The ChaLearn gesture dataset (CGD 2011)

This paper describes the data used in the ChaLearn gesture challenges that took place in 2011/2012, whose results were discussed at the CVPR 2012 and ICPR 2012 conferences. The task can be described as: user-dependent, small vocabulary, fixed camera, one-shot-learning. The data include 54,000 hand and arm gestures recorded with an RGB-D \(\hbox {Kinect}^\mathrm{TM}\) camera. The data are organized into batches of 100 gestures pertaining to a small gesture vocabulary of 8–12 gestures, recorded by the same user. Short continuous sequences of 1–5 randomly selected gestures are recorded. We provide man-made annotations (temporal segmentation into individual gestures, alignment of RGB and depth images, and body part location) and a library of function to preprocess and automatically annotate data. We also provide a subset of batches in which the user’s horizontal position is randomly shifted or scaled. We report on the results of the challenge and distribute sample code to facilitate developing new solutions. The data, datacollection software and the gesture vocabularies are downloadable from http://gesture.chalearn.org. We set up a forum for researchers working on these data http://groups.google.com/group/gesturechallenge.     

Efficient Binary Tomographic Reconstruction

Tomographic reconstruction of a binary image from few projections is considered. A novel heuristic algorithm is proposed, the central element of which is a nonlinear transformation ψ(p)=log(p/(1?p)) of the probability p that a pixel of the sought image be 1-valued. It consists of backprojections based on ψ(p) and iterative corrections. Application of this algorithm to a series of artificial test cases leads to exact binary reconstructions, (i.e., recovery of the binary image for each single pixel) from the knowledge of projection data over a few directions. Images up to 10⁶ pixels are reconstructed in a few seconds. A series of test cases is performed for comparison with previous methods, showing a better efficiency and reduced computation times.     

A web-based platform for biosignal visualization and annotation

With the advent of wearable sensing and mobile technologies, biosignals have seen an increasingly growing number of application areas, leading to the collection of large volumes of data. One of the difficulties in dealing with these data sets, and in the development of automated machine learning systems which use them as input, is the lack of reliable ground truth information. In this paper we present a new web-based platform for visualization, retrieval and annotation of biosignals by non-technical users, aimed at improving the process of ground truth collection for biomedical applications. Moreover, a novel extendable and scalable data representation model and persistency framework is presented. The results of the experimental evaluation with possible users has further confirmed the potential of the presented framework.     

Einfluß von Lösungsmitteln auf den Partikeldurchmesser von Dispersionen anionischer Polyurethanionomerer

Aqueous dispersions of anionic polyurethanes were prepared by reaction of polyethers, 1,6-hexamethylene diisocyanate, 1,2,4-benzenetricarboxylic acid anhydride and triethyl amine. Formation of the dispersion was achieved by phase reversal or by precipitation. Phase reversal occurred when the solution or the melt of the polymer was treated with water, while precipitation took place when the solution was stirred into water. The amount of acetone used has an effect on the mean particle diameter, D?_T, obtained by turbidity measurements. There was an optimal acetone concentration at which D?_T reached a minimum. A similar effect was observed when tetrahydrofuran, 2-butanone or acetonitrile were used as solvents instead of acetone. When the dispersion was formed by precipitation, only adequately diluted acetonic solutions formed colloidal dispersions, while concentrated solutions gave dispersions with a broad particle size distribution.     

A linear algebra approach to OLAP

Inspired by the relational algebra of data processing, this paper addresses the foundations of data analytical processing from a linear algebra perspective. The paper investigates, in particular, how aggregation operations such as cross tabulations and data cubes essential to quantitative analysis of data can be expressed solely in terms of matrix multiplication, transposition and the Khatri–Rao variant of the Kronecker product. The approach offers a basis for deriving an algebraic theory of data consolidation, handling the quantitative as well as qualitative sides of data science in a natural, elegant and typed way. It also shows potential for parallel analytical processing, as the parallelization theory of such matrix operations is well acknowledged.     

Fast online graph clustering via Erd?s-Rényi mixture

In the context of graph clustering, we consider the problem of simultaneously estimating both the partition of the graph nodes and the parameters of an underlying mixture of affiliation networks. In numerous applications the rapid increase of data size over time makes classical clustering algorithms too slow because of the high computational cost. In such situations online clustering algorithms are an efficient alternative to classical batch algorithms. We present an original online algorithm for graph clustering based on a Erd?s-Rényi graph mixture. The relevance of the algorithm is illustrated, using both simulated and real data sets. The real data set is a network extracted from the French political blogosphere and presents an interesting community organization.     

Experimental measurement of a time-varying optical path difference by the small-aperture beam technique

We discuss the use of time series of the jitter angle of multiple, small-aperture probe beams (the small-aperture beam technique, or SABT) as they emerge from a turbulent, optically active flow-field to quantify the time-varying optical path difference (OPD). The flow field studied is that for the transitionally turbulent region of a two-dimensional heated jet. Techniques to construct a complete time series of instantaneous realizations of the OPD are first applied to a numerically generated flow field and then to an experimental flow field. The SABT sensor's measurement accuracy is assessed, and its application to flow fields that differ from the numerical heated jet is discussed.     

Health learning materials support in South Africa compared with other developing countries

An investigation was carried out in to thyroid hormones (TSH, T3, T4) and lipid parameters (total cholesterol, LDL cholesterol, HDL cholesterol, triglyceride) in 136 adolescents (94 femals, average age 13 years). An iodine deficiency (grade II-II) with respect to the daily urine excretion per 1,73 m2 BSA was found in 75%. With few exceptions the serum levels of TSH and T4 were in the normal range. In 36% of the patients we noticed compensatory elevated T3 levels. Correlations between thyroid hormones TSH, T4, renal iodine excretion and the volume of thyroid glands were not detectable, only T3 showed a dignificant positive correlation to the thyroid gland volume. The average values of lipids in patients were found to be higher than in normals. We consider the changed lipids as a sign of a disturbed efficacy of thyroid hormones. The regional insufficient iodine supply causes goiters and to a high degree the observed hyperchole-sterolemia, too. Our results underline the necessity of a common iodine salt prophylaxis as well as the treatment of "harmless" goiters in puberty.     

Iris recognition: Analysis of the error rates regarding the accuracy of the segmentation stage

Iris recognition has been widely used in several scenarios with very satisfactory results. As it is one of the earliest stages, the image segmentation is in the basis of the process and plays a crucial role in the success of the recognition task. In this paper we analyze the relationship between the accuracy of the iris segmentation process and the error rates of three typical iris recognition methods. We selected 5000 images of the UBIRIS, CASIA and ICE databases that the used segmentation algorithm can accurately segment and artificially simulated four types of segmentation inaccuracies. The obtained results allowed us to conclude about a strong relationship between translational segmentation inaccuracies – that lead to errors in phase – and the recognition error rates.     

Outliers detection in environmental monitoring databases

Environmental monitoring is nowadays an important task in many industrial operations. In order to comply with strong environmental laws, they have implemented monitoring systems based on a network of air quality and meteorological stations providing real-time measurements of key variables associated to the distribution of pollutants in surrounding areas. These measurements can be contaminated by outliers, which must be discarded in order to have a consistent set of data. This work presents a nonlinear procedure for outliers detection based on residual analysis of regression with Partial Least Squares and Artificial Neural Networks. In order to minimize the negative effect of outliers in the training dataset a learning algorithm with regularization is proposed. This algorithm is based on a Quasi-Newton optimization method and it was tested on a simulated nonlinear process, on real data from environmental monitoring contaminated with synthetic outliers, and finally applied to a real environmental monitoring data obtained from a monitoring station and having natural outliers. The results are encouraging and further developments are foreseen for including information from neighboring stations and emission source operation.