Institution
Bar-Ilan University
Education•Ramat 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.
Topics: Population, Poison control, Judaism, Anxiety, Electrolyte
Papers published on a yearly basis
Papers
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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
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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
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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
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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
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12 Jul 2015TL;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
Name | H-index | Papers | Citations |
---|---|---|---|
H. Eugene Stanley | 154 | 1190 | 122321 |
Albert-László Barabási | 152 | 438 | 200119 |
Shlomo Havlin | 131 | 1013 | 83347 |
Stuart A. Aaronson | 129 | 657 | 69633 |
Britton Chance | 128 | 1112 | 76591 |
Mark A. Ratner | 127 | 968 | 68132 |
Doron Aurbach | 126 | 797 | 69313 |
Jun Yu | 121 | 1174 | 81186 |
Richard J. Wurtman | 114 | 933 | 53290 |
Amir Lerman | 111 | 877 | 51969 |
Zhu Han | 109 | 1407 | 48725 |
Moussa B.H. Youdim | 107 | 574 | 42538 |
Juan Bisquert | 107 | 450 | 46267 |
Rachel Yehuda | 106 | 461 | 36726 |
Michael F. Green | 106 | 485 | 45707 |