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IEEE International Conference on Robotics and Biomimetics

作者： Leyuan Sun Guanqun Ding Yusuke Yoshiyasu Fumio Kanehiro Department of Intelligent and Mechanical Interaction Systems Graduate School of Science and Technology University of Tsukuba Tsukuba Ibaraki Japan CNRS-AIST Joint Robotics Laboratory (JRL) IRL National Institute of Advanced Industrial Science and Technology (AIST). Digital Architecture Research Center (DARC) National Institute of Advanced Industrial Science and Technology (AIST) Tokyo Japan Computer Vision Research Team Artificial Intelligence Research Center (AIRC) National Institute of Advanced Industrial Science and Technology (AIST) Japan

ISBN: (纸本)9781665481106

As a basic and indispensable module, LiDAR odom-etry estimation is widely used in robotics. In recent years, learning-based modeling approaches for odometry estimation have been validated to be feasible. However, it is necessary to consider security factors as the highest priorities when we apply the learning-based model to certain high-risk real-world scenarios, such as autonomous driving. The odometry uncertainty estimation provides more valuable information for downstream tasks, such as route planning and navigation. In this paper, we propose an end-to-end neural network (namely CertainOdom) to solve odometry and uncertainty estimation tasks by applying multi-task learning. Instead of using the manually-tuned hyper-parameters, we employ the learnable uncertainties to weigh the balance between the error of translation and orientation in the loss function. We evaluate the estimated trajectory and uncertainty on KITTI dataset. We also compare the robustness against the traditional geometry-based methods on our artificially degraded KITTI LiDAR dataset. Extensive experimental results show that our model with uncertainty weighted loss achieves competitive performance in LiDAR odometry estimation. We also explain our uncertainties qualitatively and quantitatively.

关键词： Uncertainty Laser radar Biological system modeling Estimation Multitasking Robustness Trajectory

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Unlocking the Emotional World of Visual Media: An Overview of the science, Research, and Impact of Understanding Emotion

arXiv

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arXiv 2023年

作者： Wang, James Z. Zhao, Sicheng Wu, Chenyan Adams, Reginald B. Newman, Michelle G. Shafir, Tal Tsachor, Rachelle The Data Science and Artificial Intelligence Area the Human-Computer Interaction Area the College of Information Sciences and Technology The Pennsylvania State University University ParkPA16802 United States The Department of Psychology The Pennsylvania State University University ParkPA16802 United States BNRist Tsinghua University Beijing100084 China The Emily Sagol Creative Arts Therapies Research Center University of Haifa 3498838 Israel The School of Theatre and Music University of Illinois ChicagoIL60607 United States

The emergence of artificial emotional intelligence technology is revolutionizing the fields of computers and robotics, allowing for a new level of communication and understanding of human behavior that was once thought impossible. Whereas recent advancements in deep learning have transformed the field of computer vision, automated understanding of evoked or expressed emotions in visual media remains in its infancy. This foundering stems from the absence of a universally accepted definition of "emotion," coupled with the inherently subjective nature of emotions and their intricate nuances. In this paper, we provide a comprehensive, multidisciplinary overview of the field of emotion analysis in visual media, drawing on insights from psychology, engineering, and the arts. We begin by exploring the psychological foundations of emotion and the computational principles that underpin the understanding of emotions from images and videos. We then review the latest research and systems within the field, accentuating the most promising approaches. We also discuss the current technological challenges and limitations of emotion analysis, underscoring the necessity for continued investigation and innovation. We contend that this represents a "Holy Grail" research problem in computing and delineate pivotal directions for future inquiry. Finally, we examine the ethical ramifications of emotion-understanding technologies and contemplate their potential societal impacts. Overall, this article endeavors to equip readers with a deeper understanding of the domain of emotion analysis in visual media and to inspire further research and development in this captivating and rapidly evolving field. Copyright © 2023, The Authors. All rights reserved.

关键词： Intelligent robots

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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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Influence of multi-modality on moving target selection in virtual reality

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Virtual Reality & Intelligent Hardware 2019年第3期1卷 303-315页

作者： Yang LI Dong WU Jin HUANG Feng TIAN Hong'an WANG Guozhong DAI Beijing Key Laboratory of Human-Computer Interaction Institute of SoftwareChinese Academy of SciencesBeijing 100190China State Key Laboratory of Computer Science Chinese Academy of SciencesBeijing 100190China Institute of Remote Sensing and Digital Earth Chinese Academy of SciencesBeijing 100190China University Chinese Academy Sciences Beijing 100190China

Background Owing to recent advances in virtual reality(VR)technologies,effective user interaction with dynamic content in 3D scenes has become a research *** target selection is a basic interactive task in which the user performance research in tasks is significant to user interface design in *** from the existing static target selection studies,the moving target selection in VR is affected by the change in target speed,angle and size,and lack of research on some key *** This study designs an experimental scenario in which the users play badminton under the condition of *** adding seven kinds of modal clues such as vision,audio,haptics,and their combinations,five kinds of moving speed and four kinds of serving angles,and the effect of these factors on the performance and subjective feelings in moving target selection in VR,is *** The results show that the moving speed of the shuttlecock has a significant impact on the user *** angle of service has a significant impact on hitting rate,but has no significant impact on the hitting *** acquisition of the user performance by the moving target is mainly influenced by vision under the combined modalities;adding additional modalities can improve user *** the hitting distance of the target is increased in the trimodal condition,the hitting rate *** This study analyses the results of user performance and subjective perception,and then provides suggestions on the combination of modality clues in different scenarios.

关键词： Multimodal Moving target selection Virtual reality

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Do I enjoy my friends? Friendship and enjoyment during recess in autistic and non-autistic children

Research in Autism

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Research in Autism 2025年 124卷

作者： Jiayin Zhao Boya Li Yung-Ting Tsou Mitra Baratchi Els Blijd-Hoogewys Özgür Gülen Carolien Rieffe Unit of Developmental and Educational Psychology Institute of Psychology Leiden University Leiden the Netherlands Department of Computer Science Faculty of Science Vrije Universiteit Amsterdam Amsterdam the Netherlands Leiden Institute of Advanced Computer Science (LIACS) Leiden University Leiden the Netherlands Developmental Psychology Faculty of Behavioral and Social Sciences. University of Groningen Groningen the Netherlands Psychiatrie Noord Groningen the Netherlands Department of Psychology Faculty of Economics Administrative and Social Sciences Bahçeşehir University İstanbul Turkey Department of Human Media Interaction Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede the Netherlands Department of Psychology and Human Development Institute of Education University College London London UK

Friendships play a crucial role in children’s well-being and school experiences. This study aimed to gain a better understanding of how autistic and non-autistic children's friendships are related to their enjoyment of the school time spent with peers, i.e. at recess time (school break time). A multi-method approach, including self-reports, peer nominations, and objective measures based on sensor data was used. Forty-five autistic children and 45 non-autistic children from two special education schools participated, aged between 8 to 14 years. Outcomes showed that autistic and non-autistic children did not differ regarding the number of reciprocal and non-reciprocated friends. Yet, autistic children spent less time in contact with their reciprocal friends during recess at the schoolyard compared to their non-autistic peers. Also, while non-autistic pupils spent more time with reciprocal friends than with non-reciprocated ones, this difference was not found among autistic pupils. Notably, spending more time with non-reciprocated friends during recess was related to lower levels of enjoyment in both autistic and non-autistic children. Our findings suggest that autistic children may approach friendships with different priorities. Furthermore, this study underscores the need to consider broader factors beyond reciprocity when assessing children’s social experience at school.

关键词： Peer contact Social Functioning School environment RFID data Schoolyard

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Face-sketch learning with human sketch-drawing order enforcement

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science China(Information sciences) 2020年第11期63卷 298-311页

作者： Liang CHANG Lihua JIN Lifen WENG Wentao CHAO Xuguang WANG Xiaoming DENG Qiulei DONG School of Artificial Intelligence Beijing Normal University Department of Design Art Xiamen University of Technology Department of Automation North China Electric Power University Beijing Key Laboratory of Human Computer Interactions Institute of Software Chinese Academy of Sciences National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences

Dear editor,Although face-sketch synthesis generates a sketch from a given face photo automatically [1], it is an open research problem in computer vision [2–4]. Recently, several deep neural network (DNN)methods for... 详细信息

关键词： face sketch synthesis deep neural network order enforcement image synthesis generative adversarial network

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Explaining autonomous drones: An XAI journey

Applied AI Letters

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Applied AI Letters 2021年第4期2卷

作者： Stefik, Mark Youngblood, Michael Pirolli, Peter Lebiere, Christian Thomson, Robert Price, Robert Nelson, Lester D. Krivacic, Robert Le, Jacob Mitsopoulos, Konstantinos Somers, Sterling Schooler, Joel Intelligent Systems Laboratory PARC A Xerox Company Palo Alto CA United States The Institute for Human and Machine Cognition Pensacola FL United States Human-Computer Interaction Institute School of Computer Science Carnegie Mellon University Pittsburgh PA United States Army Cyber Institute United States Military Academy New York NY United States

COGLE (COmmon Ground Learning and Explanation) is an explainable artificial intelligence (XAI) system where autonomous drones deliver supplies to field units in mountainous areas. The mission risks vary with topography, flight decisions, and mission goals. The missions engage a human plus AI team where users determine which of two AI-controlled drones is better for each mission. This article reports on the technical approach and findings of the project and reflects on challenges that complex combinatorial problems present for users, machine learning, user studies, and the context of use for XAI systems. COGLE creates explanations in multiple modalities. Narrative “What” explanations compare what each drone does on a mission and “Why” based on drone competencies determined from experiments using counterfactuals. Visual “Where” explanations highlight risks on maps to help users to interpret flight plans. One branch of the research studied whether the explanations helped users to predict drone performance. In this branch, a model induction user study showed that post-decision explanations had only a small effect in teaching users to determine by themselves which drone is better for a mission. Subsequent reflection suggests that supporting human plus AI decision making with pre-decision explanations is a better context for benefiting from explanations on combinatorial tasks. © 2021 PARC, a Xerox Company. Applied AI Letters published by John Wiley & Sons Ltd. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

关键词： autonomous systems explainable artificial intelligence hierarchical multifunction modeling

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AI-based Bayesian structural time series modeling for assessing PM 2.5 air quality improvements during the Beijing 2022 Winter Olympics

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Atmospheric Environment 2025年 358卷

作者： Weizhao Liang Yuan Li Xuan Liu Qili Dai Yinchang Feng Key Laboratory of Urban Air Particulate Pollution Prevention and Control of Ministry of Ecology and Environment College of Environmental Science and Engineering Nankai University Tianjin 300350 China China Meteorological Administration-Nankai University Cooperative Laboratory for Atmospheric Environment-Health Research Tianjin 300350 China Tianjin Key Laboratory of Software Experience and Human Computer Interaction Tianjin 300457 China

To prevent poor air quality during the Beijing Winter Olympics (BWO), substantial air pollution control measures were implemented in Beijing and surrounding cities. However, the actual causal effects of these measures remain uncertain, primarily due to the difficulty of establishing an appropriate control group under complex meteorological conditions and high anthropogenic emissions in cold season. This study addresses that challenge by employing an artificial intelligence (AI)-based Bayesian Structural Time Series (BSTS) model to estimate the causal effects of intervention measures. Utilizing temporal-related proxies, meteorological data, and PM 2.5 concentration records from 2015 to 2021, counterfactual PM 2.5 concentrations were first predicted using a Random Forest-Long Short-Term Memory (RF-LSTM) hybrid model, which generated a covariate for the BSTS model to enable robust causal inference with minimal parametric assumptions. The results indicate that, in the absence of interventions, Beijing's PM 2.5 during the Winter Olympics would have been 20 μg/m 3 (34 %) higher. The implemented measures significantly reduced peak PM 2.5 levels during the Winter Olympics and Winter Paralympics. Rigorous validation confirmed the accuracy of the predicted counterfactual PM 2.5 concentration, thereby strengthening the reliability of the causal inference results. Overall, the AI-based BSTS approach effectively quantified the PM 2.5 reductions attributable to the air quality assurance measures during the BWO, providing strong evidence for the effectiveness of pollution control efforts.

关键词： Air pollution Bayesian structural time series Beijing Winter Olympics Intervention evaluation Long short-term memory

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A SURVEY ON BRAIN-INSPIRED DEEP LEARNING VIA PREDICTIVE CODING

arXiv

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arXiv 2023年

作者： Salvatori, Tommaso Mali, Ankur Buckley, Christopher L. Lukasiewicz, Thomas Rao, Rajesh P.N. Friston, Karl Ororbia, Alexander VERSES AI Research Lab Los AngelesCA United States Institute of Logic and Computation Vienna University of Technology Austria University of South Florida TampaFL33620 United States Sussex AI Group Department of Informatics University of Sussex Brighton United Kingdom Department of Computer Science University of Oxford United Kingdom Paul G. Allen School of Computer Science and Engineering University of Washington SeattleWA United States The Wellcome Centre for Human Neuroimaging Queen Square Institute of Neurology University College London United Kingdom The Neural Adaptive Computing Laboratory Rochester Institute of Technology RochesterNY14623 United States

Artificial intelligence (AI) is rapidly becoming one of the key technologies of this century. The majority of results in AI thus far have been achieved using deep neural networks trained with the error backpropagation learning algorithm. However, such an algorithm has always been considered biologically implausible. To this end, recent works have studied learning algorithms for deep neural networks inspired by the neurosciences. One such theory, called predictive coding (PC), has shown promising properties that make it potentially valuable for the machine learning community: it can model information processing in different areas of the brain, can be used in cognitive control and robotics, has a solid mathematical foundation in variational inference, and performs its computations asynchronously. Inspired by such properties, works that propose novel PC-like algorithms are starting to be present in multiple sub-fields of machine learning and artificial intelligence at large. Here, we survey such efforts by first providing a broad overview of the history of PC to provide a common ground for the understanding of the recent developments, then by surveying current efforts and results, and concluding with a large discussion of possible implications and ways forward. Copyright © 2023, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Deep learning for drug repurposing: methods, databases, and applications

arXiv

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arXiv 2022年

作者： Pan, Xiaoqin Lin, Xuan Cao, Dongsheng Zeng, Xiangxiang Yu, Philip S. He, Lifang Nussinov, Ruth Cheng, Feixiong School of Computer Science and Engineering Hunan University China School of Computer Science Xiangtan University China Xiangya School of Pharmaceutical Sciences Central South University China Department of Computer Science University of Illinois at Chicago United States Department of Computer Science and Engineering Lehigh University United States Computational Structural Biology Section Basic Science Program Frederick National Laboratory for Cancer Research National Cancer Institute at Frederick FrederickMD21702 United States Department of Human Molecular Genetics and Biochemistry Sackler School of Medicine Tel Aviv University Tel Aviv69978 Israel Genomic Medicine Institute Lerner Research Institute Cleveland Clinic ClevelandOH44195 United States Department of Molecular Medicine Cleveland Clinic Lerner College of Medicine Case Western Reserve University ClevelandOH44195 United States Case Comprehensive Cancer Center Case Western Reserve University School of Medicine ClevelandOH44106 United States

Drug development is time-consuming and expensive. Repurposing existing drugs for new therapies is an attractive solution that accelerates drug development at reduced experimental costs, specifically for Coronavirus Disease 2019 (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, comprehensively obtaining and productively integrating available knowledge and big biomedical data to effectively advance deep learning models is still challenging for drug repurposing in other complex diseases. In this review, we introduce guidelines on how to utilize deep learning methodologies and tools for drug repurposing. We first summarized the commonly used bioinformatics and pharmacogenomics databases for drug repurposing. Next, we discuss recently developed sequence-based and graph-based representation approaches as well as state-of-the-art deep learning-based methods. Finally, we present applications of drug repurposing to fight the COVID-19 pandemic, and outline its future challenges. © 2022, CC BY-NC-SA.

关键词： Coronavirus

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