Related papers: Using Principal Component Analysis to Distinguish …
The universal behaviour of superconductors near the phase transition is described by the three-dimensional field theory of scalar quantum electrodynamics. We approximately solve the model with the help of non-perturbative flow equations. A…
We show that periodically driven superconducting vortices in the presence of quenched disorder exhibit a transition from reversible to irreversible flow under increasing vortex density or cycle period. This type of behavior has recently…
Principal components analysis (PCA) is a classical method for the reduction of dimensionality of data in the form of n observations (or cases) of a vector with p variables. For a simple model of factor analysis type, it is proved that…
In the framework of London theory we study the novel magnetic state in two-component superconductors with finite density of fractional flux vortices stabilized near the surface. We show that the process of vortex entry into the…
We explore the dynamics of driven magnetic flux lines in disordered type-II superconductors in the presence of twin boundaries oriented parallel to the direction of the applied magnetic field, using a three-dimensional elastic line model…
The influence of fractal clusters of a normal phase on distinctive features of current-voltage characteristic of percolative type-II superconductors is considered. The results of high-resolution measurements of the differential resistance…
A very important issue concerning protostellar jets is the mechanism behind their formation. Obtaining information on the region at the base of a jet can shed light into the subject and some years ago this has been done through a search for…
We propose novel methods for predictive (sparse) PCA with spatially misaligned data. These methods identify principal component loading vectors that explain as much variability in the observed data as possible, while also ensuring the…
We study the zero-temperature dynamic transition from the disordered flow to an ordered flow state in driven vortices in type-II superconductors. The transition current $I_{p}$ is marked by a sharp kink in the $V(I)$ characteristic with a…
Model-independent analysis (MIA) methods are generally useful for analysing complex systems in which relationships between the observables are non-trivial and noise is present. Principle Component Analysis (PCA) is one of MIA methods…
We examine the depinning transitions and the temperature versus driving force phase diagram for magnetically interacting pancake vortices in layered superconductors. For strong disorder the initial depinning is plastic followed by a sharp…
The phenomenon of superconductivity was discovered in 1911, however, the methodology to classify and distinguish type-II superconductivity was established only in late fifties after Abrikosov's prediction of a flux line lattice in 1957. The…
We carry out a principal component analysis of fluctuations in a hydrodynamic simulation of heavy-ion collisions, and compare with experimental data from the CMS collaboration. The leading and subleading principal components of elliptic and…
The dynamics of moving vortex lattice is considered in the framework of the time dependent Ginzburg - Landau equation neglecting effects of pinning. At high flux velocities the pinning dominated dynamics is expected to cross over into the…
We examine the statics and dynamics of vortices in the presence of a periodic quasi-one dimensional substrate, focusing on the limit where the vortex lattice constant is smaller than the substrate lattice period. As a function of the…
The principal component analysis (PCA) is a staple statistical and unsupervised machine learning technique in finance. The application of PCA in a financial setting is associated with several technical difficulties, such as numerical…
Existence of thermodynamically stable composite vortices in a two-component superconductor may form distinctive vortex patterns, and may lead to type-1.5 superconductivity. Here we study the surface energy of the two-component…
Multiway data are becoming more and more common. While there are many approaches to extending principal component analysis (PCA) from usual data matrices to multiway arrays, their conceptual differences from the usual PCA, and the…
Principal component analysis (PCA) is a most frequently used statistical tool in almost all branches of data science. However, like many other statistical tools, there is sometimes the risk of misuse or even abuse. In this paper, we…
Principal component analysis (PCA) is a widely used dimension reduction method, but its performance is known to be non-robust to outliers. Recently, product-PCA (PPCA) has been shown to possess the efficiency-loss free ordering-robustness…