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From FAIR to CURE: Guidelines for Computational Models of Biological systems

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arXiv 2025年

作者： Sauro, Herbert M. Agmon, Eran Blinov, Michael L. Gennari, John H. Hellerstein, Joe Heydarabadipour, Adel Hunter, Peter Jardine, Bartholomew E. May, Elebeoba Nickerson, David P. Smith, Lucian P. Bader, Gary D. Bergmann, Frank Boyle, Patrick M. Dräger, Andreas Faeder, James R. Feng, Song Freire, Juliana Fröhlich, Fabian Glazier, James A. Gorochowski, Thomas E. Helikar, Tomas Hoops, Stefan Imoukhuede, Princess Keating, Sarah M. Konig, Matthias Laubenbacher, Reinhard Loew, Leslie M. Lopez, Carlos F. Lytton, William W. McCulloch, Andrew Mendes, Pedro Myers, Chris J. Myers, Jerry G. Mulugeta, Lealem Niarakis, Anna van Niekerk, David D. Olivier, Brett G. Patrie, Alexander A. Quardokus, Ellen M. Radde, Nicole Rohwer, Johann M. Sahle, Sven Schaff, James C. Sego, T.J. Shin, Janis Snoep, Jacky L. Vadigepalli, Rajanikanth Wiley, H. Steve Waltemath, Dagmar Moraru, Ion Department of Bioengineering University of Washington SeattleWA98195-5061 United States Intelligent Systems Engineering and Biocomplexity Institute Indiana University Street BloomingtonIN47408 United States Center for Cardiovascular Biology University of Washington SeattleWA98195-5061 United States eScience Institute University of Washington SeattleWA98195-5061 United States Institute for Stem Cell and Regenerative Medicine University of Washington SeattleWA98195-5061 United States School of Biological Sciences University of Bristol 24 Tyndall Avenue BristolBS8 1TQ United Kingdom Departments of Physiology & Pharmacology Neurology Downstate Health Science University BrooklynNY11203 United States Department of Neurology Kings County Hospital BrooklynNY11203 United States NASA-John H. Glenn Research Center MS 110-3 21000 Brookpark Road ClevelandOH44135 United States Center for Cell Analysis and Modeling UConn Health 263 Farmington Avenue FarmingtonCT06030-6406 United States Amsterdam Institute for Life and Environment Vrije Universiteit Amsterdam De Boelelaan 1108 Amsterdam1081 HZ Netherlands Department of Pathology and Genomic Medicine Thomas Jefferson University 1020 Locust St PhiladelphiaPA19107 United States Biological Sciences Division Pacific Northwest National Laboratory 902 Battelle Blvd RichlandWA99354 United States Auckland Bioengineering Institute University of Auckland Auckland1010 New Zealand Center of Integrative Biology University of Toulouse III-Paul Sabatier 165 Rue Marianne Grunberg-Manago Toulouse31400 France Lifeware Group Inria Building Alan Turing 1 Rue Honoré d’Estienne d’Orves Palaiseau91120 France Multiscale Modeling Group Altos Labs Redwood CityCA94065 United States partner site Tübingen Tübingen Germany Ottfried-Müller-Str. 37 Tübingen72076 Germany 06120 Germany Department of Computer Science Center for Data Science New York University New YorkNY11201 United States Department of Bi

Guidelines for managing scientific data have been established under the FAIR principles requiring that data be Findable, Accessible, Interoperable, and Reusable. In many scientific disciplines, especially computational biology, both data and models are key to progress. For this reason, and recognizing that such models are a very special type of "data", we argue that computational models, especially mechanistic models prevalent in medicine, physiology and systems biology, deserve a complementary set of guidelines. We propose the CURE principles, emphasizing that models should be Credible, Understandable, Reproducible, and Extensible. We delve into each principle, discussing verification, validation, and uncertainty quantification for model credibility;the clarity of model descriptions and annotations for understandability;adherence to standards and open science practices for reproducibility;and the use of open standards and modular code for extensibility and reuse. We outline recommended and baseline requirements for each aspect of CURE, aiming to enhance the impact and trustworthiness of computational models, particularly in biomedical applications where credibility is paramount. Our perspective underscores the need for a more disciplined approach to modeling, aligning with emerging trends such as Digital Twins and emphasizing the importance of data and modeling standards for interoperability and reuse. Finally, we emphasize that given the non-trivial effort required to implement the guidelines, the community moves to automate as many of the guidelines as possible. © 2025, CC BY.

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Robot-Assisted Navigation for a Robotic Walker with Aided User Intent

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Robot-Assisted Navigation for a Robotic Walker with Aided Us...

IEEE International Symposium on Robot and Human Interactive Communication

作者： Luis Garrote Joao Paulo Joao Perdiz Paulo Peixoto Urbano J. Nunes Institute of Systems and Robotics (ISR-UC) Electrical and Computer Engineering Department (DEEC-UC) University of Coimbra

ISBN: (纸本)9781538679814

This paper presents an approach to robot-assisted navigation on a mobility assistance context, by learning from the user while helping him navigate efficiently and safely in complex environments. Assistive robots such as robotic walkers provide the ability to support a user's body weight on the upper limbs while walking. However, walkers can add an extra layer of distress due to their specific manipulation constraints. For the users of such devices, lack of dexterous upper limb control can be a considerable problem as it means they may be unable to operate these devices efficiently;users may also have visual impairments that reduce their navigational efficiency. The proposed approach uses a Reinforcement Learning (RL) model and a dynamic window-based local motion planning algorithm. It aims to learn needed corrections in the motion command based on the surrounding environment and the user's intent. The proposed solution handles corrections and guides the user through the environment without collisions while learning and aiding the user when he is unable to operate the device efficiently. The RL based approach was tested in indoor scenarios with a robotic walker platform showing preliminary promising results.

关键词： Consumers Walkers Learning Upper Extremity Visual Impairment ambient environment navigation Pedestrians Distress Stressing Environments

Nonlinear signatures of Floquet band topology

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arXiv 2021年

作者： Maluckov, Aleksandra Smolina, Ekaterina Leykam, Daniel Gündogdu, Sinan Angelakis, Dimitris G. Smirnova, Daria A. Vinča Institute of Nuclear Sciences University of Belgrade National Institute of the Republic of Serbia P.O.B. 522 Belgrade11001 Serbia Institute of Applied Physics Russian Academy of Science Nizhny Novgorod603950 Russia Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 117543 Singapore Center for Theoretical Physics of Complex Systems Institute for Basic Science Daejeon34126 Korea Republic of Department of Physics Humboldt-Universität zu Berlin Newtonstrasse 15 Berlin12489 Germany School of Electrical and Computer Engineering Technical University of Crete Chania73100 Greece Nonlinear Physics Centre Australian National University CanberraACT2601 Australia

We study how the nonlinear propagation dynamics of bulk states may be used to distinguish topological phases of slowly-driven Floquet lattices. First, we show how instabilities of nonlinear Bloch waves may be used to populate Floquet bands and measure their Chern number via the emergence of nontrivial polarization textures in a similar manner to static (undriven) lattices. Second, we show how the nonlinear dynamics of non-stationary superposition states may be used to identify dynamical symmetry inversion points in the intra-cycle dynamics, thereby allowing anomalous Floquet phases to be distinguished from the trivial phase. The approaches may be readily implemented using light propagation in nonlinear waveguide arrays. Copyright © 2021, The Authors. All rights reserved.

关键词： Dynamics

Energy End-Use Flexibility of the Next Generation of Decision-Makers in a Smart Grid Setting: An Exploratory Study 1

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5th EAI International Conference on Green Energy and Networking, GreeNets 2018

作者： Reis, Inês F. G. Lopes, Marta A. R. Ferreira, Paula F. V. Antunes, Carlos Henggeler Araújo, Madalena INESC Coimbra - Institute for Systems Engineering and Computers DEEC R. Sílvio Lima Coimbra3030-290 Portugal Department of Environment - ESAC Polytechnic Institute of Coimbra Coimbra3045-601 Portugal ALGORITMI Research Centre University of Minho Campus Azurém Guimarães4800-058 Portugal Department of Electrical and Computer Engineering University of Coimbra Pólo II R. Sílvio Lima Coimbra3030-290 Portugal

ISBN: (数字)9783030129507

ISBN: (纸本)9783030129491

Demand Response (DR) mechanisms have been developed to reshape consumption patterns in face of price signals, enabling to deal with the increasing penetration of intermittent renewable resources and balance electricity demand and supply. Although DR mechanisms have been in place for some time, it is still unclear to what extent end-users are ready, or willing, to embrace DR programs that can be complex and imply adjustments of daily routines. This work aims to understand how the next generation of Portuguese decision makers, namely young adults in higher education, are prepared to deal with energy decisions in the context of the challenges brought by the smart grids. Results demonstrate that cost savings and the contribution to environmental protection are found to be important motivating factors to enroll into DR programs, which should be further exploited in future actions for the promotion of end-user engagement. Moreover, DR solutions are well-accepted by higher education students, although with limited flexibility levels. In addition, there is room to exploit the willingness to adopt time-differentiated tariffs, yet savings should be clearer and more attractive to end-users. Also, the framing effect should be considered when promoting this type of time-differentiated tariffs. © 2019, ICST institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Smart power grids

Localized heat diffusion in topological thermal materials

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arXiv 2021年

作者： Qi, Minghong Wang, Dong Cao, Pei-Chao Zhu, Xue-Feng Qiu, Cheng-Wei Chen, Hongsheng Li, Ying Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou310027 China International Joint Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang The Electromagnetics Academy Zhejiang University Zhejiang University Haining314400 China School of Physics Innovation Institute Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

Various unusual behaviors of artificial materials are governed by their topological properties, among which the edge state at the boundary of a photonic or phononic lattice has been captivated as a popular notion. However, this remarkable bulk-boundary correspondence and the related phenomena are missing in thermal materials. One reason is that heat diffusion is described in a non-Hermitian framework because of its dissipative nature. The other is that the relevant temperature field is mostly composed of modes that extend over wide ranges, making it difficult to be rendered within the tight-binding theory as commonly employed in wave physics. Here, we overcome the above challenges and perform systematic studies on heat diffusion in thermal lattices. Based on a continuum model, we introduce a state vector to link the Zak phase with the existence of the edge state, and thereby analytically prove the thermal bulk-boundary correspondence. We experimentally demonstrate the predicted edge states with a topologically protected and localized heat dissipation capacity. Our finding sets up a solid foundation to explore the topology in novel heat transfer manipulations. © 2021, CC BY.

关键词： Topology

Adaptive leader-follower formation control and obstacle avoidance via deep reinforcement learning

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arXiv 2019年

作者： Zhou, Yanlin Lu, Fan Pu, George Ma, Xiyao Sun, Runhan Chen, Hsi-Yuan Li, Xiaolin Wu, Dapeng National Science Foundation Center for Big Learning Large-scale Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Florida GainesvilleFL United States Department of Mechanical and Aerospace Engineering University of Florida GainesvilleFL United States Amazon Robotics North Reading AI Institute Tongdun Technology

We propose a deep reinforcement learning (DRL) methodology for the tracking, obstacle avoidance, and formation control of nonholonomic robots. By separating vision-based control into a perception module and a controller module, we can train a DRL agent without sophisticated physics or 3D modeling. In addition, the modular framework averts daunting retrains of an image-to-action end-to-end neural network, and provides flexibility in transferring the controller to different robots. First, we train a convolutional neural network (CNN) to accurately localize in an indoor setting with dynamic foreground/background. Then, we design a new DRL algorithm named Momentum Policy Gradient (MPG) for continuous control tasks and prove its convergence. We also show that MPG is robust at tracking varying leader movements and can naturally be extended to problems of formation control. Leveraging reward shaping, features such as collision and obstacle avoidance can be easily integrated into a DRL controller. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

Self-propelled detachment upon coalescence of surface bubbles

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arXiv 2021年

作者： Lv, Pengyu Peñas, Pablo Le The, Hai Eijkel, Jan van den Berg, Albert Zhang, Xuehua Lohse, Detlef State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science BIC-ESAT College of Engineering Peking University Beijing100871 China Physics of Fluids group Faculty of Science and Technology Max Planck - University of Twente Center for Complex Fluid Dynamics MESA+ Institute J. M. Burgers Centre for Fluid Dynamics University of Twente P.O. Box 217 Enschede7500 AE Netherlands BIOS Lab-on-a-Chip group Faculty of Electrical Engineering Max Planck - University of Twente Center for Complex Fluid Dynamics Mathematics and Computer Science MESA+ Institute University of Twente P.O. Box 217 Enschede7500 AE Netherlands Department of Chemical & Materials Engineering University of Alberta EdmontonABT6G1H9 Canada Max Planck Institute for Dynamics and Self-Organization Göttingen37077 Germany

The removal of microbubbles from substrates is crucial for the efficiency of many catalytic and electrochemical gas evolution reactions in liquids. The current work investigates the coalescence and detachment of bubbles generated from catalytic decomposition of hydrogen peroxide. Self-propelled detachment, induced by the coalescence of two bubbles, is observed at sizes much smaller than those determined by buoyancy. Upon coalescence, the released surface energy is partly dissipated by the bubble oscillations, working against viscous drag. The remaining energy is converted to the kinetic energy of the out-of-plane jumping motion of the merged bubble. The critical ratio of the parent bubble sizes for the jumping to occur is theoretically derived from an energy balance argument and found to be in agreement with the experimental results. The present results provide both physical insight for the bubble interactions and practical strategies for applications in chemical engineering and renewable energy technologies like electrolysis. Copyright © 2021, The Authors. All rights reserved.

关键词： Coalescence

Designing and Training of A Dual CNN for Image Denoising

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arXiv 2020年

作者： Zuo, Wangmeng Zhang, David Lin, Chia-Wen Xu, Yong Tian, Chunwei Du, Bo Bio-Computing Research Center Harbin Institute of Technology Shenzhen China Shenzhen Key Laboratory of Visual Object Detection and Recognition ShenzhenGuangdong518055 China Peng Cheng Laboratory Shenzhen518055 China School of Computer Science and Technology Harbin Institute of Technology 150001 HarbinHeilongjiang China Peng Cheng Laboratory Shenzhen518055 China School of Computer Science Wuhan University 430072 WuhanHubei China Department of Electrical Engineering Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan 518172 ShenzhenGuangdong China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

Deep convolutional neural networks (CNNs) for image denoising have recently attracted increasing research interest. However, plain networks cannot recover fine details for a complex task, such as real noisy images. In this paper, we propose a Dual denoising Network (DudeNet) to recover a clean image. Specifically, DudeNet consists of four modules: a feature extraction block, an enhancement block, a compression block, and a reconstruction block. The feature extraction block with a sparse mechanism extracts global and local features via two sub-networks. The enhancement block gathers and fuses the global and local features to provide complementary information for the latter network. The compression block refines the extracted information and compresses the network. Finally, the reconstruction block is utilized to reconstruct a denoised image. The DudeNet has the following advantages: (1) The dual networks with a parse mechanism can extract complementary features to enhance the generalized ability of denoiser. (2) Fusing global and local features can extract salient features to recover fine details for complex noisy images. (3) A Small-size filter is used to reduce the complexity of denoiser. Extensive experiments demonstrate the superiority of DudeNet over existing current state-of-the-art denoising methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Recovery

Signal space cognitive cooperation

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arXiv 2019年

作者： Erdogan, Eylem Afana, Ali Umit Sokun, Hamza Ikki, Salama Durak-Ata, Lutfiye Yanikomeroglu, Halim Department of Electrical and Electronics Engineering Istanbul Medeniyet University Uskudar Istanbul Turkey Department of Electrical Engineering Lakehead University Thunder BayON Canada Informatics Institute Istanbul Technical University Maslak Istanbul Turkey Systems and Computer Engineering Carleton University OttawaON Canada

—In this work, a new transmission scheme, signal space cognitive cooperation, is introduced by applying the idea of signal space diversity in an underlay spectrum sharing decode- and-forward multi-relay cooperative network. In the proposed structure, the secondary source signal is rotated by a certain angle and then the source and the secondary best relay transmit the in-phase and the quadrature components of the rotated signal. As a consequence, two source signals, rather than one, are transmitted in two-time slots which improves data rates considerably, compared to the conventional cognitive cooperative schemes. In this work, proactive relaying mode is used in which the best relay is selected based on the max-min selection criterion before executing the transmission. Considering both statistical and the instantaneous channel state information of the feedback channel between the primary receiver and the secondary network, two power allocation methods are adopted at the source. For both methods, closed-form expressions of error probability are derived. Moreover, asymptotic analysis is performed and diversity gain is obtained to provide further insights about the system performance. Finally, analytical expressions are verified by Monte-Carlo simulations. Copyright © 2019, The Authors. All rights reserved.

关键词： Channel state information