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International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

作者： Chanwoong Kwak Jaeyoon Jang Hosub Yoon Department of Computer Software and Engineering Korea University of Science and Technology Daejeon Republic of Korea Human-Machine Interaction Research Group Electronics and Telecommunications Research Institute Daejeon Republic of Korea

ISBN: (数字)9781728157153

ISBN: (纸本)9781728157160

Facial landmark localization is essential for robot-human interaction. In particular, the human eye is more important because it can grasp a person's interests. However, the traditional method does not consider eye changes from the dataset, so the limitation is clear, this paper presents a data augmentation method for acquiring various eye images and a method for creating a robust eye landmark model with 2-stage training. Experiments on augmented 300W-LP datasets show that our method outperforms performance than the previous method.

关键词： Face Training Three-dimensional displays Two dimensional displays Measurement computer vision

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PULASki: Learning inter-rater variability using statistical distances to improve probabilistic segmentation

arXiv

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arXiv 2023年

作者： Chatterjee, Soumick Gaidzik, Franziska Sciarra, Alessandro Mattern, Hendrik Janiga, Gábor Speck, Oliver Nürnberger, Andreas Pathiraja, Sahani Data and Knowledge Engineering Group Faculty of Computer Science Otto von Guericke University Magdeburg Germany Genomics Research Centre Human Technopole Milan Italy Laboratory of Fluid Dynamics and Technical Flows Otto von Guericke University Magdeburg Germany Department of Biomedical Magnetic Resonance Faculty of Natural Sciences Otto von Guericke University Magdeburg Germany Department of Neurology Medical Faculty University Hospital Magdeburg Germany German Centre for Neurodegenerative Disease Magdeburg Germany Centre for Behavioural Brain Sciences Magdeburg Germany School of Mathematics & Statistics UNSW Sydney UNSW Data Science Hub Australia

In the domain of medical imaging, many supervised learning based methods for segmentation face several challenges such as high variability in annotations from multiple experts, paucity of labelled data and class imbalanced datasets. These issues may result in segmentations that lack the requisite precision for clinical analysis and can be misleadingly overconfident without associated uncertainty quantification. We propose the PULASki as a computationally efficient generative tool for biomedical image segmentation that accurately captures variability in expert annotations, even in small datasets. Our approach makes use of an improved loss function based on statistical distances in a conditional variational autoencoder structure (Probabilistic UNet), which improves learning of the conditional decoder compared to the standard cross-entropy particularly in class imbalanced problems. We analyse our method for two structurally different segmentation tasks (intracranial vessel and multiple sclerosis (MS) lesion) and compare our results to four well-established baselines in terms of quantitative metrics and qualitative output. These experiments are characterised by class-imbalanced, low signal to noise ratio data sets with highly ambiguous features. Empirical results demonstrate the PULASKi method outperforms all baselines at the 5% significance level. Our experiments are also of the first to present a comparative study of the computationally feasible segmentation of complex geometries using 3D patches and the traditional use of 2D slices. The generated segmentations are shown to be much more anatomically plausible than in the 2D case, particularly for the vessel task. Our method can also be applied to a wide range of multi-label segmentation tasks and and is useful for downstream tasks such as hemodynamic modelling (computational fluid dynamics and data assimilation), clinical decision making, and treatment planning. Copyright © 2023, The Authors. All rights reserved.

关键词： Medical imaging

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In vivo high-resolution multimodal nonlinear optical microscopy of spinal cord in mice

In vivo high-resolution multimodal nonlinear optical microsc...

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Advanced Chemical Microscopy for Life science and Translational Medicine 2020

作者： Wu, Wanjie Qin, Zhongya Wu, Junqiang Chen, Congping Chen, Weitao Liu, Kai Qu, Jianan Y. Biophotonics Research Laboratory Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Division of Life Science State Key Laboratory of Molecular Neuroscience Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Center of Systems Biology and Human Health Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

ISBN: (纸本)9781510632677

Chronic in vivo optical imaging of the spinal cord is an effective way to study the biological processes during and after spinal cord injury (SCI) in mouse models. It normally relies on an implanted spinal chamber to provide continuous optical access to the spinal cord. However, the chronic window consists of multiple layers of transparent materials with various optical properties and irregular thickness, which induce large optical aberration. Therefore, the image quality of multiphoton microscopy as well as the precision of femtosecond laser axotomy were dramatically degraded. In this work, we developed an adaptive optics (AO) microscope system integrating stimulated Raman scattering (SRS) and twophoton excited fluorescence (TPEF). Using our system, the aberrations induced by the spinal cord window were measured and compensated accordingly, enabling both high-resolution imaging and precise laser axotomy of the mouse spinal cord. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Adaptive optics

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EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review

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Computational Intelligence and Neuroscience 2022年第0期2022卷 6486570页

作者： Ahmad, Ijaz Wang, Xin Zhu, Mingxing Wang, Cheng Pi, Yao Khan, Javed Ali Khan, Siyab Samuel, Oluwarotimi Williams Chen, Shixiong Li, Guanglin CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences Shenzhen China School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen China School of Biomedical Engineering Sun Yat-Sen University Guangzhou China Department of Software Engineering University of Science and Technology Khyber Pakhtunkhwa Bannu Pakistan Institute of Computer Science and Information Technology The University of Agriculture Khyber Pakhtunkhwa Peshawar Pakistan

Epileptic seizure is one of the most chronic neurological diseases that instantaneously disrupts the lifestyle of affected individuals. Toward developing novel and efficient technology for epileptic seizure management, recent diagnostic approaches have focused on developing machine/deep learning model (ML/DL)-based electroencephalogram (EEG) methods. Importantly, EEG's noninvasiveness and ability to offer repeated patterns of epileptic-related electrophysiological information have motivated the development of varied ML/DL algorithms for epileptic seizure diagnosis in the recent years. However, EEG's low amplitude and nonstationary characteristics make it difficult for existing ML/DL models to achieve a consistent and satisfactory diagnosis outcome, especially in clinical settings, where environmental factors could hardly be avoided. Though several recent works have explored the use of EEG-based ML/DL methods and statistical feature for seizure diagnosis, it is unclear what the advantages and limitations of these works are, which might preclude the advancement of research and development in the field of epileptic seizure diagnosis and appropriate criteria for selecting ML/DL models and statistical feature extraction methods for EEG-based epileptic seizure diagnosis. Therefore, this paper attempts to bridge this research gap by conducting an extensive systematic review on the recent developments of EEG-based ML/DL technologies for epileptic seizure diagnosis. In the review, current development in seizure diagnosis, various statistical feature extraction methods, ML/DL models, their performances, limitations, and core challenges as applied in EEG-based epileptic seizure diagnosis were meticulously reviewed and compared. In addition, proper criteria for selecting appropriate and efficient feature extraction techniques and ML/DL models for epileptic seizure diagnosis were also discussed. Findings from this study will aid researchers in deciding the most efficient ML/DL m

关键词： Electroencephalography

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Assessment of multiple-biomarker classifiers: Fundamental principles and a proposed strategy

arXiv

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arXiv 2019年

作者： Yousef, Waleed A. Computer Science Department Faculty of Computers and Information Helwan University Egypt Human Computer Interaction Laboratory [HCI Lab. Egypt

The multiple-biomarker classifier problem and its assessment are reviewed against the background of some fundamental principles from the field of statistical pattern recognition, machine learning, or the recently so-called "data science". A narrow reading of that literature has led many authors to neglect the contribution to the total uncertainty of performance assessment from the finite training sample. Yet the latter is a fundamental indicator of the stability of a classifier;thus its neglect may be contributing to the problematic status of many studies. A three-level strategy is proposed for moving forward in this field. The lowest level is that of construction, where candidate features are selected and the choice of classifier architecture is made. At that point, the effective dimensionality of the classifier is estimated and used to size the next level of analysis, a pilot study on previously unseen cases. The total (training and testing) uncertainty resulting from the pilot study is, in turn, used to size the highest level of analysis, a pivotal study with a target level of uncertainty. Some resources available in the literature for implementing this approach are reviewed. Although the concepts explained in the present article may be fundamental and straightforward for many researchers in the machine learning community they are subtle for many practitioners, for whom we provided a general advice for the best practice in [1] and elaborate here in the present paper. Copyright © 2019, The Authors. All rights reserved.

关键词： Uncertainty analysis

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Advancing Treatment in Bullous Pemphigoid: A Comprehensive Review of Novel Therapeutic Targets and Approaches

引用

Clinical Reviews in Allergy and Immunology 2023年第3期65卷 331-353页

作者： Chen, Hsuan-Chi Wang, Chuang-Wei Toh, Wu Han Lee, Hua-En Chung, Wen-Hung Chen, Chun-Bing Department of Medical Education Linkou Chang Gung Memorial Hospital Taoyuan Taiwan Department of Dermatology Drug Hypersensitivity Clinical and Research Center Chang Gung Memorial Hospital Keelung Linkou Taipei Taiwan Cancer Vaccine and Immune Cell Therapy Core Laboratory Department of Medical Research Chang Gung Memorial Hospital Linkou Taiwan Chang Gung Immunology Consortium Chang Gung Memorial Hospital and Chang Gung University Kaohsiung Taiwan Department of Dermatology Xiamen Chang Gung Hospital Xiamen China Xiamen Chang Gung Allergology Consortium Xiamen Chang Gung Hospital Xiamen China Institute for NanoBioTechnology Johns Hopkins University Baltimore MD United States Translational Tissue Engineering Center Johns Hopkins University School of Medicine Baltimore MD United States Department of Computer Science Johns Hopkins University Baltimore MD United States Department of Biology Johns Hopkins University Baltimore MD United States School of Medicine National Tsing Hua University Hsinchu Taiwan College of Medicine Chang Gung University Taoyuan Taiwan Graduate Institute of Clinical Medical Sciences College of Medicine Chang Gung University Taoyuan Taiwan Immune-Oncology Center of Excellence Chang Gung Memorial Hospital Linkou Taiwan Whole-Genome Research Core Laboratory of Human Diseases Chang Gung Memorial Hospital Keelung Taiwan Department of Dermatology Beijing Tsinghua Chang Gung Hospital School of Clinical Medicine Tsinghua University Beijing China Department of Dermatology Ruijin Hospital School of Medicine Shanghai Jiao Tong University Shanghai China Genomic Medicine Core Laboratory Chang Gung Memorial Hospital Linkou Taiwan

Bullous pemphigoid is one of the most common autoimmune bullous diseases occurring primarily in the elderly. Pathogenic autoantibodies against BP180 and BP230 at the dermal–epidermal junction cause subepidermal blisters, erosions, and intense pruritus, all of which adversely affect the patients’ quality of life and may increase their morbidity and mortality. Current systemic treatment options for bullous pemphigoid are limited to corticosteroids and immunosuppressants, which can have substantial side effects on these vulnerable patients that even exceed their therapeutic benefits. Therefore, more precisely, targeting therapies to the pathogenic cells and molecules in bullous pemphigoid is an urgent issue. In this review, we describe the pathophysiology of bullous pemphigoid, focusing on autoantibodies, complements, eosinophils, neutrophils, proteases, and the T helper 2 and 17 axes since they are crucial in promoting proinflammatory environments. We also highlight the emerging therapeutic targets for bullous pemphigoid and their latest discoveries in clinical trials or experimental studies. Further well-designed studies are required to establish the efficacy and safety of these prospective therapeutic options. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Autoimmune bullous dermatosis Biomarker Bullous pemphigoid Pathogenesis Therapeutic target

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QA-KGNet: A Language Model-Driven Knowledge Graph Question-Answering Model

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Ruan Jian Xue Bao/Journal of Software 2023年第10期34卷

作者： Qiao, Shao-Jie Yang, Guo-Ping Yu, Yong Han, Nan Qin, Xiao Qu, Lu-Lu Ran, Li-Qiong Li, He School of Software Engineering Chengdu University of Information Technology Chengdu610225 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China School of Management Chengdu University of Information Technology Chengdu610225 China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning530100 China School of Computer Science and Technology XiDian University Xi’an710071 China

The question-answering system based on knowledge graphs can analyze user questions, and has become an effective way to retrieve relevant knowledge and automatically answer the given questions. The knowledge graph-based question-answering system usually uses a neural program induction model to convert natural language question into a logical form, and the answer can be obtained by executing the logical form on the knowledge graph. However, the knowledge question-answering system by using pre-trained language models and knowledge graphs involves two challenges: 1) given the QA (Question-answering) context, relevant knowledge needs to be identified from a large KG (Knowledge Graph);2) we need to perform the joint reasoning on QA context and KG. Based on these challenges, a language model-driven knowledge graph question-answering model is proposed, which connects the QA context and KG to form a joint graph, and uses a language model to calculate the relevance of the given QA context nodes and KG nodes, and a multi-head graph attention network is employed to update the node representation. Extensive experiments on the CommonsenseQA, OpenBookQA and MedQA-USMLE real datasets are conducted to evaluate the performance of QA-KGNet and the experimental results show that QA-KGNet outperforms existing benchmark models and exhibits excellent structured reasoning capability. © 2023 Chinese Academy of sciences. All rights reserved.

关键词： Knowledge graph

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An investigation on deep learning with Beta stabilizer

arXiv

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arXiv 2020年

作者： Liu, Qi Tan, Tian Yu, Kai Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai China

Artificial neural networks (ANN) have been used in many applications such like handwriting recognition and speech recognition. It is well-known that learning rate is a crucial value in the training procedure for artificial neural networks. It is shown that the initial value of learning rate can confoundedly affect the final result and this value is always set manually in practice. A new parameter called beta stabilizer has been introduced to reduce the sensitivity of the initial learning rate. But this method has only been proposed for deep neural network (DNN) with sigmoid activation function. In this paper we extended beta stabilizer to long short-term memory (LSTM) and investigated the effects of beta stabilizer parameters on different models, including LSTM and DNN with relu activation function. It is concluded that beta stabilizer parameters can reduce the sensitivity of learning rate with almost the same performance on DNN with relu activation function and LSTM. However, it is shown that the effects of beta stabilizer on DNN with relu activation function and LSTM are fewer than the effects on DNN with sigmoid activation function. Copyright © 2020, The Authors. All rights reserved.

关键词： Long short-term memory

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In-situ data scheduling optimization based on rainbow DQN for IIoT

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Future Generation computer Systems 2026年 174卷

作者： Peiying Zhang Siyi Li Lizhuang Tan Neeraj Kumar Jian Wang Kai Liu Qingdao Institute of Software College of Computer Science and Technology China University of Petroleum (East China) Qingdao 266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao 266580 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan 250014 China Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala 147004 India Department of Computer Science University of Economics and Human Sciences Warszawa 01-043 Poland Department of Networks and Communications College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam 31441 Saudi Arabia College of Science China University of Petroleum (East China) Qingdao 266580 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing 100084 China State Key Laboratory of Space Network and Communications Tsinghua University Beijing 100084 China

In industrial systems, in-situ computing refers to a mode of local or proximate data processing within the cloud–edge-terminal architecture. In the Industrial Internet of Things (IIoT) environment, servers are typically deployed near terminal devices to coordinate and manage the scheduling of in-situ data, forming a comprehensive in-situ server system. With the rapid growth of IIoT terminal devices, challenges such as high processing delays and severe demand congestion arise in the in-situ data scheduling process. To address this challenge, this paper proposes a scheduling optimization scheme based on Rainbow DQN. The scheme designs a scheduling buffer and classification strategy to distinguish in-situ data based on storage demand characteristics, thereby optimizing the scheduling process. At the same time, a multi-level storage structure is introduced to store in-situ data classified according to the scheduling strategy. Additionally, the Rainbow DQN algorithm is utilized to assist in the scheduling decision-making of in-situ data retrieval, effectively alleviating system congestion while ensuring certain throughput performance. Extensive simulation experiments show that the Rainbow DQN algorithm achieves a reduction of 60.82% in average waiting delay, 62.94% in average unprocessed demand, and a 58.27% reduction in average completion delay.

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人类泛基因组参考图谱发布开启基因组学领域的新篇章

引用

science Bulletin 2023年第14期68卷 1484-1487页

作者：王博党宁馨杨晓飞徐书华叶凯 School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China MOE Key Lab for Intelligent Networks&Networks Security Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China Genome Institute the First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China School of Computer Science and Technology Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China State Key Laboratory of Genetic Engineering Human Phenome InstituteZhangjiang Fudan International Innovation CenterCenter for Evolutionary BiologySchool of Life SciencesFudan UniversityShanghai 200032China Ministry of Education Key Laboratory of Contemporary Anthropology Collaborative Innovation Center for Genetics and DevelopmentFudan UniversityShanghai 200032China Department of Liver Surgery and Transplantation Liver Cancer InstituteZhongshan HospitalFudan UniversityShanghai 200032China School of Life Science and Technology Shanghai Tech UniversityShanghai 201210China

The significance of DNA sequencing in advancing medicalresearch,improving healthcare practices,and enhancing ourunderstanding of human evolution cannot be *** yearmarks the 20th anniversary of the completion of the human Genome Project(HGP)in 2003,with the draft human genome reference published in 2001[1,2].However,even with the latesthuman reference genome,GRCh38,there remain gaps totalingapprox.150 Mb,involving complex sequence in the pericentromeric and subtelomeric regions,as well as recent segmentalduplications and both ampliconic gene and ribosomal DNA(rDNA)arrays[3].A groundbreaking achievement was recently accomplished by the Telomere-to-Telomere(T2T)consortium whosuccessfully closed the remaining gaps and completed the firstend-to-end assembly of a human haploid genome,known as T2TCHM13[3].

关键词：基因组学 breaking accomplished

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