scholarly journals Predicting fault slip via transfer learning

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Kun Wang ◽  
Christopher W. Johnson ◽  
Kane C. Bennett ◽  
Paul A. Johnson
Keyword(s):  
Machine Learning ◽  
Numerical Simulations ◽  
Transfer Learning ◽  
Laboratory Experiments ◽  
Fault Slip ◽  
Geophysical Data ◽  
Training Data ◽  
Data Sets ◽  
Earthquake Cycle ◽  
Fault Friction

AbstractData-driven machine-learning for predicting instantaneous and future fault-slip in laboratory experiments has recently progressed markedly, primarily due to large training data sets. In Earth however, earthquake interevent times range from 10’s-100’s of years and geophysical data typically exist for only a portion of an earthquake cycle. Sparse data presents a serious challenge to training machine learning models for predicting fault slip in Earth. Here we describe a transfer learning approach using numerical simulations to train a convolutional encoder-decoder that predicts fault-slip behavior in laboratory experiments. The model learns a mapping between acoustic emission and fault friction histories from numerical simulations, and generalizes to produce accurate predictions of laboratory fault friction. Notably, the predictions improve by further training the model latent space using only a portion of data from a single laboratory earthquake-cycle. The transfer learning results elucidate the potential of using models trained on numerical simulations and fine-tuned with small geophysical data sets for potential applications to faults in Earth.

scholarly journals Predicting Fault Slip via Transfer Learning

2021 ◽  
Author(s):  
Kun Wang ◽  
Christopher Johnson ◽  
Kane Bennett ◽  
Paul Johnson
Keyword(s):  
Machine Learning ◽  
Numerical Simulations ◽  
Transfer Learning ◽  
Laboratory Experiments ◽  
Laboratory Data ◽  
Fault Slip ◽  
Geophysical Data ◽  
Training Data ◽  
Data Sets ◽  
Earthquake Cycle

Abstract Data-driven machine-learning for predicting instantaneous and future fault-slip in laboratory experiments has recently progressed markedly due to large training data sets. In Earth however, earthquake interevent times range from 10's-100's of years and geophysical data typically exist for only a portion of an earthquake cycle. Sparse data presents a serious challenge to training machine learning models. Here we describe a transfer learning approach using numerical simulations to train a convolutional encoder-decoder that predicts fault-slip behavior in laboratory experiments. The model learns a mapping between acoustic emission histories and fault-slip from numerical simulations, and generalizes to produce accurate results using laboratory data. Notably slip-predictions markedly improve using the simulation-data trained-model and training the latent space using a portion of a single laboratory earthquake-cycle. The transfer learning results elucidate the potential of using models trained on numerical simulations and fine-tuned with small geophysical data sets for potential applications to faults in Earth.

scholarly journals Reconstructing rock art chronology with transfer learning: A case study from Arnhem Land, Australia

2021 ◽  
Author(s):  
Jarrad Kowlessar ◽  
James Keal ◽  
Daryl Wesley ◽  
Ian Moffat ◽  
Dudley Lawrence ◽  
...  
Keyword(s):  
Machine Learning ◽  
Material Culture ◽  
Transfer Learning ◽  
Rock Art ◽  
Training Data ◽  
Data Sets ◽  
Learning Approaches ◽  
Arnhem Land ◽  
Learning Analysis

In recent years, machine learning approaches have been used to classify and extract style from media and have been used to reinforce known chronologies from classical art history. In this work we employ the first ever machine learning analysis of Australian rock art using a data efficient transfer learning approach to identify features suitable for distinguishing styles of rock art. These features are evaluated in a one-shot learning setting. Results demonstrate that known Arnhem Land Rock art styles can be resolved without knowledge of prior groupings. We then analyse the activation space of learned features and report on the relationships between styles and arrange these classes into a stylistic chronology based on distance within the activation space. By generating a stylistic chronology, it is shown that the model is sensitive to both temporal and spatial patterns in the distribution of rock art in the Arnhem Land Plateau region. More broadly, this approach is ideally suited to evaluating style within any material culture assemblage and overcomes the common constraint of small training data sets in archaeological machine learning studies.

Application of multi-sensor unmanned aerial system for identification of hydrothermal alteration zones

2020 ◽  
Author(s):  
Yosoon Choi ◽  
Jieun Baek ◽  
Jangwon Suh ◽  
Sung-Min Kim
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Training Data ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Integrated Analysis ◽  
Unmanned Aerial System ◽  
Data Sets ◽  
Learning Techniques ◽  
Hydrothermal Alteration Zones

<p>In this study, we proposed a method to utilize a multi-sensor Unmanned Aerial System (UAS) for exploration of hydrothermal alteration zones. This study selected an area (10m × 20m) composed mainly of the andesite and located on the coast, with wide outcrops and well-developed structural and mineralization elements. Multi-sensor (visible, multispectral, thermal, magnetic) data were acquired in the study area using UAS, and were studied using machine learning techniques. For utilizing the machine learning techniques, we applied the stratified random method to sample 1000 training data in the hydrothermal zone and 1000 training data in the non-hydrothermal zone identified through the field survey. The 2000 training data sets created for supervised learning were first classified into 1500 for training and 500 for testing. Then, 1500 for training were classified into 1200 for training and 300 for validation. The training and validation data for machine learning were generated in five sets to enable cross-validation. Five types of machine learning techniques were applied to the training data sets: k-Nearest Neighbors (k-NN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Deep Neural Network (DNN). As a result of integrated analysis of multi-sensor data using five types of machine learning techniques, RF and SVM techniques showed high classification accuracy of about 90%. Moreover, performing integrated analysis using multi-sensor data showed relatively higher classification accuracy in all five machine learning techniques than analyzing magnetic sensing data or single optical sensing data only.</p>

scholarly journals Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features

2015 ◽  
Vol 22 (3) ◽  
pp. 671-681 ◽  
Author(s):  
Azadeh Nikfarjam ◽  
Abeed Sarker ◽  
Karen O’Connor ◽  
Rachel Ginn ◽  
Graciela Gonzalez
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Language Processing ◽  
High Performance ◽  
Conditional Random Fields ◽  
Training Data ◽  
Data Sets ◽  
Social Media Mining ◽  
Medical Concepts ◽  
Media Mining

Abstract Objective Social media is becoming increasingly popular as a platform for sharing personal health-related information. This information can be utilized for public health monitoring tasks, particularly for pharmacovigilance, via the use of natural language processing (NLP) techniques. However, the language in social media is highly informal, and user-expressed medical concepts are often nontechnical, descriptive, and challenging to extract. There has been limited progress in addressing these challenges, and thus far, advanced machine learning-based NLP techniques have been underutilized. Our objective is to design a machine learning-based approach to extract mentions of adverse drug reactions (ADRs) from highly informal text in social media. Methods We introduce ADRMine, a machine learning-based concept extraction system that uses conditional random fields (CRFs). ADRMine utilizes a variety of features, including a novel feature for modeling words’ semantic similarities. The similarities are modeled by clustering words based on unsupervised, pretrained word representation vectors (embeddings) generated from unlabeled user posts in social media using a deep learning technique. Results ADRMine outperforms several strong baseline systems in the ADR extraction task by achieving an F-measure of 0.82. Feature analysis demonstrates that the proposed word cluster features significantly improve extraction performance. Conclusion It is possible to extract complex medical concepts, with relatively high performance, from informal, user-generated content. Our approach is particularly scalable, suitable for social media mining, as it relies on large volumes of unlabeled data, thus diminishing the need for large, annotated training data sets.

scholarly journals Materials Representation and Transfer Learning for Multi-Property Prediction

2021 ◽  
Author(s):  
Shufeng Kong ◽  
Dan Guevarra ◽  
Carla P. Gomes ◽  
John Gregoire
Keyword(s):  
Machine Learning ◽  
Optical Absorption ◽  
Transfer Learning ◽  
Materials Science ◽  
Training Data ◽  
Target Domain ◽  
Generative Adversarial Network ◽  
Property Prediction ◽  
Adversarial Network ◽  
Correlation Learning

The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the underlying interactions of multiple elements, as well as the relationships among multiple properties, to facilitate property prediction in new composition spaces. To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates (i) prediction using only a material’s composition, (ii) learning and exploitation of correlations among target properties in multitarget regression, and (iii) leveraging training data from tangential domains via generative transfer learning. The model is demonstrated for prediction of spectral optical absorption of complex metal oxides spanning 69 3-cation metal oxide composition spaces. H-CLMP accurately predicts non-linear composition-property relationships in composition spaces for which no training data is available, which broadens the purview of machine learning to the discovery of materials with exceptional properties. This achievement results from the principled integration of latent embedding learning, property correlation learning, generative transfer learning, and attention models. The best performance is obtained using H-CLMP with Transfer learning (H-CLMP(T)) wherein a generative adversarial network is trained on computational density of states data and deployed in the target domain to augment prediction of optical absorption from composition. H-CLMP(T) aggregates multiple knowledge sources with a framework that is well-suited for multi-target regression across the physical sciences.

scholarly journals Enhancing Image Diagnosis by the Implementation of Transfer Classifiers

2019 ◽  
Vol 8 (3) ◽  
pp. 999-1002
Keyword(s):  
Supervised Learning ◽  
Transfer Learning ◽  
Training Data ◽  
Data Sets ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Image Diagnosis ◽  
Sensitivity Problem ◽  
Common Application ◽  
Target Data

Images generated from a variety of sources and foundations today can pose difficulty for a user to interpret similarity in them or analyze them for further use because of their segmentation policies. This unconventionality can generate many errors, because of which the previously used traditional methodologies such as supervised learning techniques less resourceful, which requires huge quantity of labelled training data which mirrors the desired target data. This paper thus puts forward the mechanism of an alternative technique i.e. transfer learning to be used in image diagnosis so that efficiency and accuracy among images can be achieved. This type of mechanism deals with variation in the desired and actual data used for training and the outlier sensitivity, which ultimately enhances the predictions by giving better results in various areas, thus leaving the traditional methodologies behind. The following analysis further discusses about three types of transfer classifiers which can be applied using only small volume of training data sets and their contrast with the traditional method which requires huge quantities of training data having attributes with slight changes. The three different separators were compared amongst them and also together from the traditional methodology being used for a very common application used in our daily life. Also, commonly occurring problems such as the outlier sensitivity problem were taken into consideration and measures were taken to recognise and improvise them. On further research it was observed that the performance of transfer learning exceeds that of the conventional supervised learning approaches being used for small amount of characteristic training data provided reducing the stratification errors to a great extent

scholarly journals Gender bias in machine learning for sentiment analysis

2018 ◽  
Vol 42 (3) ◽  
pp. 343-354 ◽  
Author(s):  
Mike Thelwall
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Gender Bias ◽  
Training Data ◽  
Data Sets ◽  
Content Type ◽  
Female Authors ◽  
Gender Biases ◽  
Mixed Gender ◽  
Gender Specific

Purpose The purpose of this paper is to investigate whether machine learning induces gender biases in the sense of results that are more accurate for male authors or for female authors. It also investigates whether training separate male and female variants could improve the accuracy of machine learning for sentiment analysis. Design/methodology/approach This paper uses ratings-balanced sets of reviews of restaurants and hotels (3 sets) to train algorithms with and without gender selection. Findings Accuracy is higher on female-authored reviews than on male-authored reviews for all data sets, so applications of sentiment analysis using mixed gender data sets will over represent the opinions of women. Training on same gender data improves performance less than having additional data from both genders. Practical implications End users of sentiment analysis should be aware that its small gender biases can affect the conclusions drawn from it and apply correction factors when necessary. Users of systems that incorporate sentiment analysis should be aware that performance will vary by author gender. Developers do not need to create gender-specific algorithms unless they have more training data than their system can cope with. Originality/value This is the first demonstration of gender bias in machine learning sentiment analysis.

Facial Attractiveness: Beauty and the Machine

2006 ◽  
Vol 18 (1) ◽  
pp. 119-142 ◽  
Author(s):  
Yael Eisenthal ◽  
Gideon Dror ◽  
Eytan Ruppin
Keyword(s):  
Machine Learning ◽  
Facial Attractiveness ◽  
Training Data ◽  
Data Sets ◽  
Learning Context ◽  
Data Set ◽  
Attractiveness Rating ◽  
Facial Beauty ◽  
Facial Images

This work presents a novel study of the notion of facial attractiveness in a machine learning context. To this end, we collected human beauty ratings for data sets of facial images and used various techniques for learning the attractiveness of a face. The trained predictor achieves a significant correlation of 0.65 with the average human ratings. The results clearly show that facial beauty is a universal concept that a machine can learn. Analysis of the accuracy of the beauty prediction machine as a function of the size of the training data indicates that a machine producing human-like attractiveness rating could be obtained given a moderately larger data set.

scholarly journals Machine learning based energy-free structure predictions of molecules, transition states, and solids

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Dominik Lemm ◽  
Guido Falk von Rudorff ◽  
O. Anatole von Lilienfeld
Keyword(s):  
Machine Learning ◽  
Ab Initio ◽  
Chemical Space ◽  
Transition States ◽  
Computational Prediction ◽  
Training Data ◽  
Data Sets ◽  
Prediction Errors ◽  
Optimization Task ◽  
Out Of Sample

AbstractThe computational prediction of atomistic structure is a long-standing problem in physics, chemistry, materials, and biology. Conventionally, force-fields or ab initio methods determine structure through energy minimization, which is either approximate or computationally demanding. This accuracy/cost trade-off prohibits the generation of synthetic big data sets accounting for chemical space with atomistic detail. Exploiting implicit correlations among relaxed structures in training data sets, our machine learning model Graph-To-Structure (G2S) generalizes across compound space in order to infer interatomic distances for out-of-sample compounds, effectively enabling the direct reconstruction of coordinates, and thereby bypassing the conventional energy optimization task. The numerical evidence collected includes 3D coordinate predictions for organic molecules, transition states, and crystalline solids. G2S improves systematically with training set size, reaching mean absolute interatomic distance prediction errors of less than 0.2 Å for less than eight thousand training structures — on par or better than conventional structure generators. Applicability tests of G2S include successful predictions for systems which typically require manual intervention, improved initial guesses for subsequent conventional ab initio based relaxation, and input generation for subsequent use of structure based quantum machine learning models.

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