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Photoinduced anomalous supercurrent Hall effect

Physical Review B 2023年第18期108卷 L180509-L180509页

作者： A. V. Parafilo V. M. Kovalev I. G. Savenko Center for Theoretical Physics of Complex Systems Institute for Basic Science (IBS) Daejeon 34126 Korea Novosibirsk State Technical University Novosibirsk 630073 Russia Department of Physics Guangdong Technion–Israel Institute of Technology 241 Daxue Road Shantou Guangdong 515063 China Technion–Israel Institute of Technology 32000 Haifa Israel Guangdong Provincial Key Laboratory of Materials and Technologies for Energy Conversion Guangdong Technion–Israel Institute of Technology Guangdong 515063 China

We predict a photoinduced Hall effect in an isotropic conventional two-dimensional superconductor with a built-in supercurrent exposed to a circularly polarized light. This second-order with respect to the electromagnetic field amplitude effect occurs when the frequency of the field exceeds double the value of the superconducting gap. It reveals itself in the emergence of a Cooper-pair condensate flow in the direction transverse to the initial built-in supercurrent, which arises to compensate for the light-induced electric current of quasiparticles photoexcited across the gap. The initial supercurrent breaks both the time-reversal and inversion symmetries, while the presence of dilute disorder in the sample provides the breaking of the Galilean invariance. We develop a microscopic theory of the supercurrent Hall effect in the case of weak disorder and show that the Hall supercurrent is directly proportional to the quasiparticle recombination time, which can acquire large values.

关键词： Anomalous Hall effect Cooper pairs Optical conductivity Photoinduced effect Superconducting fluctuations Superconducting gap Superconducting phase transition 2-dimensional systems Superconductors Feynman diagrams Methods in superconductivity

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Matrix-Driven Adaptive Dual-Space Evolutionary Algorithm for Many-Task Optimization

Matrix-Driven Adaptive Dual-Space Evolutionary Algorithm for...

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Congress on Evolutionary Computation

作者： Yaxue Liu Jingchuan Zhang Xianpeng Wang National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Ministry of Education Shenyang China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Shenyang China

ISBN: (数字)9798331534318

ISBN: (纸本)9798331534325

Evolutionary multi-task optimization is an emerging research topic in the field of evolutionary computation. It aims to achieve simultaneous optimization of different tasks by dynamically leveraging the synergies that exist between them. As the number of tasks being optimized simultaneously increases, the differences between the tasks can vary significantly, and their iterative trends may also diverge. These factors contribute to a higher likelihood of negative transfer during the knowledge transfer process. To address these challenges, this paper proposes a matrix-driven adaptive dual-space evolutionary optimization algorithm, referred to as MaKAM. The MaKAM algorithm aims to compute the similarity between tasks using dual measurement criteria from both the objective space and decision space, effectively avoiding negative transfer between tasks. For many-task single-objective optimization problems, comparative algorithms are evaluated using the CEC19MaTSO and WCCI20MaTSO test suites. For many-task multi-objective optimization problems, comparisons are made using the CEC19MaTMO and WCCI20MaTMO test suites. The results indicate that the overall performance of the proposed MaKAM algorithm significantly surpasses that of the compared algorithms in the literature.

关键词： Filters Information sharing Collaboration Evolutionary computation Multitasking Market research Extraterrestrial measurements Iterative methods Optimization Knowledge transfer

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MRO Configuration Management for complex Products

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Journal of systems science and systems Engineering 2022年第3期31卷 359-380页

作者： Chunliu Zhou Lian Qi Xiaobing Liu Hongguang Bo Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Anhui University of TechnologyMaanshan 243002China School of Management Science and Engineering Anhui University of TechnologyMaanshan 243032China Rutgers Business School RutgersThe State University of New JerseyPiscatawayNJ 08854United State School of Economics and Management Dalian University of TechnologyDalian 116023China

Configuration management(CM)is an effective method for data modeling and change management in product development,but the study of how it can be adapted for complex product maintenance,repair and overhaul(MRO)has thus far been *** propositions are put forward on the basis of a literature review and verified by a case of a high-speed train MRO *** findings show that CM can be used in MRO data management with adjustments in its ***,MRO-CM planning considers both multistage service and single-stage service to keep data consistent and ***,MRO data integration with the design/production phase should be considered during configuration ***,baselines and change rules are developed for change ***,status accounting and auditing can be repeatedly used in MRO data management.A systematic MRO-CM framework is generated to assist MRO practitioners in understanding the relationships among different MRO-CM *** paper contributes to expanding the theory of CM in MRO for complex products and provides an effective method for managing MRO data from systematic engineering insights.

关键词： Configuration management MRO Service data management case study

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Motion Planning of 3D Nonholonomic Robots via Curvature-Constrained Vector Fields

Motion Planning of 3D Nonholonomic Robots via Curvature-Cons...

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IEEE Conference on Decision and Control

作者： Yike Qiao Xiaodong He Zhongkui Li Department of Mechanics and Engineering Science College of Engineering State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Vector-field-based methods are typical feedback planning algorithms, especially eligible for the motion planning of nonholonomic robots. Nevertheless, most existing vector fields (VF) do not account for the prevalent constraints on robot’s kinematics. This paper addresses the motion planning problem for 3D nonholonomic robots with trajectories featuring upper bounded curvature. To this end, a curvature-constrained VF over $\mathbb{R}^{3}$ is proposed, whose integral curves guarantee an upperbound of curvature as well as an almost-global attraction region towards the desired position with a specified heading direction. Moreover, a control strategy is presented to determine the robot’s control inputs subject to the curvature constraint. Under the designed control laws, the robot is guaranteed to track the VF while ensuring that the actual trajectory adheres to the curvature constraint. Finally, the efficacy of the presented motion planning algorithm is validated by numerical simulations.

关键词： Three-dimensional displays Tracking Velocity control Kinematics Numerical simulation Vectors Planning Trajectory Collision avoidance Robots

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From Fourier to neural ODEs: flow matching for modeling complex systems 24

From Fourier to neural ODEs: flow matching for modeling comp...

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Proceedings of the 41st International Conference on Machine Learning

作者： Xin Li Jingdong Zhang Qunxi Zhu Chengli Zhao Xue Zhang Xiaojun Duan Wei Lin College of Science National University of Defense Technology Changsha Hunan China School of Mathematical Sciences LMNS and SCMS and Research Institute of Intelligent Complex Systems Fudan University China Research Institute of Intelligent Complex Systems Fudan University China School of Mathematical Sciences LMNS and SCMS and Research Institute of Intelligent Complex Systems and State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Institutes of Brain Science Fudan University China and Shanghai Artificial Intelligence Laboratory China

Modeling complex systems using standard neural ordinary differential equations (NODEs) often faces some essential challenges, including high computational costs and susceptibility to local optima. To address these challenges, we propose a simulation-free framework, called Fourier NODEs (FNODEs), that effectively trains NODEs by directly matching the target vector field based on Fourier analysis. Specifically, we employ the Fourier analysis to estimate temporal and potential high-order spatial gradients from noisy observational data. We then incorporate the estimated spatial gradients as additional inputs to a neural network. Furthermore, we utilize the estimated temporal gradient as the optimization objective for the output of the neural network. Later, the trained neural network generates more data points through an ODE solver without participating in the computational graph, facilitating more accurate estimations of gradients based on Fourier analysis. These two steps form a positive feedback loop, enabling accurate dynamics modeling in our framework. Consequently, our approach outperforms state-of-the-art methods in terms of training time, dynamics prediction, and robustness. Finally, we demonstrate the superior performance of our framework using a number of representative complex systems.

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Class Incremental Learning for Knowledge Acquisition Based on Prototype Network Gating Mechanism

Class Incremental Learning for Knowledge Acquisition Based o...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Yuming Feng Xiaolong Jin Haozhe Liang Jiafeng Guo School of Computer Science and Technology University of Chinese Academy of Sciences CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Institute of Systems Engineering Academy of Military Science

ISBN: (数字)9798350377613

ISBN: (纸本)9798350377620

With the development of information technology, the medical field has accumulated a large amount of medical data, which continues to grow. Efficient extraction and effective utilization of knowledge from this vast amount of medical data are crucial for realizing its immense value. Traditional knowledge acquisition algorithms face challenges in effectively identifying and acquiring new types of knowledge in practical applications. To address this, this paper proposes a class-incremental learning method based on a prototype network gating mechanism, called PGMoE. This method integrates multiple experts through a prototype network-based gating mechanism, dynamically adjusting the model's response to new and old knowledge, thereby effectively mitigating the issue of catastrophic forgetting in class-incremental learning. PGMoE not only improves the stability and accuracy of the model in continuous learning processes but also ensures the model's ability to remember historical knowledge. Comparative experiments on multiple datasets with existing class-incremental learning methods demonstrate that PGMoE has significant performance advantages in handling continuous learning tasks.

关键词： Learning systems Accuracy Incremental learning Knowledge acquisition Scalability Heuristic algorithms Prototypes Logic gates Stability analysis Information technology

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Combining strongly lensed and unlensed fast radio bursts: To be a more precise late-universe probe

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science CHINA Physics, Mechanics & Astronomy 2025年第8期68卷 1-21页

作者： Ji-Guo Zhang Yi-Fan Jiang Ze-Wei Zhao Jing-Zhao Qi Jing-Fei Zhang Xin Zhang Liaoning Key Laboratory of Cosmology and Astrophysics & Department of Physics College of Sciences Northeastern University Shenyang 110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry (Ministry of Education) Northeastern University Shenyang 110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang 110819 China

The Macquart relation and time-delay cosmography are now two promising ways to fast radio burst (FRB) *** this work,we propose a joint method that combines strongly lensed and unlensed FRBs for improvingcosmological parameter estimation using simulated FRB data from the future sensitivecoherent all-sky monitor survey, which is expected to detect a large number of FRBsincluding galaxy-galaxy strongly lensed events. We find that using a detectable sampleof 100000 localized FRBs including $40$ lensed events can simultaneously constrain theHubble constant and the equation of state of dark energy, with high precisionof $\varepsilon(H_0)=0.4%$ and $\varepsilon(w)=4.5%$ in the simplest dynamical darkenergy model. The joint analysis of unlensed and lensed FRBs significantly improves theconstraint on $H_0$, which could be more effective than combining either the unlensed FRBswith future gravitational wave (GW) standard sirens or the lensed FRBs with CMB. Furthermore,combining the full FRB sample with the CMB+BAO+SNe datayields $\sigma(H_0)=0.29~{\rm~km~s^{-1}Mpc^{-1}}$, $\sigma(w_0)=0.046$,and $\sigma(w_a)=0.15$ in the two-parameter dynamical dark energy model,which outperforms the results from the CMB+BAO+SNe+GW *** reinforces the cosmological implications of a multi-wavelength observational strategyin optical and radio bands. We conclude that the future FRB observations will shed light onthe nature of dark energy and also the Hubble tension if enough events with long-durationlensing are incorporated.

关键词： fast radio bursts strong gravitational lensing cosmology the Hubble constant dark energy

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Discovering API Directives from API Specifications with Text Classification

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Journal of Computer science & Technology 2021年第4期36卷 922-943页

作者： Jing-Xuan Zhang Chuan-Qi Tao Zhi-Qiu Huang Xin Chen College of Compute Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 210016China Key Laboratory of Safety-Critical Software(Nanjing University of Aeronautics and Astronautics) Ministry of Industry and Information TechnologyNanjing 210016China Key Laboratory of Complex Systems Modeling and Simulation(Hangzhou Dianzi University) Ministry of Education Hangzhou 310018China School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou 310018China

Application programming interface(API)libraries are extensively used by *** correctly program with APIs and avoid bugs,developers shall pay attention to API directives,which illustrate the constraints of ***,API directives usually have diverse morphologies,making it time-consuming and error-prone for developers to discover all the relevant API *** this paper,we propose an approach leveraging text classification to discover API directives from API ***,given a set of training sentences in API specifications,our approach first characterizes each sentence by three groups of ***,to deal with the unequal distribution between API directives and non-directives,our approach employs an under-sampling strategy to split the imbalanced training set into several subsets and trains several *** a new sentence in an API specification,our approach synthesizes the trained classifiers to predict whether it is an API *** have evaluated our approach over a publicly available annotated API directive *** experimental results reveal that our approach achieves an F-measure value of up to 82.08%.In addition,our approach statistically outperforms the state-of-the-art approach by up to 29.67%in terms of F-measure.

关键词： Application programming interface(API)directive API specification imbalanced learning text classification

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DSNRNet: A Network of LiDAR data Denoising by Differential Stability in Underground Mine

DSNRNet: A Network of LiDAR Data Denoising by Differential S...

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International Conference on Intelligent Transportation

作者： Dejiang Meng Bin Tian Ziyu Pan Junhui Wang Long Chen The Waytous Inc. Beijing China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The School of Data and Computer Science Sun Yat-sen University China

Some sprinklers in underground mines spray water mist to lessen the amount of dust in the air. But water mist causes LiDAR to provide inaccurate point measurements, which are referred to as water mist noise that undermines the effectiveness of LiDAR-based localization and object recognition. Therefore, in order to reduce water mist noise, we have developed a new noise segmentation network that can operate on a CPU in real time-differential stability noise removal network (DSNRNet). This network consists of two sub-networks. The first sub-network is aimed at extracting differential stability features, so we named it the differential stability feature extraction network (sub-network 1). The second sub-network: a fully connected neural network (sub-network 2), is used to segment noise. To evaluate the DSNRNet's performance, we built a LiDAR semantic segmentation dataset of underground mines and run the DSNRNet in an Intel i7-11800H CPU. The experimental results demonstrate that this method is able to strike a better balance between speed (26.3 milliseconds) and accuracy (97.8 % ) compared with the other two most possible methods-DSOR (24.2 milliseconds, 3.2 % ) and WeatherNet (436.8ms, 98.5 % ).

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Sequence-to-Sequence Neural Diarization with Automatic Speaker Detection and Representation

arXiv

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arXiv 2024年

作者： Cheng, Ming Lin, Yuke Li, Ming The School of Computer Science Wuhan University Wuhan430072 China Suzhou Municipal Key Laboratory of Multimodal Intelligent Systems Data Science Research Center Duke Kunshan University Kunshan215316 China

This paper proposes a novel Sequence-to-Sequence Neural Diarization (SSND) framework to perform online and offline speaker diarization. It is developed from the sequence-to-sequence architecture of our previous target-speaker voice activity detection system and then evolves into a new diarization paradigm by addressing two critical problems. 1) Speaker Detection: The proposed approach can utilize incompletely given speaker embeddings to discover the unknown speaker and predict the target voice activities in the audio signal. It does not require a prior diarization system for speaker enrollment in advance. 2) Speaker Representation: The proposed approach can adopt the predicted voice activities as reference information to extract speaker embeddings from the audio signal simultaneously. The representation space of speaker embedding is jointly learned within the whole diarization network without using an extra speaker embedding model. During inference, the SSND framework can process long audio recordings blockwise. The detection module utilizes the previously obtained speaker-embedding buffer to predict both enrolled and unknown speakers' voice activities for each coming audio block. Next, the speaker-embedding buffer is updated according to the predictions of the representation module. Assuming that up to one new speaker may appear in a small block shift, our model iteratively predicts the results of each block and extracts target embeddings for the subsequent blocks until the signal ends. Finally, the last speaker-embedding buffer can re-score the entire audio, achieving highly accurate diarization performance as an offline system. Experimental results show that our proposed SSND framework achieves new state-of-the-art diarization error rates (DERs) for online inference on the DIHARD-II (24.41%) and DIHARD-III (17.12%) evaluation sets without using oracle voice activity detection. At the same time, it also refreshes the state-of-the-art performance for offline infere

关键词： Audio recordings

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