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Hyperbolic multiplex network embedding with maps of random walk

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arXiv 2019年

作者： Sun, Peiyuan Li, Jianxin Beijing Advanced Institution on Big Data and Brain Computing Beihang University Beijing China

Recent research on network embedding in hyperbolic space have proven successful in several applications. However, nodes in real world networks tend to interact through several distinct channels. Simple aggregation or ignorance of this multiplexity will lead to misleading results. On the other hand, there exists redundant information between different interaction patterns between nodes. Recent research reveals the analogy between the community structure and the hyperbolic coordinate. To learn each node's effective embedding representation while reducing the redundancy of multiplex network, we then propose a unified framework combing multiplex network hyperbolic embedding and multiplex community detection. The intuitive rationale is that high order node embedding approach is expected to alleviate the observed network's sparse and noisy structure which will benefit the community detection task. On the contrary, the improved community structure will also guide the node embedding task. To incorporate the common features between channels while preserving unique features, a random walk approach which traversing in latent multiplex hyperbolic space is proposed to detect the community across channels and bridge the connection between node embedding and community detection. The proposed framework is evaluated on several network tasks using different real world dataset. The results demonstrates that our framework is effective and efficiency compared with state-of-the-art approaches. Copyright © 2019, The Authors. All rights reserved.

关键词： Network embeddings

Recurrent interaction network for jointly extracting entities and classifying relations

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arXiv 2020年

作者： Sun, Kai Zhang, Richong Mensah, Samuel Mao, Yongyi Liu, Xudong Sklsde School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Institution on Big Data and Brain Computing Beihang University Beijing China School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

Named entity recognition (NER) and Relation extraction (RE) are two fundamental tasks in natural language processing applications. In practice, these two tasks are often to be solved simultaneously. Traditional multi-Task learning models implicitly capture the correlations between NER and RE. However, there exist intrinsic connections between the output of NER and RE. In this study, we argue that an explicit interaction between the NER model and the RE model will better guide the training of both models. Based on the traditional multi-Task learning framework, we design an interactive feature encoding method to capture the intrinsic connections between NER and RE tasks. In addition, we propose a recurrent interaction network to progressively capture the correlation between the two models. Empirical studies on two real-world datasets confirm the superiority of the proposed model. Copyright © 2020, The Authors. All rights reserved.

关键词： Natural language processing systems

Strong coupling of antiferromagnetic resonance with sub-THz cavity fields

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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

computing-in-Memory Architecture Based on Field-Free SOT-MRAM with Self-Reference Method

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Computing-in-Memory Architecture Based on Field-Free SOT-MRA...

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

Picosecond Optospintronic Tunnel Junctions for Non-volatile Photonic Memories

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Research Square

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

ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network

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

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

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

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

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

SUGAR: Subgraph neural network with reinforcement pooling and self-supervised mutual information mechanism

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arXiv 2021年

作者： Sun, Qingyun Li, Jianxin Peng, Hao Wu, Jia Ning, Yuanxing Yu, Phillip S. He, Lifang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China Department of Computing Macquarie University Sydney Australia Department of Computer Science University of Illinois at Chicago Chicago United States Department of Computer Science and Engineering Lehigh University BethlehemPA United States

Graph representation learning has attracted increasing research attention. However, most existing studies fuse all structural features and node attributes to provide an overarching view of graphs, neglecting finer substructures' semantics, and suffering from interpretation enigmas. This paper presents a novel hierarchical subgraph-level selection and embedding-based graph neural network for graph classification, namely SUGAR, to learn more discriminative subgraph representations and respond in an explanatory way. SUGAR reconstructs a sketched graph by extracting striking subgraphs as the representative part of the original graph to reveal subgraph-level patterns. To adaptively select striking subgraphs without prior knowledge, we develop a reinforcement pooling mechanism, which improves the generalization ability of the model. To differentiate subgraph representations among graphs, we present a self-supervised mutual information mechanism to encourage subgraph embedding to be mindful of the global graph structural properties by maximizing their mutual information. Extensive experiments on six typical bioinformatics datasets demonstrate a significant and consistent improvement in model quality with competitive performance and interpretability. Copyright © 2021, The Authors. All rights reserved.

关键词： Reinforcement learning

Modeling Taxi Customer Searching Behavior Using High-Resolution GPS data

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

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