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TSENG-TYPE SUBGRADIENT METHODS FOR SOLVING NONMONOTONE VARIATIONAL INEQUALITIES

UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2023年第2期85卷 3-16页

作者： Li, Xiangyao Zhu, Li-Jun Postolache, Mihai School of Mathematics and Information Science North Minzu University Yinchuan750021 China Health Big Data Research Institute of North Minzu University Yinchuan750021 China The Key Laboratory of Intelligent Information and Big Data Processing of NingXia Province North Minzu University Yinchuan750021 China Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy Bucharest050711 Romania University "Politehnica" of Bucharest Department of Mathematics and Informatics Bucharest060042 Romania

In this article, we introduce two new approaches for solving variational inequalities without monotonicity. The first algorithm simplifies the projection region of each iteration in Ye and He [Comput. Optim. Appl., 60 (2015), 141-150], that is, it becomes the intersection of multiple half-spaces and no longer needs to be intersected with the feasible set. By a selection technique, the second algorithm replaces the projection on the common region of the feasible set and multiple half-spaces with a specific half-spaces in each iteration. The strong convergence of these two algorithms have been demonstrated under the assumption that the Minty variational inequality has a solution. Finally, some numerical examples are given to illustrate the advantages of the proposed algorithms. © 2023, Politechnica University of Bucharest. All rights reserved.

关键词： Variational techniques

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9th International Conference on Fuzzy Systems and data Mining, FSDM 2023

作者： Xu, Changlin Wang, Kuan School of Mathematics and Information Science North Minzu University Yinchuan750021 China The Key Laboratory of Intelligent Information and Big Data Processing of Ningxia Province North Minzu University Ningxia Yinchuan750021 China

ISBN: (纸本)9781643684703

Uncertainty exists widely in nature and human society. The handling and analysis of these uncertain phenomena have long been a focal point of research in natural sciences, representing a hot and crucial topic. The cloud model uses three numerical characteristics to realize qualitative and quantitative transformation of uncertain knowledge. Normal cloud is one of the more important and common cloud models. The similarity measurement of cloud model is used to judge the similarity of two cloud concepts, and the purpose is to understand the correlation between different cloud concepts, so as to improve the efficiency of classification and clustering. As a deformation extension of f divergence, Hellinger distance has good properties and is suitable for describing the difference between two probability distributions. HECMk and HCCMk algorithms are proposed by combining Hellinger distance and expectation curve cluster with entropy. Compared with previous algorithms, these two algorithms have higher distinguishing ability. Among them, HCCMk algorithm has better comprehensive performance and is feasible in actual similarity measurement. © 2023 The authors and IOS Press.

关键词： Entropy

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Dominate Tree Species Classification on Large-Scale Mountainous Areas Using Voting Strategy-Based Ensemble Learning Method 8th

Dominate Tree Species Classification on Large-Scale Mountain...

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8th China High Resolution Earth Observation Conference, CHREOC 2022

作者： Zheng, Pengfei Fang, Panfei Liu, Peiwei Xu, Weiheng Wang, Guojun Wang, Leiguang Faculty of Forestry Southwest Forestry University Kunming China College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China Key Laboratory of National Forestry and Grassland Administration on Forestry and Ecological Big Data Southwest Forestry University Kunming China Yunnan Tobacco Company Qujing Company Qujing China

ISBN: (纸本)9789811982019

Remote sensing technology provides an economical and efficient means for obtaining the distribution of dominant tree species. However, remote sensing mapping of dominant tree species have remained an ongoing challenge, such as massive data and low accuracy of a single machine learning method, especially over large-scale mountainous areas. In this study, the multi-source variables and multi-classifier decision fusion was used to separate the dominant tree species on the Google Earth Engine cloud computing platform. We combined with multi-source data such as Sentinel-2 satellite imagery, bioclimate, topography, and forest inventory data and constructed time series for NDVI and REP to evaluate different variables combinations. At the same time, tree species classification was carried out following two approaches: non-stratified and stratified. Four different Machine learning algorithms, Random Forest (RF), Support Vector Machine (SVM), XGBoost, and Maximum Entropy (MaxEnt) were used as component classifiers to construct two decision fusion models of serial integration and Bayesian parallel integration respectively and completed the spatial distribution of 10 main dominant tree species mapping in the mountainous areas of northwest Yunnan. The nine tree species were classified with an overall accuracy of 74.44% using serial integration of MaxEnt and RF. This study confirmed that combination of multi-source data multi-source variables and the decision fusion method can be used to provide a reference for the classification of dominant tree species in large-scale mountainous areas. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Engines

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Discriminative distribution alignment for domain adaptive object detection

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NEUROCOMPUTING 2022年 474卷 48-59页

作者： Huang, Junchu Shen, Shifu Zhou, Zhiheng Zhang, Pengyu Fan, Kefeng South China Univ Technol 381 Wushan Rd Guangzhou Guangdong Peoples R China South China Univ Technol Key Lab Big Data & Intelligent Robot Minist Educ Guangzhou Guangdong Peoples R China China Elect Standardizat Inst Beijing Peoples R China

Domain adaptive object detection has achieved appealing performance by constructing an effective transferable model for unlabeled target images, which takes advantage of the well-labeled source images with different distributions. However, two crucial factors are overlooked by most current methods: 1) different areas of an image should not be equally aligned since some areas may contribute more to distribution alignment if they contain more discriminative information for classifying the objects;and 2) the objectives of feature alignment and classification should not be independently optimized since it will fail to capture the discriminative information of data. To address these issues, we propose a new domain adaptive object detection model, referred to as discriminative distribution alignment domain adaptive detector. To be specific, the proposed method first makes the model focus on the areas that are quantified with high localization probability at the image level to enhance discrimination between foregrounds and backgrounds. Then the source and target images are aligned at the category level to learn class-invariant features by two adversarial regions-of-interest classifiers. Comprehensive experiments on several visual tasks verify that the proposed method outperforms the competitive domain adaptive object detection methods significantly in unsupervised domain adaptation setting. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Object detection Domain adaptation Domain adaptive object detection

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DAS: Densely-Anchored Sampling for Deep Metric Learning 17th

DAS: Densely-Anchored Sampling for Deep Metric Learning

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17th European Conference on Computer Vision (ECCV)

作者： Liu, Lizhao Huang, Shangxin Zhuang, Zhuangwei Yang, Ran Tan, Mingkui Wang, Yaowei South China Univ Technol Guangzhou Peoples R China PengCheng Lab Shenzhen Peoples R China Minist Educ Key Lab Big Data & Intelligent Robot Guangzhou Peoples R China

ISBN: (纸本)9783031198083;9783031198090

Deep Metric Learning (DML) serves to learn an embedding function to project semantically similar data into nearby embedding space and plays a vital role in many applications, such as image retrieval and face recognition. However, the performance of DML methods often highly depends on sampling methods to choose effective data from the embedding space in the training. In practice, the embeddings in the embedding space are obtained by some deep models, where the embedding space is often with barren area due to the absence of training points, resulting in so called "missing embedding" issue. This issue may impair the sample quality, which leads to degenerated DML performance. In this work, we investigate how to alleviate the "missing embedding" issue to improve the sampling quality and achieve effective DML. To this end, we propose a Densely-Anchored Sampling (DAS) scheme that considers the embedding with corresponding data point as "anchor" and exploits the anchor's nearby embedding space to densely produce embeddings without data points. Specifically, we propose to exploit the embedding space around single anchor with Discriminative Feature Scaling (DFS) and multiple anchors with Memorized Transformation Shifting (MTS). In this way, by combing the embeddings with and without data points, we are able to provide more embeddings to facilitate the sampling process thus boosting the performance of DML. Our method is effortlessly integrated into existing DML frameworks and improves them without bells and whistles. Extensive experiments on three benchmark datasets demonstrate the superiority of our method.

关键词： Deep metric learning Missing embedding Embedding space exploitation Densely-Anchored Sampling

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Multimodal Biological Knowledge Graph Completion via Triple Co-Attention Mechanism 39

Multimodal Biological Knowledge Graph Completion via Triple ...

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39th IEEE International Conference on data Engineering, ICDE 2023

作者： Xu, Derong Zhou, Jingbo Xu, Tong Xia, Yuan Liu, Ji Chen, Enhong Dou, Dejing University of Science and Technology of China State Key Laboratory of Cognitive Intelligence China Baidu Research Business Intelligence Lab Baidu Intelligent Healthcare Unit Baidu Research Big Data Lab BCG X

ISBN: (纸本)9798350322279

Biological Knowledge Graphs (BKGs) can help to model complex biological systems in a structural way to support various tasks. Nevertheless, the incompleteness problem may limit the performance of existing BKGs, which still deserves new methods to reveal the missing relations. Though great efforts have been made to knowledge graph completion, existing methods are not easy to be adapted to the multimodal biological information such as molecular structures and textual descriptions. To this end, we propose a novel co-attention-based multimodal embedding framework, named CamE, for the multimodal BKG completion task. Specifically, we design a Triple Co-Attention (TCA) operator to capture and highlight the same semantic features among different modalities. Based on TCA, we further propose two components to handle multimodal fusion and multimodal entity-relation interaction, respectively. One is the multimodal TCA fusion module to achieve a multimodal joint representation for each entity in the BKG. It aims to project different modal information into a common space by capturing the same semantic features and overcoming the modality gap. The other is the relation-aware interactive TCA module to learn interactive representation by modelling the deep interaction between multimodal entities and relations. Extensive experiments on two real-world multimodal BKG datasets demonstrate that our method significantly outperforms several state-of-the-art baselines, including 10.3% and 16.2% improvement w.r.t MRR and Hits@1 metrics over its best competitors on public DRKG-MM dataset. © 2023 IEEE.

关键词： Knowledge graph

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Federated Learning Based User Scheduling for Real-Time Multimedia Tasks in Edge Devices 1

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3rd EAI International Conference on Edge Computing and IoT, EAI ICECI 2022

作者： Wen, Wenkan Liu, Yiwen Gao, Yanxia Zhu, Zhirong Shi, Yuanquan Peng, Xiaoning School of Computer and Artificial Intelligence Huaihua University Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (数字)9783031289903

ISBN: (纸本)9783031289897

Edge networks are highly volatile and the quality of device communication and computational resources change not only over time but also according to the movement of users. Current federation learning suffers from poor device network state and failure of devices to upload models in a timely manner. To address these problems, an intelligent scheduling mechanism that uses the predicted device state based on device information to select the appropriate device for federated learning is proposed in this paper. By focusing on information such as communication quality, computational resources, and location information, the information of edge devices is collected to analyze and predict the device network and computing resources to further analyze the state of devices in depth. Experiments are conducted on real datasets, and the experimental results show that the proposed scheduling method can make the global model fit faster than without the algorithm, which significantly improves the training efficiency of federated learning. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Machine learning

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TF-DTA: A Deep Learning Approach Using Transformer Encoder to Predict Drug-Target Binding Affinity

TF-DTA: A Deep Learning Approach Using Transformer Encoder t...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Li, Wenjun Zhou, Yiqiang Tang, Xiwei Changsha University of Science and Technology Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Hunan Changsha China Hunan First Normal University School of Computer Science Hunan Changsha China

ISBN: (纸本)9798350337488

Bioinformatics is a rapidly growing field that involves the application of computational methods to analyze and interpret biological data. One important task in bioinformatics is predicting the drug-target affinity (DTA), which plays a significant role in drug discovery through virtual screening. Effectively predicting the association between drug molecules and target molecules can speed up the drug discovery process. The DTA can be quantitatively measured. This quantifiable affinity is more precise than a simple binary relationship. In this study, we propose a deep learning model for DTA prediction that utilizes the encoder module of the Transformer architecture. Our proposed model utilizes Convolutional Neural Networks (CNNs) and the encoder module of Transformer to characterize protein and drug sequences. The model outperforms some methods such as KronRLS, SimBoost and DeepDTA as evidenced by superior evaluation metrics such as Mean Squared Error (MSE), Concordance Index (CI), and Regression toward the Mean Index (rm2). These results demonstrate the effectiveness of the Transformer's encoder in extracting meaningful representations from sequences, thereby improving the accuracy of DTA prediction. Deep learning models for DTA prediction can accelerate drug discovery by identifying drug candidates with high binding affinity to specific targets. Compared to traditional methods, the use of machine learning technology enables more effective and efficient drug discovery. © 2023 IEEE.

关键词： deep learning drug-target affinity multi-head attention mechanism

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9th International Conference on Fuzzy Systems and data Mining, FSDM 2023

作者： Xu, Changlin Wu, Wenyuan School of Mathematics and Information Science North Minzu University Yinchuan750021 China The Key Laboratory of Intelligent Information and Big Data Processing of Ningxia Province North Minzu University Ningxia Yinchuan750021 China

ISBN: (纸本)9781643684703

The similarity of triangular fuzzy numbers could be served as a measurement of similarity between two triangular cloud models. However, it has been found that existing methods for describing the similarity of triangular cloud models has the drawbacks that can't make full use of utilizing graphic information and assigning inappropriate weights to distances through research and analysis. The method proposed in this literature has adjusted the weight of distance similarity aim to ensure that the characteristic information of the model will not be covered. In addition, the proposed method provides a measurement to describe geometric shape bases on the expected curve included angle of the triangular cloud model. On the foundation of taking abundantly the similarity of distance and shape into account, we provide the measurement to achieve better accuracy. The feasibility and effectiveness of the proposed method have been demonstrated through the time series of KNN algorithm. © 2023 The authors and IOS Press.

关键词： Cloud computing

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Transverse Velocity Field Measurements in High-resolution Solar Images Based on Deep Learning

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Research in Astronomy and Astrophysics 2023年第6期23卷 86-97页

作者： Zhen-Hong Shang Si-Yu Mu Kai-Fan Ji Zhen-Ping Qiang Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and TechnologyKunming 650500China Yunnan Observatories Chinese Academy of SciencesKunming 650216China College of Big Data and Intelligent Engineering Southwest Forestry UniversityKunming 650224China

To address the problem of the low accuracy of transverse velocity field measurements for small targets in highresolution solar images,we proposed a novel velocity field measurement method for high-resolution solar images based on *** method transforms the transverse velocity field measurements into an optical flow field prediction *** evaluated the performance of the proposed method using the Hαand TiO data sets obtained from New Vacuum Solar Telescope *** experimental results show that our method effectively predicts the optical flow of small targets in images compared with several typical machine-and deeplearning *** the Hαdata set,the proposed method improves the image structure similarity from 0.9182 to0.9587 and reduces the mean of residuals from 24.9931 to 15.2818;on the TiO data set,the proposed method improves the image structure similarity from 0.9289 to 0.9628 and reduces the mean of residuals from 25.9908 *** optical flow predicted using the proposed method can provide accurate data for the atmospheric motion information of solar *** code implementing the proposed method is available on https://***/lygmsy123/transverse-velocity-field-measurement.

关键词： methods:data analysis techniques:image processing Sun:fundamental parameters

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