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arXiv 2023年

作者： Dou, Huanzhang Zhang, Pengyi Su, Wei Yu, Yunlong Lin, Yining Li, Xi College of Computer Science & Technology Zhejiang University China College of Information Science & Electronic Engineering Zhejiang University China SupreMind China Shanghai Institute for Advanced Study Zhejiang University China Shanghai Ai Laboratory China Zhejiang - Singapore Innovation and Ai Joint Research Lab China

Gait is one of the most promising biometrics that aims to identify pedestrians from their walking patterns. However, prevailing methods are susceptible to confounders, resulting in the networks hardly focusing on the regions that reflect effective walking patterns. To address this fundamental problem in gait recognition, we propose a Generative Counterfactual Intervention framework, dubbed GaitGCI, consisting of Counterfactual Intervention Learning (CIL) and Diversity-Constrained Dynamic Convolution (DCDC). CIL eliminates the impacts of confounders by maximizing the likelihood difference between factual/counterfactual attention while DCDC adaptively generates sample-wise factual/counterfactual attention to efficiently perceive the sample-wise properties. With matrix decomposition and diversity constraint, DCDC guarantees the model to be efficient and effective. Extensive experiments indicate that proposed GaitGCI: 1) could effectively focus on the discriminative and interpretable regions that reflect gait pattern;2) is model-agnostic and could be plugged into existing models to improve performance with nearly no extra cost;3) efficiently achieves state-of-the-art performance on arbitrary scenarios (in-the-lab and in-the-wild). Copyright © 2023, The Authors. All rights reserved.

关键词： Gait analysis

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A Study on the Threshold Voltage Shift under Gate-pulse Stress in D-mode GaN MIS-HEMTs 15

A Study on the Threshold Voltage Shift under Gate-pulse Stre...

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15th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2020

作者： Zhang, Xinxin Niu, Rongkang Huang, Zhangwei Dai, Tianjiao Chang, Kuan-Chang Lin, Xinnan Shenzhen Key Lab of Advanced Electron Device and Integration School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China

ISBN: (纸本)9781728162355

This work investigates the mechanism of threshold voltage Vth shift of D-Mode GaN-on-Si high electron mobility transistors (HEMTs) based on double pulse IDS-VGS measurements. The influence of applied drain voltage VDS on shift of Vth is also studied. The Vth shift is attributed to the traps located at the dielectric and dielectric/III-nitride interface. More specifically, (1) the presence of relatively fast traps located in the SiNx capture electrons tunneling from the gate metal in the quiescent gate pulse points, which cannot be released during the measurement, inducing the positive shift of Vth;(2) the higher VDS with higher electric field promote the release of electrons from the interface states to the channel, which is more obvious on the on-state gate pulse, leading to a smaller positive shift of Vth when comparing to off-state. © 2020 IEEE.

关键词： Gallium nitride

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Charge Detrapping on Gate Edge of AlGaN/GaN HEMT under Drain Stress 15

Charge Detrapping on Gate Edge of AlGaN/GaN HEMT under Drain...

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15th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2020

作者： Niu, Rongkang Dai, Tianjiao Zhang, Xinxin Chang, Kuanchang Lin, Xinnan Shenzhen Key Lab of Advanced Electron Device and Integration School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China

ISBN: (纸本)9781728162355

This work reports on the mechanism of abnormal dynamic on-resistance (dynamic Ron) in AlGaN/GaN high electron mobility transistors (HEMT) under drain stress (VDS, stress). Based on the capacitance change, it is proposed that the defects in GaN/SiN interface have been considered as the main cause of dynamic Ron decreasing. The time constant of charge detrapping at the edge of the gate (access region near the gate) is greater than 0.05s. It is found that field plate can suppress the unstable factors of dynamic Ron by reducing the peak electric field to suppress electron detrapping. © 2020 IEEE.

关键词： III-V semiconductors

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Mesenchymal stem cells loaded on 3D-printed gradient poly(e-caprolactone)/methacrylated alginate composite scaffolds for cartilage tissue engineering

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Regenerative Biomaterials 2021年第3期8卷 70-83页

作者： Yanyan Cao Peng Cheng Shengbo Sang Chuan Xiang Yang An Xiaochun Wei Zhizhong Shen Yixia Zhang Pengcui Li Key Lab of Advanced Transducers and Intelligent Control System of the Ministry of Education MicroNano System Research CenterCollege of Information and ComputerTaiyuan University of TechnologyTaiyuan 030024China College of Information Science and Engineering Hebei North UniversityZhangjiakou 075000China Shanxi Key Laboratory of Bone and Soft Tissue Injury Repair Department of OrthopedicsThe Second Hospital of Shanxi Medical UniversityTaiyuan 030001China Department of Plastic Surgery Peking University Third HospitalBeijing 100191China Department of Biomedical Engineering College of Biomedical EngineeringTaiyuan University of TechnologyTaiyuan 030024China

Cartilage has limited self-repair ability due to its avascular,alymphatic and aneural *** combination of three-dimensional(3D)printing and tissue engineering provides an up-and-coming approach to address this ***,we designed and fabricated a tri-layered(superficial layer(SL),middle layer(ML)and deep layer(DL))stratified scaffold,inspired by the architecture of collagen fibers in native cartilage *** scaffold was composed of 3D printed depth-dependent gradient poly(e-caprolactone)(PCL)impregnated with methacrylated alginate(ALMA),and its morphological analysis and mechanical properties were *** prove the feasibility of the composite scaffolds for cartilage regeneration,the viability,proliferation,collagen deposition and chondrogenic differentiation of embedded rat bone marrow mesenchymal stem cells(BMSCs)in the scaffolds were assessed by Live/dead assay,CCK-8,DNA content,cell morphology,immunofluorescence and real-time reverse transcription polymerase chain ***-loaded gradient PCL/ALMA scaffolds showed excellent cell survival,cell proliferation,cell morphology,collagen II deposition and hopeful chondrogenic differentiation compared with three individual-layer ***,our study demonstrates the potential use of the gradient PCL/ALMA construct for enhanced cartilage tissue engineering.

关键词： three-dimensional printing cartilage tissue engineering PCL methacrylated alginate bone marrow mesenchymal stem cells

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LHNN: Lattice Hypergraph Neural Network for VLSI Congestion Prediction

arXiv

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arXiv 2022年

作者： Wang, Bowen Shen, Guibao Li, Dong Hao, Jianye Liu, Wulong Huang, Yu Wu, Hongzhong Lin, Yibo Chen, Guangyong Heng, Pheng Ann Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology China Huawei Noah’s Ark Lab Hong Kong Huawei Hisilicon China Department of Computer Science Peking University China

Precise congestion prediction from a placement solution plays a crucial role in circuit placement. This work proposes the lattice hypergraph (LH-graph), a novel graph formulation for circuits, which preserves netlist data during the whole learning process, and enables the congestion information propagated geometrically and topologically. Base on the formulation, we further developed a heterogeneous graph neural network architecture LHNN, jointing the routing demand regression to support the congestion spot classification. LHNN constantly achieves more than 35% improvements compared with U-nets and Pix2Pix on the F1 score. We expect our work shall highlight essential procedures using machine learning for congestion prediction. Copyright © 2022, The Authors. All rights reserved.

关键词： VLSI circuits

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Why Blockchain Needs Graph: A Survey on Studies, Scenarios, and Solutions

SSRN

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SSRN 2023年

作者： Song, Jie Zhang, Pengyi Qu, Qiang Bai, Yongjie Gu, Yu Yu, Ge Software College Northeastern University Shenyang110819 China Shenzhen Institute of Advanced Technology Chinese Academy of Science Shenzhen518055 China Blockchain Lab Huawei Cloud Tech Co. Ltd Shenzhen518101 China School of Computer Science and Engineering Northeastern University Shenyang110819 China

The popularity of blockchain platforms and their applications in industry and academia keeps rising. The multifarious requirements stimulate another technique, graph data and algorithms, to join the blockchains;thus, studies, scenarios, and solutions about graph-related blockchains have emerged. State-of-the-art studies have reported on ``blockchain + graph'' solutions, but few of them natively integrate graph computing into a blockchain. Here, the ``graph integration'' means processing on-chain graph data, which contains blockchain information, in a real-time, distributed, and consensual manner. Aiming to answer why a blockchain needs graphs, and why a Graph-integrated Blockchain Platform (GiBP for short) is necessary and challengeable, we analyze literature about blockchain and graph, as well as use cases on the application-oriented and graph-related scenarios collected from practical blockchain projects. We draw the findings for three graph-related blockchain scenarios: graph algorithms for blockchains, graph data in blockchains, and graph applications on blockchains. Based on these findings, we explain why a GiBP is inevitable, the challenges of a GiBP, and the GiBPs’ main functions and features people expect. We also propose the prospection of a GiBP for future research. © 2023, The Authors. All rights reserved.

关键词： Blockchain

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NTIRE 2024 Restore Any Image Model (RAIM) in the Wild Challenge

arXiv

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arXiv 2024年

作者： Liang, Jie Yi, Qiaosi Liu, Shuaizheng Sun, Lingchen Zhang, Xindong Zeng, Hui Zhang, Lei Timofte, Radu Huang, Yibin Liu, Shuai Li, Yongqiang Feng, Chaoyu Wang, Xiaotao Lei, Lei Chen, Yuxiang Chen, Xiangyu Chen, Qiubo Chen, Jiaxu Sun, Fengyu Cui, Mengying Hu, Zhenyu Liu, Jingyun Ma, Wenzhuo Wang, Ce Zheng, Hanyou Sun, Wanjie Chen, Zhenzhong Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Dun, Xiong Ji, Pengzhou Xing, Yujie Wang, Xuquan Wang, Zhanshan Cheng, Xinbin Xiao, Jun He, Chenhang Wang, Xiuyuan Liu, Zhi-Song Miao, Zimeng Yin, Zhicun Liu, Ming Zuo, Wangmeng Wu, Rongyuan Li, Shuai OPPO Research Institute China The Hong Kong Polytechnic University Hong Kong Computer Vision Lab University of Würzburg Germany Xiaomi Inc. China Xiaohongshu China University of Macau China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China China School of Remote Sensing and Information Engineering Wuhan University China Department of Information Technology Uppsala University Sweden Institute of Precision Optical Engineering School of Physics Science and Engineering Tongji University China Lappeenranta-Lahti University of Technology Finland School of Computer Science and Technology Harbin Institute of Technology China

In this paper, we review the NTIRE 2024 challenge on Restore Any Image Model (RAIM) in the Wild. The RAIM challenge constructed a benchmark for image restoration in the wild, including real-world images with/without reference ground truth in various scenarios from real applications. The participants were required to restore the realcaptured images from complex and unknown degradation, where generative perceptual quality and fidelity are desired in the restoration result. The challenge consisted of two tasks. Task one employed real referenced data pairs, where quantitative evaluation is available. Task two used unpaired images, and a comprehensive user study was conducted. The challenge attracted more than 200 registrations, where 39 of them submitted results with more than 400 submissions. Top-ranked methods improved the state-of-the-art restoration performance and obtained unanimous recognition from all 18 judges. The proposed datasets are available at https://***/file/d/ 1DqbxUoiUqkAIkExu3jZAqoElr_nu1IXb/view? usp=sharing and the homepage of this challenge is at https://***/ competitions/17632. © 2024, CC BY-SA.

关键词： Image reconstruction

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Impact-aware Maneuver Decision with Enhanced Perception for Autonomous Vehicle

Impact-aware Maneuver Decision with Enhanced Perception for ...

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International Conference on Data engineering

作者： Shuncheng Liu Yuyang Xia Xu Chen Jiandong Xie Han Su Kai Zheng School of Computer Science and Engineering University of Electronic Science and Technology of China China Huawei Cloud Database Innovation Lab China Yangtze Delta Region Institute (Quzhou) University of Electronic Science and Technology of China China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China China

Autonomous driving is an emerging technology that has developed rapidly over the last decade. There have been numerous interdisciplinary challenges imposed on the current transportation system by autonomous vehicles. In this paper, we conduct an algorithmic study on the autonomous vehicle decision-making process, which is a fundamental problem in the vehicle automation field and the root cause of most traffic congestion. We propose a perception-and-decision framework, called HEAD, which consists of an enHanced pErception module and a mAneuver Decision module. HEAD aims to enable the autonomous vehicle to perform safe, efficient, and comfortable maneuvers with minimal impact on other vehicles. In the enhanced perception module, a graph-based state prediction model with a strategy of phantom vehicle construction is proposed to predict the one-step future states for multiple surrounding vehicles in parallel, which deals with sensor limitations such as limited detection range and poor detection accuracy under occlusions. Then in the maneuver decision module, a deep reinforcement learning-based model is designed to learn a policy for the autonomous vehicle to perform maneuvers in continuous action space w.r.t. a parameterized action Markov decision process. A hybrid reward function takes into account aspects of safety, efficiency, comfort, and impact to guide the autonomous vehicle to make optimal maneuver decisions. Extensive experiments offer evidence that HEAD can advance the state of the art in terms of both macroscopic and microscopic effectiveness.

关键词：

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Machine Learning-Integrated Modeling of Thermal Properties and Relaxation Dynamics in Metallic Glasses

arXiv

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arXiv 2025年

作者： Que, Ngo T. Phan, Anh D. Tran, Truyen Huy, Pham T. Trang, Mai X. Luong, Thien V. Phenikaa Institute for Advanced Study Phenikaa University Hanoi12116 Viet Nam Faculty of Materials Science and Engineering Phenikaa University Hanoi12116 Viet Nam Deakin University Australia Faculty of Computer Science Phenikaa University Hanoi12116 Viet Nam Business AI Lab Faculty of Data Science and Artificial Intelligence National Economics University Hanoi10000 Viet Nam

Metallic glasses are a promising class of materials celebrated for their exceptional thermal and mechanical properties. However, accurately predicting and understanding the melting temperature (Tm) and glass transition temperature (Tg) remains a significant challenge. In this study, we present a comprehensive approach that integrates machine learning (ML) models with theoretical methods to predict and analyze these key thermal properties in metallic glasses. Our ML models using distributional data derived from elemental composition-based features obtain high accuracy while minimizing data preprocessing complexity. Furthermore, we explore the correlation between Tm and Tg to elucidate their dependence on alloy composition and thermodynamic behavior. When the Tgvalue of metallic glasses is known, further analysis using the Elastically Collective Nonlinear Langevin Equation (ECNLE) theory provides a deeper understanding of structural relaxation dynamics. This integrated framework establishes a quantitative description consistent with experimental data and previous works and paves the way for the efficient design and discovery of advanced materials with tailored thermal properties. Copyright © 2025, The Authors. All rights reserved.

关键词： Amorphous alloys

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A 64-channel Fully Implantable Brain-computer Microsystem with Custom Microelectrode Array

A 64-channel Fully Implantable Brain-computer Microsystem wi...

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IEEE Biomedical Circuits and Systems (BIOCAS)

作者： Yaoyao Hao Leixiao Han Youwei Jin Qin Chen Chenbin Yu Bowen Yu Penghai Li Jingzhuo Han Yueming Wang The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China Nanhu Brain-computer Interface Institute Hangzhou China Department of Biomedical Engineering Zhejiang University Hangzhou China Qiushi Academy for Advanced Studies Zhejiang University Hangzhou China MOE Frontier Science Center for Brain Science and Brain-Machine Integration Zhejiang University Hangzhou China

ISBN: (数字)9798350354959

ISBN: (纸本)9798350354966

This paper presents the development of a 64-channel fully implantable neural signal recording and processing system. The system is designed with hermetically sealed, commercial off-the-shelf components to enable neural signal acquisition, wireless transmitting and wireless charging. Additionally, an artificial neural network was implemented on the hardware to create a standalone, comprehensive brain signal decoding system. A silicon-based microelectrode array, fabricated with MEMS process and assembled into 3D floating form by stacking planar silicon probes, was attached to the system to sense neural signals. The whole system was militarized packaged into a medical-scale titanium case and embedded onto skull for brain-computer interface (BCI) applications. The study demonstrates the feasibility and potential of this advanced technology for neural signal recording and processing in a fully implantable system.

关键词： Micromechanical devices Wireless communication Microelectrodes Wireless sensor networks Three-dimensional displays Array signal processing Titanium Hardware Recording Decoding

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