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Disruptive Technologies (ICDT), International Conference on

作者： Amrita Rajiv Kumar Shivani Joshi Daksh Raj Centre for Cyber Security and Cryptology Computer Science & Engineering Sharda School of Engineering & Technology Sharda University Greater Noida U.P. India Department of Applied Computational Science and Engineering NVIDIA AI Learning & Research Center G L Bajaj Institute of Technology and Management Greater Noida U.P. India Department of Computer Science and Engineering G L Bajaj Institute of Technology and Management Greater Noida U.P. India Department of Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida U.P. India

With the increasing popularity of live streaming platforms, there is an imperative need for robust methods for identifying individuals in real-time. The proposed system utilizes Convolutional Neural Networks (CNN) computer vision algorithms to identify faces in live streams and match them against a pre-existing database of known individuals. Additionally, the system employs deep learning and supervised learning techniques along with various types of CNN to achieve robust facial recognition in diverse lighting and environmental conditions. The system's accuracy is evaluated using various performance metrics, including precision, recall, and F1-score. The results demonstrate that the proposed system achieves high accuracy in person identification during real-time live streams. This system holds potential applications in a variety of domains, including security, entertainment, and social media.

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A Hybrid Reactive Routing Protocol for Decentralized UAV Networks

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Garg, Shivam Ihler, Alexander Bentley, Elizabeth Serena Kumar, Sunil San Diego State University Computational Science Research Center San DiegoCA92182 United States University of California Irvine Donald Bren Hall School of Information and Computer Sciences IrvineCA92697 United States Air Force Research Laboratory RomeNY13441 United States Electrical and Computer Engineering Department San DiegoCA92182 United States

Wireless networks consisting of low SWaP (size, weight, and power), fixed-wing UAVs (unmanned aerial vehicles) are used in many applications, such as monitoring, search, and surveillance of inaccessible areas. A decentralized and autonomous approach ensures robustness to failures;the UAVs explore and sense within the area and forward their information, in a multihop manner, to nearby aerial gateway nodes. However, the unpredictable nature of the events, relatively high speed of the UAVs and dynamic trajectories cause the network topology to change significantly over time, resulting in frequent route breaks. A holistic routing approach is needed to support multiple traffic flows in these networks to provide mobility- and congestion-aware, high-quality routes when needed, with low control and computational overhead, using the information collected in a distributed manner. Existing routing schemes do not address all the mentioned issues. This paper presents a hybrid reactive routing protocol for decentralized UAV networks called Hyd-AODV. It searches routes on-demand (using a multi-metric route selection), monitors a region around the selected route (the "pipe"), and proactively switches to an alternative route before the current route's quality degrades below a threshold. The impact of pipe width is empirically and theoretically evaluated to find alternate high-quality routes within the pipe and the overhead required to maintain the pipe. A queue management scheme is also incorporated to prioritize packet transmissions based on their age of information (AoI). Compared to existing reactive routing schemes, the proposed approach achieves higher throughput and reduces the number of route discoveries, overhead, and resulting flow interruptions at different traffic loads, node density, and speeds. Despite having limited network topology information and low overhead and route computation complexity, the proposed scheme achieves a superior throughput to proactive optimized l

关键词： Micro air vehicle (MAV)

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Comparative Analysis of Laboratory and Radiation Methods of Studies with the Degree of Severity of Patients with COVID-19

Comparative Analysis of Laboratory and Radiation Methods of ...

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Digital Signal Processing and its Applications (DSPA), International Conference on

作者： Angelica Kents Aleksandr Zotin Konstantin Simonov X-ray Department Center for Radiation Diagnostics Federal Siberian Research and Clinical Center of FMBA of Russia Krasnoyarsk Russian Federation Department of Informatics and Computer Techniques Candidate of Technical Sciences Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russian Federation Department of Computational Mechanics of Deformable Media Doctor of Technical Sciences Institute of Computational Modeling SB RAS Krasnoyarsk Russian Federation

The study is devoted to a comparative analysis and retrospective evaluation of laboratory and instrumental data with the severity of lung tissue damage in COVID-19 of patients with COVID-19. An improvement was made in the methodology for interpreting and analyzing dynamic changes associated with COVID-19 on CT images of the lungs. The technique includes the following steps: pre-processing, segmentation with color coding, calculation and evaluation of signs to highlight areas with probable pathology (including combined evaluation of signs). Analysis and interpretation is carried out on the emerging database of patients. At the same time the following indicators are distinguished: the results of the analysis of CT images of the lungs in dynamics; the results of the analysis of clinical and laboratory data (severity course of the disease, temperature, saturation, etc.). The results of laboratory studies are analyzed with an emphasis on the values of the main indicator - interleukin-6. This indicator is a marker of significant and serious changes characterizing the severity of the patient's condition.

关键词： COVID-19 Pathology Temperature distribution Correlation Tissue damage Computed tomography Instruments

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A Variational Bayesian Perspective on MIMO Detection with Low-Resolution ADCs

A Variational Bayesian Perspective on MIMO Detection with Lo...

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Asilomar Conference on Signals, Systems & computers

作者： Ly V. Nguyen A. Lee Swindlehurst Duy H.N. Nguyen Computational Science Research Center San Diego State University CA USA Department of Electrical Engineering and Computer Science University of California Irvine CA USA Department of Electrical and Computer Engineering San Diego State University CA USA

This paper proposes data detection methods for massive multiple-input multiple-output (MIMO) systems with low-resolution analog-to-digital converters (ADCs) based on the variational Bayes (VB) inference framework. We derive matched-filter quantized VB (MF-QVB) and linear minimum mean-squared error quantized VB (LMMSE-QVB) detection methods assuming the channel state information (CSI) is available. Unlike conventional VB-based detection methods that assume knowledge of the second-order statistics of the additive noise, we propose to float the noise variance/covariance matrix as an unknown random variable that is used to account for both the noise and the residual inter-user interference. Finally, we show via numerical results that the proposed VB-based methods provide robust performance and also significantly outperform existing methods.

关键词： computers Additive noise Channel estimation Interference MIMO Random variables Bayes methods

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A Full wCDM Analysis of KiDS-1000 Weak Lensing Maps using Deep Learning

arXiv

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arXiv 2022年

作者： Fluri, Janis Kacprzak, Tomasz Lucchi, Aurelien Schneider, Aurel Refregier, Alexandre Hofmann, Thomas Institute of Particle Physics and Astrophysics Department of Physics ETH Zurich Switzerland Data Analytics Lab Department of Computer Science ETH Zurich Switzerland Swiss Data Science Center Paul Scherrer Institute Switzerland Department of Mathematics and Computer Science University of Basel Switzerland Center for Theoretical Astrophysics and Cosmology Institute for Computational Science University of Zurich Switzerland

We present a full forward-modeled wCDM analysis of the KiDS-1000 weak lensing maps using graph-convolutional neural networks (GCNN). Utilizing the CosmoGrid, a novel massive simulation suite spanning six different cosmological parameters, we generate almost one million tomographic mock surveys on the sphere. Due to the large data set size and survey area, we perform a spherical analysis while limiting our map resolution to HEALPix nside = 512. We marginalize over systematics such as photometric redshift errors, multiplicative calibration and additive shear bias. Furthermore, we use a map-level implementation of the non-linear intrinsic alignment model along with a novel treatment of baryonic feedback to incorporate additional astrophysical nuisance parameters. We also perform a spherical power spectrum analysis for comparison. The constraints of the cosmological parameters are generated using a likelihood-free inference method called Gaussian Process Approximate Bayesian Computation (GPABC). Finally, we check that our pipeline is robust against choices of the simulation parameters. We find constraints on the degeneracy parameter of S8 ≡ σ8pΩM/0.3 = 0.78+0−00606 for our power spectrum analysis and S8 = 0.79+0−00505 for our GCNN analysis, improving the former by 16%. This is consistent with earlier analyses of the 2-point function, albeit slightly higher. Baryonic corrections generally broaden the constraints on the degeneracy parameter by about 10%. These results offer great prospects for full machine learning based analyses of on-going and future weak lensing surveys. Copyright © 2022, The Authors. All rights reserved.

关键词： Surveys

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Efficient Training Approaches for Performance Anomaly Detection Models in Edge Computing Environments

arXiv

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arXiv 2024年

作者： Fernando, Duneesha Rodriguez, Maria A. Arroba, Patricia Ismail, Leila Buyya, Rajkumar Laboratory School of Computing and Information Systems The University of Melbourne Parkville Australia The CCS Center for Computational Simulation Universidad Politécnica de Madrid Madrid Spain Lab Department of Computer Science and Software Engineering United Arab Emirates University United Arab Emirates

Microservice architectures are increasingly used to modularize IoT applications and deploy them in distributed and heterogeneous edge computing environments. Over time, these microservice-based IoT applications are susceptible to performance anomalies caused by resource hogging (e.g., CPU or memory), resource contention, etc., which can negatively impact their Quality of Service and violate their Service Level Agreements. Existing research on performance anomaly detection for edge computing environments focuses on model training approaches that either achieve high accuracy at the expense of a time-consuming and resource-intensive training process or prioritize training efficiency at the cost of lower accuracy. To address this gap, while considering the resource constraints and the large number of devices in modern edge platforms, we propose two clustering-based model training approaches: (1) intra-cluster parameter transfer learning-based model training (ICPTL) and (2) cluster-level model training (CM). These approaches aim to find a trade-off between the training efficiency of anomaly detection models and their accuracy. We compared the models trained under ICPTL and CM to models trained for specific devices (most accurate, least efficient) and a single general model trained for all devices (least accurate, most efficient). Our findings show that ICPTL's model accuracy is comparable to that of the model per device approach while requiring only 40% of the training time. In addition, CM further improves training efficiency by requiring 23% less training time and reducing the number of trained models by approximately 66% compared to ICPTL, yet achieving a higher accuracy than a single general model. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Automated Generation of Scientific Workflow Generators with Wfchef

SSRN

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SSRN 2023年

作者： Coleman, Tainã Casanova, Henri da Silva, Rafael Ferreira University of Southern California Department of Computer Science Los AngelesCA United States Information and Computer Sciences University of Hawaii HonoluluHI United States National Center for Computational Sciences Oak Ridge National Laboratory Oak RidgeTN United States

Scientific workflow applications have become mainstream and their automated and efficient execution on large-scale compute platforms is the object of extensive research and development. For these efforts to be successful, a solid experimental methodology is needed to evaluate workflow algorithms and systems. A foundation for this methodology is the availability of realistic workflow instances. Workflow instances for a few scientific applications are available in public repositories. While these are invaluable, they are limited: workflow instances are not available for all application scales of interest. To address this limitation, previous work has developed generators of synthetic, but representative, workflow instances of arbitrary scales. These generators are popular, but implementing them is a manual, labor-intensive process that requires expert application knowledge. As a result, these generators only target a handful of applications, even though there are hundreds of workflow applications in *** this work, we present WfChef, a framework that fully automates the process of constructing a synthetic workflow generator for any scientific application. Based on an input set of workflow instances for a particular application, WfChef automatically produces a synthetic workflow generator. We define and evaluate several metrics for quantifying the realism of the generated workflows. Using these metrics, we compare the realism of the workflows generated by WfChef generators to that of the workflows generated by the previously available, hand-crafted generators. We find that the WfChef generators not only require zero development effort (because they are automatically produced), but also generate workflows that are more realistic than those generated by hand-crafted generators. © 2023, The Authors. All rights reserved.

关键词： Workflow management

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Evaluating Return on Investment for Cyberinfrastructure Using the International Integrated Reporting Framework

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SN computer science 2024年第5期5卷 558页

作者： Snapp-Childs, Winona G. Hart, David L. Costa, Claudia M. Wernert, Julie A. Jankowski, Harmony E. Towns, John W. Stewart, Craig A. Pervasive Technology Institute Indiana University 2709 E. Tenth St. Bloomington 47408 IN United States Research Technologies University Information Technology Services Indiana University 2709 E. Tenth St. Bloomington 47408 IN United States Computational and Information Systems Lab National Center for Atmospheric Research Boulder 803807 CO United States National Center for Supercomputing Applications University of Illinois 1205 W. Clark St. Urbana 61801 IL United States Department of Computer Science Indiana University 700 N. Woodlawn Ave. Bloomington 47408 IN United States

This paper investigates the return on investment (ROI) in cyberinfrastructure (CI) facilities and services by comparing the value of end products created to the cost of operations. We assessed the cost of a US CI facility called XSEDE and the value of the end products created using this facility, categorizing end products according to the International Integrated Reporting Framework. The US federal government invested approximately $0.3B in operating the XSEDE ecosystem from 2016–2022. The estimated value of end products facilitated by XSEDE ranges from around $4.7B to $22.7B or more. Credit for the majority of these end products is shared among various contributors, including the XSEDE ecosystem. Granting the XSEDE ecosystem a seemingly reasonable percentage of credit for its contributions to end product creation suggests that the return on federal investment in the XSEDE ecosystem, in terms of value of end products created, was greater than one and possibly far greater than one. The Framework proved useful for addressing this question. Earlier work showed that the value of services provided by XSEDE was significantly greater than the cost of those services to the US federal government—a positive return on investment for delivery of services. Analyzing the financial efficiency of operations and the financial value of end products are two means for assessing the success of CI facilities in financial terms. Financial analyses should be used as one of many approaches for evaluating the success of CI facilities. © The Author(s) 2024.

关键词： Cyberinfrastructure IR framework Return on investment ROI science gateways XSEDE

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Toward a 2D Local Implementation of Quantum LDPC Codes

arXiv

引用

arXiv 2024年

作者： Berthusen, Noah Devulapalli, Dhruv Schoute, Eddie Childs, Andrew M. Gullans, Michael J. Gorshkov, Alexey V. Gottesman, Daniel Joint Center for Quantum Information and Computer Science NIST University of Maryland College ParkMD20742 United States Computer Computational and Statistical Sciences Division Los Alamos National Laboratory Los AlamosNM87545 United States Department of Computer Science Institute for Advanced Computer Studies University of Maryland College ParkMD20742 United States Joint Quantum Institute NIST University of Maryland College ParkMD20742 United States

Geometric locality is an important theoretical and practical factor for quantum low-density parity-check (qLDPC) codes which affects code performance and ease of physical realization. For device architectures restricted to 2D local gates, naively implementing the high-rate codes suitable for low-overhead fault-tolerant quantum computing incurs prohibitive overhead. In this work, we present an error correction protocol built on a bilayer architecture that aims to reduce operational overheads when restricted to 2D local gates by measuring some generators less frequently than others. We investigate the family of bivariate bicycle qLDPC codes and show that they are well suited for a parallel syndrome measurement scheme using fast routing with local operations and classical communication (LOCC). Through circuit-level simulations, we find that in some parameter regimes bivariate bicycle codes implemented with this protocol have logical error rates comparable to the surface code while using fewer physical qubits. © 2024, CC BY.

关键词： Quantum computers

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Critical Growth of Cerebral Tissue in Organoids: Theory and Experiments

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Physical Review Letters 2023年第17期131卷 178402-178402页

作者： Egor I. Kiselev Florian Pflug Arndt von Haeseler Center for Integrative Bioinformatics Vienna (CIBIV) Max Perutz Laboratories University of Vienna and Medical University of Vienna Vienna Bio Center (VBC) 1030 Vienna Austria Physics Department Technion 320003 Haifa Israel Biological Complexity Unit Okinawa Institute of Science and Technology Graduate University Onna Okinawa 904-0495 Japan Bioinformatics and Computational Biology Faculty of Computer Science University of Vienna 1090 Vienna Austria

We develop a Fokker-Planck theory of tissue growth with three types of cells (symmetrically dividing, asymmetrically dividing, and nondividing) as main agents to study the growth dynamics of human cerebral organoids. Fitting the theory to lineage tracing data obtained in next generation sequencing experiments, we show that the growth of cerebral organoids is a critical process. We derive analytical expressions describing the time evolution of clonal lineage sizes and show how power-law distributions arise in the limit of long times due to the vanishing of a characteristic growth scale. We discuss that the independence of critical growth on initial conditions could be biologically advantageous.

关键词： Anomalous diffusion Genomics Levy flights Population genetics Brain Tissues Fokker–Planck equation Self-organized criticality

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