VictorJuiz/thesis huggingface dataset:
Computation in constrained stochastic model predictive control of linear systems [electronic resource]

Despite its sub-optimality, Model Predictive Control (MPC) has received much attention over the recent decades due to its ability to handle constraints. In particular, stochastic MPC, which includes uncertainty in the system dynamics, is one of the most active recent research topics in MPC. In this dissertation, we focus on (1) increasing computation speed of constrained stochastic MPC of linear systems with additive noise and, (2) improving the accuracy of an approximate solution involving systems with additive and multiplicative noise. Constrained MPC for linear systems with additive noise has been successfully formulated as a semidefinite programming problem (SDP) using the Youla parameterization or innovation feedback and linear matrix inequalities. Unfortunately, this method can be prohibitively slow even for problems with moderate size state. Thus, in this thesis we develop an interior point algorithm which can more efficiently solve the problem. This algorithm converts the stochastic problem into a deterministic one using the mean and the covariance matrix as the system state and using affine feedback. A line search interior point method is then directly applied to the nonlinear deterministic optimization problem. In the process, we take advantage of a recursive structure that appears when a control problem is solved via the line search interior point method in order to decrease the algorithmic complexity of the solution. We compare the computation time and complexity of our algorithm against an SDP solver. The second part of the dissertation deals with systems with additive and multiplicative noise under probabilistic constraints. This class of systems differs from the additive noise case in that the probability distribution of a state is neither Gaussian nor known in closed form. This causes a problem when the probability constraints are dealt with. In previous studies, this problem has been tackled by approximating the state as a Gaussian random variable or by approximating the probability bound as an ellipsoid. In this dissertation, we use the Cornish-Fisher expansion to approximate the probability bounds of the constraints. Since the Cornish-Fisher expansion utilizes quantile values with the first several moments, the probabilistic constraints have the same form as those in the additive noise case when the constraints are converted to deterministic ones. This makes the procedure smooth when we apply the developed algorithm to a linear system with multiplicative noise. Moreover, we can easily extend the application of the algorithm to a linear system with additive plus multiplicative noise.

Keywords: Aeronautics and Astronautics
Advisor: Primbs James primary advisor. Thesis advisor
Lai T. L. advisor. Thesis advisor
Lall Sanjay advisor. Thesis advisor
Computational analysis of the mammalian cis-regulatory landscape [electronic resource]

Improvements in DNA sequencing technologies have made it possible to determine the genetic makeup of many organisms. Computational analyses of the massive amounts of sequence data available have produced many insights into evolutionary and developmental biology. For example, comparison of the full genome sequences of human and mouse discovered that the majority of functional sequence in the human genome does not code for protein. Much of this functional non-coding sequence appears to act in a regulatory role, dictating the precise tissues and developmental time points in which each protein should be produced. This dissertation describes three major contributions to the computational analysis of regulatory elements. First, I describe the Genomic Regions Enrichment of Annotations Tool (GREAT), a novel statistical method and associated web-based tool developed to infer the biological functions of regulatory elements based on the functions of their putative target genes. I demonstrate its marked improvement over current methods at interpreting functional enrichment signals for a variety of regulatory element types. Next, I discuss a computational methodology developed to identify medium- to large-scale (10-100,000 nucleotide) genomic deletions from whole genome sequences of multiple mammals. Using this methodology, I quantify the dispensability of highly conserved non-coding elements (CNEs) as their likelihood to be deleted in a subset of species. Despite their genomic prevalence and apparent redundancy in function, CNEs are very rarely lost in extant species. Even more surprisingly, there is a very weak relationship between dispensability and nucleotide conservation level. Sequences under purifying selection at moderate levels of nucleotide conservation are lost at a rate similar to those at perfect sequence conservation. Instead, evolutionary resistance to loss is more strongly correlated with depth of sequence homology, as ancient enhancers are more resistant to deletion than ones that arose more recently in evolution. Finally, I present the discovery and analysis of human-specific genomic deletions. By comparing the genome sequences of five species including human and our nearest ape relative, the chimpanzee, I identified 583 regions present in non-human species that contain highly-conserved sequence but are surprisingly deleted in humans. Statistical analyses indicate that these deletions occur preferentially near steroid hormone receptor genes and brain-expressed genes that are known to inhibit proliferation. Experimental results provide particular examples that may have contributed to unique human traits: the loss of an AR enhancer is correlated with the human loss of penile spines and sensory vibrissae, and the loss of a GADD45G enhancer is correlated with the human expansion of the cerebral cortex.

Advisor: Bejerano Gill
Batzoglou Serafim advisor. Thesis advisor
Kingsley David M. (David Mark) advisor. Thesis advisor
A computational approach to identification and comparison of cell subsets in flow cytometry data [electronic resource]

Changes in frequency and/or biomarker expression in small subsets of peripheral blood cells provide key diagnostics for disease presence, status and prognosis. At present, flow cytometry instruments that measure the joint expression of up to 20 markers in/on large numbers of individual cells are used to measure surface and internal marker expression. This technology is routinely used to determine the frequencies of various marker-defined cell subsets in patient samples and is often used to inform therapeutic decision-making. Nevertheless, quantitative methods for comparing data between samples are sorely lacking. There are no reliable computational methods for determining the magnitude of differences among samples from different patients, among samples obtained from the same patient on different days, or between aliquots of the same sample measured before and after response to stimulation or other treatment. This thesis describes novel computational methods that provide reliable indices of change in subset representation and/or marker expression by individual subsets of cells. The methods we have developed utilize a non-parametric clustering algorithm, Density-Based Merging (DBM), that we developed to identify subsets (clusters) of cells that express a common set of markers measured independently for each cell by flow cytometry. To quantitate differences between these subsets, we introduce the application of Earth Movers Distance (EMD), an algorithm used to compare multivariate distributions borrowed from the image retrieval literature. The resultant methods are highly sensitive and reliable for identifying small marker expression differences between subset of cells in flow cytometry data sets. We show that these methods are easily applied and readily interpreted. Importantly, we demonstrate their practical utility with data from an allergy study in which the expression of two markers on very rare blood cells (basophils) in response to stimulation with an offending allergen indicates whether the patient is allergic to the stimulating antigen. In addition, we have developed novel evaluation criteria for assessing the performance of clustering algorithms on flow cytometry data by combining mixtures of cells identifiable by dimensions ``hidden'' from the algorithm that provide true cluster membership. Thus, we expect that the methods described here will introduce a new approach to using flow cytometry to measure biomarker changes as indices of drug response, disease susceptibility, disease progress and prognosis.

Advisor: Das Amar K. (Amar Kumar) primary advisor. Thesis advisor
Walther Guenther primary advisor. Thesis advisor
Herzenberg Leonore A. advisor. Thesis advisor
Computational fluid dynamic simulations investigating the diagnosis, treatment, and long term outcomes associated with aortic coarctation [electronic resource]

Coarctation of the aorta is a congenital heart defect where a narrowing occurs in the thoracic aorta. Aortic coarctation accounts for approximately 5-10% of all congenital heart defects, affecting approximately 1 in 3000 newborns. While the condition has been recognized for over 70 years, many clinical questions still remain about the management of patients with aortic coarctation. There exists a need to investigate the complex hemodynamics associated with this disease using computational fluid dynamics to improve the diagnosis, treatment, and long-term management of these patients. First, computational simulations were used to determine the hemodynamic significance of various aortic arch obstructions, specifically to determine the significance of aortic hypoplasia, defined as long segment aortic narrowing, compared with aortic coarctation, defined as discrete narrowing. Ventricular workload was used as a metric for the degree of significance imposed by the narrowing. Results indicated that aortic hypoplasia was more significant that aortic coarctation when the same percent narrowing was considered. In addition, the simulations revealed that aortic hypoplasia consisting of 25-50% narrowing is not likely to be hemodynamically significant under resting conditions. Next, various treatment options were investigated by using simulations to compare surgical and stent-based treatment. In particular, stents placed in the aorta create a compliance mismatch because the stent is rigid and the aorta is deformable, so computational simulations were used to determine the significance of the mismatch. The results showed that the compliance mismatch associated with a rigid stent did not produce a clinically significant change in the required ventricular workload. This study refuted the claim that the compliance mismatch associated with stenting in aortic coarctation patients negatively impacts patient hemodynamics. Finally, patients with corrected aortic coarctation often suffer from hypertension and associated cardiovascular problems in the coronary and cerebral vessels. Computational simulations were used to determine the hemodynamics in the aorta, coronary, and cerebral vessels associated with hypertension and subsequent vascular remodeling. The results showed that hypertension induced with aortic coarctation is associated with nearly equal increases in mean pressure but different increases in pulse pressure throughout the model. This study revealed interesting and important information related to the hemodynamics in a large vascular model under the effects of hypertension and vascular wall remodeling. These studies showed the power of computational simulations to aid in the investigation of clinical questions regarding aortic coarctation.

Keywords: Bioengineering.
Advisor: Feinstein Jeffrey A. primary advisor. Thesis advisor
Taylor Charles A. (Charles Anthony) primary advisor. Thesis advisor
Chan Frandics advisor. Thesis advisor
Pelc Norbert J. advisor. Thesis advisor
A computational framework based on an embedded boundary method for nonlinear multi-phase fluid-structure interactions [electronic resource]

Nonlinear fluid-structure interaction (FSI) is a dominating feature in many important engineering applications. Examples include underwater implosions, pipeline explosions, flapping wings for micro aerial vehicles, and shock wave lithotripsy. Due to the inherent nonlinearity and system complexity, such problems have not been thoroughly analyzed, which greatly hinders the advance of related engineering fields. This thesis focuses on the development, verification, and validation of a fluid-structure coupled computational framework for the solution of nonlinear multi-phase FSI problems involving high compressions and shock waves, large structural displacements and deformations, self-contact, and possibly the initiation and propagation of cracks in the structure. First, an embedded boundary method for solving 3D multi-phase compressible inviscid flows on arbitrary (i.e. structured and unstructured) non body-conforming CFD grids is presented. Key components include: (1) robust and efficient computational algorithms for tracking open, closed, and cracking fluid-structure interfaces with respect to the fixed, non body-conforming CFD grid; (2) a numerical algorithm based on the exact solution of local, one-dimensional fluid-structure Riemann problems to enforce the no-interpenetration transmission condition at the fluid-structure interface; and (3) two consistent and conservative algorithms for enforcing the equilibrium transmission condition at the same interface. Next, the multi-phase compressible flow solver equipped with the aforementioned embedded boundary method is carefully coupled with an extended finite element method (XFEM) based structure solver, using a partitioned procedure and provably second-order explicit-explicit and implicit-explicit time-integrators. In particular, the interface tracking algorithms in the embedded boundary method are adapted to tracking embedded discrete interfaces with phantom elements and carrying implicitly represented cracks. Finally, the resulting fluid-structure coupled computational framework is applied to the solution of several challenging FSI problems in the fields of aeronautics, underwater implosions and explosions, and pipeline explosions to assess its performance. In particular, two laboratory experiments are considered for validation purpose: the first one concerns the implosive collapse of an air-filled aluminum cylinder; the second one studies the dynamic fracture of pre-flawed aluminum pipes driven by detonation waves. In both cases, the numerical simulation correctly reproduces in a quantitative sense the important features in the experiment.

Advisor: Farhat Charbel primary advisor. Thesis advisor
Iaccarino Gianluca advisor. Thesis advisor
Lew Adrian advisor. Thesis advisor
Computational methods for the analysis of musical structure [electronic resource]

Music is an art form which is realized in time. This dissertation presents computational methods for examining the temporality of music at multiple time-scales so that both short-term surface features and deeper long-term structures can be studied and related to each other. The methods are applied in particular to musical key analysis (Chapters 2-4) and also adapted for use in performance analysis (Chapters 5-6). The essential methodology is to examine all sequential time-scales within a piece using some analytic process and then arrange a summary of the analytic results into a maximally overlapped arrangement. Chapter 2 defines a two-dimensional plotting domain for displaying musical features at all possible time-scales which forms a basis for further analysis methods. The resulting structures in the plots can be examined subjectively as a navigational aid in the music as illustrated in Chapters 3 and 5. They can also be used to extract musically relevant information as discussed in Chapters 4 and 6.

Keywords: Music
Advisor: Smith Julius O. (Julius Orion) primary advisor. Thesis advisor
Chafe Chris advisor. Thesis advisor
Selfridge-Field Eleanor
Computational models of blood flow in deformable arteries incorporating viscoelastic wall properties [electronic resource]

It is well known that blood vessels exhibit viscoelastic properties. Vessel wall viscoelasticity is an important source of physical damping and dissipation in the cardiovascular system. There is a growing need to incorporate viscoelasticity of arteries in computational models of blood flow which are utilized for applications such as disease research, treatment planning and medical device evaluation. However, thus far the use of viscoelastic wall properties in blood flow modeling has been limited. As part of the present work, arterial wall viscoelasticity was incorporated into two computational models of blood flow: (1) a nonlinear one-dimensional (1-D) model and (2) a three-dimensional (3-D) fluid-solid interaction (FSI) model of blood flow. 1-D blood flow model: In blood flow simulations different viscoelastic wall models may produce significantly different flow, pressure and wall deformation solutions. To highlight these differences a novel comparative study of two viscoelastic wall models and an elastic model is presented in this work. The wall models were incorporated in a nonlinear 1-D model of blood flow, which was solved using a space-time finite element method. The comparative study involved the following applications: (i) Wave propagation in an idealized vessel with reflection-free outflow boundary condition; (ii) Carotid artery model with non-periodic boundary conditions; (iii) Subject-specific abdominal aorta model under rest and exercise conditions. 3-D FSI blood flow model: 3-D blood flow models enable physiologic simulations in complex, subject-specific anatomies. In the present work, a viscoelastic constitutive relationship for the arterial wall was incorporated in the 3-D Coupled Momentum Method for Fluid-Solid Interaction problems (CMM-FSI). Results in an idealized carotid artery stenosis geometry show that higher frequency components of flow rate, pressure and vessel wall motion are damped in the viscoelastic case. These results indicate that the dissipative nature of viscoelastic wall properties has an important impact in 3-D simulations of blood flow. Future work will include simulations of blood flow in patient-specific geometries such as aortic coarctation (a congenital disease) to assess the impact of wall viscoelasticity in clinically relevant scenarios. In the present work, arterial viscoelasticity has been incorporated in 1-D and 3-D computational models of blood flow. The biomechanical effects of wall viscoelasticity have been investigated through idealized and subject-specific blood flow simulations. These contributions are significant and suggest the potential importance of wall viscoelasticity in blood flow simulations for clinically relevant applications.

Keywords: Mechanical Engineering
Advisor: Taylor Charles A. (Charles Anthony) primary advisor. Thesis advisor
Kuhl Ellen
Lew Adrian advisor. Thesis advisor
Computed tomography based estimation of bone rigidity and strength under combined bending and torsion [electronic resource]

A new method to evaluate bone rigidity and strength using tomographic bone images obtained via QCT (Quantitative Computed Tomography) is introduced. A newly developed computer program named VA-BATTS is used for image processing, bone segmentation, mesh creation, material assignment and calculation of far field normal and shear stresses as well as other cross sectional properties. In order to calculate torsional and transverse shear stresses in prismatic bodies having inhomogenous material properties, a new two-dimensional finite element formulation to estimate is presented. The formulation combines the torsional and transverse shear problem solutions and adds terms to account for the material inhomogeneity into one Weak Form of the problem, further discretized to yield a numerical approximation of the shear stresses problem. Results were validated using analytical models as well as three dimensional commercial code test cases yielding mean errors over the entire domain of less than 1%. This semi-automated application is publicly distributed and can be downloaded from https://simtk.org/home/va-batts. VA-BATTS implements an elliptical stress failure criterion to predict bone strength. To validate, fifty-two fresh frozen femurs were tested under combined three-point bending and torsion to failure. VA-BATTS was able to predict bone failure under combined bending and torsion (R2=0.68) as well as bone torsional (R2=0.80) and bending (R2=0.50) rigidity. Using multivariate analysis that combined the elliptical stress failure and the torsional and bending rigidities, the prediction confidence level was raised (R2=0.87), comparable to existing more complex three dimensional finite element studies. The elliptical stress criterion suggests that the distal femur is weaker, in absolute terms, than the midshaft femur suggesting an explanation of the increased rate of distal femur fractures in patients with Spinal Cord Injury. In general, the newly introduced method proved to yield more accurate predictions compared to DXA derived Bone Mineral Density measurements (R2=0.56). Fracture patterns were analyzed to show mostly spiral patterns where torsional loads were applied. In addition, the accuracy of three point bending experiments was examined. Three parameters that may introduce errors in the predictions - transverse shear, local deformation (indentation) as well as cross sectional deformation effect -- were studied using a parametric finite element model. The model shows that depending on the geometric properties of the bone, errors as high as 75% may be introduced in the estimation of the bone elastic modulus. Bone rigidity estimates may now be corrected using the correction factors supplied in this study.

Keywords: Mechanical Engineering
Advisor: Carter Dennis R. (Dennis Robert) primary advisor. Thesis advisor
Beaupré Gary Scott advisor. Thesis advisor
Nelson Drew advisor. Thesis advisor
Computing rare-event probabilities for affine models and general state space Markov processes [electronic resource]

Rare-event simulation concerns computing small probabilities, i.e. rare-event probabilities. This dissertation investigates efficient simulation algorithms based on importance sampling for computing rare-event probabilities for different models, and establishes their efficiency via asymptotic analysis. The first part discusses asymptotic behavior of affine models. Stochastic stability of affine jump diffusions are carefully studied. In particular, positive recurrence, ergodicity, and exponential ergodicity are established for such processes under various conditions via a Foster-Lyapunov type approach. The stationary distribution is characterized in terms of its characteristic function. Furthermore, the large deviations behavior of affine point processes are explicitly computed, based on which a logarithmically efficient importance sampling algorithm is proposed for computing rare-event probabilities for affine point processes. The second part is devoted to a much more general setting, i.e. general state space Markov processes. The current state-of-the-art algorithm for computing rare-event probabilities in this context heavily relies on the solution of a certain eigenvalue problem, which is often unavailable in closed form unless certain special structure is present (e.g. affine structure for affine models). To circumvent this difficulty, assuming the existence of a regenerative structure, we propose a bootstrap-based algorithm that conducts the importance sampling on the regenerative cycle-path space instead of the original one-step transition kernel. The efficiency of this algorithm is also discussed.

Keywords: Management Science and Engineering
Advisor: Glynn Peter W. primary advisor. Thesis advisor
Bambos Nicholas advisor. Thesis advisor
Lai T. L. advisor. Thesis advisor