检索结果-内蒙古大学图书馆

SSrN 2023年

作者： Jo, Su In Kim, Suree Lim, Jung Mi rhee, Sue Goo Jeong, Bo-Gyeong Cha, Sun-Shin Chang, Jae-Byum Kang, dongmin Department of Life Science Ewha Womans University Seoul Korea Republic of Fluorescence Core Imaging Center Bioimaging Data Curation Center Ewha Womans University Seoul Korea Republic of Biochemistry and Biophysics Center NHLBI National Institutes of Health Bethesda MD20892 United States R&D Division Todd Pharm Co. Ltd. Seoul Korea Republic of Department of Chemistry and Nanoscience Ewha Womans University Seoul Korea Republic of Department of Materials Science and Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of

Phosphatidylinositol 4,5-bisphosphate (PtdIns[4,5]P2) is implicated in various processes, including hormone-induced signal transduction, endocytosis, and exocytosis in the plasma membrane. However, how H2O2 accumulation regulates the levels of PtdIns(4,5)P2 in the plasma membrane in cells stimulated with epidermal growth factors (EGFs) is not known. We show that a plasma membrane PtdIns(4,5)P2-degrading enzyme, synaptojanin (Synj) phosphatase, is inactivated through oxidation by H2O2. Intriguingly, H2O2 inhibits the 4-phosphatase activity of Synj but not the 5-phosphatase activity. In EGF-activated cells, the oxidation of Synj dual phosphatase is required for the transient increase in the plasma membrane levels of PtdIns(4)P, which can control EGF receptor-mediated endocytosis. These results indicate that intracellular H2O2 molecules act as signaling mediators to fine-tune endocytosis by controlling the stability of plasma membrane PtdIns(4)P, an intermediate product of Synj phosphoinositide dual phosphatase. © 2023, The Authors. All rights reserved.

关键词： Cell membranes

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Semi-supervised Learning to rank with Uncertain data 16th

Semi-supervised Learning to Rank with Uncertain Data

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16th Web Information Systems and Applications Conference, WISA 2019

作者： Zhang, Xin Zhao, ZhongQi Liu, ChengHou Zhang, Chen Cheng, Zhi Anhui Provincial Engineering Laboratory of Big Data Technology Application for Urban Infrastructure Technology Department of Computer Science and Technology Hefei University Hefei China Suzhou Vocational University Suzhou China Changan Automobile R&D Center Hefei China

ISBN: (纸本)9783030309510

Although, semi-supervised learning with a small amount of labeled data can be utilized to improve the effectiveness of learning to rank in information retrieval, the pseudo labels created by semi-supervised learning may not reliable. The uncertain data nearby the boundaries of relevant and irrelevant documents for a given query has a significant impact on the effectiveness of learning to rank. Therefore, how to utilize the uncertain data to bring benefit for semi-supervised learning to rank is an excellent challenge. In this paper, we propose a semi-supervised learning to rank algorithm, that builds a query-quality predictor by utilizing uncertain data. Specially, this approach selects the training queries following the empirical observation that the relevant documents of high quality training queries are highly coherent. This approach learns from the uncertain data to predict the retrieval performance gain of a given training query by making use of query features. Then the pseudo labels for learning to rank are aggregated iteratively by semi-supervised learning with the selected queries. Experimental results on the standard LETOr dataset show that our proposed approaches outperform the strong baselines. © 2019, Springer Nature Switzerland AG.

关键词： Iterative methods

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SynFacePAd 2023: Competition on Face Presentation Attack detection Based on Privacy-aware Synthetic Training data

arXiv

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arXiv 2023年

作者： Fang, Meiling Huber, Marco Fierrez, Julian ramachandra, raghavendra damer, Naser Alkhaddour, Alhasan Kasantcev, Maksim Pryadchenko, Vasiliy Yang, Ziyuan Huangfu, Huijie Chen, Yingyu Zhang, Yi Pan, Yuchen Jiang, Junjun Liu, Xianming Sun, Xianyun Wang, Caiyong Liu, Xingyu Chang, Zhaohua Zhao, Guangzhe Tapia, Juan Gonzalez-Soler, Lazaro Aravena, Carlos Schulz, daniel Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain Norway ID R&D Inc. New York United States School of Cyber Science and Engineering Sichuan University Chengdu China School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture China Biometrics and Security Research Group Hochschule Darmstadt Darmstadt Germany I+D Vision Center Santiago Chile

This paper presents a summary of the Competition on Face Presentation Attack detection Based on Privacy-aware Synthetic Training data (SynFacePAd 2023) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition attracted a total of 8 participating teams with valid submissions from academia and industry. The competition aimed to motivate and attract solutions that target detecting face presentation attacks while considering synthetic-based training data motivated by privacy, legal and ethical concerns associated with personal data. To achieve that, the training data used by the participants was limited to synthetic data provided by the organizers. The submitted solutions presented innovations and novel approaches that led to outperforming the considered baseline in the investigated benchmarks. © 2023, CC BY-NC-SA.

关键词： Competition

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Fire-Image-denseNet (FIdN) for predicting wildfire burnt area using remote sensing data

arXiv

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arXiv 2024年

作者： Pang, Bo Cheng, Sibo Huang, Yuhan Jin, Yufang Guo, Yike Prentice, I. Colin Harrison, Sandy P. Arcucci, rossella Department of Earth Science and Engineering Imperial College London London United Kingdom Data Science Institute Department of Computing Imperial College London London United Kingdom CEREA École des Ponts and EDF R&D Institut Polytechnique de Paris Île-de-France France Department of Land Air and Water Resources University of California DavisCA United States Georgina Mace Centre for the Living Planet Department of Life Sciences Imperial College London London United Kingdom Geography & Environmental Science University of Reading Reading United Kingdom Department of Computer Science and Engineering Hong Kong university of science and technology Hong Kong Computer Network Information Center Chinese Academy of Sciences Beijing China

Predicting the extent of massive wildfires once ignited is essential to reduce the subsequent socioeconomic losses and environmental damage, but challenging because of the complexity of fire behaviour. Existing physicsbased models are limited in predicting large or long-duration wildfire events. Here, we develop a deep-learning-based predictive model, Fire-Image-denseNet (FIdN), that uses spatial features derived from both near real-time and reanalysis data on the environmental and meteorological drivers of wildfire. We trained and tested this model using more than 300 individual wildfires that occurred between 2012 and 2019 in the western US. In contrast to existing models, the performance of FIdN does not degrade with fire size or duration. Furthermore, it predicts final burnt area accurately even in very heterogeneous landscapes in terms of fuel density and flammability. The FIdN model showed higher accuracy, with a mean squared error (MSE) about 82% and 67% lower than those of the predictive models based on cellular automata (CA) and the minimum travel time (MTT) approaches, respectively. Its structural similarity index measure (SSIM) averages 97%, outperforming the CA and FlamMap MTT models by 6% and 2%, respectively. Additionally, FIdN is approximately three orders of magnitude faster than both CA and MTT models. The enhanced computational efficiency and accuracy advancements offer vital insights for strategic planning and resource allocation for firefighting operations. © 2024, CC BY.

关键词： Travel time

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Geometric Optimisation of Unmanned Aerial Vehicle Trajectories in Uncertain Environments

引用

Vehicular Communications 2025年 54卷

作者： Akshya, J. Sundarrajan, M. Amutha, S. dhanaraj, rajesh Kumar Khadidos, Adil O. Khadidos, Alaa O. Selvarajan, Shitharth Department of Computational Intelligence SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India Department of Networking and Communications SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Symbiosis Institute of Computer Studies and Research (SICSR) Symbiosis International (Deemed University) Pune India Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz University Jeddah Saudi Arabia School of Built Environment Engineering and Computing Leeds Beckett University LS6 3HF Leeds United Kingdom Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India

The problem of efficient trajectory optimisation for Unmanned Aerial Vehicles (UAVS) in dynamic and constrained environments is one where energy efficiency, spatial coverage, and path smoothness need to be balanced. The existing methods, namely rrT*, A*, and dijkstra, are popular but generally heuristic and do not provide globally optimal solutions. They face significant limitations while dealing with complex geometries, dynamic obstacles, and multi-objective requirements. These challenges call for a mathematically sound framework that seamlessly integrates convex analysis and computational geometry to provide an optimal trajectory planning framework. This research work introduces a convex optimisation framework for UAV trajectory planning which unifies multiple objectives, like minimising energy consumption, maximising spatial coverage, and ensuring the smoothness of the path, into a single convex objective function. More importantly, it indicates that obstacle dynamics and uncertain environmental conditions are handled better by it, so it is relatively easier for safe and efficient navigation. Proven to converge faster and with higher precision than rrT*, A*, and dijkstra, the approach proposed here enjoys intrinsic convex properties, which ensure global optimality. Qualitative measurements show the efficiency of the proposed framework. The result is energy efficiency of 90%, with 92% coverage, 98% constraint satisfaction, and 95% path smoothness, which is 15-25% better on all metrics than traditional approaches can offer. By bridging between theory in convex optimisation and practice for solving multi-objective problems in a dynamic setting, this study provides a more robust solution for UAV trajectory planning. © 2025 The Author(s)

关键词： Computational Geometry Convex Optimisation delaunay Triangulations Spatial Coverage UAV Trajectory Voronoi diagrams

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Weakly supervised learning for textbook question answering

TechRxiv

引用

Techrxiv 2021年

作者： Ma, Jie Chai, Qi Huang, Jingyue Liu, Jun You, Yang Zheng, Qinghua National Engineering Lab for Big Data Analytics School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&D School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China The High Performance Computing for Artificial intelligence Lab Department of Computer Science National University of Singapore 117417 Singapore

Textbook Question Answering (TQA) is the task of answering diagram and non-diagram questions given large multimodal contexts consisting of abundant text and diagrams. deep text understandings and effective learning of diagram semantics are important for this task due to its specificity. In this paper, we propose a Weakly Supervised learning method for TQA (WSTQ), which regards the incompletely accurate results of essential intermediate procedures for this task as supervision to develop Text Matching (TM) and relation detection (rd) tasks and then employs the tasks to motivate itself to learn strong text comprehension and excellent diagram semantics respectively. Specifically, we apply the result of text retrieval to build positive as well as negative text pairs. In order to learn deep text understandings, we first pre-train the text understanding module of WSTQ on TM and then fine-tune it on TQA. We build positive as well as negative relation pairs by checking whether there is any overlap between the items/regions detected from diagrams using object detection. The rd task forces our method to learn the relationships between regions, which are crucial to express the diagram semantics. We train WSTQ on rd and TQA simultaneously, i.e., multitask learning, to obtain effective diagram semantics and then improve the TQA performance. Extensive experiments are carried out on CK12-QA and AI2d to verify the effectiveness of WSTQ. Experimental results show that our method achieves significant accuracy improvements of 5.02% and 4.12% on test splits of the above datasets respectively than the current state-of-the-art baseline. We have released our code on https://***/dr-majie/WSTQ. © 2021, CC BY.

关键词： Semantics

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design and Comparison of Collaborative Filtering technology for Product Suggestions in E-Commerce

Design and Comparison of Collaborative Filtering Technology ...

引用

Automation, Computing and renewable Systems (ICACrS), International Conference on

作者： P. rajasekar B. Mohanraj S. N. Padhi N. Sivakumar Lavanya J Charles Prabu V Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Information Technology Karpagam College of Engineering Coimbatore Tamil Nadu India Department of Mechanical Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science and Engineering Panimalar Engineering College Chennai City Campus Chennai Tamil Nadu India Department of Fashion Designing SRM Institute of Science and Technology CSH Kattankulathur Campus Chennai Tamil Nadu India Department of Computer Science Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (纸本)9781665460859

E-commerce websites and internet purchasing have never been more necessary. Product recommendations are a key tactic employed by online buying platforms. This study compares two different filtering algorithms that are used in product recommendationson websites like Amazon and Flipkart. reviews of electrical devices, user data, and timestamps were all gathered from Kaggle and put into a dataset. These specifics then undergo several preprocessing steps. The steps are data visualization, duplicate value analysis, missing value analysis, and datatype analysis. The exhibited data is then used to train and test the machine learning models. For this purpose, two machine learning models are developed using two different algorithms. The algorithms K-Nearest Neighbor(KNN) and Support Vector decomposition (SVd) are employed. The models are then trained to acquire used in the prediction procedure after that. The best models for the prediction process are then determined by scrutinizing the results of both models. The root Mean Square Error (rMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE) values are located and compared because the findings of the analysis weren't *** erroneous data are plotted into a graph and evaluated to further demonstrate the point. As the SVd method is discovered to be the best of the group, the algorithm's greatest and worst predictions are discovered and examined. Even the poorest model constructed for the study's predictions turned out to be superior to several other prediction algorithms used for product suggestions. This algorithm can be deployed into the backend of any e-commerce website making it more effective.

关键词： Training Support vector machines Analytical models Machine learning algorithms data visualization Machine learning Predictive models

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real-Time rotated Object detection Using Angle decoupling

Real-Time Rotated Object Detection Using Angle Decoupling

引用

Chinese Automation Congress (CAC)

作者： Zheng Wang Lei Shen Bin Li Jing Yang Fusen Yang Kun Yuan Chao Fang Yi Fanwang State Key Laboratory of Public Big Data(Guizhou University) R & D Center (Beijing Daheng Image Vision Co. Ltd.) Beijing China R&D Center (Beijing Daheng Image Vision Co. Ltd.) Beijing China Mechanical Engineering College GuiZhou University GuiYang China State Key Laboratory of Public Big Data(Guizhou University) School of Mechanical Engineering (Guizhou University) GuiYang China Beijing Daheng Image Vision Co. Ltd. Beijing China Key Laboratory of Advanced Manufactiring Technology of the Ministry of Education Guizhou University GuiYang China

ISBN: (纸本)9781665426480

real-time object detection and angle fitting of rotating objects in industrial scenes are challenging computer vision tasks at present. Current object-detection models in natural scenes cannot meet the needs of the industry for accurate angle fitting. Thus, we propose a rotating detection box representation based on angle information decoupling (rbbox), and designed rotated-YOLO (You Only Look Once) deep learning rotating object detection model for rbbox representation. First, an angle information encoding and decoding method is proposed by analyzing the problem of calculating angle error mutation in the existing angle information expression methods. Second, based on the YOLO series neural network model, the detection head and loss function are improved. Finally, the cosine similarity evaluation index was proposed to solve the problem in which intersection over union could not clearly express the accuracy of angle fitting in the object detection task in natural scenes. Experiments on industrial datasets show that the algorithm performs well on open-source industrial datasets. The average angle cosine similarity between rbbox and label box is 0.98781, and mAP@.5 is 0.995. The reasoning speed of NVIdIA A100 GPU is 76.923 fps, which can meet the needs of real-time reasoning in industry.

关键词： Industries Computational modeling Fitting Neural networks Object detection real-time systems Cognition

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Hypercomplex Low rank reconstruction for Nmr Spectroscopy with Cloud Computing ⋆

SSRN

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SSrN 2022年

作者： Guo, Yi Zhan, Jiaying Tu, Zhangren Zhou, Yirong Wu, Jianfan Hong, Qing Orekhov, Vladislav Qu, Xiaobo Guo, di School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361005 China China Mobile Group Xiamen361005 China Swedish NMR Center Department of Chemistry and Molecular Biology University of Gothenburg Gothenburg41390 Sweden

Nuclear magnetic resonance (NMr) spectroscopy serves as an important tool to analyze chemicals and proteins in bioengineering. Multi-dimensional NMr offers a major improvement in resolution with multi-dimensional spectrum, but significantly increases data acquisition time and produces hypercomplex data that is difficult to be handled. To reduce this time, non-uniformly sampling can be applied to obtain undersampled data and using a reconstruction approach, such as the state-of-the-art low-rank method, to remove the spectral artifacts introduced by undersampling. However, only complex format of signal, including the real and imaginary parts, is considered in previous low-rank approach, which is less efficient when dealing with hypercomplex data that has multiple components. To deal with this problem, a hypercomplex low-rank model is proposed by introducing an adjoint matrix operation and then solved with a fast matrix factorization algorithm. This method efficiently uses redundant information among all the components of hypercomplex signal. Using simulated data and real protein data, we demonstrate that the proposed method provides a fast and high-fidelity reconstruction of hypercomplex NMr spectroscopy. Furthermore, an open-access and easy-to-use cloud-computing platform is developed for this method. © 2022, The Authors. All rights reserved.

关键词： Nuclear magnetic resonance spectroscopy

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A Faithful deep Sensitivity Estimation for Accelerated Magnetic resonance Imaging

arXiv

引用

arXiv 2022年

作者： Wang, Zi Fang, Haoming Qian, Chen Shi, Boxuan Bao, Lijun Zhu, Liuhong Zhou, Jianjun Wei, Wenping Lin, Jianzhong Guo, di Qu, Xiaobo Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University Xiamen361005 China Fudan University Xiamen361006 China Department of Radiology The First Affiliated Hospital of Xiamen University Xiamen361005 China Department of Radiology Zhongshan Hospital affiliated to Xiamen University Xiamen361005 China School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China

Magnetic resonance imaging (MrI) is an essential diagnostic tool that suffers from prolonged scan time. To alleviate this limitation, advanced fast MrI technology attracts extensive research interests. recent deep learning has shown its great potential in improving image quality and reconstruction speed. Faithful coil sensitivity estimation is vital for MrI reconstruction. However, most deep learning methods still rely on pre-estimated sensitivity maps and ignore their inaccuracy, resulting in the significant quality degradation of reconstructed images. In this work, we propose a Joint deep Sensitivity estimation and Image reconstruction network, called JdSI. during the image artifacts removal, it gradually provides more faithful sensitivity maps with high-frequency information, leading to improved image reconstructions. To understand the behavior of the network, the mutual promotion of sensitivity estimation and image reconstruction is revealed through the visualization of network intermediate results. results on in vivo datasets and radiologist reader study demonstrate that, for both calibration-based and calibrationless reconstruction, the proposed JdSI achieves the state-of-the-art performance visually and quantitatively, especially when the acceleration factor is high. Additionally, JdSI owns nice robustness to patients and autocalibration signals. Copyright © 2022, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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