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Exploring quantitative measures in metacognition of emotion

Scientific Reports 2024年第1期14卷 1-10页

作者： Lee, Hsing-Hao Liu, Gabrielle Kaili-May Chen, Yi-Chuan Yeh, Su-Ling Department of Psychology New York University New York NY United States Department of Psychology National Taiwan University Taipei Taiwan Department of Computer Science Yale University New Haven CT United States Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge MA United States Department of Mathematics Massachusetts Institute of Technology Cambridge MA United States Department of Medicine MacKay Medical College Taipei Taiwan Graduate Institute of Brain and Mind Sciences National Taiwan University Taipei Taiwan Neurobiology and Cognitive Science Center National Taiwan University Taipei Taiwan Center for Artificial Intelligence and Advanced Robotics National Taiwan University Taipei Taiwan National Humanities Center Research Triangle Park NC United States

Metacognition of emotion (meta-emotion) refers to the ability to evaluate and identify one’s emotional feelings. No previous study has defined and measured this construct through objective and quantitative procedures. We established a reliable method to measure meta-emotion. With a two-interval forced-choice procedure, participants selected which of two pictures elicited stronger positive emotion;via the Law of Comparative Judgment, their responses were used to compute individual psychological distances for the emotional responses triggered by the pictures. Then, participants were asked to judge whether a pre-exposed picture induced a stronger positive emotion than the median of that elicited by the whole picture set, followed by a confidence rating. By utilizing each individual’s psychological distance, the correctness of a participant’s emotional experience was quantified by dʹ, and meta-emotion was quantified using meta-dʹ, M-ratio, and M-diff as indices of metacognitive sensitivity and efficiency based on Signal-Detection Theory. Test–retest reliabilities, validated by Spearman correlation, were observed in meta-dʹ, M-ratio, and marginally with M-diff, suggesting the stability of meta-emotion in the current design. This study unveils a validated procedure to quantify meta-emotion, extendable for assessing metacognition of other subjective feelings. Nevertheless, caution is warranted in interpretation, as the measured processes may be influenced by non-metacognitive factors. © 2024, The Author(s).

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Design of a Printed Circuit Board (PCB) for Electrical Integration on the Agile Ground Robot (AGRO)

Design of a Printed Circuit Board (PCB) for Electrical Integ...

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2020 IEEE MIT Undergraduate research Technology Conference, URTC 2020

作者： Rodriguez, Andres H. Gonzalez, Daniel J. Lesak, Mark C. Robotics Research Center Department of Electrical Engineering and Computer Science United States Military Academy West PointNY10996 United States United States Military Academy Army Cyber Institute West PointNY10996 United States

ISBN: (纸本)9781728175713

A custom Printed Circuit Board (PCB) was designed and fabricated to integrate several electrical sensing and actuation components and their communication circuits for the rapid development of robots such as the Agile Ground Robot (AGRO). AGRO is a hybrid wheeled/legged robot designed to be quickly deployed by emergency teams and operate autonomously or by remote control to perform tasks such as reconnaissance, inspection, mapping, or object detection. The central computer for such a robot requires the use of motor controllers, servomechanisms, and sensors such as Inertial Measurement Units (IMUs) to operate. AGRO in particular requires inter-device digital communication using CAN, RS232, and S-Bus interfaces. The PCB was designed to meet these requirements through component selection, schematic design, circuit layout, and fabrication. The PCB is mountable as a 'shield' on top the AGRO's Mbed microcontroller, controlling all on-board components and systems through its firmware. The board also features additional pins, switches, and power breakouts for expanding its capabilities. © 2020 IEEE.

关键词： Printed circuit boards

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Enhance to read better: A multi-task adversarial network for handwritten document image enhancement

arXiv

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arXiv 2021年

作者： Jemni, Sana Khamekhem Souibgui, Mohamed Ali Kessentini, Yousri Fornés, Alicia MIR@CL: Multimedia InfoRmation systems and Advanced Computing Laboratory Computer Vision Center Computer Science Department Universitat Autònoma de Barcelona Spain Digital Research Center of Sfax B.P. 275 Sakiet Ezzit Sfax3021 Tunisia SM@RTS : Laboratory of Signals SysteMs ARtificial Intelligence and neTworkS

Handwritten document images can be highly affected by degradation for different reasons: Paper ageing, daily-life scenarios (wrinkles, dust, etc.), bad scanning process and so on. These artifacts raise many readability issues for current Handwritten Text Recognition (HTR) algorithms and severely devalue their efficiency. In this paper, we propose an end to end architecture based on Generative Adversarial Networks (GANs) to recover the degraded documents into a clean and readable form. Unlike the most well-known document binarization methods, which try to improve the visual quality of the degraded document, the proposed architecture integrates a handwritten text recognizer that promotes the generated document image to be more readable. To the best of our knowledge, this is the first work to use the text information while binarizing handwritten documents. Extensive experiments conducted on degraded Arabic and Latin handwritten documents demonstrate the usefulness of integrating the recognizer within the GAN architecture, which improves both the visual quality and the readability of the degraded document images. Moreover, we outperform the state of the art in H-DIBCO challenges, after fine tuning our pre-trained model with synthetically degraded Latin handwritten images, on this task. © 2021, CC BY.

关键词： Recurrent neural networks

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6D pose estimation with combined deep learning and 3D vision techniques for a fast and accurate object grasping

arXiv

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arXiv 2021年

作者： Le, Tuan-Tang Le, Trung-Son Chen, Yu-Ru Vidal, Joel Lin, Chyi-Yeu Department of Mechanical Engineering National Taiwan University of Science and Technology Taipei106 Taiwan Computer Vision and Robotics Institute University of Girona Girona17003 Spain Taiwan Building Technology Center National Taiwan University of Science and Technology Taipei106 Taiwan Center for Cyber-Physical System National Taiwan University of Science and Technology Taipei106 Taiwan

Real-time robotic grasping, supporting a subsequent precise object-in-hand operation task, is a priority target towards highly advanced autonomous systems. However, such an algorithm which can perform sufficiently-accurate grasping with time efficiency is yet to be found. This paper proposes a novel method with a 2-stage approach that combines a fast 2D object recognition using a deep neural network and a subsequent accurate and fast 6D pose estimation based on Point Pair Feature framework to form a real-time 3D object recognition and grasping solution capable of multi-object class scenes. The proposed solution has a potential to perform robustly on real-time applications, requiring both efficiency and accuracy. In order to validate our method, we conducted extensive and thorough experiments involving laborious preparation of our own dataset. The experiment results show that the proposed method scores 97.37% accuracy in 5cm5deg metric and 99.37% in Average Distance metric. Experiment results have shown an overall 62% relative improvement (5cm5deg metric) and 52.48% (Average Distance metric) by using the proposed method. Moreover, the pose estimation execution also showed an average improvement of 47.6% in running time. Finally, to illustrate the overall efficiency of the system in real-time operations, a pick-and-place robotic experiment is conducted and has shown a convincing success rate with 90% of accuracy. This experiment video is available at https://***/view/dl-ppf6dpose/. © 2021, CC BY.

关键词： Real time systems

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Robust and Precise Facial Landmark Detection by Self-Calibrated Pose Attention Network

arXiv

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arXiv 2021年

作者： Wan, Jun Xi, Hui Zhou, Jie Lai, Zhihui Pedrycz, Witold Wang, Xu Sun, Hang School of Information and Safety Engineering Zhongnan University of Economics and Law Wuhan430073 China College of Computer Science and Software Engineering Shen zhen University Shenzhen518060 China Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Department of Electrical & Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland College of Computer and Information Technology China Three Gorges University Yichang HuBei China

Current fully-supervised facial landmark detection methods have progressed rapidly and achieved remarkable performance. However, they still suffer when coping with faces under large poses and heavy occlusions for inaccurate facial shape constraints and insufficient labeled training samples. In this paper, we propose a semi-supervised framework, i.e., a Self-Calibrated Pose Attention Network (SCPAN) to achieve more robust and precise facial landmark detection in challenging scenarios. To be specific, a Boundary-Aware Landmark Intensity (BALI) field is proposed to model more effective facial shape constraints by fusing boundary and landmark intensity field information. Moreover, a Self-Calibrated Pose Attention (SCPA) model is designed to provide a self-learned objective function that enforces intermediate supervision without label information by introducing a self-calibrated mechanism and a pose attention mask. We show that by integrating the BALI fields and SCPA model into a novel self-calibrated pose attention network, more facial prior knowledge can be learned and the detection accuracy and robustness of our method for faces with large poses and heavy occlusions have been improved. The experimental results obtained for challenging benchmark datasets demonstrate that our approach outperforms state-of-the-art methods in the literature. Copyright © 2021, The Authors. All rights reserved.

关键词： Face recognition

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Structure-aware long short-term memory network for 3D cephalometric landmark detection

arXiv

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arXiv 2021年

作者： Chen, Runnan Ma, Yuexin Chen, Nenglun Liu, Lingjie Cui, Zhiming Lin, Yanhong Wang, Wenping department of computer science University of Hong Kong Hong Kong Hong Kong school of information science and technology ShanghaiTech University Shanghai Engineering Research Center of Intelligent Vision and Imaging Shanghai China Max Planck Institute for Informatics Saarbrücken Germany

Detecting 3D landmarks on cone-beam computed tomography (CBCT) is crucial to assessing and quantifying the anatomical abnormalities in 3D cephalometric analysis. However, the current methods are time-consuming and suffer from large biases in landmark localization, leading to unreliable diagnosis results. In this work, we propose a novel Structure-Aware Long Short-Term Memory framework (SA-LSTM) for efficient and accurate 3D landmark detection. To reduce the computational burden, SA-LSTM is designed in two stages. It first locates the coarse landmarks via heatmap regression on a down-sampled CBCT volume and then progressively refines landmarks by attentive offset regression using high-resolution cropped patches. To boost accuracy, SA-LSTM captures global-local dependence among the cropping patches via self-attention. Specifically, a graph attention module implicitly encodes the landmark’s global structure to rationalize the predicted position. Furthermore, a novel attention-gated module recursively filters irrelevant local features and maintains high-confident local predictions for aggregating the final result. Experiments show that our method significantly outperforms state-of-the-art methods in terms of efficiency and accuracy on an in-house dataset and a public dataset, achieving 1.64 mm and 2.37 mm average errors, respectively, and using only 0.5 seconds for inferring the whole CBCT volume of resolution 768×768×576. Moreover, all predicted landmarks are within 8 mm error, which is vital for acceptable cephalometric analysis. © 2021, CC BY-NC-SA.

关键词： Brain

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Task Scheduling Strategies for Utility Maximization in a Renewable-Powered IoT Node

Task Scheduling Strategies for Utility Maximization in a Ren...

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作者： Leithon, Johann Suarez, Luis A. Anis, Muhammad Moiz Jayakody, Dushantha Nalin K. Department of Signal Processing and Acoustics Aalto University Espoo126107 Finland Moscow Research Center Huawei Technologies Ltd. Moscow75290 Russia Dhanani School of Science and Engineering Habib University Karachi634050 Pakistan School of Computer Science and Robotics National Research Tomsk Polytechnic University Tomsk11500 Russia

In this paper, we propose a task scheduling strategy for an Internet of Things (IoT) node powered by renewable energy (RE). The node is assumed to have a rechargeable battery and an RE harvester. Moreover, the node is requested to perform {M} tasks over a planning period of {N}~\geq ~{M} time slots. For each task, a priority rating and a reward are assigned. With these considerations we develop a mathematical framework to optimize the utility of the node, defined as the sum of rewards over the specified planning horizon. Using the proposed framework, we derive a genie-aided strategy, which serves as a performance benchmark for online algorithms. We then propose two online task scheduling strategies of different complexity level, which correspond to a Mixed Integer Linear Programming (MILP) based approach and later on, a simpler sorting-based mechanism is also introduced. The presented techniques use existing forecasting methods to estimate future RE production. We finally evaluate the performance of the proposed strategies and their robustness to forecasting errors through extensive simulations. The impact of system parameters such as battery size and RE harvesting capacity are also examined numerically. © 2017 IEEE.

关键词： Energy harvesting

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The IDEAL framework for surgical robotics: development, comparative evaluation and long-term monitoring (vol 30, pg 61, 2024)

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NATURE MEDICINE 2024年第4期30卷 1213-1213页

作者： Marcus, Hani J. Ramirez, Pedro T. Khan, Danyal Z. Horsfall, Hugo Layard Hanrahan, John G. Williams, Simon C. Beard, David J. Bhat, Rani Catchpole, Ken Cook, Andrew Hutchison, Katrina Martin, Janet Melvin, Tom Stoyanov, Danail Rovers, Maroeska Raison, Nicholas Dasgupta, Prokar Noonan, David Stocken, Deborah Sturt, Georgia Vanhoestenberghe, Anne Vasey, Baptiste McCulloch, Peter Department of Neurosurgery National Hospital of Neurology and Neurosurgery London UK Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS) London UK Department of Obstetrics and Gynaecology Houston Methodist Hospital Neal Cancer Center Houston TX USA RCS Surgical Interventional Trials Unit (SITU) & Robotic and Digital Surgery Initiative (RADAR) Nuffield Dept Orthopaedics Rheumatology and Musculo-skeletal Sciences University of Oxford Oxford UK Department of Gynaecological Oncology Apollo Hospital Bengaluru India Department of Anaesthesia and Perioperative Medicine Medical University of South Carolina Charleston SC USA NIHR Coordinating Centre and Clinical Trials Unit University of Southampton Southampton UK Department of Philosophy Macquarie University Sydney Australia Department of Anesthesia & Perioperative Medicine University of Western Ontario Ontario Canada Department of Medical Gerontology School of Medicine Trinity College Dublin Dublin Republic of Ireland Department of Medical Imaging Radboudumc Nijmegen the Netherlands Department of Biomedical Engineering and Imaging Sciences King’s College London London UK King’s Health Partners Academic Surgery King’s College London London UK Moon Surgical Paris France RCSEng Surgical Trials Centre Leeds Institute of Clinical Trials Research University of Leeds Leeds UK Bowel Research UK London UK School of Biomedical Engineering & Imaging Sciences King’s College London London UK Department of Surgery Geneva University Hospital Geneva Switzerland Nuffield Department of Surgical Sciences University of Oxford John Radcliffe Hospital Oxford UK PRISMA Lab University of Naples Federico II Naples Italy Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich Switzerland School of Computer Science University of Birmingham Birmingham UK Division of Cardiac Surgery Brigham and Women’s Hospital Harvard Medical Schoo

The next generation of surgical robotics is poised to disrupt healthcare systems worldwide, requiring new frameworks for evaluation. However, evaluation during a surgical robot’s development is challenging due to their complex evolving nature, potential for wider system disruption and integration with complementary technologies like artificial intelligence. Comparative clinical studies require attention to intervention context, learning curves and standardized outcomes. Long-term monitoring needs to transition toward collaborative, transparent and inclusive consortiums for real-world data collection. Here, the Idea, Development, Exploration, Assessment and Long-term monitoring (IDEAL) robotics Colloquium proposes recommendations for evaluation during development, comparative study and clinical monitoring of surgical robots—providing practical recommendations for developers, clinicians, patients and healthcare systems. Multiple perspectives are considered, including economics, surgical training, human factors, ethics, patient perspectives and sustainability. Further work is needed on standardized metrics, health economic assessment models and global applicability of recommendations.

关键词： Medical research Surgery

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MutualNet: Adaptive ConvNet via Mutual Learning from Different Model Configurations

arXiv

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arXiv 2021年

作者： Yang, Taojiannan Zhu, Sijie Mendieta, Matias Wang, Pu Balakrishnan, Ravikumar Lee, Minwoo Han, Tao Shah, Mubarak Chen, Chen The Center for Research in Computer Vision University of Central Florida OrlandoFL32816 United States The Department of Computer Science University of North Carolina at Charlotte CharlotteNC28223 United States Intel Labs New Jersey Institute of Technology NewarkNJ07102 United States The Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

Most existing deep neural networks are static, which means they can only perform inference at a fixed complexity. But the resource budget can vary substantially across different devices. Even on a single device, the affordable budget can change with different scenarios, and repeatedly training networks for each required budget would be incredibly expensive. Therefore, in this work, we propose a general method called MutualNet to train a single network that can run at a diverse set of resource constraints. Our method trains a cohort of model configurations with various network widths and input resolutions. This mutual learning scheme not only allows the model to run at different width-resolution configurations but also transfers the unique knowledge among these configurations, helping the model to learn stronger representations overall. MutualNet is a general training methodology that can be applied to various network structures (e.g., 2D networks: MobileNets, ResNet, 3D networks: SlowFast, X3D) and various tasks (e.g., image classification, object detection, segmentation, and action recognition), and is demonstrated to achieve consistent improvements on a variety of datasets. Since we only train the model once, it also greatly reduces the training cost compared to independently training several models. Surprisingly, MutualNet can also be used to significantly boost the performance of a single network, if dynamic resource constraints are not a concern. In summary, MutualNet is a unified method for both static and adaptive, 2D and 3D networks. Code and pre-trained models are available at https://***/taoyang1122/MutualNet. Copyright © 2021, The Authors. All rights reserved.

关键词： Budget control

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Bacteria Flagella-Mimicking Polymer Multilayer Magnetic Microrobots

SSRN

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SSRN 2024年

作者： Wu, Zhiguang Lu, Liang Bai, Shuang Shi, Jiaqi Zhang, Zhuotao Pan, Yunlu Wang, Wei Wu, Wenfeng Sun, Shoubin Li, Xu Hou, Gang Huang, Tianyun Jia, Yuxin Zuo, Kaiwen Granovsky, Alexander Wu, He Nikolai, Perov School of Medicine and Health Harbin Institute of Technology Harbin China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China The First Affiliated Hospital of Harbin Medical University Harbin China National Center for Respiratory Medicine State Key Laboratory of Respiratory Health and Multimorbidity National Clinical Research Center for Respiratory Diseases Institute of Respiratory Medicine Chinese Academy of Medical Sciences Department of Pulmonary and Critical Care Medicine Center of Respiratory Medicine China-Japan Friendship Hospital Beijing China College of Science Sichuan Agricultural University Chengdu China The Fourth Affiliated Hospital of Harbin Medical University Harbin China Department of Ophthalmology The Second Hospital Jilin University Changchun China Department of Advanced Manufacturing and Robotics College of Engineering State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China Department of Mathematics Imperial College London United Kingdom Department of Computer Science University of Warwick Coventry United Kingdom Magnetism Department Faculty of Physics Lomonosov Moscow State University Moscow Russia Harbin Medical University China

Learning from natural bacteria flagellum, we demonstrate a magnetic polymer multilayer conical microrobot that bestow the controllable propulsion upon external rotating magnetic field with uniform intensity. The magnetic microrobots were constructed by template-assisted Layer-by-Layer technique technique and subsequent functionalization of magnetic particle onto the big opening of the microrobots. Geometric variables of the polymer microrobots, such as the diameter and wall thickness, can be controlled by selection of porous template and layers of assembly. The microrobots perform controllable propulsion through the manipulation of magnetic field. The comparative analysis of the movement behavior reveals that the deformation of microrobots may attributed to the propulsion upon rotating magnetic field, which is similar as that of natural bacteria. The influence of actuation frequencies and frequencies on the velocity of the microrobots is studied. Such use of polymer polymer multilayer tubular micro/nanoarchitectures results in significant savings in the processing costs and provides a simple and cost-effective fabrication route for the mass production of magnetic polymer microrobots. © 2024, The Authors. All rights reserved.

关键词： Bacteria

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