WhoLoDancE: Deliverable 4.1 - Data Integration Algorithm and System Analysis and Framework Description

The WhoLoDancE search and similarity engine exists in order to support data mining and real-time querying of the motion captured data collected within the project. In order to reduce the computational burden while retaining most of the semantically relevant content, the search and similarity algorithms are applied not to the raw video or the point cloud data collected during the recording sessions, but first of all to the algorithmically reconstructed skeletal configurations of the dancers, with the further addition of low-, mid- and high-level features (HLFs) derived from these data. We refer to each skeletal coordinate (for example, a right elbow angle) as a dimension. Thus the recordings are for our purposes multi-dimensional time series of floating-point numbers and we are solving a problem of providing an adequate infrastructure for search and similarity measurements over high-dimensional time series. The dimensionality of recordings collected within WhoLoDancE is not constant and ranges between 72 and 168, depending on the amount of detail collected during the motion capture sessions. For example, the Flamenco recordings feature finger motions, while recordings of other genres, for which finger motions are less relevant and haven’t been captured, do not. We approach the problem of search in multidimensional time series with a variable number of dimensions by first performing the similarity computations on the one-dimensional time series constituting the data in our database and then aggregating these results across multiple dimensions in a suitable manner. This allows for the parallelization of most of the similarity computations across different dimensions, which is a useful feature given the large number of dimensions and the availability of parallel computing infrastructure (multi-core servers, GPUs, and compute clusters). The computation of similarity on a single dimension can also be parallelized naturally by partitioning the data, albeit at a slight loss in efficiency. These properties make our approach scalable and capable of supporting orders of magnitude more data than have been collected within the project

Practicalities of Corpus Building: Creating and exploring digital data

In this panel, we present a number of approaches to the creation of music corpora, using manual and fullyand partially-automated methods, and we consider the effects these approaches have on the nature, size and data encoding of the respective collections We also explore how the process is affected by the nature of the source materials used. We illustrate the impact of these approaches for later use, such as simple web or paper publication or searching and analyzing the newly-available musical information

Practicalities of Corpus Building: Creating and exploring digital data

In this panel, we present a number of approaches to the creation of music corpora, using manual and fullyand partially-automated methods, and we consider the effects these approaches have on the nature, size and data encoding of the respective collections We also explore how the process is affected by the nature of the source materials used. We illustrate the impact of these approaches for later use, such as simple web or paper publication or searching and analyzing the newly-available musical information

Vladimir I. Arnold: collected works : hydrodynamics, bifurcation theory, algebraic geometry : 1965-1972

Vladimir Arnold was one of the great mathematical scientists of our time. He is famous for both the breadth and the depth of his work. At the same time he is one of the most prolific and outstanding mathematical authors. This second volume of his ""Collected Works"" focuses on hydrodynamics, bifurcation theory, and algebraic geometry