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2024 International Conference on Optoelectronic Information and computer Engineering, OICE 2024

作者： Liu, Lusheng Le, Song Zhang, Yuan Zhang, Jing Wang, Aiping Bin, Jianxiong Niu, Lan Zhang, Lihua Kang, Xiaoyang Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Shanghai Engineering Research Center of AI & Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of AI and Robotics Academy for Engineering & Technology Fudan University Shanghai200433 China Ji Hua Laboratory Guangdong Province Foshan528200 China Yiwu Research Institute of Fudan University Zhejiang Yiwu City322000 China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou311121 China

ISBN: (纸本)9781510682504

Optogenetics technology has greatly promoted the development of neuroscience. Flexible gene manipulation tools and sensitive light activation methods have provided convenience for neural function research. However, the thermal effect generated in the process of light stimulation affects the activation of neurons by optogenetic technology, which makes the results of light-activated light-sensitive proteins to generate neural currents inaccurate. Here, we established an in vivo activation model of LED-based optogenetic technology through simulation and analyzed the thermal effect produced by it. We propose to use microchannels to dissipate heat from implanted devices, reducing the thermal effect of light stimulation for neural activation. Through simulation, we propose the use of the vortex-like and cobweb-like microchannels for heat dissipation, which can control the temperature rise within 0.28K and 0.31K, respectively. © 2024 SPIE.

关键词： Heat losses

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VIS+AI: integrating visualization with artificial intelligence for efficient data analysis

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Frontiers of computer science 2023年第6期17卷 13-24页

作者： Xumeng WANG Ziliang WU Wenqi HUANG Yating WEI Zhaosong HUANG Mingliang XU Wei CHEN College of Computer Science Nankai UniversityTianjin 300381China State Key Lab of CAD&CG Zhejiang UniversityHangzhou 310058China Digital Grid Research Institute China Southern Power GridGuangzhou 510670China Huawei Technologies Co. Ltd.Hangzhou 310052China School of Computer and Artificial Intelligence Zhengzhou UniversityZhengzhou 450001China Engineering Research Center of Ministry of Education on Intelligent Swarm Systems Zhengzhou UniversityZhengzhou 450001China National Supercomputing Center in Zhengzhou Zhengzhou 450001China Laboratory of Art and Archaeology Image Zhejiang UniversityHangzhou 310058China

Visualization and artificial intelligence(AI)are well-applied approaches to data *** one hand,visualization can facilitate humans in data understanding through intuitive visual representation and interactive *** the other hand,AI is able to learn from data and implement bulky tasks for *** complex data analysis scenarios,like epidemic traceability and city planning,humans need to understand large-scale data and make decisions,which requires complementing the strengths of both visualization and *** studies have introduced AI-assisted visualization as AI4VIS and visualization-assisted AI as ***,how can AI and visualization complement each other and be integrated into data analysis processes are still *** this paper,we define three integration levels of visualization and *** highest integration level is described as the framework of VIS+AI,which allows AI to learn human intelligence from interactions and communicate with humans through visual *** also summarize future directions of VIS+AI to inspire related studies.

关键词： visualization artificial intelligence data analysis knowledge generation

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Clothed Human Performance Capture with a Double-layer Neural Radiance Fields

Clothed Human Performance Capture with a Double-layer Neural...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Kangkan Wang Guofeng Zhang Suxu Cong Jian Yang Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of CAD&CG Zhejiang University China

This paper addresses the challenge of capturing performance for the clothed humans from sparse-view or monocular videos. Previous methods capture the performance of full humans with a personalized template or recover the garments from a single frame with static human poses. However, it is inconvenient to extract cloth semantics and capture clothing motion with one-piece template, while single frame-based methods may suffer from instable tracking across videos. To address these problems, we propose a novel method for human performance capture by tracking clothing and human body motion separately with a double-layer neural radiance fields (NeRFs). Specifically, we propose a double-layer NeRFsfor the body and garments, and track the densely deforming template of the clothing and body by jointly optimizing the deformation fields and the canonical double-layer NeRFs. In the optimization, we introduce a physics-aware cloth simulation network which can help generate physically plausible cloth dynamics and body-cloth interactions. Compared with existing methods, our method is fully differentiable and can capture both the body and clothing motion robustly from dynamic videos. Also, our method represents the clothing with an independent NeRFs, allowing us to model implicit fields of general clothes feasibly. The experimental evaluations validate its effectiveness on real multi-view or monocular videos.

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Emerging topic identification from app reviews via adaptive online biterm topic modeling

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Frontiers of Information Technology & Electronic Engineering 2022年第5期23卷 678-691页

作者： Wan ZHOU Yong WANG Cuiyun GAO Fei YANG School of Information and Computer Anhui Polytechnic UniversityWuhu 241000China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210000China School of Computer Science and Technology Harbin Institute of Technology(Shenzhen)Shenzhen 518000China Zhejiang Lab Hangzhou 310000China

Emerging topics in app reviews highlight the topics(e.g.,software bugs)with which users are concerned during certain *** emerging topics accurately,and in a timely manner,could help developers more effectively update *** for identifying emerging topics in app reviews based on topic models or clustering methods have been proposed in the ***,the accuracy of emerging topic identification is reduced because reviews are short in length and offer limited *** solve this problem,an improved emerging topic identification(IETI)approach is proposed in this ***,we adopt natural language processing techniques to reduce noisy data,and identify emerging topics in app reviews using the adaptive online biterm topic *** we interpret the implicature of emerging topics through relevant phrases and *** adopt the official app changelogs as ground truth,and evaluate IETI in six common *** experimental results indicate that IETI is more accurate than the baseline in identifying emerging topics,with improvements in the F1 score of 0.126 for phrase labels and 0.061 for sentence ***,we release the codes of IETI on Github(https://***/wanizhou/IETI).

关键词： App reviews Emerging topic identification Topic model Natural language processing

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NFTVis: Visual Analysis of NFT Performance

NFTVis: Visual Analysis of NFT Performance

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Pacific (formerly Asia-Pacific APVIS) Visualization Symposium

作者： Fan Yan Xumeng Wang Ketian Mao Wei Zhang Wei Chen State Key Lab of CAD&CG Zhejiang University College of Computer Science Nankai University Ministry of Education Laboratory of Art and Archaeology Image (Zhejiang University) China

A non-fungible token (NFT) is a data unit stored on the blockchain. Nowadays, more and more investors and collectors (NFT traders), who participate in transactions of NFTs, have an urgent need to assess the performance of NFTs. However, there are two challenges for NFT traders when analyzing the performance of NFT. First, the current rarity models have flaws and are sometimes not convincing. In addition, NFT performance is dependent on multiple factors, such as images (high-dimensional data), history transactions (network), and market evolution (time series). It is difficult to take comprehensive consideration and analyze NFT performance efficiently. To address these challenges, we propose NFTVis, a visual analysis system that facilitates assessing individual NFT performance. A new NFT rarity model is proposed to quantify NFTs with images. Four well-coordinated views are designed to represent the various factors affecting the performance of the NFT. Finally, we evaluate the usefulness and effectiveness of our system using two case studies and user studies.

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SUSHI: Ultra-High-Speed and Ultra-Low-Power Neuromorphic Chip Using Superconducting Single-Flux-Quantum Circuits

SUSHI: Ultra-High-Speed and Ultra-Low-Power Neuromorphic Chi...

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IEEE/ACM International Symposium on Microarchitecture (MICRO)

作者： Zeshi Liu Shuo Chen Peiyao Qu Huanli Liu Minghui Niu Liliang Ying Jie Ren Guangming Tang Haihang You State Key Lab of Processors Institute of Computing Technology Chinese Academy of Science School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Shanghai Institute of Microsystem and Information Technology Chinese Academy of Science Shanghai China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Science Beijing China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Science Zhongguancun Laboratory Beijing China

The rapid single-flux-quantum (RSFQ) superconducting technology is highly promising due to its ultra-high-speed computation with ultra-low-power consumption, making it an ideal solution for the post-Moore era. In superconducting technology, information is encoded and processed based on pulses that resemble the neuronal pulses present in biological neural systems. This has led to a growing research focus on implementing neuromorphic processing using superconducting technology. However, current research on superconducting neuromorphic processing does not fully leverage the advantages of superconducting circuits due to incomplete neu-romorphic design and approach. Although they have demonstrated the benefits of using superconducting technology for neuromor-phic hardware, their designs are mostly incomplete, with only a few components validated, or based solely on simulation. This paper presents SUSHI (Superconducting neUromorphic proceSsing cHIp) to fully leverage the potential of superconducting neuromorphic processing. Based on three guiding principles and our architectural and methodological designs, we address existing challenges and enables the design of verifiable and fabricable superconducting neuromorphic chips. We fabricate and verify a chip of SUSHI using superconducting circuit technology. Successfully obtaining the correct inference results of a complete neural network on the chip, this is the first instance of neural networks being completely executed on a superconducting chip to the best of our knowledge. Our evaluation shows that using approximately 10 5 Josephson junctions, SUSHI achieves a peak neuromorphic processing performance of 1,355 giga-synaptic operations per second (GSOPS) and a power efficiency of 32,366 GSOPS per Watt (GSOPS/W). This power efficiency outperforms the state-of-the-art neuromorphic chips TrueNorth and Tianjic by 81 and 50 times, respectively.

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VFGCN: A Vertical Federated Learning Framework With Privacy Preserving for Graph Convolutional Network

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Wang, Gang Li, Qingming Liu, Ximeng Yan, Xiaoran Dong, Qingkuan Wu, Huiwen Kong, Xiangjie Zhou, Li Zhejiang Lab HangZhou310000 China Zhejiang University College of Computer Science and Technology Hangzhou310058 China Fuzhou University Key Laboratory of Information Security of Network Systems College of Computer and Big Data Fuzhou350108 China Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China Zhejiang University of Technology College of Computer Science and Technology Hangzhou310023 China Dalian University of Technology School of Software Dalian116620 China

Due to the robust representational capabilities of graph data, employing graph neural networks for its processing has demonstrated superior performance over conventional deep learning algorithms. Graph data encompasses abundant features and structural information;however, its large-scale collection is often challenging in practice. This difficulty arises because data predominantly exists in isolated compartments, making it arduous to harmonize information across various organizations or to enable multiple organizations to collaborate effectively while safeguarding local data privacy. In light of an extreme data distribution scenario, where each client possesses distinct nodes with partially overlapping segments yet divergent data features, we introduce a dual-cloud server architecture. This framework encompasses the design of four secure subprotocols: ReEnc (secure re-encryption), SecPSI (secure outsourcing of PSI), SecWeight (secure weight calculation), and SecAgg (secure aggregation). Together, these components facilitate a vertical federated learning framework for graph convolutional networks, ensuring privacy preservation. We provide a security proof for the entire system and extensive evaluation on three benchmark datasets (Cora, Citeseer, and Pubmed) illustrates that our Vertical Federated Graph Convolutional Network (VFGCN) surpasses existing privacy-preserving methodologies. © 2004-2012 IEEE.

关键词： Privacy by design

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Decomposition of dynamic transcriptomic responses during effector-triggered immunity reveals conserved responses in two distinct plant cell populations

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Plant Communications 2024年第8期5卷 1-17页

作者： Xiaotong Liu Daisuke Igarashi Rachel A.Hillmer Thomas Stoddard You Lu Kenichi Tsuda Chad L.Myers Fumiaki Katagiri Department of Plant and Microbial Biology University of Minnesota-Twin CitiesSt PaulMN 55108USA Department of Computer Science and Engineering University of Minnesota-Twin CitiesMinneapolisMN 55455USA Bioinformatics and Computational Biology Graduate Program University of Minnesota-Twin CitiesMinneapolisMN 55455USA Institute for Innovation Ajinomoto Co.Inc.KawasakiJapan State Key Laboratory of Agricultural Microbiology Hubei Hongshan LaboratoryHubei Key Lab of Plant PathologyCollege of Plant Science and TechnologyHuazhong Agricultural UniversityWuhan 430070China Present address:225 S 6th St MinneapolisMN 55402USA Present address:Biotalys Inc.2520 Meridian Parkway Suite 480DurhamNC 27713USA Present address:Life Sciences Institute and Department of Cell and Developmental Biology University of Michigan Medical SchoolAnn ArborMI 48109USA

Rapid plant immune responses in the appropriate cells are needed for effective defense against *** transcriptome analysis is often used to describe overall immune responses,collection of transcriptome data with sufficient resolution in both space and time is *** reanalyzed public Arabidopsis time-course transcriptome data obtained after low-dose inoculation with a Pseudomonas syringae strain expressing the effector AvrRpt2,which induces effector-triggered immunity in ***-peak time-course patterns are prevalent among thousands of upregulated *** implemented a multicompartment modeling approach to decompose the double-peak pattern into two single-peak patterns for each *** decomposed peaks reveal an“echoing”pattern:the peak times of the first and second peaks correlate well across most upregulated *** demonstrated that the two peaks likely represent responses of two distinct cell populations that respond either cell autonomously or indirectly to ***,the peak decomposition has extracted spatial information from the time-course *** echoing pattern also indicates a conserved transcriptome response with different initiation times between the two cell populations despite different elicitor types.A gene set highly overlapping with the conserved gene set is also upregulated with similar kinetics during pattern-triggered *** of a WRKY network via different entry-point WRKYs can explain the similar but not identical transcriptome responses elicited by different elicitor *** discuss potential benefits of the properties of the WRKY activation network as an immune signaling network in light of pressure from rapidly evolving pathogens.

关键词： Arabidopsis Pseudomonas syringae plant immune response multi-compartment model WRKY transcription factors resilient immune signaling network

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Spin-UP: Spin Light for Natural Light Uncalibrated Photometric Stereo

arXiv

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arXiv 2024年

作者： Li, Zongrui Lu, Zhan Yan, Haojie Shi, Boxin Pan, Gang Zheng, Qian Jiang, Xudong Lab Interdisciplinary Graduate Programme Nanyang Technological University Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore College of Computer Science and Technology Zhejiang University China The State Key Lab of Brain-Machine Intelligence Zhejiang University China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China National Engineering Research Center of Visual Technology School of Computer Science Peking University China

Natural Light Uncalibrated Photometric Stereo (NaUPS) relieves the strict environment and light assumptions in classical Uncalibrated Photometric Stereo (UPS) methods. However, due to the intrinsic ill-posedness and high-dimensional ambiguities, addressing NaUPS is still an open question. Existing works impose strong assumptions on the environment lights and objects' material, restricting the effectiveness in more general scenarios. Alternatively, some methods leverage supervised learning with intricate models while lacking interpretability, resulting in a biased estimation. In this work, we propose Spin Light Uncalibrated Photometric Stereo (Spin-UP), an unsupervised method to tackle NaUPS in various environment lights and objects. The proposed method uses a novel setup that captures the object's images on a rotatable platform, which mitigates NaUPS's ill-posedness by reducing unknowns and provides reliable priors to alleviate NaUPS's ambiguities. Leveraging neural inverse rendering and the proposed training strategies, Spin-UP recovers surface normals, environment light, and isotropic reflectance under complex natural light with low computational cost. Experiments have shown that Spin-UP outperforms other supervised/unsupervised NaUPS methods and achieves state-of-the-art performance on synthetic and real-world datasets. Codes and data are available at https://***/LMozart/CVPR2024-SpinUP. Copyright © 2024, The Authors. All rights reserved.

关键词： Photometry

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Strong nonlinear optical processes with extraordinary polarization anisotropy in inversionsymmetry broken two-dimensional PdPSe

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Light(science & Applications) 2024年第8期13卷 1524-1533页

作者： Song Zhu Ruihuan Duan Xiaodong Xu Fangyuan Sun Wenduo Chen Fakun Wang Siyuan Li Ming Ye Xin Zhou Jinluo Cheng Yao Wu Houkun Liang Junichiro Kono Xingji Li Zheng Liu Qi Jie Wang School of Electrical and Electronic Engineering Nanyang Technological University639798 SingaporeSingapore School of Material Science and Engineering Nanyang Technological University639798 SingaporeSingapore CINTRA CNRS/NTU/THALES UMI 3288Research Techno PlazaNanyang Technological University637371 SingaporeSingapore School of Materials Science and Engineering Harbin Institute of Technology150001 HarbinChina Department of Chemistry National University of Singapore117543 SingaporeSingapore GPL Photonics Lab State Key Laboratory of Applied OpticsChangchun Institute of OpticsFine Mechanics and PhysicsChinese Academy of Sciences130033 ChangchunChina School of Electronics and Information Engineering Sichuan University610064 ChengduSichuanChina School of Physical and Mathematical Sciences Nanyang Technological University637371 SingaporeSingapore Departments of Electrical and Computer Engineering Physics and Astronomyand Materials Science and NanoEngineeringRice UniversityHoustonTXUSA

Nonlinear optical activities,especially second harmonic generation(SHG),are key phenomena in inversion-symmetrybroken two-dimensional(2D)transition metal dichalcogenides(TMDCs).On the other hand,anisotropic nonlinear optical processes are important for unique applications in nano-nonlinear photonic devices with polarization functions,having become one of focused research topics in the field of nonlinear ***,the strong nonlinearity and strong optical anisotropy do not exist simultaneously in common 2D ***,we demonstrate strong secondorder and third-order susceptibilities of 64 pm/V and 6.2×10^(-19)m^(2)/V^(2),respectively,in the even-layer PdPSe,which has not been discovered in other common TMDCs(e.g.,MoS2).Strikingly,it also simultaneously exhibited strong SHG anisotropy with an anisotropic ratio of~45,which is the largest reported among all 2D materials to date,to the best of our *** addition,the SHG anisotropy ratio can be harnessed from 0.12 to 45(375 times)by varying the excitation wavelength due to the dispersion ofχ^(2)*** an illustrative example,we further demonstrate polarized SHG imaging for potential applications in crystal orientation identification and polarization-dependent spatial *** findings in 2D PdPSe are promising for nonlinear nanophotonic and optoelectronic applications.

关键词： materials anisotropy nonlinear

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