A summary of existing and planned experiment hardware for low-gravity fluids research

An overview is presented of (1) existing ground-based, low gravity research facilities, with examples of hardware capabilities, and (2) existing and planned space-based research facilities, with examples of current and past flight hardware. Low-gravity, ground-based facilities, such as drop towers and aircraft, provide the experimenter with quick turnaround time, easy access to equipment, gravity levels ranging from 10(exp -2) to 10(exp -6) G, and low-gravity durations ranging from 2 to 30 sec. Currently, the only operational space-based facility is the Space Shuttle. The Shuttle's payload bay and middeck facilities are described. Existing and planned low-gravity fluids research facilities are also described with examples of experiments and hardware capabilities

Compressibility effects on fluid entrainment by turbulent mixing layers

Compressibility effects on fluid entrainment by supersonic turbulent mixing layer

The utilization of neural nets in populating an object-oriented database

Existing NASA supported scientific data bases are usually developed, managed and populated in a tedious, error prone and self-limiting way in terms of what can be described in a relational Data Base Management System (DBMS). The next generation Earth remote sensing platforms (i.e., Earth Observation System, (EOS), will be capable of generating data at a rate of over 300 Mbs per second from a suite of instruments designed for different applications. What is needed is an innovative approach that creates object-oriented databases that segment, characterize, catalog and are manageable in a domain-specific context and whose contents are available interactively and in near-real-time to the user community. Described here is work in progress that utilizes an artificial neural net approach to characterize satellite imagery of undefined objects into high-level data objects. The characterized data is then dynamically allocated to an object-oriented data base where it can be reviewed and assessed by a user. The definition, development, and evolution of the overall data system model are steps in the creation of an application-driven knowledge-based scientific information system

Lambda-Cold Dark Matter, Stellar Feedback, and the Galactic Halo Abundance Pattern

(Abridged) The hierarchical formation scenario for the stellar halo requires the accretion and disruption of dwarf galaxies, yet low-metallicity halo stars are enriched in alpha-elements compared to similar, low-metallicity stars in dwarf spheroidal (dSph) galaxies. We address this primary challenge for the hierarchical formation scenario for the stellar halo by combining chemical evolution modelling with cosmologically-motivated mass accretion histories for the Milky Way dark halo and its satellites. We demonstrate that stellar halo and dwarf galaxy abundance patterns can be explained naturally within the LCDM framework. Our solution relies fundamentally on the LCDM model prediction that the majority of the stars in the stellar halo were formed within a few relatively massive, ~5 x 10^10 Msun, dwarf irregular (dIrr)-size dark matter halos, which were accreted and destroyed ~10 Gyr in the past. These systems necessarily have short-lived, rapid star formation histories, are enriched primarily by Type II supernovae, and host stars with enhanced [a/Fe] abundances. In contrast, dwarf spheroidal galaxies exist within low-mass dark matter hosts of ~10^9 Msun, where supernovae winds are important in setting the intermediate [a/Fe] ratios observed. Our model includes enrichment from Type Ia and Type II supernovae as well as stellar winds, and includes a physically-motivated supernovae feedback prescription calibrated to reproduce the local dwarf galaxy stellar mass - metallicity relation. We use representative examples of the type of dark matter halos we expect to host a destroyed ``stellar halo progenitor'' dwarf, a surviving dIrr, and a surviving dSph galaxy, and show that their derived abundance patterns, stellar masses, and gas masses are consistent with those observed for each type of system.Comment: 10 pages, 3 figures, version accepted by Ap

Word Embeddings for Entity-annotated Texts

Learned vector representations of words are useful tools for many information retrieval and natural language processing tasks due to their ability to capture lexical semantics. However, while many such tasks involve or even rely on named entities as central components, popular word embedding models have so far failed to include entities as first-class citizens. While it seems intuitive that annotating named entities in the training corpus should result in more intelligent word features for downstream tasks, performance issues arise when popular embedding approaches are naively applied to entity annotated corpora. Not only are the resulting entity embeddings less useful than expected, but one also finds that the performance of the non-entity word embeddings degrades in comparison to those trained on the raw, unannotated corpus. In this paper, we investigate approaches to jointly train word and entity embeddings on a large corpus with automatically annotated and linked entities. We discuss two distinct approaches to the generation of such embeddings, namely the training of state-of-the-art embeddings on raw-text and annotated versions of the corpus, as well as node embeddings of a co-occurrence graph representation of the annotated corpus. We compare the performance of annotated embeddings and classical word embeddings on a variety of word similarity, analogy, and clustering evaluation tasks, and investigate their performance in entity-specific tasks. Our findings show that it takes more than training popular word embedding models on an annotated corpus to create entity embeddings with acceptable performance on common test cases. Based on these results, we discuss how and when node embeddings of the co-occurrence graph representation of the text can restore the performance.Comment: This paper is accepted in 41st European Conference on Information Retrieva

Dynamics of axial separation in long rotating drums

We propose a continuum description for the axial separation of granular materials in a long rotating drum. The model, operating with two local variables, concentration difference and the dynamic angle of repose, describes both initial transient traveling wave dynamics and long-term segregation of the binary mixture. Segregation proceeds through ultra-slow logarithmic coarsening.Comment: 4 pages, 3 Postscript figures; submitted to PR

Simultaneous Kummer congruences and E∞\mathbb{E}_\infty-orientations of KO and tmf

Building on results of M. Ando, M.J. Hopkins and C. Rezk, we show the existence of uncountably many $\mathbb{E}_\infty$-String orientations of real K-theory KO and of topological modular forms tmf, generalizing the $\hat{A}$- (resp. the Witten) genus. Furthermore, the obstruction to lifting an $\mathbb{E}_\infty$-String orientations from KO to tmf is identified with a classical Iwasawa-theoretic condition. The common key to all these results is a precise understanding of the classical Kummer congruences, imposed for all primes simultaneously. This result is of independent arithmetic interest.Comment: final versio

Tests of a simple data merging algorithm for the GONG project

The GONG (Global Oscillation Network Group) project proposes to reduce the impact of diurnal variations on helioseismic measurements by making long-term observations of solar images from six sites placed around the globe. The sun will be observed nearly constantly for three years, resulting in the acquisition of l+ terabyte of image data. To use the solar network to maximum advantage, the images from the sites must be combined into a single time series to determine mode frequencies, amplitudes, and line widths. Initial versions of combined, i.e., merged, time series were made using a simple weighted average of data from different sites taken simultaneously. In order to accurately assess the impact of the data merge on the helioseismic measurements, a set of artificial solar disk images was made using a standard solar model and containing a well known set of oscillation modes and frequencies. This undegraded data set and data products computed from it were used to judge the relative merits of various data merging schemes. The artificial solar disk images were subjected to various instrumental and atmospheric degradations, dependent on site and time, in order to create a set of images simulating those likely to be taken at the site. The degraded artificial solar disk images for the six observing sites were combined in various ways to form merged time series of images and mode coefficients. Various forms of a weighted average were used, including an equally-weighted average, an average with weights dependent upon air mass and averages with weights dependent on various quality assurance parameters. Both the undegraded solar disk image time series and several time series made up of various combinations of the degraded solar disk images from the six sites were subjected to standard helioseismic measurement processing. This processing consisted of coordinate remapping, detrending, spherical harmonic transformation, computation of power series for the oscillation mode coefficients, and mode frequency identification. Visual and statistical evaluation of the merged data sets themselves and differences between the merged and undegraded data set shows good agreement between the two data sets. Some slight differences in image scale and registration appear between the undegraded data set and the various merged data sets. In the set of power series made from the mode coefficients of the merged data sets, some power leakage is observed into the background and into slightly lower l-value modes, especially at higher l-value mode frequencies. The results of the comparison of time series and mode oscillation frequencies of the undegraded data with those of the data merged using weighted averages indicate that, at least for p-mode solar oscillations, a weighted average of either the detrended remapped images or the mode coefficients gives good determination of the mode frequencies and adequate-to-good determination of the amplitudes and widths of the mode frequency lines. This conclusion is most advantageous to the analysis of the massive amounts of data to be received by the GONG network