检索结果-内蒙古大学图书馆

TechRxiv 2021年

作者： Ma, Jie Chai, Qi Huang, Jingyue Liu, Jun You, Yang Zheng, Qinghua National Engineering Lab for Big Data Analytics School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&D School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China The High Performance Computing for Artificial intelligence Lab Department of Computer Science National University of Singapore 117417 Singapore

Textbook Question Answering (TQA) is the task of answering diagram and non-diagram questions given large multimodal contexts consisting of abundant text and diagrams. Deep text understandings and effective learning of diagram semantics are important for this task due to its specificity. In this paper, we propose a Weakly Supervised learning method for TQA (WSTQ), which regards the incompletely accurate results of essential intermediate procedures for this task as supervision to develop Text Matching (TM) and Relation Detection (RD) tasks and then employs the tasks to motivate itself to learn strong text comprehension and excellent diagram semantics respectively. Specifically, we apply the result of text retrieval to build positive as well as negative text pairs. In order to learn deep text understandings, we first pre-train the text understanding module of WSTQ on TM and then fine-tune it on TQA. We build positive as well as negative relation pairs by checking whether there is any overlap between the items/regions detected from diagrams using object detection. The RD task forces our method to learn the relationships between regions, which are crucial to express the diagram semantics. We train WSTQ on RD and TQA simultaneously, i.e., multitask learning, to obtain effective diagram semantics and then improve the TQA performance. Extensive experiments are carried out on CK12-QA and AI2D to verify the effectiveness of WSTQ. Experimental results show that our method achieves significant accuracy improvements of 5.02% and 4.12% on test splits of the above datasets respectively than the current state-of-the-art baseline. We have released our code on https://***/dr-majie/WSTQ. © 2021, CC BY.

关键词： Semantics

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Research on freeway travel speed prediction based on commercial vehicle data 12

Research on freeway travel speed prediction based on commerc...

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12th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2020

作者： Cheng, Genyuan Xie, Dekun Cheng, Xiaochun Chen, Qin Zhao, Jiandong Tangshan Municipal Transportation Bureau Hebei Province Tangshan063000 China Tandon School of Engineering New York University BrooklynNY11201 United States Beijing National Day School Beijing100039 China Beijing Jiaotong University 100044 China Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9781728170817

With the rapid development of China's economy and the continuous improvement of people's living standards, the public's travel demand is increasing. However, rapid traffic demand growth is bound to bring problems such as traffic congestion and traffic accidents. Accurate travel speed prediction can provide a basis for traveler route selection and traffic guidance. This study uses the Beidou positioning navigation system data to take the Shanghai-Kunming freeway section in Jiangxi Province as a test section to study the travel speed prediction. The contents include:(1) Summarizing the main methods of short-term traffic flow prediction. (2) Design the preprocessing process for Beidou system data. (3) A travel speed prediction model based on support vector regression is established. (4) The SVR model is used to conduct empirical prediction research on different traffic flow states. © 2020 IEEE.

关键词： Support vector machines

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Crystallization Regulation by Self-Assembling Liquid Crystal Template Enables Efficient and Stable Perovskite Solar Cells

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Angewandte Chemie 2023年第52期135卷

作者： Meizi Wu Hongyan Wang Yong Li Dr. Ran Chen Hui Zhou Shaomin Yang Dongfang Xu Kun Li Prof. Dr. Zhongwei An Shengzhong (Frank) Liu Prof. Dr. Zhike Liu Key Laboratory of Applied Surface and Colloid Chemistry Ministry of Education Shaanxi Key Laboratory for Advanced Energy Devices Shaanxi Engineering Lab for Advanced Energy Technology International Joint Research Center of Shaanxi Province for Photoelectric Materials Science School of Materials Science & Engineering Shaanxi Normal University Xi'an 710119 P. R. China Contribution: Data curation (lead) Formal analysis (lead) Investigation (lead) Writing - original draft (lead) Contribution: Data curation (equal) Formal analysis (equal) Contribution: Data curation (equal) Investigation (equal) School of Materials Science and Engineering Xi' an University of Science and Technology Xi'an 710054 P. R. China Contribution: Conceptualization (lead) Methodology (equal) Contribution: Formal analysis (equal) Investigation (equal) Contribution: Methodology (equal) Contribution: Data curation (equal) Contribution: Investigation (equal) Contribution: Conceptualization (equal) Methodology (equal) Contribution: Conceptualization (equal) Methodology (equal) Writing - review & editing (lead)

It is found that the disordered growth of bottom perovskite film deteriorates the buried interface of perovskite solar cells (PSCs), so developing a new material to modify the buried interface for regulating the crystal growth and defect passivation is an effective approach for improving the photovoltaic performance of PSCs. Here, we developed a new ionic liquid crystal (ILC, 1-Dodecyl-3-methylimidazolium tetrafluoroborate) as both crystal regulator and defect passivator to modify the buried interface of PSCs. The high lattice matching between this ILC and perovskite promotes preferential growth of perovskite film along [001] direction, while the oriented ILC with mesomorphic phase has a strong chemical interaction with perovskite to passivate the interface defect, as a result, the modified buried interface exhibits suppressed defects, improved band alignment, reduced nonradiative recombination losses, and enhanced charge extraction. The ILC-modified PSC delivers a power conversion efficiency of 24.92 % and maintains 94 % of the original value after storage in ambient for 3000 h.

关键词： Crystallization Regulation Ionic Liquid Crystals Perovskite Solar Cells Self-Assembling

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ADAM CHALLENGE: DETECTING AGE-RELATED MACULAR DEGENERATION FROM FUNDUS IMAGES

arXiv

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arXiv 2022年

作者： Fang, Huihui Li, Fei Fu, Huazhu Sun, Xu Cao, Xingxing Lin, Fengbin Son, Jaemin Kim, Sunho Quellec, Gwenole Matta, Sarah Shankaranarayana, Sharath M. Chen, Yi-Ting Wang, Chuen-Heng Shah, Nisarg A. Lee, Chia-Yen Hsu, Chih-Chung Xie, Hai Lei, Baiying Baid, Ujjwal Innani, Shubham Dang, Kang Shi, Wenxiu Kamble, Ravi Singhal, Nitin Wang, Ching-Wei Lo, Shih-Chang Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The ADAM Challenge State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore CONICET Yatiris lab of Pladema Institute Tandil Argentina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Inserm UMR 1101 BrestF-29200 France Univ Bretagne Occidentale BrestF-29200 France Zasti India Pvt. Ltd. India Graphen.Inc Muen Biomedical and Optoelectronics Technologies Inc Taiwan Department of Electrical Engineering Indian Institute of Technology Rajasthan Jodhpur India Department of Electrical Engineering National United University Taiwan Institute of Data Science Department of Statistics National Cheng Kung University Taiwan Shenzhen University Shenzhen China SGGS Institute of Engineering and Technology Nanded India Suzhou Tisu Information Technology Co. Ltd China AIRA Matrix Mumbai India Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taiwan

Age-related macular degeneration (AMD) is the leading cause of visual impairment among elderly in the world. Early detection of AMD is of great importance, as the vision loss caused by this disease is irreversible and permanent. Color fundus photography is the most cost-effective imaging modality to screen for retinal disorders. Cutting edge deep learning based algorithms have been recently developed for automatically detecting AMD from fundus images. However, there are still lack of a comprehensive annotated dataset and standard evaluation benchmarks. To deal with this issue, we set up the Automatic Detection challenge on Age-related Macular degeneration (ADAM), which was held as a satellite event of the ISBI 2020 conference. The ADAM challenge consisted of four tasks which cover the main aspects of detecting and characterizing AMD from fundus images, including detection of AMD, detection and segmentation of optic disc, localization of fovea, and detection and segmentation of lesions. As part of the ADAM challenge, we have released a comprehensive dataset of 1200 fundus images with AMD diagnostic labels, pixel-wise segmentation masks for both optic disc and AMD-related lesions (drusen, exudates, hemorrhages and scars, among others), as well as the coordinates corresponding to the location of the macular fovea. A uniform evaluation framework has been built to make a fair comparison of different models using this dataset. During the ADAM challenge, 610 results were submitted for online evaluation, with 11 teams finally participating in the onsite challenge. This paper introduces the challenge, the dataset and the evaluation methods, as well as summarizes the participating methods and analyzes their results for each task. In particular, we observed that the ensembling strategy and the incorporation of clinical domain knowledge were the key to improve the performance of the deep learning models. © 2022, CC BY-NC-ND.

关键词： Cost effectiveness

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A Markov Traffic Model for Signalized Traffic Networks Based on Bayesian Estimation

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IFAC-PapersOnLine 2020年第2期53卷 15029-15034页

作者： S.Y. Liu S. Lin Y.B. Wang B. De Schutter W.H.K. Lam School of Computer Science and Technology University of Chinese Academy of Sciences Beijing 100089 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences College of Civil Engineering and Architecture Zhejiang University PR China Delft Center for Systems and Control Delft University of Technology The Netherlands Department of Civil and Structural Engineering The Hong Kong Polytechnic University Hong Kong SAR China

In order to better understand the stochastic dynamic features of signalized traffic networks, we propose a Markov traffic model to simulate the dynamics of traffic link flow density for signalized urban traffic networks with demand uncertainty. In this model, we have four different state modes for the link according to different congestion levels of the link. Each link can only be in one of the four link state modes at any time, and the transition probability from one state to the other state is estimated by Bayesian estimation based on the distributions of the dynamic traffic flow densities, and the posterior probabilities. Therefore, we use a first-order Markov Chain Model to describe the dynamics of the traffic flow evolution process. We illustrate our approach for a small traffic network. Compared with the data from the microscopic traffic simulator SUMO, the proposed model can estimate the link traffic densities accurately and can give a reliable estimation of the uncertainties in the dynamic process of signalized traffic networks.

关键词： Markov traffic model Traffic signals Bayesian Urban traffic network

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Adaptive gradient methods with dynamic bound of learning rate 7

Adaptive gradient methods with dynamic bound of learning rat...

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7th International Conference on Learning Representations, ICLR 2019

作者： Luo, Liangchen Xiong, Yuanhao Liu, Yan Sun, Xu MOE Key Lab of Computational Linguistics School of EECS Peking University China College of Information Science and Electronic Engineering Zhejiang University China Department of Computer Science University of Southern California United States Center for Data Science Beijing Institute of Big Data Research Peking University China

Adaptive optimization methods such as ADAGRAD, RMSPROP and ADAM have been proposed to achieve a rapid training process with an element-wise scaling term on learning rates. Though prevailing, they are observed to generalize poorly compared with SGD or even fail to converge due to unstable and extreme learning rates. Recent work has put forward some algorithms such as AMSGRAD to tackle this issue but they failed to achieve considerable improvement over existing methods. In our paper, we demonstrate that extreme learning rates can lead to poor performance. We provide new variants of ADAM and AMSGRAD, called ADABOUND and AMSBOUND respectively, which employ dynamic bounds on learning rates to achieve a gradual and smooth transition from adaptive methods to SGD and give a theoretical proof of convergence. We further conduct experiments on various popular tasks and models, which is often insufficient in previous work. Experimental results show that new variants can eliminate the generalization gap between adaptive methods and SGD and maintain higher learning speed early in training at the same time. Moreover, they can bring significant improvement over their prototypes, especially on complex deep networks. The implementation of the algorithm can be found at https://***/Luolc/AdaBound. © 7th International Conference on Learning Representations, ICLR 2019. All Rights Reserved.

关键词： Gradient methods

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SMAP: A Joint Dimensionality Reduction Scheme for Secure Multi-Party Visualization

SMAP: A Joint Dimensionality Reduction Scheme for Secure Mul...

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IEEE Symposium on Visual Analytics Science and Technology (VAST)

作者： Jiazhi Xia Tianxiang Chen Lei Zhang Wei Chen Yang Chen Xiaolong Zhang Cong Xie Tobias Schreck School of Computer Science and Engineering Central South University State Key Lab of CAD&CG Zhejiang University I4 data Pennsylvania State University University Park PA Facebook Graz University of Technology

ISBN: (数字)9781728180090

ISBN: (纸本)9781728180106

Nowadays, as data becomes increasingly complex and distributed, data analyses often involve several related datasets that are stored on different servers and probably owned by different stakeholders. While there is an emerging need to provide these stakeholders with a full picture of their data under a global context, conventional visual analytical methods, such as dimensionality reduction, could expose data privacy when multi-party datasets are fused into a single site to build point-level relationships. In this paper, we reformulate the conventional t-SNE method from the single-site mode into a secure distributed infrastructure. We present a secure multi-party scheme for joint t-SNE computation, which can minimize the risk of data leakage. Aggregated visualization can be optionally employed to hide disclosure of point-level relationships. We build a prototype system based on our method, SMAP, to support the organization, computation, and exploration of secure joint embedding. We demonstrate the effectiveness of our approach with three case studies, one of which is based on the deployment of our system in real-world applications.

关键词： data visualization Dimensionality reduction data privacy Task analysis Privacy Hospitals Distributed databases

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Auto-Generating Neural Networks with Reinforcement Learning for Multi-Purpose Image Forensics

Auto-Generating Neural Networks with Reinforcement Learning ...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yujun Wei Yifang Chen Xiangui Kang Z. Jane Wang Liang Xiao Guangdong Key Lab of Information Security School of Data and Computer Science Sun Yat-Sen University Guangzhou China Department of ECE University of British Colombia Vancouver Canada Department of Communication Engineering Xiamen University Xiamen China

ISBN: (数字)9781728113319

ISBN: (纸本)9781728113326

Designing a forensic convolutional neural network (CNN) is usually based on some ad-hoc intuition and domain knowledge. Many methods to automate neural network design have been proposed for computer vision tasks, but they may not be directly applied to image forensic problems, which tend to detect weak traces signals left by image operations rather than strong image content signals. In this paper, we propose an approach to learn an optimal forensic CNN structure with reinforcement learning for detecting multiple image tampering operations. A learning agent is introduced to select CNN layers sequentially in a limited state-action space using Q-learning with an $\epsilon$-greedy strategy and experience replay. The experiments demonstrate that the auto-generated network performs better than other classic image forensic methods and shows more robustness against JPEG compression. To our knowledge, this is the first attempt to design forensic deep neural networks automatically with reinforcement learning.

关键词： Image forensics Learning (artificial intelligence) Task analysis Neural networks Feature extraction Transform coding

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CBLNER: A Multi-models Biomedical Named Entity Recognition System Based on Machine Learning 1

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15th International Conference on Intelligent Computing, ICIC 2019

作者： Lejun, Gong Xiaolin, Liu Xuemin, Yang Lipeng, Zhang Yao, Jia Ronggen, Yang Jiangsu Key Lab of Big Data Security and Intelligent Processing School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Faculty Intelligent Science and Control Engineering Jinling Institute of Technology Nanjing211169 China

ISBN: (数字)9783030269692

ISBN: (纸本)9783030269685

Biomedical named entities is fundamental recognition task in biomedical text mining. This paper developed a system for identifying biomedical entities with four models including CRF, LSTM, Bi-LSTM and BiLSTM-CRF. The system achieved the following performance in test data Genia V3.02: CRF with an F score of 75.91%, LSTM with an F score of 71.69%, BiLSTM with a F score of 74.37%, BiLSTM-CRF with a F score of 76.81%. Experimental results show the performance of BiLSTM-CRF model is better than other three models. Compared with CRF model, Bi-LSTM-CRF model has better recognition effect for biological entities in long text and entities that modified by modifiers. Therefore, CBLNER system lays a foundation for further relationship and event extraction, and could also provide reference for entity recognition research in other fields. © 2019, Springer Nature Switzerland AG.

关键词： Character recognition

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Learning with Group Noise

arXiv

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arXiv 2021年

作者： Wang, Qizhou Yao, Jiangchao Gong, Chen Liu, Tongliang Gong, Mingming Yang, Hongxia Han, Bo Department of Computer Science Hong Kong Baptist University Hong Kong Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of MoE School of Computer Science and Engineering Nanjing University of Science and Technology China Data Analytics and Intelligence Lab Alibaba Group China Department of Computing Hong Kong Polytechnic University Hong Kong Trustworthy Machine Learning Lab School of Computer Science University of Sydney Australia School of Mathematics and Statistics University of Melbourne Australia

Machine learning in the context of noise is a challenging but practical setting to plenty of real-world applications. Most of the previous approaches in this area focus on the pairwise relation (casual or correlational relationship) with noise, such as learning with noisy labels. However, the group noise, which is parasitic on the coarse-grained accurate relation with the fine-grained uncertainty, is also universal and has not been well investigated. The challenge under this setting is how to discover true pairwise connections concealed by the group relation with its fine-grained noise. To overcome this issue, we propose a novel Max-Matching method for learning with group noise. Specifically, it utilizes a matching mechanism to evaluate the relation confidence of each object (cf. Figure 1) w.r.t. the target, meanwhile considering the Non-IID characteristics among objects in the group. Only the most confident object is considered to learn the model, so that the fine-grained noise is mostly dropped. The performance on a range of real-world datasets in the area of several learning paradigms demonstrates the effectiveness of Max-Matching. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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