ConeQuest: A Benchmark for Cone Segmentation on Mars

Over the years, space scientists have collected terabytes of Mars data from satellites and rovers. One important set of features identified in Mars orbital images is pitted cones, which are interpreted to be mud volcanoes believed to form in regions that were once saturated in water (i.e., a lake or ocean). Identifying pitted cones globally on Mars would be of great importance, but expert geologists are unable to sort through the massive orbital image archives to identify all examples. However, this task is well suited for computer vision. Although several computer vision datasets exist for various Mars-related tasks, there is currently no open-source dataset available for cone detection/segmentation. Furthermore, previous studies trained models using data from a single region, which limits their applicability for global detection and mapping. Motivated by this, we introduce ConeQuest, the first expert-annotated public dataset to identify cones on Mars. ConeQuest consists of >13k samples from 3 different regions of Mars. We propose two benchmark tasks using ConeQuest: (i) Spatial Generalization and (ii) Cone-size Generalization. We finetune and evaluate widely-used segmentation models on both benchmark tasks. Results indicate that cone segmentation is a challenging open problem not solved by existing segmentation models, which achieve an average IoU of 52.52% and 42.55% on in-distribution data for tasks (i) and (ii), respectively. We believe this new benchmark dataset will facilitate the development of more accurate and robust models for cone segmentation. Data and code are available at https://github.com/kerner-lab/ConeQuest.Comment: Accepted at WACV 202

Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

Machine learning algorithms for parsing remote sensing data have a wide range of societally relevant applications, but labels used to train these algorithms can be difficult or impossible to acquire. This challenge has spurred research into self-supervised learning for remote sensing data aiming to unlock the use of machine learning in geographies or application domains where labelled datasets are small. Current self-supervised learning approaches for remote sensing data draw significant inspiration from techniques applied to natural images. However, remote sensing data has important differences from natural images -- for example, the temporal dimension is critical for many tasks and data is collected from many complementary sensors. We show that designing models and self-supervised training techniques specifically for remote sensing data results in both smaller and more performant models. We introduce the Pretrained Remote Sensing Transformer (Presto), a transformer-based model pre-trained on remote sensing pixel-timeseries data. Presto excels at a wide variety of globally distributed remote sensing tasks and outperforms much larger models. Presto can be used for transfer learning or as a feature extractor for simple models, enabling efficient deployment at scale

The GadX regulon affects virulence gene expression and adhesion of porcine enteropathogenic Escherichia coli in vitro

The ability of enteropathogenic Escherichia coli (EPEC) to express virulence factor genes and develop attaching and effacing (AE) lesions is inhibited in acidic environmental conditions. This inhibition is due to the activation of transcription factor GadX, which upregulates expression of glutamic acid decarboxylase (Gad). Gad, in turn, produces γ-aminobutyric acid (GABA), which was recently shown to have a beneficial effect on the jejunal epithelium in vitro due to increased mucin-1 levels. In the present study, we sought to test whether forced GadX activation/overexpression abolishes virulence associated features of EPEC and provokes increased GABA production. EPEC strains were isolated from diarrheic pigs and submitted to activation of GadX by acidification as well as gadX overexpression via an inducible expression vector plasmid. GABA concentrations in the growth medium, ability for adhesion to porcine intestinal epithelial cells (IPEC-J2) and virulence gene expression were determined. Growth in acidified media led to increased GABA levels, upregulated gadA/B expression and downregulated mRNA synthesis of the bacterial adhesin intimin. EPEC strains transformed with the gadX gene produced 2.1 to 3.4-fold higher GABA levels than empty-vector controls and completely lost their ability to adhere to IPEC-J2 cells and to induce actin accumulation. We conclude that intensified gadX activation can abolish the ability of EPEC to adhere to the intestinal epithelium by reducing the expression of major virulence genes

The Lunar Polar Hydrogen Mapper (LunaH-Map) CubeSat Mission

The Lunar Polar Hydrogen Mapper (LunaH-Map) is a 6U CubeSat mission recently selected by NASA\u27s Science Mission Directorate to fly as a secondary payload on first Exploration Mission (EM-1) of the Space Launch System (SLS), scheduled to launch in July 2018. LunaH-Map is led by a small team of researchers and students at Arizona State University, in collaboration with NASA centers, JPL, universities, and commercial space businesses. The LunaH-Map mission will reveal hydrogen abundances at spatial scales below 10 km in order to understand the relationship between hydrogen and permanently shadowed regions, particularly craters, at the Moon\u27s South Pole. The mission\u27s primary payload is designed to use the scintillator material Cs2YLiCl6:Ce, or CLYC to measure count rates of thermal and epithermal neutrons. Enabled by a low-thrust ion propulsion system, LunaH-Map will achieve lunar orbit insertion within ~12 months of SLS separation and maneuver into a highly elliptical, low-perilune (5-10 km) orbit centered around the South Pole of the Moon. In this orbit, LunaH-Map will achieve over 140 low-altitude fly-bys of the South Pole during its two month science phase. LunaH-Map and two fellow secondary payloads selected by NASA to fly on SLS EM-1 will be the first CubeSats to explore the Moon and interplanetary space

GEO-Bench: Toward Foundation Models for Earth Monitoring

Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to substantial increases in generalization to downstream tasks. Such models, recently coined foundation models, have been transformational to the field of natural language processing. Variants have also been proposed for image data, but their applicability to remote sensing tasks is limited. To stimulate the development of foundation models for Earth monitoring, we propose a benchmark comprised of six classification and six segmentation tasks, which were carefully curated and adapted to be both relevant to the field and well-suited for model evaluation. We accompany this benchmark with a robust methodology for evaluating models and reporting aggregated results to enable a reliable assessment of progress. Finally, we report results for 20 baselines to gain information about the performance of existing models. We believe that this benchmark will be a driver of progress across a variety of Earth monitoring tasks

Integrating Machine Learning for Planetary Science: Perspectives for the Next Decade

Machine learning (ML) methods can expand our ability to construct, and draw insight from large datasets. Despite the increasing volume of planetary observations, our field has seen few applications of ML in comparison to other sciences. To support these methods, we propose ten recommendations for bolstering a data-rich future in planetary science.Comment: 10 pages (expanded citations compared to 8 page submitted version for decadal survey), 3 figures, white paper submitted to the Planetary Science and Astrobiology Decadal Survey 2023-203

Pan-viral serology implicates enteroviruses in acute flaccid myelitis.

Since 2012, the United States of America has experienced a biennial spike in pediatric acute flaccid myelitis (AFM)1-6. Epidemiologic evidence suggests non-polio enteroviruses (EVs) are a potential etiology, yet EV RNA is rarely detected in cerebrospinal fluid (CSF)2. CSF from children with AFM (n = 42) and other pediatric neurologic disease controls (n = 58) were investigated for intrathecal antiviral antibodies, using a phage display library expressing 481,966 overlapping peptides derived from all known vertebrate and arboviruses (VirScan). Metagenomic next-generation sequencing (mNGS) of AFM CSF RNA (n = 20 cases) was also performed, both unbiased sequencing and with targeted enrichment for EVs. Using VirScan, the viral family significantly enriched by the CSF of AFM cases relative to controls was Picornaviridae, with the most enriched Picornaviridae peptides belonging to the genus Enterovirus (n = 29/42 cases versus 4/58 controls). EV VP1 ELISA confirmed this finding (n = 22/26 cases versus 7/50 controls). mNGS did not detect additional EV RNA. Despite rare detection of EV RNA, pan-viral serology frequently identified high levels of CSF EV-specific antibodies in AFM compared with controls, providing further evidence for a causal role of non-polio EVs in AFM