Measuring the saturation scale in nuclei

The saturation momentum seeing in the nuclear infinite momentum frame is directly related to transverse momentum broadening of partons propagating through the medium in the nuclear rest frame. Calculation of broadening within the color dipole approach including the effects of saturation in the nucleus, gives rise to an equation which describes well data on broadening in Drell-Yan reaction and heavy quarkonium production.Comment: 11 pages, 5 figures, based on the talk presented by B.K. at the INT workshop "Physics at a High Energy Electron Ion Collider", Seattle, October 200

Giant microwave-induced BB-periodic magnetoresistance oscillations in a two-dimensional electron gas with a bridged-gate tunnel point contact

We have studied the magnetoresistance of the quantum point contact fabricated on the high mobility two-dimensional electron gas (2DEG) exposed to microwave irradiation. The resistance reveals giant $B$-periodic oscillations with the relative amplitude $\Delta R/R$ of up to $700$\% resulting from the propagation and interference of the edge magnetoplasmons (EMPs) in the sample. This giant photoconductance is attributed to the considerably large local electron density modulation in the vicinity of the point contact. We have also analyzed the oscillation periods $\Delta B$ of the resistance oscillations and, comparing the data with the EMP theory, extracted the EMP interference length $L$. We have found that the length $L$ substantially exceeds the distance between the contact leads but rather corresponds to the distance between metallic contact pads measured along the edge of the 2DEG. This resolves existing controversy in the literature and should help to properly design highly sensitive microwave and terahertz spectrometers based on the discussed effect.Comment: 5 pages, 5 figure

Stereo Computation for a Single Mixture Image

This paper proposes an original problem of \emph{stereo computation from a single mixture image}-- a challenging problem that had not been researched before. The goal is to separate (\ie, unmix) a single mixture image into two constitute image layers, such that the two layers form a left-right stereo image pair, from which a valid disparity map can be recovered. This is a severely illposed problem, from one input image one effectively aims to recover three (\ie, left image, right image and a disparity map). In this work we give a novel deep-learning based solution, by jointly solving the two subtasks of image layer separation as well as stereo matching. Training our deep net is a simple task, as it does not need to have disparity maps. Extensive experiments demonstrate the efficacy of our method.Comment: Accepted by European Conference on Computer Vision (ECCV) 201