genes is an area where IOB has carved out a niche for itself. This is not a vain boast. Take for instance the Institute's Human Protein Reference Database an unprecedented online compendium of curated protein information. Containing information about more than 27000 proteins and 39000 protein-protein interactions the database is the fruit of several years of effort from curators who sifted through more than 2000000 research papers. Unlike most other protein databases IOB's creation encapsulates virtually every relevant feature of each protein—function sequence domains motifs interactions expression localization modifications disease associations—and includes results obtained w

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