Structure and energetics of Si(111)-(5x2)-Au

We propose a new structural model for the Si(111)-(5x2)-Au reconstruction. The model incorporates a new experimental value of 0.6 monolayer for the coverage of gold atoms, equivalent to six gold atoms per 5x2 cell. Five main theoretical results, obtained from first-principles total-energy calculations, support the model. (1) In the presence of silicon adatoms the periodicity of the gold rows spontaneously doubles, in agreement with experiment. (2) The dependence of the surface energy on the adatom coverage indicates that a uniformly covered phase is unstable and will phase-separate into empty and covered regions, as observed experimentally. (3) Theoretical scanning tunneling microscopy images are in excellent agreement with experiment. (4) The calculated band structure is consistent with angle-resolved photoemission spectra; analysis of their correspondence allows the straightforward assignment of observed surface states to specific atoms. (5) The calculated activation barrier for diffusion of silicon adatoms along the row direction is in excellent agreement with the experimentally measured barrier.Comment: 11 pages, 7 figures, also available with higher-resolution figures from http://cst-www.nrl.navy.mil/users/erwin/ausi111.v5.pd

Graph Signal Processing: Overview, Challenges and Applications

Research in Graph Signal Processing (GSP) aims to develop tools for processing data defined on irregular graph domains. In this paper we first provide an overview of core ideas in GSP and their connection to conventional digital signal processing. We then summarize recent developments in developing basic GSP tools, including methods for sampling, filtering or graph learning. Next, we review progress in several application areas using GSP, including processing and analysis of sensor network data, biological data, and applications to image processing and machine learning. We finish by providing a brief historical perspective to highlight how concepts recently developed in GSP build on top of prior research in other areas.Comment: To appear, Proceedings of the IEE

Kernel Belief Propagation

We propose a nonparametric generalization of belief propagation, Kernel Belief Propagation (KBP), for pairwise Markov random fields. Messages are represented as functions in a reproducing kernel Hilbert space (RKHS), and message updates are simple linear operations in the RKHS. KBP makes none of the assumptions commonly required in classical BP algorithms: the variables need not arise from a finite domain or a Gaussian distribution, nor must their relations take any particular parametric form. Rather, the relations between variables are represented implicitly, and are learned nonparametrically from training data. KBP has the advantage that it may be used on any domain where kernels are defined (Rd, strings, groups), even where explicit parametric models are not known, or closed form expressions for the BP updates do not exist. The computational cost of message updates in KBP is polynomial in the training data size. We also propose a constant time approximate message update procedure by representing messages using a small number of basis functions. In experiments, we apply KBP to image denoising, depth prediction from still images, and protein configuration prediction: KBP is faster than competing classical and nonparametric approaches (by orders of magnitude, in some cases), while providing significantly more accurate results