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arXiv 2021年

作者： Zhang, Xianzhi Zhou, Yipeng Wu, Di Hu, Miao Xi Zheng, James Chen, Min Guo, Song Department of Computer Science Sun Yat-sen University Guangzhou510006 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China Peng Cheng Laboratory Shenzhen518000 China Department of Computing FSE Macquarie University 2122 Australia School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computing The Polytechnic University of Hong Kong Hong Kong

It is always a challenging problem to deliver a huge volume of videos over the Internet. To meet the high bandwidth and stringent playback demand, one feasible solution is to cache video contents on edge servers based on predicted video popularity. Traditional caching algorithms (e.g., LRU, LFU) are too simple to capture the dynamics of video popularity, especially long-tailed videos. Recent learning-driven caching algorithms (e.g., DeepCache) show promising performance, however, such black-box approaches are lack of explainability and interpretability. Moreover, the parameter tuning requires a large number of historical records, which are difficult to obtain for videos with low popularity. In this paper, we optimize video caching at the edge using a white-box approach, which is highly efficient and also completely explainable. To accurately capture the evolution of video popularity, we develop a mathematical model called HRS model, which is the combination of multiple point processes, including Hawkes’ self-exciting, reactive and self-correcting processes. The key advantage of the HRS model is its explainability, and much less number of model parameters. In addition, all its model parameters can be learned automatically through maximizing the Log-likelihood function constructed by past video request events. Next, we further design an online HRS-based video caching algorithm. To verify its effectiveness, we conduct a series of experiments using real video traces collected from Tencent Video, one of the largest online video providers in China. Experiment results demonstrate that our proposed algorithm outperforms the state-of-the-art algorithms, with 12.3% improvement on average in terms of cache hit rate under realistic settings. Copyright © 2021, The Authors. All rights reserved.

关键词： Gradient methods

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Surface soil moisture inversion based on DEFS-GA-BP model using Sentinel-1/2 remote sensing data 5

Surface soil moisture inversion based on DEFS-GA-BP model us...

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IET International Radar Conference 2020, IET IRC 2020

作者： Zhang, Chenyang Min, Lin Zhao, Jianhui College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng475004 China Network Information Center Office Henan University Kaifeng475004 China

ISBN: (纸本)9781839535635

The major work is to retrieve the surface soil moisture under crop cover in this paper. This paper proposes an improved GA-BP model called as DEFS-GA-BP model. This model is proposed to retrieve the surface soil moisture distribution using Sentinel-1 and Sentinel-2 remote sensing data. After extracting nine polarization features and one roughness parameter from Sentinel-1 data, five vegetation indexes from Sentinel-2 data. The differential evolution feature selection (DEFS) algorithm is applied to select the optimal feature subset. By using the genetic algorithm (GA) to optimize the node weights, the back propagation (BP) neural network is trained using the optimal feature subset and then used to generate the surface soil moisture distribution. The results of inversion algorithm indicate that the proposed DEFS-GA-BP model has a better inversion precision than GA-BP model, and demonstrates its application potential in surface soil moisture inversion. © 2020 Institution of Engineering and Technology. All rights reserved.

关键词： Genetic algorithms

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Multi-Grained Selection and Fusion for Fine-Grained Image Representation

Multi-Grained Selection and Fusion for Fine-Grained Image Re...

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International Joint Conference on Neural Networks (IJCNN)

作者： Jianrong Jiang Hongxing Wang Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education China School of Big Data & Software Engineering Chongqing University Chongqing China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

How to learn a good fine-grained image representation is a key problem for fine-grained tasks. Most previous supervised methods suffer from insufficient training data, which require laborious annotations of fine-grained objects. In this paper, we propose an annotation-free method for fine-grained image representation, dubbed Multi-Grained Selection and Fusion (MGSF). The proposed MGSF extracts two types of visual features, i.e., fine-grained discriminative features that highlight informative convolutional parts by spatial selection and channel selection, and coarse-grained scene-level features that provide context information for fine-grained objects. Extensive experiments in fine-grained image retrieval demonstrate the superiority of our proposed representation compared with the state-of-the-art approaches on several fine-grained datasets.

关键词： Feature extraction Image retrieval Image representation Semantics Visualization Image classification Task analysis

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Design and vibration performance study of spherical shell high-intensity focused ultrasonic transducer in longitudinal-radial composite vibration

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Journal of Applied Physics 2025年第22期137卷

作者： Zhang, Haidao Xu, Long Li, Jun Li, Fengming Zhao, Peng Wang, Zhaohuan Zhang, Tianteng Cai, Yue Chen, Chen Key Laboratory of Functional Materials and Devices for Informatics of Anhui Higher Education Institutes Fuyang Normal University Fuyang236037 China College of Sciences China Jiliang University Hangzhou310018 China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang University Hangzhou310018 China College of Metrology Measurement and Instrument China Jiliang University Hangzhou310018 China

This study presents the development and analysis of a high-intensity focused ultrasonic transducer featuring a spherical shell in longitudinal-radial composite vibration. The transducer consists of a longitudinal sandwich piezoelectric converter, a stepped horn, and a metal spherical shell. The longitudinal sandwich piezoelectric converter, in conjunction with the stepped horn, generates longitudinal vibration that excites radial vibration within the metal spherical shell, thereby enabling high-intensity focused ultrasound inside the metal spherical shell. The electromechanical equivalent circuits for both the metal spherical shell and the composite transducer were derived to provide a theoretical foundation for the analysis. Numerical simulations were utilized to investigate the vibration and ultrasound field features of the transducer, followed by the design and fabrication of a prototype. Experimental measurements of the impedance characteristics and radiation field were performed, with results demonstrating good agreement with both analytical predictions and numerical simulations. This validates the potential of the transducer for applications requiring high-intensity ultrasound with a larger radiating surface. © 2025 Author(s).

关键词： Numerical models

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Sharpening semantic gradient in a planarized sentence representation

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Neural networks : the official journal of the International Neural Network Society 2025年 190卷 107687页

作者： Caiwei Yang Yanping Chen Shuai Yu Bo Dong Jiwei Qin Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Guizhou University Guiyang 550025 Guizhou PR China State Key Laboratory of Public Big Data Guizhou university Guiyang 550025 Guizhou PR China. Electronic address: gs.cwyang21@***. State Key Laboratory of Public Big Data Guizhou university Guiyang 550025 Guizhou PR China. Electronic address: ypench@***. State Key Laboratory of Public Big Data Guizhou university Guiyang 550025 Guizhou PR China. Electronic address: gs.syu21@***. Department of Computer Science and Technology Xi'an Jiaotong University Xi'an 710049 Shanxi PR China. Electronic address: dong.bo@***. College of Information Science and Engineering Xinjiang University Urumqi 830046 Xinjiang PR China. Electronic address: jwqin_xju@***.

Mapping a sentence into a two-dimensional representation has the advantage of unfolding nested semantic structures in a sentence and encoding the interaction between tokens. In the planarized sentence representation, neighboring elements denote overlapped linguistic units in a sentence. An important phenomenon is that the semantic information of a true linguistic unit may penetrate neighboring elements, which blurs the semantic edge of a linguistic unit and disturbs the planarized sentence representation. Therefore, sharpening the semantic gradient helps aggravate semantic information from neighborhoods and depressing noises in neighboring elements. This paper reveals the mechanism of sharpening semantic gradient in the planarized sentence representation. Our method is evaluated on six evaluation datasets. The results show impressive improvement on three information extraction tasks. The success indicates that representing and processing sentences in a two-dimensional representation has a great potential to decode the sentential semantic structure and support sentence-level information extraction. Our code to implement the model is available at: https://***/caiwyang/Semantic_Gradient.

关键词： Information extraction Planarized sentence representation Semantic gradient

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Interpreting dense retrieval as mixture of topics

arXiv

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arXiv 2021年

作者： Zhan, Jingtao Mao, Jiaxin Liu, Yiqun Guo, Jiafeng Zhang, Min Ma, Shaoping Department of Computer Science and Technology Institute for Artificial Intelligence Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Beijing Key Laboratory of Big Data Management and Analysis Methods Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

Dense Retrieval (DR) reaches state-of-the-art results in first-stage retrieval, but little is known about the mechanisms that contribute to its success. Therefore, in this work, we conduct an interpretation study of recently proposed DR models. Specifically, we first discretize the embeddings output by the document and query encoders. Based on the discrete representations, we analyze the attribution of input tokens. Both qualitative and quantitative experiments are carried out on public test collections. Results suggest that DR models pay attention to different aspects of input and extract various high-level topic representations. Therefore, we can regard the representations learned by DR models as a mixture of high-level topics. Copyright © 2021, The Authors. All rights reserved.

关键词： Mixtures

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Improving Breast Cancer Diagnosis in Ultrasound Images Using Deep Learning with Feature Fusion and Attention Mechanism

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Academic Radiology 2025年

作者： Asif, Sohaib Yan, Yuqi Feng, Bojian Wang, Meiling Zheng, Yuxin Jiang, Tian Fu, Ruyi Yao, Jincao Lv, Lujiao Song, Mei Sui, Lin Yin, Zheng Wang, Vicky Yang Xu, Dong Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital) Zhejiang Taizhou 317502 China Center of Intelligent Diagnosis and Therapy (Taizhou) Hangzhou Institute of Medicine (HIM) Chinese Academy of Sciences Zhejiang Taizhou 317502 China Wenling Institute of Big Data and Artificial Intelligence in Medicine Zhejiang Taizhou 317502 China Department of Diagnostic Ultrasound Imaging & Interventional Therapy Zhejiang Cancer Hospital Hangzhou Institute of Medicine (HIM) Chinese Academy of Sciences Zhejiang Hangzhou 310022 China Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology Hangzhou 310022 China Second Clinical College Zhejiang University of Traditional Chinese Medicine Hangzhou China Hangzhou Fuyang Hospital of Orthopedics of Traditional Chinese Medicine Special Inspection Department Zhejiang China Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province Hangzhou 310022 China

Rationale and Objectives: Early detection of malignant lesions in ultrasound images is crucial for effective cancer diagnosis and treatment. While traditional methods rely on radiologists, deep learning models can improve accuracy, reduce errors, and enhance efficiency. This study explores the application of a deep learning model for classifying benign and malignant lesions, focusing on its performance and interpretability. Materials and Methods: In this study, we proposed a feature fusion-based deep learning model for classifying benign and malignant lesions in ultrasound images. The model leverages advanced architectures such as MobileNetV2 and DenseNet121, enhanced with feature fusion and attention mechanisms to boost classification accuracy. The clinical dataset comprises 2171 images collected from 1758 patients between December 2020 and May 2024. Additionally, we utilized the publicly available BUSI dataset, consisting of 780 images from female patients aged 25 to 75, collected in 2018. To enhance interpretability, we applied Grad-CAM, Saliency Maps, and shapley additive explanations (SHAP) techniques to explain the model's decision-making. A comparative analysis with radiologists of varying expertise levels is also conducted. Results: The proposed model exhibited the highest performance, achieving an AUC of 0.9320 on our private dataset and an area under the curve (AUC) of 0.9834 on the public dataset, significantly outperforming traditional deep convolutional neural network models. It also exceeded the diagnostic performance of radiologists, showcasing its potential as a reliable tool for medical image classification. The model's success can be attributed to its incorporation of advanced architectures, feature fusion, and attention mechanisms. The model's decision-making process was further clarified using interpretability techniques like Grad-CAM, Saliency Maps, and SHAP, offering insights into its ability to focus on relevant image features for accurate cla

关键词： Attention mechanisms Deep learning Feature fusion Medical diagnostics Ultrasound imaging

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Research on Accurate Model of Lithium Battery

Research on Accurate Model of Lithium Battery

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第32届中国控制与决策会议

作者： Yongxue Ye Hongyan Ma Jingjian Yang Beijing University of Civil Engineering and Architecture Beijing Key Laboratory of Intelligent Processing for Building Big Data College of Electrical and Information EngineeringBeijing University of Civil Engineering and Architecture

In order to build accurately the external characteristic curve of lithium-ion batteries,this paper improve existing lithium-ion battery models,and verify the improved lithium-ion battery model in *** this paper,for reflecting the accuracy of the improved external characteristics of the lithium-ion battery model,the external characteristic curves of lithium-ion battery model simulation before and after improvement are compared under different temperature and discharge current *** comparison results show that under different operating conditions,the improved model can more accurately reflect the experimental conditions of lithium battery discharge in the concave function discharge area than before the improvement.

关键词： lithium battery mathematical model research Simulink modeling data comparison

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Learning discrete representations via constrained clustering for effective and efficient dense retrieval

arXiv

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arXiv 2021年

Dense Retrieval (DR) has achieved state-of-the-art first-stage ranking effectiveness. However, the efficiency of most existing DR models is limited by the large memory cost of storing dense vectors and the time-consuming nearest neighbor search (NNS) in vector space. Therefore, we present RepCONC, a novel retrieval model that learns discrete Representations via CONstrained Clustering. RepCONC jointly trains dual-encoders and the Product Quantization (PQ) method to learn discrete document representations and enables fast approximate NNS with compact indexes. It models quantization as a constrained clustering process, which requires the document embeddings to be uniformly clustered around the quantization centroids and supports end-to-end optimization of the quantization method and dual-encoders. We theoretically demonstrate the importance of the uniform clustering constraint in RepCONC and derive an efficient approximate solution for constrained clustering by reducing it to an instance of the optimal transport problem. Besides constrained clustering, RepCONC further adopts a vector-based inverted file system (IVF) to support highly efficient vector search on CPUs. Extensive experiments on two popular ad-hoc retrieval benchmarks show that RepCONC achieves better ranking effectiveness than competitive vector quantization baselines under different compression ratio settings. It also substantially outperforms a wide range of existing retrieval models in terms of retrieval effectiveness, memory efficiency, and time efficiency. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal encoding

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Scheduling Rechargeable UAVs for Long Time Barrier Coverage

Scheduling Rechargeable UAVs for Long Time Barrier Coverage

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Zhouqing Han Xiaojun Zhu Lijie Xu Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications China

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

We consider barrier coverage applications where a set of UAVs are deployed to monitor whether intruders pass through a line, i.e., the barrier. Due to limited energy supply of UAVs, a charging pile is used to recharge UAVs. The problem is to place UAVs on top of the barrier and schedule them to the charging pile such that the barrier is seamlessly covered and the total number of UAVs is minimized. We decompose the problem into subproblems by dividing the barrier into disjoint subsegments and covering each subsegment independently. We prove a theoretical lower bound on the minimum number of UAVs required to cover the barrier forever. We then propose two scheduling strategies. In the first strategy, only fully recharged backup UAVs will be scheduled to take over UAVs running out of energy. If there are enough UAVs, this strategy can cover the barrier forever. The second strategy is proposed to deal with the situation that the number of UAVs is insufficient. Under the strategy, if no backup UAV is fully recharged, the one with the most battery energy will be selected to take over the monitoring task. When the number of UAVs is insufficient, the barrier can still be covered for a long time. We analytically derive the number of UAVs required by the first strategy, and the monitoring duration of the second strategy in case of insufficient UAVs. Simulations verify the effectiveness of the proposed solutions.

关键词： Schedules Conferences Force Batteries Task analysis Monitoring

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