Improved Delsarte bounds for spherical codes in small dimensions

We present an extension of the Delsarte linear programming method. For several dimensions it yields improved upper bounds for kissing numbers and for spherical codes. Musin's recent work on kissing numbers in dimensions three and four can be viewed in our framework.Comment: 16 pages, 3 figures. Substantial changes after referee's comments, one new lemm

Cartesian genetic programming for trading: a preliminary investigation

In this paper, a preliminary investigation of Cartesian Genetic Programming (CGP) for algorithmic intraday trading is conducted. CGP is a recent new variant of genetic programming that differs from traditional approaches in a number of ways, including being able to evolve programs with limited size and with multiple outputs. CGP is used to evolve a predictor for intraday price movements, and trading strategies using the evolved predictors are evaluated along three dimensions (return, maximum drawdown and recovery factor) and against four different financial datasets (the Euro/US dollar exchange rate and the Dow Jones Industrial Average during periods from 2006 and 2010). We show that CGP is capable in many instances of evolving programs that, when used as trading strategies, lead to modest positive returns

Mutually Unbiased Bases and Semi-definite Programming

A complex Hilbert space of dimension six supports at least three but not more than seven mutually unbiased bases. Two computer-aided analytical methods to tighten these bounds are reviewed, based on a discretization of parameter space and on Grobner bases. A third algorithmic approach is presented: the non-existence of more than three mutually unbiased bases in composite dimensions can be decided by a global optimization method known as semidefinite programming. The method is used to confirm that the spectral matrix cannot be part of a complete set of seven mutually unbiased bases in dimension six.Comment: 11 pages

Graph kernels between point clouds

Point clouds are sets of points in two or three dimensions. Most kernel methods for learning on sets of points have not yet dealt with the specific geometrical invariances and practical constraints associated with point clouds in computer vision and graphics. In this paper, we present extensions of graph kernels for point clouds, which allow to use kernel methods for such ob jects as shapes, line drawings, or any three-dimensional point clouds. In order to design rich and numerically efficient kernels with as few free parameters as possible, we use kernels between covariance matrices and their factorizations on graphical models. We derive polynomial time dynamic programming recursions and present applications to recognition of handwritten digits and Chinese characters from few training examples

The Impact of Teaching Programming by using Scratch on Self-motivation towards Learning Programming for Primary School Students in Riyadh

The study aimed to measure the effect of teaching programming using Scratch on Self-motivation towards learning programming for primary school students in Riyadh. Twenty-five 4th-grade students participated in the study. A quasi-experimental design was applied. Pre and post-tests were utilized using a motivational scale towards learning programming designed by the researchers to include three dimensions: perseverance, ambition and enjoyment. Participants received Scratch lessons for the duration of a semester. The results showed significant differences in favor of the post-test in all dimensions of the scale. In addition, students' self-motivation towards learning programming has improved by 22.8% in the average rate of the scale. The results showed the improvement rate of students' self-motivation towards learning programming to the scale axes (perseverance, ambition, enjoyment) as follows: 20.9%, 23.8%, and 23.6% respectively. Moreover, the order of items and the improvement rate were different according to the pre-test and post-test; although all the items were positive

CLUSTEREASY: A Program for Simulating Scalar Field Evolution on Parallel Computers

We describe a new, parallel programming version of the scalar field simulation program LATTICEEASY. The new C++ program, CLUSTEREASY, can simulate arbitrary scalar field models on distributed-memory clusters. The speed and memory requirements scale well with the number of processors. As with the serial version of LATTICEEASY, CLUSTEREASY can run simulations in one, two, or three dimensions, with or without expansion of the universe, with customizable parameters and output. The program and its full documentation are available on the LATTICEEASY website at http://www.science.smith.edu/departments/Physics/fstaff/gfelder/latticeeasy/. In this paper we provide a brief overview of what CLUSTEREASY does and the ways in which it does and doesn't differ from the serial version of LATTICEEASY.Comment: 3 pages, 1 figur