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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Chen, Min Zhang, Yanyan National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education Shen Yang China

ISBN: (纸本)9798400704956

We propose a dynamic multi-objective optimization algorithm for visualizing and clustering topological features of decision space variables. It maps all the obtained historical optimal solutions to the two-dimensional space through t-SNE, clusters the points, and selects a corresponding proportion of individuals from different clusters to form a new population. Throughout the search process, the topological characteristics of the reduced historical optimal solutions can be visually displayed. We conducted a preliminary experimental analysis on the standard test function to verify the effectiveness of the proposed method. © 2024 Copyright held by the owner/author(s).

关键词： Topology

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Stiffness and toughness of soft/stiff suture joints in biological composites

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Applied Mathematics and Mechanics(English Edition) 2022年第10期43卷 1469-1484页

作者： Dong WU Yixing HUANG Ming LEI Zeang ZHAO Xiaogang GUO Daining FANG State Key Laboratory for Turbulence and Complex Systems&Center for Applied Physics and Technology College of EngineeringPeking UniversityBeijing 100871China Beijing Key Laboratory of Lightweight Multi-functional Composite Materials and Structures Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Explosion Science and Technology Beijing Institute of TechnologyBeijing 100081China School of Astronautics Northwestern Polytechnical UniversityXi’an 710072China

Biological composites can overcome the conflict between strength and toughness to achieve unprecedented mechanical properties in engineering *** suture joint,as a kind of heterogeneous architecture widely existing in biological tissues,is crucial to connect dissimilar components and to attain a tradeoff of all-sided functional ***,the suture joints have attracted many researchers to theoretically investigate their mechanical ***,most of the previous models focus on the sutural interface between two chemically similar stiff phases with(or without)a thin adhesive layer,which are under the framework of linear elasticity and small ***,a general model based on the finite deformation framework is proposed to explore the stiffness and toughness of chemically dissimilar suture joints connecting soft and stiff *** tension tests are conducted to investigate the tensile response of the suture joints,and finite element simulations are implemented to explore the underlying mechanisms,considering both material nonlinearity and cohesive properties of the *** failure modes are quantitively captured by our *** stored elastic energy in the soft phase competes with the energy dissipation due to the interface debonding,which controls the transition among different failure *** toughness of the suture joints depends on not only the intrinsic strengths of the constituent materials and their cohesive strength,but also the interfacial *** work provides the structureproperty relationships of the soft/stiff suture joints and gives a foundational guidance of mechanical design towards high-performance bioinspired composites.

关键词： suture joint biological composite finite deformation toughness

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The Impact of Medical Resources and Oral Antiviral Drugs on SARS-CoV-2 Mortality—Hong Kong SAR,China,2022

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China CDC weekly 2024年第21期6卷 469-477,I0001-I0005页

作者： Zhiqi Zeng Jingyi Liang Zhijie Lin Wenda Guan Wei He Qianying Li Lixi Liang Weiqi Pan Zige Liu Guibin Lu Xiaoyan Deng Eric HY Lau Chitin Hon Zifeng Yang Guangzhou key laboratory for clinical rapid diagnosis and early warning of infectious diseases KingMed School of Laboratory MedicineGuangzhou Medical UniversityGuangzhou CityGuangdong ProvinceChina Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao SARChina Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovative EngineeringMacao University of Science and TechnologyMacao SARChina State Key Laboratory of Respiratory Disease National Clinical Research Center for Respiratory DiseaseGuangzhou Institute of Respiratory Healththe First Affiliated Hospital of Guangzhou Medical UniversityGuangzhou CityGuangdong ProvinceChina School of Public Health The University of Hong KongHong Kong SARChina Laboratory of Data Discovery for Health Hong Kong SARChina Guangzhou Laboratory Guangzhou CityGuangdong ProvinceChina

Introduction:The Omicron variant of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)demonstrates increased transmissibility compared to earlier strains,contributing to a significant number of fatalities in Hong Kong Special Administrative Region(HKSAR),*** medical resources and medications are essential in mitigating these *** study evaluates the effects of supplementary resources from the Chinese mainland during the fifth wave of the pandemic in ***:Vector autoregression(VAR)was employed to analyze data from the Oxford coronavirus disease 2019(COVID-19)Government Response Tracker to assess the effectiveness of control measures during five waves of the pandemic in ***,a transmission dynamics model was created to investigate the influence of supplementary medical resources from the Chinese mainland and oral medications on ***:In the initial four waves,workplace closures,restrictions on public events,international travel bans,and shielding the elderly significantly influenced pandemic ***,during the fifth wave,these measures showed no notable *** comparing a situation without extra medical resources or COVID-19 oral medication,there was a 17.7%decrease in COVID-19 fatalities with mainland medical resources and an additional 10.2%reduction with oral ***,they contributed to a 26.6%decline in ***:With the rapid spread of the virus,regional reallocation of medical resources may reduce mortality even when the local healthcare system is overstretched.

关键词： mainland Impact travel

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Late Breaking Results: Coulomb Force-Based Routability-Driven Placement Considering Global and Local Congestion 24

Late Breaking Results: Coulomb Force-Based Routability-Drive...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Meng, Jihai Weng, Shaohong Cai, Zhijie Chen, Yilu Lin, Zhifeng Chen, Jianli Center for Discrete Mathematics and Theoretical Computer Science Fuzhou University Fuzhou350108 China College of Computer and Data Science Fuzhou University Fuzhou350108 China State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai200433 China

ISBN: (纸本)9798400706011

Placement is a critical stage for VLSI routability optimization. A placement engine without considering the layout congestion might lead to poor solutions with routing failures. This paper introduces a Coulomb force-based global placement framework that addresses global and local routing congestions. We first present a routing path-based cell padding strategy for local congestion mitigation. Then, we construct a routability-aware placement model that utilizes virtual Coulomb forces to eliminate crucial global congestion. Compared with a leading academic placer, RePlAce, and the advanced commercial tool, Innovus, the experimental results on industrial benchmark suites show that our proposed algorithm achieves the best routability within the shortest runtime. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Traffic congestion

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Towards understanding residual and dilated dense neural networks via convolutional sparse coding

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National science Review 2021年第3期8卷 127-139页

作者： Zhiyang Zhang Shihua Zhang National Center for Mathematics and Interdisciplinary Sciences Center for Excellence in Mathematical Science Key Laboratory of Random Complex Structures and Data ScienceAcademy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences Center for Excellence in Animal Evolution and Genetics Chinese Academy of Sciences Key Laboratory of Systems Biology Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Chinese Academy of Sciences

Convolutional neural network(CNN) and its variants have led to many state-of-the-art results in various ***,a clear theoretical understanding of such networks is still ***,a multilayer convolutional sparse coding(ML-CSC) model has been proposed and proved to equal such simply stacked networks(plain networks).Here,we consider the initialization,the dictionary design and the number of iterations to be factors in each layer that greatly affect the performance of the ML-CSC *** by these considerations,we propose two novel multilayer models:the residual convolutional sparse coding(Res-CSC) model and the mixed-scale dense convolutional sparse coding(MSD-CSC) *** are closely related to the residual neural network(ResNet) and the mixed-scale(dilated) dense neural network(MSDNet),***,we derive the skip connection in the ResNet as a special case of a new forward propagation rule for the ML-CSC *** also find a theoretical interpretation of dilated convolution and dense connection in the MSDNet by analyzing the MSD-CSC model,which gives a clear mathematical understanding of *** implement the iterative soft thresholding algorithm and its fast version to solve the Res-CSC and MSD-CSC *** unfolding operation can be employed for further ***,extensive numerical experiments and comparison with competing methods demonstrate their effectiveness.

关键词： convolutional neural network convolutional sparse coding residual neural network mixed-scale dense neural network dilated convolution dense connection

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Enhanced carbon capture with motif-rich amino acid loaded defective robust metal-organic frameworks

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Nano Research 2024年第3期17卷 2004-2010页

作者： Qi-Ye Ju Jia-Jia Zheng Li Xu Hai-Yan Jiang Zi-Qian Xue Lu Bai Yang-Yang Guo Ming-Shui Yao Ting-Yu Zhu State Key Laboratory of Multi-phase Complex Systems CAS Key Laboratory of Green Process and EngineeringBeijing Key Laboratory of Ionic Liquids Clean ProcessInstitute of Process EngineeringChinese Academy of SciencesBeijing 100190China Laboratory of Theoretical and Computational Nanoscience National Center for Nanoscience and TechnologyChinese Academy of SciencesBeijing 100190China School of Rare Earths University of Science and Technology of ChinaHefei 230026China Ganjiang Innovation Academy Chinese Academy of SciencesGanzhou 341000China University of Chinese Academy of Sciences Chinese Academy of SciencesBeijing 100049China Institute for Integrated Cell-Material Sciences Kyoto UniversityInstitute for Advanced StudyKyoto UniversityYoshidaUshinomiya-choSakyokuKyoto 606-8501Japan

The use of metal-organic frameworks(MOFs)as solid adsorption materials for carbon capture is promising,but achieving efficient and reversible adsorption with a balance of capacity and selectivity for carbon dioxide(CO_(2))over N_(2) remains a *** take full advantage of the strong channel traffic and robustness of MOFs with relatively small pores,it is highly necessary to employ a defect-engineering strategy to construct a broader channel structure that can facilitate the loading of functional motif-rich amino acids(AAs).This strategy can greatly enhance the CO_(2) adsorption performance of *** this study,motif-rich amino acids are loaded into the defective and robust porous frameworks via combined defect-engineering and post-synthetic *** defective Zr/Hf-MOF-808s modified with AAs,especially for the 18 mol%4-nitroisophthalic acid,generated defective products allowing for the loading of L-serine(L-Ser).This modification resulted in a significant improvement in both the adsorption capacity(248%improvement at 298 K,100 kPa)and the selectivity of CO_(2)/N_(2) using the ideal adsorbed solution theory(IAST),with the selectivity increasing to 120.55 and 38.27 at 15 and 100 kPa,respectively,while maintaining good cycling *** functional theory(DFT)simulation,CO_(2) temperature-programmed desorption(CO_(2)-TPD),and in situ Fourier transform infrared spectroscopy(FTIR)were further employed to have a better understanding of the enhanced CO_(2) adsorption ***,unlike the AAs loaded pristine MOF-808s that showed the best CO_(2) adsorption capacity with the loading of short and small glycine(Gly),the broadened channel size in our work enables the loading of functional motif-rich L-serine,which brings more active binding sites,improving CO_(2) adsorption.

关键词： carbon capture and storage metal-organic framework(MOF) amino acids(AAs) defect-engineering motif-rich

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COVID-19 Knowledge Mining Based on Large Language Models and Chain-of-Thought Reasoning

COVID-19 Knowledge Mining Based on Large Language Models and...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Fushuai Ling, Yunchao Yang, Jiaxin Zhang, Peng Zhang, Guoqing University of Chinese Academy of Sciences Key Laboratory of Systems Health Science of Zhejiang Province School of Life Science Hangzhou Institute for Advanced Study Hangzhou China University of Chinese Academy of Sciences Chinese Academy of Sciences Cas Key Laboratory of Computational Biology Bio-Med Big Data Center Shanghai Institute of Nutrition and Health Shanghai China

ISBN: (纸本)9798350386226

This research aims to extract knowledge related to COVID-19 clinical diagnosis and molecular mechanisms from COVID-19 literature by applying large language models, deepening our quantitative understanding of COVID-19 and its causative virus SARS-CoV-2, and providing foundational data resources for subsequent COVID-19 research. The knowledge extraction process is divided into three stages:The first phase: A total of 131 key documents, including COVID-19 research, vaccine clinical trials, and clinical guidelines, were selected for content extraction. Entities, relationships, entity types, and conditions were annotated, and knowledge was described in the form of triplets, using events as the unit. By summarizing the important and frequently occurring events, entities, and relationships, as well as analyzing the clinical diagnosis knowledge of COVID-19, nine COVID-19 clinical diagnosis knowledge patterns were identified. Subsequently, a corpus containing 342,287 articles was constructed using search strategies from PubMed and the CORD-19 *** Second phase: Chain-of-thought reasoning was applied to enhance the data, simulating human experts in determining the knowledge patterns of COVID-19 sentences and extracting the COVID-19 entities and their types through a step-by-step reasoning *** Third phase: The focus was on training large language models to achieve superior Knowledge Pattern Discrimination (KPD) and Named Entity Recognition (NER) performance, the Accuracy of the KPD model and the F1-score of the NER model reached 0.869 and 0.887 respectively, surpassing existing methods. Additionally, ablation studies indicated that the data formats used in chain-of-thought reasoning and model fine-tuning affect the performance of the models. © 2024 IEEE.

关键词： COVID-19

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Essential Forward Weak KAM Solution for the Convex Hamilton–Jacobi Equation

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Acta Mathematica Sinica,English Series 2022年第5期38卷 797-806页

作者： Xi Feng SU Jian Lu ZHANG Laboratory of Mathematics and Complex Systems(Ministry of Education) School of Mathematical SciencesBeijing Normal UniversityBeijing 100875P.R.China Hua Loo-Keng Key Laboratory of Mathematics&Mathematics Institute Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190P.R.China

For a convex,coercive continuous Hamiltonian on a closed Riemannian manifold M,we construct a unique forward weak KAM solution of H(x,d_(x)u)=c(H)by a vanishing discount approach,where c(H)is the Ma?écritical ***... 详细信息

关键词： Hamilton-Jacobi equation discounted equation weak KAM solution Aubry-Mather theory viscosity solution

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Analysis of Factors Influencing Driver Lane-Changing Intentions Based on a Naturalistic Trajectory data Set on Highways 24

Analysis of Factors Influencing Driver Lane-Changing Intenti...

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24th COTA International Conference of Transportation Professionals: Resilient, Intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Wang, Guangchen Lu, Guangquan Wang, Jinghua Liu, Miaomiao School of Transportation Science and Engineering Beihang Univ. Beijing China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang Univ. Beijing China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

Accurately analyzing and predicting driver lane-changing intentions is of paramount importance, as it significantly enhances the safety of self-driving vehicles in their decision-making processes, holding great promise for the future of intelligent transportation systems. This study extracts fundamental lane-changing characteristic parameters from the Next Generation Simulation (NGSIM) trajectory data set and employs Structural Equation Modeling (SEM) to construct a model that elucidates the factors influencing drivers’ lane-changing intentions. Furthermore, it provides insight into how lane-changing intentions are influenced by lane-changing behavior and explores the intricate connection between intention and associated risks. The results emphasize the substantial negative impact of the longitudinal speed difference between the self-vehicle and the preceding vehicle on lane-changing intentions. Additionally, the consideration of lane-changing risk plays a pivotal role in drivers’ decisions to engage in lane-changing behavior. These findings have significant implications for a deeper comprehension of lane-changing intentions, guiding lane-changing decisions, and enhancing overall traffic safety. © ASCE.

关键词： Autonomous vehicles

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Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection 24

Dual-Modeling Decouple Distillation for Unsupervised Anomaly...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Liu, Xinyue Wang, Jianyuan Leng, Biao Zhang, Shuo School of Computer Science and Engineering Beihang University Beijing China Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China Beijing Key Lab of Traffic Data Analysis and Mining School of Computer & Technology Beijing Jiaotong University Beijing China

ISBN: (纸本)9798400706868

Knowledge distillation based on student-teacher network is one of the mainstream solution paradigms for the challenging unsupervised Anomaly Detection task, utilizing the difference in representation capabilities of the teacher and student networks to implement anomaly localization. However, over-generalization of the student network to the teacher network may lead to negligible differences in representation capabilities of anomaly, thus affecting the detection effectiveness. Existing methods address the possible over-generalization by using differentiated students and teachers from the structural perspective or explicitly expanding distilled information from the content perspective, which inevitably results in an increased likelihood of underfitting of the student network and poor anomaly detection capabilities in anomaly center or edge. In this paper, we propose Dual-Modeling Decouple Distillation (DMDD) for the unsupervised Anomaly Detection. In DMDD, a Decouple Student-Teacher Network is proposed to decouple the initial student features into normality and abnormality features. We further introduce Dual-Modeling Distillation based on normal-anomalous image pairs, fitting normality features of anomalous image and the teacher features of the corresponding normal image, widening the distance between abnormality features and the teacher features in anomalous regions. Synthesizing these two distillation ideas, we achieve anomaly detection which focuses on both edge and center of anomaly. Finally, a Multi-perception Segmentation Network is proposed to achieve focused anomaly map fusion based on multiple attention. Experimental results on MVTec AD show that DMDD surpasses SOTA localization performance of previous knowledge distillation-based methods, reaching 98.85% on pixel-level AUC and 96.13% on PRO. © 2024 ACM.

关键词： Students

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