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Impairments in Spatial Representations of Place Cells and Neural rhythms In APP knock-in rat model

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Impairments in Spatial Representations of Place Cells and Ne...

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Xueling Wang Yimeng Wang Keliang Pang Bai Lu Yutao Tian Chenguang Zheng Academy of Medical Engineering and Translational Medicine Medical College Tianjin University Tianjin China Aging and Longevity Institute & Institute of Biological Science Zhongshan Hospital Fudan University Shanghai China School of Pharmaceutical Sciences IDG/McGovern Institute for Brain Research Tsinghua University-Peking University Joint Center for Life Sciences Tsinghua University Beijing China Tianjin Key Laboratory of Brain Science and Neuroengineering Tianjin China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Alzheimer's disease (AD) is a neurodegenerative disease that severely impacts spatial memory. Place cells in the hippocampus play an important role in spatial memory. However, neuronal activity in the hippocampus is difficult to monitor and study in human AD patients. Thus, it is critical to have an animal model that resembles closely human AD. The newly developed APP NL-G-F rat model, which is a knock-in rat line of the amyloid precursor protein (APP) with Swedish-Beyreuther/Iberian-Arctic mutations, presents a unique opportunity. In this study, we investigated how hippocampal place cells and neural rhythms were impaired in APP rats. Place cells of APP rats showed a less defined spatial tunning and disrupted remapping. In addition, APP rats exhibited decreased power of theta rhythm and slow gamma rhythm. These results suggest that the impairment of spatial memory in APP rats may be caused by the damage of place cells' ability to represent spatial location and abnormal neural rhythm in the hippocampus.

关键词： Proteins Rats Alzheimer's disease Engineering in medicine and biology Biomedical monitoring Hippocampus Monitoring

Multimodal prototypical networks with Co-metric fusion for few-shot hyperspectral image classification

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Neurocomputing 2025年 648卷

作者： Yuhang Li Jinrong He Hanchi Liu Yurong Zhang Zhaokui Li College of Mathematics and Computer Science Yan'an University Yan’an 716000 China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning 530100 China School of Computer Shenyang Aerospace University Shenyang 110136 China

In the field of Hyperspectral image (HSI) classification, prototype-based network methods have achieved significant research progress. These methods utilize pixel-level information from images to construct central prototypes for each class, providing effective solutions for few-shot learning. However, traditional prototype networks have some inherent flaws; they primarily rely on a single image modality and fail to fully leverage the potential complementarity between different modalities, using only a single modality to generate class prototypes, which limits the model's performance in representing class prototypes and enhancing discriminative capabilities. And subtle inter-class differences are also a challenging task in cross-domain scenarios. To overcome these challenges, this study proposes an innovative Multimodal Prototypical Networks with Co-metric Fusion (MPCF). By integrating prototype information from both image and text modalities, MPCF significantly enhances the performance of few-shot learning. The method not only captures the spectral and spatial features of images to construct image prototypes but also extracts textual features from category descriptions to generate text prototypes. Furthermore, by integrating contrastive learning strategies with the Co-metric fusion mechanism, the method effectively harnesses the information from different modalities. This integration allows for the capture of category information across multiple dimensions, significantly boosting the model's discriminative power among various classes and enhancing its capacity to address few-shot learning scenarios. Experiments conducted on several public benchmark HSI datasets (Indian Pines-84.06 %, Houston-80.41 %, Salinas-92.63 %) demonstrate that MPCF exhibits excellent performance under few-shot and cross-domain conditions, achieving higher classification accuracy and stronger robustness compared to state-of-the-art methods. The related code will be made publicly available at t

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Hierarchical generative modelling for autonomous robots

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arXiv 2023年

作者： Yuan, Kai Sajid, Noor Friston, Karl Li, Zhibin Embodied AI Lab Intel Labs Germany Wellcome Centre for Human Neuroimaging Queen Square Institute of Neurology University College London United Kingdom Department of Computer Science UCL Robotics Institute University College London United Kingdom

humans can produce complex whole-body motions when interacting with their surroundings, by planning, executing and combining individual limb movements. We investigated this fundamental aspect of motor control in the setting of autonomous robotic operations. We approach this problem by hierarchical generative modelling equipped with multi-level planning—for autonomous task completion—that mimics the deep temporal architecture of human motor control. Here, temporal depth refers to the nested time scales at which successive levels of a forward or generative model unfold, for example, delivering an object requires a global plan to contextualise the fast coordination of multiple local movements of limbs. This separation of temporal scales also motivates robotics and control. Specifically, to achieve versatile sensorimotor control, it is advantageous to hierarchically structure the planning and low-level motor control of individual limbs. We use numerical and physical simulation to conduct experiments and to establish the efficacy of this formulation. Using a hierarchical generative model, we show how a humanoid robot can autonomously complete a complex task that necessitates a holistic use of locomotion, manipulation, and grasping. Specifically, we demonstrate the ability of a humanoid robot that can retrieve and transport a box, open and walk through a door to reach the destination, approach and kick a football, while showing robust performance in presence of body damage and ground irregularities. Our findings demonstrated the effectiveness of using human-inspired motor control algorithms, and our method provides a viable hierarchical architecture for the autonomous completion of challenging goal-directed tasks. © 2023, CC BY.

关键词： Anthropomorphic robots

Skeleton-Embedded Network for action recognition

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Skeleton-Embedded Network for action recognition

IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Li Luo Ziliang Ren Yong Qin Qieshi Zhang Xiangyang Gao Department of Computer Science Dongguan University of Technology Dongguan China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China The Chinese University of Hong Kong China

Multimodality-based human action recognition is becoming an increasingly attractive topic as different modalities can provide complementary information. RGB and skeleton data have their pros and limitations for action recognition because they describe the action from distinctive views. Therefore, the fusion of these two modalities using their complementarity is meaningful to action recognition. However, it is difficult to completely utilize the supplementarity of RGB and skeleton data. In this paper, to take advantage of their complementary at the semantic level, A skeleton-embedded network(SE-Net) is proposed by us. The architecture is based on integrating their feature in the network not only a post-hoc fusion. With a sparse sampling strategy, which is efficient for learning using the whole action video. To examine the performance of our method, we conducted experiments on the wildly used datasets, obtaining an accuracy of 86.4% in C-sub and 88.2% in C-view on NTU-RGB+D 60. For NTU-RGB+D 120, the accuracy of 81.3% in C-sub and 83.4% in C-set is obtained.

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On the Limits of Lossy Compression for human Activity Recognition in Sensor Networks

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On the Limits of Lossy Compression for Human Activity Recogn...

Conference on Local computer Networks (LCN)

作者： Jonas Schulz Hristina Radak Phuong T. Nguyen Giang T. Nguyen Frank H. P. Fitzek Deutsche Telekom Chair of Communication Networks TU Dresden Germany Department of Information Engineering Computer Science and Mathematics University of L’Aquila Italy Haptic Communication Systems TU Dresden Germany Centre for Tactile Internet with Human-in-the-Loop (CeTI)

human activity recognition is crucial for tactile internet, virtual reality, and digital-twin applications. Previous works have analyzed machine learning for this purpose but often need to pay more attention to typical challenges when deploying these machine learning models in production. First, data must be transmitted to the network node on which the machine-learning model is running. However, scaling human activity recognition by the number of devices and users puts additional constraints on the available transmission channel. While transmitting less data saves bandwidth, removing redundant information from the data can also benefit machine learning. This paper addresses the problem of transmitting wearable sensor data for human activity recognition. To this end, we analyze the extent to which wearable sensor data can be compressed via sparse coding without sacrificing loss in recognition performance. We empirically illustrate, on various datasets, that only a fraction of sensor information is relevant for human activity recognition.

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Bilingual Lexicon Induction for Low-Resource Languages using Graph Matching via Optimal Transport

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arXiv 2022年

作者： Marchisio, Kelly Saad-Eldin, Ali Duh, Kevin Priebe, Carey Koehn, Philipp Department of Computer Science United States Department of Applied Mathematics and Statistics United States Department of Biomedical Engineering United States Human Language Technology Center of Excellence Johns Hopkins University United States

Bilingual lexicons form a critical component of various natural language processing applications, including unsupervised and semisupervised machine translation and crosslingual information retrieval. We improve bilingual lexicon induction performance across 40 language pairs with a graph-matching method based on optimal transport. The method is especially strong with low amounts of supervision. Copyright © 2022, The Authors. All rights reserved.

关键词： Natural language processing systems

Mental Models of a Mobile Shoe Rack

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ACM Transactions on human-Robot interaction 2021年第2期10卷

作者： Rueben, Matthew Klow, Jeffrey Duer, Madelyn Zimmerman, Eric Piacentini, Jennifer Browning, Madison Bernieri, Frank J. Grimm, Cindy M. Smart, William D. University of Southern California Los AngelesCA90089 United States Oregon State University CorvallisOR97330 United States Institute School of Psychological Science Interaction Lab in the Department of Computer Science

Most people do not have direct access to knowledge about the inner workings of robots. Instead, they must develop mental models of the robot, a process that is not well understood. This article presents findings from a long-term, in-the-wild, qualitative, hypothesis-generating study of the mental model formation process. The focus was on how (qualitatively) users form mental models of the robot-specifically its perceptual capabilities, rules of behavior, and communication with other humans. Participants of diverse ages had multiple interactions with the robot over six weeks in a non-laboratory setting. The robot's rules of behavior were changed every two weeks. A novel, non-anthropomorphic robot was created for the study with a realistic use case: Storing people's shoes during a yoga class. This article reports findings from a case study analysis of 28 interviews conducted over six weeks with six participants. These findings are organized into six topics: (1) variability in the rate at which mental models are updated to be more predictive, (2) types of reasoning and hypothesizing about the robot, (3) borrowing from existing mental models and use of imagination, (4) attributing sensing capabilities where there are no visible sensors, (5) judgments about whether the robot is autonomous or teleoperated, and (6) experimenting with the robot. Specific suggestions for future research are given throughout, culminating in a set of study design recommendations. This work demonstrates the fruitfulness of long-term, in-the-wild studies of human-robot interaction, of which mental model formation is a foundational aspect. © 2021 Copyright held by the owner/author(s).

关键词： Anthropomorphic robots

VaccineLies: A Natural Language Resource for Learning to Recognize Misinformation about the COVID-19 and HPV Vaccines

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arXiv 2022年

作者： Weinzierl, Maxwell A. Harabagiu, Sanda M. Human Language Technology Research Institute Department of Computer Science The University of Texas at Dallas 800 W. Campbell Rd. RichardsonTX75080 United States

Billions of COVID-19 vaccines have been administered, but many remain hesitant. Misinformation about the COVID-19 vaccines and other vaccines, propagating on social media, is believed to drive hesitancy towards vaccination. The ability to automatically recognize misinformation targeting vaccines on Twitter depends on the availability of data resources. In this paper we present VACCINELIES, a large collection of tweets propagating misinformation about two vaccines: the COVID-19 vaccines and the human Papillomavirus (HPV) vaccines. Misinformation targets are organized in vaccine-specific taxonomies, which reveal the misinformation themes and concerns. The ontological commitments of the Misinformation taxonomies provide an understanding of which misinformation themes and concerns dominate the discourse about the two vaccines covered in VACCINELIES. The organization into training, testing and development sets of VACCINELIES invites the development of novel supervised methods for detecting misinformation on Twitter and identifying the stance towards it. Furthermore, VACCINELIES can be a stepping stone for the development of datasets focusing on misinformation targeting additional vaccines. © 2022, CC BY.

关键词： Vaccines

A Pilot Study of Sidewalk Equity in Seattle Using Crowdsourced Sidewalk Assessment Data

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arXiv 2022年

作者： Li, Chu Orii, Lisa Saugstad, Michael Mooney, Stephen J. Eisenberg, Yochai Labbé, Delphine Hammel, Joy Froehlich, Jon E. Allen School of Computer Science University of Washington United States Epidemiology University of Washington United States Disability and Human Development Department University of Illinois Chicago United States

We examine the potential of using large-scale open crowdsourced sidewalk data from Project Sidewalk to study the distribution and condition of sidewalks in Seattle, WA. While potentially noisier than professionally gathered sidewalk datasets, crowdsourced data enables large, cross-regional studies that would be otherwise expensive and difficult to manage. As an initial case study, we examine spatial patterns of sidewalk quality in Seattle and their relationship to racial diversity, income level, built density, and transit modes. We close with a reflection on our approach, key limitations, and opportunities for future work. © 2022, CC BY.

关键词： Crowdsourcing