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Institution

Jožef Stefan Institute

FacilityLjubljana, 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
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Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
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

Book ChapterDOI
21 Aug 2005
TL;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

NameH-indexPapersCitations
Vladimir Cindro129115782000
Igor Mandić128106579498
Jure Leskovec12747389014
Matej Orešič8235226830
P. Križan7874926408
Jose Miguel Miranda7633618080
Vito Turk7427123205
Andrii Tykhonov7327024864
Masashi Yokoyama7331018817
Kostya Ostrikov7276321442
M. Starič7153019136
Boris Turk6723127006
Bostjan Kobe6627917592
Jure Zupan6122812054
Mario Sannino6028117144
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202331
202268
2021755
2020770
2019653
2018576