Upscaling Microscale Flow Effects in Fractures using Differentiable Programming
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Computer Science↗
Engineering topics
O'Malley, Daniel
Publications and source records attributed to O'Malley, Daniel.
Implications of new Reasoning Capabilities for Science and Security: Results from a Quick Initial Study
On Thursday, September 12 OpenAI released “a new series of models designed to spend more time thinking… they can reason through complex tasks and solve harder problems than previous models in science, coding, and math.” These models are referred to as o1-preview and o1-mini and appear to be first results of what had been a closely held project called Strawberry within OpenAI. The models are not described as successors in the earlier GPT series because they provide a qualitatively different type of capability, especially step-by-step reasoning.
97 MATHEMATICS AND COMPUTING↗
Sparce, Distributed, Automatic Jacobians for Large-scale Scientific Machine Learning
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Earth Sciences↗
Enhancing Code Translation in Language Models with Few-Shot Learning via Retrieval-Augmented Generation
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Computer Science↗
Aerial imagery dataset of lost oil wells
Orphaned wells are wells for which the operator is unknown or insolvent. The location of hundreds of thousands of these wells remain unknown in the United States alone. Cost-effective techniques are essential to locate orphaned wells to address environmental problems. In this paper, we present a dataset consisting of 120,948 aerial images of recently documented orphan wells. Each of these 512 × 512 images is paired with segmentation masks that indicate the presence or absence of such well. These images, sourced from the National Agriculture Imagery Program, cover the continental United States with spatial resolutions ranging from 30 centimeters to 1 meter. Additionally, we included negative examples by selecting locations uniformly across the United States. Accompanying metadata includes the IDs and spatial resolution of the original images, which are available for free through the United States Geological Survey, and the pixel coordinates of documented orphaned wells identified in these images. This dataset is intended to support the development of deep-learning models that can help locating undocumented orphan wells from such imagery, thereby blunting the environmental damage they do.
Climate-change mitigation↗