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arXiv 2023年

作者： Tosta e Melo, I. Ducoin, J.-G. Vidadi, Z. Andrade, C. Rupchandani, V. Agayeva, S. Abdelhadi, J. Abe, L. Aguerre-Chariol, O. Aivazyan, V. Alishov, S. Antier, S. Bai, J.-M. Baransky, A. Bednarz, S. Bendjoya, Ph. Benkhaldoun, Z. Beradze, S. Bizouard, M.A. Bhardwaj, U. Blazek, M. Boër, M. Broens, E. Burkhonov, O. Christensen, N. Cooke, J. Corradi, W. Coughlin, M.W. Culino, T. Daigne, F. Dornic, D. Duverne, P.-A. Ehgamberdiev, S. Eymar, L. Fouad, A. Freeberg, M. Gendre, B. Guo, F. Gokuldass, P. Guessoum, N. Gurbanov, E. Hainich, R. Hasanov, E. Hello, P. Inasaridze, R. Iskandar, A. Ismailov, N. Janati, A. Jegou du Laz, T. Kann, D.A. Karpov, S. Kiendrebeogo, R.W. Klotz, A. Kneip, R. Kochiashvili, N. Kaeouach, A. Kruiswijk, K. Lamoureux, M. Leroy, N. Lin, W.L. Mao, J. Marchais, D. Mašek, M. Midavaine, T. Moller, A. Morris, D. Natsvlishvili, R. Navarete, F. Nicuesa Guelbenzu, A. Noonan, K. Noysena, K. Oksanen, A. Orange, N.B. Pellouin, C. Peloton, J. Peng, H.W. Pilloix, M. Popowicz, A. Pradier, T. Pyshna, O. Raaijmakers, G. Rajabov, Y. Rau, A. Rinner, C. Rivet, J.-P. Ryh, A.S. Sabil, M. Sadibekova, T. Sasaki, N. Serrau, M. Simon, A. Shokry, Ahmed Smith, K. Sokoliuk, O. Song, X. Takey, A. Thierry, P. Tillayev, Y. Turpin, D. de Ugarte Postigo, A. Vasylenko, V. Vernet, D. Wang, L. Vachier, F. Vignes, J.P. Wang, X.F. Zeng, X. Zhang, J. Zhu, Y. Department of Physics and Astronomy University of Catania Catania95125 Italy Sorbonne Université CNRS UMR 7095 Institut d'Astrophysique de Paris 98 bis bd Arago Paris75014 France N.Tusi Shamakhy Astrophysical Observatory Azerbaijan National Academy of Sciences settl.Y. Mammadaliyev ShamakhyAZ 5626 Azerbaijan School of Physics and Astronomy University of Minnesota MinneapolisMN55455 United States Brown University RI ProvidenceRI02912 United States Oukaimeden Observatory High Energy Physics and Astrophysics Laboratory FSSM Cadi Ayyad University Av. Prince My Abdellah MarrakeshBP 2390 Morocco Université Côte d'Azur Observatoire de la Côte d'Azur CNRS Laboratoire J.-L. Lagrange Boulevard de l'Observatoire Nice06304 France 14 rue Saint-Hubert Orry-la-VilleF-60560 France E. Kharadze Georgian National Astrophysical Observatory Mt.Kanobili Abastumani Adigeni0301 Georgia Samtskhe-Javakheti State University Rustaveli Str. 113 Akhaltsikhe0080 Georgia N.Tusi Shamakhy astrophysical Observatory Azerbaijan National Academy of Sciences settl.Mamedaliyev ShamakhyAZ 5626 Azerbaijan Université Côte d'Azur Observatoire de la Côte d'Azur CNRS Artemis Boulevard de l'Observatoire Nice06304 France Astronomical Observatory Taras Shevshenko National University of Kyiv Observatorna str. 3 Kyiv04053 Ukraine Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Akademicka 16 Gliwice44-100 Poland GRAPPA Anton Pannekoek Institute for Astronomy Institute of High-Energy Physics University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands FZU - Institute of Physics The Czech Academy of Sciences Na Slovance 1999/2 PrahaCZ-182 21 Czech Republic Vereniging or Sterrenkunde Balen-Neetlaan 18A Mol2400 Belgium Ulugh Beg Astronomical Institute Uzbekistan Academy of Sciences Astronomy str. 33 Tashkent100052 Uzbekistan Centre for Astrophysics and Supercomputing Swinburne University of Technology HawthornVIC3122

Aims. We present a campaign designed to train the GRANDMA network and its infrastructure to follow up on transient alerts and detect their early afterglows. In preparation for O4 II campaign, we focused on GRB alerts as they are expected to be an electromagnetic counterpart of gravitational-wave events. Our goal was to improve our response to the alerts and start prompt observations as soon as possible to better prepare the GRANDMA network for the fourth observational run of LIGO-Virgo-Kagra (which started at the end of May 2023), and future missions such as SM. Methods. To receive, manage and send out observational plans to our partner telescopes we set up dedicated infrastructure and a rota of follow-up adcates were organized to guarantee round-the-clock assistance to our telescope teams. To ensure a great number of observations, we focused on Swift GRBs whose localization errors were generally smaller than the GRANDMA telescopes' field of view. This allowed us to bypass the transient identification process and focus on the reaction time and efficiency of the network. Results. During'Ready for O4 II', 11 Swift/INTEGRAL GRB triggers were selected, nine fields had been observed, and three afterglows were detected (GRB 220403B, GRB 220427A, GRB 220514A), with 17 GRANDMA telescopes and 17 amateur astronomers from the citizen science project Kilonova-Catcher. Here we highlight the GRB 220427A analysis where our long-term follow-up of the host galaxy allowed us to obtain a photometric redshift of z = 0.82 ± 0.09, its lightcurve elution, fit the decay slope of the afterglows, and study the properties of the host galaxy. Conclusions. During this 8-week-long GRB follow-up campaign, we successfully fulfilled our goal of training telescope teams for O4 and the improvement of the associated technical toolkits. For seven of the GRB alerts, our network was able to start the first observations less than one hour after the GRB trigger time. We also characterized the network effic

关键词： Telescopes

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Precision Studies of QCD in the Low Energy Domain of the EIC

arXiv

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arXiv 2022年

作者： Burkert, V.D. Elouadrhiri, L. Afanasev, A. Arrington, J. Contalbrigo, M. Cosyn, W. Deshpande, A. Glazier, D.I. Ji, X. Liuti, S. Oh, Y. Richards, D. Satogata, T. Vossen, A. Abdolmaleki, H. Albataineh, A. Aidala, C.A. Alexandrou, C. Avagyan, H. Bacchetta, A. Baker, M. Benmokhtar, F. Bernauer, J.C. Bissolotti, C. Briscoe, W. Byers, D. Cao, Xu Carlson, C.E. Cichy, K. Cloet, I.C. Cocuzza, C. Cole, P.L. Constantinou, M. Courtoy, A. Dahiyah, H. Dehmelt, K. Diehl, S. Dilks, C. Djalali, C. Dupré, R. Dusa, S.C. El-Bennich, B. Fassi, L. El Frederico, T. Freese, A. Gamage, B.R. Gamberg, L. Ghoshal, R.R. Girod, F.X. Goncalves, V.P. Gotra, Y. Guo, F.K. Guo, X. Hattawy, M. Hatta, Y. Hayward, T. Hen, O. Huber, G.M. Hyde, C. Isupov, E.L. Jacak, B. Jacobs, W. Jentsch, A. Ji, C.R. Joosten, S. Kalantarians, N. Kang, Z. Kim, A. Klein, S. Kriesten, B. Kumano, S. Kumar, A. Kumericki, K. Kuchera, M. Lai, W.K. Li, Jin Li, Shujie Li, W. Li, X. Lin, H.-W. Liu, K.F. Liu, Xiaohui Markowitz, P. Mathieu, V. McEneaney, M. Mekki, A. de Melo, J.P.B.C. Meziani, Z.E. Milner, R. Mkrtchyan, H. Mochalov, V. Mokeev, V. Morozov, V. Moutarde, H. Murray, M. Mtingwa, S. Nadel-Turonski, P. Okorokov, V.A. Onyie, E. Pappalardo, L.L. Papandreou, Z. Pecar, C. Pilloni, A. Pire, B. Polys, N. Prokudin, A. Przybycien, M. Qiu, J.-W. Radici, M. Reed, R. Ringer, F. Roy, B.J. Sato, N. Schäfer, A. Schmookler, B. Schnell, G. Schweitzer, P. Seidl, R. Semenov-Tian-Shansky, K.M. Serna, F. Shaban, F. Shabestari, M.H. Shiells, K. Signori, A. Spiesberger, H. Strakovsky, I. Sufian, R.S. Szczepaniak, A. Teodorescu, L. Terry, J. Teryaev, O. Tessarotto, F. Timmer, C. Tawfik, Abdel Nasser Cazares, L. Valenzuela Vladimirov, A. Voutier, E. Watts, D. Wilson, D. Winney, D. Xiao, B. Ye, Z. Ye, Zh. Yuan, F. Zachariou, N. Zahed, I. Zhang, J.L. Zhang, Y. Zhou, J. Thomas Jefferson National Accelerator Facility Newport NewsVA23606 United States The George Washington University Department of Physics WashingtonDC20052 United States Lawrence Berkeley National Laboratory BerkeleyCA94720 United States INFN Ferrara Ferrara44122 Italy Florida International University Department of Physics MiamiFL33199 United States Ghent University Department of Physics and Astronomy GhentB9000 Belgium Stony Brook University Stony BrookNY11794 United States SUPA School of Physics and Astronomy University of Glasgow GlasgowG12 8QQ United Kingdom University of Maryland College ParkMD20742 United States Center for Nuclear Femtography 1201 New York Avenue WashingtonDC20005 United States University of Virginia Physics Department 382 McCormick Rd CharlottesvilleVA22904 United States Department of Physics Kyungpook National University Daegu41566 Korea Republic of Asia Pacific Center for Theoretical Physics Gyeongbuk Pohang37673 Korea Republic of Duke University DurhamNC27708 United States P.O.Box 19395-5531 Tehran Iran Yarmouk University Irbid21163 Jordan Department of Physics University of Michigan Ann ArborMI48109 United States University of Cyprus Department of Physics The Cyprus Institute CY-1678 Nicosia NicosiaCY-1645 Cyprus Dipartimento di Fisica Università di Pavia Pavia Italy Duquesne University PittsburghPA15282 United States RIKEN BNL Research Center Brookhaven National Laboratory UptonNY11973-5000 United States Institute of Modern Physics Chinese Academy of Sciences Lanzhou730000 China Department of Physics and Astronomy College of William and Mary WilliamsburgVA United States Adam Mickiewicz University ul. Uniwersytetu Poznanskiego 2 Poznan61-614 Poland Argonne National Laboratory LemontIL60439 United States Temple University PhiladelphiaPA19122 United States Department of Physics Lamar University TX United States Instituto de Física Universidad Nacional Autónoma de México Apar

The manuscript focuses on the high impact science of the EIC with objective to identify a portion of the science program for QCD precision studies that requires or greatly benefits from high luminosity and low center-of-mass energies. The science topics include (1) Generalized Parton Distributions, 3D imagining and mechanical properties of the nucleon (2) mass and spin of the nucleon (3) Momentum dependence of the nucleon in semi-inclusive deep inelastic scattering (4) Exotic meson spectroscopy (5) science highlights of nuclei (6) Precision studies of Lattice QCD in the EIC era (7) science of far-forward particle detection (8) Radiative effects and corrections (9) Artificial Intelligence (10) EIC interaction regions for high impact science program with discovery potential. This paper documents the scientific basis for supporting such a program and helps to define the path toward the realization of the second EIC interaction region. © 2022, CC0.

关键词： Forward scattering

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Drone squadron optimization: A self-adaptive algorithm for global numerical optimization

arXiv

引用

arXiv 2017年

作者： de Melo, Vinícius V. Banzhaf, Wolfgang Institute of Science and Technology Federal University of São Paulo São José dos Campos SP Brazil Department of Computer Science and Engineering BEACON Center for the Study of Evolution in Action Michigan State University East LansingMI48864 United States

This paper proposes Drone Squadron Optimization, a new self-adaptive metaheuristic for global numerical optimization which is updated online by a hyper-heuristic. DSO is an artifact-inspired technique, as opposed to many algorithms used nowadays, which are nature-inspired. DSO is very flexible because it is not related to behaviors or natural phenomena. DSO has two core parts: The semi-autonomous drones that fly over a landscape to explore, and the Command Center that processes the retrieved data and updates the drones' firmware whenever necessary. The self-adaptive aspect of DSO in this work is the perturbation/movement scheme, which is the procedure used to generate target coordinates. This procedure is evolved by the Command Center during the global optimization process in order to adapt DSO to the search landscape. DSO was evaluated on a set of widely employed benchmark functions. The statistical analysis of the results shows that the proposed method is competitive with the other methods in the comparison, the performance is promising, but several future improvements are planned. Copyright © 2017, The Authors. All rights reserved.

关键词： Global optimization

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Main belt asteroids taxonomical information from Dark Energy Survey data

arXiv

引用

arXiv 2023年

作者： Carruba, Valerio Camargo, Júlio I.B. Aljbaae, Safwan Ferreira, F.S. Lin, E. Figueiredo-Peixoto, V. Banda-Huarca, M.V. Pieres, A. Boufleur, R.C. da Costa, L.N. Abbott, T.M.C. Aguena, M. Allam, Sahar S. Alves, O. Bernardinelli, P.H. Bertin, E. Brooks, D. Carnero Rosell, A. Carretero, J. Pereira, M.E.S. Davis, T.M. De Vicente, J. Desai, S. Doel, P. Ferrero, I. Friedel, D. Frieman, J. García-Bellido, J. Gatti, M. Giannini, G. Gruen, D. Gruendl, R.A. Herner, K. Hinton, S.R. Hollowood, D.L. James, D.J. Kent, S. Kuehn, K. Lahav, O. Marshall, J.L. Mena-Fernández, J. Miquel, R. Palmese, A. Plazas Malagón, A.A. Rodríguez-Monroy, M. Sanchez, E. Santiago, B. Schubnell, M. Smith, M. Suchyta, E. Swanson, M.E.C. Tarle, G. Walker, A.R. Weaverdyck, N. Wiseman, P. School of Natural Sciences and Engineering Brazil Laboratório Interinstitucional de e-Astronomia - LIneA Brazil Observatório Nacional Rua Gal. José Cristino 77 RJ Rio de Janeiro20921-400 Brazil Division of Space Mechanics and Control C.P. 515 SP São José dos Campos12227-310 Brazil Department of Physics University of Michigan Ann ArborMI48109 United States IGeo - Universidade Federal do Rio de Janeiro Av. Athos da Silveira Ramos 274 RJ Rio de Janeiro21941-916 Brazil Cerro Tololo Inter-American Observatory NSF’s National Optical-Infrared Astronomy Research Laboratory Casilla 603 La Serena Chile Fermi National Accelerator Laboratory P. O. Box 500 BataviaIL60510 United States Astronomy Department University of Washington Box 351580 SeattleWA98195 United States CNRS UMR 7095 Institut d’Astrophysique de Paris ParisF-75014 France Sorbonne Universités UPMC Univ Paris 06 UMR 7095 Institut d’Astrophysique de Paris ParisF-75014 France Department of Physics & Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom Instituto de Astrofisica de Canarias Tenerife La LagunaE-38205 Spain Universidad de La Laguna Dpto. AstrofÃsica Tenerife La LagunaE-38206 Spain The Barcelona Institute of Science and Technology Campus UAB Bellaterra Barcelona08193 Spain Hamburger Sternwarte Universität Hamburg Gojenbergsweg 112 Hamburg21029 Germany School of Mathematics and Physics University of Queensland BrisbaneQLD4072 Australia Madrid Spain Department of Physics IIT Hyderabad Telangana Kandi502285 India Institute of Theoretical Astrophysics University of Oslo. P.O. Box 1029 Blindern OsloNO-0315 Norway Center for Astrophysical Surveys National Center for Supercomputing Applications 1205 West Clark St. UrbanaIL61801 United States Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States Instituto de Fisica Teorica UAM/CSIC Universidad Autonoma de Madrid Madrid28049 Spain Department

While proper orbital elements are currently available for more than 1 million asteroids, taxonomical information is still lagging behind. Surveys like SDSS-MOC4 provided preliminary information for more than 100,000 objects, but many asteroids still lack even a basic taxonomy. In this study, we use Dark Energy Survey (DES) data to provide new information on asteroid physical properties. By cross-correlating the new DES database with other databases, we investigate how asteroid taxonomy is reflected in DES data. While the resolution of DES data is not sufficient to distinguish between different asteroid taxonomies within the complexes, except for V-type objects, it can provide information on whether an asteroid belongs to the C- or S-complex. Here, machine learning methods optimized through the use of genetic algorithms were used to predict the labels of more than 68 000 asteroids with no prior taxonomic information. Using a high-quality, limited set of asteroids with data on gri slopes and i − z colors, we detected 409 new possible V-type asteroids. Their orbital distribution is highly consistent with that of other known V-type objects. © 2023, CC BY.

关键词： Taxonomies

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Supervised and unsupervised learning in animal classification

Supervised and unsupervised learning in animal classificatio...

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International Conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： N. Manohar Y.H. Sharath Kumar G. Hemantha Kumar DoS in Computer Science University of Mysore Mysuru India MIT Mysore India DoS in Computer Science University of Mysore Mysore Karnataka IN

ISBN: (纸本)9781509020300

In this work, we have developed a supervised and unsupervised based classification system to classify the animals. Initially, the animal images are segmented using maximal region merging segmentation algorithm. The Gabor features are extracted from segmented images. Further, the extracted features are reduced based on supervised and unsupervised methods. In supervised method, we have used Linear Discriminate Analysis (LDA) dimension reduction technique to reduce the features. The reduced features are fed into symbolic classifier for the purpose of classification. In unsupervised method, we have used Principle component analysis (PCA) dimension reduction technique to reduce the features. The reduced features are fed into K-means algorithm for the purpose of grouping. Experimentation has been conducted on a dataset of 2000 animal images consisting of 20 different categories of animals with varying percentages of training samples. From the proposed model, it is observed that supervised classification system performs better compared to unsupervised method.

关键词： Animals Feature extraction Image segmentation Gabor filters Merging Classification algorithms Shape

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Regularity theory and global existence of small data solutions to semi-linear de sitter models with power non-linearity

arXiv

引用

arXiv 2017年

作者： Ebert, Marcelo Rempel Reissig, Michael FFCLRP Av. dos Bandeirantes 3900 Ribeirão Preto - SPCEP 14040-901 Brazil Faculty for Mathematics and Computer Science Technical University Bergakademie Freiberg Prüferstr.9 FREIBERG09596 Germany

In this paper we study the Cauchy problem for semi-linear de Sitter models with power non-linearity. The model of interest is (equation presented), where m2 is a non-negative constant. We study the global (in time) existence of small data solutions. In particular, we show the interplay between the power p, admissible data spaces and admissible spaces of solutions (in weak sense, in sense of energy solutions or in classical sense). Copyright © 2017, The Authors. All rights reserved.

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Alignment Free Promoter Sequence Analysis using Feature Reduced Cumulative Distribution of Motifs

Alignment Free Promoter Sequence Analysis using Feature Redu...

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International Conference on Advances in Computing, Communications and Informatics

作者： Kouser, K. Lalitha Rangarajan DoS in Computer Science University of Mysore Mysore India

ISBN: (纸本)9781509020300

Advancement in the DNA sequencing machinery has led to tremendous accumulation of sequence data. This has encouraged researchers to develop more robust analysis methods. Promoter sequences are an important part of these DNA sequences which take part in gene expression and regulation. Here, we attempt to perform alignment free variance based feature selection on Position Specific Motif Matrices of promoter sequences. Then, we analyze similarity/differences existing in these sequences using the cumulative distribution of the selected features/motifs. To demonstrate the efficacy of the proposed technique we use promoter data from NCBI database. The similarity/dissimilarity values get enhanced when we use only the selected features/motifs instead of all the features. Hence, the combination of feature selection and analysis using cumulative distribution of motifs proposed for promoter sequence analysis has the potential to enhance the similarity/differences that exist between promoter sequences.

关键词： Alignment free Cumulative distribution Feature selection Motifs Position Specific Motif Matrices (PSMMs) Promoter sequences Sequence analysis Variance

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Study of the flexibility of a learning analytics tool to evaluate teamwork competence acquisition in different contexts

Study of the flexibility of a learning analytics tool to eva...

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2017 Learning Analytics Summer Institute Spain: Advances in Learning Analytics, LASI-SPAIN 2017

作者： Conde, Miguel Ángel García-Peñalvo, Francisco J. Fidalgo-Blanco, Ángel Sein-Echaluce, María Luisa Department of Mechanics Computer Science and Aerospace Engineering Robotics Group University of León - Campus de Vegazana S/N León24071 Spain Research Institute for Educational Sciences GRIAL research group Faculty of Science - Plaza de Los Caídos S/N. 37008 Salamanca University of Salamanca Spain Laboratory of Innovation in Information Technologies Politechnical University of Madrid Calle de Ríos Rosas 21 Madrid28003 Spain Department of Applied Mathematics Campus Rio Ebro University of Zaragoza Calle de María de Luna 3 Zaragoza50018 Spain

Learning analytics tools and methodologies aim to facilitate teachers and/or decision makers with information and knowledge about what is happening in virtual learning environments in a straightforward and effortless way. However, it is necessary to apply these tools and methodologies in different contexts with a similar success, that is, that they should be flexible and portable enough. There exist several learning analytics tools that only works properly with very specific versions of learning platforms. In this paper, the authors aim to evaluate the flexibility and portability of a methodology and a learning analytics tool that supports individual assessment of teamwork competence. In order to do so the methodology and the tool are applied in a similar course from two different academic contexts. After the experiment, it is possible to see that the learning analytics tool seems to work properly and the suggested new functionalities are similar in both contexts. The methodology can be also applied but results could be improved if some meetings are carried out to check how team works are progressing with their tasks.

关键词： computer aided instruction

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Modelling Energy Consumption based on Resource Utilization

arXiv

引用

arXiv 2017年

作者： Povoa, Lucas Venezian Marcondes, Cesar Senger, Hermes Federal Institute for Education Science and Technology of São Paulo CaraguatatubaSão Paulo Brazil Aeronautics Institute of Technology São José dos CamposSão Paulo Brazil Computer Science Department Federal University of São Carlos São CarlosSão Paulo Brazil

Power management is an expensive and important issue for large computational infrastructures such as datacenters, large clusters, and computational grids. However, measuring energy consumption of scalable systems may be impractical due to both cost and complexity for deploying power metering devices on a large number of machines. In this paper, we propose the use of information about resource utilization (e.g. processor, memory, disk operations, and network traffic) as proxies for estimating power consumption. We employ machine learning techniques to estimate power consumption using such information which are provided by common operating systems. Experiments with linear regression, regression tree, and multilayer perceptron on data from different hardware resulted into a model with 99.94% of accuracy and 6.32 watts of error in the best case. Copyright © 2017, The Authors. All rights reserved.

关键词： computer architecture

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Energy efficient and secured routing scheme in hybrid network

Energy efficient and secured routing scheme in hybrid networ...

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IEEE International Conference on Computational Intelligence and Computing Research

作者： S Manjula Suresha DoS in Computer Science University of Mysore Manasagangotri Mysore Kamataka India

ISBN: (纸本)9781509006137

Inside MANET, mobile devices can move independently devoid of pre established infrastructure. In this type of network there is no fixed topology for the network. To increase the coverage range and to obtain the global type of services these MANETs can be joined to other type of networks. For combination of heterogeneous type of networks, intermediate one which is a gateway is used to form a hybrid network. In MANET, security issues also play a vital part during data communication since sender to sink. So in our work, we are combining MANET with Internet to form a hybrid network and for the interconnection between heterogeneous networks gateways are adopted and for identification and selection of those gateways, adaptive gateway discovery approach is employed and to enhance security feature encryption and decryption with authentication is taken into consideration.

关键词： Logic gates Receivers Mobile ad hoc networks Routing protocols Routing Peer-to-peer computing

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