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IEEE International Conference on Big Data

作者： Hao Gao Yongqing Wang Jiangli Shao Huawei Shen Xueqi Cheng Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665445993

User identity linkage aims to link users with the same identities across different social networks. Recently, re- searchers model the similarities of users’ behaviors such as Point of Interests(PoIs) or User Generated Contents(UGCs) to predict the identities of users. However, it is non-trivial to solve the problem due to the following challenges: 1) PoIs are always sparse in the non-location-based social platforms, and it is impractical to measure the similarities of users solely with PoIs; 2) The similarities of hierarchical are hierarchical from the view of word, phrase, and sentence. How to model the hierarchical structure remains a key challenge; 3) The unreliable semantics of words. Two different words may refer to the same physical appearance of users, indicating that users are with the same *** tackle the above problems, we propose UGCLink, a knowledge distillation framework that models UGCs to predict user identities. Two main components are included in the framework, where the student network aims to model the similarities of UGCs and the teacher network guides the student network to learn better word embeddings that reveal the physical appearance of users. Besides, the teacher network, a document classification model that classifies UGCs into the categories of PoIs, is trained to guide the word embedding learning process in the student network to circumvent the unreliable semantic problem. We demonstrate that our proposed method outperforms the state- of-the-art methods by more than 11% in terms of AUC score.

关键词： Couplings Social networking (online) Conferences Semantics User-generated content Predictive models Big Data

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Decision-based black-box attack against vision transformers via patch-wise adversarial removal 22

Decision-based black-box attack against vision transformers ...

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Proceedings of the 36th International Conference on Neural Information Processing systems

作者： Yucheng Shi Yahong Han Yu-an Tan Xiaohui Kuang College of Intelligence and Computing and Tianjin Key Lab of Machine Learning Tianjin University Tianjin China and Engineering Research Center of City Intelligence and Digital Governance Ministry of Education of the People's Republic of China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China National Key Laboratory of Science and Technology on Information System Security Beijing China

ISBN: (纸本)9781713871088

Vision transformers (ViTs) have demonstrated impressive performance and stronger adversarial robustness compared to Convolutional Neural Networks (CNNs). On the one hand, ViTs' focus on global interaction between individual patches reduces the local noise sensitivity of images. On the other hand, the neglect of noise sensitivity differences between image regions by existing decision-based attacks further compromises the efficiency of noise compression, especially for ViTs. Therefore, validating the black-box adversarial robustness of ViTs when the target model can only be queried still remains a challenging problem. In this paper, we theoretically analyze the limitations of existing decision-based attacks from the perspective of noise sensitivity difference between regions of the image, and propose a new decision-based black-box attack against ViTs, termed Patch-wise Adversarial Removal (PAR). PAR divides images into patches through a coarse-to-fine search process and compresses the noise on each patch separately. PAR records the noise magnitude and noise sensitivity of each patch and selects the patch with the highest query value for noise compression. In addition, PAR can be used as a noise initialization method for other decision-based attacks to improve the noise compression efficiency on both ViTs and CNNs without introducing additional calculations. Extensive experiments on three datasets demonstrate that PAR achieves a much lower noise magnitude with the same number of queries.

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iGniter: Interference-Aware GPU Resource Provisioning for Predictable DNN Inference in the Cloud

arXiv

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arXiv 2022年

作者： Xu, Fei Xu, Jianian Chen, Jiabin Chen, Li Shang, Ruitao Zhou, Zhi Liu, Fangming The Shanghai Key Laboratory of Multidimensional Information Processing School of Computer Science and Technology East China Normal University 3663 N. Zhongshan Road Shanghai200062 China The School of Computing and Informatics University of Louisiana at Lafayette 301 East Lewis Street LafayetteLA70504 United States The Guangdong Key Laboratory of Big Data Analysis and Processing School of Computer Science and Engineering Sun Yat-sen University 132 E. Waihuan Road Guangzhou510006 China The National Engineering Research Center for Big Data Technology and System The Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology 1037 Luoyu Road Wuhan430074 China

GPUs are essential to accelerating the latency-sensitive deep neural network (DNN) inference workloads in cloud datacenters. To fully utilize GPU resources, spatial sharing of GPUs among co-located DNN inference workloads becomes increasingly compelling. However, GPU sharing inevitably brings severe performance interference among co-located inference workloads, as motivated by an empirical measurement study of DNN inference on EC2 GPU instances. While existing works on guaranteeing inference performance service level objectives (SLOs) focus on either temporal sharing of GPUs or reactive GPU resource scaling and inference migration techniques, how to proactively mitigate such severe performance interference has received comparatively little attention. In this paper, we propose iGniter, an interference-aware GPU resource provisioning framework for cost-efficiently achieving predictable DNN inference in the cloud. iGniter is comprised of two key components: (1) a lightweight DNN inference performance model, which leverages the system and workload metrics that are practically accessible to capture the performance interference;(2) A cost-efficient GPU resource provisioning strategy that jointly optimizes the GPU resource allocation and adaptive batching based on our inference performance model, with the aim of achieving predictable performance of DNN inference workloads. We implement a prototype of iGniter based on the NVIDIA Triton inference server hosted on EC2 GPU instances. Extensive prototype experiments on four representative DNN models and datasets demonstrate that iGniter can guarantee the performance SLOs of DNN inference workloads with practically acceptable runtime overhead, while saving the monetary cost by up to 25% in comparison to the state-of-the-art GPU resource provisioning strategies. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Hierarchical Learning for IRS-Assisted MEC systems with Rate-Splitting Multiple Access

arXiv

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arXiv 2024年

作者： Wu, Yinyu Zhang, Xuhui Ren, Jinke Shen, Yanyan Yang, Bo Wang, Shuqiang Guan, Xinping Niyato, Dusit The Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong518055 China The University of Chinese Academy of Sciences Beijing100049 China The Shenzhen Future Network of Intelligence Institute The School of Science and Engineering The Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Guangdong Shenzhen518172 China Shenzhen University of Advanced Technology Guangdong518055 China The Department of Automation The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

Intelligent reflecting surface (IRS)-assisted mobile edge computing (MEC) systems have shown notable improvements in efficiency, such as reduced latency, higher data rates, and better energy efficiency. However, the resource competition among users will lead to uneven allocation, increased latency, and lower throughput. Fortunately, the rate-splitting multiple access (RSMA) technique has emerged as a promising solution for managing interference and optimizing resource allocation in MEC systems. This paper studies an IRS-assisted MEC system with RSMA, aiming to jointly optimize the passive beamforming of the IRS, the active beamforming of the base station, the task offloading allocation, the transmit power of users, the ratios of public and private information allocation, and the decoding order of the RSMA to minimize the average delay from a novel uplink transmission perspective. Since the formulated problem is non-convex and the optimization variables are highly coupled, we propose a hierarchical deep reinforcement learning-based algorithm to optimize both continuous and discrete variables of the problem. Additionally, to better extract channel features, we design a novel network architecture within the policy and evaluation networks of the proposed algorithm, combining convolutional neural networks and densely connected convolutional network for feature extraction. Simulation results indicate that the proposed algorithm not only exhibits excellent convergence performance but also outperforms various benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Efficient thermal dissipation in wafer-scale heterogeneous integration of single-crystalline𝛽β-Ga_(2)O_(3)thin film on SiC

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Fundamental Research 2021年第6期1卷 691-696页

作者： Wenhui Xu Tiangui You Yibo Wang Zhenghao Shen Kang Liu Lianghui Zhang Huarui Sun Ruijie Qian Zhenghua An Fengwen Mu Tadatomo Suga Genquan Han Xin Ou Yue Hao Xi Wang State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200050China The State Key Discipline Laboratory of Wide Band Gap Semiconductor Technology School of MicroelectronicsXidian UniversityXi’an 710071China School of Science and Ministry of Industry and Information Technology Key Laboratory of Micro-Nano Optoelectronic Information System Harbin Institute of TechnologyShenzhen 518055China Department of Physics State Key Laboratory of Surface PhysicsInstitute of Nanoelectronic Devices and Quantum ComputingFudan UniversityShanghai 200433China High-Frequency High-Voltage Device and Integrated Circuits R&D Center Institute of MicroelectronicsChinese Academy of SciencesBeijing 100029China Collaborative Research Center Meisei UniversityHinoJapan Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China

The semiconductor,β-Ga_(2)O_(3)is attractive for applications in high power electronic devices with low conduction loss due to its ultra-wide bandgap(∼4.9 eV)and large Baliga’s figure of ***,the thermal conductivity of𝛽β-Ga_(2)O_(3)is much lower than that of other wide/ultra-wide bandgap semiconductors,such as SiC and GaN,which results in the deterioration of𝛽β-Ga_(2)O_(3)-based device performance and reliability due to *** overcome this problem,a scalable thermal management strategy was proposed by heterogeneously integrating wafer-scale single-crystalline𝛽β-Ga_(2)O_(3)thin films on a highly thermally conductive SiC *** of the transferred𝛽β-Ga_(2)O_(3)thin film indicated a uniform thickness to within±2.01%,a smooth surface with a roughness of 0.2 nm,and good crystalline quality with an X-ray rocking curves(XRC)full width at half maximum of 80 *** thermoreflectance measurements were employed to investigate the thermal *** thermal performance of the fabricated𝛽β-Ga_(2)O_(3)/SiC heterostructure was effectively improved in comparison with that of the𝛽β-Ga_(2)O_(3)bulk wafer,and the effective thermal boundary resistance could be further reduced to 7.5 m 2 K/GW by a post-annealing *** barrier diodes(SBDs)were fabricated on both a𝛽β-Ga_(2)O_(3)/SiC heterostructured material and a𝛽β-Ga_(2)O_(3)bulk *** thermal imaging revealed the temperature increase of the SBDs on𝛽β-Ga_(2)O_(3)/SiC to be one quarter that on the𝛽β-Ga_(2)O_(3)bulk wafer with the same applied power,which suggests that the combination of the𝛽-Ga_(2)O_(3)thin film and SiC substrate with high thermal conductivity promotes heat dissipation in𝛽β-Ga_(2)O_(3)-based devices.

关键词： Thermal management Heterogeneous integration Wafer scale𝛽β-Ga_(2)O_(3)on SiC Ion-cutting technique Schottky barrier diodes(SBDs) Transient thermoreflectance(TTR) measurements

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Modeling analysis and comparision of neural network simulation based on ECM and LSTM 4

Modeling analysis and comparision of neural network simulati...

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2021 4th International Conference on Applied Mathematics, Modeling and Simulation, AMMS 2021

作者： Duan, Lingyun Liu, Ziyuan Yu, Wen Chen, Wei Jin, Dongyan Li, Denghua Sun, Suhua Dai, Ruixi Agricultural Information Institute Chinese Academy of Agricultural Sciences Beijing100081 China Key Laboratory of Agricultural Information Service Technology Ministry of Agriculture and Rural Affairs Beijing100081 China Key Laboratory of Intelligent Agricultural Early Warning Technology and System Chinese Academy of Agricultural Sciences Beijing100081 China Beijing Engineering Research Center for Agricultural Monitoring and Early Warning Beijing100081 China Graduate School of Chinese Academy of Agricultural Sciences Beijing100081 China

Comparing the prediction effects of traditional econometric algorithm model and deep learning algorithm model, taking regional GDP as an example, two prediction models of ARMA-ECM and LSTM-SVR are established for prediction, and the prediction results of different models are compared and analyzed. The results show that there are some deviations in the prediction results of the two models, but the prediction trends are the same. The prediction accuracy of LSTM-SVR model will decrease significantly with the reduction of time series data samples, while ARMA-ECM model is not so sensitive. © Journal of Physics: Conference Series 2021.

关键词： Forecasting

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Reconfigurable logic circuits and rectifier based on two-terminal ionic homojunctions

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Device 2025年第5期3卷

作者： Zhong, Zhipeng Cheng, Xin Song, Wenqing Yang, Qianyi Li, Xiang Wang, Yuyang Wang, Wan Zhuang, Yezhao Chen, Yan Shi, Wu Lin, Tie Meng, Xiangjian Huang, Hai Wang, Jianlu Chu, Junhao State Key Laboratory of Photovoltaic Science and Technology Shanghai Frontiers Science Research Base of Intelligent Optoelectronic and Perception Institute of Optoelectronics and Department of Material Science Fudan University Shanghai 200433 China Zhangjiang Fudan International Innovation Center Fudan University Shanghai 201210 China State Key Laboratory of Surface Physics and Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai 200433 China State Key Laboratory of Infrared Physics Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai 200083 China Frontier Institute of Chip and System Fudan University Shanghai 200433 China

Reconfigurable logic devices that can perform multiple functions with fewer components offer a solution for increasing data processing demands. Achieving reconfigurability without complex designs, however, remains challenging. In this study, we present a two-terminal reconfigurable homojunction device based on ionic-conducting CuInP₂S₆ (CIPS). The device achieves tunable and reversible rectification by controlling Cu migration under an electric field. The homojunction is formed by modulating the contact potential of the CIPS channel, resulting in an asymmetric contact barrier. The resulting device has a rectification ratio of up to 4 × 10⁴, an ultra-low leakage current (∼100 fA), and a high breakdown voltage of 170 V, maintaining read stability over 3,000 cycles. It operates at frequencies up to 10 kHz and can be reconfigured as OR and AND logic states depending on the Cu migration direction of the CIPS homojunction. © 2025 The Author(s)

关键词： copper indium thiophosphate DTI-2: Explore ionic conductor photovoltaic reconfigurable homojunction

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Contrasting lake changes in Tibet revealed by recent multi-modal satellite observations

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Science of the Total Environment 2024年 908卷 168342页

作者： Ran, Jiangjun Liu, Lin Zhang, Guoqing Shum, C.K. Qiu, Jiahui Hu, Ruigang Li, Jianping Peng, Junhuan Hwang, Cheinway Luan, Yi Sun, Yue Xu, Min Chen, Dingmei Ding, Jun Zhong, Yulong Department of Earth and Space Sciences Southern University of Science and Technology Shenzhen China Earth System Science Programme Faculty of Science The Chinese University of Hong Kong Hong Kong Institute of Tibetan Plateau Research Chinese Academy of Sciences Beijing China Division of Geodetic Science School of Earth Sciences Ohio State University ColumbusOH United States State Key Laboratory of Geodesy and Earth's Dynamics Innovation Academy for Precision Measurement Science and Technology Chinese Academy of Sciences Wuhan China Key Laboratory of Physical Oceanography Academy of the Future Ocean College of Oceanic and Atmospheric Science Ocean University of China Qingdao China Laboratory for Ocean Dynamics and Climate Pilot Qingdao National Laboratory for Marine Science and Technology Qingdao China School of Land Science and Technology China University of Geosciences Beijing China Department of Civil Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan State Key Laboratory of Cryospheric Science Northwest Institute of Eco-Environment and Resources Chinese Academy of Sciences Lanzhou China Shannan Meteorological Service of Tibet Shannan China Wuhan China

The limited anthropogenic activities on the Tibetan Plateau make this an ideal natural laboratory to elucidate how climate change impacts lake changes. Previous studies have mainly focused on decadal lake changes, yet their rapid evolutions at short temporal intervals and the associated atmospheric origins remain elusive. Here, we produce a new lake area change dataset at monthly sampling over 2015–2020 from 16,801 satellite images. Our estimates achieve an accuracy of © 2023

关键词： Lakes

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Bridge the vision gap from field to command: A deep learning network enhancing illumination and details

arXiv

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arXiv 2021年

作者： Jiang, Zhuqing Liu, Chang Wang, Ya'nan Li, Kai Men, Aidong Wang, Haiying Luo, Haiyong Beijing University of Posts and Telecommunications China Beijing Key Laboratory of Network System and Network Culture China Institute of Computing Technology Chinese Academy of Sciences China

With the goal of tuning up the brightness, low-light image enhancement enjoys numerous applications, such as surveillance, remote sensing and computational photography. Images captured under low-light conditions often suffer from poor visibility and blur. Solely brightening the dark regions will inevitably amplify the blur, thus may lead to detail loss. In this paper, we propose a simple yet effective two-stream framework named NEID to tune up the brightness and enhance the details simultaneously without introducing many computational costs. Precisely, the proposed method consists of three parts: Light Enhancement (LE), Detail Refinement (DR) and Feature Fusing (FF) module, which can aggregate composite features oriented to multiple tasks based on channel attention mechanism. Extensive experiments conducted on several benchmark datasets demonstrate the efficacy of our method and its superiority over state-of-the-art methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Luminance

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DSP Assembler Auto-Generation Technique and ToolChain Integration

DSP Assembler Auto-Generation Technique and ToolChain Integr...

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International Conference on Communication technology (ICCT)

作者： Wanli Wang Jun Wu Haoqi Ren Zhifeng Zhang Electronics and Information Engineering College Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China School of Computer Science Fudan University Shanghai China

ISBN: (纸本)9781665432078

To promote the application of DSP, a fast and accurate toolchain generation method must be realized. Unlike the traditional manual method, in this paper, we propose a toolchain generation algorithm based on the architecture description language, and migrate the toolchain to adapt to the specified processor architecture. The algorithm uses the DSP architecture description as input to generate intermediate expressions, then uses lexical analysis and syntax analysis tools to migrate the assembly toolchain. In addition, an integrated development environment is developed based on Eclipse, which creates a friendly human-computer interface. Experiments show that this toolchain generation algorithm runs fast and accurately, which solves problems of the development and application of the DSP toolchain, and creates favorable conditions for the promotion of DSP.

关键词： Human computer interaction Microprocessors Conferences Manuals Syntactics Libraries Communications technology

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