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Black-hole-inspired thermal trapping with graded heat-conduction metadevices

National Science Review 2023年第2期10卷 131-138页

作者： Liujun Xu Jinrong Liu Peng Jin Guoqiang Xu Jiaxin Li Xiaoping Ouyang Ying Li Cheng-Wei Qiu Jiping Huang Department of Physics State Key Laboratory of Surface Physics and Key Laboratory of Micro and Nano Photonic Structures (MOE) Fudan University Department of Electrical and Computer Engineering National University of Singapore Graduate School of China Academy of Engineering Physics School of Materials Science and Engineering Xiangtan University Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical InstrumentationZJU-Hangzhou Global Scientific and Technological Innovation CenterZhejiang University International Joint Innovation Center Key Laboratory of Advanced Micro/Nano Electronic Devices and Smart Systems of Zhejiang The Electromagnetics Academy of Zhejiang University Zhejiang University

The curved space-time produced by black holes leads to the intriguing trapping *** far,metadevices have enabled analogous black holes to trap light or sound in laboratory ***,trapping heat in a conductive environment is still challenging because diffusive behaviors are *** by black holes,we construct graded heat-conduction metadevices to achieve thermal trapping,resorting to the imitated advection produced by graded thermal conductivities rather than the trivial solution of using insulation materials to confine thermal *** experimentally demonstrate thermal trapping for guiding hot spots to diffuse towards the *** heat-conduction metadevices have advantages in energy-efficient thermal regulation because the imitated advection has a similar temperature field effect to the realistic advection that is usually driven by external energy *** results also provide an insight into correlating transformation thermotics with other disciplines,such as cosmology,for emerging heat control schemes.

关键词： thermal trapping graded metadevices imitated advection

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Boundary Control of Coupled Wave Systems with Spatially-Varying Coefficients

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Journal of Systems Science & Complexity 2022年第4期35卷 1310-1329页

作者： FENG Xiaodan ZHANG Zhifei School of Mathematics and Statistics Huazhong University of Science and TechnologyWuhan430074China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and TechnologyWuhan430074China

This paper considers the stabilization of the coupled wave systems with spatially-varying *** authors design a state feedback controller by backstepping *** contrast to the previous work in the literature,the kernel equations become more complicated and the main difficulty lies in proving the existence and uniqueness of the solution to the kernel ***,using the backstepping approach,the authors verify the kernel equations,which is a system of coupled hyperbolic equations with spatially-varying ***,the existence and uniqueness of the kernel matrices is ***,the authors use a Lyapunov function to get the exponential stabilization of the closed-loop system.A numerical example is presented to illustrate the effectiveness of the proposed controller.

关键词： Backstepping boundary control coupled wave systems exponential stabilization spatially-varying coefficients

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CA-Res2UNet++: a Deep Residual UNet Based Method for Brain Tumor Segmentation in Multimodal MRI 2

CA-Res2UNet++: a Deep Residual UNet Based Method for Brain T...

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2nd International Conference on Biomedical and Intelligent Systems, IC-BIS 2023

作者： Hu, Yujiao Chen, Weiting Wu, Wenjing Long, Jianghao MOE Research Center of Software / Hardware Co-Design Engineering Shanghai Key Laboratory of Trustworthy Computing East China Normal University China

ISBN: (纸本)9781510666733

In recent years, segmentation of the multimodal brain tumor image puts forward high requirements for performance. To meet the accuracy requirements, we propose a multimodal brain tumor image segmentation method based on UNet and deep residual learning, which can make full use of multimodal information in order to obtain details. The model achieves the information extraction and integration of multimodal images at different scales by introducing a multiscale feature extraction module, a coordinate attention module, and a dense atrous spatial pyramid pooling module. The results show that the proposed method can fully exploit the multi-scale context information of multimodal images, alleviate the problem of blurred edges in segmentation regions, and achieve good performance on popular evaluation metrics. © 2023 SPIE.

关键词： Magnetic resonance imaging

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Multi-scale Transformation Based Image Representation and Analysis of Amplitude-integrated EEG 2

Multi-scale Transformation Based Image Representation and An...

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2nd International Conference on Biomedical and Intelligent Systems, IC-BIS 2023

作者： Wu, Wenjing Chen, Weiting Hu, Yujiao MOE Research Center of Software / Hardware Co-Design Engineering Shanghai Key Laboratory of Trustworthy Computing East China Normal University China

ISBN: (纸本)9781510666733

Amplitude-integrated electroencephalography (aEEG) is widely adopted for recognizing neonatal neurological disorders in clinics. Previous work has mainly analyzed aEEGs from a time series perspective, while clinicians are more concerned about images. This paper studies the efficacy of image representations in aEEG interpretation. To this end, we employ Amplitude-frequency contour map (AFCM) to express information on local amplitude distributions and overall temporal trend. During its generation, Multi-scale Box-Cox transformation is introduced to flexibly control the degree of amplitude compression and capture details in different amplitude regions. Moreover, one- and two-dimensional features are extracted and combined to enhance model predictions. The experimental results show the potential of image representation for aEEG recognition. This method can provide new ideas to analyze time series signals like aEEGs from the perspective of combining time series and image. © 2023 SPIE.

关键词： Electroencephalography

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GIRP: Energy-Efficient QoS-Oriented Microservice Resource Provisioning via Multi-Objective Multi-Task Reinforcement Learning

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IEEE Transactions on Mobile computing 2025年第7期24卷 5793-5807页

作者： Yuan, Honggang Wang, Ting Fu, Min Shi, Yuanming East China Normal University MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Laboratory of Trustworthy Computing Shanghai200062 China National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore ShanghaiTech University School of Information Science and Technology Shanghai201210 China

Microservice architecture has revolutionized web service development by facilitating loosely coupled and independently developable components distributed as containers or virtual machines. While existing studies emphasize end-to-end latency, this paper investigates energy-efficient quality-of-service (QoS)-oriented microservice provisioning, focusing on both QoS satisfaction and power consumption (PC) conservation. We propose the Green and Intelligent Resource Provision (GIRP) architecture, integrating a data-driven energy-latency-aware resource allocation and scheduling manager to balance latency and PC. To reconcile the trade-offs involved, a dual-objective optimization problem is formulated to minimize latency and energy use by selecting proper servers, allocating CPU cores, and determining service replicas. To address challenges with discrete variables, dual objectives, and implicit mappings, we leverage a model-free deep deterministic policy gradient-based reinforcement learning algorithm. Specifically, we develop a multi-task agent via the Multi-gate Mixture-of-Experts model to simultaneously make two separate actions regarding CPU core numbers and service replica numbers, followed by a single-task agent to determine service scheduling. Extensive experiments on the DeathStarBenchmark testbed validate GIRP's effectiveness, demonstrating approximately 52% resource savings and a 43% reduction in PC compared to leading methods like Sinan, Firm, and heuristic-based algorithms. These results highlight GIRP's capability to optimize microservice orchestration by balancing end-to-end latency and power efficiency. © 2025 IEEE. All rights reserved.

关键词： Quality of service

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Byzantine-robust distributed support vector machine

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Science China Mathematics 2025年第3期68卷 707-728页

作者： Xiaozhou Wang Weidong Liu Xiaojun Mao School of Statistics East China Normal UniversityShanghai 200062China Key Laboratory of Advanced Theory and Application in Statistics and Data Science(Ministry of Education) East China Normal UniversityShanghai 200062China School of Mathematical Sciences Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of Artificial Intelligence(Ministry of Education) Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of Scientific and Engineering Computing(Ministry of Education) Shanghai Jiao Tong UniversityShanghai 200240China

The development of information technology brings diversification of data sources and large-scale data sets and calls for the exploration of distributed learning algorithms. In distributed systems, some local machines may behave abnormally and send arbitrary information to the central machine(known as Byzantine failures), which can invalidate the distributed algorithms based on the assumption of faultless systems. This paper studies Byzantine-robust distributed algorithms for support vector machines(SVMs) in the context of binary classification. Despite a vast literature on Byzantine problems, much less is known about the theoretical properties of Byzantine-robust SVMs due to their unique challenges. In this paper, we propose two distributed gradient descent algorithms for SVMs. The median and trimmed mean operations in aggregation can effectively defend against Byzantine failures. Theoretically, we show the convergence of the proposed estimators and provide the statistical error rates. After a certain number of iterations, our estimators achieve near-optimal rates. Simulation studies and real data analysis are conducted to demonstrate the performance of the proposed Byzantine-robust distributed algorithms.

关键词： Byzantine robustness convergence distributed learning support vector machine

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Interannual succession of phytoplankton community in a canyon-shaped drinking water reservoir during the initial impoundment period:Taxonomic versus functional groups

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Journal of Environmental Sciences 2025年第5期151卷 454-468页

作者： Yi Hou Ya Cheng Kai Li Meng Yang Kangzhe Huang Gang Ji Ruikang Xue Tinglin Huang Gang Wen Shaanxi Provincial Field Scientific Observation and Research Station of Water Quality in Qinling Mountains Xi’an University of Architecture and TechnologyXi’an 710055China Key Laboratory of Northwest Water Resource Environment and EcologyMOEXi’an University of Architecture and TechnologyXi’an 710055China School of Environmental and Municipal Engineering Xi’an University of Architecture and TechnologyXi’an 710055China

During the initial impoundment period of a canyon-shaped reservoir,the water body fluctuated violently regarding water level,hydrological condition,and thermal *** variations may alter the structure of phytoplankton community,resulting in algal blooms and seriously threatening the ecological security of the *** is of great significance to understand the continuous changes of phytoplankton in the initial impoundment period for the protection of reservoir water ***,a two-year in-situ monitoring study was conducted on water quality and phytoplankton in a representative canyonshaped reservoir named Sanhekou and the interannual changes of phytoplankton community and its response to environmental changes during the initial impoundment period were discussed at taxonomic versus functional classification *** results showed that the total nitrogen and permanganate index levels were relatively high in the first year due to rapid water storage and heavy rainfall input,and the more stable hydrological conditions in the second year promoted the increase of algae density and the transformation of community,and the proportion of cyanobacteria increased *** succession order of phytoplankton in the first year of the initial impoundment periodwas Chlorophyta-Bacillariophyta-Chlorophyta,or J/F/X1-P/MP/W1-A/X1/MP,*** the succession order in the second year was Cyanobacteria/Chlorophyta-Bacillariophyta-Chlorophyta,or L_(M)/G/P-P/A/X1-X1/J/*** temperature,relativewater column stability,mixing depth,and pHwere crucial factors affecting phytoplankton community *** study revealed the interannual succession law and driving factors of phytoplankton in the initial impoundment period and provided an important reference for the operation management and ecological protection of canyon-shaped reservoirs.

关键词： Canyon-shaped reservoir Functional groups Initial impoundment Interannual succession Water quality

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A Derivative-free Trust-region Method for Optimization on the Ellipsoid

A Derivative-free Trust-region Method for Optimization on th...

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2023 International Conference on Advances in Computer Science and engineering Technology, ACSE 2023

作者： Xie, Pengcheng State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences University of Chinese Academy of Sciences ZhongGuanCun East Road No. 55 Beijing China

Optimization methods play a crucial role in various fields and applications. In some optimization problems, the derivative information of the objective function is unavailable. Such black-box optimization problems need to be solved by derivative-free optimization methods. At the same time, optimization problems with ellipsoidal constraints are important and have widespread applications in various fields as well. Following the development of the late professor M. J. D. Powell's efficient derivative-free trust-region optimization methods, this paper considers solving derivative-free optimization problems on the ellipsoid. Our new optimization solver EC-NEWUOA for problems on the ellipsoid in ., n is designed based on Powell's derivative-free software NEWUOA for unconstrained optimization problems. The proposed techniques for our new method mainly include using the Courant penalty function, the augmented Lagrangian method, and the projection technique. Details about the method and theoretical analysis are included in this paper. We also compare our new method with other algorithms by solving test problems and then show the numerical advantages of our new method. © Published under licence by IOP Publishing Ltd.

关键词： Lagrange multipliers

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Two-step Runge–Kutta methods for Volterra integro-differential equations

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International Journal of Computer Mathematics 2024年第1期101卷 37-55页

作者： Wen, Jiao Huang, Chengming Guan, Hongbo College of Mathematics and Information Science Zhengzhou University of Light Industry Zhengzhou China School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Wuhan China

In this paper, we investigate two-step Runge–Kutta methods to solve Volterra integro-differential equations. Two-step Runge–Kutta methods increase the order of convergence in comparing the classical Runge–Kutta method without extra computational cost. First, the local order conditions and convergence theorem are derived. Then, stability properties of two-step Runge–Kutta methods corresponding to the basic and convolution test equations are analysed. Furthermore, one-stage method with order four and two-stage method with order six are constructed and we plot the stability regions. Numerical examples are presented to confirm the theoretical analyses. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Stability

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Continual learning with Bayesian model based on a fixed pre-trained feature extractor

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Visual Intelligence 2023年第1期1卷 1-14页

作者： Yang, Yang Cui, Zhiying Xu, Junjie Zhong, Changhong Zheng, Wei-Shi Wang, Ruixuan School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Network Intelligence Peng Cheng Laboratory Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing MOE Guangzhou China

Deep learning has shown its human-level performance in various applications. However, current deep learning models are characterized by catastrophic forgetting of old knowledge when learning new classes. This poses a challenge such as in intelligent diagnosis systems where initially only training data of a limited number of diseases are available. In this case, updating the intelligent system with data of new diseases would inevitably downgrade its performance on previously learned diseases. Inspired by the process of learning new knowledge in human brains, we propose a Bayesian generative model for continual learning built on a fixed pre-trained feature extractor. In this model, knowledge of each old class can be compactly represented by a collection of statistical distributions, e.g., with Gaussian mixture models, and naturally kept from forgetting in continual learning over time. Unlike existing class-incremental learning methods, the proposed approach is not sensitive to the continual learning process and can be additionally well applied to the data-incremental learning scenario. Experiments on multiple medical and natural image classification tasks reveal that the proposed approach outperforms state-of-the-art approaches that even keep some images of old classes during continual learning of new classes. © The Author(s) 2023.

关键词： Bayesian model Continual learning Fixed feature extractor Generative approach

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