Gluons, quarks, and the transition from nonperturbative to perturbative QCD

Lattice-based investigations of two fundamental QCD quantities are described, namely the gluon and quark propagators in Landau gauge. We have studied the Landau gauge gluon propagator using a variety of lattices with spacings from a = 0.17 to 0.41 fm. We demonstrate that it is possible to obtain scaling behavior over a very wide range of momenta and lattice spacings and to explore the infinite volume and continuum limits. These results confirm that the Landau gauge gluon propagator is infrared finite. We study the Landau gauge quark propagator in quenched QCD using two forms of the O(a)-improved propagator and we find good agreement between these. The extracted value of the infrared quark mass in the chiral limit is found to be 300 +/- 30 MeV. We conclude that the momentum regime where the transition from nonperturbative to perturbative QCD occurs is Q^2 approx 4GeV^2.Comment: 8 pages, 6 figures, 1 table. Talk presented by AGW at the Workshop on Lepton Scattering, Hadrons and QCD, March 26-April 5, 2001, CSSM, Adelaide, Australia. To appear in the proceeding

Deep Learning For Smile Recognition

Inspired by recent successes of deep learning in computer vision, we propose a novel application of deep convolutional neural networks to facial expression recognition, in particular smile recognition. A smile recognition test accuracy of 99.45% is achieved for the Denver Intensity of Spontaneous Facial Action (DISFA) database, significantly outperforming existing approaches based on hand-crafted features with accuracies ranging from 65.55% to 79.67%. The novelty of this approach includes a comprehensive model selection of the architecture parameters, allowing to find an appropriate architecture for each expression such as smile. This is feasible because all experiments were run on a Tesla K40c GPU, allowing a speedup of factor 10 over traditional computations on a CPU.Comment: Proceedings of the 12th Conference on Uncertainty Modelling in Knowledge Engineering and Decision Making (FLINS 2016

Morphological Complexity and Conceptualization : The Human Body

In this squib, I want to argue that the morphological structure of words is, at least to some extent, motivated. As an example I have choosen the partonomic (and for the less part taxonomic) nomenclature of the human body. While important work by Brown et alii (1973), Anderson (1978) and Schladt (1997) exists on this topic, these analyses focus on the conceptualization of body-parts and their semantics, but not on their morphological representation. In the following, I want to check two predictions about the morphological complexity of lexical items denoting parts of the human body. The first assumption is that the most canonical body-parts are always expressed by mono-lexematic items. The second one consists in the assumption that body-parts of the lowest levels in the hierarchy are always morphologically complex. A set of six body-parts has been analysed in 27 languages. The set consists of two canonical (HEAD and EAR) and of one from the lowest level of the hierarchy (TOENAIL). For this I have adopted a sample from Schladt (1997) and a small one compiled by mysel

A Simple and Correct Even-Odd Algorithm for the Point-in-Polygon Problem for Complex Polygons

Determining if a point is in a polygon or not is used by a lot of applications in computer graphics, computer games and geoinformatics. Implementing this check is error-prone since there are many special cases to be considered. This holds true in particular for complex polygons whose edges intersect each other creating holes. In this paper we present a simple even-odd algorithm to solve this problem for complex polygons in linear time and prove its correctness for all possible points and polygons. We furthermore provide examples and implementation notes for this algorithm.Comment: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017), Volume 1: GRAP

Patrick O. Rawlins v. NJ Transit

USDC for the District of New Jerse

Profile Likelihood Biclustering

Biclustering, the process of simultaneously clustering the rows and columns of a data matrix, is a popular and effective tool for finding structure in a high-dimensional dataset. Many biclustering procedures appear to work well in practice, but most do not have associated consistency guarantees. To address this shortcoming, we propose a new biclustering procedure based on profile likelihood. The procedure applies to a broad range of data modalities, including binary, count, and continuous observations. We prove that the procedure recovers the true row and column classes when the dimensions of the data matrix tend to infinity, even if the functional form of the data distribution is misspecified. The procedure requires computing a combinatorial search, which can be expensive in practice. Rather than performing this search directly, we propose a new heuristic optimization procedure based on the Kernighan-Lin heuristic, which has nice computational properties and performs well in simulations. We demonstrate our procedure with applications to congressional voting records, and microarray analysis.Comment: 40 pages, 11 figures; R package in development at https://github.com/patperry/biclustp