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Physical Review X 2014年第4期4卷 041005-041005页

作者： Joaquín Sanz Cheng-Yi Xia Sandro Meloni Yamir Moreno Institute for Biocomputation and Physics of Complex Systems (BIFI) University of Zaragoza Zaragoza 50018 Spain Department of Theoretical Physics University of Zaragoza Zaragoza 50009 Spain Key Laboratory of Computer Vision and System (Ministry of Education) and Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin 300191 People’s Republic of China Complex Networks and Systems Lagrange Lab Institute for Scientific Interchange Turin 10126 Italy

Current modeling of infectious diseases allows for the study of complex and realistic scenarios that go from the population to the individual level of description. However, most epidemic models assume that the spreading process takes place on a single level (be it a single population, a metapopulation system, or a network of contacts). In particular, interdependent contagion phenomena can be addressed only if we go beyond the scheme-one pathogen-one network. In this paper, we propose a framework that allows us to describe the spreading dynamics of two concurrent diseases. Specifically, we characterize analytically the epidemic thresholds of the two diseases for different scenarios and compute the temporal evolution characterizing the unfolding dynamics. Results show that there are regions of the parameter space in which the onset of a disease’s outbreak is conditioned to the prevalence levels of the other disease. Moreover, we show, for the susceptible-infected-susceptible scheme, that under certain circumstances, finite and not vanishing epidemic thresholds are found even at the limit for scale-free networks. For the susceptible-infected-removed scenario, the phenomenology is richer and additional interdependencies show up. We also find that the secondary thresholds for the susceptible-infected-susceptible and susceptible-infected-removed models are different, which results directly from the interaction between both diseases. Our work thus solves an important problem and paves the way toward a more comprehensive description of the dynamics of interacting diseases.

关键词： Complex systems Interdisciplinary physics Statistical physics

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Instruction level test for parallel multipliers

Instruction level test for parallel multipliers

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IEEE International Conference on Electronics, Circuits and systems (ICECS)

作者： Ma Lin Gao Yan Chinese Academy of Sciences Beijing China Key Laboratory of Computer System and Architecturen+î Institute of Computing Technology Chinese Academy and Sciences Beijing China College of Electronics and Information Engineering University of Tongji Shanghai China

Multiplication operations are the normal operations in operating systems or scientific calculations. Multipliers embedded in processors, DSP or SoC are well optimized for best performance, and they are sensitive to test overhead. Instruction level test is a popular functional test approach for microprocessors test, and it can get satisfactory test results. But for the multipliers, one important part of microprocessors, there is no detail on how to testing them. This paper presents an instruction level test approach for the multiplier test. The proposed approach does not modify the multipliers and does not need any extra logic just using instructions of the processors to test processorspsila multipliers. Sequentially it does not have any test cost on area or timing. Moreover the instruction-level test is suited for at-speed test in nature. Experimental results on the real processorpsilas circuits show that the instruction level test approach has good effect on parallel multipliers.

关键词： Circuit testing Built-in self-test system testing Circuit faults Sequential analysis Microprocessors Automatic testing Costs Very large scale integration Integrated circuit testing

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DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-Preserving Talking Face Synthesis

arXiv

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arXiv 2024年

作者： Deng, Kaijun Zheng, Dezhi Xie, Jindong Wang, Jinbao Xie, Weicheng Shen, Linlin Song, Siyang Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China Department of Computer Science University of Exeter United Kingdom

Accurately synthesizing talking face videos and capturing fine facial features for individuals with long hair presents a significant challenge. To tackle these challenges in existing methods, we propose a decomposed per-embedding Gaussian fields (DEGSTalk), a 3D Gaussian Splatting (3DGS)-based talking face synthesis method for generating realistic talking faces with long hairs. Our DEGSTalk employs Deformable Pre-Embedding Gaussian Fields, which dynamically adjust pre-embedding Gaussian primitives using implicit expression coefficients. This enables precise capture of dynamic facial regions and subtle expressions. Additionally, we propose a Dynamic Hair-Preserving Portrait Rendering technique to enhance the realism of long hair motions in the synthesized videos. Results show that DEGSTalk achieves improved realism and synthesis quality compared to existing approaches, particularly in handling complex facial dynamics and hair preservation. Our code will be publicly available at https://***/CVI-SZU/DEGSTalk. Copyright © 2024, The Authors. All rights reserved.

关键词： Gaussian distribution

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Epitaxial growth of NaCl on Fe (100) and characterization of Fe/NaCl/Fe magnetic tunnel junctions

Epitaxial growth of NaCl on Fe (100) and characterization of...

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IEEE Conference on Nanotechnology

作者： Yuan-Tao Ji Qiang Li Yong-Chao Tang Lin Li Guo-Xing Miao State Key Laboratory for Manufacturing Systems Engineering and Systems Engineering Institute Xi’an Jiaotong University Xi’an China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (纸本)9781479956234

Growth of NaCl and Fe/NaCl/Fe Magnetic tunneling junctions on Si (100) has been achieved by using a high vacuum electron-beam deposition system. Epitaxial tunnel junctions turn out to be prone to pinholes as well as electrode oxidation. Instead, the best tunneling magnetoresistance we have achieved in this system is on polycrystalline tunnel barriers with thin Mg insertion, and reaching 22.3% at room temperature.

关键词： Iron Junctions Magnetic tunneling Epitaxial growth Tunneling magnetoresistance Electrodes Silicon

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Random Occlusion Recovery with Noise Channel for Person Re-identification 16th

Random Occlusion Recovery with Noise Channel for Person Re-i...

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16th International Conference on Intelligent computing, ICIC 2020

作者： Zhang, Kun Wu, Di Yuan, Changan Qin, Xiao Wu, Hongjie Zhao, Xingming Zhang, Lijun Du, Yuchuan Wang, Hanli Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University Shanghai China Guangxi Academy of Science Nanning530025 China School of Computer and Information Engineering Nanning Normal University Nanning530299 China School of Computer Science and Technology Soochow University Suzhou215006 China School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou215009 China Fudan University Shanghai200433 China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Ministry of Education Shanghai China Collaborative Innovation Center of Intelligent New Energy Vehicle and School of Automotive Studies Tongji University Shanghai201804 China The Key Laboratory of Road and Traffic Engineering of the Ministry of Education Department of Transportation Engineering Tongji University Shanghai201804 China Department of Computer Science and Technology the Key Laboratory of Embedded System and Service Computing and Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China

ISBN: (纸本)9783030607982

Person re-identification, as the basic task of a multi-camera surveillance system, plays an important role in a variety of surveillance applications. However, the current mainstream person re-identification model based on deep learning requires a lot of labeled data, which takes a lot of time and manpower. In this study, we proposed a person re-identification method based on random occlusion recovery with noise channel. We add random occlusion blocks to the original image, use the GAN model for repair, and use the repaired image to expand the original training set. After that, the generated image is adjusted through the noise channel. Finally, we use the enhanced data set to train the baseline model. Our model achieves the state-of-the-art on Market-1501 dataset, proving that the method is effective. © 2020, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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New Big Data Collecting Method Based on Compressive Sensing in WSN

New Big Data Collecting Method Based on Compressive Sensing ...

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International Conference on computer Communications and Networks (ICCCN)

作者： De-gan Zhang Xiao-hua Liu Yu-ya Cui Hong-tao Peng School of Computer Science &Engineering Tianjin University of Technology Tianjin China Computing & Novel software Technology Tianjin Key Lab of Intelligent Tianjin China Ministry of Education Key Laboratory of Computer Vision and System(TJUT) Tianjin China National Petroleum Corporation (CNPC) Managers Training Institute Beijing China

Considered the wireless sensor network clustering structure, a new big data collecting method based on compressive sensing is proposed. The collection process is as follows: in the cluster, the sink node sets the corresponding seed vector based on the distribution of network, and then sends it to each cluster head. Cluster head can generate corresponding own random spacing sparse matrix based on its received seed vector, and collect data through compressive sensing technology; Among clusters, clusters forward measurement values to sink node along multi-hop routing tree which we built before. Performance analyzing and comparison of results show that this method is superior to other methods regardless of in a cluster or inter-cluster.

关键词： Wireless sensor networks Compressed sensing Sparse matrices Big Data Routing Energy consumption Training

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SFDA-rPPG: Source-Free Domain Adaptive Remote Physiological Measurement with Spatio-Temporal Consistency

arXiv

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arXiv 2024年

作者： Xie, Yiping Yu, Zitong Wu, Bingjie Xie, Weicheng Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Singapore

Remote Photoplethysmography (rPPG) is a non-contact method that uses facial video to predict changes in blood volume, enabling physiological metrics measurement. Traditional rPPG models often struggle with poor generalization capacity in unseen domains. Current solutions to this problem is to improve its generalization in the target domain through Domain Generalization (DG) or Domain Adaptation (DA). However, both traditional methods require access to both source domain data and target domain data, which cannot be implemented in scenarios with limited access to source data, and another issue is the privacy of accessing source domain data. In this paper, we propose the first Source-free Domain Adaptation benchmark for rPPG measurement (SFDA-rPPG), which overcomes these limitations by enabling effective domain adaptation without access to source domain data. Our framework incorporates a Three-Branch Spatio-Temporal Consistency Network (TSTC-Net) to enhance feature consistency across domains. Furthermore, we propose a new rPPG distribution alignment loss based on the Frequency-domain Wasserstein Distance (FWD), which leverages optimal transport to align power spectrum distributions across domains effectively and further enforces the alignment of the three branches. Extensive cross-domain experiments and ablation studies demonstrate the effectiveness of our proposed method in source-free domain adaptation settings. Our findings highlight the significant contribution of the proposed FWD loss for distributional alignment, providing a valuable reference for future research and applications. The source code is available at https://***/XieYiping66/SFDA-rPPG. Copyright © 2024, The Authors. All rights reserved.

关键词： Photoplethysmography

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A highly-efficient and green data flow engine for solving euler atmospheric equations

A highly-efficient and green data flow engine for solving eu...

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International Conference on Field Programmable Logic and Applications

作者： Lin Gan Haohuan Fu Chao Yang Wayne Luk Wei Xue Oskar Mencer Xiaomeng Huang Guangwen Yang Department-of Computer-Science and Technology Tsinghua University Tsinghua National Laboratory for Information Science and Technology (TNList) Ministrv of Education Key Laboratory for Earth System Modeling Tsinghua University Institute of Software Chinese Academy of Sciences Department of Computing Imperial College London Mexeler Technologies

Atmospheric modeling is an essential issue in the study of climate change. However, due to the complicated algorithmic and communication models, scientists and researchers are facing tough challenges in finding efficient solutions to solve the atmospheric equations. In this paper, we accelerate a solver for the three-dimensional Euler atmospheric equations through reconfigurable data flow engines. We first propose a hybrid design that achieves efficient resource allocation and data reuse. Furthermore, through algorithmic offsetting, fast memory table, and customizable-precision arithmetic, we map a complex Euler kernel into a single FPGA chip, which can perform 956 floating point operations per cycle. In a 1U-chassis, our CPU-DFE unit with 8 FPGA chips is 18.5 times faster and 8.3 times more power efficient than a multicore system based on two 12-core Intel E5-2697 (Ivy Bridge) CPUs, and is 6.2 times faster and 5.2 times more power efficient than a hybrid unit equipped with two 12-core Intel E5-2697 (Ivy Bridge) CPUs and three Intel Xeon Phi 5120d (MIC) cards.

关键词： Mathematical model Atmospheric modeling Equations Field programmable gate arrays Computational modeling Kernel Three-dimensional displays

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F-CNN: An FPGA-based framework for training Convolutional Neural Networks

F-CNN: An FPGA-based framework for training Convolutional Ne...

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International Conference on Application Specific systems (ASAP), Architectures and Processors

作者： Wenlai Zhao Haohuan Fu Wayne Luk Teng Yu Shaojun Wang Bo Feng Yuchun Ma Guangwen Yang Ministry of Education Key Laboratory for Earth System Modeling and Center for Earth System Science Tsinghua University China Tsinghua National Laboratory for Information Science and Technology China Department of Computing Imperial college London UK Department of Automatic Test and Control Harbin Institute of Technology China Department of Computer Science and Technology Tsinghua University China

ISBN: (纸本)9781509015047

This paper presents a novel reconfigurable framework for training Convolutional Neural Networks (CNNs). The proposed framework is based on reconfiguring a streaming datapath at runtime to cover the training cycle for the various layers in a CNN. The streaming datapath can support various parameterized modules which can be customized to produce implementations with different trade-offs in performance and resource usage. The modules follow the same input and output data layout, simplifying configuration scheduling. For different layers, instances of the modules contain different computation kernels in parallel, which can be customized with different layer configurations and data precision. The associated models on performance, resource and bandwidth can be used in deriving parameters for the datapath to guide the analysis of design trade-offs to meet application requirements or platform constraints. They enable estimation of the implementation specifications given different layer configurations, to maximize performance under the constraints on bandwidth and hardware resources. Experimental results indicate that the proposed module design targeting Maxeler technology can achieve a performance of 62.06 GFLOPS for 32-bit floating-point arithmetic, outperforming existing accelerators. Further evaluation based on training LeNet-5 shows that the proposed framework achieves about 4 times faster than CPU implementation of Caffe and about 7.5 times more energy efficient than the GPU implementation of Caffe.

关键词： Training Field programmable gate arrays Convolution Computational modeling Bandwidth Neural networks Runtime

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FIRE: A Dataset for Feedback Integration and Refinement Evaluation of Multimodal Models

arXiv

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arXiv 2024年

作者： Li, Pengxiang Gao, Zhi Zhang, Bofei Yuan, Tao Wu, Yuwei Harandi, Mehrtash Jia, Yunde Zhu, Song-Chun Li, Qing Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China State Key Laboratory of General Artificial Intelligence BIGAI China State Key Laboratory of General Artificial Intelligence Peking University China Department of Electrical and Computer System Engineering Monash University Australia Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China Department of Automation Tsinghua University China

Vision language models (VLMs) have achieved impressive progress in diverse applications, becoming a prevalent research direction. In this paper, we build FIRE, a feedback-refinement dataset, consisting of 1.1M multi-turn conversations that are derived from 27 source datasets, empowering VLMs to spontaneously refine their responses based on user feedback across diverse tasks. To scale up the data collection, FIRE is collected in two components: FIRE-100K and FIRE-1M, where FIRE-100K is generated by GPT-4V, and FIRE-1M is freely generated via models trained on FIRE-100K. Then, we build FIRE-Bench, a benchmark to comprehensively evaluate the feedback-refining capability of VLMs, which contains 11K feedback-refinement conversations as the test data, two evaluation settings, and a model to provide feedback for VLMs. We develop the FIRE-LLaVA model by fine-tuning LLaVA on FIRE-100K and FIRE-1M, which shows remarkable feedback-refining capability on FIRE-Bench and outperforms untrained VLMs by 50%, making more efficient user-agent interactions and underscoring the significance of the FIRE dataset. Copyright © 2024, The Authors. All rights reserved.

关键词： Fireclay

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