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Proceedings of the Winter Simulation Conference

作者： Yiqi Seow Xiao Feng Yin Haiyan Xu Xiuju Fu Zheng Qin Hong Kiat Tan Li Yang Hsu Kiesha Prem Sze Wee Tan Molecular Engineering Laboratory Institute of Bioengineering and Nanotechnology A*STAR Singapore Systems Science Department Institute of High Performance Computing A*STAR Singapore Industry Development Group A*STAR Singapore Saw Swee Hock School of Public Health National University of Singapore and National University Health System Singapore Saw Swee Hock School of Public Health National University of Singapore and National University Health System Singapore and Centre for Mathematical Modelling of Infectious Diseases London School of Hygiene & Tropical London United Kingdom Enterprise Division A*STAR Singapore

Global travel and trade have been hit hard by the COVID-19 pandemic. Border closures have impacted both leisure and business travel. The socioeconomic costs of border closure are particularly severe for individuals living and working across state lines, for which previously unhindered passage has been curtailed, and daily commute across borders is now virtually impossible. Here, we examine how the periodic screening of daily cross-border commuters across territories with relatively low COVID-19 incidence will impact the transmission of SARS-CoV-2 across borders using agent-based simulation. We find that periodic testing at practical frequencies of once every 7, 14 or 21 days would reduce the number of infected individuals crossing the border. The unique transmission characteristics of SARS-CoV-2 suggest that periodic testing of populations with low incidence is of limited use in reducing cross-border transmission and is not as cost-effective as other mitigation measures for preventing transmission.

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Surgical video workflow analysis via visual-language learning

npj Health Systems

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npj Health Systems 2025年第1期2卷 1-12页

作者： Pengpeng Li Xiangbo Shu Jinhui Tang Chun-Mei Feng Yifei Feng Wangmeng Zuo School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) Singapore Republic of Singapore The First School of Clinical Medicine Nanjing Medical University Nanjing China Department of General Surgery The First Affiliated Hospital of Nanjing Medical University Nanjing China Faculty of Computing Harbin Institute of Technology Harbin China

Surgical video workflow analysis has made intensive development in computer-assisted surgery by combining deep learning models, aiming to enhance surgical scene analysis and decision-making. However, previous research has primarily focused on coarse-grained analysis of surgical videos, e.g., phase recognition, instrument recognition, and triplet recognition that only considers relationships within surgical triplets. In order to provide a more comprehensive fine-grained analysis of surgical videos, this work focuses on accurately identifying triplets from surgical videos. Specifically, we propose a vision-language deep learning framework that incorporates intra- and inter- triplet modeling, termed I2TM, to explore the relationships among triplets and leverage the model understanding of the entire surgical process, thereby enhancing the accuracy and robustness of recognition. Besides, we also develop a new surgical triplet semantic enhancer (TSE) to establish semantic relationships, both intra- and inter-triplets, across visual and textual modalities. Extensive experimental results on surgical video benchmark datasets demonstrate that our approach can capture finer semantics, achieve effective surgical video understanding and analysis, with potential for widespread medical applications.

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Revealing Interfacial Properties of Inorganic Molecular Crystal Sb2o3 and Monolayer Mos2

SSRN

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SSRN 2023年

作者： Liu, Qin Zuo, Yang He, Jingyu Zeng, Minggang Yang, Tong Zhou, Jun Yang, Yulin Song, Ting Ting Wang, Shijie Yang, Ming College of Physics and Space Science China West Normal University Nanchong637002 China Department of Applied Physics The Hong Kong Polytechnic University Hung Hom Hong Kong Institute of High-Performance Computing A∗STAR 1 Fusionopolis Way Singapore138632 Singapore Institute of Materials Research and Engineering A ∗ STAR 2 Fusionopolis Way Singapore138634 Singapore Fujian Provincial Key Laboratory of Optoelectronic Technology and Devices School of Optoelectronic and Communication Engineering Xiamen University of Technology Xiamen361024 China Research Centre on Data Sciences & Artificial Intelligence The Hong Kong Polytechnic University Hung Hom Hong Kong

Inorganic molecule crystal Sb2O3 has been identified as a promising high-k dielectric for direct integration with the two-dimensional semiconductor MoS2. However, a comprehensive understanding of this van der Waals (vdW) interface is still lacking, impeding its applications in high-performance nanoelectronics. In this study, we employ first-principles calculations to elucidate the interfacial interaction and electronic properties of the Sb2O3/monolayer MoS2 interface. We find that a high-performance quasi-vdW interface can be formed between Sb2O3 and monolayer MoS2, as evidenced by weak interfacial interaction, a dangling-bond-free interface, insignificant electron-hole puddle redistribution, and the preserved semiconducting properties of monolayer MoS2. Notably, the interface exhibits a remarkable defect tolerance capability during integration, as Sb2O3 cluster vacancies (the dominant defect in Sb2O3) neither introduce mid-gap states nor significantly affect the interface properties. Besides, our study reveals an asymmetric type-I band alignment at the interface, where the conduction band offset is sufficiently large to minimize electron tunneling. Our work offers a comprehensive understanding of the vdW interface between Sb2O3 and monolayer MoS2, which could potentially advance the development of inorganic molecular crystals as high-k dielectrics for high-performance two-dimensional electronic devices. © 2023, The Authors. All rights reserved.

关键词： Antimony compounds

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Susceptible-infected-susceptible model on networks with eigenvector localization

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Physical Review E 2020年第4期101卷 042310-042310页

作者： Zong-Wen Wei Bing-Hong Wang Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 China Department of Modern Physics University of Science and Technology of China Hefei 230027 China

It is a longstanding debate on the absence of threshold for susceptible-infected-susceptible (SIS) model on networks with finite second order moment of degree distribution. The eigenvector localization of the adjacency matrix for a network gives rise to the inactive Griffiths phase featuring slow decay of the activity localized around highly connected nodes due to the dynamical fluctuation. We show how it dramatically changes our understanding of the SIS model, opening up new possibilities for the debate. We derive the critical condition for Griffiths to active phase transition: on average, an infected node can further infect another one in the characteristic lifespan of the star subgraph composed of the node and its nearest neighbors. The system approaches the critical point of avoiding the irreversible dynamical fluctuation and the trap of absorbing state. As a signature of the phase transition, the infection density of a node is not only proportional to its degree, but also proportional to the exponentially growing lifespan of the star. And the divergence of the average lifespan of the stars is responsible for the vanishing threshold in the thermodynamic limit. The eigenvector localization exponentially reinforces the infection of highly connected nodes, while it inversely suppresses the infection of small-degree nodes.

关键词： Complex systems Epidemic Network phase transitions Nonequilibrium statistical mechanics Spreading

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Entanglement-Induced Barren Plateaus

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PRX Quantum 2021年第4期2卷 040316-040316页

作者： Carlos Ortiz Marrero Mária Kieferová Nathan Wiebe Data Sciences and Analytics Group Pacific Northwest National Laboratory Richland Washington 99354 USA Centre for Quantum Computation and Communication Technology Centre for Quantum Software and Information University of Technology Sydney New South Wales 2007 Australia Department of Computer Science University of Toronto Ontario M5S 1A1 Canada High Performance Computing Group Pacific Northwest National Laboratory Richland Washington 99354 USA

We argue that an excess in entanglement between the visible and hidden units in a quantum neural network can hinder learning. In particular, we show that quantum neural networks that satisfy a volume law in the entanglement entropy will give rise to models that are not suitable for learning with high probability. Using arguments from quantum thermodynamics, we then show that this volume law is typical and that there exists a barren plateau in the optimization landscape due to entanglement. More precisely, we show that for any bounded objective function on the visible layers, the Lipshitz constants of the expectation value of that objective function will scale inversely with the dimension of the hidden subsystem with high probability. We show how this can cause both gradient-descent and gradient-free methods to fail. We note that similar problems can happen with quantum Boltzmann machines, although stronger assumptions on the coupling between the hidden and/or visible subspaces are necessary. We highlight how pretraining such generative models may provide a way to navigate these barren plateaus.

关键词： Machine learning Quantum algorithms

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Implementing the matrix multiplication with dfc on kunlun small scale computer 20

Implementing the matrix multiplication with dfc on kunlun sm...

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20th International Conference on Parallel and Distributed computing, Applications and Technologies, PDCAT 2019

作者： Du, Zheng Zhang, Jing Sha, Shihao Luo, Qiuming Department of Computer Science and Technology Tsinghua University China NHPCC/Guangdong Key Laboratory of Popular HPC Guangdong Province Engineering Center of China-made High Performance Data Computing System College of Computer Science Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781728126166

In this paper, we demonstrate a new dataflow platform of DFC, which can handle the successive dataflow computing passes with tagged data. By implementing the matrix multiplication in DFC, we show that DFC can exploit the parallelism automatically with a much simple dataflow graph constructed by DF functions of DFC. Different from the other dataflow execution platform, DFC support multiple worker threads for one dataflow node of DF functions. By running the matrix multiplication program of DFC on Kunlun system, it was verified that DFC get a reasonable speedup for large scale computing for thread number up to 512. © 2019 IEEE.

关键词： Matrix algebra

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Evaluating the Covid-19 Screening Regime for Cross-Border Workers

Evaluating the Covid-19 Screening Regime for Cross-Border Wo...

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Simulation Winter Conference

作者： Yiqi Seow Xiao Feng Yin Haiyan Xu Xiuju Fu Zheng Qin Hong Kiat Tan Li Yang Hsu Kiesha Prem Sze Wee Tan Molecular Engineering Laboratory Institute of Bioengineering and Nanotechnology A*STAR Singapore Systems Science Department Institute of High Performance Computing A*STAR Singapore Industry Development Group A*STAR Singapore Saw Swee Hock School of Public Health National University of Singapore and National University Health System Singapore Centre for Mathematical Modelling of Infectious Diseases London School of Hygiene & Tropical Medicine London United Kingdom Enterprise Division A*STAR Singapore

ISBN: (纸本)9781665476621

关键词： COVID-19 Schedules Pandemics Sociology Social factors Frequency measurement Statistics

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NTIRE 2024 Quality Assessment of AI-Generated Content Challenge

arXiv

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arXiv 2024年

作者： Liu, Xiaohong Min, Xiongkuo Zhai, Guangtao Li, Chunyi Kou, Tengchuan Sun, Wei Wu, Haoning Gao, Yixuan Cao, Yuqin Zhang, Zicheng Wu, Xiele Timofte, Radu Gankhuyag, Ganzorig Yoon, Kihwan Xu, Yifang Fan, Haotian Kong, Fangyuan Zhao, Shiling Dong, Weifeng Yin, Haibing Zhu, Li Wang, Zhiling Huang, Bingchen Peng, Fei Fu, Huiyuan Ming, Anlong Wang, Chuanming Ma, Huadong He, Shuai Dou, Zifei Chen, Shu Yu, Zihao Guan, Fengbin Motamednia, Hossein Hosseini-Benvidi, S. Farhad Mahmoudi-Aznaveh, Ahmad Mansouri, Azadeh Saha, Avinab Mishra, Sandeep Gupta, Shashank Sureddi, Rajesh Saha, Oindrila Celona, Luigi Bianco, Simone Napoletano, Paolo Schettini, Raimondo Yang, Junfeng Fu, Jing Zhang, Wei Cao, Wenzhi Liu, Limei Peng, Han Yuan, Weijun Li, Zhan Cheng, Yihang Deng, Yifan Zhang, Huacong Xie, Haiyi Wang, Chengwei Chen, Baoying Zeng, Jishen Yang, Jianquan Wang, Weigang Fang, Xi Lv, Xiaoxin Yan, Jun Li, Haohui Qu, Bowen Li, Yao Luo, Shuqing Wang, Shunzhou Gao, Wei Lu, Zihao Conde, Marcos V. Timofte, Radu Lu, Yiting Li, Xin Wang, Xinrui Guan, Fengbin Chen, Zhibo Liao, Ruling Ye, Yan Wang, Qiulin Li, Bing Zhou, Zhaokun Geng, Miao Chen, Rui Tao, Xin Liang, Xiaoyu Sun, Shangkun Fang, Xi Wang, Weigang Yan, Jun Ma, Xingyuan Li, Jiaze Yang, Mengduo Xu, Haoran Zhou, Jie Zhu, Shiding Yu, Bohan Zhi, Tianwu Zhang, Yabin Li, Yaohui Li, Yang Xu, Jingwen Liu, Jianzhao Liao, Yiting Li, Junlin Chen, Baoying Zeng, Jishen Xie, Haiyi Wang, Chengwei Yang, Jianquan Chen, Pengfei Xu, Xinrui Shen, Jiabin Duan, Zhichao Liu, Jiahe Yan, Qi Qu, Youran Zeng, Xiaohui Wang, Lele Liao, Renjie Shanghai Jiao Tong University China Nanyang Technological University Singapore ETH Zürich Switzerland University of Würzburg Germany Korea Electronics Technology Institute Korea Republic of ByteDance China Hangzhou Dianzi University China Sankuai China Beijing University of Posts and Telecommunications China Beijing Xiaomi Mobile Software Co. Ltd. China University of Science and Technology of China China High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran Cyber Research Institute Shahid Beheshti University Tehran Iran University of Texas Austin United States University of Massachusetts Amher United States Department of Informatics Systems and Communication Xiangjiang Laboratory Hunan University of Technology and Business China Jinan University China Sun Yat-sen University China Alibaba Group China Cisco Systems Inc. United States DP Technology Ltd. Shopee Pte. Ltd Tongji University China School of Electronic and Computer Engineering Shenzhen Graduate School Peking University China University of Würzburg Germany Kuaishou Technology China Peking University China Zhejiang University China Bytedance Inc China Xidian University China University of British Columbia Canada University of Toronto Canada

This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2024. This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC). The challenge is divided into the image track and the video track. The image track uses the AIGIQA-20K, which contains 20,000 AI-Generated Images (AIGIs) generated by 15 popular generative models. The image track has a total of 318 registered participants. A total of 1,646 submissions are received in the development phase, and 221 submissions are received in the test phase. Finally, 16 participating teams submitted their models and fact sheets. The video track uses the T2VQA-DB, which contains 10,000 AI-Generated Videos (AIGVs) generated by 9 popular Text-to-Video (T2V) models. A total of 196 participants have registered in the video track. A total of 991 submissions are received in the development phase, and 185 submissions are received in the test phase. Finally, 12 participating teams submitted their models and fact sheets. Some methods have achieved better results than baseline methods, and the winning methods in both tracks have demonstrated superior prediction performance on AIGC. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Circuit area optimization of multi-output MPRM based on ERWOA algorithm

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Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics 2023年第5期49卷 1193-1200页

作者： He, Juncai He, Zhenxue Wang, Fushun Huo, Zhisheng Xiao, Limin Intelligent Agricultural Equipment Research Institute Hebei Agricultural University Baoding071001 China Key Laboratory of Agricultural Big Data of Hebei Province Hebei Agricultural University Baoding071001 China High Performance Computing Platform Beihang University Beijing100191 China School of Software Beihang University Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China

Mixed polarity Reed-Muller (MPRM) circuit area optimization has become a research hotspot in the field of integrated circuit design. It is a combinatorial optimization, aiming at finding the MPRM expression with the least number of terms among many MPRM expressions. A explosion strategy and restart strategy based whale optimization algorithm (ERWOA) is proposed. In addition, a multi-output MPRM circuit area optimization method is proposed, which uses the improved whale algorithm and the improved polarity conversion algorithm to search for the MPRM circuit with the least number of AND terms. Results on the MCNC Benchmark circuits show that the proposed algorithm increases the conversion efficiency by 99.93% and 99.96% at most, compared with the mixed polarity and inter-polarity conversion algorithms based on the list technology, respectively. Compared with the genetic algorithm and the artificial bee colony algorithm, the improved whale optimization algorithm saves the circuit area up to 18.32% with an average of 5.54%, and 14.41% with an average of 5.00%, respectively. © 2023 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.

关键词： Genetic algorithms

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Caroli–de Gennes–Matricon Analogs in Full-Shell Hybrid Nanowires

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Physical Review Letters 2025年第20期134卷 206302-206302页

作者： M. T. Deng Carlos Payá Pablo San-Jose Elsa Prada C. M. Marcus S. Vaitiekėnas Center for Quantum Devices Niels Bohr Institute University of Copenhagen 2100 Copenhagen Denmark Institute for Quantum Information and State Key Laboratory of High Performance Computing College of Computer Science and Technology NUDT Changsha 410073 China Instituto de Ciencia de Materiales de Madrid (ICMM) CSIC 28049 Madrid Spain Materials Science and Engineering and Department of Physics University of Washington Seattle Washington 98195 USA

We report tunneling spectroscopy of Andreev subgap states in hybrid nanowires with a thin superconducting full shell surrounding a semiconducting core. The combination of the quantized fluxoid of the shell and the Andreev reflection at the superconductor-semiconductor interface gives rise to analogs of Caroli–de Gennes–Matricon states found in Abrikosov vortices in type-II superconductors. Unlike in metallic superconductors, Caroli–de Gennes–Matricon analogs in full-shell hybrid nanowires manifest as one-dimensional Van Hove singularities with energy spacings comparable to the superconducting gap and independent of the Fermi energy, making them readily observable. Evolution of these analogs with axial magnetic field, skewed within the Little-Parks lobe structure, is consistent with theory and yields information about the radial distribution and angular momenta of the corresponding subbands.

关键词： Nanowires

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