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arXiv 2024年

作者： Ren, Yingying Li, Qiuli Guo, Yangyang Pedrycz, Witold Xing, Lining Liu, Anfeng Song, Yanjie School of Computer Electronics and Information Guangxi University Nanning China Guangxi Key Laboratory of Multimedia Communications and Network Technology China Key Laboratory of Parallel Distributed and Intelligent Computing Guangxi University China School of Systems Science Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey School of Electronic Engineering Xidian University Xian China School of Electronic Engineering Central South University Changsha China National Engineering Research Center of Maritime Navigation System Dalian Maritime University Dalian China

With the rapid development of the satellite industry, the information transmission network based on communication satellites has gradually become a major and important part of the future satellite ground integration network. However, the low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system and needs to be solved urgently. Effectively planning the task of satellite ground networking by reasonably scheduling resources is crucial for the efficient transmission of task data. In this paper, we hope to provide a task execution scheme that maximizes the profit of the networking task for satellite ground network planning considering feeding mode (SGNPFM). To solve the SGNPFM problem, a mixed-integer planning model with the objective of maximizing the gain of the link-building task is constructed, which considers various constraints of the satellite in the feed-switching mode. Based on the problem characteristics, we propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks. To obtain more high-quality solutions, different similarity evaluation methods are designed to assist the algorithm in intelligently screening individuals. The DSGAalso uses an adaptive crossover strategy based on similarity mechanism, which guides the algorithm to achieve efficient population search. In addition, a task scheduling algorithm considering the feed-switching mode is designed for decoding the algorithm to generate a highquality scheme. The results of simulation experiments show that the DSGA can effectively solve the SGNPFM problem. Compared to other algorithms, the proposed algorithm not only obtains higher quality planning schemes but also has faster algorithm convergence speed. The proposed algorithm improves data trans

关键词： Profitability

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Tailoring magnetism in self-intercalated Cr1+δTe2 epitaxial films

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Physical Review Materials 2020年第11期4卷 114001-114001页

作者： Y. Fujisawa M. Pardo-Almanza J. Garland K. Yamagami X. Zhu X. Chen K. Araki T. Takeda M. Kobayashi Y. Takeda C. H. Hsu F. C. Chuang R. Laskowski K. H. Khoo A. Soumyanarayanan Y. Okada Quantum Materials Science Unit Okinawa Institute of Science and Technology (OIST) Okinawa 904-0495 Japan Applied Physics & Mathematics Department Northeastern University Boston Massachusetts 02115 USA Institute of Materials Research and Engineering Agency for Science Technology and Research 138634 Singapore Department of Electrical Engineering and Information Systems The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan Center for Spintronics Research Network The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan Materials Sciences Research Center Japan Atomic Energy Agency Sayo Hyogo 679-5148 Japan Department of Physics National Sun Yat-sen University Kaohsiung 80424 Taiwan Institute of High Performance Computing Agency for Science Technology and Research 138632 Singapore Department of Physics National University of Singapore 117551 Singapore

Magnetic transition metal dichalcogenide (TMD) films have recently emerged as promising candidates in hosting novel magnetic phases relevant to next-generation spintronic devices. However, systematic control of the magnetization orientation, or anisotropy, and its thermal stability characterized by Curie temperature (TC), remains to be achieved in such films. Here we present self-intercalated epitaxial Cr1+δTe2 films as a platform for achieving systematic/smooth magnetic tailoring in TMD films. Using a molecular-beam epitaxy based technique, we have realized epitaxial Cr1+δTe2 films with smoothly tunable δ over a wide range (0.33–0.82), while maintaining NiAs-type crystal structure. With increasing δ, we found monotonic enhancement of TC from 160 to 350 K, and the rotation of magnetic anisotropy from out-of-plane to in-plane easy-axis configuration for fixed film thickness. Contributions from conventional dipolar and orbital moment terms are insufficient to explain the observed evolution of magnetic behavior with δ. Instead, ab initio calculations suggest that the emergence of antiferromagnetic interactions with δ, and its interplay with conventional ferromagnetism, may play a key role in the observed trends. This demonstration of tunable TC and magnetic anisotropy across room temperature in TMD films paves the way for engineering different magnetic phases for spintronic applications.

关键词： Magnetism

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Deterministic realization of collective measurements via photonic quantum walks

arXiv

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arXiv 2017年

作者： Hou, Zhibo Tang, Jun-Feng Shang, Jiangwei Zhu, Huangjun Li, Jian Yuan, Yuan Wu, Kang-Da Xiang, Guo-Yong Li, Chuan-Feng Guo, Guang-Can Key Laboratory of Quantum Information University of Science and Technology of China CAS Hefei230026 China Synergetic Innovation Center of Quantum Information and Quantum Physics University of Science and Technology of China Hefei230026 China Naturwissenschaftlich-Technische Fakultät Universität Siegen Siegen57068 Germany Beijing Key Laboratory of Nanophotonics Ultrafine Optoelectronic Systems School of Physics Beijing Institute of Technology Beijing100081 China Institute for Theoretical Physics University of Cologne Cologne50937 Germany Department of Physics Center for Field Theory and Particle Physics Fudan University Shanghai200433 China Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai200433 China State Key Laboratory of Surface Physics Fudan University Shanghai200433 China Collaborative Innovation Center of Advanced Microstructures Nanjing210093 China Institute of Signal Processing Transmission Nanjing University of Posts and Telecommunications Nanjing210003 China Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Ministry of Education Nanjing210003 China

Collective measurements on identically prepared quantum systems can extract more information than local measurements, thereby enhancing information-processing efficiency. Although this nonclassical phenomenon has been known for two decades, it has remained a challenging task to demonstrate the advantage of collective measurements in experiments. Here we introduce a general recipe for performing deterministic collective measurements on two identically prepared qubits based on quantum walks. Using photonic quantum walks, we realize experimentally an optimized collective measurement with fidelity 0.9946 without post selection. As an application, we achieve the highest tomographic efficiency in qubit state tomography to date. Our work offers an effective recipe for beating the precision limit of local measurements in quantum state tomography and metrology. In addition, our study opens an avenue for harvesting the power of collective measurements in quantum information processing and for exploring the intriguing physics behind this power. Copyright © 2017, The Authors. All rights reserved.

关键词： Quantum optics

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When Fine-Tuning LLMs Meets Data Privacy: An Empirical Study of Federated Learning in LLM-Based Program Repair

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ACM Transactions on Software Engineering and Methodology 1000年

作者： Wenqiang Luo Jacky Keung Boyang Yang He Ye Claire Le Goues Tegawendé F. Bissyandé Haoye Tian Xuan Bach D. Le Department of Computer Science City University of Hong Kong China Jisuan Institute of Technology Beijing JudaoYouda Network Technology Co. Ltd. China School of Computer Science Carnegie Mellon University USA SnT University of Luxembourg Luxembourg School of Computing and Information Systems University of Melbourne Australia

Software systems have been evolving rapidly and inevitably introducing bugs at an increasing rate, leading to significant maintenance costs. While large language models (LLMs) have demonstrated remarkable potential in enhancing software development and maintenance practices, particularly in automated program repair (APR), they rely heavily on high-quality code repositories. Most code repositories are proprietary assets that capture the diversity and nuances of real-world industry software practices, which public datasets cannot fully represent. However, obtaining such data from various industries is hindered by data privacy concerns, as companies are reluctant to share their proprietary codebases. There has also been no in-depth investigation of collaborative software development by learning from private and decentralized data while preserving data privacy for program *** address the gap, we investigate federated learning as a privacy-preserving method for fine-tuning LLMs on proprietary and decentralized data to boost collaborative software development and maintenance. We use the private industrial dataset TutorCode for fine-tuning and the EvalRepair-Java benchmark for evaluation, and assess whether federated fine-tuning enhances program repair. We then further explore how code heterogeneity (i.e., variations in coding style, complexity, and embedding) and different federated learning algorithms affect bug fixing to provide practical implications for real-world software development collaboration. Our evaluation reveals that federated fine-tuning can significantly enhance program repair, achieving increases of up to 16.67% for Top@10 and 18.44% for Pass@10, even comparable to the bug-fixing capabilities of centralized learning. Moreover, the negligible impact of code heterogeneity implies that industries can effectively collaborate despite diverse data distributions. Different federated algorithms also demonstrate unique strengths across LLMs, suggesting that

关键词： Program Repair Federated Learning Large Language Models

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Segment Anything Model is a Good Teacher for Local Feature Learning

arXiv

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arXiv 2023年

作者： Wu, Jingqian Xu, Rongtao Wood-Doughty, Zach Wang, Changwei Xu, Shibiao Lam, Edmund Y. The University of Hong Kong Pokfulam Hong Kong The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100190 China Northwestern University EvanstonIL60201 United States The Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250013 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China The School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China

Local feature detection and description play an important role in many computer vision tasks, which are designed to detect and describe keypoints in any scene and any downstream task. Data-driven local feature learning methods need to rely on pixel-level correspondence for training. However, a vast number of existing approaches ignored the semantic information on which humans rely to describe image pixels. In addition, it is not feasible to enhance generic scene keypoints detection and description simply by using traditional common semantic segmentation models because they can only recognize a limited number of coarse-grained object classes. In this paper, we propose SAMFeat to introduce SAM (segment anything model), a foundation model trained on 11 million images, as a teacher to guide local feature learning. SAMFeat learns additional semantic information brought by SAM and thus is inspired by higher performance even with limited training samples. To do so, first, we construct an auxiliary task of Attention-weighted Semantic Relation Distillation (ASRD), which adaptively distillates feature relations with category-agnostic semantic information learned by the SAM encoder into a local feature learning network, to improve local feature description using semantic discrimination. Second, we develop a technique called Weakly Supervised Contrastive Learning Based on Semantic Grouping (WSC), which utilizes semantic groupings derived from SAM as weakly supervised signals, to optimize the metric space of local descriptors. Third, we design an Edge Attention Guidance (EAG) to further improve the accuracy of local feature detection and description by prompting the network to pay more attention to the edge region guided by SAM. SAMFeat’s performance on various tasks such as image matching on HPatches, and long-term visual localization on Aachen Day-Night showcases its superiority over previous local features. The release code is available at https://***/vignywang/SAMFeat

关键词： Computer vision

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Big Data: Prospects and Challenges

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Vikalpa 2015年第1期40卷 74 - 96页

作者： Janakiraman Moorthy Rangin Lahiri Neelanjan Biswas Dipyaman Sanyal Jayanthi Ranjan Krishnadas Nanath Pulak Ghosh (Coordinator) Director and Professor of Marketing at the Institute of Management Technology Dubai. Earlier he was Professor of Marketing at the IIM Calcutta and IIM Lucknow. He received his PhD from IIM Ahmedabad. His recent research papers were published in the leading scholarly ournals such as Marketing Science British Food Journal Journal of Information Technology Case and Application Research Journal of Database Marketing & Customer Strategy Management. He has wide experience in the banking and investment industry. He was earlier the Global Research and Project Director of the Institute for Customer Relationship Management Atlanta USA. He was the Convener of the prestigious CAT Exam 2011. e-mail: Practice Director leading Atos India's CRM practice while supporting Strategic Business Development for North American Market. With an experience of more than 15 years Rangin has worked extensively as a Business Consultant in Information Technology (Sales Automation Marketing & Service Management area) Customer Data Management and CRM Analytics. e-mail: Business Consultant at Atos with extensive experience in Business Analysis Risk Management Analytics Business Development Presales Solution Ideation on Enterprise Data Management Enterprise Reference Data and Master Data Management area. e-mail: founder and CEO of dono consulting a boutique quantitative analytics and investment research firm. He has worked for leading financial firms in New York and India including Dow Jones Blackstone Sorin Capital (VP Quantitative Modeling) and Thomson Reuters (Head of Real Estate Analytics). A CFA charter holder and Commonwealth Scholar Deep has an MS (Applied Economics) from University of Texas Dallas and an MA (Economics) from Jadavpur University e-mail: Professor in the Information Systems Group of the Institute of Management Technology Ghaziabad. Her PhD is in the field of data mining from Jamia Millia Islamia Central University India. She has published five edited books. She is serving on the editorial b

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Tailoring magnetism in self-intercalated Cr1+δTe2 epitaxial films

arXiv

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arXiv 2020年

作者： Fujisawa, Y. Pardo-Almanza, M. Garland, J. Yamagami, K. Zhu, X. Chen, X. Araki, K. Takeda, T. Kobayashi, M. Takeda, Y. Hsu, C.H. Chuang, F.C. Laskowski, R. Khoo, K.H. Soumyanarayanan, A. Okada, Yoshinori Okinawa904-0495 Japan Applied Physics & Mathematics Department Northeastern University BostonMA02115 United States Institute of Materials Research and Engineering Agency for Science Technology and Research Singapore138634 Singapore Department of Electrical Engineering and Information Systems University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Center for Spintronics Research Network University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Condensed Matter Science Division Quantum Beam Science Directorate Japan Atomic Energy Agency 1-1-1 Kouto Sayo-cho Sayo-gunHyogo679-5148 Japan Department of Physics National Sun Yat-sen University Kaohsiung80424 Taiwan Institute of High Performance Computing Agency for Science Technology and Research 138632 Singapore Department of Physics National University of Singapore 117551 Singapore

Magnetic transition metal dichalcogenide (TMD) films have recently emerged as promising candidates to host novel magnetic phases relevant to next-generation spintronic devices. However, systematic control of the magnetization orientation, or anisotropy, and its thermal stability, characterized by Curie temperature (Tc) – remains to be achieved in such films. Here we present self-intercalated epitaxial Cr1+δTe2 films as a platform for achieving systematic/smooth magnetic tailoring in TMD films. Using a molecular beam epitaxy (MBE) based technique, we have realized epitaxial Cr1+δTe2 films with smoothly tunable δ over a wide range (0.33-0.82), while maintaining NiAs-type crystal structure. With increasing δ, we found monotonic enhancement of Tc from 160 to 350 K, and the rotation of magnetic anisotropy from out-of-plane to in-plane easy axis configuration for fixed film thickness. Contributions from conventional dipolar and orbital moment terms are insufficient to explain the observed evolution of magnetic behavior with δ. Instead, ab initio calculations suggest that the emergence of antiferromagnetic interactions with δ, and its interplay with conventional ferromagnetism, may play a key role in the observed trends. To our knowledge, this constitutes the first demonstration of tunable Tc and magnetic anisotropy across room temperature in TMD films, and paves the way for engineering novel magnetic phases for spintronic applications. Copyright © 2020, The Authors. All rights reserved.

关键词： Transition metals

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Correction to: Social influence and unfollowing accelerate the emergence of echo chambers

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Journal of Computational Social Science 2022年第2期5卷 1779-1779页

作者： Sasahara, Kazutoshi Chen, Wen Peng, Hao Ciampaglia, Giovanni Luca Flammini, Alessandro Menczer, Filippo Graduate School of Informatics Nagoya University Nagoya Japan JST PRESTO Saitama Japan Center for Complex Networks and Systems Research Indiana University Bloomington Bloomington USA Luddy School of Informatics Computing and Engineering Indiana University Bloomington USA School of Information University of Michigan Ann Arbor USA Network Science Institute Indiana University Bloomington USA Department of Computer Science and Engineering University of South Florida Tampa USA

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INTRODUCTION

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Bulletin of the American Meteorological Society 2024年第8期105卷 S1-S11页

作者： Boyer, T. Blunden, J. Dunn, R.J.H. NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Scripps Institution of Oceanography University of California San Diego La Jolla California NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Brandenburg University of Technology (BTU) Cottbus-Senftenberg Germany Servicio Meteorológico Nacional Buenos Aires Argentina NOAA/OAR Physical Sciences Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research and School of Physics University of Costa Rica San José Costa Rica Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Hampton University Hampton Virginia Seychelles Meteorological Authority Mahe Seychelles Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder Colorado Servicio Nacional de Meteorología e Hidrología del Perú Lima Perú Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Department o

In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and Russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. A

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第Sup1期18卷 1-79页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Bra

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