The average time necessary to add numbers by local parallel computation is directly related to the length of the longest carry propagation chain in the sum. The mean length of longest carry propagation chain when adding two independent uniform random n bit numbers is a well studied topic, and useful approximations as well as an exact expression for this value have been found. My thesis searches for similar formulas for mean length of the longest carry propagation chain in sums that arise when a random n-digit number is multiplied by a number of the form 1 + 2d. Letting Cn, d represent the desired mean, my thesis details how to find formulas for Cn,d using probability, generating functions and linear algebra arguments. I also find bounds on Cn,d to prove that Cn,d = log2 n + O(1), and show work towards finding an even more exact approximation for Cn,d.

Each year, crowdsourcing organizational research grows increasingly popular. However, this source of sampling receives much scrutiny focused on data quality and related research methods. Specific to the present research, survey attentiveness poses a unique dilemma. Research on updated conceptualizations of attentiveness--insufficient effort responding (IER)--shows that it carries substantial concerns for data quality beyond random noise, which further warrants deleting inattentive participants. However, personal characteristics predict IER, so deleting data may cause sampling bias. Therefore, preventing IER becomes paramount, but research seems to ignore whether IER prevention itself may create systematic error. This study examines the detection and prevention of IER in Amazon's Mechanical Turk (Mturk) by evaluating three IER detection methods pertinent to concerns of attentiveness on the platform and using two, promising, IER prevention approaches--Mturk screening features and IER preventive warning messages. I further consider how these issues relate to organizational research and answer the call for a more nuanced understanding of the Mturk population by focusing on psychological phenomena often studied/measured in organizational literature--the congruency effect and approach-avoidance motivational theories, Big Five personality, positive and negative affectivity, and core self-evaluations. I collected survey data from screened and non-screened samples and manipulated warning messages using four conditions--no warning, gain-framed, loss-framed, and combined-framed messages. I used logistic regression to compare the prevalence of IER across conditions and the effectiveness of warning messages given positively or negatively valenced motivational tendencies. I also used 4x2 factorial ANCOVAs to test for differences in personal characteristics across conditions. The sample consisted of 1071 Mturk workers (turkers). Results revealed differences in IER prevalence among detection methods and between prevention conditions, counter-intuitive results for congruency effects and motivational theories, and differences across conditions for agreeableness, conscientiousness, and positive and negative affectivity. Implications, future research, and recommendations are discussed.

Thesis (M. Phil.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 55-57).

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Uncertainty is inherent in neural processing due to noise in sensation and the sensory transmission processes, the ill-posed nature of many perceptual tasks, and temporal dynamics of the natural environment, to name a few causes. A wealth of evidence from physiological and behavioral experiments show that these various forms of uncertainty have major effects on perceptual learning and inference. In order to use sensory inputs efficiently to make decisions and guide behavior, neural systems must represent and manipulate information about uncertainty in their computations. In this thesis, we first consider how spiking neural populations might encode and decode information about continuous dynamic stimulus variables including the uncertainty about them. We explore the efficacy of a complex encoder that is paired with a simple decoder which allows computationally straightforward representation and manipulation of dynamically changing uncertainty. The encoder we present takes the form of a biologically plausible recurrent spiking neural network where the output population recodes its inputs to produce spikes that are independently decodeable. We show that this network can be learned in a supervised manner, by a simple, local learning rule. We also demonstrate that the coding scheme can be applied recursively to carry out meaningful uncertainty-sensitive computations such as dynamic cue combination. Next, we explore the computational principles that underlie non-linear response characteristics such as perceptual bias and uncertainty observed in audiovisual spatial illusions that involve multisensory interactions with conflicting cues. We examine in detail, the explanatory power of one particular causal model in characterizing the impact of conflicting inputs on perception and behavior. We also attempt to understand from a computational perspective, whether and how different task instructions might modulate the interaction of information from individual (visual and auditory) senses. Our analyses reveal some new properties of the sensory likelihoods and stimulus prior which were thought to be well described by Gaussian functions. Our results conclude that task-specific expectations can influence perception in ways that relate to a choice of inference strategy.

Uncertainty is inherent in neural processing due to noise in sensation and the sensory transmission processes, the ill-posed nature of many perceptual tasks, and temporal dynamics of the natural environment, to name a few causes. A wealth of evidence from physiological and behavioral experiments show that these various forms of uncertainty have major effects on perceptual learning and inference. In order to use sensory inputs efficiently to make decisions and guide behavior, neural systems must represent and manipulate information about uncertainty in their computations. In this thesis, we first consider how spiking neural populations might encode and decode information about continuous dynamic stimulus variables including the uncertainty about them. We explore the efficacy of a complex encoder that is paired with a simple decoder which allows computationally straightforward representation and manipulation of dynamically changing uncertainty. The encoder we present takes the form of a biologically plausible recurrent spiking neural network where the output population recodes its inputs to produce spikes that are independently decodeable. We show that this network can be learned in a supervised manner, by a simple, local learning rule. We also demonstrate that the coding scheme can be applied recursively to carry out meaningful uncertainty-sensitive computations such as dynamic cue combination. Next, we explore the computational principles that underlie non-linear response characteristics such as perceptual bias and uncertainty observed in audiovisual spatial illusions that involve multisensory interactions with conflicting cues. We examine in detail, the explanatory power of one particular causal model in characterizing the impact of conflicting inputs on perception and behavior. We also attempt to understand from a computational perspective, whether and how different task instructions might modulate the interaction of information from individual (visual and auditory) senses. Our analyses reveal some new properties of the sensory likelihoods and stimulus prior which were thought to be well described by Gaussian functions. Our results conclude that task-specific expectations can influence perception in ways that relate to a choice of inference strategy.

Woven string kernels are a form of evolvable, directed, acyclic graphs specialized to perform DNA classification. They are introduced in this thesis, given a rigorous theoretical treatment as a mathematical object, and shown to have a number of interesting properties. Two forms of woven string kernels, uniform and non-uniform, are discussed. The non-uniform woven string kernels are repurposed for use as updating rules for cellular automata. The details of their representation and implementation are presented. A chapter of this thesis is devoted to a visualization technique called non-linear projection, an evolvable form of multidimensional scaling that is used in the analysis of experimental results. The woven string kernels are tested on simple and complex synthetic data as well as biological data, using an evolutionary algorithm to find woven string kernels that are acceptable solutions for classification. They perform marginally on the simplest synthetic data - based on GC content - for which they are not entirely appropriate. They exhibit perfect classification on the more complex synthetic data and on the biological data. Woven string kernels have a number of parameters including their height, the number of initial strings from which they are built, and the amount of weaving used to generate the final structure. A parameter study shows that these parameters must be set based on the type of data under analysis. Experimentation with woven string kernels as rules for updating cellular automata show that having a larger population and more available colour states are correlated with an increase in performance as apoptotic one dimensional cellular automata. This thesis concludes with directions for future work related to theory and experimentation, for both uniform and non-uniform woven string kernels.

No description available.

The objective of Evolutionary Computation is to solve practical problems (e.g.optimization, data mining) by simulating the mechanisms of natural evolution. This thesis addresses several topics related to adaptation and self-organization in evolving systems with the overall aims of improving the performance of Evolutionary Algorithms (EA), understanding its relation to natural evolution, and incorporating new mechanisms for mimicking complex biological systems. Part I of this thesis presents a new mechanism for allowing an EA to adapt its behavior in response to changes in the environment. Using the new approach, adaptation of EA behavior (i.e. control of EA design parameters) is driven by an analysis of population dynamics, as opposed to the more traditional use of fitness measurements. Comparisons with a number of adaptive control methods from the literature indicate substantial improvements in algorithm performance for a range of artificial and engineering design problems. Part II of this thesis involves a more thorough analysis of EA behavior based on the methods derived in Part 1. In particular, several properties of EA population dynamics are measured and compared with observations of evolutionary dynamics in nature. The results demonstrate that some large scale spatial and temporal features of EA dynamics are remarkably similar to their natural counterpart. Compatibility of EA with the Theory of Self-Organized Criticality is also discussed. Part III proposes fundamentally new directions in EA research which are inspired by the conclusions drawn in Part II. These changes involve new mechanisms which allow selforganization of the EA to occur in ways which extend beyond its common convergence in parameter space. In particular, network models for EA populations are developed where the network structure is dynamically coupled to EA population dynamics. Results indicate strong improvements in algorithm performance compared to cellular Genetic Algorithms and non-distributed EA designs. Furthermore, topological analysis indicates that the population network can spontaneously evolve to display similar characteristics to the interaction networks of complex biological systems.

Thesis (Ph. D.)--University of Texas at Austin, 2004. / Supervisor: Risto Miikkulainen. Vita. Includes bibliographical references. Also available from UMI.