Motion synthesis and editing for the generation of new sign language content

Machine Translation - Existing work on the animation of signing avatars often relies on pure procedural techniques or on the playback of Motion Capture (MoCap) data. While the first solution...     

Flow profile measurement in microchannel using the optical feedback interferometry sensing technique

The need to accurately measure flow profiles in microfluidic channels is well recognised. In this work, we present a new optical feedback interferometry (OFI) flow sensor that accurately measures local velocity in fluids and enables reconstruction of a velocity profile inside a microchannel. OFI is a self-aligned interferometric technique that uses the laser as both the transmitter and the receiver thus offering high sensitivity, fast response, and a simple and compact optical design. The system described here is based on a commercial semiconductor laser and has been designed to achieve a micrometer-range spatial resolution. The sensor performance was validated by reconstructing the velocity profile inside a circular cross-section flow-channel with 320  $\upmu $ m internal diameter, with a relative error smaller than 1.8 %. The local flow velocity is directly measured, thus avoiding the need for model based profile calculation and uncertainties inherent to this approach. The system was validated by successfully extracting the flow profiles in both Newtonian and shear-thinning liquids.     

Critical power as an endurance index

The relationship between exhaustion time (t(lim)) and the work performed at the end of constant-power exercises can be described by a linear relationship (Wlim = a + b t(lim)) for work involving the whole body (eg cycling) or part of the body (eg knee extensions). The slope b in the equation is termed the critical power and has been proposed as an index of the capacity to perform work over a long period of time. The first objective of the present study was to compare the values of slopes b calculated from whole-body work of short duration, ie maximal and supra-maximal cycling exercises (slope b1), with the values calculated from the same work, the durations of which were between 3.5 and 35 min (slope b3), as in the protocols used by Scherrer and Monod (1960) for body-part work. Slope b1 was significantly higher than slope b3 in 10 subjects who performed 5 cycling exhausting exercises (60, 73, 86, 100 and 120% of maximal aerobic power (MAP) in watts). Exhaustion times corresponding to power outputs equivalent to b1 and b3 were equal to 29.0 +/- 19.1 min and 48.6 +/- 9.8 min respectively. Moreover, the exhaustion times at 60 and 73%,MAP were significantly correlated with slope b3 (expressed in %MAP) but not with slope b1. Consequently, slope b3 should be considered as the critical power instead of slope b1 as in some studies in the literature (Moritani et al, 1981). The second objective was to study the physiological significance of the critical power (slope b3) of whole-body work (cycling). The workload that corresponded to a lactate steady state was not significantly different from b3 (68.8 +/- 6.0 vs 68.7 +/- 6.3% MAP). Nevertheless, slope b3 represents a workload corresponding to a slight but significant drift of heart rate or oxygen uptake. These results probably explain why b3 is a power which can be maintained for a long time but not beyond about l h in an average subject.     

Nested multigrid methods for time-periodic,parabolic optimal control problems

We present a nested multigrid method to optimize time-periodic, parabolic, partial differential equations (PDE). We consider a quadratic tracking objective with a linear parabolic PDE constraint. The first order optimality conditions, given by a coupled system of boundary value problems can be rewritten as an Fredholm integral equation of the second kind, which is solved by a multigrid of the second kind. The evaluation of the integral operator consists of solving sequentially a boundary value problem for respectively the state and the adjoints. Both problems are solved efficiently by a time-periodic space-time multigrid method.     

Neural Network Method for Solving Partial Differential Equations

A method is presented to solve partial differential equations (pde's) and its boundary and/or initial conditions by using neural networks. It uses the fact that multiple input, single output, single hidden layer feedforward networks with a linear output layer with no bias are capable of arbitrarily well approximating arbitrary functions and its derivatives, which is proven by a number of authors and well known in literature. Knowledge about the pde and its boundary and/or initial conditions is incorporated into the structures and the training sets of several neural networks. In this way we obtain networks of which some are specifically structured. To find the solution of the pde and its boundary and/or initial conditions we have to train all obtained networks simultaneously. Therefore we use an evolutionary algorithm to train the networks. We demonstrate the working of our method by applying it to two problems.     

Neural computing models for prediction of permeability coefficient of coarse-grained soils

Correlations are very significant from the earliest days; in some cases, it is essential as it is difficult to measure the amount directly, and in other cases it is desirable to ascertain the results with other tests through correlations. Soft computing techniques are now being used as alternate statistical tool, and new techniques such as artificial neural networks, fuzzy inference systems, genetic algorithms, and their hybrids were employed for developing the predictive models to estimate the needed parameters, in the recent years. Determination of permeability coefficient (k) of soils is very important for the definition of hydraulic conductivity and is difficult, expensive, time-consuming, and involves destructive tests. In this paper, use of some soft computing techniques such as ANNs (MLP, RBF, etc.) and ANFIS (adaptive neuro-fuzzy inference system) for prediction of permeability of coarse-grained soils was described and compared. As a result of this paper, it was obtained that the all constructed soft computing models exhibited high performance for predicting k. In order to predict the permeability coefficient, ANN models having three inputs, one output were applied successfully and exhibited reliable predictions. However, all four different algorithms of ANN have almost the same prediction capability, and accuracy of MLP was relatively higher than RBF models. The ANFIS model for prediction of permeability coefficient revealed the most reliable prediction when compared with the ANN models, and the use of soft computing techniques will provide new approaches and methodologies in prediction of some parameters in soil mechanics.     

Science and technology in the region: The output of regional science and technology, its strengths and its leading institutions

Summary We operationalize scientific output in a region by means of the number of articles (as in the SciSearch database) per year and technology output by means of the number of patent applications (as in the database of the European Patent Office) per priority year. All informetric analyses were done using the DIALOG online-system. The main research questions are the following: Which scientific and technological fields or topics are most influent within a region and which institutions or companies are mainly publishing articles or holding patents? Do the distributions of regional science and technology fields and of publishing institutions follow the well-known informetric function? Are there - as it is expected - only few fields and few institutions which dominate the region? Is there a connection between the economic power of a region and the regional publication and patent output? Examples studied in detail are seven German regions: Aachen, Düsseldorf, Hamburg, Köln (Cologne), Leipzig - Halle - Dessau, München (Munich), and Stuttgart. Three different indicators were used, science and technology attraction of a region (number of scientific articles and patents), science and technology intensity (articles and patents per 1,000 inhabitants), and science and technology density (articles and patents per 1 billion EURO gross value added). Top region concerning both attraction and intensity is Munich, concerning density it is Aachen.     

Benchmarking Least Squares Support Vector Machine Classifiers

In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LS-SVMs), a least squares cost function is proposed so as to obtain a linear set of equations in the dual space. While the SVM classifier has a large margin interpretation, the LS-SVM formulation is related in this paper to a ridge regression approach for classification with binary targets and to Fisher's linear discriminant analysis in the feature space. Multiclass categorization problems are represented by a set of binary classifiers using different output coding schemes. While regularization is used to control the effective number of parameters of the LS-SVM classifier, the sparseness property of SVMs is lost due to the choice of the 2-norm. Sparseness can be imposed in a second stage by gradually pruning the support value spectrum and optimizing the hyperparameters during the sparse approximation procedure. In this paper, twenty public domain benchmark datasets are used to evaluate the test set performance of LS-SVM classifiers with linear, polynomial and radial basis function (RBF) kernels. Both the SVM and LS-SVM classifier with RBF kernel in combination with standard cross-validation procedures for hyperparameter selection achieve comparable test set performances. These SVM and LS-SVM performances are consistently very good when compared to a variety of methods described in the literature including decision tree based algorithms, statistical algorithms and instance based learning methods. We show on ten UCI datasets that the LS-SVM sparse approximation procedure can be successfully applied.     

COVID-19, Vaccination,and Female Fertility in the Czech Republic

The fast-track process to approve vaccines against COVID-19 has raised questions about their safety, especially in relation to fertility. Over the last 2 years, studies have appeared monitoring female fertility, especially from assisted reproduction centers or in animal experiments. However, studies monitoring healthy populations are still limited. The aim of our study was to monitor the relevant parameters of female fertility (sex and other steroids, LH, FSH, SHBG, Antimüllerian hormone and antral follicle count) before and then 2–4 months after the third dose of vaccination against COVID-19 in a group of 25 healthy fertile woman. In addition, anti-SARS-CoV-2 and anti-SARS-CoV-2S antibodies were determined. We did not observe significant changes in the measured parameters before and after the third dose of vaccination. By comparing levels of the analytes with antibodies indicating a prior COVID-19 infection, we found that women who had experienced the disease had statistically lower levels of estrone, estradiol, SHBG and 5α-dihydroprogesterone, and conversely, higher levels of androgen active dehydroepiandrosterone and dihydrotestosterone. Our results confirm that vaccination does not affect female fertility, and that what fertile women should be worried about is not vaccination, but rather COVID-19 infection itself.