检索结果-内蒙古大学图书馆

SSRN 2024年

作者： Qi, Yaping Hu, Dan Zheng, Ming Jiang, Yucheng Chen, Yong P. Tohoku University Sendai980-8577 Japan Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology Av. Wai Long 999078 China Institute of Physics and Astronomy Villum Center for Hybrid Quantum Materials and Devices Aarhus University Aarhus-C8000 Denmark School of Materials Science and Physics China University of Mining and Technology Xuzhou221116 China Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application School of Physical Science and Technology Suzhou University of Science and Technology Jiangsu Suzhou215009 China Department of Physics and Astronomy Elmore Family School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States

Two-dimensional (2D) materials have attracted extensive attention due to their unique characteristics and application potentials. Raman spectroscopy, as a rapid and non-destructive probe, exhibits distinct features and holds notable advantages in the structural characterization of 2D materials. However, traditional data analysis of Raman spectra relies on manual interpretation and feature extraction, which are both time-consuming and subjective. In this work, we employ deep learning techniques, including classificatory and generative deep learning, to assist the analysis of Raman spectra of typical 2D materials. For the limited and unevenly distributed Raman spectral data, we propose a data augmentation approach based on Denoising Diffusion Probabilistic Models (DDPM) to augment the training dataset and construct a four-layer Convolutional Neural Network (CNN) for 2D material classification. The proposed CNN model achieves an impressive accuracy of 98.8%. Experiments illustrate the effectiveness of DDPM in addressing data limitations and significantly improved the performance of the classification model. Notably, the DDPM-CNN method shows high reliability, with 100% classification accuracy. Our work demonstrates the practicality of deep learning-assisted Raman spectroscopy for high-precision recognition and classification of 2D materials, presenting a promising avenue for rapid and automated materials analysis via spectroscopy. © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Construction of Tibetan text proofreading evaluation set 20

Construction of Tibetan text proofreading evaluation set

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20th Chinese National Conference on Computational Linguistics, CCL 2021

作者： San, Maocuo Cai, Zhi-Jie Dao, Jizhaxi College of Computer Science and Technology Qinghai Normal University Qinghai Xining810016 China Key Laboratory of Tibetan Informati on Processing Ministry of Education Qinghai Xining810008 China Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province Qinghai Xining810008 China School of Computer Science and Technology Southwest Minzu University Sichuan Chengdu610041 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Xining810008 China

Text proofreading evaluation set is the basis of spelling research, including traditional text proofreading evaluation set and standard text proofreading evaluation set. The traditional text proofreading evaluation set is a kind of evaluation set obtained by artificial forgery of the correct data set through subjective experience. It is a common text proofreading evaluation method, but it also has many defects. Standard text proofreading evaluation set is an evaluation set obtained by selecting research objects to obtain reliable real data sets. Based on the analysis of the construction methods of English and Chinese text proofreading evaluation set, combined with the characteristics of Tibetan, this paper studies the construction methods of Tibetan text proofreading evaluation set, constructs a standard text proofreading evaluation set for evaluating the performance of Tibetan text proofreading, and analyzes the error types and distribution in the evaluation set, so as to verify the effectiveness of the standard text proofreading evaluation set constructed in this paper Availability and usability. © 2021 China National Conference on Computational Linguistics Published under Creative Commons Attribution 4.0 International License

关键词： Natural language processing systems

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When Deep Learning Meets Information Retrieval-based Bug Localization: A Survey

arXiv

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arXiv 2025年

作者： Niu, Feifei Li, Chuanyi Liu, Kui Xia, Xin Lo, David State Key Laboratory for Novel Software Technology Software Institute Nanjing University Nanjing China Software Engineering Application Technology Lab Huawei Hangzhou China College of Computer Science and Technology Zhejiang University China School of Computing and Information Systems Singapore Management University Singapore

Bug localization is a crucial aspect of software maintenance, running through the entire software lifecycle. Information retrieval-based bug localization (IRBL) identifies buggy code based on bug reports, expediting the bug resolution process for developers. Recent years have witnessed significant achievements in IRBL, propelled by the widespread adoption of deep learning (DL). To provide a comprehensive overview of the current state of the art and delve into key issues, we conduct a survey encompassing 61 IRBL studies leveraging DL. We summarize best practices in each phase of the IRBL workflow, undertake a meta-analysis of prior studies, and suggest future research directions. This exploration aims to guide further advancements in the field, fostering a deeper understanding and refining practices for effective bug localization. Our study suggests that the integration of DL in IRBL enhances the model’s capacity to extract semantic and syntactic information from both bug reports and source code, addressing issues such as lexical gaps, neglect of code structure information, and cold-start problems. Future research avenues for IRBL encompass exploring diversity in programming languages, adopting fine-grained granularity, and focusing on real-world applications. Most importantly, although some studies have started using large language models for IRBL, there is still a need for more in-depth exploration and thorough investigation in this area. © 2025, CC BY.

关键词： Online searching

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Sputtered Sn-doped Ga2O3 films under balance controlled of energy supply and ion bombardment for solar-blind detection application

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Vacuum 2024年 225卷

作者： Wang, Chen Fan, Wei-Hang Cao, Rong-Jun Fan, Hui-Chen Xu, Yi-Hong Kang, Pin-Chun Wu, Ting-Zhu Kuo, Hao-Chung Lien, Shui-Yang Zhu, Wen-Zhang 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 National Measurement and Testing Center for Flat Panel Display Industry Xiamen Institute of Measurement and Testing Xiamen361004 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 Biomedical Engineering Da-Yeh University Changhua51591 Taiwan

The characteristics of amorphous Sn-doped Ga2O3 films deposited using radio frequency magnetron sputtering at room temperature under different sputter powers have been demonstrated. A balance mechanism of energy supply and ion bombardment controlled by sputter power has been found during the deposition. The increasing growth rate as power can be due to the increased yield sputtering species indicated by an in situ optical emission spectroscopy. As the power increases to 700 W, an obvious change from discrete nanoparticles to flat-continuous film can be observed because of sufficient energy supply for oxidation reaction, which leads to the maximum ratio of lattice oxygen and Sn4+ indicating the optimal film quality and conductive property. However, a non-negligible high energy ion bombardment presented at 900 W leads to the degraded film quality. The influence of sputtering power on Sn-doped Ga2O3 solar-blind photodetectors is demonstrated. It can obtain a good performance with an ultra-low dark current density of 2.2 × 10−11 A/cm2, a substantial light-dark current ratio of 1.08 × 103 and fast rise/decay time of 1.59 s/0.54 s at 700 W. The revelation of the role of sputtering power on the properties of Sn-doped Ga2O3 films may offer an interesting direction for low-cost and high-performance solar-blind photodetectors. © 2024 Elsevier Ltd

关键词： Gallium compounds

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Learning the compositional visual coherence for complementary recommendations 29

Learning the compositional visual coherence for complementar...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

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 China Columbia University United States School of Computer Science and Technology University of Science and Technology of China China College of Management and Economics Tianjin University China JD AI Research

ISBN: (纸本)9780999241165

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 Networks (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. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Semantics

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XCrossNet: Feature structure-oriented learning for click-through rate prediction

arXiv

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arXiv 2021年

作者： Yu, Runlong Ye, Yuyang Liu, Qi Wang, Zihan Yang, Chunfeng Hu, Yucheng Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Hefei China Management Science and Information Systems Rutgers Business School Rutgers University Newark United States MOE Key Laboratory of Computational Linguistics School of Electronics Engineering and Computer Science Peking University Beijing China Tencent Inc Shenzhen China

Click-Through Rate (CTR) prediction is a core task in nowadays commercial recommender systems. Feature crossing, as the mainline of research on CTR prediction, has shown a promising way to enhance predictive performance. Even though various models are able to learn feature interactions without manual feature engineering, they rarely attempt to individually learn representations for different feature structures. In particular, they mainly focus on the modeling of cross sparse features but neglect to specifically represent cross dense features. Motivated by this, we propose a novel Extreme Cross Network, abbreviated XCrossNet, which aims at learning dense and sparse feature interactions in an explicit manner. XCrossNet as a feature structure-oriented model leads to a more expressive representation and a more precise CTR prediction, which is not only explicit and interpretable, but also time-efficient and easy to implement. Experimental studies on Criteo Kaggle dataset show significant improvement of XCrossNet over state-of-the-art models on both effectiveness and efficiency. © 2021, CC BY.

关键词： Forecasting

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Mccseg: Morphological Guided Causal Constraint Network for Medical Image Multi-Object Segmentation

SSRN

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SSRN 2023年

作者： Gao, Yifan Wei, Lifang Li, Jun Chang, Xinyue Zhang, Yulong Chen, Riqing Yang, Changcai Wei, Yi Dong, Heng Digital Fujian Research Institute of Big Data for Agriculture and Forestry College of Computer and Information Science Fujian Agriculture and Forestry University Fuzhou350002 China Institute for AI in Medicine School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210000 China Department of Obstetrics & Gynecology College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics Fujian Medical University Fuzhou350001 China School of Mathematics and Computer Science Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry Wuyi University Fujian354300 China

Medical image multi-object segmentation aims to accurately extract each object that is great significance for the medical image analysis. Although several methods based on deep leaning have been widely developed, it is still challenging due to the similarities of different objects, the significant variability of morphology, data heterogeneity and the unbalanced distribution of samples. To address the above issues, we propose a novel Morphological Guided Causal Constraint segmentation network (MCCSeg) for medical image multi-object segmentation. We build a hybrid CNN-Transformer encoder module to obtain local and global features. Specifically, a Causal Constraint Module (CCM) is proposed for feature decorrelation by sample reweighting, which utilizes weight allocation to train the weight of training samples. The Random Fourier Features (RFF) are used to solve nonlinear dependency problems and remove correlations between features for eliminating the impact of spurious correlations. The morphological guidance module (MG) is designed to extract the boundary as prior morphology information for enhancing feature representation, which are concatenated into the deep supervision module in decoder for further optimizing feature extraction. The experiments demonstrate that MCCSeg outperforms other state-of-the-art methods, obtaining up 3.76% and 5.41% improvements in DICE and HD95 scores on Synapse dataset, respectively. And the ablation experiments further verify the effectiveness of MCCSeg in improving generalization. The source code will be released at https://***/YfGAO/MCCSeg. © 2023, The Authors. All rights reserved.

关键词： Morphology

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Lesionmix Data Enhancement and Entropy Minimization for Semi-Supervised Lesion Segmentation of Lung Cancer

SSRN

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SSRN 2024年

作者： Pan, Xipeng Chen, Mingwei Lin, Huan Bian, Xinjun Feng, Siyang Chen, Jiale Wang, Lin Chen, Xin Liu, Zaiyi Lan, Rushi School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Guilin541004 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 Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Guangdong Guangzhou510080 China State Key Laboratory of Oncology in South China Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Guangdong Provincial Clinical Research Center for Cancer Sun Yat-sen University Cancer Center Guangzhou510060 China Department of Nasopharyngeal Carcinoma Sun Yat-sen University Cancer Center Guangzhou510060 China Department of Radiology Guangzhou First People’s Hospital School of Medicine South China University of Technology Guangdong Guangzhou510180 China Guangdong Provincial People's Hospital China School of Medicine China

Determining the location and contour of the lesion is an important prerequisite for medical diagnosis, subsequent personalized treatment plan and prognostic prediction. Semi-supervised learning and data enhancement methods facilitate deep learning to be used in many fields of medical imaging. In this paper, we propose a data enhancement method named LesionMix, which performs lesion extraction and reuses through a small amount of labeled CT data to improve the utilization efficiency of the labeled data. Meanwhile, we propose a two-stage semi-supervised training strategy called Entropy Minimization LesionMix, in which features containing lesion contour information are quickly learned through LesionMix data augmentation in the first stage. Entropy minimization strategy optimizes the model parameters to alleviate overfitting as much as possible in the second stage and improves prediction confidence. Our proposed method is validated on an public LIDC-IDRI dataset and an in-house GDPHLUAD dataset. Extensive experiments demonstrate that our method achieves promising performance and outperforms seven state-of-the-art semi-supervised models;Moreover, ablation experiments validate the effectiveness of various aspects of our approach. © 2024, The Authors. All rights reserved.

关键词： Diseases

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Morphological Guided Causal Constraint Network for Medical Image Multi-Object Segmentation

Morphological Guided Causal Constraint Network for Medical I...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yifan Gao Jun Li Xinyue Chang Yulong Zhang Riqing Chen Changcai Yang Yi Wei Heng Dong Lifang Wei College of Computer and Information Science Fujian Agriculture and Forestry University Fujian China Institute for AI in Medicine School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics Fujian Medical University Fujian China Digital Fujian Research Institute of Big Data for Agriculture and Forestry Fujian Agriculture and Forestry University Fujian China School of Mathematics and Computer Science Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry Wuyi University Fujian China

Multi-objective segmentation (MOS) in medical images is to simultaneously extract multiple regions of interest in the medical images. Due to the unbalanced distribution of samples and the similarity and significant differences between features in medical images, current methods still struggle to achieve satisfactory results. In this context, we propose a novel Morphological Guided Causal Constrain segmentation network (MCCSeg) for medical image multi-object segmentation. We introduced a Causal Constrain Module (CCM) for feature decorrelation by sample reweighting. The morphological guidance module (MG) is designed to extract the boundary features as the prior shape information for enhancing feature representation. Our experiments demonstrate that MCCSeg outperforms other state-of-the-art methods, obtaining up 3.76% and 5.41% improvements in DICE and HD95 scores on Synapse dataset, respectively.

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Boosting Factorization Machines via Saliency-Guided Mixup

arXiv

引用

arXiv 2022年

作者： Wu, Chenwang Lian, Defu Ge, Yong Zhou, Min Chen, Enhong Tao, Dacheng School of Data Science University of Science and Technology of China Anhui Hefei230000 China Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230000 China Eller College of Management University of Arizona United States Huawei Noah’s Ark Lab Hong Kong JD Explore Academy in *** China University of Sydney Australia

Factorization machines (FMs) are widely used in recommender systems due to their adaptability and ability to learn from sparse data. However, for the ubiquitous non-interactive features in sparse data, existing FMs can only estimate the parameters corresponding to these features via the inner product of their embeddings. Undeniably, they cannot learn the direct interactions of these features, which limits the model’s expressive power. To this end, we first present MixFM, inspired by Mixup, to generate auxiliary training data to boost FMs. Unlike existing augmentation strategies that require labor costs and expertise to collect additional information such as position and fields, these extra data generated by MixFM only by the convex combination of the raw ones without any professional knowledge support. More importantly, if the parent samples to be mixed have non-interactive features, MixFM will establish their direct interactions. Second, considering that MixFM may generate redundant or even detrimental instances, we further put forward a novel Factorization Machine powered by Saliency-guided Mixup (denoted as SMFM). Guided by the customized saliency, SMFM can generate more informative neighbor data. Through theoretical analysis, we prove that the proposed methods minimize the upper bound of the generalization error, which hold a beneficial effect on enhancing FMs. Significantly, we give the first generalization bound of FM, implying the generalization requires more data and a smaller embedding size under the sufficient representation capability. Finally, extensive experiments on five datasets confirm that our approaches are superior to baselines. Besides, the results show that"poisoning" mixed data is likewise beneficial to the FM variants. Copyright © 2022, The Authors. All rights reserved.

关键词： Recommender systems

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