Continuous monitoring of the lunar or Martian subsurface using on-board pattern recognition and neural processing of Rover geophysical data

The ultimate goal is to create an extraterrestrial unmanned system for subsurface mapping and exploration. Neural networks are to be used to recognize anomalies in the profiles that correspond to potentially exploitable subsurface features. The ground penetrating radar (GPR) techniques are likewise identical. Hence, the preliminary research focus on GPR systems will be directly applicable to seismic systems once such systems can be designed for continuous operation. The original GPR profile may be very complex due to electrical behavior of the background, targets, and antennas, much as the seismic record is made complex by multiple reflections, ghosting, and ringing. Because the format of the GPR data is similar to the format of seismic data, seismic processing software may be applied to GPR data to help enhance the data. A neural network may then be trained to more accurately identify anomalies from the processed record than from the original record

On the third critical field in Ginzburg-Landau theory

Using recent results by the authors on the spectral asymptotics of the Neumann Laplacian with magnetic field, we give precise estimates on the critical field, $H_{C_3}$, describing the appearance of superconductivity in superconductors of type II. Furthermore, we prove that the local and global definitions of this field coincide. Near $H_{C_3}$ only a small part, near the boundary points where the curvature is maximal, of the sample carries superconductivity. We give precise estimates on the size of this zone and decay estimates in both the normal (to the boundary) and parallel variables

How to realize Lie algebras by vector fields

An algorithm for embedding finite dimensional Lie algebras into Lie algebras of vector fields (and Lie superalgebras into Lie superalgebras of vector fields) is offered in a way applicable over ground fields of any characteristic. The algorithm is illustrated by reproducing Cartan's interpretations of the Lie algebra of G(2) as the Lie algebra that preserves certain non-integrable distributions. Similar algorithm and interpretation are applicable to other exceptional simple Lie algebras, as well as to all non-exceptional simple ones and many non-simple ones, and to many Lie superalgebras.Comment: 17 pages, LaTe

High-resolution subsurface imaging and neural network recognition: Non-intrusive buried substance location. Final report, January 26, 1997

A high-frequency, high-resolution electromagnetic (EIVI) imaging system has been developed for environmental geophysics surveys. Some key features of this system include: (1) rapid surveying to allow dense spatial sampling over a large area, (2) high-accuracy measurements which are used to produce a high-resolution image of the subsurface, (3) measurements which have excellent signal-to-noise ratio over a wide bandwidth (31 kHz to 32 MHZ), (4) elimination of electric-field interference at high frequencies, (5) large-scale physical modeling to produce accurate theoretical responses over targets of interest in environmental geophysics surveys, (6) rapid neural network interpretation at the field site, and (7) visualization of complex structures during the survey. Four major experiments were conducted with the system: (1) Data were collected for several targets in our physical modeling facility. (2) We tested the system over targets buried in soil. (3) We conducted an extensive survey at the Idaho National Engineering Laboratory (INEL) Cold Test Pit (CTP). The location of the buried waste, category of waste, and thickness of the clay cap were successfully mapped. (4) We ran surveys over the acid pit at INEL. This was an operational survey over a hot site. The interpreted low-resistivity region correlated closely with the known extent of the acid pit

Central Nervous System Parasitosis and Neuroinflammation Ameliorated by Systemic IL-10 Administration in Trypanosoma brucei-Infected Mice

Peer reviewedPublisher PD

High-Resolution Subsurface Imaging and Neural Network Recognition: Non-Intrusive Buried Substance Location. Final Report

A high-frequency, high-resolution electromagnetic (EM) imaging system has been developed for environmental geophysics surveys. Some key features of this system include: (1) rapid surveying to allow dense spatial sampling over a large area, (2) high-accuracy measurements which are used to produce a high-resolution image of the subsurface, (3) measurements which have excellent signal-to-noise ratio over a wide bandwidth (31 kHz to 32 MHz), (4) elimination of electric-field interference at high frequencies, (5) large-scale physical modeling to produce accurate theoretical responses over targets of interest in environmental geophysics surveys, (6) rapid neural network interpretation at the field site, and (7) visualization of complex structures during the survey. Four major experiments were conducted with the system: (1) Data were collected for several targets in our physical modeling facility. (2) The authors tested the system over targets buried in soil. (3) The authors conducted an extensive survey at the Idaho National Engineering Laboratory (INEL) Cold Test Pit (CTP). The location of the buried waste, category of waste, and thickness of the clay cap were successfully mapped. (4) The authors ran surveys over the acid pit at INEL. This was an operational survey over a hot site. The interpreted low-resistivity region correlated closely with the known extent of the acid pit

The LASI high-frequency electromagnetic subsurface-imaging system: System description and demonstration site-characterization survey at the Idaho National Engineering Laboratory

A high-frequency, high-resolution, electromagnetic imaging system has been developed for environmental geophysics surveys. Some key features include: (1) rapid surveying to allow dense spatial sampling over a large area, (2) high-accuracy measurements which are used to produce a high-resolution image of the subsurface, (3) measurements which have excellent signal-to-noise ratio over a wide bandwidth (31 kHz to 32 MHz), (4) large-scale physical modeling to produce accurate theoretical responses over targets of interest in environmental geophysics surveys, (5) rapid neural network interpretation at the field site, and (6) visualization of complex structures during the survey. A field survey was conducted at INEL, Nov. 28-Dec. 1, 1995, over the Cold Test Pit, which simulates waste pits at INEL and other DOE sites

High-resolution subsurface imaging and neural network recognition: Non-intrusive buried substance location. Final report, January 26, 1997

A high-frequency, high-resolution electromagnetic (EIVI) imaging system has been developed for environmental geophysics surveys. Some key features of this system include: (1) rapid surveying to allow dense spatial sampling over a large area, (2) high-accuracy measurements which are used to produce a high-resolution image of the subsurface, (3) measurements which have excellent signal-to-noise ratio over a wide bandwidth (31 kHz to 32 MHZ), (4) elimination of electric-field interference at high frequencies, (5) large-scale physical modeling to produce accurate theoretical responses over targets of interest in environmental geophysics surveys, (6) rapid neural network interpretation at the field site, and (7) visualization of complex structures during the survey. Four major experiments were conducted with the system: (1) Data were collected for several targets in our physical modeling facility. (2) We tested the system over targets buried in soil. (3) We conducted an extensive survey at the Idaho National Engineering Laboratory (INEL) Cold Test Pit (CTP). The location of the buried waste, category of waste, and thickness of the clay cap were successfully mapped. (4) We ran surveys over the acid pit at INEL. This was an operational survey over a hot site. The interpreted low-resistivity region correlated closely with the known extent of the acid pit

Predicting gene essentiality in Caenorhabditis elegans by feature engineering and machine-learning

Defining genes that are essential for life has major implications for understanding critical biological processes and mechanisms. Although essential genes have been identified and characterised experimentally using functional genomic tools, it is challenging to predict with confidence such genes from molecular and phenomic data sets using computational methods. Using extensive data sets available for the model organism Caenorhabditis elegans, we constructed here a machine-learning (ML)-based workflow for the prediction of essential genes on a genome-wide scale. We identified strong predictors for such genes and showed that trained ML models consistently achieve highly-accurate classifications. Complementary analyses revealed an association between essential genes and chromosomal location. Our findings reveal that essential genes in C. elegans tend to be located in or near the centre of autosomal chromosomes; are positively correlated with low single nucleotide polymorphim (SNP) densities and epigenetic markers in promoter regions; are involved in protein and nucleotide processing; are transcribed in most cells; are enriched in reproductive tissues or are targets for small RNAs bound to the argonaut CSR-1. Based on these results, we hypothesise an interplay between epigenetic markers and small RNA pathways in the germline, with transcription-based memory; this hypothesis warrants testing. From a technical perspective, further work is needed to evaluate whether the present ML-based approach will be applicable to other metazoans (including Drosophila melanogaster) for which comprehensive data set (i.e. genomic, transcriptomic, proteomic, variomic, epigenetic and phenomic) are available