1,042 research outputs found
sj-zip-1-smr-10.1177_00491241231200194 - Supplemental material for High-Dimensional Imputation for the Social Sciences: A Comparison of State-of-The-Art Methods
No full textSupplemental material, sj-zip-1-smr-10.1177_00491241231200194 for High-Dimensional Imputation for the Social Sciences: A Comparison of State-of-The-Art Methods by Edoardo Costantini, Kyle M. Lang, Tim Reeskens and Klaas Sijtsma in Sociological Methods & Research</p
Supplemental material for High-Dimensional Imputation for the Social Sciences: A Comparison of State-of-The-Art Methods
No full textSupplemental material, sj-zip-1-smr-10.1177_00491241231200194 for High-Dimensional Imputation for the Social Sciences: A Comparison of State-of-The-Art Methods by Edoardo Costantini, Kyle M. Lang, Tim Reeskens and Klaas Sijtsma in Sociological Methods & Researc
First person – Kyle Wegner
No full textFirst Person is a series of interviews with the first authors of a selection of papers published in Biology Open, helping early-career researchers promote themselves alongside their papers. Kyle Wegner is first author on ‘Edar is a downstream target of beta-catenin and drives collagen accumulation in the mouse prostate’, published in BIO. Kyle is a PhD candidate in the lab of Chad M. Vezina at the University of Wisconsin-Madison, investigating principles of toxicology and urology to evaluate mechanisms of urinary dysfunction in aging men
sj-zip-1-smr-10.1177_00491241231200194 - Supplemental material for High-Dimensional Imputation for the Social Sciences: A Comparison of State-of-The-Art Methods
No full textSupplemental material, sj-zip-1-smr-10.1177_00491241231200194 for High-Dimensional Imputation for the Social Sciences: A Comparison of State-of-The-Art Methods by Edoardo Costantini, Kyle M. Lang, Tim Reeskens and Klaas Sijtsma in Sociological Methods & Researc
209 - Kyle Matthew Nardi
No full textIncludes bibliographical references.Atmospheric rivers (ARs), narrow corridors of high atmospheric water vapor transport, influence large regions of the West Coast of North America, from southern California to British Columbia and Alaska. Regardless of location, areas influenced by landfalling ARs face various threats and disruptions from excessive rainfall and associated runoff. Therefore, improving forecasts of AR occurrence and characteristics is of great importance to those responsible for protecting life and property. When providing the public with outlooks and warnings related to ARs, forecasters must confront the challenge of assessing the output of different numerical weather prediction (NWP) models. Specifically, forecasters must understand how performance varies across different time scales, geographical regions, and individual models. Prior work, such as Wick et al. (2013), has examined the forecast skill
of several NWP models at different lead times, yet as models are continuously updated, a fresh perspective on AR forecast performance is desired. This study aims to assess how different weather forecast models perform at varying lead times and for distinct regions of the West Coast of North America. Re-forecasts from several operational NWP models, obtained from the International S2S Project Database, are run out to approximately 60 days. An atmospheric river detection algorithm is applied to the model output in order to quantify how the models handle such features. The study examines atmospheric river re-forecasts for the West Coast of North America as well as three non-overlapping sub-regions along the coast. The first sub-region extends from southern California to the Oregon border. The second sub-region covers the Pacific Northwest from southern Oregon to the northern extent of Vancouver Island. The third and final sub-region consists of the coasts of British Columbia and southeastern Alaska. Together, these regions represent a large fraction of the AR landfall locations for western North America. Model performance is studied through the lens of AR occurrence, intensity, and location. Results indicate variations in re-forecast skill as a function of lead time, geographic region, and model used. A desired near-term outcome of this work is an increased awareness of both the utility and limitations of NWP models in the prediction of atmospheric river events at short, medium, and long-range leads. A desired long-term outcome is the use of these results as a bridge to understanding what gives rise to the differing characters of atmospheric rivers over the northeast Pacific and how models can improve their depictions of such features
The assembled body: Anatomical enumeration and embodiment in Anglo-Saxon devotional texts
Full text link"""The Assembled Body: Anatomical Enumeration and Embodiment in Anglo-Saxon Devotional Texts"" argues that Anglo-Saxon Christians viewed the material body as a potent site for spiritual transformation. This notion finds its fullest expression in the rhetorical scheme of anatomical enumeration which appears across a diverse collection of Old English and Anglo-Latin devotional forms that range from the seventh to eleventh century, such as anonymous personal protective charms and prayers, confessional formulae, monastic execrations, scientific writing and diagrams produced Byrhtferth, as well as a number of Ælfric of Eynsham's vernacular homilies. This project demonstrates how Anglo-Saxon authors employed such enumerative anatomical catalogs to highlight the vibrancy of the flesh at moments spiritual uncertainty. Casting the material body as an assemblage of agents, this rhetorical disarticulation of the flesh enables readers to envision the realignment and reintegration of their disordered and disobedient limbs into the unity of Christ's spiritual body."Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2020-05-01The student, Kyle Williams, accepted the attached license on 2018-03-20 at 11:29.The student, Kyle Williams, submitted this Dissertation for approval on 2018-03-20 at 11:41.This Dissertation was approved for publication on 2018-03-30 at 15:42.DSpace SAF Submission Ingestion Package generated from Vireo submission #12078 on 2018-08-31 at 17:25:34Made available in DSpace on 2018-09-04T20:46:51Z (GMT). No. of bitstreams: 2
WILLIAMS-DISSERTATION-2018.pdf: 5298733 bytes, checksum: 029b45c559412aeb5ca7681ebe6395b4 (MD5)
LICENSE.txt: 4210 bytes, checksum: a9a07b045dba23f0f2e6ec11fb6ed911 (MD5)
Previous issue date: 2018-03-30Embargo set by: Seth Robbins for item 107350
Lift date: 2020-09-04T20:47:38Z
Reason: Author requested closed access (OA after 2yrs) in Vireo ETD systemEmbargo set by: Seth Robbins for item 107350
Lift date: 2020-09-04T20:50:11Z
Reason: Author requested closed access (OA after 2yrs) in Vireo ETD systemLimited Restriction Lifted for Item 107350 on 2020-09-05T09:15:26Z
desihub/speclite: Bug fix release: General clean-up prior to refactoring package infrastructure
No full textThis release includes a number of bug fixes. The intention is that this release will be the last before a major refactor of the package infrastructure, e.g. preparing to eliminate the use of setup.py. See PR #91 and #92 for further details.David Kirkby, Thomas Robitaille, Benjamin Alan Weaver, Moustakas, Erik Tollerud, Michael Droettboom, Brigitta Sipőcz, E. M. Bray, Larry Bradley, Andy Park, J. Michael Burgess, Marcelo Alvarez, Stephen Bailey, Sergey Koposov, Dustin Lang, Matt Craig, Christoph Deil, P. L. Lim, Kyle Barbary, … Hans Moritz Günther. (2024). desihub/speclite: Bug fix release: General clean-up prior to refactoring package infrastructure (v0.20). Zenodo. https://doi.org/10.5281/zenodo.1322553
Store House
No full textThesis: M. Arch., Massachusetts Institute of Technology, Department of Architecture, 2014.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (page 234).Since 1950 the average US home has grown from 1100 square feet to over 2600 square feet. During this same period the average family size shrunk by a person, meaning that per capita residential square footage has more than tripled in less than 60 years. What's more, if one looks at residential storage capacity as an indicator of consumption, its notable that the average citizen has 830% more storage space today than they did in the fifties. Paradoxically, in the last decade other forms of ownership have lost favor. The appetite for conventional ownership has been, in part, supplanted by a disinterest in maintenance and responsibility. Subscription services have begun to replace the conventional retail transaction. At first people rented the intangible and ephemeral but in the last few years they have begun renting things that would have seemed technologically impossible, or at a minimum improbable, ten years ago. This new mode of collective ownership represents a societal shift that architecture is lagging behind. This thesis aspires to use the spatial generosity of storage and the burgeoning sharing economy to re-imagine a suburb that promotes the sharing of rarely used objects & spaces amongst neighbors to foster community and reduce consumption.by Kyle BarkerM. Arch
Message passing neural networks for molecular property prediction
No full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis: M. Eng. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 81-84).Developing new drugs relies heavily on understanding the various molecular properties of potential drug candidates. While experimental assays performed in the lab are the best source of information about molecular properties, these assays are slow and expensive. For this reason, there has been great interest in the potential of machine learning models to predict molecular properties without the need for experimental assays. However, recent literature has not yet clearly determined which machine learning models are optimal for molecular property prediction. In this thesis, I apply the Direct Message Passing Neural Network (D-MPNN) from [47, 48] to 19 publicly available property prediction datasets, and I demonstrate that it consistently outperforms prior machine learning models. Additionally, I introduce several optimizations to the D-MPNN which further enhance its performance and lead to new state-of-the-art results.by Kyle Swanson.M. Eng. in Computer Science and EngineeringM.Eng.inComputerScienceandEngineering Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienc
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