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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Kai Lei Meng Qin Bo Bai Gong Zhang Min Yang PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China ICNLAB School of Electronics and Computer Engineering (SECE) Peking University Shenzhen China Future Network Theory Lab 2012 Labs Huawei Technologies Co. Ltd Hong Kong China Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen China

In this paper, we generally formulate the dynamics prediction problem of various network systems (e.g., the prediction of mobility, traffic and topology) as the temporal link prediction task. Different from conventional techniques of temporal link prediction that ignore the potential non-linear characteristics and the informative link weights in the dynamic network, we introduce a novel non-linear model GCN-GAN to tackle the challenging temporal link prediction task of weighted dynamic networks. The proposed model leverages the benefits of the graph convolutional network (GCN), long short-term memory (LSTM) as well as the generative adversarial network (GAN). Thus, the dynamics, topology structure and evolutionary patterns of weighted dynamic networks can be fully exploited to improve the temporal link prediction performance. Concretely, we first utilize GCN to explore the local topological characteristics of each single snapshot and then employ LSTM to characterize the evolving features of the dynamic networks. Moreover, GAN is used to enhance the ability of the model to generate the next weighted network snapshot, which can effectively tackle the sparsity and the wide-value-range problem of edge weights in real-life dynamic networks. To verify the model's effectiveness, we conduct extensive experiments on four datasets of different network systems and application scenarios. The experimental results demonstrate that our model achieves impressive results compared to the state-of-the-art competitors.

关键词： Predictive models Hidden Markov models Task analysis Topology Network topology Generative adversarial networks Gallium nitride

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Quantum Entanglement Based on Two-Photon Joint Subtraction from Two-Mode Squeezed Vacuum States

Quantum Entanglement Based on Two-Photon Joint Subtraction f...

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OptoElectronics and Communications Conference, OECC

作者： Hongbin Song Heyu Huang Hao Liu Ai Zhou Guofeng Zhang Hidehiro Yonezawa The General Education Division and Guangdong Key Laboratory of Optoelectronic Materials and Chips and Shenzhen Key Lab of Semiconductor Lasers The Chinese University of Hong Kong Shenzhen Shenzhen China School of Materials Science and Engineering and National Engineering Research Center of Fiber Optic Sensing Technology and Networks Wuhan University of Technology Wuhan China National Engineering Research Center of Fiber Optic Sensing Technology and Networks Wuhan University of Technology Wuhan China Department of Applied Mathematics The Hong Kong Polytechnic University Hongkong China Optical Quantum Control Research Team RIKEN Center for Quantum Computing Saitama Japan

We present and analyze the quantum entanglement generated from two-photon joint subtraction from two-mode squeezed vacuum states (TMSV). Larger entanglement negativity than single-photon jointly subtracted TMSV, photo... 详细信息

ISBN: (数字)9798350308396

ISBN: (纸本)9798350308402

关键词： Protocols Correlation Quantum entanglement Simulation Elementary particle vacuum Quantum networks Photonics

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Performance evaluation for caching-based content distribution in backhaul-limited small cell networks

Performance evaluation for caching-based content distributio...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Ronghui Hou Yu Cheng Lin X. Cai Hongcheng Zhuang State Key Lab of Integrated Service Networks Xidian University Xian China Department of Electrical and Computer Engineering Illinois Institute of Technology Chicago USA R&D Centre Huawei Technologies Shenzhen China

ISBN: (纸本)9781467376884

Distributed caching at the edge can reduce the backhaul resource consumption, which is considered as a major way to improve network capacity in dense small cell networks. In this paper, we consider the caching-based content distribution in dense small cell networks. In such a content distribution system, the most critical metric is the user request blocking probability. A user's request will be rejected if the content is not cached at the associated SBS and there is no backhaul resource available at the SBS. In this paper, we develop an analytic model to derive the blocking probability as the function of cache capacity, available backhaul resource, and request arrival rates. Based on the analysis, the optimal resource configuration is determined to minimize the blocking probability. Extensive simulations are conducted to validate our analysis.

关键词： Analytical models Probability Capacity planning Electronic mail Wireless communication Markov processes Steady-state

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MedTruth: A semi-supervised approach to discovering knowledge condition information from multi-source medical data

arXiv

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arXiv 2018年

作者： Deng, Yang Li, Yaliang Shen, Ying Du, Nan Fan, Wei Yang, Min Lei, Kai School of Electronics and Computer Engineering Peking University PCL Research Center of Networks and Communications Peng Cheng Laboratory Chinese University of Hong Kong Alibaba Group Tencent Medical AI Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences

Knowledge Graph (KG) contains entities and the relations between entities. Due to its representation ability, KG has been successfully applied to support many medical/healthcare tasks. However, in the medical domain, knowledge holds under certain conditions. For example, symptom runny nose highly indicates the existence of disease whooping cough when the patient is a baby rather than the people at other ages. Such conditions for medical knowledge are crucial for decision-making in various medical applications, which is missing in existing medical KGs. In this paper, we aim to discovery medical knowledge conditions from texts to enrich KGs. Electronic Medical Records (EMRs) are systematized collection of clinical data and contain detailed information about patients, thus EMRs can be a good resource to discover medical knowledge conditions. Unfortunately, the amount of available EMRs is limited due to reasons such as regularization. Meanwhile, a large amount of medical question answering (QA) data is available, which can greatly help the studied task. However, the quality of medical QA data is quite diverse, which may degrade the quality of the discovered medical knowledge conditions. In the light of these challenges, we propose a new truth discovery method, MedTruth, for medical knowledge condition discovery, which incorporates prior source quality information into the source reliability estimation procedure, and also utilizes the knowledge triple information for trustworthy information computation. We conduct series of experiments on real-world medical datasets to demonstrate that the proposed method can discover meaningful and accurate conditions for medical knowledge by leveraging both EMR and QA data. Further, the proposed method is tested on synthetic datasets to validate its effectiveness under various scenarios. Copyright © 2018, The Authors. All rights reserved.

关键词： Medical computing

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CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning

arXiv

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arXiv 2022年

作者： Shu, Jun Yuan, Xiang Meng, Deyu Xu, Zongben School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi China Peng Cheng Laboratory Guangdong Shenzhen China The Macau Institute of Systems Engineering Macau University of Science and Technology Taipa China

Modern deep neural networks (DNNs) can easily overfit to biased training data containing corrupted labels or class imbalance. Sample re-weighting methods are popularly used to alleviate this data bias issue. Most current methods, however, require manually pre-specifying the weighting schemes as well as their additional hyper-parameters relying on the characteristics of the investigated problem and training data. This makes them fairly hard to be generally applied in practical scenarios, due to their significant complexities and inter-class variations of data bias situations. To address this issue, we propose a meta-model capable of adaptively learning an explicit weighting scheme directly from data. Specifically, by seeing each training class as a separate learning task, our method aims to extract an explicit weighting function with sample loss and task/class feature as input, and sample weight as output, expecting to impose adaptively varying weighting schemes to different sample classes based on their own intrinsic bias characteristics. Synthetic and real data experiments substantiate the capability of our method on achieving proper weighting schemes in various data bias cases, like the class imbalance, feature-independent and dependent label noise scenarios, and more complicated bias scenarios beyond conventional cases. Besides, the task-transferability of the learned weighting scheme is also substantiated, by readily deploying the weighting function learned on relatively smaller-scale CIFAR-10 dataset on much larger-scale full WebVision dataset. A performance gain can be readily achieved compared with previous state-of-the-art ones without additional hyper-parameter tuning and meta gradient descent step. The general availability of our method for multiple robust deep learning issues, including partial-label learning, semi-supervised learning and selective classification, has also been validated. Code for reproducing our experiments is available at https://github

关键词： Deep neural networks

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Robust IRS-aided Secrecy Transmission with Location Optimization

arXiv

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arXiv 2022年

作者： Bai, Jiale Wang, Hui-Ming Liu, Peng The School of Information and Communications Engineering Xi'an Jiaotong University Xi’an710049 China The Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi'an Jiaotong University Xi’an710049 China The Wireless Technology Lab Huawei Technologies 2012 Labs Shenzhen China

In this paper, we propose a robust secrecy transmission scheme for intelligent reflecting surface (IRS) aided communication systems. Different from all the existing works where IRS has already been deployed at a fixed location, we take the location of IRS as a variable to maximize the secrecy rate (SR) under the outage probability constraint by jointly optimizing the location of IRS, transmit beamformer and IRS phase shifts with imperfect channel state information (CSI) of Eve, where we consider two cases: a) the location of Eve is known;b) only a suspicious area of Eve is available. We show a critical observation that CSI models are different before and after IRS deployment, thus the optimization problem could be decomposed and solved via a two-stage framework. For case a), in the first stage, universal upper bounds of outage probabilities only related to the location of IRS are derived which can be optimized via successive convex approximation (SCA) method. In the second stage, we develop an alternative optimization (AO) algorithm to optimize beamformer and phase shifts iteratively. For case b), we propose a Max-Min SR scheme based on two-stage framework, where the location of IRS is optimized based on the worst location of Eve. Simulation results indicate the importance of the location of IRS optimization. © 2022, CC BY.

关键词： Location

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Intelligent Reflecting Surface Enabled Sensing: Cramér-Rao Lower Bound Optimization

arXiv

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arXiv 2022年

作者： Song, Xianxin Xu, Jie Liu, Fan Han, Tony Xiao Eldar, Yonina C. School of Science and Engineering Future Network of Intelligence Institute Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Shenzhen China Department of Electrical and Electronic Engineering Southern University of Science and Technology China Wireless Technology Lab Laboratories 2012 Huawei China Faculty of Mathematics and Computer Science Weizmann Institute of Science Israel

This paper investigates intelligent reflecting surface (IRS) enabled non-line-of-sight (NLoS) wireless sensing, in which an IRS is deployed to assist an access point (AP) to sense a target in its NLoS region. It is assumed that the AP is equipped with multiple antennas and the IRS is equipped with a uniform linear array. The AP aims to estimate the target's direction-of-arrival (DoA) with respect to the IRS, based on the echo signals from the AP-IRS-target-IRS-AP link. Under this setup, we jointly design the transmit beamforming at the AP and the reflective beamforming at the IRS to minimize the Cramér-Rao lower bound (CRLB) on estimation error. Towards this end, we first obtain the CRLB expression for estimating the DoA in closed form. Next, we optimize the joint beamforming design to minimize the CRLB, via alternating optimization, semi-definite relaxation, and successive convex approximation. Numerical results show that the proposed design based on CRLB minimization achieves improved sensing performance in terms of mean squared error, as compared to the traditional schemes with signal-to-noise ratio maximization and separate beamforming. Copyright © 2022, The Authors. All rights reserved.

关键词： Beamforming

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Intelligent Reflecting Surface Enabled Sensing: Cramér-Rao Lower Bound optimization

Intelligent Reflecting Surface Enabled Sensing: Cramér-Rao ...

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IEEE Globecom Workshops

作者： Xianxin Song Jie Xu Fan Liu Tony Xiao Han Yonina C. Eldar School of Science and Engineering Future Network of Intelligence Institute and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong (Shenzhen) China Department of Electrical and Electronic Engineering Southern University of Science and Technology China wireless Technology Lab 2012 Laboratories Huawei China Faculty of Mathematics and Computer Science Weizmann Institute of Science Israel

ISBN: (纸本)9781665459761

This paper investigates intelligent reflecting surface (IRS) enabled non-line-of-sight (NLoS) wireless sensing, in which an IRS is deployed to assist an access point (AP) to sense a target in its NLoS region. It is assumed that the AP is equipped with multiple antennas and the IRS is equipped with a uniform linear array. The AP aims to estimate the target’s direction-of-arrival (DoA) with respect to the IRS, based on the echo signals from the AP-IRS-target-IRS-AP link. Under this setup, we jointly design the transmit beamforming at the AP and the reflective beamforming at the IRS to minimize the CramérRao lower bound (CRLB) on estimation error. Towards this end, we first obtain the CRLB expression for estimating the DoA in closed form. Next, we optimize the joint beamforming design to minimize the CRLB, via alternating optimization, semi-definite relaxation, and successive convex approximation. Numerical results show that the proposed design based on CRLB minimization achieves improved sensing performance in terms of mean squared error, as compared to the traditional schemes with signal-to-noise ratio maximization and separate beamforming.

关键词： Wireless communication Wireless sensor networks Estimation error Direction-of-arrival estimation Array signal processing Minimization Sensors

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Multi-view Vector-valued Manifold Regularization for Multi-label Image Classification

arXiv

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arXiv 2019年

作者： Luo, Yong Tao, Dacheng Xu, Chang Xu, Chao Liu, Hong Wen, Yonggang School of Electronics Engineering and Computer Science Peking University Beijing100871 China Centre for Quantum Computation and Intelligent Systems University of Technology Sydney Jones Street Ultimo SydneyNSW2007 Australia Engineering Lab on Intelligent Perception for Internet of Things Shenzhen Graduate School Peking University China Division of Networks and Distributed Systems School of Computer Engineering Nanyang Technological University Singapore Singapore

—In computer vision, image datasets used for classification are naturally associated with multiple labels and comprised of multiple views, because each image may contain several objects (e.g. pedestrian, bicycle and tree) and is properly characterized by multiple visual features (e.g. color, texture and shape). Currently available tools ignore either the label relationship or the view complementary. Motivated by the success of the vector-valued function that constructs matrix-valued kernels to explore the multi-label structure in the output space, we introduce multi-view vector-valued manifold regularization (MV3MR) to integrate multiple features. MV3MR exploits the complementary property of different features and discovers the intrinsic local geometry of the compact support shared by different features under the theme of manifold regularization. We conducted extensive experiments on two challenging, but popular datasets, PASCAL VOC’ 07 (VOC) and MIR Flickr (MIR), and validated the effectiveness of the proposed MV3MR for image classification. Copyright © 2019, The Authors. All rights reserved.

关键词： Image classification

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Variational inverting network for statistical inverse problems of partial differential equations

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 9597-9656页

作者： Junxiong Jia Yanni Wu Peijun Li Deyu Meng School of Mathematics and Statistics Xi 'an Jiaotong University Xi ' an China Department of Mathematics Purdue University West Lafayette Indiana School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an Shaanxi China Peng Cheng Laboratory Shenzhen Guangdong China Macau Institute of Systems Engineering Macau University of Science and Technology Taipa Macau China

To quantify uncertainties in inverse problems of partial differential equations (PDEs), we formulate them into statistical inference problems using Bayes' formula. Recently, well-justified infinite-dimensional Bayesian analysis methods have been developed to construct dimension-independent algorithms. However, there are three challenges for these infinite-dimensional Bayesian methods: prior measures usually act as regularizers and are not able to incorporate prior information efficiently; complex noises, such as more practical noni.i.d. distributed noises, are rarely considered; and time-consuming forward PDE solvers are needed to estimate posterior statistical quantities. To address these issues, an infinite-dimensional inference framework has been proposed based on the infinite-dimensional variational inference method and deep generative models. Specifically, by introducing some measure equivalence assumptions, we derive the evidence lower bound in the infinite-dimensional setting and provide possible parametric strategies that yield a general inference framework called the Variational Inverting Network (VINet). This inference framework can encode prior and noise information from learning examples. In addition, relying on the power of deep neural networks, the posterior mean and variance can be efficiently and explicitly generated in the inference stage. In numerical experiments, we design specific network structures that yield a computable VINet from the general inference framework. Numerical examples of linear inverse problems of an elliptic equation and the Helmholtz equation are presented to illustrate the effectiveness of the proposed inference framework.

关键词： infinite-dimensional variational inference inverse problems Bayesian analysis for functions partial differential equations deep neural networks

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