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Articles 1 - 5 of 5
Full-Text Articles in Engineering
Towards Carbon-Aware Spatial Computing: Challenges And Opportunities, Bharat Jayaprakash, Matthew Eagon, Mingzhou Yang, William F. Northrop, Shashi Shekhar
Towards Carbon-Aware Spatial Computing: Challenges And Opportunities, Bharat Jayaprakash, Matthew Eagon, Mingzhou Yang, William F. Northrop, Shashi Shekhar
I-GUIDE Forum
Carbon-aware spatial computing (CASC) is focused on reducing the carbon footprint of spatial computing itself and leveraging spatial computing techniques to minimize carbon emissions in other domains. The significance of CASC lies in its potential to mitigate anthropogenic climate change by offering numerous societal applications, such as carbon-aware supply chain development and carbon-aware site selection. CASC is challenging because of the spatiotemporal variability and the high dimensionality of carbon emissions data, involving spatial coordinates and timestamps. Related work, known as carbon-aware computing, mostly focuses on job scheduling of cloud computing, and there is a lack of surveys and review papers …
Streamlined Hpc Environments With Cvmfs And Cybergis-Compute, Alexander C. Michels, Mit Kotak, Anand Padmanabhan, Shaowen Wang
Streamlined Hpc Environments With Cvmfs And Cybergis-Compute, Alexander C. Michels, Mit Kotak, Anand Padmanabhan, Shaowen Wang
I-GUIDE Forum
High-Performance Computing (HPC) resources provide the potential for complex, large-scale modeling and analysis, fueling scientific progress over the last few decades, but these advances are not equally distributed across disciplines. Those in computational disciplines are often trained to have the necessary technical skills to utilize HPC (e.g. familiarity with the terminal), but many disciplines face technical hurdles when trying to apply HPC resources to their work. This unequal familiarity with HPC is increasingly a problem as cross-discipline teams work to tackle critical interdisciplinary issues like climate change and sustainability. CyberGIS-Compute is middle-ware designed to democratize to HPC services with the …
Fine Tuning Mobilenet Neural Networks For Oil Spill Detection, Caixia Wang, Andrew Coulson
Fine Tuning Mobilenet Neural Networks For Oil Spill Detection, Caixia Wang, Andrew Coulson
I-GUIDE Forum
The monitoring of open water and early identification of oil spills in the Alaska Arctic has become increasingly critical due to the rise in oil and gas exploration and shipping activities, facilitated by the increasing number of ice-free days resulting from global warming. This escalating risk of oil spills is further compounded by potential accidents in offshore operations, illicit oil discharges, and knowledge gaps in Arctic coastlines, rapidly changing due to rising seas, permafrost melting, and coastal erosion. To address these pressing challenges, we propose a deep learning model based on MobileNet neural networks to detect oil spills in remotely …
I-Guide Climbers: A Model For Multidisciplinary Academic Labs For Early Career Development, Iman Haqiqi, Wei Hu, Ramya Kumaran, Pin-Ching Li, Nicholas Manning, Alex Michels, Ayman Nassar, Jinwoo Park, Jimeng Shi, Adam Tonks, Zhaonan Wang
I-Guide Climbers: A Model For Multidisciplinary Academic Labs For Early Career Development, Iman Haqiqi, Wei Hu, Ramya Kumaran, Pin-Ching Li, Nicholas Manning, Alex Michels, Ayman Nassar, Jinwoo Park, Jimeng Shi, Adam Tonks, Zhaonan Wang
I-GUIDE Forum
In this paper, we propose a new form of multidisciplinary academic collaboration that goes beyond the traditional modes of knowledge exchange. We argue that most research collaboration today is based on interactions between closely related disciplines, in which researchers share data, methods, and insights within a common framework or problem. However, such collaboration may not foster the development of the communication and management skills essential to a multi-disciplinary research career. Therefore, we suggest establishing a network of researchers from divergent, yet complementary, disciplines who are interested in improving these skills through regular interactions and feedback. The main goal of this …
Reducing Uncertainty In Sea-Level Rise Prediction: A Spatial-Variability-Aware Approach, Subhankar Ghosh, Shuai An, Arun Sharma, Jayant Gupta, Shashi Shekhar, Aneesh Subramanian
Reducing Uncertainty In Sea-Level Rise Prediction: A Spatial-Variability-Aware Approach, Subhankar Ghosh, Shuai An, Arun Sharma, Jayant Gupta, Shashi Shekhar, Aneesh Subramanian
I-GUIDE Forum
Given multi-model ensemble climate projections, the goal is to accurately and reliably predict future sea-level rise while lowering the uncertainty. This problem is important because sea-level rise affects millions of people in coastal communities and beyond due to climate change's impacts on polar ice sheets and the ocean. This problem is challenging due to spatial variability and unknowns such as possible tipping points (e.g., collapse of Greenland or West Antarctic ice-shelf), climate feedback loops (e.g., clouds, permafrost thawing), future policy decisions, and human actions. Most existing climate modeling approaches use the same set of weights globally, during either regression or …