Where is my mind? : a propósito da necessidade do teatro, dos Pixies, de Pasolini e da Polónia

Crítica a vários espectáculos apresentados no Warszawskie Spotkania Teatralne, em Varsóvia (Polónia), 2012.

O que é que tem o actor?: figurações de personagens shakespearianas por actores e actrizes portugueses ao longo do século XX (... e um bocadinho do XXI)

O presente artigo pretende dar conta do caminho percorrido pelo teatro português ao longo do século XX, tendo como eixo estruturante a figuração de algumas personagens de William Shakespeare por actores portugueses. Para tal, considerarei o papel que o actor pode assumir dentro de uma estrutura de produção teatral e o significado que pode ter a presença de Shakespeare no reportório de vários grupos com propósitos e orgânicas diversas. Tomarei como exemplos alguns espectáculos representantes de vários momentos e de correntes diversas no teatro português, levantando a hipótese de que se poderá perceber na figuração de personagens shakespearianas alguns dos sentidos do que tem sido a evolução do teatro em Portugal.

O estranho caso do romancista e dos livros pretos que ele escreveu: a propósito da obra de Gonçalo M. Tavares e dos espectáculos "Sobreviver" e "Jerusalém"

Trazer a matéria romanesca para o palco tem sido uma das mais dilectas tentações do drama contemporâneo. Em Portugal, há um universo de um romancista em particular que tem despertado o interesse de alguns criadores teatrais: Gonçalo M. Tavares (n.1970). Sendo também um dramaturgo, é no romance que a excelência superlativa da sua escrita se tem afirmado e tem sido reconhecida. Neste artigo abordo dois espectáculos que partiram de romances de Gonçalo M. Tavares que fazem parte do ciclo “O Reino-Livros Pretos”: Sobreviver, encenado por Lúcia Sigalho /Sensurround, apresentado no S. Luiz Teatro Municipal (Lisboa), em Fevereiro de 2006; e Jerusalém, encenado por João Brites / Teatro o bando, apresentado no Centro Cultural de Belém (Lisboa), em Outubro de 2008. Sobreviver, de Sigalho, foi o resultado de um trabalho de escrita cénica, realizado pela encenadora e pelo colectivo de actores, sobre os livros pretos de Gonçalo M. Tavares Um homem: Klaus Klump, A máquina de Joseph Walser e Jerusalém (e também um excerto de O senhor Brecht). Jerusalém, de Brites partia da obra homónima de M. Tavares, estando a sua matriz performativa mais próxima da narrativa original. A dramaturgia do espectáculo construía-se sobre a mesma cartografia de horror e mal que o romance habitava. Havia, contudo, um ímpeto no sentido de poder expandir os significados do texto e encontrar ecos de guerra e violência noutras latitudes.

A Casa da Comédia (1946-1975): de Fernando Amado a Bertolt Brecht

Tomando como ponto de partida o movimento de teatro experimental português do pós-Segunda Guerra Mundial, a presente investigação centra-se no trajecto da Casa da Comédia, fundada por Fernando Amado em 1946, e que retoma a actividade em 1962. Procuramos identificar as ideias de teatro que animam o percurso deste grupo, dar eco da recepção aos seus espectáculos, bem como inserir o seu projecto no âmbito dos esforços de renovação cénica que vão tendo lugar um pouco por toda a Europa.

Performance of concrete made with aggregates recycled from precasting industry waste: influence of the crushing process

The aim of this paper is to evaluate the influence of the crushing process used to obtain recycled concrete aggregates on the performance of concrete made with those aggregates. Two crushing methods were considered: primary crushing, using a jaw crusher, and primary plus secondary crushing (PSC), using a jaw crusher followed by a hammer mill. Besides natural aggregates (NA), these two processes were also used to crush three types of concrete made in laboratory (L20, L45 e L65) and three more others from the precast industry (P20, P45 e P65). The coarse natural aggregates were totally replaced by coarse recycled concrete aggregates. The recycled aggregates concrete mixes were compared with reference concrete mixes made using only NA, and the following properties related to the mechanical and durability performance were tested: compressive strength; splitting tensile strength; modulus of elasticity; carbonation resistance; chloride penetration resistance; water absorption by capillarity; water absorption by immersion; and shrinkage. The results show that the PSC process leads to better performances, especially in the durability properties.

As ferramentas do corpo numa performance inclusiva: relatório de estágio

Relatório de Estágio submetido à Escola Superior de Teatro e Cinema para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Artes Performativas – especialização em Teatro-­‐Música.

Ai de mim!

Dissertação submetida à Escola Superior de Teatro e Cinema para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Teatro, especialização em Encenação.

A many-core co-processor for embedded parallel computing on FPGA

Single processor architectures are unable to provide the required performance of high performance embedded systems. Parallel processing based on general-purpose processors can achieve these performances with a considerable increase of required resources. However, in many cases, simplified optimized parallel cores can be used instead of general-purpose processors achieving better performance at lower resource utilization. In this paper, we propose a configurable many-core architecture to serve as a co-processor for high-performance embedded computing on Field-Programmable Gate Arrays. The architecture consists of an array of configurable simple cores with support for floating-point operations interconnected with a configurable interconnection network. For each core it is possible to configure the size of the internal memory, the supported operations and number of interfacing ports. The architecture was tested in a ZYNQ-7020 FPGA in the execution of several parallel algorithms. The results show that the proposed many-core architecture achieves better performance than that achieved with a parallel generalpurpose processor and that up to 32 floating-point cores can be implemented in a ZYNQ-7020 SoC FPGA.

Sparse matrix multiplication on a reconfigurable many-core architecture

Sparse matrix-vector multiplication (SMVM) is a fundamental operation in many scientific and engineering applications. In many cases sparse matrices have thousands of rows and columns where most of the entries are zero, while non-zero data is spread over the matrix. This sparsity of data locality reduces the effectiveness of data cache in general-purpose processors quite reducing their performance efficiency when compared to what is achieved with dense matrix multiplication. In this paper, we propose a parallel processing solution for SMVM in a many-core architecture. The architecture is tested with known benchmarks using a ZYNQ-7020 FPGA. The architecture is scalable in the number of core elements and limited only by the available memory bandwidth. It achieves performance efficiencies up to almost 70% and better performances than previous FPGA designs.

Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.