Elliptical bodies. Avant-garde, and the physical shape of flamenco rhythms

Cuban writer and arts critic Severo Sarduy theorized that essential baroque qualities are defined by the ellipse with one focus invisible so that the visible focus is exaggerated. An analysis of rhythmic and visual aesthetics of two Flamenco artists, Vicente Escudero and his contemporary Israel Galván, brings to light how these artists refine the double foci in works that often reach into other disciplines and avant-garde movements of expressionism, cubism, and aleatoric music. The results are baroque expressions that are in contrast to artistic norms that preceded these artists and depended on balance, order, and predictability associated with classicism. In the case of Escudero, a number of his practices, including the posture of a male dancer, use of contra-tiempo, and isolating bursts of footwork, have become standards of virtuosity among dancers today and shape the contemporary baroque identity of Flamenco

EEF: Exponentially Embedded Families with Class-Specific Features for Classification

In this letter, we present a novel exponentially embedded families (EEF) based classification method, in which the probability density function (PDF) on raw data is estimated from the PDF on features. With the PDF construction, we show that class-specific features can be used in the proposed classification method, instead of a common feature subset for all classes as used in conventional approaches. We apply the proposed EEF classifier for text categorization as a case study and derive an optimal Bayesian classification rule with class-specific feature selection based on the Information Gain (IG) score. The promising performance on real-life data sets demonstrates the effectiveness of the proposed approach and indicates its wide potential applications.Comment: 9 pages, 3 figures, to be published in IEEE Signal Processing Letter. IEEE Signal Processing Letter, 201

Improved Auto-Encoding using Deterministic Projected Belief Networks

In this paper, we exploit the unique properties of a deterministic projected belief network (D-PBN) to take full advantage of trainable compound activation functions (TCAs). A D-PBN is a type of auto-encoder that operates by "backing up" through a feed-forward neural network. TCAs are activation functions with complex monotonic-increasing shapes that change the distribution of the data so that the linear transformation that follows is more effective. Because a D-PBN operates by "backing up", the TCAs are inverted in the reconstruction process, restoring the original distribution of the data, thus taking advantage of a given TCA in both analysis and reconstruction. In this paper, we show that a D-PBN auto-encoder with TCAs can significantly out-perform standard auto-encoders including variational auto-encoders

Water content of roasted coffee: impact on grinding behaviour, extraction, and aroma retention

Normal and long time roasting trials were carried out on industrial scale. Different amounts of water were applied during quenching, resulting in water contents in the range of 2.3-8.8g/100gwb. Coffees were ground immediately after cooling, and after equilibration times of 6 and 24h. Particle size distribution of ground coffees, percolation time, and extraction properties were investigated on an espresso coffee machine. Coffees ground after 24h resting time were subjected to storage trials to determine aroma stability as influenced by water content. Coffees with high moisture content exhibited coarser particles upon grinding, and equilibration time prior to grinding was needed for coffees with high water content to improve grinding results. Coffees with low water content did not exhibit this time dependency prior to grinding. Coffees with low water content were extracted more effectively than high moisture coffees, and percolation was slower. During open and closed storage, evolution of hexanal and sulfides was highly sensitive to water content. However, differences in evolution of other aroma compounds were found during closed storage only, where moisture content had a negative impact on aroma stability of the coffees subjected to investigatio

A Comparison of PDF Projection with Normalizing Flows and SurVAE

Normalizing flows (NF) recently gained attention as a way to construct generative networks with exact likelihood calculation out of composable layers. However, NF is restricted to dimension-preserving transformations. Surjection VAE (SurVAE) has been proposed to extend NF to dimension-altering transformations. Such networks are desirable because they are expressive and can be precisely trained. We show that the approaches are a re-invention of PDF projection, which appeared over twenty years earlier and is much further developed