An Introduction to Convolutional Neural Networks

The field of machine learning has taken a dramatic twist in recent times, with the rise of the Artificial Neural Network (ANN). These biologically inspired computational models are able to far exceed the performance of previous forms of artificial intelligence in common machine learning tasks. One of the most impressive forms of ANN architecture is that of the Convolutional Neural Network (CNN). CNNs are primarily used to solve difficult image-driven pattern recognition tasks and with their precise yet simple architecture, offers a simplified method of getting started with ANNs. This document provides a brief introduction to CNNs, discussing recently published papers and newly formed techniques in developing these brilliantly fantastic image recognition models. This introduction assumes you are familiar with the fundamentals of ANNs and machine learning.Comment: 10 pages, 5 figure

Computer Vision for Carriers: PATRIOT

Deck tracking performed on carriers currently involves a team of sailors manually identifying aircraft and updating a digital user interface called the Ouija Board. Improvements to the deck tracking process would result in increased Sortie Generation Rates, and therefore applying automation is seen as a critical method to improve deck tracking. However, the requirements on a carrier ship do not allow for the installation of hardware-based location sensing technologies like Global Positioning System (GPS) sensors. PATRIOT (Panoramic Asset Tracking of Real-Time Information for the Ouija Tabletop) is a research effort and proposed solution to performing deck tracking with passive sensing and without the need for GPS sensors. PATRIOT is a prototype system which takes existing camera feeds, calculates aircraft poses, and updates a virtual Ouija board interface with the current status of the assets. PATRIOT would allow for faster, more accurate, and less laborious asset tracking for aircraft, people, and support equipment. PATRIOT is anticipated to benefit the warfighter by reducing cognitive workload, reducing manning requirements, collecting data to improve logistics, and enabling an automation gateway for future efforts to improve efficiency and safety. The authors have developed and tested algorithms to perform pose estimations of assets in real-time including OpenPifPaf, High-Resolution Network (HRNet), HigherHRNet (HHRNet), Faster R-CNN, and in-house developed encoder-decoder network. The software was tested with synthetic and real-world data and was able to accurately extract the pose of assets. Fusion, tracking, and real-world generality are planned to be improved to ensure a successful transition to the fleet.Comment: 8 pages, 18 figures. Published in the Proceedings of the ASNE 2023 Technology, Systems & Ships Symposium. Reproduced with permission from the American Society of Naval Engineers. Distribution Statement A: Approved for public release; distribution is unlimited, as submitted under NAVAIR Public Release Authorization 2023-01

Assurance for Deployed Continual Learning Systems

The future success of the Navy will depend, in part, on artificial intelligence. In practice, many artificially intelligent algorithms, and in particular deep learning models, rely on continual learning to maintain performance in dynamic environments. The software requires adaptation to maintain its initial level of performance in unseen situations. However, if not monitored properly, continual learning may lead to several issues including catastrophic forgetting in which a trained model forgets previously learned tasks when being retrained on new data. The authors created a new framework for safely performing continual learning with the goal of pairing this safety framework with a deep learning computer vision algorithm to allow for safe and high-performing automatic deck tracking on carriers and amphibious assault ships. The safety framework includes several features, such as an ensemble of convolutional neural networks to perform image classification, a manager to record confidences and determine the best answer from the ensemble, a model of the environment to predict when the system may fail to meet minimum performance metrics, a performance monitor to log system and domain performance and check against requirements, and a retraining component to update the ensemble and manager to maintain performance. The authors validated the proposed method using extensive simulation studies based on dynamic image classification. The authors showed the safety framework could probabilistically detect out of distribution data. The results also show the framework can detect when the system is no longer performing safely and can significantly extend the working envelope of an image classifier.Comment: 8 pages, 11 figures. Published in the Proceedings of the ASNE 2023 Technology, Systems & Ships Symposium. Reproduced with permission from the American Society of Naval Engineers. Distribution Statement A: Approved for public release; distribution is unlimited, as submitted under NAVAIR Public Release Authorization 2023-02

The Gut Microbiota Composition in Dichorionic Triplet Sets Suggests a Role for Host Genetic Factors

peer-reviewedMonozygotic and dizygotic twin studies investigating the relative roles of host genetics and environmental factors in shaping gut microbiota composition have produced conflicting results. In this study, we investigated the gut microbiota composition of a healthy dichorionic triplet set. The dichorionic triplet set contained a pair of monozygotic twins and a fraternal sibling, with similar pre- and post-natal environmental conditions including feeding regime. V4 16S rRNA and rpoB amplicon pyrosequencing was employed to investigate microbiota composition, and the species and strain diversity of the culturable bifidobacterial population was also examined. At month 1, the monozygotic pair shared a similar microbiota distinct to the fraternal sibling. By month 12 however, the profile was more uniform between the three infants. Principal coordinate analysis (PCoA) of the microbiota composition revealed strong clustering of the monozygotic pair at month 1 and a separation of the fraternal infant. At months 2 and 3 the phylogenetic distance between the monozygotic pair and the fraternal sibling has greatly reduced and by month 12 the monozygotic pair no longer clustered separately from the fraternal infant. Pulse field gel electrophoresis (PFGE) analysis of the bifidobacterial population revealed a lack of strain diversity, with identical strains identified in all three infants at month 1 and 12. The microbiota of two antibiotic-treated dichorionic triplet sets was also investigated. Not surprisingly, in both triplet sets early life antibiotic administration appeared to be a major determinant of microbiota composition at month 1, irrespective of zygosity. By month 12, early antibiotic administration appeared to no longer exert such a strong influence on gut microbiota composition. We hypothesize that initially host genetics play a significant role in the composition of an individual’s gut microbiota, unless an antibiotic intervention is given, but by month 12 environmental factors are the major determinant.This study was performed as part of the INFANTMET project (10/RD/Infantmet/MFRC/705) and was funded by the Government of Ireland's Department of Agriculture Fisheries and in part by Alimentary Pharmabiotic Centre. KM is a Teagasc Walsh Fellow. CS, RPR and PWOT are members of The Alimentary Pharmabiotic Centre, which is a Centre for Science and Technology (CSET) funded by the Science Foundation Ireland (SFI), through the Irish Government’s National Development Plan (Grant no. 02/CE/B124 and 07/CE/B1368)

Structure and stereochemistry of the base excision repair glycosylase MutY reveal a mechanism similar to retaining glycosidases.

MutY adenine glycosylases prevent DNA mutations by excising adenine from promutagenic 8-oxo-7,8-dihydroguanine (OG):A mismatches. Here, we describe structural features of the MutY active site bound to an azaribose transition state analog which indicate a catalytic role for Tyr126 and approach of the water nucleophile on the same side as the departing adenine base. The idea that Tyr126 participates in catalysis, recently predicted by modeling calculations, is strongly supported by mutagenesis and by seeing close contact between the hydroxyl group of this residue and the azaribose moiety of the transition state analog. NMR analysis of MutY methanolysis products corroborates a mechanism for adenine removal with retention of stereochemistry. Based on these results, we propose a revised mechanism for MutY that involves two nucleophilic displacement steps akin to the mechanisms accepted for 'retaining' O-glycosidases. This new-for-MutY yet familiar mechanism may also be operative in related base excision repair glycosylases and provides a critical framework for analysis of human MutY (MUTYH) variants associated with inherited colorectal cancer

Coincident Learning for Unsupervised Anomaly Detection

Anomaly detection is an important task for complex systems (e.g., industrial facilities, manufacturing, large-scale science experiments), where failures in a sub-system can lead to low yield, faulty products, or even damage to components. While complex systems often have a wealth of data, labeled anomalies are typically rare (or even nonexistent) and expensive to acquire. Unsupervised approaches are therefore common and typically search for anomalies either by distance or density of examples in the input feature space (or some associated low-dimensional representation). This paper presents a novel approach called CoAD, which is specifically designed for multi-modal tasks and identifies anomalies based on \textit{coincident} behavior across two different slices of the feature space. We define an \textit{unsupervised} metric, $\hat{F}_\beta$, out of analogy to the supervised classification $F_\beta$ statistic. CoAD uses $\hat{F}_\beta$ to train an anomaly detection algorithm on \textit{unlabeled data}, based on the expectation that anomalous behavior in one feature slice is coincident with anomalous behavior in the other. The method is illustrated using a synthetic outlier data set and a MNIST-based image data set, and is compared to prior state-of-the-art on two real-world tasks: a metal milling data set and a data set from a particle accelerator

The impact of the supersonic baryon-dark matter velocity difference on the z~20 21cm background

Recently, Tseliakhovich and Hirata (2010) showed that during the cosmic Dark Ages the baryons were typically moving supersonically with respect to the dark matter with a spatially variable Mach number. Such supersonic motion may source shocks that heat the Universe. This motion may also suppress star formation in the first halos. Even a small amount of coupling of the 21cm signal to this motion has the potential to vastly enhance the 21cm brightness temperature fluctuations at 15<z<40 as well as to imprint acoustic oscillations in this signal. We present estimates for the size of this coupling, which we calibrate with a suite of cosmological simulations. Our simulations, discussed in detail in a companion paper, are initialized to self-consistently account for gas pressure and the dark matter-baryon relative velocity, v_bc (in contrast to prior simulations). We find that the supersonic velocity difference dramatically suppresses structure formation at 10-100 comoving kpc scales, it sources shocks throughout the Universe, and it impacts the accretion of gas onto the first star-forming minihalos (even for halo masses as large as ~10^7 Msun). However, we find that the v_bc-sourced temperature fluctuations can contribute only as much as ~10% of the fluctuations in the 21cm signal. We do find that v_bc could source an O(1) component in the power spectrum of the 21cm signal via the X-ray (but not ultraviolet) backgrounds produced once the first stars formed. In a scenario in which ~10^6 Msun minihalos reheated the Universe via their X-ray backgrounds, we find that the pre-reionization 21cm signal would be larger than previously anticipated and exhibit significant acoustic features. We show that structure formation shocks are unable to heat the Universe sufficiently to erase a strong 21cm absorption trough at z ~ 20 that is found in most models of the sky-averaged 21cm intensity.Comment: 17 pages, 11 figures, accepted to ApJ; for movies see http://astro.berkeley.edu/~mmcquinn/firstligh

Expressive and receptive language skills in preschool children from a socially disadvantaged area

Purpose: Evidence suggests that children present with receptive language skills that are equivalent to or more advanced than expressive language skills. This profile holds true for typical and delayed language development. This study aimed to determine if such a profile existed for preschool children from an area of social deprivation and to investigate if particular language skills influence any differences found between expressive and receptive skills. Method: Data from 187 CELF P2 UK assessments conducted on preschool children from two socially disadvantaged areas in a city in southern Ireland. Result: A significant difference was found between Receptive Language Index (RLI) and Expressive Language Index (ELI) scores with Receptive scores found to be lower than Expressive scores. The majority (78.6%) of participants had a lower Receptive Language than Expressive score (RLI ELI), with very few (3.2%) having the same Receptive and Expressive scores (RLI = ELI). Scores for the Concepts and Following Directions (receptive) sub-test were significantly lower than for the other receptive sub tests, while scores for the Expressive Vocabulary sub-test were significantly higher than for the other expressive sub tests. Conclusion: The finding of more advanced expressive than receptive language skills in socially deprived preschool children is previously unreported and clinically relevant for speech-language pathologists in identifying the needs of this population