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arXiv 2022年

作者： Żelasko, Piotr Feng, Siyuan Velázquez, Laureano Moro Abavisani, Ali Bhati, Saurabhchand Scharenborg, Odette Hasegawa-Johnson, Mark Dehak, Najim Center of Language and Speech Processing The Johns Hopkins University 3400 North Charles Street BaltimoreMD21218 United States Human Language Technology Center of Excellence The Johns Hopkins University 810 Wyman Park Drive BaltimoreMD21218 United States Multimedia Computing Group Delft University of Technology Van Mourik Broekmanweg 6 Delft2628 XE Netherlands Department of Electrical and Computer Engineering University of Illinois 405 N Mathews UrbanaIL61801 United States

The high cost of data acquisition makes Automatic Speech Recognition (ASR) model training problematic for most existing languages, including languages that do not even have a written script, or for which the phone inventories remain unknown. Past works explored multilingual training, transfer learning, as well as zero-shot learning in order to build ASR systems for these low-resource languages. While it has been shown that the pooling of resources from multiple languages is helpful, we have not yet seen a successful application of an ASR model to a language unseen during training. A crucial step in the adaptation of ASR from seen to unseen languages is the creation of the phone inventory of the unseen language. The ultimate goal of our work is to build the phone inventory of a language unseen during training in an unsupervised way without any knowledge about the language. In this paper, we 1) investigate the influence of different factors (i.e., model architecture, phonotactic model, type of speech representation) on phone recognition in an unknown language;2) provide an analysis of which phones transfer well across languages and which do not in order to understand the limitations of and areas for further improvement for automatic phone inventory creation;and 3) present different methods to build a phone inventory of an unseen language in an unsupervised way. To that end, we conducted mono-, multi-, and crosslingual experiments on a set of 13 phonetically diverse languages and several in-depth analyses. We found a number of universal phone tokens (IPA symbols) that are well-recognized cross-linguistically. Through a detailed analysis of results, we conclude that unique sounds, similar sounds, and tone languages remain a major challenge for phonetic inventory discovery. © 2022, CC BY-NC-ND.

关键词： Telephone sets

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SUMMARY ON THE ICASSP 2022 MULTI-CHANNEL MULTI-PARTY MEETING TRANSCRIPTION GRAND CHALLENGE

arXiv

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arXiv 2022年

作者： Yu, Fan Zhang, Shiliang Guo, Pengcheng Fu, Yihui Du, Zhihao Zheng, Siqi Huang, Weilong Xie, Lei Tan, Zheng-Hua Wang, DeLiang Qian, Yanmin Lee, Kong Aik Yan, Zhijie Ma, Bin Xu, Xin Bu, Hui Speech Lab Alibaba Group China Speech Lab Alibaba Group Singapore AISHELL Foundation China Beijing Shell Shell Technology Co. Ltd. Beijing China Department of Electronic Systems Aalborg University Aalborg Denmark Department of Computer Science and Engineering Center for Cognitive and Brain Sciences The Ohio State University United States SpeechLab Department of Computer Science and Engineering Shanghai Jiao Tong University China Aural and Language Intelligence Department Institute for Infocomm Research A*STAR Singapore

The ICASSP 2022 Multi-channel Multi-party Meeting Transcription Grand Challenge (M2MeT) focuses on one of the most valuable and the most challenging scenarios of speech technologies. The M2MeT challenge has particularly set up two tracks, speaker diarization (track 1) and multi-speaker automatic speech recognition (ASR) (track 2). Along with the challenge, we released 120 hours of real-recorded Mandarin meeting speech data with manual annotation, including far-field data collected by 8-channel microphone array as well as near-field data collected by each participants’ headset microphone. We briefly describe the released dataset, track setups, baselines and summarize the challenge results and major techniques used in the submissions. © 2022, CC BY-SA.

关键词： Speech recognition

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Large Generative Model Assisted 3D Semantic Communication

arXiv

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arXiv 2024年

作者： Jiang, Feibo Peng, Yubo Dong, Li Wang, Kezhi Yang, Kun Pan, Cunhua You, Xiaohu Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China School of Information Science and Engineering Hunan Normal University Changsha China Changsha Social Laboratory of Artificial Intelligence Hunan University of Technology and Business Changsha China The Department of Computer Science Brunel University London United Kingdom The School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Changchun Institute of Technology China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The Frontiers Science Center for Mobile Information Communication and Security National Mobile Communications Research Laboratory Southeast University Nanjing China The Purple Mountain Laboratories Nanjing China

Semantic Communication (SC) is a novel paradigm for data transmission in 6G. However, there are several challenges posed when performing SC in 3D scenarios: 1) 3D semantic extraction;2) Latent semantic redundancy;and 3) Uncertain channel estimation. To address these issues, we propose a Generative AI Model assisted 3D SC (GAM-3DSC) system. Firstly, we introduce a 3D Semantic Extractor (3DSE), which employs generative AI models, including Segment Anything Model (SAM) and Neural Radiance Field (NeRF), to extract key semantics from a 3D scenario based on user requirements. The extracted 3D semantics are represented as multi-perspective images of the goal-oriented 3D object. Then, we present an Adaptive Semantic Compression Model (ASCM) for encoding these multi-perspective images, in which we use a semantic encoder with two output heads to perform semantic encoding and mask redundant semantics in the latent semantic space, respectively. Next, we design a conditional Generative adversarial network and Diffusion model aided-Channel Estimation (GDCE) to estimate and refine the Channel State Information (CSI) of physical channels. Finally, simulation results demonstrate the advantages of the proposed GAM-3DSC system in effectively transmitting the goal-oriented 3D scenario. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Adaptive optics two-photon microscopy enables near-diffraction-limited and functional retinal imaging in vivo

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Light(Science & Applications) 2020年第1期9卷 1298-1308页

作者： Zhongya Qin Sicong He Chao Yang Jasmine Sum-Yee Yung Congping Chen Christopher Kai-Shun Leung Kai Liu Jianan Y.Qu Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina Division of Life Science The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina Department of Ophthalmology and Visual Sciences The Chinese University of Hong KongHong KongChina State Key Laboratory of Molecular Neuroscience The Hong Kong University of Science and TechnologyClear Water BayKowloonHongKongChina Center of Systems Biology and Human Health The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina

In vivo fundus imaging offers non-invasive access to neuron structures and biochemical processes in the ***,optical aberrations of the eye degrade the imaging resolution and prevent visualization of subcellular retinal *** developed an adaptive optics two-photon excitation fluorescence microscopy(AO-TPEFM)system to correct ocular aberrations based on a nonlinear fluorescent guide star and achieved subcellular resolution for in vivo fluorescence imaging of the mouse *** accurate wavefront sensing and rapid aberration correction,AOTPEFM permits structural and functional imaging of the mouse retina with submicron ***,simultaneous functional calcium imaging of neuronal somas and dendrites was ***,the timelapse morphological alteration and dynamics of microglia were characterized in a mouse model of retinal *** addition,precise laser axotomy was achieved,and degeneration of retinal nerve fibres was *** highresolution AO-TPEFM is a promising tool for non-invasive retinal imaging and can facilitate the understanding of a variety of eye diseases as well as neurodegenerative disorders in the central nervous system.

关键词： optics functional photon

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Intelligent upper-limb exoskeleton integrated with soft wearable bioelectronics and deep-learning for human intention-driven strength augmentation based on sensory feedback

arXiv

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arXiv 2023年

作者： Lee, Jinwoo Kwon, Kangkyu Soltis, Ira Matthews, Jared Lee, Yoon-Jae Kim, Hojoong Romero, Lissette Zavanelli, Nathan Kwon, Youngjin Kwon, Shinjae Lee, Jimin Na, Yewon Lee, Sung Hoon Yu, Ki Jun Shinohara, Minoru Hammond, Frank L. Yeo, Woon-Hong Department of Mechanical Robotics and Energy Engineering Dongguk University Seoul04620 Korea Republic of IEN Center for Human-Centric Interfaces and Engineering Institute for Electronics and Nanotechnology Georgia Institute of Technology AtlantaGA30332 United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology AtlantaGA30332 United States School of Industrial Design Georgia Institute of Technology AtlantaGA30332 United States School of Electrical and Electronic Engineering Yonsei University Seoul03722 Korea Republic of School of Biological Sciences Georgia Institute of Technology AtlantaGA30332 United States Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University School of Medicine AtlantaGA30332 United States Institute for Materials Parker H. Petit Institute for Bioengineering and Biosciences Neural Engineering Center Institute for Robotics and Intelligent Machines Georgia Institute of Technology AtlantaGA30332 United States

The age and stroke-associated decline in musculoskeletal strength degrades the ability to perform daily human tasks using the upper extremities. Here, we introduce an intelligent upper-limb exoskeleton system that uses cloud-based deep learning to predict human intention for strength augmentation. The embedded soft wearable sensors provide sensory feedback by collecting real-time muscle signals, which are simultaneously computed to determine the user's intended movement. The cloud-based deep-learning predicts four upper-limb joint motions with an average accuracy of 96.2% at a 500-550 millisecond response rate, suggesting that the exoskeleton operates just by human intention. In addition, an array of soft pneumatics assists the intended movements by providing 897 newton of force while generating displacement of 87 millimeter at maximum. Collectively, the intent-driven exoskeleton can reduce human muscle activities by 3.7 times on average compared to the unassisted *** Codes 68T40 (Primary) 92C55, 68T99 (Secondary) Copyright © 2023, The Authors. All rights reserved.

关键词： Sensory feedback

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Deep Learning for Brain Age Estimation: A Systematic Review

arXiv

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arXiv 2022年

作者： Tanveer, M. Ganaie, M.A. Beheshti, Iman Goel, Tripti Ahmad, Nehal Lai, Kuan-Ting Huang, Kaizhu Zhang, Yu-Dong Ser, Javier Del Lin, Chin-Teng Department of Mathematics Indian Institute of Technology Indore Simrol Indore453552 India Department of Robotics University of Michigan Ann ArborMI48109 United States Department of Human Anatomy and Cell Science Rady Faculty of Health Sciences Max Rady College of Medicine University of Manitoba WinnipegMB Canada Biomedical Imaging Lab National Institute of Technology Assam Silchar788010 India Department of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Data Science Research Center Duke Kunshan University China School of Computing and Mathematical Sciences University of Leicester United Kingdom Derio48160 Spain Bilbao48013 Spain School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia

Over the years, Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deviations from the healthy brain aging pattern are associated to the accelerated brain aging and brain abnormalities. Hence, efficient and accurate diagnosis techniques are required for eliciting accurate brain age estimations. Several contributions have been reported in the past for this purpose, resorting to different data-driven modeling methods. Recently, deep neural networks (also referred to as deep learning) have become prevalent in manifold neuroimaging studies, including brain age estimation. In this review we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data. We detail and analyze different deep learning architectures used for this application, pausing at research works published to date quantitatively exploring their application. We also examine different brain age estimation frameworks, comparatively exposing their advantages and weaknesses. Finally, the review concludes with an outlook towards future directions that should be followed by prospective studies. The ultimate goal of this paper is to establish a common and informed reference for newcomers and experienced researchers willing to approach brain age estimation by using deep learning models. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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High-resolution two-photon transcranial imaging of brain using direct wavefront sensing

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Photonics Research 2021年第6期9卷 1144-1156页

作者： CONGPING CHEN ZHONGYA QIN SICONG HE SHAOJUN LIU SHUN-FAT LAU WANJIE WU DAN ZHU NANCY Y.IP JIANAN Y.QU Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina State Key Laboratory of Molecular Neuroscience The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina Center of Systems Biology and Human Health The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhanChina MoE Key Laboratory for Biomedical Photonics Collaborative Innovation Center for Biomedical EngineeringSchool of Engineering SciencesHuazhong University of Science and TechnologyWuhanChina Division of Life Science The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina Molecular Neuroscience Center The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina

Imaging of the brain in its native state at high spatial resolution poses major challenges to visualization ***-photon microscopy integrated with the thinned-skull or optical clearing skull technique provides a minimally invasive tool for in vivo imaging of the cortex of mice without activating immune response and inducing brain ***,the imaging contrast and spatial resolution are severely compromised by the optical heterogeneity of the skull,limiting the imaging depth to the superficial *** this work,an optimized configuration of an adaptive optics two-photon microscope system and an improved wavefront sensing algorithm are proposed for accurate correction for the aberrations induced by the skull window and brain *** this system,we achieved subcellular resolution transcranial imaging of layer 5 pyramidal neurons up to 700μm below pia in living *** addition,we investigated microglia–plaque interaction in living brain of Alzheimer’s disease and demonstrated high-precision laser dendrotomy and single-spine ablation.

关键词： resolution photon system

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QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge

arXiv

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arXiv 2024年

作者： Li, Hongwei Bran Navarro, Fernando Ezhov, Ivan Bayat, Amirhossein Das, Dhritiman Kofler, Florian Shit, Suprosanna Waldmannstetter, Diana Paetzold, Johannes C. Hu, Xiaobin Wiestler, Benedikt Zimmer, Lucas Amiranashvili, Tamaz Prabhakar, Chinmay Berger, Christoph Weidner, Jonas Alonso-Basanta, Michelle Rashid, Arif Baid, Ujjwal Adel, Wesam Alis, Deniz Baheti, Bhakti Bai, Yingbin Bhat, Ishaan Cetindag, Sabri Can Chen, Wenting Cheng, Li Dutande, Prasad Dular, Lara Elattar, Mustafa A. Feng, Ming Gao, Shengbo Huisman, Henkjan Hu, Weifeng Innani, Shubham Ji, Wei Karimi, Davood Kuijf, Hugo J. Kwak, Jin Tae Le, Hoang Long Li, Xiang Lin, Huiyan Liu, Tongliang Ma, Jun Ma, Kai Ma, Ting Oksuz, Ilkay Holland, Robbie Oliveira, Arlindo L. Pal, Jimut Bahan Pei, Xuan Qiao, Maoying Saha, Anindo Selvan, Raghavendra Shen, Linlin Silva, Joao Lourenco Spiclin, Ziga Talbar, Sanjay Wang, Dadong Wang, Wei Wang, Xiong Wang, Yin Xi, Ruiling Xu, Kele Yang, Yanwu Yergin, Mert Yu, Shuang Zeng, Lingxi Zhang, YingLin Zhao, Jiachen Zheng, Yefeng Zukovec, Martin Do, Richard Becker, Anton Simpson, Amber Konukoglu, Ender Jakab, Andras Bakas, Spyridon Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School United States Department of Quantitative Biomedicine University of Zurich Switzerland University Children’s Hospital Zurich University of Zurich Switzerland Department of Radioncology and Radiation Theraphy Klinikum rechts der Isar Technical University of Munich Germany Department of Information Technology and Electrical Engineering ETH-Zurich Switzerland Department of Radiology Memorial Sloan Kettering Cancer Center New York City United States Department of Biomedical and Molecular Sciences Queen’s University Canada TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany McGovern Institute Massachusetts Institute of Technology United States Institute for Diagnostic and Interventional Radiology Unveristy Zurich Hospital Switzerland BioMedIA Imperial College London United Kingdom Department of Radiation Oncology University of Pennsylvania PA United States University of Pennsylvania PA United States Department of Radiation Oncology Winship Cancer Institute of Emory University Georgia United States Nile University Cairo Egypt Department of Medical Sciences Acibadem University Istanbul Turkey Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Trustworthy Machine Learning Lab University of Sydney Australia Image Sciences Institute University Medical Center Utrecht Netherlands Computer Engineering Department Istanbul Technical University Istanbul Turkey School of Computer Science Shenzhen University Shenzhen China University of Alberta United States University of Ljubljana Faculty of Electrical Engineering Ljubljana Slovenia Tongji University Shanghai China OPPO Research Institute Shanghai China School of Biological and Medical Engineering Beihang University Beijing China Harvard Medical School Boston

Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a significant challenge in achieving consistent and reliable image segmentation. This variability not only reflects the inherent complexity and subjective nature of medical image interpretation but also directly impacts the development and evaluation of automated segmentation algorithms. Accurately modeling and quantifying this variability is essential for enhancing the robustness and clinical applicability of these algorithms. We report the set-up and summarize the benchmark results of the Quantification of Uncertainties in Biomedical Image Quantification Challenge (QUBIQ), which was organized in conjunction with International Conferences on Medical Image Computing and computer-Assisted Intervention (MICCAI) 2020 and 2021. The challenge focuses on the uncertainty quantification of medical image segmentation which considers the omnipresence of inter-rater variability in imaging datasets. The large collection of images with multi-rater annotations features various modalities such as MRI and CT;various organs such as the brain, prostate, kidney, and pancreas;and different image dimensions 2D-vs-3D. A total of 24 teams submitted different solutions to the problem, combining various baseline models, Bayesian neural networks, and ensemble model techniques. The obtained results indicate the importance of the ensemble models, as well as the need for further research to develop efficient 3D methods for uncertainty quantification methods in 3D segmentation tasks. © 2024, CC BY-NC-ND.

关键词： Image segmentation

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Using ASR methods for OCR 15

Using ASR methods for OCR

引用

15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019

作者： Arora, Ashish Garcia, Paola Watanabe, Shinji Manohar, Vimal Shao, Yiwen Khudanpur, Sanjeev Chang, Chun Chieh Rekabdar, Babak Babaali, Bagher Povey, Daniel Etter, David Raj, Desh Hadian, Hossein Trmal, Jan Center for Language and Speech Processing Johns Hopkins University Baltimore United States Human Language Technology Center of Excellence Johns Hopkins University Baltimore United States Department of Computer Engineering Sharif University of Technology Iran School of Mathematics Statistics and Computer Sciences College of Science University of Tehran Iran

ISBN: (纸本)9781728128610

Hybrid deep neural network hidden Markov models (DNN-HMM) have achieved impressive results on large vocabulary continuous speech recognition (LVCSR) tasks. However, the recent approaches using DNN-HMM models are not explored much for text recognition. Inspired by the current work in automatic speech recognition (ASR) and machine translation, we present an open vocabulary sub-word text recognition system. The sub-word lexicon and sub-word language model (LM) helps in overcoming the challenge of recognizing out of vocabulary (OOV) words, and a time delay neural network (TDNN) and convolution neural network (CNN) based DNN-HMM optical model (OM) efficiently models the sequence dependency in the line image. We present results on 12 datasets with training data varying from 6k lines to 600k lines. The system is built for 8 languages, i.e., English, French, Arabic, Chinese, Farsi, Tamil, Russian, and Korean. We report competitive results on several commonly used handwritten and printed text datasets. © 2019 IEEE.

关键词： Hidden Markov models

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Long-term in vivo imaging of mouse spinal cord through an optically cleared intervertebral window (vol 13, 1959, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 1959-1959页

作者： Wu, Wanjie He, Sicong Wu, Junqiang Chen, Congping Li, Xuesong Liu, Kai Qu, Jianan Y. Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong P. R. China Department of Biology School of Life Sciences Southern University of Science and Technology Shenzhen China Division of Life Science The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong P. R. China State Key Laboratory of Molecular Neuroscience The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong P. R. China Center of Systems Biology and Human Health The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong P. R. China Molecular Neuroscience Center The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong P. R. China

The spinal cord accounts for the main communication pathway between the brain and the peripheral nervous system. Spinal cord injury is a devastating and largely irreversible neurological trauma, and can result in lifelong disability and paralysis with no available cure. In vivo spinal cord imaging in mouse models without introducing immunological artifacts is critical to understand spinal cord pathology and discover effective treatments. We developed a minimally invasive intervertebral window by retaining the ligamentum flavum to protect the underlying spinal cord. By introducing an optical clearing method, we achieve repeated two-photon fluorescence and stimulated Raman scattering imaging at subcellular resolution with up to 15 imaging sessions over 6–167 days and observe no inflammatory response. Using this optically cleared intervertebral window, we study neuron-glia dynamics following laser axotomy and observe strengthened contact of microglia with the nodes of Ranvier during axonal degeneration. By enabling long-term, repetitive, stable, high-resolution and inflammation-free imaging of mouse spinal cord, our method provides a reliable platform in the research aiming at interpretation of spinal cord physiology and pathology. Wu et al. developed a technique for longitudinal imaging with subcellular resolution of the spinal cord without causing inflammation or microglia activation in live mouse through an optically cleared intervertebral window.

关键词： Multiphoton microscopy Cellular neuroscience Optical imaging

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