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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Jia Liu Xingyu Chen Shigang Chen Xiulong Liu Yanyan Wang Lijun Chen State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer & Information Science & Engineering University of Florida USA Department of Computing The Hong Kong Polytechnic University China

Sleep monitoring plays an important role in many medical applications, including SIDS prevention, care of patients with pressure ulcers, and assistance to patients with sleep apnea, where studies have shown that autonomous and continuous monitoring of sleep postures provides useful information for lowering health risk. Existing systems are designed based on electrocardiogram, cameras and pressure sensors, which are expensive to deploy, intrusive to privacy, or uncomfortable to use. This paper presents TagSheet, the first sleep monitoring system based on passive RFID tags, which provides a convenient, non-intrusive, and comfortable way of monitoring the sleeping postures. It does not require attaching any tag directly to a patient's body. Tags are taped under a bed sheet. With a combination of hierarchical recognition, image processing and polynomial fitting, the proposed system identifies body postures based on the observed variation caused by the patient body to the backscattered signals from tags. The system does not require any personalized data training, making it plug-n-play in use. One additional advantage is that the system can also estimate the patient's respiration rate. This is particularly helpful in assisting patients with sleep apnea. We have implemented a prototype system, and experiments show that the system performs posture identification with an accuracy up to 96.7% and in the meantime it measures the respiration rate with a small error of about 0.7 bpm (breath per minute).

关键词： Sleep apnea Monitoring Diversity reception Cameras Pressure sensors Training Pediatrics

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AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results 16th

AIM 2020 Challenge on Efficient Super-Resolution: Methods an...

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Workshops held at the 16th European Conference on computer Vision, ECCV 2020

作者： Zhang, Kai Danelljan, Martin Li, Yawei Timofte, Radu Liu, Jie Tang, Jie Wu, Gangshan Zhu, Yu He, Xiangyu Xu, Wenjie Li, Chenghua Leng, Cong Cheng, Jian Wu, Guangyang Wang, Wenyi Liu, Xiaohong Zhao, Hengyuan Kong, Xiangtao He, Jingwen Qiao, Yu Dong, Chao Luo, Xiaotong Chen, Liang Zhang, Jiangtao Suin, Maitreya Purohit, Kuldeep Rajagopalan, A.N. Li, Xiaochuan Lang, Zhiqiang Nie, Jiangtao Wei, Wei Zhang, Lei Muqeet, Abdul Hwang, Jiwon Yang, Subin Kang, JungHeum Bae, Sung-Ho Kim, Yongwoo Qu, Yanyun Jeon, Geun-Woo Choi, Jun-Ho Kim, Jun-Hyuk Lee, Jong-Seok Marty, Steven Marty, Eric Xiong, Dongliang Chen, Siang Zha, Lin Jiang, Jiande Gao, Xinbo Lu, Wen Wang, Haicheng Bhaskara, Vineeth Levinshtein, Alex Tsogkas, Stavros Jepson, Allan Kong, Xiangzhen Zhao, Tongtong Zhao, Shanshan Hrishikesh, P.S. Puthussery, Densen Jiji, C.V. Nan, Nan Liu, Shuai Cai, Jie Meng, Zibo Ding, Jiaming Ho, Chiu Man Wang, Xuehui Yan, Qiong Zhao, Yuzhi Chen, Long Sun, Long Wang, Wenhao Liu, Zhenbing Lan, Rushi Umer, Rao Muhammad Micheloni, Christian Computer Vision Lab ETH Zurich Zürich Switzerland State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China MAICRO Nanjing China University of Electronic Science and Technology of China Nanjing China McMaster University Hamilton Canada Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Beijing China Xiamen University Xiamen China Indian Institute of Technology Madras Chennai India Nanjing University of Aeronautics and Astronautics Nanjing China School of Computer Science Northwestern Polytechnical University Xi’an China Kyung Hee University Seoul Korea Republic of Sang Myung University Seoul Korea Republic of Yonsei University Seoul Korea Republic of ETH Zurich Zürich Switzerland Zhejiang University Hangzhou China Shandong China Xidian University Xi’an China Samsung AI Centre Toronto Canada Wuhan China Dalian Maritime University Dalian China China Everbright Bank Co. Ltd. Beijing China College of Engineering Trivandrum India Jinan China InnoPeak Technology Inc. Palo Alto United States SenseTime Research Hong Kong China Sun Yat-sen University Guangzhou China City University of Hong Kong Kowloon Hong Kong Guilin University of Electronic Technology Guilin541004 China University of Udine Udine Italy

ISBN: (纸本)9783030670696

This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor × 4 based on a set of prior examples of low and corresponding high resolution images. The goal is to devise a network that reduces one or several aspects such as runtime, parameter count, FLOPs, activations, and memory consumption while at least maintaining PSNR of MSRResNet. The track had 150 registered participants, and 25 teams submitted the final results. They gauge the state-of-the-art in efficient single image super-resolution. © 2020, Springer Nature Switzerland AG.

关键词： Optical resolving power

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Uncertainty relations in the presence of quantum memory for mutually unbiased measurements

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Physical Review A 2018年第3期98卷 032329-032329页

作者： Kun Wang Nan Wu Fangmin Song State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing 210093 China

In a work by Berta et al. [Phys. Rev. A 90, 062127 (2014)], uncertainty relations in the presence of quantum memory were formulated for mutually unbiased bases using conditional collision entropy. In this paper, we generalize their results to the mutually unbiased measurements. Our primary result is an equality between the amount of uncertainty for a set of measurements and the amount of entanglement of the measured state, both of which are quantified by the conditional collision entropy. Implications of this equality relation are discussed. We further show that similar equality relations can be obtained for generalized symmetric informationally complete measurements. We also derive an interesting equality for arbitrary orthogonal basis of the space of Hermitian, traceless operators.

关键词： Entanglement detection Quantum correlations in quantum information Quantum memories

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

arXiv

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arXiv 2023年

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

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

关键词： Multi-task learning

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A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning

arXiv

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arXiv 2021年

作者： Li, Zekun Zhao, Wei Shi, Feng Qi, Lei Xie, Xingzhi Wei, Ying Ding, Zhongxiang Gao, Yang Wu, Shangjie Liu, Jun Shi, Yinghuan Shen, Dinggang State Key Laboratory for Novel Software Technology Nanjing University China National Institute of Healthcare Data Science Nanjing University Nanjing210046 China School of Computer Science and Artificial Intelligence Southeast University Nanjing210018 China Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai201807 China School of Biomedical Engineering ShanghaiTech University Shanghai China Department of Artificial Intelligence Korea University Seoul02841 Korea Republic of Department of Radiology Second Xiangya Hospital Central South University Hunan410011 China Department of Radiology Quality Control Center Changsha410011 China Department of Radiology Hangzhou First People's Hospital Zhejiang University School of Medicine Hangzhou Zhejiang China Department of Pulmonary and Critical Care Medicine Second Xiangya Hospital Central South University Changsha410011 China

How to fast and accurately assess the severity level of COVID-19 is an essential problem, when millions of people are suffering from the pandemic around the world. Currently, the chest CT is regarded as a popular and informative imaging tool for COVID-19 diagnosis. However, we observe that there are two issues - weak annotation and insufficient data that may obstruct automatic COVID-19 severity assessment with CT images. To address these challenges, we propose a novel three-component method, i.e., 1) a deep multiple instance learning component with instance-level attention to jointly classify the bag and also weigh the instances, 2) a bag-level data augmentation component to generate virtual bags by reorganizing high confidential instances, and 3) a self-supervised pretext component to aid the learning process. We have systematically evaluated our method on the CT images of 229 COVID-19 cases, including 50 severe and 179 non-severe cases. Our method could obtain an average accuracy of 95.8%, with 93.6% sensitivity and 96.4% specificity, which outperformed previous works. Copyright © 2021, The Authors. All rights reserved.

关键词： computerized tomography

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An Approach to Locating Delayed Activities in software Processes

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International Journal of Automation and computing 2018年第1期15卷 115-124页

作者： Yun-Zhi Jin Hua Zhou Hong-Ji Yang Si-Jing Zhang Ji-Dong Ge School of Software Yunnan University Kunming 650091 China Key Laboratory for Software Engineering of Yunnan Province Kunming 650000 China Research Center of Cloud Computing of Yunnan Province Kunming 650000 China Centre for Creative Computing Bath Spa University Corsham SN13 0BZ UK Department of Computer Science and Technology University of Bedfordshire LU13JU UK State Key Laboratory for Novel Software Technology Software Institute Nanjing University Nanjing 210093 China

Activity is now playing a vital role in software processes. To ensure the high-level efficiency of software processes, a key point is to locate those activities that own bigger resource occupation probabilities with respect to average execution time, called delayed activities, and then improve them. To this end, we firstly propose an approach to locating delayed activities in software processes. Furthermore, we present a case study, which exhibits the high-level efficiency of the approach, to concretely illustrate this new solution. Some beneficial analysis and reasonable modification are developed in the end.

关键词： Locating of the delayed activities software process stochastic Petri-nets Markov random fields metrics.

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Pairwise Teacher-Student Network for Semi-Supervised Hashing

Pairwise Teacher-Student Network for Semi-Supervised Hashing

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Shifeng Zhang Jianmin Li Bo Zhang Department of Computer Science and Technology Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Hashing method maps similar high-dimensional data to binary hashcodes with smaller hamming distance, and it has received broad attention due to its low storage cost and fast retrieval speed. Pairwise similarity is easily obtained and widely used for retrieval, and most supervised hashing algorithms are carefully designed for the pairwise supervisions. As labeling all data pairs is difficult, semi-supervised hashing is proposed which aims at learning efficient codes with limited labeled pairs and abundant unlabeled ones. Existing methods build graphs to capture the structure of dataset, but they are not working well for complex data as the graph is built based on the data representations and determining the representations of complex data is difficult. In this paper, we propose a novel teacher-student semi-supervised hashing framework in which the student is trained with the pairwise information produced by the teacher network. The network follows the smoothness assumption, which achieves consistent distances for similar data pairs so that the retrieval results are similar for neighborhood queries. Experiments on large-scale datasets show that the proposed method reaches impressive gain over the supervised baselines and is superior to state-of-the-art semi-supervised hashing methods.

关键词： Training Hamming distance Semantics Data models Semisupervised learning Perturbation methods labeling

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A Spatially Programmable DNA Nanorobot Arm to Modulate Anisotropic Gold Nanoparticle Assembly by Enzymatic Excision

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Angewandte Chemie 2023年第43期135卷

作者： Prof. Jing Yang Yuan Liang Xiang Li Yongpeng Zhang Prof. Long Qian Prof. Yonggang Ke Prof. Cheng Zhang School of Computer Science Key Lab of High Confidence Software Technologies Peking University Beijing 100871 China School of Control and Computer Engineering North China Electric Power University Beijing 102206 China Center for Quantitative Biology Peking University Beijing 100871 China Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University Emory University School of Medicine Atlanta GA 30322 USA Department of Chemistry Emory University Atlanta GA 30322 USA

Programmable assembly of gold nanoparticle superstructures with precise spatial arrangement has drawn much attention for their unique characteristics in plasmonics and biomedicine. Bio-inspired methods have already provided programmable, molecular approaches to direct AuNP assemblies using biopolymers. The existing methods, however, predominantly use DNA as scaffolds to directly guide the AuNP interactions to produce intended superstructures. New paradigms for regulating AuNP assembly will greatly enrich the toolbox for DNA-directed AuNP manipulation and fabrication. Here, we developed a strategy of using a spatially programmable enzymatic nanorobot arm to modulate anisotropic DNA surface modifications and assembly of AuNPs. Through spatial controls of the proximity of the reactants, the locations of the modifications were precisely regulated. We demonstrated the control of the modifications on a single 15 nm AuNP, as well as on a rectangular DNA origami platform, to direct unique anisotropic AuNP assemblies. This method adds an alternative enzymatic manipulation to DNA-directed AuNP superstructure assembly.

关键词： AuNPs Assembly DNA Origami Enzymatic Excision Reactions Molecular Spatial Programmability Nanorobot

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An empirical study on constraint optimization techniques for test generation

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science China(Information sciences) 2017年第1期60卷 69-83页

作者： Zhiyi ZHANG Zhenyu CHEN Ruizhi GAO Eric WONG Baowen XU State Key Laboratory for Novel Software Technology Nanjing University Department of Computer Science University of Texas at Dallas

Constraint solving is a frequent, but expensive operation with symbolic execution to generate tests for a program. To improve the efficiency of test generation using constraint solving, four optimization techniques are usually applied to existing constraint solvers, which are constraint independence, constraint set simplification, constraint caching, and expression rewriting. In this paper, we conducted an empirical study, using these four constraint optimization techniques in a well known test generation tool KLEE with 77 GNU Coreutils applications, to systematically investigate how these optimization techniques affect the efficiency of test generation. The experimental results show that these constraint optimization techniques as well as their combinations cannot improve the efficiency of test generation significantly for ALL-SIZED programs. Moreover, we studied the constraint optimization techniques with respect to two static metrics, lines of code(LOC) and cyclomatic complexity(CC), of programs. The experimental results show that the "constraint set simplification" technique can improve the efficiency of test generation significantly for the programs with high LOC and CC values. The"constraint caching" optimization technique can improve the efficiency of test generation significantly for the programs with low LOC and CC values. Finally, we propose four hybrid optimization strategies and practical guidelines based on different static metrics.

关键词： test generation symbolic execution constraint solving constraint optimization static metric

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ARAML: A stable adversarial training framework for text generation

arXiv

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arXiv 2019年

作者： Ke, Pei Huang, Fei Huang, Minlie Zhu, Xiaoyan Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing100084 China

Most of the existing generative adversarial networks (GAN) for text generation suffer from the instability of reinforcement learning training algorithms such as policy gradient, leading to unstable performance. To tackle this problem, we propose a novel framework called Adversarial Reward Augmented Maximum Likelihood (ARAML). During adversarial training, the discriminator assigns rewards to samples which are acquired from a stationary distribution near the data rather than the generator’s distribution. The generator is optimized with maximum likelihood estimation augmented by the discriminator’s rewards instead of policy gradient. Experiments show that our model can outperform state-of-the-art text GANs with a more stable training *** Codes 68T50 Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

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