Complex takes shape

View of the progress of the Mackenzie Chown Complex several months into construction.

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.

Optimization of the valence energy variance of the CuH molecule

We developed the concept of split-'t to deal with the large molecules (in terms of the number of electrons and nuclear charge Z). This naturally leads to partitioning the local energy into components due to each electron shell. The minimization of the variation of the valence shell local energy is used to optimize a simple two parameter CuH wave function. Molecular properties (spectroscopic constants and the dipole moment) are calculated for the optimized and nearly optimized wave functions using the Variational Quantum Monte Carlo method. Our best results are comparable to those from the single and double configuration interaction (SDCI) method.

Optimization of trial wave functions for use in quantum Monte Carlo with application to LiH

Methods for both partial and full optimization of wavefunction parameters are explored, and these are applied to the LiH molecule. A partial optimization can be easily performed with little difficulty. But to perform a full optimization we must avoid a wrong minimum, and deal with linear-dependency, time step-dependency and ensemble-dependency problems. Five basis sets are examined. The optimized wavefunction with a 3-function set gives a variational energy of -7.998 + 0.005 a.u., which is comparable to that (-7.990 + 0.003) 1 of Reynold's unoptimized \fin ( a double-~ set of eight functions). The optimized wavefunction with a double~ plus 3dz2 set gives ari energy of -8.052 + 0.003 a.u., which is comparable with the fixed-node energy (-8.059 + 0.004)1 of the \fin. The optimized double-~ function itself gives an energy of -8.049 + 0.002 a.u. Each number above was obtained on a Bourrghs 7900 mainframe computer with 14 -15 hrs CPU time.

Investigations into the determination of polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs) by capillary gas chromatography

Factors involved in the determination of PAHs (16 priority PAHs as an example) and PCBs (10 PCB congeners, representing 10 isomeric groups) by capillary gas chromatography coupled with mass spectrometry (GC/MS, for PAHs) and electron capture detection (GC/ECD , for PCBs) were studied, with emphasis on the effect of solvent. Having various volatilities and different polarities, solvent studied included dichloromethane, acetonitrile, hexan e, cyclohexane, isooctane, octane, nonane, dodecane, benzene, toluene, p-xylene, o-xylene, and mesitylene. Temperatures of the capillary column, the injection port, the GC/MS interface, the flow rates of carrier gas and make-up gas, and the injection volume were optimized by one factor at a time method or simplex optimization method. Under the optimized conditions, both peak height and peak area of 16 PAHs, especially the late-eluting PAHs, were significantly enhanced (1 to 500 times) by using relatively higher boiling point solvents such as p-xylene and nonane, compared with commonly used solvents like benzene and isooctane. With the improved sensitivity, detection limits of between 4.4 pg for naphthalene and 30.8 pg for benzo[g,h,i]perylene were obtained when p-xylene was used as an injection solvent. Effect of solvent on peak shape and peak intensity were found to be greatly dependent on temperature parameters, especially the initial temperature of the capillary column. The relationship between initial temperature and shape of peaks from 16 PAHs and 10 PCBs were studied and compared when toluene, p-xylene, isooctane, and nonane were used as injection solvents. If a too low initial temperature was used, fronting or split of peaks was observed. On the other hand, peak tailing occurred at a too high initial column temperature. The optimum initial temperature, at which both peak fronting and tailing were avoided and symmetrical peaks were obtained, depended on both solvents and the stationary phase of the column used. On a methyl silicone column, the alkane solvents provided wider optimum ranges of initial temperature than aromatic solvents did, for achieving well-shaped symmetrical GC peaks. On a 5% diphenyl: 1% vinyl: 94% dimethyl polysiloxane column, when the aromatic solvents were used, the optimum initial temperature ranges for solutes to form symmetrical peaks were improved to a similar degree as those when the alkanes were used as injection solvents. A mechanism, based on the properties of and possible interactions among the analyte, the injection solvent, and the stationary phase of the capillary column, was proposed to explain these observations. The effect of initial temperature on peak height and peak area of the 16 PAHs and the 10 PCBs was also studied. The optimum initial temperature was found to be dependent on the physical properties of the solvent used and the amount of the solvent injected. Generally, from the boiling point of the solvent to 10 0C above its boiling point was an optimum range of initial temperature at which cthe highest peak height and peak area were obtained.

Particle swarm optimization for two-connected networks with bounded rings

The Two-Connected Network with Bounded Ring (2CNBR) problem is a network design problem addressing the connection of servers to create a survivable network with limited redirections in the event of failures. Particle Swarm Optimization (PSO) is a stochastic population-based optimization technique modeled on the social behaviour of flocking birds or schooling fish. This thesis applies PSO to the 2CNBR problem. As PSO is originally designed to handle a continuous solution space, modification of the algorithm was necessary in order to adapt it for such a highly constrained discrete combinatorial optimization problem. Presented are an indirect transcription scheme for applying PSO to such discrete optimization problems and an oscillating mechanism for averting stagnation.

Bio-inspired optimization & sampling technique for side-chain packing in MCCE

The prediction of proteins' conformation helps to understand their exhibited functions, allows for modeling and allows for the possible synthesis of the studied protein. Our research is focused on a sub-problem of protein folding known as side-chain packing. Its computational complexity has been proven to be NP-Hard. The motivation behind our study is to offer the scientific community a means to obtain faster conformation approximations for small to large proteins over currently available methods. As the size of proteins increases, current techniques become unusable due to the exponential nature of the problem. We investigated the capabilities of a hybrid genetic algorithm / simulated annealing technique to predict the low-energy conformational states of various sized proteins and to generate statistical distributions of the studied proteins' molecular ensemble for pKa predictions. Our algorithm produced errors to experimental results within .acceptable margins and offered considerable speed up depending on the protein and on the rotameric states' resolution used.

(A) Optimization of solid body phase synthesis of RNA using the Cpep chemistry. (B) Synthesis of BODIPY labeled oligonucleotides

(A) Solid phase synthesis of oligonucleotides are well documented and are extensively studied as the demands continue to rise with the development of antisense, anti-gene, RNA interference, and aptamers. Although synthesis of RNA sequences faces many challenges, most notably the choice of the 2' -hydroxy protecting group, modified 2' -O-Cpep protected ribonucleotides were synthesized as alternitive building blocks. Altering phosphitylation procedures to incorporate 3' -N,N-diethyl phosphoramidites enhanced the overall reactivity, thus, increased the coupling efficiency without loss of integrety. Furthermore, technical optimizations of solid phase synthesis cycles were carried out to allow for successful synthesis of a homo UIO sequences with a stepwise coupling efficiency reaching 99% and a final yield of 91 %. (B) Over the past few decades, dipyrrometheneboron difluoride (BODIPY) has gained recognition as one of the most versatile fluorophores. Currently, BODIPY labeling of oligonucleotides are carried out post-synthetically and to date, there lacks a method that allows for direct incorporation of BODIPY into oligonucleotides during solid phase synthesis. Therefore, synthesis of BODIPY derived phosphoramidites will provide an alternative method in obtaining fluorescently labelled oligonucleotides. A method for the synthesis and incorporation of the BODIPY analogues into oligonucleotides by phosphoramidite chemistry-based solid phase DNA synthesis is reported here. Using this approach, BODIPY-labeled TlO homopolymer and ISIS 5132 were successfully synthesized.

Generating Aesthetically Pleasing Images in a Virtual Environment using Particle Swarm Optimization

This research focuses on generating aesthetically pleasing images in virtual environments using the particle swarm optimization (PSO) algorithm. The PSO is a stochastic population based search algorithm that is inspired by the flocking behavior of birds. In this research, we implement swarms of cameras flying through a virtual world in search of an image that is aesthetically pleasing. Virtual world exploration using particle swarm optimization is considered to be a new research area and is of interest to both the scientific and artistic communities. Aesthetic rules such as rule of thirds, subject matter, colour similarity and horizon line are all analyzed together as a multi-objective problem to analyze and solve with rendered images. A new multi-objective PSO algorithm, the sum of ranks PSO, is introduced. It is empirically compared to other single-objective and multi-objective swarm algorithms. An advantage of the sum of ranks PSO is that it is useful for solving high-dimensional problems within the context of this research. Throughout many experiments, we show that our approach is capable of automatically producing images satisfying a variety of supplied aesthetic criteria.

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.

Characterizing Dynamic Optimization Benchmarks for the Comparison of Multi-Modal Tracking Algorithms

Population-based metaheuristics, such as particle swarm optimization (PSO), have been employed to solve many real-world optimization problems. Although it is of- ten sufficient to find a single solution to these problems, there does exist those cases where identifying multiple, diverse solutions can be beneficial or even required. Some of these problems are further complicated by a change in their objective function over time. This type of optimization is referred to as dynamic, multi-modal optimization. Algorithms which exploit multiple optima in a search space are identified as niching algorithms. Although numerous dynamic, niching algorithms have been developed, their performance is often measured solely on their ability to find a single, global optimum. Furthermore, the comparisons often use synthetic benchmarks whose landscape characteristics are generally limited and unknown. This thesis provides a landscape analysis of the dynamic benchmark functions commonly developed for multi-modal optimization. The benchmark analysis results reveal that the mechanisms responsible for dynamism in the current dynamic bench- marks do not significantly affect landscape features, thus suggesting a lack of representation for problems whose landscape features vary over time. This analysis is used in a comparison of current niching algorithms to identify the effects that specific landscape features have on niching performance. Two performance metrics are proposed to measure both the scalability and accuracy of the niching algorithms. The algorithm comparison results demonstrate the algorithms best suited for a variety of dynamic environments. This comparison also examines each of the algorithms in terms of their niching behaviours and analyzing the range and trade-off between scalability and accuracy when tuning the algorithms respective parameters. These results contribute to the understanding of current niching techniques as well as the problem features that ultimately dictate their success.

A Scalability Study and New Algorithms for Large-Scale Many-Objective Optimization

Many real-world optimization problems contain multiple (often conflicting) goals to be optimized concurrently, commonly referred to as multi-objective problems (MOPs). Over the past few decades, a plethora of multi-objective algorithms have been proposed, often tested on MOPs possessing two or three objectives. Unfortunately, when tasked with solving MOPs with four or more objectives, referred to as many-objective problems (MaOPs), a large majority of optimizers experience significant performance degradation. The downfall of these optimizers is that simultaneously maintaining a well-spread set of solutions along with appropriate selection pressure to converge becomes difficult as the number of objectives increase. This difficulty is further compounded for large-scale MaOPs, i.e., MaOPs possessing large amounts of decision variables. In this thesis, we explore the challenges of many-objective optimization and propose three new promising algorithms designed to efficiently solve MaOPs. Experimental results demonstrate the proposed optimizers to perform very well, often outperforming state-of-the-art many-objective algorithms.