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Advancing wavefront sensing:meta Shack-Hartmann sensor enhances phase imaging

Light(Science & Applications) 2024年第12期13卷 2874-2876页

作者： Xiaoyuan Liu Zihan Geng Mu Ku Chen The State Key Laboratory of Terahertz and Millimeter Waves City University of Hong KongKowloonHong Kong 999077China Department of Electrical Engineering City University of Hong KongKowloonHong Kong 999077China Institute of Data and Information Tsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhenGuangdong 518071China

A meta-lens array-based Shack-Hartmann wavefront sensor has been developed to break the limits imposed by the size and curvature of traditional micro-lenses,which significantly improves both sampling density and angular resolution of phase *** advances the field of optical phase measurement to smaller-scale complex wavefront characterization.

关键词： measurement. phase curvature

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Parameterized Approximation Schemes for Fair-Range Clustering 38

Parameterized Approximation Schemes for Fair-Range Clusterin...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhang, Zhen Chen, Xiaohong Liu, Limei Chen, Jie Huang, Junyu Feng, Qilong National Key Laboratory Cultivation Base for Data Intelligence and Smart Society Hunan University of Technology and Business Changsha410205 China Xiangjiang Laboratory Changsha410205 China School of Computer Science and Engineering Central South University Changsha410083 China

Fair-range clustering extends classical clustering formulations by associating each data point with one or more demographic labels. It imposes lower and upper bound constraints on the number of facilities opened for each label, ensuring fair representation of all demographic groups by the selected facilities. In this paper we focus on the fair-range k-median and k-means problems in Euclidean spaces. We give (1 + Ε)-approximation algorithms with fixed-parameter tractable running times for both problems, parameterized by the numbers of opened facilities and demographic labels. For Euclidean metrics, these are the first parameterized approximation schemes for the problems, improving upon the previously known O(1)-approximation ratios given by Thejaswi et al. (KDD 2022). © 2024 Neural information processing systems foundation. All rights reserved.

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Parallelism or Fairness? How to Be Friendly for SSDs in Cloud Environments

Parallelism or Fairness? How to Be Friendly for SSDs in Clou...

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2024 IEEE International Conference on Cluster Computing, CLUSTER 2024

作者： Zhou, Yang Wang, Fang Shi, Zhan Feng, Dan Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350358711

Modern SSDs achieve low latency and high throughput by utilizing multiple levels of SSD parallelism. Fairness is also a critical design consideration in cloud environments and has inspired great interest in recent years to study it. However, we find that excessive striving for SSD parallelism can inadvertently harm the SSD fairness due to the striped data layout. We observe from our experimental analysis that some common access patterns in cloud loads (e.g., high-intensity I/O) are not addressed by SSD parallelism, which can affect both SSD parallelism and fairness. In this paper, we analyze two address mapping policies with orthogonal characteristics from SSD parallelism and fairness perspectives. Based on extensive experiments and observations, we schedule a hybrid address mapping policy to achieve fairness of SSDs in cloud environments by distinguishing different I/O access modes and cold/hot data, while ensuring SSD performance. Experimental results on the latest block-level I/O traces show that our approach significantly improves performance and fairness by up to 77.3% and 62.8%, respectively, over the previous schemes with negligible cost. Meanwhile, it can also reduce the write amplification by 23.5% to 33.5%. © 2024 IEEE.

关键词： Cloud platforms

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key Clues Guided Video Character Social Relationship Recognition Enhanced by LLM

Key Clues Guided Video Character Social Relationship Recogni...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Dong, Wenlong Zhu, Qing Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China

ISBN: (纸本)9798350368741

Video Character Social Relationship Recognition (VCSRR) requires a comprehensive consideration about spatio-temporal and multi-modal clues in videos. Most existing methods mainly focus on integrating multi-modal clues and modeling interactions among characters. However, they fail to discover key clues in the complex video data or fully understand the clues related to social relationships. In this article, we propose a novel Large Language Model Enhanced key Clues Selection (LEKCS) framework to address the aforementioned issues. The core of LE-KCS is to mine multi-scale key clues from the perspectives of time, space and multi-modality, then transfer the knowledge about social relationships of the Large Language Model to VCSRR for understanding the selected clues. We evaluated LEKCS on the MovieGraphs dataset and the experimental results indicate that our proposed LE-KCS achieves state-of-the-art performance. © 2025 IEEE.

关键词： large language model Social relationship recognition video understanding

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data science:Trends, perspectives, and prospects

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data Science and Informetrics 2022年第3期2卷 1-21页

作者： Chaolemen Borjigin Chen Zhang Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of ChinaBeijingChina Information Resource Management School Renmin University of ChinaBeijingChina

data science is a rapidly growing academic field with significant implications for all conventional scientific studies. However, most relevant studies have been limited to one or several facets of data science from a specific application domain perspective and less to discuss its theoretical framework. data science is unique in that its research goals, perspectives, and body of knowledge are distinct from other sciences. The core theories of data science are the DIKW pyramid, data-intensive scientific discovery, data science life cycle, data wrangling or munging,big data analytics, data management, and governance, data products Dev Ops, and big data visualization. Six main trends characterize the recent theoretical studies on data science are:(1)the growing significance of data Ops,(2) the rise of citizen data scientists,(3) enabling augmented data science,(4) integrating data warehouse with data lake,(5) diversity of domain-specific data science, and(6) implementing data stories as data products. Further development of data science should prioritize four ways to turn challenges into opportunities:(1) accelerating theoretical studies of data science,(2) the trade-off between explainability and performance,(3) achieving data ethics, privacy and trust, and(4) aligning academic curricula with industrial needs.

关键词： CCS concepts General and reference Surveys and overviews data science Big data data products data-driven management The DIKW pyramid

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Meta-transfer learning for predicting position-dependent milling stability across varying overhang lengths under limited experimental data

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Journal of Manufacturing Processes 2025年 149卷 43-56页

作者： Deng, Congying Wu, Qiang Miao, Jianguo Lu, Sheng Xie, Luofeng School of Integrated Circuits Chongqing University of Posts and Telecommunications Chongqing400065 China College of Automation Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China School of Mechanical Engineering Sichuan University Chengdu610065 China

Stability lobe diagram (SLD) serves as a pivotal reference in determining appropriate chatter-free milling parameters. The tool tip frequency response functions (FRFs), as crucial prerequisites for deriving theoretical SLDs, vary with the position and overhang length. The acquisition of tool tip FRFs under different machining positions and overhang lengths necessitates repeated time-consuming experiments or simulations. To guarantee the efficiency and accuracy in predicting SLDs, this paper puts forward a meta-transfer learning method utilizing a deep neural network to refine the prediction of stability limits with limited experimental data. Initially, a source overhang length is defined to perform the impact testing under adequate machining positions for acquiring tool tip dynamics and constructing a theoretical stability dataset. Fewer impact tests for a target overhang length are conducted under insufficient machining positions arranged through a proposed similarity-based sampling strategy, and a target stability dataset is constructed using limited tool tip FRFs. Secondly, the source dataset is updated by domain adaptation on the modal parameters with a multi-fidelity modeling. Subsequently, meta-learning is carried out on multiple meta-tasks randomly determined from the updated source dataset. The ultimately trained deep neural network during the meta-learning phase undergoes fine-tuning with the target dataset for predicting the stability limits at various machining positions. Finally, a three-axis vertical machining center is taken to conduct a detailed experimental validation. Comparative analyses indicate that the proposed methodology outperforms others in accurately predicting stability limits, even with a constrained number of labeled samples. © 2025 The Society of Manufacturing Engineers

关键词： Milling (machining)

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Glacial Lake Area Changes in High Mountain Asia during 1990-2020 Using Satellite Remote Sensing

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Research 2023年第2期2022卷 1-23页

作者： Meimei Zhang Fang Chen Huadong Guo Lu Yi Jiangyuan Zeng Bin Li International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesNo.9 Dengzhuang South RoadBeijing 100094China University of Chinese Academy of Sciences Beijing 100049China Key Laboratory of Coastal Environment and Resources Research of Zhejiang Province School of EngineeringWestlake UniversityHangzhou 310024China State Key Laboratory of Remote Sensing Science Aerospace Information Research InstituteChinese Academy of Sciences Beijing 100094China

Changes in a large-scale glacial lake area directly reflect the regional glacier status and climate ***,long time series of glacial lake dataset and comprehensive investigation of the spatiotemporal changes in the gla... 详细信息

关键词： glacial glacier lake

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An Area Optimization Approach for Large-Scale RM-TB Dual Logic Circuits Based on a Multitasking Optimization Algorithm

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IEEE Transactions on Computers 2025年第07期74卷 2348-2363页

作者： Wu, Xiaoqian Wang, Peng Li, Shaoquan Liu, Huaxiao Liu, Lei Jilin University College of Computer Science and Technology Changchun China Key Laboratory of Symbolic Computation China Knowledge Engineering of Ministry of Education China

Logic synthesis is a crucial step in integrated circuit design, and area optimization is an indispensable part of this process. However, the area optimization problem for large-scale Fixed Polarity Reed-Muller (FPRM) circuits is an NP-hard problem. To address this problem, we divide Boolean circuits into small-scale circuits based on the idea of divide-and-conquer using the proposed grouping decomposition mechanism. Each small-scale Boolean circuit is transformed into an FPRM circuit by a polarity transformation algorithm. To ensure the circuit’s functionality remains unaffected, we integrate FPRM circuits into an FPRM and Boolean (RM-TB) dual logic circuit based on the proposed gate-level integration. However, the area optimization problem of RM-TB dual logic circuits is a multi-task, high-dimensional, and multi-extremal combinatorial optimization problem. Therefore, we propose a Multipopulation Multitasking Optimization Algorithm (MMuOA) that integrates self-evolution with a multitasking equilibrium optimizer and cross-task evolution through knowledge sharing and transfer. This forms a dynamic optimization framework for simultaneously searching for the optimal polarity corresponding to the minimal area of RM-TB dual logic circuits. Moreover, we propose an Area Optimization Approach (AOA) for an RM-TB dual logic circuit with the minimum area using the MMuOA. Experimental results based on the Microelectronics Center of North Carolina (MCNC) Benchmark test circuits demonstrate the effectiveness and superiority of the AOA compared to the state-of-the-art area optimization approach. © 1968-2012 IEEE.

关键词： Combinatorial optimization

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Two-Dimensional MXenes:Innovative Materials for Efficient Thermal Management and Safety Solutions

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Research 2024年第4期2024卷 80-100页

作者： XiaoYan Hu Qi Fan Shengchao Wang Yanxin Chen Degao Wang Ke Chen Fangfang Ge Wenhu Zhou Kun Liang School of Materials Science and Chemical Engineering Ningbo UniversityNingboZhejiang 315211P.R.China Zhejiang Key Laboratory of Data-Driven High-Safety Energy Materials and Applications Ningbo Key Laboratory of Special Energy Materials and ChemistryNingbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingbo 35201P.R.China University of Chinese Academy of Sciences Beijing 100049P.R.China Advanced Interdisciplinary Sciences Research(AIR)Center Ningbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingbo 315201P.R.China Qianwan Institute of CNITECH Ningbo 315336P.R.China Xiangya School of Pharmaceutical Sciences Central South UniversityChangsha 410013P.R.China

MXenes,a class of 2-dimensional transition metal carbides and nitrides,have garnered important attention due to their remarkable electrical and thermal conductivity,high photothermal conversion efficiency,and *** review explores the potential of MXene materials in various thermal applications,including thermal energy storage,heat dissipation in electronic devices,and the mitigation of electromagnetic interference in wearable *** advancements in MXene composites,such as MXene/bacterial cellulose aerogel films and MXene/polymer composites,have demonstrated enhanced performance in phase change thermal storage and electromagnetic interference shielding,underscoring their versatility and *** notable progress has been made,challenges remain,including the need for a deeper understanding of photothermal conversion mechanisms,improvements in mechanical properties,exploration of diverse MXene types,and the development of sustainable synthesis *** paper discusses these aspects and outlines future research directions,emphasizing the growing importance of MXenes in addressing energy efficiency,health,and safety concerns in modern applications.

关键词： MXene composites wear

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Backdoor Attacks on Bimodal Salient Object Detection with RGB-Thermal data 24

Backdoor Attacks on Bimodal Salient Object Detection with RG...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Yin, Wen Zhu, Bin Benjamin Xie, Yulai Zhou, Pan Feng, Dan School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Microsoft Corporation Beijing China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9798400706868

RGB-Thermal Salient Object Detection (RGBT-SOD) plays a critical role in complex scene recognition applications, such as autonomous driving. However, security research in this domain is still in its infancy. This paper presents the first backdoor attack on RGBT-SOD systems, generating saliency maps on triggered inputs that depict non-existent salient objects chosen by the attacker or falsely mark an entire image as fully salient or entirely non-salient. We uncover that triggers have an influence range for generating non-existent salient objects, supported by a theoretical analysis. Extensive experiments show the effectiveness of our attack in both digital and physical-world scenarios. Notably, our dual-modality backdoor attack achieves an Attack Success Rate (ASR) of 86.72% with only five pairs of poisoned images in model training. After investigating potential countermeasures, we find them inadequate in mitigating our attacks, highlighting the urgent need for robust defenses against sophisticated backdoor attacks in RGBT-SOD systems. © 2024 ACM.

关键词： Object detection

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