TS-071915 — An ultra‑efficient probit‑space ensemble expansion approach triples the size of weather‑forecast ensembles by producing hundreds of realistic “virtual” members from existing runs using minimal compute time and expert statistical knowledge.

TS-070331 — Accelerate discovery and streamline workflows with a next-generation platform for high-throughput nanopore data extraction.

TS-067434 — The Need Spatial aggregation is crucial in numerous industries where data from low-level spatial units, such as census blocks, must be grouped into larger, meaningful regions. Traditional approaches often struggle with the computational complexity of these tasks and tend to focus on finding a singl…

TS-062576 — The Need Like a molecular “fingerprint”, NMR spectra provide valuable insights into the properties and structures of molecular compounds. However, analyzing and interpreting NMR spectra is inherently challenging as comprehensive and explicit identification of a compound is tedious and t…

TS-062574 — The Need Like a molecular “fingerprint”, NMR spectra provide valuable insights into the properties and structures of molecular compounds. However, analyzing and interpreting NMR spectra is inherently challenging as comprehensive and explicit identification of a compound is tedious and t…

TS-062335 — The Need: In the field of ophthalmology and vision care, there is a critical need for accurate and comprehensive measurements of visual function over the entire visual field. Conditions such as glaucoma, age-related macular degeneration (AMD), diabetes, stroke, pituitary disease, brain tumors, and n…

TS-051417 — Traditional regression analysis has been a staple in predicting cause-effect relationships. Counltess industries use these methods to predict rare events, from economic issues to cancer research, causality is often a desired result. Given the vast amount of potential variables it sometimes becomes…

TS-042266 — A monaural machine-learning algorithm for classifying time-frequency units in an unknown signal, which results in marked speech-intelligibly improvements in noisy signals.

TS-038074 — A monaural machine-learning algorithm for classifying time-frequency units in an unknown signal, which results in marked speech-intelligibly improvements in noisy signals.