Top Quark Mass Measurement in the lepton+jets Channel Using a Matrix Element Method and in situ Jet Energy Calibration

A precision measurement of the top quark mass m_t is obtained using a sample of ttbar events from ppbar collisions at the Fermilab Tevatron with the CDF II detector. Selected events require an electron or muon, large missing transverse energy, and exactly four high-energy jets, at least one of which is tagged as coming from a b quark. A likelihood is calculated using a matrix element method with quasi-Monte Carlo integration taking into account finite detector resolution and jet mass effects. The event likelihood is a function of m_t and a parameter DJES to calibrate the jet energy scale /in situ/. Using a total of 1087 events, a value of m_t = 173.0 +/- 1.2 GeV/c^2 is measured.

Top quark mass measurement in the lepton plus jets channel using a modified matrix element method

We report a measurement of the top quark mass, m_t, obtained from ppbar collisions at sqrt(s) = 1.96 TeV at the Fermilab Tevatron using the CDF II detector. We analyze a sample corresponding to an integrated luminosity of 1.9 fb^-1. We select events with an electron or muon, large missing transverse energy, and exactly four high-energy jets in the central region of the detector, at least one of which is tagged as coming from a b quark. We calculate a signal likelihood using a matrix element integration method, with effective propagators to take into account assumptions on event kinematics. Our event likelihood is a function of m_t and a parameter JES that determines /in situ/ the calibration of the jet energies. We use a neural network discriminant to distinguish signal from background events. We also apply a cut on the peak value of each event likelihood curve to reduce the contribution of background and badly reconstructed events. Using the 318 events that pass all selection criteria, we find m_t = 172.7 +/- 1.8 (stat. + JES) +/- 1.2 (syst.) GeV/c^2.

Top quark mass measurement in the tt̅ all hadronic channel using a matrix element technique in pp̅ collisions at √s=1.96 TeV

We present a measurement of the top quark mass in the all-hadronic channel (\tt $\to$ \bb$q_{1}\bar{q_{2}}q_{3}\bar{q_{4}}$) using 943 pb$^{-1}$ of \ppbar collisions at $\sqrt {s} = 1.96$ TeV collected at the CDF II detector at Fermilab (CDF). We apply the standard model production and decay matrix-element (ME) to $\ttbar$ candidate events. We calculate per-event probability densities according to the ME calculation and construct template models of signal and background. The scale of the jet energy is calibrated using additional templates formed with the invariant mass of pairs of jets. These templates form an overall likelihood function that depends on the top quark mass and on the jet energy scale (JES). We estimate both by maximizing this function. Given 72 observed events, we measure a top quark mass of 171.1 $\pm$ 3.7 (stat.+JES) $\pm$ 2.1 (syst.) GeV/$c^{2}$. The combined uncertainty on the top quark mass is 4.3 GeV/$c^{2}$.

Topics in Spatial Econometrics - With Applications to House Prices

Topics in Spatial Econometrics — With Applications to House Prices Spatial effects in data occur when geographical closeness of observations influences the relation between the observations. When two points on a map are close to each other, the observed values on a variable at those points tend to be similar. The further away the two points are from each other, the less similar the observed values tend to be. Recent technical developments, geographical information systems (GIS) and global positioning systems (GPS) have brought about a renewed interest in spatial matters. For instance, it is possible to observe the exact location of an observation and combine it with other characteristics. Spatial econometrics integrates spatial aspects into econometric models and analysis. The thesis concentrates mainly on methodological issues, but the findings are illustrated by empirical studies on house price data. The thesis consists of an introductory chapter and four essays. The introductory chapter presents an overview of topics and problems in spatial econometrics. It discusses spatial effects, spatial weights matrices, especially k-nearest neighbours weights matrices, and various spatial econometric models, as well as estimation methods and inference. Further, the problem of omitted variables, a few computational and empirical aspects, the bootstrap procedure and the spatial J-test are presented. In addition, a discussion on hedonic house price models is included. In the first essay a comparison is made between spatial econometrics and time series analysis. By restricting the attention to unilateral spatial autoregressive processes, it is shown that a unilateral spatial autoregression, which enjoys similar properties as an autoregression with time series, can be defined. By an empirical study on house price data the second essay shows that it is possible to form coordinate-based, spatially autoregressive variables, which are at least to some extent able to replace the spatial structure in a spatial econometric model. In the third essay a strategy for specifying a k-nearest neighbours weights matrix by applying the spatial J-test is suggested, studied and demonstrated. In the final fourth essay the properties of the asymptotic spatial J-test are further examined. A simulation study shows that the spatial J-test can be used for distinguishing between general spatial models with different k-nearest neighbours weights matrices. A bootstrap spatial J-test is suggested to correct the size of the asymptotic test in small samples.

The role of inflammation and matrix proteins in airway remodelling

Asthma is a chronic inflammatory disorder of the airways. Remodelling in asthma is defined as the structural changes seen in the airways of asthmatics in comparison to healthy controls. Progressive loss of lung function also seen in asthma might be caused by remodelling. The research aims of this thesis were to investigate inflammation and remodelling in the airways of different types of asthmatics and smokers. The association between inflammation and remodelling was also examined in a mouse model of allergic airway inflammation. Healthy smokers showed increased numbers of macrophages in the BAL with no changes in the inflammatory cells in biopsies. Macrophages seemed to be quite quiescent, since mRNA expression for a wide variety of inflammatory mediators, especially chemokines CCL3, CCL4, CCL5 and CCL20, secreted by macrophages was significantly lower than in healthy non-smokers. Attenuated macrophage activity in the airway lumen may render smokers more susceptible to airway infections and have an impact on the development of other airway pathology. Patients with diisocyanate-induced asthma (DIA) on inhaled corticosteroids (ICS) who still had non-specific bronchial hyperreactivity (NSBHR) at the end of the follow-up showed increased expression of TNF-α, IL-6 and IL-15 mRNA in BAL cells compared to those without NSBHR. In addition to being markers for poor prognosis and possible slight glucocorticoid resistance, these cytokines might aid in guiding the treatment of DIA. The increase in the thickness of tenascin-C layer in the bronchial basement membrane (BM) was much less than usually seen in other types of asthma, which might not make tenascin-C a good marker for DIA. OVA-induced tenascin-C expression in the lung was attenuated in STAT4-/- mice with impaired Th1-type immunity compared to WT mice. Interestingly, STAT6-/- mice with impaired Th2-type immunity showed tenascin-C expression levels similar to those of WT mice. The clearest difference between these two knockout strains in response to OVA was that STAT4-/- mice exhibited no upregulation of IFN-γ and TNF-α mRNA expression. Thus, tenascin-C expression was unexpectedly more related to Th1 type reactions. In vitro studies confirmed the results. Human fibroblasts stimulated by TNF-α and IFN-γ showed increased expression of tenascin-C. Patients with newly diagnosed asthma showed increased expression of laminin α2 in the bronchial BM in comparison to patients with asthma symptoms only and healthy controls. Both patients with asthma and those with only asthma symptoms showed increased expression of the laminin β2 chain in comparison to controls. Thus, laminin α2 expression differentiated patients with clinical asthma from patients with symptoms only. Furthermore, the expression of laminin α2 and β2 was associated with NSBHR, linking very specific remodelling events to clinical findings.

Exact bounds for distributed graph colouring

A distributed system is a collection of networked autonomous processing units which must work in a cooperative manner. Currently, large-scale distributed systems, such as various telecommunication and computer networks, are abundant and used in a multitude of tasks. The field of distributed computing studies what can be computed efficiently in such systems. Distributed systems are usually modelled as graphs where nodes represent the processors and edges denote communication links between processors. This thesis concentrates on the computational complexity of the distributed graph colouring problem. The objective of the graph colouring problem is to assign a colour to each node in such a way that no two nodes connected by an edge share the same colour. In particular, it is often desirable to use only a small number of colours. This task is a fundamental symmetry-breaking primitive in various distributed algorithms. A graph that has been coloured in this manner using at most k different colours is said to be k-coloured. This work examines the synchronous message-passing model of distributed computation: every node runs the same algorithm, and the system operates in discrete synchronous communication rounds. During each round, a node can communicate with its neighbours and perform local computation. In this model, the time complexity of a problem is the number of synchronous communication rounds required to solve the problem. It is known that 3-colouring any k-coloured directed cycle requires at least ½(log* k - 3) communication rounds and is possible in ½(log* k + 7) communication rounds for all k ≥ 3. This work shows that for any k ≥ 3, colouring a k-coloured directed cycle with at most three colours is possible in ½(log* k + 3) rounds. In contrast, it is also shown that for some values of k, colouring a directed cycle with at most three colours requires at least ½(log* k + 1) communication rounds. Furthermore, in the case of directed rooted trees, reducing a k-colouring into a 3-colouring requires at least log* k + 1 rounds for some k and possible in log* k + 3 rounds for all k ≥ 3. The new positive and negative results are derived using computational methods, as the existence of distributed colouring algorithms corresponds to the colourability of so-called neighbourhood graphs. The colourability of these graphs is analysed using Boolean satisfiability (SAT) solvers. Finally, this thesis shows that similar methods are applicable in capturing the existence of distributed algorithms for other graph problems, such as the maximal matching problem.

Algorithms for Exact Structure Discovery in Bayesian Networks

Bayesian networks are compact, flexible, and interpretable representations of a joint distribution. When the network structure is unknown but there are observational data at hand, one can try to learn the network structure. This is called structure discovery. This thesis contributes to two areas of structure discovery in Bayesian networks: space--time tradeoffs and learning ancestor relations. The fastest exact algorithms for structure discovery in Bayesian networks are based on dynamic programming and use excessive amounts of space. Motivated by the space usage, several schemes for trading space against time are presented. These schemes are presented in a general setting for a class of computational problems called permutation problems; structure discovery in Bayesian networks is seen as a challenging variant of the permutation problems. The main contribution in the area of the space--time tradeoffs is the partial order approach, in which the standard dynamic programming algorithm is extended to run over partial orders. In particular, a certain family of partial orders called parallel bucket orders is considered. A partial order scheme that provably yields an optimal space--time tradeoff within parallel bucket orders is presented. Also practical issues concerning parallel bucket orders are discussed. Learning ancestor relations, that is, directed paths between nodes, is motivated by the need for robust summaries of the network structures when there are unobserved nodes at work. Ancestor relations are nonmodular features and hence learning them is more difficult than modular features. A dynamic programming algorithm is presented for computing posterior probabilities of ancestor relations exactly. Empirical tests suggest that ancestor relations can be learned from observational data almost as accurately as arcs even in the presence of unobserved nodes.