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SSRN 2025年

作者： Xu, Fashu Wang, Junqing Tong, Bingkui Fang, Xuyi Zhang, Hui Deng, Tao Wei, Qu Nie, Yong Li, Kang Department of Orthopedic Surgery West China Biomedical Big Data Center West China Hospital Sichuan University Sichuan Chengdu China Department of Orthopedic Surgery Orthopedic Research Institute West China Hospital Sichuan University Sichuan Chengdu China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Shenyang China College of Computer Science Sichuan University Sichuan Chengdu China College of Electrical Engineering Sichuan University Sichuan Chengdu China School of Mechanical Engineering Sichuan University Sichuan Chengdu China

Background and objectiveKnee contact force (KCF) is pivotal in both the study and degenerative pathologies like Knee Osteoarthritis. Traditionally, they are primarily estimated using musculoskeletal modeling and machine learning predictive algorithms. However, musculoskeletal modeling is limited by intricate experimental protocols, prohibitive equipment cost, and scalability challenges. While machine learning methods, though computationally efficient, exhibit suboptimal predictive accuracy. This study innovatively developed a Transformer model for predicting KCF, benchmarked against long short-term memory (LSTM) and multilayer perceptron (MLP) *** comprehensive database was constructed with gait data from 513 participants, incorporating three degrees of freedom of hip, knee flexion/extension, and ankle dorsiflexion/plantarflexion, constituting four input combinations for the models. Contact forces derived from OpenSim served as the ground truth. Root mean square error (RMSE) and coefficient of determination (R2) were used to evaluate the performance of *** results indicated that the Transformer model demonstrated superior performance, achieving a significantly lower mean root mean square error of 0.02 body weight (BW) compared to MLP (0.54 BW) and LSTM (0.45 BW). Furthermore, the mean R2 of the Transformer network (0.99) was markedly superior to that of MLP (0.45) and LSTM (0.58).ConclusionsThe application of Transformer networks significantly improved the accuracy of KCF prediction, addressing the limitations of traditional musculoskeletal modeling and existing machine learning approaches, which was of paramount importance in both clinical practice and biomechanical research. © 2025, The Authors. All rights reserved.

关键词： Mean square error

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Mathematical Modeling of 18F-Fluoromisonidazole (18F-FMISO) Radiopharmaceutical Transport in Vascularized Solid Tumors

arXiv

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arXiv 2024年

作者： Abazari, Mohammad Amin Soltani, M. Eydi, Faezeh Rahmim, Arman Kashkooli, Farshad Moradi Department of Mechanical Engineering K. N. Toosi University of Technology Tehran Iran Department of Electrical and Computer Engineering Faculty of Engineering School of Optometry and Vision Science Faculty of Science University of Waterloo WaterlooN2L 3G Canada Department of Integrative Oncology BC Cancer Research Institute VancouverBCV5Z 1L3 Canada University of Waterloo WaterlooONN2L 3G Canada Cancer Biology Research Center Cancer Institute of Iran Tehran University of Medical Sciences Tehran Iran Departments of Radiology and Physics University of British Columbia VancouverBC Canada

18F-Fluoromisonidazole (18F-FMISO) is a highly promising positron emission tomography radiopharmaceutical for identifying hypoxic regions in solid tumors. This research employs spatiotemporal multi-scale mathematical modeling to explore how different levels of angiogenesis influence the transport of radiopharmaceuticals within tumors. In this study, two tumor geometries with heterogeneous and uniform distributions of capillary networks were employed to incorporate varying degrees of microvascular density. The synthetic image of the heterogeneous and vascularized tumor was generated by simulating the angiogenesis process. The proposed multi-scale spatiotemporal model accounts for intricate physiological and biochemical factors within the tumor microenvironment, such as the transvascular transport of the radiopharmaceutical agent, its movement into the interstitial space by diffusion and convection mechanisms, and ultimately its uptake by tumor cells. Results showed that both quantitative and semi-quantitative metrics of 18F-FMISO uptake differ spatially and temporally at different stages during tumor growth. The presence of a high microvascular density in uniformly vascularized tumor increases cellular uptake, as it allows for more efficient release and rapid distribution of radiopharmaceutical molecules. This results in enhanced uptake compared to the heterogeneous vascularized tumor. In both heterogeneous and uniform distribution of microvessels in tumors, the diffusion transport mechanism has a more pronounced than convection. The findings of this study shed light on the transport phenomena behind 18F-FMISO radiopharmaceutical distribution and its delivery in the tumor microenvironment, aiding oncologists in their routine decision-making processes. © 2024, CC BY.

关键词： Tumors

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Inaugural Issue of the International Journal of Artificial Intelligence and robotics research (IJAIRR): The Emergence of an Interdisciplinary Nexus

International Journal of Artificial Intelligence and Robotic...

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International Journal of Artificial Intelligence and robotics research 2024年第1期1卷

作者： Yu Sun Dong Xu Xiaorui Zhu Computer Science and Engineering Department University of South Florida Tampa FL USA Department of Electrical Engineering and Computer Science University of Missouri-Columbia Columbia MO USA Christopher S. Bond Life Sciences Center University of Missouri–Columbia Columbia MO USA Galaxy Artificial Intelligence and Robotics Research Institute Singapore

For the inaugural issue of the International Journal of Artificial Intelligence and robotics research (IJAIRR), I am honored to present an editorial that encapsulates the essence and ambition of this cutting-edge publication. IJAIRR emerges at a time when Artificial Intelligence and robotics (AIR) are not merely technological novelties but fundamental drivers of progress across various scientific and practical domains. This journal aims to be at the forefront of documenting, analyzing, and guiding the interdisciplinary integration of AI, robotics, and fundamental sciences.

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Exploring quantitative measures in metacognition of emotion

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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

引用

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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