The National Aeronautics and Space Administration (NASA)/Goddard Space Flight Center (GSFC) sounding-rocket program

An overall introduction to the NASA sounding rocket program as managed by the Goddard Space Flight Center is presented. The various sounding rockets, auxiliary systems (telemetry, guidance, etc.), launch sites, and services which NASA can provide are briefly described

Status of superpressure balloon technology in the United States

Superpressure mylar balloon technology in United States - applications, balloon size criteria, and possible improvement

Carboniferous metamorphism on the north (upper) side of the Sebago Batholith

Guidebook for field trips in southwestern Maine: New England Intercollegiate Geological Conference, 78th annual meeting, Bates College, Lewiston, Maine, October 17, 18, and 19, 1986: Trip C-

Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability

Post-hoc model-agnostic interpretation methods such as partial dependence plots can be employed to interpret complex machine learning models. While these interpretation methods can be applied regardless of model complexity, they can produce misleading and verbose results if the model is too complex, especially w.r.t. feature interactions. To quantify the complexity of arbitrary machine learning models, we propose model-agnostic complexity measures based on functional decomposition: number of features used, interaction strength and main effect complexity. We show that post-hoc interpretation of models that minimize the three measures is more reliable and compact. Furthermore, we demonstrate the application of these measures in a multi-objective optimization approach which simultaneously minimizes loss and complexity

Prediction of Cognitive Decline in Healthy Older Adults using fMRI

Few studies have examined the extent to which structural and functional MRI, alone and in combination with genetic biomarkers, can predict future cognitive decline in asymptomatic elders. This prospective study evaluated individual and combined contributions of demographic information, genetic risk, hippocampal volume, and fMRI activation for predicting cognitive decline after an 18-month retest interval. Standardized neuropsychological testing, an fMRI semantic memory task (famous name discrimination), and structural MRI (sMRI) were performed on 78 healthy elders (73% female; mean age = 73 years, range = 65 to 88 years). Positive family history of dementia and presence of one or both apolipoprotein E (APOE) ε4 alleles occurred in 51.3% and 33.3% of the sample, respectively. Hippocampal volumes were traced from sMRI scans. At follow-up, all participants underwent a repeat neuropsychological examination. At 18 months, 27 participants (34.6%) declined by at least 1 SD on one of three neuropsychological measures. Using logistic regression, demographic variables (age, years of education, gender) and family history of dementia did not predict future cognitive decline. Greater fMRI activity, absence of an APOE ε4 allele, and larger hippocampal volume were associated with reduced likelihood of cognitive decline. The most effective combination of predictors involved fMRI brain activity and APOE ε4 status. Brain activity measured from task-activated fMRI, in combination with APOE ε4 status, was successful in identifying cognitively intact individuals at greatest risk for developing cognitive decline over a relatively brief time period. These results have implications for enriching prevention clinical trials designed to slow AD progression

Comparison of Semantic and Episodic Memory BOLD fMRI Activation in Predicting Cognitive Decline in Older Adults

Previous studies suggest that task-activated functional magnetic resonance imaging (fMRI) can predict future cognitive decline among healthy older adults. The present fMRI study examined the relative sensitivity of semantic memory (SM) versus episodic memory (EM) activation tasks for predicting cognitive decline. Seventy-eight cognitively intact elders underwent neuropsychological testing at entry and after an 18-month interval, with participants classified as cognitively “Stable” or “Declining” based on ≥1.0 SD decline in performance. Baseline fMRI scanning involved SM (famous name discrimination) and EM (name recognition) tasks. SM and EM fMRI activation, along with Apolipoprotein E (APOE) ε4 status, served as predictors of cognitive outcome using a logistic regression analysis. Twenty-seven (34.6%) participants were classified as Declining and 51 (65.4%) as Stable. APOE ε4 status alone significantly predicted cognitive decline (R2 = .106; C index = .642). Addition of SM activation significantly improved prediction accuracy (R2 = .285; C index = .787), whereas the addition of EM did not (R2 = .212; C index = .711). In combination with APOE status, SM task activation predicts future cognitive decline better than EM activation. These results have implications for use of fMRI in prevention clinical trials involving the identification of persons at-risk for age-associated memory loss and Alzheimer\u27s disease. (JINS, 2012, 18, 1–11

Exploring Interpretability for Predictive Process Analytics

Modern predictive analytics underpinned by machine learning techniques has become a key enabler to the automation of data-driven decision making. In the context of business process management, predictive analytics has been applied to making predictions about the future state of an ongoing business process instance, for example, when will the process instance complete and what will be the outcome upon completion. Machine learning models can be trained on event log data recording historical process execution to build the underlying predictive models. Multiple techniques have been proposed so far which encode the information available in an event log and construct input features required to train a predictive model. While accuracy has been a dominant criterion in the choice of various techniques, they are often applied as a black-box in building predictive models. In this paper, we derive explanations using interpretable machine learning techniques to compare and contrast the suitability of multiple predictive models of high accuracy. The explanations allow us to gain an understanding of the underlying reasons for a prediction and highlight scenarios where accuracy alone may not be sufficient in assessing the suitability of techniques used to encode event log data to features used by a predictive model. Findings from this study motivate the need and importance to incorporate interpretability in predictive process analytics.Comment: 15 pages, 7 figure

Recognition of Famous Names Predicts Cognitive Decline in Healthy Elders

Objective: The ability to recognize familiar people is impaired in both Mild Cognitive Impairment (MCI) and Alzheimer’s Dementia (AD). In addition, both groups often demonstrate a time-limited temporal gradient (TG) in which well known people from decades earlier are better recalled than those learned recently. In this study, we examined the TG in cognitively intact elders for remote famous names (1950–1965) compared to more recent famous names (1995–2005). We hypothesized that the TG pattern on a famous name recognition task (FNRT) would predict future cognitive decline, and also show a significant correlation with hippocampal volume. Method: Seventy-eight healthy elders (ages 65–90) with age-appropriate cognitive functioning at baseline were administered a FNRT. Follow-up testing 18 months later produced two groups: Declining (≥ 1 SD reduction on at least one of three measures) and Stable (\u3c 1 SD). Results: The Declining group (N = 27) recognized fewer recent famous names than the Stable group (N = 51), although recognition for remote names was comparable. Baseline MRI volumes for both the left and right hippocampi were significantly smaller in the Declining group than the Stable group. Smaller baseline hippocampal volume was also significantly correlated with poorer performance for recent, but not remote famous names. Logistic regression analyses indicated that baseline TG performance was a significant predictor of group status (Declining vs. Stable) independent of chronological age and APOE ε4 inheritance. Conclusions: The TG for famous name recognition may serve as an early preclinical cognitive marker of cognitive decline in healthy older individual

A COBRA/TRAC, Best-Estimate Analysis of a Large-Break Accident in a PWR Equipped with Upper Head Injection

This report is about the Best-Estimate Analysis of a Large-Break Accident in a PWR Equipped with Upper Head Injection. It also documents about the simulation of a double ended (200 percent), cold leg break, loss-of-coolant accident in a PWR Equipped with Upper Head Injection

A COBRA/TRAC, Best-Estimate Analysis of a Large-Break Accident in a PWR Equipped with Upper Head Injection

This report is about the Best-Estimate Analysis of a Large-Break Accident in a PWR Equipped with Upper Head Injection. It also documents about the simulation of a double ended (200 percent), cold leg break, loss-of-coolant accident in a PWR Equipped with Upper Head Injection