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426 results on '"Baran I"'

1. Reusable MLOps: Reusable Deployment, Reusable Infrastructure and Hot-Swappable Machine Learning models and services

Author

Panchal, D, Verma, P, Baran, I, Musgrove, D, and Lu, D

Subjects
Computer Science - Software Engineering, Computer Science - Computers and Society
Abstract

Although Machine Learning model building has become increasingly accessible due to a plethora of tools, libraries and algorithms being available freely, easy operationalization of these models is still a problem. It requires considerable expertise in data engineering, software development, cloud and DevOps. It also requires planning, agreement, and vision of how the model is going to be used by the business applications once it is in production, how it is going to be continuously trained on fresh incoming data, and how and when a newer model would replace an existing model. This leads to developers and data scientists working in silos and making suboptimal decisions. It also leads to wasted time and effort. We introduce the Acumos AI platform we developed and we demonstrate some unique novel capabilities that the Acumos model runner possesses, that can help solve the above problems. We introduce a new sustainable concept in the field of AI/ML operations - called Reusable MLOps - where we reuse the existing deployment and infrastructure to serve new models by hot-swapping them without tearing down the infrastructure or the microservice, thus achieving reusable deployment and operations for AI/ML models while still having continuously trained models in production.

Published
2024

2. Subgradient methods for non-smooth optimization problems with some relaxation of sharp minimum

Author

Ablaev, S. S., Makarenko, D. V., Stonyakin, F. S., Alkousa, M. S., and Baran, I. V.

Subjects
Mathematics - Optimization and Control
Abstract

In this paper we propose a generalized condition for a sharp minimum, somewhat similar to the inexact oracle proposed recently by Devolder-Glineur-Nesterov. The proposed approach makes it possible to extend the class of applicability of subgradient methods with the Polyak step-size, to the situation of inexact information about the value of the minimum, as well as the unknown Lipschitz constant of the objective function. Moreover, the use of local analogs of the global characteristics of the objective function makes it possible to apply the results of this type to wider classes of problems. We show the possibility of applying the proposed approach to strongly convex non-smooth problems, also, we make an experimental comparison with the known optimal subgradient method for such a class of problems. Moreover, there were obtained some results connected to the applicability of the proposed technique to some types of problems with convexity relaxations: the recently proposed notion of weak $\beta$-quasi-convexity and ordinary quasi-convexity. Also in the paper, we study a generalization of the described technique to the situation with the assumption that the $\delta$-subgradient of the objective function is available instead of the usual subgradient. For one of the considered methods, conditions are found under which, in practice, it is possible to escape the projection of the considered iterative sequence onto the feasible set of the problem., Comment: in Russian language

Published
2022
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9. Whole genomes from bacteria collected at diagnostic units around the world 2020

Author

Nag, S, Larsen, G, Szarvas, J, Birkedahl, L, Gulyas, G, Ciok, W, Lagermann, T, Tafaj, S, Bradbury, S, Collignon, P, Daley, D, Dougnon, V, Fabiyi, K, Coulibaly, B, Dembele, R, Nikiema, G, Magloire, N, Ouindgueta, I, Hossain, Z, Begum, A, Donchev, D, Diggle, M, Turnbull, L, Levesque, S, Berlinger, L, Sogaard, K, Guevara, P, Valderrama, C, Maikanti, P, Amlerova, J, Drevinek, P, Tkadlec, J, Dilas, M, Kaasch, A, Westh, H, Bachtarzi, M, Amhis, W, Salazar, C, Villacis, J, Luzon, M, Palau, D, Duployez, C, Paluche, M, Asante-Sefa, S, Moller, M, Ip, M, Marekovic, I, Pal-Sonnevend, A, Cocuzza, C, Dambrauskiene, A, Macanze, A, Cossa, A, Mandomando, I, Nwajiobi-Princewill, P, Okeke, I, Kehinde, A, Adebiyi, I, Akintayo, I, Popoola, O, Onipede, A, Blomfeldt, A, Nyquist, N, Bocker, K, Ussher, J, Ali, A, Ullah, N, Khan, H, Gustafson, N, Jarrar, I, Al-Hamad, A, Luvira, V, Paveenkittiporn, W, Baran, I, Mwansa, J, Sikakwa, L, Yamba, K, Hendriksen, R, Aarestrup, F, Nag S., Larsen G., Szarvas J., Birkedahl L. E. K., Gulyas G. M., Ciok W. J., Lagermann T. M., Tafaj S., Bradbury S., Collignon P., Daley D., Dougnon V., Fabiyi K., Coulibaly B., Dembele R., Nikiema G., Magloire N., Ouindgueta I. J., Hossain Z. Z., Begum A., Donchev D., Diggle M., Turnbull L. A., Levesque S., Berlinger L., Sogaard K. K., Guevara P. D., Valderrama C. D., Maikanti P., Amlerova J., Drevinek P., Tkadlec J., Dilas M., Kaasch A., Westh H. T., Bachtarzi M. A., Amhis W., Salazar C. E. S., Villacis J. E., Luzon M. A. D., Palau D. B., Duployez C., Paluche M., Asante-Sefa S., Moller M., Ip M., Marekovic I., Pal-Sonnevend A., Cocuzza C. E., Dambrauskiene A., Macanze A., Cossa A., Mandomando I., Nwajiobi-Princewill P., Okeke I. N., Kehinde A. O., Adebiyi I., Akintayo I., Popoola O., Onipede A., Blomfeldt A., Nyquist N. E., Bocker K., Ussher J., Ali A., Ullah N., Khan H., Gustafson N. W., Jarrar I., Al-Hamad A., Luvira V., Paveenkittiporn W., Baran I., Mwansa J. C. L., Sikakwa L., Yamba K., Hendriksen R. S., Aarestrup F. M., Nag, S, Larsen, G, Szarvas, J, Birkedahl, L, Gulyas, G, Ciok, W, Lagermann, T, Tafaj, S, Bradbury, S, Collignon, P, Daley, D, Dougnon, V, Fabiyi, K, Coulibaly, B, Dembele, R, Nikiema, G, Magloire, N, Ouindgueta, I, Hossain, Z, Begum, A, Donchev, D, Diggle, M, Turnbull, L, Levesque, S, Berlinger, L, Sogaard, K, Guevara, P, Valderrama, C, Maikanti, P, Amlerova, J, Drevinek, P, Tkadlec, J, Dilas, M, Kaasch, A, Westh, H, Bachtarzi, M, Amhis, W, Salazar, C, Villacis, J, Luzon, M, Palau, D, Duployez, C, Paluche, M, Asante-Sefa, S, Moller, M, Ip, M, Marekovic, I, Pal-Sonnevend, A, Cocuzza, C, Dambrauskiene, A, Macanze, A, Cossa, A, Mandomando, I, Nwajiobi-Princewill, P, Okeke, I, Kehinde, A, Adebiyi, I, Akintayo, I, Popoola, O, Onipede, A, Blomfeldt, A, Nyquist, N, Bocker, K, Ussher, J, Ali, A, Ullah, N, Khan, H, Gustafson, N, Jarrar, I, Al-Hamad, A, Luvira, V, Paveenkittiporn, W, Baran, I, Mwansa, J, Sikakwa, L, Yamba, K, Hendriksen, R, Aarestrup, F, Nag S., Larsen G., Szarvas J., Birkedahl L. E. K., Gulyas G. M., Ciok W. J., Lagermann T. M., Tafaj S., Bradbury S., Collignon P., Daley D., Dougnon V., Fabiyi K., Coulibaly B., Dembele R., Nikiema G., Magloire N., Ouindgueta I. J., Hossain Z. Z., Begum A., Donchev D., Diggle M., Turnbull L. A., Levesque S., Berlinger L., Sogaard K. K., Guevara P. D., Valderrama C. D., Maikanti P., Amlerova J., Drevinek P., Tkadlec J., Dilas M., Kaasch A., Westh H. T., Bachtarzi M. A., Amhis W., Salazar C. E. S., Villacis J. E., Luzon M. A. D., Palau D. B., Duployez C., Paluche M., Asante-Sefa S., Moller M., Ip M., Marekovic I., Pal-Sonnevend A., Cocuzza C. E., Dambrauskiene A., Macanze A., Cossa A., Mandomando I., Nwajiobi-Princewill P., Okeke I. N., Kehinde A. O., Adebiyi I., Akintayo I., Popoola O., Onipede A., Blomfeldt A., Nyquist N. E., Bocker K., Ussher J., Ali A., Ullah N., Khan H., Gustafson N. W., Jarrar I., Al-Hamad A., Luvira V., Paveenkittiporn W., Baran I., Mwansa J. C. L., Sikakwa L., Yamba K., Hendriksen R. S., and Aarestrup F. M.

Abstract

The Two Weeks in the World research project has resulted in a dataset of 3087 clinically relevant bacterial genomes with pertaining metadata, collected from 59 diagnostic units in 35 countries around the world during 2020. A relational database is available with metadata and summary data from selected bioinformatic analysis, such as species prediction and identification of acquired resistance genes.

Published
2023

26. List of contributors

Author

Akkerman, R., primary, Arold, B., additional, Baran, I., additional, Bel, S., additional, Bernhart, G., additional, Birkefeld, K., additional, Boisse, P., additional, Bougherara, H., additional, Boussu, F., additional, Cerisier, A., additional, Charmetant, A., additional, Collombet, F., additional, Crosky, A., additional, Crouzeix, L., additional, Davila, Y., additional, Douchin, B., additional, Dufour, C., additional, Dusserre, G., additional, Farzaneh, S., additional, Fitoussi, J., additional, Gessler, A., additional, Grant, C., additional, Gries, T., additional, Grunevald, Y.-H., additional, Haanappel, S.P., additional, Hearle, J.W.S., additional, Kelly, D., additional, Khelladi, S., additional, Lefebvre, M., additional, Legrand, X., additional, Metzner, C., additional, Naouar, N., additional, Nony, F., additional, Park, C.H., additional, Pearce, G., additional, Potter, K., additional, Ramaswamy, S., additional, Schäfer, J., additional, Tcharkhtchi, A., additional, Thévenin, R., additional, Veyet, F., additional, Vignoles, G.L., additional, Ward, C., additional, Wendland, B., additional, and Zitoune, R., additional

Published
2015
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47. Micro-CT-based analysis of fibre-reinforced composites: Applications

Author

Straumit, I., Baran, I., Gorbatikh, L., Farkas, L., Hahn, C., Ilin, K., Ivens, J., Lessard, L., Liu, Y., Nguyen, N., Matveeva, A., Mehdikhani, M., Shishkina, O., Soete, J., Takahashi, J., Vandepitte, D., Dmytro Vasiukov, Wan, Y., Winterstein, E., Wevers, M., Lomov, S. V., Production Technology, Department of Geo-information Processing, Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), University of Twente [Netherlands], Siemens Industry Software S.r.l., Technische Universität München [München] (TUM), Central China Normal University [Wuhan, China], Physique Corpusculaire et Cosmologie - Collège de France (PCC), Collège de France (CdF (institution))-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Ecole nationale supérieure Mines-Télécom Lille Douai (IMT Lille Douai), Institut Mines-Télécom [Paris] (IMT), CENTEXBEL ZWIJNAARDE BEL, Partenaires IRSTEA, and Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)

Subjects
Micro-computed tomography, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, [INFO]Computer Science [cs], Mechanical properties, Internal structure, Variability, ComputingMilieux_MISCELLANEOUS, ComputingMethodologies_COMPUTERGRAPHICS
Abstract

The paper presents an overview of cases in which the analysis of the internal structure and mechanical properties of fibre reinforced composites is performed based on the micro-computed X-ray tomography (micro-CT) reconstruction of the composite reinforcement geometry. In all the cases, the analysis relies on structure tensor-based algorithms for quantification of the micro-CT image, implemented in VoxTex software.

Published
2018

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