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International Joint Conference on Neural Networks (IJCNN)

作者： Le-Yan Tao Bao-Liang Lu Department of Computer Science and Engineering Center for Brain-like Computing and Machine Intelligence Shanghai China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China Brain Science and Technology Research Center Shanghai China Qing Yuan Research Institute Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Emotion recognition under sleep deprivation is instructive for the study of mental disorders such as major depressive disorder. Previous studies on emotion recognition under sleep deprivation have been mainly based on psychological research techniques. In this paper, we introduce a multilayer weight-sharing multimodal residual LSTM network for emotion recognition under sleep deprivation. The advantage of our proposed method is that it allows for the combination of three different features: the electroencephalography (EEG) single-channel differential entropy (DE) features, EEG functional strength features with topological correlation connectivity, and eye movement features. The experiments under the conditions of sleep deprivation, sleep recovery and baseline are designed and conducted. The experimental results demonstrate that the proposed method significantly enhances the performance compared with the simple concatenation of the features of different modalities, and the best mean accuracies of 86.86% and 82.03% are achieved for four emotions (happiness, sadness, fear, and neutral) in subject-dependent and cross-subject emotion recognition tasks under 30 hours of sleep deprivation, respectively. The classification accuracy of the happiness emotion is obviously impaired under sleep deprivation, indicating that sleep deprivation impairs the stimulation of the happiness emotion, and one night of sleep recovery can reactivate the elicitation of the happiness emotion to the baseline level. Furthermore, we study the brain neural patterns of the four emotional states. The prefrontal area becomes less activated for the happiness emotion and sadness emotion in the gamma band under sleep deprivation, while the neural pattern of the fear emotion is highly robust with respect to sleep deprivation.

关键词： Feature extraction Electroencephalography Sleep Emotion recognition Correlation Physiology Task analysis

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A Learning Model to Improve Learning Outcome on Experiential Learning in a Multi-Phase Internship: A Case Study of the Internship Program of a Thai University. 8

A Learning Model to Improve Learning Outcome on Experiential...

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8th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2019

作者： Kingkaew, Chanakarn Supnithi, Thepchai Theeramunkong, Thanaruk Morita, Kai Tanaka, Koji Ikeda, Mitsuru School of Information Computer and Communication Technology Sirindhorn International Institute of Technology Thammasat University Pathum Thani Thailand Language and Semantic Technology Laboratory National Electronics and Computer Technology Center Pathum Thani Thailand School of Knowledge Science Japan Advanced Institute of Science and Technology Nomi Japan Department of Psychological Science College of Informatics and Human Communication Kanazawa Institute of Technology Nonoichi Japan

ISBN: (纸本)9781728126272

The internship is one of the crucial learning processes that provide a unique experience to combine what they learned in university courses and how to use it in the real society during their university lives as it provides the opportunity to learn through work experience offered by the external organization. Although learner can learn career skills through the internship, most learner cannot make good reflection and cannot learn how to learn from experience. We think the cause of this problem is that most learner cannot reflect their experience and cannot clear the abstract concept and apply it to the next trial. This implies that those learners can improve their learning through internship by acquiring experiential learning skills. In the internship in general, the mentor has the important education roles. One is to foster career skills learning, and the other is to promote experiential learning. However, in most case, the mentor is very good at career skill teaching, but most of the mentor is not well prepared to support experiential learning. In response to the above problems, to support learners to realized good learning, we provide a learning environment for the learner to support experiential learning. A guideline for the learner to let them aware of the critical concept of internship and experiential learning. Also, we provide a web system to practice reflective writing to understand and able to apply experiential learning concepts. In this paper, as the preliminary research to develop learning environment, the analysis of the learner learning activity of the current internship program and show the prototype of the conceptualization of the educational concepts to support the experiential learning. © 2019 IEEE.

关键词： Knowledge management

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Enhancing Pre-trained Language Model with Lexical Simplification

arXiv

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arXiv 2020年

作者： Bao, Rongzhou Wang, Jiayi Zhang, Zhuosheng Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

For both human readers and pre-trained language models (PrLMs), lexical diversity may lead to confusion and inaccuracy when understanding the underlying semantic meanings of given sentences. By substituting complex words with simple alternatives, lexical simplification (LS) is a recognized method to reduce such lexical diversity, and therefore to improve the understandability of sentences. In this paper, we leverage LS and propose a novel approach which can effectively improve the performance of PrLMs in text classification. A rule-based simplification process is applied to a given sentence. PrLMs are encouraged to predict the real label of the given sentence with auxiliary inputs from the simplified version. Using strong PrLMs (BERT and ELECTRA) as baselines, our approach can still further improve the performance in various text classification tasks. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantics

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Retraction notice to “Neural network-based optimization of hydrogen fuel production energy system with proton exchange electrolyzer supported nanomaterial” [Fuel 332 (2023) 125827]

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Fuel 2025年 391卷

作者： Tao Hai Kosar Hikmat Hama Aziz Jincheng Zhou Hayder A. Dhahad Kamal Sharma Sattam Fahad Almojil Abdulaziz Ibrahim Almohana Abdulrhman Fahmi Alali Teeba Ismail Kh Sadok Mehrez Anas Abdelrahman School of Computer and Information Qiannan Normal University for Nationalities Duyun Guizhou 558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun Guizhou 558000 China Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Shah Alam Selangor 40450 Malaysia Department of Chemistry College of Science University of Sulaimani Qlyasan Street Kurdistan Region 46001 Iraq Department of Medical Laboratory of Science College of Health Sciences University of Human Development Sulaimaniyah Iraq Key Laboratory of Complex Systems and Intelligent Optimization of Qiannan Duyun 558000 China Mechanical Engineering Department University of Technology Baghdad Iraq Institute of Engineering and Technology GLA University Mathura Uttar Pradesh 281406 India Department of Civil Engineering College of Engineering King Saud University P.O. Box 800 Riyadh 11421 Saudi Arabia Department of Computer Engineering College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq Department of Mechanical Engineering College of Engineering at Al Kharj Prince Sattam bin Abdulaziz University 16273 Saudi Arabia Department of Mechanical Engineering University of Tunis El Manar ENIT BP 37 Le Belv’ed‘ere Tunis 1002 Tunisia Department of Chemical Engineering Faculty of Engineering & Technology Future University in Egypt New Cairo 11500 Tunisia

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Accurate word representations with universal visual guidance

arXiv

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arXiv 2020年

作者： Zhang, Zhuosheng Yu, Haojie Zhao, Hai Wang, Rui Utiyama, Masao Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

Word representation is a fundamental component in neural language understanding models. Recently, pre-trained language models (PrLMs) offer a new performant method of contextualized word representations by leveraging the sequence-level context for modeling. Although the PrLMs generally give more accurate contextualized word representations than non-contextualized models do, they are still subject to a sequence of text contexts without diverse hints for word representation from multimodality. This paper thus proposes a visual representation method to explicitly enhance conventional word embedding with multiple-aspect senses from visual guidance. In detail, we build a small-scale word-image dictionary from a multimodal seed dataset where each word corresponds to diverse related images. The texts and paired images are encoded in parallel, followed by an attention layer to integrate the multimodal representations. We show that the method substantially improves the accuracy of disambiguation. Experiments on 12 natural language understanding and machine translation tasks further verify the effectiveness and the generalization capability of the proposed approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Modeling languages

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Identifying and validating subtypes within major psychiatric disorders based on frontal-posterior functional imbalance via deep learning (October, 10.1038/s41380-020-00892-3, 2020)

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MOLECULAR PSYCHIATRY 2021年第7期26卷 3003-3003页

作者： Chang, Miao Womer, Fay Y. Gong, Xiaohong Chen, Xi Tang, Lili Feng, Ruiqi Dong, Shuai Duan, Jia Chen, Yifan Zhang, Ran Wang, Yang Ren, Sihua Wang, Yi Kang, Jujiao Yin, Zhiyang Wei, Yange Wei, Shengnan Jiang, Xiaowei Xu, Ke Cao, Bo Zhang, Yanbo Zhang, Weixiong Tang, Yanqing Zhang, Xizhe Wang, Fei Department of Radiology The First Affiliated Hospital of China Medical University Shenyang China Department of Psychiatry Washington University School of Medicine St. Louis MO USA State Key Laboratory of Genetic Engineering and Human Phenome Institute School of Life Sciences Fudan University Shanghai China School of Computer Science and Engineering Northeastern University Shenyang Liaoning China Department of Psychiatry The First Affiliated Hospital of China Medical University Shenyang China Shanghai Center for Mathematical Science Fudan University Shanghai China Department of Psychiatry University of Alberta Edmonton AB Canada Department of Psychiatry College of Medicine University of Saskatchewan Ellis Hall Royal University Hospital Saskatoon SK Canada Department of Computer Science and Engineering Washington University St. Louis MO USA Department of Genetics Washington University School of Medicine St. Louis MO USA School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing Jiangsu China Nanjing Brain Hospital Nanjing Medical University Nanjing Jiangsu China

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

关键词： Neuroscience Prognostic markers Psychology Medicine/Public Health general Psychiatry Neurosciences Behavioral sciences Pharmacotherapy Biological Psychology

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Transdisciplinary reflections on social robotics in academia and beyond 3

Transdisciplinary reflections on social robotics in academia...

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3rd International Conference on Robophilosophy 2018 / Transor 2018: Envisioning Robots in Society - Politics, Power, and Public Space

作者： Hannibal, Glenda Lindner, Felix Vienna University of Technology Institute of Visual Computing and Human-Centered Technology Research Division of Human Computer Interaction Argentinierstraße 8 Vienna1040 Austria Department of Computer Science University of Freiburg Germany

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Document-level neural machine translation with document embeddings

arXiv

引用

arXiv 2020年

作者： Jiang, Shu Zhao, Hai Li, Zuchao Lu, Bao-Liang Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

Standard neural machine translation (NMT) is on the assumption of document-level context independent. Most existing document-level NMT methods are satisfied with a smattering sense of brief document-level information, while this work focuses on exploiting detailed document-level context in terms of multiple forms of document embeddings, which is capable of sufficiently modeling deeper and richer document-level context. The proposed document-aware NMT is implemented to enhance the Transformer baseline by introducing both global and local document-level clues on the source end. Experiments show that the proposed method significantly improves the translation performance over strong baselines and other related studies. Copyright © 2020, The Authors. All rights reserved.

关键词： Neural machine translation

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Pregnancy alters fatty acid metabolism, glucose regulation, and detoxification of the liver in synchrony with biomechanical property changes

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Heliyon 2024年第20期10卷 e39674页

作者： Guo, Jing Krehl, Karolina Safraou, Yasmine Wallach, Iwona Braun, Jürgen Meierhofer, David Sack, Ingolf Berndt, Nikolaus Department of Radiology Charité – Universitätsmedizin Berlin corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin Berlin Germany Department of Veterinary Medicine Institute of Animal Welfare Animal Behavior and Laboratory Animal Science Freie Universität Berlin Berlin Germany Institute of Computer-assisted Cardiovascular Medicine Deutsches Herzzentrum der Charité Berlin Germany Charité – Universitätsmedizin Berlin corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin Berlin Germany Institute of Medical Informatics Charité – Universitätsmedizin Berlin corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin Berlin Germany Mass Spectrometry Facility Max Planck Institute for Molecular Genetics Berlin Germany Department of Molecular Toxicology German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE) Nuthetal Germany

Pregnancy places a metabolic burden on the body including the liver, which is responsible for ensuring adequate nutrition for the maternal and fetal systems. To gain a better understanding of liver adaptation, this study investigates metabolic shifts occurring in livers of pregnant rats. Metabolic capacities of the livers of pregnant and non-pregnant female Wistar rats were assessed using comprehensive metabolic models. Kinetic metabolic models were generated for each animal based on protein abundance data from proteomics analysis allowing for a subject-specific assessment of hepatic metabolic functions. Data are available via ProteomeXchange with identifier PXD050758. Additionally, tissue stiffness, viscosity, and water diffusion obtained from magnetic resonance imaging and elastography were correlated with metabolic capabilities to study the relationship between metabolic function and biophysical properties. Proteome profiling revealed differences in protein expression in the livers of pregnant and non-pregnant animals. Functional analysis showed significant variations in metabolic capacities. Livers of pregnant rats had reduced capacities in carbohydrate and fatty acid metabolism, along with altered urea synthesis. Additionally, there were associations between metabolic functions and biophysical properties highlighting potential links between changes in liver structure and metabolic capacities during pregnancy. In summary, our work reveals extensive hepatic metabolic changes in pregnant rats. The liver adapts its metabolic capacities to ensure whole-body metabolic homeostasis but may struggle to counteract nutritional challenges, such as hypoglycemia. The study, employing a personalized approach combining proteomics, kinetic modeling, and advanced imaging, sheds light on the intricate interplay between hepatic adaptations and medical imaging markers, providing a foundation for further investigations into the implications for maternal and fetal health. © 2024 The

关键词： Kinetic models Magnetic resonance elastography Metabolic liver reprogramming Proteomics analysis Tissue biomechanics

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Transgenic rhesus monkeys carrying the human MCPH1 gene copies show human-like neoteny of brain development

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National science Review 2019年第3期6卷 480-493页

作者： Lei Shi Xin Luo Jin Jiang Yongchang Chen Cirong Liu Ting Hu Min Li Qiang Lin Yanjiao Li Jun Huang Hong Wang Yuyu Niu Yundi Shi Martin Styner Jianhong Wang Yi Lu Xuejin Sun Hualin Yu Weizhi Ji Bing Su State Key Laboratory of Genetic Resources and Evolution Kunming Institute of Zoology Chinese Academy of Sciences Primate Research Center Kunming Institute of Zoology Chinese Academy of Sciences Center for Excellence in Animal Evolution and Genetics Chinese Academy of Sciences Kunming College of Life Science University of Chinese Academy of Sciences Yunnan Key Laboratory of Primate Biomedical Research Institute of Primate Translation Medicine Kunming University of Science and Technology Department of Psychiatry University of North CarolinaChapel Hill Department of Computer Science University of North CarolinaChapel Hill Key Laboratory of Animal Models and Human Disease Mechanisms Kunming Institute of Zoology Chinese Academy of Sciences Department of Medical Imaging the First Affiliated Hospital of Kunming Medical University Department of Minimally Invasive Neurosurgery the First Affiliated Hospital of Kunming Medical University

Brain size and cognitive skills are the most dramatically changed traits in humans during evolution and yet the genetic mechanisms underlying these human-specific changes remain elusive. Here, we successfully generated 11 transgenic rhesus monkeys(8 first-generation and 3 second-generation) carrying human copies of MCPH1, an important gene for brain development and brain evolution. Brain-image and tissue-section analyses indicated an altered pattern of neural-cell differentiation, resulting in a delayed neuronal maturation and neural-fiber myelination of the transgenic monkeys, similar to the known evolutionary change of developmental delay(neoteny) in humans. Further brain-transcriptome and tissue-section analyses of major developmental stages showed a marked human-like expression delay of neuron differentiation and synaptic-signaling genes, providing a molecular explanation for the observed brain-developmental delay of the transgenic monkeys. More importantly, the transgenic monkeys exhibited better short-term memory and shorter reaction time compared with the wild-type controls in the delayed-matching-to-sample task. The presented data represent the first attempt to experimentally interrogate the genetic basis of human brain origin using a transgenic monkey model and it values the use of non-human primates in understanding unique human traits.

关键词： human evolution brain development MCPH1 transgenic monkey neoteny cognition

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