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arXiv 2022年

作者： Lebedev, Alexey Lebedev, Vladimir Vorobyev, Ilya Deppe, Christian Kharkevich Institute for Information Transmission Problems Russian Academy of Sciences Moscow Russia Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow Russia Institute for Communications Engineering Department of Electrical and Computer Engineering Technical University of Munich Munich Germany

In this paper, we developed new coding strategies for the Z-channel. In particular, we look at the case with two-stage encoding. In this case, the encoder uses noiseless feedback once and adjusts the further encoding strategy based on the previous partial output of the channel. Nevertheless, the developed codes improve the known results with full feedback for small length and 1 error. A tool for the two-stage strategy is the development of a new optimality condition for non-adaptive codes. Copyright © 2022, The Authors. All rights reserved.

关键词： Signal encoding

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Momentum transfer dependence of kaon semileptonic form factor on (10 fm)4 at the physical point 39

Momentum transfer dependence of kaon semileptonic form facto...

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39th International Symposium on Lattice Field Theory, LATTICE 2022

作者： Yamazaki, Takeshi Ishikawa, Ken-Ichi Ishizuka, Naruhito Kuramashi, Yoshinobu Namekawa, Yusuke Taniguchi, Yusuke Ukita, Naoya Yoshié, Tomoteru Faculty of Pure and Applied Sciences University of Tsukuba Ibaraki Tsukuba305-8571 Japan Center for Computational Sciences University of Tsukuba Ibaraki Tsukuba305-8577 Japan Core of Research for the Energetic Universe Graduate School of Advanced Science and Engineering Hiroshima University Higashi-Hiroshima739-8526 Japan Education and Research Center for Artificial Intelligence and Data Innovation Hiroshima University Higashi-Hiroshima739-8521 Japan

We calculate the kaon semileptonic form factors using the two sets of the PACS10 configuration, whose physical volumes are more than (10 fm)4 at the physical point. The lattice spacings are 0.063 and 0.085 fm. The configurations were generated using the Iwasaki gauge action and Nf = 2 + 1 stout-smeared nonperturbatively O(a)-improved Wilson quark action. From the momentum transfer dependence of the form factors, we evaluate the slope and curvature for the form factors at the zero momentum transfer. Furthermore, we calculate the phase space factor, which is used to obtain |Vus| through the kaon semileptonic decay. These results are compared with previous lattice results and experimental values. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).

关键词： Momentum transfer

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Medical Personal Protective Equipment Detection Using YOLOV7

Medical Personal Protective Equipment Detection Using YOLOV7

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Md. Anas Ali Mahmudul Haque Saadia Binte Alam Rashedur Rahman Mashraful Amin Syoji Kobashi Graduate School of Engineering University of Hyogo Himeji Hyogo Japan Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh Center for Computational & Data Sciences Independent University Bangladesh Dhaka Bangladesh

In recent years, healthcare and safety have been a major focus of deep learning research. This paper focuses on the detection of Medical Personal Protective Equipment (MPPE) in the health-care sector using YOLOv7. Improper use of personal protective equipment (PPE) can result in the contamination and cross-contamination of infectious diseases, so it is crucial for healthcare professionals to use it correctly. The CPPE-5 dataset was used to train the model, which contains 1029 high-quality images divided into five categories: coveralls, face shield, gloves, masks, and goggles. The objective of this research is to create an accurate model for future applications and development using a suitable medical PPE dataset. The proposed model outperforms previous studies, with an optimal mAP of 90.93 %, indicating that it is a promising method for detecting MPPE in the healthcare sector.

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CFL-Net: Image Forgery Localization Using Contrastive Learning

arXiv

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arXiv 2022年

作者： Niloy, Fahim Faisal Bhaumik, Kishor Kumar Woo, Simon S. Center for Computational & Data Sciences Independent University Bangladesh Computer Science and Engineering Department Sungkyunkwan University Suwon Korea Republic of

Conventional forgery localizing methods usually rely on different forgery footprints such as JPEG artifacts, edge inconsistency, camera noise, etc., with cross-entropy loss to locate manipulated regions. However, these methods have the disadvantage of over-fitting and focusing on only a few specific forgery footprints. On the other hand, real-life manipulated images are generated via a wide variety of forgery operations and thus, leave behind a wide variety of forgery footprints. Therefore, we need a more general approach for image forgery localization that can work well on a variety of forgery conditions. A key assumption in underlying forged region localization is that there remains a difference of feature distribution between untampered and manipulated regions in each forged image sample, irrespective of the forgery type. In this paper, we aim to leverage this difference of feature distribution to aid in image forgery localization. Specifically, we use contrastive loss to learn mapping into a feature space where the features between untampered and manipulated regions are well-separated for each image. Also, our method has the advantage of localizing manipulated region without requiring any prior knowledge or assumption about the forgery type. We demonstrate that our work outperforms several existing methods on three benchmark image manipulation datasets. Code is available at https://***/niloy193/CFLNet. © 2022, CC BY.

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Relative magnetic field and slipping effect on Casson dusty fluid of two phase fluctuating flow over inclined parallel plate

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South African Journal of Chemical engineering 2023年 44卷 135-146页

作者： khan, Dolat Kumam, Poom Kumam, Wiyada Suttiarporn, Panawan Rehman, Ali Fixed Point Research Laboratory Fixed Point Theory and Applications Research Group Center of Excellence in Theoretical and Computational Science (TaCS-CoE) Faculty of Science King Mongkut's University of Technology Thonburi (KMUTT) 126 Pracha Uthit Rd. Bang Mod Bangkok Thung Khru 10140 Thailand Center of Excellence in Theoretical and Computational Science (TaCS-CoE) Faculty of Science King Mongkut's University of Technology Thonburi (KMUTT) 126 Pracha Uthit Rd. Bang Mod Bangkok Thung Khru 10140 Thailand Applied Mathematics for Science and Engineering Research Unit (AMSERU) Program in Applied Statistics Department of Mathematics and Computer Science Faculty of Science and Technology Rajamangala University of Technology Thanyaburi (RMUTT) Pathum Thani 12110 Thailand Faculty of Science Energy and Environment King Mongkut's University of Technology North Bangkok Rayong Campus Rayong 21120 Thailand Special Interest Group for Modelling and Data Analytics Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Kuala Nerus 21030 Malaysia

Due to its significant applications in magnetic devices for cell separation, magnetic drugs for cancer tumor treatment, blood flow adjustment during surgery, magnetic endoscopy, and fluid pumping in industrial and engineering processes, magnetohydrodynamics (MHD) has a wide range of applications. Therefore, the aim of this study to investigate the Relative Magnetic Field and slipping effect on Casson dusty fluid of two-phase fluctuating flow over inclined parallel plate. More exactly, the relative magnetic phenomena are fixed relatively to the plate (MFFRP) or fixed relatively to the fluid (MFFRF). The mathematical modeling was established as the set of partial differential equations for dusty Casson fluid. Buckingham's pi theorem is used to find out the dimensionless variables, to make our system dimensionless. The solution in perturb form is to find out by incorporating Poincare–Lighthill perturbation technique (PLPT). To know in-depth different parameters the graphical results for both velocities are plotted with the help of software Mathcad- 15. It is clearly detected that the relative magnetic field performs a vital part in the fluid as well as particle motion. Moreover, the relative magnetic field play a vital role in the decreasing of skin friction of both plates. © 2023

关键词： Brinkman fluid Dust particles Fluctuating vertical plate Inclined relative magnetic field PLPT

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FedMonitor: An Implementation Architecture for Monitoring of Resources and Events'Notifications on Federated Cloud Computing Infrastructures 4

FedMonitor: An Implementation Architecture for Monitoring of...

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4th International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024

作者： Adetiba, Emmanuel Nnaji, Uche Wejin, John S. Ojesanmi, Festus Akanle, Matthew B. Ajayi, Priscilla O. Sweetwiliams, Faith O. Thakur, Surendra C. Adebiyi, Ezekiel F. Department of Electrical and Information Engineering and Covenant Applied Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban South Africa Covenant University Department of Computer and Information Science & CApIC-ACE Ota Nigeria Covenant University Department of Electrical and Information Engineering & CApIC-ACE Jalingo Nigeria Tareba State University Department of Computer Science Jalingo Nigeria Ota Nigeria Covenant University & CApIC-ACE Covenant University Department of Electrical and Information Engineering Ota Nigeria Covenant University Center for Systems and Information Services & CApIC-ACE Ota Nigeria Durban University of Technology e-Skills Co-Laboratory Department of Information Technology and KZN Durban4001 South Africa Covenant University Ogun State Ota Nigeria Makerere University African Center of Excellence in Bioinformatics & Data Intensive Science Kampala10218 Uganda

ISBN: (纸本)9798350353266

In the evolving landscape of cloud computing, federated cloud infrastructures present unique challenges and opportunities for resource and application monitoring. Monitoring the diverse array of cloud resources and applications within a federated cloud environment is crucial for delivering consistent Quality of Service (QoS) and continuous oversight across various cloud applications and infrastructure. Despite the various successes documented in the literature on multi-cloud monitoring, existing frameworks have not fully explored or provided enough insights into centralized monitoring of resources, applications, and network devices in a federated cloud environment. This paper introduces FedMonitor, an implementation architecture designed to enhance the monitoring and notification capabilities in federated cloud environments by building on the theoretical ideals in our earlier work (i.e. FEDARGOS-V1). FedMonitor leverages Prometheus and Grafana to crawl, manage and visualize metrics from diverse software services, hardware resources, and network devices within a federated cloud environment. The results demonstrate hat FedMonitor improves the effectiveness of resource and application monitoring in federated cloud environments. © 2024 IEEE.

关键词： Cloud platforms

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Development of Predictive Mathematical Model for Millimeter Wave Degradation in Sandstorm Regions

Development of Predictive Mathematical Model for Millimeter ...

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IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE)

作者： Esmail Abuhdima Abdulmajid Mrebit Ricsheia Barr Avery Basden Jian Liu Amirhossein Nazeri Gurcan Comert Chin-Tser Huang Pierluigi Pisu Engineering Technology ECPI University Newport News VA USA Engineering and Computer Science Benedict College Columbia SC USA Computer Science and Engineering University of South Carolina Columbia SC USA International Center for Automotive Clemson University Greenville SC USA Computational Data Science and Engineering North Carolina A&T State University Greensboro NC USA International Center for Automotive Clemson University Greenville USA

ISBN: (数字)9798350351118

ISBN: (纸本)9798350351125

The advent of millimeter wave (mm-Wave) technology in modern communication systems, including 5G networks, has brought about unprecedented data transmission speeds and bandwidths. However, environmental factors highly affect mm-Wave signals, particularly in regions susceptible to dust and sand storms. Dust storms, characterized by high concentrations of suspended particles, lead to significant signal attenuation and degradation during the absorbed and scattered incident wave. This attenuation poses challenges to the reliability and performance of mm-Wave communication systems. The previous research used Mie theory to compute the specific attenuation due to dusty storms because it provides a complete analytical solution to Maxwell’s equations compared to other analytical and numerical methods. However, the Mie scattering model lacks accuracy due to consideration of only the amplitude of the attenuation factor with respect to the dust and sand environment. This paper presents the development of predictive mathematical models designed to estimate mm-Wave signal degradation in dust and sand storm conditions. The models integrate key physical parameters such as dust particle size distribution, storm intensity, signal frequency, and atmospheric *** predictive model demonstrates a significant accuracy in estimating signal attenuation by considering the phase shift in signal by introducing complex attenuation factor. We mathematically demonstrated that dust and sandstorms can cause mm-Wave signal attenuation but also cause a significant signal phase shift. This complex attenuation factor provides valuable insights for network engineers to design and optimize mm-Wave communi cation systems in dust-prone environments. Comparative analysis with existing models underscores the proposed models’ enhanced predictive capability and flexibility in adapting to diverse dust storm *** research outcomes contribute to the ongoing efforts to improve mm-Wave communicat

关键词： Degradation Adaptation models Storms Millimeter wave technology Predictive models Attenuation Mathematical models Environmental factors Numerical models Millimeter wave communication

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Cluster-aware semi-supervised learning: relational knowledge distillation provably learns clustering 23

Cluster-aware semi-supervised learning: relational knowledge...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Yijun Dong Kevin Miller Qi Lei Rachel Ward Courant Institute of Mathematical Sciences New York University New York NY Oden Institute for Computational Engineering & Science University of Texas at Austin Austin TX Courant Institute of Mathematical Sciences & Center of Data Science New York University New York NY Oden Institute for Computational Engineering & Science University of Texas at Austin Austin TX

Despite the empirical success and practical significance of (relational) knowledge distillation that matches (the relations of) features between teacher and student models, the corresponding theoretical interpretations remain limited for various knowledge distillation paradigms. In this work, we take an initial step toward a theoretical understanding of relational knowledge distillation (RKD), with a focus on semi-supervised classification problems. We start by casting RKD as spectral clustering on a population-induced graph unveiled by a teacher model. Via a notion of clustering error that quantifies the discrepancy between the predicted and ground truth clusterings, we illustrate that RKD over the population provably leads to low clustering error. Moreover, we provide a sample complexity bound for RKD with limited unlabeled samples. For semi-supervised learning, we further demonstrate the label efficiency of RKD through a general framework of cluster-aware semi-supervised learning that assumes low clustering errors. Finally, by unifying data augmentation consistency regularization into this cluster-aware framework, we show that despite the common effect of learning accurate clusterings, RKD facilitates a "global" perspective through spectral clustering, whereas consistency regularization focuses on a "local" perspective via expansion.

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Pelvic bone region segmentation (PBRS) from X-ray image using convolutional neural network (CNN)

Pelvic bone region segmentation (PBRS) from X-ray image usin...

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International Conference on Computer and Information Technology (ICCIT)

作者： Syed Alif Ul Alam Saadia Binte Alam Sourav Saha Mahmudul Haque Rashedur Rahman Syoji Kobashi Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh Center for Computational & Data Sciences Independent University Bangladesh Dhaka Bangladesh Graduate School of Engineering University of Hyogo Shosha Himeji Hyogo Japan

Pelvic and hip fractures offer considerable public health risks with high morbidity and mortality rates. Because of the complicated bone structure of the pelvic bone region, detecting fractures is difficult. Though X-ray imaging is routinely utilised for detecting fractures, manual fracture diagnosis is prone to inaccuracies. This paper proposes the use of deep learning algorithms for automated segmentation of the pelvic bone region in X-ray images. In our work, we have investigated U-Net based pelvic area segmentation models with various convolutional neural network (CNN) backbones. The DenseNet121-based U-Net design emerged as the most optimal model, establishing a compromise between performance and computational efficiency. Although it had a modest loss in IoU and F1 scores when compared to InceptionNetV3, it had a remarkable 59.44% reduction in the number of parameters.

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Topological Correction of Subject-Level Intrinsic Connectivity Networks

Topological Correction of Subject-Level Intrinsic Connectivi...

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IEEE International Symposium on Biomedical Imaging

作者： Noah Lewis Armin Iraji Robyn Miller Vince D. Calhoun School of Computational Science and Engineering Georgia Institute of Technology Tri-Institutional Center for Translational Research in Neuroimaging Data Science (TReNDS) Georgia Institute of Technology Georgia State University Emory University School of Electrical and Computer Engineering Georgia Institute of Technology

Over the last several decades, researchers have sought to capture the underlying functional activity of the human brain from functional magnetic resonance imaging (fMRI). One well-studied and promising avenue of research is independent component analysis (ICA) to capture the maximally independent set of elements known as functional networks. These functional networks are represented as spatial maps or 3D images in which each voxel has a value associated with the given network. The current state-of-the-art methods use group-level spatial maps as a reference to provide estimates of subject-level maps, which are vulnerable to the low contrast-to-noise (CNR) ratio of fMRI signals. However, such approaches do not account for all group-level spatial information. As such, their subject-level estimate is still quite noisy. This work presents a novel method that leverages the topological properties of the group maps to improve subject-level estimations. We show that adding topological similarity constraints also improves subject-specific information.

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