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arXiv 2020年

作者： Zhang, Yu Tiňo, Peter Leonardis, Aleš Tang, Ke The Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Research Institute of Trust-Worthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China The School of Computer Science University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom

Along with the great success of deep neural networks, there is also growing concern about their black-box nature. The interpretability issue affects people's trust on deep learning systems. It is also related to many ethical problems, e.g., algorithmic discrimination. Moreover, interpretability is a desired property for deep networks to become powerful tools in other research fields, e.g., drug discovery and genomics. In this survey, we conduct a comprehensive review of the neural network interpretability research. We first clarify the definition of interpretability as it has been used in many different contexts. Then we elaborate on the importance of interpretability and propose a novel taxonomy organized along three dimensions: type of engagement (passive vs. active interpretation approaches), the type of explanation, and the focus (from local to global interpretability). This taxonomy provides a meaningful 3D view of distribution of papers from the relevant literature as two of the dimensions are not simply categorical but allow ordinal subcategories. Finally, we summarize the existing interpretability evaluation methods and suggest possible research directions inspired by our new taxonomy. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

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Editorial: Focus on organic materials, bio-interfacing and processing in neuromorphic computing and artificial sensory applications

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Neuromorphic Computing and Engineering 2023年第4期3卷 040202-040202页

作者： van de Burgt, Yoeri Santoro, Francesca Tee, Benjamin Alibart, Fabien Institute for Complex Molecular Systems Eindhoven University of Technology Eindhoven Netherlands Microsystems Department of Mechanical Engineering Eindhoven University of Technology Eindhoven Netherlands Faculty of Electrical Engineering and IT RWTH Aachen Aachen Germany Institute of Biological Information Processing—Bioelectronics Forschungszentrum Juelich Germany Department of Materials Science and Engineering (MSE) National University of Singapore Singapore Singapore Department of Electrical and Computer Engineering (ECE) National University of Singapore Singapore Singapore Institut Interdisciplinaire d’Innovation Technologique (3IT) Universiteé de Sherbrooke Sherbrooke Canada Laboratoire Nanotechnologies Nanosystèmes (LN2) CNRS UMI-3463 Universiteé de Sherbrooke Sherbrooke Canada Institute of Electronics Microelectronics and Nanotechnology (IEMN) Universiteé de Lille Villeneuve d’Ascq France

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Hierarchical data visualization of experimental erythrocyte aggregation employing cross correlation and optical flow applications

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Microvascular Research 2022年 143卷 104386页

作者： Gaynes, Bruce I. Shapiro, Mark B. Augustine, Abel Saju Xu, Yang Lin, Yang Mirbod, Parisa Dieter, Robert S. Cheng, Yang Wu, Mengren Venkataraman, Harish Gao, Yuan Petrov, Plamen Xu, Jie Loyola University Chicago Stritch School of Medicine Maywood IL United States Edward Hines Jr. VA Medical Center Hines IL United States Anthem Inc. 220 Virginia Ave Indianapolis 46204 IN United States University of Illinois at Chicago Richard and Loan Hill Department of Biomedical Engineering 851 S Morgan St Chicago 60607 IL United States San Diego State University Department of Computer Science 5500 Campanile Dr San Diego 92182 CA United States University of Rhode Island Department of Mechanical Industrial & Systems Engineering 2 East Alumni Avenue Kingston 02881 RI United States University of Illinois at Chicago Department of Mechanical and Industrial Engineering 842 W Taylor Street Chicago 60607 IL United States University of Southern California Viterbi Department of Computer Science 941 Bloom Walk Los Angeles 90089 CA United States University of Illinois at Chicago Department of Computer Science 851 S Morgan St Chicago 60607 IL United States Hydrogen Health 125 W 25th St New York 10001 NY United States

Appraisal of microvascular erythrocyte velocity as well as aggregation are critical features of hemorheological assessment. Examination of erythrocyte velocity-aggregate characteristics is critical in assessing disorders associated with coagulopathy. Microvascular erythrocyte velocity can be assessed using various methodologic approaches;however, the shared assessment of erythrocyte velocity and aggregation has not been well described. The purpose of this study therefore is to examine three independent erythrocyte assessment strategies with and without experimentally induced aggregation in order to elucidate appropriate analytic strategy for combined velocity/aggregation assessment applicable to in-vivo capillaroscopy. We employed a hierarchical microfluidic model combined with Bland-Altman analysis to examine agreement between three methodologies to assess erythrocyte velocity appropriate for interpretation of cinematography of in-vivo microvascular hemorheology. We utilized optical and manual techniques as well as a technique which we term transversal temporal cross-correlation (TTC) to observe and measure both erythrocyte velocity and aggregation. In general, optical, manual and TTC agree in estimation of velocity at relatively low flow rate, however with an increase in infusion rate the optical flow method yielded the velocity estimates that were lower than the TTC and manual velocity estimates. We suggest that this difference was due to the fact that slower moving particles close to the channel wall were better illuminated than faster particles deeper in the channel which affected the optical flow analysis. Combined velocity/aggregation appraisal using TTC provides an efficient approach for estimating erythrocyte aggregation appropriate for in-vivo applications. We demonstrated that the optical flow and TTC analyses can be used to estimate erythrocyte velocity and aggregation both in ex-vivo microfluidics laboratory experiments as well as in-vivo recordings. Th

关键词： Aggregation Cross-correlation Erythrocyte Hemorheology Microfluidics

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Information systems for Processes Maintenance in Socio-communication and Resource Networks of the Smart Cities

Information Systems for Processes Maintenance in Socio-commu...

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International Conference on computer science and Information Technologies, CSIT 2018

作者： Tabachyshyn, Danylo Kunanets, Nataliia Karpinski, Mykolay Duda, Oleksiy Matsiuk, Oleksandr Information Systems and Networks Department Lviv Polytechnic National University St. Bandera street 12 Lviv Ukraine Computer Science University of Bielsko-Biala Willowa Street 2 Bielsko-Biala Poland Computer Science Ternopil Ivan Puluj National Technical University Ruska street 56 Ternopil Ukraine

ISBN: (纸本)9783030010683

Practical implementation of the Smart city concept is based on the heavy use of so-called "soft" domains characterizing the formation of comfortable urban socio-communicative environment and "hard" domains providing the operation of diverse community services, provision of appropriate services by means of modern information technologies. The analysis of numerous publications provides a strong evidence that at present there are no systematic information-technological developments concerning processes support in social communication and resource networks of the Smart cities. The authors developed a number of information systems based particularly on consolidated socio-communicative resources and database containing information concerning accounting and cost analysis in resource networks of the Smart City. The context diagrams of the consolidated socio-communicative resource and the system of accounting and cost analysis in resource networks of the Smart City are constructed. Function sets of information systems possibilities are formed and corresponding diagrams of UseCases are designed. The database structures of the consolidated socio-communicative resources system and the information system of accounting and cost analysis in resource networks of the Smart City are formed. Relative data base sizing is carried out. The current state of practical implementation of software-algorithmic tools is presented. The obtained results are analyzed, the relevant conclusions are made and the promising trends of further researches are defined. © 2019, Springer Nature Switzerland AG.

关键词： Database systems

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Structural block driven - Enhanced convolutional neural representation for relation extraction

arXiv

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arXiv 2021年

作者： Wang, Dongsheng Tiwari, Prayag Garg, Sahil Zhu, Hongyin Bruza, Peter Department of Computer Science University of Copenhagen Copenhagen Denmark Department of Information Engineering University of Padova Padova Italy École de Technologie Supérieure MontréalQCH3C 1K3 Canada Institute of Automation Chinese Academy of Sciences Beijing China School of Information Systems Queensland University of Technology 2 George St Brisbane CityQLD4000 Australia

In this paper, we propose a novel lightweight relation extraction approach of structural block driven - convolutional neural learning. Specifically, we detect the essential sequential tokens associated with entities through dependency analysis, named as a structural block, and only encode the block on a block-wise and an inter-block-wise representation, utilizing multi-scale Convolutional Neural Networks (CNNs). This is to 1) eliminate the noisy from irrelevant part of a sentence;meanwhile 2) enhance the relevant block representation with both block-wise and inter-block-wise semantically enriched representation. Our method has the advantage of being independent of long sentence context since we only encode the sequential tokens within a block boundary. Experiments on two datasets i.e., SemEval2010 and KBP37, demonstrate the significant advantages of our method. In particular, we achieve the new state-of-the-art performance on the KBP37 dataset;and comparable performance with the state-of-the-art on the SemEval2010 dataset. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolution

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Challenges and possibilities of overtaking strategies for autonomous vehicles

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Periodica Polytechnica Transportation Engineering 2020年第4期48卷 320-326页

作者： Hegedűs, Tamás Németh, Balázs Gáspár, Péter Department of Control for Transportation and Vehicle Systems Faculty of Transportation Engineering and Vehicle Engineering Budapest University of Technology and Economics Stoczek street 2 BudapestH-1111 Hungary Institute for Computer Science and Control Kende street 13-17 BudapestH-1111 Hungary

This paper present three distinct probability-based methods for decision making and trajectory planning layers of overtaking maneuvering functionality for autonomous vehicles. The computation time of the proposed decision-making algorithms may be high, because the number of describing parameters of the traffic situations may vary in a high range. The presented clustering-based, graph-based and dynamic-based methods differ in the complexity of their computation algorithms. Since the decision-making process may require considerable online computation effort, a neural-network-based approach is presented for implementation purposes. © 2020 Budapest University of Technology and Economics. All rights reserved.

关键词： Decision making

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Why is the Winner the Best?

Why is the Winner the Best?

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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DriveSOTIF: Advancing Perception SOTIF Through Multimodal Large Language Models

arXiv

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arXiv 2025年

作者： Huang, Shucheng Shi, Freda Sun, Chen Zhong, Jiaming Ning, Minghao Yang, Yufeng Lu, Yukun Wang, Hong Khajepour, Amir MVSLab Department of Mechanical and Mechatronics Engineering University of Waterloo 200 University Ave West WaterlooONN2L3G1 Canada CompLING Lab David R. Cheriton School of Computer Science University of Waterloo Vector Institute 200 University Ave West WaterlooONN2L3G1 Canada Department of Data and Systems Engineering University of Hong Kong Pok Fu Lam Hong Kong School of Vehicle and Mobility Tsinghua University Beijing100084 China

Human drivers naturally possess the ability to perceive driving scenarios, predict potential hazards, and react instinctively due to their spatial and causal intelligence, which allows them to perceive, understand, predict, and interact with the 3D world both spatially and temporally. Autonomous vehicles, however, lack these capabilities, leading to challenges in effectively managing perception-related Safety of the Intended Functionality (SOTIF) risks, particularly in complex and unpredictable driving conditions. To address this gap, we propose an approach that fine-tunes multimodal language models (MLLMs) on a customized dataset specifically designed to capture perception-related SOTIF scenarios. Model benchmarking demonstrates that this tailored dataset enables the models to better understand and respond to these complex driving situations. Additionally, in real-world case studies, the proposed method correctly handles challenging scenarios that even human drivers may find difficult. Real-time performance tests further indicate the potential for the models to operate efficiently in live driving environments. This approach, along with the dataset generation pipeline, shows significant promise for improving the identification, cognition, prediction, and reaction to SOTIF-related risks in autonomous driving systems. The dataset and information are available: https://***/s95huang/*** © 2025, CC BY-NC-ND.

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Micro-scale Electrostatic Attach-detach Device for Self-reconfigurable Modular Robotic System

Micro-scale Electrostatic Attach-detach Device for Self-reco...

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Design, Test, Integration and Packaging of MEMS/MOEMS

作者： Kei Misumi Gwenn Ulliac Naoto Usami Benoit Piranda Yoshio Mita Akio Higo Julien Bourgeois Department of Electrical Engineering and Information Systems The University of Tokyo Tokyo Japan MN2S FEMTO-ST Besancon France Department of Aeronautics and Astronautics The University of Tokyo Tokyo Japan Computer Science Department the University of Franche-Comté Besancon France Systems Design Lab Graduate School of Engineering The University of Tokyo Tokyo Japan

ISBN: (数字)9781728189017

ISBN: (纸本)9781728189024

We propose a micro-scale electrostatic attach-detach actuator device as an enabling component of micro selfre-configurable modular robotic system (SMR), or programmable matter. The system is composed of a number of identical tiny agents that can adhere each other to form arbitral shapes. The actuator is composed of Au electrodes sandwiched by Parylene-C flexible substrate films. The film is made planar, and the SMR agent is realized by wrapping the actuator film around a shell of a unit robot, made by precise 3-D printing. We have measured the adhesive strength to be 99.3 μN at 50 V, that is sufficient to perform attach demonstration.

关键词： Self-reconfigurable robots electrostatic actuator micro robot programmable matter Microbots Electrostatic actuators Reconfigurable robot actuators Modularity Films Electrostatics bond strength robotic surgical system Printing, Three-Dimensional

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CARDIOCARE platform: A beyond the state of the art approach for the management of elderly multimorbid patients with breast cancer therapy induced cardiac toxicity*

CARDIOCARE platform: A beyond the state of the art approach ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Kostas M. Tsiouris Grigoris Kalliatakis Ketti Mazzocco Bostjan Šeruga Kostas Marias Georgia Karanasiou Athos Antoniades Andri Papakonstantinou Constanza Conti Manolis Tsiknakis Stelios Sfakianakis Lampros Lakkas Gerasimos Filippatos Anca Bucur Dimitrios I. Fotiadis George Manikis Davide Mauri Anastasia Constantinidou Elsa Pacella Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina Greece Computational Bio-Medicine Laboratory Institute of Computer Science Foundation for Research and Technology – Hellas Heraklion Greece Cardiology Division European Institute of Oncology Milan Italy Department of Medical Oncology Institute of Oncology Ljubljana Ljubljana Slovenia Stremble Ventures Ltd Limassol Cyprus Dept. for Breast Cancer Endocrine Tumors and Sarcoma Theme Cancer Karolinska University Hospital Sweden Istituto di Management Sanitario S.r.l Milano Italy Institute of Computer Science Foundation for Research and Technology - Hellas Heraklion Greece 2nd Dept. of Cardiology School of Medicine University of Ioannina Ioannina Greece National and Kapodistrian University of Athens Athens Greece Dept. of Cardiology School of Medicine Attikon University Hospital Athens Greece Philips Research Europe Eindhoven Netherlands Dept. of Medical Oncology University of Ioannina Ioannina Greece Bank of Cyprus Oncology Centre Nicosia Cyprus European Society of Cardiology Brussels Belgium

Breast cancer (BC) is the most common cancer in women in Europe and worldwide, with a high prevalence in middle-aged and older women. The last years, the evolution in the existing treatment approaches have contributed to improved clinical outcomes and survival rates. Nevertheless, BC therapy-related cardiotoxicity, poses a severe impact in the short- and long-term Quality of Life (QoL) and associated survival of the BC patients. This study demonstrates how the CARDIOCARE platform and the developed risk stratification models provides healthcare professionals with a valuable tool for effectively managing BC patients, preventing treatment induced cardiotoxicity and improving their QoL. This is accomplished through the integration of multi-source patient-specific data from patient-oriented mobile applications and wearable sensors, and by the employment of beyond the state-of-the-art data mining and machine learning approaches.

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