674 research outputs found
Microscopic Scenario for Striped Superconductors
We argue that the superconducting state found in high- cuprates is
inhomogeneous with a corresponding inhomogeneous superfluid density. We
introduce two classes of microscopic models which capture the magnetic and
superconducting properties of these strongly correlated materials. We start
from a generalized t-J model, in which appropriate inhomogeneous terms mimic
stripes. We find that inhomogeneous interactions that break magnetic symmetries
are essential to induce substantial pair binding of holes in the thermodynamic
limit. We argue that this type of model reproduces the ARPES and neutron
scattering data seen experimentally.Comment: 4 pages, 2 psfigures. To appear in Physica
EPASAD: Ellipsoid decision boundary based Process-Aware Stealthy Attack Detector
Due to the importance of Critical Infrastructure (CI) in a nation's economy,
they have been lucrative targets for cyber attackers. These critical
infrastructures are usually Cyber-Physical Systems (CPS) such as power grids,
water, and sewage treatment facilities, oil and gas pipelines, etc. In recent
times, these systems have suffered from cyber attacks numerous times.
Researchers have been developing cyber security solutions for CIs to avoid
lasting damages. According to standard frameworks, cyber security based on
identification, protection, detection, response, and recovery are at the core
of these research. Detection of an ongoing attack that escapes standard
protection such as firewall, anti-virus, and host/network intrusion detection
has gained importance as such attacks eventually affect the physical dynamics
of the system. Therefore, anomaly detection in physical dynamics proves an
effective means to implement defense-in-depth. PASAD is one example of anomaly
detection in the sensor/actuator data, representing such systems' physical
dynamics. We present EPASAD, which improves the detection technique used in
PASAD to detect these micro-stealthy attacks, as our experiments show that
PASAD's spherical boundary-based detection fails to detect. Our method EPASAD
overcomes this by using Ellipsoid boundaries, thereby tightening the boundaries
in various dimensions, whereas a spherical boundary treats all dimensions
equally. We validate EPASAD using the dataset produced by the TE-process
simulator and the C-town datasets. The results show that EPASAD improves
PASAD's average recall by 5.8% and 9.5% for the two datasets, respectively.Comment: Submitte
Active Brownian Particles. From Individual to Collective Stochastic Dynamics
We review theoretical models of individual motility as well as collective
dynamics and pattern formation of active particles. We focus on simple models
of active dynamics with a particular emphasis on nonlinear and stochastic
dynamics of such self-propelled entities in the framework of statistical
mechanics. Examples of such active units in complex physico-chemical and
biological systems are chemically powered nano-rods, localized patterns in
reaction-diffusion system, motile cells or macroscopic animals. Based on the
description of individual motion of point-like active particles by stochastic
differential equations, we discuss different velocity-dependent friction
functions, the impact of various types of fluctuations and calculate
characteristic observables such as stationary velocity distributions or
diffusion coefficients. Finally, we consider not only the free and confined
individual active dynamics but also different types of interaction between
active particles. The resulting collective dynamical behavior of large
assemblies and aggregates of active units is discussed and an overview over
some recent results on spatiotemporal pattern formation in such systems is
given.Comment: 161 pages, Review, Eur Phys J Special-Topics, accepte
Understanding High Dimensional Spaces through Visual Means Employing Multidimensional Projections
Data visualisation helps understanding data represented by multiple
variables, also called features, stored in a large matrix where individuals are
stored in lines and variable values in columns. These data structures are
frequently called multidimensional spaces.In this paper, we illustrate ways of
employing the visual results of multidimensional projection algorithms to
understand and fine-tune the parameters of their mathematical framework. Some
of the common mathematical common to these approaches are Laplacian matrices,
Euclidian distance, Cosine distance, and statistical methods such as
Kullback-Leibler divergence, employed to fit probability distributions and
reduce dimensions. Two of the relevant algorithms in the data visualisation
field are t-distributed stochastic neighbourhood embedding (t-SNE) and
Least-Square Projection (LSP). These algorithms can be used to understand
several ranges of mathematical functions including their impact on datasets. In
this article, mathematical parameters of underlying techniques such as
Principal Component Analysis (PCA) behind t-SNE and mesh reconstruction methods
behind LSP are adjusted to reflect the properties afforded by the mathematical
formulation. The results, supported by illustrative methods of the processes of
LSP and t-SNE, are meant to inspire students in understanding the mathematics
behind such methods, in order to apply them in effective data analysis tasks in
multiple applications
KC Two-Way Clustering Algorithms For Multi-Child Semantic Maps In Image Mining
Image mining is now a thriving and expanding field of computer science research. Image mining is linked to the advancement of data mining in image preparation. Image mining is used to extract hidden information and in other situations where the photos do not clearly describe the situation. Image mining combines machine learning, data handling, application autonomy, and image preparation concepts. Semantic maps are used to visualize image data stored in image databases. We recommend using Multi-Child Semantic Maps to build semantic maps which fully display the image. In this study, we propose two path clustering on Multi-Child Semantic Maps (MCSM) using the K-C Means Clustering Algorithm, also known as the MCSMK-C algorithm. This algorithm causes image clustering and instructs the mining system to look at the image's top area. When mining, the MCSMK-C algorithm considers the X and Y coordinates. The system looks for groups by examining each object's territory in the database, and it saves a region if it contains more objects than the required number
Big data and virtual communities: methodological issues
Virtual communities represent today en emergent phenomenon through which users get together to
create ideas, to obtain help from one another, or just to casually engage in discussions. Their increasing popularity
as well as their utility as a source of business value and marketing strategies justify the necessity of
defi ning some specifi c methodologies for analyzing them. The aim of this paper is providing new insights
into virtual communities from a methodological viewpoint, highlighting the main trends and challenge
Advanced data mining in field ion microscopy
Field ion microscopy (FIM) allows to image individual surface atoms by exploiting the effect of an intense electric field. Widespread use of atomic resolution imaging by FIM has been hampered by a lack of efficient image processing/data extraction tools. Recent advances in imaging and data mining techniques have renewed the interest in using FIM in conjunction with automated detection of atoms and lattice defects for materials characterization. After a brief overview of existing routines, we review the use of machine learning (ML) approaches for data extraction with the aim to catalyze new data-driven insights into high electrical field physics. Apart from exploring various supervised and unsupervised ML algorithms in this context, we also employ advanced image processing routines for data extraction from large sets of FIM images. The outcomes and limitations of such routines are discussed, and we conclude with the possible application of energy minimization schemes to the extracted point clouds as a way of improving the spatial resolution of FIM
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