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Institution

Bar-Ilan University

EducationRamat Gan, Israel
About: Bar-Ilan University is a education organization based out in Ramat Gan, Israel. It is known for research contribution in the topics: Population & Poison control. The organization has 12835 authors who have published 34964 publications receiving 995648 citations. The organization is also known as: Bar Ilan University & BIU.


Papers
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Journal ArticleDOI
TL;DR: This analysis indicates that heavy-tailed degree distribution is causally determined by similarly skewed distribution of human activity, which cannot be explained by interactive models, like preferential attachment, since the observed actions are not likely to be caused by interactions with other people.
Abstract: The probability distribution of number of ties of an individual in a social network follows a scale-free power-law. However, how this distribution arises has not been conclusively demonstrated in direct analyses of people's actions in social networks. Here, we perform a causal inference analysis and find an underlying cause for this phenomenon. Our analysis indicates that heavy-tailed degree distribution is causally determined by similarly skewed distribution of human activity. Specifically, the degree of an individual is entirely random - following a “maximum entropy attachment” model - except for its mean value which depends deterministically on the volume of the users' activity. This relation cannot be explained by interactive models, like preferential attachment, since the observed actions are not likely to be caused by interactions with other people.

193 citations

Journal ArticleDOI
TL;DR: The use of NMR spectroscopy for the characterization of polymer hydrogels and organogels has recently seen enormous growth as mentioned in this paper, with special emphasis on the interplay between the morphology and molecular mobility of constituents and the intermolecular interactions.

193 citations

Journal ArticleDOI
TL;DR: In this paper, the authors considered the classical mathematical model with saturation response of the infection rate and obtained sufficient conditions on the parameters for the global stability of the infected steady state and the infection-free steady state.

193 citations

Journal ArticleDOI
15 Feb 2010-ACS Nano
TL;DR: In this article, a dye-sensitized solar cell involves colloidal semiconductor quantum dots that serve as antennas, funneling absorbed light to the charge separating dye molecules via nonradiative energy transfer.
Abstract: A new design of dye-sensitized solar cells involves colloidal semiconductor quantum dots that serve as antennas, funneling absorbed light to the charge separating dye molecules via nonradiative energy transfer. The colloidal quantum dot donors are incorporated into the solid titania electrode resulting in high energy transfer efficiency and significant improvement of the cell stability. This design practically separates the processes of light absorption and charge carrier injection, enabling us to optimize each of these separately. Incident photon-to-current efficiency measurements show a full coverage of the visible spectrum despite the use ofaredabsorbingdye,limitedonlybytheefficiencyofchargeinjectionfromthedyetothetitaniaelectrode.Time resolved luminescence measurements clearly relate this to Forster resonance energy transfer from the quantum dots to the dye. The presented design introduces new degrees of freedom in the utilization of quantum dot sensitizers for photovoltaic cells. In particular, it opens the way toward the utilization of new materials whose band offsets do not allow direct charge injection.

193 citations

Proceedings ArticleDOI
12 Jul 2015
TL;DR: The results presented in this paper show that signatures generated by the DBN allow for an accurate classification of new malware variants, and the presented method achieves 98.6% classification accuracy using the signatures Generating malware signatures.
Abstract: This paper presents a novel deep learning based method for automatic malware signature generation and classification. The method uses a deep belief network (DBN), implemented with a deep stack of denoising autoencoders, generating an invariant compact representation of the malware behavior. While conventional signature and token based methods for malware detection do not detect a majority of new variants for existing malware, the results presented in this paper show that signatures generated by the DBN allow for an accurate classification of new malware variants. Using a dataset containing hundreds of variants for several major malware families, our method achieves 98.6% classification accuracy using the signatures generated by the DBN. The presented method is completely agnostic to the type of malware behavior that is logged (e.g., API calls and their parameters, registry entries, websites and ports accessed, etc.), and can use any raw input from a sandbox to successfully train the deep neural network which is used to generate malware signatures.

193 citations


Authors

Showing all 13037 results

NameH-indexPapersCitations
H. Eugene Stanley1541190122321
Albert-László Barabási152438200119
Shlomo Havlin131101383347
Stuart A. Aaronson12965769633
Britton Chance128111276591
Mark A. Ratner12796868132
Doron Aurbach12679769313
Jun Yu121117481186
Richard J. Wurtman11493353290
Amir Lerman11187751969
Zhu Han109140748725
Moussa B.H. Youdim10757442538
Juan Bisquert10745046267
Rachel Yehuda10646136726
Michael F. Green10648545707
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023117
2022330
20212,286
20202,157
20191,920
20181,768