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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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Hand tracking accuracy enhancement by data fusion using leap motion and myo armband

Hand tracking accuracy enhancement by data fusion using leap...

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2019 IEEE International Conference on Unmanned Systems and Artificial Intelligence, ICUSAI 2019

作者： Chen, Jingxiang Liu, Chao Cui, Rongxin Yang, Chenguang Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China LIRMM CNRS-University of Montpellier France School of Marine Engineering Northwestern Polytechnical University Xi'an China Bristol Robotics Laboratory University of the West of England BristolBS16 1QY United Kingdom

ISBN: (纸本)9781728158594

In this paper, by using the combination of Leap Motion and Myo armband, two methods for hand tracking and online hand gesture identification are proposed. With the proposed methods, We have improved the measurement accuracy of the palm direction and solved the problem of insufficient accuracy when the palm is at the limit of the measurement range. We use the Kalman filter algorithm and the neural network classification method to process the data measured by Leap Motion and Myo, so that the tracking of the operator's hand gesture is more accurate and robust even when the hand is at positions close to the measurement limit of one single sensor. The methods, which improve the hand tracking accuracy, can be used for robotic control, demonstration or teleoperation. The effectiveness of the proposed methods has been demonstrated through comparative experiments. © 2019 IEEE.

关键词： Palmprint recognition

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Fusion of metadata and dermoscopic images for melanoma detection: Deep learning and feature importance analysis

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Information Fusion 2025年

作者： Misbah Ahmad Imran Ahmed Abdellah Chehri Gwangill Jeon Centre for Machine Vision Bristol Robotics Laboratory University of West of England Frenchy Campus Bristol BS16 1QY Bristol UK Department of Animal and Agriculture Hartpury University Hartpury House Gloucester GL19 3BE Gloucestershire UK School of Computing and Information Science Anglia Ruskin University East Road Cambridge CB1 1PT Cambridgeshire UK Department of Mathematics and Computer Science Royal Military College of Canada (RMC) P.O. Box 17000 Station Forces Kingston K7K 7B4 Ontario Canada Department of Embedded Systems Engineering Incheon National University 119 Academy-ro Incheon 22012 Incheon Republic of Korea

In the era of smart healthcare, integrating multimodal data is essential for improving diagnostic accuracy and enabling personalized care. This study presented a deep learning-based multimodal approach for melanoma detection, leveraging both dermoscopic images and clinical metadata to enhance classification performance. The proposed model integrated a multi-layer convolutional neural network (CNN) to extract image features and combined them with structured metadata, including patient age, gender, and lesion location, through feature-level fusion. The fusion process occurred at the final CNN layer, where high-dimensional image feature vectors were concatenated with processed metadata. The metadata was handled separately through a fully connected neural network comprising multiple dense layers. The final fused representation was passed through additional dense layers, culminating in a classification layer that outputted the probability of melanoma presence. The model was trained end-to-end using the SIIM-ISIC dataset, allowing it to learn a joint representation of image and metadata features for optimal classification. Various data augmentation techniques were applied to dermoscopic images to mitigate class imbalance and improve model robustness. Additionally, exploratory data analysis (EDA) and feature importance analysis were conducted to assess the contribution of each metadata feature to the overall classification. Our fusion-based deep learning architecture outperformed single-modality models, boosting classification accuracy. The presented model achieved an accuracy of 94.5% and an overall F1-score of 0.94, validating its effectiveness in melanoma detection. This study aims to highlight the potential of deep learning-based multimodal fusion in enhancing diagnostic precision, offering a scalable and reliable solution for improved melanoma detection in smart healthcare systems.

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Flow separation control by spanwise alternating wall actuation 11

Flow separation control by spanwise alternating wall actuati...

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11th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2019

作者： Ni, Weidan Moulinec, Charles Yao, Yufeng Fang, Jian Emerson, David R. Lu, Lipeng National Key Laboratory of Science and Technology on Aero-Engine Aero-Thermodynamics School of Energy and Power Engineering Beihang University Beijing100191 China Daresbury Laboratory WarringtonWA4 4AD United Kingdom Faculty of Environment and Technology Department of Engineering Design and Mathematics University of the West of England Bristol BS16 1QY United Kingdom

An implicit large-eddy simulation (ILES) is carried out to study the turbulent boundary layer separation from a backward-facing rounded ramp with active wall actuation control. This method, called spanwise alternating distributed strips (SADS) control, is imposed onto the flat plate surface upstream of the rounded ramp by alternatively applying out-of-phase control (OPC) and in-phase control (IPC) to the wall-normal velocity component in the lateral direction. As a result, the local turbulence is alternatively suppressed by the OPC strips and enhanced by the IPC strips, leading to the creation of a vertical shear layer, which is responsible for the presence of large-scale streamwise vortices (LSSVs). These LSSVs, thought to be similar to Prandtl’s second kind of secondary flow, can be further sustained by the SADS control, exerting a predominant influence on the suppression of the flow separation. The interaction between the LSSVs and the downstream recirculation zone and free shear layer is studied by examining flow statistics, including skin-friction and pressure coefficients, skin friction streamlines, mean streamwise velocity, turbulent kinetic energy and Reynolds stresses. It is found that in comparison with the non-controlled case, the flow separation is delayed, the reattachment point is shifted upstream, and the size of the mean recirculation zone is therefore reduced. A better performance with regards to the separation point is achieved by the case with L+IPC =387 (Case WE). The case with L+IPC =155 (Case W5) behaves better in terms of the reattachment point and the length of the separated zone. The analyses show that the delay of the flow separation is attributed to the activation of the near-wall turbulence by the IPC strips and the improvement of the reattachment location is mainly due to the LSSVs. © 2019 International Symposium on Turbulence and Shear Flow Phenomena, TSFP. All rights reserved.

关键词： Kinetic energy

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Distributedly Solving Boolean Equations over Networks

Distributedly Solving Boolean Equations over Networks

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IEEE Conference on Decision and Control

作者： Hongsheng Qi Bo Li Rui-Juan Jing Alexandre Proutiere Guodong Shi Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China Key Laboratory of Mathematics Mechanization Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Faculty of Science Jiangsu University Zhenjiang China KTH Royal Institute of Technology Stockholm Sweden Australian Center for Field Robotics School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney NSW Australia

ISBN: (数字)9781728174471

ISBN: (纸本)9781728174488

In this paper, we propose distributed algorithms that solve a system of Boolean equations over a network, where each node in the network possesses only one Boolean equation from the system. The Boolean equation assigned at any particular node is a private equation known to this node only, and the nodes aim to compute the exact set of solutions to the system without exchanging their local equations. We show that each private Boolean equation can be locally lifted to a linear algebraic equation under a basis of Boolean vectors, leading to a network linear equation that is distributedly solvable. A number of exact or approximate solutions to the induced linear equation are then computed at each node from different initial values. The solutions to the original Boolean equations are eventually computed locally via a Boolean vector search algorithm. We prove that given solvable Boolean equations, when the initial values of the nodes for the distributed linear equation solving step are i.i.d selected according to a uniform distribution in a high-dimensional cube, our algorithms return the exact solution set of the Boolean equations at each node with high probability.

关键词： Signal processing algorithms Heuristic algorithms Consensus algorithm Distributed algorithms Approximation algorithms Signal processing Convergence

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A multi-objective deep reinforcement learning algorithm for spatio-temporal latency optimization in mobile IoT-enabled edge computing networks

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Simulation Modelling Practice and Theory 2025年 143卷

作者： Parisa Khoshvaght Amir Haider Amir Masoud Rahmani Farhad Soleimanian Gharehchopogh Ferzat Anka Jan Lansky Mehdi Hosseinzadeh Institute of Research and Development Duy Tan University Da Nang Vietnam School of Engineering & Technology Duy Tan University Da Nang Vietnam Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India Department of AI and Robotics Sejong University Seoul 05006 Republic of Korea Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Engineering Ur. C. Islamic Azad University Urmia Iran Data Science Application and Research Center (VEBIM) Fatih Sultan Mehmet Vakif University Istanbul Türkiye Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Pattern Recognition and Machine Learning Laboratory School of Computing Gachon University Seongnam Republic of Korea

The rapid increase in Mobile Internet of Things (IoT) devices requires novel computational frameworks. These frameworks must meet strict latency and energy efficiency requirements in Edge and Mobile Edge Computing (MEC) systems. Spatio-temporal dynamics, which include the position of edge servers and the timing of task schedules, pose a complex optimization problem. These challenges are further exacerbated by the heterogeneity of IoT workloads and the constraints imposed by device mobility. The balance between computational overhead and communication challenges is also a problem. To solve these issues, advanced methods are needed for resource management and dynamic task scheduling in mobile IoT and edge computing environments. In this paper, we propose a Deep Reinforcement Learning (DRL) multi-objective algorithm, called a Double Deep Q-Learning (DDQN) framework enhanced with Spatio-temporal mobility prediction, latency-aware task offloading, and energy-constrained IoT device trajectory optimization for federated edge computing networks. DDQN was chosen for its optimize stability and reduced overestimation in Q-values. The framework employs a reward-driven optimization model that dynamically prioritizes latency-sensitive tasks, minimizes task migration overhead, and balances energy efficiency across devices and edge servers. It integrates dynamic resource allocation algorithms to address random task arrival patterns and real-time computational demands. Simulations demonstrate up to a 35 % reduction in end-to-end latency, a 28 % improvement in energy efficiency, and a 20 % decrease in the deadline-miss ratio compared to benchmark algorithms.

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Nonlinear Dynamics of a Magnetically Coupled Dielectric Elastomer Actuator

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Physical Review Applied 2019年第4期12卷 044033-044033页

作者： Chong-Jing Cao Thomas L. Hill Andrew T. Conn Bo Li Xing Gao Research Center for Medical Robotics and Minimally Invasive Surgical Devices Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China Department of Aerospace Engineering University of Bristol Bristol BS8 1TH UK Bristol Robotics Laboratory Bristol BS16 1QY UK Department of Mechanical Engineering University of Bristol Bristol BS8 1TH UK Shaanxi Key Lab of Intelligent Robots School of Mechanical Engineering Xi’an Jiaotong University Xi’an 710049 China

A magnetically coupled dielectric elastomer actuator (MCDEA) is an emerging two-degree-of-freedom DEA system that demonstrates rich dynamic behaviors and promising potential applications in robotics, energy harvesting, and smart structures. However, the three-dimensional geometry, nonlinearity, and viscoelasticity in the system lead to complex nonlinear dynamic behavior that is challenging to predict. In this work, we develop a numerical model that can accurately characterize its dynamic responses. This model, along with experimental results, demonstrates the complex dynamic phenomena of this MCDEA system, including superharmonic, primary-harmonic, and subharmonic resonances, bifurcations, and multiple modes of oscillation. Dynamic control strategies including phase tuning and frequency control are proposed in this work, allowing control of the amplitude and the appearance of a specific resonance and the occurrence of emerging dynamic behaviors, such as beat phenomena. These findings have numerous additional applications in areas including active vibrational control, energy harvesting, and programmable soft motors for on-demand locomotion.

关键词： Actuating materials Bifurcations Devices Dielectrics Coupled oscillators Dynamical systems Elastomers Ferromagnets

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Dynamic contextualization and comparison as the basis of biologically inspired action understanding

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Paladyn 2018年第1期9卷 19-59页

作者： Alkurdi, Laith Busch, Christian Peer, Angelika Department of Electrical Electronic and Computer Engineering Technische Universität München München Germany Department of Electrical Electronic and Computer Engineering Technische Universität München München Germany Bristol Robotics Laboratory Faculty of Environment and Technology Department of Engineering Design and Mathematics University of the West of England Bristol United Kingdom

People exhibit a robust ability to understand the actions of others around them. In this work, we identify two biologically inspired mechanisms that we hypothesize to be central in the function of action understanding. The first module is a contextual predictor of the observed action, given the goal-directed movement towards objects, and the actions that are allowed to be performed on the object. The second module is a kinematic trajectory parser that validates the previous prediction against a set of learned templates. We model both mechanisms and link them to the environment using the cognitive framework of Dynamic Field Theory and present our first steps into integrating the aforementioned modules into a consistent framework for the purpose of action understanding. The two modules and the combined architecture as a whole are experimentally validated using a recording of an actor performing a series of intentional actions testing the ability of the architecture to understand context and parse actions dynamically. Our initial qualitative results show that action understanding benefits from the combination of the two modules, while any module alone would be insufficient to resolve ambiguity in the perceived actions. © 2018 De Gruyter Open Ltd. All rights reserved.

关键词： Ability testing

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Diurnal variations in the stratosphere of the ultrahot giant exoplanet WASP-121b

arXiv

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arXiv 2022年

作者： Mikal-Evans, Thomas Sing, David K. Barstow, Joanna K. Kataria, Tiffany Goyal, Jayesh Lewis, Nikole Taylor, Jake Mayne, Nathan J. Daylan, Tansu Wakeford, Hannah R. Marley, Mark S. Spake, Jessica J. Max Planck Institute for Astronomy Heidelberg Germany Department of Physics Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA United States Department of Earth & Planetary Sciences Johns Hopkins University BaltimoreMD United States Department of Physics & Astronomy Johns Hopkins University BaltimoreMD United States School of Physical Sciences The Open University Milton Keynes United Kingdom Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States Homi Bhabha National Institute Odisha Jatni India Department of Astronomy and Carl Sagan Institute Cornell University IthacaNY United States Institute for Research on Exoplanets Department of Physics Université de Montréal Montréal Canada University of Oxford Oxford United Kingdom College of Engineering Mathematics and Physical Sciences University of Exeter Exeter United Kingdom Department of Astrophysical Sciences Princeton University PrincetonNJ United States School of Physics University of Bristol Bristol United Kingdom Lunar and Planetary Laboratory Department of Planetary Sciences University of Arizona TucsonAZ United States Division of Geological and Planetary Sciences California Institute of Technology PasadenaCA United States

The temperature profile of a planetary atmosphere is a key diagnostic of radiative and dynamical processes governing the absorption, redistribution, and emission of energy. Observations have revealed dayside stratospheres that either cool1,2 or warm3,4 with altitude for a small number of gas giant exoplanets, while other dayside stratospheres are consistent with constant temperatures.5-7 Here we report spectroscopic phase curve measurements for the gas giant WASP-121b,8 which constrain stratospheric temperatures throughout the diurnal cycle. Variations measured for a water vapour spectral feature reveal a temperature profile that transitions from warming with altitude on the dayside hemisphere to cooling with altitude on the nightside hemisphere. The data are well explained by models assuming chemical equilibrium, with water molecules thermally dissociating at low pressures on the dayside and recombining on the nightside.9,10 Nightside temperatures are low enough for perovskite (CaTiO3) to condense, which could deplete titanium from the gas phase11,12 and explain recent non-detections at the day-night terminator.13-16 Nightside temperatures are also consistent with the condensation of refractory species such as magnesium, iron, and vanadium. Detections15-18 of these metals at the day-night terminator suggest, however, that if they do form nightside clouds, cold trapping does not efficiently remove them from the upper atmosphere. Horizontal winds and vertical mixing could keep these refractory condensates aloft in the upper atmosphere of the nightside hemisphere until they are recirculated to the hotter dayside hemisphere and vaporised. © 2022, CC BY.

关键词： Perovskite

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Efficient MedSAMs: Segment Anything in Medical Images on Laptop

arXiv

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

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