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13th International Conference on Information Science and technology, ICIST 2023

作者： Zhang, Fangyu Wei, Yanjie Lin, Zhenli Pan, Yi Liu, Jin Centre for High Performance Computing Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Ophthalmology Shenzhen University General Hospital Shenzhen China Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9798350313925

Deep learning (DL) has been widely used in the detection of neurological diseases such as Autism spectrum disorder (ASD). Most studies in this field manually designed and adjusted the hyperparameters of DL models, but due to the huge search space, few studies have attempted to optimize the topology of neural networks which is another important factor related to the performance of DL models. This paper proposes a heuristic sparsification method named the Evolutionary Connection Sparsification Algorithm (ECSA) which is used to optimize the topology of fully connected neural networks (FC-NNs) and generate the corresponding sparsely connected neural networks (SC-NNs). The algorithm is based on the workflow of the genetic algorithm but with improved coding strategy, fitness evaluation approach, crossover and mutation operator, and including an effective convergence acceleration strategy. Experiments show that the SC-NNs generated by ECSA significantly outperform the original FC-NNs in terms of accuracy, F1-score, and AUC, and the model complexities are greatly reduced. © 2023 IEEE.

关键词： Topology

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DTNet: Dual-encoder generative adversarial network for generating breast cancer immunohistochemical images 5

DTNet: Dual-encoder generative adversarial network for gener...

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5th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2024

作者： Jia, Yifan Duan, Guanhua Song, Yanyun Ye, Lan Liu, Zhi Shandong Sports Rehabilitation Research Center Jinan China The Second Hospital of Shandong University The Cancer Center Jinan China The School of Information Science and Engineering Shandong University Qingdao China State Key Laboratory of High Performance Server and Storage Technology Jinan China

ISBN: (纸本)9798350373820

In order to address the coloring problem in breast cancer, we propose an innovative generator network architecture paradigm and a pooling-based loss function to tackle the challenge of strict alignment between image pairs. Our network architecture primarily consists of a content encoder, a style encoder, a decoder, and a TSA skip-connection module. The content encoder ensures that content features are preserved during coloring, while the style encoder guarantees the accuracy of coloring styles. The TSS skipconnection layer based on a transformer architecture is employed to facilitate the flow of feature information. Experimental evaluations on four coloring datasets demonstrate that our model exhibits superior performance and robustness compared to existing models. © 2024 IEEE.

关键词： Network architecture

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A Novel ACA for Analyzing Electromagnetic Field Problems of Filter with Waveports

A Novel ACA for Analyzing Electromagnetic Field Problems of ...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Yutao Hou Ruipeng Jia Shilong Du Xunwang Zhao Xifeng Pu Shaanxi Key Laboratory of Large Scale Electromagnetic Computing Xidian University Xi’an China Shaanxi Innovation Center for High Performance CAE Software Xi’an China

ISBN: (数字)9798350355581

ISBN: (纸本)9798350355598

In this paper, a fast direct method based on adaptive cross approximation (ACA) is used to calculate the electromagnetic field of the waveport which is a difficulty when analyzing metal-dielectric filter models. Different from the traditional method of moments (MoM), the system matrix is divided into blocks and then compressed in this method which greatly improves the calculation efficiency while ensuring the calculation accuracy. Numerical results are presented to demonstrate that the proposed ACA can efficiently and accurately calculate the metal-dielectric filter models.

关键词： Analytical models Adaptation models Accuracy Computational modeling Computational electromagnetics Numerical models Scattering parameters Computational efficiency Electromagnetic fields Method of moments

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Sequence-to-Sequence Knowledge Graph Completion Based on Gated Attention Unit 29

Sequence-to-Sequence Knowledge Graph Completion Based on Gat...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Yi, Fengge Wei, Xiumei Liu, Xiaojing Jiang, Xuesong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

ISBN: (纸本)9798350330717

We present GauKGT5, a sequence-to-sequence model proposed for knowledge graph completion (KGC). Our research extends the KGT5 model, a recent sequence-to-sequence link prediction (LP) model. GauKGT5 takes advantage of textual characteristics inherent in the knowledge graph, exhibiting a small model size. However, KGT5's proficiency in link prediction necessitates the ensemble with a knowledge graph embedding model, which itself poses challenges due to its substantial size and expense. By integrating the Gated Attention Unit into the KGT5 model and directly applying it to the encoder-decoder structure, we achieve improved contextual dependency capturing within the sequence, resulting in enhanced prediction accuracy, accelerated training speed, and enhanced computational efficiency. At the same time, we introduce parallel computing as a means to enhance the efficiency of model training and inference within the XPU distributed computing environment. © 2023 IEEE.

关键词： gated attention unit knowledge graph completion sequence-to-sequence model

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Research on shielding effectiveness of field-to-wire coupling model based on time-domain hybrid method

Research on shielding effectiveness of field-to-wire couplin...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Conghao Fan Wenjun Sun Haoxin Wan Xunwang Zhao Xifeng Pu Shaanxi Key Laboratory of Large-Scale Electromagnetic Computing Xidian University Xi’an China Shaanxi Innovation Center for High Performance CAE Software Xi’an China

ISBN: (数字)9798350355581

ISBN: (纸本)9798350355598

This paper adopts an efficient time-domain hybrid method to solve the complex field-to-wire coupling model. Under the irradiation of high frequency electromagnetic pulse, the transient response of different types and different lengths of cables arranged in parallel in a large engine room model is simulated. By analyzing the electromagnetic compatibility (EMC) inside and outside the model, the three-dimensional current distribution on the model surface and the shielding effectiveness of the model, the results show that the aircraft shell itself has good electromagnetic protection characteristics. In addition, the experimental results by numerical simulation show that improving the electromagnetic protection capability of the fuselage, adopting the shielded cable structure and changing the wiring mode can effectively improve the shielding effectiveness of the inner cable of the fuselage under the irradiation of high-energy electromagnetic pulse.

关键词： Couplings Solid modeling Analytical models Atmospheric modeling Numerical models Aircraft Protection Electromagnetics Time-domain analysis Cables

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An Automata-Theoretic Approach to Synthesizing Binarized Neural Networks 21st

An Automata-Theoretic Approach to Synthesizing Binarized Ne...

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21st International Symposium on Automated technology for Verification and Analysis, ATVA 2023

作者： Tao, Ye Liu, Wanwei Song, Fu Liang, Zhen Wang, Ji Zhu, Hongxu College of Computer Science and Technology National University of Defense Technology Changsha China School of Information Science and Technology ShanghaiTech University Shanghai China Institute of Software Chinese Academy of Sciences and University of Chinese Academy of Sciences Beijing China Automotive Software Innovation Center Shanghai China Institute for Quantum Information and State Key Laboratory for High Performance Computing National University of Defense Technology Changsha China

ISBN: (纸本)9783031453281

Deep neural networks, (DNNs, a.k.a. NNs), have been widely used in various tasks and have been proven to be successful. However, the accompanied expensive computing and storage costs make the deployments in resource-constrained devices a significant concern. To solve this issue, quantization has emerged as an effective way to reduce the costs of DNNs with little accuracy degradation by quantizing floating-point numbers to low-width fixed-point representations. Quantized neural networks (QNNs) have been developed, with binarized neural networks (BNNs) restricted to binary values as a special case. Another concern about neural networks is their vulnerability and lack of interpretability. Despite the active research on trustworthy of DNNs, few approaches have been proposed to QNNs. To this end, this paper presents an automata-theoretic approach to synthesizing BNNs that meet designated properties. More specifically, we define a temporal logic, called BLTL, as the specification language. We show that each BLTL formula can be transformed into an automaton on finite words. To deal with the state-explosion problem, we provide a tableau-based approach in real implementation. For the synthesis procedure, we utilize SMT solvers to detect the existence of a model (i.e., a BNN) in the construction process. Notably, synthesis provides a way to determine the hyper-parameters of the network before training. Moreover, we experimentally evaluate our approach and demonstrate its effectiveness in improving the individual fairness and local robustness of BNNs while maintaining accuracy to a great extent. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep neural networks

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3D-Printed Monolith Metallic Ni-Mo Electrodes for Ultrahigh Current Hydrogen Evolution

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Energy & Environmental Materials 2024年第5期7卷 224-233页

作者： Yanran Xun Hongmei Jin Yuemeng Li Shixiang Zhou Kaixi Zhang Xi Xu Win Jonhson Shuai Chang Teck Leong Tan Jun Ding Department of Materials Science and Engineering National University of SingaporeSingapore 117574Singapore Institute of High Performance Computing(IHPC) Agency for ScienceTechnology and Research(A^(*)STAR)1 Fusionopolis Way#16-16 ConnexisSingapore 138632Singapore Key Laboratory of Flexible Electronics of Zhejiang Province Ningbo Institute of Northwestern Polytechnical UniversityNingbo 315103China State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyHarbin 150001China

In this work,we reported a series of monolithic 3D-printed Ni-Mo alloy electrodes for highly efficient water splitting at high current density(1500 mA cm^(-2))with excellent stability,which provides a solution to scale up Ni-Mo catalysts for HER to industry *** possible Ni-Mo metal/alloy phases were achieved by tuning the atomic composition and heat treatment procedure,and they were investigated through both experiment and simulation,and the optimal NiMo phase shows the best *** functional theory(DFT)calculations elucidate that the NiMo phase has the lowest H2O dissociation energy,which further explains the exceptional performance of *** addition,the microporosity was modulated via controlled thermal treatment,indicating that the 1100℃sintered sample has the best catalytic performance,which is attributed to the high electrochemically active surface area(ECSA).Finally,the four different macrostructures were achieved by 3D printing,and they further improved the catalytic *** gyroid structure exhibits the best catalytic performance of driving 500 mA cm^(-2)at a low overpotential of 228 mV and 1500 mA cm^(-2)at 325 mV,as it maximizes the efficient bubble removal from the electrode surface,which offers the great potential for high current density water splitting.

关键词： 3D-printed electrode Ni-Mo catalyst phase tuning ultra-high current density HER

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ADAPT: Adaptive distributed optimization approach for uploading data with redundancy in cooperative mobile cloud

ADAPT: Adaptive distributed optimization approach for upload...

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作者： Wang, Ji Bao, Weidong Zhu, Xiaomin College of Systems Engineering National University of Defense Technology Changsha China State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China

With the development of information technology and the ubiquity of mobile devices, increasing amounts of data are generated, processed, and transmitted by mobile devices. To alleviate the tension between the energy poverty of mobile devices and the increasing demand for transmitting data, the energy-efficient data transmission problem attracts considerable interests. Nonetheless, how to upload data with redundancy efficiently lacks a thorough study despite the wide existence of this problem in many situations like data storage among mobile devices and mobile crowd sensing. Since uploading redundant data brings little value while still consuming precious energy, it is important to design an efficient approach for mobile devices to upload data with redundancy cooperatively. In this work, we formulate the uploading data with redundancy in cooperative mobile cloud as an energy-constrained utility maximization problem. To solve this problem, we propose an adaptive distributed optimization approach consisting of the correlated upload decision and the online distributed scheduling algorithm. By the correlated upload decision, each mobile device can make adaptive decisions on how much data to upload and which data to upload according to its own observations independently. The online distributed scheduling algorithm enables mobile devices to optimally upload data. A series of simulation experiments are conducted to demonstrate the effectiveness of our approach. Finally, we test our approach on a real demo system to verify its practicability in reality. © 2019 John Wiley & Sons, Ltd.

关键词： Digital storage

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Uncertainty-Inspired Multi-Task Learning in Arbitrary Scenarios of ECG Monitoring

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IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4046-4058页

作者： Xingyao Wang Hongxiang Gao Caiyun Ma Tingting Zhu Feng Yang Chengyu Liu Huazhu Fu Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) Singapore School of Instrument Science and Engineering and State Key Laboratory of Digital Medical Engineering Southeast University Nanjing China Department of Engineering Science University of Oxford Oxford U.K.

As the scenarios for electrocardiogram (ECG) monitoring become increasingly diverse, particularly with the development of wearable ECG, the influence of ambiguous factors in diagnosis has been amplified. Reliable ECG information must be extracted from abundant noises and confusing artifacts. To address this issue, we suggest an uncertainty-inspired model for beat-level diagnosis (UI-Beat). The base architecture of UI-Beat separates heartbeat localization and event diagnosis in two branches to address the problem of heterogeneous data sources. To disentangle the epistemic and aleatoric uncertainty within one stage in a deterministic neural network, we propose a new method derived from uncertainty formulation and realize it by introducing the class-biased transformation. Then the disentangled uncertainty can be utilized to screen out noise and identify ambiguous heartbeat synchronously. The results indicate that UI-Beat can significantly improve the performance of noise detection (from 91.60% to 97.50% for real-world noise detection and from 61.40% to 82.41% for real-world artifact detection). For multi-lead ECG analysis, UI-Beat is approaching the performance upper bound in heartbeat localization (only 15 false positives and 9 false negatives out of the 175,907 heartbeats in the INCART database) and achieving a significant performance improvement in heartbeat classification through uncertainty-based cross-lead fusion compared to single-lead prediction and other state-of-the-art methods (an average improvement of 14.28% for detecting heartbeats of S and 3.37% for detecting heartbeats of V). Considering the characteristic of one-stage ECG analysis within one model, it is suggested that the proposed UI-Beat has the potential to be employed as a general model for arbitrary scenarios of ECG monitoring, with the capacity to remove unusableepisodes, and realize heartbeat-level diagnosis with confidence provided.

关键词： Electrocardiography Heart beat Uncertainty Noise Heart rate variability Location awareness Databases Annotations Analytical models Multitasking

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Quantum frontiers in high energy physics

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Science China(Physics,Mechanics & Astronomy) 2025年第6期68卷 49-71页

作者： Yaquan Fang Christina Gao Ying-Ying Li Jing Shu Yusheng Wu Hongxi Xing Bin Xu Lailin Xu and Chen Zhou Institute of High Energy Physics Chinese Academy of SciencesBeijing 100049China University of Chinese Academy of Sciences Beijing 100049China Department of Physics Southern University of Science and TechnologyShenzhen 518055China Interdisciplinary Center for Theoretical Study University of Science and Technology of ChinaHefei 230026China Peng Huanwu Center for Fundamental Theory Hefei 230026China State Key Laboratory of Nuclear Physics and Technology School of PhysicsPeking UniversityBeijing 100871China Center for High Energy Physics Peking UniversityBeijing 100871China Beijing Laser Acceleration Innovation Center Beijing 101400China State Key Laboratory of Particle Detection and Electronics Department of Modern PhysicsUniversity of Science and Technology of ChinaHefei 230026China Key Laboratory of Atomic and Subatomic Structure and Quantum Control(MOE) Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of MatterInstitute of Quantum MatterSouth China Normal UniversityGuangzhou 510006China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Guangdong Provincial Key Laboratory of Nuclear ScienceSouthern Nuclear Science Computing CenterSouth China Normal UniversityGuangzhou 510006China Southern Center for Nuclear-Science Theory Institute of Modern PhysicsChinese Academy of SciencesHuizhou 516000China

Numerous challenges persist in high energy physics(HEP),the addressing of which requires advancements in detection technology,computational methods,data analysis frameworks,and phenomenological *** provide a concise yet comprehensive overview of recent progress across these areas,in line with advances in quantum *** will discuss the potential of quantum devices in detecting subtle effects indicative of new physics BSM,the transformative role of quantum algorithms and large-scale quantum computers in studying real-time non-perturbative dynamics in the early universe and at colliders,as well as in analyzing complex HEP ***,we emphasize the importance of integrating quantum properties into HEP experiments to test quantum mechanics at unprecedented high-energy scales and search for hints of new *** ahead,the continued integration of resources to fully harness these evolving technologies will enhance our efforts to deepen our understanding of the fundamental laws of nature.

关键词： high energy physics quantum sensing quantum simulations quantum machine learning quantum information

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