Estimation and inference for grasping and manipulation tasks using vision and kinesthetic sensors

This thesis presents a novel framework for state estimation in the context of robotic grasping and manipulation. The overall estimation approach is based on fusing various visual cues for manipulator tracking, namely appearance and feature-based, shape-based, and silhouette-based visual cues. Similarly, a framework is developed to fuse the above visual cues, but also kinesthetic cues such as force-torque and tactile measurements, for in-hand object pose estimation. The cues are extracted from multiple sensor modalities and are fused in a variety of Kalman filters.

A hybrid estimator is developed to estimate both a continuous state (robot and object states) and discrete states, called contact modes, which specify how each finger contacts a particular object surface. A static multiple model estimator is used to compute and maintain this mode probability. The thesis also develops an estimation framework for estimating model parameters associated with object grasping. Dual and joint state-parameter estimation is explored for parameter estimation of a grasped object's mass and center of mass. Experimental results demonstrate simultaneous object localization and center of mass estimation.

Dual-arm estimation is developed for two arm robotic manipulation tasks. Two types of filters are explored; the first is an augmented filter that contains both arms in the state vector while the second runs two filters in parallel, one for each arm. These two frameworks and their performance is compared in a dual-arm task of removing a wheel from a hub.

This thesis also presents a new method for action selection involving touch. This next best touch method selects an available action for interacting with an object that will gain the most information. The algorithm employs information theory to compute an information gain metric that is based on a probabilistic belief suitable for the task. An estimation framework is used to maintain this belief over time. Kinesthetic measurements such as contact and tactile measurements are used to update the state belief after every interactive action. Simulation and experimental results are demonstrated using next best touch for object localization, specifically a door handle on a door. The next best touch theory is extended for model parameter determination. Since many objects within a particular object category share the same rough shape, principle component analysis may be used to parametrize the object mesh models. These parameters can be estimated using the action selection technique that selects the touching action which best both localizes and estimates these parameters. Simulation results are then presented involving localizing and determining a parameter of a screwdriver.

Lastly, the next best touch theory is further extended to model classes. Instead of estimating parameters, object class determination is incorporated into the information gain metric calculation. The best touching action is selected in order to best discern between the possible model classes. Simulation results are presented to validate the theory.

Multi-model inference ranking and applications to physics at the Large Hadron Collider

In the measurement of the Higgs Boson decaying into two photons the parametrization of an appropriate background model is essential for fitting the Higgs signal mass peak over a continuous background. This diphoton background modeling is crucial in the statistical process of calculating exclusion limits and the significance of observations in comparison to a background-only hypothesis. It is therefore ideal to obtain knowledge of the physical shape for the background mass distribution as the use of an improper function can lead to biases in the observed limits. Using an Information-Theoretic (I-T) approach for valid inference we apply Akaike Information Criterion (AIC) as a measure of the separation for a fitting model from the data. We then implement a multi-model inference ranking method to build a fit-model that closest represents the Standard Model background in 2013 diphoton data recorded by the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC). Potential applications and extensions of this model-selection technique are discussed with reference to CMS detector performance measurements as well as in potential physics analyses at future detectors.

Metastability and dynamics of stem cells: from direct observations to inference at the single cell level

Organismal development, homeostasis, and pathology are rooted in inherently probabilistic events. From gene expression to cellular differentiation, rates and likelihoods shape the form and function of biology. Processes ranging from growth to cancer homeostasis to reprogramming of stem cells all require transitions between distinct phenotypic states, and these occur at defined rates. Therefore, measuring the fidelity and dynamics with which such transitions occur is central to understanding natural biological phenomena and is critical for therapeutic interventions.

While these processes may produce robust population-level behaviors, decisions are made by individual cells. In certain circumstances, these minuscule computing units effectively roll dice to determine their fate. And while the 'omics' era has provided vast amounts of data on what these populations are doing en masse, the behaviors of the underlying units of these processes get washed out in averages.

Therefore, in order to understand the behavior of a sample of cells, it is critical to reveal how its underlying components, or mixture of cells in distinct states, each contribute to the overall phenotype. As such, we must first define what states exist in the population, determine what controls the stability of these states, and measure in high dimensionality the dynamics with which these cells transition between states.

To address a specific example of this general problem, we investigate the heterogeneity and dynamics of mouse embryonic stem cells (mESCs). While a number of reports have identified particular genes in ES cells that switch between 'high' and 'low' metastable expression states in culture, it remains unclear how levels of many of these regulators combine to form states in transcriptional space. Using a method called single molecule mRNA fluorescent in situ hybridization (smFISH), we quantitatively measure and fit distributions of core pluripotency regulators in single cells, identifying a wide range of variabilities between genes, but each explained by a simple model of bursty transcription. From this data, we also observed that strongly bimodal genes appear to be co-expressed, effectively limiting the occupancy of transcriptional space to two primary states across genes studied here. However, these states also appear punctuated by the conditional expression of the most highly variable genes, potentially defining smaller substates of pluripotency.

Having defined the transcriptional states, we next asked what might control their stability or persistence. Surprisingly, we found that DNA methylation, a mark normally associated with irreversible developmental progression, was itself differentially regulated between these two primary states. Furthermore, both acute or chronic inhibition of DNA methyltransferase activity led to reduced heterogeneity among the population, suggesting that metastability can be modulated by this strong epigenetic mark.

Finally, because understanding the dynamics of state transitions is fundamental to a variety of biological problems, we sought to develop a high-throughput method for the identification of cellular trajectories without the need for cell-line engineering. We achieved this by combining cell-lineage information gathered from time-lapse microscopy with endpoint smFISH for measurements of final expression states. Applying a simple mathematical framework to these lineage-tree associated expression states enables the inference of dynamic transitions. We apply our novel approach in order to infer temporal sequences of events, quantitative switching rates, and network topology among a set of ESC states.

Taken together, we identify distinct expression states in ES cells, gain fundamental insight into how a strong epigenetic modifier enforces the stability of these states, and develop and apply a new method for the identification of cellular trajectories using scalable in situ readouts of cellular state.

Latent variable models for neural data analysis

The brain is perhaps the most complex system to have ever been subjected to rigorous scientific investigation. The scale is staggering: over 10^11 neurons, each making an average of 10^3 synapses, with computation occurring on scales ranging from a single dendritic spine, to an entire cortical area. Slowly, we are beginning to acquire experimental tools that can gather the massive amounts of data needed to characterize this system. However, to understand and interpret these data will also require substantial strides in inferential and statistical techniques. This dissertation attempts to meet this need, extending and applying the modern tools of latent variable modeling to problems in neural data analysis.

It is divided into two parts. The first begins with an exposition of the general techniques of latent variable modeling. A new, extremely general, optimization algorithm is proposed - called Relaxation Expectation Maximization (REM) - that may be used to learn the optimal parameter values of arbitrary latent variable models. This algorithm appears to alleviate the common problem of convergence to local, sub-optimal, likelihood maxima. REM leads to a natural framework for model size selection; in combination with standard model selection techniques the quality of fits may be further improved, while the appropriate model size is automatically and efficiently determined. Next, a new latent variable model, the mixture of sparse hidden Markov models, is introduced, and approximate inference and learning algorithms are derived for it. This model is applied in the second part of the thesis.

The second part brings the technology of part I to bear on two important problems in experimental neuroscience. The first is known as spike sorting; this is the problem of separating the spikes from different neurons embedded within an extracellular recording. The dissertation offers the first thorough statistical analysis of this problem, which then yields the first powerful probabilistic solution. The second problem addressed is that of characterizing the distribution of spike trains recorded from the same neuron under identical experimental conditions. A latent variable model is proposed. Inference and learning in this model leads to new principled algorithms for smoothing and clustering of spike data.

Liquid silicate equation of state : using shock waves to understand the properties of the deep Earth

The equations of state (EOS) of several geologically important silicate liquids have been constrained via preheated shock wave techniques. Results on molten Fe₂SiO₄ (fayalite), Mg₂SiO₄ (forsterite), CaFeSi₂O₆ (hedenbergite), an equimolar mixture of CaAl₂Si₂O₈-CaFeSi₂O₆ (anorthite-hedenbergite), and an equimolar mixture of CaAl₂Si₂O₈-CaFeSi₂O₆-CaMgSi₂O₆(anorthite-hedenbergite-diopside) are presented. This work represents the first ever direct EOS measurements of an iron-bearing liquid or of a forsterite liquid at pressures relevant to the deep Earth (> 135 GPa). Additionally, revised EOS for molten CaMgSi₂O₆ (diopside), CaAl₂Si₂O₈ (anorthite), and MgSiO₃ (enstatite), which were previously determined by shock wave methods, are also presented.

The liquid EOS are incorporated into a model, which employs linear mixing of volumes to determine the density of compositionally intermediate liquids in the CaO-MgO-Al₂O₃-SiO₂-FeO major element space. Liquid volumes are calculated for temperature and pressure conditions that are currently present at the core-mantle boundary or that may have occurred during differentiation of a fully molten mantle magma ocean.

The most significant implications of our results include: (1) a magma ocean of either chondrite or peridotite composition is less dense than its first crystallizing solid, which is not conducive to the formation of a basal mantle magma ocean, (2) the ambient mantle cannot produce a partial melt and an equilibrium residue sufficiently dense to form an ultralow velocity zone mush, and (3) due to the compositional dependence of Fe2+ coordination, there is a threshold of Fe concentration (molar X_Fe ≤ 0.06) permitted in a liquid for which its density can still be approximated by linear mixing of end-member volumes.

Markets and microstructure

This document contains three papers examining the microstructure of financial interaction in development and market settings. I first examine the industrial organization of financial exchanges, specifically limit order markets. In this section, I perform a case study of Google stock surrounding a surprising earnings announcement in the 3rd quarter of 2009, uncovering parameters that describe information flows and liquidity provision. I then explore the disbursement process for community-driven development projects. This section is game theoretic in nature, using a novel three-player ultimatum structure. I finally develop econometric tools to simulate equilibrium and identify equilibrium models in limit order markets.

In chapter two, I estimate an equilibrium model using limit order data, finding parameters that describe information and liquidity preferences for trading. As a case study, I estimate the model for Google stock surrounding an unexpected good-news earnings announcement in the 3rd quarter of 2009. I find a substantial decrease in asymmetric information prior to the earnings announcement. I also simulate counterfactual dealer markets and find empirical evidence that limit order markets perform more efficiently than do their dealer market counterparts.

In chapter three, I examine Community-Driven Development. Community-Driven Development is considered a tool empowering communities to develop their own aid projects. While evidence has been mixed as to the effectiveness of CDD in achieving disbursement to intended beneficiaries, the literature maintains that local elites generally take control of most programs. I present a three player ultimatum game which describes a potential decentralized aid procurement process. Players successively split a dollar in aid money, and the final player--the targeted community member--decides between whistle blowing or not. Despite the elite capture present in my model, I find conditions under which money reaches targeted recipients. My results describe a perverse possibility in the decentralized aid process which could make detection of elite capture more difficult than previously considered. These processes may reconcile recent empirical work claiming effectiveness of the decentralized aid process with case studies which claim otherwise.

In chapter four, I develop in more depth the empirical and computational means to estimate model parameters in the case study in chapter two. I describe the liquidity supplier problem and equilibrium among those suppliers. I then outline the analytical forms for computing certainty-equivalent utilities for the informed trader. Following this, I describe a recursive algorithm which facilitates computing equilibrium in supply curves. Finally, I outline implementation of the Method of Simulated Moments in this context, focusing on Indirect Inference and formulating the pseudo model.

Aseismic deformation in subduction megathrusts : central Andes and north-east Japan

We aim to characterize fault slip behavior during all stages of the seismic cycle in subduction megathrust environments with the eventual goal of understanding temporal and spatial variations of fault zone rheology, and to infer possible causal relationships between inter-, co- and post-seismic slip, as well as implications for earthquake and tsunami hazard. In particular we focus on analyzing aseismic deformation occurring during inter-seismic and post-seismic periods of the seismic cycle. We approach the problem using both Bayesian and optimization techniques. The Bayesian approach allows us to completely characterize the model parameter space by searching a posteriori estimates of the range of allowable models, to easily implement any kind of physically plausible a priori information and to perform the inversion without regularization other than that imposed by the parameterization of the model. However, the Bayesian approach computational expensive and not currently viable for quick response scenarios. Therefore, we also pursue improvements in the optimization inference scheme. We present a novel, robust and yet simple regularization technique that allows us to infer robust and somewhat more detailed models of slip on faults. We apply such methodologies, using simple quasi-static elastic models, to perform studies of inter- seismic deformation in the Central Andes subduction zone, and post-seismic deformation induced by the occurrence of the 2011 Mw 9.0 Tohoku-Oki earthquake in Japan. For the Central Andes, we present estimates of apparent coupling probability of the subduction interface and analyze its relationship to past earthquakes in the region. For Japan, we infer high spatial variability in material properties of the megathrust offshore Tohoku. We discuss the potential for a large earthquake just south of the Tohoku-Oki earthquake where our inferences suggest dominantly aseismic behavior.

Damage detection in civil structures using high-frequency seismograms

The dynamic properties of a structure are a function of its physical properties, and changes in the physical properties of the structure, including the introduction of structural damage, can cause changes in its dynamic behavior. Structural health monitoring (SHM) and damage detection methods provide a means to assess the structural integrity and safety of a civil structure using measurements of its dynamic properties. In particular, these techniques enable a quick damage assessment following a seismic event. In this thesis, the application of high-frequency seismograms to damage detection in civil structures is investigated.

Two novel methods for SHM are developed and validated using small-scale experimental testing, existing structures in situ, and numerical testing. The first method is developed for pre-Northridge steel-moment-resisting frame buildings that are susceptible to weld fracture at beam-column connections. The method is based on using the response of a structure to a nondestructive force (i.e., a hammer blow) to approximate the response of the structure to a damage event (i.e., weld fracture). The method is applied to a small-scale experimental frame, where the impulse response functions of the frame are generated during an impact hammer test. The method is also applied to a numerical model of a steel frame, in which weld fracture is modeled as the tensile opening of a Mode I crack. Impulse response functions are experimentally obtained for a steel moment-resisting frame building in situ. Results indicate that while acceleration and velocity records generated by a damage event are best approximated by the acceleration and velocity records generated by a colocated hammer blow, the method may not be robust to noise. The method seems to be better suited for damage localization, where information such as arrival times and peak accelerations can also provide indication of the damage location. This is of significance for sparsely-instrumented civil structures.

The second SHM method is designed to extract features from high-frequency acceleration records that may indicate the presence of damage. As short-duration high-frequency signals (i.e., pulses) can be indicative of damage, this method relies on the identification and classification of pulses in the acceleration records. It is recommended that, in practice, the method be combined with a vibration-based method that can be used to estimate the loss of stiffness. Briefly, pulses observed in the acceleration time series when the structure is known to be in an undamaged state are compared with pulses observed when the structure is in a potentially damaged state. By comparing the pulse signatures from these two situations, changes in the high-frequency dynamic behavior of the structure can be identified, and damage signals can be extracted and subjected to further analysis. The method is successfully applied to a small-scale experimental shear beam that is dynamically excited at its base using a shake table and damaged by loosening a screw to create a moving part. Although the damage is aperiodic and nonlinear in nature, the damage signals are accurately identified, and the location of damage is determined using the amplitudes and arrival times of the damage signal. The method is also successfully applied to detect the occurrence of damage in a test bed data set provided by the Los Alamos National Laboratory, in which nonlinear damage is introduced into a small-scale steel frame by installing a bumper mechanism that inhibits the amount of motion between two floors. The method is successfully applied and is robust despite a low sampling rate, though false negatives (undetected damage signals) begin to occur at high levels of damage when the frequency of damage events increases. The method is also applied to acceleration data recorded on a damaged cable-stayed bridge in China, provided by the Center of Structural Monitoring and Control at the Harbin Institute of Technology. Acceleration records recorded after the date of damage show a clear increase in high-frequency short-duration pulses compared to those previously recorded. One undamage pulse and two damage pulses are identified from the data. The occurrence of the detected damage pulses is consistent with a progression of damage and matches the known chronology of damage.

Three-dimensional nonlinear inelastic analysis of steel moment-frame buildings damaged by earthquake excitations

The Northridge earthquake of January 17, 1994, highlighted the two previously known problems of premature fracturing of connections and the damaging capabilities of near-source ground motion pulses. Large ground motions had not been experienced in a city with tall steel moment-frame buildings before. Some steel buildings exhibited fracture of welded connections or other types of structural degradation.

A sophisticated three-dimensional nonlinear inelastic program is developed that can accurately model many nonlinear properties commonly ignored or approximated in other programs. The program can assess and predict severely inelastic response of steel buildings due to strong ground motions, including collapse.

Three-dimensional fiber and segment discretization of elements is presented in this work. This element and its two-dimensional counterpart are capable of modeling various geometric and material nonlinearities such as moment amplification, spread of plasticity and connection fracture. In addition to introducing a three-dimensional element discretization, this work presents three-dimensional constraints that limit the number of equations required to solve various three-dimensional problems consisting of intersecting planar frames.

Two buildings damaged in the Northridge earthquake are investigated to verify the ability of the program to match the level of response and the extent and location of damage measured. The program is used to predict response of larger near-source ground motions using the properties determined from the matched response.

A third building is studied to assess three-dimensional effects on a realistic irregular building in the inelastic range of response considering earthquake directivity. Damage levels are observed to be significantly affected by directivity and torsional response.

Several strong recorded ground motions clearly exceed code-based levels. Properly designed buildings can have drifts exceeding code specified levels due to these ground motions. The strongest ground motions caused collapse if fracture was included in the model. Near-source ground displacement pulses can cause columns to yield prior to weaker-designed beams. Damage in tall buildings correlates better with peak-to-peak displacements than with peak-to-peak accelerations.

Dynamic response of tall buildings shows that higher mode response can cause more damage than first mode response. Leaking of energy between modes in conjunction with damage can cause torsional behavior that is not anticipated.

Various response parameters are used for all three buildings to determine what correlations can be made for inelastic building response. Damage levels can be dramatically different based on the inelastic model used. Damage does not correlate well with several common response parameters.

Realistic modeling of material properties and structural behavior is of great value for understanding the performance of tall buildings due to earthquake excitations.

Awakening a sleeping giant: The riddle of Latino political participation

For some time now, the Latino voice has been gradually gaining strength in American politics, particularly in such states as California, Florida, Illinois, New York, and Texas, where large numbers of Latino immigrants have settled and large numbers of electoral votes are at stake. Yet the issues public officials in these states espouse and the laws they enact often do not coincide with the interests and preferences of Latinos. The fact that Latinos in California and elsewhere have not been able to influence the political agenda in a way that is commensurate with their numbers may reflect their failure to participate fully in the political process by first registering to vote and then consistently turning out on election day to cast their ballots.

To understand Latino voting behavior, I first examine Latino political participation in California during the ten general elections of the 1980s and 1990s, seeking to understand what percentage of the eligible Latino population registers to vote, with what political party they register, how many registered Latinos to go the polls on election day, and what factors might increase their participation in politics. To ensure that my findings are not unique to California, I also consider Latino voter registration and turnout in Texas for the five general elections of the 1990s and compare these results with my California findings.

I offer a new approach to studying Latino political participation in which I rely on county-level aggregate data, rather than on individual survey data, and employ the ecological inference method of generalized bounds. I calculate and compare Latino and white voting-age populations, registration rates, turnout rates, and party affiliation rates for California's fifty-eight counties. Then, in a secondary grouped logit analysis, I consider the factors that influence these Latino and white registration, turnout, and party affiliation rates.

I find that California Latinos register and turn out at substantially lower rates than do whites and that these rates are more volatile than those of whites. I find that Latino registration is motivated predominantly by age and education, with older and more educated Latinos being more likely to register. Motor voter legislation, which was passed to ease and simplify the registration process, has not encouraged Latino registration . I find that turnout among California's Latino voters is influenced primarily by issues, income, educational attainment, and the size of the Spanish-speaking communities in which they reside. Although language skills may be an obstacle to political participation for an individual, the number of Spanish-speaking households in a community does not encourage or discourage registration but may encourage turnout, suggesting that cultural and linguistic assimilation may not be the entire answer.

With regard to party identification, I find that Democrats can expect a steady Latino political identification rate between 50 and 60 percent, while Republicans attract 20 to 30 percent of Latino registrants. I find that education and income are the dominant factors in determining Latino political party identification, which appears to be no more volatile than that of the larger electorate.

Next, when I consider registration and turnout in Texas, I find that Latino registration rates are nearly equal to those of whites but that Texas Latino turnout rates are volatile and substantially lower than those of whites.

Low turnout rates among Latinos and the volatility of these rates may explain why Latinos in California and Texas have had little influence on the political agenda even though their numbers are large and increasing. Simply put, the voices of Latinos are little heard in the halls of government because they do not turn out consistently to cast their votes on election day.

While these findings suggest that there may not be any short-term or quick fixes to Latino participation, they also suggest that Latinos should be encouraged to participate more fully in the political process and that additional education may be one means of achieving this goal. Candidates should speak more directly to the issues that concern Latinos. Political parties should view Latinos as crossover voters rather than as potential converts. In other words, if Latinos were "a sleeping giant," they may now be a still-drowsy leviathan waiting to be wooed by either party's persuasive political messages and relevant issues.

An experimental study of the mobility of edge dislocations in pure copper single crystals

The velocity of selectively-introduced edge dislocations in 99.999 percent pure copper crystals has been measured as a function of stress at temperatures from 66°K to 373°K by means of a torsion technique. The range of resolved shear stress was 0 to 15 megadynes/ cm^2 for seven temperatures (66°K, 74°K, 83°K, 123°K, 173°K, 296°K, 296°K, 373°K.

Dislocation mobility is characterized by two distinct features; (a) relatively high velocity at low stress (maximum velocities of about 9000 em/sec were realized at low temperatures), and (b) increasing velocity with decreasing temperature at constant stress.

The relation between dislocation velocity and resolved shear stress is:

v = v_o(τ_r/τ_o)^n

where v is the dislocation velocity at resolved shear stress τ_r, v_o is a constant velocity chosen equal to 2000 cm/ sec, τ_o is the resolved shear stress required to maintain velocity v_o, and n is the mobility coefficient. The experimental results indicate that τ_o decreases from 16.3 x 10^6 to 3.3 x 10^6 dynes/cm^2 and n increases from about 0.9 to 1.1 as the temperature is lowered from 296°K to 66°K.

The experimental dislocation behavior is consistent with an interpretation on the basis of phonon drag. However, the complete temperature dependence of dislocation mobility could not be closely approximated by the predictions of one or a combination of mechanisms.

Surface effects on spinwave resonance in thin magnetic films

Over the past few decades, ferromagnetic spinwave resonance in magnetic thin films has been used as a tool for studying the properties of magnetic materials. A full understanding of the boundary conditions at the surface of the magnetic material is extremely important. Such an understanding has been the general objective of this thesis. The approach has been to investigate various hypotheses of the surface condition and to compare the results of these models with experimental data. The conclusion is that the boundary conditions are largely due to thin surface regions with magnetic properties different from the bulk. In the calculations these regions were usually approximated by uniform surface layers; the spins were otherwise unconstrained except by the same mechanisms that exist in the bulk (i.e., no special "pinning" at the surface atomic layer is assumed). The variation of the ferromagnetic spinwave resonance spectra in YIG films with frequency, temperature, annealing, and orientation of applied field provided an excellent experimental basis for the study.

This thesis can be divided into two parts. The first part is ferromagnetic resonance theory; the second part is the comparison of calculated with experimental data in YIG films. Both are essential in understanding the conclusion that surface regions with properties different from the bulk are responsible for the resonance phenomena associated with boundary conditions.

The theoretical calculations have been made by finding the wave vectors characteristic of the magnetic fields inside the magnetic medium, and then combining the fields associated with these wave vectors in superposition to match the specified boundary conditions. In addition to magnetic boundary conditions required for the surface layer model, two phenomenological magnetic boundary conditions are discussed in detail. The wave vectors are easily found by combining the Landau-Lifshitz equations with Maxwell's equations. Mode positions are most easily predicted from the magnetic wave vectors obtained by neglecting damping, conductivity, and the displacement current. For an insulator where the driving field is nearly uniform throughout the sample, these approximations permit a simple yet accurate calculation of the mode intensities. For metal films this calculation may be inaccurate but the mode positions are still accurately described. The techniques necessary for calculating the power absorbed by the film under a specific excitation including the effects of conductivity, displacement current and damping are also presented.

In the second part of the thesis the properties of magnetic garnet materials are summarized and the properties believed associated with the two surface regions of a YIG film are presented. Finally, the experimental data and calculated data for the surface layer model and other proposed models are compared. The conclusion of this study is that the remarkable variety of spinwave spectra that arises from various preparation techniques and subsequent treatments can be explained by surface regions with magnetic properties different from the bulk.

The Voting Rights Act, Shelby County, and Redistricting: Improving Estimates of Racially Polarized Voting in a Multiple-Election Context

The Supreme Court’s decision in Shelby County has severely limited the power of the Voting Rights Act. I argue that Congressional attempts to pass a new coverage formula are unlikely to gain the necessary Republican support. Instead, I propose a new strategy that takes a “carrot and stick” approach. As the stick, I suggest amending Section 3 to eliminate the need to prove that discrimination was intentional. For the carrot, I envision a competitive grant program similar to the highly successful Race to the Top education grants. I argue that this plan could pass the currently divided Congress.

Without Congressional action, Section 2 is more important than ever before. A successful Section 2 suit requires evidence that voting in the jurisdiction is racially polarized. Accurately and objectively assessing the level of polarization has been and continues to be a challenge for experts. Existing ecological inference methods require estimating polarization levels in individual elections. This is a problem because the Courts want to see a history of polarization across elections.

I propose a new 2-step method to estimate racially polarized voting in a multi-election context. The procedure builds upon the Rosen, Jiang, King, and Tanner (2001) multinomial-Dirichlet model. After obtaining election-specific estimates, I suggest regressing those results on election-specific variables, namely candidate quality, incumbency, and ethnicity of the minority candidate of choice. This allows researchers to estimate the baseline level of support for candidates of choice and test whether the ethnicity of the candidates affected how voters cast their ballots.

Approximate theory for the flow through a cascade of cambered airfoils

An approximate theory for steady irrotational flow through a cascade of thin cambered airfoils is developed. Isolated thin airfoils have only slight camber is most applications, and the well known methods that replace the source and vorticity distributions of the curved camber line by similar distributions on the straight chord line are adequate. In cascades, however, the camber is usually appreciable, and significant errors are introduced if the vorticity and source distributions on the camber line are approximated by the same distribution on the chord line.

The calculation of the flow field becomes very clumsy in practice if the vorticity and source distributions are not confined to a straight line. A new method is proposed and investigated; in this method, at each point on the camber line, the vorticity and sources are assumed to be distributed along a straight line tangent to the camber line at that point, and corrections are determined to account for the deviation of the actual camber line from the tangent line. Hence, the basic calculation for the cambered airfoils is reduced to the simpler calculation of the straight line airfoils, with the equivalent straight line airfoils changing from point to point.

The results of the approximate method are compared with numerical solutions for cambers as high as 25 per cent of the chord. The leaving angles of flow are predicted quite well, even at this high value of the camber. The present method also gives the functional relationship between the exit angle and the other parameters such as airfoil shape and cascade geometry.

Neural and computational representations of decision variables

These studies explore how, where, and when representations of variables critical to decision-making are represented in the brain. In order to produce a decision, humans must first determine the relevant stimuli, actions, and possible outcomes before applying an algorithm that will select an action from those available. When choosing amongst alternative stimuli, the framework of value-based decision-making proposes that values are assigned to the stimuli and that these values are then compared in an abstract “value space” in order to produce a decision. Despite much progress, in particular regarding the pinpointing of ventromedial prefrontal cortex (vmPFC) as a region that encodes the value, many basic questions remain. In Chapter 2, I show that distributed BOLD signaling in vmPFC represents the value of stimuli under consideration in a manner that is independent of the type of stimulus it is. Thus the open question of whether value is represented in abstraction, a key tenet of value-based decision-making, is confirmed. However, I also show that stimulus-dependent value representations are also present in the brain during decision-making and suggest a potential neural pathway for stimulus-to-value transformations that integrates these two results.

More broadly speaking, there is both neural and behavioral evidence that two distinct control systems are at work during action selection. These two systems compose the “goal-directed system”, which selects actions based on an internal model of the environment, and the “habitual” system, which generates responses based on antecedent stimuli only. Computational characterizations of these two systems imply that they have different informational requirements in terms of input stimuli, actions, and possible outcomes. Associative learning theory predicts that the habitual system should utilize stimulus and action information only, while goal-directed behavior requires that outcomes as well as stimuli and actions be processed. In Chapter 3, I test whether areas of the brain hypothesized to be involved in habitual versus goal-directed control represent the corresponding theorized variables.

The question of whether one or both of these neural systems drives Pavlovian conditioning is less well-studied. Chapter 4 describes an experiment in which subjects were scanned while engaged in a Pavlovian task with a simple non-trivial structure. After comparing a variety of model-based and model-free learning algorithms (thought to underpin goal-directed and habitual decision-making, respectively), it was found that subjects’ reaction times were better explained by a model-based system. In addition, neural signaling of precision, a variable based on a representation of a world model, was found in the amygdala. These data indicate that the influence of model-based representations of the environment can extend even to the most basic learning processes.

Knowledge of the state of hidden variables in an environment is required for optimal inference regarding the abstract decision structure of a given environment and therefore can be crucial to decision-making in a wide range of situations. Inferring the state of an abstract variable requires the generation and manipulation of an internal representation of beliefs over the values of the hidden variable. In Chapter 5, I describe behavioral and neural results regarding the learning strategies employed by human subjects in a hierarchical state-estimation task. In particular, a comprehensive model fit and comparison process pointed to the use of "belief thresholding". This implies that subjects tended to eliminate low-probability hypotheses regarding the state of the environment from their internal model and ceased to update the corresponding variables. Thus, in concert with incremental Bayesian learning, humans explicitly manipulate their internal model of the generative process during hierarchical inference consistent with a serial hypothesis testing strategy.