scholarly journals Cataloguing the radio-sky with unsupervised machine learning: a new approach for the SKA era

2020 ◽  
Vol 497 (3) ◽  
pp. 2730-2758 ◽  
Author(s):  
T J Galvin ◽  
M T Huynh ◽  
R P Norris ◽  
X R Wang ◽  
E Hopkins ◽  
...  
Keyword(s):  
Machine Learning ◽  
A Priori ◽  
Radio Continuum ◽  
Host Galaxy ◽  
Wide Field ◽  
Self Organizing Map ◽  
Unsupervised Machine Learning ◽  
New Approach ◽  
Som Algorithm ◽  
Infrared Sources

ABSTRACT We develop a new analysis approach towards identifying related radio components and their corresponding infrared host galaxy based on unsupervised machine learning methods. By exploiting Parallelized rotation and flipping INvariant Kohonen maps (pink), a self-organizing map (SOM) algorithm, we are able to associate radio and infrared sources without the a priori requirement of training labels. We present an example of this method using 894 415 images from the Faint Images of the Radio-Sky at Twenty centimeters (FIRST) and Wide-field Infrared Survey Explorer (WISE) surveys centred towards positions described by the FIRST catalogue. We produce a set of catalogues that complement FIRST and describe 802 646 objects, including their radio components and their corresponding AllWISE infrared host galaxy. Using these data products, we (i) demonstrate the ability to identify objects with rare and unique radio morphologies (e.g. ‘X’-shaped galaxies, hybrid FR I/FR II morphologies), (ii) can identify the potentially resolved radio components that are associated with a single infrared host, (iii) introduce a ‘curliness’ statistic to search for bent and disturbed radio morphologies, and (iv) extract a set of 17 giant radio galaxies between 700 and 1100 kpc. As we require no training labels, our method can be applied to any radio-continuum survey, provided a sufficiently representative SOM can be trained.

scholarly journals Quantifying Geometric Accuracy With Unsupervised Machine Learning: Using Self-Organizing Map on Fused Filament Fabrication Additive Manufacturing Parts

2017 ◽  
Vol 140 (3) ◽  
Author(s):  
Mojtaba Khanzadeh ◽  
Prahalada Rao ◽  
Ruholla Jafari-Marandi ◽  
Brian K. Smith ◽  
Mark A. Tschopp ◽  
...  
Keyword(s):  
Machine Learning ◽  
Additive Manufacturing ◽  
Process Conditions ◽  
Complex Geometries ◽  
Self Organizing Map ◽  
Geometric Accuracy ◽  
Fused Filament Fabrication ◽  
Unsupervised Machine Learning ◽  
Geometric Deviations ◽  
Self Organizing

Although complex geometries are attainable with additive manufacturing (AM), a major barrier preventing its use in mission-critical applications is the lack of geometric accuracy of AM parts. Existing geometric dimensioning and tolerancing (GD&T) characteristics are defined based on simple landmark features, and thus, need to be customized to capture the subtle difference in parts with complex geometries. Hence, the objective of this work is to quantify the geometric deviations of additively manufactured parts from a large data set of laser-scanned coordinates using an unsupervised machine learning (ML) approach called the self-organizing map (SOM). The central hypothesis is that clusters recognized by the SOM correspond to specific types of geometric deviations, which in turn are linked to certain AM process conditions. This hypothesis is tested on parts made while varying process conditions in the fused filament fabrication (FFF) AM process. The outcomes of this research are as follows: (1) visualizing and quantifying the link between process conditions and geometric accuracy in FFF and (2) significantly reducing the amount of point cloud data required for characterizing of geometric accuracy. The significance of this research is that this unsupervised ML approach resulted in less than 3% of over 1 million data points being required to fully quantify the part geometric accuracy.

Mathematical Knowledge and the Interplay of Practices

2015 ◽  
Author(s):  
José Ferreirós
Keyword(s):  
Mathematical Knowledge ◽  
A Priori ◽  
Philosophy Of Mathematics ◽  
Advanced Mathematics ◽  
New Approach ◽  
Agent Based ◽  
Epistemology Of Mathematics ◽  
Actual Experience ◽  
Elementary Math ◽  
Mathematical Tradition

This book presents a new approach to the epistemology of mathematics by viewing mathematics as a human activity whose knowledge is intimately linked with practice. Charting an exciting new direction in the philosophy of mathematics, the book uses the crucial idea of a continuum to provide an account of the development of mathematical knowledge that reflects the actual experience of doing math and makes sense of the perceived objectivity of mathematical results. Describing a historically oriented, agent-based philosophy of mathematics, the book shows how the mathematical tradition evolved from Euclidean geometry to the real numbers and set-theoretic structures. It argues for the need to take into account a whole web of mathematical and other practices that are learned and linked by agents, and whose interplay acts as a constraint. It demonstrates how advanced mathematics, far from being a priori, is based on hypotheses, in contrast to elementary math, which has strong cognitive and practical roots and therefore enjoys certainty. Offering a wealth of philosophical and historical insights, the book challenges us to rethink some of our most basic assumptions about mathematics, its objectivity, and its relationship to culture and science.

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

Analysis of the Bath Motion in the MM-SQC Dynamics Using Unsupervised Machine Learning Dimensionality Reduction Approaches: Principal Component Analysis

2020 ◽  
Author(s):  
Jiawei Peng ◽  
Yu Xie ◽  
Deping Hu ◽  
Zhenggang Lan
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Collective Motion ◽  
Principal Component ◽  
Component Analysis ◽  
Nonadiabatic Dynamics ◽  
Trajectory Data ◽  
Unsupervised Machine Learning ◽  
Physical Knowledge ◽  
Vibronic Couplings

The system-plus-bath model is an important tool to understand nonadiabatic dynamics for large molecular systems. The understanding of the collective motion of a huge number of bath modes is essential to reveal their key roles in the overall dynamics. We apply the principal component analysis (PCA) to investigate the bath motion based on the massive data generated from the MM-SQC (symmetrical quasi-classical dynamics method based on the Meyer-Miller mapping Hamiltonian) nonadiabatic dynamics of the excited-state energy transfer dynamics of Frenkel-exciton model. The PCA method clearly clarifies that two types of bath modes, which either display the strong vibronic couplings or have the frequencies close to electronic transition, are very important to the nonadiabatic dynamics. These observations are fully consistent with the physical insights. This conclusion is obtained purely based on the PCA understanding of the trajectory data, without the large involvement of pre-defined physical knowledge. The results show that the PCA approach, one of the simplest unsupervised machine learning methods, is very powerful to analyze the complicated nonadiabatic dynamics in condensed phase involving many degrees of freedom.

scholarly journals Testing the evolution of correlations between supermassive black holes and their host galaxies using eight strongly lensed quasars

2020 ◽  
Vol 501 (1) ◽  
pp. 269-280
Author(s):  
Xuheng Ding ◽  
Tommaso Treu ◽  
Simon Birrer ◽  
Adriano Agnello ◽  
Dominique Sluse ◽  
...  
Keyword(s):  
Black Holes ◽  
Hubble Space Telescope ◽  
High Redshift ◽  
Galactic Nuclei ◽  
Host Galaxy ◽  
Wide Field ◽  
Host Galaxies ◽  
Field Surveys ◽  
Instrumental Resolution ◽  
The Moment

ABSTRACT One of the main challenges in using high-redshift active galactic nuclei (AGNs) to study the correlations between the mass of a supermassive black hole ($\mathcal {M}_{\rm BH}$) and the properties of its active host galaxy is instrumental resolution. Strong lensing magnification effectively increases instrumental resolution and thus helps to address this challenge. In this work, we study eight strongly lensed AGNs with deep Hubble Space Telescope imaging, using the lens modelling code lenstronomy to reconstruct the image of the source. Using the reconstructed brightness of the host galaxy, we infer the host galaxy stellar mass based on stellar population models. $\mathcal {M}_{\rm BH}$ are estimated from broad emission lines using standard methods. Our results are in good agreement with recent work based on non-lensed AGNs, demonstrating the potential of using strongly lensed AGNs to extend the study of the correlations to higher redshifts. At the moment, the sample size of lensed AGNs is small and thus they provide mostly a consistency check on systematic errors related to resolution for non-lensed AGNs. However, the number of known lensed AGNs is expected to increase dramatically in the next few years, through dedicated searches in ground- and space-based wide-field surveys, and they may become a key diagnostic of black holes and galaxy co-evolution.

scholarly journals Eye-blink artifact removal from single channel EEG with k-means and SSA

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ajay Kumar Maddirala ◽  
Kalyana C Veluvolu
Keyword(s):  
Machine Learning ◽  
Single Channel ◽  
Learning Algorithm ◽  
Singular Spectrum Analysis ◽  
Machine Learning Algorithm ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Unsupervised Machine Learning ◽  
Eye Blink ◽  
Blink Artifact

AbstractIn recent years, the usage of portable electroencephalogram (EEG) devices are becoming popular for both clinical and non-clinical applications. In order to provide more comfort to the subject and measure the EEG signals for several hours, these devices usually consists of fewer EEG channels or even with a single EEG channel. However, electrooculogram (EOG) signal, also known as eye-blink artifact, produced by involuntary movement of eyelids, always contaminate the EEG signals. Very few techniques are available to remove these artifacts from single channel EEG and most of these techniques modify the uncontaminated regions of the EEG signal. In this paper, we developed a new framework that combines unsupervised machine learning algorithm (k-means) and singular spectrum analysis (SSA) technique to remove eye blink artifact without modifying actual EEG signal. The novelty of the work lies in the extraction of the eye-blink artifact based on the time-domain features of the EEG signal and the unsupervised machine learning algorithm. The extracted eye-blink artifact is further processed by the SSA method and finally subtracted from the contaminated single channel EEG signal to obtain the corrected EEG signal. Results with synthetic and real EEG signals demonstrate the superiority of the proposed method over the existing methods. Moreover, the frequency based measures [the power spectrum ratio ($$\Gamma $$ Γ ) and the mean absolute error (MAE)] also show that the proposed method does not modify the uncontaminated regions of the EEG signal while removing the eye-blink artifact.

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