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Mixed reality based respiratory liver tumor puncture navigation

Computational Visual Media 2019年第4期5卷 363-374页

作者： Ruotong Li Weixin Si Xiangyun Liao Qiong Wang Reinhard Klein Pheng-Ann Heng Institute of Computer Science II University of Bonn53115 BonnGermany Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesChina Department of Computer Science and Engineering Chinese University of Hong KongHong KongChina

This paper presents a novel mixed reality based navigation system for accurate respiratory liver tumor punctures in radiofrequency ablation(RFA).Our system contains an optical see-through head-mounted display device(OST-HMD),Microsoft Holo Lens for perfectly overlaying the virtual information on the patient,and a optical tracking system NDI Polaris for calibrating the surgical utilities in the surgical *** with traditional navigation method with CT,our system aligns the virtual guidance information and real patient and real-timely updates the view of virtual guidance via a position tracking *** addition,to alleviate the difficulty during needle placement induced by respiratory motion,we reconstruct the patientspecific respiratory liver motion through statistical motion model to assist doctors precisely puncture liver *** proposed system has been experimentally validated on vivo pigs with an accurate real-time registration approximately 5-mm mean FRE and TRE,which has the potential to be applied in clinical RFA guidance.

关键词： mixed reality human computer interaction statistical motion model

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Toward Efficient Automated Feature Engineering

Toward Efficient Automated Feature Engineering

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International Conference on Data Engineering

作者： Kafeng Wang Pengyang Wang Chengzhong Xu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Macau China University of Chinese Academy of Sciences Macau China University of Macau Macau China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China Department of Computer and Information Science State Key Laboratory of Internet of Things for Smart City University of Macau Macau China

Automated Feature Engineering (AFE) refers to automatically generate and select optimal feature sets for downstream tasks, which has achieved great success in real-world applications. Current AFE methods mainly focus on improving the effectiveness of the produced features, but ignoring the low-efficiency issue for large-scale deployment. Therefore, in this work, we propose a generic framework to improve the efficiency of AFE. Specifically, we construct the AFE pipeline based on reinforcement learning setting, where each feature is assigned an agent to perform feature transformation and selection, and the evaluation score of the produced features in downstream tasks serve as the reward to update the policy. We improve the efficiency of AFE in two perspectives. On the one hand, we develop a Feature Pre-Evaluation (FPE) Model to reduce the sample size and feature size that are two main factors on undermining the efficiency of feature evaluation. On the other hand, we devise a two-stage policy training strategy by running FPE on the pre-evaluation task as the initialization of the policy to avoid training policy from scratch. We conduct comprehensive experiments on 36 datasets in terms of both classification and regression tasks. The results show 2.9% higher performance in average and 2x higher computational efficiency comparing to state-of-the-art AFE methods.

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Technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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The understanding of congruent and incongruent referential gaze in 17-month-old infants: an eye-tracking study comparing human and robot (vol 10, pg 11918, 2020)

引用

SCIENTIFIC REPORTS 2020年第1期10卷 1-10页

作者： Manzi, F. Ishikawa, M. Di Dio, C. Itakura, S. Kanda, T. Ishiguro, H. Massaro, D. Marchetti, A. Research Unit on Theory of Mind Department of Psychology Università Cattolica del Sacro Cuore Milan Italy School of Graduated Letter Department of Psychology Kyoto University Kyoto Japan Centre for Baby Science Doshisha University Kyoto Japan Human-Robot Interaction Laboratory Department of Computer Science Kyoto University Kyoto Japan Advanced Telecommunications Research Institute International IRC/HIL Keihanna Science City Kyoto Japan Department of Systems Innovation Osaka University Toyonaka Japan

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

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Augmented reality and robotics in education: A systematic literature review

Computers in Human Behavior: Artificial Humans

引用

computers in human Behavior: Artificial humans 2025年 4卷

作者： Christina Pasalidou Chris Lytridis Avgoustos Tsinakos Nikolaos Fachantidis Laboratory of Informatics & Robotic Applications in Education and Society Department of Educational and Social Policy University of Macedonia Thessaloniki Greece Human-Machines Interaction (HUMAIN) Lab Department of Computer Science Democritus University of Thrace Kavala Greece Advanced Educational Technologies and Mobile Applications (AETMA) Lab Department of Computer Science Democritus University of Thrace Kavala Greece

Integrating cutting-edge technologies into education has been a continuous goal to enhance teaching and learning experiences. Augmented Reality (AR) and robotics are two emerging technologies that have shown promise in transforming educational environments. This paper presents a systematic review of the literature on the combination of AR and robotics for educational purposes, identifying key applications, benefits, and trends. Using the PRISMA methodology, 69 relevant studies from five major databases were analysed and categorised into three themes: (a) AR and Socially Assistive Robots (SAR), (b) AR-assisted educational robotics, and (c) AR in robotics/engineering education. The review provides insights into how different AR-enhanced robotics applications across primary, secondary, and higher education, provide visualizations, multimodal feedback, and immersive experiences. Key findings suggest that while interactive features of AR and the embodiment of robots show promising results for learning, fostering motivation, excitement, positive attitudes, and enriched educational experiences, challenges such as technological complexity and cost remain barriers to widespread adoption. Future research should focus on pedagogical frameworks and large-scale implementations to optimize AR-robotics integration in diverse educational settings.

关键词： Robotics Augmented reality HRI Education

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A Simulation Study of Transcranial Magnetoacoustic Stimulation of the Basal Ganglia Thalamic Neural Network to Improve Parkinson's Disease Status

SSRN

引用

SSRN 2023年

作者： Zhang, Yanqiu Zhang, Hao Xu, Tianya Liu, Jiahe Mu, Jiayang Chen, Rongjie Yang, Jiumin Wang, Peiguo Jian, Xiqi School of Biomedical Engineering and Technology Tianjin Medical University Tianjin300070 China Academy of Medical Engineering and Translational Medicine Tianjin International Joint Research Centre for Neural Engineering Tianjin Key Laboratory of Brain Science and Neural Engineering Tianjin University Tianjin300072 China Haihe Laboratory of Brain-computer Interaction and Human-machine Integration Tianjin300392 China Neurology Department Tianjin Huanhu Hospital Tianjin300350 China Department of Oncology Radiotherapy Cancer Institute Hospital of Tianjin Medical University Tianjin300202 China

Background: Parkinson's disease (PD) is a common neurodegenerative disease. Transcranial magnetoacoustic stimulation (TMAS) is a new therapy that combines a transcranial focused acoustic pressure field with a magnetic field to excite or inhibit neurons in targeted area, which suppresses the abnormally elevated beta band amplitude in PD states, with high spatial resolution and non-invasively. Objective: To study the effective stimulation parameters of TMAS mononuclear and multinuclear stimulation for the treatment of PD with reduced beta band energy, improved abnormal synchronization, and no thermal damage. Methods: The TMAS model is constructed based on the volunteer's computed tomography, 128 arrays of phase-controlled transducers, and permanent magnets. A basal ganglia-thalamic (BG-Th) neural network model of the PD state was constructed on the basis of the Izhikevich model and the acoustic model. An ultrasound stimulation neuron model is constructed based on the Hodgkin-Huxley model. Numerical simulations of transcranial focused acoustic pressure field, temperature field and induced electric field at single and dual targets were performed using the locations of STN, GPi, and GPe in the human brain as the main stimulation target areas. And the acoustic and electric parameters at the focus were extracted to stimulate mononuclear and multinuclear in the BG-Th neural network. Results: When the stimulating effect of ultrasound is ignored, TMAS-STN suppresses the beta band amplitude of each nucleus, whereas TMAS-GPi affects only some nuclei. TMAS-STN&GPi can reduce the beta band amplitude. TMAS-STN&GPi&GPe is more effective. When considering the stimulatory effect of ultrasound, the stimulatory effect of lower sound pressures is negligible, but higher sound pressures may promote or inhibit the stimulatory effect of induced currents. Conclusions: TMAS stimulation of multinuclear with appropriate ultrasound intensity is effective in PD and may be clinically useful. © 202

关键词： Ultrasonics

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Self-Prompting Large Language Models for Zero-Shot Open-Domain QA

arXiv

引用

arXiv 2022年

作者： Li, Junlong Wang, Jinyuan Zhang, Zhuosheng Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China SJTU-Paris Elite Institute of Technology Shanghai Jiao Tong University China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China

Open-Domain Question Answering (ODQA) aims to answer questions without explicitly providing specific background documents. This task becomes notably challenging in a zeroshot setting where no data is available to train tailored retrieval-reader models. While recent Large Language Models (LLMs) like GPT-3 have demonstrated their effectiveness in zeroshot ODQA using direct prompting methods, these methods still fall short of fully harnessing the potential of LLMs when implicitly invoked. In this paper, we propose a Self- Prompting framework to explicitly utilize the massive knowledge encoded in the parameters of LLMs and their strong instruction understanding abilities. Concretely, we prompt LLMs step by step to generate multiple pseudo QA pairs with background passages and explanations entirely from scratch. These generated elements are then utilized for in-context learning. Experimental results show that our method significantly surpasses previous stateof- the-art zero-shot methods on three widelyused ODQA datasets and even achieves comparable performance with various customized fine-tuned models on full training data. Our code is available at https://***/ lockon-n/self-prompting. © 2022, CC BY-NC-SA.

关键词： Computational linguistics

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Edge Learning for Large-Scale Internet of Things With Task-Oriented Efficient Communication

arXiv

引用

arXiv 2023年

作者： Xie, Haihui Xia, Minghua Wu, Peiran Wang, Shuai Poor, H. Vincent The School of Electronics and Information Technology Sun Yat-Sen University Guangzhou510006 China The Southern Marine Science and Engineering Guangdong Laboratory Zhuhai519082 China The Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

In the Internet of Things (IoT) networks, edge learning for data-driven tasks provides intelligent applications and services. As the network size becomes large, different users may generate distinct datasets. Thus, to suit multiple edge learning tasks for large-scale IoT networks, this paper performs efficient communication under the task-oriented principle by using the collaborative design of wireless resource allocation and edge learning error prediction. In particular, we start with multi-user scheduling to alleviate co-channel interference in dense networks. Then, we perform optimal power allocation in parallel for different learning tasks. Thanks to the high parallelization of the designed algorithm, extensive experimental results corroborate that the multi-user scheduling and task-oriented power allocation improve the performance of distinct edge learning tasks efficiently compared with the state-of-the-art benchmark algorithms. © 2023, CC BY-NC-SA.

关键词： Internet of things

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Toward Efficient Automated Feature Engineering

arXiv

引用

arXiv 2022年

作者： Wang, Kafeng Wang, Pengyang Xu, Chengzhong Shenzhen Institute of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences University of Macau China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China

utomated Feature Engineering (AFE) refers to automatically generate and select optimal feature sets for downstream tasks, which has achieved great success in real-world applications. Current AFE methods mainly focus on improving the effectiveness of the produced features, but ignoring the low-efficiency issue for large-scale deployment. Therefore, in this work, we propose a generic framework to improve the efficiency of AFE. Specifically, we construct the AFE pipeline based on reinforcement learning setting, where each feature is assigned an agent to perform feature transformation and selection, and the evaluation score of the produced features in downstream tasks serve as the reward to update the policy. We improve the efficiency of AFE in two perspectives. On the one hand, we develop a Feature Pre-Evaluation (FPE) Model to reduce the sample size and feature size that are two main factors on undermining the efficiency of feature evaluation. On the other hand, we devise a two-stage policy training strategy by running FPE on the pre-evaluation task as the initialization of the policy to avoid training policy from scratch. We conduct comprehensive experiments on 36 datasets in terms of both classification and regression tasks. The results show 2.9% higher performance in average and 2x higher computational efficiency comparing to state-of-the-art AFE methods. © 2022, CC BY.

关键词： Reinforcement learning

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Difficulty-aware meta-learning for rare disease diagnosis 23rd

Difficulty-aware meta-learning for rare disease diagnosis

引用

23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Li, Xiaomeng Yu, Lequan Jin, Yueming Fu, Chi-Wing Xing, Lei Heng, Pheng-Ann Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin Hong Kong Department of Radiation Oncology Stanford University Stanford United States Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9783030597092

Rare diseases have extremely low-data regimes, unlike common diseases with large amount of available labeled data. Hence, to train a neural network to classify rare diseases with a few per-class data samples is very challenging, and so far, catches very little attention. In this paper, we present a difficulty-aware meta-learning method to address rare disease classifications and demonstrate its capability to classify dermoscopy images. Our key approach is to first train and construct a meta-learning model from data of common diseases, then adapt the model to perform rare disease classification. To achieve this, we develop the difficulty-aware meta-learning method that dynamically monitors the importance of learning tasks during the meta-optimization stage. To evaluate our method, we use the recent ISIC 2018 skin lesion classification dataset, and show that with only five samples per class, our model can quickly adapt to classify unseen classes by a high AUC of 83.3%. Also, we evaluated several rare disease classification results in the public Dermofit Image Library to demonstrate the potential of our method for real clinical practice. © Springer Nature Switzerland AG 2020.

关键词： Learning systems

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