Holographic thermalization in N=4 Super Yang-Mills theory at finite coupling

We investigate the behavior of energy momentum tensor correlators in holographic $\mathcal{N}=4$ super Yang-Mills plasma, taking finite coupling corrections into account. In the thermal limit we determine the flow of quasinormal modes as a function of the 't Hooft coupling. Then we use a specific model of holographic thermalization to study the deviation of the spectral densities from their thermal limit in an out-of-equilibrium situation. The main focus lies on the thermalization pattern with which the plasma constituents approach their thermal distribution as the coupling constant decreases from the infinite coupling limit. All obtained results point towards the weakening of the usual top-down thermalization pattern.Comment: 18 pages, 7 figures, v3: major revisio

Impact of microphysics on the growth of one-dimensional breath figures

Droplet patterns condensing on solid substrates (breath figures) tend to evolve into a self-similar regime, characterized by a bimodal droplet size distribution. The distributions comprise a bell-shaped peak of monodisperse large droplets, and a broad range of smaller droplets. The size distribution of the latter follows a scaling law characterized by a non-trivial polydispersity exponent. We present here a numerical model for three-dimensional droplets on a one-dimensional substrate (fiber) that accounts for droplet nucleation, growth and merging. The polydispersity exponent retrieved using this model is not universal. Rather it depends on the thickness of the fiber and on details of the droplet interaction leading to merging. In addition, its values consistently differ from the theoretical prediction by Blackman (Phys. Rev. Lett., 2000). Possible causes of this discrepancy are pointed out

DORMAN computer program (study 2.5). Volume 1: Executive summary

The DORCA Applications study has been directed at development of a data bank management computer program identified as DORMAN. Because of the size of the DORCA data files and the manipulations required on that data to support analyses with the DORCA program, automated data techniques to replace time-consuming manual input generation are required. The Dynamic Operations Requirements and Cost Analysis (DORCA) program was developed for use by NASA in planning future space programs. Both programs are designed for implementation on the UNIVAC 1108 computing system. The purpose of this Executive Summary Report is to define for the NASA management the basic functions of the DORMAN program and its capabilities

Simultaneous Hand Pose and Skeleton Bone-Lengths Estimation from a Single Depth Image

Articulated hand pose estimation is a challenging task for human-computer interaction. The state-of-the-art hand pose estimation algorithms work only with one or a few subjects for which they have been calibrated or trained. Particularly, the hybrid methods based on learning followed by model fitting or model based deep learning do not explicitly consider varying hand shapes and sizes. In this work, we introduce a novel hybrid algorithm for estimating the 3D hand pose as well as bone-lengths of the hand skeleton at the same time, from a single depth image. The proposed CNN architecture learns hand pose parameters and scale parameters associated with the bone-lengths simultaneously. Subsequently, a new hybrid forward kinematics layer employs both parameters to estimate 3D joint positions of the hand. For end-to-end training, we combine three public datasets NYU, ICVL and MSRA-2015 in one unified format to achieve large variation in hand shapes and sizes. Among hybrid methods, our method shows improved accuracy over the state-of-the-art on the combined dataset and the ICVL dataset that contain multiple subjects. Also, our algorithm is demonstrated to work well with unseen images.Comment: This paper has been accepted and presented in 3DV-2017 conference held at Qingdao, China. http://irc.cs.sdu.edu.cn/3dv

Learning quadrangulated patches for 3D shape parameterization and completion

We propose a novel 3D shape parameterization by surface patches, that are oriented by 3D mesh quadrangulation of the shape. By encoding 3D surface detail on local patches, we learn a patch dictionary that identifies principal surface features of the shape. Unlike previous methods, we are able to encode surface patches of variable size as determined by the user. We propose novel methods for dictionary learning and patch reconstruction based on the query of a noisy input patch with holes. We evaluate the patch dictionary towards various applications in 3D shape inpainting, denoising and compression. Our method is able to predict missing vertices and inpaint moderately sized holes. We demonstrate a complete pipeline for reconstructing the 3D mesh from the patch encoding. We validate our shape parameterization and reconstruction methods on both synthetic shapes and real world scans. We show that our patch dictionary performs successful shape completion of complicated surface textures.Comment: To be presented at International Conference on 3D Vision 2017, 201