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arXiv 2024年

作者： Ou, Shaoyuan Xue, Kaiwen Zhou, Lian Lee, Chun-Ho Sludds, Alexander Hamerly, Ryan Zhang, Ke Feng, Hanke Kopparapu, Reshma Zhong, Eric Wang, Cheng Englund, Dirk Yu, Mengjie Chen, Zaijun Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States Research Laboratory of Electronics MIT CambridgeMA02139 United States PHI Laboratories NTT Research Inc. 940 Stewart Drive SunnyvaleCA94085 United States Department of Electrical Engineering State Key Laboratory of Terahertz and Millimeter Waves City University of Hong Kong Kowloon Hong Kong School of Advanced Computing University of Southern California Los AngelesCA90089 United States Opticore Inc. Los AngelesCA90007 United States

The escalating data volume and complexity resulting from the rapid expansion of artificial intelligence (AI), internet of things (IoT) and 5G/6G mobile networks is creating an urgent need for energy-efficient, scalable computing hardware. Here we demonstrate a hypermultiplexed integrated-photonics-based tensor optical processor (HITOP) that can perform trillions of operations per second (TOPS) at the energy efficiency of 40 TOPS/W. Space-time-wavelength three-dimensional (3D) optical parallelism enables O(N2) operations per clock-cycle using O(N) modulator devices. The system is built with wafer-fabricated III/V micron-scale lasers and high-speed thin-film Lithium-Niobate electro-optics for encoding at 10s femtojoule/symbol. Lasing threshold incorporates analog inline rectifier (ReLu) nonlinearity for low-latency activation. The system scalability is verified with machine learning models of 405,000 parameters. A combination of high clockrates, energy-efficient processing and programmability unlocks the potential of light for large-scale AI accelerators in applications ranging from training of large AI models to real-time decision making in edge deployment. © 2024, CC0.

关键词： Photonic integrated circuits

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PreZ-DGGAN: A Drug Graph GAN Based on Pre-Learning of Implicit Variables 2nd

PreZ-DGGAN: A Drug Graph GAN Based on Pre-Learning of Implic...

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2nd International Conference on Applied Intelligence, ICAI 2024

作者： Liu, Yixin Fan, Yueqin Li, Zhipeng Zhang, Qinhu Big Data and Intelligent Computing Research Center Guangxi Academy of Science Nanning530007 China School of Mechanical Engineering Guangxi University Nanning530004 China Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo315201 China Institute for Regenerative Medicine Medical Innovation Center and State Key Laboratory of Cardiology School of Medicine Shanghai East Hospital Tongji University Shanghai200123 China College of Advanced Agricultural Sciences Zhejiang Agriculture and Forestry University Hangzhou311300 China

ISBN: (纸本)9789819619061

In the field of drug discovery and development, deep learning techniques have become a powerful tool to accelerate the discovery and development of new drugs. In the design and optimization of lead molecules, generative adversarial network and graph neural network are one of the prime techniques for accelerating research and development, because the former is based on the idea of two-player game to learn drug features to generate effective molecules, while the unstructured inputs of the latter are well-suited for the analysis of molecular data. Therefore, this study develops a new graph generative adversarial model, called PreZ-DGGAN, by inserting the graph neural network layer into the generative adversarial network. Within PreZ-DGGAN, the latent variable input of the generator is pre-learned by an improved graph variational autoencoder, which effectively avoids the mode collapse of the generative adversarial network, whereas the hidden layer of the discriminator is graph neural network layer, which is able to better learn the real molecules and judge the generated molecules as true or false. Experiments on two molecule datasets show that the molecules generated by PreZ-DGGAN have higher validity (100% on average) and uniqueness (91.5% on average), and the generated molecules are more diverse in structure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Anti-JPEG Compression Steganography Based on the High Tense Region Locating Method

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Computers, Materials & Continua 2019年第4期59卷 199-214页

作者： Yang Wu Weiping Shang Jiahao Chen State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450002China School of Telecommunications Engineering Xidian UniversityXi’an710071China Zhuhai Campus of Beijing Normal University Zhuhai519000China

Robust data hiding techniques attempt to construct covert communication in a lossy public ***,the existing robust JPEG steganographic algorithms cannot overcome the side-information missing ***,this paper proposes a new robust JPEG steganographic algorithm based on the high tense region location method which needs no side-information of lossy ***,a tense region locating method is proposed based on the Harris-Laplacian feature ***,robust cover object generating processes are ***,the advanced embedding cost function is proposed.A series of experiments are conducted on various JPEG image sets and the results show that the proposed steganographic algorithm can resist JPEG compression efficiently with acceptable performance against steganalysis statistical detection libraries GFR(Gabor Filters Rich model)and DCTR(Discrete Cosine Transform Residual).

关键词： Robust data hiding steganography JPEG compression resistant Harris-Laplacian feature

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Practical Software Diversification Tool Chain for Dissimilar Redundancy Architecture

Practical Software Diversification Tool Chain for Dissimilar...

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IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

作者： Gaofei Zhang Zheng Zhang Jiang Wang Xinsheng Ji State key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Information Engineering University Zhengzhou China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

With the rapid development of the information society, there are corresponding tool solutions for various daily problems. Aiming at the complex generation process and inconvenient operation of heterogeneous components in a security defense system constructed with dissimilar redundancy, in this paper, we propose a practical basic software application isomerization tool chain, which integrates a series of key technologies of heterogeneous structures. At the same time, corresponding automated tests are provided for users to conduct comprehensive evaluation. Experimental evaluation shows that the tool chain will be able to provide heterogeneous component support for security defense technologies based on dissimilarity redundancy construction and improve the security of software systems.

关键词： Redundancy Computer architecture Tools Software Hardware Security Testing

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Optimization of performance of the KM2A full array using the Crab Nebula

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Chinese Physics C 2024年第6期48卷 169-178页

作者：曹臻 F.Aharonian 安琪阿西克古白云翔包逸炜 D.Bastieri 毕效军毕玉江蔡金庭曹晴曹文羽曹喆常进常劲帆陈尚明陈恩生陈亮陈林陈龙陈明君陈玛丽陈起辉陈素弘陈天禄陈阳程宁程耀东崔明阳崔树旺崔晓红崔昱东戴本忠代洪亮戴子高单增罗布 D.della Volpe 董绪强段凯凯樊军辉范一中方军方堃冯存峰封莉冯少辉丰晓婷冯有亮 S.Gabici 高博高川东高林青高启高卫高伟康葛茂茂耿利斯 G.Giacinti 龚光华苟全补顾旻皓郭福来郭晓磊郭义庆郭莹莹韩毅昂何会海贺昊宁何佳银何新波何钰 M.Heller 贺远强侯博文侯超侯贤胡红波胡铨胡世聪黄代绘黄天奇黄文俊黄性涛黄晓渊黄勇黄志成季筱璐贾焕玉贾康江琨姜晓巍姜泽军金敏康明铭柯通 D.Kuleshov.K.Kurinov 李兵兵李澄李骢李丹李飞李海波李会财李华阳李军李剑李捷李凯李文龙李文莲李秀荣李昕李一卓李哲黎卓梁恩维梁云峰林苏杰刘冰刘成刘栋刘虎刘海东刘佳刘江来刘金艳刘茂元柳若愚刘四明刘伟刘怡刘以农鲁睿罗晴吕洪魁马伯强马玲玲马欣华毛基荣闵振 W.Mitthumsiri 穆慧君南云程 A.Neronov 区子维庞彬宇 P.Pattarakijwanich 裴致远齐孟尧祁业情乔冰强秦家军 D.Ruffolo A.Sáiz D.Semikoz 邵澄宇邵琅 O.Shchegolev 盛祥东舒富文宋慧超 Yu.V.Stenkin V.Stepanov 苏扬孙秦宁孙晓娜孙志斌谭柏轩唐庆文唐泽波田文武王超王昌贝王广威王洪光王惠惠王建成汪凯王利苹王玲玉王培汉王冉王为王祥高王祥玉王阳王玉东王岩谨王忠海王仲翔王振王铮韦大明魏俊杰魏永健文韬吴超勇吴含荣武莎吴雪峰吴雨生席邵强夏捷夏君集项光漫肖迪泫肖刚辛广广辛玉良邢祎熊峥徐东莲徐仁峰徐仁新徐伟立薛良闫大海颜景志颜田杨朝文杨帆杨冯帆杨何文杨佳盈杨莉莉杨明洁杨睿智杨深邦姚玉华姚志国叶一锰尹丽巧尹娜游晓浩游智勇于艳红袁强岳华曾厚敦曾婷轩曾玮查敏张彬彬张丰张海明张恒英张建立张丽霞张力张鹏飞张佩佩张瑞张少博张少如张寿山张潇张笑鹏张云峰张毅张勇赵兵赵静赵雷赵立志赵世平郑福周斌周浩周佳能周猛周平周荣周勋秀祝成光祝凤荣朱辉朱科军左雄 LHAASO Collaboration Key Laboratory of Particle Astrophyics&Experimental Physics Division&Computing Center Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China University of Chinese Academy of Sciences Beijing 100049China Tianfu Cosmic Ray Research Center Chengdu 610000China Dublin Institute for Advanced Studies 31 Fitzwilliam Place2 DublinIreland Max-Planck-Institut for Nuclear Physics P.O.Box 10398069029 HeidelbergGermany State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China Hefei 230026China School of Physical Science and Technology&School of Information Science and Technology Southwest Jiaotong UniversityChengdu 610031China School of Astronomy and Space Science Nanjing UniversityNanjing 210023China Center for Astrophysics Guangzhou UniversityGuangzhou 510006China Hebei Normal University Shijiazhuang 050024China Key Laboratory of Dark Matter and Space Astronomy&Key Laboratory of Radio Astronomy Purple Mountain ObservatoryChinese Academy of SciencesNanjing 210023China Tsung-Dao Lee Institute&School of Physics and Astronomy Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical ObservatoryChinese Academy of SciencesShanghai 200030China Key Laboratory of Cosmic Rays(Tibet University) Ministry of EducationLhasa 850000TibetChina National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China School of Physics and Astronomy(Zhuhai)&School of Physics(Guangzhou)&Sino-French Institute of Nuclear Engineering and Technology(Zhuhai) Sun Yat-sen UniversityZhuhai 519000&Guangzhou 510275China School of Physics and Astronomy Yunnan UniversityKunming 650091China Département de Physique Nucléaire et Corpusculaire Facultéde SciencesUniversitéde Genève24 Quai Ernest Ansermet1211 GenevaSwitzerland Institute of Frontier and Interdisciplinary Science Shandong UniversityQingdao 266237China APC Universit'e Paris Cit'eCNRS/IN2P3CEA/IRFUObservat

The full array of the Large High Altitude Air Shower Observatory(LHAASO)has been in operation since July *** its kilometer-square array(KM2A),we optimized the selection criteria for very high and ultrahigh energyγ-rays using data collected from August 2021 to August 2022,resulting in an improvement in significance of the detection in the Crab Nebula of approximately 15%,compared with that of previous *** the implementation of these new selection criteria,the angular resolution was also significantly improved by approximately 10%at tens of *** aspects of the full KM2A array performance,such as the pointing error,were also calibrated using the Crab *** resulting energy spectrum of the Crab Nebula in the energy range of 10-1000 TeV are well fitted by a log-parabola model,which is consistent with the previous results from LHAASO and other experiments.

关键词： γ-ray Crab Nebula significance

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Opcode sequence analysis of Android malware by a convolutional neural network

Opcode sequence analysis of Android malware by a convolution...

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作者： Li, Dan Zhao, Lichao Cheng, Qingfeng Lu, Ning Shi, Wenbo School of Computer Science and Engineering Northeastern University Shenyang China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China School of Computer Science and Technology Xidian University Xi'an China School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao China

The number of malware has exploded due to the openness of the Android platform, and the endless stream of malware poses a threat to the privacy, tariffs, and device of mobile phone users. A novel Android mobile malware detection system is proposed, which employs an optimized deep convolutional neural network to learn from opcode sequences. The optimized convolutional neural network is trained multiple times by the raw opcode sequences extracted from the decompiled Android file, so that the feature information can be effectively learned and the malicious program can be detected more accurately. More critically, the k-max pooling method with better results is adopted in the pooling operation phase, which improves the detection effect of the proposed method. The experimental results show that the detection system achieved the accuracy of 99%, which is 2%-11% higher than the accuracy of the machine learning detection algorithms when using the same data set. It also ensures that the indicators, such as F1-score, recall, and precision, are maintained above 97%. Based on the detection system, a multi–data set comparison experiment is carried out. The introduced k-max pooling is deeply studied, and the effect of k of k-max pooling on the overall detection effect is observed. © 2019 John Wiley & Sons, Ltd.

关键词： Android malware

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DULDA: Dual-domain Unsupervised Learned Descent Algorithm for PET image reconstruction

arXiv

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arXiv 2023年

作者： Hu, Rui Chen, Yunmen Kim, Kyungsang Rockenbach, Marcio Aloisio Bezerra Cavalcanti Li, Quanzheng Liu, Huafeng State Key Laboratory of Modern Optical Instrumentation Department of Optical Engineering Zhejiang University Hangzhou310027 China Department of Mathematics University of Florida GainesvilleFL32611 United States The Center for Advanced Medical Computing and Analysis Massachusetts General Hospital Harvard Medical School BostonMA02114 United States Center for Clinical Data Science Massachusetts General Brigham BostonMA02116 United States

Deep learning based PET image reconstruction methods have achieved promising results recently. However, most of these methods follow a supervised learning paradigm, which rely heavily on the availability of high-quality training labels. In particular, the long scanning time required and high radiation exposure associated with PET scans make obtaining these labels impractical. In this paper, we propose a dual-domain unsupervised PET image reconstruction method based on learned descent algorithm, which reconstructs high-quality PET images from sinograms without the need for image labels. Specifically, we unroll the proximal gradient method with a learnable l2,1 norm for PET image reconstruction problem. The training is unsupervised, using measurement domain loss based on deep image prior as well as image domain loss based on rotation equivariance property. The experimental results demonstrate the superior performance of proposed method compared with maximum-likelihood expectation–maximization (MLEM), total-variation regularized EM (EM-TV) and deep image prior based method (DIP). Copyright © 2023, The Authors. All rights reserved.

关键词： Image reconstruction

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Gender-Based Deep Learning Firefly Optimization Method for Test Data Generation

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Computational Intelligence and Neuroscience 2021年 2021卷 8056225-8056225页

作者： Zhang, Wenning Jiao, Chongyang Zhou, Qinglei Liu, Yang Xu, Ting State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan450000 China Software College Zhongyuan University of Technology Zhengzhou Henan450000 China School of Information Engineering Zhengzhou University Zhengzhou Henan450000 China The Jackson Laboratory for Genomic Medicine FarmingtonCT06032 United States

Software testing is a widespread validation means of software quality assurance in industry. Intelligent optimization algorithms have been proved to be an effective way of automatic test data generation. Firefly algorithm has received extensive attention and been widely used to solve optimization problems because of less parameters and simple implement. To overcome slow convergence rate and low accuracy of the firefly algorithm, a novel firefly algorithm with deep learning is proposed to generate structural test data. Initially, the population is divided into male subgroup and female subgroup. Following the randomly attracted model, each male firefly will be attracted by another randomly selected female firefly to focus on global search in whole space. Each female firefly implements local search under the leadership of the general center firefly, constructed based on historical experience with deep learning. At the final period of searching, chaos search is conducted near the best firefly to improve search accuracy. Simulation results show that the proposed algorithm can achieve better performance in terms of success coverage rate, coverage time, and diversity of solutions. © 2021 Wenning Zhang et al.

关键词： Software testing

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Pushing AI to wireless network edge: An overview on integrated sensing, communication, and computation towards 6G

arXiv

引用

arXiv 2022年

作者： Zhu, Guangxu Lyu, Zhonghao Jiao, Xiang Liu, Peixi Chen, Mingzhe Xu, Jie Cui, Shuguang Zhang, Ping Shenzhen Research Institute of Big Data Shenzhen518172 China Shenzhen518172 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables33146 United States State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Peng Cheng Laboratory Shenzhen518066 China

Pushing artificial intelligence (AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things (AIoT) in the sixth-generation (6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the huge amount of data scattered at wireless network edge. In general, realizing edge intelligence corresponds to the process of sensing, communication, and computation, which are coupled ingredients for data generation, exchanging, and processing, respectively. However, conventional wireless networks design the sensing, communication, and computation separately in a task-agnostic manner, which encounters difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications such as auto-driving. This thus prompts a new design paradigm of seamless integrated sensing, communication, and computation (ISCC) in a task-oriented manner, which comprehensively accounts for the use of the data in the downstream AI applications. In view of its growing interest, this article provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art development, and shedding light on the road ahead. Copyright © 2022, The Authors. All rights reserved.

关键词： Artificial intelligence

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Origin Pilot: A quantum operating system for effecient usage of quantum resources

arXiv

引用

arXiv 2021年

作者： Kong, Weicheng Wang, Junchao Han, Yongjian Wu, Yuchun Zhang, Yu Dou, Menghan Fang, Yuan Guo, Guoping Origin Quantum Computing Company Limited Hefei230026 China CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei230026 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450002 China School of Computer Science and Technology University of Science and Technology of China Hefei230027 China

The operating system is designed to manage the hardware and software resources of a computer. With the development of quantum computing, the management of quantum resources and cooperation between quantum systems and other computing resources (e.g. CPU, GPU and FPGA etc.) become the key challenge for the application of quantum computing to solve real world problems. In this paper we propose a quantum operating system, Origin Pilot. Origin Pilot includes the module of quantum task scheduling, quantum resource management, quantum program compilation and qubits' automatic calibration. With these modules, Origin Pilot can manage the quantum computing resources and solve the multi-quantum processor scheduling problem. It can also allow the parallel execution of multiple quantum programs and calibrate the quantum resource effectively. Thus, the performance of resources is guaranteed and the resource utilization is improved. By comparing the results with and without Origin Pilot, we evaluate the impact on a quantum circuit's fidelity of qubits mapping algorithm. We also evaluate the effectiveness of automatic calibration and parallel execution of multi-quantum processors. Finally, Origin Pilot can be easily customized for hybrid computing resources. © 2021, CC BY-NC-SA.

关键词： Scheduling

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