Bar-Ilan University

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.

Hydrogen bonds in water

Abstract: Equilibrium and dynamical properties of the hydrogen bonds in liquid water are analysed using the results of molecular dynamics simulations of the MCY-CI model. Properties of the hydrogen bond clusters as functions of temperature are described. The connectivity of the clusters is analysed in terms of the bridgeless polygons which are formed by the bonds. The problem of obtaining a meaningful definition of bond lifetime is discussed, and the results of lifetime measurements based on alternative definitions are shown.

context2vec: Learning Generic Context Embedding with Bidirectional LSTM

Abstract: Context representations are central to various NLP tasks, such as word sense disambiguation, named entity recognition, coreference resolution, and many more. In this work we present a neural model for efficiently learning a generic context embedding function from large corpora, using bidirectional LSTM. With a very simple application of our context representations, we manage to surpass or nearly reach state-of-the-art results on sentence completion, lexical substitution and word sense disambiguation tasks, while substantially outperforming the popular context representation of averaged word embeddings. We release our code and pretrained models, suggesting they could be useful in a wide variety of NLP tasks.

Conservation of angular momentum, transverse shift, and spin Hall effect in reflection and refraction of an electromagnetic wave packet.

Abstract: We present a solution to the problem of reflection and refraction of a polarized Gaussian beam on the interface between two transparent media. The transverse shifts of the beams' centers of gravity are calculated. They always satisfy the total angular momentum conservation law for beams, but, in general, do not satisfy the conservation laws for individual photons as a consequence of the lack of the "which path" information in a two-channel wave scattering. The field structure for the reflected and refracted beams is analyzed. In the scattering of a linearly polarized beam, photons of opposite helicities are accumulated at the opposite edges of the beam: this is the spin Hall effect for photons, which can be registered in the cross-polarized component of the scattered beam.

Correlated and uncorrelated regions in heart-rate fluctuations during sleep.

Abstract: Healthy sleep consists of several stages: deep sleep, light sleep, and rapid eye movement (REM) sleep. Here we show that these sleep stages can be characterized and distinguished by correlations of heart rates separated by $n$ beats. Using the detrended fluctuation analysis (DFA) up to fourth order we find that long-range correlations reminiscent to the wake phase are present only in the REM phase. In the non-REM phases, the heart rates are uncorrelated above the typical breathing cycle time, pointing to a random regulation of the heartbeat during non-REM sleep.

Computational methods in authorship attribution

Abstract: Statistical authorship attribution has a long history, culminating in the use of modern machine learning classification methods. Nevertheless, most of this work suffers from the limitation of assuming a small closed set of candidate authors and essentially unlimited training text for each. Real-life authorship attribution problems, however, typically fall short of this ideal. Thus, following detailed discussion of previous work, three scenarios are considered here for which solutions to the basic attribution problem are inadequate. In the first variant, the profiling problem, there is no candidate set at all; in this case, the challenge is to provide as much demographic or psychological information as possible about the author. In the second variant, the needle-in-a-haystack problem, there are many thousands of candidates for each of whom we might have a very limited writing sample. In the third variant, the verification problem, there is no closed candidate set but there is one suspect; in this case, the challenge is to determine if the suspect is or is not the author. For each variant, it is shown how machine learning methods can be adapted to handle the special challenges of that variant. © 2009 Wiley Periodicals, Inc.