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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Li, Xun Xue, Feng Chen, Chao Yuan, Xiaohui Lu, Qiang Ministry of Education Hefei China School of Computer Science and Information Hefei University of Technology Hefei China Hefei China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei China School of Software Hefei University of Technology Hefei China Department of Science and Engineering University of North Texas DentonTX United States

ISBN: (纸本)9781665438643

Due to the increasing popularity of 3D sensors, it has become easier and easier to obtain point cloud data. In many fields such as autonomous driving, how to fully extract the features of point cloud to better understand and perceive 3D scenes requires further research. Therefore, this paper proposes a novel end-to-end deep learning network for the features of disorder and irregularity of 3D point cloud data. Our network uses an encoder-decoder network architecture, and a three-layer structure is used in both stages. Each encoder layer consists of graph attention convolution and graph attention pooling. Graph attention convolution reflects the spatial distribution relationship in the neighborhood area. And graph attention pooling merges the spatial distribution information in the neighborhood into the feature of the sampling point. The experimental results show that our method achieves the best results in shape classification and has competitive performance in other tasks. © 2021 IEEE

关键词： Deep neural networks

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PIG: Prompt Images Guidance for Night-Time Scene Parsing

arXiv

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arXiv 2024年

作者： Xie, Zhifeng Qiu, Rui Wang, Sen Tan, Xin Xie, Yuan Ma, Lizhuang Department of Film and Television Engineering Shanghai University Shanghai200072 China Shanghai Key Laboratory of Computer Software Testing and Evaluating China Hanghai Engineering Research Center of Motion Picture Special Effects Shanghai200072 China School of Computer Science and Technology China Normal University Shanghai200062 China Chongqing Institute of East China Normal University Chongqing401120 China School of Computer Science and Technology East China Normal University Shanghai200062 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China

Night-time scene parsing aims to extract pixel-level semantic information in night images, aiding downstream tasks in understanding scene object distribution. Due to limited labeled night image datasets, unsupervised domain adaptation (UDA) has become the predominant method for studying night scenes. UDA typically relies on paired day-night image pairs to guide adaptation, but this approach hampers dataset construction and restricts generalization across night scenes in different datasets. Moreover, UDA, focusing on network architecture and training strategies, faces difficulties in handling classes with few domain similarities. In this paper, we leverage Prompt Images Guidance (PIG) to enhance UDA with supplementary night knowledge. We propose a Night-Focused Network (NFNet) to learn night-specific features from both target domain images and prompt images. To generate high-quality pseudo-labels, we propose Pseudo-label Fusion via Domain Similarity Guidance (FDSG). Classes with fewer domain similarities are predicted by NFNet, which excels in parsing night features, while classes with more domain similarities are predicted by UDA, which has rich labeled semantics. Additionally, we propose two data augmentation strategies: the Prompt Mixture Strategy (PMS) and the Alternate Mask Strategy (AMS), aimed at mitigating the overfitting of the NFNet to a few prompt images. We conduct extensive experiments on four night-time datasets: NightCity, NightCity+, Dark Zurich, and ACDC. The results indicate that utilizing PIG can enhance the parsing accuracy of UDA. The code is available at https://***/qiurui4shu/PIG. Copyright © 2024, The Authors. All rights reserved.

关键词： Network architecture

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An empirical study on issue knowledge transfer from python to R for machine learning software 32

An empirical study on issue knowledge transfer from python t...

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32nd International Conference on software Engineering and Knowledge Engineering, SEKE 2020

作者： Huang, Wenchin Ji, Zhenlan Li, Yanhui State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China School of Management and Engineering Nanjing University China

ISBN: (纸本)1891706500

Background: With the blowout of programming languages, developers employ different languages to solve similar problems (e.g., to implement machine learning algorithms) separately and frequently, which gives rise to knowledge transfer across different language development. Since GitHub provides an issue tracking system for developers and users to follow with issues, knowledge about how to deal with issues is a main part of available knowledge on GitHub. Such issue knowledge could be directly transferred to help developers handle new issues on current projects from similar projects in different languages. Aims: Inspired by a large amount of developed and developing machine learning software written in Python and R on GitHub, we aim to discover how much issue knowledge can be transferred from Python projects to R projects. Method: We investigate totally 1161 issues from 15 popular machine learning projects in R and 7496 issues from Scikit-Learn in Python on GitHub. After computing the text similarity between issues from R and Python projects, we match top 5 similar Scikit-Learn issues for each R issue and manually judge 1161×5 issue-pairs to label and group them. Results: We observe that a) 13% (149/1161) of R issues can refer to related Python issues;b) 47% (71/149) of related R issues can be linked to Python issues by the text mining technique BM25 at the very early stage;c) 83% (124/149) of related Python issues support code and description about the similar machines learning problems;d) reference knowledge is considered as the most useful knowledge from Python issues. Conclusion: We put forward the following suggestions: a) referring to the corresponding cross languages issues is an efficient way for developers, especially there is the lack of related information in current language;b) the text mining technique BM25 is helpful for developers to start earlier for searching similar issues cross languages. © 2020 Knowledge Systems Institute Graduate School. All rights

关键词： Python

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An Inception Module CNN Classifiers Fusion Method on Pulmonary Nodule Diagnosis by Signs

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Tsinghua science and technology 2020年第3期25卷 368-383页

作者： Guangyuan Zheng Guanghui Han Nouman Qadeer Soomro the Beijing Key Laboratory of Intelligent Information Technology School of Computer Science and TechnologyBeijing Institute of TechnologyBeijing 100081China the School of Information Technology Shangqiu Normal UniversityShangqiu 476000China the School of Biomedical Engineering Sun Yat-sen UniversityGuangzhou 510006China the Department of Software Engineering Mehran University of Engineering and TechnologySZAB CampusKhairpur Mir's66020Pakistan

A "sign" on a lung CT image refers to a radiologic finding that suggests a pathological progression of some specific disease. Analysis of CT signs is helpful to understand the pathological origin of the lesion. In-depth study of lung nodules classification with different CT signs will help to distinguish benign and malignant nodules more clearly and accurately. To this end, we propose an Inception module-based ensemble classification method for pulmonary nodule diagnosis with different nodule signs. We first construct a Convolutional Neural Network(CNN) classifier adopting Inception modules and pre-train it on ImageNet. We then fine-tune this pre-trained classifier on 10 different lung nodule sign sample sets, and fuse these 10 classifiers with an artificial immune ensemble algorithm. The overall sensitivity, specificity, and accuracy of our proposed Artificial Immune Algorithm-based Inception Networks Fusion(AIA-INF) algorithm are 82.22%, 93.17%, and 88.67%, respectively, which are significantly higher than those of the alternative Bagging and Boosting methods. The experimental results show that our Inception-based ensemble classifier offers promising performance, and compared with other CADx systems, this scheme can offer a more detailed reference for diagnosis, and can be valuable for junior radiologist training.

关键词： sign lung cancer pulmonary nodule Convolutional Neural Network(CNN) Artificial Immune Algorithm(AIA)

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Measuring discrimination to boost comparative testing for multiple deep learning models

arXiv

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arXiv 2021年

作者： Meng, Linghan Li, Yanhui Chen, Lin Wang, Zhi Wu, Di Zhou, Yuming Xu, Baowen State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China Momenta Suzhou China

The boom of DL technology leads to massive DL models built and shared, which facilitates the acquisition and reuse of DL models. For a given task, we encounter multiple DL models available with the same functionality, which are considered as candidates to achieve this task. Testers are expected to compare multiple DL models and select the more suitable ones w.r.t. the whole testing context. Due to the limitation of labeling effort, testers aim to select an efficient subset of samples to make an as precise rank estimation as possible for these models. To tackle this problem, we propose Sample Discrimination based Selection (SDS) to select efficient samples that could discriminate multiple models, i.e., the prediction behaviors (right/wrong) of these samples would be helpful to indicate the trend of model performance. To evaluate SDS, we conduct an extensive empirical study with three widely-used image datasets and 80 real world DL models. The experimental results show that, compared with state-of-the-art baseline methods, SDS is an effective and efficient sample selection method to rank multiple DL models. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

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Experimental verification of group non-membership in optical circuits

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Photonics Research 2021年第9期9卷 1745-1751页

作者： KAI SUN ZI-JIAN ZHANG FEI MENG BIN CHENG ZHU CAO JIN-SHI XU MAN-HONG YUNG CHUAN-FENG LI GUANG-CAN GUO CAS Key Laboratory of Quantum Information University of Science and Technology of ChinaHefei 230026China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of ChinaHefei 230026China Department of Physics Southern University of Science and TechnologyShenzhen 518055China Department of Computer Science The University of Hong KongPokfulamHong Kong SARChina Centre for Quantum Software and Information Faculty of Engineering and Information TechnologyUniversity of Technology SydneySydneyNSW 2007Australia Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and TechnologyShanghai 200237China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Shenzhen Key Laboratory of Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China

The class quantum Merlin–Arthur(QMA),as the quantum analog of nondeterministic polynomial time,contains the decision problems whose YES instance can be verified efficiently with a quantum *** problem of deciding the group non-membership(GNM)of a group element is conjectured to be a member of *** works on the verification of GNM,which still lacks experimental demonstration,required a quantum circuit with O(n~5)group oracle ***,we provide an efficient way to verify GNM problems,in which each quantum circuit only contains O(1)group of oracle calls,and the number of qubits in each circuit is reduced by *** on this protocol,we then experimentally demonstrate the new verification process with a four-element group in an all-optical *** new protocol is validated experimentally by observing a significant completeness-soundness gap between the probabilities of accepting elements in and outside the *** work efficiently simplifies the verification of GNM and is helpful in constructing more quantum protocols based on the near-term quantum devices.

关键词： quantum element polynomial

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2D-3D Feature Co-Embedding Network with Sparse Annotation for 3D Medical Image Segmentation

2D-3D Feature Co-Embedding Network with Sparse Annotation fo...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Mingzhu Zhu Shuyu Guo Ziwei Wang Jianhang Jiao Tian Bai College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Software Jilin University Changchun China Department of Orthopedics The Second Norman Bethune Hospital of Jilin University Changchun China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Supervised methods on 3D medical image segmentation need large amounts of annotated data, but annotating is time-consuming. Also, existing 3D segmentation methods capture more global structural information but overlook local detailed features, which negatively impacts the segmentation of small tissues. In this paper, we propose a novel weakly-supervised 2D-3D Feature Co-Embedding Network (2D-3D CoENet) that includes 2D and 3D encoding layers, simultaneously extracting 2D local detailed and 3D global structural features. To reduce annotation costs, we use fewer labeled slices as ground truth and pseudo-labels are generated by 2D-3D CoENet for other slices. Additionally, multi-view learning is introduced to capture more 2D local detailed information, and a Multi-view Semantic Consistency loss (MSC loss) is proposed to constrain features from multiple perspectives. To further enhance the local detailed texture features, we propose an Edge Enhancement Module (EEM) in the 3D segmentation network to enhance the edge detail features. Our experimental results on the SKI10 dataset and OAI ZIB dataset demonstrate that our method outperforms the SOTA weakly-supervised segmentation methods. Moreover, our approach achieves results that are comparable to the fully-supervised upper bound results.

关键词： Image segmentation Three-dimensional displays Costs Upper bound Annotations Image edge detection Semantics Feature extraction Encoding Biomedical imaging

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DearLLM: Enhancing Personalized Healthcare via Large Language Models-Deduced Feature Correlations 39

DearLLM: Enhancing Personalized Healthcare via Large Languag...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xu, Yongxin Jiang, Xinke Chu, Xu Qiu, Rihong Feng, Yujie Ding, Hongxin Zhao, Junfeng Wang, Yasha Xie, Bing School of Computer Science School of Software & Microelectronics Peking University Beijing China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China National Engineering Research Center For Software Engineering Peking University Beijing China Center on Frontiers of Computing Studies Peking University Beijing China Peking University Information Technology Institute Binhai Tianjin China Nanhu Laboratory Jiaxing China Department of Computing The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)157735897X

Exploring the correlations between medical features is essential for extracting patient health patterns from electronic health records (EHR) data, and strengthening medical predictions and decision-making. To constrain the hypothesis space of pure data-driven deep learning in the context of limited annotated data, a common trend is to incorporate external knowledge, especially knowledge priors related to personalized health contexts, to optimize model training. However, most existing methods lack flexibility and are constrained by the uncertainties brought about by fixed feature correlation priors. In addition, in utilizing knowledge, these methods overlook the knowledge informative for personalized healthcare. To this end, we propose DearLLM, a novel and effective framework that leverages feature correlations deduced by large language models (LLMs) to enhance personalized healthcare. Concretely, DearLLM captures and learns quantitative correlations between medical features by calculating the conditional perplexity of LLMs’ deduction based on personalized patient backgrounds. Then, DearLLM enhances healthcare predictions by emphasizing knowledge that carries unique patient information through a feature-frequency-aware graph pooling method. Extensive experiments on two real-world benchmark datasets show significant performance gains brought by DearLLM. Furthermore, the discovered findings align well with medical literature, offering meaningful clinical interpretations. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Electronic health record

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Measuring Discrimination to Boost Comparative Testing for Multiple Deep Learning Models

Measuring Discrimination to Boost Comparative Testing for Mu...

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International Conference on software Engineering (ICSE)

作者： Linghan Meng Yanhui Li Lin Chen Zhi Wang Di Wu Yuming Zhou Baowen Xu State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China Momenta Suzhou China

The boom of DL technology leads to massive DL models built and shared, which facilitates the acquisition and reuse of DL models. For a given task, we encounter multiple DL models available with the same functionality, which are considered as candidates to achieve this task. Testers are expected to compare multiple DL models and select the more suitable ones w.r.t. the whole testing context. Due to the limitation of labeling effort, testers aim to select an efficient subset of samples to make an as precise rank estimation as possible for these models. To tackle this problem, we propose Sample Discrimination based Selection (SDS) to select efficient samples that could discriminate multiple models, i.e., the prediction behaviors (right/wrong) of these samples would be helpful to indicate the trend of model performance. To evaluate SDS, we conduct an extensive empirical study with three widely-used image datasets and 80 real world DL models. The experiment results show that, compared with state-of-the-art baseline methods, SDS is an effective and efficient sample selection method to rank multiple DL models.

关键词： Voting Predictive models Labeling Task analysis Testing Context modeling software engineering

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Phenotyping of Salvia miltiorrhiza Roots Reveals Associations between Root Traits and Bioactive Components

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Plant Phenomics 2023年第4期5卷 746-756页

作者： Junfeng Chen Yun Wang Peng Di Yulong Wu Shi Qiu Zongyou Lv Yuqi Qiao Yajing Li Jingfu Tan Weixu Chen Ma Yu Ping Wei Ying Xiao Wansheng Chen The SATCM Key Laboratory for New Resources&Quality Evaluation of Chinese Medicine Institute of Chinese Materia MedicaShanghai University of Traditional Chinese MedicineShanghai 201203China School of Medicine Shanghai UniversityShanghai 200444China State Local Joint Engineering Research Center of Ginseng Breeding and Application Jilin Agricultural UniversityChangchun 130118China School of Computer Science Sichuan Normal UniversityChengdu 610066China Shangyao Huayu(Linyi)Traditional Chinese Resources Co. Ltd.Linyi 276000China School of Life Science and Engineering Southwest University of Science and TechnologyMianyang 621010SichuanChina Sichuan Academy of Traditional Chinese Medicine Chengdu 610041China Department of Pharmacy Changzheng HospitalSecond Military Medical UniversityShanghai 200003China

Plant phenomics aims to perform high-throughput,rapid,and accurate measurement of plant traits,facilitating the identification of desirable traits and optimal genotypes for crop *** miltiorrhiza(Danshen)roots possess remarkable therapeutic effect on cardiovascular diseases,with huge market *** great advances have been made in metabolic studies of the bioactive metabolites,investigation for *** roots on other physiological aspects is ***,we developed a framework that utilizes image feature extraction software for in-depth phenotyping of *** *** employing multiple software programs,*** roots were described from 3 aspects:agronomic traits,anatomy traits,and root system *** K-means clustering based on the diameter ranges of each root branch,all roots were categorized into 3 groups,with primary root-associated key *** a proof of concept,we examined the phenotypic components in a series of randomly collected *** roots,demonstrating that the total surface of root was the best parameter for the biomass prediction with high linear regression correlation(R^(2)=0.8312),which was sufficient for subsequently estimating the production of bioactive metabolites without content *** study provides an important approach for further grading of medicinal materials and breeding practices.

关键词： Root utilize anatomy

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