Analytical, Theoretical and Empirical Advances in Genome-Scale Algorithmics

Ever-increasing amounts of complex biological data continue to come on line daily. Examples include proteomic, transcriptomic, genomic and metabolomic data generated by a plethora of high-throughput methods. Accordingly, fast and effective data processing techniques are more and more in demand. This issue is addressed in this dissertation through an investigation of various algorithmic alternatives and enhancements to routine and traditional procedures in common use. In the analysis of gene co-expression data, for example, differential measures of entropy and variation are studied as augmentations to mere differential expression. These novel metrics are shown to help elucidate disease-related genes in wide assortments of case/control data. In a more theoretical spirit, limits on the worst-case behavior of density based clustering methods are studied. It is proved, for instance, that the well-known paraclique algorithm, under proper tuning, can be guaranteed never to produce subgraphs with density less than 2/3. Transformational approaches to efficient algorithm design are also considered. Classic graph search problems are mapped to and from well-studied versions of satisfiability and integer linear programming. In so doing, regions of the input space are classified for which such transforms are effective alternatives to direct graph optimizations. In all these efforts, practical implementations are emphasized in order to advance the boundary of effective computation

Scalable Inference for Multi-Target Tracking of Proliferating Cells

With the continuous advancements in microscopy techniques such as improved image quality, faster acquisition and reduced photo-toxicity, the amount of data recorded in the life sciences is rapidly growing. Clearly, the size of the data renders manual analysis intractable, calling for automated cell tracking methods. Cell tracking – in contrast to other tracking scenarios – exhibits several difficulties: low signal to noise ratio in the images, high cell density and sometimes cell clusters, radical morphology changes, but most importantly cells divide – which is often the focus of the experiment. These peculiarities have been targeted by tracking-byassignment methods that first extract a set of detection hypotheses and then track those over time. Improving the general quality of these cell tracking methods is difficult, because every cell type, surrounding medium, and microscopy setting leads to recordings with specific properties and problems. This unfortunately implies that automated approaches will not become perfect any time soon but manual proof reading by experts will remain necessary for the time being. In this thesis we focus on two different aspects, firstly on scaling previous and developing new solvers to deal with longer videos and more cells, and secondly on developing a specialized pipeline for detecting and tracking tuberculosis bacteria. The most powerful tracking-by-assignment methods are formulated as probabilistic graphical models and solved as integer linear programs. Because those integer linear programs are in general NP-hard, increasing the problem size will lead to an explosion of computational cost. We begin by reformulating one of these models in terms of a constrained network flow, and show that it can be solved more efficiently. Building on the successful application of network flow algorithms in the pedestrian tracking literature, we develop a heuristic to integrate constraints – here for divisions – into such a network flow method. This allows us to obtain high quality approximations to the tracking solution while providing a polynomial runtime guarantee. Our experiments confirm this much better scaling behavior to larger problems. However, this approach is single threaded and does not utilize available resources of multi-core machines yet. To parallelize the tracking problem we present a simple yet effective way of splitting long videos into intervals that can be tracked independently, followed by a sparse global stitching step that resolves disagreements at the cuts. Going one step further, we propose a microservices based software design for ilastik that allows to distribute all required computation for segmentation, object feature extraction, object classification and tracking across the nodes of a cluster or in the cloud. Finally, we discuss the use case of detecting and tracking tuberculosis bacteria in more detail, because no satisfying automated method to this important problem existed before. One peculiarity of these elongated cells is that they build dense clusters in which it is hard to outline individuals. To cope with that we employ a tracking-by-assignment model that allows competing detection hypotheses and selects the best set of detections while considering the temporal context during tracking. To obtain these hypotheses, we develop a novel algorithm that finds diverseM- best solutions of tree-shaped graphical models by dynamic programming. First experiments with the pipeline indicate that it can greatly reduce the required amount of human intervention for analyzing tuberculosis treatment

Sources d'Alimentation Électrique pour l'Étude et l'Utilisation Efficace des Lampes Excimer DBD

Avec l'objectif d'améliorer le rendement des lampes à excimères (Excilampe) à décharge à barrière diélectrique (DBD), un convertisseur en mode de courant, qui permet un ajustement précis de la puissance électrique injectée dans ce type des lampes, à été conçu et mis en oeuvre. Ce convertisseur fournit à la lampe un courant de forme d'onde carrée contrôlé au moyen de trois paramètres: l'amplitude, la fréquence et le rapport cyclique, pour obtenir un contrôle total de l'énergie électrique transmise à l'excilampe DBD. La mise en oeuvre intègre un transformateur élévateur comme interface entre la lampe et un commutateur. Les expériences démontrent le principe de fonctionnement de ce convertisseur, y compris les mesures de puissance du rayonnement UV. Les degrés de liberté du convertisseur sont utilisées pour analyser le comportement de la lampe sous différentes combinaisons de ces trois paramètres, et sont utilisés pour déterminer le point de fonctionnement optimal de la lampe. Ensuite, un convertisseur à résonance du type onduleur série, est proposé pour alimenter la lampe avec une grande efficacité électrique. Afin de contrôler effectivement la puissance de la lampe, le mode de fonctionnement de ce convertisseur utilise le mode de conduction discontinue et la commutation douce (ZCS), avec lequel on obtient aussi de faibles émissions électromagnétiques et l'on réduit les pertes de commutation. Les relations mathématiques obtenus à partir de l'analyse du diagramme de phase, ont été validées par des simulations et avec des résultats expérimentaux. Enfin, différentes topologies d'alimentations pour DBD sont comparées analytiquement et expérimentalement pour évaluer objectivement les avantages de chaque approche. Une des perspectives de ce travail est l'application de l'alimentation en créneaux pour l'étude de la performance d'autres types de réacteurs et d'excilampes DBD. ABSTRACT : With the aim to provide a scientific tool for the enhancement of the Dielectric Barrier Discharge (DBD) Excimer Lamps (Excilamp) performance, a current-mode converter that allows an accurate adjustment of the electrical power injected into one of those lamps, is designed and implemented. With the proposed converter, the current supplied to the lamp has a square shape, controlled by means of three parameters: amplitude, duty cycle and frequency, which provides full control of the lamp electrical power. Implementation is made considering a step-up transformer interfacing the high-voltage lamp with the converter. Experiments demonstrate the operating principle of this converter, including UV power measurements for a DBD XeCl Excilamp. The capabilities of the converter are used to analyze the lamp behavior under different combinations of these three parameters, illustrating its capabilities for finding the optimal operating point. Then a series-resonant inverter for the supply of DBD) excilamp is proposed. In order to effectively control the lamp power, the operating mode of this converter combines discontinuous current-mode and soft-commutation (ZCS), obtaining as well low electromagnetic emissions, and reduced switching losses. The mathematical relationships obtained from state plane analysis, are validated with simulations and experimental results. Finally, several topologies of DBDs power supplies are compared analytical and experimentally to elucidate the advantages of each approach. After this work, one of the perspectives is the application of the square-shape supply in the performance study of other types of DBD excilamps and DBD reactors

Learning with Joint Inference and Latent Linguistic Structure in Graphical Models

Constructing end-to-end NLP systems requires the processing of many types of linguistic information prior to solving the desired end task. A common approach to this problem is to construct a pipeline, one component for each task, with each system\u27s output becoming input for the next. This approach poses two problems. First, errors propagate, and, much like the childhood game of telephone , combining systems in this manner can lead to unintelligible outcomes. Second, each component task requires annotated training data to act as supervision for training the model. These annotations are often expensive and time-consuming to produce, may differ from each other in genre and style, and may not match the intended application. In this dissertation we present a general framework for constructing and reasoning on joint graphical model formulations of NLP problems. Individual models are composed using weighted Boolean logic constraints, and inference is performed using belief propagation. The systems we develop are composed of two parts: one a representation of syntax, the other a desired end task (semantic role labeling, named entity recognition, or relation extraction). By modeling these problems jointly, both models are trained in a single, integrated process, with uncertainty propagated between them. This mitigates the accumulation of errors typical of pipelined approaches. Additionally we propose a novel marginalization-based training method in which the error signal from end task annotations is used to guide the induction of a constrained latent syntactic representation. This allows training in the absence of syntactic training data, where the latent syntactic structure is instead optimized to best support the end task predictions. We find that across many NLP tasks this training method offers performance comparable to fully supervised training of each individual component, and in some instances improves upon it by learning latent structures which are more appropriate for the task

Zoom Out and See Better: Scalable Message Tracing for Post-Silicon SoC Debug

We present a method for selecting trace messages for post-silicon validation of System-on-Chip (SoC). Our message selection is guided by specifications of interacting flows in common user applications. In current practice, such messages are selected based on designer expertise. We formulate the problem as an optimization of mutual information gain and trace buffer utilization. Our approach scales to systems far beyond the capacity of current signal selection techniques. We achieve an average trace buffer utilization of 98.96% with an average flow specification coverage of 94.3% and an average bug localization to only 21.11% of the potential root causes in our large-scale debugging effort. We present efficacy of our selected messages in debugging and root cause analysis using five realistic case studies consisting of complex and subtle bugs from the OpenSPARC T2 processor.IBMOpe

Development of a sensitivity analysis technique for multiloop flight control systems

This report presents the development and application of a sensitivity analysis technique for multiloop flight control systems. This analysis yields very useful information on the sensitivity of the relative-stability criteria of the control system, with variations or uncertainties in the system and controller elements. The sensitivity analysis technique developed is based on the computation of the singular values and singular-value gradients of a feedback-control system. The method is applicable to single-input/single-output as well as multiloop continuous-control systems. Application to sampled-data systems is also explored. The sensitivity analysis technique was applied to a continuous yaw/roll damper stability augmentation system of a typical business jet, and the results show that the analysis is very useful in determining the system elements which have the largest effect on the relative stability of the closed-loop system. As a secondary product of the research reported here, the relative stability criteria based on the concept of singular values were explored