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30th Great Lakes Symposium on VLSI, GLSVLSI 2020

作者： Bai, Yining Zhang, Yue Wang, Jinkai Wang, Guanda Zhang, Zhizhong Zheng, Zhenyi Zhang, Kun Zhao, Weisheng Fert Beijing Institute School of Microelectronics Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China Nanoelectronics Science and Technology Center Hefei Innovation Research Institute Beihang University Hefei China School of Electronics Engineering Beihang University Beijing China

ISBN: (纸本)9781450379441

This paper proposes a novel in-memory computing (IMC) scheme based on toggle spin torque magnetic random access memory (TST-MRAM), called TST-IMC, which makes full use of the unique TST writing mechanism. In this scheme, all of the computing results are directly written in bit-cells without transferring data out of the memory array. Varied Boolean logic operations, such as, NAND, NOR and XOR, can be achieved by specially configuring decision cells. We can also implement three-input majority logic through replacing a decision cell with a datum cell, which can further be used to realize the carry of full-adder. By using 28 nm CMOS technology node and 50 nmdiameter TST-MRAM, we perform mixed simulations to validate the functionality of the proposed TST-IMC scheme. Simulation results show that XOR logic operation can be carried out within 4 ns at 1.8 V supply voltage while the other basic logic operations can be faster, i.e. within 2 ns. In addition, TST-IMC 33% less time and 44% energy saved comparing with existing IMC schemes. © 2020 Association for computing Machinery.

关键词： Cells

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Robust optimization for quantum reinforcement learning control using partial observations

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Physical Review A 2022年第6期105卷 062443-062443页

作者： Chen Jiang Yu Pan Zheng-Guang Wu Qing Gao Daoyi Dong State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou 310027 China The School of Automation Science and Electrical Engineering and Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China The School of Engineering and Information Technology University of New South Wales Canberra Australian Capital Territory 2600 Australia

The current quantum reinforcement learning control models often assume that the quantum states are known a priori for control optimization. However, full observation of the quantum state is experimentally infeasible due to the exponential scaling of the number of required quantum measurements on the number of qubits. In this paper, we investigate a robust reinforcement learning method using partial observations to overcome this difficulty. This control scheme is compatible with near-term quantum devices, where the noise is prevalent and predetermining the dynamics of the quantum state is practically impossible. We show that this simplified control scheme can achieve similar or even better performance when compared to the conventional methods relying on full observation. We demonstrate the effectiveness of this scheme on examples of quantum state control and the quantum approximate optimization algorithm (QAOA). It has been shown that high-fidelity state control can be achieved even if the noise amplitude is at the same level as the control amplitude. Besides, an acceptable level of optimization accuracy can be achieved for a QAOA with a noisy control Hamiltonian. This robust control optimization model can be trained to compensate for the uncertainties in practical quantum computing.

关键词： Machine learning Quantum control Quantum engineering

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Dynamical reciprocity in interacting games: Numerical results and mechanism analysis

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Physical Review E 2022年第5期105卷 054302-054302页

作者： Rizhou Liang Qinqin Wang Jiqiang Zhang Guozhong Zheng Lin Ma Li Chen School of Physics and Information Technology Shaanxi Normal University Xi'an 710062 People's Republic of China School of Physics and Electronic-Electrical Engineering Ningxia University Yinchuan 750021 People's Republic of China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 People's Republic of China Robert Koch-Institute Nordufer 20 13353 Berlin Germany

We study the evolution of two mutually interacting pairwise games on different topologies. On two-dimensional square lattices, we reveal that the game-game interaction can promote the cooperation prevalence in all cases, and the cooperation-defection phase transitions even become absent and fairly high cooperation is expected when the interaction becomes very strong. A mean-field theory is developed that points out dynamical routes arising therein. Detailed analysis shows indeed that there are rich categories of interactions in either the individual or bulk scenario: invasion, neutral, and catalyzed types; their combination puts cooperators at a persistent advantage position, which boosts the cooperation. The robustness of the revealed reciprocity is strengthened by the studies of model variants, including the public goods game, asymmetrical or time-varying interactions, games of different types, games with timescale separation, different updating rules, etc. The structural complexities of the underlying population, such as Newman-Watts small world networks, Erdős-Rényi random networks, and Barabási-Albert networks, also do not alter the working of the dynamical reciprocity. In particular, as the number of games engaged increases, the cooperation level continuously improves in general. However, our analysis shows that the dynamical reciprocity works only in structured populations, otherwise the game-game interaction has no any impact on the cooperation at all. In brief, our work uncovers a cooperation mechanism in the structured populations, which indicates the great potential for human cooperation since concurrent issues are so often seen in the real world.

关键词： Patterns in complex systems Population dynamics Social systems

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3D Face Mask Anti-spoofing via Deep Fusion of Dynamic Texture and Shape Clues

3D Face Mask Anti-spoofing via Deep Fusion of Dynamic Textur...

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International Conference on Automatic Face and Gesture Recognition

作者： Song Chen Weixin Li Hongyu Yang Di Huang Yunhong Wang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

ISBN: (数字)9781728130798

ISBN: (纸本)9781728130804

Face anti-spoofing has recently become more important to the wide application of Face Recognition (FR) techniques. Compared to Spoofing Attacks (SAs) of printed photos and replayed videos, 3D masks bring more challenges to FR systems. This paper proposes a novel approach to 3D face mask anti-spoofing, namely Multi-Modal Dynamics Fusion Network (MM-DFN), and different from the overwhelming majority of the methods in the literature that only employ RGB data, it highlights the credit of the geometry information delivered by depth sensors or reconstructed from RGB images. Dynamic texture and shape clues are respectively encoded by a two-branch deep CNN model at different rates so that discriminative details are sufficiently captured, and they are combined at intervals for more comprehensive description. Moreover, a 3D model guided data augmentation method is applied to generate a diversity of samples with various poses, which further enhances the anti-spoofing model. The proposed method is extensively evaluated on three public benchmarks, i.e. 3DMAD, HKBU-MARs V1 and SMAD, and the results achieved are state-of-the-art, demonstrating its effectiveness for this issue.

关键词： Three-dimensional displays Faces Videos Feature extraction Shape Face recognition Two dimensional displays

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Hybrid Resource Orchestration and Scheduling for Cyber-Physical-Human Systems

Hybrid Resource Orchestration and Scheduling for Cyber-Physi...

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IEEE International Conference on High Performance computing and Communications (HPCC)

作者： Jianyong Zhu Xu Wang Tianyu Wo Chunming Hu Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China

ISBN: (纸本)9781728176499;9781728176505

Recently Cyber-Physical-Human Systems (CPHS) have been attracted much attention. Unlike existing computing paradigms such as Cloud computing and Edge computing, CPHS usually comprise many types of hybrid resources such as software and hardware in the cloud layer, network devices and edge servers in the network layer, as well as the physical devices and human participation. Such hybrid resource makes it challenging to efficient resource scheduling for CPHS. Previous studies on CPHS often focus on improving scheduling efficiency for specific application scenarios and thus are limited for other CPHS. In this work, we present a unified hybrid resource scheduling framework for CPHS, including a hybrid resource model for multiple types of resource providers and participants in CPHS, a hybrid resource orchestration tool that can describe our hybrid resource model as the resource requirements, and a scheduling mechanism to satisfy the resource requirements especially by considering the resource constraints and crosslayer scheduling in CPHS. We perform the case study of typical CPHS scenarios and show that our hybrid resource model and scheduling framework is effective, and provide a new view for improving the resource provision efficiency in CPHS.

关键词： Cloud computing Processor scheduling High performance computing Conferences Computational modeling Tools Software

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Intensity Enhancement Via Gan for Multimodal Facial Expression Recognition

Intensity Enhancement Via Gan for Multimodal Facial Expressi...

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IEEE International Conference on Image Processing

作者： Kangkang Zhu Yunhong Wang Hongyu Yang Di Huang Liming Chen Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Face expression recognition (FER) on low intensity is not well studied in the literature. This paper investigates this new problem and presents a novel Generative Adversarial Network (GAN) based multimodal approach to it. The method models the tasks of intensity enhancement and expression recognition jointly, ensuring that the synthesize faces not only present expression of high intensity, but also truly contribute to promoting the performance of FER. Extensive experiments are conducted on the BU-3DFE and BU-4DFE datasets. State-of-the-art FER performance clearly validates the effectiveness of the proposed method.

关键词： Training Image recognition Face recognition Conferences Generative adversarial networks Gallium nitride Task analysis

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Optimal Location for Electric Vehicles Charging Stations Based on P-Median Model 6th

Optimal Location for Electric Vehicles Charging Stations Bas...

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6th International Conference on Artificial Intelligence and Security,ICAIS 2020

作者： Tian, Daxin Yan, Huiwen Duan, Xuting Cao, Yue Zhou, Jianshan Hao, Wei Long, Kejun Gu, Jian Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China School of Transportation Science and Engineering Beihang University Beijing100191 China School of Traffic and Transportation Engineering Changsha University of Science and Technology Changsha410114 China

ISBN: (纸本)9789811580857

To some certain extent, the development of electric vehicles relies on the availability of infrastructure, especially charging facility. There have been a large amount of studies on the location of electric vehicle charging stations before. In this paper, we establish an improved p-median model that aims to minimize the time costs. This model has some constraints such as the capacity of charging stations and the demand of customers, etc. All of these constraints should be satisfied at the same time. In order to solve this problem, a greedy heuristic algorithm is proposed. Then, we use MATLAB to simulate the case of a real-city charging station layout, and the result indicates that our method is effective and reasonable. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Heuristic algorithms

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In Memory of Leslie G. Ungerleider

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Neuroscience Bulletin 2021年第4期37卷 592-595页

作者： Ning Liu Hui Zhang Xilin Zhang Jiongjiong Yang Xuchu Weng Lin Chen State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing100101China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine and EngineeringBeihang UniversityBeijing100191China Key Laboratory of Brain Cognition and Education SciencesMinistry of EducationSouth China Normal UniversityGuangzhou510631GuangdongChina School of Psychology South China Normal UniversityGuangzhou510631GuangdongChina School of Psychological and Cognitive Sciences Peking UniversityBeijing100871China Institute for Brain Research and Rehabilitation South China Normal UniversityGuangzhou510631GuangdongChina University of Chinese Academy of Sciences Beijing100049China

Dr. Leslie Ungerleider, who together with Mortimer Mishkin established the model of ‘‘two functionally dissociated cortical visual pathways in the primate brain’’[1], passed away on December 11, 2020. Ungerleider... 详细信息

关键词： visual timer eider

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Self-Supervised Continual Graph Learning in Adaptive Riemannian Spaces

arXiv

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arXiv 2022年

作者： Sun, Li Ye, Junda Peng, Hao Wang, Feiyang Yu, Philip S. School of Control and Computer Engineering North China Electric Power University Beijing102206 China School of Computer Science Beijing University of Posts and Telecommunications Beijing100876 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Department of Computer Science University of Illinois ChicagoIL United States

Continual graph learning routinely finds its role in a variety of real-world applications where the graph data with different tasks come sequentially. Despite the success of prior works, it still faces great challenges. On the one hand, existing methods work with the zero-curvature Euclidean space, and largely ignore the fact that curvature varies over the coming graph sequence. On the other hand, continual learners in the literature rely on abundant labels, but labeling graph in practice is particularly hard especially for the continuously emerging graphs on-the-fly. To address the aforementioned challenges, we propose to explore a challenging yet practical problem, the self-supervised continual graph learning in adaptive Riemannian spaces. In this paper, we propose a novel self-supervised Riemannian Graph Continual Learner (RieGrace). In RieGrace, we first design an Adaptive Riemannian GCN (AdaRGCN), a unified GCN coupled with a neural curvature adapter, so that Riemannian space is shaped by the learnt curvature adaptive to each graph. Then, we present a Label-free Lorentz Distillation approach, in which we create teacher-student AdaRGCN for the graph sequence. The student successively performs intra-distillation from itself and inter-distillation from the teacher so as to consolidate knowledge without catastrophic forgetting. In particular, we propose a theoretically grounded Generalized Lorentz Projection for the contrastive distillation in Riemannian space. Extensive experiments on the benchmark datasets show the superiority of RieGrace, and additionally, we investigate on how curvature changes over the graph sequence. © 2022, CC BY-NC-SA.

关键词： Distillation

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How to use open-pFind in deep proteomics data analysis?—A protocol for rigorous identification and quantitation of peptides and proteins from mass spectrometry data

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Biophysics Reports 2021年第3期7卷 207-226页

作者： Guangcan Shao Yong Cao Zhenlin Chen Chao Liu Shangtong Li Hao Chi Meng-Qiu Dong School of Life Sciences Peking University National Institute of Biological Sciences Beijing Tsinghua Institute of Multidisciplinary Biomedical Research Tsinghua University Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) University of CASInstitute of Computing TechnologyCAS University of Chinese Academy of Sciences Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine and EngineeringBeihang University

High-throughput proteomics based on mass spectrometry（MS） analysis has permeated biomedical science and propelled numerous research projects. p Find 3 is a database search engine for high-speed and in-depth proteomics data analysis. p Find 3 features a swift open search workflow that is adept at uncovering less obvious information such as unexpected modifications or mutations that would have gone unnoticed using a conventional data analysis pipeline. In this protocol, we provide step-by-step instructions to help users mastering various types of data analysis using p Find 3 in conjunction with p Parse for data pre-processing and if needed, p Quant for quantitation. This streamlined p Parse-p Findp Quant workflow offers exceptional sensitivity, precision, and speed. It can be easily implemented in any laboratory in need of identifying peptides, proteins, or post-translational modifications, or of quantitation based on15N-labeling, SILAC-labeling, or TMT/i TRAQ labeling.

关键词： Mass spectrometry Protein identification Search engine Open-pFind Quantitation

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