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AdaptHM: A Fully Adaptive data Migration Strategy for Hybrid Memory systems

IEEE Transactions on Computer-Aided Design of Integrated Circuits and systems 2025年第3期44卷 923-936页

作者： Peng, Zhouxuan Feng, Dan Chen, Jianxi Hu, Jing Liu, Yachun Hu, Jinlei Zhang, Jintong Wan, Tianyu Chen, Zuoning Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology MoE of China Wuhan430074 China Chinese Academy of Engineering Beijing100088 China

data migration strategies (DMSs) improve the overall performance of hybrid memory systems by migrating frequently accessed (hot) data to faster memory. However, designing an efficient DMS is challenging since the key metrics of DMS - hot data selection, migration granularity, and migration frequency - are sensitive to access patterns of workloads. Most existing strategies focus on only one of these metrics and often overlook the crucial impact of access patterns, resulting in suboptimal performance and unnecessary migration traffic. In this article, we propose AdaptHM, a fully access-pattern-aware Adaptive DMS for Hybrid Memory systems. AdaptHM achieves adaptability on all three metrics through its unique multilevel data framework. First, AdaptHM adopts a group-level competition policy to select hot blocks, which responds faster to access patterns than threshold-based policies. Second, AdaptHM enables segment-level dynamic migration granularity by decoupling migration from remapping, which shows better-access pattern resilience than existing schemes with fixed-size global migration granularity. Third, AdaptHM adjusts the migration frequency at set-level by periodically assessing the migration benefit, avoiding unnecessary migrations. Experimental results demonstrate that AdaptHM improves the performance by an average of 12.78% and reduces energy consumption by up to 37.24% compared to the state-of-the-art scheme. © 1982-2012 IEEE.

关键词： Frequency modulation

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Continuously Controllable Facial Expression Editing in Talking Face Videos

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IEEE TRANSACTIONS ON AFFECTIVE COMPUTING 2024年第3期15卷 1400-1413页

作者： Sun, Zhiyao Wen, Yu-Hui Lv, Tian Sun, Yanan Zhang, Ziyang Wang, Yaoyuan Liu, Yong-Jin Tsinghua Univ Dept Comp Sci & Technol MOE Key Lab Pervas Comp Beijing 100084 Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing Key Lab Traff Data Anal & Min Beijing 100044 Peoples R China Huawei Technol Co Ltd Adv Comp & Storage Lab Shenzhen 518129 Peoples R China

Recently audio-driven talking face video generation has attracted considerable attention. However, very few researches address the issue of emotional editing of these talking face videos with continuously controllable expressions, which is a strong demand in the industry. The challenge is that speech-related expressions and emotion-related expressions are often highly coupled. Meanwhile, traditional image-to-image translation methods cannot work well in our application due to the coupling of expressions with other attributes such as poses, i.e., translating the expression of the character in each frame may simultaneously change the head pose due to the bias of the training data distribution. In this paper, we propose a high-quality facial expression editing method for talking face videos, allowing the user to control the target emotion in the edited video continuously. We present a new perspective for this task as a special case of motion information editing, where we use a 3DMM to capture major facial movements and an associated texture map modeled by a StyleGAN to capture appearance details. Both representations (3DMM and texture map) contain emotional information and can be continuously modified by neural networks and easily smoothed by averaging in coefficient/latent spaces, making our method simple yet effective. We also introduce a mouth shape preservation loss to control the trade-off between lip synchronization and the degree of exaggeration of the edited expression. Extensive experiments and a user study show that our method achieves state-of-the-art performance across various evaluation criteria.

关键词： Faces Videos Shape Three-dimensional displays Lips Image reconstruction Solid modeling Continuously controllable expressions facial expression editing talking face video generation

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Perovskite photodetector-based single pixel color camera for artificial vision

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Light(Science & Applications) 2023年第4期12卷 509-511页

作者： Chaohao Chen Ziyuan Lie Lan Fu School of Electrical and Data Engineering Faculty of Engineering and Information TechnologyThe University of Technology SydneySydneyNSW 2007Australia Australlian Research Council Centre of Excellence for Transformative Meta-Optical Systems Department of Electronic Materials EngineeringResearch School of PhysicsThe Australian National UniversityCanberraACT 2600Australia MoE Key Lab of Photoelectronic Imaging Technology and System School of Optics and PhotonicsBeijing Institute of TechnologyBeijing 100081China

Narrowband red,green,blue self-filtering perovskite photodetectors and a broadband white photodetector are incorporated into a single pixel imaging camera to mimic the long-,medium-,and short-wavelength cone cells and... 详细信息

关键词： artificial pixel filtering

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Nanoscale phase management of the 2D/3D heterostructure toward efficient perovskite solar cells

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Science Bulletin 2024年第18期69卷 2853-2861页

作者： Hao Gu Annan Zhu Junmin Xia Wang Li Jiahao Zheng Tao Yang Shengwen Li Nan Zhang Shiliang Mei Yongqing Cai Shi Chen Chao Liang Guichuan Xing Joint Key Laboratory of the Ministry of Education Institute of Applied Physics and Materials EngineeringUniversity of MacaoMacao 999078China State Key Laboratory of Organic Electronics and Information Displays&Institute of Advanced Materials(IAM) Jiangsu National Synergetic Innovation Center for Advanced Materials(SICAM)Nanjing University of Posts&Telecommunications(NUPT)Nanjing 210023China MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter School of PhysicsNational Innovation Platform(Center)for Industry-Education Integration of Energy Storage TechnologyXi’an Jiaotong UniversityXi’an 710049China Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic SystemsHarbin Institute of TechnologyShenzhen 518055China Institute for Electric Light Sources School of Information Science and TechnologyFudan UniversityShanghai 200433China

The stabilization of the formamidinium lead iodide(FAPbI_(3))structure is pivotal for the development of efficient photovoltaic *** two-dimensional(2D)layers to passivate the threedimensional(3D)perovskite is essential for maintaining the a-phase of FAPbI_(3) and enhancing the power conversion efficiency(PCE)of perovskite solar cells(PSCs).However,the role of bulky ligands in the phase management of 2D perovskites,crucial for the stabilization of FAPbI_(3),has not yet been *** this study,we synthesized nanoscale 2D perovskite capping crusts with=1 and 2 Ruddlesden-Popper(RP)perovskite layers,respectively,which form a type-Ⅱ 2D/3D *** heterostructure stabilizes the a-phase of FAPbI_(3),and facilitates ultrafast carrier extraction from the 3D perovskite network to transport contact *** introduced tri-fluorinated ligands to mitigate defects caused by the halide vacancies and uncoordinated Pb^(2+)ions,thereby reducing nonradiative carrier recombination and extending carrier *** films produced were incorporated into PSCs that not only achieved a PCE of 25.39%but also maintained 95%of their initial efficiency after 2000 h of continuous light exposure without *** findings underscore the effectiveness of a phase-pure 2D/3D heterostructure-terminated film in inhibiting phase transitions passivating the iodide anion vacancy defects,facilitating the charge carrier extraction,and boosting the performance of optoelectronic devices.

关键词： Phase-pure 2D perovskite 2D/3D heterostructure Type-II energy alignment Carrier dynamic FAPbI_(3)

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Facilitating database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation 48th

Facilitating Database Tuning with Hyper-Parameter Optimizati...

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48th International Conference on Very Large databases (VLDB)

作者： Zhang, Xinyi Chang, Zhuo Li, Yang Wu, Hong Tan, Jian Li, Feifei Cui, Bin Peking Univ Sch CS Key Lab High Confidence Software Technol MOE Beijing Peoples R China Damo Acad Database & Storage Lab Alibaba Grp Hangzhou Peoples R China Peking Univ Ctr Data Sci Beijing Peoples R China Peking Univ Qingdao Inst Computat Social Sci Qingdao Peoples R China

Recently, using automatic configuration tuning to improve the performance of modern database management systems (DBMSs) has attracted increasing interest from the database community. This is embodied with a number of systems featuring advanced tuning capabilities being developed. However, it remains a challenge to select the best solution for database configuration tuning, considering the large body of algorithm choices. In addition, beyond the applications on database systems, we could find more potential algorithms designed for configuration tuning. To this end, this paper provides a comprehensive evaluation of configuration tuning techniques from a broader perspective, hoping to better benefit the database community. In particular, we summarize three key modules of database configuration tuning systems and conduct extensive ablation studies using various challenging cases. Our evaluation demonstrates that the hyper-parameter optimization algorithms can be borrowed to further enhance the database configuration tuning. Moreover, we identify the best algorithm choices for different modules. Beyond the comprehensive evaluations, we offer an efficient and unified database configuration tuning benchmark via surrogates that reduces the evaluation cost to a minimum, allowing for extensive runs and analysis of new techniques.

关键词： database systems

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A Universal data-Driven Spatial Interpolation Framework for the Measurements of Environmental Sites

SSRN

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SSRN 2023年

作者： Cheng, Zhen Guo, Yuzhe Xue, Wenbo Du, Qinyi Shanghai Environmental Protection Key Lab of Environmental Big Data and Intelligent Decision-making School of Environmental Science and Engineering Shanghai Jiao Tong University Shanghai China Center of Air Quality Simulation and System Analysis Chinese Academy of Environmental Planning Beijing100043 China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China

The accurate spatial distribution of environmental pollutant metrics is crucial for evaluating human exposure and managing the environment. Spatial interpolation, which relies on the measurements of limited environmental sites, is often necessary. However, commonly used geostatistical methods often introduce significant prediction biases, leading to misleading assessments and decision-making processes. Reported machine learning studies usually required intensive spatial datasets and sites’ measurements, as well as special complex algorithm for target pollutant. In this study, we propose and validate a simple and universal data-driven spatial interpolation framework for environmental sites’ measurements. We employ a conditional generative adversarial network (cGAN) as the data-driven algorithm to leverage spatial dataset. We demonstrate the implementation and validation of the framework using two representative ambient pollutants, namely ozone and PM2.5. The results reveal a relative mean absolute error of only 9% for ozone and 13% for PM2.5. Compared to the traditional inverse distance weight method and ordinary kriging method, the proposed framework reduces errors by 70% and 79% respectively. Furthermore, the R-squared values between independent measurements from additional geographic locations and the corresponding predicted results using the proposed method reach 0.96 for ozone and 0.97 for PM2.5, showcasing its excellent and consistent performance. The unique input dataset and universal algorithm architecture illustrate the distinct advantages compared to previous machine learning studies. With the rapid increase of remote sensing and numerical modeling datasets in the environmental field, we anticipate that data-driven methods will replace traditional mathematical approaches. This study provides a simple yet universal interpolation framework and is expected to accelerate the adoption of these advancements. © 2023, The Authors. All rights reserved.

关键词： Spatial distribution

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Continuously Controllable Facial Expression Editing in Talking Face Videos

arXiv

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arXiv 2022年

作者： Sun, Zhiyao Wen, Yu-Hui Lv, Tian Sun, Yanan Zhang, Ziyang Wang, Yaoyuan Liu, Yong-Jin BNRist the Department of Computer Science and Technology MOE-Key Laboratory of Pervasive Computing Tsinghua University Beijing100084 China Beijing Key Laboratory of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Advanced Computing and Storage Lab Huawei Technologies Company Ltd. Shenzhen518129 China

关键词： Quality control

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GradSA: Gradient Sparsification and Accumulation for Communication-Efficient Distributed Deep Learning 15th

GradSA: Gradient Sparsification and Accumulation for Communi...

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15th International Conference on Green, Pervasive, and Cloud Computing, GPC 2020

作者： Liu, Bo Jiang, Wenbin Zhao, Shaofeng Jin, Hai He, Bingsheng 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 Wuhan China Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Huazhong University of Science and Technology Wuhan China Library Henan University of Economics and Law Zhengzhou China Department of Computer Science School of Computing National University of Singapore Singapore Singapore

ISBN: (纸本)9783030642426

Large-scale distributed deep learning is of great importance in various applications. For distributed training, the inter-node gradient communication often becomes the performance bottleneck. Gradient sparsification has been proposed to reduce the communication overhead. However, the sparsification only arbitrarily selects a small fraction of gradients, while the efficiency of selection dimension has been overlooked. Furthermore, gradient staleness is inevitable after applying the sparsification which will ultimately lead to model divergence. In this paper, we propose a staleness-compensated sparse stochastic gradient descent algorithm, GradSA, to improve the training efficiency. Layer-level gradients are sparsified to reduce the communication overhead, which conform to the characteristics of the network structure, and historical accumulation of the approximated gradients is utilized to speed up convergence. We demonstrate the model convergence acceleration and the efficiency of our layer-level selection over existing state-of-the-art works, such as DGC, TernGrad, and 8-Bit quantization. © 2020, Springer Nature Switzerland AG.

关键词： Efficiency

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Facilitating database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation

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PROCEEDINGS OF THE VLDB ENDOWMENT 2022年第9期15卷 1808-1821页

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A deep learning system for predicting time to progression of diabetic retinopathy

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NATURE MEDICINE 2024年第2期30卷 358-359页

作者： [Anonymous] Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Ophthalmology Huadong Sanatorium Wuxi China Department of Ophthalmology Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong China Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Department of Ophthalmology Peking Union Medical College Hospital Peking Union Medical College Chinese Academy of Medical Sciences Beijing China Medical Records and Statistics Office Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Geriatrics Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Tech

We developed and validated a deep learning system (termed DeepDR Plus) in a diverse, multiethnic, multi-country dataset to predict personalized risk and time to progression of diabetic retinopathy. We show that DeepDR Plus can be integrated into the clinical workflow to promote individualized intervention strategies for the management of diabetic retinopathy.

关键词： Diabetes complications Machine learning Predictive markers

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