Computer-Aided Discovery: Toward Scientific Insight Generation with Machine Support

Pankratius, Victor; Li, Justin; Gowanlock, Michael; Blair, D. M.; Rude, Cody; Herring, T. A.; Lind, Frank D.; Erickson, P. J.; Lonsdale, Colin J.

doi:10.1109/mis.2016.60

Cited by 21 publications

(17 citation statements)

References 8 publications

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“…ARGSENSE employs a voting method based on Subjective Logic to rank the debate topics according to their belief, disbelief or popularity within a community of arguers. Thus, ARGSENSE is in line with the recent trend to support scientific discovery [25] and to enhance the climate science cyber-infrastructure from useful to usable decision support tools [3].…”

Section: Discussionmentioning

confidence: 77%

Climate change opinions in online debate sites

et al. 2020

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Debate sites in social media provide a unified platform for citizens to discuss controversial questions and to put forward their ideas and arguments on the issues of common interest. Opinions of citizens may provide useful knowledge to stakeholders but manual analysis of arguments in debate sites is tedious, while computational support to this end has been rather scarce. We focus here on developing a technical instrumentation for making sense of a set of online arguments and aggregating them into usable results for policy making and climate science communication. Our objectives are: (i) to aggregate arguments posted for a certain debate topic, (ii) to consolidate opinions posted under several but related topics either in the same or different debate site, and (iii) to identify possible linguistic characteristics of the argumentative texts. For the first objective, we propose a voting method based on subjective logic [13]. For the second objective, we assess the semantic similarity between two debate topics based on textual entailment [28]. For the third objective, we employ various existing methods for lexical analysis such as frequency analysis or readability indexes. Although we focused here on the climate change, the method can be applied to any domain.

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Section: Discussionmentioning

confidence: 77%

Climate change opinions in online debate sites

et al. 2020

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“…In particular, our approach opens the door for a variety of future uses that distinguish this work from previous simulators (e.g., Biggs et al, 2007;Lee et al, 2012). With this computer-aided discovery approach (Pankratius et al, 2016), one can proceed to automatically exchange algorithms and pipeline parameters of the InSAR workflow and ultimately synthesize an algorithmic pipeline in an iterative fashion. Error characterizations on the output can be based on metrics such as the structural similarity index (Wang et al, 2004) between the artificial and real image.…”

Section: Discussionmentioning

confidence: 99%

“…Error characterizations on the output can be based on metrics such as the structural similarity index (Wang et al, 2004) between the artificial and real image. With this computer-aided discovery approach (Pankratius et al, 2016), one can proceed to automatically exchange algorithms and pipeline parameters of the InSAR workflow and ultimately synthesize an algorithmic pipeline in an iterative fashion. Another key application for this generator is a machine learning setting similar to generative adversarial networks (Goodfellow et al, 2014), where the generator is trained to mimic real interferograms in the best possible way, while iteratively measuring error and adjusting the generator's parameter values.…”

Section: Discussionmentioning

confidence: 99%

Generative Modeling of InSAR Interferograms

Rongier

Rude

Herring

et al. 2019

Earth and Space Science

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Interferometric synthetic aperture radar (InSAR) has become an essential technique to detect surface variations due to volcanoes, earthquakes, landslides, glaciers, and aquifers. However, Earth's ionosphere, atmosphere, vegetation, surface runoff, etc., introduce noise that requires post-processing to separate its components. This work defines a generator to create interferograms that include each of those components. Our approach leverages deformation models with real data, either directly or through machine learning using geostatistical methods. These methods result from previous developments to more efficiently and better simulate spatial variables and could replace some statistical approaches used in InSAR processing. We illustrate the use of the generator to simulate an artificial interferogram based on the 2015 Illapel earthquake and discuss the improved performance offered by geostatistical approaches compared with classical statistical ones. The generator establishes a tool for multiple applications (1) to evaluate InSAR correction workflows in controlled scenarios with known ground truth; (2) to develop training sets and generative methods for machine learning algorithms; and (3) to educate on InSAR and its principles.

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“…The computer aided discovery system utilized here is an implementation under continuing development (Pankratius et al, July/August 2016). The overall approach is to create configurable data processing frameworks that then generate a specific analysis pipeline configuration.…”

Section: The Computer-aided Discovery Pipelinementioning

confidence: 99%

Computer aided detection of transient inflation events at Alaskan volcanoes using GPS measurements from 2005–2015

Rude

Blair

et al. 2016

Journal of Volcanology and Geothermal Research

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Analysis of transient deformation events in time series data observed via networks of continuous Global Positioning System (GPS) ground stations provide insight into the magmatic and tectonic processes that drive volcanic activity. Typical analyses of spatial positions originating from each station require careful tuning of algorithmic parameters and selection of time and spatial regions of interest to observe possible transient events. This iterative, manual process is tedious when attempting to make new discoveries and does not easily scale with the number of stations. Addressing this challenge, we introduce a novel approach based on a computer-aided discovery system that facilitates the discovery of such potential transient events. The advantages of this approach are demonstrated by actual detections of transient deformation events at volcanoes selected from the Alaska Volcano Observatory database using data recorded by GPS stations from the Plate Boundary Observatory network. Our technique successfully reproduces the analysis of a transient signal detected in the first half of 2008 at Akutan volcano and is also directly applicable to 3 additional volcanoes in Alaska, with the new detection of 2 previously unnoticed inflation events: in early 2011 at Westdahl and in early 2013 at Shishaldin. This study also discusses the benefits of our computer-aided discovery approach for volcanology in general. Advantages include the rapid analysis on multi-scale resolutions of transient deformation events at a large number of sites of interest and the capability to enhance reusability and reproducibility in volcano studies.

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Computer-Aided Discovery: Toward Scientific Insight Generation with Machine Support

Cited by 21 publications

References 8 publications

Climate change opinions in online debate sites

Climate change opinions in online debate sites

Generative Modeling of InSAR Interferograms

Computer aided detection of transient inflation events at Alaskan volcanoes using GPS measurements from 2005–2015

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