AI - Data - Apps - Cybernetics Archives - Page 24 of 40

AI-ML Virtual Seminar: A Gentle Introduction to Machine Learning for Astrobiology

by Keith CowingSeptember 4, 2024September 4, 2024

Everyone is invited to the monthly virtual seminar on artificial intelligence and machine learning (AI-ML) hosted by the Exobiology Branch at NASA Ames Research Center.

Body-wide tissue stress map with cfRNA. a, Cell-type deconvolution using Bayes Prism with Tabula Sapiens as a reference. Top ten cell types by average fraction across all samples with all remaining cell types summed together as ‘other’. b, Cell type of origin for hepatocytes, endothelial cells, haematopoietic stem cells and melanocytes, which all show increased abundance during post-flight and recovery timepoints. c, Cell proportion changes in different layers of the skin from spatially resolved transcriptomics on skin biopsies. Predicted melanocyte abundance changes are significant in the inner epidermal and outer dermal skin compartments. In panels b and c, n = 4 independent participants across 7 timepoints. The centre of the boxplots represent the median, the box hinges encompass the first and third quartiles, and the whiskers extend to the smallest and largest values no further than 1.5 × IQR away from the hinges. NS, not significant; **P ≤ 0.01; ***P ≤ 0.001. -- Nature via PubMed

AI - Data - Apps - Cybernetics, Astronauts and Explorers, Genomics, Proteomics, Bioinformatics, Space Life Science, Space Physiology & Medicine, Status Report

The Space Omics and Medical Atlas (SOMA) and International Astronaut Biobank

by Keith CowingSeptember 3, 2024June 12, 2025

Spaceflight induces molecular, cellular and physiological shifts in astronauts and poses myriad biomedical challenges to the human body, which are becoming increasingly relevant as more humans venture into space.

All-sky distribution of the 124 new SMDET discoveries in equatorial coordinates with the Aitoff projection. Sources are color-coded based on spectral type estimates from §4, while those with a black interior are either candidate subdwarfs discussed in §6 or lack a photometric spectral type estimate. -- astro-ph.SR

AI - Data - Apps - Cybernetics, Astronomy & Telescopes, Imaging & Spectroscopy, Status Report

Discovery Of 118 New Ultracool Dwarf Candidates Using Machine Learning Techniques

by Keith CowingAugust 28, 2024August 27, 2024

We present the discovery of 118 new ultracool dwarf candidates, discovered using a new machine learning tool, named SMDET, applied to time series images from the Wide-field Infrared Survey Explorer.

Gel plays Pong CREDIT Cell Reports Physical Science/Strong et al.

AI - Data - Apps - Cybernetics, Commercial & Private Sector, Intelligence & Language, Nanotechnology & SynBio, Origin & Evolution of Life, Press Release

Hydrogel Material Shows Unexpected Learning Abilities: It Learned To Play ‘Pong’

by Keith CowingAugust 23, 2024August 23, 2024

In a study published today (22 August) in Cell Reports Physical Science, a team led by Dr Yoshikatsu Hayashi demonstrated that a simple hydrogel – a type of soft, flexible […]

Dung beetles use the Milky Way at night to navigate. Engineers have used the same technique to develop an AI sensor for navigation in low light. Credit University of South Australia

AI - Data - Apps - Cybernetics, Astronomy & Telescopes, Imaging & Spectroscopy, Intelligence & Language, Press Release, Stellar Cartography

Insect Astronomy: How Dung Beetles and the Milky Way Are Helping To Improve Navigation Systems

by Keith CowingAugust 22, 2024August 22, 2024

An insect species that evolved 130 million years ago is the inspiration for a new research study to improve navigation systems in drones, robots, and orbiting satellites.

Phylogenetic inference using Assembly Theory. A) Algorithmic pipeline for the generation of a phylogenetic tree from MS data using Joint Assembly. Recursive MA is employed to calculate approximated Joint MA indices for single and paired samples, which are used in JAO calculations (see methods). The JAO matrix represents the overlap of joint assembly spaces of samples, which is then transformed into a tree through an inference algorithm (WPGMA). B) Comprison of the assembly-based JAO phylogenetic tree model with the consensual genome-based model. Colors refer to the predefined cladal domains, as in Figures 4, and indicate the cladal cohesiveness of the clustering. Full annotations of the trees are included in Figure 7 and SI. -- q-bio.PE

AI - Data - Apps - Cybernetics, Biochemistry & Organic Chemistry, Biophysics, Biosignatures & Paleobiology, Fossils & Paleontology, Genomics, Proteomics, Bioinformatics, Imaging & Spectroscopy, Nomenclature, Systematics, Natural History, Origin & Evolution of Life, Status Report

Constructing the Molecular Tree of Life using Assembly Theory and Mass Spectrometry

by Keith CowingAugust 21, 2024August 21, 2024

The connected nature of all life on earth, dating back to the last universal common ancestor (LUCA), has been explored using knowledge of taxonomy and biochemistry, with significant insights enabled […]

Comparison between the performance of the orbital outcome regressor (top row) and a non-ML baseline model (bottom row) on the validation set of 103,404 three-planet systems. Predicted orbital elements (i.e., semi-major axis, eccentricity, and inclination) for the new, merged planets are plotted against their true orbital elements. The ML model predicts orbital elements with somewhat less scatter and bias about the true values than the baseline model, approaching the accuracy limits imposed by chaos (see Table 1). -- astro-ph.EP

AI - Data - Apps - Cybernetics, Astrogeology, Atmospheres, Climate, Weather, Biogeochemical Cycles & Geobiology, Comets and Asteroids, Extinction events, Habitable Zones, Impact events, Status Report

Accelerating Giant Impact Simulations With Machine Learning

by Keith CowingAugust 20, 2024August 20, 2024

Constraining planet formation models based on the observed exoplanet population requires generating large samples of synthetic planetary systems, which can be computationally prohibitive.

An overview of the inputs for each step in our processing pipeline. a) A region in the HiRISE image of ESP 018707 2205 is shown at the native resolution (0.3m/px) with a blue box highlighting the window size for our classifier network. b) A 256 x 256 pixel window is used as input for our spatial classifier algorithm however the window is at 1/16 the original resolution (1.2m/px). A grid of 8x8 squares is displayed showing how that image gets tiled and preprocessed using parts of a JPEG encoder which involves the discrete cosine transform (d). c) A single 8x8 tile flattened into a 1d array which is used for the DCT transform (d). e) A block of Fourier coefficients is rearranged into a data cube and used as input for our Fourier classifier. Reducing the image size and ultimately the channel size after the first conv. layer significantly shortens the network’s processing time compared to the spatial image input. -- astro-ph.EP

AI - Data - Apps - Cybernetics, Astrogeology, Away Teams & Field Reports, Caves, Craters, Mountains, Subsurface, Lava Tubes, Exploration Gear & Tech, Fossils & Paleontology, Imaging & Spectroscopy, Mapping, Geodesy, Cartography, Bathymetry, Mars, Missions & Hardware, Robotics/Rovers/UAVs, Sample Return, Status Report, Tricorders & Sensors

Mapping “Brain Terrain” Regions on Mars Using Deep Learning

by Keith CowingAugust 13, 2024August 13, 2024

One of the main objectives of the Mars Exploration Program is to search for evidence of past or current life on the planet.

AI - Data - Apps - Cybernetics, Missions & Hardware, Status Report, Video, Audio, Webcasts, Podcasts

Video: Building an Onboard AI to Act as an Advance Science Team

by Keith CowingAugust 13, 2024August 13, 2024

Dr. Ryan Felton hosts Dr. Bethany Theiling from the NASA Goddard Space Flight Center. Bethany discusses her research and endeavors to make AI/ML in-situ autonomous capabilities during planetary missions a […]

Example solution for the 𝛼(𝑥, 𝑦) shown in Fig. 1 for the direction of the light in positive x-direction (𝜙 = 0). Top: The complete solution (left), the solution if scattering would be treated as absorption (middle) and the difference between the two: the scattered light component (right), all in units of 𝑢0 := 𝑢(𝑥 = −1, 𝜙 = 0) = 1. Bottom: The logarithm (log10) of the respective residuals of the differential equations. The middle plot shows the residual of 𝑢𝑎 with respect to the RTE without scattering and the right one the residual of (𝑢𝑎 + 𝑢𝑠 ) with respect to the complete RTE. The left plot is calculated from the two residuals by taking their root mean square. -- astro-ph.EP

AI - Data - Apps - Cybernetics, Atmospheres, Climate, Weather, Biogeochemical Cycles & Geobiology, Exoplanets, -moons, -comets, Habitable Zones, Press Release, Water/Hycean Worlds & Oceanography

Approximating Rayleigh Scattering in Exoplanetary Atmospheres using Physics-informed Neural Networks (PINNs)

by Keith CowingAugust 11, 2024August 10, 2024

This research introduces an innovative application of physics-informed neural networks (PINNs) to tackle the intricate challenges of radiative transfer (RT) modeling in exoplanetary atmospheres, with a special focus on efficiently […]

AI – Data – Apps – Cybernetics

AI-ML Virtual Seminar: A Gentle Introduction to Machine Learning for Astrobiology

The Space Omics and Medical Atlas (SOMA) and International Astronaut Biobank

Discovery Of 118 New Ultracool Dwarf Candidates Using Machine Learning Techniques

Hydrogel Material Shows Unexpected Learning Abilities: It Learned To Play ‘Pong’

Insect Astronomy: How Dung Beetles and the Milky Way Are Helping To Improve Navigation Systems

Constructing the Molecular Tree of Life using Assembly Theory and Mass Spectrometry

Accelerating Giant Impact Simulations With Machine Learning

Mapping “Brain Terrain” Regions on Mars Using Deep Learning

Video: Building an Onboard AI to Act as an Advance Science Team

Approximating Rayleigh Scattering in Exoplanetary Atmospheres using Physics-informed Neural Networks (PINNs)

A Martian Horizon Last Monday Afternoon

Habitable-zone Earths At The Detection Frontier: Measured Completeness And False-alarm Rate Of A Transit Pipeline For The PLATO M-dwarf Sample

A Homogeneous Survey Of JWST MIRI Transmission Spectra Of 10 Exoplanets

Retrieving Ocean Glint Reflectance Signatures From Directly Imaged Earth-like Exoplanets

Analytical Platform (Microsampling, Catapulting, Trapping, and LIBS) for the Characterization of Discrete Domains in Samples of Astrochemical Interest