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arXiv 2020年

作者： Papadopoulos, Alexandros Kyritsis, Konstantinos Klingelhoefer, Lisa Bostanjopoulou, Sevasti Chaudhuri, K. Ray Delopoulos, Anastasios Multimedia Understanding Group Information Processing Laboratory Dept. of Electrical and Computer Engineering Aristotle University of Thessaloniki Greece Department of Neurology Technical University of Dresden Dresden Germany Third Neurological Clinic Papanikolaou Hospital Thessaloniki Greece International Parkinson Excellence Research Centre King’s College Hospital NHS Foundation Trust London United Kingdom

Parkinson’s Disease (PD) is a slowly evolving neurological disease that affects about 1% of the population above 60 years old, causing symptoms that are subtle at first, but whose intensity increases as the disease progresses. Automated detection of these symptoms could offer clues as to the early onset of the disease, thus improving the expected clinical outcomes of the patients via appropriately targeted interventions. This potential has led many researchers to develop methods that use widely available sensors to measure and quantify the presence of PD symptoms such as tremor, rigidity and braykinesia. However, most of these approaches operate under controlled settings, such as in lab or at home, thus limiting their applicability under free-living conditions. In this work, we present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device. We propose a Multiple-Instance Learning approach, wherein a subject is represented as an unordered bag of accelerometer signal segments and a single, expert-provided, tremor annotation. Our method combines deep feature learning with a learnable pooling stage that is able to identify key instances within the subject bag, while still being trainable end-to-end. We validate our algorithm on a newly introduced dataset of 45 subjects, containing accelerometer signals collected entirely in-the-wild. The good classification performance obtained in the conducted experiments suggests that the proposed method can efficiently navigate the noisy environment of in-the-wild recordings. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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parallel Dispatch:A New Paradigm of electrical Power System Dispatch

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IEEE/CAA Journal of Automatica Sinica 2018年第1期5卷 311-319页

作者： Jun Jason Zhang Fei-Yue Wang Qiang Wang Dazhi Hao Xiaojing Yang David Wenzhong Gao Xiangyang Zhao Yingchen Zhang Department of Electrical and Computer Engineering Ritchie School of Engineering and Computer Science University of Denver Denver 80210 CO USA State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Science (SKL-MCCS CASIA) Beijing 100190 China Research Center of Computational Experiments and Parallel Systems The National University of Defense Technology Changsha 410073 China State Grid Tianjin Electric Power Company Tianjin 300010 China School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China National Renewable Energy Laboratory Golden CO 80401 USA IEEE

Modern power systems are evolving into sociotechnical systems with massive complexity, whose real-time operation and dispatch go beyond human capability. Thus,the need for developing and applying new intelligent power system dispatch tools are of great practical significance. In this paper, we introduce the overall business model of power system dispatch, the top level design approach of an intelligent dispatch system, and the parallel intelligent technology with its dispatch applications. We expect that a new dispatch paradigm,namely the parallel dispatch, can be established by incorporating various intelligent technologies, especially the parallel intelligent technology, to enable secure operation of complex power grids,extend system operators' capabilities, suggest optimal dispatch strategies, and to provide decision-making recommendations according to power system operational goals.

关键词： ACP knowledge automation power dispatch parallel dynamic programming parallel intelligence paralle learning situational awareness

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Promoting diversity for end-to-end conversation response generation

arXiv

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arXiv 2019年

作者： Ruan, Yu-Ping Ling, Zhen-Hua Liu, Quan Gu, Jia-Chen Zhu, Xiaodan National Engineering Laboratory for Speech and Language Information Processing University of Science and Technology of China Hefei China iFLYTEK Research Hefei China Department of Electrical and Computer Engineering Queen's University Kingston Canada

We present our work on Track 2 in the Dialog System Technology Challenges 7 (DSTC7). The DSTC7-Track 2 aims to evaluate the response generation of fully data-driven conversation models in knowledge-grounded settings, which provides the contextual-relevant factual texts. The Sequence-to-Sequence models have been widely used for end-to-end generative conversation modelling and achieved impressive results. However, they tend to output dull and repeated responses in previous studies. Our work aims to promote the diversity for end-to-end conversation response generation, which follows a two-stage pipeline: 1) Generate multiple responses. At this stage, two different models are proposed, i.e., a variational generative (VariGen) model and a retrieval based (Retrieval) model. 2) Rank and return the most related response by training a topic coherence discrimination (TCD) model for the ranking process. According to the official evaluation results, our proposed Retrieval and VariGen systems ranked first and second respectively on objective diversity metrics, i.e., Entropy, among all participant systems. And the VariGen system ranked second on NIST and METEOR metrics. Copyright © 2019, The Authors. All rights reserved.

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Systematic Resource Allocation in Cloud RAN with Caching as a Service under Two Timescales

Systematic Resource Allocation in Cloud RAN with Caching as ...

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作者： Tang, Jianhua Quek, Tony Q. S. Chang, Tsung-Hui Shim, Byonghyo Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Normal University Changsha410081 China Institute of New Media and Communications Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen518172 China Department of Electrical and Computer Engineering Institute of New Media and Communications Seoul National University Seoul08826 Korea Republic of

Recently, cloud radio access network (C-RAN) with caching as a service (CaaS) was proposed to merge the functionalities of communication, computing, and caching (CCC) together. In this paper, we dissect the interactions of CCC in C-RAN with CaaS from two dimensions: physical resource dimension and time dimension. In the physical resource dimension, we identify how to segment the baseband unit (BBU) pool resources (i.e., computation and storage) into different types of virtual machines (VMs). In the time dimension, we address how the long-term resource segmentation in the BBU pool impacts on the short-term transmit beamforming at the remote radio heads. We formulate the problem as a stochastic mixed-integer nonlinear programming (SMINLP) to minimize the system cost, including the server cost, VM cost and wireless transmission cost. After a series of approximation, including sample average approximation, successive convex approximation, and semidefinite relaxation, the SMINLP is approximated as a global consensus problem. The alternating direction method of multipliers (ADMM) is utilized to obtain the solution in a parallel fashion. Simulation results verify the convergence of our proposed algorithm, and also confirm that the proposed scheme is more cost-saving than that without considering the integration of CCC. © 1972-2012 IEEE.

关键词： Integer programming

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Exploring unsupervised pretraining and sentence structure modelling for winograd schema challenge

arXiv

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arXiv 2019年

作者： Ruan, Yu-Ping Zhu, Xiaodan Ling, Zhen-Hua Shi, Zhan Liu, Quan Wei, Si National Engineering Laboratory for Speech and Language Information Processing University of Science and Technology of China Hefei China Department of Electrical and Computer Engineering Queen's University KingstonON Canada iFLYTEK Research Hefei China

Winograd Schema Challenge (WSC) was proposed as an AI-hard problem in testing computers' intelligence on common sense representation and reasoning. This paper presents the new state-of-theart on WSC, achieving an accuracy of 71.1%. We demonstrate that the leading performance benefits from jointly modelling sentence structures, utilizing knowledge learned from cutting-edge pretraining models, and performing fine-tuning. We conduct detailed analyses, showing that fine-tuning is critical for achieving the performance, but it helps more on the simpler associative problems. Modelling sentence dependency structures, however, consistently helps on the harder non-associative subset of WSC. Analysis also shows that larger fine-tuning datasets yield better performances, suggesting the potential benefit of future work on annotating more Winograd schema sentences. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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TransMut: a program to predict HLA-I peptide binding and optimize mutated peptides for vaccine design by the Transformer-derived self-attention model

Research Square

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Research Square 2021年

作者： Chu, Yanyi Zhang, Yan Wang, Qiankun Zhang, Lingfeng Wang, Xuhong Wang, Yanjing Wang, Jianmin Jiang, Xue Salahub, Dennis Russell Xiong, Yi Wei, Dong-Qing State Key Laboratory of Microbial Metabolism Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances Joint International Research Laboratory of Metabolic & Developmental Sciences School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai200030 China Department of Chemistry CMS - Centre for Molecular Simulation IQST - Institute for Quantum Science and Technology University of Calgary 2500 University Drive NW CalgaryABT2N 1N4 Canada Department of Clinical Oncology The University of Hong Kong-Shenzhen Hospital Shenzhen China School of Electrical Engineering and Computer Science University of Ottawa 75 Laurier Ave. OttawaONK1N 6N5 Canada The Ministry of Education Key Laboratory of System control and information processing Department of Automation School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200030 China Integrative Biotechnology & Translational Medicine Yonsei University Incheon21983 Korea Republic of Peng Cheng Laboratory Vanke Cloud City Phase I Building 8 Xili Street Nanshan District Guangdong Shenzhen518055 China

Computational prediction of the interaction between human leukocyte antigen (HLA) and peptide (pHLA) can speed up epitope screening and vaccine design. Here, we develop the TransMut framework composed of TransPHLA for pHLA binding prediction and AOMP for mutated peptide optimization, which can be generalized to any binding and mutation task of biomolecules. Firstly, TransPHLA is developed by using a Transformer-derived self-attention model to predict pHLA binding, which is significantly superior to 11 previous methods on pHLA binding prediction, neoantigen and human papilloma virus vaccine identification. For vaccine design, the AOMP program is then developed to automatically optimize mutated peptides to search for mutant peptides with higher affinity to the target HLA and with high homology to the source peptide. Among 3660 non-binding pHLAs, 3630 were successfully mutated. Of these, 94% were verified by the IEDB recommended method, and 88% have homology higher than 80% to the source peptide. © 2021, CC BY.

关键词： Peptides

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AIM 2020 Challenge on Image Extreme Inpainting 16th

AIM 2020 Challenge on Image Extreme Inpainting

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Workshops held at the 16th European Conference on computer Vision, ECCV 2020

作者： Ntavelis, Evangelos Romero, Andrés Bigdeli, Siavash Timofte, Radu Hui, Zheng Wang, Xiumei Gao, Xinbo Shin, Chajin Kim, Taeoh Son, Hanbin Lee, Sangyoun Li, Chao Li, Fu He, Dongliang Wen, Shilei Ding, Errui Bai, Mengmeng Li, Shuchen Zeng, Yu Lin, Zhe Yang, Jimei Zhang, Jianming Shechtman, Eli Lu, Huchuan Zeng, Weijian Ni, Haopeng Cai, Yiyang Li, Chenghua Xu, Dejia Wu, Haoning Han, Yu Nadim, Uddin S. M. Jang, Hae Woong Ahmed, Soikat Hasan Yoon, Jungmin Jung, Yong Ju Li, Chu-Tak Liu, Zhi-Song Wang, Li-Wen Siu, Wan-Chi Lun, Daniel P. K. Suin, Maitreya Purohit, Kuldeep Rajagopalan, A.N. Narang, Pratik Mandal, Murari Chauhan, Pranjal Singh Computer Vision Lab ETH Zürich Zürich Switzerland CSEM Neuchâtel Switzerland School of Electronic Engineering Xidian University Xi’an China Image and Video Pattern Recognition Laboratory School of Electrical and Electronic Engineering Yonsei University Seoul Korea Republic of Baidu Inc. Beijing China Beijing China Dalian University of Technology Dalian China Adobe San Jose United States Rensselaer Polytechnic Institute Troy United States Peking University Beijing China Lab Gachon University Seongnam Korea Republic of Centre for Multimedia Signal Processing Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong China Indian Institute of Technology Madras Chennai India BITS Pilani Pilani India MNIT Jaipur Jaipur India

ISBN: (纸本)9783030670696

This paper reviews the AIM 2020 challenge on extreme image inpainting. This report focuses on proposed solutions and results for two different tracks on extreme image inpainting: classical image inpainting and semantically guided image inpainting. The goal of track 1 is to inpaint large part of the image with no supervision. Similarly, the goal of track 2 is to inpaint the image by having access to the entire semantic segmentation map of the input. The challenge had 88 and 74 participants, respectively. 11 and 6 teams competed in the final phase of the challenge, respectively. This report gauges current solutions and set a benchmark for future extreme image inpainting methods. © 2020, Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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Beamforming Design and Association Scheme for Multi-RIS Multi-User mmWave Systems Through Graph Neural Networks

arXiv

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arXiv 2025年

作者： Liu, Mengbing Huang, Chongwen Alhammadi, Ahmed Di Renzo, Marco Debbah, Mérouane Yuen, Chau School of Electrical and Electronics Engineering Nanyang Technological University 639798 Singapore College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China Technology Innovation Institute Masdar City Abu Dhabi United Arab Emirates Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et Systèmes 3 Rue Joliot-Curie Gif-sur-Yvette91192 France King’s College London Centre for Telecommunications Research Department of Engineering LondonWC2R 2LS United Kingdom KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi127788 United Arab Emirates CentraleSupelec University Paris-Saclay Gif-sur-Yvette91192 France

Reconfigurable intelligent surface (RIS) is emerging as a promising technology for next-generation wireless communication networks, offering a variety of merits such as the ability to tailor the communication environment. Moreover, deploying multiple RISs helps mitigate severe signal blocking between the base station (BS) and users, providing a practical and efficient solution to enhance the service coverage. However, fully reaping the potential of a multi-RIS aided communication system requires solving a non-convex optimization problem. This challenge motivates the adoption of learning-based methods for determining the optimal policy. In this paper, we introduce a novel heterogeneous graph neural network (GNN) to effectively leverage the graph topology of a wireless communication environment. Specifically, we design an association scheme that selects a suitable RIS for each user. Then, we maximize the weighted sum rate (WSR) of all the users by iteratively optimizing the RIS association scheme, and beamforming designs until the considered heterogeneous GNN converges. Based on the proposed approach, each user is associated with the best RIS, which is shown to significantly improve the system capacity in multi-RIS multiuser millimeter wave (mmWave) communications. Specifically, simulation results demonstrate that the proposed heterogeneous GNN closely approaches the performance of the high-complexity alternating optimization (AO) algorithm in the considered multi-RIS aided communication system, and it outperforms other benchmark schemes. Moreover, the performance improvement achieved through the RIS association scheme is shown to be of the order of 30%. Copyright © 2025, The Authors. All rights reserved.

关键词： Beamforming

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Stacked auto encoder based Deep Reinforcement Learning for online resource scheduling in large-scale MEC networks

arXiv

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arXiv 2020年

作者： Jiang, Feibo Wang, Kezhi Dong, Li Pan, Cunhua Yang, Kun Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Department of Computer and Information Sciences Northumbria University United Kingdom Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom University of Electronic Science and Technology of China Chengdu China

An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the mobile users, by optimizing offloading decision, transmission power, and resource allocation in the mobile edge computing (MEC) system. Towards this end, a deep reinforcement learning (DRL) method is proposed to obtain an online resource scheduling policy. Firstly, a related and regularized stacked auto encoder (2r-SAE) with unsupervised learning is proposed to perform data compression and representation for high dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Secondly, we present an adaptive simulated annealing based approach (ASA) as the action search method of DRL, in which an adaptive h-mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Thirdly, a preserved and prioritized experience replay (2p-ER) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. Numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks. It also shows that the proposed framework is suitable for resource scheduling problem in large-scale MEC networks, especially in the dynamic environment. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Block Sparse Bayesian Learning for Bursty Impulsive Noise Detection in Broadband PLC 2

Block Sparse Bayesian Learning for Bursty Impulsive Noise De...

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2nd International Conference on Imaging, Signal processing and Communication, ICISPC 2018

作者： Mariyam, Prita Dewi Liu, Jing Juwono, Filbert H. Gunawan, Dadang Universitas Indonesia Department of Electrical Engineering Depok West Java16424 Indonesia National Laboratory of Radar Signal Processing Xidian University Xi'an710071 China Curtin University Malaysia Department of Electrical and Computer Engineering Miri Sarawak98009 Malaysia

ISBN: (纸本)9781538669822

Broadband power-line communications (PLC) is considered as an enabling technology for the Internet-of-Things (IoT) systems. However, PLC faces some transmission issues as a power line was not designed to transmit communication data. Impulsive noise is one of the major issues in broadband PLC systems. In practice, impulsive noise may occur in bursts. The performance of the PLC systems degrades significantly under bursty impulsive noise environment. We propose to use a compressive sensing algorithm to detect and mitigate the bursty impulsive noise. In this paper, block sparse Bayesian learning (BSBL) algorithms are used to detect and estimate the bursty impulsive noise. The estimated impulsive noise is then subtracted from the contaminated signal. BSBL has relatively better performance than other block compressive sensing algorithms. We examine the performance of two types of BSBL algorithms, i.e. BSBL-bound optimization (BSBL-BO) and BSBL-expectation-maximization (BSBL-EM) under various bursty impulsive noise conditions. The results show that both algorithms have comparable performance in terms of bit error rate (BER). However, BSBL-EM gives better minimum square error (MSE) but much slower in the CPU processing time than BSBL-BO. © 2018 IEEE.

关键词： Impulse noise

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