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.

Nature and Nurture in Own-Race Face Processing

Abstract: A standard visual preference task was used to examine 3-month-olds' looking times at own-race versus other-race faces as a function of environmental exposure to faces from the two categories. Participants were Caucasian infants living in a Caucasian environment, African infants living in an African environment, and African infants living in a predominantly Caucasian environment. The results indicate that preference for own-race faces is present as early as 3 months of age, but that this preference results from exposure to the prototypical facial environment.

Boundary value problems for fractional diffusion equations

Abstract: The fractional diffusion equation is solved for different boundary value problems, these being absorbing and reflecting boundaries in half-space and in a box. Thereby, the method of images and the Fourier–Laplace transformation technique are employed. The separation of variables is studied for a fractional diffusion equation with a potential term, describing a generalisation of an escape problem through a fluctuating bottleneck. The results lead to a further understanding of the fractional framework in the description of complex systems which exhibit anomalous diffusion.

Lattices which are good for (almost) everything

Abstract: We define an ensemble of lattices, and show that for asymptotically high dimension most of its members are simultaneously good as sphere packings, sphere coverings, additive white Gaussian noise (AWGN) channel codes and mean-squared error (MSE) quantization codes. These lattices are generated by applying Construction A to a random linear code over a prime field of growing size, i.e., by "lifting" the code to /spl Ropf//sup n/.

Unsupervised Cross-Domain Image Generation

Abstract: We study the problem of transferring a sample in one domain to an analog sample in another domain. Given two related domains, S and T, we would like to learn a generative function G that maps an input sample from S to the domain T, such that the output of a given function f, which accepts inputs in either domains, would remain unchanged. Other than the function f, the training data is unsupervised and consist of a set of samples from each domain. The Domain Transfer Network (DTN) we present employs a compound loss function that includes a multiclass GAN loss, an f-constancy component, and a regularizing component that encourages G to map samples from T to themselves. We apply our method to visual domains including digits and face images and demonstrate its ability to generate convincing novel images of previously unseen entities, while preserving their identity.