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CARL: Camera-Agnostic Representation Learning for Spectral Image Analysis

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arXiv 2025年

作者： Baumann, Alexander Ayala, Leonardo Seidlitz, Silvia Sellner, Jan Studier-Fischer, Alexander Özdemir, Berkin Maier-Hein, Lena Ilic, Slobodan Siemens AG Munich Germany Heidelberg Germany Medical Faculty Faculty of Mathematics and Computer Science Heidelberg University Germany NCT Heidelberg Germany HIDSS4Health Heidelberg Germany Department of Urology and Urosurgery University Medical Center Mannheim Germany Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Germany Division of Intelligent Systems and Robotics in Urology DKFZ Heidelberg Germany DKFZ Hector Cancer Institute University Medical Center Mannheim Germany

Spectral imaging offers promising applications across diverse domains, including medicine and urban scene understanding, and is already established as a critical modality in remote sensing. However, variability in channel dimensionality and captured wavelengths among spectral cameras impede the development of AI-driven methodologies, leading to camera-specific models with limited generalizability and inadequate cross-camera applicability. To address this bottleneck, we introduce CARL, a model for Camera-Agnostic Representation Learning across RGB, multispectral, and hyperspectral imaging modalities. To enable the conversion of a spectral image with any channel dimensionality to a camera-agnostic embedding, we introduce wavelength positional encoding and a self-attention-cross-attention mechanism to compress spectral information into learned query representations. Spectral-spatial pre-training is achieved with a novel spectral self-supervised JEPA-inspired strategy tailored to CARL. Large-scale experiments across the domains of medical imaging, autonomous driving, and satellite imaging demonstrate our model’s unique robustness to spectral heterogeneity, outperforming on datasets with simulated and real-world cross-camera spectral variations. The scalability and versatility of the proposed approach position our model as a backbone for future spectral foundation models. © 2025, CC BY.

关键词： Image coding

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

Assessing Trustworthy AI in times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

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IEEE Transactions on Technology and Society

IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

Globally exponentially stable attitude observer with Earth velocity estimation

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Globally exponentially stable attitude observer with Earth v...

作者： Batista, Pedro Silvestre, Carlos Oliveira, Paulo Institute for Systems and Robotics Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Department of Electrical and Computer Engineering Faculty of Science and Technology of the University of Macau China Department of Mechanical Engineering Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal

This paper presents a novel observer for attitude estimation based on a triad of high-grade rate gyros aided by a body-fixed vector measurement of a constant inertial vector, departing from the majority of solutions that consider at least two of these vectors. A cascade approach is proposed, where the first block estimates a vector that is related to the angular velocity of the Earth and a second block estimates the attitude. While the topological characteristics are relaxed in the attitude observer so that global exponential stability is achieved, an additional stage is also devised that yields estimates directly on the special orthogonal group, preserving global convergence of the estimation error. Simulation results with realistic sensor noise were performed, including extensive Monte Carlo runs. These results illustrate the convergence of the proposed solution, as well as the achievable performance and robustness to sensor noise. © 2019 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd

关键词： Angular velocity

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

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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

Passive Quadrupedal Gait Synchronization for Extra Robotic Legs Using a Dynamically Coupled Double Rimless Wheel Model

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arXiv 2020年

作者： Gonzalez, Daniel J. Harry Asada, H. Robotics Research Center Department of Electrical Engineering and Computer Science United States Military Academy West PointNY10996 United States d’Arbeloff Laboratory for Information Systems and Technology Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States

The Extra Robotic Legs (XRL) system is a robotic augmentation worn by a human operator consisting of two articulated robot legs that walk with the operator and help bear a heavy backpack payload. It is desirable for the Human-XRL quadruped system to walk with the rear legs lead the front by 25% of the gait period, minimizing the energy lost from foot impacts while maximizing balance stability. Unlike quadrupedal robots, the XRL cannot command the human’s limbs to coordinate quadrupedal locomotion. Using a pair of Rimless Wheel models, it is shown that the systems coupled with a spring and damper converge to the desired 25% phase difference. A Poincaré return map was generated using numerical simulation to examine the convergence properties to different coupler design parameters, and initial conditions. The Dynamically Coupled Double Rimless Wheel system was physically realized with a spring and dashpot chosen from the theoretical results, and initial experiments indicate that the desired synchronization properties may be achieved within several steps using this set of passive components alone. Copyright © 2020, The Authors. All rights reserved.

关键词： Exoskeleton (robotics)

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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Anchor-based spatio-temporal attention 3D convolutional networks for dynamic 3D point cloud sequences

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arXiv 2020年

作者： Wang, Guangming Chen, Muyao Liu, Hanwen Yang, Yehui Liu, Zhe Wang, Hesheng The Department of Automation Institute of Medical Robotics Key Laboratory of System Control and Information Processing Ministry of Education Key Laboratory of Marine Intelligent Equipment System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The Department of Computer Science and Technology University of Cambridge

With the rapid development of measurwement technology, LiDAR and depth cameras are widely used in the perception of the 3D environment. Recent learning based methods for robot perception most focus on the image or video, but deep learning methods for dynamic 3D point cloud sequences are underexplored. Therefore, developing efficient and accurate perception method compatible with these advanced instruments is pivotal to autonomous driving and service robots. An Anchor-based Spatio-Temporal Attention 3D Convolution operation (ASTA3DConv) is proposed in this paper to process dynamic 3D point cloud sequences. The proposed convolution operation builds a regular receptive field around each point by setting several virtual anchors around each point. The features of neighborhood points are firstly aggregated to each anchor based on the spatio-temporal attention mechanism. Then, anchor-based 3D convolution is adopted to aggregate these anchors' features to the core points. The proposed method makes better use of the structured information within the local region and learns spatio-temporal embedding features from dynamic 3D point cloud sequences. Anchor-based Spatio-Temporal Attention 3D Convolutional Neural Networks (ASTA3DCNNs) are built for classification and segmentation tasks based on the proposed ASTA3DConv and evaluated on action recognition and semantic segmentation tasks. The experiments and ablation studies on MSRAction3D and Synthia datasets demonstrate the superior performance and effectiveness of our method for dynamic 3D point cloud sequences. Our method achieves the state-of-the-art performance among the methods with dynamic 3D point cloud sequences as input on MSRAction3D and Synthia datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Embeddings

Intrinsic motivation and episodic memories for robot exploration of high-dimensional sensory spaces

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arXiv 2020年

作者： Schillaci, Guido Villalpando, Antonio Pico Hafner, Verena V. Hanappe, Peter Colliaux, David Wintz, Timothee BioRobotics Institute Scuola Superiore Sant'Anna Pisa Italy Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Pisa Italy Adaptive Systems Group Humboldt-Universität zu Berlin Germany Sony Computer Science Laboratories Paris France

This work presents an architecture that generates curiosity-driven goal-directed exploration behaviours for an image sensor of a microfarming robot. A combination of deep neural networks for offline unsupervised learning of lowdimensional features from images, and of online learning of shallow neural networks representing the inverse and forward kinematics of the system have been used. The artificial curiosity system assigns interest values to a set of pre-defined goals, and drives the exploration towards those that are expected to maximise the learning progress. We propose the integration of an episodic memory in intrinsic motivation systems to face catastrophic forgetting issues, typically experienced when performing online updates of artificial neural networks. Our results show that adopting an episodic memory system not only prevents the computational models from quickly forgetting knowledge that has been previously acquired, but also provides new avenues for modulating the balance between plasticity and stability of the models. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks