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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Chao Wang Zhaohao Wang Yansong Xu Jianlei Yang Youguang Zhang Weisheng Zhao Fert Beijing Research Institute Beihang University School of Electronics and Information Engineering Beihang University School of Microelectronics Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University School of Computer Science and Engineering Beihang University Beijing 100191 China

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

On the current computing platforms, the memory wall between processor and memory has become the toughest challenge for the traditional Von-Neumann computer architecture. computing-in-Memory (CIM) is taken as a promising approach to solving the above bottleneck in computing systems. In this paper, we propose a CIM platform with field-free spinorbit torque magnetic random access memory (SOT-MRAM). The self-reference (SelfRef) method is designed to enhance the read reliability and directly obtain logic results through memory-like read operations without adding logic cells. Memory read/write and logic operations, including NOT, AND/NAND and OR/NOR, can be implemented in the same SOT-MRAM chip. The speed and power penalties caused by SelfRef scheme are acceptable thanks to the ultrafast switching of the SOT. The read reliability and logic correctness of the proposed CIM are demonstrated by hybrid simulation on a 40 nm technology node.

关键词： Reliability Switches Resistance Magnetic tunneling Magnetization Torque Semiconductor device modeling

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Strong coupling of antiferromagnetic resonance with sub-THz cavity fields

arXiv

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arXiv 2020年

作者： Bialek, Marcin Zhang, J. Yu, H. Ansermet, J.-Ph. Lausanne1015 Switzerland Fert Beijing Institute School of Microelectronics Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

Strong coupling of electromagnetic cavity fields with antiferromagnetic spin waves in hematite (α-Fe2O3) was achieved above room temperature. A cube of hematite was placed in a metallic tube and transmission was measured, using a continuous-wave THz spectrometer. Spectra, collected as a function of temperature, reveal the formation of magnetic polaritons. © 2020, CC BY-SA.

关键词： Hematite

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MERITS: Medication Recommendation for Chronic Disease with Irregular Time-Series

MERITS: Medication Recommendation for Chronic Disease with I...

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IEEE International Conference on data Mining (ICDM)

作者： Shuai Zhang Jianxin Li Haoyi Zhou Qishan Zhu Shanghang Zhang Danding Wang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China School of Computer Science and Engineering Beihang University China Shenyuan Honors College Beihang University China School of Software Beihang University China National University of Singapore >Singapore

ISBN: (纸本)9781665423991

Medication recommendation for chronic diseases based on the complex historical electronic medical records (EMR) is an important and challenging research problem in medical informatics because the medical records are often irregularly sampled and contain many missing data. However, most existing approaches fail to explore the irregular time-series dependencies and ignore the consecutive correlation in dynamic prescription history. To fill this gap, we propose the MEdication Recommendation network on Irregular Time-Series (MERITS), which captures the irregular time-series dependencies with the neural ordinary differential equations (Neural ODE). Meanwhile, it leverages a drug-drug interaction knowledge graph and two learned medication relation graphs to explore the co-occurrence and sequential correlations of the medications. We further propose an attention-based encoder-decoder framework to combine the historical information of patients and medications from EMR. Besides, we collect and annotate a diabetes inpatient medication dataset and demonstrate the effectiveness of MERITS by comparing it with several state-of-the-art methods of medication recommendations.

关键词： Correlation data visualization Ordinary differential equations data models Diabetes History Electronic medical records

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Deep Reinforcement Learning Guided Graph Neural Networks for brain Network Analysis

arXiv

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arXiv 2022年

作者： Zhao, Xusheng Wu, Jia Peng, Hao Beheshti, Amin Monaghan, Jessica J.M. McAlpine, David Hernandez-Perez, Heivet Dras, Mark Dai, Qiong Li, Yangyang Yu, Philip S. He, Lifang Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Computing Macquarie University Sydney Australia Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China National Acoustic Laboratories Sydney Australia Department of Linguistics The Australian Hearing Hub Macquarie University Sydney Australia CAEIT Beijing China Department of Computer Science University of Illinois ChicagoIL United States Computer Science & Engineering Lehigh University PA United States

Modern neuroimaging techniques enable us to construct human brains as brain networks or connectomes. Capturing brain networks' structural information and hierarchical patterns is essential for understanding brain functions and disease states. Recently, the promising network representation learning capability of graph neural networks (GNNs) has prompted related methods for brain network analysis to be proposed. Specifically, these methods apply feature aggregation and global pooling to convert brain network instances into vector representations encoding brain structure induction for downstream brain network analysis tasks. However, existing GNN-based methods often neglect that brain networks of different subjects may require various aggregation iterations and use GNN with a fixed number of layers to learn all brain networks. Therefore, how to fully release the potential of GNNs to promote brain network analysis is still non-trivial. In our work, a novel brain network representation framework, BN-GNN, is proposed to solve this difficulty, which searches for the optimal GNN architecture for each brain network. Concretely, BN-GNN employs deep reinforcement learning (DRL) to automatically predict the optimal number of feature propagations (reflected in the number of GNN layers) required for a given brain network. Furthermore, BN-GNN improves the upper bound of traditional GNNs' performance in eight brain network disease analysis tasks. © 2022, CC BY.

关键词： Reinforcement learning

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Picosecond Optospintronic Tunnel Junctions for Non-volatile Photonic Memories

Research Square

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Research Square 2021年

作者： Wang, Luding Cheng, Houyi Li, Pingzhi Liu, Yang van Hees, Youri L.W. Lavrijsen, Reinoud Lin, Xiaoyang Cao, Kaihua Koopmans, Bert Zhao, Weisheng Fert Beijing Institute School of Integrated Circuit Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Department of Applied Physics Institute for Photonic Integration Eindhoven University of Technology P.O. Box 513 Eindhoven5600 MB Netherlands

Perpendicular magnetic tunnel junctions are one of the building blocks for spintronic memories, which allow fast nonvolatile data access, offering substantial potentials to revolutionize the mainstream computing architecture1-3. However, conventional switching mechanisms of such devices are fundamentally hindered by spin polarized currents4, either spin transfer torque5,6 or spin orbit torque7,8 with spin precession time limitation and excessive power dissipation. These physical constraints significantly stimulate the advancement of modern spintronics1,4. Here, we report an optospintronic tunnel junction using a photonic-spintronic combination. This composite device incorporates an all-optically switchable Co/Gd bilayer coupled to a CoFeB/MgO-based perpendicular magnetic tunnel junction by the Ruderman-Kittel-Kasuya-Yosida interaction. A picosecond all-optical operation of the optospintronic tunnel junction is explicitly confirmed by time-resolved measurements. Moreover, the device shows a considerable tunnel magnetoresistance and thermal stability. This proof-of-concept device represents an essential step towards ultrafast photonic memories with THz data access, as well as ultralow power consumption. © 2021, CC BY.

关键词： Tunnel junctions

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System architecture for high-performance permissioned blockchains

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Frontiers of Computer Science 2019年第6期13卷 1151-1165页

作者： Libo FENG Hui ZHANG Wei-Tek TSAI Simeng SUN State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China Digital Society&Blockchain Laboratory School of Computer ScienceBeihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100191China School of Computing Informatics and Decision Systems EngineeringArizona State UniversityTempeUSA

Blockchain(BC),as an emerging distributed database technology with advanced security and reliability,has attracted much attention from experts who devoted to efinance,intellectual property protection,the internet of things(IoT)and so ***,the inefficient transaction processing speed,which hinders the BC’s widespread,has not been well tackled *** this paper,we propose a novel architecture,called Dual-Channel Parallel Broadcast model(DCPB),which could address such a problem to a greater extent by using three methods which are dual communication channels,parallel pipeline processing and block broadcast *** the dual-channel model,one channel processes transactions,and the other engages in the execution of *** parallel pipeline processing allows the system to operate *** block generation strategy improves the efficiency and speed of *** experiments have been applied to BeihangChain,a simplified prototype for BC system,illustrates that its transaction processing speed could be improved to 16K transaction per second which could well support many real-world scenarios such as BC-based energy trading system and Micro-film copyright trading system in CCTV.

关键词： blockchain concurrency performance dualchannel model parallel pipeline consensus algorithm

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ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network

ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Ne...

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IEEE International Conference on data Mining (ICDM)

作者： Xingcheng Fu Jianxin Li Jia Wu Qingyun Sun Cheng Ji Senzhang Wang Jiajun Tan Hao Peng Philip S. Yu Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China Macquarie University Sydney Australia School of Computer Science and Engineering Central South University Changsha China University of Illinois at Chicago Chicago IL USA

ISBN: (纸本)9781665423991

Graph Neural Networks (GNNs) have been widely studied in various graph data mining tasks. Most existing GNNs embed graph data into Euclidean space and thus are less effective to capture the ubiquitous hierarchical structures in real-world networks. Hyperbolic Graph Neural Networks (HGNNs) extend GNNs to hyperbolic space and thus are more effective to capture the hierarchical structures of graphs in node representation learning. In hyperbolic geometry, the graph hierarchical structure can be reflected by the curvatures of the hyperbolic space, and different curvatures can model different hierarchical structures of a graph. However, most existing HGNNs manually set the curvature to a fixed value for simplicity, which achieves a suboptimal performance of graph learning due to the complex and diverse hierarchical structures of the graphs. To resolve this problem, we propose an Adaptive Curvature Exploration Hyperbolic Graph Neural Network named ACE-HGNN to adaptively learn the optimal curvature according to the input graph and downstream tasks. Specifically, ACE-HGNN exploits a multi-agent reinforcement learning framework and contains two agents, ACE-Agent and HGNN-Agent for learning the curvature and node representations, respectively. The two agents are updated by a Nash Q-leaning algorithm collaboratively, seeking the optimal hyperbolic space indexed by the curvature. Extensive experiments on multiple real-world graph datasets demonstrate a significant and consistent performance improvement in model quality with competitive performance and good generalization ability.

关键词： Representation learning Geometry Adaptation models Adaptive systems Conferences Reinforcement learning Graph neural networks

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Identifying topologies and system parameters of uncertaintime-varying delayed complex networks

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Science China(Technological Sciences) 2019年第1期62卷 94-105页

作者： WANG Xiong GU HaiBo WANG QianYao Lü JinHu LSC Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

Node dynamics and network topologies play vital roles in determining the network features and network dynamical *** it is of great theoretical significance and practical value to recover the topology structures and system parameters of uncertain complex networks with available information. This paper presents an adaptive anticipatory synchronization-based approach to identify the unknown system parameters and network topological structures of uncertain time-varying delayed complex networks in the presence of noise. Moreover, during the identification process, our proposed scheme guarantees anticipatory synchronization between the uncertain drive and constructed auxiliary response network simultaneously. Particularly, our method can be extended to several special cases. Furthermore, numerical simulations are provided to verify the effectiveness and applicability of our method for reconstructing network topologies and node parameters. We hope our method can provide basic insight into future research on addressing reconstruction issues of uncertain realistic and large-scale complex networks.

关键词： system parameters and network topologies identification anticipatory synchronization uncertain time-varying delayed complex networks noise-perturbed complex networks

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Modeling Taxi Customer Searching Behavior Using High-Resolution GPS data

Modeling Taxi Customer Searching Behavior Using High-Resolut...

引用

International Conference on Intelligent Transportation

作者： Zhen Guo Mengyan Hao Bin Yu School of Transportation Science and Engineering Beihang University Beijing PR China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing PR China

ISBN: (数字)9781728141497

ISBN: (纸本)9781728141503

Occupancy of vacant taxis brings waste of resources and heavy traffic pressure to urban traffic network. To enable better efficiency of taxis and less traffic congestion, a logit-based model is developed to describe vacant taxi drivers' route choice behavior and decision-making mechanism when searching for next customer. The proposed model is based on a multinomial logit model (MNL), considering two novel influencing indicators including the path unreliability (PU) and expected rate of return (EROR). In order to simplify the model construction and reduce computational cost, we divide the research area into identical squares with a 0.5-km resolution. Then the customer searching movements are extracted from the high-resolution GPS data of more than 8000 taxis in Shanghai to validate the logit model. The model results show that the customer searching behavior of vacant taxi drivers is significantly influenced by PU and EROR. Moreover, the effect of these two indicators on customer searching behavior varies with the time of day.

关键词： Public transportation Global Positioning System Trajectory data mining Computed tomography Delays Vehicles

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An Improved Deep Learning Approach for Thyroid Nodule Diagnosis

An Improved Deep Learning Approach for Thyroid Nodule Diagno...

引用

IEEE International Symposium on Biomedical Imaging

作者： Xiangdong Guo Haifeng Zhao Zhenyu Tang School of Computer Science and Technology Anhui University Hefei Anhui China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

ISBN: (数字)9781538693308

ISBN: (纸本)9781538693315

Although thyroid ultrasonography (US) has been widely applied, it is still difficult to distinguish benign and malignant nodules. Currently, convolutional neural network (CNN) based methods have been proposed and shown promising performance for benign and malignant nodules classification. It is known that the US images are usually captured in multi-angles, and the same thyroid in different US images have inconsistent content. However, most of the existing CNN based methods extract features using fixed convolution kernels, which could be a big issue for processing US images. Moreover, fully-connected (FC) layers are usually adopted in CNN, which could cause the loss of inter-pixel relations. In this paper, we propose a new CNN which is integrated with squeeze-and-excitation (SE) module and maximum retention of inter-pixel relations module (CNN-SE-MPR). It can adaptively select features from different US images and preserve the inter-pixel relations. Moreover, we introduce transfer learning to avoid problems such as local optimum and data insufficiency. The proposed network is tested on 407 thyroid US images collected from cooperated hospitals. Confirmed by ablation experiments and the comparison experiments under the state-of-the-art methods, it is shown that our method improves the accuracy of the diagnosis results.

关键词： Feature extraction Cancer Convolution Training Sensitivity Machine learning Ultrasonography

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