The Cell processor is an example of the trade-offs made when designing a mass market power efficient multi-core machine, but the machine-exposing architecture and raw communication mechanisms of Cell are hard to manage for a programmer. Cell's design is simple and causes software complexity to go up in the areas of achieving low threading overhead, good bandwidth efficiency, and load balance. Several attempts have been made to produce efficient and effective programming systems for Cell, but the attempts have been too specialized and thus fall short. We present Jack Rabbit, an efficient thread pool work queue implementation, with load balancing mechanisms and double buffering. Our system incurs low threading overhead, gets good load balance, and achieves bandwidth efficiency. Our system represents a step towards an effective way to program Cell and any similar current or future processors. / text

Reducing interference noise in a noisy speech recording has been a challenging task for many years yet has a variety of applications, for example, in handsfree mobile communications, in speech recognition, and in hearing aids. Traditional single-channel noise reduction schemes, such as Wiener filtering, do not work satisfactorily in the presence of non-stationary background noise. Alternatively, supervised approaches, where the noise type is known in advance, lead to higher-quality enhanced speech signals. This dissertation proposes supervised and unsupervised single-channel noise reduction algorithms. We consider two classes of methods for this purpose: approaches based on nonnegative matrix factorization (NMF) and methods based on hidden Markov models (HMM).  The contributions of this dissertation can be divided into three main (overlapping) parts. First, we propose NMF-based enhancement approaches that use temporal dependencies of the speech signals. In a standard NMF, the important temporal correlations between consecutive short-time frames are ignored. We propose both continuous and discrete state-space nonnegative dynamical models. These approaches are used to describe the dynamics of the NMF coefficients or activations. We derive optimal minimum mean squared error (MMSE) or linear MMSE estimates of the speech signal using the probabilistic formulations of NMF. Our experiments show that using temporal dynamics in the NMF-based denoising systems improves the performance greatly. Additionally, this dissertation proposes an approach to learn the noise basis matrix online from the noisy observations. This relaxes the assumption of an a-priori specified noise type and enables us to use the NMF-based denoising method in an unsupervised manner. Our experiments show that the proposed approach with online noise basis learning considerably outperforms state-of-the-art methods in different noise conditions.  Second, this thesis proposes two methods for NMF-based separation of sources with similar dictionaries. We suggest a nonnegative HMM (NHMM) for babble noise that is derived from a speech HMM. In this approach, speech and babble signals share the same basis vectors, whereas the activation of the basis vectors are different for the two signals over time. We derive an MMSE estimator for the clean speech signal using the proposed NHMM. The objective evaluations and performed subjective listening test show that the proposed babble model and the final noise reduction algorithm outperform the conventional methods noticeably. Moreover, the dissertation proposes another solution to separate a desired source from a mixture with arbitrarily low artifacts.  Third, an HMM-based algorithm to enhance the speech spectra using super-Gaussian priors is proposed. Our experiments show that speech discrete Fourier transform (DFT) coefficients have super-Gaussian rather than Gaussian distributions even if we limit the speech data to come from a specific phoneme. We derive a new MMSE estimator for the speech spectra that uses super-Gaussian priors. The results of our evaluations using the developed noise reduction algorithm support the super-Gaussianity hypothesis. / <p>QC 20130916</p>

By counting 1's in the "right half" of 2w consecutive rows, we locate the main diagonal of any doubly infinite permutation matrix with bandwidth w. Then the matrix can be correctly centered and factored into block-diagonal permutation matrices. Part II of the paper discusses the same questions for the much larger class of band-dominated matrices. The main diagonal is determined by the Fredholm index of a singly infinite submatrix. Thus the main diagonal is determined "at infinity" in general, but from only 2w rows for banded permutations.

In 2010 Chung-Graham-Knuth proved an interesting symmetric identity for the Eulerian numbers and asked for a q-analog version. Using the q-Eulerian polynomials introduced by Shareshian-Wachs we find such a q-identity. Moreover, we provide a bijective proof that we further generalize to prove other symmetric qidentities using a combinatorial model due to Foata-Han. Meanwhile, Hyatt has introduced the colored Eulerian quasisymmetric functions to study the joint distribution of the excedance number and major index on colored permutations. Using the Decrease Value Theorem of Foata-Han we give a new proof of his main generating function formula for the colored Eulerian quasisymmetric functions. Furthermore, certain symmetric q-Eulerian identities are generalized and expressed as identities involving the colored Eulerian quasisymmetric functions. Next, generalizing the recent works of Savage-Visontai and Beck-Braun we investigate some q-descent polynomials of general signed multipermutations. The factorial and multivariate generating functions for these q-descent polynomials are obtained and the real rootedness results of some of these polynomials are given. Finally, we study the diagonal generating function of the Jacobi-Stirling numbers of the second kind by generalizing the analogous results for the Stirling and Legendre-Stirling numbers of the second kind. It turns out that the generating function is a rational function, whose numerator is a polynomial with nonnegative integral coefficients. By applying Stanley's theory of P-partitions we find combinatorial interpretations of those coefficients

Concentrations of elements and ions measured in samples collected between February 1993 and December 2000 at a rural site in central Anatolia were investigated to evaluate the chemical composition of atmosphere at central Anatolia, to determine pollution level of the region, to study temporal variability of the pollutants and to investigate the sources and source regions of air pollutants in the region. Level of pollution at central Anatolia was found to be lower than the pollution level at other European countries and Mediterranean and Black Sea regions of Turkey. Enrichment factor calculations revealed that SO42-, Pb and Ca are highly enriched in the aerosol / whereas, soil component has dominating contribution on observed concentrations of V, Mg, Ca and K. SO42-/(SO2+SO42-) ratio observed in &Ccedil / ubuk station indicates that contribution of distant sources is more important than the contribution of local sources on observed SO42- levels. SO42-/NO3- ratio calculations showed that Central Anatolia is receipt of SO42- from Eastern European countries. Positive Matrix Factorization (PMF) analysis revealed 6 source groups, namely motor vehicle source, mixed urban factor, long range transport factor, soil factor, NO3- factor and Cd factor. Distribution of Potential Source Contribution Function (PSCF) values showed that main source areas of SO42-, NH4+ and Cd are western parts of Turkey, Balkan countries, central and western Europe, central Russian Federation and north of Sweden and Finland / NO3- are the regions located around the Mediterranean Sea / and there is no very strong potential source area observed for NH3 and Pb.

Measurement of chemical composition of precipitation is important both to understand acidification of terrestrial and aquatic ecosystems and neutralization process in the atmosphere. Such data are scarce in the Mediterranean region. In this study, chemical composition of daily, wet-only, 387 number of rain water samples collected between 1991 and 1999 were investigated to determine levels, temporal variation and long-term trends in concentrations of major ions and trace elements between 1991 and 1999. Samples had already been collected and some of the analysis had been completed. The anions SO42-, NO3- and Cl- were analyzed by HPLC coupled with UV-VIS detector, NH4+ was analyzed by colorimetry and H+ ion was analyzed by pH meter. The major ions and trace metals were analyzed by using Atomic Absorption Spectrometry (AAS) and Graphite Furnace Atomic Absorption Spectrometry (GFAAS). In this study complete data set were generated by analyzing samples that had not been previously analyzed for major ions and trace elements with Inductively Coupled Plasma with Optical Emission Spectrometry (ICP-OES). Statistical tools were used to determine the distribution of the pollutants. The rain water data tends to be log-normally distributed since data show large variations due to meteorological conditions, physical and chemical transformations and air mass transport patterns. The median pH of the rain water was found to be 5.29, which indicates that the rain water is not strongly acidic. This case is not a result of lacking of acidic compounds but rather indicates extended neutralization process in rain water. Eastern Mediterranean atmosphere is under the influence of three general source types: (1) anthropogenic sources, which are located to the north and northwest of the basin brings low pH values to the region (SO42-, NO3- ions) / (2) a strong crustal source, which is dried and suspended local soil and air masses transported from North Africa transport which have high pH values (Ca2+, Al, Fe ions) and (3) a marine source, which is the Mediterranean Sea itself (Na+, Cl- ions). In the region, the main acid forming compounds are H2SO4 and HNO3 whereas / CaCO3 and NH3 are responsible for the neutralization process. To describe the level of pollutant concentrations and the factors that affect their variations in rain water / ion compositions, neutralization of acidity, short and long-term variability of ions and elements, their time trend analysis and wet deposition fluxes were investigated briefly. Positive matrix factorization (PMF) was used to determine components of ionic mass in the precipitation. In Antalya Station the rain water has five factors: free acidity factor, crustal factor, marine factor, NO3- factor and SO42- factor. Potential Source Contribution Function (PSCF) and trajectory statistics were used to determine source regions generating these components. NO3- has potential source regions of Western Mediterranean countries and North Africa, whereas SO42- has additional southeasterly trajectory components of Israel and south east of Turkey.

Hierarchical Markovian Models (HMMs) are composed of multiple low level models (LLMs) and high level model (HLM) that defines the interaction among LLMs. The essence of the HMM approach is to model the system at hand in the form of interacting components so that its (larger) underlying continous-time Markov chain (CTMC) is not generated but implicitly represented as a sum of Kronecker products of (smaller) component matrices. The Kronecker structure of an HMM induces nested block partitionings in its underlying CTMC. These partitionings may be used in block versions of classical iterative methods based on splittings, such as block SOR (BSOR), to solve the underlying CTMC for its stationary vector. Therein the problem becomes that of solving multiple nonsingular linear systems whose coefficient matrices are the diagonal blocks of a particular partitioning. This paper shows that in each HLM state there may be diagonal blocks with identical off-diagonal parts and diagonals differing from each other by a multiple of the identity matrix. Such diagonal blocks are named candidate blocks. The paper explains how candidate blocks can be detected and how the can mutually benefit from a single real Schur factorization. It gives sufficient conditions for the existence of diagonal blocks with real eigenvalues and shows how these conditions can be checked using component matrices. It describes how the sparse real Schur factors of candidate blocks satisfying these conditions can be constructed from component matrices and their real Schur factors. It also demonstrates how fill in- of LU factorized (non-candidate) diagonal blocks can be reduced by using the column approximate minimum degree algorithm (COLAMD). Then it presents a three-level BSOR solver in which the diagonal blocks at the first level are solved using block Gauss-Seidel (BGS) at the second and the methods of real Schur and LU factorizations at the third level. Finally, on a set of numerical experiments it shows how these ideas can be used to reduce the storage required by the factors of the diagonal blocks at the third level and to improve the solution time compared to an all LU factorization implementation of the three-level BSOR solver.

Kronecker structured representations are used to cope with the state space explosion problem in Markovian modeling and analysis. Currently an open research problem is that of devising strong preconditioners to be used with projection methods for the computation of the stationary vector of Markov chains (MCs) underlying such representations. This paper proposes a block SOR (BSOR) preconditioner for hierarchical Markovian Models (HMMs) that are composed of multiple low level models and a high level model that defines the interaction among low level models. The Kronecker structure of an HMM yields nested block partitionings in its underlying continuous-time MC which may be used in the BSOR preconditioner. The computation of the BSOR preconditioned residual in each iteration of a preconditioned projection method becoms the problem of solving multiple nonsingular linear systems whose coefficient matrices are the diagonal blocks of the chosen partitioning. The proposed BSOR preconditioner solvers these systems using sparse LU or real Schur factors of diagonal blocks. The fill-in of sparse LU factorized diagonal blocks is reduced using the column approximate minimum degree algorithm (COLAMD). A set of numerical experiments are presented to show the merits of the proposed BSOR preconditioner.

From the time of the Greeks, primality testing and factoring have fascinated mathematicians, and for centuries following the Greeks primality testing and factorization were pursued by enthusiasts and professional mathematicians for their intrisic value. There was little practical application. One example application was to determine whether or not the Fermat numbers, that is, numbers of the form F;, = 2'" + 1 were prime. Fermat conjectured that for all n they were prime. For n = 1,2,3,4, the Fermat numbers are prime, but Euler showed that F; was not prime and to date no F,, n 2 5 has been found to be prime. Thus, for nearly 2000 years primality testing and factorization was largely pure mathematics. This all changed in the mid 1970's with the advent of public key cryptography. Large prime numbers are used in generating keys in many public key cryptosystems and the security of many of these cryptosystems depends on the difficulty of factoring numbers with large prime factors. Thus, the race was on to develop new algorithms to determine the primality or otherwise of a given large integer and to determine the factors of given large integers. The development of such algorithms continues today. This thesis develops both of these themes. The first part of this thesis deals with primality testing and after a brief introduction to primality testing a new probabilistic primality algorithm, ALI, is introduced. It is analysed in detail and compared to Fermat and Miller-Rabin primality tests. It is shown that the ALI algorithm is more efficient than the Miller-Rabin algorithm in some aspects. The second part of the thesis deals with factoring and after looking closely at various types of algorithms a new algorithm, RAK, is presented. It is analysed in detail and compared with Fermat factorization. The RAK algorithm is shown to be significantly more efficient than the Fermat factoring algorithm. A number of enhancements is made to the basic RAK algorithm in order to improve its performance. The RAK algorithm with its enhancements is known as IMPROVEDRAK. In conjunction with this work on factorization an improvement to Shor's factoring algorithm is presented. For many integers Shor's algorithm uses a quantum computer multiple times to factor a composite number into its prime factors. It is shown that Shor's alorithm can be modified in a way such that the use of a quantum computer is required just once. The common thread throughout this thesis is the application of factoring and primality testing techniques to integer types which commonly occur in public key cryptosystems. Thus, this thesis contributes not only in the area of pure mathematics but also in the very contemporary area of cryptology.

En imagerie nucléaire (scintigraphie, TEMP, TEP), les diagnostics sont fréquemment faits à l'aide des courbes d'activité temporelles des différents organes et tissus étudiés. Ces courbes représentent l'évolution de la distribution d'un traceur radioactif injecté dans le patient. Leur obtention est compliquée par la superposition des organes et des tissus dans les séquences d'images 2D, et il convient donc de séparer les différentes contributions présentes dans les pixels. Le problème de séparation de sources sous-jacent étant sous-déterminé, nous proposons d'y faire face dans cette thèse en exploitant différentes connaissances a priori d'ordre spatial et temporel sur les sources. Les principales connaissances intégrées ici sont les régions d'intérêt (ROI) des sources qui apportent des informations spatiales riches. Contrairement aux travaux antérieurs qui ont une approche binaire, nous intégrons cette connaissance de manière robuste à la méthode de séparation, afin que cette dernière ne soit pas sensible aux variations inter et intra-utilisateurs dans la sélection des ROI. La méthode de séparation générique proposée prend la forme d'une fonctionnelle à minimiser, constituée d'un terme d'attache aux données ainsi que de pénalisations et de relâchements de contraintes exprimant les connaissances a priori. L'étude sur des images de synthèse montrent les bons résultats de notre approche par rapport à l'état de l'art. Deux applications, l'une sur les reins, l'autre sur le cœur illustrent les résultats sur des données cliniques réelles. / In nuclear imaging (scintigraphy, SPECT, PET), diagnostics are often made with time activity curves (TAC) of organs and tissues. These TACs represent the dynamic evolution of tracer distribution inside patient's body. Extraction of TACs can be complicated by overlapping in the 2D image sequences, hence source separation methods must be used in order to extract TAC properly. However, the underlying separation problem is underdetermined. We propose to overcome this difficulty by adding some spatial and temporal prior knowledge about sources on the separation process. The main knowledge used in this work is region of interest (ROI) of organs and tissues. Unlike state of the art methods, ROI are integrated in a robust way in our method, in order to face user-dependancy in their selection. The proposed method is generic and minimize an objective function composed with a data fidelity criterion, penalizations and relaxations expressing prior knowledge. Results on synthetic datasets show the efficiency of the proposed method compare to state of the art methods. Two clinical applications on the kidney and on the heart are also adressed.