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AHFE International Conference on Human Factors in simulation and Modeling, 2019

作者： Files, Benjamin T. Oiknine, Ashley H. Thomas, Jerald Khooshabeh, Peter Sinatra, Anne M. Pollard, Kimberly A. US Army Research Laboratory Los Angeles United States DCS Corporation Los Angeles United States Psychological and Brain Sciences University of California at Santa Barbara Santa Barbara United States Computer Science University of Minnesota Minneapolis United States Natick Soldier Research Development & Engineering Center – Simulation & Training Technology Center Orlando United States

ISBN: (纸本)9783030201470

We introduce a novel framework for creating and evaluating multiple virtual reality environments (VEs) that are naturalistic and similar in navigational complexity. We developed this framework in support of a spatial-learning study using a within-subjects design. We generated three interior environments and used graph-theoretic methods to ensure similar complexity. We then developed a scavenger-hunt task that ensured participants would visit all parts of the environments. Here, we describe VE development and a user study evaluating the relative task difficulty in the environments. Our results showed that our techniques were generally successful: the average time to complete the task was similar across environments. Some participants took longer to complete the task in one of the environments, indicating room for refinement of our framework. The methods described here should be of use for future studies using VEs, especially in within-subjects design. © Springer Nature Switzerland AG 2020.

关键词： Virtual reality

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A Model Based Heterogeneous Data Collaboration Method

A Model Based Heterogeneous Data Collaboration Method

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IEEE International Conference on Electronics and Communication Engineering (ICECE)

作者： Junjie Xue Junhua Zhou Guoqiang Shi Yanhong Jiang Bowen Wei Hongyan Quan Beijing Complex Product Advanced Manufacturing Engineering Research Center Beijing Simulation Center Beijing China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System Engineering Beijing China Beijing Simulation Center Science and Technology on Space System Simulation Laboratory Beijing China School of Software Engineering East China Normal University Shanghai China School of Computer Science and Technology East China Normal University Shanghai China

ISBN: (数字)9781728192581

ISBN: (纸本)9781728192598

Model based heterogeneous data collaboration is important in the integrated design and manufacturing technology. In this work, we propose a systematic method of collaborative design method for heterogeneous data sources. In our method, we use the Open Model Based Engineering Environment (OpenMBEE) to study. In our work, we setup environment and construct the integrated multi-tools achieving consistency design, and then we successfully design and implement a reasonable scheme to parameterize the model in requirement analysis and store the extracted parameters in Model Management System (MMS) for transporting to the visualization tool for further simulation. We integrate the mathematical analysis tools of MATLAB, and successfully design the interface in the tool integration, and realize the model consistency in the problem of integrating multiple tools in the design based on model center. The effectiveness of the proposed integration strategies is verified by some practical instances. That is verified that our provided study can an effective design idea for complex production.

关键词： Analytical models Collaboration Production Tools Data models Mathematical model Matlab

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TMBO-AOD: Transparent Mask Background Optimization for Accurate Object Detection in Large-Scale Remote-Sensing Images

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Remote Sensing 2025年第10期17卷 1762-1762页

作者： Tianyi Fu Hongbin Dong Benyi Yang Baosong Deng National Engineering Laboratory for E-Government Modeling and Simulation Harbin Engineering University Nantong Street Harbin 150001 China College of Computer Science and Technology Harbin Engineering University Nantong Street Harbin 150001 China Defense Innovation Institute Chinese Academy of Military Science Beijing 100071 China Intelligent Game and Decision Laboratory Chinese Academy of Military Science Beijing 100071 China

Recent advancements in deep-learning and computer vision technologies, coupled with the availability of large-scale remote-sensing image datasets, have accelerated the progress of remote-sensing object detection. However, large-scale remote-sensing images typically feature extensive and complex backgrounds with small and sparsely distributed objects, which pose significant challenges to detection performance. To address this, we propose a novel framework for accurate object detection, termed transparent mask background optimization for accurate object detection (TMBO-AOD), which incorporates a clear focus module and an adaptive filtering framework. The clear focus module constructs an empirical background pool using a Gaussian distribution and introduces transparent masks to prepare for subsequent optimization stages. The adaptive filtering framework can be applied to anchor-based or anchor-free models. It dynamically adjusts the number of candidates generated based on background flags, thereby optimizing the label assignment process. This approach not only alleviates the imbalance between positive and negative samples but also enhances the efficiency of candidate generation. Furthermore, we introduce a novel separated loss function that strengthens both foreground and background consistencies. Specifically, it focuses the model’s attention on foreground objects while enabling it to learn the consistency of background features, thus improving its ability to distinguish objects from the background. We employ YOLOv8 combined with our proposed optimizations to evaluate our model in many datasets, demonstrating improvements in both accuracy and efficiency. Additionally, we validate the effectiveness of our adaptive filtering framework in both anchor-based and anchor-free methods. When implemented with YOLOv5 (anchor based), the framework reduces the candidate generation time by 48.36%, while the YOLOv8 (anchor-free) implementation achieves a 46.81% reduction, both with

关键词： convolutional neural networks remote sensing diversity feature extraction object detection

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Spontaneous Chirality Flipping in an Orthogonal Spin-Charge Ordered Topological Magnet

arXiv

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arXiv 2023年

作者： Miao, H. Bouaziz, J. Fabbris, G. Meier, W.R. Yang, F.Z. Li, H.X. Nelson, C. Vescovo, E. Zhang, S. Christianson, A. Lee, H.N. Zhang, Y. Batista, C.D. Blügel, S. Materials Science and Technology Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Peter Grünberg Institut Institute for Advanced Simulation Forschungszentrum Jülich &JARA JülichD-52425 Germany Advanced Photon Source Argonne National Laboratory ArgonneIL60439 United States Department of Physics and Astronomy The University of Tennessee KnoxvilleTN37996 United States Guangzhou China National Synchrotron Light Source II Brookhaven National Laboratory UptonNY11973 United States Max-Planck-Institut fur Physik komplexer Systeme Nothnitzer Straße 38 Dresden01187 Germany Min H. Kao Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN37996 United States Quantum Condensed Matter Division Shull-Wollan Center Oak Ridge National Laboratory Oak RidgeTN37831 United States

The asymmetric distribution of chiral objects with opposite chirality is of great fundamental interests ranging from molecular biology to particle physics1. In quantum materials, chiral states can build on inversion-symmetry-breaking lattice structures or emerge from spontaneous magnetic ordering induced by competing interactions. Although the handedness of a chiral state can be changed through external fields2,3, a spontaneous chirality flipping has yet to be discovered. In this letter, we present experimental evidence of chirality flipping via changing temperature in a topological magnet EuAl4, which features orthogonal spin and charge density waves (SDW/CDW). Using circular dichroism of Bragg peaks in the resonant magnetic x-ray scattering, we find that the chirality of the helical SDW flips through a first order phase transition with modified SDW wavelength. Intriguingly, we observe that the CDW couples strongly with the SDW and displays a rare commensurate-to-incommensurate transition at the chirality flipping temperature. Combining with first principles calculations and angle resolved photoemission spectroscopy, our results support a Fermi surface origin of the helical SDW with intertwined spin, charge, and lattice degrees of freedom in EuAl4. Our results reveal an unprecedented spontaneous chirality flipping and lays the groundwork for a new functional manipulation of chirality through momentum dependent spin-charge-lattice interactions. © 2023, CC BY.

关键词： Topology

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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

arXiv

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China 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 Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

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Granular-ball fuzzy information-based outlier detector

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International Journal of Approximate Reasoning 2025年 185卷

作者： Li, Qilin Yuan, Zhong Peng, Dezhong Song, Xiaomin Zheng, Huiming Su, Xinyu Marketing Service Center of State Grid Sichuan Electric Power Corporation Chengdu610045 China College of Computer Science Sichuan University Chengdu610065 China Sichuan National Innovation New Vision UHD Video Technology Co. Ltd Chengdu610095 China Tianfu Jincheng Laboratory Chengdu610093 China National Key Laboratory of Fundamental Algorithms and Models for Engineering Numerical Simulation Chengdu610207 China

Outlier detection is an important part of the process of carrying out data mining and analysis and has been applied to many fields. Existing methods are typically anchored in a single-sample processing paradigm, where the processing unit is each individual and single-granularity sample. This processing paradigm is inefficient and ignores the multi-granularity features inherent in data. In addition, these methods often overlook the uncertainty information present in the data. To remedy the above-mentioned shortcomings, we propose an unsupervised outlier detection method based on Granular-Ball Fuzzy Granules (GBFG). GBFG adopts a granular-ball-based computing paradigm, where the fundamental processing units are granular-balls. This shift from individual samples to granular-balls enables GBFG to capture the overall data structure from a multi-granularity perspective and improve the performance of outlier detection. Subsequently, we calculate the outlier factor based on the outlier degrees of the granular-ball fuzzy granules to which the sample belongs, serving as a measure of the outlier degrees of samples. The experimental results prove that GBFG has a remarkable performance compared with the existing excellent algorithms. The code of GBFG is publicly available on https://***/Mxeron/GBFG. © 2025 Elsevier Inc.

关键词： Granular computing

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Lattice quantum chromodynamics at large isospin density

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Physical Review D 2023年第11期108卷 114506-114506页

作者： Ryan Abbott William Detmold Fernando Romero-López Zohreh Davoudi Marc Illa Assumpta Parreño Robert J. Perry Phiala E. Shanahan Michael L. Wagman Center for Theoretical Physics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA The NSF AI Institute for Artificial Intelligence and Fundamental Interactions Department of Physics and Maryland Center for Fundamental Physics University of Maryland College Park Maryland 20742 USA Joint Center for Quantum Information and Computer Science NIST/University of Maryland College Park Maryland 20742 USA InQubator for Quantum Simulation (IQuS) Department of Physics University of Washington Seattle Washington 98195 USA Departament de Física Quàntica i Astrofísica and Institut de Ciències del Cosmos Universitat de Barcelona Martí i Franquès 1 E08028 Spain Fermi National Accelerator Laboratory Batavia Illinois 60510 USA

We present an algorithm to compute correlation functions for systems with the quantum numbers of many identical mesons from lattice quantum chromodynamics (QCD). The algorithm is numerically stable and allows for the computation of n-pion correlation functions for n∈{1,…,N} using a single N×N matrix decomposition, improving on previous algorithms. We apply the algorithm to calculations of correlation functions with up to 6144 charged pions using two ensembles of gauge field configurations generated with quark masses corresponding to a pion mass mπ=170 MeV and spacetime volumes of (4.43×8.8) fm4 and (5.83×11.6) fm4. We also discuss statistical techniques for the analysis of such systems, in which the correlation functions vary over many orders of magnitude. In particular, we observe that the many-pion correlation functions are well-approximated by log-normal distributions, allowing the extraction of the energies of these systems. Using these energies, the large-isospin-density, zero-baryon-density region of the QCD phase diagram is explored. A peak is observed in the energy density at an isospin chemical potential μI∼1.5mπ, signaling the transition into a Bose-Einstein condensed phase. The isentropic speed of sound, cs, in the medium is seen to exceed the ideal-gas (conformal) limit (cs2≤1/3) over a wide range of chemical potential before falling towards the asymptotic expectation at μI∼15mπ. These, and other thermodynamic observables, indicate that the isospin chemical potential must be large for the system to be well described by an ideal gas or perturbative QCD.

关键词： Lattice QCD QCD phase transitions

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LODUS: A Multi-Level Framework for Simulating Environment and Population - A Contagion Experiment on a Pandemic World

LODUS: A Multi-Level Framework for Simulating Environment an...

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IEEE International Smart Cities Conference (ISC2)

作者： Gabriel Fonseca Silva Vinícius Cassol Amyr Borges Fortes Neto Andre Antonitsch Diogo Schaffer Soraia Raupp Musse Rodrigo de Marsillac Linn Virtual Humans Simulation Laboratory Graduate Course in Computer Science School of Technology Pontifical Catholic University of Rio Grande do Sul Municipal Secretary of Planning and Management Porto Alegre City Hall - RS

ISBN: (数字)9781728182940

ISBN: (纸本)9781728182957

Nowadays we are experiencing a way of life that never existed before. The pandemic has sharply changed our habits, customs, and behavior. In addition, a lot of work was suddenly requested for city managers challenging them to develop strategies to try stopping the pandemic progression. Urban environments must be dynamic and managers need fast decisions when working on crisis situations. In this paper we present LODUS, a framework able to simulate urban environments on a multi-level approach, combining macro and micro simulation information in order to provide accurate information about population dynamics. Furthermore, the framework LODUS is a powerful tool when performing an urban viability study, since the simulation results are able to highlight and predict attention points prior to an urban environment to be built.

关键词： Pandemics Smart cities simulation Conferences Sociology Tools Statistics

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VRAS: A virtual rehearsal assistant system for live performance 2nd

VRAS: A virtual rehearsal assistant system for live performa...

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2nd International Conference on Smart Vehicular technology, Transportation, Communication and Applications, VTCA 2018

作者： Wu, Yufeng Ding, Gangyi Li, Hongsong Xue, Tong Jiao, Di Huang, Tianyu Zhang, Longfei Zhang, Fuquan Xu, Lin Digital Performance and Simulation Key Laboratory School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China Key Laboratory of Nondestructive Testing Fuqing Branch of Fujian Normal University Fuzhou350300 China

ISBN: (纸本)9783030045845

The VR technologies are widely adopted for training purposes by providing the users with educational virtual experience. In this work, we propose an immersive VR system that help the choreographers and the dancers to facilitate their dance rehearsal experience. The system integrates motion capture devices and head-mounted displays (HMDs). The motions of the dancers, their partners, and the choreographers are captured and projected into a virtual dancing scene in an interactive frame rate. The dancers who are wearing the HMDs, are allowed to observe the synthesized virtual performances within a virtual stage space from several selected third-person views. These are the audience’s view, the dancing partner’s view, and the choreographer’s view. Such synthesized external self-images augment dancer’s perception of their dance performance and their understanding of the choreography. Feedbacks from the participants indicate the proposed system is effective and the preliminary experimental results agree with our observations. © Springer Nature Switzerland AG 2019.

关键词： Helmet mounted displays

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A chunk-based multi-strategy machine translation method 5th

A chunk-based multi-strategy machine translation method

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5th Euro-China Conference on Intelligent Data Analysis and Applications, ECC 2018

作者： Wang, Yiou Zhang, Fuquan Beijing Institute of Science and Technology Information Beijing100044 China Digital Performance and Simulation Technology Lab School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China

ISBN: (纸本)9783030037659

In this paper, a chunk-based multi-strategy machine translation method is proposed. Firstly, an English-Chinese bilingual tree-bank is constructed. Then, a translation strategy based on the chunk that combines statistics and rules is used in the translation stage. Through hierarchical sub-chunks, the input sentence is divided into a set of chunk sequence. Each chunk searches the corresponding instance in the corpus. Translation is completed by recursive refinement from chunks to words. Conditional Random Fields model is used to divide chunks. An experimental English-Chinese translation system is deployed, and experimental results show that the system performs better than the Systran system. © Springer Nature Switzerland AG 2019.

关键词： Machine translation

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