Formation And Characterization of Amorphous Erbium-Based Alloys Prepared By Near-Isothermal Cold-Rolling of Elemental Composites

The article originally appeared in Journal of Applied Physics 58, 3885 (1985) and may also be found at The article originally appeared in Journal of Applied Physics 58, 3865 (1985) and may also be found at ttp://jap.aip.org/resource/1/japiau/v58/i10/p3865_s1Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83409/1/Atzmon_Johnson_JAP1985.pd

Low Molecular Weight Fluorescent Probes (LMFPs) to Detect the Group 12 Metal Triad

Fluorescence sensing, of d-block elements such as Cu2+, Fe3+, Fe2+, Cd2+, Hg2+, and Zn2+ has significantly increased since the beginning of the 21st century. These particular metal ions play essential roles in biological, industrial, and environmental applications, therefore, there has been a drive to measure, detect, and remediate these metal ions. We have chosen to highlight the low molecular weight fluorescent probes (LMFPs) that undergo an optical response upon coordination with the group 12 triad (Zn2+, Cd2+, and Hg2+), as these metals have similar chemical characteristics but behave differently in the environment

Convolutional neural networks can decode eye movement data: A black box approach to predicting task from eye movements

Previous attempts to classify task from eye movement data have relied on model architectures designed to emulate theoretically defined cognitive processes and/or data that have been processed into aggregate (e.g., fixations, saccades) or statistical (e.g., fixation density) features. Black box convolutional neural networks (CNNs) are capable of identifying relevant features in raw and minimally processed data and images, but difficulty interpreting these model architectures has contributed to challenges in generalizing lab-trained CNNs to applied contexts. In the current study, a CNN classifier was used to classify task from two eye movement datasets (Exploratory and Confirmatory) in which participants searched, memorized, or rated indoor and outdoor scene images. The Exploratory dataset was used to tune the hyperparameters of the model, and the resulting model architecture was retrained, validated, and tested on the Confirmatory dataset. The data were formatted into timelines (i.e., x-coordinate, y-coordinate, pupil size) and minimally processed images. To further understand the informational value of each component of the eye movement data, the timeline and image datasets were broken down into subsets with one or more components systematically removed. Classification of the timeline data consistently outperformed the image data. The Memorize condition was most often confused with Search and Rate. Pupil size was the least uniquely informative component when compared with the x- and y-coordinates. The general pattern of results for the Exploratory dataset was replicated in the Confirmatory dataset. Overall, the present study provides a practical and reliable black box solution to classifying task from eye movement data

The Small Magellanic Cloud Investigation of Dust and Gas Evolution (SMIDGE): The Dust Extinction Curve from Red Clump Stars

We use Hubble Space Telescope (HST) observations of red clump stars taken as part of the Small Magellanic Cloud Investigation of Dust and Gas Evolution (SMIDGE) program to measure the average dust extinction curve in a ~ 200 pc x 100 pc region in the southwest bar of the Small Magellanic Cloud (SMC). The rich information provided by our 8-band ultra-violet through near-infrared photometry allows us to model the color-magnitude diagram of the red clump accounting for the extinction curve shape, a log-normal distribution of $A_{V}$, and the depth of the stellar distribution along the line of sight. We measure an extinction curve with $R_{475} = A_{475}/(A_{475}-A_{814})$ = 2.65 $\pm$ 0.11. This measurement is significantly larger than the equivalent values of published Milky Way $R_{V}$ = 3.1 ($R_{475} = 1.83$) and SMC Bar $R_{V}$ = 2.74 ($R_{475} = 1.86$) extinction curves. Similar extinction curve offsets in the Large Magellanic Cloud (LMC) have been interpreted as the effect of large dust grains. We demonstrate that the line-of-sight depth of the SMC (and LMC) introduces an apparent "gray" contribution to the extinction curve inferred from the morphology of the red clump. We show that no gray dust component is needed to explain extinction curve measurements when a full-width half-max depth of 10 $\pm$ 2 kpc in the stellar distribution of the SMC (5 $\pm$ 1 kpc for the LMC) is considered, which agrees with recent studies of Magellanic Cloud stellar structure. The results of our work demonstrate the power of broad-band HST imaging for simultaneously constraining dust and galactic structure outside the Milky Way.Comment: 16 pages, 12 figures, 5 tables. Accepted for publication in Ap

Potential mammalian filovirus reservoirs

Centers for Disease Control and Prevention http://www.cdc.gov/ncidod/EID/vol10no12/04-0346.htmEbola and Marburg viruses are maintained in unknown reservoir species; spillover into human populations results in occasional human cases or epidemics. We attempted to narrow the list of possibilities regarding the identity of those reservoir species. We made a series of explicit assumptions about the reservoir: it is a mammal; it supports persistent, largely asymptomatic filovirus infections; its range subsumes that of its associated filovirus; it has coevolved with the virus; it is of small body size; and it is not a species that is commensal with humans. Under these assumptions, we developed priority lists of mammal clades that coincide distributionally with filovirus outbreak distributions and compared these lists with those mammal taxa that have been tested for filovirus infection in previous epidemiologic studies. Studying the remainder of these taxa may be a fruitful avenue for pursuing the identity of natural reservoirs of filoviruses

Geographic potential for outbreaks of Marburg hemorrhagic fever

Am. J. Trop. Med. Hyg., 75(1), 2006, pp. 9–15 Copyright © 2006 by The American Society of Tropical Medicine and Hygiene 9 http://www.ajtmh.org/cgi/content/abstract/75/1/9Marburg virus represents one of the least well-known of the hemorrhagic fever-causing viruses worldwide; in particular, its geographic potential in Africa remains quite mysterious. Ecologic niche modeling was used to explore the geographic and ecologic potential of Marburg virus in Africa. Model results permitted a reinterpretation of the geographic point of infection in the initiation of the 1975 cases in Zimbabwe, and also anticipated the potential for cases in Angola, where a large outbreak recently (2004–2005) occurred. The geographic potential for additional outbreaks is outlined, including in several countries in which the virus is not known. Overall, results demonstrate that ecologic niche modeling can be a powerful tool in understanding geographic distributions of species and other biologic phenomena such as zoonotic disease transmission from natural reservoir populations

Toward Agent-based Modeling of the U.S. Department of Defense Acquisition System

The systems development, procurement and sustainment of a nation\u27s military equipment is vital to its national interests, but the process is complex, constantly changing and highly adaptive, as well as time consuming and costly. The U.S. Department of Defense (DoD) expends both large amounts of capital and manpower to equip its armed forces. This research seeks to identify opportunities to gain better insight into the functioning of the defense acquisition system, building on previous simulations. A case is made that the DoD Requirements, Planning Acquisition, Technology and Logistics System is a complex adaptive system that has characteristics appropriate for exploration with agent-based modeling. This paper reviews relevant literature, existing models and presents preliminary analysis on an agent-based simulation of the DoD acquisition system. This research finds that agent-based modeling can provide insights to inform new acquisition theory

Deep-Learning-Based Multivariate Pattern Analysis (dMVPA): A Tutorial and a Toolbox

In recent years, multivariate pattern analysis (MVPA) has been hugely beneficial for cognitive neuroscience by making new experiment designs possible and by increasing the inferential power of functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and other neuroimaging methodologies. In a similar time frame, “deep learning” (a term for the use of artificial neural networks with convolutional, recurrent, or similarly sophisticated architectures) has produced a parallel revolution in the field of machine learning and has been employed across a wide variety of applications. Traditional MVPA also uses a form of machine learning, but most commonly with much simpler techniques based on linear calculations; a number of studies have applied deep learning techniques to neuroimaging data, but we believe that those have barely scratched the surface of the potential deep learning holds for the field. In this paper, we provide a brief introduction to deep learning for those new to the technique, explore the logistical pros and cons of using deep learning to analyze neuroimaging data – which we term “deep MVPA,” or dMVPA – and introduce a new software toolbox (the “Deep Learning In Neuroimaging: Exploration, Analysis, Tools, and Education” package, DeLINEATE for short) intended to facilitate dMVPA for neuroscientists (and indeed, scientists more broadly) everywhere