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arXiv 2020年

作者： Ma, Qian Yeh, Edmund Huang, Jianwei School of Intelligent Systems Engineering Sun Yat-sen University China Department of Electrical and Computer Engineering Northeastern University Boston02215 United States Shenzhen Institute of Artificial Intelligence and Robotics for Society School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 Hong Kong

Caching networks can reduce the routing costs of accessing contents by caching contents closer to users. However, cache nodes may belong to different entities and behave selfishly to maximize their own benefits, which often lead to performance degradation for the overall network. While there has been extensive literature on allocating contents to caches to maximize the social welfare, the analysis of selfish caching behaviors remains largely unexplored. In this paper, we model the selfish behaviors of cache nodes as selfish caching games on arbitrary directed graphs with heterogeneous content popularity. We study the existence of a pure strategy Nash equilibrium (PSNE) in selfish caching games, and analyze its efficiency in terms of social welfare. We show that a PSNE does not always exist in arbitrary-topology caching networks. However, if the network does not have a mixed request loop, i.e., a directed loop in which each edge is traversed by at least one content request, we show that a PSNE always exists and can be found in polynomial time. Furthermore, we can avoid mixed request loops by properly choosing request forwarding paths. We then show that the efficiency of Nash equilibria, captured by the price of anarchy (PoA), can be arbitrarily poor if we allow arbitrary content request patterns, and adding extra cache nodes can make the PoA worse, i.e., cache paradox happens. However, when cache nodes have homogeneous request patterns, we show that the PoA is bounded even allowing arbitrary topologies. We further analyze the selfish caching games for cache nodes with limited computational capabilities, and show that an approximate PSNE exists with bounded PoA in certain cases of interest. Simulation results show that increasing the cache capacity in the network improves the efficiency of Nash equilibria, while adding extra cache nodes can degrade the efficiency of Nash equilibria. Copyright © 2020, The Authors. All rights reserved.

关键词： Game theory

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Diffusive nonreciprocity and thermal diode

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Physical Review B 2021年第1期103卷 014307-014307页

作者： Ying Li Jiaxin Li Minghong Qi Cheng-Wei Qiu Hongsheng Chen Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation College of Information Science and Electronic Engineering Zhejiang University Hangzhou 310027 China ZJU-Hangzhou Global Science and Technology Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Zhejiang University Hangzhou 310027 China International Joint Innovation Center ZJU-UIUC Institute The Electromagnetics Academy at Zhejiang University Zhejiang University Haining 314400 China Department of Electrical and Computer Engineering National University of Singapore Singapore 117583 Singapore State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin 150001 China

Wave propagation and diffusion in linear materials preserve local reciprocity in terms of a symmetric Green's function. For wave propagations, the relation between the fields entering and leaving a system is more relevant than the detailed information about the fields inside it. In such cases, the global reciprocity of the scattering off a system through several ports is more important, which is defined as the symmetric transmission between the scattering channels. When a two-port system supports nonreciprocal (electromagnetic, acoustic) wave propagation, it is a (optical, phonon) diode directly following the definition. However, to date no concrete definition or discussion has been made on the global reciprocity of diffusive processes through a multiple-port system. It thus remains unclear what are the differences and relations between the three concepts, namely, local nonreciprocity, global nonreciprocity, and diode effect in diffusion. Here, we provide theoretical analysis on the frequency-domain Green's function and define the global reciprocity of heat diffusion through a two-port system, which has a different setup from that of a thermal diode. We further prove the equivalence between a heat transfer system with broken steady-state global reciprocity and a thermal diode, assuming no temperature-dependent heat generation. The validities of some typical mechanisms in breaking the diffusive reciprocity and making a thermal diode have been discussed. Our results set a general background for future studies on symmetric and asymmetric diffusive processes.

关键词： Diffusion Heat transfer Metamaterials Diodes Green's function methods

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Roadmap on soft robotics: multifunctionality, adaptability and growth without borders

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Multifunctional Materials 2022年第3期5卷 032001-032001页

作者： Mazzolai, Barbara Mondini, Alessio Del Dottore, Emanuela Margheri, Laura Carpi, Federico Suzumori, Koichi Cianchetti, Matteo Speck, Thomas Smoukov, Stoyan K. Burgert, Ingo Keplinger, Tobias Siqueira, Gilberto De Freitas Vanneste, Felix Goury, Olivier Duriez, Christian Nanayakkara, Thrishantha Vanderborght, Bram Brancart, Joost Terryn, Seppe Rich, Steven I. Liu, Ruiyuan Fukuda, Kenjiro Someya, Takao Calisti, Marcello Laschi, Cecilia Sun, Wenguang Wang, Gang Wen, Li Baines, Robert Patiballa, Sree Kalyan Kramer-Bottiglio, Rebecca Rus, Daniela Fischer, Peer Simmel, Friedrich C Lendlein, Andreas Bioinspired Soft Robotics Laboratory Istituto Italiano di Tecnologia Italy Department of Industrial Engineering University of Florence Via di S. Marta 3 Florence50139 Italy Department of Mechanical Engineering Tokyo Institute of Technology Ookayama Meguro Tokyo152-8550 Japan The BioRobotics Institute Scuola Superiore Sant’Anna Pisa56025 Italy Department of Excellence in Robotics & AI Scuola Superiore Sant’Anna Pisa56025 Italy Plant Biomechanics Group & Botanic Garden and Cluster of Excellence livMatS @ FIT University of Freiburg Germany Active and Intelligent Materials Laboratory Queen Mary University of London LondonE1 4NS United Kingdom Wood Materials Science Institute for Building Materials ETH Zurich Zurich8093 Switzerland Cellulose and Wood Materials Laboratory Empa Swiss Federal Laboratories for Materials Science and Technology Dübendorf8600 Switzerland University of Lille Inria CNRS Centrale Lille UMR 9189 CRIStAL LilleF-59000 France Dyson School of Design Engineering Imperial College London United Kingdom Vrije Universiteit Brussel and imec Brussels Belgium Thin-Film Device Laboratory RIKEN 2-1 Hirosawa Saitama Wako351-0198 Japan Center for Emergent Matter Science RIKEN 2-1 Hirosawa Saitama Wako351-0198 Japan Electrical and Electronic Engineering and Information Systems The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan The BioRobotics Institute Scuola Superiore Sant’Anna Italy Lincoln Institute for Agri-food Technology University of Lincoln Lincoln United Kingdom Department of Mechanical Engineering National University of Singapore Singapore School of Mechanical Engineering and Automation Beihang University Beijing100191 China Department of Mechanical Engineering and Materials Science Yale University New HavenCT06511 United States CSAIL MIT Schwarzman College of Computing United States Max Planck Institute for Intelligent Systems Heisenbergstr. 3 Stuttgart70569 Germany Institute of Physical Chemistry

Soft robotics aims at creating systems with improved performance of movement and adaptability in unknown, challenging, environments and with higher level of safety during interactions with humans. This Roadmap on Soft robotics covers selected aspects for the design of soft robots significantly linked to the area of multifunctional materials, as these are considered a fundamental component in the design of soft robots for an improvement of their peculiar abilities, such as morphing, adaptivity and growth. The roadmap includes different approaches for components and systems design, bioinspired materials, methodologies for building soft robots, strategies for the implementation and control of their functionalities and behavior, and examples of soft-bodied systems showing abilities across different environments. For each covered topic, the author(s) describe the current status and research directions, current and future challenges, and perspective advances in science and technology to meet the challenges. © 2022 The Author(s). Published by IOP Publishing Ltd.

关键词： Machine design

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Continuity scaling: A rigorous framework for detecting and quantifying causality accurately

arXiv

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arXiv 2022年

作者： Ying, Xiong Leng, Si-Yang Ma, Huan-Fei Nie, Qing Lai, Ying-Cheng Lin, Wei School of Mathematical Sciences SCMS SCAM Fudan University Shanghai200433 China Research Institute for Intelligent Complex Systems CCSB LCNBI Fudan University Shanghai200433 China State Key Laboratory of Medical Neurobiology MOE Frontiers Center for Brain Science Institutes of Brain Science Fudan University Shanghai200032 China Institute of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai200433 China School of Mathematical Sciences Soochow University Suzhou215006 China Department of Mathematics Department of Developmental and Cell Biology NSF-Simons Center for Multiscale Cell Fate Research University of California IrvineCA92697-3875 United States School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287-5706 United States Shanghai Artificial Intelligence Laboratory Shanghai200232 China

Data based detection and quantification of causation in complex, nonlinear dynamical systems is of paramount importance to science, engineering and beyond. Inspired by the widely used methodology in recent years, the cross-map-based techniques, we develop a general framework to advance towards a comprehensive understanding of dynamical causal mechanisms, which is consistent with the natural interpretation of causality. In particular, instead of measuring the smoothness of the cross map as conventionally implemented, we define causation through measuring the scaling law for the continuity of the investigated dynamical system directly. The uncovered scaling law enables accurate, reliable, and efficient detection of causation and assessment of its strength in general complex dynamical systems, outperforming those existing representative methods. The continuity scaling based framework is rigorously established and demonstrated using datasets from model complex systems and the real world. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamical systems

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Naturally embedded SSVEP phase tagging in a P300-based BCI: LSC-4Q speller*

Naturally embedded SSVEP phase tagging in a P300-based BCI: ...

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IEEE International Conference on systems, Man and Cybernetics

作者： Gabriel Pires Mine Yasemin Urbano J. Nunes Institute for Systems and Robotics University of Coimbra Portugal Engineering Department of the Polytechnic Institute of Tomar Tomar Portugal Department of Electrical and Computer Engineering of the University of Coimbra Portugal

This paper proposes a P300-based BCI speller called LSC-4Q that takes advantage of steady state visual evoked potentials (SSVEP) that appear naturally in the brain as a side effect of the inter-stimulus interval of P300 visual paradigms. The LSC-4Q speller has a circular layout divided into quadrants, and symbols flash individually with a given pseudo-random strategy. Controlling the sequence of the events such that consecutive flashes alternate between sides or quadrants of the speller, we research the possibility of detecting the SSVEP phase associated with the side or quadrant for which the user is focusing on the target symbol, without explicitly incorporating a SSVEP flickering stimulator. The SSVEP phase is extracted using a statistical spatio-spectral Fisher criterion beamformer (SSFCB) implemented in the frequency domain. Results show that SSFCB efficiently extracts phase tags from the SSVEPs embedded on the visual evoked potentials of the oddball paradigm. Preliminary results with 4 participants suggest that it is possible to detect with high accuracy the side of the screen to which the user is looking, and with less precision the detection of the quadrant. Main issues are related to phase variability across sessions. Online results show that some participants can benefit from the combined P300-Lateral approach, improving the overall classification when P300 misclassifications occur.

关键词： Feature extraction Electroencephalography Visualization Time-frequency analysis Layout Standards

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Retraction notice to "Modeling of adsorptive removal of azithromycin from aquatic media by CoFe2O4/NiO anchored microalgae-derived nitrogen-doped porous activated carbon adsorbent and colorimetric quantifying of azithromycin in pharmaceutical products" [Chemosphere 329 (2023) 138635]

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Chemosphere 2025年 375卷 144147页

作者： Fuad Ameen Reza Mostafazadeh Yasamin Hamidian Nevin Erk Afsaneh L Sanati Ceren Karaman Ali Ayati Department of Botany and Microbiology College of Science King Saud University Riyadh 11451 Saudi Arabia. Ankara University Faculty of Pharmacy Department of Analytical Chemistry 06560 Ankara Turkey. Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Polo II 3030-290 Coimbra Portugal. Akdeniz University Vocational School of Technical Sciences Department of Electricity and Energy Antalya 07070 Turkey. ChemBio Cluster ITMO University Lomonosova Street 9 Saint Petersburg 191002 Russia.

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://***/about/policies/article-withdrawal). This article has been retracted at the request of the Editors-in-Chief. A journal-wide investigation by Elsevier's Research Integrity & Publishing Ethics team identified violations of the journal's policies on conflict of interest related to the submission and review of this paper. Review of this submission was handled by Guest Editor Elena-Niculina Dragoi despite an extensive recent record of collaboration, including co-publication, with two of the paper co-authors (Ceren Karaman, Nevin Erk). In addition, there was a significant increase of citations of papers authored by the Guest Editor and by author Karaman between the original submission and the revised version; 6 papers by Dragoi and 8 by Karaman were added in the revised version of the paper. This compromised the editorial process and breached the journal's policies. The authors disagree with this retraction and dispute the grounds for it.

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Learning data-adaptive non-parametric kernels

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 8590-8628页

作者： Fanghui Liu Xiaolin Huang Chen Gong Jie Yang Li Li Department of Electrical Engineering ESAT-STADIUS KU Leuven Belgium Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China and Department of Computing Hong Kong Polytechnic University Hong Kong SAR China Department of Automation BNRist Tsinghua University China

In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllable trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalability issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.

关键词： support vector machines non-parametric kernel learning gradient-Lipschitz continuous

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Assessing the effects of time-dependent restrictions and control actions to flatten the curve of COVID-19 in Kazakhstan

arXiv

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arXiv 2020年

作者： Do, Ton Duc Gui, Meei Mei Ng, Kok Yew Department of Robotics and Mechatronics School of Engineering and Digital Sciences Nazarbayev University Nur-Sultan Kazakhstan School of Chemistry and Chemical Engineering Queen's University Belfast Belfast United Kingdom Engineering Research Institute University of Ulster Belfast United Kingdom Electrical and Computer Systems Engineering School of Engineering Monash University Malaysia Bandar Sunway Malaysia

This paper presents the assessment of time-dependent national-level restrictions and control actions and their effects in fighting the COVID-19 pandemic. By analysing the transmission dynamics during the first wave of COVID-19 in the country, the effectiveness of the various levels of control actions taken to flatten the curve can be better quantified and understood. This in turn can help the relevant authorities to better plan for and control the subsequent waves of the pandemic. To achieve this, a deterministic population model for the pandemic is firstly developed to take into consideration the time-dependent characteristics of the model parameters, especially on the ever-evolving value of the reproduction number, which is one of the critical measures used to describe the transmission dynamics of this pandemic. The reproduction number alongside other key parameters of the model can then be estimated by fitting the model to real-world data using numerical optimisation techniques or by inducing ad-hoc control actions as recorded in the news platforms. In this paper, the model is verified using a case study based on the data from the first wave of COVID-19 in the Republic of Kazakhstan. The model is fitted to provide estimates for two settings in simulations;time-invariant and time-varying (with bounded constraints) parameters. Finally, some forecasts are made using four scenarios with time-dependent control measures so as to determine which would reflect on the actual situations better. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

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Correction to: MR beyond diagnostics at the ESMRMB annual meeting: MR theranostics and intervention

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Magma (New York, N.Y.) 2024年第5期37卷 947-948页

作者： Milan Hájek Ulrich Flögel Adriana A S Tavares Lucia Nichelli Aneurin Kennerley Thomas Kahn Jurgen J Futterer Aikaterini Fitsiori Holger Grüll Nandita Saha Felipe Couñago Dogu Baran Aydogan Maria Eugenia Caligiuri Cornelius Faber Laura C Bell Patrícia Figueiredo Joan C Vilanova Francesco Santini Ralf Mekle Sonia Waiczies Department of Diagnostic and Interventional Radiology Institute for Clinical and Experimental Medicine Prague Czech Republic. Experimental Cardiovascular Imaging Institute for Molecular Cardiology Heinrich Heine University Düsseldorf Düsseldorf Germany. Centre for Cardiovascular Sciences and Edinburgh Imaging University of Edinburgh Edinburgh UK. Sorbonne Université Inserm CNRS UMR S 1127 Paris Brain Institute ICM Paris France. Department of Neuroradiology AP-HP Pitié-Salpêtrière Hospital Paris France. Department of Sports and Exercise Science Institute of Sport Manchester Metropolitan University Manchester UK. Department of Biology University of York York UK. Department of Diagnostic and Interventional Radiology University of Leipzig Leipzig Germany. Minimally Invasive Image-Guided Intervention Center (MAGIC) Department of Medical Imaging Radboudumc Nijmegen The Netherlands. Unit of Diagnostic and Interventional Neuroradiology Diagnostic Department University Hospitals of Geneva Geneva Switzerland. Institute of Diagnostic and Interventional Radiology Faculty of Medicine University Hospital of Cologne University of Cologne Cologne Germany. Department of Chemistry Faculty of Mathematics and Natural Sciences University of Cologne Cologne Germany. Max-Delbrück-Centrum Für Molekulare Medizin (MDC) Berlin Ultrahigh Field Facility Berlin Germany. Charité-Universitätsmedizin Berlin Berlin Germany. Department of Radiation Oncology Hospital Universitario San Francisco de Asís Hospital Universitario Vithas La Milagrosa GenesisCare 28010 Madrid Spain. A.I. Virtanen Institute for Molecular Sciences University of Eastern Finland Kuopio Finland. Neuroscience Research Center Department of Medical and Surgical Sciences Università Degli Studi "Magna Graecia" Catanzaro Italy. Translational Research Imaging Center (TRIC) Clinic of Radiology University of Münster Münster Germany. Early Clinical Development Genentech Inc. South San Francisco USA. Institute for S

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Mobile Robot Localization with Reinforcement Learning Map Update Decision aided by an Absolute Indoor Positioning System

Mobile Robot Localization with Reinforcement Learning Map Up...

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2019 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Luís Garrote Miguel Torres Tiago Barros João Perdiz Cristiano Premebida Urbano J. Nunes Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Portugal Dept. of Aeronautical and Automotive Engineering Loughborough University UK

This paper introduces a new mobile robot localization solution consisting of two main modules: a Particle-Filter based Localization (PFL) and a Reinforcement-Learning based map updating, integrating relative measurements and absolute indoor positioning sensor (A-IPS) data. Concerning localization using 2D-LiDARs, featureless areas are known to be problematic. To solve this problem a classic PFL approach was modified to incorporate A-IPS position measurements in the prediction and update stages. The localization approach has the particularity of including the possibility of updating the map whenever major modifications are detected in the environment in relation to the current localization map. Due to the random sampling-based nature of the PFL, an associated map update solution is not trivial since small inconsistencies in the estimated pose can lead to erroneous map associations. The proposed method learns to decide by assigning higher rewards the greater is the overlap between the map and the 2DLIDAR scans, via RL, and then a proper update of the map is achieved. Validation of the proposed pipeline was carried out in a differential drive platform with algorithms developed in ROS. Tests were performed in two scenarios in order to assess the performance of both the localization module and the map update stage. The results show that the proposed localization method offers improvements in relation to known approaches, and consequently suggest promising perspectives for the proposed map update decision framework.

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