This dataset includes core physical properties (e.g., bulk density, porosity, P-wave velocity) and magnetic susceptibility data for SPR0901-04BC (34.2816°N, 120.0415°W, 588 m water depth) measured on the multisensor track (MST). SPR0901-04BC was collected by the research vessel R/V Sproul off Southern California in 2009.1. The study is supported by NSF OCE-0752093.
The data and the scripts are to show that seizure onset dynamics and evoked responses change over the progression of epileptogenesis defined in this intrahippocampal tetanus toxin rat model. All tests explored in this study can be repeated with the data and scripts included in this repository. and Dataset citation: Crisp, D.N., Cheung, W., Gliske, S.V., Lai, A., Freestone, D.R., Grayden, D.B., Cook, MJ., Stacey, W.C. (2019). Epileptogenesis modulates spontaneous and responsive brain state dynamics [Data set]. University of Michigan Deep Blue Data Repository. https://doi.org/10.7302/r6vg-9658
This collection represents various raw data and analysis of cores extracted during the November 2008 mission of R/V Melville in the Santa Barbara Basin., The core included is the jumbo piston core MV0811-14JC. Core photos, physical properties and magnetic susceptibility from the multisensor track (MST), and the scanning X-ray fluorescence (XRF) data are included in the collection., and Cruise DOI: 10.7284/903459
The research is funded by NSF OCE-1304327.
The goal of this research was to understand structures where the solar wind plasma contribution to the total plasma was equal to the ionospheric plasma. This simulation was performed over a simulation time of 12 hours for 4 different plasma compositions for 2 different solar wind profiles., The SWMF used the Block Adaptive Tree Solar wind Roe-type Upwind Scheme version 9.20. It can be found at http://csem.engin.umich.edu/tools/swmf/downloads.php. These data can be processed using the simulation code deposited at the Deep Blue Data record indicated in the "Citation to related material" field., and To cite this data set: Trung, H.-S., Liemohn, M., W., Ilie, R. (2019). 12 hour data for magnetospheric simulations for a multifluid plasma for 8 different configurations [Data set]. University of Michigan Deep Blue Data Repository. https://doi.org/10.7302/fwq2-ey41
The work on accelerating authenticated boot for embedded system resulted in designing an algorithm in python to perform the random address generation and cryptographic MAC calculation.
The Sampled Boot schemes implemented in this package allow a significant reduction of the time
needed to authenticate firmware images during startup, while still retaining a high degree of trust.
This is particularly useful for automotive applications in which startup time constraints make secure boot a time prohibitive process. and Citation for this dataset: Nasser, A., Gumise, W. (2019). Authenticated Boot Acceleration Algorithm [Code and data]. University of Michigan Deep Blue Data Repository. https://doi.org/10.7302/yeh1-1x17
The outputs include the steady state solutions for all Galileo flybys, the particle information for plotting the distribution functions near the reconnection site, the particle and field data for mapping the energetic flux densities, and 3D files for visualizing the whole simulation domain. More details can be found in Readme.txt.
The relationship between words in a sentence often tell us more about the underlying semantic content of a document than its actual words, individually. Recent publications in the natural language processing arena, more specifically using word embeddings, try to incorporate semantic aspects into their word vector representation by considering the context of words and how they are distributed in a document collection. In this work, we propose two novel algorithms, called Flexible Lexical Chain II and Fixed Lexical Chain II that combine the semantic relations derived from lexical chains, prior knowledge from lexical databases, and the robustness of the distributional hypothesis in word embeddings into a single decoupled system. In short, our approach has three main contributions: (i) unsupervised techniques that fully integrate word embeddings and lexical chains; (ii) a more solid semantic representation that considers the latent relation between words in a document; and (iii) lightweight word embeddings models that can be extended to any natural language task. Knowledge-based systems that use natural language text can benefit from our approach to mitigate ambiguous semantic representations provided by traditional statistical approaches. The proposed techniques are tested against seven word embeddings algorithms using five different machine learning classifiers over six scenarios in the document classification task. Our results show that the integration between lexical chains and word embeddings representations sustain state-of-the-art results, even against more complex systems.
Manganese in the sedimentary record has been interpreted by many as a powerful redox proxy for paleoenvironments, and yet very little work has been done to ensure that the manganese-rich minerals in the rock record are actually recording primary signals. In the accompanying manuscript, we present an in-depth characterization of the manganese mineralogy from two correlated regions recording the Transvaal Supergroup in South Africa with markedly different alteration histories to investigate if there can be post-depositional emplacement of manganese-rich minerals. The data uploaded here are X-ray absorption spectra of (1) manganese standard minerals that were useful in our analyses and (2) minerals from an important well-characterized sample that may be useful as comparative standards in future studies.
SWMF is used to study the segmentation of SED plume into polar cap patches during the geomagnetic storm on Sep 7, 2017. The database includes the 3D output in the upper atmosphere from GITM, the 2D output from Ionospheric Electrodynamics (IE) and 3D output from BATSRUS. The output from GITM can be read with thermo_batch_new.pro. The output from IE can be opened with Spacepy at https://pythonhosted.org/SpacePy/. The output from BATSRUS can be opened with tecplot.
More details can be found in Readme.txt.
Model simulations were conducted to investigate the role of soil moisture on the terrestrial carbon and water cycles. The data are composed of NetCDF files generated by the simulations that contain the data variables analyzed in the paper. and CLM5 Documentation - http://www.cesm.ucar.edu/models/cesm2/land/.