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TechRxiv 2023年

作者： Scherer, William T. Tolk, Andreas Loper, Margaret L. Barry, Philip Rabadi, Ghaith Yilmaz, Levent The MITRE Corporation Modeling and Analysis Innovation Center 1001 Research Park Blvd #200 CharlottesvilleVA22911 United States Georgia Tech Research Institute Georgia Institute of Technology AtlantaGA30308 United States University of Virgina Department of Systems and Information Engineering CharlottesvilleVA22904 United States L3Harris Corporation Mission Analysis Group 2235 Monroe Street HerndonVA20171 United States University of Central Florida School of Modeling Simulation and Training 3100 Technology Pkwy OrlandoFL32826 United States Auburn University Samuel Ginn College of Engineering Computer Science and Software Engineering AuburnAL36849 United States

The position paper summarizes the inputs of a set of experts from academia and industry presenting their view on chances and challenges of using ChatGPT within the Modelling and simulation education. They also address the need to evaluate continuous education as well as education of faculty members to address scholastic challenges and opportunities while meeting the expectation of industry. © 2023, CC BY.

关键词： modeling languages

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Building Transparent and Personalized AI Support in Air Traffic Control

Building Transparent and Personalized AI Support in Air Traf...

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Conference on Digital Avionics Systems (DASC)

作者： Carl Westin Brian Hilburn Clark Borst Erik-Jan van Kampen Magnus Bång Science and Technology Linköping University Linköping Sweden Center for Human Performance Research Den Haag The Netherlands Control and Simulation Technical University Delft Delft The Netherlands Computer and Information Science Linköping University Linköping Sweden

ISBN: (数字)9781728198255

ISBN: (纸本)9781728180885

Artificial intelligence is considered a key enabler for realizing a more efficient future air traffic management system. As the automation designed to support us grows more sophisticated and complex, our understanding of it tends to suffer. Recent research has addressed this issue in two ways: either through increased automation transparency or increased personalization. This paper overviews recent work in these two areas of strategic conformance (i.e., personalization) and automation transparency (e.g., explainable artificial intelligence and machine learning interpretability). We discuss how to achieve and how to balance conformance and transparency in the context of a machine learning system for conflict detection and resolution in air traffic control. In the MAHALO project, we aim to build, and empirically evaluate, a personalized and transparent decision support system by combining supervised and reinforcement learning approaches. We believe that such a system could strive for optimal performance while accommodating individual differences. By knowing the individual's preferences, the system would be able to afford transparency by explaining both why it suggests another solution (that deviates from the individual's), and why this solution is considered to be better.

关键词： Automation Artificial intelligence Decision making Cognition Uncertainty human factors Europe

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A Coupled Transformer-CNN Network: Advancing Sea Surface Temperature Forecast Accuracy

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Zhang, Tao Lin, Pengfei Liu, Hailong Wang, Pengfei Wang, Ya Xu, Kai Zheng, Weipeng Li, Yiwen Jiang, Jinrong Zhao, Lian Chen, Jian State Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Sciences Beijing100029 China College of Earth and Planetary Sciences University of Chinese Academy of Sciences Beijing100049 China Laoshan Laboratory Qingdao266237 China Institute of Atmospheric Physics Chinese Academy of Sciences Beijing100029 China Earth System Numerical Simulation Science Center Institute of Atmospheric Physics Chinese Academy of Sciences Beijing100029 China School of Ocean Sciences China University of Geosciences Beijing100083 China Computer Network Information Center Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Geo-Information Engineering Xi'an710054 China

Sea surface temperature (SST) is critically important for understanding ocean dynamics and supporting various marine activities, making accurate short-term SST forecasting highly significant. However, accurately modeling the multi-scale variability of SST remains challenging for existing deep learning (DL) models. This study introduces the Coupled Transformer-CNN Network (CoTCN), a hybrid architecture designed to leverage the multi-scale variability of SST. The CoTCN combines the strengths of Transformers and convolutional neural networks (CNNs), significantly enhancing SST forecasts' spatial continuity and predictive accuracy. Compared to five state-of-the-art DL models based on Transformer or CNN that include ConvLSTM, ConvGRU, AFNO, PredRNN, and SwinLSTM, CoTCN demonstrates superior performance in global and local areas of SST forecasting. At 1-day lead time, CoTCN reduces the global average root mean square error (RMSE) by over 15%, with forecast errors ranging from 0.20°C to 0.53°C across 1-10 day lead times. Moreover, the CoTCN effectively mitigates the checkerboard artifacts inherent to the Vision Transformer architecture. These findings highlight the effectiveness of CoTCN in capturing SST's multi-scale features and underscore the promising potential of hybrid architectures for future DL models. © 1980-2012 IEEE.

关键词： Convolutional neural networks

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DeePMD-kit v2: A software package for Deep Potential models

arXiv

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arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

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Outcomes from a Workshop on a National center for Quantum Education

arXiv

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arXiv 2024年

作者： Barnes, Edwin Bennett, Michael B. Boltasseva, Alexandra Borish, Victoria Brown, Bennett Carr, Lincoln D. Ceballos, Russell R. Dukes, Faith Easton, Emily W. Economou, Sophia E. Edwards, E.E. Finkelstein, Noah D. Fracchiolla, C. Franklin, Diana Freericks, J.K. Goss, Valerie Hannum, Mark Holincheck, Nancy Kelly, Angela M. Lanes, Olivia Lewandowski, H.J. Matsler, Karen Jo Mercurio, Emily Montaño, Inès Murdock, Maajida Peltz, Kiera Perron, Justin K. Richardson, Christopher J.K. Rosenberg, Jessica L. Ross, Richard S. Ryu, Minjung Samuel, Raymond E. Schrode, Nicole Schwamberger, Susan Searles, Thomas A. Singh, Chandralekha Tingle, Alexandra Zwickl, Benjamin M. Department of Physics Virginia Tech Center for Quantum Information Science and Engineering Virginia Tech 850 West Campus Drive BlacksburgVA24061 United States Q-SEnSE NSF QLCI University of Colorado 440 UCB BoulderCO80301 United States Elmore Family School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue Quantum Science and Engineering Institute Purdue University 610 Purdue Mall West LafayetteIN47907 United States Quantum Science Center A National Quantum Information Science Research Center of The U.S. Department of Energy Oak Ridge National Laboratory 1 Bethel Valley Road Oak RidgeTN37830 United States JILA National Institute of Standards and Technology the University of Colorado BoulderCO80309 United States Department of Physics University of Colorado 390 UCB BoulderCO80309 United States QuSTEAM 510 Ronalds St. Iowa CityIA52245 United States Quantum Engineering Program Department of Physics Colorado School of Mines 1523 Illinois St. GoldenCO80401 United States QuSTEAM Non-Profit Wilbur Wright College City Colleges of Chicago 4300 N Narragansett Ave ChicagoIL60634 United States Government and Community Relations Office - K-12 Programs Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA United States Chicago Quantum Exchange 5235 S. Harper Court ChicagoIL60615 United States Department of Physics Virginia Tech 850 West Campus Drive BlacksburgVA24060 United States Department of Electrical and Computer Engineering Duke Quantum Center Duke University Duke University 130 Hudson Hall DurhamNC27708 United States American Physical Society 555 12th St. NW WashingtonDC20004 United States Computer Science University of Chicago 5730 S. Ellis Ave. ChicagoIL60637 United States Department of Physics Georgetown University 552 Reiss Science Building WashingtonDC20057 United States Chemistry Physics and Engineering Studies Chicago State University 9501 S. King Drive WSC 309 ChicagoIL606

In response to numerous programs seeking to advance quantum education and workforce development in the United States, experts from academia, industry, government, and professional societies convened for a National science Foundation-sponsored workshop in February 2024 to explore the benefits and challenges of establishing a national center for quantum education. Broadly, such a center would foster collaboration and build the infrastructure required to develop a diverse and quantum-ready workforce. The workshop discussions centered around how a center could uniquely address gaps in public, K-12, and undergraduate quantum information science and engineering (QISE) education. Specifically, the community identified activities that, through a center, could lead to an increase in student awareness of quantum careers, boost the number of educators trained in quantum-related subjects, strengthen pathways into quantum careers, enhance the understanding of the U.S. quantum workforce, and elevate public engagement with QISE. Core proposed activities for the center include professional development for educators, coordinated curriculum development and curation, expanded access to educational laboratory equipment, robust evaluation and assessment practices, network building, and enhanced public engagement with quantum science. © 2024, CC BY.

关键词： Students

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Experiments in Routing Vehicles for Municipal Services

Experiments in Routing Vehicles for Municipal Services

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International Conference on High Performance Computing and simulation

作者： Imran Mahmood Sahar Idwan Junaid Ahmed Zubairi Izzeddin Matar Center for Research in Modeling & Simulation School of Electrical Engineering and Computer Science National University of Sciences and Technology CS and Engineering Department School of Engineering American U of Ras Al Khaimah Ras al Khaimah UAE and Comp Sci & Apps Dept Hashemite University Computer and Information Sciences Department College of Liberal Arts and Sciences State University of New York at Fredonia Faculty of Information Technology Software Engineering Department University of Petra

ISBN: (纸本)9781538678800

In this paper, route planning of waste collection trucks using R&R (Ruin and Recreate) approach is explored. We assume the trucks are guided by the central depot in selecting the optimal route for waste collection. Heuristic algorithms are simulated to find the optimal routes for the waste management fleet. Through the use of smart dumpsters that can communicate the current level of waste using sensors and communication modules, we aim to reduce the number of trucks used, the total time taken and total distance traveled by the fleet in a day. Our work is based on variations of CVRPTW (Capacitated Vehicle Routing Problem with Time Window Constraint). The central management system selects the dumpsters, based on their waste levels, in descending order, and dispatches appropriate number of trucks, with path assignments using Ruin and Recreate (R&R) approach of the VRPTW strategy. The municipal authority saves transit time, fuel cost and service time by using our approach, through a simulation of smart waste collection.

关键词： Smart Dumpster Capacitated Vehicle Routing Problem with Time Window Constraint Ruin & Recreate Search heuristic Waste management simulation

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Vehicular ad hoc network (VANET): A survey, challenges, and applications

Advances in Intelligent Systems and Computing

引用

Advances in Intelligent Systems and Computing 2017年 548卷 39-51页

作者： Rasheed, Asim Gillani, Saira Ajmal, Sana Qayyum, Amir Capital University of Science & Technology Islamabad Pakistan Department of Computer Science COMSATS Institute of Information Technology Islamabad Pakistan Center of Modeling and Simulation National University of Sciences & Technology Islamabad Pakistan

An ad hoc network consisting of vehicles has emerged as an interesting but challenging domain where a lot of new application may find their place. Though research in this field is on since last two decades, large-scale practical implementation still require some time. In this paper, a survey of current challenges and potential applications, incorporating medium access control schemes, routing approaches, hardware and spectrum issues, and security and privacy issues for VANETs, is presented. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Surveys

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Randomized reference models for temporal networks

arXiv

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arXiv 2018年

作者： Gauvin, Laetitia Génois, Mathieu Karsai, Márton Kivelä, Mikko Takaguchi, Taro Valdano, Eugenio Vestergaard, Christian L. Data Science Lab ISI Foundation Turin Italy Aix Marseille Univ. Univ. Toulon CNRS CPT Marseille France Department of Network and Data Science Central European University Vienna1100 Austria Université de Lyon ENS de Lyon INRIA CNRS LIP UMR 5668 IXXI Lyon69364 Cedex 07 France Alfréd Rényi Insititute of Mathematics BudapestH-1053 Hungary Department of Computer Science Aalto University School of Science P.O. Box 12200 AaltoFI-00076 Finland National Institute of Information and Communications Technology 4-2-1 Nukui-Kitamachi Koganei Tokyo184-8795 Japan Center for Biomedical Modeling The Semel Institute for Neuroscience and Human Behavior David Geffen School of Medicine University of California Los Angeles 760 Westwood Plaza Los AngelesCA90024 United States Decision and Bayesian Computation Department of Computational Biology Department of Neuroscience CNRS USR 3756 UMR 3571 Institut Pasteur 25 rue du Docteur Roux Paris75015 France

Many dynamical systems can be successfully analyzed by representing them as networks. Empirically measured networks and dynamic processes that take place in these situations show heterogeneous, non-Markovian, and intrinsically correlated topologies and dynamics. This makes their analysis particularly challenging. Randomized reference models (RRMs) have emerged as a general and versatile toolbox for studying such systems. Defined as random networks with given features constrained to match those of an input (empirical) network, they may for example be used to identify important features of empirical networks and their effects on dynamical processes unfolding in the network. RRMs are typically implemented as procedures that reshuffle an empirical network, making them very generally applicable. However, the effects of most shuffling procedures on network features remain poorly understood, rendering their use non-trivial and susceptible to misinterpretation. Here we propose a unified framework for classifying and understanding microcanonical RRMs (MRRMs) that sample networks with uniform probability. Focusing on temporal networks, we survey applications of MRRMs found in literature, and we use this framework to build a taxonomy of MRRMs that proposes a canonical naming convention, classifies them, and deduces their effects on a range of important network features. We furthermore show that certain classes of MRRMs may be applied in sequential composition to generate new MRRMs from the existing ones surveyed in this article. We finally provide a tutorial showing how to apply a series of MRRMs to analyze how different network features affect a dynamic process in an empirical temporal network. Our taxonomy provides a reference for the use of MRRMs, and the theoretical foundations laid here may further serve as a base for the development of a principled and automatized way to generate and apply randomized reference models for the study of networked systems. Copyright © 2018,

关键词： Taxonomies

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An efficient storage technology for object-based distributed storage system

International Journal of Control and Automation

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International Journal of Control and Automation 2015年第11期8卷 337-348页

作者： Zhang, Jilin Su, Yucheng Wan, Jian Zhou, Li Wang, Jue Zhang, Lei College of Electrical Engineering Zhejiang University Hangzhou China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education China Supercomputing Center of Computer Network Information Center Chinese Academy of Sciences Beijing China Computer Science Department Beijing University of Civil Engineering and Architecture Beijing China

There have two effective ways to improve the overall efficiency of the object-based distributed storage system, one is network caching technology and the other is metadata server cluster load balancing strategy. Under the heterogeneous storage environments, object-based distributed file system face the key challenge of lacking caching mechanism for the high-frequency access files, metadata server load imbalance also reduce the overall efficiency. To address these challenges, we discuss the design and implementation of an object-based distributed file storage caching system, and propose a weight-based metadata server cluster load balancing strategy. It effectively improve the metadata processing capacity. Based on the results, it demonstrated the feasibility and the efficiency of our methods. © 2015 SERSC.

关键词： Metadata

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Research and implementation of CDP disaster recovery system based on oracle

International Journal of Control and Automation

引用

International Journal of Control and Automation 2015年第11期8卷 325-336页

作者： Ren, Yongjian Ye, Jiaolong Zhang, Jilin Zhou, Li Wang, Jue Zhang, Lei School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education China College of Electrical Engineering Zhejiang University Hangzhou China Supercomputing Center of Computer Network Information Center Chinese Academy of Sciences Beijing China Computer Science Department Beijing University of Civil Engineering and Architecture Beijing China

The OraCDP disaster recovery system uses CDP disaster recovery technology based on the combination of block level, combining with the communication coupling between the underlying I/O and Oracle database. The experimental results show that the OraCDP system can not only maintain the normal operation of the original system without any negative influence, but also guarantee the recovery of data and service at specific time quickly, fully and accurately. © 2015 SERSC.

关键词： Recovery

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