Trajectory generation is an important concern in pedestrian, vehicle, and wildlife movement studies. Generated trajectories help enrich the training corpus in relation to deep learning applications, and may be used to facilitate simulation tasks. This is especially significant in the wildlife domain, where the cost of obtaining additional real data can be prohibitively expensive, time-consuming, and bear ethical considerations. In this paper, we introduce WildGEN: a conceptual framework that addresses this challenge by employing a Variational Auto-encoders (VAEs) based method for the acquisition of movement characteristics exhibited by wild geese over a long horizon using a sparse set of truth samples. A subsequent post-processing step of the generated trajectories is performed based on smoothing filters to reduce excessive wandering. Our evaluation is conducted through visual inspection and the computation of the Hausdorff distance between the generated and real trajectories. In addition, we utilize the Pearson Correlation Coefficient as a way to measure how realistic the trajectories are based on the similarity of clusters evaluated on the generated and real trajectories.

Some humorous texts can be detected by stereotyped patterns and terminology. But a humorous story or situation is often an exaggeration of patterns that also occur in serious texts: novelty, unusual plot twists, and situations that disrupt normal social conventions. The same methods for detecting novelty in serious texts can be adapted to detecting novelty in a humorous situation, but with additional tests for features that make it humorous. To interpret and reason about natural language texts, VivoMind Research has developed a cognitive architecture based on societies of heterogeneous intercommunicating agents that use conceptual graphs (CGs) as the knowledge representation. CGs are designed for representing semantics at the level of sentences and paragraphs, but they must be related to larger patterns that span an entire story, article, or book. For detecting and analyzing large-scale patterns, catastrophe theoretical semantics has proved to be surprisingly effective. This article discusses applications to both fictional and nonfictional documents of various kinds, both serious and humorous.