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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chao Li Shaokang Dong Shangdong Yang Hongye Cao Wenbin Li Yang Gao State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science Nanjing University of Posts and Telecommunications Guangxi Key Lab of Multi-Source Information Mining & Security Guangxi Normal University

Most real-world multi-agent tasks exhibit the characteristic of sparse interaction, wherein agents interact with each other in a limited number of crucial states while largely acting independently. Effectively modeling the sparse interaction and leveraging the learned interaction structure to instruct agents’ learning processes can enhance the efficiency of multi-agent reinforcement learning algorithms. However, it remains unclear how to identify these specific interactive states solely through trials and errors within current multi-agent tasks. To address this challenge, this paper introduces a novel algorithm called Sparse Interaction as Anomaly (SIA), which innovatively casts the sparse interaction modeling into an anomaly detection problem. The underlying intuition is that interactive states appear rarely in agents’ trajectories and exhibit distinct dynamics compared to other commonplace states. Building upon this insight, SIA first employs variational inference to model the latent dynamics of agents’ trajectories. It then designates states with anomalous dynamics as the elusive interactive states and subsequently instructs agents to explore these states more extensively. This facilitates the emergence of interactive behaviors and promotes the learning of multi-agent policies. Experimental evaluation of SIA across various multi-agent tasks demonstrates its superior performance against multiple baselines, highlighting its effectiveness.

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Automated and Agile Design of Layout Hotspot Detector via Neural Architecture Search

Automated and Agile Design of Layout Hotspot Detector via Ne...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Zihao Chen Fan Yang Li Shang Xuan Zeng State Key Lab of ASIC & System School of Microelectronics Fudan University Shanghai China China and Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China

This paper presents a neural architecture search scheme for chip layout hotspot detection. In this work, hotspot detectors, in the form of neural networks, are modeled as weighted directed acyclic graphs. A variational autoencoder maps the discrete graph topological space into a continuous embedding space. Bayesian Optimization performs neural architecture search in this embedding space, where an architecture performance predictor is employed to accelerate the search process. Experimental studies on ICCAD 2012 and ICCAD 2019 Contest benchmarks demonstrate that, the proposed scheme significantly improves the agility of previous neural architecture search schemes, and generates hotspot detectors with competitive detection accuracy, false alarm rate, and inference time.

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CAMInterHand: Cooperative Attention for Multi-View Interactive Hand Pose and Mesh Reconstruction

CAMInterHand: Cooperative Attention for Multi-View Interacti...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Guwen Han Qi Ye Anjun Chen Jiming Chen College of Computer Science and Technology Zhejiang University Hangzhou China College of Control Science and Engineering Zhejiang University Hangzhou China The College of Control Science and Engineeringthe State Key Laboratory of Industrial Control Technology Zhejiang University and the Key Key Lab of CS&AUS of Zhejiang Province

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Interactive hand mesh reconstruction from singleview images poses a significant challenge with the severe occlusion and depth ambiguity inherent in interactive hand gestures. Recent approaches that employ probabilistic models and tokenpruned techniques have shown decent results in multi-view human body reconstruction. Nevertheless, these methods have not fully utilized multi-scale semantic information from multiview images and are not applicable in scenarios involving severe occlusion during dual-hand interactions. Simultaneously, current single-view methods independently reconstruct the left and right hands, which are ineffective in enhancing the interaction between both hands. To address these challenges, we propose CAMInterHand, a cooperative attention-based method for multi-view interactive hand pose and mesh reconstruction. Specifically, CAMInterHand extracts local pyramid features and global vertex features from multi-scale feature maps of multi-view images, enabling the exploration of rich local semantic information and facilitating effective feature alignment. Furthermore, CAMInterHand employs the cooperative attention fusion module to fuse all features from multi-view images, enhancing interactions among vertices of dual hands within global and local contexts. We conduct extensive experiments on the large-scale multi-view dataset InterHand2.6M and CAMInterHand achieves a substantial performance improvement over existing methods for multi-view and single-view interactive hand reconstruction.

关键词： Fuses Semantics Feature extraction Probabilistic logic Data mining Robotics and automation Image reconstruction

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Self‑Generated Buried Submicrocavities for High‑Performance Near‑Infrared Perovskite Light‑Emitting Diode

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Nano-Micro Letters 2023年第8期15卷 355-367页

作者： Jiong Li Chenghao Duan Qianpeng Zhang Chang Chen Qiaoyun Wen Minchao Qin Christopher C.S.Chan Shibing Zou Jianwu Wei Zuo Xiao Chuantian Zuo Xinhui Lu Kam Sing Wong Zhiyong Fan keyou Yan School of Environment and Energy State Key Lab of Luminescent Materials and DevicesGuangdong Provincial Key Laboratory of Solid Wastes Pollution Control and RecyclingSouth China University of TechnologyGuangzhou 510000People’s Republic of China Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongPeople’s Republic of China Department of Physics The Chinese University of Hong KongShatinHong KongPeople’s Republic of China Department of Physics The Hong Kong University of Science and TechnologyClear Water BayKowloon 999077Hong KongPeople’s Republic of China School of Chemistry and Chemical Engineering Guangxi UniversityNanning 530004People’s Republic of China Center for Excellence in Nanoscience(CAS) Key Laboratory of Nanosystem and Hierarchical Fabrication(CAS)National Center for Nanoscience and TechnologyBeijing 100190People’s Republic of China

ABSTRACT Embedding submicrocavities is an effective approach to improve the light out-coupling efficiency(LOCE)for planar perovskite light-emitting diodes(PeLEDs).In this work,we employ phenethylammonium iodide(PEAI)to trigger the Ostwald ripening for the downward recrystallization of perovskite,resulting in spontaneous formation of buried submicrocavities as light output *** simulation suggests the buried submicrocavities can improve the LOCE from 26.8 to 36.2%for near-infrared ***,PeLED yields peak external quantum efficiency(EQE)increasing from 17.3%at current density of 114 mA cm^(−2)to 25.5%at current density of 109 mA cm^(−2)and a radiance increasing from 109 to 487 W sr^(−1)m^(−2)with low *** turn-on voltage decreased from 1.25 to 1.15 V at 0.1 W sr^(−1)m^(−2).Besides,downward recrystallization process slightly reduces the trap density from 8.90×10^(15)to 7.27×10^(15)cm^(−3).This work provides a self-assembly method to integrate buried output coupler for boosting the performance of PeLEDs.

关键词： Perovskite light-emitting diodes Downward recrystallization Buried submicrocavities Light out-coupling efficiency Radiative recombination

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PPIRB:Achieving an privacy-preserving image retrieval scheme Based on Blockchain 5

PPIRB:Achieving an privacy-preserving image retrieval scheme...

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5th International Conference on Data science and Information Technology, DSIT 2022

作者： Yuan, Bo Peng, Jiahui Li, Chunpei Qiu, Wangjie School of Computer Science and Engineering State Key Laboratory of Software Development Environment Beihang University Beijing100191 China Guangxi Key Lab of Multi-source Information Mining Security Guangxi Normal University GuiLin China State Key Laboratory of Software Development Environment Beihang University Advanced Innovation Center for Future Blockchain and Privacy Computing Beijing100191 China

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

With the proliferation of cloud services, the work of large-scale image retrieval is carried out on remote cloud become a trend in order to get rid of the storage burden and computation. However, traditional retrieval work based on centralized cloud services is considered to take risk in giving away users' privacy in many researches. We need a secure way for retrieving and storing images by decentralized approach. In this paper, we propose a decentralized scheme named PPIRB(An Privacy-Preserving Image Retrieval Scheme Base on Blockchain) for retrieving images based on blockchain. We used the data structure called amplifying locality sensitive hashing (ALSH) to improve search accuracy. Searchable symmetric en-cryption(SSE) and ALSH used to realize the encryption retrieval of image index on the blockchain. Our scheme also uses IPFS to replace the cloud server to realize distributed storage, and uses secret sharing scheme to realize the access control. We implemented and evaluated the scheme in Ethereum, and the results show that our scheme is efficient and accurate in finding similar images while preserving privacy. The overall overhead is reasonable. © 2022 IEEE.

关键词： Blockchain

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Attention Bootstrapping for Multi-Modal Test-Time Adaptation 39

Attention Bootstrapping for Multi-Modal Test-Time Adaptation

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhao, Yusheng Luo, Junyu Luo, Xiao Huang, Jinsheng Yuan, Jingyang Xiao, Zhiping Zhang, Ming State Key Laboratory for Multimedia Information Processing School of Computer Science PKU-Anker LLM Lab Peking University Beijing China Department of Computer Science University of California Los Angeles CA United States Paul G. Allen School of Computer Science and Engineering University of Washington Seattle WA United States

ISBN: (纸本)157735897X

Test-time adaptation aims to adapt a well-trained model to potential distribution shifts at test time using only unlabeled test data, without access to the original training data. While previous efforts mainly focus on a single modality, test-time distribution shift in the multi-modal setting is more complex and calls for new solutions. This paper tackles the problem of multi-modal test-time adaptation by proposing a novel method named Attention Bootstrapping with Principal Entropy Minimization (ABPEM). We observe that test-time distribution shift causes misalignment across modalities, leading to a large gap between intra-modality discrepancies (measured by self-attention) and inter-modality discrepancies (measured by cross-attention). We name this the attention gap. This attention gap widens with more severe distribution shifts, hindering effective modality fusion. To mitigate this attention gap and encourage better modality fusion, we propose attention bootstrapping that promotes cross-attention with the guidance of self-attention. Moreover, to reduce the gradient noise in the commonly-used entropy minimization, we adopt principal entropy minimization, a refinement of entropy minimization that reduces gradient noise by focusing on the principal parts of entropy, excluding less reliable gradient information. Extensive experiments on the benchmarks validate the effectiveness of the proposed ABPEM in comparison with competing baselines. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Modal analysis

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Attention Bootstrapping for Multi-Modal Test-Time Adaptation

arXiv

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arXiv 2025年

作者： Zhao, Yusheng Luo, Junyu Luo, Xiao Huang, Jinsheng Yuan, Jingyang Xiao, Zhiping Zhang, Ming State Key Laboratory for Multimedia Information Processing School of Computer Science PKU-Anker LLM Lab Peking University Beijing China Department of Computer Science University of California Los AngelesCA United States Paul G. Allen School of Computer Science and Engineering University of Washington SeattleWA United States

关键词： Benchmarking

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IVE: Accelerating Enumeration-Based Subgraph Matching via Exploring Isolated Vertices

IVE: Accelerating Enumeration-Based Subgraph Matching via Ex...

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International Conference on Data Engineering

作者： Zite Jiang Shuai Zhang Xingzhong Hou Mengting Yuan Haihang You State Key Lab of Processors Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijinz China Zhongguancun Laboratory Beijing China School of Computer Science Wuhan University Wuhan China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

The performance of the enumeration-based sub-graph matching, which searches all isomorphic subgraphs in the data graph, is crucial to various applications. The upper bound of the complexity for the enumeration-based method is exponential to the number of query graph vertices, denoted as $n$ . We propose a novel subgraph matching algorithm called the Isolated Vertices Exploration (IVE). The IVE leverages isolated vertices during the reordering and enumeration phases, thereby significantly accelerating the subgraph matching process. During the enumeration, the isolated vertices can be matched by using a quick bipartite graph matching algorithm. Consequently, the complexity of matching the remaining non-isolated vertices is exponential to the number of non-isolated vertices, denoted as $n^{\prime}$ . For the reordering, we designed the Maximum Deleted Edges (MDE) to minimize $n^{\prime}$ . MDE iteratively selects the query vertex with the maximum edges. According to the experimental results, $n^{\prime}$ is less than $0.8n$ for 99.8% of arbitrary graphs. Moreover, IVE outperforms the state-of-the-art algorithms in various scenarios with different sizes, sparsities and fields, achieving a performance speedup of up to 80.3x.

关键词： Backtracking Upper bound Design methodology Data engineering Complexity theory Bipartite graph

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New Algorithms for Distributed Fair k-Center Clustering: Almost Accurate as Sequential Algorithms 24

New Algorithms for Distributed Fair k-Center Clustering: Alm...

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Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems

作者： Xiaoliang Wu Qilong Feng Ziyun Huang Jinhui Xu Jianxin Wang School of Computer Science and Engineering Central South University & Xiangjiang Laboratory Changsha China Department of Computer Science and Software Engineering Penn State Erie The Behrend College Erie PA USA Department of Computer Science and Engineering State University of New York at Buffalo Buffalo NY USA Hunan Provincial Key Lab on Bioinformatics Central South University & Xiangjiang Laboratory Changsha China

ISBN: (纸本)9798400704864

Fair clustering problems have been paid lots of attention recently. In this paper, we study the k-Center problem under the group fairness and data summarization fairness constraints, denoted as Group Fair k-Center (GFkC) and Data Summarization Fair k-Center (DSFkC), respectively, in the massively parallel computational (MPC) distributed model. The previous best results for the above two problems in the MPC model are a 9-approximation with violation 7 (WWW 2022) and a (17+ε)-approximation without fairness violation (ICML 2020), respectively. In this paper, we obtain a (3+ε)-approximation with violation 1 for the GFkC problem in the MPC model, which is almost as accurate as the best known approximation ratio 3 with violation 1 for the sequential algorithm of the GFkC problem. Moreover, for the DSFkC problem in the MPC model, we obtain a (4+ε)-approximation without fairness violation, which is very close to the best known approximation ratio 3 for the sequential algorithm of the DSFkC problem. Empirical experiments show that our distributed algorithms perform better than existing state-of-the-art distributed methods for the above two problems.

关键词： clustering fairness machine learning

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FlyLISL: Traffic Balance Awared Routing for Large-scale Mixed-Reality Telepresence over Reconfigurable Mega-Constellation 42

FlyLISL: Traffic Balance Awared Routing for Large-scale Mixe...

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42nd IEEE International Conference on Distributed Computing Systems Workshops, ICDCSW 2022

作者： Zhang, Ruoyi Deng, Jing Liu, Qi Xie, Xinlei Gong, Qingyuan Zhu, Chao Beijing Institute of Technology School of Cyberspace Science and Technology China Communication University of China State Key Laboratory of Media Convergence and Communication China School of Information and Electronics Beijing Institute of Technology China School of Computer Science Fudan University Shanghai Key Lab of Intelligent Information Processing China

ISBN: (数字)9781665488792

ISBN: (纸本)9781665488792

With the emerging telepresence applications, such as meta universe, tele-education and tele-social, there exist a large number of demands for sufficient network resources for visual streaming. However, transmitting the huge volume of the generated visual data through the meandering terrestrial networks would overwhelm the backbone networks and cause non-negligible latency. Mega-constellations, such as OneWeb and Starlink, is developing as one of 'New Infrastructure Construction', and could provide high-capacity and low-latency communication in a world-wide range. However, most of the existing mega-constellation networks' topology is fixed and would easily fall into congestion with the bursting traffic generated by the large-scale telepresence applications. To address this challenge, we propose a reconfigurable mega-constellation architecture by utilizing the permanent and temporary laser inter-satellite links (LISLs). Moreover, based on the mixed-integer linear programming (MILP), we design a routing system, FlyLISL, aiming at balancing the global traffic load by minimizing the maximum link utilization of all LISLs. To evaluate the effectiveness of FlyLISL, we simulate the performance of FlyLISL by comparing with the particle swarm optimization (PSO) and Random based routing strategies. Compared with reference works, FlyLISL reduces the maximum link utilization by up to 72.6%. © 2022 IEEE.

关键词： Mixed reality

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