Genome sequence data contain abundant information about genealogical history, but methods for extracting and interpreting this information are not yet fully developed. We analyzed genome sequences for multiple accessions of the selfing plant, Arabidopsis thaliana, with the goal of better understanding its genealogical history. As expected from accessions of the same species, we found much disco...

We explore the use of two dependency parsers, Malt and MST, in a Lexical Functional Grammar parsing pipeline. We compare this to the traditional LFG parsing pipeline which uses constituency parsers. We train the dependency parsers not on classical LFG f-structures but rather on modified dependency-tree versions of these in which all words in the input sentence are represented and multiple heads...

Leak detection techniques based on Machine Learning (ML) models can assist or even replace manual work in leak operations water distribution systems (WDSs). However, studies leakage on-site signals are limited compared to lab-scale detection. The have stronger interference and randomness, while the laboratory relatively simpler. To better operations, present paper develops compares three ML-bas...

In this work we propose a new method for designing modulo 2+1 multipliers for diminished-1 operands. Our multipliers compared to the already known tree architecture offer enhanced operation speed for the majority of n values, with similar area complexities. They also have very regular structure, and can be pipelined at the full-adder level.

Alfalfa is critical to global food security, and its data abundant in the U.S. nationally, but often scarce locally, limiting potential performance of machine learning (ML) models predicting alfalfa biomass yields. Training ML on local-only results very low estimation accuracy when datasets are small. Therefore, we explore synthesizing non-local estimate yields labeled as high, medium, or low. ...