scholarly journals Rapid identification of strongly lensed gravitational-wave events with machine learning

2021 ◽  
Vol 104 (12) ◽  
Author(s):  
Srashti Goyal ◽  
Harikrishnan D. ◽  
Shasvath J. Kapadia ◽  
Parameswaran Ajith
Keyword(s):  
Machine Learning ◽  
Gravitational Wave ◽  
Rapid Identification

scholarly journals Optimization of model independent gravitational wave search for binary black hole mergers using machine learning

2021 ◽  
Vol 104 (2) ◽  
Author(s):  
T. Mishra ◽  
B. O’Brien ◽  
V. Gayathri ◽  
M. Szczepańczyk ◽  
S. Bhaumik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Black Hole ◽  
Gravitational Wave ◽  
Binary Black Hole ◽  
Model Independent

Machine Learning Arrives in Archaeology

2021 ◽  
Vol 9 (2) ◽  
pp. 186-191
Author(s):  
Simon H. Bickler
Keyword(s):  
Machine Learning ◽  
Rapid Development ◽  
Rapid Identification ◽  
First Century ◽  
Traditional Methods ◽  
Archaeological Practice ◽  
The World ◽  
Archaeological Work ◽  
Archaeological Analysis

OverviewMachine learning (ML) is rapidly being adopted by archaeologists interested in analyzing a range of geospatial, material cultural, textual, natural, and artistic data. The algorithms are particularly suited toward rapid identification and classification of archaeological features and objects. The results of these new studies include identification of many new sites around the world and improved classification of large archaeological datasets. ML fits well with more traditional methods used in archaeological analysis, and it remains subject to both the benefits and difficulties of those approaches. Small datasets associated with archaeological work make ML vulnerable to hidden complexity, systemic bias, and high validation costs if not managed appropriately. ML's scalability, flexibility, and rapid development, however, make it an essential part of twenty-first-century archaeological practice. This review briefly describes what ML is, how it is being used in archaeology today, and where it might be used in the future for archaeological purposes.

scholarly journals Classifying Lensed Gravitational Waves in the Geometrical Optics Limit with Machine Learning

2019 ◽  
Vol 16 (2) ◽  
pp. 5-16
Author(s):  
Amit Singh ◽  
Ivan Li ◽  
Otto Hannuksela ◽  
Tjonnie Li ◽  
Kyungmin Kim
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Gravitational Wave ◽  
Gravitational Waves ◽  
Geometrical Optics ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Multi Layer Perceptron ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

Gravitational waves are theorized to be gravitationally lensed when they propagate near massive objects. Such lensing effects cause potentially detectable repeated gravitational wave patterns in ground- and space-based gravitational wave detectors. These effects are difficult to discriminate when the lens is small and the repeated patterns superpose. Traditionally, matched filtering techniques are used to identify gravitational-wave signals, but we instead aim to utilize machine learning techniques to achieve this. In this work, we implement supervised machine learning classifiers (support vector machine, random forest, multi-layer perceptron) to discriminate such lensing patterns in gravitational wave data. We train classifiers with spectrograms of both lensed and unlensed waves using both point-mass and singular isothermal sphere lens models. As the result, classifiers return F1 scores ranging from 0:852 to 0:996, with precisions from 0:917 to 0:992 and recalls ranging from 0:796 to 1:000 depending on the type of classifier and lensing model used. This supports the idea that machine learning classifiers are able to correctly determine lensed gravitational wave signals. This also suggests that in the future, machine learning classifiers may be used as a possible alternative to identify lensed gravitational wave events and to allow us to study gravitational wave sources and massive astronomical objects through further analysis. KEYWORDS: Gravitational Waves; Gravitational Lensing; Geometrical Optics; Machine Learning; Classification; Support Vector Machine; Random Tree Forest; Multi-layer Perceptron

scholarly journals Rapid identification of wood species using XRF and neural network machine learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Aaron N. Shugar ◽  
B. Lee Drake ◽  
Greg Kelley
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Wood Species ◽  
Cost Effective ◽  
Rapid Identification ◽  
Invasive Technique ◽  
X Ray ◽  
Non Invasive ◽  
Cost Effective Alternative

AbstractAn innovative approach for the rapid identification of wood species is presented. By combining X-ray fluorescence spectrometry with convolutional neural network machine learning, 48 different wood specimens were clearly differentiated and identified with a 99% accuracy. Wood species identification is imperative to assess illegally logged and transported lumber. Alternative options for identification can be time consuming and require some level of sampling. This non-invasive technique offers a viable, cost-effective alternative to rapidly and accurately identify timber in efforts to support environmental protection laws and regulations.

scholarly journals Efficient gravitational-wave glitch identification from environmental data through machine learning

2020 ◽  
Vol 101 (10) ◽  
Author(s):  
Robert E. Colgan ◽  
K. Rainer Corley ◽  
Yenson Lau ◽  
Imre Bartos ◽  
John N. Wright ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gravitational Wave ◽  
Environmental Data

scholarly journals Vital signs assessed in initial clinical encounters predict COVID-19 mortality in an NYC hospital system

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Elza Rechtman ◽  
Paul Curtin ◽  
Esmeralda Navarro ◽  
Sharon Nirenberg ◽  
Megan K. Horton
Keyword(s):  
Machine Learning ◽  
New York ◽  
Clinical Decision Making ◽  
Care Delivery ◽  
Vital Signs ◽  
Clinical Decision ◽  
Rapid Identification ◽  
Gradient Boosting ◽  
Hospital System ◽  
Response Strategies

AbstractTimely and effective clinical decision-making for COVID-19 requires rapid identification of risk factors for disease outcomes. Our objective was to identify characteristics available immediately upon first clinical evaluation related COVID-19 mortality. We conducted a retrospective study of 8770 laboratory-confirmed cases of SARS-CoV-2 from a network of 53 facilities in New-York City. We analysed 3 classes of variables; demographic, clinical, and comorbid factors, in a two-tiered analysis that included traditional regression strategies and machine learning. COVID-19 mortality was 12.7%. Logistic regression identified older age (OR, 1.69 [95% CI 1.66–1.92]), male sex (OR, 1.57 [95% CI 1.30–1.90]), higher BMI (OR, 1.03 [95% CI 1.102–1.05]), higher heart rate (OR, 1.01 [95% CI 1.00–1.01]), higher respiratory rate (OR, 1.05 [95% CI 1.03–1.07]), lower oxygen saturation (OR, 0.94 [95% CI 0.93–0.96]), and chronic kidney disease (OR, 1.53 [95% CI 1.20–1.95]) were associated with COVID-19 mortality. Using gradient-boosting machine learning, these factors predicted COVID-19 related mortality (AUC = 0.86) following cross-validation in a training set. Immediate, objective and culturally generalizable measures accessible upon clinical presentation are effective predictors of COVID-19 outcome. These findings may inform rapid response strategies to optimize health care delivery in parts of the world who have not yet confronted this epidemic, as well as in those forecasting a possible second outbreak.

Rapid Identification of Marine Plastic Debris via Spectroscopic Techniques and Machine Learning Classifiers

2020 ◽  
Vol 54 (17) ◽  
pp. 10630-10637 ◽  
Author(s):  
Anna P. M. Michel ◽  
Alexandra E. Morrison ◽  
Victoria L. Preston ◽  
Charles T. Marx ◽  
Beckett C. Colson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Rapid Identification ◽  
Spectroscopic Techniques ◽  
Plastic Debris ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Marine Plastic Debris

A Rapid Identification and Counting Method for Particle Sensing Using Machine Learning

2020 ◽  
Vol MA2020-02 (65) ◽  
pp. 3304-3304
Author(s):  
Muhammed Oztemel ◽  
Jason D Park ◽  
Jin-Woo Choi
Keyword(s):  
Machine Learning ◽  
Rapid Identification ◽  
Counting Method
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