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15th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th International Conference on Harmony Search, Soft computing and Applications, ICHSA 2019

作者： Zhang, Ning Zhao, Chenli Zhang, Xue Yi, Dongyun College of Liberal Arts and Sciences National University of Defense Technology Changsha410073 China State Key Laboratory of High Performance Computing National University of Defense Technology Changsha410073 China

ISBN: (纸本)9783030325909

Intention usually refers to the intention to achieve a certain purpose, which is the realistic power to motivate people to act. At present, with the development of the Internet and social media, the scope of human activities is becoming larger and deeper. In the academic circle, the cooperation between scientific research workers is becoming closer and closer, and the activities of human beings and even the cooperative behaviors of scientific research workers contain the intentions of individuals or organizations. By extracting scientific citation data on the network, this paper constructs a spatial-temporal related scientific collaboration network, proposes a spatial-temporal distribution method of resources, and analyses and evaluates the behavioral intention of collaboration network. It mainly includes the visualization method of spatial-temporal distribution of resources of paper information, the spatial-temporal network modeling of paper cooperative intention, the analysis of intention evaluation model and so on. Finally, the problems and possible challenges in the future research are prospected. © 2020, Springer Nature Switzerland AG.

关键词： Behavioral research

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Optimal Identification of Multiple Diffusion Sources in Complex Networks with Partial Observations 15th

Optimal Identification of Multiple Diffusion Sources in Comp...

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作者： Li, Xiang Wang, Xiaojie Zhao, Chengli Zhang, Xue Yi, Dongyun College of Liberal Arts and Sciences National University of Defense Technology Changsha410073 China State Key Laboratory of High Performance Computing National University of Defense Technology Changsha410073 China

ISBN: (纸本)9783030324551

Source localization is a typical inverse problem in complex networks, which is widely used in disease outbreak, rumor propagation and pollutants spread. In this paper, we propose that, based on network topology and the times at which the diffusion reached partial nodes, it is easy to identify the source. The results show that in six different networks, although the number of observers is small, the precision of source localization can be high. Precision increases with network size increasing and source number decreasing. Furthermore, our method makes the sources localization precision very robust, not only with the condition of three different given observers selection strategies, but with three various intensity noise on the diffusion path. © Springer Nature Switzerland AG, 2020.

关键词： Complex networks

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Characterizing Human Collective Behaviors During COVID-19 — Hong Kong SAR, China, 2020

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China CDC weekly 2023年第4期5卷 71-75,I0001-I0003页

作者： Zhanwei Du Xiao Zhang Lin Wang Sidan Yao Yuan Bai Qi Tan Xiaoke Xu Sen Pei Jingyi Xiao Tim K.Tsang Qiuyan Liao Eric H.YLau Peng Wu Chao Gao Benjamin J.Cowling WHO Collaborating Centre for Infectious Disease Epidemiology and Control School of Public HealthLi Ka Shing Faculty of MedicineThe University of Hong KongHong Kong Special Administrative RegionChina Laboratory of Data Discovery for Health Hong Kong Science and Technology ParkNew TerritoriesHong Kong Special Administrative RegionChina Department of Genetics University of CambridgeCambridgeCB23EHUK Institute of High Performance Computing(IHPC) Agency for ScienceTechnology and Research(A*STAR)Singapore College of Information and Communication Engineering Dalian Minzu UniversityDalian CityLiaoning ProvinceChina Department of Environmental Health Sciences Mailman School of Public HealthColumbia UniversityNew York CityNYUSA School of Artificial Intelligence Opticsand Electronics(iOpen)Northwestern Polytechnical UniversityXi’an CityShaanxi ProvinceChina

Summary What is already known about this topic?People are likely to engage in collective behaviors online during extreme events,such as the coronavirus disease 2019(COVID-19)crisis,to express awareness,take action,and... 详细信息

关键词： collective likely extreme

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The Joint Model of Multi-Intent Detection and Slot Filling Based on Bidirectional Interaction Structure

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Wuhan University Journal of Natural Sciences 2025年第1期30卷 21-31页

作者： WANG Changjing ZENG Xianghui WANG Yuxin SUN Yuxin ZUO Zhengkang School of Digital Industry Jiangxi Normal UniversityShangrao 334000JiangxiChina School of Computer Information Engineering Jiangxi Normal UniversityNanchang 330022JiangxiChina Jiangxi Provincial Key Laboratory for High Performance Computing Jiangxi Normal UniversityNanchang 330022JiangxiChina State International Science and Technology Cooperation Base of Networked Supporting Software Jiangxi Normal UniversityNanchang 330022JiangxiChina

Intent detection and slot filling are two important components of natural language *** their relevance,joint training is often performed to improve *** studies mostly use a joint model of multi-intent detection and slot-filling with unidirectional interaction,which improves the overall performance of the model by fusing the intent information in the slot-filling *** this basis,in order to further improve the overall performance of the model by exploiting the correlation between the two,this paper proposes a joint multi-intent detection and slot-filling model based on a bidirectional interaction structure,which fuses the intent encoding information in the encoding part of slot filling and fuses the slot decoding information in the decoding part of intent *** results on two public multi-intent joint training datasets,MixATIS and MixSNIPS,show that the bidirectional interaction structure proposed in this paper can effectively improve the performance of the joint *** addition,in order to verify the generalization of the bidirectional interaction structure between intent and slot,a joint model for single-intent scenarios is proposed on the basis of the model in this *** model also achieves excellent performance on two public single-intent joint training datasets,CAIS and SNIPS.

关键词： natural language understanding multi-intent detection slot filling bidirectional interaction joint training

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Distance Maximization and Defences on Deep Hashing Based Image Retrieval

Distance Maximization and Defences on Deep Hashing Based Ima...

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IEEE International Conference on Big Knowledge (ICBK)

作者： Junda Lu Yukai Miao Mingyang Chen Bo Huang Bing Li Wei Wang Dinusha Vatsalan Mohamed Ali Kaafar Macquarie University Australia The University of New South Wales Australia Zhongguancun Laboratory China The Hong Kong University of Science and Technology Guangzhou China The Hong Kong University of Science and Technology Hong Kong SAR China Agency for Science Technology and Research (A *STAR) Institute of High Performance Computing (IHPC) Singapore Centre for Frontier AI Research (CFAR) Agency for Science Technology and Research (A *STAR) Singapore Guangzhou Municipal Key Laboratory of Materials Informatics HKUST Guangzhou China

Though being remarkably efficient in computation and storage, recent research demonstrates deep hashing based image retrieval models are also vulnerable to adversarial attacks. A substantial amount of defence techniques against adversarial attacks have been developed recently, the overwhelming majority focus on adversarial training. However, adversarial defence focusing on the data side, i.e., cluster-contrasting, still remains a paucity. Albeit a pioneer work makes attempt to maximise the average distances of image clusters by anchoring predefined hash centers, it is still insufficient for a robust defence due to no optimal guarantee and over-sticking to the global average distance. Further, the laser-focus of either adversarial training or cluster contrasting of existing methods hinder them from further robustness boosting by judiciously incorporating them together. In this paper, we propose a novel distance maximization algorithm for defence on deep hashing based image retrieval systems. The model finds the optimal maximum average distance between cluster centers, and then uses a heuristic method to increase the minimum distance in the worst-case by solving a maximum Boolean satisfiability (max-SAT) problem. Our proposed distance maximization algorithm is a boosting algorithm that can be incorporated into adversarial training to enhance or boost the robustness of retrieval systems. Experiments conducted on two datasets demonstrate that our algorithm can generate clusters with a maximised average distance, while the minimum distance is also increased by up to 33% over the state-of-the-art method, and robustness is improved by up to 23 %.

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The Onsager principle and structure preserving numerical schemes

arXiv

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arXiv 2024年

作者： Chen, Huangxin Liu, Hailiang Xu, Xianmin School of Mathematical Sciences Fujian Provincial Key Laboratory on Mathematical Modeling and High Performance Scientific Computing Xiamen University Fujian361005 China Iowa State University Department of Mathematics AmesIA50011 United States LSEC ICMSEC NCMIS Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

We present a natural framework for constructing energy-stable time discretization schemes. By leveraging the Onsager principle, we demonstrate its efficacy in formulating partial differential equation models for diverse gradient flow systems. Furthermore, this principle provides a robust basis for developing numerical schemes that uphold crucial physical properties. Within this framework, several widely used schemes emerge naturally, showing its versatility and *** Codes 65M12, 65M22, 76M30 Copyright © 2024, The Authors. All rights reserved.

关键词： Partial differential equations

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The Onsager Principle and Physics Preserving Numerical Schemes

SSRN

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SSRN 2024年

We present a natural framework for constructing energy-stable time discretization schemes. By leveraging the Onsager principle,we demonstrate its efficacy in formulating partial differential equation models for diverse gradient flow systems. Furthermore, this principle provides a robust basis for developing numerical schemes that uphold crucial physical properties. Within this framework, several widely used schemes emerge naturally, showing its versatility and applicability. © 2024, The Authors. All rights reserved.

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Characterization of Intensity Correlation via Single-photon Detection in Quantum key Distribution

arXiv

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arXiv 2024年

作者： Xing, Tianyi Liu, Junxuan Zhang, Likang Wang, Min-Yan Li, Yu-Huai Liu, Ruiyin Peng, Qingquan Wang, Dongyang Wang, Yaxuan Liu, Hongwei Li, Wei Cao, Yuan Huang, Anqi Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Hefei National Research Center for Physical Sciences The Microscale and School of Physical Sciences University of Science and Technology of China Hefei230026 China Shanghai Research Center for Quantum Science CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai201315 China Hefei National Laboratory University of Science and Technology of China Hefei230088 China China Information Technology Security Evaluation Center Beijing100085 China

One of the most significant vulnerabilities in the source unit of quantum key distribution (QKD) is the correlation between quantum states after modulation, which shall be characterized and evaluated for its practical security performance. In this work, we propose a methodology to characterize the intensity correlation according to the single-photon detection results in the measurement unit without modifying the configuration of the QKD system. In contrast to the previous research that employs extra classical optical detector to measure the correlation, our method can directly analyse the detection data generated during the raw key exchange, enabling to characterize the feature of correlation in real-time system operation. The basic method is applied to a BB84 QKD system and the characterized correlation significantly decreases the secure key rate shown by the security proof. Furthermore, the method is extended and applied to characterize the correlation from the result of Bell-state measurement, which demonstrates its applicability to a running full-scheme MDI QKD system. This study provides an approach for standard certification of a QKD system. Copyright © 2024, The Authors. All rights reserved.

关键词： Photons

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Maximal coin-walker entanglement in a ballistic quantum walk

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Physical Review A 2022年第4期105卷 042216-042216页

作者： Rong Zhang Ran Yang Jian Guo Chang-Wei Sun Jia-Chen Duan Heng Zhou Zhenda Xie Ping Xu Yan-Xiao Gong Shi-Ning Zhu National Laboratory of Solid State Microstructure School of Physics School of Electronic Science and Engineering and Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing 210093 China College of Electronic and Optical Engineering Nanjing University of Posts and Telecommunication Nanjing 210023 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu 611731 China Institute for Quantum Information and State Key Laboratory of High Performance Computing College of Computing National University of Defense Technology Changsha 410073 China

We report that the position-inhomogeneous quantum walk (IQW) can be utilized to produce the maximal high-dimensional entanglement while maintaining the quadratic speedup spread of the wave function. Our calculations show that the maximal coin-walker entanglement can be generated in any odd steps or asymptotically in even steps, and the nearly maximal entanglement can be obtained in even steps after 2. We implement the IQW by a stable resource-saving time-bin optical network, in which a polarization Sagnac loop is employed to realize the precisely tunable phase shift. Our approach opens up an efficient way for high-dimensional entanglement engineering as well as promotes investigations on the role of coin-walker interactions in QW-based applications.

关键词： Entanglement manipulation Optical quantum information processing Quantum optics Quantum walks

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SMART: towards pre-trained missing-aware model for patient health status prediction 24

SMART: towards pre-trained missing-aware model for patient h...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Zhihao Yu Xu Chu Yujie Jin Yasha Wang Junfeng Zhao School of Computer Science Peking University and National Research and Engineering Center of Software Engineering Peking University School of Computer Science Peking University and Center on Frontiers of Computing Studies Peking University Beijing China and Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China and Peking University Information Technology Institute (Tianjin Binhai) National Research and Engineering Center of Software Engineering Peking University and Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China and Peking University Information Technology Institute (Tianjin Binhai) School of Computer Science Peking University and National Research and Engineering Center of Software Engineering Peking University and Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China and Nanhu Laboratory Jiaxing China

ISBN: (纸本)9798331314385

Electronic health record (EHR) data has emerged as a valuable resource for analyzing patient health status. However, the prevalence of missing data in EHR poses significant challenges to existing methods, leading to spurious correlations and suboptimal predictions. While various imputation techniques have been developed to address this issue, they often obsess difficult-to-interpolate details and may introduce additional noise when making clinical predictions. To tackle this problem, we propose SMART, a Self-Supervised Missing-Aware RepresenTation Learning approach for patient health status prediction, which encodes missing information via missing-aware temporal and variable attentions and learns to impute missing values through a novel self-supervised pre-training approach which reconstructs missing data representations in the latent space rather than in input space as usual. By adopting elaborated attentions and focusing on learning higher-order representations, SMART promotes better generalization and robustness to missing data. We validate the effectiveness of SMART through extensive experiments on six EHR tasks, demonstrating its superiority over state-of-the-art methods. Our code is available at https://***/yzhHoward/SMART.

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