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.     

Application of a glyphosate-based herbicide to Phragmites australis: Impact on groundwater and near-shore lake water at a beach on Georgian Bay

During the past decade, the invasive Phragmites australis (common reed) has established itself along beaches of the Great Lakes, causing detrimental impacts to both the natural ecological integrity of the shoreline and the recreational value of beaches. The herbicide Roundup®, containing the active ingredient glyphosate, was applied to Phragmites along a beach on the southern shore of Georgian Bay, Canada, to eradicate the Phragmites which was destroying the natural beach ecosystem. Groundwater and lake water were tested to determine if glyphosate enters the groundwater and lake at the beach and how long glyphosate will persist. Two days after application, the geometric mean concentration of glyphosate in the groundwater below the Phragmites was 0.060 μg/L with a maximum of 12.50 μg/L. Concentrations rapidly declined over the next two to three weeks to below minimum detection limits (< 0.020 μg/L). Glyphosate was also detected in the nearshore lake water with concentrations peaking at a geometric mean of 0.14 μg/L one week after application, and declining to 0.039 μg/L four weeks after application. Concentrations of glyphosate never exceeded the Canadian water quality guideline for the protection of aquatic life (65 μg/L) in either the groundwater or lake water. An approximate half-life for the dissipation of glyphosate by degradation and dilution/flushing as groundwater flows toward the lake, assuming a first order kinetic reaction, yielded a half-life of 3.5 during the 4 weeks after the herbicide was applied. The application of Roundup® resulted in an 90% reduction in the size of the stand of Phragmites.     

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.     

Exploring educational immersive videogames: an empirical study with a 3D multimodal interaction prototype

ABSTRACT

Gestural interaction devices emerged and originated various studies on multimodal human–computer interaction to improve user experience (UX). However, there is a knowledge gap regarding the use of these devices to enhance learning. We present an exploratory study which analysed the UX with a multimodal immersive videogame prototype, based on a Portuguese historical/cultural episode. Evaluation tests took place in high school environments and public videogaming events. Two users would be present simultaneously in the same virtual reality (VR) environment: one as the helmsman aboard Vasco da Gama’s fifteenth-century Portuguese ship and the other as the mythical Adamastor stone giant at the Cape of Good Hope. The helmsman player wore a VR headset to explore the environment, whereas the giant player used body motion to control the giant, and observed results on a screen, with no headset. This allowed a preliminary characterisation of UX, identifying challenges and potential use of these devices in multi-user virtual learning contexts. We also discuss the combined use of such devices, towards future development of similar systems, and its implications on learning improvement through multimodal human–computer interaction.     

Principal motion components for one-shot gesture recognition

This paper introduces principal motion components (PMC), a new method for one-shot gesture recognition. In the considered scenario a single training video is available for each gesture to be recognized, which limits the application of traditional techniques (e.g., HMMs). In PMC, a 2D map of motion energy is obtained per each pair of consecutive frames in a video. Motion maps associated to a video are processed to obtain a PCA model, which is used for recognition under a reconstruction-error approach. The main benefits of the proposed approach are its simplicity, easiness of implementation, competitive performance and efficiency. We report experimental results in one-shot gesture recognition using the ChaLearn Gesture Dataset; a benchmark comprising more than 50,000 gestures, recorded as both RGB and depth video with a Kinect?camera. Results obtained with PMC are competitive with alternative methods proposed for the same data set.