Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa

1 code implementation • 24 Jan 2023 • John Lagergren, Mirko Pavicic, Hari B. Chhetri, Larry M. York, P. Doug Hyatt, David Kainer, Erica M. Rutter, Kevin Flores, Jack Bailey-Bale, Marie Klein, Gail Taylor, Daniel Jacobson, Jared Streich

In this way, the current work is designed to provide the plant phenotyping community with (i) methods for fast and accurate image-based feature extraction that require minimal training data, and (ii) a new population-scale data set, including 68 different leaf phenotypes, for domain scientists and machine learning researchers.

Few-Shot Learning Plant Phenotyping

Learning from learning machines: a new generation of AI technology to meet the needs of science

no code implementations • 27 Nov 2021 • Luca Pion-Tonachini, Kristofer Bouchard, Hector Garcia Martin, Sean Peisert, W. Bradley Holtz, Anil Aswani, Dipankar Dwivedi, Haruko Wainwright, Ghanshyam Pilania, Benjamin Nachman, Babetta L. Marrone, Nicola Falco, Prabhat, Daniel Arnold, Alejandro Wolf-Yadlin, Sarah Powers, Sharlee Climer, Quinn Jackson, Ty Carlson, Michael Sohn, Petrus Zwart, Neeraj Kumar, Amy Justice, Claire Tomlin, Daniel Jacobson, Gos Micklem, Georgios V. Gkoutos, Peter J. Bickel, Jean-Baptiste Cazier, Juliane Müller, Bobbie-Jo Webb-Robertson, Rick Stevens, Mark Anderson, Ken Kreutz-Delgado, Michael W. Mahoney, James B. Brown

We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery.

Varied phenomenology of models displaying dynamical large-deviation singularities

no code implementations • 16 Dec 2020 • Stephen Whitelam, Daniel Jacobson

Singularities of dynamical large-deviation functions are often interpreted as the signal of a dynamical phase transition and the coexistence of distinct dynamical phases, by analogy with the correspondence between singularities of free energies and equilibrium phase behavior.

Statistical Mechanics

Evolutionary reinforcement learning of dynamical large deviations

no code implementations • 2 Sep 2019 • Stephen Whitelam, Daniel Jacobson, Isaac Tamblyn

We show how to calculate the likelihood of dynamical large deviations using evolutionary reinforcement learning.

reinforcement-learning Reinforcement Learning (RL)

Parallel Accelerated Custom Correlation Coefficient Calculations for Genomics Applications

1 code implementation • 23 May 2017 • Wayne Joubert, James Nance, Sharlee Climer, Deborah Weighill, Daniel Jacobson

The massive quantities of genomic data being made available through gene sequencing techniques are enabling breakthroughs in genomic science in many areas such as medical advances in the diagnosis and treatment of diseases.

Distributed, Parallel, and Cluster Computing Data Structures and Algorithms Performance 65Y05 [Computer aspects of numerical algorithms: Parallel computation], 68W10 [Algorithms: Parallel algorithms]

Parallel Accelerated Vector Similarity Calculations for Genomics Applications

1 code implementation • 23 May 2017 • Wayne Joubert, James Nance, Deborah Weighill, Daniel Jacobson

The surge in availability of genomic data holds promise for enabling determination of genetic causes of observed individual traits, with applications to problems such as discovery of the genetic roots of phenotypes, be they molecular phenotypes such as gene expression or metabolite concentrations, or complex phenotypes such as diseases.

Distributed, Parallel, and Cluster Computing Data Structures and Algorithms Performance 65Y05, 68W10