Measuring the local dark matter density

We examine systematic problems in determining the local matter density from the vertical motion of stars, i.e. the 'Oort limit'. Using collisionless simulations and a Monte Carlo Markov Chain technique, we determine the data quality required to detect local dark matter at its expected density. We find that systematic errors are more important than observational errors and apply our technique to Hipparcos data to reassign realistic error bars to the local dark matter density.Comment: 3 pages, 1 figure, to be published in "Hunting for the Dark: The Hidden Side of Galaxy Formation", Malta, 19-23 Oct. 2009, eds. V.P. Debattista & C.C. Popescu, AIP Conf. Se

Dynamic Inventory Control with Satisfaction-Dependent Demand

In this paper, we consider the discrete multiperiod newsvendor dynamic inventory control problem where customers follow a simple satisfaction-based demand process, where their probability of demand depends on whether their demand was satised the last time they demanded a product, and observe the differences between optimal policies and myopic policies which do not directly consider how inventory policies can affect future demand. We conrm the intuitive result that inventory managers should tend to order more than the myopic policy when satised customers are more likely to demand product, and less than the myopic policy when satised customers are less likely to demand. Moreover, we and that, when choosing a fixed order policy, even an empirically myopic solution with perfect demand distribution information will move away from the optimum towards a suboptimal solution.

Semigroup Well-posedness of A Linearized, Compressible Fluid with An Elastic Boundary

We address semigroup well-posedness of the fluid-structure interaction of a linearized compressible, viscous fluid and an elastic plate (in the absence of rotational inertia). Unlike existing work in the literature, we linearize the compressible Navier-Stokes equations about an arbitrary state (assuming the fluid is barotropic), and so the fluid PDE component of the interaction will generally include a nontrivial ambient flow profile $\mathbf{U}$. The appearance of this term introduces new challenges at the level of the stationary problem. In addition, the boundary of the fluid domain is unavoidably Lipschitz, and so the well-posedness argument takes into account the technical issues associated with obtaining necessary boundary trace and elliptic regularity estimates. Much of the previous work on flow-plate models was done via Galerkin-type constructions after obtaining good a priori estimates on solutions (specifically \cite {Chu2013-comp}---the work most pertinent to ours here); in contrast, we adopt here a Lumer-Phillips approach, with a view of associating solutions of the fluid-structure dynamics with a $C_{0}$-semigroup $\left\{ e^{ \mathcal{A}t}\right\} _{t\geq 0}$ on the natural finite energy space of initial data. So, given this approach, the major challenge in our work becomes establishing of the maximality of the operator $\mathcal{A}$ which models the fluid-structure dynamics. In sum: our main result is semigroup well-posedness for the fully coupled fluid-structure dynamics, under the assumption that the ambient flow field $\mathbf{U}\in \mathbf{H}^{3}(\mathcal{O})$ has zero normal component trace on the boundary (a standard assumption with respect to the literature). In the final sections we address well-posedness of the system in the presence of the von Karman plate nonlinearity, as well as the stationary problem associated with the dynamics.Comment: 1 figur

Variable selection for BART: An application to gene regulation

We consider the task of discovering gene regulatory networks, which are defined as sets of genes and the corresponding transcription factors which regulate their expression levels. This can be viewed as a variable selection problem, potentially with high dimensionality. Variable selection is especially challenging in high-dimensional settings, where it is difficult to detect subtle individual effects and interactions between predictors. Bayesian Additive Regression Trees [BART, Ann. Appl. Stat. 4 (2010) 266-298] provides a novel nonparametric alternative to parametric regression approaches, such as the lasso or stepwise regression, especially when the number of relevant predictors is sparse relative to the total number of available predictors and the fundamental relationships are nonlinear. We develop a principled permutation-based inferential approach for determining when the effect of a selected predictor is likely to be real. Going further, we adapt the BART procedure to incorporate informed prior information about variable importance. We present simulations demonstrating that our method compares favorably to existing parametric and nonparametric procedures in a variety of data settings. To demonstrate the potential of our approach in a biological context, we apply it to the task of inferring the gene regulatory network in yeast (Saccharomyces cerevisiae). We find that our BART-based procedure is best able to recover the subset of covariates with the largest signal compared to other variable selection methods. The methods developed in this work are readily available in the R package bartMachine.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS755 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Doctor of Philosophy

dissertationIn this thesis I present novel findings of microstructural remodeling that occurs during dyssynchronous heart failure (DHF) and the ability for cardiac resynchronization therapy (CRT) to reverse this remodeling. DHF is an advanced disease state that occurs in a large portion of patients suffering from heart failure. Mechanical dyssynchrony between the left and right ventricles of the heart, the hallmark of DHF, results in significantly increased heterogeneity of stress in the cardiac wall. DHF severely limits cardiac performance, decreasing quality of life and increasing mortality. The main therapy for treating DHF is CRT, a therapy in which mechanical synchrony is restored to the ventricles via electrical pacing. The success of CRT varies widely. Scientific knowledge surrounding DHF and CRT is surprisingly sparse for how widespread the disease and therapy are. A better understanding of the subcellular structure and function altered during DHF will improve our understanding of the disease and potentially help develop novel therapies and even lead to development of assays capable of better predicting success of current therapies. Here we use confocal microscopy to explore protein distributions within isolated cardiomyocytes and intact tissue, Ca2+ handling during activation and relaxation of stimulated cardiomyocytes, and to develop a method for quantifying strain in 2D image sequences of contracting cardiomyocytes at an unprecedented spatiotemporal resolution. Specifically I will demonstrate that ?-actinin, the protein comprising the majority of the sarcomeric Z-disk, is significantly altered during DHF and that CRT is able to partially reverse this remodeling. I will then present findings on remodeling of the transverse tubular system and associated ryanodine receptor clusters, both crucial components of excitation-contraction coupling. In particular, I will show that these structures exhibit subcellular heterogeneity during DHF, affecting excitation-contraction coupling. This heterogeneity is reduced after CRT, indicating previously unknown capabilities of restoration. Finally, I will present a novel method to characterize strain within contracting cardiomyocytes. This method expands on previous methods by providing a regional 2D strain tensor at unprecedented spatiotemporal resolution, allowing more accurate description of the mechanical properties of the cell. Together, this work makes a significant contribution to the understanding of DHF and CRT

Exploiting Latent Features of Text and Graphs

As the size and scope of online data continues to grow, new machine learning techniques become necessary to best capitalize on the wealth of available information. However, the models that help convert data into knowledge require nontrivial processes to make sense of large collections of text and massive online graphs. In both scenarios, modern machine learning pipelines produce embeddings --- semantically rich vectors of latent features --- to convert human constructs for machine understanding. In this dissertation we focus on information available within biomedical science, including human-written abstracts of scientific papers, as well as machine-generated graphs of biomedical entity relationships. We present the Moliere system, and our method for identifying new discoveries through the use of natural language processing and graph mining algorithms. We propose heuristically-based ranking criteria to augment Moliere, and leverage this ranking to identify a new gene-treatment target for HIV-associated Neurodegenerative Disorders. We additionally focus on the latent features of graphs, and propose a new bipartite graph embedding technique. Using our graph embedding, we advance the state-of-the-art in hypergraph partitioning quality. Having newfound intuition of graph embeddings, we present Agatha, a deep-learning approach to hypothesis generation. This system learns a data-driven ranking criteria derived from the embeddings of our large proposed biomedical semantic graph. To produce human-readable results, we additionally propose CBAG, a technique for conditional biomedical abstract generation