检索结果-内蒙古大学图书馆

SSRN 2022年

作者： Wang, Jing Wan, Feng Dong, Chunjiao Yin, Chaoying Chen, Xiaoyu Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Ministry of Transport Beijing Jiaotong University Beijing100044 China Hikvision Research Institute Hangzhou310012 China College of Automobile and Traffic Engineering Nanjing Forestry University Nanjing210037 China School of Computer and Information Technology Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China

Although many studies have explored the impacts of the built environment on rail transit station ridership, spatiotemporal heterogeneity of the effects and different ridership patterns are rarely considered. Using multi-source data from China, three direct models (DRMs) based on the geographically and temporally weighted regression (GTWR) model are proposed to reveal the spatiotemporal effects of daily, weekday hourly and weekend hourly ridership patterns. The results show that employment density, entertainment places, housing prices, and entrances/exits are the dominant variables affecting ridership in the three models;however, their effects vary in spatiotemporal ranges. For example, the positive effect of employment density decreases from Monday to Sunday, and its coefficients during the peak hours are highest in the northwest center, where is the largest technology park situated. The results also highlight the importance of bus stations and parking lots in attracting commuter ridership. The findings provide insights into urban rail transit planning. © 2022, The Authors. All rights reserved.

关键词： Housing

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Short-term Traffic Flow Prediction by Graph Deep Learning with Spatial Temporal Modeling

Short-term Traffic Flow Prediction by Graph Deep Learning wi...

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Electronic Information technology and Smart Agriculture (ICEITSA), International Conference on

作者： Lilian Yuan Weilin Fang Hongbo Xiao Jianhua Xiao Yuanquan Shi Yujun Yang School of Computer Science and Engineering Huaihua University Huaihua Hunan P. R. China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua Hunan P. R. China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua Hunan P. R. China

ISBN: (纸本)9781665464024

An extensive amount of research applications on traffic flow prediction models taking into account spatio-temporal modeling have been undertaken in recent years. Relative to traditional spatio-temporal modeling approaches, good application results have been achieved in predicting traffic flow. However, in real life, traffic flow proves to be time- and space-dependent, which poses difficulties for accurate modeling of traffic flow prediction. To address these problems, we propose the ST-GNN-GRU model, which mainly contains S-GNN and T-GRU modules. The model uses a combination of spatio-temporal modeling and deep learning, which can not only cope with the spatio-temporal modeling problem, but also improve the traffic flow prediction accuracy. The experiments are conducted using the METR-LA dataset, and the outcomes show that the proposed method performs more effectively than other baseline approaches.

关键词： Deep learning Smart agriculture Predictive models Prediction algorithms Information technology Testing

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Rethinking Mitosis Detection: Towards Diverse Data and Feature Representation

arXiv

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arXiv 2023年

作者： Wang, Hao Lin, Jiatai Li, Danyi Wang, Jing Zhao, Bingchao Shi, Zhenwei Pan, Xipeng Wang, Huadeng Li, Bingbing Liang, Changhong Han, Guoqiang Liang, Li Han, Chu Liu, Zaiyi The School of Computer Science and Engineering South China University of Technology Guangzhou510006 China Department of Radiology Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Southern Medical University Guangzhou510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou510080 China The Department of Pathology Nanfang Hospital Basic Medical College Southern Medical University Guangdong Guangzhou China Guangdong Province Key Laboratory of Molecular Tumor Pathology Guangdong Guangzhou China The Department of Pathology Guangdong Provincial People’s Hospital Ganzhou Hospital Ganzhou China The School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China

Mitosis detection is one of the fundamental tasks in computational pathology, which is extremely challenging due to the heterogeneity of mitotic cell. Most of the current studies solve the heterogeneity in the technical aspect by increasing the model complexity. However, lacking consideration of the biological knowledge and the complex model design may lead to the overfitting problem while limited the generalizability of the detection model. In this paper, we systematically study the morphological appearances in different mitotic phases as well as the ambiguous non-mitotic cells and identify that balancing the data and feature diversity can achieve better generalizability. Based on this observation, we propose a novel generalizable framework (MitDet) for mitosis detection. The data diversity is considered by the proposed diversity-guided sample balancing (DGSB). And the feature diversity is preserved by inter- and intra- class feature diversity-preserved module (InCDP). Stain enhancement (SE) module is introduced to enhance the domain-relevant diversity of both data and features simultaneously. Extensive experiments have demonstrated that our proposed model outperforms all the SOTA approaches in several popular mitosis detection datasets in both internal and external test sets using minimal annotation efforts with point annotations only. Comprehensive ablation studies have also proven the effectiveness of the rethinking of data and feature diversity balancing. By analyzing the results quantitatively and qualitatively, we believe that our proposed model not only achieves SOTA performance but also might inspire the future studies in new perspectives. Source code is at https://***/Onehour0108/MitDet. © 2023, CC BY.

关键词： Chemical detection

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Learning from noisy labels via dynamic loss thresholding

arXiv

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arXiv 2021年

作者： Yang, Hao Jin, Youzhi Li, Ziyin Wang, Deng-Bao Miao, Lei Geng, Xin Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China AI Technology Application Department Huawei Technologies Collaborative Innovation Center of Wireless Communications Technology China

Numerous researches have proved that deep neural networks (DNNs) can fit everything in the end even given data with noisy labels, and result in poor generalization performance. However, recent studies suggest that DNNs tend to gradually memorize the data, moving from correct data to mislabeled data. Inspired by this finding, we propose a novel method named Dynamic Loss Thresholding (DLT). During the training process, DLT records the loss value of each sample and calculates dynamic loss thresholds. Specifically, DLT compares the loss value of each sample with the current loss threshold. Samples with smaller losses can be considered as clean samples with higher probability and vice versa. Then, DLT discards the potentially corrupted labels and further leverages supervised learning techniques. Experiments on CIFAR-10/100 and Clothing1M demonstrate substantial improvements over recent state-of-the-art methods. In addition, we investigate two real-world problems for the first time. Firstly, we propose a novel approach to estimate the noise rates of datasets based on the loss difference between the early and late training stages of DNNs. Secondly, we explore the effect of hard samples (which are difficult to be distinguished) on the process of learning from noisy labels. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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Quadratic stabilisation of switched affine systems

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Journal of Control and Decision 2020年第1期7卷 1-23页

作者： Minqing Xiao Guisheng Zhai Chi Huang College of Mathematics and Informatics Fujian Key Laboratory of Mathematical Analysis and ApplicationFujian Normal UniversityFuzhouPeople’s Republic of China Department of Mathematical Sciences College of Systems Engineering and ScienceShibaura Institute of TechnologyTokyoJapan Department of Computer Science and Technology School of Economic Information EngineeringSouthwestern University of Finance and EconomicsChengduPeople’s Republic of China

Minqing Xiao received the *** from Chongqing University,Chongqing,China,in *** is currently a Professor with the College of Mathematics and Informatics,Fujian Normal University,Fuzhou,*** current research interests include robust control/filter theory,delta operator systems,networked control systems,and switched systems;Guisheng Zhai received his *** from Fudan University,China,in 1988,and he received his *** ***,both in system science,from Kobe University,Japan,in 1993 and 1996,*** two years of industrial experience,*** moved to Wakayama University,Japan,in 1998,and then to Osaka Prefecture University,Japan,in *** held visiting professor positions at University of Notre Dame from August 2001 to July 2002,and at Purdue University from March 2016 through February *** April 2010,he joined the faculty board of Shibaura Institute of technology,Japan,where he currently is a full Professor of Mathematical *** research interests include large scale and decentralised control systems,robust control,switched systems and switching control,networked control and multi-agent systems,neural networks and signal processing,*** is on the editorial board of several academic journals including IET Control Theory&applications,International Journal of Applied Mathematics and computer Science,Journal of Control and Decision,and Frontiers of Mechanical *** is a Senior Member of IEEE,a member of ISCIE,SICE,JSST and JSME.

关键词： Switched affine systems(SAS) switched uncertain affine systems(SUAS) quadratic stabilisation convex combination state-dependent switching output-dependent switching convergence set LMIs norm bounded uncertainties

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Language-specific Characteristic Assistance for Code-switching Speech Recognition

arXiv

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arXiv 2022年

作者： Song, Tongtong Xu, Qiang Ge, Meng Wang, Longbiao Shi, Hao Lv, Yongjie Lin, Yuqin Dang, Jianwu Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Graduate School of Informatics Kyoto University Sakyo-ku Kyoto Japan Japan Advanced Institute of Science and Technology Ishikawa Japan

Dual-encoder structure successfully utilizes two language-specific encoders (LSEs) for code-switching speech recognition. Because LSEs are initialized by two pre-trained language-specific models (LSMs), the dual-encoder structure can exploit sufficient monolingual data and capture the individual language attributes. However, most existing methods have no language constraints on LSEs and underutilize language-specific knowledge of LSMs. In this paper, we propose a language-specific characteristic assistance (LSCA) method to mitigate the above problems. Specifically, during training, we introduce two language-specific losses as language constraints and generate corresponding language-specific targets for them. During decoding, we take the decoding abilities of LSMs into account by combining the output probabilities of two LSMs and the mixture model to obtain the final predictions. Experiments show that either the training or decoding method of LSCA can improve the model's performance. Furthermore, the best result can obtain up to 15.4% relative error reduction on the code-switching test set by combining the training and decoding methods of LSCA. Moreover, the system can process code-switching speech recognition tasks well without extra shared parameters or even retraining based on two pre-trained LSMs by using our method. Copyright © 2022, The Authors. All rights reserved.

关键词： Speech recognition

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Class-Imbalanced Semi-Supervised Learning for Large-Scale Point Cloud Semantic Segmentation Via Decoupling Optimization

SSRN

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SSRN 2023年

作者： Li, Mengtian Lin, Shaohui Wang, Zihan Shen, Yunhang Zhang, Baochang Ma, Lizhuang Visual Media Intelligent Processing Research Group Department of Film and Television Engineering Shanghai University Shanghai200072 China School of Computer Science and Technology East China Normal University Shanghai200062 China Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Youtu Lab Tencent China

Semi-supervised learning (SSL), thanks to the significant reduction of data annotation costs, has been an active research topic for large-scale 3D scene understanding. However, the existing SSL-based methods suffer from severe training bias, mainly due to class imbalance and long-tail distributions of the point cloud data. As a result, they lead to a biased prediction for the tail class segmentation. In this paper, we introduce a new decoupling optimization framework, which disentangles feature representation learning and classifier in an alternative optimization manner to shift the bias decision boundary effectively. In particular, we first employ two-round pseudo-label generation to select unlabeled points across head-to-tail classes. We further introduce multi-class imbalanced focus loss to adaptively pay more attention to feature learning across head-to-tail classes. We fix the backbone parameters after feature learning and retrain the classifier using ground-truth points to update its parameters. Ex-✩Fully documented templates are available in the elsarticle package on CTAN. © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Efficiently Retrieving Differences between Remote Sets using Counting Bloom Filter

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International Journal of Performability Engineering 2019年第7期15卷 1947-1954页

作者： Tian, Xiaomei Zhao, Huihuang Sun, Yaqi Liang, Xiaoman College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

Retrieving differences between remote sets is widely used in set reconciliation and data deduplication. Set reconciliation and data deduplication between two nodes are widely used in various network applications. The basic idea of the difference retrieving problem is that each member of a node pair has an object set and seeks to find all differences between the two remote sets. There are many methods for retrieving difference sets, such as the standard Bloom filter (SBF), counting Bloom filter (CBF), and invertible Bloom filter (IBF). In these methods, based on the standard Bloom filter or its variants, each node represents its objects using a standard Bloom filter or other Bloom filter, which is then exchanged. A receiving node retrieves different objects between the two sets according to the received SBF, CBF, or IBF. We propose a new algorithm for retrieving differences that finds differences between remote sets using counting Bloom filters' deletion operation. The theoretical analyses and experimental results show that the differences can be retrieved efficiently. Only a very small number of differences are missing in the retrieving process, and this false negative rate can be decreased to 0% by adjusting the counting Bloom filter's parameters. © 2019 Totem Publisher, Inc. All rights reserved.

关键词： Data structures

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Error Correction of Tibetan Verbs Based on Deep Learning

Error Correction of Tibetan Verbs Based on Deep Learning

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Artificial Intelligence and computer Engineering (ICAICE), International Conference on

作者： HUA Guo‒ cai– rang Secha Jia BAN Ma– bao CAI Rang– jia School of computer science Qinghai Normal University China Qinghai Key Laboratory of Tibetan information processing and machine translation China Qinghai Tibetan information processing engineering technology research center China State Key Laboratory of Tibetan intelligent information processing and application China

Verbs are the core of the semantic structure represented by Tibetan sentences, and automatic error correction of Tibetan verbs is one of the important research topics in Tibetan language processing. In this paper, we propose a Bi-LSTM neural network model for Tibetan verb error correction by analyzing the usage rules of verbs in Tibetan, summarizing the grammatical, semantic and spelling features of verbs, and proposes a Bi-LSTM neural network model for Tibetan verb error correction based on these features, which not only extracts various features of verbs, but also capture the contextual information of verbs through Bi-LSTM neural network. The method proposed in this work solves the drawbacks of the traditional methods of low generalization and inability to obtain long-distance contextual implicit information. The experimental results show that the accuracy, recall and F1 values of the proposed method on the test set reach 97.3%,95.7% and 96.9%, respectively, indicating the effectiveness of the proposed method on the task of automatic error correction for Tibetan verbs.

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Spectrometric Characterization of Suspension Liquid and Light Extinction Model Update

SSRN

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SSRN 2022年

作者： Liu, Sheng Shen, Yang Gao, Zihui Gao, Hongwen School of Computer Science and Technology Huaibei Normal University Huaibei235000 China State Key Laboratory of Enviromental Pollution Control and Resource Reuse Tongji University Shanghai200092 China Shanghai GreenEmpire Environmental Science and Technology Corporation Shanghai200092 China Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior Huaibei235000 China

On the basis of the classical light scattering models, the light extinction model is the first to establish as [[EQUATION]] (, N and γ – average diameter in μm, number and relative refractive index of the suspending particles, λ , A and δ – incident light wavelength in μm, absorbance and optical path in cm of the suspension liquid) by spectrometric characterization of ten standard suspension liquids. It has been used to determine the suspending particles in the calcium oxalate, Formazine, soil, milk and sewage suspension water samples. As the result, the light extinction model method brought out less than 12% error of and 18% error of the suspending particles’ quality by comparing with the conventional methods. It provides a simple and reliable spectroptometric determination of a suspension liquid. Also, it is very potential to in-situ monitor the growth and working state of the suspending particles using in synthesis of materials, culture of cells, treatment of wastewater and safety of drinking water and foods. © 2022, The Authors. All rights reserved.

关键词： Suspensions (fluids)

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