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15th International Workshop on OpenMP (IWOMP)

作者： Mayer, Florian Knaust, Marius Philippsen, Michael Friedrich Alexander Univ Erlangen Nurnberg FAU Programming Syst Grp Erlangen Germany Zuse Inst Berlin Berlin Germany

ISBN: (纸本)9783030285968;9783030285951

Due to the ubiquity of OpenMP and the rise of FPGA-based accelerators in the HPC world, several research groups have attempted to bring the two together by building OpenMP-to-FPGA compilers. This paper is a survey of the current state of the art (with a focus on the OpenMP target pragma). It first introduces and explains a design space for the compilers. Design space dimensions include how FPGA infrastructure is generated, how work is distributed, and where/how target outlining is done. A table concisely condenses the available information on the surveyed projects which are also summarized and compared. The paper concludes with possible future research directions.

关键词： Field programmable gate arrays (FPGA)

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Toward digital twins' workload allocation on clouds with low-cost microservices streaming interaction

Toward digital twins' workload allocation on clouds with low...

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Ivannikov Ispras Open Conference (ISPRAS)

作者： Tchernykh, Andrei Facio-Medina, Adrian Pulido-Gaytan, Bernardo Rivera-Rodriguez, Raul Cortes-Mendoza, Jorge M. Radchenko, Gleb Babenko, Mikhail Chernykh, Igor Kulikov, Igor Nesmachnow, Sergio Ivannikov Inst Syst Programming Moscow Russia CICESE Res Ctr Ensenada Baja California Mexico South Ural State Univ Chelyabinsk Russia North Caucasus Fed Univ Stavropol Russia ICMMG SB RAS Novosibirsk Russia Univ Republica Montevideo Uruguay

ISBN: (纸本)9781665412919

A Digital Twin (DT) is a set of computational models representing real-time physical objects and processes in a digital world. The increasing adoption of this paradigm by the major industrial equipment vendors to simulate real-time working conditions and perform smart decision-making, established the Smart Factory Digital Twins architectures, where a set of DTs published as microservices interact with each other exchanging information by streaming technology. In this sense, a fundamental problem consists of selecting adequate computational resources to simulate the physical objects. In an Infrastructure as a Service (IaaS) cloud for DTs, the allocation focuses on distributing the jobs of the set into the virtual machine instances in a way that computational resources demand is satisfied and the cost is minimized In this paper, we propose a set of algorithms based on heuristics, metaheuristics, and Mixed Integer programming to find low-cost solutions. The performance of algorithms is evaluated using Amazon EC2 instances and DT jobs with randomly generated bandwidth, memory, and processor requirements. The experimental results show that the proposed approaches based on bin packing, genetic algorithms, partition, filtering, set coverage, and branch and bound strategies present a competitive performance in the workload allocation of the computational set of jobs of a DT into an IaaS cloud environment. Our allocation heuristic-based techniques allow considerable cost savings in medium and large periods concerning standard approaches such as local search.

关键词： instance Provisioning Digital Twin Smart Factory EC2 Mixed Integer programming Evolutionary Algorithms Metaheuristics

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Formation of Fused Images of the Land Surface from Radar and Optical Images in Spatially Distributed On-Board Operational Monitoring systems

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JOURNAL OF IMAGING 2021年第12期7卷 251-251页

作者： Nenashev, Vadim A. Khanykov, Igor G. St Petersburg State Univ Aerosp Instrumentat Inst Comp Syst & Programming Lab Intelligent Technol & Modelling Complex Syst 67 B Morskaia St St Petersburg 190000 Russia Russian Acad Sci St Petersburg Fed Res Ctr Lab Big Data Technol Sociocyberphys Syst 14 Line VO 39 St Petersburg 199178 Russia

This paper considers the issues of image fusion in a spatially distributed small-size on-board location system for operational monitoring. The purpose of this research is to develop a new method for the formation of fused images of the land surface based on data obtained from optical and radar devices operated from two-position spatially distributed systems of small aircraft, including unmanned aerial vehicles. The advantages of the method for integrating information from radar and optical information-measuring systems are justified. The combined approach allows removing the limitations of each separate system. The practicality of choosing the integration of information from several widely used variants of heterogeneous sources is shown. An iterative approach is used in the method for combining multi-angle location images. This approach improves the quality of synthesis and increases the accuracy of integration, as well as improves the information content and reliability of the final fused image by using the pixel clustering algorithm, which produces many partitions into clusters. The search for reference points on isolated contours is carried out on a pair of left and right images of the docked image from the selected partition. For these reference points, a functional transformation is determined. Having applied it to the original multi-angle heterogeneous images, the degree of correlation of the fused image is assessed. Both the position of the reference points of the contour and the desired functional transformation itself are refined until the quality assessment of the fusion becomes acceptable. The type of functional transformation is selected based on clustered images and then applied to the original multi-angle heterogeneous images. This process is repeated for clustered images with greater granularity in case if quality assessment of the fusion is considered to be poor. At each iteration, there is a search for pairs of points of the contour of the isolated

关键词： image segmentation pixel clustering fused image small-sized on-board systems contour points location information unmanned aerial vehicle spatially distributed systems

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Designing Interfaces for Classes of a Neural Network Graph Model

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programming AND COMPUTER SOFTWARE 2020年第7期46卷 463-472页

作者： Karpov, Yu L. Volkova, I. A. Vylitok, A. A. Karpov, L. E. Smetanin, Yu G. Luxoft Profess LLC 1 I Volokolamskii Proezd 10 Moscow 123060 Russia Moscow MV Lomonosov State Univ Moscow 119991 Russia Russian Acad Sci Ivannikov Inst Syst Programming Ul Solzhenitsyna 25 Moscow 109004 Russia Russian Acad Sci Fed Res Ctr Informat & Control Ul Vavilova 44-2 Moscow 119333 Russia Tech Univ Moscow Inst Phys & Technol Inst Skii 9 Dolgoprudnyi 141701 Moscow Oblast Russia

This paper describes an approach to testing artificial neural networks that is implemented in a C++ program as a set of data structures and algorithms for their processing. C++ classes are used as data structures that implement the processing of the following objects: vertex, edge, directed and undirected graphs, spanning tree, and circuit. Interfaces for the most important overloaded operations on these objects are described. The implementation of a testing procedure that uses overloaded operations on graph model objects is illustrated.

关键词： Directed graphs

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Hybrid attention-based deep neural networks for short-term wind power forecasting using meteorological data in desert regions

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SCIENTIFIC REPORTS 2024年第1期14卷 1-17页

作者： Belletreche, Moussa Bailek, Nadjem Abotaleb, Mostafa Bouchouicha, Kada Zerouali, Bilel Guermoui, Mawloud Kuriqi, Alban Alharbi, Amal H. Khafaga, Doaa Sami EL-Shimy, Mohamed El-kenawy, El-Sayed M. Ahmed Draia Univ Adrar Fac Matter Sci Math & Comp Sci Dept Matter Sci Adrar 01000 Algeria Ahmed Draia Univ Adrar Fac Matter Sci Math & Comp Sci Dept Math & Comp Sci Lab Math Modeling & Applicat Adrar 01000 Algeria Univ Tamanghasset Fac Sci & Technol Energies & Mat Res Lab Tamanghasset 10034 Algeria South Ural State Univ Dept Syst Programming Chelyabinsk 454080 Russia Ctr Dev Energies Renouvelables CDER BP 62 Route Observ Bouzareah Algiers 16340 Algeria Hassiba Benbouali Univ Chlef Fac Civil Engn & Architecture Dept Hydraul Lab Architecture Cities & Environm BP 78C Ouled Fares 02180 Chlef Algeria Ctr Dev Energies Renouvelables CDER Unite Rech Appliquee Energies Renouvelables URAER Ghardaia 47133 Algeria Univ Lisbon CERIS Inst Super Tecn Ave RoviscoPais 1 P-1049001 Lisbon Portugal Princess Nourah Bint Abdulrahman Univ PNU Coll Comp & Informat Sci Comp Sci Dept POB 84428 Riyadh 11671 Saudi Arabia Ain Shams Univ Fac Engn Elect Power & Machines Dept Cairo 11566 Egypt Delta Higher Inst Engn & Technol Dept Commun & Elect Mansoura 35111 Egypt

This study introduces an optimized hybrid deep learning approach that leverages meteorological data to improve short-term wind energy forecasting in desert regions. Over a year, various machine learning and deep learning models have been tested across different wind speed categories, with multiple performance metrics used for evaluation. Hyperparameter optimization for the LSTM and Conv-Dual Attention Long Short-Term Memory (Conv-DA-LSTM) architectures was performed. A comparison of the techniques indicates that the deep learning methods consistently outperform the classical techniques, with Conv-DA-LSTM yielding the best overall performance with a clear margin. This method obtained the lowest error rates (RMSE: 71.866) and the highest level of accuracy (R2: 0.93). The optimization clearly works for higher wind speeds, achieving a remarkable improvement of 22.9%. When we look at the monthly performance, all the months presented at least some level of consistent enhancement (RRMSE reductions from 1.6 to 10.2%). These findings highlight the potential of advanced deep learning techniques in enhancing wind energy forecasting accuracy, particularly in challenging desert environments. The hybrid method developed in this study presents a promising direction for improving renewable energy management. This allows for more efficient resource allocation and improves wind resource predictability.

关键词： Wind power forecasting Convolutional neural network Hybrid deep learning Energy transition Long short-term memory Attention mechanism Hyperparameter optimization Renewable energy Wind energy

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On the Representation of Results of Binary Code Reverse Engineering

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programming AND COMPUTER SOFTWARE 2018年第3期44卷 200-206页

作者： Padaryan, V. A. Ledovskikh, I. N. Russian Acad Sci Inst Syst Programming Moscow 109004 Russia

A representation of algorithms extracted from binary code by reverse engineering is discussed. Both intermediate representations designed for automatic analysis and final representations passed to the end user are considered. The two main tasks of reverse engineering-automatic detection of exploitable vulnerabilities and discovery of undocumented features- are analyzed. The basic scheme of the system implementing the automatic detection of exploitable vulnerabilities is presented and the key properties of the intermediate representation designed for solving this problem using an efficient generation of a system of equations for an SMT solver are described. The workflow for discovering undocumented features is described. These steps are the localization of the algorithm, its representation in the form that is convenient for analysis, and investigation of its properties. To automate the first phase, a combined static and dynamic representation is constructed, which includes OS-level events and calls to library functions;they serve as anchor points used by the analyst for the algorithm localization. The further support of localization uses code slicing and navigation algorithms. Once the algorithm is localized, the further work goes in two directions: interactive construction of a compact annotated representation of the algorithm by a flowchart and automated investigation of the algorithm properties aimed at determining declared and undeclared data flows. The representation of the algorithm is based on the construction of simplified models of functions taking into account input and output buffers and on the automatic detection of data dependences between buffers of various function calls. The overall scenario of the analyst' work with such a flowchart in the context of discovering undocumented features is described;this scenario is based on annotating the declared data flows and on the automatic detection of undeclared data flows. In conclusion, an example of the resul

关键词： binary code combined analysis intermediate representation

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Time for lipid cell biology

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NATURE CELL BIOLOGY 2025年第2期27卷 169-174页

作者： Chen, Xiao-Wei Don, Anthony S. Fedorova, Maria Harayama, Takeshi Krahmer, Natalie Nagata, Shigekazu Narayan, Priyanka Nicholas, Dequina Nowinski, Sara M. Saheki, Yasunori Semenkovich, Clay F. Zhao, Xiaoai Zou, Yilong Peking Univ NHC Key Lab Cardiovasc Mol Biol & Regulatory Peptides NHC Key Lab Cardiovasc Mol Biol & Regulatory Pept Beijing Peoples R China Univ Sydney Fac Med & Hlth Sch Med Sci Sydney NSW Australia Univ Sydney Charles Perkins Ctr Sydney NSW Australia Univ Sydney Brain & Mind Ctr Sydney NSW Australia Univ Hosp Carl Gustav Carus Ctr Membrane Biochem & Lipid Res Dresden Germany Tech Univ Dresden Fac Med Dresden Germany Univ Cote Azur Inst Pharmacol Mol & Cellulaire CNRS UMR7275 Inserm U1323 Valbonne France Helmholtz Zentrum Munchen Inst Diabet & Obes Neuherberg Germany German Ctr Diabet Res DZD Neuherberg Germany Osaka Univ Immunol Frontier Res Ctr Osaka Japan Natl Inst Diabet & Digest & Kidney Dis NIDDK NIH Bethesda MD 20892 USA Univ Calif Irvine Mol Biol & Biochem Irvine CA 92697 USA Van Andel Inst Dept Metab & Nutr Programming Grand Rapids MI 49503 USA Nanyang Technol Univ Lee Kong Chian Sch Med Neurosci & Mental Hlth Programme Singapore Singapore Kumamoto Univ Inst Resource Dev & Anal Kumamoto Japan Washington Univ St Louis St Louis MO 63130 USA Yale Sch Med Dept Comparat Med Yale Ctr Mol & Syst Metab New Haven CT 06520 USA Westlake Univ Sch Life Sci Hangzhou Peoples R China Westlake Lab Life Sci & Biomed Westlake Four Dimens Dynam Metabol Meta4D Lab Hangzhou Peoples R China

It is an exciting time for lipid metabolism and membrane cell biologists as technological progress has increased our ability to study lipids in cells. We asked leaders studying lipid cell biology from different perspectives to share what questions they are most interested in and what tools they believe the field is currently lacking. © Springer Nature America, Inc. 2025.

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Building the Software-Defined Data Center

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programming AND COMPUTER SOFTWARE 2019年第8期45卷 458-466页

作者： Shabanov, B. M. Samovarov, O., I Russian Acad Sci Sci Res Inst Syst Dev Nakhimovskii Pr 36-1 Moscow 117218 Russia Russian Acad Sci Ivannikov Inst Syst Programming Ul Solzhenitsyna 25 Moscow 109004 Russia Plekhanov Russian Univ Econ Stremyannyi Per 36 Moscow 117997 Russia

Data center is the most effective way of providing computational resources to a large number of users. The software-defined model is a modern approach to the creation of the computing infrastructure for the data center, which allows user tasks to be processed in acceptable time and at acceptable cost. This paper formulates the general design requirements for the interagency data center and describes some problems and methods of planning and building software-defined data centers (deployment of computing systems optimized for maximum hardware utilization, software support for different classes of tasks, etc.).

关键词： Automation

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Bayesian and deep-learning models applied to the early detection of ovarian cancer using multiple longitudinal biomarkers

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CANCER MEDICINE 2024年第7期13卷 e7163页

作者： Abrego, Luis Zaikin, Alexey Marino, Ines P. Krivonosov, Mikhail I. Jacobs, Ian Menon, Usha Gentry-Maharaj, Aleksandra Blyuss, Oleg UCL EGA Inst Womens Hlth Dept Womens Canc London England UCL Dept Math London WC1E 6BT England Univ Rey Juan Carlos Dept Biol Geol Phys & Inorgan Chem Madrid Spain Russian Acad Sci Ivannikov Inst Syst Programming Res Ctr Trusted Artificial Intelligence Moscow Russia Lobachevsky State Univ Inst Biogerontol Nizhnii Novgorod Russia UCL MRC Clin Trials Unit London England Queen Mary Univ London Wolfson Inst Populat Hlth London England Sechenov First Moscow State Med Univ Sechenov Univ Inst Childs Hlth Dept Paediat & Paediat Infect Dis Moscow Russia Queen Mary Univ London Wolfson Inst Populat Hlth Ctr Canc Screening Prevent & Early Detect Charterhouse Sq London EC1M 6BQ England

BackgroundOvarian cancer is the most lethal of all gynecological cancers. Cancer Antigen 125 (CA125) is the best-performing ovarian cancer biomarker which however is still not effective as a screening test in the general population. Recent literature reports additional biomarkers with the potential to improve on CA125 for early detection when using longitudinal multimarker *** data comprised 180 controls and 44 cases with serum samples sourced from the multimodal arm of UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). Our models were based on Bayesian change-point detection and recurrent neural *** obtained a significantly higher performance for CA125-HE4 model using both methodologies (AUC 0.971, sensitivity 96.7% and AUC 0.987, sensitivity 96.7%) with respect to CA125 (AUC 0.949, sensitivity 90.8% and AUC 0.953, sensitivity 92.1%) for Bayesian change-point model (BCP) and recurrent neural networks (RNN) approaches, respectively. One year before diagnosis, the CA125-HE4 model also ranked as the best, whereas at 2 years before diagnosis no multimarker model outperformed *** study identified and tested different combination of biomarkers using longitudinal multivariable models that outperformed CA125 alone. We showed the potential of multivariable models and candidate biomarkers to increase the detection rate of ovarian cancer. Using the data from largest prospective ovarian cancer clinical trial, we assessed, for the first time, the performance of deep and statistical learning approaches in evaluating of the panel longitudinal biomarkers. Our results demonstrate that these models outperform CA125, the single best ovarian cancer biomarker. These findings underscore the potential of multimarker models in improving the detection rate of ovarian cancer and have significant implications for the field of cancer screening and early ***

关键词： CA125 change-point detection longitudinal biomarkers ovarian cancer recurrent neural networks

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Random Graph Modeling: A Survey of the Concepts

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ACM COMPUTING SURVEYS 2020年第6期52卷 131-131页

作者： Drobyshevskiy, Mikhail Turdakov, Denis Russian Acad Sci Ivannikov Inst Syst Programming Alexander Solzhenitsyn St 25 Moscow 109004 Russia Moscow Inst Phys & Technol Alexander Solzhenitsyn St 25 Moscow 109004 Russia

Random graph (RG) models play a central role in complex networks analysis. They help us to understand, control, and predict phenomena occurring, for instance, in social networks, biological networks, the Internet, and so on. Despite a large number of RG models presented in the literature, there are few concepts underlying them. instead of trying to classify awide variety of very dispersedmodels, we capture and describe concepts they exploit considering preferential attachment, copying principle, hyperbolic geometry, recursively defined structure, edge switching, Monte Carlo sampling, and so on. We analyze RG models, extract their basic principles, and build a taxonomy of concepts they are based on. We also discuss how these concepts are combined in RG models and how they work in typical applications like benchmarks, null models, and data anonymization.

关键词： Random graph models patterns

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