检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Zhang, Deyun Geng, Shijia Zhou, Yang Xu, Weilun Wei, Guodong Wang, Kai Yu, Jie Zhu, Qiang Li, Yongkui Zhao, Yonghong Chen, Xingyue Zhang, Rui Fu, Zhaoji Zhou, Rongbo Yanqi, E. Fan, Sumei Zhao, Qinghao Cheng, Chuandong Peng, Nan Zhang, Liang Zheng, Linlin Chu, Jianjun Xu, Hongbin Tan, Chen Liu, Jian Tao, Huayue Liu, Tong Chen, Kangyin Jiang, Chenyang Liu, Xingpeng Hong, Shenda HeartVoice Medical Technology Hefei China HeartRhythm-HeartVoice Joint Laboratory Beijing China Heart Center Beijing Chao-Yang Hospital Capital Medical University Beijing China HeartRhythm Medical Beijing China Division of Life Sciences and Medicine University of Science and Technology of China Hefei China Department of Physiology School of Basic Medical Sciences Anhui Medical University Hefei China Department of Anatomy and Histoembryology School of Basic Medical Sciences Anhui Medical University Hefei China Department of Cardiology Peking University People's Hospital Beijing China Department of Neurosurgery The First Affiliated Hospital University of Science and Technology of China Hefei China Heart Center Anhui Chest Hospital Anhui Medical University Hefei China Department of Electrocardiography The First Affiliated Hospital Anhui Medical University Hefei China The Second People's Hospital of Hefei City Hefei China Information Center/Medical Big Data Office The First Affiliated Hospital Anhui Medical University Hefei China Department of Cardiology Hebei Yan Da Hospital Langfang China Network Information Center Second Hospital of Tianjin Medical University Tianjin China Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease Department of Cardiology Tianjin Institute of Cardiology Second Hospital of Tianjin Medical University Tianjin China Key Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province Department of Cardiology Sir Run Run Shaw Hospital Zhejiang University School of Medicine Hangzhou China National Institute of Health Data Science Peking University Beijing China Institute of Medical Technology Health Science Center Peking University Beijing China

The artificial intelligence (AI) system has achieved expert-level performance in electrocardiogram (ECG) signal analysis. However, in underdeveloped countries or regions where the healthcare information system is imperfect, only paper ECGs can be provided. Analysis of real-world ECG images (photos or scans of paper ECGs) remains challenging due to complex environments or interference. In this study, we present an AI system developed to detect and screen cardiac abnormalities (cas) from real-world ECG images. The system was evaluated on a large dataset of 52,357 patients from multiple regions and populations across the world. On the detection task, the AI system obtained area under the receiver operating curve (AUC) of 0.996 (hold-out test), 0.994 (external test 1), 0.984 (external test 2), and 0.979 (external test 3), respectively. Meanwhile, the detection results of AI system showed a strong correlation with the diagnosis of cardiologists (cardiologist 1 (R=0.794, p Copyright © 2023, The Authors. All rights reserved.

关键词： Electrocardiograms

来源：评论

学校读者我要写书评

暂无评论

A symmetric uniform formula and sole index method for sieving (twin) primes

TechRxiv

引用

TechRxiv 2020年

作者： Ren, Wei School of Computer Science China University of Geosciences Wuhan430074 China Key Laboratory of Network Assessment Technology CAS Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Guizhou Provincial Key Laboratory of Public Big Data GuiZhou University Guizhou China

All primes can be indexed by k, as primes must be in the form of 6k+1 or 6k-1. In this paper, we explore for the set of k such that either 6k+1 or 6k-1 is not a prime. Our method provides a uniform formula for k that can sieve primes and twin primes as well. The uniform presents symmetry in terms of k that works as single index in sieving. We also propose a new conjecture that is equivalent to Twin Prime Conjecture but possibly be easier to approach by merely exploring of sole index in term of k. The uniform formula for prime sieving are as follows: k ∈ Sl ⇒ 6k−1 6∈ , where Sl = [−I]6I+1 = [I]6I−1\min([I]6I−1), I ∈ . k ∈ Sr ⇒ 6k + 1 6∈ , where Sr = [−I]6I−1 ∪ ([I]6I+1\min([I]6I+1)), I ∈ . © 2020, CC BY.

关键词： Number theory

来源：评论

学校读者我要写书评

暂无评论

Role of Left-Hand Cut Contributions on Pole Extractions from Lattice data: case Study for Tcc(3875)+

引用

Physical Review Letters 2023年第13期131卷 131903-131903页

作者： Meng-Lin Du Arseniy Filin Vadim Baru Xiang-Kun Dong Evgeny Epelbaum Feng-Kun Guo Christoph Hanhart Alexey Nefediev Juan Nieves Qian Wang School of Physics University of Electronic Science and Technology of China Chengdu 611731 China Institut für Theoretische Physik II Ruhr-Universität Bochum D-44780 Bochum Germany CAS Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing 100190 China School of Physical Sciences University of Chinese Academy of Sciences Beijing 100049 China Peng Huanwu Collaborative Center for Research and Education Beihang University Beijing 100191 China Institute for Advanced Simulation Institut für Kernphysik and Jülich Center for Hadron Physics Forschungszentrum Jülich D-52425 Jülich Germany Jozef Stefan Institute Jamova 39 1000 Ljubljana Slovenia CeFEMA Center of Physics and Engineering of Advanced Materials Instituto Superior Técnico Avenida Rovisco Pais 1 1049-001 Lisboa Portugal Instituto de Física Corpuscular (centro mixto CSIC-UV) Institutos de Investigación de Paterna Apartado 22085 46071 Valencia Spain Guangdong Provincial Key Laboratory of Nuclear Science Institute of Quantum Matter South China Normal University Guangzhou 510006 China Institute of High Energy Physics Chinese Academy of Sciences Beijing 100049 China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Southern Nuclear Science Computing Center South China Normal University Guangzhou 510006 China

We discuss recent lattice data for the Tcc(3875)+ state to stress, for the first time, a potentially strong impact of left-hand cuts from the one-pion exchange on the pole extraction for near-threshold exotic states. In particular, if the left-hand cut is located close to the two-particle threshold, which happens naturally in the DD* system for the pion mass exceeding its physical value, the effective-range expansion is valid only in a very limited energy range up to the cut and as such is of little use to reliably extract the poles. Then, an accurate extraction of the pole locations requires the one-pion exchange to be implemented explicitly into the scattering amplitudes. Our findings are general and potentially relevant for a wide class of hadronic near-threshold states.

关键词： Hadron-hadron interactions Heavy quark effective theory Lattice QCD Phenomenology Exotic mesons

来源：评论

学校读者我要写书评

暂无评论

Signed graph attention networks

arXiv

引用

arXiv 2019年

作者： Huang, Junjie Shen, Huawei Hou, Liang Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Graph or network data is ubiquitous in the real world, including social networks, information networks, traffic networks, biological networks and various technical networks. The non-Euclidean nature of graph data poses the challenge for modeling and analyzing graph data. Recently, Graph Neural network (GNNs) are proposed as a general and powerful framework to handle tasks on graph data, e.g., node embedding, link prediction and node classification. As a representative implementation of GNNs, Graph Attention networks (GATs) are successfully applied in a variety of tasks on real datasets. However, GAT is designed to networks with only positive links and fails to handle signed networks which contain both positive and negative links. In this paper, we propose Signed Graph Attention networks (SiGATs), generalizing GAT to signed networks. SiGAT incorporates graph motifs into GAT to capture two well-known theories in signed network research, i.e., balance theory and status theory. In SiGAT, motifs offer us the flexible structural pattern to aggregate and propagate messages on the signed network to generate node embeddings. We evaluate the proposed SiGAT method by applying it to the signed link prediction task. Experimental results on three real datasets demonstrate that SiGAT outperforms feature-based method, network embedding method and state-of-the-art GNN-based methods like signed graph convolutional network (SGCN). Copyright © 2019, The Authors. All rights reserved.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

A Pitch-aware Speaker Extraction Serial network

A Pitch-aware Speaker Extraction Serial Network

引用

Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Yu Jiang Meng Ge Longbiao Wang Jianwu Dang Kiyoshi Honda Sulin Zhang Bo Yu Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Japan Advanced Institute of Science and Technology Ishikawa Japan Automotive Data of China Co. Ltd

ISBN: (数字)9789881476883

ISBN: (纸本)9781728181301

Despite deep learning has an excellent performance in monaural speaker extraction, it's still a challenge to extract speakers when facing the same gender, i.e., male-male and female-female. On the other hand, it has been proved that pitch tracking is effective for same-gender speech separation. In this study, we proposed a pitch-aware speaker extraction serial network (PSESNet) to improve extraction performance. We designed a serial system and compared it with multi-task learning, we tried to use the target speaker's pitch information to optimize the loss function rather than as input to the extraction network. The extraction part uses SpeakerBeam-FE (SBF) with magnitude and temporal spectrum approximation loss (MTSAL) and speaker embedding concatenation. After extracting the spectrogram of the target speaker, we connected the spectrogram to predict the pitch information to do further optimization. Experimental results show that serial system performs better than multi-task learning and proposed method improves performance in both same and opposite gender conditions. On average, PSESNet achieves 4.7% and 3.8% relative improvements on WSJ0 dataset over the SBF-MTSAL-Concat baseline on signal-to-distortion ratio (SDR) under both closed and open condition.

关键词： data mining Spectrogram Estimation Training Target tracking Optimization Acceleration

来源：评论

学校读者我要写书评

暂无评论

Hierarchical deep CNN feature set-based representation learning for robust cross-resolution face recognition

arXiv

引用

arXiv 2021年

作者： Gao, Guangwei Yu, Yi Yang, Jian Qi, Guo-Jun Yang, Meng Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210023 China Digital Content and Media Sciences Research Division National Institute of Informatics Tokyo101-8430 Japan School of Computer Science and Technology Nanjing University of Science and Technology Nanjing210094 China Department of Computer Science University of Central Florida OrlandoFL32816 United States School of Data and Computer Science Sun Yat-sen University Guangzhou510006 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University Guangzhou510006 China

Cross-resolution face recognition (CRFR), which is important in intelligent surveillance and biometric forensics, refers to the problem of matching a low-resolution (LR) probe face image against high-resolution (HR) gallery face images. Existing shallow learning-based and deep learning-based methods focus on mapping the HR-LR face pairs into a joint feature space where the resolution discrepancy is mitigated. However, little works consider how to extract and utilize the intermediate discriminative features from the noisy LR query faces to further mitigate the resolution discrepancy due to the resolution limitations. In this study, we desire to fully exploit the multilevel deep convolutional neural network (CNN) feature set for robust CRFR. In particular, our contributions are threefold. (i) To learn more robust and discriminative features, we desire to adaptively fuse the contextual features from different layers. (ii) To fully exploit these contextual features, we design a feature set-based representation learning (FSRL) scheme to collaboratively represent the hierarchical features for more accurate recognition. Moreover, FSRL utilizes the primitive form of feature maps to keep the latent structural information, especially in noisy cases. (iii) To further promote the recognition performance, we desire to fuse the hierarchical recognition outputs from different stages. Meanwhile, the discriminability from different scales can also be fully integrated. By exploiting these advantages, the efficiency of the proposed method can be delivered. Experimental results on several face datasets have verified the superiority of the presented algorithm to the other competitive CRFR approaches. Copyright © 2021, The Authors. All rights reserved.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

DLEDNet: A deep learning-based image encryption and decryption network for internet of medical things

arXiv

引用

arXiv 2020年

作者： Ding, Yi Wu, Guozheng Chen, Dajiang Zhang, Ning Gong, Linpeng Cao, Mingsheng Qin, Zhiguang Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu Sichuan610054 China Institute of Electronic and Information Engineering of UESTC in Guangdong Dongwan Guangdong China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu Sichuan610054 China National Natural Science Foundation of China Beijing China Department of Computing Science Texas A&M University-Corpus Christi Corpus ChristiTX78412 United States

With the rapid development of Internet of Medical Things (IoMT) technology, many medical imaging equipments are connected to the medical information network to facilitate the process of diagnosing and treating for doctors. As medical image contains sensitive information, it is of importance yet very challenging to encrypt the medical image to safeguard the privacy or security of the patient. In this work, a deep learning based encryption and decryption network (DLEDNet) is proposed to fulfill the process of encrypting and decrypting the medical image. Specifically, in DLEDNet, the Cycle-GAN network is employed as the main learning network to transfer the medical image from its original domain to the target domain. Target domain is regarded as a "Hidden Factors" to guide the learning model to realize the encryption process. The encrypted image is restored to the original (plaintext) image through a reconstruction network to achieve an image decryption process. In order to realize the data mining process direct from the privacy-protected environment, a ROI (Region of interest)-mining-network is proposed to extract the interested object directly from the encrypted image. The proposed DLEDNet is evaluated on the chest X-ray dataset. Extensive experimental results show that the proposed method can achieve a high level of security with a good performance in efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

TraCemop: Towards Federated Learning with Traceable Contribution Evaluation and Model Ownership Protection

引用

IEEE Transactions on Mobile computing 2025年

作者： Liu, Jingwei Zhou, Zihan Sun, Rong Liu, Lei Lu, Rongxing Dustdar, Schahram Niyato, Dusit Ministry of Education Engineering Research Center of Blockchain Technology Application and Evaluation China Xidian University Shaanxi Key Laboratory of Blockchain and Secure Computing Xi'an710071 China Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China Xidian University Guangzhou Institute of Technology Guangzhou510555 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250014 China Queen's University School of Computing KingstonONK7L 2N8 Canada TU Wien Distributed Systems Group Austria UPF ICREA Barcelona Spain Nanyang Technological University School of Computer Science and Engineering Singapore

Federated Learning (FL) allows multiple clients to collaboratively train machine learning models without the need to share their local private data. As a result, it can effectively address the issue of data fragmentation. Nevertheless, insufficient evaluation of individual contributions and the lack of protections for both the intellectual property rights (IPR) of models and client privacy can greatly reduce clients' motivations in federated training. To address these challenges, this paper introduces the Traceable Contribution Evaluation and Model Ownership Protection (TraCemop) framework for federated learning, which allows each client to swiftly assess the contributions of others in each round, with integrated support for the traceability of evaluation results. To safeguard the intellectual property of models, a collective watermark is embedded in the global model. Additionally, a secure mechanism for verifying model ownership is also available in case of disputes. Security analysis indicates that TraCemop is capable of resisting data reconstruction attacks as well as various types of model copyright infringements. Finally, we evaluate the proposed framework using two commonly-used datasets, and the experimental results show a significant improvement in the efficiency of contribution evaluation compared to existing methods. Meanwhile, IPR infringement tests on TraCemop reveal that the proposed framework is resilient against malicious efforts to monopolize model ownership. © 2002-2012 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

ReCoSa: Detecting the relevant contexts with self-attention for multi-turn dialogue generation

arXiv

引用

arXiv 2019年

作者： Zhang, Hainan Y., Lan L., Pang J., Guo X., Cheng CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

In multi-turn dialogue generation, response is usually related with only a few contexts. Therefore, an ideal model should be able to detect these relevant contexts and produce a suitable response accordingly. However, the widely used hierarchical recurrent encoder-decoder models just treat all the contexts indiscriminately, which may hurt the following response generation process. Some researchers try to use the cosine similarity or the traditional attention mechanism to find the relevant contexts, but they suffer from either insufficient relevance assumption or position bias problem. In this paper, we propose a new model, named ReCoSa, to tackle this problem. Firstly, a word level LSTM encoder is conducted to obtain the initial representation of each context. Then, the self-attention mechanism is utilized to update both the context and masked response representation. Finally, the attention weights between each context and response representations are computed and used in the further decoding process. Experimental results on both Chinese customer services dataset and English Ubuntu dialogue dataset show that ReCoSa significantly outperforms baseline models, in terms of both metric-based and human evaluations. Further analysis on attention shows that the detected relevant contexts by ReCoSa are highly coherent with human's understanding, validating the correctness and interpretability of ReCoSa. Copyright © 2019, The Authors. All rights reserved.

关键词： Signal encoding

来源：评论

学校读者我要写书评

暂无评论

Graph wavelet neural network

arXiv

引用

arXiv 2019年

作者： Xu, Bingbing Shen, Huawei Cao, Qi Qiu, Yunqi Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China

We present graph wavelet neural network (GWNN), a novel graph convolutional neural network (CNN), leveraging graph wavelet transform to address the shortcomings of previous spectral graph CNN methods that depend on graph Fourier transform. Different from graph Fourier transform, graph wavelet transform can be obtained via a fast algorithm without requiring matrix eigendecomposition with high computational cost. Moreover, graph wavelets are sparse and localized in vertex domain, offering high efficiency and good interpretability for graph convolution. The proposed GWNN significantly outperforms previous spectral graph CNNs in the task of graph-based semi-supervised classification on three benchmark datasets: Cora, Citeseer and Pubmed. Copyright © 2019, The Authors. All rights reserved.

关键词： Wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：