Institution
University of Warsaw
Education•Warsaw, Poland•
About: University of Warsaw is a education organization based out in Warsaw, Poland. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 20832 authors who have published 56617 publications receiving 1185084 citations. The organization is also known as: Uniwersytet Warszawski & Warsaw University.
Papers published on a yearly basis
Papers
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TL;DR: In this article, the authors present a detailed analysis of the performance of the Large Hadron Collider (CMS) at 14 TeV and compare it with the state-of-the-art analytical tools.
Abstract: CMS is a general purpose experiment, designed to study the physics of pp collisions at 14 TeV at the Large Hadron Collider (LHC). It currently involves more than 2000 physicists from more than 150 institutes and 37 countries. The LHC will provide extraordinary opportunities for particle physics based on its unprecedented collision energy and luminosity when it begins operation in 2007. The principal aim of this report is to present the strategy of CMS to explore the rich physics programme offered by the LHC. This volume demonstrates the physics capability of the CMS experiment. The prime goals of CMS are to explore physics at the TeV scale and to study the mechanism of electroweak symmetry breaking--through the discovery of the Higgs particle or otherwise. To carry out this task, CMS must be prepared to search for new particles, such as the Higgs boson or supersymmetric partners of the Standard Model particles, from the start-up of the LHC since new physics at the TeV scale may manifest itself with modest data samples of the order of a few fb−1 or less. The analysis tools that have been developed are applied to study in great detail and with all the methodology of performing an analysis on CMS data specific benchmark processes upon which to gauge the performance of CMS. These processes cover several Higgs boson decay channels, the production and decay of new particles such as Z' and supersymmetric particles, Bs production and processes in heavy ion collisions. The simulation of these benchmark processes includes subtle effects such as possible detector miscalibration and misalignment. Besides these benchmark processes, the physics reach of CMS is studied for a large number of signatures arising in the Standard Model and also in theories beyond the Standard Model for integrated luminosities ranging from 1 fb−1 to 30 fb−1. The Standard Model processes include QCD, B-physics, diffraction, detailed studies of the top quark properties, and electroweak physics topics such as the W and Z0 boson properties. The production and decay of the Higgs particle is studied for many observable decays, and the precision with which the Higgs boson properties can be derived is determined. About ten different supersymmetry benchmark points are analysed using full simulation. The CMS discovery reach is evaluated in the SUSY parameter space covering a large variety of decay signatures. Furthermore, the discovery reach for a plethora of alternative models for new physics is explored, notably extra dimensions, new vector boson high mass states, little Higgs models, technicolour and others. Methods to discriminate between models have been investigated. This report is organized as follows. Chapter 1, the Introduction, describes the context of this document. Chapters 2-6 describe examples of full analyses, with photons, electrons, muons, jets, missing ET, B-mesons and τ's, and for quarkonia in heavy ion collisions. Chapters 7-15 describe the physics reach for Standard Model processes, Higgs discovery and searches for new physics beyond the Standard Model
973 citations
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14 Jun 2016TL;DR: This paper shows how the design of an optimization algorithm can be cast as a learning problem, allowing the algorithm to learn to exploit structure in the problems of interest in an automatic way.
Abstract: The move from hand-designed features to learned features in machine learning has been wildly successful. In spite of this, optimization algorithms are still designed by hand. In this paper we show how the design of an optimization algorithm can be cast as a learning problem, allowing the algorithm to learn to exploit structure in the problems of interest in an automatic way. Our learned algorithms, implemented by LSTMs, outperform generic, hand-designed competitors on the tasks for which they are trained, and also generalize well to new tasks with similar structure. We demonstrate this on a number of tasks, including simple convex problems, training neural networks, and styling images with neural art.
972 citations
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TL;DR: In this paper, a C*-algebre A engendree par deux elements α et γ satisfaisant une relation de commutation simple dependante de ν is presented.
Abstract: Pour un nombre ν de l'intervalle [−1, 1], on introduit et on etudie une C*-algebre A engendree par deux elements α et γ satisfaisant une relation de commutation simple dependante de ν
972 citations
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TL;DR: The Heisenberg uncertainty relation and the Gross-Nelson inequality in quantum mechanics are derived in this paper, which express restrictions imposed by quantum theory on probability distributions of canonically conjugate variables in terms of corresponding information entropies.
Abstract: New uncertainty relations in quantum mechanics are derived. They express restrictions imposed by quantum theory on probability distributions of canonically conjugate variables in terms of corresponding information entropies. The Heisenberg uncertainty relation follows from those inequalities and so does the Gross-Nelson inequality.
964 citations
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TL;DR: In tolerance approximation spaces the lower and upper set approximations are defined and the tolerance relation defined by the so called uncertainty function or the positive region of a given partition of objects have been chosen as invariants in the attribute reduction process.
Abstract: We generalize the notion of an approximation space introduced in [8] In tolerance approximation spaces we define the lower and upper set approximations We investigate some attribute reduction problems for tolerance approximation spaces determined by tolerance information systems The tolerance relation defined by the so called uncertainty function or the positive region of a given partition of objects have been chosen as invariants in the attribute reduction process We obtain the solutions of the reduction problems by applying boolean reasoning [1] The solutions are represented by tolerance reducts and relative tolerance reducts
955 citations
Authors
Showing all 21191 results
Name | H-index | Papers | Citations |
---|---|---|---|
Alexander Malakhov | 139 | 1486 | 99556 |
Emmanuelle Perez | 138 | 1550 | 99016 |
Piotr Zalewski | 135 | 1388 | 89976 |
Krzysztof Doroba | 133 | 1440 | 89029 |
Hector F. DeLuca | 133 | 1303 | 69395 |
Krzysztof M. Gorski | 132 | 380 | 105912 |
Igor Golutvin | 131 | 1282 | 88559 |
Jan Krolikowski | 131 | 1289 | 83994 |
Michal Szleper | 130 | 1238 | 82036 |
Anatoli Zarubin | 129 | 1204 | 86435 |
Malgorzata Kazana | 129 | 1175 | 81106 |
Artur Kalinowski | 129 | 1162 | 81906 |
Predrag Milenovic | 129 | 1185 | 81144 |
Marcin Konecki | 128 | 1178 | 79392 |
Karol Bunkowski | 128 | 1192 | 79455 |