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Multidisciplinary integration of frontier technologies facilitating the development of anesthesiology and perioperative medicine in aging society

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Fundamental Research 2024年第4期4卷 795-796页

作者： Lize Xiong Junli Cao Hailong Dong Weihong Song Dong Ming Translational Research Institute of Brain and Brain-Like Intelligence and Department of Anesthesiology and Perioperative Medicine Shanghai Fourth People's Hospital Affiliated to Tongji University School of MedicineShanghai 200434China Jiangsu Province Key Laboratory of Anesthesiology Xuzhou Medical UniversityXuzhou 221004China Department of Anesthesiology and Perioperative Medicine Xijing Hospitalthe Fourth Military Medical UniversityXi'an 710032China The Second Affliated Hospital and Yuying Children's Hospital Institute of AgingKey Laboratory of Alzheimer's Disease of Zhejiang ProvinceWenzhou Medical UniversityWenzhou325035China State Key Laboratory of Advanced Medical Materials and Devices Medical SchoolTianjin UniversityTianjin 300072China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin 300380China

The population aged 60 and over in China reached approximately 297 million in 2023 according to the National Bureau of Statistics,accounting for 21.1%of the national *** population aged 65 and over was about 217 million,accounting for 15.4%[1].It is estimated that by 2035,China will have an elderly population of over 400 million,accounting for more than 30%of the total population,thereby entering a stage of severe *** brings many challenges to the society,especially in terms of medical care for elderly *** continuous advancement of science and technology,especially the breakthrough in basic research as well as new technologies and interdisciplinary integration,has brought new opportunities for the development of various professional *** better address the challenges of anesthesia and perioperative medicine in the aging era and to accelerate the rehabilitation of patients while maintaining life and health services,it is crucial to promote interdisciplinary integration.

关键词： breakthrough frontier thereby

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Energy Efficiency Optimization for Full-Duplex D2D Communications Underlaying Distributed Antenna Systems

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年

作者： Liu, Zhan Liao, Zhiyuan Li, Chunquan Zhang, Zhijun Yu, Junzhi Liu, P.X. Hunan University of Humanities Science and Technology School of Information Loudi417000 China Nanchang University School of Information Engineering Nanchang330031 China Jiangxi Provincial Key Laboratory of Intelligent Systems and Human-Machine Interaction Nanchang330031 China South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

In this paper, we investigate the total system energy efficiency (EE) of full-duplex (FD) device-to-device (D2D) communications underlaying distributed antenna systems (DAS), where remote access units (RAUs), D2D users (DUs), and cellular users (CUs) are all capable of FD operation. Specifically, we jointly optimize subcarrier assignment and power allocation under the quality of service (QoS) requirements of CUs and DUs and the maximum power constraints of RAUs, CUs, and DUs. In addition, we propose a novel spectrum sharing strategy that allows each subcarrier to be assigned to multiple CUs and/or multiple D2D pairs (DPs) for flexibility. To solve the formulated non-convex optimization problem, we first employ fractional programming to transform the objective function in the optimization problem from the fractional form into the equivalent subtractive form. Then, an efficient iterative resource allocation algorithm is proposed, which needs to solve an inner problem in each iteration. After relaxing the variables and introducing penalty factors, the non-convex inner problem is transformed into a convex problem through the successive convex approximation (SCA) method and solved by iterative algorithm. Simulation results demonstrate that the proposed algorithm can considerably improve the system EE compared to other benchmark schemes. Furthermore, the proposed spectrum sharing strategy is superior to the existing sharing strategies. © 2017 IEEE.

关键词： Convex optimization

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Exploiting aliasing for manga restoration

arXiv

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arXiv 2021年

作者： Xie, Minshan Xia, Menghan Wong, Tien-Tsin Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology SIAT CAS

As a popular entertainment art form, manga enriches the line drawings details with bitonal screentones. However, manga resources over the Internet usually show screentone artifacts because of inappropriate scanning/rescaling resolution. In this paper, we propose an innovative twostage method to restore quality bitonal manga from degraded ones. Our key observation is that the aliasing induced by downsampling bitonal screentones can be utilized as informative clues to infer the original resolution and screentones. First, we predict the target resolution from the degraded manga via the Scale Estimation Network (SENet) with spatial voting scheme. Then, at the target resolution, we restore the region-wise bitonal screentones via the Manga Restoration Network (MR-Net) discriminatively, depending on the degradation degree. Specifically, the original screentones are directly restored in pattern-identifiable regions, and visually plausible screentones are synthesized in pattern-agnostic regions. Quantitative evaluation on synthetic data and visual assessment on real-world cases illustrate the effectiveness of our method. Copyright © 2021, The Authors. All rights reserved.

关键词： Restoration

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Adversarial Self-Attention for Language Understanding

arXiv

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arXiv 2022年

作者： Wu, Hongqiu Ding, Ruixue Zhao, Hai Xie, Pengjun Huang, Fei Zhang, Min 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 China School of Computer Science and Technology Soochow University China Damo Academy Alibaba Group China

Deep neural models (e.g. Transformer) naturally learn spurious features, which create a "shortcut" between the labels and inputs, thus impairing the generalization and robustness. This paper advances the self-attention mechanism to its robust variant for Transformer-based pre-trained language models (e.g. BERT). We propose Adversarial Self-Attention mechanism (ASA), which adversarially biases the attentions to effectively suppress the model reliance on features (e.g. specific keywords) and encourage its exploration of broader semantics. We conduct a comprehensive evaluation across a wide range of tasks for both pre-training and fine-tuning stages. For pre-training, ASA unfolds remarkable performance gains compared to naive training for longer steps. For fine-tuning, ASA-empowered models outweigh naive models by a large margin considering both generalization and robustness. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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Detecting Major Depression Disorder with Multiview Eye Movement Features in a Novel Oil Painting Paradigm

Detecting Major Depression Disorder with Multiview Eye Movem...

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International Joint Conference on Neural Networks (IJCNN)

作者： Tian-Fang Ma Lu-Yu Liu Li-Ming Zhao Dan Peng Yong Lu Wei-Long Zheng Bao-Liang Lu Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China Emotion Helper Shanghai China RuiJin-Mihoyo Laboratory Clinical Neuroscience Center RuiJin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Major Depressive Disorder (MDD) is a debilitating condition marked by persistent low mood, reduced interest, cognitive impairments, and vegetative neurological symptoms such as sleep and appetite disturbances. In this paper, we collected eye movement signals from 40 patients diagnosed with MDD and 40 healthy controls to study the relation between eye movements and cognitive processes for depression detection. The eye movement data were captured during a novel emotional cognition task using oil paintings. Subsequently, the data were transformed into multiview eye movement features, including heatmaps, trajectories, and statistical vectors. Rigorous statistical analyses were then conducted on these features to identify significant patterns and correlations between eye movements and depressive symptoms. A multiview invariant & specific eye movement model (MISEYE) was proposed to fuse different eye movement features. The proposed achieved an accuracy rate of 79.88% in depression detection. This performance surpassed not only the outcomes of single-mode approaches and combinations of any two features but also outperformed other fusion methodologies. These findings not only shed light on the intricate relationship between eye movement patterns and MDD but also underscore the potential of eye-tracking technology in psychiatric research.

关键词： Heating systems Accuracy Statistical analysis Oils Depression Feature extraction Vectors

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Forging Multiple Training Objectives for Pre-trained Language Models via Meta-Learning

arXiv

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arXiv 2022年

作者： Wu, Hongqiu Ding, Ruixue Zhao, Hai Chen, Boli Xie, Pengjun Huang, Fei Zhang, Min 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 China School of Computer Science and Technology Soochow University China Damo Academy Alibaba Group China

Multiple pre-training objectives fill the vacancy of the understanding capability of single-objective language modeling, which serves the ultimate purpose of pre-trained language models (PrLMs), generalizing well on a mass of scenarios. However, learning multiple training objectives in a single model is challenging due to the unknown relative significance as well as the potential contrariety between them. Empirical studies have shown that the current objective sampling in an ad-hoc manual setting makes the learned language representation barely converge to the desired optimum. Thus, we propose MOMETAS, a novel adaptive sampler based on meta-learning, which learns the latent sampling pattern on arbitrary pre-training objectives. Such a design is lightweight with negligible additional training overhead. To validate our approach, we adopt five objectives and conduct continual pre-training with BERT-base and BERT-large models, where MOMETAS demonstrates universal performance gain over other rule-based sampling strategies on 14 natural language processing tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Modeling languages

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CoCo-Agent: A Comprehensive Cognitive MLLM Agent for Smartphone GUI Automation

arXiv

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arXiv 2024年

作者： Ma, Xinbei Zhang, Zhuosheng Zhao, Hai School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China 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 China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China

Multimodal large language models (MLLMs) have shown remarkable potential as human-like autonomous language agents to interact with real-world environments, especially for graphical user interface (GUI) automation. However, those GUI agents require comprehensive cognition ability including exhaustive perception and reliable action response. We propose a Comprehensive Cognitive LLM Agent, CoCo-Agent, with two novel approaches, comprehensive environment perception (CEP) and conditional action prediction (CAP), to systematically improve the GUI automation performance. First, CEP facilitates the GUI perception through different aspects and granularity, including screenshots and complementary detailed layouts for the visual channel and historical actions for the textual channel. Second, CAP decomposes the action prediction into sub-problems: action type prediction and action target conditioned on the action type. With our technical design, our agent achieves new state-of-the-art performance on AITW and META-GUI benchmarks, showing promising abilities in realistic scenarios. Code is available at https://***/xbmxb/CoCo-Agent. Copyright © 2024, The Authors. All rights reserved.

关键词： Graphical user interfaces

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S-INF: Towards Realistic Indoor Scene Synthesis via Scene Implicit Neural Field

arXiv

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arXiv 2024年

作者： Liang, Zixi Xu, Guowei Wu, Haifeng Huang, Ye Li, Wen Duan, Lixin Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China China School of Computer Science and Engineering University of Electronic Science and Technology of China China Sichuan Provincial Key Laboratory for Human Disease Gene Study the Center for Medical Genetics Department of Laboratory Medicine Sichuan Academy of Medical Sciences Sichuan Provincial People’s Hospital UESTC China

Learning-based methods have become increasingly popular in 3D indoor scene synthesis (ISS), showing superior performance over traditional optimization-based approaches. These learning-based methods typically model distributions on simple yet explicit scene representations using generative models. However, due to the oversimplified explicit representations that overlook detailed information and the lack of guidance from multimodal relationships within the scene, most learning-based methods struggle to generate indoor scenes with realistic object arrangements and styles. In this paper, we introduce a new method, Scene Implicit Neural Field (S-INF), for indoor scene synthesis, aiming to learn meaningful representations of multimodal relationships, to enhance the realism of indoor scene synthesis. S-INF assumes that the scene layout is often related to the object-detailed information. It disentangles the multimodal relationships into scene layout relationships and detailed object relationships, fusing them later through implicit neural fields (INFs). By learning specialized scene layout relationships and projecting them into S-INF, we achieve a realistic generation of scene layout. Additionally, S-INF captures dense and detailed object relationships through differentiable rendering, ensuring stylistic consistency across objects. Through extensive experiments on the benchmark 3D-FRONT dataset, we demonstrate that our method consistently achieves state-of-the-art performance under different types of ISS. © 2024, CC BY.

关键词： Contrastive Learning

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Pushing the Boundaries of Computational Empowerment of Children 25

Pushing the Boundaries of Computational Empowerment of Child...

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Proceedings of the 24th interaction Design and Children

作者： Netta Iivari Ole Sejer Iversen Yasmin Kafai Alissa N. Antle Marianne Graves Petersen Marianne Kinnula Christian Dindler Fares Kayali Elizabeth Bonsignore Charu Monga, Ms. Marie-Monique Schaper Sumita Sharma INTERACT Research Unit University of Oulu Oulu Finland Center for Computational Thinking and Design Aarhus University Aarhus Denmark Graduate School of Education University of Pennsylvania Philadelphia Pennsylvania USA School of Interactive Arts and Technology Simon Fraser University Vancouver British Columbia Canada Computer Science Aarhus University Aarhus Århus Denmark University of Vienna Vienna Austria Human-Computer Interaction Lab University of Maryland College Park Maryland USA Department of Design Indian Institute of Technology Delhi Delhi India Department for Computer Engineering and Digital Design University of Lleida Lleida Spain

ISBN: (纸本)9798400714733

Emerging technologies such as artificial intelligence (AI) and mixed reality (MR) increasingly pervade the world of children, from their schoolwork to leisure activities and social relationships. This has led to Child computer interaction (CCI) research supporting children's critical understanding of digital technologies and their ability to influence the design of future digital technologies. Despite these efforts, there are several limitations and challenges in terms of Computational Empowerment (CE) of children. This workshop unites such research 1) to create a shared manifesto for CE of children and 2) to jointly develop a research ‘roadmap’ to push the boundaries of CE of children.

关键词： Child computer interaction Computational Empowerment Participatory Design Critical Computing Emerging Technologies

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Heart-brain axis:low blood pressure during off-pump CABG surgery is associated with postoperative heart failure

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Military Medical Research 2025年第5期 799-802页

作者： Xiu-Yun Liu Jing-Jing Mu Jian-Ge Han Mei-Jun Pang Kuo Zhang Wen-Qian Zhai Nan Su Guang-Jian Ni Zhi-Gang Guo Dong Ming Academy of Medical Engineering and Translational Medicine Tianjin University State Key Laboratory of Advanced Medical Materials and Devices Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Department of Anesthesiology Tianjin University Chest HospitalTianjin Key Laboratory of Cardiovascular Emergency and Critical CareTianjin Municipal Science and Technology Bureau Department of Cardiac Surgery Tianjin University Chest HospitalTianjin Key Laboratory of Cardiovascular Emergency and Critical CareTianjin Municipal Science and Technology Bureau

Dear Editor,The primary objective of the letter is to emphasize the importance of personalized management of arterial blood pressure (ABP) in the context of off-pump coronary artery bypass grafting (CABG) *** artery disease,a leading global cause of mortality,necessitates a substantial number of cardiac surgeries,with approximately 400,000CABG operations conducted annually in the United *** heart failure (HF) is a common occurrence a?t er CABG surgery,with readmission rates within 30 d due to HF ranging from 12%to 16%.

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