Technology Readiness Levels for Machine Learning Systems

The development and deployment of machine learning (ML) systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. The lack of diligence can lead to technical debt, scope creep and misaligned objectives, model misuse and failures, and expensive consequences. Engineering systems, on the other hand, follow well-defined processes and testing standards to streamline development for high-quality, reliable results. The extreme is spacecraft systems, where mission critical measures and robustness are ingrained in the development process. Drawing on experience in both spacecraft engineering and ML (from research through product across domain areas), we have developed a proven systems engineering approach for machine learning development and deployment. Our "Machine Learning Technology Readiness Levels" (MLTRL) framework defines a principled process to ensure robust, reliable, and responsible systems while being streamlined for ML workflows, including key distinctions from traditional software engineering. Even more, MLTRL defines a lingua franca for people across teams and organizations to work collaboratively on artificial intelligence and machine learning technologies. Here we describe the framework and elucidate it with several real world use-cases of developing ML methods from basic research through productization and deployment, in areas such as medical diagnostics, consumer computer vision, satellite imagery, and particle physics

Free electron concentration dependent sub-bandgap optical absorption characterization of bulk GaN crystals

Optical transmission measurements were performed on high quality bulk gallium nitride (GaN) crystals grown by sodium flux, hydride vapor phase epitaxy, and the ammonothermal method with varying free electron concentrations ranging from 4×1016 cm-3 to 9×1018 cm-3. The quality of the crystals was analyzed by x-ray diffraction, threading dislocation density determination, impurity concentrations, and Hall mobility measurements. The sub-bandgap absorption coefficient and index of refraction was determined based on illumination wavelengths ranging from 360 nm to 800 nm. Phonon-assisted free carrier absorption was determined to be the dominant absorption mechanism above approximately 0.1 cm-1. The absorption coefficient at 450 nm varied linearly from 0.1 cm- 1 to 5 cm-1 for free electron concentrations ranging from 1×1017 cm-3 to 9×1018 cm-3. The ammonothermal GaN samples exhibited a strong defect related onset of absorption above 2.9 eV which can be explained by the presence of appreciable hydrogenated gallium vacancies having defect states close to the valance band within the electric bandgap of GaN. The presence of hydrogenated gallium vacancies was experimentally confirmed by Fourier transform infrared absorbance measurements and double hydrogenated gallium vacancy defect are speculated to be prominent in ammonothermal GaN.Peer reviewe

Global geomagnetic perturbation forecasting using Deep Learning

Geomagnetically Induced Currents (GICs) arise from spatio-temporal changes to Earth's magnetic field which arise from the interaction of the solar wind with Earth's magnetosphere, and drive catastrophic destruction to our technologically dependent society. Hence, computational models to forecast GICs globally with large forecast horizon, high spatial resolution and temporal cadence are of increasing importance to perform prompt necessary mitigation. Since GIC data is proprietary, the time variability of horizontal component of the magnetic field perturbation (dB/dt) is used as a proxy for GICs. In this work, we develop a fast, global dB/dt forecasting model, which forecasts 30 minutes into the future using only solar wind measurements as input. The model summarizes 2 hours of solar wind measurement using a Gated Recurrent Unit, and generates forecasts of coefficients which are folded with a spherical harmonic basis to enable global forecasts. When deployed, our model produces results in under a second, and generates global forecasts for horizontal magnetic perturbation components at 1-minute cadence. We evaluate our model across models in literature for two specific storms of 5 August 2011 and 17 March 2015, while having a self-consistent benchmark model set. Our model outperforms, or has consistent performance with state-of-the-practice high time cadence local and low time cadence global models, while also outperforming/having comparable performance with the benchmark models. Such quick inferences at high temporal cadence and arbitrary spatial resolutions may ultimately enable accurate forewarning of dB/dt for any place on Earth, resulting in precautionary measures to be taken in an informed manner.Comment: 23 pages, 8 figures, 5 tables; accepted for publication in AGU: Spaceweathe

TLR7 and TLR8 Differentially Activate the IRF and NF-κB Pathways in Specific Cell Types to Promote Inflammation

TLR7 and TLR8 are pattern recognition receptors that reside in the endosome and are activated by ssRNA molecules. TLR7 and TLR8 are normally part of the antiviral defense response, but they have also been implicated as drivers of autoimmune diseases such as lupus. The receptors have slightly different ligand-binding specificities and cellular expression patterns that suggest they have nonredundant specialized roles. How the roles of TLR7 and TLR8 differ may be determined by which cell types express each TLR and how the cells respond to activation of each receptor. To provide a better understanding of the effects of TLR7/8 activation, we have characterized changes induced by TLR-specific agonists in different human immune cell types and defined which responses are a direct consequence of TLR7 or TLR8 activation and which are secondary responses driven by type I IFN or cytokines produced subsequent to the primary response. Using cell sorting, gene expression analysis, and intracellular cytokine staining, we have found that the IFN regulatory factor (IRF) and NF-κB pathways are differentially activated downstream of the TLRs in various cell types. Studies with an anti-IFNAR Ab in human cells and lupus mice showed that inhibiting IFN activity can block secondary IFN-induced gene expression changes downstream of TLR7/8 activation, but not NF-κB–regulated genes induced directly by TLR7/8 activation at earlier timepoints. In summary, these results elucidate the different roles TLR7 and TLR8 play in immunity and inform strategies for potential treatment of autoimmune diseases driven by TLR7/8 activation

Survival of Del17p CLL Depends on Genomic Complexity and Somatic Mutation.

PURPOSE: Chronic lymphocytic leukemia (CLL) with 17p deletion typically progresses quickly and is refractory to most conventional therapies. However, some del(17p) patients do not progress for years, suggesting that del(17p) is not the only driving event in CLL progression. We hypothesize that other concomitant genetic abnormalities underlie the clinical heterogeneity of del(17p) CLL. EXPERIMENTAL DESIGN: We profiled the somatic mutations and copy number alterations (CNA) in a large group of del(17p) CLLs as well as wild-type CLL and analyzed the genetic basis of their clinical heterogeneity. RESULTS: We found that increased somatic mutation number associates with poor overall survival independent of 17p deletion (P = 0.003). TP53 mutation was present in 81% of del(17p) CLL, mostly clonal (82%), and clonal mutations with del(17p) exhibit shorter overall survival than subclonal mutations with del(17p) (P = 0.019). Del(17p) CLL has a unique driver mutation profile, including NOTCH1 (15%), RPS15 (12%), DDX3X (8%), and GPS2 (6%). We found that about half of del(17p) CLL cases have recurrent deletions at 3p, 4p, or 9p and that any of these deletions significantly predicts shorter overall survival. In addition, the number of CNAs, but not somatic mutations, predicts shorter time to treatment among patients untreated at sampling. Indolent del(17p) CLLs were characterized by absent or subclonal TP53 mutation and few CNAs, with no difference in somatic mutation number. CONCLUSIONS: We conclude that del(17p) has a unique genomic profile and that clonal TP53 mutations, 3p, 4p, or 9p deletions, and genomic complexity are associated with shorter overall survival. Clin Cancer Res; 23(3); 735-45. ©2016 AACR