A review of reinforcement learning for natural language processing and applications in healthcare.

Importance: Reinforcement learning (RL) represents a pivotal avenue within natural language processing (NLP), offering a potent mechanism for acquiring optimal strategies in task completion. This literature review studies various NLP applications where RL has demonstrated efficacy, with notable applications in healthcare settings.

Objectives: To systematically explore the applications of RL in NLP, focusing on its effectiveness in acquiring optimal strategies, particularly in healthcare settings, and provide a comprehensive understanding of RL's potential in NLP tasks.

Flock2: A model for orientation-based social flocking.

The aerial flocking of birds, or murmurations, has fascinated observers while presenting many challenges to behavioral study and simulation. We examine how the periphery of murmurations remain well bounded and cohesive. We also investigate agitation waves, which occur when a flock is disturbed, developing a plausible model for how they might emerge spontaneously.

Frustum-Traced Irregular Z-Buffers: Fast, Sub-pixel Accurate Hard Shadows.

We further describe and analyze a real-time system for rendering antialiased hard shadows using irregular z-buffers (IZBs) that we first presented in Wyman et al. [1]. We focus on identifying bottlenecks, exploring these from an algorithmic complexity standpoint, and presenting techniques to improve performance.

Graphics performance in rich Internet applications.

Rendering performance for rich Internet applications (RIAs) has recently focused on the debate between using Flash and HTML5 for streaming video and gaming on mobile devices. A key area not widely explored, however, is the scalability of raw bitmap graphics performance for RIAs. Does Flash render animated sprites faster than HTML5? How much faster is WebGL than Flash? Answers to these questions are essential for developing large-scale data visualizations, online games, and truly dynamic websites.