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Institution

Jagiellonian University

EducationKrakow, Poland
About: Jagiellonian University is a education organization based out in Krakow, Poland. It is known for research contribution in the topics: Population & Catalysis. The organization has 17438 authors who have published 44092 publications receiving 862633 citations. The organization is also known as: Academia Cracoviensis & Akademia Krakowska.


Papers
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Journal ArticleDOI
TL;DR: Patients with ULMCA disease treated with PCI had favorable early outcomes in comparison with the CABG group, and MACCE-free survival was similar in both groups with a trend toward improved survival after PCI.

408 citations

Journal ArticleDOI
TL;DR: This paper investigates how particular choices of loss functions affect deep models and their learning dynamics, as well as resulting classifiers robustness to various effects, and shows that L1 and L2 losses are justified classification objectives for deep nets, by providing probabilistic interpretation in terms of expected misclassification.
Abstract: Deep neural networks are currently among the most commonly used classifiers. Despite easily achieving very good performance, one of the best selling points of these models is their modular design – one can conveniently adapt their architecture to specific needs, change connectivity patterns, attach specialised layers, experiment with a large amount of activation functions, normalisation schemes and many others. While one can find impressively wide spread of various configurations of almost every aspect of the deep nets, one element is, in authors’ opinion, underrepresented – while solving classification problems, vast majority of papers and applications simply use log loss. In this paper we try to investigate how particular choices of loss functions affect deep models and their learning dynamics, as well as resulting classifiers robustness to various effects. We perform experiments on classical datasets, as well as provide some additional, theoretical insights into the problem. In particular we show that L1 and L2 losses are, quite surprisingly, justified classification objectives for deep nets, by providing probabilistic interpretation in terms of expected misclassification. We also introduce two losses which are not typically used as deep nets objectives and show that they are viable alternatives to the existing ones. Keywords: loss function, deep learning, classification theory

407 citations

Journal ArticleDOI
A. Abada1, Marcello Abbrescia2, Marcello Abbrescia3, Shehu S. AbdusSalam4  +1501 moreInstitutions (239)
TL;DR: In this article, the physics opportunities of the Future Circular Collider (FC) were reviewed, covering its e+e-, pp, ep and heavy ion programs, and the measurement capabilities of each FCC component, addressing the study of electroweak, Higgs and strong interactions.
Abstract: We review the physics opportunities of the Future Circular Collider, covering its e+e-, pp, ep and heavy ion programmes. We describe the measurement capabilities of each FCC component, addressing the study of electroweak, Higgs and strong interactions, the top quark and flavour, as well as phenomena beyond the Standard Model. We highlight the synergy and complementarity of the different colliders, which will contribute to a uniquely coherent and ambitious research programme, providing an unmatchable combination of precision and sensitivity to new physics.

407 citations

Journal ArticleDOI
TL;DR: In this article, the CO 3 2 − ions were introduced using NH 4 HCO 3 and NaHCO 3 in the amount of 0.1 or 0.05 m. The significant decomposition of carbonated hydroxyapatite powders is observed when they are heated at the temperature of 800 ˚C.

406 citations

Journal ArticleDOI
Antonio Terracciano1, Ahmed M. Abdel-Khalek, N. Ádám2, L. Adamovová3, C.-k. Ahn4, H.-n. Ahn4, B. M. Alansari, Lidia Alcalay5, Jüri Allik6, Alois Angleitner, María Dolores Avia7, Lindsay E. Ayearst8, Claudio Barbaranelli9, Andrew Beer10, M. A. Borg-Cunen11, Denis Bratko, Marina Brunner-Sciarra12, L. Budzinski13, N. Camart14, Donatien Dahourou15, F. De Fruyt, M. I. P. de Lima16, G. E. H. del Pilar17, Ed Diener18, Ruth Falzon11, K. Fernando19, Emília Ficková3, Ronald Fischer20, Carmen Flores-Mendoza, M. A. Ghayur21, Sami Gülgöz22, Bo Hagberg23, Jamin Halberstadt19, Magdalena S. Halim24, Martina Hřebíčková25, J. Humrichouse10, Hans Henrik Jensen26, D. D. Jocic, F. H. Jónsson27, Brigitte Khoury28, W. Klinkosz24, Goran Knežević29, Mary Anne Lauri11, N. Leibovich30, Thomas A. Martin31, Iris Marušić, Khairul Anwar Mastor32, David Matsumoto33, Margaret McRorie34, B. Meshcheriakov35, Erik Lykke Mortensen26, M. Munyae36, János Nagy2, Katsuharu Nakazato37, Florence Nansubuga38, Shigehiro Oishi39, A. O. Ojedokun40, Fritz Ostendorf, Delroy L. Paulhus41, S. Pelevin35, J.-M. Petot14, N. Podobnik, Jose Porrata42, V. S. Pramila43, G. Prentice34, Anu Realo6, Norma Reátegui12, Jean-Pierre Rolland14, Jérôme Rossier44, Willibald Ruch, Velko S. Rus45, M.L. Sánchez-Bernardos7, Vanina Schmidt30, S. Sciculna-Calleja11, A. Sekowski24, Jane Shakespeare-Finch46, Yoshiko Shimonaka47, Franco Simonetti5, Tilahun Sineshaw48, Jerzy Siuta49, Peter B. Smith50, Paul D. Trapnell51, K. K. Trobst8, Lei Wang52, Michelle Yik53, A. Zupančič, Robert R. McCrae1 
National Institutes of Health1, Eötvös Loránd University2, Slovak Academy of Sciences3, Pusan National University4, Pontifical Catholic University of Chile5, University of Tartu6, Complutense University of Madrid7, Keele University8, Sapienza University of Rome9, University of Iowa10, University of Malta11, Cayetano Heredia University12, University of Melbourne13, University of Paris14, University of Ouagadougou15, University of Coimbra16, University of the Philippines Diliman17, University of Illinois at Urbana–Champaign18, University of Otago19, Victoria University of Wellington20, Al Akhawayn University21, Koç University22, Lund University23, The Catholic University of America24, Academy of Sciences of the Czech Republic25, University of Copenhagen26, University of Iceland27, American University of Beirut28, University of Belgrade29, University of Buenos Aires30, Susquehanna University31, National University of Malaysia32, San Francisco State University33, Queen's University Belfast34, International University, Cambodia35, University of Botswana36, Iwate Prefectural University37, Makerere University38, University of Virginia39, University of Ibadan40, University of British Columbia41, University of Puerto Rico, Río Piedras42, Andhra University43, University of Lausanne44, University of Ljubljana45, Queensland University of Technology46, Bunkyo Gakuin University47, Ramapo College48, Jagiellonian University49, University of Sussex50, University of Winnipeg51, Peking University52, Hong Kong University of Science and Technology53
07 Oct 2005-Science
TL;DR: Perceptions of national character appear to be unfounded stereotypes that may serve the function of maintaining a national identity.
Abstract: Most people hold beliefs about personality characteristics typical of members of their own and others' cultures. These perceptions of national character may be generalizations from personal experience, stereotypes with a "kernel of truth," or inaccurate stereotypes. We obtained national character ratings of 3989 people from 49 cultures and compared them with the average personality scores of culture members assessed by observer ratings and self-reports. National character ratings were reliable but did not converge with assessed traits. Perceptions of national character thus appear to be unfounded stereotypes that may serve the function of maintaining a national identity.

403 citations


Authors

Showing all 17729 results

NameH-indexPapersCitations
Roxana Mehran141137899398
Brad Abbott137156698604
M. Morii1341664102074
M. Franklin134158195304
John Huth131108785341
Wladyslaw Dabrowski12999079728
Rostislav Konoplich12881173790
Michel Vetterli12890176064
Francois Corriveau128102275729
Christoph Falk Anders12673468828
Tomasz Bulik12169886211
Elzbieta Richter-Was11879369127
S. H. Robertson116131158582
S. J. Chen116155962804
David M. Stern10727147461
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023162
2022510
20212,769
20202,776
20192,736
20182,735