152 research outputs found
Environments associated with giant hail-producing thunderstorms in Northern Argentina
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Previous issue date: 2019-12-11Several studies have shown that South America is one of the prime locations for the production of intense hailstorms in the world, with previous research showing South America to be a region of large hail production using TRMM and other satellite proxies. Until recently, there has been limited research on the hail production in Argentina due to a lack of consistent and quality-controlled observations, yet the damage from hail caused approximately 50 million USD in wine production losses annually.
This research utilizes Twitter to identify hail events, which are verified using GOES-13 satellite imagery and/or C-band radar signatures in Northern Argentina. ERA-5 reanalysis is then used to reconstruct the environment of giant hail (diameter > 70 mm) events. We found that areas surrounding Córdoba and Mendoza, Argentina have (1) enhanced synoptic-scale ascent through the overlapping of upper-level divergence from two jet streaks downstream of an anomalous trough; (2) a deep layer of moisture supplied from the Amazon basin transport by the South American low-level jet below mid-level cool, dry westerlies; and (3) composite CAPE > 1200 J/kg capped by CIN ~100 J/kg. Even though (1) and (2) are similar to the environment of hailstorms in the U.S., the relatively lower CAPE and higher CIN for giant hail production in Argentina poses interesting questions for future work.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-12-01The student, Shaowen Chen, accepted the attached license on 2019-12-10 at 22:12.The student, Shaowen Chen, submitted this Thesis for approval on 2019-12-10 at 22:41.This Thesis was approved for publication on 2019-12-11 at 16:01.DSpace SAF Submission Ingestion Package generated from Vireo submission #14782 on 2020-02-28 at 17:24:22Embargo set by: Seth Robbins for item 113937
Lift date: 2022-03-02T22:18:25Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemU of I Only Restriction Lifted for Item 113937 on 2022-03-03T10:15:19Z
Data-intensive crop knowledge discovery in the era of cybergis and machine intelligence
No full textWith an increasing global population and continuing climate change, food security has become a grand scientific and societal challenge. To tackle this challenge, it is critically important to obtain timely crop information such as yield potential and growing conditions as crop information is often time sensitive for societal applications. With rapidly advancing remote sensing technologies such as satellite- and UAV-based approaches of fine-resolution, and continuous observation in visible bands, NIR, thermal, microwave for large geographic areas, timely crop knowledge discovery based on massive remote sensing data provides a promising means to tackle the food security challenge. Furthermore, to integrate remote sensing data of crops with related environmental data (e.g., temperature, precipitation, and radiation) can help understand crop changes in various environmental conditions. How to harness such rich data sources to achieve timely crop knowledge discovery based on advanced computing and geospatial approaches such as deep learning and cyberGIS for multiple agricultural applications is the primary focus of this dissertation research. Specifically, several interrelated studies have been conducted to achieve high-performance and in-season crop type classification at both the county and state scales in Illinois, USA; integrate climate and satellite data for wheat yield prediction in Australia; and detect in-season crop nitrogen stress using UAV- and CubeSat-based multispectral sensing at a field level. These studies are enabled by cutting-edge machine learning methods (e.g. deep neural networks) and advanced cyberGIS capabilities (e.g. ROGER supercomputer). Collectively, findings from the studies promise to transform data-intensive crop knowledge discovery.Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2020-02-28 without embargo termsThe student, Yaping Cai, accepted the attached license on 2019-12-02 at 17:07.The student, Yaping Cai, submitted this Dissertation for approval on 2019-12-02 at 17:20.This Dissertation was approved for publication on 2019-12-03 at 16:05.DSpace SAF Submission Ingestion Package generated from Vireo submission #14630 on 2020-02-28 at 17:14:38Made available in DSpace on 2020-03-02T21:58:19Z (GMT). No. of bitstreams: 3
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Previous issue date: 2019-12-0
Critical Theory and Interaction Design
No full textClassic texts by thinkers from Althusser to Žižek alongside essays by leaders in interaction design and HCI show the relevance of critical theory to interaction design.
Why should interaction designers read critical theory? Critical theory is proving unexpectedly relevant to media and technology studies. The editors of this volume argue that reading critical theory—understood in the broadest sense, including but not limited to the Frankfurt School—can help designers do what they want to do; can teach wisdom itself; can provoke; and can introduce new ways of seeing. They illustrate their argument by presenting classic texts by thinkers in critical theory from Althusser to Žižek alongside essays in which leaders in interaction design and HCI describe the influence of the text on their work. For example, one contributor considers the relevance Umberto Eco's “Openness, Information, Communication” to digital content; another reads Walter Benjamin's “The Author as Producer” in terms of interface designers; and another reflects on the implications of Judith Butler's Gender Trouble for interaction design. The editors offer a substantive introduction that traces the various strands of critical theory.
Taken together, the essays show how critical theory and interaction design can inform each other, and how interaction design, drawing on critical theory, might contribute to our deepest needs for connection, competency, self-esteem, and wellbeing.
Contributors Jeffrey Bardzell, Shaowen Bardzell, Olav W. Bertelsen, Alan F. Blackwell, Mark Blythe, Kirsten Boehner, John Bowers, Gilbert Cockton, Carl DiSalvo, Paul Dourish, Melanie Feinberg, Beki Grinter, Hrönn Brynjarsdóttir Holmer, Jofish Kaye, Ann Light, John McCarthy, Søren Bro Pold, Phoebe Sengers, Erik Stolterman, Kaiton Williams., Peter Wright
Classic texts Louis Althusser, Aristotle, Roland Barthes, Seyla Benhabib, Walter Benjamin, Judith Butler, Arthur Danto, Terry Eagleton, Umberto Eco, Michel Foucault, Wolfgang Iser, Alan Kaprow, Søren Kierkegaard, Bruno Latour, Herbert Marcuse, Edward Said, James C. Scott, Slavoj Žiže
Data-intensive crop knowledge discovery in the era of cybergis and machine intelligence
No full textWith an increasing global population and continuing climate change, food security has become a grand scientific and societal challenge. To tackle this challenge, it is critically important to obtain timely crop information such as yield potential and growing conditions as crop information is often time sensitive for societal applications. With rapidly advancing remote sensing technologies such as satellite- and UAV-based approaches of fine-resolution, and continuous observation in visible bands, NIR, thermal, microwave for large geographic areas, timely crop knowledge discovery based on massive remote sensing data provides a promising means to tackle the food security challenge. Furthermore, to integrate remote sensing data of crops with related environmental data (e.g., temperature, precipitation, and radiation) can help understand crop changes in various environmental conditions. How to harness such rich data sources to achieve timely crop knowledge discovery based on advanced computing and geospatial approaches such as deep learning and cyberGIS for multiple agricultural applications is the primary focus of this dissertation research. Specifically, several interrelated studies have been conducted to achieve high-performance and in-season crop type classification at both the county and state scales in Illinois, USA; integrate climate and satellite data for wheat yield prediction in Australia; and detect in-season crop nitrogen stress using UAV- and CubeSat-based multispectral sensing at a field level. These studies are enabled by cutting-edge machine learning methods (e.g. deep neural networks) and advanced cyberGIS capabilities (e.g. ROGER supercomputer). Collectively, findings from the studies promise to transform data-intensive crop knowledge discovery
スペクトル観点から効果的かつ効率的な個人推薦に向けて
No full text京都大学新制・課程博士博士(情報学)甲第25430号情博第868号新制||情||145(附属図書館)京都大学大学院情報学研究科社会情報学専攻(主査)教授 伊藤 孝行, 教授 田島 敬史, 教授 鹿島 久嗣, 教授 杉山 一成(大阪成蹊大学)学位規則第4条第1項該当Doctor of InformaticsKyoto UniversityDFA
Graph Neural Networks for Personalized Recommendation
No full textvideo/mp4Graph Neural Networks (GNNs) have shown great success in recommender systems in recent years. In this presentation, I will first give a brief introduction of GNN and personalized recommendation followed by some successful instantiations of GNN-based recommendation methods. Then, I will also introduce some challenges and further potential of GNN-based recommendation.講演日: 2024年6月20日講演場所: エーアイ大講義室, AI Inc. Seminar Hall （L1）vide
Auto-tuned optimized parallel I/O for GIScience and spatial applications
No full textReading and writing big data is increasingly becoming a major bottleneck of using high-performance computing systems as we are heading towards the Exascale era. An unprecedented amount of data is being produced everyday
by different sources. On the other hand, the computation power of HPC
systems is getting scaled to hundreds of thousands cores. However, for an application to be able to utilize this much data and computation power, using I/O effectively is a must. One of the fields dealing with huge amount of data is geographic information science.
In this thesis, we have implemented a parallel I/O library specialized for spatial data analysis in GIScience, capable of treating different I/O patterns such as Row-Wise, Column-Wise and Block-Wise I/O. We then establish an auto-tuning framework for finding optimal parallel I/O configurations. This auto-tuning framework is based on genetic
algorithm and works on a range of configurations from the parallel file system all the way up to spatial data-analysis applications. The results and findings of a set of I/O intensive experiments executed on large HPC systems are also presented to demonstrate the effectiveness of the framework.Item withdrawn by Mark Zulauf ([email protected]) on 2013-04-22T18:10:42Z
Item was in collections:
University of Illinois Theses & Dissertations (ID: 1)
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Original Data
Group with Access UIUC Users [automated]
Release Date: 2015-05-24 17:18:31 UTC
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemItem marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Seth Robbins ([email protected]) on 2013-05-24T22:18:40Z
Item is restricted until 2015-05-24T22:18:31ZU of I Only Restriction Lifted for Item 44387 on 2015-05-24T10:01:23Z
Data-intensive spatial pattern discovery based on generalized spatial point representations
No full textGeospatial big data consisting of records at the individual level or with fine spatial resolutions, such as geo-referenced social media posts and movement records collected using GPS, provide tremendous opportunities to understand complex geographic phenomena and their space-time dynamics. Such data have been widely used in many real-world applications, such as event detection and population migration analyses. These applications require not only efficient data handling and processing capabilities, but also innovative data models and analytical approaches that satisfy application-specific requirements. The aim of this dissertation research is to establish a suite of innovative methods for analyzing geospatial big data that can be modeled as generalized spatial points while addressing the following key research questions: how to estimate the spatial and spatiotemporal patterns of geographic phenomena from geospatial big data based on spatial point models? How to compare these patterns to gain insights into complex geographic phenomena? How to estimate the computational intensity of the methods? How can cyberGIS be advanced to resolve the computational intensity? Specifically, novel methods are designed in this dissertation research to exploit spatial data characteristics, innovate spatial point pattern analytics, and resolve computational intensity through high-performance spatial algorithms. Such methods are evaluated in the context of several real-world applications, including event detection from social media data and spatial movement pattern detection. Experiment results demonstrated that fine-scale spatial patterns can be revealed from geospatial big data using the proposed approaches. Novel cyberGIS software capabilities are also created as a result of this dissertation research.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2020-08-01The student, Yizhao Gao, accepted the attached license on 2018-07-10 at 20:22.The student, Yizhao Gao, submitted this Dissertation for approval on 2018-07-10 at 20:34.This Dissertation was approved for publication on 2018-07-13 at 09:28.DSpace SAF Submission Ingestion Package generated from Vireo submission #12804 on 2018-09-27 at 11:18:50Made available in DSpace on 2018-09-27T16:30:30Z (GMT). No. of bitstreams: 4
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Previous issue date: 2018-07-13Embargo set by: Seth Robbins for item 107796
Lift date: 2020-09-27T16:30:34Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemEmbargo set by: Seth Robbins for item 107796
Lift date: 2020-09-27T16:31:43Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemEmbargo set by: Seth Robbins for item 107796
Lift date: 2020-09-27T16:34:29Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemU of I Only Restriction Lifted for Item 107796 on 2020-09-28T09:15:22Z
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