检索结果-内蒙古大学图书馆

SSRN 2024年

作者： Fan, Hui-Chen Wang, Chen Xu, Yi-Hong Fan, Teng-Min Huang, Pao-Hsun Ruan, Yu-Jiao Wu, Tingzhu Kuo, Hao-Chung Wuu, Dong-Sing Lai, Feng-Min Lien, Shui-Yang Xiamen Key Laboratory of Development and Application for Advanced Semiconductor Coating Technology School of Opto-Electronic and Communication Engineering Xiamen University of Technology Xiamen361024 China School of Ocean Information Engineering Jimei University Jimei District Fujian Xiamen361021 China National Measurement and Testing Center for Flat Panel Display Industry Xiamen Institute of Measurement and Testing Xiamen361024 China National Integrated Circuit Industry and Education Integration Innovation Platform Department of Electronic Science Xiamen University Xiamen361005 China Department of Photonics Graduate Institute of Electro-Optical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Department of Applied Materials and Optoelectronic Engineering National Chi Nan University Nantou54561 Taiwan Department of Biomedical Engineering Da-Yeh University Changhua51591 Taiwan

In this study, plasma enhanced atomic layer deposition was used to prepare ZnGaO thin films with different ZnO cycle ratios from 0% to 50%, which were employed to fabricate the MSM-type and TFT-type solar-blind photodetector. The properties of the films and the performance of the solar blind photodetector are greatly affected by the ZnO cycle ratio. Appropriate Zn doping content decreases the ratio of oxygen vacancies and Zn interstitials in Ga2O3, while excessive Zn doping degrades the quality of the film. Compared to MSM-type photodetector, an obviously improved performance of ZnGaO TFT-type photodetector has been observed due to its self-amplified photocurrent and the reduced dark current controlled by gate voltage. At a ZnO cycle ratio of 40%, a superior performance of TFT-type solar-blind photodetector with an ultra-low dark current ~4.5×10-13 A, an excellent light switching ratio ~2.2×107, and an outstandingly high sensitivity ~34.31 A/W has been obtained. These results demonstrate that the incorporation of Zn dopants can effectively modulate the performance of Ga2O3 TFT-type photodetector providing a great perspective for cost-effective and high-performance solar-blind PDs. © 2024, The Authors. All rights reserved.

关键词： Zinc oxide

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Reverse-engineering bar charts using neural networks

arXiv

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arXiv 2020年

作者： Zhou, Fangfang Zhao, Yong Chen, Wenjiang Tan, Yijing Xu, Yaqi Chen, Yi Liu, Chao Zhao, Ying School of Computer Science and Engineer Central South University Changsha China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University Beijing China Institute of Systems Engineering Academy of Military Sciences Beijing China Rail Data Research and Application Key Laboratory of Hunan Province Changsha China

Reverse-engineering bar charts extracts textual and numeric information from the visual representations of bar charts to support application scenarios that require the underlying information. In this paper, we propose a neural network-based method for reverse-engineering bar charts. We adopt a neural network-based object detection model to simultaneously localize and classify textual information. This approach improves the efficiency of textual information extraction. We design an encoder–decoder framework that integrates convolutional and recurrent neural networks to extract numeric information. We further introduce an attention mechanism into the framework to achieve high accuracy and robustness. Synthetic and real-world datasets are used to evaluate the effectiveness of the method. To the best of our knowledge, this work takes the lead in constructing a complete neural network-based method of reverse-engineering bar charts. Copyright © 2020, The Authors. All rights reserved.

关键词： Reverse engineering

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An Ultra-compact Broadband TE-pass Nanofocusing Structure

An Ultra-compact Broadband TE-pass Nanofocusing Structure

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Kejian Zhu Xu Pengfei Pengfei Sun Xingpeng Liu Haiou Li Zhiping Zhou Nano-optoelectronics Frontier Center of Ministry of Education Peking University Beijing China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University Beijing China Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic Technology Guilin China

We propose a broadband structure which integrates polarization and nanofocusing functions into a single device with 3.8x4 μm 2 footprint. The bandwidth is over 200 nm with insert loss (IL) smaller than 1.68 dB. ... 详细信息

ISBN: (数字)9781943580828

ISBN: (纸本)9781728176567

关键词： Bandwidth Nanoscale devices Optical fiber communication Broadband communication System-on-chip Photonics Periodic structures

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CS-Net: A Two-Step Epithelium Tissue Segmentation Regression Network with CS-Gate Attention on Histology Images

SSRN

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SSRN 2022年

作者： Gong, Zhengze Pan, Xipeng Han, Chu Qiu, Bingjiang Zhao, Bingchao Liu, Yu Chen, Xinyi Liu, Wenbin Chen, Zhihua Lu, Cheng Liu, Zaiyi Fang, Gang Institute of Computing Science and Technology Guangzhou University Guangzhou510006 China Department of Radiology Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China Guangdong Cardiovascular Institute Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China

Background and Objective: The segmentation of oropharyngeal epithelium tissue within Haematoxylin&Eosin(HE)-stained pathological images is of great clinical significance for the pathological analysis and diagnosis. However, due to the imperfection of staining, staining fainting, or variations between staining methods in different labs, the contrast between epithelium and stroma may not be obvious in the HE-stained *** and methods: To address these challenges, we propose a two-step method, including a stain-style transfer step and a threshold segmentation step. In the first step, we propose a stain-style transfer network named CS-Net, together with our proposed attention module named CS-Gate, to convert the HE-stained image into an Immunohistochemistry (IHC)-stained image, aiming to enhance the appearance of epithelial tissue. In the second step, we perform threshold segmentation on the synthetic IHC-stained image to finally obtain a binary mask of oropharyngeal epithelium ***: We conduct experiments on Oropharyngeal Cancer Tissue-Microarray Image dataset, and the results show that CS-Net can generate a more precise binary mask than UNet-based segmentation networks(achieving an accuracy of 92.48%), while restoring colors and details better than general GAN-based networks (about 10% higher in Structural Similarity). Then we use Breast Cancer Cases (BCSS) public dataset as external validation, and find out that CS-Net has a stronger generalization capability. The code of CS-Net is available at https://***/frankie98kung/CS-Net. © 2022, The Authors. All rights reserved.

关键词： Tissue

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Predicted high-temperature superconductivity in rare earth hydride ErH2at moderate pressure

arXiv

引用

arXiv 2022年

作者： Liu, Yiding Fan, Qiang Yang, Jianhui Wang, Lili Zhang, Weibin Yao, Gang College of Mathematics and Physics Leshan Normal University Leshan614004 China Institute of Atomic and Molecular Physics Sichuan University Chengdu610065 China School of New Energy Materials and Chemistry Leshan Normal University Leshan614004 China Institute of Computer Application China Academy of Engineering Physics Mianyang621900 China College of Physics and Electronics Information Yunnan Key Laboratory of Optoelectronic Information Technology Yunnan Normal University Kunming650500 China Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai200240 China

Hydrides offer an opportunity to study high-temperature (Tc) superconductivity at experimentally achievable pressures. However, they remained extremely high. Using density functional theory calculations, herein we demonstrated that a newly rare earth hydride, namely bulk ErH2, could be superconducting with a Tcaround 80 K at 14.5 GPa. To date, the drived pressure is the lowest reported value for compressed hydrides. Besides superconductivity, Fermi Surface nesting and Kondo effect were manifested at this pressure. Intriguingly, due to Kondo destruction, superconductivity was prone to exist at 15 GPa. Under the rest of applied pressures, we also revealed a gap of band structure at 20 GPa on the background of normal metallic states. At 20 GPa, this compressed system could act as a host of superconductor being judged from a sharp jump of spontaneous magnetic susceptibility with an evanescent spin density of state at Fermi level along with the competition between spin density wave and superconductivity. Finally, electron pairing glue for ErH2at these three typical pressures was attributed to the antiferromagnetic spin fluctuation. Copyright © 2022, The Authors. All rights reserved.

关键词： Density functional theory

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Learning the Compositional Visual Coherence for Complementary Recommendations

arXiv

引用

arXiv 2020年

作者： Li, Zhi Wu, Bo Liu, Qi Wu, Likang Zhao, Hongke Mei, Tao Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science University of Science and Technology of China Columbia University School of Computer Science and Technology University of Science and Technology of China College of Management and Economics Tianjin University JD AI Research

Complementary recommendations, which aim at providing users product suggestions that are supplementary and compatible with their obtained items, have become a hot topic in both academia and industry in recent years. Existing work mainly focused on modeling the co-purchased relations between two items, but the compositional associations of item collections are largely unexplored. Actually, when a user chooses the complementary items for the purchased products, it is intuitive that she will consider the visual semantic coherence (such as color collocations, texture compatibilities) in addition to global impressions. Towards this end, in this paper, we propose a novel Content Attentive Neural Network (CANN) to model the comprehensive compositional coherence on both global contents and semantic contents. Specifically, we first propose a Global Coherence Learning (GCL) module based on multi-heads attention to model the global compositional coherence. Then, we generate the semantic-focal representations from different semantic regions and design a Focal Coherence Learning (FCL) module to learn the focal compositional coherence from different semantic-focal representations. Finally, we optimize the CANN in a novel compositional optimization strategy. Extensive experiments on the large-scale real-world data clearly demonstrate the effectiveness of CANN compared with several state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Textures

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Software trustworthiness evaluation using structural equation modeling

引用

International Journal of Performability Engineering 2019年第10期15卷 2628-2635页

作者： Deng, Rumei Chen, Yixiang Wu, Hengyang Tao, Hongwei MoE Engineering Center for Software Hardware Co-Design Technology and Application East China Normal University Shanghai200062 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China School of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou450002 China

Software trustworthiness evaluation results can provide guidance for existing software development. Therefore, it is of great significance to study software trustworthiness evaluation. Weights and the relationship of trustworthy attributes have a huge effect on the results of software trustworthiness evaluation. However, few studies have considered weights and the quantitative relationship among trustworthy attributes at the same time. Structural equation modeling (SEM) provides a more scientific and reasonable method to obtain weights, where the objectification of subjective weights is guaranteed. In this paper, we propose a SEM-based method to evaluate software trustworthiness. Firstly, we establish the trustworthy evaluation indicator system for software. Based on the survey data, we construct the SEM for software trustworthiness evaluation to obtain the weights of trustworthy attributes and the relationship among them. Lastly, we apply the trustworthy measurement model to calculate the trustworthiness value of software to be surveyed. These trustworthiness values demonstrate the reasonability of our method. © 2019 Totem Publisher, Inc. All rights reserved.

关键词： Surveys

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Shadow Detection Of Moving Objects In Traffic Monitoring Video

Shadow Detection Of Moving Objects In Traffic Monitoring Vid...

引用

IEEE Joint International Information technology and Artificial Intelligence Conference (ITAIC)

作者： Mingrui Zhang Wenbing Zhao Xiying Li Dan Wang College of Computer Science and Technology Beijing University of Technology Beijing China Intelligent Transportation Research Center School of Intelligent System Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Intelligent Transportation System of Guangdong Province Guangzhou China Key Laboratory of Video Image Intelligent Analysis and Application Technology Ministry of Public Security Guangzhou China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

Moving object detection is an important application of computer vision. Commonly used foreground separation algorithms such as Gaussian mixture modeling, ViBe, frame difference method, etc., do not consider the color of shadow and recognize the shadow of a moving object as a part of the moving object. In many cases, the shadow detection effect is not good. Focus on the detection of moving object shadows in traffic surveillance videos, this paper improves the existing ViBe algorithm, considers the color characteristics of the shadows, recognizes the shadows as part of the background, gains a smaller amount of calculation and better effect of shadow detection with the advantages of ViBe.

关键词： Surveillance Conferences Color Object detection Traffic control Information technology Videos

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Predicting Large-scale Protein-protein Interactions by Extracting Coevolutionary Patterns with MapReduce Paradigm

Predicting Large-scale Protein-protein Interactions by Extra...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Lun Hu Bo-Wei Zhao Shicheng Yang Xin Luo MengChu Zhou Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of Sciences Urumqi China School of Computer Science and Technology Wuhan University of Technology Wuhan China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China Hengrui (Chongqing) Artificial Intelligence Research Center Cloudwalk China New Jersey Institute of Technology Newark NJ USA

ISBN: (纸本)9781665442084

Protein-protein interactions are of great significance for us to understand the functional mechanisms of proteins. With the rapid development of high-throughput genomic technology, the amount of protein-protein interaction data has become so big that most of existing prediction algorithms are no longer applicable. To address this problem, we develop a distributed framework by reimplementing one of state-of-the-art algorithms, i.e., CoFex, by using MapReduce. In particular, we adopt a novel tree-based data structure to reduce the heavy memory consumption cased by the huge sequence information of proteins. After that, the procedure of CoFex is modified by following the paradigm of MapReduce such that the prediction task can be completed in a distributed manner, thus fulfilling the demanding requirements of large-scale protein-protein interaction prediction. A series of experiments have been conducted to evaluate the performance of the proposed distributed framework in terms of both efficiency and effectiveness. Experimental results demonstrate that the proposed framework can considerably improve the efficiency of CoFex by achieving more than two-orders-of-magnitude improvement in computational efficiency while retaining a comparable level of accuracy.

关键词： Proteins Memory management Genomics Predictive models Prediction algorithms Data structures Computational efficiency

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R2-Net: Relation of relation learning network for sentence semantic matching

arXiv

引用

arXiv 2020年

作者： Zhang, Kun Wu, Le Lv, Guangyi Wang, Meng Chen, Enhong Ruan, Shulan School of Computer Science and Information Engineering Hefei University of Technology China Institute of Artificial Intelligence Hefei Comprehensive National Science Center China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China China

Sentence semantic matching is one of the fundamental tasks in natural language processing, which requires an agent to determine the semantic relation among input sentences. Recently, deep neural networks have achieved impressive performance in this area, especially BERT. Despite their effectiveness, most of these models treat output labels as meaningless one-hot vectors, underestimating the semantic information and guidance of relations that these labels reveal, especially for tasks with a small number of labels. To address this problem, we propose a Relation of Relation Learning Network (R2-Net) for sentence semantic matching. Specifically, we first employ BERT to encode the input sentences from a global perspective. Then a CNN-based encoder is designed to capture keywords and phrase information from a local perspective. To fully leverage labels for better relation information extraction, we introduce a self-supervised relation of relation classification task for guiding R2-Net to consider more about relations. Meanwhile, a triplet loss is employed to distinguish the intra-class and inter-class relations in a finer granularity. Empirical experiments on two sentence semantic matching tasks demonstrate the superiority of our proposed model. As a byproduct, we have released the codes to facilitate other researches. © 2020, CC BY.

关键词： Natural language processing systems

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