A Data-driven, Piecewise Linear Approach to Modeling Human Motions

Motion capture, or mocap, is a prevalent technique for capturing and analyzing human articulations. Nowadays, mocap data are becoming one of the primary sources of realistic human motions for computer animation as well as education, training, sports medicine, video games, and special effects in movies. As more and more applications rely on high-quality mocap data and huge amounts of mocap data become available, there are imperative needs for more effective and robust motion capture techniques, better ways of organizing motion databases, as well as more efficient methods to compress motion sequences. I propose a data-driven, segment-based, piecewise linear modeling approach to exploit the redundancy and local linearity exhibited by human motions and describe human motions with a small number of parameters. This approach models human motions with a collection of low-dimensional local linear models. I first segment motion sequences into subsequences, i.e. motion segments, of simple behaviors. Motion segments of similar behaviors are then grouped together and modeled with a unique local linear model. I demonstrate this approach's utility in four challenging driving problems: estimating human motions from a reduced marker set; missing marker estimation; motion retrieval; and motion compression

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested

Proceedings of the Seventh Congress of the European Society for Research in Mathematics Education

International audienceThis volume contains the Proceedings of the Seventh Congress of the European Society for Research in Mathematics Education (ERME), which took place 9-13 February 2011, at Rzeszñw in Poland

SIMULATING SEISMIC WAVE PROPAGATION IN TWO-DIMENSIONAL MEDIA USING DISCONTINUOUS SPECTRAL ELEMENT METHODS

We introduce a discontinuous spectral element method for simulating seismic wave in 2- dimensional elastic media. The methods combine the flexibility of a discontinuous finite element method with the accuracy of a spectral method. The elastodynamic equations are discretized using high-degree of Lagrange interpolants and integration over an element is accomplished based upon the Gauss-Lobatto-Legendre integration rule. This combination of discretization and integration results in a diagonal mass matrix and the use of discontinuous finite element method makes the calculation can be done locally in each element. Thus, the algorithm is simplified drastically. We validated the results of one-dimensional problem by comparing them with finite-difference time-domain method and exact solution. The comparisons show excellent agreement