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SSRN 2025年

作者： Awedat, Khalfalla Comert, Gurcan Ayad, Mustafa Mrebit, Abdulmajid Computer Information Technology Department SUNY Morrisville College 80 Eaton St MorrisvilleNY13408 United States Computational Data Science and Engineering Department North Carolina A&T State University 1601 E. Market Street GreensboroNC27411 United States Electrical and Computer Engineering Department SUNY Oswego 430 Shineman Center OswegoNY13126 United States School of Art and Sciences Computer Science Physics & Engineering Department Benedict College 1600 Harden Street ColumbiaSC29204 United States

Fault detection in photovoltaic (PV) panels using thermal images remains a significant challenge due to the com-plexity of thermal patterns, environmental noise, and the subtle nature of anomalies. This paper introduces anadvanced deep learning framework that enhances the U-Net architecture by integrating Residual Blocks, AtrousSpatial Pyramid Pooling (ASPP), and Attention Mechanisms. These enhancements collectively improve featureextraction, contextual understanding, and fault localization, addressing the limitations of traditional segmentationapproaches and reducing false positives. Extensive experiments demonstrate that the proposed method significantlyoutperforms all benchmarked algorithms across key segmentation metrics, including standard U-Net, U-Net withASPP, and DeepLabV3+. Compared to standard U-Net, the proposed model achieves more than a 29% increasein F1-score and a 62% improvement in Intersection over Union (IoU) while reducing segmentation loss by 71%.Its ability to accurately detect faults under challenging conditions establishes the framework as a state-of-the-artsolution for real-time PV monitoring. These results demonstrate the effectiveness of the proposed approach in ad-dressing the challenges of PV fault detection, offering a practical and reliable solution for ensuring the operationalperformance of renewable energy systems. © 2025, The Authors. All rights reserved.

关键词： Deep learning

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HET: Scaling out huge embedding model training via cache-enabled distributed framework

arXiv

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arXiv 2021年

作者： Miao, Xupeng Zhang, Hailin Shi, Yining Nie, Xiaonan Yang, Zhi Tao, Yangyu Cui, Bin Peking University China Institute of Computational Social Science Peking University Qingdao China Tencent Inc Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications China

Embedding models have been an effective learning paradigm for high-dimensional data. However, one open issue of embedding models is that their representations (latent factors) often result in large parameter space. We observe that existing distributed training frameworks face a scalability issue of embedding models since updating and retrieving the shared embedding parameters from servers usually dominates the training cycle. In this paper, we propose HET, a new system framework that significantly improves the scalability of huge embedding model training. We embrace skewed popularity distributions of embeddings as a performance opportunity and leverage it to address the communication bottleneck with an embedding cache. To ensure consistency across the caches, we incorporate a new consistency model into HET design, which provides fine-grained consistency guarantees on a per-embedding basis. Compared to previous work that only allows staleness for read operations, HET also utilizes staleness for write operations. Evaluations on six representative tasks show that HET achieves up to 88% embedding communication reductions and up to 20.68× performance speedup over the state-of-the-art baselines. Copyright © 2021, The Authors. All rights reserved.

关键词： Embeddings

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Realizing Body-Machine Interface for Quadrotor Control Through Kalman Filters and Recurrent Neural Network

Realizing Body-Machine Interface for Quadrotor Control Throu...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Alexander Menshchikov Daniil Lopatkin Evgeny Tsykunov Dzmitry Tsetserukou Andrey Somov Center for Computational and Data-Intensive Science and Engineering (CDISE) Skolkovo Institute of Science and Technology Moscow Russia Space Center Skolkovo Institute of Science and Technology Moscow Russia

ISBN: (数字)9781728189567

ISBN: (纸本)9781728189574

Unmanned Aerial Vehicles (UAV) have been recently applied in several various civilian applications. Based on this, there is a growing need for intuitive UAV control interfaces. In this work, we report on the Body-Machine Interface (BMI), helping a human operator to control a quadrotor through the gesture commands. We perform the human motion capture through wearable sensors and Kalman filter to reduce the noise. For the gesture command recognition, we designed the Recurrent Neural Network recognizing gestures within 65 ms. For the quadrotor orientation estimation, we designed the Extended Kalman Filter (EKF). We assess the proposed BMI via the simulations and experiments: the standard deviation of the trajectories varies for up to 10 cm.

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Duplication with transposition distance to the root for q-ary strings

Duplication with transposition distance to the root for q-ar...

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IEEE International Symposium on Information Theory

作者： Nikita Polyanskii Ilya Vorobyev Institute for Communications Engineering Technical University of Munich Munich Germany Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow Russia

ISBN: (数字)9781728164328

ISBN: (纸本)9781728164335

We study the duplication with transposition distance between strings of length n over a q-ary alphabet and their roots. In other words, we investigate the number of duplication operations of the form x = (abcd) →y = (abcbd), where x and y are strings and a, b, c and d are their substrings, needed to get a q-ary string of length n starting from the set of strings without duplications. For exact duplication, we prove that the maximal distance between a string of length at most n and its root has the asymptotic order n/logn. For approximate duplication, where a β-fraction of symbols may be duplicated incorrectly, we show that the maximal distance has a sharp transition from the order n/logn to logn at β = (q - 1)/q. The motivation for this problem comes from genomics, where such duplications represent a special kind of mutation and the distance between a given biological sequence and its root is the smallest number of transposition mutations required to generate the sequence.

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Spreading dynamics of information on online social networks

arXiv

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arXiv 2025年

作者： Meng, Fanhui Xie, Jiarong Sun, Jiachen Xu, Cong Zeng, Yutian Wang, Xiangrong Jia, Tao Huang, Shuhong Deng, Youjin Hu, Yanqing School of Systems Science and Engineering Sun Yat-sen University Guangzhou510275 China Center for Computational Communication Research Beijing Normal University Zhuhai519087 China School of Journalism and Communication Beijing Normal University Beijing100875 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Department of Statistics and Data Science College of Science Southern University of Science and Technology Shenzhen518055 China College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China College of Computer and Information Science Chongqing Normal University Chongqing401331 China College of Computer and Information Science Southwest University Chongqing400715 China Institute of Neuroscience Technical University of Munich Munich80802 Germany Department of Modern Physics University of Science and Technology of China Hefei230026 China MinJiang Collaborative Center for Theoretical Physics College of Physics and Electronic Information Engineering Minjiang University Fuzhou350108 China Center for Complex Flows and Soft Matter Research Southern University of Science and Technology Shenzhen518055 China

Social media is profoundly changing our society with its unprecedented spreading power. Due to the complexity of human behaviors and the diversity of massive messages, the information spreading dynamics are complicated, and the reported mechanisms are different and even controversial. Based on data from mainstream social media platforms, including WeChat, Weibo, and Twitter, cumulatively encompassing a total of 7.45 billion users, we uncover a ubiquitous mechanism that the information spreading dynamics are basically driven by the interplay of social reinforcement and social weakening effects. Accordingly, we propose a concise equation, which, surprisingly, can well describe all the empirical large-scale spreading trajectories. Our theory resolves a number of controversial claims and satisfactorily explains many phenomena previously observed. It also reveals that the highly clustered nature of social networks can lead to rapid and high-frequency information bursts with relatively small coverage per burst. This vital feature enables social media to have a high capacity and diversity for information dissemination, beneficial for its ecological development. © 2025, CC BY.

关键词： Tweets

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Explore user neighborhood for real-time e-commerce recommendation

arXiv

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arXiv 2021年

作者： Xie, Xu Sun, Fei Yang, Xiaoyong Yang, Zhao Gao, Jinyang Ou, Wenwu Cui, Bin Peking University China Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications China Institute of Computational Social Science Peiking University Qingdao China Alibaba Group China

Recommender systems play a vital role in modern online services, such as Amazon and Taobao. Traditional personalized methods, which focus on user-item (UI) relations, have been widely applied in industrial settings, owing to their efficiency and effectiveness. Despite their success, we argue that these approaches ignore local information hidden in similar users. To tackle this problem, user-based methods exploit similar user relations to make recommendations in a local perspective. Nevertheless, traditional user-based methods, like userKNN and matrix factorization, are intractable to be deployed in the real-time applications since such transductive models have to be recomputed or retrained with any new interaction. To overcome this challenge, we propose a framework called self-complementary collaborative filtering (SCCF) which can make recommendations with both global and local information in real time. On the one hand, it utilizes UI relations and user neighborhood to capture both global and local information. On the other hand, it can identify similar users for each user in real time by inferring user representations on the fly with an inductive model. The proposed framework can be seamlessly incorporated into existing inductive UI approach and benefit from user neighborhood with little additional computation. It is also the first attempt to apply user-based methods in real-time settings. The effectiveness and efficiency of SCCF are demonstrated through extensive offline experiments on four public datasets, as well as a large scale online A/B test in Taobao. © 2021, CC BY-SA.

关键词： Collaborative filtering

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Automatic detection of lung nodule in CT scan slices using CNN segmentation schemes: A study

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Procedia Computer science 2023年 218卷 2786-2794页

作者： Seifedine Kadry Enrique Herrera-Viedma Rubén González Crespo Sujatha Krishnamoorthy Venkatesan Rajinikanth Faculty of Applied Computing and Technology Noroff University College Kristiansand 94612 Norway Research Institute in Data Science and Computational Intelligence University of Granada Granada Andalusian Spain Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja 26006 Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Chennai 600119 Tamil Nadu India

The lung is one of the prime respiratory organs in human physiology, and its abnormality will severely disrupt the respiratory system. Lung Nodule (LN) is one of the abnormalities, and early screening and treatment are necessary to reduce its harshness. The proposed work aims to implement the Convolutional-Neural-Network (CNN) segmentation methodology to extract the LN in various lung CT slices, such as axial, coronal, and sagittal planes. This work consists of the following phases; (i) Image collection and pre-processing, (ii) Ground-truth generation, (iii) CNN-supported segmentation, and (iv) Performance evaluation and validation. In this work, the merit of pre-trained CNN segmentation schemes is verified using (i) One-fold training and (ii) Two-fold training methods. The test images for this study are collected from The Cancer Imaging Archive (TCIA) database. The experimental investigation is executed using Python®, and the outcome of this study confirms that the VGG-SegNet helps to get better values of Jaccard (>88%), Dice (>93%), and Accuracy (>96%) compared to other CNN methods.

关键词： Lung nodule CT scan CNN segmentation VGG-SegNet Accuracy Detection

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Synthesising 3D Cardiac CINE-MR Images and Corresponding Segmentation Masks using a Latent Diffusion Model

Synthesising 3D Cardiac CINE-MR Images and Corresponding Seg...

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IEEE International Symposium on Biomedical Imaging

作者： Nina Cheng Zhengji Liu Yash Deo Haoran Dou Ning Bi Kun Wu Fengming Lin Zeike A Taylor Nishant Ravikumar Alejandro F Frangi CISTIB Centre for Computational Imaging and Simulation Technologies in Biomedicine University of Leeds School of Optometry The Hong Kong Polytechnic University NIHR Leeds Biomedical Research Centre Leeds UK Alan Turing Institute London UK Division of Informatics Imaging and Data Science Schools of Computer Science and Health Sciences University of Manchester Manchester UK Cardiovascular Sciences Departments Medical Imaging Research Center (MIRC) Electrical Engineering KU Leuven Leuven Belgium

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

We propose a novel pipeline for the generation of synthetic full spatial cine cardiac magnetic resonance (CMR) images via a latent Denoising Diffusion Implicit Models (DDIMs). These synthetic images can be used as viable alternatives to real data in deep learning model training for downstream cardiac image analysis tasks such as cardiac segmentation. To demonstrate the effectiveness of this approach, we generated synthetic CMR images along with their corresponding segmentation masks. We evaluated model performance using a variety of methods, including generated image fidelity, diversity and calculated the volumes of the generated segmentation masks and compare it with the real segmentation masks. The proposed pipeline has the potential to be widely applied to other tasks in various medical imaging modalities. Effective and efficient generation of 3D cine cardiac images with corresponding segmentation masks can supplement real patient datasets and help reduce the burden of manually annotating images.

关键词： Training Image segmentation Solid modeling Three-dimensional displays Biological system modeling Pipelines Noise reduction

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Deep and handcrafted feature supported diabetic retinopathy detection: A study

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Procedia Computer science 2023年 218卷 2675-2683页

作者： Seifedine Kadry Rubén González Crespo Enrique Herrera-Viedma Sujatha Krishnamoorthy Venkatesan Rajinikanth Faculty of Applied Computing and Technology Noroff University College Kristiansand 94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja 26006 Andalusian Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Chennai 600119 Tamil Nadu India

The eye is the prime sensory organ in physiology, and the abnormality in the eye severely influences the vision system. Therefore, eye irregularity is commonly assessed using imaging schemes, and Fundus Retinal Image (FRI) supported eye screening is one of the ophthalmological practices. This work proposed a Deep-Learning Procedure (DLP) to recognize Diabetic Retinopathy (DR) in FI. The proposed work presents the experimental work with different DLP methods found in the literature. This work is executed with two modes; (i) DR detection using conventional deep-features and (ii) DR discovery using deep ensemble features. To demonstrate this work, 1800 fundus images (900 regular and 900 DR class) are considered for the assessment, and the advantage of proposed plan is confirmed using various performance metrics. The experimental outcome of this study confirms that the AlexNet-based detection provides a better detection (>96%), and the deep ensemble features of AlexNet, VGG16, and ResNet18 provide a detection accuracy of >98% on the chosen FRI database.

关键词： Eye abnormality Diabetic retinopathy Fundus imaging Deep-Learning Classification

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Interlayer antisynchronization in degree-biased duplex networks

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Physical Review E 2023年第3期107卷 034313-034313页

作者： Sayantan Nag Chowdhury Sarbendu Rakshit Chittaranjan Hens Dibakar Ghosh Department of Environmental Science and Policy University of California Davis California 95616 USA Technology Innovation Hub (TIH) IDEAS (Institute of Data Engineering Analytics and Science Foundation) Indian Statistical Institute 203 B. T. Road Kolkata 700108 India Department of Mechanical Engineering University of California Riverside California 92521 USA Center for Computational Natural Sciences and Bioinformatics International Institute of Information Technology Gachibowli Hyderabad 500032 India Physics and Applied Mathematics Unit Indian Statistical Institute 203 B. T. Road Kolkata 700108 India

With synchronization being one of nature's most ubiquitous collective behaviors, the field of network synchronization has experienced tremendous growth, leading to significant theoretical developments. However, most previous studies consider uniform connection weights and undirected networks with positive coupling. In the present article, we incorporate the asymmetry in a two-layer multiplex network by assigning the ratio of the adjacent nodes' degrees as the weights to the intralayer edges. Despite the presence of degree-biased weighting mechanism and attractive-repulsive coupling strengths, we are able to find the necessary conditions for intralayer synchronization and interlayer antisynchronization and test whether these two macroscopic states can withstand demultiplexing in a network. During the occurrence of these two states, we analytically calculate the oscillator's amplitude. In addition to deriving the local stability conditions for interlayer antisynchronization via the master stability function approach, we also construct a suitable Lyapunov function to determine a sufficient condition for global stability. We provide numerical evidence to show the necessity of negative interlayer coupling strength for the occurrence of antisynchronization, and such repulsive interlayer coupling coefficients cannot destroy intralayer synchronization.

关键词： Coupled oscillators Dynamics of networks Synchronization

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