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arXiv 2019年

作者： Peng, Hao Li, Jianxin Gong, Qiran Song, Yangqiu Ning, Yuanxing Lai, Kunfeng Yu, Philip S. Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment Beihang University Department of Computer Science and Engineering Hong Kong University of Science and Technology Platform and Content Group Tencent Institute for Data Science Tsinghua University Department of Computer Science University of Illinois Chicago United States

Events are happening in real-world and real-time, which can be planned and organized occasions involving multiple people and objects. Social media platforms publish a lot of text messages containing public events with comprehensive topics. However, mining social events is challenging due to the heterogeneous event elements in texts and explicit and implicit social network structures. In this paper, we design an event meta-schema to characterize the semantic relatedness of social events and build an event-based heterogeneous information network (HIN) integrating information from external knowledge base, and propose a novel Pairwise Popularity Graph Convolutional Network (PP-GCN) based fine-grained social event categorization model. We propose a Knowledgeable meta-paths Instances based social Event Similarity (KIES) between events and build a weighted adjacent matrix as input to the PP-GCN model. Comprehensive experiments on real data collections are conducted to compare various social event detection and clustering tasks. Experimental results demonstrate that our proposed framework outperforms other alternative social event categorization techniques. Copyright © 2019, The Authors. All rights reserved.

关键词： Information services

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Power Supply Noise Aware Task Scheduling on Homogeneous 3D MPSoCs Considering the Thermal Constraint

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Journal of Computer Science & Technology 2018年第5期33卷 966-983页

作者： Ying-Lin Zhao Jian-Lei Yang Wei-Sheng Zhao Aida Todri-Sanial Yuan-Qing Cheng Fert Beijing Research Institute Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing 100191 China School of Electronic and Information Engineering Beihang University Beijing 100191 China Qingdao Research Institute Beihang University Qingdao 266041 China School of Computer Science and Engineering Beihang University Beijing 100191 China Laboratory of Informaties Robotics and Mieroeleetronics University of Montpellier Montpellier 34095 France National Center for Scientific Research MontpeUier 34095 France

Thanks to the emerging 3D integration technology, The multiprocessor system on chips （MPSoCs） can now integrate more IP cores on chip with improved energy efficiency. However, several severe challenges also rise up for 3D ICs due to the die-stacking architecture. Among them, power supply noise becomes a big concern. In the paper, we investigate power supply noise （PSN） interactions among different cores and tiers and show that PSN variations largely depend on task assignments. On the other hand, high integration density incurs a severe thermal issue on 3D ICs. In the paper, we propose a novel task scheduling framework considering both the PSN and the thermal issue. It mainly consists of three parts. First, we extract current stimuli of running tasks by analyzing their power traces derived from architecture level simulations. Second, we develop an efficient power delivery network （PDN） solver to evaluate PSN magnitudes efficiently. Third, we propose a heuristic algorithm to solve the formulated task scheduling problem. Compared with the state-of-the-art task assignment algorithm, the proposed method can reduce PSN by 12% on a 2 × 2 × 2 3D MPSoCs and by 14% on a 3 × 3 × 3 3D MPSoCs. The end-to-end task execution time also improves as much as 5.5% and 7.8% respectively due to the suppressed PSN.

关键词： MPSoCs power supply noise （PSN） power delivery network （PDN） task scheduling algorithm temperature 3D

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Master stability functions for complete, intralayer, and interlayer synchronization in multiplex networks of coupled Rössler oscillators

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Physical Review E 2019年第1期99卷 012304-012304页

作者： Longkun Tang Xiaoqun Wu Jinhu Lü Jun-an Lu Raissa M. D'Souza Fujian Province University Key Laboratory of Computation Science School of Mathematical Science Huaqiao University Quanzhou 362021 China School of Mathematics and Statistics Wuhan University Wuhan 430072 China Department of Computer Science University of California Davis California 95616 USA 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 Beijing 100191 China

Synchronization phenomena are of broad interest across disciplines and increasingly of interest in a multiplex network setting. For the multiplex network of coupled Rössler oscillators, here we show how the master stability function, a celebrated framework for analyzing synchronization on a single network, can be extended to certain classes of multiplex networks with different intralayer and interlayer coupling functions. We derive three master stability equations that determine, respectively, the necessary regions of complete synchronization, intralayer synchronization, and interlayer synchronization. We calculate these three regions explicitly for the case of a two-layer network of Rössler oscillators and show that the overlap of the regions determines the type of synchronization achieved. In particular, if the interlayer or intralayer coupling function is such that the interlayer or intralayer synchronization region is empty, complete synchronization cannot be achieved regardless of the coupling strength. Furthermore, for any network structure, the occurrence of intralayer and interlayer synchronization depends mainly on the coupling functions of nodes within a layer and across layers, respectively. Our mathematical analysis requires that the intralayer and interlayer supra-Laplacians commute. But, we show this is only a sufficient, and not necessary, condition and that the results can be applied more generally.

关键词： Collective dynamics Synchronization Synchronization transition

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Synergistic learning of lung lobe segmentation and hierarchical multi-instance classification for automated severity assessment of covid-19 in ct images

arXiv

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arXiv 2020年

作者： He, Kelei Zhao, Wei Xie, Xingzhi Ji, Wen Liu, Mingxia Tang, Zhenyu Shi, Feng Gao, Yang Liu, Jun Zhang, Junfeng Shen, Dinggang Medical School of Nanjing University Nanjing China National Institute of Healthcare Data Science Nanjing University China Department of Radiology Second Xiangya Hospital Central South University Changsha Hunan China Department of Radiology Quality Control Center Changsha Hunan China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Biomedical Research Imaging Center Department of Radiology University of North Carolina Chapel HillNC United States Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai200232 China

Understanding chest CT imaging of the coronavirus disease 2019 (COVID-19) will help detect infections early and assess the disease progression. Especially, automated severity assessment of COVID-19 in CT images plays an essential role in identifying cases that are in great need of intensive clinical care. However, it is often challenging to accurately assess the severity of this disease in CT images, due to variable infection regions in the lungs, similar imaging biomarkers, and large inter-case variations. To this end, we propose a synergistic learning framework for automated severity assessment of COVID-19 in 3D CT images, by jointly performing lung lobe segmentation and multi-instance classification. Considering that only a few infection regions in a CT image are related to the severity assessment, we first represent each input image by a bag that contains a set of 2D image patches (with each cropped from a specific slice). A multi-task multi-instance deep network (called M2UNet) is then developed to assess the severity of COVID-19 patients and also segment the lung lobe simultaneously. Our M2UNet consists of a patch-level encoder, a segmentation subnetwork for lung lobe segmentation, and a classification subnetwork for severity assessment (with a unique hierarchical multi-instance learning strategy). Here, the context information provided by segmentation can be implicitly employed to improve the performance of severity assessment. Extensive experiments were performed on a real COVID-19 CT image dataset consisting of 666 chest CT images, with results suggesting the effectiveness of our proposed method compared to several state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

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Laser pulse induced efficient terahertz emission from Co/Al heterostructures

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Physical Review B 2020年第2期102卷 024435-024435页

作者： Hui Zhang Zheng Feng Jine Zhang He Bai Huaiwen Yang Jianwang Cai Weisheng Zhao Wei Tan Fengxia Hu Baogen Shen Jirong Sun Fert Beijing Institute School of Microelectronics Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijng 100191 People's Republic of China Microsystem and Terahertz Research Center China Academy of Engineering Physics Chengdu Sichuan 610200 People's Republic of China Institute of Electronic Engineering China Academy of Engineering Physics Mianyang Sichuan 621999 People's Republic of China Beijing National Laboratory for Condensed Matter Physics & Institute of Physics Chinese Academy of Sciences Beijing 100190 People's Republic of China School of Physical Sciences University of Chinese Academy of Sciences Beijing 100049 Peoples’ Republic of China Songshan Lake Materials Laboratory Dongguan Guangdong 523808 People's Republic of China Spintronics Institute University of Jinan Jinan Shandong 250022 People's Republic of China

Investigation on terahertz (THz) emission is important not only for fundamental research but also for practical application. A rapidly developing approach to generate THz emission is using a heterostructure composed of ferromagnetic metal (FM) and nonmagnetic metal (NM) which is pumped by femtosecond laser pulse. Previous works in this regard mainly focused on bilayer with heavy NM (such as Pt) with a large spin Hall angle. Here we present a comprehensive investigation on THz emission from Co/Al heterostructures stemming from the inverse spin Hall effect. It is surprising to find that although the spin Hall angle of Al is two orders of magnitude smaller than that of Pt, the measured THz signals are close to one-third of that of the typical Co/Pt heterostructures. To explore the underlying physics, theoretical models are employed to investigate the spin-related properties of Al. We obtain that the upper limit of the spin Hall angle of Al is 0.55% and the spin-diffusion length is 2.2 ± 0.2 nm; the diffusion length is comparable to that of Pt (2.3 ± 0.1 nm). This work shows that it is worth revisiting light metals for the spintronic THz emitter.

关键词： Spin-orbit coupling Spintronics Ultrafast magnetic effects Metals Terahertz techniques

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SVM-based deep stacking networks

arXiv

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arXiv 2019年

作者： Wang, Jingyuan Feng, Kai Wu, Junjie MOE Engineering Research Center of Advanced Computer Application Technology School of Computer Science Engineering Beihang University Beijing100191 China Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operations School of Economics and Management Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

The deep network model, with the majority built on neural networks, has been proved to be a powerful framework to represent complex data for high performance machine learning. In recent years, more and more studies turn to nonneural network approaches to build diverse deep structures, and the Deep Stacking Network (DSN) model is one of such approaches that uses stacked easy-to-learn blocks to build a parameter-training-parallelizable deep network. In this paper, we propose a novel SVM-based Deep Stacking Network (SVM-DSN), which uses the DSN architecture to organize linear SVM classifiers for deep learning. A BP-like layer tuning scheme is also proposed to ensure holistic and local optimizations of stacked SVMs simultaneously. Some good math properties of SVM, such as the convex optimization, is introduced into the DSN framework by our model. From a global view, SVM-DSN can iteratively extract data representations layer by layer as a deep neural network but with parallelizability, and from a local view, each stacked SVM can converge to its optimal solution and obtain the support vectors, which compared with neural networks could lead to interesting improvements in anti-saturation and interpretability. Experimental results on both image and text data sets demonstrate the excellent performances of SVM-DSN compared with some competitive benchmark models. Copyright © 2019, The Authors. All rights reserved.

关键词： Convex optimization

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PSGAN: A GENERATIVE ADVERSARIAL NETWORK FOR REMOTE SENSING IMAGE PAN-SHARPENING

PSGAN: A GENERATIVE ADVERSARIAL NETWORK FOR REMOTE SENSING I...

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IEEE International Conference on Image Processing

作者： Xiangyu Liu Yunhong Wang Qingjie Liu Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Key Laboratory of Digital Media Beihang University Beijing China

Remote sensing image fusion (also known as pan-sharpening) aims to generate a high resolution multi-spectral image from inputs of a high spatial resolution single band panchromatic (PAN) image and a low spatial resolution multi-spectral (MS) image. In this paper, we propose PSGAN, a generative adversarial network (GAN) for remote sensing image pan-sharpening. To the best of our knowledge, this is the first attempt at producing high quality pan-sharpened images with GANs. The PSGAN consists of two parts. Firstly, a two-stream fusion architecture is designed to generate the desired high resolution multi-spectral images, then a fully convolutional network serving as a discriminator is applied to distinct "real" or "pan-sharpened" MS images. Experiments on images acquired by Quickbird and GaoFen-1 satellites demonstrate that the proposed PSGAN can fuse PAN and MS images effectively and significantly improve the results over the state of the art traditional and CNN based pan-sharpening methods.

关键词： Generators Gallium nitride Spatial resolution Remote sensing Task analysis Training

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MemNetAR: Memory Network with Adversative Relation for Target-Level Sentiment Classification

MemNetAR: Memory Network with Adversative Relation for Targe...

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2019 IEEE Global Communications Conference (GLOBECOM)

作者： Yiwei Gao Jianwei Niu Xuefeng Liu Kaili Mao Shui Yu State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beihang University Hangzhou Innovation Research Institute Beihang University School of Computer Science and Cyber Engineering Guangzhou University Guangdong China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Target-level sentiment classification aims to identify the sentiment of multiple targets in a sentence. Although existing approaches based on neural network have achieved good performance in this task, we find that many approaches tend to give the same predictions for instances that have multiple targets, and this tendency can make a low accuracy for those instances that have different classes for different targets. Based on this observation, we propose MemNetAR, a memory network which can explicitly leverage the adversative relation among multiple targets in a sentence. Specifically, we add an adversative loss to the cross-entropy loss when there are adversative words between targets. The experimental results on public laptop and restaurant datasets prove that our model can improve 0.84% and 0.8% on total test dataset, and improve 2.97% and 2.68% on the dataset consisting of those instances with multiple targets but different classes by leveraging this new adversative information.

关键词： Task analysis Feature extraction Sentiment analysis Portable computers Machine learning Training Predictive models

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An Adaptive Control Method for Arterial Signal Coordination Based on Deep Reinforcement Learning*

An Adaptive Control Method for Arterial Signal Coordination ...

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International Conference on Intelligent Transportation

作者： Peng Chen Zemao Zhu Guangquan Lu Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Hai Dian District Beijing Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Hai Dian District Beijing

ISBN: (纸本)9781538670255

Traffic signal control is a complex problem and it is difficult to determine an optimal strategy to control multi-directional traffic at multiple intersections. Recent years have witnessed numerous successes of deep learning neural networks in the fields of artificial intelligence. Motivated by the dominant performance of neural networks, this study attempts to develop a novel adaptive signal control approach by fusing deep learning (DL) and reinforcement learning (RL), i.e., deep reinforcement learning (DRL), for arterial signal coordination. DRL can considerably improve the ability to deal with large amounts of data processing, systematic perception and expression, which is key to coordinated control of arterial intersections. The proposed algorithm is implemented by utilizing real-time traffic detection data and aims to optimize the hybrid global and local reward functions. The experimental results obtained by traffic simulation software SUMO demonstrate the advantage of the proposed approach, as well as its efficiency and effectiveness compared with fixed-time and actual signal control methods.

关键词： Aerospace electronics Machine learning Adaptive control Traffic control Roads Optimization

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The Future of Graph database Applications: An Electric Utility Perspective 2

The Future of Graph Database Applications: An Electric Utili...

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2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC 2017)

作者： Siyan Liu Guoqiang Lin Bo Chai Jiangpeng Dai Aihua Zhou Feng Chen Jing Pan Advanced Computing and Big Data Laboratory Global Energy Interconnection Research Institute State Grid Anhui Electric Power Company

ISBN: (纸本)9781509064151;9781509064144

With the development of smart grid, renewable energy, as well as electric vehicle technique, plenty of primary and secondary devices are plugged into the power grid. However, the large amount of data generated by these devices cause problems. Relational databases can hardly deal with large-scale topological data. Therefore, graph database may be a better alternative to relational database. This paper describes graph database application scenarios in electric utilities, including topological search, vulnerability analysis, and enterprise relationship management. Among the scenarios, topological search is our existing study. Despite the lack of systematic implementation, we introduced graph database as a novel database management system into electrical engineering.

关键词： graph database enterprise software topology analysis

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