In recent years wavelets were shown to be effective data synopses. We are concerned with the problem of finding efficiently wavelet synopses for massive data sets, in situations where information about query workload is available. We present linear time, I/O optimal algorithms for building optimal workload-based wavelet synopses for point queries. The synopses are based on a novel construction of weighted inner-products and use weighted wavelets that are adapted to those products. The synopses are optimal in the sense that the subset of retained coefficients is the best possible for the bases in use with respect to either the mean-squared absolute or relative errors. For the latter, this is the first optimal wavelet synopsis even for the regular, non-workload-based case. Experimental results demonstrate the advantage obtained by the new optimal wavelet synopses, as well as the robustness of the synopses to deviations in the actual query workload. * Research partly supported by a grant from the Israel Science Foundation. † Contact author 1 that uses only M coefficients (and assumes that all other coefficients are zero) defines a new vector that approximates the original vector, using less space. This is called M-term approximation, which defines a wavelet synopsis of size M. Wavelet synopses. Wavelets were traditionally used to compress some data set where the purpose is to reconstruct, in a later time, an approximation of the whole data using the set of retained coefficients. The situation is a little different when using wavelets for building synopses in database systems [16]: in this case only portions of the data are reconstructed each time, in response to user queries, rather than the whole data at once. As a result, portions of the data that are used for answering frequent queries are reconstructed more frequently than portions of the data that correspond to rare queries. Therefore, the approximation error is measured over the multi-set of actual queries, rather than over the data itself. Another aspect of the use of wavelets in database systems is that due to the large data-sizes in databases (giga-, tera-and peta-bytes), the efficiency of building wavelet synopses is of primary importance. Disk I/Os should be minimized as much as possible, and non-linear-time algorithms may be unacceptable.

The RoboCup simulation league is traditionally the league with the largest number of teams participating, both at the international competitions and worldwide. 2011 was no exception, with a total of 39 teams entering the 2D and 3D simulation competitions. This paper presents the champions of the competitions, WrightEagle from the University of Science and Technology of China in the 2D competition, and UT Austin Villa from the University of Texas at Austin in the 3D competition.

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This paper presents the design and learning architecture for an omnidirectional walk used by a humanoid robot soccer agent acting in the RoboCup 3D simulation environment. The walk, which was originally designed for and tested on an actual Nao robot before being employed in the 2011 RoboCup 3D simulation competition, was the crucial component in the UT Austin Villa team winning the competition in 2011. To the best of our knowledge, this is the first time that robot behavior has been conceived and constructed on a real robot for the end purpose of being used in simulation.  The walk is based on a double linear inverted pendulum model, and multiple sets of its parameters are optimized via a novel framework. The framework optimizes parameters for different tasks in conjunction with one another, a little-understood problem with substantial practical significance.  Detailed experiments show that the UT Austin Villa agent significantly outperforms all the other agents in the competition with the optimized walk being the key to its success.

The problem of multiagent patrol has gained considerable attention during the past decade, with the immediate applicability of the problem being one of its main sources of interest. In this paper we concentrate on frequency-based patrol, in which the agents' goal is to optimize a frequency criterion, namely, minimizing the time between visits to a set of interest points. We consider multiagent patrol in environments with complex environmental conditions that affect the cost of traveling from one point to another. For example, in marine environments, the travel time of ships depends on parameters such as wind, water currents, and waves. We demonstrate that in such environments there is a need to consider a new multiagent patrol strategy which divides the given area into parts in which more than one agent is active, for improving frequency. We show that in general graphs this problem is intractable, therefore we focus on simplified (yet realistic) cyclic graphs with possible inner edges. Although the problem remains generally intractable in such graphs, we provide a heuristic algorithm that is shown to significantly improve point-visit frequency compared to other patrol strategies. For evaluation of our work we used a custom developed ship simulator that realistically models ship movement constraints such as engine force and drag and reaction of the ship to environmental changes.