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Anomalous negative magnetoresistance in quantum dot Josephson junctions with Kondo correlations

science China(Physics,Mechanics & Astronomy) 2024年第8期67卷 3-13页

作者： Mingtang Deng Chunlin Yu Guangyao Huang Rosa López Philippe Caroff Sepideh Ghalamestani Gloria Platero Hongqi Xu Institute for Quantum Information&State Key Laboratory of High Performance Computing College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China NanoLund and Division of Solid State Physics Lund UniversityLund S-22100Sweden China Greatwall Quantum Laboratory Changsha 410006China Institut de Física Interdisciplinària i de Sistemes Complexos IFISC Palma de Mallorca E-07122Spain Instituto de Ciencia de Materiales de Madrid Madrid 28049Spain Beijing Key Laboratory of Quantum Devices Key Laboratory for the Physics and Chemistry of Nanodevicesand Department of ElectronicsPeking UniversityBeijing 100871China Beijing Academy of Quantum Information Sciences Beijing 100193China Hefei National Laboratory Hefei 230088China

The interplay between superconductivity and the Kondo effect has stimulated significant interest in condensed matter *** compete when their critical temperatures are close and can give rise to a quantum phase transition that can mimic Majorana zero ***,we have fabricated and measured Al-InSb nanowire quantum dot-Al *** the Kondo regime,a supercurrent-induced zero-bias conductance peak *** zero-bias peak shows an anomalous negative magnetoresistance(NMR)at weak magnetic *** attribute this anomalous NMR to quasiparticle trapping at vortices in the superconductor leads as a weak magnetic field is *** trapping effect lowers the quasiparticle-caused dissipation and thus enhances the Josephson *** work connects the vortex physics and the supercurrent tunneling in Kondo regimes and can help further understand the physics of Josephson quantum dot system.

关键词： quantum dot Kondo correlation InSb nanowire quasiparticle trapping

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A Test Suite and An Optimizer for Dietary Nutrition Optimization Problem: From Constrained Many-Objective Perspective

A Test Suite and An Optimizer for Dietary Nutrition Optimiza...

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2023 IEEE Congress on Evolutionary Computation, CEC 2023

作者： Ti, Yaqi Li, Changhe Yang, Shengxiang Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China School of Computer Science and Informatics De Montfort University Leicester United Kingdom

ISBN: (纸本)9798350314588

With increasing people who suffer from diet-related diseases, providing suggestions for personal daily nutrient-dense intake is highly expected. However, current dietary nutrition models are less precise, and dietary nutrition optimizers usually fail to give satisfactory solutions. Therefore, we construct a constrained many-objective nutrition model with more precise nutrient assessments and a scalable constrained many-objective benchmark set. This test suite has great flexibility in evaluating algorithms' performance on high dimensional search and objective spaces with some feasible region fragments. We also propose a kd-tree based dynamic constrained many-objective evolutionary algorithm to search for customized food combinations according to personal daily consumption and intake preference. Experiments show that our algorithm has better diversity maintenance ability in high dimension space. © 2023 IEEE.

关键词： Nutrition

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On interleaving space exploration of multi-threaded programs

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Frontiers of computer science 2021年第4期15卷 17-28页

作者： Dongjie CHEN Yanyan JIANG Chang XU Xiaoxing MA State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China Department of Computer Science and Technology Nanjing UniversityNanjing 210023China

Exploring the interleaving space of a multithreaded program to efficiently detect concurrency bugs is important but also difficult because of the astronomically many thread *** paper presents a novel framework to decompose a thread schedule generator that explores the interleaving space into the composition of a basic generator and its extension under the“small interleaving hypothesis”.Under this framework,we in-depth analyzed research work on interleaving space exploration,illustrated how to design an effective schedule generator,and shed light on future research opportunities.

关键词： survey testing concurrency bugs interleaving space

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Quantitatively mapping local quality of super-resolution microscopy by rolling Fourier ring correlation

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Light(science & Applications) 2023年第12期12卷 2826-2844页

作者： Weisong Zhao Xiaoshuai Huang Jianyu Yang Liying Qu Guohua Qiu Yue Zhao Xinwei Wang Deer Su Xumin Ding Heng Mao Yaming Jiu Ying Hu Jiubin Tan Shiqun Zhao Leiting Pan Liangyi Chen Haoyu Li Innovation Photonics and Imaging Center School of Instrumentation Science and EngineeringHarbin Institute of TechnologyHarbinChina Key Laboratory of Ultra-Precision Intelligent Instrumentation of Ministry of Industry and Information Technology Harbin Institute of TechnologyHarbinChina Biomedical Engineering Department International Cancer InstitutePeking University Cancer Hospital and InstituteHealth Science CenterPeking UniversityBeijingChina The Key Laboratory of Weak-Light Nonlinear Photonics of Education Ministry School of Physics and TEDA Institute of Applied PhysicsFrontiers Science Center for Cell ResponsesNankai UniversityTianjinChina State Key Laboratory of Membrane Biology Beijing Key Laboratory of Cardiometabolic Molecular MedicineInstitute of Molecular MedicineNational Biomedical Imaging CenterSchool of Future TechnologyPeking UniversityBeijingChina Department of Control Science and Engineering Harbin Institute of TechnologyHarbinChina School of Mathematical Sciences Peking UniversityBeijingChina Unit of Cell Biology and Imaging Study of Pathogen Host Interaction The Center for MicrobesDevelopment and HealthKey Laboratory of Molecular Virology and ImmunologyShanghai Institute of Immunity and InfectionChinese Academy of SciencesShanghaiChina School of Life Science and Technology Harbin Institute of TechnologyHarbinChina PKU-IDG/McGovern Institute for Brain Research BeijingChina Beijing Academy of Artificial Intelligence BeijingChina Frontiers Science Center for Matter Behave in Space Environment Harbin Institute of TechnologyHarbinChina Key Laboratory of Micro-Systems and Micro-Structures Manufacturing of Ministry of Education Harbin Institute of TechnologyHarbinChina

In fluorescence microscopy,computational algorithms have been developed to suppress noise,enhance contrast,and even enable super-resolution(SR).However,the local quality of the images may vary on multiple scales,and these differences can lead to *** mapping methods fail to finely estimate the local quality,challenging to associate the SR scale ***,we develop a rolling Fourier ring correlation(rFRC)method to evaluate the reconstruction uncertainties down to SR *** visually pinpoint regions with low reliability,a filtered rFRC is combined with a modified resolution-scaled error map(RSM),offering a comprehensive and concise map for further *** demonstrate their performances on various SR imaging modalities,and the resulting quantitative maps enable better SR images integrated from different ***,we expect that our framework can become a routinely used tool for biologists in assessing their image datasets in general and inspire further advances in the rapidly developing field of computational imaging.

关键词： resolution local enable

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Magnetically Driven Capsule Robot for Multi-Targeted Biopsy and Drug Delivery in Stomach

Magnetically Driven Capsule Robot for Multi-Targeted Biopsy ...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Xu, Zhihui Wei, Tanyong Li, Zongze Huang, Ding Liu, Shoubin Hu, Chengzhi Southern University of Science and Technology Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering Shenzhen China Southern University of Science and Technology Guangdong Prov. Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Shenzhen China Harbin Institute of Technology School of Mechanical Engineering and Automation Shenzhen China

ISBN: (纸本)9798350325706

Capsule robots have emerged as a promising solution for non-invasive and painless screening of gastrointestinal diseases, offering significant advantages in patient comfort and reduced risk of cross-infection. Despite these advantages, existing capsule robots fall short in their ability to conduct targeted biopsies and deliver drugs to specific gastric lesions. To address this limitation, this paper presents the design and development of a magnetically driven capsule robot that leverages visual feedback to achieve accurate targeting for biopsy and drug administration in gastric lesions. Internal magnets, responsive to an external gradient magnetic field, enable the controlled propulsion of the capsule robot. A kinetic model of the capsule robot is analyzed, allowing it to recognize lesion contours through the endoscopic camera. This recognition determines the rotation angle or small adjustments required for the capsule robot's attitude and positioning. Experimental results in a simplified in vitro stomach model showcase the capability of the capsule robot to achieve targeted biopsy and drug delivery. These results highlight the potential feasibility of the proposed capsule robot in enhancing diagnostic accuracy and treatment efficacy with broader applications within the realm of gastric healthcare. © 2023 IEEE.

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Negatively Correlated Search for Constrained Optimization

Negatively Correlated Search for Constrained Optimization

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2023 IEEE Congress on Evolutionary Computation, CEC 2023

作者： Li, Yuan Lu, Xiaofen Yao, Xin Southern University of Science and Technology The Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China School of Computer Science University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom

ISBN: (纸本)9798350314588

Evolutionary algorithms (EAs) combined with constraint handling techniques (CHTs) are very effective in solving constrained optimization problems (COPs). Recently, negatively correlated search (NCS) has been shown to be powerful for real-world multimodal optimization problems (MMOP). However, there has been few works on utilizing NCS to solve COPs. In this paper, we present a novel constrained optimization evolutionary algorithm (COEA) named NCS-E which combined NCS with a new CHT ensemble method to deal with COPs. It integrates three complementary CHTs as voters, and individuals are first voted by each voter, then the individual with more weighted votes is considered better. In addition, the negative correlation information of the voted individuals is utilized to adaptively adjust the voting weights. To demonstrate the performance of NCS-E, NCS-E is compared with four NCS-based COEAs and two state-of-the-art methods, MAgES and MAgES-VMCH, on 57 real-world COPs. The statistical results show that NCS-E exhibits the best performance among the compared methods. Moreover, NCS-E was applied to solve the Deep Neural Network pruning problem and compared with the state-of-the-art pruning method OLMP on three reference models. Empirical results show that the proposed method improves the performance of OLMP on all three DNN pruning tasks. © 2023 IEEE.

关键词： Constrained optimization

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Using Deep Reinforcement Learning in Optimal Energy Management for Residential House Aggregators with Uncertain User Behaviors 7

Using Deep Reinforcement Learning in Optimal Energy Manageme...

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7th IEEE Student Conference on Electric Machines and systems, SCEMS 2024

作者： Lin, Yujun Yan, Linfang Hui, Hongxun Chen, Yin Chen, Xia Wen, Jinyu School of Electrical and Electronic Engineering Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Technology Wuhan China City & Energy Research Institute Suzhou China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Electrical and Computer Engineering China University of Strathclyde Department of Electronic and Electrical Engineering Glasgow United Kingdom

ISBN: (纸本)9798350366945

In this study, the home energy management problem, which can be regarded as a high-dimensional optimization problem, for numerous residential houses, is addressed. The concept of the aggregator is utilized to reduce the state and action space and to handle the high dimensionality. A two-stage deep reinforcement learning (DRL)-based approach is proposed for the aggregators to track the schedule from a superior grid and guarantee the operation constraints. In the first stage, a DRL control agent is set to learn the optimal scheduling strategy interacting with the environment based on the soft-actor-critic framework and generate the aggregate control actions. In the second stage, the aggregate control actions are disaggregated to individual appliances considering the users' behaviors. The uncertainty of an electric vehicle's charging demand is quantitatively expressed based on the driver's experience. An aggregate anxiety concept is introduced to characterize the driver's anxiety on the electric vehicle's range and uncertain events. Finally, simulations are conducted to verify the effectiveness of the proposed approach under dynamic user behaviors, and comparisons show the superiority of the proposed approach over other benchmark methods. © 2024 IEEE.

关键词： Deep reinforcement learning

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TOWARD ADVERSARIAL TRAINING ON CONTEXTUALIZED LANGUAGE REPRESENTATION 11

TOWARD ADVERSARIAL TRAINING ON CONTEXTUALIZED LANGUAGE REPRE...

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11th International Conference on Learning Representations, ICLR 2023

作者： Wu, Hongqiu Liu, Yongxiang Shi, Hanwen Zhao, Hai Zhang, Min Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China School of Computer Science and Technology Soochow University Suzhou China

Beyond the success story of adversarial training (AT) in the recent text domain on top of pre-trained language models (PLMs), our empirical study showcases the inconsistent gains from AT on some tasks, e.g. commonsense reasoning, named entity recognition. This paper investigates AT from the perspective of the contextualized language representation outputted by PLM encoders. We find the current AT attacks lean to generate sub-optimal adversarial examples that can fool the decoder part but have a minor effect on the encoder. However, we find it necessary to effectively deviate the latter one to allow AT to gain. Based on the observation, we propose simple yet effective Contextualized representation-Adversarial Training (CreAT), in which the attack is explicitly optimized to deviate the contextualized representation of the encoder. It allows a global optimization of adversarial examples that can fool the entire model. We also find CreAT gives rise to a better direction to optimize the adversarial examples, to let them less sensitive to hyperparameters. Compared to AT, CreAT produces consistent performance gains on a wider range of tasks and is proven to be more effective for language pretraining where only the encoder part is kept for downstream tasks. We achieve the new state-of-the-art performances on a series of challenging benchmarks, e.g. AdvGLUE (59.1 → 61.1), HellaSWAG (93.0 → 94.9), ANLI (68.1 → 69.3). © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Global optimization

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An automatic isotropic/anisotropic hybrid grid generation technique for viscous flow simulations based on an artificial neural network

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Chinese Journal of Aeronautics 2022年第4期35卷 102-117页

作者： Peng LU Nianhua WANG Xinghua CHANG Laiping ZHANG Yadong WU School of Information Engineering Southwest University of Science and TechnologyMianyang 621010China School of Intelligent Manufacturing Engineering Chongqing University of Arts and ScienceChongqing 402160China Stake Key Laboratory of Aerodynamics China Aerodynamics Research and Development CenterMianyang 621000China Unmanned Systems Research Center National Innovation Institute of Defense TechnologyBeijing 100071China School of Computer Science and Technology Sichuan University of Science&EngineeringYibin 644005China

Based on the author’s previous research, a novel hybrid grid generation technique is developed by introducing an Artificial Neural Network(ANN) approach for realistic viscous flow simulations. An initial hybrid grid over a typical geometry with anisotropic quadrilaterals in the boundary layer and isotropic triangles in the off-body region is generated by the classical mesh generation method to train two ANNs on how to predict the advancing direction of the new point and to control the grid size. After inputting the initial discretized fronts, the ANN-based Advancing Layer Method(ALM) is adopted to generate the anisotropic quadrilaterals in boundary layers. When the high aspect ratio of the anisotropic grid reaches a specified value, the ANN-based Advancing Front Method(AFM) is adopted to generate isotropic triangles in the off-body computational *** initial isotropic triangles are smoothed to further improve the grid quality. Three typical cases are tested and compared with experimental data to validate the effectiveness of grids generated by the ANN-based hybrid grid generation method. The experimental results show that the two ANNs can predict the advancing direction and the grid size very well, and improve the adaptability of the isotropic/anisotropic hybrid grid generation for viscous flow simulations.

关键词： Advancing front method Advancing layer method Anisotropic quadrilateral grid generation Artificial neural network Isotropic triangular grid generation Machine learning

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Design of Capsule Robot for Magnetic Gastric Biopsy and Drug Administration 20

Design of Capsule Robot for Magnetic Gastric Biopsy and Drug...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Xu, Zhihui Li, Zongze Liu, Shoubin Hu, Chengzhi Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen China Guangdong Prov. Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China Harbin Institute of Technology School of Mechanical and Electrical Engineering Shenzhen China

ISBN: (纸本)9798350320831

Capsule robots have emerged as a promising tool for the early diagnosis of gastrointestinal diseases. Unlike conventional gastroscopy, they offer the potential for minimally invasive or even non-invasive treatment, and can alleviate discomfort and pain in patients. However, current capsule robots have some limitations. For instance, they lack stomach tissue biopsy and drug administration functions, and the capsule robot used for biopsy does not consider the tolerance of gastrointestinal tissue. Similarly, the capsule robot used for drug administration does not account for the outlet speed of drug administration. This paper proposes a new biopsy method using a magnetically manipulated capsule robot and analyzes the principles governing the robot's manipulation during the biopsy and drug administration processes. Furthermore, an ANSYS gastric model was established and used to simulate and experimentally analyze damage to the gastric tissue model from different types of biopsy needles and their moving speeds. The appropriate type of biopsy needle and moving speed were determined to prevent damage to the gastric tissue. The study also used FLUENT to determine the appropriate thrust at the entrance of the drug cavity and the impact of the drug density on the drug outlet velocity to ensure complete adherence of the drug to the lesion. © 2023 IEEE.

关键词： Biopsy

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