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6th International Conference on Advanced Machine Learning Technologies and applications, AMLTA 2021

作者： Xu, GuanWei Zheng, RongJin Chang, Kuo-Chi School of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Institute of Engineering Smart Grid Simulation Analysis and Integrated Control of Fujian University Engineering Research Center Fuzhou Fujian China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Business Administration North Borneo University College Sabah Malaysia

ISBN: (纸本)9783030697167

Leakage current of low-voltage power grid lines will cause major safety hazards such as personal electric shock and electrical fire. Traditional leakage current protectors can not play their due protection role due to their quality problems and aging, and other reasons. When leakage current accidents occur, traditional manual troubleshooting is inefficient. Aiming at the safety of leakage current in a low-voltage power grid, a new leakage current monitoring system is designed based on NB-IoT technology. The system includes the detection and transmission of leakage current and the construction of the cloud platform, which can realize the real-time monitoring of leakage current in low-voltage power grid and prevent and solve the leakage current safety problems. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Leakage currents

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Enabling and Scaling the HPCG Benchmark on the Newest Generation Sunway Supercomputer with 42 Million Heterogeneous Cores

Enabling and Scaling the HPCG Benchmark on the Newest Genera...

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Supercomputing Conference

作者： Qianchao Zhu Hao Luo Chao Yang Mingshuo Ding Wanwang Yin Xinhui Yuan Center for Data Science Peking University Beijing China School of Mathematical Sciences Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China School of Electronic Engineering and Computer Science Peking University Beijing China National Research Center of Parallel Computer Engineering & Technology Beijing China

ISBN: (数字)9781450384421

ISBN: (纸本)9781665483902

We study and evaluate performance optimization techniques for the HPCG benchmark on the newest generation Sunway super-computer. Specifically, a two-level blocking scheme is proposed to expose adequate parallelism in the symmetric Gauss-Seidel kernel while keeping a fast convergence rate, a fine-grained kernel fusion technique is developed to alleviate the bandwidth load on local storage with small capacity, and a low overhead thread collaboration method is presented to efficiently move data between threads and hide its cost with data transfer operations. Test results show that the optimized HPCG code is able to exploit 73.0% of the theoretical memory bandwidth, and scale to over 42 million heterogeneous cores with 95.5% weak-scaling efficiency and 5.91 Pflop/s performance. We also study how the performance can be improved if the specific rules of HPCG are not fully obeyed, and design dependency preserving parallelization and vectorization methods, further boosting performance to 27.6 Pflop/ s.

关键词： Instruction sets High performance computing Memory management Bandwidth Benchmark testing Parallel processing data transfer

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Spatial Commonsense Reasoning for Machine Reading Comprehension 9th

Spatial Commonsense Reasoning for Machine Reading Comprehen...

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19th International Conference on Advanced data Mining and applications, ADMA 2023

作者： Lin, Miaopei Wang, Meng-xiang Yu, Jianxing Wang, Shiqi Lai, Hanjiang Liu, Wei Yin, Jian School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Artificial Intelligence Sun Yat-sen University Zhuhai China China National Institute of Standardization Beijing100088 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

ISBN: (纸本)9783031466632

This paper studies the problem of spatial commonsense reasoning for the machine reading comprehension task. Spatial commonsense is the human-shared but latent knowledge of object shape, size, distance, and position. Reasoning this abstract knowledge can facilitate machines better perceive their surroundings, which is crucial for general intelligence. However, this valuable topic is challenging and has been less studied. To bridge this research gap, we focus on this topic and propose a new method to realize spatial reasoning. Given a text, we first build a potential reasoning graph based on its parsing tree. To better support spatial reasoning, we retrieve the related commonsense entities and relations from external knowledge sources, including the pre-trained language model (LM) and knowledge graph (KG). LM covers all kinds of factual knowledge and KG has abundant commonsense relations. We then propose a new fusion method called LEGRN (LM Edge-GNN Reasoner Networks) to fuse the text and graph. LEGRN adopts layer-based attention to integrate the LM text encoder and KG graph encoder, which can capture correlations between LM text context and KG graph structure. Considering that spatial relations involve a variety of attributes, we propose an attribute-aware inferential network to deduce the correct answers. To evaluate our approach, we construct a new large-scale dataset named CRCSpatial, consisting of 40k spatial reasoning questions. Experiment results illustrated the effectiveness of our approach. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Large dataset

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Polarization imaging bi-attentional recursive residual super-resolution method 9

Polarization imaging bi-attentional recursive residual super...

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9th Symposium on Novel Photoelectronic Detection Technology and applications

作者： Liu, Jiaqing Li, Yi Liang, Dong Xu, Guoming Zhou, Pucheng Ma, Jian School of Internet Anhui University Hefei China National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Anhui University Hefei China Anhui Provincial Key Laboratory of Polarized Light Imaging Detection Hefei China School of Electrical and Electronics Engineering Anhui Wenda University of Information Engineering Hefei China

ISBN: (纸本)9781510664432

Due to the limitation of the detector, the spatial resolution of the polarization image obtained by space-modulated full-polarization computed imaging is low. In general, there are a lot of valuable high-frequency components in low resolution space, but it is difficult to reconstruct high frequency information by deep learning method. Through the research of the previous super-resolution reconstruction method based on deep learning, it is found that it is difficult to obtain better lifting effect only by stacking residual blocks to build a deeper network. A polarization imaging bi-attention recursive residual network super-resolution reconstruction method is proposed. In the network structure, bi-attention recursive residual group is used as the deep feature extraction module, and the bi-attention module contained in this module is used to adaptively learn image features of different channels and different transformation Spaces in the deep network. In order to provide the concentrated learning efficiency of high-frequency information, jump connections are introduced into the network structure, and the shallow feature extraction and reconstruction of images are completed by a convolution layer and a sub-pixel convolution algorithm respectively. The experimental verification is carried out under the actual imaging system and simulation data, and compared with other methods in terms of visual effect and quantitative results. Experimental results verify that the proposed method can effectively reconstruct the contour structure and texture details of the detection target in subjective image quality, and is superior to the comparison method in objective evaluation index. © 2023 SPIE.

关键词： Feature extraction

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Molecular epidemiology and phylogenetic analysis of influenza viruses A (H3N2) and B/ Victoria during the COVID-19 pandemic in Guangdong, China

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Infectious Diseases of Poverty 2024年第4期13卷 99-100页

作者： Zhiqi Zeng Yong Liu Wenxiang Jin Jingyi Liang Jinbin Chen Ruihan Chen Qianying Li Wenda Guan Lixi Liang Qiubao Wu Yuanfang Lai Xiaoyan Deng Zhengshi Lin Chitin Hon Zifeng Yang State Key Laboratory of Respiratory Disease National Clinical Research Center for Respiratory Disease Guangzhou Institute of Respiratory Health the First Affiliated Hospital of Guangzhou Medical University Guangzhou Guangdong 510180 P.R. China Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases KingMed School of Laboratory Medicine Guangzhou Medical University Guangzhou People’s Republic of China Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovative Engineering Macau University of Science and Technology Macau SAR China Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology Taipa Macau China Guangzhou Laboratory Guangzhou China Kingmed Virology Diagnostic and Translational Center Guangzhou Kingmed Center for Clinical Laboratory Co. Ltd. Guangzhou China

Background Non-pharmaceutical measures and travel restrictions have halted the spread of coronavirus disease 2019(COVID-19)and ***,with COVID-19 restrictions lifted,an unanticipated outbreak of the influ-enza B/Nictoria virus in late 2021 and another influenza H3N2 outbreak in mid-2022 occurred in Guangdong,southern *** mechanism underlying this phenomenon remains *** better prepare for potential influenza out-breaks during COVID-19 pandemic,we studied the molecular epidemiology and phylogenetics of influenza A(H3N2)and BNictoria that circulated during the COVID-19 pandemic in this *** From January 1,2018 to December 31,2022,we collected throat swabs from 173,401 patients in Guang-dong who had acute respiratory tract *** viruses in the samples were tested using reverse tran-scription-polymerase chain reaction,followed by subtype identification and sequencing of hemagglutinin(HA)and neuraminidase(NA)*** and genetic diversity analyses were performed on both genes from 403 samples.A rigorous molecular clock was aligned with the phylogenetic tree to measure the rate of viral evolution and the root-to-tip distance within strains in different years was assessed using regression curve models to determine the *** During the early period of COVID-19 control,various influenza viruses were nearly undetectable in respiratory *** control measures were relaxed in January 2020,the influenza infection rate peaked at 4.94%(39/789)in December 2021,with the influenza B/Victoria accounting for 87.18%(34/39)of the total influenza *** months later,the influenza infection rate again increased and peaked at 11.34%(255/2248)in June 2022;influenza A/H3N2 accounted for 94.51%(241/255)of the total influenza cases in autumn *** diverse geographic distribution of HA genes of B/Nictoria and A/H3N2 had drastically reduced,and most strains originated from *** rate of B/Victoria HA e

关键词： Insidious transmission Influenza COVID-19 control Genetic diversity Evolution

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Learning Memory Augmented Cascading Network for Compressed Sensing of Images 16th

Learning Memory Augmented Cascading Network for Compressed S...

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16th European Conference on Computer Vision, ECCV 2020

作者： Chen, Jiwei Sun, Yubao Liu, Qingshan Huang, Rui Jiangsu Key Laboratory of Big Data Analysis Technology Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and Technology Nanjing China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9783030585419

In this paper, we propose a cascading network for compressed sensing of images with progressive reconstruction. Specifically, we decompose the complex reconstruction mapping into the cascade of incremental detail reconstruction (IDR) modules and measurement residual updating (MRU) modules. The IDR module is designed to reconstruct the remaining details from the residual measurement vector, and MRU is employed to update the residual measurement vector and feed it into the next IDR module. The contextual memory module is introduced to augment the capacity of IDR modules, therefore facilitating the information interaction among all the IDR modules. The final reconstruction is calculated by accumulating the outputs of all the IDR modules. Extensive experiments on natural images and magnetic resonance images demonstrate the proposed method achieves better performance against the state-of-the-art methods. © 2020, Springer Nature Switzerland AG.

关键词： Compressed sensing

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Designer topological flat bands in one-dimensional armchair graphene antidot lattices

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Physical Review B 2024年第11期110卷 115138页

作者： Jianing Wang Weiwei Chen Zhengya Wang Jie Meng Ruoting Yin Miaogen Chen Shijing Tan Chuanxu Ma Qunxiang Li Bing Wang Hefei National Research Center for Physical Sciences at the Microscale Synergetic Innovation Center of Quantum Information & Quantum Physics and New Cornerstone Science Laboratory Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province College of Sciences Hefei National Laboratory

We report a viable strategy to realize topological flat bands in one-dimensional armchair graphene antidot lattices. The quantum destructive interference effect leads to largely quenched intra-antidot hopping with nearly zero values, creating flat bands with a nontrivial topology, as unveiled by an effective Su-Schrieffer-Heeger model under extreme conditions. As a proof of concept, we demonstrate our approach in the on-surface synthesized porous seven-carbon-wide armchair graphene nanoribbons with periodic divacancy-type antidots, and showcase the robust flatness of the designer topological flat bands with a high tunability through strain and structural engineering which are investigated by combining tight-binding and density functional theory calculations with scanning probe microscopy measurements. We show that such available one-dimensional graphene nanoribbons can provide a rich platform for exploiting novel physics at the confluence of strong correlation and topology, opening up new avenues for research in the field of topological materials and their potential applications in quantum devices.

关键词： Electronic structure First-principles calculations Flat bands Symmetry protected topological states Topological materials Atomic force microscopy Density functional theory Scanning techniques Scanning tunneling microscopy Scanning tunneling spectroscopy Su-Schrieffer-Heeger model Tight-binding model

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Backdoor Attacks on Bimodal Salient Object Detection with RGB-Thermal data 24

Backdoor Attacks on Bimodal Salient Object Detection with RG...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Yin, Wen Zhu, Bin Benjamin Xie, Yulai Zhou, Pan Feng, Dan School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Microsoft Corporation Beijing China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9798400706868

RGB-Thermal Salient Object Detection (RGBT-SOD) plays a critical role in complex scene recognition applications, such as autonomous driving. However, security research in this domain is still in its infancy. This paper presents the first backdoor attack on RGBT-SOD systems, generating saliency maps on triggered inputs that depict non-existent salient objects chosen by the attacker or falsely mark an entire image as fully salient or entirely non-salient. We uncover that triggers have an influence range for generating non-existent salient objects, supported by a theoretical analysis. Extensive experiments show the effectiveness of our attack in both digital and physical-world scenarios. Notably, our dual-modality backdoor attack achieves an Attack Success Rate (ASR) of 86.72% with only five pairs of poisoned images in model training. After investigating potential countermeasures, we find them inadequate in mitigating our attacks, highlighting the urgent need for robust defenses against sophisticated backdoor attacks in RGBT-SOD systems. © 2024 ACM.

关键词： Object detection

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LECF: recommendation via learnable edge collaborative filtering

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science China(Information sciences) 2022年第1期65卷 116-130页

作者： Shitao XIAO Yingxia SHAO Yawen LI Hongzhi YIN Yanyan SHEN Bin CUI School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing Key Lab of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications School of Information Technology and Electrical Engineering The University of Queensland Department of Computer Science and Engineering Shanghai Jiao Tong University Department of Computer Science and Technology Peking University National Engineering Laboratory for Big Data Analysis and Applications Peking University

The core of recommendation models is estimating the probability that a user will like an item based on historical interactions. Existing collaborative filtering(CF) algorithms compute the likelihood by utilizing simple relationships between objects, e.g., user-item, item-item, or user-user. They always rely on a single type of object-object relationship, ignoring other useful relationship information in data. In this paper, we model an interaction between user and item as an edge and propose a novel CF framework, called learnable edge collaborative filtering(LECF). LECF predicts the existence probability of an edge based on the connections among edges and is able to capture the complex relationship in data. Specifically, we first adopt the concept of line graph where each node represents an interaction edge; then calculate a weighted sum of similarity between the query edge and the observed edges(i.e., historical interactions) that are selected from the neighborhood of query edge in the line graph for a recommendation. In addition, we design an efficient propagation algorithm to speed up the training and inference of LECF. Extensive experiments on four public datasets demonstrate LECF can achieve better performance than the state-of-the-art methods.

关键词： collaborative filtering recommender system implicit feedback edge-edge similarity line graph

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Application of a neural network model with multimodal fusion for fluorescence spectroscopy

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Nuclear science and Techniques 2024年第10期35卷 135-148页

作者： Lin Tang Shuang Zhou Kai-Bo Shi Hong-Tao Shen Lei You College of Electronic Information and Electrical Engineering Chengdu UniversityChengdu 610106China Guangxi Key Laboratory of Nuclear Physics and Nuclear Technology Guangxi Normal UniversityGuilin 541004China National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefei 230039China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore Vanadium and Titanium Resource Comprehensive Utilization Key Laboratory of Sichuan Province Panzhihua UniversityPanzhihua 617000China Key Laboratory of Interior Layout optimization and Security Institutions of Higher Education of Sichuan ProvinceChengdu Normal UniversityChengdu 611130China

In energy-dispersive X-ray fluorescence spectroscopy,the estimation of the pulse amplitude determines the accuracy of the spectrum *** error generated by the amplitude estimation of the pulse output distorted by the measurement system leads to false peaks in the measured *** eliminate these false peaks and achieve an accurate estimation of the distorted pulse amplitude,a composite neural network model is proposed,which embeds long and short-term memory(LSTM)into the UNet *** UNet network realizes the fusion of pulse sequence features and the LSTM model realizes pulse amplitude *** model is trained using simulated pulse datasets with different amplitudes and distortion *** the pulse height estimation,the average relative error of the trained model on the test set was approximately 0.64%,which is 27.37% lower than that of the traditional trapezoidal shaping *** processing of a standard iron source further validated the pulse height estimation performance of the UNet-LSTM *** estimating the amplitude of the distorted pulses using the model,the false peak area was reduced by approximately 91% over the full spectrum and was corrected to the characteristic peak region of interest(ROI).The corrected peak area accounted for approximately 1.32%of the characteristic peak ROI *** results indicate that the model can accurately estimate the height of distorted pulses and has substantial corrective effects on false peaks.

关键词： UNet Long-and short-term memory Pulse distortion Pulse height estimation Fluorescent spectroscopy

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