Decision Tree-based Syntactic Language Modeling

Statistical Language Modeling is an integral part of many natural language processing applications, such as Automatic Speech Recognition (ASR) and Machine Translation. N-gram language models dominate the field, despite having an extremely shallow view of language---a Markov chain of words. In this thesis, we develop and evaluate a joint language model that incorporates syntactic and lexical information in a effort to ``put language back into language modeling.'' Our main goal is to demonstrate that such a model is not only effective but can be made scalable and tractable. We utilize decision trees to tackle the problem of sparse parameter estimation which is exacerbated by the use of syntactic information jointly with word context. While decision trees have been previously applied to language modeling, there has been little analysis of factors affecting decision tree induction and probability estimation for language modeling. In this thesis, we analyze several aspects that affect decision tree-based language modeling, with an emphasis on syntactic language modeling. We then propose improvements to the decision tree induction algorithm based on our analysis, as well as the methods for constructing forest models---models consisting of multiple decision trees. Finally, we evaluate the impact of our syntactic language model on large scale Speech Recognition and Machine Translation tasks. In this thesis, we also address a number of engineering problems associated with the joint syntactic language model in order to make it tractable. Particularly, we propose a novel decoding algorithm that exploits the decision tree structure to eliminate unnecessary computation. We also propose and evaluate an approximation of our syntactic model by word n-grams---the approximation that makes it possible to incorporate our model directly into the CDEC Machine Translation decoder rather than using the model for rescoring hypotheses produced using an n-gram model

Proceedings of the 35th WIC Symposium on Information Theory in the Benelux and the 4th joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux, Eindhoven, the Netherlands May 12-13, 2014

Compressive sensing (CS) as an approach for data acquisition has recently received much attention. In CS, the signal recovery problem from the observed data requires the solution of a sparse vector from an underdetermined system of equations. The underlying sparse signal recovery problem is quite general with many applications and is the focus of this talk. The main emphasis will be on Bayesian approaches for sparse signal recovery. We will examine sparse priors such as the super-Gaussian and student-t priors and appropriate MAP estimation methods. In particular, re-weighted l2 and re-weighted l1 methods developed to solve the optimization problem will be discussed. The talk will also examine a hierarchical Bayesian framework and then study in detail an empirical Bayesian method, the Sparse Bayesian Learning (SBL) method. If time permits, we will also discuss Bayesian methods for sparse recovery problems with structure; Intra-vector correlation in the context of the block sparse model and inter-vector correlation in the context of the multiple measurement vector problem

Proceedings of the 35th WIC Symposium on Information Theory in the Benelux and the 4th joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux, Eindhoven, the Netherlands May 12-13, 2014

Compressive sensing (CS) as an approach for data acquisition has recently received much attention. In CS, the signal recovery problem from the observed data requires the solution of a sparse vector from an underdetermined system of equations. The underlying sparse signal recovery problem is quite general with many applications and is the focus of this talk. The main emphasis will be on Bayesian approaches for sparse signal recovery. We will examine sparse priors such as the super-Gaussian and student-t priors and appropriate MAP estimation methods. In particular, re-weighted l2 and re-weighted l1 methods developed to solve the optimization problem will be discussed. The talk will also examine a hierarchical Bayesian framework and then study in detail an empirical Bayesian method, the Sparse Bayesian Learning (SBL) method. If time permits, we will also discuss Bayesian methods for sparse recovery problems with structure; Intra-vector correlation in the context of the block sparse model and inter-vector correlation in the context of the multiple measurement vector problem

On the designs of early phase oncology studies

This thesis focuses on the design, statistical operating characteristics and interpretation of early phase oncology clinical trials. Anti-cancer drugs are generally highly toxic and it is imperative to deliver a dose to the patient that is low enough to be safe but high enough to produce a clinically meaningful response. Thus, a study of dose limiting toxicities (DLTs) and a determination of the maximum tolerated dose (MTD) of a drug that can be used in later phase trials is the focus of most Phase I oncology trials. We first comprehensively compare the statistical operating characteristics of various early phase oncology designs, finding that all the designs examined select the MTD more accurately when there is a clear separation between the true DLT rate at the MTD and the rates at the dose levels immediately above and below. Among the rule-based designs studied, we found that the 3+3 design under-doses a large percentage of patients and is not accurate in selecting the MTD for all the cases considered. The 5+5 a design picks the MTD as accurately as the model based designs for the true DLT rates generated using the chosen log-logistic and linear dose-toxicity curves, but requires enrolling a larger number of patients. The model based designs examined, mTPI, TEQR, BOIN, CRM and EWOC designs, perform well on the whole, assign the maximum percentage of patients to the MTD, and pick the MTD fairly accurately. However, the limited sample size of these Phase I oncology trials makes it difficult to accurately predict the MTD. Hence, we next study the effect of sample size and cohort size on the accuracy of dose selection in early phase oncology designs, finding that an adequate sample size is crucial. We then propose some integrated Phase 1/2 oncology designs, namely the 20+20 accelerated titration design and extensions of the mTPI and TEQR designs, that consider both toxicity and efficacy in dose selection, utilizing a larger sample size. We demonstrate that these designs provide an improvement over the existing early phase designs.2019-12-01T00:00:00

High Voltage DC-biased Oil Type Medium Frequency Transformer; A Green Solution for Series DC Wind Park Concept

The electric energy generated by remote offshore wind parks is transported to the consumers using high voltage submarine cables. On the generation site, such transmissions are realized today by collecting the energy produced by several wind turbines in a bulky and expensive transformer placed on a dedicated platform. An alternative solution has been proposed recently, which allows to reduce the installation and maintenance costs by eliminating such a platform. It is suggested to equip each wind turbine in the wind park by an individual DC/DC converter and connect them in series to reach the DC voltage level required for an efficient HVDC energy transportation to the shore. The DC/DC converter is supposed to be a Dual Active Bridge (DAB) converter, which can be made reasonably small to be placed on the wind turbine tower or even in its nacelle. The key element of the converter defining its size and mass is a special transformer, which operates at voltages comprising a high (switching) frequency component superimposed on a high DC offset voltage. DC insulation design of such a transformer and investigation of the effects of a high DC insulation level on the other electromagnetic properties of the transformer is the subject of the present research.In order to verify the concept a prototype of the transformer was built, and its evaluation presented. The unit has been manufactured for the rated power of 50 kW and rated voltages 0.4/5 kV including DC offset of 125 kV and square-shaped oscillations with the frequency of 5 kHz. The magnetic system was made of ferrite material and consisted of 10 shell-type core segments. The magnetic properties have been verified by measuring magnetization and losses at various frequencies in the range 1-10 kHz to cover the operational range of the DAB. The types and dimensions of the windings and their conductors were chosen to minimize the proximity and eddy current effects at higher frequencies. To reduce the size of the transformer and to allow for its efficient cooling, the active part was immersed in oil and cellulose-based materials (paper and pressboard) were used to build the high voltage insulation system. The principles for dimensioning the insulation of the transformer are discussed. The criteria used for selecting insulating distances were based on the consideration of the electric field strength obtained from FEM simulations and using the non-linear Maxwell-Wagner model accounting for local variations of the electric field caused by accumulation of interfacial charges induced by DC stresses. The properties of the materials needed for the calculations were obtained by measuring their dielectric constants and electric conductivities. The methodology used for the measurements conducted for conventional mineral oil and eco-friendly biodegradable transformer oils and, respectively, for oil-impregnated paper/pressboard, is presented. The methodologies used for obtaining parameters of the built transformer prototype needed for its integration in the power electric circuit of the DAB are introduced. A method developed for accurate calculations of the leakage inductance for the shell-type multi core transformers with circular windings is described. Two innovative methods for evaluations of parasitic capacitances based on high frequency equivalent circuits of the transformer are presented. The results of their verifications against performed Frequency Response Analysis measurements and FEM calculations as well as their accuracy are discussed.Thermal performance of the developed transformer prototype is analysed based on the results of computer simulations of heat transfer in its active part under rated load. Identified hot spots and solutions for their elimination are presented.Finally, the expected dimensions, weight, and efficiency of an actual DC/DC converter with the rated parameters corresponding to a 6 MW, 1.8 kV real wind turbine having a 250 kV offset DC voltage are estimated assuming that the developed transformer prototype is scalable. It is shown that the proposed solution allows for installing the full-scale converter having 2.2 Tons in weight and 1.8 m3 in volume on the bottom of the wind turbine’s tower

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

A multiobjective, multidisciplinary design optimization methodology for the conceptual design of distributed satellite systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2002.Includes bibliographical references (p. 427-438).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.A multiobjective, multidisciplinary design optimization methodology for mathematically modeling the distributed satellite system (DSS) conceptual design problem as an optimization problem has been developed to advance the state-of-the-art in complex distributed satellite network design. An increasing number of space missions are utilizing DSS architectures in which multiple satellites work in a coordinated fashion to improve system performance, cost, and survivability. The trade space for distributed satellite systems can be enormous - too large to enumerate, analyze, and compare all possible architectures. The seven-step methodology enables an efficient search of the trade space for the best families of architectures, and explores architectures that might not otherwise be considered during the conceptual design phase, the phase of a DSS program in which the majority of lifecycle cost gets locked in. Four classes of multidisciplinary design optimization (MDO) techniques are investigated - Taguchi, heuristic, gradient, and univariate methods. The heuristic simulated annealing (SA) algorithm found the best DSS architectures with the greatest consistency due to its ability to escape local optima within a nonconvex trade space. Accordingly, this SA algorithm forms the core single objective MDO algorithm in the methodology. The DSS conceptual design problem scope is then broadened by expanding from single objective to multiobjective optimization problems, and two variant multiobjective SA algorithms are developed.(cont.) The utility in knowing the global Pareto boundary of a DSS trade space is presented, and several methods are explored for approximating the true global Pareto boundary with only a limited knowledge of the full DSS trade space. Finally, methods for improving the performance of the SA algorithm are tested, and it was found that the 2-DOF variant of the SA algorithm is most effective at both single objective and multiobjective searches of a DSS trade space. The versatility of the methodology is demonstrated through its application to the conceptual design of three separate distributed satellite systems - the civil NASA Origins Terrestrial Planet Finder mission, the military TechSat 21 GMTI space-based radar mission, and the commercial broadband satellite communications mission. In each case, the methodology identifies more cost-effective system architectures than those previously considered for the single objective optimization problem, and a Pareto optimal set of architectures for the multiobjective optimization problem. In this manner, the methodology serves as a powerful, versatile systems engineering tool for the conceptual design of distributed satellite systems.by Cyrus D. Jilla.Ph.D

Proceedings of the 7th International Conference EEDAL 2013 Energy Efficiency in Domestic Appliances and Lighting

This book contains the papers presented at the seventh international conference on Energy Efficiency in Domestic Appliances and Lighting. EEDAL'2013 was organised in Coimbra, Portugal in September 2013. This major international conference, which was previously been staged in Florence 1997, Naples 2000, Turin 2003, London 2006, B2e0r0l9in, Copenhagen 2011 has been very successful in attracting an international community of stakeholders dealing with residential appliances, equipment, metering liagnhdti ng (including manufacturers, retailers, consumers, governments, international organisations aangde ncies, academia and experts) to discuss the progress achieved in technologies, behavioural aspects and poliacineds , the strategies that need to be implemented to further progress this important work. Potential readers who may benefit from this book include researchers, engineers, policymakers, and all those who can influence the design, selection, application, and operation of electrical appliances and lighting.JRC.F.7-Renewables and Energy Efficienc