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computer simulation of heart-pacemaker interaction

Journal of Electrocardiology 1990年第SUPPL.期23卷 156-156页

作者： Greenhut, Saul E. MacDonald, Robert S. Jenkins, Janice M. Department of Electrical Engineering Computer Science The University of Michigan Ann Arbor MI United States the Bioengineering Program The University of Michigan Ann Arbor MI United States

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Learning universal representations from word to sentence

arXiv

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arXiv 2020年

作者： Li, Yian 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 China

Despite the well-developed cut-edge representation learning for language, most language representation models usually focus on specific level of linguistic unit, which cause great inconvenience when being confronted with handling multiple layers of linguistic objects in a unified way. Thus this work introduces and explores the universal representation learning, i.e., embeddings of different levels of linguistic unit in a uniform vector space through a task-independent evaluation. We present our approach of constructing analogy datasets in terms of words, phrases and sentences and experiment with multiple representation models to examine geometric properties of the learned vector space. Then we empirically verify that well pre-trained Transformer models incorporated with appropriate training settings may effectively yield universal representation. Especially, our implementation of fine-tuning ALBERT on NLI and PPDB datasets achieves the highest accuracy on analogy tasks in different language levels. Further experiments on the insurance FAQ task show effectiveness of universal representation models in real-world applications. Copyright © 2020, The Authors. All rights reserved.

关键词： Linguistics

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Improving Out-of-Distribution Generalization in Graphs via Hierarchical Semantic Environments

arXiv

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arXiv 2024年

作者： Piao, Yinhua Lee, Sangseon Lu, Yijingxiu Kim, Sun Department of Computer Science and Engineering Seoul National University Korea Republic of Institute of Computer Technology Seoul National University Korea Republic of AIGENDRUG Co. Ltd. Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of

Out-of-distribution (OOD) generalization in the graph domain is challenging due to complex distribution shifts and a lack of environmental contexts. Recent methods attempt to enhance graph OOD generalization by generating flat environments. However, such flat environments come with inherent limitations to capture more complex data distributions. Considering the DrugOOD dataset, which contains diverse training environments (e.g., scaffold, size, etc.), flat contexts cannot sufficiently address its high heterogeneity. Thus, a new challenge is posed to generate more semantically enriched environments to enhance graph invariant learning for handling distribution shifts. In this paper, we propose a novel approach to generate hierarchical semantic environments for each graph. Firstly, given an input graph, we explicitly extract variant subgraphs from the input graph to generate proxy predictions on local environments. Then, stochastic attention mechanisms are employed to re-extract the subgraphs for regenerating global environments in a hierarchical manner. In addition, we introduce a new learning objective that guides our model to learn the diversity of environments within the same hierarchy while maintaining consistency across different hierarchies. This approach enables our model to consider the relationships between environments and facilitates robust graph invariant learning. Extensive experiments on real-world graph data have demonstrated the effectiveness of our framework. Particularly, in the challenging dataset DrugOOD, our method achieves up to 1.29% and 2.83% improvement over the best baselines on IC50 and EC50 prediction tasks, respectively. © 2024, CC BY.

关键词： Semantics

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Robust Saliency-Aware Distillation for Few-shot Fine-grained Visual Recognition

arXiv

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arXiv 2023年

作者： Liu, Haiqi Chen, C.L. Philip Gong, Xinrong Zhang, Tong The School of Computer Science and Engineering South China University of Technology Guangzhou510006 China The Brain and Affective Cognitive Research Center Pazhou Lab. Guangzhou510335 China The Engineering Research Center The Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou510006 China

Recognizing novel sub-categories with scarce samples is an essential and challenging research topic in computer vision. Existing literature addresses this challenge by employing local-based representation approaches, which may not sufficiently facilitate meaningful object-specific semantic understanding, leading to a reliance on apparent background correlations. Moreover, they primarily rely on high-dimensional local descriptors to construct complex embedding space, potentially limiting the generalization. To address the above challenges, this article proposes a novel model, Robust Saliency-aware Distillation (RSaD), for few-shot fine-grained visual recognition. RSaD introduces additional saliency-aware supervision via saliency detection to guide the model toward focusing on the intrinsic discriminative regions. Specifically, RSaD utilizes the saliency detection model to emphasize the critical regions of each sub-category, providing additional object-specific information for fine-grained prediction. RSaD transfers such information with two symmetric branches in a mutual learning paradigm. Furthermore, RSaD exploits inter-regional relationships to enhance the informativeness of the representation and subsequently summarize the highlighted details into contextual embeddings to facilitate the effective transfer, enabling quick generalization to novel sub-categories. The proposed approach is empirically evaluated on three widely used benchmarks, demonstrating its superior performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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A distributed neural system for top-down face processing

引用

Neuroscience Letters 2009年第1期451卷 6-10页

作者： Li, Jun Liu, Jiangang Liang, Jimin Zhang, Hongchuan Zhao, Jizheng Huber, David E. Rieth, Cory A. Lee, Kang Tian, Jie Shi, Guangming Life Science Research Center School of Electronic Engineering Xidian University Xi'an Shaanxi 710071 China School of Computer and Information Technology Beijing Jiaotong University Beijing 1000441 China Institute of Automation Chinese Academy of Sciences Beijing 100190 China Developmental Cognitive Neuroscience Laboratory Northwestern University IL United States Department of Psychology University of California San Diego La Jolla CA United States Institute of Child Study University of Toronto Toronto Ont. Canada

Evidence suggests that the neural system associated with face processing is a distributed cortical network containing both bottom-up and top-down mechanisms. While bottom-up face processing has been the focus of many studies, the neural areas involved in the top-down face processing have not been extensively investigated due to difficulty in isolating top-down influences from the bottom-up response engendered by presentation of a face. In the present study, we used a novel experimental method to induce illusory face-detection. This method allowed for directly examining the neural systems involved in top-down face processing while minimizing the influence of bottom-up perceptual input. A distributed cortical network of top-down face processing was identified by analyzing the functional connectivity patterns of the right fusiform face area (FFA). This distributed cortical network model for face processing includes both "core" and "extended" face processing areas. It also includes left anterior cingulate cortex (ACC), bilateral orbitofrontal cortex (OFC), left dorsolateral prefrontal cortex (DLPFC), left premotor cortex, and left inferior parietal cortex. These findings suggest that top-down face processing contains not only regions for analyzing the visual appearance of faces, but also those involved in processing low spatial frequency (LSF) information, decision-making, and working memory. © 2008 Elsevier Ireland Ltd.

关键词： Distributed cortical network Face processing fMRI Psychophysiological interaction (PPI) Top-down processing

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A Wearable Device Network to Track Animal Behavior and Relationships in the Wild

A Wearable Device Network to Track Animal Behavior and Relat...

引用

IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Luis Camal Anup Kirtane Teresa Blanco Roberto Casas Federico Rossano Baris Aksanli Electrical and Computer Engineering San Diego State University San Diego CA USA GeoSpatiumLab S.L. Aragon Institute of Engineering Research Zaragoza Spain Aragon Institute of Engineering Research University of Zaragoza Zaragoza Spain Cognitive Science University of California San Diego San Diego CA USA

ISBN: (纸本)9781728138862

The advancements in sensor technology have made it possible to design wearable devices specifically designed for animals. These wearable devices can be used for locating individual animals, monitor their status, and track their trajectories in the wild. Some animal groups (such as chimpanzees) exhibit complex group behavior and these group dynamics play an important role in the physical and mental health of the animals. Scientists have traditionally been monitoring group dynamics manually in the wild. This requires extensive field trips, costing a lot of time and money. This calls for using the recent developments in technology, such as smart wearable devices for this purpose. However, lack of infrastructure support (limited connectivity, limited power, etc.) in the wilderness makes this a tedious task. In this work-in-progress paper, we present our technological approach and how we address the issues of wilderness to study animal behavior. We demonstrate how we build a network of lightweight wearable devices, and how the digital output of these devices can be used to analyze animal relationship. We show an initial, exploratory experiment, outlining the capabilities of the devices and technologies used in terms of communication efficiency, and the potential of the devices that can be used in the wilderness. Our initial results show that up to 90% of the proximity-based interactions can be captured.

关键词： behavioural sciences computerised monitoring wearable computers wireless sensor networks zoology Behavioral sciences computerized monitoring Wearable computers Zoology animal behaviour wilderness Wearable group dynamics Wireless sensor networks field trips Technology Development Animals

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Deep Kernel Embedded Clustering Network

SSRN

引用

SSRN 2024年

作者： Ren, Lina Huang, Ruizhang Chen, Yanping Lin, Chuan Qin, Yongbin State Key Laboratory of Public Big Data Text Computing and Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and Technology Guizhou University Guiyang550025 China Department of Information Engineering Guizhou Light Industry Technical College Guiyang550025 China

In this paper, we propose a deep kernel embedded clustering network, namely DKEC, which learns data partitions with kernelized semantic embeddings of data samples via a self-supervised deep neural network. A kernelized semantic embedding learner is designed to learn high quality data representations, which not only capture the data semantic characteristics but also show good structural separable characteristics. Other than directly apply traditional predefined kernel mapping methods, the kernelized semantic embedding learner, investigated by a deep variational Gaussian process, is able to learn the kernel mapping according to the nature of various datasets automatically, while maintain reasonable computational complexity. The DKEC, which is an end-to-end deep clustering network, is invested based on the kernelized semantic embedding learner. A triplet self-supervised optimization objective is designed in the DKEC model to guide the clustering partition, the separable factor of the kernelized semantic embedding, and the data reconstruction factor of the kernelized semantic embedding, simultaneously. We conducted extensive experiments on $ 7 $ real-world datasets. The results unequivocally showcase the exceptional clustering performance of the proposed DKEC. © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Load Manipulation by a Triangular Formation of Quadrotors

Load Manipulation by a Triangular Formation of Quadrotors

引用

International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Valentim Ernandes Neto Alexandre Santos Brandão Mário Sarcinelli-Filho Graduate Program on Electrical Engineering Federal University of Espírito Santo Vitória ES Brazil Núcleo de Especialização em Robótica Department of Electrical Engineering and Graduate Program on Computer Science Federal University of Viçosa Viçosa MG Brazil

ISBN: (数字)9781728142784

ISBN: (纸本)9781728142791

This work discusses a trajectory tracking task accomplished by a group of three Bebop 2 quadrotors working cooperatively to transport a load. Such unmanned aerial vehicles (UAVs) are considered as a triangular formation, and the load, a triangle-shaped structure, is attached to them through massless flexible cables. The reason to use three quadrotors is the gain in terms of the payload capability and the stability of the load, since it is attached to three vehicles, staying vertically aligned with the center of mass of the triangle correspondent to the formation. A formation controller, actually a kinematic controller applied to a virtual massless robot correspondent to the center of mass of the triangle, is adopted, which generates reference velocities for the three UAVs, whose movement generates the movement and reshaping of the triangular formation. Then, individual dynamic compensators are adopted for each UAV, whose input is the reference velocity delivered by the formation controller. Finally, experimental results are discussed, which validate the proposal.

关键词： Vehicle dynamics Aerodynamics Atmospheric modeling Aircraft Robot kinematics Mathematical model

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Reference language based unsupervised neural machine translation

arXiv

引用

arXiv 2020年

作者： Li, Zuchao Zhao, Hai Wang, Rui Utiyama, Masao Sumita, Eiichiro Department of Computer Science and Engineering Shanghai Jiao Tong University 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 Kyoto Japan

Exploiting common language as an auxiliary for better translation has a long tradition in machine translation, which lets supervised learning based machine translation enjoy the enhancement delivered by the well-used pivot language, in case that the prerequisite of parallel corpus from source language to target language cannot be fully satisfied. The rising of unsupervised neural machine translation (UNMT) seems completely relieving the parallel corpus curse, though still subject to unsatisfactory performance so far due to vague clues available used for its core back-translation training. Further enriching the idea of pivot translation by freeing the use of parallel corpus other than its specified source and target, we propose a new reference language based UNMT framework, in which the reference language only shares parallel corpus with the source, indicating clear enough signal to help the reconstruction training of UNMT through a proposed reference agreement mechanism. Experimental results show that our methods improve the quality of UNMT over that of a strong baseline in terms of only one auxiliary language, demonstrating the usefulness of the proposed reference language based UNMT with a good start. Copyright © 2020, The Authors. All rights reserved.

关键词： Neural machine translation

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Do humans and Convolutional Neural Networks attend to similar areas during scene classification: Effects of task and image type

arXiv

引用

arXiv 2023年

作者： Müller, Romy Dürschmidt, Marcel Ullrich, Julian Knoll, Carsten Weber, Sascha Seitz, Steffen Faculty of Psychology Chair of Engineering Psychology and Applied Cognitive Research TUD Dresden University of Technology Dresden Germany Faculty of Electrical and Computer Engineering Chair of Fundamentals of Electrical Engineering TUD Dresden University of Technology Dresden Germany Faculty of Mathematics and Natural Sciences Department of Computer Science Machine Learning Group Heinrich-Heine-Universität Düsseldorf Düsseldorf Germany

Deep Learning models like Convolutional Neural Networks (CNN) are powerful image classifiers, but what factors determine whether they attend to similar image areas as humans do? While previous studies have focused on technological factors, little is known about the role of factors that affect human attention. In the present study, we investigated how the tasks used to elicit human attention maps interact with image characteristics in modulating the similarity between humans and CNN. We varied the intentionality of human tasks, ranging from spontaneous gaze during categorization over intentional gaze-pointing up to manual area selection. Moreover, we varied the type of image to be categorized, using either singular, salient objects, indoor scenes consisting of object arrangements, or landscapes without distinct objects defining the category. The human attention maps generated in this way were compared to the CNN attention maps revealed by explainable artificial intelligence (Grad-CAM). The influence of human tasks strongly depended on image type: For objects, human manual selection produced maps that were most similar to CNN, while the specific eye movement task has little impact. For indoor scenes, spontaneous gaze produced the least similarity, while for landscapes, similarity was equally low across all human tasks. To better understand these results, we also compared the different human attention maps to each other. Our results highlight the importance of taking human factors into account when comparing the attention of humans and CNN. © 2023, CC BY.

关键词： Eye movements

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