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Superior performance of ZnGaO solar-blind photodetectors by Implementing TFT structure and tunable ZnO cycle ratio

Materials Today Chemistry 2024年 38卷

作者： Fan, Hui-Chen Wang, Chen Xu, Yi-Hong Fan, Teng-Min Huang, Pao-Hsun Ruan, Yu-Jiao Wu, Ting-Zhu 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 Xiamen 361024 China School of Ocean Information Engineering Jimei University Jimei District Xiamen 361021 Fujian China National Measurement and Testing Center for Flat Panel Display Industry Xiamen Institute of Measurement and Testing Xiamen 361024 China National Integrated Circuit Industry and Education Integration Innovation Platform Department of Electronic Science Xiamen University Xiamen 361005 China Department of Photonics & Graduate Institute of Electro-Optical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu 30010 Taiwan Department of Applied Materials and Optoelectronic Engineering National Chi Nan University Nantou 54561 Taiwan Department of Biomedical Engineering Da-Yeh University Changhua 51591 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 Elsevier Ltd

关键词： High performance Plasma enhanced atomic layer deposition TFT-Type solar-blind photodetector ZnGaO film ZnO cycle ratio

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Adaptive Data Replication Optimization Based on Reinforcement Learning

Adaptive Data Replication Optimization Based on Reinforcemen...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Chee Keong Wee Richi Nayak Digital Application Services Business Applications Technology Services eHealth Queensland Queensland Australia School of Electrical Engineering & Computer Science Science & Engineering Faculty Queensland University of Technology Brisbane Queensland Australia

ISBN: (数字)9781728125473

ISBN: (纸本)9781728125480

Data replication plays an important role in enterprise IT landscapes, where data is shared among multiple IT systems. IT administrators need to tune the replicating software's configuration setting for it to perform at its optimum level. It is a challenge to continue optimizing the software's configuration to keep up with the fluctuating workload in a dynamic business environment. We propose a novel approach of using reinforcement learning with meta-heuristics to create an adaptive optimization method for data replication software. The experimental results show the replicating software managed by the proposed approach can perform at an optimum level despite consistently working under changing workloads.

关键词： Software Business Throughput Reinforcement learning Tuning Machine learning algorithms Adaptation models

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Pairwise Relation Learning for Semi-supervised Gland Segmentation 23rd

Pairwise Relation Learning for Semi-supervised Gland Segment...

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23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Xie, Yutong Zhang, Jianpeng Liao, Zhibin Verjans, Johan Shen, Chunhua Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xian710072 China The University of Adelaide Adelaide Australia South Australian Health and Medical Research Institute Adelaide Australia Research & Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen518057 China

ISBN: (纸本)9783030597214

Accurate and automated gland segmentation on histology tissue images is an essential but challenging task in the computer-aided diagnosis of adenocarcinoma. Despite their prevalence, deep learning models always require a myriad number of densely annotated training images, which are difficult to obtain due to extensive labor and associated expert costs related to histology image annotations. In this paper, we propose the pairwise relation-based semi-supervised (PRS2) model for gland segmentation on histology images. This model consists of a segmentation network (S-Net) and a pairwise relation network (PR-Net). The S-Net is trained on labeled data for segmentation, and PR-Net is trained on both labeled and unlabeled data in an unsupervised way to enhance its image representation ability via exploiting the semantic consistency between each pair of images in the feature space. Since both networks share their encoders, the image representation ability learned by PR-Net can be transferred to S-Net to improve its segmentation performance. We also design the object-level Dice loss to address the issues caused by touching glands and combine it with other two loss functions for S-Net. We evaluated our model against five recent methods on the GlaS dataset and three recent methods on the CRAG dataset. Our results not only demonstrate the effectiveness of the proposed PR-Net and object-level Dice loss, but also indicate that our PRS2 model achieves the state-of-the-art gland segmentation performance on both benchmarks. © 2020, Springer Nature Switzerland AG.

关键词： Image enhancement

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StarGAT: Star-Shaped Hierarchical Graph Attentional Network for Heterogeneous Network Representation Learning

StarGAT: Star-Shaped Hierarchical Graph Attentional Network ...

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IEEE International Conference on Data Mining (ICDM)

作者： Wen-Zhi Li Ling Huang Chang-Dong Wang Yu-Xin Ye School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Computational Science Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China College of Mathematics and Informatics South China Agricultural University Guangzhou China Guangdong Provincial Key Laboratory of Public Finance and Taxation with Big Data Application Guangzhou China College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)9781665423991

Many real-world graphs can be viewed as Heterogeneous Networks or Heterogeneous Information Networks (HINs) for that they comprise a diversity of node types and relation types. Due to the efficient representation ability of Graph Neural Network and the idea of random walk, many recent studies apply graph representation learning to HINs and achieve satisfactory results. However, these works either treat different node types in a metapath equally, which is inconsistent with the original graph semantic information for that different node types should have different statuses, or only consider the first-order (node-level) and second-order (metapath-level, a.k.a. link-level) information aggregation while ignoring the higher-order relations. To tackle these two problems, we propose a novel Star-Shaped Hierarchical Graph Attentional Network (StarGAT) model to boost representation learning in HINs. Specifically, we assume nodes in HINs can be categorized into a star-shaped structure including one center node type and a bunch of auxiliary node types in a specific task; and we encode node-level, link-level and motif-level attentions in a hierarchical manner to capture richer semantic information. Extensive experiments on three datasets illustrate the model effectiveness.

关键词： Representation learning Conferences Semantics Heterogeneous networks Graph neural networks Data mining Task analysis

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The 3D Projection Function on NetPad

The 3D Projection Function on NetPad

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International Conference on computer science & Education (ICCSE)

作者： Qixin Zhou Chuanzhong Li Ruiqi Cai Miaoxuan Li Chuanjun Zhang Hao Guan Institute of Computing Science and Technology Guangzhou University Guangzhou China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China School of Mathematics and Big Data Guizhou Education University Guiyang China Chengdu Institute of Computer Application The Chinese Academy of Science Chengdu China

ISBN: (数字)9781728172675

ISBN: (纸本)9781728172682

Projection of 3D geometric graphics is an introduction to solid geometry, which has indispensable functional requirements for 3D Dynamic Geometry Software. To optimize the 3D dynamic geometry software in the construction of projection steps and support more types of projected objects, this paper presents a method of construction of 3D projection. The key steps as follows: Firstly, discretize the projection object. Secondly, transform the set of the discrete point according to the projection type selected by the user. Thirdly, construct a new geometric object as the projection using the set of transformed point. The experimental results show that using the method can support the projection of various geometric objects, dynamically show the projection process, and reduce the construction burden of the user.

关键词： Geometry Three-dimensional displays Education Software computer science Solids Light sources

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Ordinal Preferences Driven Reputation Measurement for Online Services with User Incentive

Ordinal Preferences Driven Reputation Measurement for Online...

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IEEE International Conference on Web Services (ICWS)

作者： Xiaodong Fu Li Liu Lijun Liu Yong Feng Kun Yue Yunnan Provincial Key Laboratory of Computer Technology Application Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China School of Information Science and Engineering Yunnan University Kunming China

ISBN: (数字)9781728187860

ISBN: (纸本)9781728187877

A core source of raw information used as inputs to the reputation systems of online services is the feedback ratings provided by users. However, it is impossible that all users rate services with the same criteria and so ratings of different users are incommensurable. Meanwhile, users are not necessarily willing to provide honest feedbacks. Thus, aggregating dishonest cardinal ratings into reputation will potentially lead to unreliable and misleading reputation. In this paper, we propose a reputation model that aggregates ordinal user preferences rather than cardinal ratings for online services with user incentive. A distance metric is defined to measure the discrepancy between ordinal preferences. Then an optimal reputation model with the attributes of incentive compatible and individually rational is proposed. We design a B&B algorithm to solve the optimization problem so that a reputation vector that maximizes the total value of all users can be found efficiently. A comprehensive experimental study and performance analysis are conducted to evaluate the effectiveness and efficiency of the proposed method.

关键词： Measurement Web services Conferences Aggregates Performance analysis Optimization

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Assessing the memory ability of recurrent neural networks

arXiv

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arXiv 2020年

作者： Zhang, Cheng Li, Qiuchi Hua, Lingyu Song, Dawei Tianjin Key Laboratory of Cognitive Computing and Application Tianjin University Tianjin China Department of Information Engineering University of Padua Padua Italy School of Computer Science Beijing Institute of Technology Beijing China School of Computer Science Beijing Institute of Technology Beijing China

It is known that Recurrent Neural Networks (RNNs) can remember, in their hidden layers, part of the semantic information expressed by a sequence (e.g., a sentence) that is being processed. Different types of recurrent units have been designed to enable RNNs to remember information over longer time spans. However, the memory abilities of different recurrent units are still theoretically and empirically unclear, thus limiting the development of more effective and explainable RNNs. To tackle the problem, in this paper, we identify and analyze the internal and external factors that affect the memory ability of RNNs, and propose a Semantic Euclidean Space to represent the semantics expressed by a sequence. Based on the Semantic Euclidean Space, a series of evaluation indicators are defined to measure the memory abilities of different recurrent units and analyze their limitations. These evaluation indicators also provide a useful guidance to select suitable sequence lengths for different RNNs during training. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantics

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Big data driven product design: A survey

arXiv

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arXiv 2021年

作者： Quan, Huafeng Li, Shaobo Zeng, Changchang Wei, Hongjing Hu, Jianjun College of Big Data and Statistics Guizhou Universify of Finance and Economics Guiyang550050 China State Key Laboratory of Public Big Data Guizhou University Guiyang550050 China Chengdu Institute of Computer Application Chinese Academy of Sciences Chengdu610041 China School of Mechanical Engineering Guizhou Institute of Technology Guiyang550050 China Department of Computer Science and Engineering University of South Carolina ColumbiaSC29201 United States

With the improvement of living standards, user requirements of modern products are becoming increasingly more diversified and personalized. Traditional product design methods can no longer satisfy the market needs due to their strong subjectivity, small survey scope, poor real-time data, and lack of visual display, which calls for the development of big data driven product design methodology. Big data in the product lifecycle contains valuable information for guiding product design, such as customer preferences, market demands, product evaluation, and visual display: online product reviews reflect customer evaluations and requirements;product images contain information of shape,color, and texture which can inspire designers to get initial design schemes more quickly or even directly generate new product images. How to efficiently collect product design related data and exploit them effectively during the whole product design process is thus critical to modern product design. This paper aims to conduct a comprehensive survey on big data driven product design. It will help researchers and practitioners to comprehend the latest development of relevant studies and applications centered on how big data can be processed, analyzed, and exploited in aiding product design. We first introduce several representative traditional product design methods and highlight their limitations. Then we discuss current and potential applications of textual data, image data, audio data, and video data in product design cycles. Finally, major deficiencies of existing data driven product design studies and future research directions are summarized. We believe that this study can draw increasing attention to modern data driven product design. © 2021, CC BY-NC-SA.

关键词： Product design

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MMLUP: Multi-Source & Multi-Task Learning for User Profiles in Social Network

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computers, Materials & Continua 2019年第9期61卷 1105-1115页

作者： Dongjie Zhu Yuhua Wang Chuiju You Jinming Qiu Ning Cao Chenjing Gong Guohua Yang Helen Min Zhou School of Computer Science and Technology Harbin Institute of TechnologyWeihai264209China College of Information Engineering Sanming University365004SanmingChina Fujian Province University Key Lab for Industry Big Data Analysis and Application FujianChina College of Information Engineering Qingdao Binhai UniversityQingdaoChina Jiangsu Province Wireless Sensing System Application Engneering Technology Research and Development Centre China School of Engineering Manukau Institute of TechnologyAuckland2241New Zealand

With the rapid development of the mobile Internet,users generate massive data in different forms in social network every day,and different characteristics of users are reflected by these social media *** to integrate multiple heterogeneous information and establish user profiles from multiple perspectives plays an important role in providing personalized services,marketing,and recommendation *** this paper,we propose Multi-source&Multi-task Learning for User Profiles in Social Network which integrates multiple social data sources and contains a multi-task learning framework to simultaneously predict various attributes of a ***,we design their own feature extraction models for multiple heterogeneous data ***,we design a shared layer to fuse multiple heterogeneous data sources as general shared representation for multi-task ***,we design each task’s own unique presentation layer for discriminant output of ***,we design a weighted loss function to improve the learning efficiency and prediction accuracy of each *** experimental results on more than 5000 Sina Weibo users demonstrate that our approach outperforms state-of-the-art baselines for inferring gender,age and region of social media users.

关键词： User profiles multi-source multi-task learning social network

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DVS: Deep Visibility Series and its application in Construction Cost Index Forecasting

arXiv

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arXiv 2021年

作者： Zhan, Tianxiang He, Yuanpeng Li, Hanwen Xiao, Fuyuan College of Computer and Information Science College of Software Southwest University Chongqing400715 China School of Big Data and Software Engineering Chongqing University Chongqing401331 China National Eng. Lab. for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China

Time series forecasting is a hot spot in recent years. Visibility Graph (VG) algorithm is used for time series forecasting in previous research, but the forecasting effect is not as good as deep learning prediction methods such as methods based on Artificial Neural Network (ANN), Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM). The visibility graph generated from specific time series contains abundant network information, but the previous forecasting method did not effectively use the network information to forecast, resulting in relatively large prediction errors. To optimize the forecasting method based on VG, this article proposes the Deep Visibility Series (DVS) module through the bionic design of VG and the expansion of the past research. By applying the bionic design of biological vision to VG, DVS has obtained superior forecasting accuracy. At the same time, this paper applies the DVS forecasting method to the construction cost index forecast, which has practical significance. Copyright © 2021, The Authors. All rights reserved.

关键词： Visibility

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