Cybersecurity, Artificial Intelligence, and Risk Management: Understanding Their Implementation in Military Systems Acquisitions

Excerpt from the Proceedings of the Nineteenth Annual Acquisition Research SymposiumThis research has the explicit goal of proposing a reusable, extensible, adaptable, and comprehensive advanced analytical modeling process to help the U.S. Navy in quantifying, modeling, valuing, and optimizing a set of nascent Artificial Intelligence and Machine Learning (AI/ML) applications in the aerospace, automotive and transportation industries and developing a framework with a hierarchy of functions by technology category and developing a unique-to-Navy-ship construct that, based on weighted criteria, scores the return on investment of developing naval AI/ML applications that enhance warfighting capabilities. This current research proposes to create a business case for making strategic decisions under uncertainty. Specifically, we will look at a portfolio of nascent artificial intelligence and machine learning applications, both at the PEO-SHIPS and extensible to the Navy Fleet. This portfolio of options approach to business case justification will provide tools to allow decision-makers to decide on the optimal flexible options to implement and allocate in different types of artificial intelligence and machine learning applications, subject to budget constraints, across multiple types of ships. The concept of the impact of innovative technology on productivity has applicability beyond the Department of Defense (DoD). Private industry can greatly benefit from the concepts and methodologies developed in this research to apply to the hiring and talent management of scientists, programmers, engineers, analysts, and senior executives in the workforce to increase innovation productivity.Approved for public release; distribution is unlimited

Cybersecurity, Artificial Intelligence, and Risk Management: Understanding Their Implementation in Military Systems Acquisitions

Excerpt from the Proceedings of the Nineteenth Annual Acquisition Research SymposiumThis research has the explicit goal of proposing a reusable, extensible, adaptable, and comprehensive advanced analytical modeling process to help the U.S. Navy in quantifying, modeling, valuing, and optimizing a set of nascent Artificial Intelligence and Machine Learning (AI/ML) applications in the aerospace, automotive and transportation industries and developing a framework with a hierarchy of functions by technology category and developing a unique-to-Navy-ship construct that, based on weighted criteria, scores the return on investment of developing naval AI/ML applications that enhance warfighting capabilities. This current research proposes to create a business case for making strategic decisions under uncertainty. Specifically, we will look at a portfolio of nascent artificial intelligence and machine learning applications, both at the PEO-SHIPS and extensible to the Navy Fleet. This portfolio of options approach to business case justification will provide tools to allow decision-makers to decide on the optimal flexible options to implement and allocate in different types of artificial intelligence and machine learning applications, subject to budget constraints, across multiple types of ships. The concept of the impact of innovative technology on productivity has applicability beyond the Department of Defense (DoD). Private industry can greatly benefit from the concepts and methodologies developed in this research to apply to the hiring and talent management of scientists, programmers, engineers, analysts, and senior executives in the workforce to increase innovation productivity.Approved for public release; distribution is unlimited

Correlation between superfluid density and Tc of underdoped YBa2Cu3O6+x near the superconductor-insulator transition

We report measurements of the ab-plane superfluid density Ns (magnetic penetration depth, \lambda) of severely underdoped films of YBa2Cu3O6+x, with Tc's from 6 to 50 K. Tc is not proportional to Ns(0); instead, we find Tc ~ Ns^{1/2.3 +/- 0.4}. At the lowest dopings, Tc is as much as 5 times larger than the upper limit set by the KTB transition temperature of individual CuO2 bilayers.Comment: 4 pages, 2 figures, Submitted to Phys. Rev. Let

Fermi surface reconstruction in (Ba1−x_{1-x}Kx_x)Fe2_2As2_2 (0.44 ≤x≤\leq x \leq 1) probed by thermoelectric power measurements

We report in-plane thermoelectric power measurements on single crystals of (Ba$_{1-x}$K$_x$)Fe$_2$As$_2$ (0.44 $\leq x \leq$ 1). We observe a minimum in the S$|_{T=const}$ versus x at x ~ 0.55 that can be associated with the change in the topology of the Fermi surface, a Lifshitz transition, related to the electron pockets at the center of M point crossing the Fermi level. This feature is clearly observable below ~ 75 K. Thermoelectric power also shows a change in the x ~ 0.8 - 0.9 range, where maximum in the thermoelectric power collapses into a plateau. This Lifshitz transition is most likely related to the reconstruction of the Fermi surface associated with the transformation of the hole pockets at the M point into four blades as observed by ARPES measurements.Comment: Accepted for publication in Phys. Rev.

AEGIS Platforms: The Potential Impact of Open Architecture in Sustaining Engineering

Sponsored Report (for Acquisition Research Program)This proof-of-concept case study analyzes the potential benefits of open architecture (OA) in the AEGIS software maintenance and upgrade process. In a multi-phased approach, the Knowledge value Added/Real-Options (KVA+RO) framework was applied to sustaining engineering on specific AEGIS software processes.Naval Postgraduate School Acquisition Research ProgramApproved for public release; distribution is unlimited

and Cost/Benefits Opportunities

Acquisition Research Program Sponsored Report SeriesSponsored Acquisition Research & Technical ReportsThe acquisition of artificial intelligence (AI) systems is a relatively new challenge for the U.S. Department of Defense (DoD). Given the potential for high-risk failures of AI system acquisitions, it is critical for the acquisition community to examine new analytical and decision-making approaches to managing the acquisition of these systems in addition to the existing approaches (i.e., Earned Value Management, or EVM). In addition, many of these systems reside in small start-up or relatively immature system development companies, further clouding the acquisition process due to their unique business processes when compared to the large defense contractors. This can lead to limited access to data, information, and processes that are required in the standard DoD acquisition approach (i.e., the 5000 series). The well-known recurring problems in acquiring information technology automation within the DoD will likely be exacerbated in acquiring complex and risky AI systems. Therefore, more robust, agile, and analytically driven acquisition methodologies will be required to help avoid costly disasters in acquiring these kinds of systems. This research provides a set of analytical tools for acquiring organically developed AI systems through a comparison and contrast of the proposed methodologies that will demonstrate when and how each method can be applied to improve the acquisitions lifecycle for AI systems, as well as provide additional insights and examples of how some of these methods can be applied. This research identifies, reviews, and proposes advanced quantitative, analytically based methods within the integrated risk management (IRM)) and knowledge value added (KVA) methodologies to complement the current EVM approach. This research examines whether the various methodologies—EVM, KVA, and IRM—could be used within the Defense Acquisition System (DAS) to improve the acquisition of AI. While this paper does not recommend one of these methodologies over the other, certain methodologies, specifically IRM, may be more beneficial when used throughout the entire acquisition process instead of within a portion of the system. Due to this complexity of AI system, this research looks at AI as a whole and not specific types of AI.Approved for public release; distribution is unlimited.Approved for public release; distribution is unlimited

High Levels of Sequence Diversity in the 5′ UTRs of Human-Specific L1 Elements

Approximately 80 long interspersed element (LINE-1 or L1) copies are able to retrotranspose actively in the human genome, and these are termed retrotransposition-competent L1s. The 5′ untranslated region (UTR) of the human-specific L1 contains an internal promoter and several transcription factor binding sites. To better understand the effect of the L1 5′ UTR on the evolution of human-specific L1s, we examined this population of elements, focusing on the sequence diversity and accumulated substitutions within their 5′ UTRs. Using network analysis, we estimated the age of each L1 component (the 5′ UTR, ORF1, ORF2, and 3′ UTR). Through the comparison of the L1 components based on their estimated ages, we found that the 5′ UTR of human-specific L1s accumulates mutations at a faster rate than the other components. To further investigate the L1 5′ UTR, we examined the substitution frequency per nucleotide position among them. The results showed that the L1 5′ UTRs shared relatively conserved transcription factor binding sites, despite their high sequence diversity. Thus, we suggest that the high level of sequence diversity in the 5′ UTRs could be one of the factors controlling the number of retrotransposition-competent L1s in the human genome during the evolutionary battle between L1s and their host genomes

Magnetic Penetration Depth Measurements of Pr2−x_{2-x}Cex_xCuO4−δ_{4-\delta} Films on Buffered Substrates: Evidence for a Nodeless Gap

We report measurements of the inverse squared magnetic penetration depth, $\lambda^{-2}(T)$, in Pr$_{2-x}$Ce$_{x}$CuO$_{4-\delta}$ ($0.115 \leq x \leq 0.152$) superconducting films grown on SrTiO$_3$ (001) substrates coated with a buffer layer of insulating Pr$_{2}$CuO$_{4}$. $\lambda^{-2}(0)$, $T_c$ and normal-state resistivities of these films indicate that they are clean and homogeneous. Over a wide range of Ce doping, $0.124\leq x \leq 0.144$, $\lambda^{-2}(T)$ at low $T$ is flat: it changes by less than 0.15% over a factor of 3 change in $T$, indicating a gap in the superconducting density of states. Fits to the first 5% decrease in $\lambda^{-2}(T)$ produce values of the minimum superconducting gap in the range of $0.29\leq\Delta_{\rm min}/k_BT_c\leq1.01$.Comment: 4 pages 5 figure

Promoter hypomethylation of NY-ESO-1, association with clinicopathological features and PD-L1 expression in non-small cell lung cancer.

Cancer-Testis antigens (CTA) are immunogenic molecules with normal tissue expression restricted to testes but with aberrant expression in up to 30% of non-small cell lung cancers (NSCLCs). Regulation of CTA expression is mediated in part through promoter DNA methylation. Recently, immunotherapy has altered treatment paradigms in NSCLC. Given its immunogenicity and ability to be re-expressed through demethylation, NY-ESO-1 promoter methylation, protein expression and its association with programmed death receptor ligand-1 (PD-L1) expression and clinicopathological features were investigated. Lung cancer cell line demethylation resulting from 5-Aza-2'-deoxycytidine treatment was associated with both NY-ESO-1 and PD-L1 re-expression in vitro but not increased chemosensitivity. NY-ESO-1 hypomethylation was observed in 15/94 (16%) of patient samples and associated with positive protein expression (P < 0.0001). In contrast, PD-L1 expression was observed in 50/91 (55%) but strong expression in only 12/91 (13%) cases. There was no association between NY-ESO-1 and PD-L1 expression, despite resultant re-expression of both by 5-Aza-2'-deoxycytidine. Importantly, NY-ESO-1 hypomethylation was found to be an independent marker of poor prognosis in patients not treated with chemotherapy (HR 3.59, P = 0.003) in multivariate analysis. In patients treated with chemotherapy there were no differences in survival associated with NY-ESO-1 hypomethylation. Collectively, these results provided supporting evidence for the potential use of NY-ESO-1 hypomethylation as a prognostic biomarker in stage 3 NSCLCs. In addition, these data highlight the potential to incorporate demethylating agents to enhance immune activation, in tumours currently devoid of immune infiltrates and expression of immune checkpoint genes