Estimating an Executive Summary of a Time Series: The Tendency

no code implementations • 8 Jan 2024 • Caio Alves, Juan M. Restrepo, Jorge M. Ramirez

Through a series of real-world applications, we demonstrate the computational robustness and practical utility of our proposed tendencies, emphasizing their adaptability and relevance in diverse time series contexts.

Time Series

Probabilistic Measures for Biological Adaptation and Resilience

no code implementations • 13 Jul 2023 • Jorge M. Ramirez, Juan M. Restrepo, Valerio Lucarini, David Weston

This paper introduces a novel approach to quantifying ecological resilience in biological systems, particularly focusing on noisy systems responding to episodic disturbances with sudden adaptations.

Randomized Algorithms for Scientific Computing (RASC)

no code implementations • 19 Apr 2021 • Aydin Buluc, Tamara G. Kolda, Stefan M. Wild, Mihai Anitescu, Anthony DeGennaro, John Jakeman, Chandrika Kamath, Ramakrishnan Kannan, Miles E. Lopes, Per-Gunnar Martinsson, Kary Myers, Jelani Nelson, Juan M. Restrepo, C. Seshadhri, Draguna Vrabie, Brendt Wohlberg, Stephen J. Wright, Chao Yang, Peter Zwart

Randomized algorithms have propelled advances in artificial intelligence and represent a foundational research area in advancing AI for Science.

Gradient Sensing via Cell Communication

no code implementations • 19 Jun 2018 • Dallas Foster, Collin Victor, Brian Frost, Juan M. Restrepo

Higher order approximations to the communication mechanism were better able to sense an external gradient.