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arXiv 2024年

作者： Zhang, Yuelin Ding, Qingpeng Lei, Long Shan, Jiwei Xie, Wenxuan Zhang, Tianyi Yan, Wanquan Tang, Raymond Shing-Yan Cheng, Shing Shin Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Medical Physics Graduate Program Duke Kunshan University Jiangsu Kunshan China Department of Medicine and Therapeutics Institute of Digestive Disease The Chinese University of Hong Kong Hong Kong Department of Mechanical and Automation Engineering T Stone Robotics Institute Shun Hing Institute of Advanced Engineering Multi-Scale Medical Robotics Center Institute of Medical Intelligence and XR The Chinese University of Hong Kong Hong Kong

Ultrasound (US)-guided needle insertion is widely employed in percutaneous interventions. However, providing feedback on the needle tip position via US image presents challenges due to noise, artifacts, and the thin imaging plane of US, which degrades needle features and leads to intermittent tip visibility. In this paper, a Mamba-based US needle tracker MambaXCTrack utilizing structured state space models crosscorrelation (SSMX-Corr) and implicit motion prompt is proposed, which is the first application of Mamba in US needle tracking. The SSMX-Corr enhances cross-correlation by longrange modeling and global searching of distant semantic features between template and search maps, benefiting the tracking under noise and artifacts by implicitly learning potential distant semantic cues. By combining with cross-map interleaved scan (CIS), local pixel-wise interaction with positional inductive bias can also be introduced to SSMX-Corr. The implicit lowlevel motion descriptor is proposed as a non-visual prompt to enhance tracking robustness, addressing the intermittent tip visibility problem. Extensive experiments on a dataset with motorized needle insertion in both phantom and tissue samples demonstrate that the proposed tracker outperforms other stateof- the-art trackers while ablation studies further highlight the effectiveness of each proposed tracking module. © 2024, CC BY.

关键词： Needles

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Dynamic Hierarchical Skeleton-Based Network for Human Action Recognition in Video Content

SSRN

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SSRN 2024年

作者： Yang, Hualin Li, Xiying Chen, Calvin Yu-Chian Artificial Intelligence Medical Research Center School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University Guangdong Shenzhen518107 China - Preferred Program Peking University Shenzhen Graduate School Guangdong Shenzhen518055 China School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Guangdong Shenzhen518055 China Department of Medical Research China Medical University Hospital Taichung40447 Taiwan Department of Bioinformatics and Medical Engineering Asia University Taichung41354 Taiwan Guangdong Provincial Key Laboratory of Intelligent Transportation System Sun Yat-sen University Guangzhou China School of Intelligent Systems Engineering Sun Yat-sen University Guangdong Shenzhen518107 China

Human skeleton-based action recognition represents a pivotal field of study, capturing the intricate interplay between physical dynamics and intentional actions. Current research primarily focuses on extracting structural and temporal information from static skeleton-based graphs, but they grapple with a myriad of challenges. These include 1) an absence of hierarchical structure in encoding the skeleton-based graphs;2) A requirement for substantial prior knowledge to interpret the diverse spatial dynamics within singular action labels;3) An intricate task of representing the multifaceted temporal dynamics of individual actions. To counter this issue, we propose DHS-Net, an innovative dynamic hierarchical skeleton-based network in video content. The proposed DHS-Net incorporates the DHS Block as a central element, effectively facilitating information exchange through node concentration and diffusion across both structural and temporal dimensions. Through this architectural design, DHS-Net extracts hierarchical representations, enhancing the comprehensive understanding of spatio-temporal dynamics and marking a significant advancement in human action recognition. Our rigorous experimental evaluations further substantiate DHS-Net's supremacy, outperforming several state-of-the-art benchmarks in skeleton-based action recognition and achieving remarkable prediction accuracy. © 2024, The Authors. All rights reserved.

关键词： Dynamics

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Giant Deep-Ultraviolet Circular Dichroism in Synthetic Architectures of Ordered Carbon Nanotubes

Giant Deep-Ultraviolet Circular Dichroism in Synthetic Archi...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Doumani, Jacques Lou, Minhan Dewey, Oliver Hong, Nina Fan, Jichao Baydin, Andrey Pasquali, Matteo Yomogida, Yohei Yanagi, Kazuhiro Saito, Riichiro Kono, Junichiro Gao, Weilu Department of Electrical and Computer Engineering Rice University HoustonTX United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX United States Department of Electrical and Computer Engineering The University of Utah Salt Lake CityUT United States Carbon Hub Rice University HoustonTX United States Department of Chemical and Biomolecular Engineering Rice University HoustonTX United States J.A. Woollam Co. LincolnNE United States The Smalley-Curl Institute Rice University HoustonTX United States Department of Chemistry Rice University HoustonTX United States Department of Materials Science and NanoEngineering Rice University HoustonTX United States Department of Physics and Astronomy Tokyo Metropolitan University Tokyo Japan Department Physics Tohoku University Sendai Japan Department Physics and Astronomy Rice University HoustonTX United States

We demonstrate tunable, giant, and structure-induced deep-ultraviolet circular dichroism in macroscopically chiral assemblies of racemic carbon nanotubes prepared using two approaches: mechanical-rotation-assisted vac... 详细信息

ISBN: (纸本)9781957171258

关键词： Dichroism

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Inverse designing metamaterials with programmable nonlinear functional responses in graph space

arXiv

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arXiv 2024年

作者： Maurizi, Marco Xu, Derek Wang, Yu-Tong Yao, Desheng Hahn, David Oudich, Mourad Satpati, Anish Bauchy, Mathieu Wang, Wei Sun, Yizhou Jing, Yun Zheng, Xiaoyu Rayne Department of Materials Science and Engineering University of California Berkeley Berkeley94720 United States Department of Computer Science University of California Los Angeles Los Angeles90095 United States Graduate Program in Acoustics Penn State University PA16802 United States Universite’ de Lorraine CNRS Institut Jean Lamour NancyF-54000 France Department of Civil and Environmental Engineering University of California Los Angeles Los Angeles90095 United States

Material responses to static and dynamic stimuli, represented as nonlinear curves, are design targets for engineering functionalities like structural support, impact protection, and acoustic and photonic bandgaps. Three-dimensional metamaterials offer significant tunability due to their internal structure, yet existing methods struggle to capture their complex behavior-to-structure relationships. We present GraphMetaMat, a graph-based framework capable of designing three-dimensional metamaterials with programmable responses and arbitrary manufacturing constraints. Integrating graph networks, physics biases, reinforcement learning, and tree search, GraphMetaMat can target stress-strain curves spanning four orders of magnitude and complex behaviors, as well as viscoelastic transmission responses with varying attenuation gaps. GraphMetaMat can create cushioning materials for protective equipment and vibration-damping panels for electric vehicles, outperforming commercial materials, and enabling the automatic design of materials with on-demand functionalities. Copyright © 2024, The Authors. All rights reserved.

关键词： Metamaterials

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Identification of Features in ANN's Black Box using Dimensional Reduction and Correlation Analysis: A Case Study of Cardiovascular Disease Dataset

Identification of Features in ANN's Black Box using Dimensio...

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International Conference on Advanced Mechatronics, Intelligent Manufacture, and Industrial Automation (ICAMIMIA)

作者： Boby Siswanto Yaya Heryadi Wayan Suparta Antoni Wibowo Haryono Soeparno Lukas Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Atma Jaya University Jakarta Indonesia

ISBN: (纸本)9781665499705

Artificial Neural Network (ANN) is a machine learning algorithm that can perform classification. ANN has limitations; namely, it has a black box working principle, which is unsure which feature is the most influential. This study is to identify the most influential features inside the ANN's black box using a classification model by applying Principal Component Analysis (PCA) dimension reduction combined with Pearson correlation analysis. The result of the proposed model can identify the name of the main features of the data inside the ANN's black box. This study uses two public Kaggle cardiovascular datasets. The first dataset consists of 13 features, and the second dataset consists of 12 features. The result is height and gender are the most influential features in the first dataset with the correlation value of 0.734; sex and smoking are the most influential features in the second dataset with the correlation value of 0.728. Black box model result with 2 PCA's features against a model with height and gender features in the first dataset resulting from the same accuracy on the test dataset of the classification prediction results with the value of 49.90%, while on the second dataset 58.30%.

关键词： Dimensionality reduction Analytical models Correlation Mechatronics Machine learning algorithms Artificial neural networks Predictive models

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Multiscale Numerical Modeling for Near-Field Microwave Impedance Microscopy

Multiscale Numerical Modeling for Near-Field Microwave Imped...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Diego Tami Douglas A. A. Ohlberg Jhonattan C. Ramirez Cássio Gonçalves Do Rego Gilberto Medeiros-Ribeiro Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Brazil Microscopy Center Universidade Federal de Minas Gerais Brazil Department of Electronic Engineering Universidade Federal de Minas Gerais Brazil Computer Science Department Universidade Federal de Minas Gerais Brazil

In this work, we analyzed numerically a multiscale nanosystem based on sMIM on TBG. Spontaneous formation of a water-meniscus by the approximation between the tip-sample concentrates the microwave fields, reaching res... 详细信息

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Microscopy Masers Lasers and electrooptics Laser modes Numerical models Impedance Electrooptical waveguides

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Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding

Chat-UniVi: Unified Visual Representation Empowers Large Lan...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Peng Jin Ryuichi Takanobu Wancai Zhang Xiaochun Cao Li Yuan School of Electronic and Computer Engineering Peking University Shenzhen China Peng Cheng Laboratory Shenzhen China AI for Science (AI4S)-Preferred Program Peking University Shenzhen Graduate School Shenzhen China Nari Technology Co. Ltd. China School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University Shenzhen China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Large language models have demonstrated impressive universal capabilities across a wide range of open-ended tasks and have extended their utility to encompass multi-modal conversations. However, existing methods encounter challenges in effectively handling both image and video understanding, particularly with limited visual tokens. In this work, we introduce Chat-UniVi, a Unified Vision-language model capable of comprehending and engaging in conver-sations involving images and videos through a unified visual representation. Specifically, we employ a set of dynamic visual tokens to uniformly represent images and videos. This representation framework empowers the model to ef-ficiently utilize a limited number of visual tokens to simul-taneously capture the spatial details necessary for images and the comprehensive temporal relationship required for videos. Moreover, we leverage a multi-scale representation, enabling the model to perceive both high-level seman-tic concepts and low-level visual details. Notably, Chat-UniVi is trained on a mixed dataset containing both images and videos, allowing direct application to tasks involving both mediums without requiring any modifications. Exten-sive experimental results demonstrate that Chat- UniVi con-sistently outperforms even existing methods exclusively de-signed for either images or videos. Code is available at https://***/PKu-Yuan Group/Chat-UniVi.

关键词： Bridges Visualization computer vision Codes Large language models Computational modeling Oral communication

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Nanoslot-Induced Ultrastrong Phonon-Photon and Phonon-Phonon Coupling in Hybrid Organic-Inorganic Perovskites 16

Nanoslot-Induced Ultrastrong Phonon-Photon and Phonon-Phonon...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Kim, Dasom Hou, Jin Lee, Geon Agrawal, Ayush Kim, Sunghwan Zhang, Hao Bao, Di Baydin, Andrey Wu, Wenjing Tay, Fuyang Huang, Shengxi Hagenmüller, Daivd Chia, Elbert E. M. Kim, Dai-Sik Seo, Minah Mohite, Aditya D. Kono, Junichiro Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States Sensor System Research Center Korea Institute of Science and Technology Seoul02792 Korea Republic of Department of Physics and Astronomy Seoul National University Seoul08826 Korea Republic of Department of Chemical and Biomolecular Engineering Rice University HoustonTX77005 United States Ulsan44919 Korea Republic of Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 637371 Singapore Smalley-Curl Institute Rice University HoustonTX77005 United States Université de Strasbourg Cnrs Strasbourg67000 France KU-KIST Graduate School of Converging Science and Technology Korea University Seoul02841 Korea Republic of Department of Physics and Astronomy Rice University HoustonTX77005 United States

Methods for harnessing vibrational states are desired for phonon-based technologies. We realized ultrastrong coupling of two phonon modes in perovskite materials induced by ultrastrong coupling with a common photonic ... 详细信息

ISBN: (纸本)9798350372076

关键词： Organic-inorganic materials

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Mathematical representation of emotion using multimodal recognition model with deep multitask learning

IEEJ Transactions on Electronics, Information and Systems

引用

IEEJ Transactions on Electronics, Information and Systems 2020年第12期140卷 1343-1351页

作者： Harata, Seiichi Sakuma, Takuto Kato, Shohei Computer Science Program Department of Engineering Graduate School of Engineering Nagoya Institute of Technology Gokiso-cho Showa-ku Nagoya-city Aichi466-8555 Japan Frontier Research Institute for Information Science Nagoya Institute of Technology Gokiso-cho Showa-ku Nagoya-city Aichi466-8555 Japan

To emulate human emotions in robots, the mathematical representation of emotion is important for each component of affective computing, such as emotion recognition, generation, and expression. In a method that learns DNN from unimodality and represents emotions by vectors of continuous values (Emotional Space), the acquired Emotional Space is likely to depend on the modality, and considering the number of dimensions of Emotional Space is necessary. In this study, we aim at the acquisition of modality independent Emotional Space. We propose a method of acquiring Emotional Space by integrating multimodalities on a DNN and combining the emotion recognition task and unification task of Emotional Space of each modality. Through the experiments with audio-visual data, we confirmed in various dimensions of Emotional Space that there are differences in Emotional Space acquired from unimodality, and the proposed method can acquire a modality independent Emotional Space. We also investigated the compatibility of the recognition and the unification score by changing the number of dimensions of Emotional Space. Finally, we indicate the proposed method can acquire sufficient Emotional Space with a small number of dimensions, such as five or six dimensions, under this paper's experimental conditions. © 2020 The Institute of Electrical Engineers of Japan.

关键词： Deep neural networks

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Terahertz Magnetospectroscopy of Gadolinium Gallium Garnet in Fields up to 25 T

Terahertz Magnetospectroscopy of Gadolinium Gallium Garnet i...

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JSAP-OSA Joint Symposia, JSAP 2021

作者： Bao, J. Peraca, N. Marquez Tay, F. Kritzell, T.E. Li, X. Noe, G.T. Katayama, I. Takeda, J. Nojiri, H. Yamahara, H. Tabata, H. Baydin, A. Kono, J. Department of Physics and Astronomy Rice University HoustonTX United States Department of Electrical and Computer Engineering Rice University HoustonTX United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX United States Department of Physics Graduate School of Engineering Science Yokohama National University Yokohama Japan Institute for Materials Research Tohoku University Sendai Japan Department of Electrical Engineering and Information Systems Graduate School of Engineering University of Tokyo Japan Smalley-Curl Institute Rice University HoustonTX United States Department of Materials Science and NanoEngineering Rice University HoustonTX United States

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