Histogram filtering as a tool in variational Monte Carlo optimization

Optimization of wave functions in quantum Monte Carlo is a difficult task because the statistical uncertainty inherent to the technique makes the absolute determination of the global minimum difficult. To optimize these wave functions we generate a large number of possible minima using many independently generated Monte Carlo ensembles and perform a conjugate gradient optimization. Then we construct histograms of the resulting nominally optimal parameter sets and "filter" them to identify which parameter sets "go together" to generate a local minimum. We follow with correlated-sampling verification runs to find the global minimum. We illustrate this technique for variance and variational energy optimization for a variety of wave functions for small systellls. For such optimized wave functions we calculate the variational energy and variance as well as various non-differential properties. The optimizations are either on par with or superior to determinations in the literature. Furthermore, we show that this technique is sufficiently robust that for molecules one may determine the optimal geometry at tIle same time as one optimizes the variational energy.

Exploring School Principals’ Perspectives on Emotional Intelligence

Ontario school principals’ professional development currently includes leadership training that encompasses emotional intelligence. This study sought to augment the limited research in the Canadian educational context on school leaders’ understanding of emotional intelligence and its relevancy to their work. The study utilized semi-structured interviews with 6 Ontario school principals representing disparate school contexts based on socioeconomic levels, urban and rural settings, and degree of ethnic diversity. Additionally, the 4 male and 2 female participants are elementary and secondary school principals in different public school boards and represent a diverse range of age and experience. The study utilized a grounded theory approach to data analysis and identified by 5 main themes: Self-Awareness, Relationship, Support, Pressure, and Emotional Filtering and Compartmentalization. Recommendations are made to further explore the emotional support systems available to school leaders in Ontario schools.

Relationship between Surface and Indwelling EMG Spike Shape Measures

Indwelling electromyography (EMG) has great diagnostic value but its invasive and often painful characteristics make it inappropriate for monitoring human movement. Spike shape analysis of the surface electromyographic signal responds to the call for non-invasive EMG measures for monitoring human movement and detecting neuromuscular disorders. The present study analyzed the relationship between surface and indwelling EMG interference patterns. Twenty four males and twenty four females performed three isometric dorsiflexion contractions at five force levels from 20% to maximal force. The amplitude measures increased differently between electrode types, attributed to the electrode sensitivity. The frequency measures were different between traditional and spike shape measures due to different noise rejection criteria. These measures were also different between surface and indwelling EMG due to the low-pass tissue filtering effect. The spike shape measures, thought to collectively function as a means to differentiate between motor unit characteristics, changed independent of one another.

QUANTIFICATION AND EXTRACTION OF SURFACE FEATURES FROM DIGITAL TERRAIN MODELS

Digital Terrain Models (DTMs) are important in geology and geomorphology, since elevation data contains a lot of information pertaining to geomorphological processes that influence the topography. The first derivative of topography is attitude; the second is curvature. GIS tools were developed for derivation of strike, dip, curvature and curvature orientation from Digital Elevation Models (DEMs). A method for displaying both strike and dip simultaneously as colour-coded visualization (AVA) was implemented. A plug-in for calculating strike and dip via Least Squares Regression was created first using VB.NET. Further research produced a more computationally efficient solution, convolution filtering, which was implemented as Python scripts. These scripts were also used for calculation of curvature and curvature orientation. The application of these tools was demonstrated by performing morphometric studies on datasets from Earth and Mars. The tools show promise, however more work is needed to explore their full potential and possible uses.