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IEEE International Conference on data Mining (ICDM)

作者： Yangcheng Gao Zhao Zhang Haijun Zhang Mingbo Zhao Yi Yang Meng Wang School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Harbin Institute of Technology (Shenzhen) Xili University Town Shenzhen China City University of Hong Kong Hong Kong SAR Centre for Artificial Intelligence University of Technology Sydney Sydney NSW Australia

ISBN: (纸本)9781665423991

Deep neural network (DNN) compression can reduce the memory footprint of deep networks effectively, so that the deep model can be deployed on the portable devices. However, most of the existing model compression methods cost lots of time, e.g., vector quantization or pruning, which makes them inept to the real-world applications that need fast online computation. In this paper, we therefore explore how to accelerate the model compression process by reducing the computation cost. Then, we propose a new deep model compression method, termed Dictionary Pair-based data-Free Fast DNN Compression, which aims at reducing the memory consumption of DNNs without extra training and can greatly improve the compression efficiency. Specifically, our proposed method performs tensor decomposition on the DNN model with a fast dictionary pair learning-based reconstruction approach, which can be deployed on different layers (e.g., convolution and fully-connection layers). Given a pre-trained DNN model, we first divide the parameters (i.e., weights) of each layer into a series of partitions for dictionary pair-based fast reconstruction, which can potentially discover more fine-grained information and provide the possibility for parallel model compression. Then, dictionaries of less memory occupation are learned to reconstruct the weights. Extensive experiments on popular DNNs (i.e., VGG-16, ResNet-18 and ResNet-50) showed that our proposed weight compression method can significantly reduce the memory footprint and speed up the compression process, with less performance loss.

关键词： Deep learning Training Performance evaluation Dictionaries Tensors Costs Computational modeling

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Glycan immunogenicity prediction based on Graph neural network

Glycan immunogenicity prediction based on Graph neural netwo...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Wang, Yu Wang, Hui Hou, Meijie Wang, Yaojun Bu, Dongbo Zhang, Chunming Huang, Chuncui Sun, Shiwei Institute of Computing Technology Chinese Academy of Sciences Key Lab of Intelligent Information Processing 6 Kexueyuan South Road Beijing100190 China Zhongke Big Data Academy Henan Zhengzhou450046 China University of Chinese Academy of Sciences 19 Yuquan Road Beijing100049 China Institute of Biophysics Chinese Academy of Sciences 15 Datun Road Beijing100101 China College of Information and Electrical Engineering China Agricultural University 17 Qinghua Donglu Beijing100083 China Phil Rivers Technology Beijing China

ISBN: (纸本)9781665401265

Glycans play important roles in a great variety of biological processes, and these roles are closely determined by the details of their structures. It becomes possible to acquire hidden features from glycan structures using deep learning method with the great progress in recent years. Unlike the linear chain of proteins and DNAs, branching is a unique feature of glycan structures, which makes it very difficult to directly apply deep learning models on glycans. Thus, how to comprehensively and efficiently describe glycans and use them as input to deep learning models still remains challenging. Here, a graph neural network (GNN) called GlyNet was used to obtain high-dimensional representation of glycans and predict their immunogenicity. Our method was applied in SugarBase, and it works more superiorly than the state-of-art method with accuracy increased from 91.7% to 95.6%. © 2021 IEEE.

关键词： Message passing

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Multi-Scale Convolution-Transformer Fusion Network for Endoscopic Image Segmentation

Multi-Scale Convolution-Transformer Fusion Network for Endos...

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IEEE International Symposium on Biomedical Imaging

作者： Baosheng Zou Zongguang Zhou Ying Han Kang Li Guotai Wang West China Biomedical Big Data Center Sichuan University Chengdu China School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Department of General Surgery Division of Gastrointestinal Surgery West China Hospital of Sichuan University Chengdu China Institute of Digestive Surgery & State Key Laboratory of Biotherapy Sichuan University Chengdu China West China Medical Simulation Center Sichuan University West China Hospital Chengdu China Shanghai AI Lab Shanghai China

Automatic semantic segmentation of endoscopic images is an essential part of computer-assisted intervention surgery. Recently, Convolutional Neural Networks (CNNs) have been widely applied to endoscopic image segmentation, but their performance is still limited due to the weak ability to capture global long-range dependencies. This paper proposes a model that combines CNN and Transformer to deal with this problem, and it is named as Multi-scale Convolution-Transformer Fusion Network (MCTFNet) and consists of three components: 1) Multiple-parallel Multi-scale Transformer Convolution (MMTC) modules in parallel branches to extract Multi-scale information, 2) Multi-scale Information Fusion (MIF) module that fuses parallel branch information to allow interaction between different resolutions and 3) High-resolution Information Processing (HIP) module to keep high-resolution features in the image and avoid loss of details. We verified our method on HeiSurF dataset, and the results show that our method achieved an average Dice of 80.07%, which outperformed state-of-the-art CNNs including HRNet (79.93%) and Deeplabv3 (78.34%). It also outperformed several networks designed for medical image segmentation.

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Towards Interpretability and Personalization: A Predictive Framework for Clinical Time-series Analysis

Towards Interpretability and Personalization: A Predictive F...

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IEEE International Conference on data Mining (ICDM)

作者： Yang Li Xianli Zhang Buyue Qian Zeyu Gao Chong Guan Yefeng Zheng Hansen Zheng Fenglang Wu Chen Li School of Computer Science and Technology Xi’an Jiaotong University Xi’an Shaanxi China Tencent Jarvis Lab Shenzhen China The First Affiliated Hospital of Xi’an Jiaotong University Xi’an Shaanxi China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Xi’an Shaanxi China

ISBN: (纸本)9781665423991

Clinical time-series is receiving long-term attention in data mining and machine learning communities and has boosted a variety of data-driven applications. Identifying similar patients or subgroups from clinical time-series is an essential step to design tailored treatments in clinical practice. However, most of the existing methods are either purely unsupervised that tend to neglect the patient outcome information or cannot generate personalized patient representation through supervised learning, thus may fail to identify ‘truly similar patients’ (i.e., patients who similar in both outcomes and individual outcome-related clinical variables). To tackle these limitations, we propose a novel predictive clinical time-series analysis framework. Specifically, our framework uses task-specific information to rule out the task-irrelevant factors in each patient data individually and generates the contribution scores that reveal the factors’ importance for the patient outcome. Then a patient representation construction method is proposed to generate task-related and personalized representations by combining remained factors and their contribution scores. At last, similarity measurement or cluster analysis can be conducted. We evaluate our framework on three real-world clinical time-series datasets, empirically demonstrate that our framework achieves improvements in prediction performance, similarity measurement, and clustering, thus potentially benefiting patient-similarity-based precision medicine applications.

关键词： Precision medicine Conferences Time series analysis Supervised learning Semantics Machine learning Predictive models

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AdaXpert: Adapting neural architecture for growing data

arXiv

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arXiv 2021年

作者： Niu, Shuaicheng Wu, Jiaxiang Xu, Guanghui Zhang, Yifan Guo, Yong Zhao, Peilin Wang, Peng Tan, Mingkui School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Tencent AI Lab China National University of Singapore Singapore Northwestern Polytechnical University China Pazhou Laboratory China

In real-world applications, data often come in a growing manner, where the data volume and the number of classes may increase dynamically. This will bring a critical challenge for learning: given the increasing data volume or the number of classes, one has to instantaneously adjust the neural model capacity to obtain promising performance. Existing methods either ignore the growing nature of data or seek to independently search an optimal architecture for a given dataset, and thus are incapable of promptly adjusting the architectures for the changed data. To address this, we present a neural architecture adaptation method, namely Adaptation eXpert (AdaXpert), to efficiently adjust previous architectures on the growing data. Specifically, we introduce an architecture adjuster to generate a suitable architecture for each data snapshot, based on the previous architecture and the different extent between current and previous data distributions. Furthermore, we propose an adaptation condition to determine the necessity of adjustment, thereby avoiding unnecessary and time-consuming adjustments. Extensive experiments on two growth scenarios (increasing data volume and number of classes) demonstrate the effectiveness of the proposed method. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Global Collaboration to Enhance Information technology & Quantitative Management: Preface for ITQM 2023

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Procedia Computer Science 2023年 221卷 xv-xvii页

作者： Shi, Yong Filip, Florin G. He, Jing Li, Jianping Kou, Gang Tien, James Berg, Daniel Research Centre on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Romanian Academy Calea Victoriei 125 Sector 1 Bucharest010071 Romania College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States University of Oxford United Kingdom School of Business Chengdu University Sichuan 610106 China College of Engineering University of Miami Coral GablesFL33124 United States

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Structure Embedded Nucleus Classification for Histopathology Images

arXiv

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arXiv 2023年

作者： Lou, Wei Wan, Xiang Li, Guanbin Lou, Xiaoying Li, Chenghang Gao, Feng Li, Haofeng Shenzhen Research Institute of Big Data Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong at Shenzhen Shenzhen518172 China Pazhou Lab Guangzhou510330 China The School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Department of Pathology Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases The Sixth Affiliated Hospital of Sun Yat-sen University Guangzhou China Artificial Intelligence Thrust The Hong Kong University of Science and Technology at Guangzhou Guangzhou510030 China Department of Colorectal Surgery Department of General Surgery Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases The Sixth Affiliated Hospital Sun Yat-sen University Guangzhou510655 China Shanghai Artificial Intelligence Laboratory Shanghai China

Nuclei classification provides valuable information for histopathology image analysis. However, the large variations in the appearance of different nuclei types cause difficulties in identifying nuclei. Most neural network based methods are affected by the local receptive field of convolutions, and pay less attention to the spatial distribution of nuclei or the irregular contour shape of a nucleus. In this paper, we first propose a novel polygon-structure feature learning mechanism that transforms a nucleus contour into a sequence of points sampled in order, and employ a recurrent neural network that aggregates the sequential change in distance between key points to obtain learnable shape features. Next, we convert a histopathology image into a graph structure with nuclei as nodes, and build a graph neural network to embed the spatial distribution of nuclei into their representations. To capture the correlations between the categories of nuclei and their surrounding tissue patterns, we further introduce edge features that are defined as the background textures between adjacent nuclei. Lastly, we integrate both polygon and graph structure learning mechanisms into a whole framework that can extract intra and inter-nucleus structural characteristics for nuclei classification. Experimental results show that the proposed framework achieves significant improvements compared to the state-of-the-art methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

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Designing preamplifier for sensing atmospheric electrostatic field strength via supercapacitive sensor

Designing preamplifier for sensing atmospheric electrostatic...

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2020 International Symposium on Electronic Information technology and Communication engineering, ISEITCE 2020

作者： Wu, Changhao Xiong, Yi Zeng, Wei National Engineering Research Center for Agro-Ecological Big Data Analysis and Application School of Electronics and Information Engineering Anhui University Hefei230601 China Science and Technology Institute Hubei Key Laboratory of Biomass Fibers and Eco-Dyeing and Finishing Wuhan Textile University Wuhan430073 China School of Physics and Technology Moe Key Laboratory of Artificial Micro- and Nano-Structures Center for Electron Microscopy Wuhan University Wuhan430072 China

In order to effectively obtain the signal from sensor, the analogy signal needs to be amplified and then converted into a digital signal for matching to the sensor characteristics. With a supercapacitive electric field sensor based on graphene aerogel, the response current signal from the electric field sensor is weak and unstable. Herein, a high gain and low noise preamplifier is developed, and an amplifier circuit with double T-type feedback network is proposed to reduce the Johnson noise for the amplifier. This design can reduce the thermal noise of resistance by using the smaller resistance under the same gain, and it can effectively reduce the interference of peak noise by adding the feedback capacitance, so as to improve the detection accuracy. The simulation results show that under the same gain condition, the Johnson noise can be reduced by 46% and the detection accuracy can be improved by 12% compared with the traditional T-type feedback network. © Published under licence by IOP Publishing Ltd.

关键词： Feedback amplifiers

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A Heterogeneous PIM Hardware-Software Co-Design for Energy-Efficient Graph Processing

A Heterogeneous PIM Hardware-Software Co-Design for Energy-E...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Yu Huang Long Zheng Pengcheng Yao Jieshan Zhao Xiaofei Liao Hai Jin Jingling Xue National Engineering Research Center for Big Data Technology and System/Service Computing Technology and System Lab/Cluster and Grid Computing Lab Huazhong University of Science and Technology China UNSW Sydney Australia

ISBN: (数字)9781728168760

ISBN: (纸本)9781728168777

Processing-In-Memory (PIM) is an emerging technology that addresses the memory bottleneck of graph processing. In general, analog memristor-based PIM promises high parallelism provided that the underlying matrix-structured crossbar can be fully utilized while digital CMOS-based PIM has a faster single-edge execution but its parallelism can be low. In this paper, we observe that there is no absolute winner between these two representative PIM technologies for graph applications, which often exhibit irregular workloads. To reap the best of both worlds, we introduce a new heterogeneous PIM hardware, called Hetraph, to facilitate energy-efficient graph processing. Hetraph incorporates memristor-based analog computation units (for high-parallelism computing) and CMOS-based digital computation cores (for efficient computing) on the same logic layer of a 3D die-stacked memory device. To maximize the hardware utilization, our software design offers a hardware heterogeneity-aware execution model and a workload offloading mechanism. For performance speedups, such a hardware-software co-design outperforms the state-of-the-art by 7.54 ×(CPU), 1.56 ×(GPU), 4.13× (memristor-based PIM) and 3.05× (CMOS-based PIM), on average. For energy savings, Hetraph reduces the energy consumption by 57.58× (CPU), 19.93× (GPU), 14.02 ×(memristor-based PIM) and 10.48 ×(CMOS-based PIM), on average.

关键词： Hardware Computer architecture Parallel processing Three-dimensional displays Computational modeling Graphics processing units Memristors

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Cryptanalysis of a Pairing-Based Anonymous Key Agreement Scheme for Smart Grid 15th

Cryptanalysis of a Pairing-Based Anonymous Key Agreement Sch...

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15th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2019, held in conjunction with the 12th International Conference on Frontiers of Information technology, Applications and Tools, FITAT 2019

作者： Liang, Xiao-Cong Wu, Tsu-Yang Lee, Yu-Qi Chen, Chien-Ming Yeh, Jyh-Haw Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Department of Computer Science Boise State University Idaho United States

ISBN: (纸本)9789811397134

Authentication and key agreement schemes in smart grid have received much attentions by researchers in recent years. Recently, Mahmood et al. proposed a secure pairing-based anonymous key exchange scheme in smart grid. However, we demonstrate that their scheme is insecure against an ephemeral secret leakage attack and an impersonation attack according to the Canetti-Krawczyk adversarial model in this paper. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Smart power grids

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