FONTES DE FINANCIAMENTO DA UNIVERSIDADE FEDERAL DE SANTA CATARINA

O financiamento universitário é temática constantemente discutida, por sua importância na manutenção das atividades de ensino, pesquisa e extensão e na contribuição da universidade para a formação e prosperidade social dos indivíduos e da coletividade. Desde a falência do modelo de Estado de Bem-Estar Social no rico ocidente e o conseqüente desmonte de seu similar nacional, nascido na era Vargas, as universidades públicas buscam fontes alternativas de recursos, seja por diminuições nos repasses do fundo público federal, seja pelo desejo dos administradores universitários de contarem com recursos menos “amarrados” por legislação específica para exercerem suas atividades. Neste estudo exploratório, mas também descritivo, baseado em pesquisa bibliográfica e documental, auxiliada por entrevista por pautas junto à Secretaria de Planejamento da Universidade Federal de Santa Catarina, foram atingidos os objetivos de listar os entes financiadores da IES nos anos de 2009 e 2010, identificar quais recursos são oriundos de convênios e contratos e calcular sua importância relativa nos orçamentos dos dois anos, respectivamente 14,50% e 18,06%. Espera-se com isso subsidiar estudos futuros quanto às contrapartidas exigidas em tais convênios e desvendar como a universidade sucede em captar tais recursos

Analgesic Comparison of Propiram Fumarate with Pentazocine, Codeine, and Placebo in Postsurgical Pain

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97240/1/j.1552-4604.1980.tb01688.x.pd

The ANOVA decomposition and generalized sparse grid methods for the high-dimensional backward Kolmogorov equation

In this thesis, we discuss numerical methods for the solution of the high-dimensional backward Kolmogorov equation, which arises in the pricing of options on multi-dimensional jump-diffusion processes. First, we apply the ANOVA decomposition and approximate the high-dimensional problem by a sum of lower-dimensional ones, which we then discretize by a θ-scheme and generalized sparse grids in time and space, respectively. We solve the resultant systems of linear equations by iterative methods, which requires both preconditioning and fast matrix-vector multiplication algorithms. We make use of a Linear Program and an algebraic formula to compute optimal diagonal scaling parameters. Furthermore, we employ the OptiCom as non-linear preconditioner. We generalize the unidirectional principle to non-local operators and develop a new matrix-vector multiplication algorithm for the OptiCom. As application we focus on the Kou model. Using a new recurrence formula, the computational complexity of the operator application remains linear in the number of degrees of freedom. The combination of the above-mentioned methods allows us to efficiently approximate the solution of the backward Kolmogorov equation for a ten-dimensional Kou model.Die ANOVA-Zerlegung und verallgemeinerte dünne Gitter für die hochdimensionale Kolmogorov-Rückwärtsgleichung In der vorliegenden Arbeit betrachten wir numerische Verfahren zur Lösung der hochdimensionalen Kolmogorov-Rückwärtsgleichung, die beispielsweise bei der Bewertung von Optionen auf mehrdimensionalen Sprung-Diffusionsprozessen auftritt. Zuerst wenden wir eine ANOVA-Zerlegung an und approximieren das hochdimensionale Problem mit einer Summe von niederdimensionalen Problemen, die wir mit einem θ-Verfahren in der Zeit und mit verallgemeinerten dünnen Gittern im Ort diskretisieren. Wir lösen die entstehenden linearen Gleichungssysteme mit iterativen Verfahren, wofür eine Vorkonditionierung als auch schnelle Matrix-Vektor-Multiplikationsalgorithmen nötig sind. Wir entwickeln ein Lineares Programm und eine algebraische Formel, um optimale Diagonalskalierungen zu finden. Des Weiteren setzen wir die OptiCom als nicht-lineares Vorkonditionierungsverfahren ein. Wir verallgemeinern das unidirektionale Prinzip auf nicht-lokale Operatoren und entwickeln einen für die OptiCom optimierten Matrix-Vektor-Multiplikationsalgorithmus. Als Anwendungsbeispiel betrachten wir das Kou-Modell. Mit einer neuen Rekurrenzformel bleibt die Gesamtkomplexität der Operatoranwendung linear in der Anzahl der Freiheitsgrade. Unter Einbeziehung aller genannten Methoden ist es nun möglich, die Lösung der Kolmogorov-Rückwärtsgleichung für ein zehndimensionales Kou-Modell effizient zu approximieren

Dimensionality reduction of high-dimensional data with a non-linear principal component aligned generative topographic mapping

Most high-dimensional real-life data exhibit some dependencies such that data points do not populate the whole data space but lie approximately on a lower-dimensional manifold. A major problem in many data mining applications is the detection of such a manifold and the expression of the given data in terms of a moderate number of latent variables. We present a method which is derived from the generative topographic mapping (GTM) and can be seen as a non-linear generalization of the Principal Component Analysis (PCA). It can detect certain non-linearities in the data but does not suffer from the curse of dimension with respect to the latent space dimension as the original GTM and thus allows for higher embedding dimensions. We provide experiments that show that ourapproach leads to an improved data reconstruction compared to the purely linear PCA and that it can furthermore be used for classification

A sparse grid based generative topographic mapping for the dimensionality reduction of high-dimensional data

Most high-dimensional data exhibit some correlation such that data points are not distributed uniformly in the data space but lie approximately on a lower-dimensional manifold. A major problem in many data-mining applications is the detection of such a manifold from given data, if present at all. The generative topographic mapping (GTM) finds a lower-dimensional parameterization for the data and thus allows for nonlinear dimensionality reduction. We will show how a discretization based on sparse grids can be employed for the mapping between latent space and data space. This leads to efficient computations and avoids the ‘curse of dimensionality’ of the embedding dimension. We will use our modified, sparse grid based GTM for problems from dimensionality reduction and data classification

An efficient sparse grid Galerkin approach for the numerical valuation of basket options under Kou's jump-diffusion model

We use a sparse grid approach to discretize a multi-dimensional partial integro-differential equation (PIDE) for the deterministic valuation of European put options on Kou’s jump-diffusion processes. We employ a generalized generating system to discretize the respective PIDE by the Galerkin approach and iteratively solve the resulting linear system. Here, we exploit a newly developed recurrence formula, which, together with an implementation of the unidirectional principle for non-local operators, allows us to evaluate the operator application in linear time. Furthermore, we exploit that the condition of the linear system is bounded independently of the number of unknowns. This is due to the use of the Galerkin generating system and the computation of L2-orthogonal complements. Altogether, we thus obtain a method that is only linear in the number of unknowns of the respective generalized sparse grid discretization. We report on numerical experiments for option pricing with the Kou model in one, two and three dimensions, which demonstrate the optimal complexity of our approach

Nuclear translocation of cardiac G protein-Coupled Receptor kinase 5 downstream of select Gq-activating hypertrophic ligands is a calmodulin-dependent process.

G protein-Coupled Receptors (GPCRs) kinases (GRKs) play a crucial role in regulating cardiac hypertrophy. Recent data from our lab has shown that, following ventricular pressure overload, GRK5, a primary cardiac GRK, facilitates maladaptive myocyte growth via novel nuclear localization. In the nucleus, GRK5\u27s newly discovered kinase activity on histone deacetylase 5 induces hypertrophic gene transcription. The mechanisms governing the nuclear targeting of GRK5 are unknown. We report here that GRK5 nuclear accumulation is dependent on Ca(2+)/calmodulin (CaM) binding to a specific site within the amino terminus of GRK5 and this interaction occurs after selective activation of hypertrophic Gq-coupled receptors. Stimulation of myocytes with phenylephrine or angiotensinII causes GRK5 to leave the sarcolemmal membrane and accumulate in the nucleus, while the endothelin-1 does not cause nuclear GRK5 localization. A mutation within the amino-terminus of GRK5 negating CaM binding attenuates GRK5 movement from the sarcolemma to the nucleus and, importantly, overexpression of this mutant does not facilitate cardiac hypertrophy and related gene transcription in vitro and in vivo. Our data reveal that CaM binding to GRK5 is a physiologically relevant event that is absolutely required for nuclear GRK5 localization downstream of hypertrophic stimuli, thus facilitating GRK5-dependent regulation of maladaptive hypertrophy

A Map of the Nanoworld: Sizing up the Science, Politics, and Business of the Infinitesimal

Mapping out the eight main nodes of nanotechnology discourse that have emerged in the past decade, we explore how various scientific, social, and ethical islands of discussion have developed, been recognized, and are being continually renegotiated. We do so by (1) identifying the ways in which scientists, policy makers, entrepreneurs, educators, and environmental groups have drawn boundaries on issues relating to nanotechnology; (2) describing concisely the perspectives from which these boundaries are drawn; and (3) exploring how boundaries on nanotechnology are marked and negotiated by various nodes of nanotechnology discourse.Comment: 25 page

On a multilevel preconditioner and its condition numbers for the discretized Laplacian on full and sparse grids in higher dimensions

We first discretize the d-dimensional Laplacian in (0, 1)d for varying d on a full uniform grid and build a new preconditioner that is based on a multilevel generating system. We show that the resulting condition number is bounded by a constant that is independent of both, the level of discretization J and the dimension d. Then, we consider so-called sparse grid spaces, which offer nearly the same accuracy with far less degrees of freedom for function classes that involve bounded mixed derivatives. We introduce an analogous multilevel preconditioner and show that it possesses condition numbers which are at least as good as these of the full grid case. In fact, for sparse grids we even observe falling condition numbers with rising dimension in our numerical experiments. Furthermore, we discuss the cost of the algorithmic implementations. It is linear in the degrees of freedom of the respective multilevel generating system. For completeness, we also consider the case of a sparse grid discretization using prewavelets and compare its properties to those obtained with the generating system approach