We present a visualization technique for brain fiber tracts from DTI data that provides insight into the structure of white matter through visual abstraction. We achieve this abstraction by analyzing the local similarity of tract segment directions at different scales using a stepwise increase of the search range. Next, locally similar tract segments are moved toward each other in an iterative process, resulting in a local contraction of tracts perpendicular to the local tract direction at a given scale. This not only leads to the abstraction of the global structure of the white matter as represented by the tracts, but also creates volumetric voids. This increase of empty space decreases the mutual occlusion of tracts and, consequently, results in a better understanding of the brain's three-dimensional fiber tract structure. Our implementation supports an interactive and continuous transition between the original and the abstracted representations via various scale levels of similarity. We also support the selection of groups of tracts, which are highlighted and rendered with the abstracted visualization as context.

Real Time Strategy Games are one of the most popular game schemes in PC markets and offer a dynamic environment that involves several interacting agents. The core strategies that need to be developed in these games are unit micro management, building order, resource management, and the game main tactic. Unfortunately, current games only use scripted and fixed behaviors for their artificial intelligence (AI), and the player can easily learn the counter measures to defeat the AI. In this paper, we describe a system based on neural networks that controls a set of units of the same type in the popular game StarCraft. Using the neural networks, the units will either choose a unit to attack or evade from the battlefield. The system uses reinforcement learning combined with neural networks using online Sarsa and neural-fitted Sarsa, both with a short term memory reward function. We also present an incremental learning method for training the units for larger scenarios involving more units using trained neural networks on smaller scenarios. Additionally, we developed a novel sensing system to feed the environment data to the neural networks using separate vision grids. The simulation results show superior performance against the human-made AI scripts in StarCraft.