Signal Enhancement for Magnetic Navigation Challenge Problem

Harnessing the magnetic field of the earth for navigation has shown promise as a viable alternative to other navigation systems. A magnetic navigation system collects its own magnetic field data using a magnetometer and uses magnetic anomaly maps to determine the current location. The greatest challenge with magnetic navigation arises when the magnetic field data from the magnetometer on the navigation system encompass the magnetic field from not just the earth, but also from the vehicle on which it is mounted. It is difficult to separate the earth magnetic anomaly field magnitude, which is crucial for navigation, from the total magnetic field magnitude reading from the sensor. The purpose of this challenge problem is to decouple the earth and aircraft magnetic signals in order to derive a clean signal from which to perform magnetic navigation. Baseline testing on the dataset shows that the earth magnetic field can be extracted from the total magnetic field using machine learning (ML). The challenge is to remove the aircraft magnetic field from the total magnetic field using a trained neural network. These challenges offer an opportunity to construct an effective neural network for removing the aircraft magnetic field from the dataset, using an ML algorithm integrated with physics of magnetic navigation.Comment: 21 pages, 4 figures. See https://github.com/MIT-AI-Accelerator/MagNav.jl for accompanying data and cod

U.S. Waterways Experiment Station Reports

Report regarding refraction seismic and vibratory tests conducted on a railroad test embankment near Aikman, Kansas as well as attenuation and dynamic laboratory tests. The refraction seismic tests indicated the existence of two distinct velocity zones, one for the embankment and one for the underlying limestone bedrock

U.S. Waterways Experiment Station Report S-70-12

Abstract: Geologic field reconnaissance and geophysical evaluation of eight areas in the western part of the United States were made to locate a site for conducting tests of a prototype hard rock silo (HRS)

Cavity detection and delineation research : Report 2 : Seismic methodology : Medford Cave Site, Florida /

"Prepared for Office, Chief of Engineers.""June 1983."Cover title.Bibliography: page 35.Final report.This investigation was conducted by the U.S. Army Engineer Waterways Experiment Station (WES) for the Office, Chief of Engineers (OCE), U.S. Army, under theMode of access: Internet

Geophysical investigation, Prado Dam and Mentone Damsite, California /

"June 1975."Prepared for U.S. Army Engineer District, Los Angeles.Includes bibliographical references (page 23).Mode of access: Internet

Signal Enhancement for Magnetic Navigation Challenge Problem

Harnessing the magnetic field of the earth for navigation has shown promise as a viable alternative to other navigation systems. A magnetic navigation system collects its own magnetic field data using a magnetometer and uses magnetic anomaly maps to determine the current location. The greatest challenge with magnetic navigation arises when the magnetic field data from the magnetometer on the navigation system encompass the magnetic field from not just the earth, but also from the vehicle on which it is mounted. It is difficult to separate the earth magnetic anomaly field magnitude, which is crucial for navigation, from the total magnetic field magnitude reading from the sensor. The purpose of this challenge problem is to decouple the earth and aircraft magnetic signals in order to derive a clean signal from which to perform magnetic navigation. Baseline testing on the dataset shows that the earth magnetic field can be extracted from the total magnetic field using machine learning (ML). The challenge is to remove the aircraft magnetic field from the total magnetic field using a trained neural network. These challenges offer an opportunity to construct an effective neural network for removing the aircraft magnetic field from the dataset, using an ML algorithm integrated with physics of magnetic navigation

Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism

We present the largest exome sequencing study of autism spectrum disorder (ASD) to date (n = 35,584 total samples, 11,986 with ASD). Using an enhanced analytical framework to integrate de novo and case-control rare variation, we identify 102 risk genes at a false discovery rate of 0.1 or less. Of these genes, 49 show higher frequencies of disruptive de novo variants in individuals ascertained to have severe neuro-developmental delay, whereas 53 show higher frequencies in individuals ascertained to have ASD; comparing ASD cases with mutations in these groups reveals phenotypic differences. Expressed early in brain development, most risk genes have roles in regulation of gene expression or neuronal communication (i.e., mutations effect neurodevelopmental and neurophysiological changes), and 13 fall within loci recurrently hit by copy number variants. In cells from the human cortex, expression of risk genes is enriched in excitatory and inhibitory neuronal lineages, consistent with multiple paths to an excitatory-inhibitory imbalance underlying ASD