Identifying Training Gaps in RQ-7B Shadow: A U.S. Army Unmanned Aircraft System

The mission of the RQ-7B has changed radically since 2003, when unmanned aircraft system (UAS) ownership shifted from Military Intelligence (MI) to Army Aviation. Instead of passive observation, RQ-7B operators must now acquire active scout-reconnaissance skills. Initial training takes place at Ft. Huachuca, AZ, the Army’s main MI installation. Operators then report to their unit, usually a Brigade Combat Team (BCT). This research focused on (a) type training received at the schoolhouse (MI/scout-reconnaissance), (b) training received at the BCT, and (c) opportunities for training. It was found that schoolhouse training still was primarily MI, (e.g., image analysis and vehicle identification). Interviews with BCT staff officers identified 10 critical scout-reconnaissance skills not trained in the schoolhouse. These required additional training, mostly on the job in the unit, because opportunities for scout-reconnaissance training at home station were limited. The research concluded that more scout-reconnaissance training should take place at the schoolhouse

Identifying Training Gaps in RQ-7B Shadow: A U.S. Army Unmanned Aircraft System

The mission of the RQ-7B has changed radically since 2003, when unmanned aircraft system (UAS) ownership shifted from Military Intelligence (MI) to Army Aviation. Instead of passive observation, RQ-7B operators must now acquire active scout-reconnaissance skills. Initial training takes place at Ft. Huachuca, AZ, the Army’s main MI installation. Operators then report to their unit, usually a Brigade Combat Team (BCT). This research focused on (a) type training received at the schoolhouse (MI/scout-reconnaissance), (b) training received at the BCT, and (c) opportunities for training. It was found that schoolhouse training still was primarily MI, (e.g., image analysis and vehicle identification). Interviews with BCT staff officers identified 10 critical scout-reconnaissance skills not trained in the schoolhouse. These required additional training, mostly on the job in the unit, because opportunities for scout-reconnaissance training at home station were limited. The research concluded that more scout-reconnaissance training should take place at the schoolhouse

QTL linkage analysis of connected populations using ancestral marker and pedigree information

The common assumption in quantitative trait locus (QTL) linkage mapping studies that parents of multiple connected populations are unrelated is unrealistic for many plant breeding programs. We remove this assumption and propose a Bayesian approach that clusters the alleles of the parents of the current mapping populations from locus-specific identity by descent (IBD) matrices that capture ancestral marker and pedigree information. Moreover, we demonstrate how the parental IBD data can be incorporated into a QTL linkage analysis framework by using two approaches: a Threshold IBD model (TIBD) and a Latent Ancestral Allele Model (LAAM). The TIBD and LAAM models are empirically tested via numerical simulation based on the structure of a commercial maize breeding program. The simulations included a pilot dataset with closely linked QTL on a single linkage group and 100 replicated datasets with five linkage groups harboring four unlinked QTL. The simulation results show that including parental IBD data (similarly for TIBD and LAAM) significantly improves the power and particularly accuracy of QTL mapping, e.g., position, effect size and individuals’ genotype probability without significantly increasing computational demand

Mixed model approaches for the identification of QTLs within a maize hybrid breeding program

Two outlines for mixed model based approaches to quantitative trait locus (QTL) mapping in existing maize hybrid selection programs are presented: a restricted maximum likelihood (REML) and a Bayesian Markov Chain Monte Carlo (MCMC) approach. The methods use the in-silico-mapping procedure developed by Parisseaux and Bernardo (2004) as a starting point. The original single-point approach is extended to a multi-point approach that facilitates interval mapping procedures. For computational and conceptual reasons, we partition the full set of relationships from founders to parents of hybrids into two types of relations by defining so-called intermediate founders. QTL effects are defined in terms of those intermediate founders. Marker based identity by descent relationships between intermediate founders define structuring matrices for the QTL effects that change along the genome. The dimension of the vector of QTL effects is reduced by the fact that there are fewer intermediate founders than parents. Furthermore, additional reduction in the number of QTL effects follows from the identification of founder groups by various algorithms. As a result, we obtain a powerful mixed model based statistical framework to identify QTLs in genetic backgrounds relevant to the elite germplasm of a commercial breeding program. The identification of such QTLs will provide the foundation for effective marker assisted and genome wide selection strategies. Analyses of an example data set show that QTLs are primarily identified in different heterotic groups and point to complementation of additive QTL effects as an important factor in hybrid performance

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Training Manned-Unmanned Teaming Skills in Army Aviation

Current Army Aviation combat operations utilize an employment strategy that teams a rotary wing aircraft with an unmanned aircraft system (UAS), thereby leveraging strategic advantages of each aircraft’s unique capabilities, endurance, and payloads. Clear and effective communications between the airborne helicopter pilot and the ground-based UAS operator are critical for successful Manned-Unmanned Teaming (MUM-T) missions. Previous studies have recommended additional training in tactical communications for the UAS payload operator in order to support precision and timeliness in teaming engagements. In accordance with Army Learning Model 2015, an engaging, skills-adaptive, computer game was developed to train critical MUM-T skills for the UAS payload operator. The training game emphasizes the UAS operator’s concise tactical communications exercised in doctrinally correct MUM-T mission scenarios with immediate Soldier feedback. Game players are initially exposed to scripted MUM-T training mission scenarios that culminate in a freeplay mission campaign. Performance measures focus on both accomplishment of mission objectives and accurate tactical communications protocol. Game players are given individual scorecards that display skill advancement and potential remediation for knowledge and skill deficiencies. An initial user assessment is scheduled for game and feature refinement. Future implementation of aggregated soldier data will be explored with UAS course instructors. Results from the user assessments and recommendations on tactical communications training will be disseminated when available