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检索条件"任意字段=21st International Symposium on Computer Architecture and High Performance Computing"

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high-Capacity Metro Optical Nodes Enabling Efficient Dimensioning of Edge computing Platforms

High-Capacity Metro Optical Nodes Enabling Efficient Dimensi...

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international Conference on Transparent Optical Networks

作者： António Eira Nelson Costa João Pedro Infinera Unipessoal Lda Carnaxide Portugal

The convergence of optical metro networks with small data-centers at the edge is reshaping how optical nodes and networks are architected. This paper explores the architecture of the metro aggregation node and its effect on edge data-center dimensioning requirements. Through network simulation on a wide range of chain topologies and load profiles, we evaluate the extent to which direct lightpaths between aggregation nodes can be used to load balance server utilization. This enables to understand how optical layer flexibility interworks with service bandwidth and latency requirements to shape the best trade-offs between server, line system and transponder costs.

关键词： Optical fibers Integrated optics Optical fiber networks Bandwidth computer architecture Network topology

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Weakly Supervised Learning Technique for Solving Partial Differential Equations;Case study of 1-D Reaction-Diffusion Equation 2nd

Weakly Supervised Learning Technique for Solving Partial Dif...

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2nd international Congress on high-performance computing and Big Data Analysis, TopHPC 2019

作者： Zakeri, Behzad Monsefi, Amin Karimi Samsam, Sanaz Monsefi, Bahareh Karimi School of Mechanical Engineering College of Engineering University of Tehran Tehran Iran Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran Department of Aerospace Engineering Sharif University of Technology Tehran Iran Faculty of Mechanical Engineering Islamic Azad University Central Tehran Branch Tehran Iran

ISBN: (纸本)9783030334949

Deep learning as a valuable intelligence tool to deal with complicated problems plays a crucial role in the 21st century. The utility of deep learning in solving partial differential equations (PDEs) is an interesting application of AI, which has been considered in recent years. However, supervision of learning procedure needs to have considerable labeled data to train the network, and this method could not be a beneficial technique to deal with unknown PDEs which we do not have any labeled data. To tackle this issue, in this paper a new method will be presented to solve PDEs only by using boundary and initial condition. Weakly supervision as an efficient method can provide an ideal bed to tackle boundary and initial value problems. To have better judgment about this method we chose Reaction-Diffusion equation as a versatile equation in engineering and science to be solved as a case study. By using the weakly supervised method and the finite difference method reaction-diffusion equation have solved, and the results of these methods have been compared. It has been shown that the results of deep learning have high consistency with finite difference results, and weakly supervised learning can be introduced as an efficient method to solve different types of differential equations. © 2019, Springer Nature Switzerland AG.

关键词： Partial differential equations

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Autonomous Image Segmentation by Competitive Unsupervised GrowCut

Autonomous Image Segmentation by Competitive Unsupervised Gr...

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international symposium on Symbolic and Numeric Algorithms for Scientific computing (SYNASC)

作者： Radu Marginean Anca Andreica Laura Diosan Zoltán Bálint Babes-Bolyai University Cluj-Napoca Romania Faculty of Mathematics and Computer Science Babes??-Bolyai University IMOGEN Research Institute County Clinical Emergency Hospital

ISBN: (数字)9781728157245

ISBN: (纸本)9781728157252

In this paper, we introduce Competitive Unsupervised GrowCut, a cellular automata-based, unsupervised and autonomous algorithm that combines the label merging component of Unsupervised GrowCut with the soft label propagation mechanism of GrowCut. We evaluated our algorithm on two benchmark image segmentation datasets, along with two related methods proposed in the literature. We also provide a detailed comparative analysis of the three algorithms' segmentation performance and properties. Our analysis identified application-specific regimes that govern the relative performance of the analyzed algorithms.

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An Approach of Segmentation Method Using Deep Learning for CT Medical Images

An Approach of Segmentation Method Using Deep Learning for C...

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international symposium on Symbolic and Numeric Algorithms for Scientific computing (SYNASC)

作者： Sebastian-Aurelian stefaniga Mihail Gaianu Computer Science Department West University of Timisoara UVT Timisoara Romania

ISBN: (数字)9781728157245

ISBN: (纸本)9781728157252

In the last few years the filed of deep learning and in general machine learning in medical imaging has become the state-of-the-art in terms of medical image analysis and processing. In our experimental work, we try to provide a high-level approach of fully connected deep neural networks applied in medical image segmentation (lung CT scans), by using Deep Learning GPU Training System (DIGITS) avoiding rigorous mathematical formalism of neural networks. For this purpose using the concept of solving problems by learning from experiences is necessary. The goal of our experimental work is to design an analysis of particular methods in lung CT scans segmentation by using the one of the most used architecture in the filed deep learning for biomedical image segmentation techniques, U-NET architecture. This will lead to obtain in the back-end a mathematical model with the purpose of training to obtain useful results when fed input data and will demonstrate how to train a Fully Convolutional Network (FCN) model like U-NET using segmentation techniques for lung tumor tissues received from Computed Tomographic (CT) device on abdominal images using GPU processing with Nvidia DIGITS.

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Digital Tracking Cloud Distributed architecture for Detection of Faint NEAs

Digital Tracking Cloud Distributed Architecture for Detectio...

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international symposium on Symbolic and Numeric Algorithms for Scientific computing (SYNASC)

作者： Roxana E. Sichitiu Marc E. Frincu Ovidiu Vaduvescu West University of Timisoara Timisoara Romania Computer Science Department West University of Timisoara Timisoara Romania Astronomy department Isaac Newton Group Spain School of Doctoral Sciences University of Craiova Romania

ISBN: (数字)9781728157245

ISBN: (纸本)9781728157252

There is an exponential volume of captured images, millions of captures taken every night being processed and scrutinized. Big Data analysis has become essential for the study of the solar system, discovery and orbital knowledge of the asteroids. This analysis often requires more advanced algorithms capable of processing the available data and solve the essential problems in almost real time. One such problem that needs very rapid investigation involves the detection of Near Earth Asteroids(NEAs) and their orbit refinement which should answer the question "will the Earth collide in the future with any hazardous asteroid?". This paper proposes a cloud distributed architecture meant to render near real-time results, focusing on the image stacking techniques aimed to detect very faint moving objects, and pairing of unknown objects with known orbits for asteroid discovery and identification.

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Mobile Data offloading in the Vehicular Networks: A Truthful Incentive Mechanism

Mobile Data offloading in the Vehicular Networks: A Truthful...

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IEEE international Conference on high performance computing and Communications

作者： Enfang Zhong Peng Li Tao Zhang Weiyi Huang Qin Liu College of Computer Science and Technology Wuhan University of Science and Technology China Department of Computer Science New York Institute of Technology USA School of Cyber Science and Engineering Wuhan University USA

Mobile data offloading is a cost-effective and promising approach to alleviate the burden of the cellular network. In this paper, we focus on designing a truthful incentive mechanism that the auction cloud platform motivates mobile users with deadline constraints and capacity constraints to offload data. First, we formulate the mobile data offloading problem as a reverse auction. Then, we reduce this social welfare maximization reverse auction for data offloading as an integer linear programming (ILP) problem. To solve this problem, we propose a data offloading auction greedy algorithm to choose the most cost-effective bids. We decompose the data offload auction greedy algorithm into the winner determination problem (WDP) and the threshold payment scheme. Moreover, we propose a delay winner winning algorithm based on the data offloading auction greedy algorithm. Extensive simulation experiments demonstrate that the data offloading auction mechanism dramatically reduces the burden of the cellular network while meeting the offloading tasks deadline constraints and mobile user capacity constraints.

关键词： Task analysis Cellular networks Wireless fidelity Greedy algorithms Delays Data models Conferences

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Inter-Scanner Harmonization of high Angular Resolution DW-MRI Using Null Space Deep Learning

Inter-Scanner Harmonization of High Angular Resolution DW-MR...

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21st international Conference on Medical Image computing and computer-Assisted Intervention (MICCAI) / 8th Eurographics Workshop on Visual computing for Biology and Medicine (VCBM) / international Workshop on Computational Diffusion MRI (CDMRI)

作者： Nath, Vishwesh Parvathaneni, Prasanna Hansen, Colin B. Hainline, Allison E. Bermudez, Camilo Remedios, Samuel Blaber, Justin A. Schilling, Kurt G. Lyu, Ilwoo Janve, Vaibhav Gao, Yurui stepniewska, Iwona Rogers, Baxter P. Newton, Allen T. Davis, L. Taylor Luci, Jeff Anderson, Adam W. Landman, Bennett A. Vanderbilt Univ EECS Nashville TN 37203 USA Vanderbilt Univ BME Nashville TN 37203 USA Vanderbilt Univ Biostat Nashville TN 37203 USA Middle Tennessee State Univ CS Murfreesboro TN 37132 USA Vanderbilt Univ Psychol Nashville TN 37203 USA Vanderbilt Univ VUIIS Nashville TN 37232 USA Vanderbilt Univ VUMC Nashville TN 37203 USA Univ Texas Austin BME Austin TX 78712 USA

ISBN: (纸本)9783030058319;9783030058302

Diffusion-weighted magnetic resonance imaging (DW-MRI) allows for non-invasive imaging of the local fiber architecture of the human brain at a millimetric scale. Multiple classical approaches have been proposed to detect both single (e.g., tensors) and multiple (e.g., constrained spherical deconvolution, CSD) fiber population orientations per voxel. However, existing techniques generally exhibit low reproducibility across MRI scanners. Herein, we propose a data-driven technique using a neural network design which exploits two categories of data. First, training data were acquired on three squirrel monkey brains using ex-vivo DW-MRI and histology of the brain. Second, repeated scans of human subjects were acquired on two different scanners to augment the learning of the network proposed. To use these data, we propose a new network architecture, the null space deep network (NSDN), to simultaneously learn on traditional observed/truth pairs (e.g., MRI-histology voxels) along with repeated observations without a known truth (e.g., scan-rescan MRI). The NSDN was tested on twenty percent of the histology voxels that were kept completely blind to the network. NSDN significantly improved absolute performance relative to histology by 3.87% over CSD and 1.42% over a recently proposed deep neural network approach. More-over, it improved reproducibility on the paired data by 21.19% over CSD and 10.09% over a recently proposed deep approach. Finally, NSDN improved generalizability of the model to a third in-vivo human scanner (which was not used in training) by 16.08% over CSD and 10.41% over a recently proposed deep learning approach. This work suggests that data-driven approaches for local fiber reconstruction are more reproducible, informative, precise and offer a novel, practical method for determining these models.

关键词： Diffusion HARDI DW-MRI Null space CSD Harmonization Inter-scanner Deep learning Histology

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Search for a diffuse astrophysical neutrino flux from the Galactic Ridge using KM3NeT/ARCA data 38

Search for a diffuse astrophysical neutrino flux from the Ga...

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38th international Cosmic Ray Conference, ICRC 2023

作者： Filippini, Francesco Aiello, S. Albert, A. Alves Garre, S. Aly, Z. Ambrosone, A. Ameli, F. Andre, M. Androutsou, E. Anguita, M. Aphecetche, L. Ardid, M. Ardid, S. Atmani, H. Aublin, J. Bailly-Salins, L. Bardačová, Z. Baret, B. Bariego-Quintana, A. Basegmez du Pree, S. Becherini, Y. Bendahman, M. Benfenati, F. Benhassi, M. Benoit, D.M. Berbee, E. Bertin, V. Biagi, S. Boettcher, M. Bonanno, D. Boumaaza, J. Bouta, M. Bouwhuis, M. Bozza, C. Bozza, R.M. Brânzaş, H. Bretaudeau, F. Bruijn, R. Brunner, J. Bruno, R. Buis, E. Buompane, R. Busto, J. Caiffi, B. Calvo, D. Campion, S. Capone, A. Carenini, F. Carretero, V. Cartraud, T. Castaldi, P. Cecchini, V. Celli, S. Cerisy, L. Chabab, M. Chadolias, M. Chen, A. Cherubini, S. Chiarusi, T. Circella, M. Cocimano, R. Coelho, J.A.B. Coleiro, A. Coniglione, R. Coyle, P. Creusot, A. Cruz, A. Cuttone, G. Dallier, R. Darras, Y. De Benedittis, A. De Martino, B. Decoene, V. Del Burgo, R. Di Cerbo, U.M. Di Mauro, L.S. Di Palma, I. Díaz, A.F. Diaz, C. Diego-Tortosa, D. Distefano, C. Domi, A. Donzaud, C. Dornic, D. Dörr, M. Drakopoulou, E. Drouhin, D. Dvornický, R. Eberl, T. Eckerová, E. Eddymaoui, A. van Eeden, T. Eff, M. van Eijk, D. El Bojaddaini, I. El Hedri, S. Enzenhöfer, A. Ferrara, G. Filipović, M.D. Filippini, F. Franciotti, D. Fusco, L.A. Gabriel, J. Gagliardini, S. Gal, T. García Méndez, J. Garcia Soto, A. Gatius Oliver, C. Geißelbrecht, N. Ghaddari, H. Gialanella, L. Gibson, B.K. Giorgio, E. Goos, I. Goupilliere, D. Gozzini, S.R. Gracia, R. Graf, K. Guidi, C. Guillon, B. Gutiérrez, M. van Haren, H. Heijboer, A. Hekalo, A. Hennig, L. Hernández-Rey, J.J. Huang, F. Idrissi Ibnsalih, W. Illuminati, G. James, C.W. de Jong, M. de Jong, P. Jung, B.J. Kalaczyński, P. Kalekin, O. Katz, U.F. Khan Chowdhury, N.R. Khatun, A. Kistauri, G. Kopper, C. Kouchner, A. Kulikovskiy, V. Kvatadze, R. Labalme, M. Lahmann, R. Larosa, G. Lastoria, C. Lazo, A. Le stum, S. Lehaut, G. Leonora, E. Università di Bologna Dipartimento di Fisica e Astronomia v.le C. Berti-Pichat 6/2 Bologna40127 Italy INFN Sezione di Bologna v.le C. Berti-Pichat 6/2 Bologna40127 Italy INFN Sezione di Catania Via Santa Sofia 64 Catania95123 Italy Université de Strasbourg CNRS IPHC UMR 7178 StrasbourgF-67000 France IFIC - Instituto de Física Corpuscular CSIC - Universitat de València c/Catedrático José Beltrán 2 Paterna Valencia46980 Spain Aix Marseille Univ CNRS IN2P3 CPPM Marseille France INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo Via Cintia ed. G Napoli80126 Italy Università di Napoli "Federico II" Dip. Scienze Fisiche "E. Pancini" Complesso Universitario di Monte S. Angelo Via Cintia ed. G Napoli80126 Italy INFN Sezione di Roma Piazzale Aldo Moro 2 Roma00185 Italy Universitat Politècnica de Catalunya Laboratori d’Aplicacions Bioacústiques Centre Tecnològic de Vilanova i la Geltrú Avda. Rambla Exposició s/n Vilanova i la Geltrú08800 Spain NCSR Demokritos Institute of Nuclear and Particle Physics Ag. Paraskevi Attikis Athens15310 Greece University of Granada Dept. of Computer Architecture and Technology CITIC Granada18071 Spain Subatech IMT Atlantique IN2P3-CNRS Université de Nantes 4 rue Alfred Kastler - La Chantrerie BP 20722 Nantes44307 France Universitat Politècnica de València Instituto de Investigación para la Gestión Integrada de las Zonas Costeras C/ Paranimf 1 Gandia46730 Spain University Mohammed V in Rabat Faculty of Sciences 4 av. Ibn Battouta B.P. 1014 RabatR.P. 10000 Morocco Université Paris Cité CNRS Astroparticule et Cosmologie ParisF-75013 France LPC CAEN Normandie Univ ENSICAEN UNICAEN CNRS IN2P3 6 boulevard Maréchal Juin Caen14050 France Czech Technical University in Prague Institute of Experimental and Applied Physics Husova 240/5 Prague110 00 Czech Republic Comenius University in Bratislava Department of Nuclear Physics and Biophysics Mlynska dolina F1 Bratislava842 48 Slov

Several theoretical models predict and describe the properties of part of the diffuse neutrino flux, originating from the interaction of Galactic cosmic rays with the interstellar medium matter located in the centre of our Galaxy. This neutrino flux is expected to be of the same order of magnitude as the diffuse γ-ray flux measured by Fermi-LAT, close to the Galactic plane. Recently hints by the ANTARES Collaboration, at a significance level over 2 σ, and the observation by the IceCube Collaboration, at a significance level of 4.5 σ, of a high-energy neutrino emission from the Galactic plane have been reported. For these reasons, and considering the privileged position of the KM3NeT/ARCA telescope, being located in the Northern hemisphere, data have been analysed searching for a possible excess of events coming from an extended region, with Galactic coordinates |l| ◦ and |b| ◦, namely the Galactic Ridge. In this contribution, the result of the analysis exploiting data gathered with KM3NeT/ARCA in the period in which comprises 6, 8, 19 or 21 active detection units is presented, showing the capabilities and performance of KM3NeT. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmology

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A Multi-Agent Placement System for Virtual Network Functions in a Cloud-Based architecture

A Multi-Agent Placement System for Virtual Network Functions...

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international symposium on Symbolic and Numeric Algorithms for Scientific computing (SYNASC)

作者： Sara Retal Abdellah Idrissi Costin Badica Ionut Muraretu Software Engineering Department University of Craiova Romania Computer Science Department University Mohammed V in Rabat Morocco University of Craiova Craiova Romania

ISBN: (数字)9781728157245

ISBN: (纸本)9781728157252

This paper introduces a multi-agent system to enhance virtual network functions placement. The suggested system consists of a number of proactive agents aiming at providing a virtual network functions configuration depending on the state of the environment. Some of these agents are developed to determine an adequate target configuration based on available information and after a negotiation interaction; while other agents are designed to perceive the environment, as well as to act as managers in the decision process. The communication and the cooperation of these agents allow efficient placement of virtual network functions on distributed data-centers, possibly using multiple agent platforms. The results have demonstrated that our approach can enhance the solution by providing an adequate and automatic configuration without the intervention of any human decision-maker.

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Improving Detection of Malicious Office Documents Using One-Side Classifiers

Improving Detection of Malicious Office Documents Using One-...

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international symposium on Symbolic and Numeric Algorithms for Scientific computing (SYNASC)

作者： Silviu Constantin Vitel Gheorghe Balan Dumitru Bogdan Prelipcean Alexandru Ioan Cuza University Iasi Romania Faculty of Computer Science Bitdefender Cyber Threat Intelligence Lab "Al.I. Cuza" University Ia??i Rom??nia

ISBN: (数字)9781728157245

ISBN: (纸本)9781728157252

The current threat landscape is diverse and has lately been shifting from the binary executable application to a more light-coded and data-oriented approach. Considering this, the use of Microsoft Office documents in attacks has increased. The number of malicious samples is high and the complexity of evasion techniques is also challenging. The VBA macros are highly used in enterprise environments with benign purposes, so, in terms of detection, the number of false alarms should be close to zero. In this paper we discuss and propose a solution which focuses on keeping the rate of false positives as low as possible and, at the same time, maximizes the detection rate.

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