The project outputs summarize all the publications, talks, and codes we accomplished under this NSF funding. In the project, we develop methodologies to manage uncertainty in future electric power systems and quantify how uncertainty affects power system sustainability. and Talks, papers, and poster in Deep Blue: http://hdl.handle.net/2027.42/149653
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.
This is the code that resulted from NSF grant ECCS-1508943, "Inferring the behavior of distributed energy resources from incomplete measurements." The project focused on developing control, estimation, and modeling methods for residential demand response and electric distribution networks.
The talks, papers, and poster in Deep Blue: http://hdl.handle.net/2027.42/149480
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.
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.
The writing samples included in this folder were collected as part of a longitudinal study in writing development published in Developing Writers in Higher Education: A Longitudinal Study (University of Michigan Press, 2019). Writing samples were chosen and uploaded by students as part of the study and come from lower and upper level courses. To learn more about this study, please see the epublication https://doi.org/10.3998/mpub.10079890.
The interviews included in this folder were conducted as part of a longitudinal study in writing development published in Developing Writers in Higher Education: A Longitudinal Study (University of Michigan Press, 2019). Interviews were conducted upon students' entry into the study (files labelled "entry") and exit from the study (files labelled "exit"). To learn more about this study, please see the epublication https://doi.org/10.3998/mpub.10079890 and the website https://www.developingwritersbook.org/pages/about/about-the-study/.
This data set is a collection of word similarity benchmarks (RG65, MEN3K, Wordsim 353, simlex999, SCWS, yp130, simverb3500) in their original format and converted into a cosine similarity scale.
In addition, we have two Wikpedia Dumps from 2010 (April) and 2018 (January) in which we provide the original format (raw words), converted using the techniques described in the paper (MSSA, MSSA-D and MSSA-NR) (title in this repository), and also the word embeddings models for 300d and 1000d using a word2vec implementation. A readme.txt is provided with more details for each file.
SPSS is required to access processed dataset in .sav format. Model output is provided as a word document, and Qualtrics survey instrument is included as PDF and .docx, where .docx version contains survey logic and question numbers.