Current methods for sequence prediction often fail to account for higher-ordercontinuity. This results in the prediction of sequences that might be continuous but not physically viable as I investigate higher-order smoothness in terms of velocity and acceleration. Hence, I propose a Yuksel Spline-based model that is not only capable of predicting curves that are guaranteed to be C^2 continuous but, also efficient to compute as well. Characteristic properties of the models are demonstrated over toy examples and sequence prediction tasks.

Machine learning(ML) has been on the rise in many fields including agriculture. It is used for many things including crop yield prediction which is meant to help farmers decide when and what to grow based on the model. Many models have been built for various crops and areas of the world utilizing various sources of data. However, there is yet to exist a model designed to predict any crop’s yield in Yuma Arizona, one of the premier places to grow crops in America. For this, I built a dataset from farm documentation that describes the actions taken before, during, and after a crop is being grown. To supplement this data, ecological data was also used so data such as temperature, heat units, soil type, and soil water holding capacity were included. I used this dataset to train various regression models where I discovered that the farm data was useful, but only when used in conjunction with the ecological data.

Event detection refers to the task of identifying event occurrences in a given natural language text. Event detection comprises two subtasks; recognizing event mention (event identification) and the type of event (event classification). Breaking from the sequence labeling and word classification approaches, this work models event detection, and its constituent subtasks of trigger identification and trigger classification, as independent sequence generation tasks. This work proposes a prompted multi-task generative model trained on event identification, classification, and combined event detection. The model is evaluated on on general-domain and biomedical-domain event detection datasets, achieving state-of-the-art results on the general-domain Roles Across Multiple Sentences (RAMS) dataset, establishing event detection benchmark performance on WikiEvents, and achieving competitive performance on the general-domain Massive Event Detection (MAVEN) dataset and the biomedical-domain Multi-Level Event Extraction (MLEE) dataset.