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arXiv 2020年

作者： Pang, Bo Bian, Tao Jiang, Zhong-Ping Control and Networks Lab Department of Electrical and Computer Engineering Tandon School of Engineering New York University 370 Jay Street BrooklynNY11201 United States Bank of America Merrill Lynch One Bryant Park New YorkNY10036 United States

This paper studies the robustness of policy iteration in the context of continuous-time infinite-horizon linear quadratic regulation (LQR) problem. It is shown that Kleinman’s policy iteration algorithm is inherently robust to small disturbances and enjoys local input-to-state stability in the sense of Sontag. More precisely, whenever the disturbance-induced input term in each iteration is bounded and small, the solutions of the policy iteration algorithm are also bounded and enter a small neighborhood of the optimal solution of the LQR problem. Based on this result, an off-policy data-driven policy iteration algorithm for the LQR problem is shown to be robust when the system dynamics are subjected to small additive unknown bounded disturbances. The theoretical results are validated by a numerical example. Copyright © 2020, The Authors. All rights reserved.

关键词： Continuous time systems

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GZMK^high CD8⁺ T effector memory cells are associated with CD15^high neutrophil abundance in non-metastatic colorectal tumors and predict poor clinical outcome (vol 13, 6752, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 6752-6752页

作者： Tiberti, Silvia Catozzi, Carlotta Croci, Ottavio Ballerini, Mattia Cagnina, Danilo Soriani, Chiara Scirgolea, Caterina Gong, Zheng He, Jiatai Macandog, Angeli D. Nabinejad, Amir Lauson, Carina B. Nava Quinte, Arianna Bertalot, Giovanni Petz, Wanda L. Ravenda, Simona P. Licursi, Valerio Paci, Paola Rasponi, Marco Rotta, Luca Fazio, Nicola Ren, Guangwen Fumagalli-Romario, Uberto Schaefer, Martin H. Campaner, Stefano Lugli, Enrico Nezi, Luigi Manzo, Teresa Department of Experimental Oncology European Institute of Oncology- IRCCS Milano Italy Center for Genomic Science of IIT CGS@SEMM (Istituto Italiano di Tecnologia at European School of Molecular Medicine) Fondazione Istituto Italiano di Tecnologia (IIT) Milano Italy Laboratory of Translational Immunology IRCCS- Humanitas Research Hospital- Milan Italy The Jackson Laboratory Bar Harbor ME USA Department of Molecular & Biomedical Sciences University of Maine Orono ME 04469 USA Department of Pathology APSS Trento Hospital & Health Care Trento Italy Digestive Surgery Division European Institute of Oncology-IRCCS Milan Italy Division of Gastrointestinal Medical Oncology and Neuroendocrine Tumors European Institute of Oncology-IRCCS Milan Italy Institute of Molecular Biology and Pathology (IBPM) National Research Council (CNR) of Italy Rome Italy Department of Computer Control and Management Engineering Sapienza - University of Rome Rome Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy

CD8+ T cells are a major prognostic determinant in solid tumors, including colorectal cancer (CRC). However, understanding how the interplay between different immune cells impacts on clinical outcome is still in its infancy. Here, we describe that the interaction of tumor infiltrating neutrophils expressing high levels of CD15 with CD8+ T effector memory cells (T EM) correlates with tumor progression. Mechanistically, stromal cell-derived factor-1 (CXCL12/SDF-1) promotes the retention of neutrophils within tumors, increasing the crosstalk with CD8+ T cells. As a consequence of the contact-mediated interaction with neutrophils, CD8+ T cells are skewed to produce high levels of GZMK, which in turn decreases E-cadherin on the intestinal epithelium and favors tumor progression. Overall, our results highlight the emergence of GZMKhigh CD8+ TEM in non-metastatic CRC tumors as a hallmark driven by the interaction with neutrophils, which could implement current patient stratification and be targeted by novel therapeutics. The tumor immune microenvironment is an important prognostic determinant in colorectal cancer (CRC). Here the authors show that tumor infiltrating neutrophils expressing high levels of CD15 interact with CD8+ T effector memory skewing them to produce GZMK, associated with tumor progression in CRC patients.

关键词： Tumour immunology Colorectal cancer

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Observation of quantum strong Mpemba effect

arXiv

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arXiv 2024年

作者： Zhang, Jie Xia, Gang Wu, Chun-Wang Chen, Ting Zhang, Qian Xie, Yi Su, Wen-Bo Wu, Wei Qiu, Cheng-Wei Chen, Ping-Xing Li, Weibin Jing, Hui Zhou, Yan-Li Institute for Quantum Science and Technology College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Mechanism and technology of Quantum Information Changsha410073 China Hefei National Laboratory Anhui Hefei230088 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Hunan Normal University Changsha410081 China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore School of Physics and Astronomy University of Nottingham NottinghamNG7 2RD United Kingdom Centre for the Mathematics and Theoretical Physics of Quantum Non-equilibrium Systems University of Nottingham NottinghamNG7 2RD United Kingdom College of Science National University of Defense Technology Changsha410073 China

An ancient and counterintuitive phenomenon known as the Mpemba effect showcases the critical role of initial conditions in relaxation processes. How to realize and utilize this effect for speeding up relaxation is an important but challenging task in purely quantum system till now. Here, we experimentally study the strong Mpemba effect in a single trapped ion system in which an exponentially accelerated relaxation in time is observed by preparing an optimal quantum initial state with no excitation of the slowest decaying mode. Also, we demonstrate that the condition of realizing such effect coincides with the Liouvillian exceptional point, featuring the coalescence of both the eigenvalues and the eigenmodes of the systems. Our work provides an efficient strategy to engineer the dynamics of open quantum system, and suggests a link unexplored yet between the Mpemba effect and the non-Hermitian physics. Copyright © 2024, The Authors. All rights reserved.

关键词： Trapped ions

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Path Planning and controlling of Omni-Directional Robot Using Cartesian Odometry and PID Algorithm

Path Planning and Controlling of Omni-Directional Robot Usin...

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International Conference on Computing, Power and Communication Technologies (GUCON)

作者： Tejas Chaudhari Mayank Jha Rohit Bangal Ganesh Chincholkar Department of Instrumentation and Control Engineering Govt. College of Engineering and Research Pune India Department of Computer Engineering Govt. College of Engineering and Research Pune India

ISBN: (纸本)9781728100173

Omni direction is the concept of existence of motion in every direction. Here, we have controlled the resulting vectors of individual wheels of a three wheel omni robot that are ultimately responsible for a stabilized motion of the robot in any direction. This phenomenon is termed as 'Holonomic Drive' and it provides higher efficiency and controllability to non-holonomic drives, when used with odometry and PID algorithms. Odometry basically is method of acquiring the real time positional data of a system in a world frame and using it for path planning. PID algorithm when applied on any system reduces the transient response and helps to attend steady state conditions with less settling time. Here, we have successfully designed and implemented the algorithms for path planning with the help of quadrature encoders and heading control of three wheel omni robot using PID, implemented on IMU(Inertial Measurement Unit).

关键词： Wheels Mobile robots Mathematical model Robot kinematics Robot sensing systems Path planning

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Distributed Learning Model Predictive control for Linear Systems*

arXiv

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arXiv 2020年

作者： Stürz, Yvonne R. Zhu, Edward L. Rosolia, Ugo Johansson, Karl H. Borrelli, Francesco Model Predictive Control Laboratory University of California BerkeleyCA94709 United States Department of Mechanical and Civil Engineering California Institute of Technology PasadenaCA91125 United States School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

This paper presents a distributed learning model predictive control (DLMPC) scheme for distributed linear time invariant systems with coupled dynamics and state constraints. The proposed solution method is based on an online distributed optimization scheme with nearest-neighbor communication. If the control task is iterative and data from previous feasible iterations are available, local data are exploited by the subsystems in order to construct the local terminal set and terminal cost, which guarantee recursive feasibility and asymptotic stability, as well as performance improvement over iterations. In case a first feasible trajectory is difficult to obtain, or the task is non-iterative, we further propose an algorithm that efficiently explores the state-space and generates the data required for the construction of the terminal cost and terminal constraint in the MPC problem in a safe and distributed way. In contrast to other distributed MPC schemes which use structured positive invariant sets, the proposed approach involves a control invariant set as the terminal set, on which we do not impose any distributed structure. The proposed iterative scheme converges to the global optimal solution of the underlying infinite horizon optimal control problem under mild conditions. Numerical experiments demonstrate the effectiveness of the proposed DLMPC scheme. Copyright © 2020, The Authors. All rights reserved.

关键词： Model predictive control

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A PD-Type Iterative Learning Algorithm for Networked control Systems with Missing Data

A PD-Type Iterative Learning Algorithm for Networked Control...

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作者： Xiaoyan Cheng Hongbin Wang Qinzhao Wang Yahui Dong the Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Weapon and Control Department Army Academy of Armored Forces

In this paper,a PD-type iterative learning control method is proposed for a class of nonlinear discrete networked control systems with measurement signal and control signal data *** and differential items of the error signal are employed in this method to modify the current control signal,which takes full advantage of the historical error *** data dropout is described as a stochastic and independent Bernoulli process with a given *** addition,the zero-order holding method is introduced at the data receivers of the controller and ***,the stability analysis and simulation results are performed to verify the convergence and effectiveness of the proposed method.

关键词： siterative learning control networked control systems data dropout nonlinear system

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Machine learning for optimal parameter prediction in quantum key distribution

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Physical Review A 2019年第6期100卷 062334-062334页

作者： Wenyuan Wang Hoi-Kwong Lo Centre for Quantum Information and Quantum Control Department of Electrical and Computer Engineering Department of Physics University of Toronto Toronto M5S3G4 ON Canada

For a practical quantum key distribution (QKD) system, parameter optimization, the choice of intensities and the probabilities of sending them, is a crucial step in gaining optimal performance, especially when one realistically considers a finite communication time. With the increasing interest in the field to implement QKD over free space on moving platforms, such as drones, handheld systems, and even satellites, one needs to perform parameter optimization with low latency and with very limited computing power. Moreover, with the advent of the internet of things, a highly attractive direction of QKD could be a quantum network with multiple devices and numerous connections, which provides a huge computational challenge for the controller that optimizes parameters for a large-scale network. Traditionally, such an optimization relies on brute-force search or local search algorithms, which are computationally intensive, and will be slow on low-power platforms (which increases latency in the system) or infeasible for even moderately large networks. In this work we present a method that uses a neural network to directly predict the optimal parameters for QKD systems. We test our machine learning algorithm on hardware devices including a Raspberry Pi 3 single-board computer (similar devices are commonly used on drones) and a mobile phone, both of which have a power consumption of less than 5 W, and we find a speedup of up to two to four orders of magnitude when compared to standard local search algorithms. The predicted parameters are highly accurate and can preserve, e.g., over 95%–99% of the optimal secure key rate for a given protocol. Moreover, our approach is highly general and can be applied effectively to various kinds of common QKD protocols.

关键词： Machine learning Quantum communication Quantum cryptography Quantum networks

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Achieving data-driven actionability by combining learning and planning

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计算机科学前沿 2018年第5期12卷 939-949页

作者： Qiang LV Yixin CHEN Zhaorong LI Zhicheng CUI Ling CHEN Xing ZHANG Haihua SHEN College of Information Engineering Yangzhou University Yangzhou 225127 China Department of Computer Science and Engineering Washington University in St.Louis St.Louis MO 63130 USA School of Management Fudan University Shanghai 200433 China School of Computer and Control Engineering University of Chinese Academy of Science Beijing 100049 China

A main focus of machine learning research has been improving the generalization accuracy and efficiency of prediction ***,what emerges as missing in many applications is actionability,i.e.,the ability to turn prediction results into *** effort in deriving such actionable knowledge is few and limited to simple action models while in many real applications those models are often more complex and harder to extract an optimal *** this paper,we propose a novel approach that achieves actionability by combining learning with planning,two core areas of *** particular,we propose a framework to extract actionable knowledge from random forest,one of the most widely used and best off-the-shelf *** formulate the actionability problem to a sub-optimal action planning (SOAP) problem,which is to find a plan to alter certain features of a given input so that the random forest would yield a desirable output,while minimizing the total costs of ***,the SOAP problem is formulated in the SAS+ planning formalism,and solved using a Max-SAT based *** experimental results demonstrate the effectiveness and efficiency of the proposed approach on a personal credit dataset and other *** work represents a new application of automated planning on an emerging and challenging machine learning paradigm.

关键词： actionable knowledge extraction machine learning planning random forest

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Efficient Pyramid Channel Attention Network for Pathological Myopia Recognition

arXiv

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arXiv 2023年

作者： Zhang, Xiaoqing Zhao, Jilu Li, Yan Wu, Hao Zhou, Xiangtian Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China National Clinical Research Center for Ocular Diseases Eye Hospital Wenzhou Medical University Wenzhou325027 China State Key Laboratory of Ophthalmology Optometry and Visual Science Eye Hospital Wenzhou Medical University Wenzhou325027 China Research Unit of Myopia Basic Research and Clinical Prevention and Control Chinese Academy of Medical Sciences Wenzhou325027 China Singapore Eye Research Institute 169856 Singapore

Pathological myopia (PM) is the leading ocular disease for impaired vision worldwide. Clinically, the characteristic of pathology distribution in PM is global-local on the fundus image, which plays a significant role in assisting clinicians in diagnosing PM. However, most existing deep neural networks focused on designing complex architectures but rarely explored the pathology distribution prior of PM. To tackle this issue, we propose an efficient pyramid channel attention (EPCA) module, which fully leverages the potential of the clinical pathology prior of PM with pyramid pooling and multi-scale context fusion. Then, we construct EPCA-Net for automatic PM recognition based on fundus images by stacking a sequence of EPCA modules. Moreover, motivated by the recent pretraining-and-finetuning paradigm, we attempt to adapt pre-trained natural image models for PM recognition by freezing them and treating the EPCA and other attention modules as adapters. In addition, we construct a PM recognition benchmark termed PM-fundus by collecting fundus images of PM from publicly available datasets. The comprehensive experiments demonstrate the superiority of EPCA-Net over state-of-the-art methods in the PM recognition task. For example, EPCA-Net achieves 97.56% accuracy and outperforms ViT by 2.85% accuracy on the PM-fundus dataset. The results also show that our method based on the pretraining-and-finetuning paradigm achieves competitive performance through comparisons to part of previous methods based on traditional fine-tuning paradigm with fewer tunable parameters, which has the potential to leverage more natural image foundation models to address the PM recognition task in limited medical data regime. Copyright © 2023, The Authors. All rights reserved.

关键词： Pathology

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Foreword

Lecture Notes in Electrical Engineering

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Lecture Notes in Electrical engineering 2020年 554卷 v-vi页

作者： Zelinka, Ivan Brandstetter, Pavel Dao, Tran Trong Duy, Vo Hoang Kim, Sang Bong Department of Computer Science Faculty of Electrical Engineering VŠB-TUO Poruba Ostrava Czech Republic Department of Electronics Faculty of Electrical Engineering VŠB-TUO Ostrava Czech Republic International Cooperation Research and Training Institute Ton Duc Thang University Ho Chi Minh Viet Nam Department of Automatic Control Ton Duc Thang University Ho Chi Minh City Viet Nam Department of Mechanical Design Engineering Pukyong National University Busan Korea Republic of

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