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SSRN 2024年

作者： Hu, Jiyuan Zhou, Zheng Wang, Jiabei Qin, Fen Wang, Jiayao Zhang, Rong Wu, Wenhao Wang, Lijun College of Geography and Environmental Science Henan University Kaifeng475004 China Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions Henan University Ministry of Education Kaifeng475004 China Henan Water Conservancy Survey. LTD Zhengzhou450008 China Henan Industrial Technology Academy of Spatial-Temporal Big Data Henan University Zhengzhou450046 China Hunan Province Key Laboratory of Coal Resources Clean-utilization and Mine Environment Protection Hunan University of Science and Technology Xiangtan411201 China

Aquifers supporting irrigated agriculture in Henan Plain in China (HNP) are under immense stress due to water scarcity and extensive. To assist in establishing a crop planting structure in this region that aligns more with the capacity of water resources, we have developed a refined methodology for estimating Groundwater Storage Anomalies (GWSA). This method integrates Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) inversion, Gravity Recovery and Climate Experiment/Global Land data Assimilation System (GRACE/GLDAS) modelling, and hydraulic head measurements. The time-series comparison of wells and overall statistics verifies the reliability of both MT-InSAR- and GRACE/GLDAS-derived GWSA, exhibiting a mean R-square of 0.8288 and an RMSE of 7.5 cm. The corrected and integrated GWSA significantly refines the regular grid of ~27 km from GRACE to within 1 km in deep confined aquifers and effectively constrains uncertainties in the GRACE solution, probably caused by spatial leakages in the mass field. Furthermore, the gravity analysis of GWSA in HNP reveals that the central regions in HNP between the Yellow River and the Huaihe River suffer the most severe regional groundwater depletion. The depletion center has shifted to the northwest by 87 km, showing a trend of easing in the north-south direction and expanding in the east-west direction. © 2024, The Authors. All rights reserved.

关键词： Aquifers

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An optimal transport approach for selecting a representative subsample with application in efficient kernel density estimation

arXiv

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arXiv 2022年

作者： Zhang, Jingyi Meng, Cheng Yu, Jun Zhang, Mengrui Zhong, Wenxuan Ma, Ping Center for Statistical Science Tsinghua University China Center for Applied Statistics Institute of Statistics and Big Data Renmin University of China China School of Mathematics and Statistics Beijing Institute of Technology China Department of Statistics University of Georgia United States

Subsampling methods aim to select a subsample as a surrogate for the observed sample. Such methods have been used pervasively in large-scale data analytics, active learning, and privacy-preserving analysis in recent decades. Instead of model-based methods, in this paper, we study model-free subsampling methods, which aim to identify a subsample that is not confined by model assumptions. Existing model-free subsampling methods are usually built upon clustering techniques or kernel tricks. Most of these methods suffer from either a large computational burden or a theoretical weakness. In particular, the theoretical weakness is that the empirical distribution of the selected subsample may not necessarily converge to the population distribution. Such computational and theoretical limitations hinder the broad applicability of model-free subsampling methods in practice. We propose a novel model-free subsampling method by utilizing optimal transport techniques. Moreover, we develop an efficient subsampling algorithm that is adaptive to the unknown probability density function. Theoretically, we show the selected subsample can be used for efficient density estimation by deriving the convergence rate for the proposed subsample kernel density estimator. We also provide the optimal bandwidth for the proposed estimator. Numerical studies on synthetic and real-world datasets demonstrate the performance of the proposed method is superior. Copyright © 2022, The Authors. All rights reserved.

关键词： Density functional theory

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Temporal Convolution Network Based on Attention Mechanism for Well Production Prediction

SSRN

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SSRN 2022年

作者： Zhen, Yan Fang, Junyi Zhao, Xiaoming Ge, Jiawang Xiao, Yifei School of Earth Science and Technology Southwest Petroleum University Sichuan Chengdu China Natural Gas Geology Key Laboratory of Sichuan Province Southwest Petroleum University Sichuan Chengdu China Spatial Information Technology and Big Data Mining Research Center Sichuan Chengdu China

Well production forecasting has a very important guiding significance for oilfield production and management. The traditional BP neural network is difficult to deal with the data with time continuity, and the recurrent neural network (RNN) has some shortcomings such as vanishing gradients and exploding gradients. In order to avoid these problems and further improve the accuracy of neural network models in well production prediction. In this study, a prediction model combining temporal convolutional network (TCN) and attention mechanism is proposed. TCN is able to obtain more sensory fields to solve longer time series with fewer network layers by causally expanding convolution. And the residual connectivity in the model structure enables the model to transfer information across layers to solve the problem of network degradation caused by overly deep networks. The attention mechanism can reinforce the influence of important features on the prediction results to improve the prediction accuracy of the model. To evaluate the effectiveness of the model, the TCN-attention model was used to forecast the oil production of two producing wells, and the prediction results were compared with those of LSTM, TCN, RNN and ARIMA. The results showed that TCN-Attention outperformed the other models in the prediction of the two wells in all evaluation metrics. The method proposed in this paper has excellent data fitting ability, can provide accurate prediction results, and have a certain application value in oilfield production. © 2022, The Authors. All rights reserved.

关键词： Time series

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An identity-based anonymous signcryption for vehicular ad hoc networks 2nd

An identity-based anonymous signcryption for vehicular ad ho...

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2nd International Cognitive Cities Conference, IC3 2019

作者： Lu, Yong Yang, Huijie School of Management Science and Engineering Nanjing University of Information Science and Technology Nanjing China Guizhou Provincial Key Laboratory of Public Big Data Guizhou University Guiyang China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China Guangdong Provincial Key Laboratory of Data Security and Privacy Protection Guangzhou China

ISBN: (纸本)9789811561122

The vehicular ad hoc networks (VANETs) can share information of vehicles’ location, speed and route within a certain range by interconnecting vehicles, roadside units (RSU) and the cloud. As people’s daily traffic tools, vehicles reflect users’ daily routines and other privacy. Therefore, privacy protection is very critical in VANETs. However, in the existing schemes, a user’s temporary identity (TID) is generated by the user himself, which is inconvenient to be managed and can be easily forged. The reputation values of users also need to be considered. In this paper, an identity based signcryption scheme for VANETs is proposed. In the novel scheme, TID is generated by key generation center (KCG) and a personal reputation system is produced. The security analysis of the scheme shows that our scheme is practical. © Springer Nature Singapore Pte Ltd 2020.

关键词： Vehicular ad hoc networks

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Semi-autoregressive transformer for image captioning

arXiv

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arXiv 2021年

作者： Zhou, Yuanen Zhang, Yong Hu, Zhenzhen Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology Tencent AI Lab Key Laboratory of Knowledge Engineering with Big Data Ministry of Education

Current state-of-the-art image captioning models adopt autoregressive decoders, i.e. they generate each word by conditioning on previously generated words, which leads to heavy latency during inference. To tackle this issue, non-autoregressive image captioning models have recently been proposed to significantly accelerate the speed of inference by generating all words in parallel. However, these non-autoregressive models inevitably suffer from large generation quality degradation since they remove words dependence excessively. To make a better trade-off between speed and quality, we introduce a semi-autoregressive model for image captioning (dubbed as SATIC), which keeps the autoregressive property in global but generates words parallelly in local. Based on Transformer, there are only a few modifications needed to implement SATIC. Experimental results on the MSCOCO image captioning benchmark show that SATIC can achieve a good trade-off without bells and whistles. Code is available at https://***/YuanEZhou/satic. Copyright © 2021, The Authors. All rights reserved.

关键词： Economic and social effects

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Energy consumption of high-rise double skin façade buildings, a machine learning analysis

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Journal of Building Engineering 2024年 89卷

作者： Rezaie, Maedeh Kariminia, Shahab Band, Shahab S. Ameri, Rasoul Farokhi, Maryam Pai, Hao-Ting Gocer, Ozgur Rismanchi, Behzad Shooshtarian, Salman Department of Architecture Advancement in Architecture and Urban Planning Research Center Najafabad Branch Islamic Azad University Najafabad Iran Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Taiwan Department of Information Management National Yunlin University of Science and Technology Taiwan Bachelor Program of Big Data Applications in Business National Pingtung University Pingtung Taiwan School of Architecture Design and Planning The University of Sydney SydneyNSW Australia Renewable Energy and Energy Efficiency Group Department of Infrastructure Engineering Faculty of Engineering and Information Technology The University of Melbourne Australia School of Property Construction and Project Management RMIT University Melbourne Australia

Most energy exchanges take place through the building skin. The skin characteristics play a decisive role in the extent of these exchanges, but they are somewhat more varied in the double skin façade (DSF). Among these characteristics, cavity segmentation has a noticeable effect on the implementation of the DSF in different directions during the hot and cold seasons. The aim of this study was to investigate the role of DSF segmentation in energy consumption and natural ventilation of high-rise buildings in hot and dry climates. This study used DesignBuilder software to study sixty-four segmentation component scenarios in an eight-story residential building in Isfahan, Iran. Moreover, a proposed hybrid model utilizing the hybridization of the Hunger Game Search and Gradient Boosting (HGS-GB) algorithm was employed to estimate energy consumption in various scenarios involving lighting, heating, cooling, and total scenarios. The available outcomes revealed that the HGS-GB model had a better performance in comparison with other individual models, such as Gradient Boosting (GB), Random Forest (RF), and K-nearest neighbors (KNN). The R2 values for lighting, heating, cooling, and total energy estimation were 0.9993, 0.9958, 0.9991, and 0.9922, respectively. The findings of this study suggest the significance of DSF segmentation in energy consumption and natural ventilation in high-rise buildings in hot and dry climates. © 2024 Elsevier Ltd

关键词： Forestry

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Devil is in Channels: Contrastive Single Domain Generalization for Medical Image Segmentation

arXiv

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arXiv 2023年

作者： Hu, Shishuai Liao, Zehui Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi'An710072 China Ningbo Institute Northwestern Polytechnical University Ningbo315048 China Research and Development Institute Northwestern Polytechnical University in Shenzhen Shenzhen518057 China

Deep learning-based medical image segmentation models suffer from performance degradation when deployed to a new healthcare center. To address this issue, unsupervised domain adaptation and multi-source domain generalization methods have been proposed, which, however, are less favorable for clinical practice due to the cost of acquiring target-domain data and the privacy concerns associated with redistributing the data from multiple source domains. In this paper, we propose a Channel-level Contrastive Single Domain Generalization (C2SDG) model for medical image segmentation. In C2SDG, the shallower features of each image and its style-augmented counterpart are extracted and used for contrastive training, resulting in the disentangled style representations and structure representations. The segmentation is performed based solely on the structure representations. Our method is novel in the contrastive perspective that enables channel-wise feature disentanglement using a single source domain. We evaluated C2SDG against six SDG methods on a multi-domain joint optic cup and optic disc segmentation benchmark. Our results suggest the effectiveness of each module in C2SDG and also indicate that C2SDG outperforms the baseline and all competing methods with a large margin. The code will be available at https://***/ShishuaiHu/CCSDG. © 2023, CC BY-NC-ND.

关键词： Deep learning

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Study of deep learning techniques for medical image analysis: A review

引用

Materials Today: Proceedings 2022年 56卷 209-214页

作者： Ayush Singhal Manu Phogat Deepak Kumar Ajay Kumar Mamta Dahiya Virendra Kumar Shrivastava Department of Computer Science Engineering Faculty of Engineering and Technology Shree Guru Gobind Singh Tricentenary University Gurugram 122505 Haryana India Department of Mechanical Engineering Faculty of Engineering & Technology Shree Guru Gobind Singh Tricentenary University Gurugram 122505 Haryana India Department of Big Data Analytics Adani Institute of Digital Technology Management Gandhinagar Gujarat 382423 India

Now, there is a lot of research going on in the field of medical image analysis by using deep convolutional networks. Deep learning uses various models to extract the information from the images provided to deep learning model. The deep learning is now widely used in the field of medical to detect and diagnose the disease and after diagnosing classifying it into particular category of the disease. The most widely model used for medical image analysis is Convolutional neural network. So, this review paper focusses on how deep learning uses deep networks to detect the disease by retrieving or extracting the information from the images provided to the network and also give information about the clinical applications in the medical fields and the limitations of deep learning in image analysis process is also highlighted.

关键词： Deep learning Medical image analysis Convolutional neural network

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Hardware-Supported Remote Persistence for Distributed Persistent Memory

Hardware-Supported Remote Persistence for Distributed Persis...

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Supercomputing Conference

作者： Zhuohui Duan Haodi Lu Haikun Liu Xiaofei Liao Hai Jin Yu Zhang Song Wu National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computing Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781450384421

ISBN: (纸本)9781665483902

The advent of Persistent Memory (PM) necessitates an evolution of Remote Direct Memory Access (RDMA) technologies for supporting remote data persistence. Previous software-based solutions require remote CPU intervention and postpone the visibility of remote persistence. In this paper, we design several hardware-supported RDMA primitives to flush data from the volatile cache of RDMA Network Interface Cards (RNICs) to the PM. We also propose durable RPCs based on the proposed RDMA Flush primitives to support remote data persistence and fast failure recovery. We emulate the performance of RDMA Flush primitives through other RDMA prim-itives, and compare our proposals with several state-of-the-art RPCs in a real testbed equipped with PM and InfiniBand networks. Experimental results show that our proposals can improve the throughput of RPCs by up to 90%, and reduce the 99th percentile latency by up to 49%. The experimental studies also provide instructive guidelines for designing RDMA-based distributed PM systems.

关键词： High performance computing Throughput Proposals Network interfaces Guidelines

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Grace: An Efficient Parallel SPARQL Query System over Large-Scale RDF data

Grace: An Efficient Parallel SPARQL Query System over Large-...

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Xiang Kang Yuying Zhao Pingpeng Yuan Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

As a markup language for describing web resources, RDF is often used to represent graph data. SPARQL is a standard query language for RDF data, which is convenient in querying RDF. As RDF data grows rapidly, how to deal with complex queries over large-scale data in a reasonable time still remains many challenges. The existing RDF query systems often fail to respond within a reasonable time when dealing with complex SPARQL queries. Therefore, we propose an efficient parallel SPARQL query system and novel in three aspects. First, the proposed design provides a dynamic selectivity estimation strategy and generates an optimal query plan for parallel query processing. Second, the new design proposes a parallel processing model to maximize the parallelism of the system. Finally, chunk-based task distribution strategy is implemented to assist the parallel processing model. Based on the proposed design, we implement an efficient parallel query system (Grace). Extensive experiments on LUBM and BTC benchmarks show that Grace outperforms RDF-3X, Virtuoso, TripleBit and achieves a good performance of scalability on the number of threads.

关键词： Distribution strategy Scalability Query processing Markup languages Instruction sets Estimation Parallel processing

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