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Institution

Warsaw University of Technology

EducationWarsaw, Poland
About: Warsaw University of Technology is a education organization based out in Warsaw, Poland. It is known for research contribution in the topics: Microstructure & Optical fiber. The organization has 14293 authors who have published 34362 publications receiving 492211 citations. The organization is also known as: Warsaw Polytechnic & Politechnika Warszawska.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the mechanical properties of aluminium polycrystals exhibiting differences in grain boundary (GB) properties have been studied by means of the Hall-Petch analysis and the results obtained indicate that th...
Abstract: Mechanical properties of aluminium polycrystals exhibiting differences in grain boundary (GB) properties have been studied by means of the Hall-Petch analysis. The results obtained indicate that th...

187 citations

Journal ArticleDOI
K. Abe1, J. Adam2, Hiroaki Aihara1, T. Akiri3  +355 moreInstitutions (53)
TL;DR: Adding a model of multinucleon interactions that affect neutrino energy reconstruction is found to produce only small biases in neutrinos oscillation parameter extraction at current levels of statistical uncertainty.
Abstract: New data from the T2K neutrino oscillation experiment produce the most precise measurement of the neutrino mixing parameter theta_{23}. Using an off-axis neutrino beam with a peak energy of 0.6 GeV and a data set corresponding to 6.57 x 10^{20} protons on target, T2K has fit the energy-dependent nu_mu oscillation probability to determine oscillation parameters. Marginalizing over the values of other oscillation parameters yields sin^2 (theta_{23}) = 0.514 +0.055/-0.056 (0.511 +- 0.055), assuming normal (inverted) mass hierarchy. The best-fit mass-squared splitting for normal hierarchy is Delta m^2_{32} = (2.51 +- 0.10) x 10^{-3} eV^2/c^4 (inverted hierarchy: Delta m^2_{13} = (2.48 +- 0.10) x 10^{-3} eV^2/c^4). Adding a model of multinucleon interactions that affect neutrino energy reconstruction is found to produce only small biases in neutrino oscillation parameter extraction at current levels of statistical uncertainty.

187 citations

Journal ArticleDOI
TL;DR: In this article, a review of vertically expanded coumarins is presented, which serves as a guide through both synthesis strategies and structure-property relationship nuances, including the mode of fusion, the type of additional ring and the presence of electron-donating and electron-withdrawing substituents.
Abstract: Coumarins fused with other aromatic units have recently emerged as a hot topic of research. Their synthesis is partly based on classical methodologies such as Pechmann reaction or Knoevenagel condensation, but it also sparked the discovery of completely new pathways. In very recent years so-called vertically expanded coumarins were synthesized, effectively expanding the portfolio of existing architectures. A subtle relationship exists between the structure of fused coumarins and their optical properties. Although absorption of UV-radiation and light is a unifying theme among these π-expanded coumarins, the fluorescence properties strongly depend on the structure. The mode of fusion, the type of additional ring and the presence of electron-donating and electron-withdrawing substituents all influence the photophysical parameters. Recent advances made it possible to modulate their absorption from 300 nm to 550 nm, resulting in new coumarins emitting orange light. This review serves as a guide through both synthesis strategies and structure–property relationship nuances. Strong intramolecular charge-transfer character made it possible to reach suitable values of two-photon absorption cross-section. Photophysical advantages of π-expanded coumarins have been already utilized in fluorescent probes and two-photon excited fluorescence microscopy.

187 citations

Journal ArticleDOI
01 Aug 2000
TL;DR: This paper addresses an independent component analysis (ICA) learning algorithm with flexible nonlinearity that is able to separate instantaneous mixtures of sub- and super-Gaussian source signals and employs the parameterized generalized Gaussian density model for hypothesized source distributions.
Abstract: This paper addresses an independent component analysis (ICA) learning algorithm with flexible nonlinearity, so named as flexible ICA, that is able to separate instantaneous mixtures of sub- and super-Gaussian source signals. In the framework of natural Riemannian gradient, we employ the parameterized generalized Gaussian density model for hypothesized source distributions. The nonlinear function in the flexible ICA algorithm is controlled by the Gaussian exponent according to the estimated kurtosis of demixing filter output. Computer simulation results and performance comparison with existing methods are presented.

187 citations

Journal ArticleDOI
TL;DR: This tutorial is devoted to the notion of max-min fairness (MMF), associated optimization problems, and their applications to multi-commodity flow networks, and its applications to communication networks.
Abstract: This tutorial is devoted to the notion of max-min fairness (MMF), associated optimization problems, and their applications to multi-commodity flow networks.We first introduce a theoretical background for a generic MMF optimization problem and discuss its relation to lexicographic optimization. We next present resolution algorithms for convex MMF optimization, and analyze their properties. In the second half of the tutorial we discuss its applications to communication networks, in particular to routing and load-balancing. We state several properties with respect to each of the studied problems and analyze the behavior of the algorithms.

186 citations


Authors

Showing all 14420 results

NameH-indexPapersCitations
Stefano Colafranceschi129110379174
Dezso Horvath128128388111
Valentina Dutta125117976231
Viktor Matveev123121273939
Anna Zanetti120148871375
Harold A. Scheraga120115266461
J. Pluta12065952025
Adam Ryszard Kisiel11869150546
Terence G. Langdon117115861603
Andrei Starodumov11469757900
T. Pawlak11137942455
John D. Pickard10762842479
W. Peryt10737640524
William G. Stevenson10158557798
Anil Kumar99212464825
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202380
2022207
20211,596
20201,804
20191,969
20182,072