A stable cardinality distance for topological classification

Advances in Data Analysis and Classification - This work incorporates topological features via persistence diagrams to classify point cloud data arising from materials science. Persistence diagrams...     

Markov Chain Monte Carlo from Lagrangian Dynamics

Hamiltonian Monte Carlo (HMC) improves the computational efficiency of the Metropolis–Hastings algorithm by reducing its random walk behavior. Riemannian HMC (RHMC) further improves the performance of HMC by exploiting the geometric properties of the parameter space. However, the geometric integrator used for RHMC involves implicit equations that require fixed-point iterations. In some cases, the computational overhead for solving implicit equations undermines RHMC’s benefits. In an attempt to circumvent this problem, we propose an explicit integrator that replaces the momentum variable in RHMC by velocity. We show that the resulting transformation is equivalent to transforming Riemannian Hamiltonian dynamics to Lagrangian dynamics. Experimental results suggest that our method improves RHMC’s overall computational efficiency in the cases considered. All computer programs and datasets are available online (http://www.ics.uci.edu/babaks/Site/Codes.html) to allow replication of the results reported in this article.     

Large deviations for optimal filtering with fractional Brownian motion

We establish large deviation estimates for the optimal filter where the observation process is corrupted by a fractional Brownian motion. The observation process is transformed to an equivalent model which is driven by a standard Brownian motion. The large deviations in turn are established by proving qualitative properties of perturbations of the equivalent observation process.     

Classification of Greek Olive Oils from Different Regions by Machine Learning-Aided Laser-Induced Breakdown Spectroscopy and Absorption Spectroscopy

In the present work, the emission and the absorption spectra of numerous Greek olive oil samples and mixtures of them, obtained by two spectroscopic techniques, namely Laser-Induced Breakdown Spectroscopy (LIBS) and Absorption Spectroscopy, and aided by machine learning algorithms, were employed for the discrimination/classification of olive oils regarding their geographical origin. Both emission and absorption spectra were initially preprocessed by means of Principal Component Analysis (PCA) and were subsequently used for the construction of predictive models, employing Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). All data analysis methodologies were validated by both “k-fold” cross-validation and external validation methods. In all cases, very high classification accuracies were found, up to 100%. The present results demonstrate the advantages of machine learning implementation for improving the capabilities of these spectroscopic techniques as tools for efficient olive oil quality monitoring and control.     

Antibacterial Effects of Essential Oils of Seven Medicinal-Aromatic Plants Against the Fish Pathogen Aeromonas veronii bv. sobria: To Blend or Not to Blend?

Despite progress achieved, there is limited available information about the antibacterial activity of constituents of essential oils (EOs) from different medicinal-aromatic plants (MAPs) against fish pathogens and the complex interactions of blended EOs thereof. The present study aimed to investigate possible synergistic antimicrobial effects of EOs from seven Greek MAPs with strong potential against Aeromonas veronii bv. sobria, a fish pathogen associated with aquaculture disease outbreaks. The main objective was to evaluate whether blending of these EOs can lead to increased antimicrobial activity against the specific microorganism. A total of 127 combinations of EOs were prepared and their effect on A. veronii bv. sobria growth was tested in vitro. We examined both the inhibitory and bactericidal activities of the individual EOs and compared them to those of the blended EOs. The vast majority of the investigated combinations exhibited significant synergistic and additive effects, while antagonistic effects were evident only in a few cases, such as the mixtures containing EOs from rosemary, lemon balm and pennyroyal. The combination of EOs from Greek oregano and wild carrot, as well as the combinations of those two with Spanish oregano or savoury were the most promising ones. Overall, Greek oregano, savoury and Spanish oregano EOs were the most effective ones when applied either in pure form or blended with other EOs.     

The laminar hole pressure for Newtonian fluids

For flows with wall turbulence the hole pressure, P _H , was shown empirically by Franklin and Wallace (J Fluid Mech, 42, 33–48, 1970) to depend solely on R ₊, the Reynolds number constructed from the friction velocity and the hole diameter b. Here this dependence is extended to the laminar regime by numerical simulation of a Newtonian fluid flowing in a plane channel (gap H) with a deep tap hole on one wall. Calculated hole pressures are in good agreement with experimental values, and for two hole sizes are well represented by: (P _H −P _HS )/τ _w = √(k ² + c ² R ₊²)−k, where the Stokes hole pressure P _HS /τ _w = s (b/H)³, k, c, s are fitted constants, and τ _w is the wall shear stress.     

Novel methodology for the calculation of the enthalpy of enclathration of methane hydrates using molecular dynamics simulations

Methane hydrates are encountered in a plethora of industrial and geological or environmental applications. In the current study, we present a novel methodology which is based on molecular dynamics simulations for the calculation of the enthalpy of enclathration of sI methane hydrates. Simulations are performed along the three-phase (Hydrate – Liquid water – Vapour; H–L_w–V) equilibrium line in the temperature range 274–310?K. The methodology takes into account the two different types of cages that are present in the sI methane hydrate and provides results for the enthalpy of enclathration for both types of cages, while it avoids performing calculations with the metastable, completely empty hydrate lattice. The formulation proposed is general and can be also applied to sII hydrates, while it can be modified/extended appropriately for use in the case of sH hydrates. Comparison is provided with available data from the literature and good agreement is observed.     

A Family of Induced Distributions

Methodology and Computing in Applied Probability - In the present paper a family of discrete distributions is introduced through the probability generating function of any discrete distribution...