Institution
Jožef Stefan Institute
Facility•Ljubljana, Slovenia•
About: Jožef Stefan Institute is a facility organization based out in Ljubljana, Slovenia. It is known for research contribution in the topics: Liquid crystal & Dielectric. The organization has 3828 authors who have published 12614 publications receiving 291025 citations.
Papers published on a yearly basis
Papers
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TL;DR: A validated method with limits of detection at ngL-1 range was applied to WWs collected at five Slovene wastewater treatment plants and WW inflows from industrial, commercial and residential sources entering the sewerage systems of two catchments, reporting the occurrence of eight bisphenols in wastewaters.
96 citations
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TL;DR: In the present review, their properties and structural features that are important to an understanding of their biological function are presented and a newly discovered role of lysosomal cathepsins in apoptotic pathways is found.
Abstract: Among the variety of proteolytic enzymes enormous progress has been seen recently in our understanding of lysosomal cysteine proteases, also known as cysteine cathepsins. These enzymes play a crucial role in diverse biological processes in physiological and pathological states, including genetic diseases. In the present review, their properties and structural features that are important to an understanding of their biological function are presented. Special emphasis is given to the newly discovered role of lysosomal cathepsins in apoptotic pathways. IUBMB Life, 57: 347-353, 2005
96 citations
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TL;DR: In this paper, a qualitative multi-attribute model for the assessment of ecological and economic impacts at a farm-level of GM and non-GM maize crops is presented for one agricultural season.
96 citations
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TL;DR: The experiments appear to favor phonon-mediated pair-breaking mechanisms over spin-mediated couple breaking over lattice energy relaxation pathways, indicating that the quasiparticles share a large amount of energy with the boson glue bath on this time scale.
Abstract: We use ultrashort intense laser pulses to study superconducting state vaporization dynamics in ${\mathrm{La}}_{2\ensuremath{-}x}{\mathrm{Sr}}_{x}{\mathrm{CuO}}_{4}$ ($x=0.1$ and 0.15) on the femtosecond time scale. We find that the energy density required to vaporize the superconducting state is $2.0\ifmmode\pm\else\textpm\fi{}0.8$ and $2.6\ifmmode\pm\else\textpm\fi{}1.0\text{ }\text{ }\mathrm{K}/\mathrm{Cu}$ for $x=0.1$ and 0.15, respectively. This is significantly greater than the condensation energy density, indicating that the quasiparticles share a large amount of energy with the boson glue bath on this time scale. Considering in detail both spin and lattice energy relaxation pathways which take place on the relevant time scale of $\ensuremath{\sim}{10}^{\ensuremath{-}12}\text{ }\text{ }\mathrm{s}$, the experiments appear to favor phonon-mediated pair-breaking mechanisms over spin-mediated pair breaking.
95 citations
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21 Aug 2005TL;DR: The quality of collaborative filtering recommendations is highly dependent on the sparsity of available data, and it is shown that kNN is dominant on datasets with relatively low sparsity while SVM-based approaches may perform better on highly sparse data.
Abstract: With the amount of available information on the Web growing rapidly with each day, the need to automatically filter the information in order to ensure greater user efficiency has emerged. Within the fields of user profiling and Web personalization several popular content filtering techniques have been developed. In this chapter we present one of such techniques – collaborative filtering. Apart from giving an overview of collaborative filtering approaches, we present the experimental results of confronting the k-Nearest Neighbor (kNN) algorithm with Support Vector Machine (SVM) in the collaborative filtering framework using datasets with different properties. While the k-Nearest Neighbor algorithm is usually used for collaborative filtering tasks, Support Vector Machine is considered a state-of-the-art classification algorithm. Since collaborative filtering can also be interpreted as a classification/regression task, virtually any supervised learning algorithm (such as SVM) can also be applied. Experiments were performed on two standard, publicly available datasets and, on the other hand, on a real-life corporate dataset that does not fit the profile of ideal data for collaborative filtering. We conclude that the quality of collaborative filtering recommendations is highly dependent on the sparsity of available data. Furthermore, we show that kNN is dominant on datasets with relatively low sparsity while SVM-based approaches may perform better on highly sparse data.
95 citations
Authors
Showing all 3879 results
Name | H-index | Papers | Citations |
---|---|---|---|
Vladimir Cindro | 129 | 1157 | 82000 |
Igor Mandić | 128 | 1065 | 79498 |
Jure Leskovec | 127 | 473 | 89014 |
Matej Orešič | 82 | 352 | 26830 |
P. Križan | 78 | 749 | 26408 |
Jose Miguel Miranda | 76 | 336 | 18080 |
Vito Turk | 74 | 271 | 23205 |
Andrii Tykhonov | 73 | 270 | 24864 |
Masashi Yokoyama | 73 | 310 | 18817 |
Kostya Ostrikov | 72 | 763 | 21442 |
M. Starič | 71 | 530 | 19136 |
Boris Turk | 67 | 231 | 27006 |
Bostjan Kobe | 66 | 279 | 17592 |
Jure Zupan | 61 | 228 | 12054 |
Mario Sannino | 60 | 281 | 17144 |