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arXiv 2020年

作者： Qin, Haotong Gong, Ruihao Liu, Xianglong Bai, Xiao Song, Jingkuan Sebe, Nicu State Key Lab of Software Development Environment Beihang University Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing China Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Information Engineering and Computer Science University of Trento Trento Italy School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Jiangxi Research Institute Beihang University Beijing China

The binary neural network, largely saving the storage and computation, serves as a promising technique for deploying deep models on resource-limited devices. However, the binarization inevitably causes severe information loss, and even worse, its discontinuity brings difficulty to the optimization of the deep network. To address these issues, a variety of algorithms have been proposed, and achieved satisfying progress in recent years. In this paper, we present a comprehensive survey of these algorithms, mainly categorized into the native solutions directly conducting binarization, and the optimized ones using techniques like minimizing the quantization error, improving the network loss function, and reducing the gradient error. We also investigate other practical aspects of binary neural networks such as the hardware-friendly design and the training tricks. Then, we give the evaluation and discussions on different tasks, including image classification, object detection and semantic segmentation. Finally, the challenges that may be faced in future research are prospected. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

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DPA-2: a large atomic model as a multi-task learner

arXiv

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arXiv 2023年

作者： Zhang, Duo Liu, Xinzijian Zhang, Xiangyu Zhang, Chengqian Cai, Chun Bi, Hangrui Du, Yiming Qin, Xuejian Peng, Anyang Huang, Jiameng Li, Bowen Shan, Yifan Zeng, Jinzhe Zhang, Yuzhi Liu, Siyuan Li, Yifan Chang, Junhan Wang, Xinyan Zhou, Shuo Liu, Jianchuan Luo, Xiaoshan Wang, Zhenyu Jiang, Wanrun Wu, Jing Yang, Yudi Yang, Jiyuan Yang, Manyi Gong, Fu-Qiang Zhang, Linshuang Shi, Mengchao Dai, Fu-Zhi York, Darrin M. Liu, Shi Zhu, Tong Zhong, Zhicheng Lv, Jian Cheng, Jun Jia, Weile Chen, Mohan Ke, Guolin Weinan, E. Zhang, Linfeng Wang, Han AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China CAS Key Laboratory of Magnetic Materials and Devices Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of Sciences Ningbo315201 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development School of Chemistry and Molecular Engineering East China Normal University Shanghai200062 China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States Department of Chemistry Princeton University PrincetonNJ08540 United States College of Chemistry and Molecular Engineering Peking University Beijing100871 China Yuanpei College Peking University Beijing100871 China School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China Key Laboratory of Material Simulation Methods & Software of Ministry of Education College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China Key Laboratory for Quantum Materials of Zhejiang Province Department of Physics School of Science Westlake University Zhejiang Hangzhou310030 China Atomistic Simulations Italia

The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with the accuracy of ab initio electronic structure methods. However, the model generation process remains a bottleneck for large-scale applications. We propose a shift towards a model-centric ecosystem, wherein a large atomic model (LAM), pre-trained across multiple disciplines, can be efficiently fine-tuned and distilled for various downstream tasks, thereby establishing a new framework for molecular modeling. In this study, we introduce the DPA-2 architecture as a prototype for LAMs. Pre-trained on a diverse array of chemical and materials systems using a multi-task approach, DPA-2 demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies. Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi-task learning

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PIN FORMED 2 Modulates the Transport of Arsenite in Arabidopsis thaliana

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Plant Communications 2020年第3期1卷 42-56页

作者： Mohammad Arif Ashraf Kana Umetsu Olena Ponomarenko Michiko Saito Mohammad Aslam Olga Antipova Natalia Dolgova Cheyenne D.Kiani Susan Nehzati Keitaro Tanoi Katsuyuki Minegishi Kotaro Nagatsu Takehiro Kamiya Toru Fujiwara Christian Luschnig Karen Tanino Ingrid Pickering Graham N.George Abidur Rahman United Graduate School of Agricultural Sciences Iwate UniversityMoriokaIwateJapan Department of Plant Bio Sciences Faculty of AgricultureIwate UniversityMoriokaIwateJapan Molecular and Environmental Science Research Group Department of Geological SciencesUniversity of SaskatchewanSaskatoonSKCanada Argonne National Lab Advanced Photon SourceXSD-MICLemontILUSA Isotope Facility for Agricultural Education and Research Graduate School of Agricultural and Life SciencesThe University of TokyoBunkyo-kuTokyoJapan Department of Radiopharmaceuticals Development National Institute of Radiological SciencesNational Institutes for Quantum and Radiological Science and TechnologyInageChibaJapan Department of Applied Biological Chemistry Graduate School of Agricultural and Life SciencesThe University of TokyoBunkyo-kuTokyoJapan Department of Applied Genetics and Cell Biology University of Natural Resources and Life SciencesVienna(BOKU)Muthgasse 181180 WienAustria Department of Plant Sciences College of Agriculture and BioresourcesUniversity of SaskatchewanSaskatoonSKCanada Agri-Innovation Center Iwate UniversityMoriokaIwateJapan

Arsenic contamination is a major environmental issue,as it may lead to serious health *** reduced trivalent formof inorganic arsenic,arsenite,is in generalmore toxic to plants comparedwith the fully oxidized pentavalent *** arsenite inplants hasbeenshown tobemediatedthrough a large subfamily of plant aquaglyceroporins,nodulin 26-like intrinsic proteins(NIPs).However,the efflux mechanisms,as well as themechanismof arsenite-induced root growth inhibition,remain poorly *** physiology,synchrotron imaging,and root transport assay approaches,we show that the cellular transport of trivalent arsenicals inArabidopsis thalianais stronglymodulatedbyPINFORMED2(PIN2)auxin efflux *** transport assay using radioactive arsenite,X-ray fluorescence imaging(XFI)coupled with X-ray absorption spectroscopy(XAS),and inductively coupled plasma mass spectrometry analysis revealed that pin2 plants accumulate higher concentrations of arsenite in roots comparedwith the *** the cellular level,arsenite specifically targets intracellular sorting of PIN2 and thereby alters the cellular auxin ***,loss of PIN2 function results in arsenite hypersensitivity in *** coupled with XAS further revealed that loss of PIN2 function results in specific accumulation of arsenical species,but not the other metals such as iron,zinc,or calcium in the root ***,these results suggest that PIN2 likely functions as an arsenite efflux transporter for the distribution of arsenical species in planta.

关键词： auxin arsenite PIN2 trafficking transport

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Forward and backward information retention for accurate binary neural networks

arXiv

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arXiv 2019年

作者： Qin, Haotong Gong, Ruihao Liu, Xianglong Shen, Mingzhu Wei, Ziran Yu, Fengwei Song, Jingkuan State Key Lab of Software Development Environment Beihang University Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University SenseTime Research Beijing University of Posts and Telecommunications Center for Future Media University of Electronic Science and Technology of China

Weight and activation binarization is an effective approach to deep neural network compression and can accelerate the inference by leveraging bitwise operations. Although many binarization methods have improved the accuracy of the model by minimizing the quantization error in forward propagation, there remains a noticeable performance gap between the binarized model and the full-precision one. Our empirical study indicates that the quantization brings information loss in both forward and backward propagation, which is the bottleneck of training accurate binary neural networks. To address these issues, we propose an Information Retention Network (IR-Net) to retain the information that consists in the forward activations and backward gradients. IR-Net mainly relies on two technical contributions: (1) Libra Parameter Binarization (Libra-PB): simultaneously minimizing both quantization error and information loss of parameters by balanced and standardized weights in forward propagation;(2) Error Decay Estimator (EDE): minimizing the information loss of gradients by gradually approximating the sign function in backward propagation, jointly considering the updating ability and accurate gradients. We are the first to investigate both forward and backward processes of binary networks from the unified information perspective, which provides new insight into the mechanism of network binarization. Comprehensive experiments with various network structures on CIFAR-10 and ImageNet datasets manifest that the proposed IR-Net can consistently outperform state-of-the-art quantization methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Chemical activation

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Activation of subnanometric Pt on Cu-modified CeO2 via redox-coupled atomic layer deposition for CO oxidation (vol 11, 4240, 2020)

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NATURE COMMUNICATIONS 2020年第1期11卷 4240-4240页

作者： Liu, Xiao Jia, Shuangfeng Yang, Ming Tang, Yuanting Wen, Yanwei Chu, Shengqi Wang, Jianbo Shan, Bin Chen, Rong State Key Laboratory of Digital Manufacturing Equipment and Technology School of Mechanical Science and Engineering Huazhong University of Science and Technology 430074 Wuhan Hubei People’s Republic of China State Key Laboratory of Materials Processing and Die and Mould Technology School of Materials Science and Engineering Huazhong University of Science and Technology 430074 Wuhan Hubei People’s Republic of China School of Physics and Technology Center for Electron Microscopy MOE Key Laboratory of Artificial Micro- and Nano-structures and Institute for Advanced Studies Wuhan University 430072 Wuhan Hubei People’s Republic of China General Motors Global Research and Development Chemical Sciences and Materials Systems Lab 3500 Mound Road Warren Michigan 48090 USA Institute of High Energy Physics Chinese Academy of Sciences 100049 Beijing People’s Republic of China Present Address: Department of Chemical and Biomolecular Engineering Clemson University Clemson South Carolina 29634 USA

A Correction to this paper has been published: 10.1038/s41467-020-19663-3.

关键词： Heterogeneous catalysis Pollution remediation Materials for energy and catalysis Synthesis and processing

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Geo-Cloudlet: Time and Power Efficient Geospatial Query Resolution using Cloudlet

Geo-Cloudlet: Time and Power Efficient Geospatial Query Reso...

引用

International Conference on advanced Computing, ICAC

作者： Jaydeep Das Anwesha Mukherjee Soumya K. Ghosh Rajkumar Buyya Advanced Technology Development Centre Indian Institute of Technology Kharagpur West Bengal India Department of Computer Science Mahishadal Raj College Purba Medinipur West Bengal India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur West Bengal India Cloud Computing and Distributed Systems (CLOUDS) Lab School of Computing and Information Systems The University of Melbourne VIC Australia

ISBN: (数字)9781728152868

ISBN: (纸本)9781728152875

Geospatial data analysis is an emerging area of research today due to the potential to enable varied location-aware services. The existing centralized cloud-based analysis becomes time and computing-intensive for huge amount of geospatial data processing. This paper addresses the challenge of time and power-efficiency in QoS-aware geospatial query resolution. We propose a cloudlet based hierarchical paradigm, namely Geo-Cloudlet, where the cloudlets contain the geospatial data of the districts. The state and national level geospatial data are stored inside the state cloud and country cloud respectively. The query resolution is performed by either the cloudlet or by the state cloud or country cloud depending upon the geographical region related to the query. The experimental analysis illustrates that the proposed architecture Geo-Cloudlet reduces the latency up to 61.3% and power consumption up to 61.1% over the use of only remote cloud servers for geospatial query resolution.

关键词：

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A probabilistic framework of preference discovery from folksonomy corpus

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Frontiers of Computer Science 2017年第6期11卷 1075-1084页

作者： Xiaohui GUO Chunming HU Richong ZHANG Jinpeng HUAI State Key Lab of Software Development Environment Beihang University Beijing 100191 China Institute of Advanced Computing Technology School of Computer Science and Engineering Beihang University Beijing 100191 China

The increasing availability of folksonomy data makes them vital for user profiling approaches to precisely detect user preferences and better understand user interests, so as to render some personalized recommendation or re- trieval results. This paper presents a rigorous probabilis- tic framework to discover user preference from folkson- omy data. Furthermore, we incorporate three models into the framework with the corresponding inference methods, expectation-maximization or Gibbs sampling algorithms. The user preference is expressed through topical conditional distributions. Moreover, to demonstrate the versatility of our framework, a recommendation method is introduced to show the possible usage of our framework and evaluate the applica- bility of the engaged models. The experimental results show that, with the help of the proposed framework, the user pref- erence can be effectively discovered.

关键词： preference discovery tagging folksonomy so-cial annotation

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A Photoswitchable Ligand Targeting the β1-Adrenoceptor Enables Light-Control of the Cardiac Rhythm**

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Angewandte Chemie 2022年第30期134卷

作者： Anna Duran-Corbera Melissa Faria Yuanyuan Ma Eva Prats André Dias Juanlo Catena Karen L. Martinez Demetrio Raldua Amadeu Llebaria Xavier Rovira MCS Laboratory of Medicinal Chemistry Institute for Advanced Chemistry of Catalonia (IQAC) CSIC Jordi Girona 18 08034 Barcelona Spain Institute for Environmental Assessment and Water Research (IDAEA) CSIC Jordi Girona 18 08034 Barcelona Spain Department of Chemistry & Nanoscience Center University of Copenhagen Thorvaldsensvej 40 1871 Frederiksberg Denmark Research and Development Center (CID) CSIC Jordi Girona 18 08034 Barcelona Spain SIMchem Service of Synthesis of High Added Value Molecules Institute for Advanced Chemistry of Catalonia (IQAC) CSIC Jordi Girona 18 Barcelona Spain Previous address: Molecular Photopharmacology Research Group The Tissue Repair and Regeneration Laboratory (TR2Lab) Faculty of Sciences and Technology University of Vic Central University of Catalonia 08500 Vic Spain

Catecholamine-triggered β-adrenoceptor (β-AR) signaling is essential for the correct functioning of the heart. Although both β 1 - and β 2 -AR subtypes are expressed in cardiomyocytes, drugs selectively targeting β 1 -AR have proven this receptor as the main target for the therapeutic effects of beta blockers in the heart. Here, we report a new strategy for the light-control of β 1 -AR activation by means of photoswitchable drugs with a high level of β 1 -/β 2 -AR selectivity. All reported molecules allow for an efficient real-time optical control of receptor function in vitro. Moreover, using confocal microscopy we demonstrate that the binding of our best hit, pAzo-2, can be reversibly photocontrolled. Strikingly, pAzo-2 also enables a dynamic cardiac rhythm management on living zebrafish larvae using light, thus highlighting the therapeutic and research potential of the developed photoswitches. Overall, this work provides the first proof of precise control of the therapeutic target β 1 -AR in native environments using light.

关键词： Azobenzene Beta-1 Adrenoceptors Drug Design Light-Regulated Ligands Photochromism

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Results of dark matter search using the full PandaX-Ⅱ exposure

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Chinese Physics C 2020年第12期44卷 184-200页

作者： Qiuhong Wang Abdusalam Abdukerim Wei Chen Xun Chen Yunhua Chen Chen Cheng Xiangyi Cui Yingjie Fan Deqing Fang Changbo Fu Mengting Fu Lisheng Geng Karl Giboni Linhui Gu Xuyuan Guo Ke Han Changda He Di Huang Yan Huang Yanlin Huang Zhou Huang Xiangdong Ji Yonglin Ju Shuaijie Li Huaxuan Liu Jianglai Liu Wenbo Ma Yugang Ma Yajun Mao Yue Meng Kaixiang Ni Jinhua Ning Xuyang Ning Xiangxiang Ren Changsong Shang Lin Si Guofang Shen Andi Tan Anqing Wang Hongwei Wang Meng Wang Siguang Wang Wei Wang Xiuli Wang Zhou Wang Mengmeng Wu Shiyong Wu Weihao Wu Jingkai Xia Mengjiao Xiao Pengwei Xie Binbin Yan Jijun Yang Yong Yang Chunxu Yu Jumin Yuan Ying Yuan Xinning Zeng Dan Zhang Tao Zhang Li Zhao Qibin Zheng Jifang Zhou Ning Zhou Xiaopeng Zhou INPAC and School of Physics and Astronomy Shanghai Jiao Tong UniversityMOE Key Lab for Particle PhysicsAstrophysics and CosmologyShanghai Key Laboratory for Particle Physics and CosmologyShanghai 200240China Shanghai Institute of Applied Physics Chinese Academy of SciencesShanghai 201800China University of Chinese Academy of Sciences Beijing 100049China Shanghai Jiao Tong University Sichuan Research Institute Chengdu 610213China Yalong River Hydropower Development Company Ltd.288 Shuanglin RoadChengdu 610051China School of Physics Sun Yat-Sen UniversityGuangzhou 510275China Tsung-Dao Lee Institute Shanghai 200240China School of Physics Nankai UniversityTianjin 300071China Key Laboratory of Nuclear Physics and Ion-beam Application(MOE) Institute of Modern PhysicsFudan UniversityShanghai 200433China School of Physics Peking UniversityBeijing 100871China School of Physics Beihang UniversityBeijing 100191China International Research Center for Nuclei and Particles in the Cosmos&Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang UniversityBeijing 100191China School of Medical Instrument and Food Engineering University of Shanghai for Science and TechnologyShanghai 200093China Department of Physics University of MarylandCollege ParkMaryland 20742USA School of Mechanical Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Physics and Key Laboratory of Particle Physics and Particle Irradiation(MOE) Shandong UniversityJinan 250100China Shanghai Advanced Research Institute Chinese Academy of SciencesShanghai 201210China Center for High Energy Physics Peking UniversityBeijing 100871China

We report the dark matter search results obtained using the full 132 ton·day exposure of the PandaX-II experiment,including all data from March 2016 to August *** significant excess of events is identified above the expected *** limits are set on the spin-independent dark matter-nucleon *** lowest 90%confidence level exclusion on the spin-independent cross section is 2.2×10−46 cm2 at a WIMP mass of 30 GeV/c2.

关键词： dark matter direct detection liquid xenon

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Confronting the smart city governance challenge

Nature Cities

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Nature Cities 2025年 1-4页

作者： Luca Mora Paolo Gerli Michael Batty Emily Binet Royall Natalia Carfi Karl-Filip Coenegrachts Martin de Jong Marcelo Facchina Marijn Janssen Albert Meijer Giulio Pasi Massimo Perrino Rob Raven Darren Sharp Abdinassir Sagar Alessandro Sancino Paolo Santi Gregory Trencher Liesbet van Zoonen Pontus Westerberg Orlando Woods Xiaoling Zhang Gesa Ziemer The Business School Edinburgh Napier University Edinburgh UK Academy of Architecture and Urban Studies Tallinn University of Technology Tallinn Estonia The Bartlett Centre for Advanced Spatial Analysis University College London London UK City of San Antonio San Antonio TX USA Open Data Charter Mexico City Mexico Open and Agile Smart Cities Brussels Belgium g imec-SMIT Vrije Universiteit Brussel Brussels Belgium Rotterdam School of Management Erasmus University Rotterdam Rotterdam The Netherlands Smart City Institute HEC-Liège University of Liège Liège Belgium Institute for Global Public Policy Fudan University Shanghai China CAF - Development Bank of Latin America and the Caribbean Santiago Chile Faculty of Technology Policy and Management Delft University of Technology Delft The Netherlands Utrecht School of Governance Utrecht University Utrecht The Netherlands Loyola University Andalusia Córdoba Spain United Cities and Local Governments Barcelona Spain Monash Sustainable Development Institute Monash University Melbourne Victoria Australia Maastricht University Maastricht The Netherlands Department of Business and Law University of Milan-Bicocca Milan Italy Department of Public Leadership & Social Enterprise Open University Milton Keynes UK Senseable City Lab Department of Urban Studies and Planning Massachusetts Institute of Technology Cambridge MA USA Institute of Informatics and Telematics National Research Council Pisa Italy Graduate School of Global Environmental Studies Kyoto University Kyoto Japan Department of Public Administration & Sociology Erasmus University Rotterdam Rotterdam The Netherlands Teaching Public Service in the Digital Age New York City NY USA College of Integrative Studies Singapore Management University Singapore Singapore Sustainability X-Lab The University of Hong Kong Hong Kong People’s Republic of China Department of Real Estate & Construction The University of Hong Kong Hong Kong People’s Republic of C

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