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Microscopy Histopathology and Analytics, Microscopy 2024 - Part of Optica Biophotonics Congress: Biomedical Optics

作者： Zhuo, Yue Choi, Ji Sun Marin, Thibault Yu, Hojeong Harley, Brendan A. Cunningham, Brian T. Gordon Center for Medical Imaging Massachusetts General Hospital BostonMA02114 United States Center for Systems Biology Massachusetts General Hospital BostonMA02114 United States Department of Radiology Harvard Medical School BostonMA02114 United States Department of Bioengineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Chemical and Biomolecular Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Micro and Nanotechnology Laboratory University of Illinois at Urbana-Champaign UrbanaIL61801 United States Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Carl R. Woese Institute for Genomic Biology University of Illinois at Urbana-Champaign UrbanaIL61801 United States

With the Photonic Resonator Outcoupler Microscopy (PROM), it is possible to detect and monitor weak-adhesive HSC adhesion without labeling. These findings indicate that PROM can be used to quantitatively and dynamical... 详细信息

ISBN: (纸本)9781957171340

关键词： Stem cells

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A novel LMI-based method for robust stabilization of fractional-order interval systems with 1 ≤ α < 2

arXiv

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arXiv 2020年

作者： Badri, Pouya Sojoodi, Mahdi Advanced Control Systems Laboratory School of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

This paper deals with the problem of robust dynamic output feedback stabilization of interval fractional-order linear time invariant (FO-LTI) systems with the fractional order 1 ≤ α Copyright © 2020, The Author... 详细信息

关键词： Linear matrix inequalities

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Pyramid Pixel Context Adaption Network for Medical Image Classification with Supervised Contrastive Learning

arXiv

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arXiv 2023年

作者： Zhang, Xiaoqing Xiao, Zunjie Wu, Xiao Chen, Yanlin Zhao, Jilu Hu, Yan Liu, Jiang Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen518055 China Singapore Eye Research Institute 169856 Singapore

Spatial attention mechanism has been widely incorporated into deep neural networks (DNNs), significantly lifting the performance in computer vision tasks via long-range dependency modeling. However, it may perform poorly in medical image analysis. Unfortunately, existing efforts are often unaware that long-range dependency modeling has limitations in highlighting subtle lesion regions. To overcome this limitation, we propose a practical yet lightweight architectural unit, Pyramid Pixel Context Adaption (PPCA) module, which exploits multiscale pixel context information to recalibrate pixel position in a pixel-independent manner dynamically. PPCA first applies a well-designed cross-channel pyramid pooling to aggregate multiscale pixel context information, then eliminates the inconsistency among them by the well-designed pixel normalization, and finally estimates per pixel attention weight via a pixel context integration. By embedding PPCA into a DNN with negligible overhead, the PPCANet is developed for medical image classification. In addition, we introduce supervised contrastive learning to enhance feature representation by exploiting the potential of label information via supervised contrastive loss. The extensive experiments on six medical image datasets show that PPCANet outperforms state-of-the-art attention-based networks and recent deep neural networks. We also provide visual analysis and ablation study to explain the behavior of PPCANet in the decision-making process. © 2023, CC BY.

关键词： Pixels

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Adaptive model predictive control of mobile robot with local path refitting

Adaptive model predictive control of mobile robot with local...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Liquan Jiang Yuanlong Xie Zhihui Jiang Jie Meng Wenbin Li State Key Laboratory of New Textile Materials and Advanced Processing Technologies Wuhan Textile University Wuhan China School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan China School of Computer Science Yangtze University Jingzhou China Intelligent Transportation Systems Research Center Wuhan University of Technology Wuhan China

ISBN: (纸本)9781665409858

Path planning and tracking control are important modules of mobile robots, which have outstanding advantages in improving the flexibility of mobile robots. However, in the existing applications, these two modules are usually processed separately, which makes the obstacle avoidance process of the mobile robot uncontrollable and reduces the adaptability of the application scenario. In order to realize collision-free high-precision trajectory tracking control of mobile robots in dynamic scenes, this paper proposes an adaptive MPC control method based on curvature constraints, which can improve the safety of operation and reduce the chattering caused by unreasonable paths in the process of operation. Firstly, according to the kinematic constraints of general mobile robots, the constraint model of mobile robot path planning is constructed. At the same time, the adaptive parameter adjustment rules are adopted to realize the balance between the calculation efficiency and the control cycle. Finally, a smooth safe path is obtained by re-fitting the planned path and reprocessing the nodes with excessive curvature. Compared with traditional methods, the proposed method has better advantages.

关键词： Industrial electronics Electric potential Trajectory tracking Kinematics Safety Planning Mobile robots

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Janus faces of dipolar sources in directional near-field coupling with an oriented misalignment

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Physical Review A 2024年第3期109卷 033505-033505页

作者： Chenxu Bian Yuhan Zhong Xuhuinan Chen Tony Low Hongsheng Chen Baile Zhang Xiao Lin State Key Laboratory of Extreme Photonics and Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center College of Information Science and Electronic Engineering Zhejiang University Hangzhou 310027 China and International Joint Innovation Center The Electromagnetics Academy at Zhejiang University Zhejiang University Haining 314400 China Department of Electrical and Computer Engineering University of Minnesota Minneapolis Minnesota 55455 USA Key Lab of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Jinhua Institute of Zhejiang University Zhejiang University Jinhua 321099 China and Shaoxing Institute of Zhejiang University Zhejiang University Shaoxing 312000 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences and Centre for Disruptive Photonic Technologies Nanyang Technological University Singapore 637371 Singapore

Directional near-field coupling can enable many applications, such as directional routing, on-chip information processing, and chiral quantum optics. This directional near-field coupling is generally face dependent, since the induced near-field radiation pattern is dependent on which face (e.g., the upper or lower face) of the dipolar source is facing toward the outcoupler. In other words, the dipolar sources in the directional near-field coupling intrinsically have two faces (e.g., Janus faces), whose corresponding near-field coupling strength and coupling directionality might be distinct. Here, we outline a general physical framework to control these Janus faces in the directional near-field coupling and find that they can have the feature of an oriented misalignment. To be specific, the upper and lower radiation patterns of excited surface waves can have the same shape but are misaligned with a twist angle. For example, the twist angle can be any acute angle if the source is composed of a Huygens dipole and an electric dipole, and it can be any obtuse angle if the source is constructed by a circular electric dipole and a magnetic dipole.

关键词： Classical optics Light-matter interaction Metamaterials Near-field optics Photonics Plasmonics

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Age-Friendly Protocol to Support Investigations of Autonomous Driving Disengagement on Driver Safety 24th

Age-Friendly Protocol to Support Investigations of Autonomou...

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24th International Conference on Human-computer Interaction, HCI International, HCII 2022

作者： Brightman, Kirsten Van Benthem, Kathleen Wallace, Bruce Herdman, Chris Sloan, Will Sloan, Tom Lochbihler, Aidan Knoefel, Frank Marshall, Shawn Advanced Cognitive Engineering Laboratory Faculty of Arts and Social Sciences Carleton University OttawaONK1S 5B6 Canada Department of Systems and Computer Engineering Faculty of Engineering and Design Carleton University OttawaONK1S 5B6 Canada AGE-WELL NIH—SAM OttawaONK1N 5C8 Canada Bruyère Research Institute OttawaONK1N 5C8 Canada Faculty of Medicine University of Ottawa OttawaONK18L6 Canada Bruyère Continuing Care OttawaONK1N 5C8 Canada

ISBN: (纸本)9783031063930

Driving enables access to meaningful occupations which determine health in older age [1] and the automotive industry is investing significantly in technology to assist and automate the driving task [2]. Little is known regarding the challenges posed by this new technology on the engagement of older drivers [3–5]. Immediate ecologically-valid research is needed to assess the impact of autonomous driving features on the engagement and safety of drivers [6, 7]. However, driving simulation has a high prevalence of simulator sickness and dropout for older participants [8, 9]. We present a novel protocol and framework where onboard sensor data is used to create and tune risk prediction models. Our approach incorporates passive sensing and indices of older driver engagement with a goal to mitigate negative simulator effects and validate results against semi-autonomous vehicle data. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Autonomous vehicles

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Dynamical causality under invisible confounders

arXiv

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arXiv 2024年

作者： Yan, Jinling Zhang, Shao-Wu Zhang, Chihao Huang, Weitian Shi, Jifan Chen, Luonan MOE Key Laboratory of Information Fusion Technology School of Automation Northwestern Polytechnical University Xi’an710072 China Key Laboratory of Systems Biology Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences Shanghai200031 China NCMIS CEMS RCSDS Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematics Sciences University of Chinese Academy of Sciences Beijing100049 China School of Computer Science and Engineering South China University of Technology Guangdong Guangzhou510006 China Guangdong Institute of Intelligence Science and Technology Hengqin Guangdong Zhuhai519031 China Research Institute of Intelligent Complex Systems Fudan University Shanghai200433 China State Key Laboratory of Medical Neurobiology MOE Frontiers Center for Brain Science Institutes of Brain Science Fudan University Shanghai200032 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China Key Laboratory of Systems Health Science of Zhejiang Province School of Life Science Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Chinese Academy of Sciences Hangzhou310024 China

Causality inference is prone to spurious causal interactions, due to the substantial confounders in a complex system. While many existing methods based on the statistical methods or dynamical methods attempt to address misidentification challenges, there remains a notable lack of effective methods to infer causality, in particular in the presence of invisible/unobservable confounders. As a result, accurately inferring causation with invisible confounders remains a largely unexplored and outstanding issue in data science and AI fields. In this work, we propose a method to overcome such challenges to infer dynamical causality under invisible confounders (CIC method) and further reconstruct the invisible confounders from time-series data by developing an orthogonal decomposition theorem in a delay embedding space. The core of our CIC method lies in its ability to decompose the observed variables not in their original space but in their delay embedding space into the common and private subspaces respectively, thereby quantifying causality between those variables both theoretically and computationally. This theoretical foundation ensures the causal detection for any high-dimensional system even with only two observed variables under many invisible confounders, which is actually a long-standing problem in the field. In addition to the invisible confounder problem, such a decomposition actually makes the intertwined variables separable in the embedding space, thus also solving the non-separability problem of causal inference. Extensive validation of the CIC method is carried out using various real datasets, and the experimental results demonstrates its effectiveness to reconstruct real biological networks even with unobserved confounders. © 2024, CC BY-NC-ND.

关键词： Time series

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Training Human Teacher to Improve Robot Learning from Demonstration: A Pilot Study on Kinesthetic Teaching

Training Human Teacher to Improve Robot Learning from Demons...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Maram Sakr Martin Freeman H. F. Machiel Van der Loos Elizabeth Croft Collaborative Advanced Robotics and Intelligent Systems (CARIS) Laboratory Mechanical Engineering University of British Columbia Electrical and Computer Systems Engineering Monash University Stanford University

ISBN: (数字)9781728160757

ISBN: (纸本)9781728160764

Robot Learning from Demonstration (LfD) allows robots to implement autonomous manipulation by observing the movements executed by a demonstrator. As such, LfD has been established as a key element for useful user interactions in everyday environments. Kinesthetic teaching, a teaching technique within LfD, entails physically guiding the robot to achieve a task. When demonstrating complex actions on a multi-DoF manipulator, novice users typically encounter difficulties with trajectory continuity and joint orientation, necessitating training by an expert. A comparison between different training approaches is conducted in a study of nine novice users. These approaches are kinesthetic, observational and discovery-learning. The kinesthetic method utilizes record and playback functions implemented on a 7-DoF Barrett Technology WAM robot. A novice user passively holds the arm while an expert's trajectory is replayed. A visual demonstration by the expert is used for the observational training group. The discovery-learning group does not receive an expert demonstration; they use trial-and-error to produce the trajectory on their own. Task-space performance is evaluated pre- and post-training for each user to determine the relative and absolute performance improvements of the groups across the three training approaches. Absolute performance improvements are compared to the performance of an expert and a minimum-jerk trajectory to gauge how skillful the participant becomes with respect to the expert. The kinesthetic approach shows superior indicators of performance in trajectory similarity to the minimum-jerk trajectory with 39% and 13% improvement over the observational and discovery methods, respectively. Observational training shows greater improvement in terms of the smoothness of the velocity profile with 32.7% compared to 29.5% and 21.9% for both discovery and kinesthetic training, respectively.

关键词： Robots Task analysis Training Trajectory End effectors Robot learning Robot sensing systems

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A Survey on how computer vision can response to urgent need to contribute in COVID-19 pandemics

A Survey on how computer vision can response to urgent need ...

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作者： Gazzah, Sami Bencharef, Omar Latis Laboratory of Advanced Technology and Intelligent Systems National Engineering School of Sousse University of Sousse Tunisia Institut Supérieur des Métiers de la Mine Zouerate Mauritania Fstg Marrakech Computer Science Department *** 40000 Morocco

ISBN: (纸本)9781728180410

The coronavirus first outbreak in Wuhan city of China by December 2019. Due to its highly contagious power, they spread promptly in the four continents. Moreover, it devastating our daily lives and cause huge economic damage. Therefore, it is urgent to detect the positive cases at the earliest and put then under isolation. Automatic virus detection using Machine Learning will be a valuable contribution to prevent the spread of this epidemic. The purpose of this paper is to present short reviews on the coronavirus detection. In reviewing the existing works, we summarized and compared some related works performed on a collection of CT and X-ray images provided from infected patients. We conclude the paper with some discussions on how computer vision can response to urgent need to contribute in pandemics and to investigate many aspects of new viral replication and pathogenesis. © 2020 IEEE.

关键词： Coronavirus

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Learner Action Patterns in the Problem-Solving Process Related to Program Code Composition Based on Tracking System Activities

Learner Action Patterns in the Problem-Solving Process Relat...

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International Conference on Informatics and Computing (ICIC)

作者： Aulia Akhrian Syahidi Ahmad Afif Supianto Tsukasa Hirashima Yutaka Watanobe Smart City Information System Applied Undergraduate Program Politeknik Negeri Banjarmasin Banjarmasin City Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency (BRIN) Bandung City Indonesia Learning Engineering Laboratory Graduate School of Advanced Science and Engineering Hiroshima University Kagamiyama Higashihiroshima Japan Department of Computer Science and Engineering Division of Information Systems University of Aizu Aizu-Wakamatsu City Fukushima Japan

ISBN: (纸本)9798350345728

Learning activities are an indicator of the learner's desire to learn during the learning process. The pattern of learner action is related to learning activities. In this case, in extracting the learning process, it is necessary to collect a lot of data through analysis of the learning process. The purpose of this study is to recommend and report the performance of an activity tracking system equipped with visual artifacts as an educational data mining approach to analyze action patterns when learners complete the arrangement of program code lines in each programming problem. In this study, data were taken from activity recordings when learners used interactive learning media for a basic programming subject called TOLSYASUPI-EduMed which had problem-solving learning models embedded in their interactions. Learning Analytics is used as a method of this research and data relationships from learner actions during the learning process using the “if-then” rule. Log data is used for recording and detecting activities carried out by learners. We confirm that the results of the study are based on the findings of the data relationship that if the lines of program code are too long (more than five lines) then almost all learners experience a bottleneck condition. The completion time of each question and the number of iterations in completing the program code lines for each question have also been recorded and have a pattern of connection between the data. The information that has been obtained is then forwarded to teachers or stakeholders as key information to make appropriate feedback to learners based on the results as a means of analyzing and evaluating the programming learning process.

关键词： Visualization Codes Media Data models Recording Data mining Problem-solving

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