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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Jiashun Wang Chao Wen Yanwei Fu Haitao Lin Tianyun Zou Xiangyang Xue Yinda Zhang Fudan University School of Data Science and MOE Frontiers Center for Brain Science Shanghai Key Lab of Intelligent Information Processing Fudan University Academy of Engineering and Technology and Institute of AI and Robotics Fudan University Google LLC

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Pose transfer has been studied for decades, in which the pose of a source mesh is applied to a target mesh. Particularly in this paper, we are interested in transferring the pose of source human mesh to deform the target human mesh, while the source and target meshes may have different identity information. Traditional studies assume that the paired source and target meshes are existed with the point-wise correspondences of user annotated landmarks/mesh points, which requires heavy labelling efforts. On the other hand, the generalization ability of deep models is limited, when the source and target meshes have different identities. To break this limitation, we proposes the first neural pose transfer model that solves the pose transfer via the latest technique for image style transfer, leveraging the newly proposed component -- spatially adaptive instance normalization. Our model does not require any correspondences between the source and target meshes. Extensive experiments show that the proposed model can effectively transfer deformation from source to target meshes, and has good generalization ability to deal with unseen identities or poses of meshes. Code is available at https://***/jiashunwang/Neural-Pose-Transfer.

关键词： Shape Three-dimensional displays Feature extraction Strain Decoding Machine learning Task analysis

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Metal-organic-gel method derived (TiZrNbTa)C high-entropy nanopowders and ceramics with high performance

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Ceramics International 2025年

作者： Didi Cao Meiling Yu Qizheng Dong Lin Zhao Peng Wang Hongkang Wei Zhiyang Lv Xunyan Tan Jian Gu Yinsheng Li Zhipeng Xie Chang-an Wang Jiangxi Key Laboratory of Advanced Ceramic Materials School of Materials Science and Engineering Jingdezhen Ceramic University Jingdezhen 333403 China College of Materials Science and Engineering Nanjing Tech University Nanjing 211816 China Zhejiang Key Laboratory of Data-Driven High-Safety Energy Materials and Applications Ningbo Key Laboratory of Special Energy Materials and Chemistry Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo 315201 China State Key Lab of New Ceramics and Fine Processing School of Materials Science and Engineering Tsinghua University Beijing 100084 China

Multi-component high-entropy carbide ceramics have emerged as a promising class of materials due to their exceptional mechanical performance, thermal stability, and chemical corrosion resistance. In this study, we employed pyromellitic acid as the carbon source and developed a metal-organic-gel method to synthesize (TiZrNbTa)C high-entropy precursors. Through carbothermal reduction at relatively low temperature of 1500°C, homogeneous single-phase face-centered cubic solid solution were obtained successfully. Subsequently, the carbide ceramics were fabricated using SPS. Characterization results demonstrate that the synthesized powders exhibit quasi-spherical morphology with uniform particle size distribution and average diameter of 57 nm. The SPS-sintered (TiZrNbTa)C high-entropy ceramics demonstrate remarkable performance, achieving relative density as high as 99.8% with uniform microstructure and particle size of 0.44 ± 0.1 μm. The material exhibited outstanding mechanical performance, with Vickers hardness and fracture toughness reaching 21.82 ± 1.5 GPa and 4.565 ± 0.5 MPa·m 1/2 , respectively.

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Neural pose transfer by spatially adaptive instance normalization

arXiv

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arXiv 2020年

作者： Wang, Jiashun Wen, Chao Fu, Yanwei Lin, Haitao Zou, Tianyun Xue, Xiangyang Zhang, Yinda Fudan University Google LLC School of Data Science MOE Frontiers Center for Brain Science Shanghai Key Lab of Intelligent Information Processing Fudan University Academy of Engineering and Technology Institute of AI and Robotics Fudan University

Pose transfer has been studied for decades, in which the pose of a source mesh is applied to a target mesh. Particularly in this paper, we are interested in transferring the pose of source human mesh to deform the target human mesh, while the source and target meshes may have different identity information. Traditional studies assume that the paired source and target meshes are existed with the point-wise correspondences of user annotated landmarks/mesh points, which requires heavy labelling efforts. On the other hand, the generalization ability of deep models is limited, when the source and target meshes have different identities. To break this limitation, we proposes the first neural pose transfer model that solves the pose transfer via the latest technique for image style transfer, leveraging the newly proposed component - spatially adaptive instance normalization. Our model does not require any correspondences between the source and target meshes. Extensive experiments show that the proposed model can effectively transfer deformation from source to target meshes, and has good generalization ability to deal with unseen identities or poses of meshes. Code is available at https://***/jiashunwang/Neural-Pose-Transfer. Copyright © 2020, The Authors. All rights reserved.

关键词： Mesh generation

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Constructing virtual 5-dimensional tori out of lower-dimensional network cards

Constructing virtual 5-dimensional tori out of lower-dimensi...

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作者： Andújar, Francisco J. Villar, Juan A. Sánchez, José L. Alfaro, Francisco J. Duato, José Fröning, Holger Department of Computing Systems University of Castilla-La Mancha Albacete Spain Department of Systems Data Processing and Computers Polytechnic University of Valencia Valencia Spain Institute of Computer Engineering Ruprecht-Karls University of Heidelberg Mannheim Germany

In the Top500 and Graph500 lists of the last years, some of the most powerful systems implement a torus topology to interconnect the millions of computing nodes they include. Some of these torus networks are of five or six dimensions, which implies an additional difficulty as the node degree increases. In previous works, we proposed and evaluated the nD Twin (nDT) torus topology to virtually increase the dimensions a torus is able to implement. We showed that this new topology reduces the distances between nodes, increasing, therefore, global network performance. In this work, we present how to build a 5DT torus network using a specific commercial 6-port network card (EXTOLL card) to interconnect those nodes. We show, using the same number of cards, that the performance of the 5DT torus network we are able to implement using our proposal is higher than the performance of the 3D torus network for the same number of compute nodes. © 2017 John Wiley & Sons, Ltd.

关键词： Topology

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Metamaterial Concentrator for Thermal Energy Harvester 1

Metamaterial Concentrator for Thermal Energy Harvester

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1st International Conference on Renewable Energy Research and Challenge, ICoRER 2019

作者： Nichita Kaikatui, Rapha Putra Andika, Adik Letsoin, Vinsenius Mangera, Paulus Hardiantono, Damis Adhi Nugroho, Yogo Susanto, Herry Wibowo, Haryo Department of Electrical Engineering Musamus University Kamizaun Street Merauke99611 Indonesia Individual Researcher Alumni School of Ipb University Bogor Indonesia Data Processing Rumah Paper - Editage Services Jl. Tokala No.1 Malang65146 Indonesia Department of Mechanical Engineering Darma Persada University Jl. Taman Malaka Selatan No.22 Jakarta13450 Indonesia School of Renewable Energy Darma Persada University Jakarta Indonesia Institute of Energy and Power Engineering Zhejiang University of Technology No. 38 Zheda Road Hangzhou310027 China

Energy demand increases in line with rapid technological advances. Research on the harvesting of renewable energy continues to be done to make efforts to convert heat energy, which is very abundant in our daily environment. Thermoelectric technology is an alternative source in answering energy needs and can produce energy on a large and small scale. Thermoelectric technology works by converting heat energy into electricity directly, or from electricity to cold. This research presents an experimental study conducted to find out the thermoelectric characteristics of the TEC in the reversal function, with heating and cooling tests on each side of the TEC type thermoelectric element, carried out to obtain the voltage value as the electrical potential generated from this element. The result is thermoelectric potential to generate DC electricity but is very limited in the function of maintaining a heat source on the hot side element. This research then proposes thermal metamaterial that functions as a collector of thermal energy in the method of converting thermal energy into DC electrical energy for the application of low power consumption communication systems. © The Authors, published by EDP Sciences, 2020.

关键词： Thermal energy

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Inception-based network and multi-spectrogram ensemble applied for predicting respiratory anomalies and lung diseases

arXiv

引用

arXiv 2020年

作者： Pham, Lam Phan, Huy King, Ross Mertins, Alfred McLoughlin, Ian Competence Unit Data Science & Artificial Intelligence Center for Digital Safety & Security Austria Institute of Technology Austria School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Institute for Signal Processing University of Lübeck Germany Singapore Institute of Technology Singapore

This paper presents an inception-based deep neural network for detecting lung diseases using respiratory sound input. Recordings of respiratory sound collected from patients are firstly transformed into spectrograms where both spectral and temporal information are well presented, referred to as front-end feature extraction. These spectrograms are then fed into the proposed network, referred to as back-end classification, for detecting whether patients suffer from lung-relevant diseases. Our experiments, conducted over the ICBHI benchmark meta-dataset of respiratory sound, achieve competitive ICBHI scores of 0.53/0.45 and 0.87/0.85 regarding respiratory anomaly and disease detection, respectively. Copyright © 2020, The Authors. All rights reserved.

关键词： Spectrographs

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Experimental investigation of electromyographic activities of upper limb muscles without and with a passive exoskeleton with four degrees of freedoms

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International Journal Bioautomation 2019年第3期23卷 343-354页

作者： Raikova, Rositsa Angelova, Silvija Veneva, Ivanka Christov, Ivaylo Department of Motor Control Institute of Biophysics and Biomedical Engineering Bulgarian Academy of Sciences 105 Acad. G. Bonchev Str. Sofia1113 Bulgaria Department of Mechatronics Institute of Mechanics Bulgarian Academy of Sciences 4 Acad. G. Bonchev Str. Sofia1113 Bulgaria Department of Analysis and Processing of Biomedical Signals and Data Institute of Biophysics and Biomedical Engineering Bulgarian Academy of Sciences 105 Acad. G. Bonchev Str. Sofia1113 Bulgaria

The choice of suitable human surface muscles and of their electromyographic (EMG) signal processing have always been a challenge, especially when technical devices are to be controlled by these signals. Experiments with six healthy volunteers were performed with a NORAXON measuring and processing system. EMG signals from 8 muscles of the upper right arm performing the main motions in the shoulder and elbow joints were registered and processed. Four angles in the joints were also registered. Six motor tasks were performed, without and with an exoskeleton with four degrees of freedom. Since the EMG signal of the muscle pectoralis major was contaminated by noise coming from the electrical activity of the heart, first this signal was filtered using recently developed dynamic filter. Normalization according to maximal isometric tasks was performed after filtration. Rectification and smoothing ensured a suitable signal for proportional control of the exoskeleton. On/off control was simulated by setting different constants for maximal EMG amplitude levels. © 2019 by the authors.

关键词： Muscle

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A Method for Authenticity Identification of Fritillaria Cirrhosa D. Don Based on Deep Learning 4

A Method for Authenticity Identification of Fritillaria Cirr...

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4th IEEE International Conference on Image, Vision and Computing, ICIVC 2019

作者： Hu, Ke Hu, Pan Cao, Dong Yan, Xin Yu, Xi Liu, Chang Chengdu University College of Information Science and Engineering Chengdu China Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan Chengdu University Chengdu China Research Institute of Big Data Chengdu University China Chengdu Institute of Chinese Herbal Medicine China

ISBN: (纸本)9781728123257

Since the authentic Fritillaria Cirrhosa D. Don resources are scarce due to its high price and valuable medical uses, it is difficult to meet the clinical needs. Therefore, the problem of adulteration in the market is more and more serious. At present, the identification of Fritillaria mainly relies on traditional trait identification, microscopic identification, physical and chemical identification and other methods, which is subjective and requires high practical experience for operators, and the pretreatment work is cumbersome. In this paper, the concept of deep learning is introduced into the identification of Fritillaria for the first time. First, we have created the first multi-angle Fritillaria standard datasets, including Fritillaria Cirrhosa D. Don and Fritillary bulb. We then have proposed a novel deep learning framework SE-DPU to classify Fritillaria datasets, from which we can get the highest classification accuracy. The framework is a hybrid of Squeeze-and-Excitation block (SE unit), U-Net and Dual Path Network (DPN). It reuses the features and explores the new features adaptively recalibrates channel-wise feature responses obtains abundance features while training. Experimental results with Fritillaria Cirrhosa D. Don data have indicated that the proposed method have provided competitive performance. © 2019 IEEE.

关键词： Deep learning

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Efficient processing of top-k frequent spatial keyword queries

Research Square

引用

Research Square 2021年

作者： Xu, Tao Xu, Aopeng Mango, Joseph Liu, Pengfei Ma, Xiaqing Zhang, Lei Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China School of Computer and Information Engineering Henan University Kaifeng475004 China Key Laboratory of Geographical Information Science Ministry of Education East China Normal University Shanghai200241 China Key Research Institute of Yellow River Civilization and Sustainable Development Henan University Kaifeng475004 China

The rapid popularization of high-speed mobile communication technology and the continuous development of mobile network devices have given spatial textual big data (STBD) new dimensions due to their ability to record geographical objects from multiple sources and with complex attributes. data mining from spatial textual datasets has become a meaningful study. As a popular topic for STBD, the top-k spatial keyword query has been developed in various forms to deal with different retrievals requirements. However, previous research focused mainly on indexing locational attributes and retrievals of few target attributes, and these correlations between large numbers of the textual attributes have not been fully studied and demonstrated. To further explore interrelated-knowledge in the textual attributes, this paper defines the top-k frequent spatial keyword query (tfSKQ) and proposes a novel hybrid index structure, named RCL-tree, based on the concept lattice theory. We also develop the tfSKQ algorithms to retrieve the most frequent and nearest spatial objects in STBD. One existing method and two baseline algorithms are implemented, and a series of experiments are carried out using real datasets to evaluate its performance. Results demonstrated the effectiveness and efficiency of the proposed RCL-tree in tfSKQ with the complex spatial multi keyword query conditions. © 2021, CC BY.

关键词： Big data

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Interval probabilistic fuzzy WordNet

arXiv

引用

arXiv 2021年

作者： Alizadeh-Q, Yousef Minaei-Bidgoli, Behrouz Hossayni, Sayyed-Ali Akbarzadeh-T, Mohammad-R. Recupero, Diego Reforgiato Rajati, Mohammad-Reza Gangemi, Aldo Data Mining Lab School of Computer Engineering Iran University of Science and Technology Tehran Iran Department of Computer Engineering Ferdowsi University of Mashhad Mashhad Iran Agents Research Lab TECNIO Centre EASY ViCOROB Research Institute Campus de Montilivi University of Girona Girona Catalonia Spain Italy Laboratoire d'Informatique de Paris Nord Université Paris 13 Sorbonne Paris Cité-CNRS France Signal and Image Processing Institute University of Southern California Los AngelesCA90089-02564 United States

WordNet lexical-database groups English words into sets of synonyms called "synsets." Synsets are utilized for several applications in the field of text-mining. However, they were also open to criticism because although, in reality, not all the members of a synset represent the meaning of that synset with the same degree, in practice, they are considered as members of the synset, identically. Thus, the fuzzy version of synsets, called fuzzy-synsets (or fuzzy word-sense classes) were proposed and studied. In this study, we discuss why (type-1) fuzzy synsets (T1 F-synsets) do not properly model the membership uncertainty, and propose an upgraded version of fuzzy synsets in which membership degrees of word-senses are represented by intervals, similar to what in Interval Type 2 Fuzzy Sets (IT2 FS) and discuss that IT2 FS theoretical framework is insufficient for analysis and design of such synsets, and propose a new concept, called Interval Probabilistic Fuzzy (IPF) sets. Then we present an algorithm for constructing the IPF synsets in any language, given a corpus and a word-sense-disambiguation system. Utilizing our algorithm and the open-American-online-corpus (OANC) and UKB word-sense-disambiguation, we constructed and published the IPF synsets of WordNet for English language. Copyright © 2021, The Authors. All rights reserved.

关键词： Ontology

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