Background Reduction for Improved Sensitivity at the Enriched Xenon Observatory through advancements in Barium-Tagging Simulations, Prediction and Prevention of High-Voltage Breakdown in Liquid Xenon Time-Projection Chambers, and Event Classification using Ensemble Methods and Machine Learning

The subject of space charge in ionization detectors is reviewed, showing how the observations and the formalism used to describe the effects have evolved, starting with applications to calorimeters and reaching recent, large time-projection chambers. General scaling laws, and different ways to present and model the effects are presented. The relations between space-charge effects and the boundary conditions imposed on the side faces of the detector are discussed, together with a design solution that mitigates some of the effects. The implications of the relative size of drift length and transverse detector size are illustrated. Calibration methods are briefly discussed.

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Comparison of Ensemble Machine Learning Methods for Soil Erosion Pin Measurements

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10010042 ◽

2021 ◽

Vol 10 (1) ◽

pp. 42

Author(s):

Kieu Anh Nguyen ◽

Walter Chen ◽

Bor-Shiun Lin ◽

Uma Seeboonruang

Keyword(s):

Machine Learning ◽

Soil Erosion ◽

Ensemble Methods ◽

Machine Learning Algorithms ◽

Multivariate Adaptive Regression Splines ◽

Gradient Boosting ◽

Support Vector ◽

Ensemble Machine Learning ◽

Boosting Method ◽

Bagging Method

Although machine learning has been extensively used in various fields, it has only recently been applied to soil erosion pin modeling. To improve upon previous methods of quantifying soil erosion based on erosion pin measurements, this study explored the possible application of ensemble machine learning algorithms to the Shihmen Reservoir watershed in northern Taiwan. Three categories of ensemble methods were considered in this study: (a) Bagging, (b) boosting, and (c) stacking. The bagging method in this study refers to bagged multivariate adaptive regression splines (bagged MARS) and random forest (RF), and the boosting method includes Cubist and gradient boosting machine (GBM). Finally, the stacking method is an ensemble method that uses a meta-model to combine the predictions of base models. This study used RF and GBM as the meta-models, decision tree, linear regression, artificial neural network, and support vector machine as the base models. The dataset used in this study was sampled using stratified random sampling to achieve a 70/30 split for the training and test data, and the process was repeated three times. The performance of six ensemble methods in three categories was analyzed based on the average of three attempts. It was found that GBM performed the best among the ensemble models with the lowest root-mean-square error (RMSE = 1.72 mm/year), the highest Nash-Sutcliffe efficiency (NSE = 0.54), and the highest index of agreement (d = 0.81). This result was confirmed by the spatial comparison of the absolute differences (errors) between model predictions and observations using GBM and RF in the study area. In summary, the results show that as a group, the bagging method and the boosting method performed equally well, and the stacking method was third for the erosion pin dataset considered in this study.

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Machine learning applied to simulations of collisions between rotating, differentiated planets

Computational Astrophysics and Cosmology ◽

10.1186/s40668-020-00034-6 ◽

2020 ◽

Vol 7 (1) ◽

Author(s):

Miles L. Timpe ◽

Maria Han Veiga ◽

Mischa Knabenhans ◽

Joachim Stadel ◽

Stefano Marelli

Keyword(s):

Machine Learning ◽

Planet Formation ◽

Critical Role ◽

Ensemble Methods ◽

Terrestrial Planet ◽

Data Driven ◽

Analytic Methods ◽

Accurate Treatment ◽

Post Impact ◽

The Cost

AbstractIn the late stages of terrestrial planet formation, pairwise collisions between planetary-sized bodies act as the fundamental agent of planet growth. These collisions can lead to either growth or disruption of the bodies involved and are largely responsible for shaping the final characteristics of the planets. Despite their critical role in planet formation, an accurate treatment of collisions has yet to be realized. While semi-analytic methods have been proposed, they remain limited to a narrow set of post-impact properties and have only achieved relatively low accuracies. However, the rise of machine learning and access to increased computing power have enabled novel data-driven approaches. In this work, we show that data-driven emulation techniques are capable of classifying and predicting the outcome of collisions with high accuracy and are generalizable to any quantifiable post-impact quantity. In particular, we focus on the dataset requirements, training pipeline, and classification and regression performance for four distinct data-driven techniques from machine learning (ensemble methods and neural networks) and uncertainty quantification (Gaussian processes and polynomial chaos expansion). We compare these methods to existing analytic and semi-analytic methods. Such data-driven emulators are poised to replace the methods currently used in N-body simulations, while avoiding the cost of direct simulation. This work is based on a new set of 14,856 SPH simulations of pairwise collisions between rotating, differentiated bodies at all possible mutual orientations.

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40. Some observations about high voltage breakdown and vacuum conditions

Vacuum ◽

10.1016/0042-207x(67)92572-9 ◽

1967 ◽

Vol 17 (1) ◽

pp. 53

Keyword(s):

High Voltage ◽

High Voltage Breakdown ◽

Voltage Breakdown

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High voltage breakdown (1.8 kV) of hydrogenated black diamond field effect transistor

Applied Physics Letters ◽

10.1063/1.4967999 ◽

2016 ◽

Vol 109 (20) ◽

pp. 203504 ◽

Cited By ~ 19

Author(s):

M. Syamsul ◽

Y. Kitabayashi ◽

D. Matsumura ◽

T. Saito ◽

Y. Shintani ◽

...

Keyword(s):

High Voltage ◽

Field Effect ◽

Field Effect Transistor ◽

High Voltage Breakdown ◽

Effect Transistor ◽

Voltage Breakdown

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Machine Learning, Ensemble Methods in

Computational Complexity ◽

10.1007/978-1-4614-1800-9_114 ◽

2012 ◽

pp. 1781-1789 ◽

Cited By ~ 2

Author(s):

Sašo Džeroski ◽

Panče Panov ◽

Bernard Ženko

Keyword(s):

Machine Learning ◽

Ensemble Methods

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Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions

Electronics ◽

10.3390/electronics10212717 ◽

2021 ◽

Vol 10 (21) ◽

pp. 2717

Author(s):

Nusrat Rouf ◽

Majid Bashir Malik ◽

Tasleem Arif ◽

Sparsh Sharma ◽

Saurabh Singh ◽

...

Keyword(s):

Machine Learning ◽

Stock Market ◽

Digital Libraries ◽

Stock Price ◽

Ensemble Methods ◽

Machine Learning Techniques ◽

Learning Approaches ◽

Stock Market Prediction ◽

Research Areas ◽

Recent Developments

With the advent of technological marvels like global digitization, the prediction of the stock market has entered a technologically advanced era, revamping the old model of trading. With the ceaseless increase in market capitalization, stock trading has become a center of investment for many financial investors. Many analysts and researchers have developed tools and techniques that predict stock price movements and help investors in proper decision-making. Advanced trading models enable researchers to predict the market using non-traditional textual data from social platforms. The application of advanced machine learning approaches such as text data analytics and ensemble methods have greatly increased the prediction accuracies. Meanwhile, the analysis and prediction of stock markets continue to be one of the most challenging research areas due to dynamic, erratic, and chaotic data. This study explains the systematics of machine learning-based approaches for stock market prediction based on the deployment of a generic framework. Findings from the last decade (2011–2021) were critically analyzed, having been retrieved from online digital libraries and databases like ACM digital library and Scopus. Furthermore, an extensive comparative analysis was carried out to identify the direction of significance. The study would be helpful for emerging researchers to understand the basics and advancements of this emerging area, and thus carry-on further research in promising directions.

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