Tools for compositional data with a total

Compositional data analysis usually deals with relative information between parts where the total (abundances, mass, amount, etc.) is unknown or uninformative. This article addresses the question of what to do when the total is known and is of interest. Tools used in this case are reviewed and analysed, in particular the relationship between the positive orthant of D-dimensional real space, the product space of the real line times the D-part simplex, and their Euclidean space structures. The first alternative corresponds to data analysis taking logarithms on each component, and the second one to treat a log-transformed total jointly with a composition describing the distribution of component amounts. Real data about total abundances of phytoplankton in an Australian river motivated the present study and are used for illustration.

'The war of Canudos: a Euclidean interpretation.' [Guerra de Canudos: uma leitura euclidiana]

The publication of the book The interior, in 1902, would change the course of thinking about the War of Canudos, who for many years, had been known simply as' the history of Euclid. President Getulio Vargas became interested in the backwoods bloodbath after reading the book avenger-Euclidean. Liked the work he visited the place of occurrence of war promising enjoy the river poured-Barris with the construction of the weir Cocorobo. Euclides da Cunha lived and produced his work in a time of great change in thought, politics and technology. Despite having worked in the press throughout his life, was best known as an engineer, for having exercised the office during the reconstruction of the bridge, in Sao Jose do Rio Pardo. This article aims to illuminate the event of war in light of the Euclidean work. We will examine the trajectory of Euclides da Cunha in journalism. Your learning process to execute the office newsreader and war correspondent, the newspaper O Estado de S. Paul, as well as their reports and work-monument the hinterlands. Resumo: A publicação da obra Os sertões, em 1902, mudaria os rumos do pensamento sobre a Guerra de Canudos, que, por muitos anos, ficara conhecida, simplesmente, como ‘história de Euclides’. O presidente Getúlio Vargas interessou-se pela hecatombe sertaneja após ter lido o livro-vingador euclidiano. Gostou tanto da obra que visitou o lugar de acontecimento da guerra prometendo aproveitar as águas do rio Vaza-Barris com a construção do açude de Cocorobó. Euclides da Cunha viveu e produziu a sua obra em um momento de grandes transformações no pensamento, na política e na tecnologia. Apesar de ter atuado na imprensa ao longo de toda a sua vida, ficou mais conhecido como engenheiro, por ter exercido o ofício, durante a reconstrução da ponte, em São José do Rio Pardo. O presente artigo visa iluminar o acontecimento da guerra à luz da obra euclidiana. Examinaremos a trajetória de Euclides da Cunha no jornalismo. O seu processo de aprendizagem para exercer o ofício de noticiarista e correspondente de guerra, pelo jornal O Estado de S. Paulo, bem como, as suas reportagens e obra-monumento Os sertões.

Object tracking via non-Euclidean geometry : a Grassmann approach

A robust visual tracking system requires an object appearance model that is able to handle occlusion, pose, and illumination variations in the video stream. This can be difficult to accomplish when the model is trained using only a single image. In this paper, we first propose a tracking approach based on affine subspaces (constructed from several images) which are able to accommodate the abovementioned variations. We use affine subspaces not only to represent the object, but also the candidate areas that the object may occupy. We furthermore propose a novel approach to measure affine subspace-to-subspace distance via the use of non-Euclidean geometry of Grassmann manifolds. The tracking problem is then considered as an inference task in a Markov Chain Monte Carlo framework via particle filtering. Quantitative evaluation on challenging video sequences indicates that the proposed approach obtains considerably better performance than several recent state-of-the-art methods such as Tracking-Learning-Detection and MILtrack.

A new registration method based on Log-Euclidean tensor metrics and its application to genetic studies

In structural brain MRI, group differences or changes in brain structures can be detected using Tensor-Based Morphometry (TBM). This method consists of two steps: (1) a non-linear registration step, that aligns all of the images to a common template, and (2) a subsequent statistical analysis. The numerous registration methods that have recently been developed differ in their detection sensitivity when used for TBM, and detection power is paramount in epidemological studies or drug trials. We therefore developed a new fluid registration method that computes the mappings and performs statistics on them in a consistent way, providing a bridge between TBM registration and statistics. We used the Log-Euclidean framework to define a new regularizer that is a fluid extension of the Riemannian elasticity, which assures diffeomorphic transformations. This regularizer constrains the symmetrized Jacobian matrix, also called the deformation tensor. We applied our method to an MRI dataset from 40 fraternal and identical twins, to revealed voxelwise measures of average volumetric differences in brain structure for subjects with different degrees of genetic resemblance.

On the compactness of isometry groups in complex analysis

We prove that the group of continuous isometries for the Kobayashi or Caratheodory metrics of a strongly convex domain in C-n is compact unless the domain is biholomorphic to the ball. A key ingredient, proved using differential geometric ideas, is that a continuous isometry between a strongly convex domain and the ball has to be biholomorphic or anti-biholomorphic. Combining this with a metric version of Pinchuk's rescaling technique gives the main result.

End point estimates for Radon transform of radial functions on non-Euclidean spaces

We prove end point estimate for Radon transform of radial functions on affine Grasamannian and real hyperbolic space. We also discuss analogs of these results on the sphere.

Quantifying the Transmit Diversity Order of Euclidean Distance Based Antenna Selection in Spatial Modulation

In this letter, we quantify the transmit diversity order of the SM system operating in a closed-loop scenario. Specifically, the SM system relying on Euclidean distance based antenna subset selection (EDAS) is considered and the achievable diversity gain is evaluated. Furthermore, the resultant trade-off between the achievable diversity gain and switching gain is studied. Simulation results confirm our theoretical results. Specifically, at a symbol error rate of about 10(-4) the signal-to-noise ratio gain achieved by EDAS is about 7 dB in case of 16-QAM and about 5 dB in case of 64-QAM.

Learning Euclidean Embeddings for Indexing and Classification

BoostMap is a recently proposed method for efficient approximate nearest neighbor retrieval in arbitrary non-Euclidean spaces with computationally expensive and possibly non-metric distance measures. Database and query objects are embedded into a Euclidean space, in which similarities can be rapidly measured using a weighted Manhattan distance. The key idea is formulating embedding construction as a machine learning task, where AdaBoost is used to combine simple, 1D embeddings into a multidimensional embedding that preserves a large amount of the proximity structure of the original space. This paper demonstrates that, using the machine learning formulation of BoostMap, we can optimize embeddings for indexing and classification, in ways that are not possible with existing alternatives for constructive embeddings, and without additional costs in retrieval time. First, we show how to construct embeddings that are query-sensitive, in the sense that they yield a different distance measure for different queries, so as to improve nearest neighbor retrieval accuracy for each query. Second, we show how to optimize embeddings for nearest neighbor classification tasks, by tuning them to approximate a parameter space distance measure, instead of the original feature-based distance measure.