检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Li, Jinli Yuan, Ye The School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China The Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China The College of Computer and Information Science Southwest University Chongqing400715 China

High-dimensional and incomplete (HDI) matrix contains many complex interactions between numerous nodes. A stochastic gradient descent (SGD)-based latent factor analysis (LFA) model is remarkably effective in extracting valuable information from an HDI matrix. However, such a model commonly encounters the problem of slow convergence because a standard SGD algorithm only considers the current learning error to compute the stochastic gradient without considering the historical and future state of the learning error. To address this critical issue, this paper innovatively proposes an ADRC-incorporated SGD (ADS) algorithm by refining the instance learning error by considering the historical and future state by following the principle of an ADRC controller. With it, an ADS-based LFA model is further achieved for fast and accurate latent factor analysis on an HDI matrix. Empirical studies on two HDI datasets demonstrate that the proposed model outperforms the state-of-the-art LFA models in terms of computational efficiency and accuracy for predicting the missing data of an HDI matrix. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Contextual-Assisted Scratched Photo Restoration

引用

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2023年第10期33卷 5458-5469页

作者： Cai, Weiwei Zhang, Huaidong Xu, Xuemiao He, Shengfeng Zhang, Kun Qin, Jing South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China South China Univ Technol Sch Future Technol Guangzhou 510006 Peoples R China State Key Lab Subtrop Bldg Sci Guangzhou 510013 Peoples R China South China Univ Technol Minist Educ Guangdong Prov Key Lab Computat Intelligence & Cyb Key Lab Big Data & Intelligent Robot Guangzhou 510006 Peoples R China Singapore Management Univ Sch Comp & Informat Syst Singapore 188065 Singapore Carnegie Mellon Univ Philosophy Dept Pittsburgh PA 15213 USA Hong Kong Polytech Univ Ctr Smart Hlth Hong Kong Peoples R China

Printed photographs can be easily warped, wrinkled, and even deteriorated over time. Existing methods treat the restoration of scratches as a pure inpainting problem that neglects the underlying corrupted contextual knowledge. However, important underlying contents are hidden behind the scratches, which are essential hints for producing a semantically consistent result. Motivated by this insight, we explore how to harmonize the scratch-free features and noisy but essential scratch features to produce a visually consistent restoration. Specifically, in this paper, we propose an automatic retouching approach for scratched photographs with the aid of scratch/background context. We explicitly process scratch and background context in two stages. In the first stage, we mainly extract global scratch features, while the mask is introduced in the second stage to filter out and inpaint the scratches. Both contexts are carefully reciprocated for a faithful restoration. Particularly, we propose a Scratch Contextual Assisted Module (SCAM) to adaptively learn texture within the detected mask. This module utilizes the distance between the scratch mask-out feature and scratch encoder feature for modeling the pixel-wise correspondence, which determines the importance of the encoder feature within the scratch mask. Furthermore, to facilitate the evaluation of scratch restoration methods, we create two new scratched photo datasets which have 238 scratch/scratch-free photo pairs to promote the development in the scratch restoration field, namely Old Scratched Photo dataset (OSPD) and Modern Scratched Photo dataset (MSPD). Extensive experimental results on the proposed datasets demonstrate that our model outperforms existing methods. To extend the application, we also perform the proposed method on video samples and obtain visual-pleasing results. The code can be found at https://***/cwyyt/Contextual-assisted-Scratched-Photo-Restoration.

关键词： Scratched photograph restoration image processing

来源：评论

学校读者我要写书评

暂无评论

Helicity controlled spin Hall angle in the 2D Rashba altermagnets

arXiv

引用

arXiv 2024年

作者： Chen, Weiwei Zeng, Longhai Zhu, W. Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province College of Sciences China Jiliang University Hangzhou310018 China School of Science Westlake University Hangzhou310024 China

We investigate the efficiency of charge-to-spin conversion in two-dimensional Rashba altermagnets, a class of materials that merge characteristics of both ferromagnets and antiferromagnets. Utilizing quantum linear response theory, we quantify the longitudinal and spin Hall conductivities in this system and demonstrate that a substantial enhancement of the spin Hall angle is achieved below the band crossing point through the dual effects of relativistic spin-orbit interaction and nonrelativistic altermagnetic exchange interaction. Additionally, we find that skew scattering and topology-related intrinsic mechanisms are almost negligible in this system, which contrasts with conventional ferromagnetic Rashba systems. Our findings not only advance the understanding of spin dynamics in Rashba altermagnets but also pave the way for novel strategies in manipulating charge-to-spin conversion via the sophisticated control of noncollinear in-plane and collinear out-of-plane spin textures. © 2024, CC0.

关键词： Spin dynamics

来源：评论

学校读者我要写书评

暂无评论

IGFClust: Clustering Unbalanced and Complex Single-Cell Expression data by Iteration and Integrating Gini Index and Fano Factor 17th

IGFClust: Clustering Unbalanced and Complex Single-Cell Exp...

引用

17th International Conference on Computer Science and Education, ICCSE 2022

作者： Li, Han Zeng, Feng Yang, Fan Department of Automation Xiamen University Xiamen China National Institute for Data Science in Health and Medicine Xiamen University Xiamen China Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Xiamen China

ISBN: (纸本)9789819924424

With ScRNAseq, we are able to obtain genome-wide transcriptome data from single cells. However, it is very difficult to identify all cell subpopulations in single cell expression data, especially when these subpopulations are unbalanced and the number of subpopulations is unknown. In this paper, we propose a new clustering algorithm, IGFClust. We design an ensemble method to identify unbalanced subpopulations using Gini index and Fano factor. In addition, we design an iterative clustering framework to avoid the problem that only some subpopulations can be identified during the clustering process. We generated four sets of labeled simulation data and compared IGFClust with existing methods. Afterwards, we analyzed 576 glioblastoma primary tumor cells. We show that IGFClust performs accurately and robustly in identifying complex and unbalanced single-cell expression data. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

An Ultra-Wideband Cascaded Power Amplifier for Military Individual Radio

An Ultra-Wideband Cascaded Power Amplifier for Military Indi...

引用

IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Fei Gao Jingchang Nan Hongji Fan Cong Wang Panpan Tang Zhaoqi Li Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Liaoning Technical University Huludao China Southeast University Nanjing China Department of Microwave Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

In recent years, there has been a tremendous interest in military individual radio in wireless communication. To obtain sufficient operating distance and long standby time, the output power and efficiency of power amplifiers have become the research focus by some of the research teams. GaN has higher electron mobility and forbidden bandwidth than other materials. Nowadays, GaN has become the mainstream key technology for radio frequency (RF) power amplifiers in 5G communication systems. In this work, a 500MHz~2500MHz GaN-based HEMT wideband cascaded power amplifier was designed. The output power of the driver amplifier is 36.6dBm ± 0.5dB, the input return loss is < -5dB, the drain efficiency is > 20%, the DC power consumption is < 25W, the input power is 5dBm, 1dB compression point is 7.8dBm, 0 dBm signal input and 33dBm signal output. The input power of the output stage is 36dBm; the output power is 44.75dB, the efficiency is more than 30%, and the input return loss < -8dB, small signal gain >10 dB, input voltage standing wave ratio < -10dB, output voltage standing wave ratio < -3dB. The demonstrated high performance of the cascaded power amplifier validates its applications in military wireless communication.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Semantic-Constraint Matching Transformer for Weakly Supervised Object Localization

arXiv

引用

arXiv 2023年

作者： Cao, Yiwen Su, Yukun Wang, Wenjun Liu, Yanxia Wu, Qingyao The School of Software Engineering South China University of Technology Guangzhou510640 China The Key Laboratory of Big Data and Intelligent Robot Ministry of Education Guangzhou China The School of Computer Science and Engineering Nanyang Technological University Singapore The Pazhou Lab Guangzhou China

Weakly supervised object localization (WSOL) strives to learn to localize objects with only image-level supervision. Due to the local receptive fields generated by convolution operations, previous CNN-based methods suffer from partial activation issues, concentrating on the object's discriminative part instead of the entire entity scope. Benefiting from the capability of the self-attention mechanism to acquire long-range feature dependencies, Vision Transformer has been recently applied to alleviate the local activation drawbacks. However, since the transformer lacks the inductive localization bias that are inherent in CNNs, it may cause a divergent activation problem resulting in an uncertain distinction between foreground and background. In this work, we proposed a novel Semantic-Constraint Matching Network (SCMN) via transformer to converge on the divergent activation. Specifically, we first propose a local patch shuffle strategy to construct the image pairs, disrupting local patches while guaranteeing global consistency. The paired images that contain the common object in spatial are then fed into the Siamese network encoder. We further design a semantic-constraint matching module, which aims to mine the co-object part by matching the coarse class activation maps (CAMs) extracted from the pair images, thus implicitly guiding and calibrating the transformer network to alleviate the divergent activation. Extensive experimental results conducted on two challenging benchmarks including CUB-200-2011 and ILSVRC datasets show that our method can achieve the new state-of-the-art performance and outperform the previous method by a large margin. Copyright © 2023, The Authors. All rights reserved.

关键词： Chemical activation

来源：评论

学校读者我要写书评

暂无评论

Time-Aware Multi-Scale RNNs for Time Series Modeling 30

Time-Aware Multi-Scale RNNs for Time Series Modeling

引用

30th International Joint Conference on Artificial Intelligence (IJCAI)

作者： Chen, Zipeng Ma, Qianli Lin, Zhenxi South China Univ Technol Sch Comp Sci & Engn Guangzhou Peoples R China South China Univ Technol Minist Educ Key Lab Big Data & Intelligent Robot Beijing Peoples R China

ISBN: (纸本)9780999241196

Multi-scale information is crucial for modeling time series. Although most existing methods consider multiple scales in the time-series data, they assume all kinds of scales are equally important for each sample, making them unable to capture the dynamic temporal patterns of time series. To this end, we propose Time-Aware Multi-Scale Recurrent Neural Networks (TAMS-RNNs), which disentangle representations of different scales and adaptively select the most important scale for each sample at each time step. First, the hidden state of the RNN is disentangled into multiple independently updated small hidden states, which use different update frequencies to model time-series multi-scale information. Then, at each time step, the temporal context information is used to modulate the features of different scales, selecting the most important time-series scale. Therefore, the proposed model can capture the multi-scale information for each time series at each time step adaptively. Extensive experiments demonstrate that the model outperforms state-of-the-art methods on multivariate time series classification and human motion prediction tasks. Furthermore, visualized analysis on music genre recognition verifies the effectiveness of the model.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

An Encoder-Decoder Model Based On Spiking Neural Networks For Address Event Representation Object Recognition

引用

IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Du, Sichun Zhu, Haodi Zhang, Yang Hong, Qinghui Hunan University College of Computer Science and Electronic Engineering Changsha418002 China Shenzhen University Computer Vision Institute School of Computer Science and Software Engineering National Engineering Laboratory for Big Data System Computing Technology Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

Address event representation (AER) object recognition task has attracted extensive attention in neuromorphic vision processing. The spike-based and event-driven computation inherent in the spiking neural network (SNN) provides an energy-saving solution for AER object recognition. However, SNN with spike timing dependent plasticity (STDP) learning rule has not achieved satisfying AER object recognition performance. This work proposes an SNN-based encoder-decoder model to improve the recognition performance of AER objects. An STDP-based locally connected spiking neural network (LC-SNN) is proposed as an encoder to extract rich spatiotemporal features from AER event flows more flexibly. After the encoder extracts and learns primary features, we propose a fully connected spiking neural network (FC-SNN) based on the reward-modulated spike-timing-dependent plasticity (R-STDP) learning rule as a decoder to learn higher-level features for classification. In addition, we improved the winner-take-all (WTA) mechanisms and R-STDP learning rule in the decoder based on the reward and punish decision, enabling the network to perform better. The experiments are performed on the N-MNIST, MNIST-DVS, and the DVS Gesture datasets, improving the accuracy of the best existing plasticity-based SNN by 0.19%, 0.27% and 1.35%, respectively. © 2016 IEEE.

关键词： Decoding

来源：评论

学校读者我要写书评

暂无评论

A Nested Named Entity Recognition Model Based on Multi-agent Communication Mechanism 35

A Nested Named Entity Recognition Model Based on Multi-agent...

引用

35th AAAI Conference on Artificial Intelligence / 33rd Conference on Innovative Applications of Artificial Intelligence / 11th Symposium on Educational Advances in Artificial Intelligence

作者： Li, Canguang Wang, Guohua Cao, Jin Cai, Yi South China Univ Technol Sch Software Engn Guangzhou Peoples R China South China Univ Technol Key Lab Big Data & Intelligent Robot Minist Educ Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781577358664

Traditional sequence tagging methods for named entity recognition (NER) face challenges when handling nested entities, where an entity is nested in another. Most previous methods for nested NER ignore the effect of entity boundary information or type information. Considering that entity boundary information and type information can be utilized to improve the performance of boundary detection, we propose a nested NER model with a multi-agent communication module. The type tagger and boundary tagger in the multi-agent communication module iteratively utilize the information from each other, which improves the boundary detection and the final performance of nested NER. Empirical experiments conducted on two nested NER datasets show the effectiveness of our model.

关键词： Multi agent systems

来源：评论

学校读者我要写书评

暂无评论

Multi-View Disease-Symptom Association Prediction Based on Disease Knowledge Graph Embedding

Multi-View Disease-Symptom Association Prediction Based on D...

引用

Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Zongqing Yang Hanyue Liu Hong Yu Feng Hu Chongqing Key Laboratory of Computational Intelligence (Chongqing University of Posts and Telecommunications) Chongqing China Chongqing Key Laboratory of Computational Intelligence (Chongqing University of Posts and Telecommunications) Key Laboratory of Big Data Intelligent Computing (Chongqing University of Posts and Telecommunications) Chongqing China

Disease symptoms are significant in disease treatment and diagnosis, as well as in developing a hierarchical model from disease phenotypes to genes to improve our understanding of complex disease causes. While previous studies have focused on genetics and proteomics, this study proposes a novel compu-tational model, MGAEMF, based on disease knowledge graph embedding for disease-symptom association (DSA) prediction (i.e., disease nodes have connections with multiple other node types). First, the disease knowledge graph is decomposed into multiple disease-related bipartite networks, form which multiple disease similarity networks and symptom similarity networks can be extracted, and the similarity networks about diseases and symptoms are separately integrated into a single similarity network. Then, the integrated disease similarity network and symptom similarity network are combined with bipartite networks built of known disease-symptom connections to generate nonlinear feature representations of diseases and symptoms using GAE. Meanwhile, the disease-symptom association matrix is calculated using a non-negative matrix factorization to provide linear feature representations of diseases and symptoms. Finally, a fully connected neural network incorporating the linear and nonlinear representations of diseases and symptoms calculates the final prediction scores for all disease-symptom pairs. During the 10-fold cross-validation experiment, the MGAEMF model outperforms other comparable methods with a superior average AUC value of 0.8372.

关键词：

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：