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Stabilizing two-qubit entanglement with dynamically decoupled active feedback

Physical Review Applied 2024年第2期21卷 024022-024022页

作者： Sacha Greenfield Leigh Martin Felix Motzoi K. Birgitta Whaley Justin Dressel Eli M. Levenson-Falk Department of Physics and Astronomy University of Southern California Los Angeles California 90089 USA Center for Quantum Information Science and Technology University of Southern California Los Angeles California 90089 USA Institute for Quantum Studies Chapman University Orange California 92866 USA Berkeley Center for Quantum Information and Computation Berkeley California 94720 USA Department of Physics University of California Berkeley California 94720 USA Department of Chemistry University of California Berkeley California 94720 USA Peter Grünberg Institute of Quantum Control (PGI-8) Forschungszentrum Jülich GmbH Wilhelm-Johnen-Straße Jülich 52428 Germany Schmid College of Science and Technology Chapman University Orange California 92866 USA Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles California 90089 USA

We propose and analyze a protocol for stabilizing a maximally entangled state of two noninteracting qubits using active state-dependent feedback from a continuous two-qubit half-parity measurement in coordination with a concurrent, noncommuting dynamical decoupling drive. We demonstrate that such a drive can be simultaneous with the measurement and feedback, while also playing a key part in the feedback protocol itself. We show that robust stabilization with near-unit fidelity can be achieved even in the presence of realistic nonidealities, such as time delay in the feedback loop, imperfect state-tracking, inefficient measurements, dephasing from 1/f-distributed qubit-frequency noise, and relaxation. We mitigate feedback-delay error by introducing a forward-state-estimation strategy in the feedback controller that tracks the effects of control signals already in transit. More generally, the steady state is globally attractive without the need for ancillas, regardless of the error state, in contrast to most known feedback and error-correction schemes.

关键词： Measurement back action Optoelectronics Quantum control Quantum feedback Quantum information with solid state qubits Quantum measurements Superconducting qubits Microwave techniques

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Enhancing crop production in the Haihe Basin while addressing challenges related to water quantity and quality

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science of the Total Environment 2024年 955卷 176800页

作者： Wang, Gang Yang, Jing Liu, Xia Zhang, Haodi Xu, Xiangbo Luo, Jiafa Bai, Zhaohai Ma, Lin Key Laboratory of Agricultural Water Resources Hebei Key Laboratory of Soil Ecology Center for Agricultural Resources Research Institute of Genetic and Developmental Biology The Chinese Academy of Sciences 286 Huaizhong Road Hebei Shijiazhuang050021 China University of Chinese Academy of Science 19 A Yuquan Road Shijingshan District Beijing100049 China School of Mathematics and Science Hebei GEO University 136 Huai'an Road Hebei Shijiazhuang050031 China College of Computer Science and Software Engineering Shenzhen University China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China AgResearch Limited Ruakura Research Centre Private Bag 3123 Hamilton New Zealand State Key Laboratory of Pollution Control and Resource Reuse School of the Environment Nanjing University Jiangsu Nanjing210023 China

To tackle the challenge of ensuring food security while managing water availability and quality constraints in the Haihe Basin, a critical grain-producing region in China with a dense population and severe water degradation issues, we have developed a comprehensive spatial optimization (CSO) strategy. This approach integrates various crop and soil management practices, land consolidation, and cutting-edge breeding technologies. It also uses a spatial planning model based on linear programming, considering water quantity and quality constraints. Our research shows that crop yields may drop by 14 % (3780 Kt) compared to levels in 2017 due to restrictions on groundwater extraction and nitrogen (N) concentrations in leaching or runoff. However, by strategically maximizing overall crop yields through the integrated CSO strategy without changing the crop production structure, the total crop yield could potentially increase by 81 % (21,817 Kt) while using 3.0 % (86 Kha) less cultivated land than in 2017. This would help to fulfill the projected food demand in 2050. If the integrated aims to minimize the sown area after fulfilling the demand for crops in 2050, around 28 % of the sown area, or 820 Kha cropland, could be reduced compared with 2017. This reduction may save more land for natural conversation. Furthermore, total N losses, including ammonia, nitrous oxide emissions, runoff, and leaching, could be reduced by 23 %–43 % (139–252 Kt), depending on the specific optimization strategy goals. The stepwise integrated optimization strategy provides a feasible framework for the Haihe Basin, enabling the maintenance or even enhancing current crop yields while safeguarding water quality and quantity. © 2024 Elsevier B.V.

关键词： Comprehensive spatial optimization Crop yield Haihe Basin Nitrogen losses Water security

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A Machine Learning Based Risk Identification Method for Illegal Pig Slaughtering

A Machine Learning Based Risk Identification Method for Ille...

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Intelligent Communication and Networking (ICN), International Conference on

作者： Jiajun Miao Yujun Shen Yan Zhang Lingjie Liu Ye Tian Yu Zhao Chunyan Zhou College of Computer Science and Technology Harbin Engineering University Harbin China Hangzhou Hikvision Digital Technology Co. Ltd Hangzhou China China Industrial Control Systems Cyber Emergency Response Team Beijing China Environment and Food and Drug Crime Investigation Detachment of Hangzhou Public Security Bureau Hangzhou China Hangzhou Institute of Technology Xidian University Harbin China Key Laboratory of Peace-Building Big Data of Zhejiang Province Hangzhou China

With the impact of African swine fever, the survival rate of domestic pigs has sharply decreased, and pork prices continue to rise. Many unscrupulous elements obtain illegal profits by avoiding inspection and quarantine, processing sick and dead pigs, and injecting water, etc. Unquarantined meat products pose serious food safety risks. In this context, we propose a machine learning based risk identification method for illegal slaughter of live pigs. Firstly, we use the RetinaNet algorithm to detect live pig transport vehicles and use a dual residual network to Super-Resolution the vehicle area in the detected image to improve facial clarity. Subsequently, the ArcFace algorithm is used to match the facial information with the ex-convicts, and finally, the Location of vehicles passing through, time, frequency and facial analysis results are combined for calculation and scoring and provide the risk warning for illegal slaughter of live pigs.

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TL2GH²T: Triple-Path Local-to-Global Network With Hybrid Head Transformer for Hyperspectral Change Detection

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IEEE Transactions on Geoscience and Remote Sensing 2024年 62卷

作者： Zhonghao Chen Yuyang Wang Swalpa Kumar Roy Hongmin Gao Yao Ding Xiongwu Xiao Zhenfeng Shao Bing Zhang College of Information Science and Engineering Hohai University Changzhou China Department of Computer Science and Engineering Alipurduar Government Engineering and Management College Alipurduar West Bengal India Laboratory of Intelligent Control Rocket Force University of Engineering Xi’an China State Key Laboratory for Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing China College of Resources and Environment University of Chinese Academy of Sciences Beijing China

With the aid of transformers, significant progress has been achieved in hyperspectral image change detection (HSI-CD) in recent times. Nonetheless, most contemporary detection methods fail to incorporate diverse diagnostic features extracted from hyperspectral (HS) images. In addition, relying solely on algebraic-based techniques to extract information of difference is insufficient for achieving satisfactory detection performance. In this regard, we propose an innovative triple-path local-to-global network (TL2GN), complemented by a hybrid head transformer (HybridHT), called TL2GH2T, tailored for HSI-CD tasks. To be specific, TL2GH2T first investigates spatial, spectral, and spatial–spectral features from a local-to-global perspective. Then, a novel spatial and spectral token fusion (SSTF) module is developed to integrate the above three tokenized features, producing discriminative features from two HS images separately. Moreover, drawing inspiration from chromosomal crossover mechanisms, we propose a HybridHT. Its goal is to simultaneously learn cross correlation and self-correlation information of bitemporal features from a global perspective, producing highly discriminative distinctions. Our approach, validated through extensive experimentation on four varied HS benchmarks, exhibits exceptional performance in HSI-CD, outperforming contemporary methods in both visual and quantitative evaluations.

关键词： Feature extraction Transformers Task analysis Correlation Convolutional neural networks Accuracy Head

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Predicting the critical behavior of complex dynamic systems via learning the governing mechanisms

arXiv

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arXiv 2025年

作者： Wang, Xiangrong Lu, Dan Wu, Zongze Xu, Weina Hou, Hongru Hu, Yanqing Moreno, Yamir College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China School of Reliability and Systems Engineering Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China Southern University of Science and Technology Shenzhen China University of Zaragoza Zaragoza50018 Spain Department of Theoretical Physics University of Zaragoza Zaragoza50009 Spain

Critical points separate distinct dynamical regimes of complex systems, often delimiting functional or macroscopic phases in which the system operates. However, the long-term prediction of critical regimes and behaviors is challenging given the narrow set of parameters from which they emerge. Here, we propose a framework to learn the rules that govern the dynamic processes of a system. The learned governing rules further refine and guide the representative learning of neural networks from a series of dynamic graphs. This combination enables knowledge-based prediction for the critical behaviors of dynamical networked systems. We evaluate the performance of our framework in predicting two typical critical behaviors in spreading dynamics on various synthetic and real-world networks. Our results show that governing rules can be learned effectively and significantly improve prediction accuracy. Our framework demonstrates a scenario for facilitating the representability of deep neural networks through learning the underlying mechanism, which aims to steer applications for predicting complex behavior that learnable physical rules can drive. © 2025, CC BY.

关键词： Deep neural networks

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A Novel Deep Learning Approach Featuring Graph-Based Algorithm for Cell Segmentation and Tracking

A Novel Deep Learning Approach Featuring Graph-Based Algorit...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Keliang Zhao Jovial Niyogisubizo Linxia Xiao Yi Pan Dongqing Wei Didi Rosiyadi Yanjie Wei Shenzhen Key Laboratory of Intelligent Bioinformatics and Center for High Performance Computing Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China College of Computer Science and Control Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai China Research Center for Artificial Intelligence and Cybersecurity National Research and Innovation Agency Bandung Indonesia

The precise segmentation and tracking of cells in microscopy image sequences play a pivotal role in biomedical research, facilitating the study of tissue, organ, and organism development. However, manual segmentation and tracking of cells is time-consuming and often require professional experiences. Besides, segmenting cells in the images with a low signal-to-noise ratio remains difficult. While deep learning (DL) has become a common method for cell segmentation, few DL- based methods address concurrent cell segmentation and tracking. In this paper, we propose a novel DL approach featuring graph-based tracking for cell segmentation and tracking in microscopy images. We combine Deeplabv3+ for semantic segmentation and ResNet50 for enhanced feature extraction, enabling comprehensive cell detection and instance segmentation. Post-processing, involving non-maxima suppression and outlier detection, refines predictions and produces final segmentation. The tracking method is based on the relative position of graph nodes to track segmented cells, encompassing cell division and apoptosis. We conduct our experiments on the induced pluripotent stem (iPS) cell datasets, and the results show that the segmentation and tracking performance of our method yields superior performance compared to the benchmark models. More specifically, our approach achieved DET values of 0.955 and 0.913, TRA values of 0.951 and 0.906, and SEG values of 0.690 and 0.665 on two iPS dataset videos, respectively. Additionally, the performance assessment encompasses four real microscopy datasets from the Cell Tracking Challenge (CTC). Our method greatly reduces the cost of manual labeling and labor-intensive costs.

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Making Adversarial Examples More Transferable and Indistinguishable

arXiv

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arXiv 2020年

作者： Zou, Junhua Duan, Yexin Li, Boyu Zhang, Wu Pan, Yu Pan, Zhisong Command and Control Engineering College Army Engineering University Nanjing210007 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Fast gradient sign attack series are popular methods that are used to generate adversarial examples. However, most of the approaches based on fast gradient sign attack series cannot balance the indistinguishability and transferability due to the limitations of the basic sign structure. To address this problem, we propose a method, called Adam Iterative Fast Gradient Tanh Method (AI-FGTM), to generate indistinguishable adversarial examples with high transferability. Besides, smaller kernels and dynamic step size are also applied to generate adversarial examples for further increasing the attack success rates. Extensive experiments on an ImageNet-compatible dataset show that our method generates more indistinguishable adversarial examples and achieves higher attack success rates without extra running time and resource. Our best transfer-based attack NI-TI-DI-AITM can fool six classic defense models with an average success rate of 89.3% and three advanced defense models with an average success rate of 82.7%, which are higher than the state-of-the-art gradient-based attacks. Additionally, our method can also reduce nearly 20% mean perturbation. We expect that our method will serve as a new baseline for generating adversarial examples with better transferability and indistinguishability. Copyright © 2020, The Authors. All rights reserved.

关键词： Network security

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Face Mask Integrated with Flexible and Wearable Manganite Oxide Respiration Sensor

SSRN

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SSRN 2023年

作者： Ye, Lianxu Wu, Fan Xu, Ruixing Zhang, Di Lu, Juanjuan Wang, Chuanlong Dong, Anjiang Xu, Sichen Xue, Lejun Fan, Zixin Xu, Longjie Li, Kaifeng Li, Dong Kursumovic, Ahmed Zhao, Run Tang, Rujun Qiu, Lei Wang, Haiyan MacManus-Driscoll, Judith L. Jing, Qingshen Li, Weiwei Yang, Hao College of Physics MIIT Key Laboratory of Aerospace Information Materials and Physics Nanjing University of Aeronautics and Astronautics Nanjing211106 China College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China School of Microelectronics & Data Science Anhui University of Technology Maanshan243002 China School of Materials Engineering School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Jiangsu Key Laboratory of Thin Films School of Physical Science and Technology Soochow University Suzhou215006 China Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application School of Physical Science and Technology Suzhou University of Science and Technology Suzhou215009 China Department of Materials Science & Metallurgy University of Cambridge 27 Charles Babbage Road CambridgeCB3 0FS United Kingdom James Watt School of Engineering University of Glasgow GlasgowG12 8LT United Kingdom State Key Laboratory of Mechanics and Control of Mechanical Structures Nanjing University of Aeronautics and Astronautics Nanjing211106 China

Face masks are key personal protective equipment for reducing exposure to viruses and other environmental hazards such as air pollution. Integrating flexible and wearable sensors into face masks can provide valuable insights into personal and public health. The advantages that a breath-monitoring face mask requires, including multi-functional sensing ability and continuous, long-term dynamic breathing process monitoring, have been underdeveloped to date. Here, we design an effective human breath monitoring face mask based on a flexible La0.7Sr0.3MnO3 (LSMO)/Mica respiration sensor. The sensor’s capabilities and systematic measurements are investigated under two application scenes, namely clinical monitoring mode and daily monitoring mode, to monitor, recognise, and analyse different human breath status, i.e., cough, normal breath, and deep breath. This sensing system exhibits super-stability and multi-modal capabilities in continuous and long-time monitoring of the human breath. We determine that during monitoring human breath, thermal diffusion in LSMO is responsible for the change of resistance in flexible LSMO/Mica sensor. Both simulated and experimental results demonstrate good discernibility of the flexible LSMO/Mica sensor operating at different breath status. Our work opens a route for the design of novel flexible and wearable electronic devices. © 2023, The Authors. All rights reserved.

关键词： Mica

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Research on Multicloud Access control Policy Integration Framework

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China Communications 2019年第9期16卷 222-234页

作者： Peng Zhao Lifa Wu Zheng Hong He Sun Command&Control Engineering College Army Engineering University of PLANanjing 210007China School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023China PANDA Electronics Group Co. Ltd.Nanjing 210014China

Multicloud access control is important for resource sharing and security interoperability across different clouds,and heterogeneity of access control policy is an important challenge for cloud *** is widely used in distributed environment as a declaratively fine-grained,attribute-based access control policy language,but the policy integration of XACML lacks formal description and theory *** Access control Policy Integration Framework(MACPIF)is proposed in the paper,which consists of Attribute-based Policy Evaluation Model(ABPEM),Four-value Logic with Completeness(FLC)and Four-value Logic based Policy Integration Operators(FLPIOs).ABPEM evaluates access control policy and extends XACML decision to *** to policy decision set and policy integration characteristics,we construct FLC and define FLPIOs including Intersection,Union,Difference,Implication and *** prove that MACPIF can achieve policy monotonicity,functional completeness,canonical suitability and canonical *** results show that this framework can meet the requirements of policy integration in Multicloud.

关键词： Multicloud access control policyintegration four-value logic

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Initial Study on Implementation of Smoothing Filters for the Purpose of Bioimpedance Spectroscopy Parameters Classification

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IFAC-PapersOnLine 2022年第4期55卷 393-398页

作者： Aleksandra Kawala-Sterniuk Amir F. Al-Bakri Mariusz Pelc Katarzyna Cichoń Wojciech Chlewicki Stepan Ozana Radek Martinek Jakub Możaryn Volodymyr Khoma Halyna Kenyo Edward Jacek Gorzelańczyk Michał Podpora Jarosław Zygarlicki Opole University of Technology Faculty of Electrical Engineering Automatic Control and Informatics 45-758 Opole Poland Department of Biomedical Engineering College of Engineering University of Babylon 51001 Babylon Iraq University of Greenwich School of Computing and Mathematical Sciences SE10 9LS London UK West Pomeranian University of Technology in Szczecin Faculty of Electrical Engineering 70-313 Szczecin Poland Faculty of Mechatronics Warsaw University of Technology 02-525 Warsaw Poland Lviv Polytechnic National University Institute of Computer Technologies Automation and Metrology Lviv Ukraine Institute of Philosophy Kazimierz Wielki University 85-092 Bydgoszcz Poland Faculty of Mathematics and Computer Science Adam Mickiewicz University in Poznań 61-614 Poznań Poland

Bioimpedance is a commonly used method for various conditions monitoring. In this paper, the authors carried out some research where they implemented various smoothing filters to enable identification of the bioimpedance spectroscopy parameters. The proposed filtering methods may also be used for application on embedded systems, which have smaller computing power but have become recently very popular. The obtained results with the implementation of smoothing filters were promising, however, some of the obtained results were unsatisfactory. This work also contains a brief introduction to bioimpedance spectroscopy and smoothing filters.

关键词： Bioimpedance Spectroscopy smoothing-filtering Savitzky-Golay Filter Median Filtering Moving-Average Filters

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