Tel Aviv University

About: Tel Aviv University is a education organization based out in Tel Aviv, Israel. It is known for research contribution in the topics: Population & Medicine. The organization has 47791 authors who have published 115959 publications receiving 3904391 citations. The organization is also known as: TAU & Universiṭat Tel-Aviv.

Egalitarian Equivalent Allocations: A New Concept of Economic Equity

Abstract: Foreword, 671. — I. Introduction, 671. — II. The concept of Pareto-efficient-egalitarian-equivalent-allocations (PEEEA), 674. — III. PEEEA as a fair arbitration scheme for allocations, 676. — IV. Maximin properties of PEEEA, 678. — V. PEEEA in economies with production, 680. — Mathematical appendices, 682.

ASCA Observations of Seyfert 1 Galaxies. III. The Evidence for Absorption and Emission Due to Photoionized Gas

Abstract: We present the results from a detailed analysis of the 0.6-10 keV spectra of 23 ASCA observations of 18 objects. We find that in most cases the underlying continuum can be well represented by a power law with a photon index Γ ~ 2. However, we find strong evidence for photoionized gas in the line of sight to 13/18 objects. We present detailed modeling of this gas based upon the ION photoionization code. Other studies have been made of the "warm absorber" phenomenon, but this paper contains the first consideration of the importance of the covering fraction of the ionized gas and a direct comparison between models of attenuation by ionized versus neutral material. We find the X-ray ionization parameter for the ionized material is strongly peaked at UX ~ 0.1. The column densities of ionized material are typically in the range NH, z ~ 1021-1023 cm-2, although highly ionized (and hence pseudotransparent) column densities up to 1024 cm-2 cannot be excluded in some cases. We also investigate the importance of the emission spectrum from the ionized gas, finding that it significantly improves the fit to many sources with an intensity consistent with material subtending a large solid angle at the central source. Allowing a fraction of the continuum to be observed without attenuation also improves the fit to many sources and is definitely required in the case of NGC 4151. A deficit of counts is observed at ~1 keV in the sources exhibiting the strongest absorption features. We suggest this is likely to be the signature of a second zone of (more highly) ionized gas, which might have been seen previously in the deep Fe K-shell edges observed in some Ginga observations. We find evidence that the ionized material in NGC 3227 and MCG -6-30-15 contains embedded dust, while there is no such evidence in the other sources We discuss these results in the context of previous studies and briefly explore the implications in other wave bands.

Identifying facial phenotypes of genetic disorders using deep learning

Abstract: Syndromic genetic conditions, in aggregate, affect 8% of the population1. Many syndromes have recognizable facial features2 that are highly informative to clinical geneticists3–5. Recent studies show that facial analysis technologies measured up to the capabilities of expert clinicians in syndrome identification6–9. However, these technologies identified only a few disease phenotypes, limiting their role in clinical settings, where hundreds of diagnoses must be considered. Here we present a facial image analysis framework, DeepGestalt, using computer vision and deep-learning algorithms, that quantifies similarities to hundreds of syndromes. DeepGestalt outperformed clinicians in three initial experiments, two with the goal of distinguishing subjects with a target syndrome from other syndromes, and one of separating different genetic subtypes in Noonan syndrome. On the final experiment reflecting a real clinical setting problem, DeepGestalt achieved 91% top-10 accuracy in identifying the correct syndrome on 502 different images. The model was trained on a dataset of over 17,000 images representing more than 200 syndromes, curated through a community-driven phenotyping platform. DeepGestalt potentially adds considerable value to phenotypic evaluations in clinical genetics, genetic testing, research and precision medicine. A deep-learning algorithm, trained on over 17,000 real-world patient facial images, achieves high accuracy in identifying rare genetic disorders.

Pseudo-random Graphs

Abstract: Random graphs have proven to be one of the most important and fruitful concepts in modern Combinatorics and Theoretical Computer Science Besides being a fascinating study subject for their own sake, they serve as essential instruments in proving an enormous number of combinatorial statements, making their role quite hard to overestimate Their tremendous success serves as a natural motivation for the following very general and deep informal questions: what are the essential properties of random graphs? How can one tell when a given graph behaves like a random graph? How to create deterministically graphs that look random-like? This leads us to a concept of pseudo-random graphs