Quantum Stress Tensor Fluctuations and their Physical Effects

We summarize several aspects of recent work on quantum stress tensor fluctuations and their role in driving fluctuations of the gravitational field. The role of correlations and anticorrelations is emphasized. We begin with a review of the properties of the stress tensor correlation function. We next consider some illuminating examples of non-gravitational effects of stress tensors fluctuations, specifically fluctuations of the Casimir force and radiation pressure fluctuations. We next discuss passive fluctuations of spacetime geometry and some of their operational signatures. These include luminosity fluctuations, line broadening, and angular blurring of a source viewed through a fluctuating gravitational field. Finally, we discuss the possible role of quantum stress tensor fluctuations in the early universe, especially in inflation. The fluctuations of the expansion of a congruence of comoving geodesics grows during the inflationary era, due to non-cancellation of anticorrelations that would have occurred in flat spacetime. This results in subsequent non-Gaussian density perturbations and allows one to infer an upper bound on the duration of inflation. This bound is consistent with adequate inflation to solve the horizon and flatness problems.Comment: 15 pages, 1 figure; invited talk presented at the 3rd Mexican Meeting on Experimental and Theoretical Physics, Mexico City, September 10-14, 200

Vitamin Fortification of Milled Rice: A New Approach to Address Micronutrient Malnutrition

To address micronutrient deficiencies in the susceptible sector of society, it is recommended to fortify the commercial milled rice with vitamins using the new technique of fortification. The simple approach, which involves rice surface modification and vitamin absorption, is deemed economical compared to traditional fortification processes. Moreover, the susceptibility of losing vitamins due to washing processes is resolved in this improved grain fortification as ~90% of the vitamins are retained. The results shown in this study indicate the successful fortification of vitamins on rice and that fortification is more pronounced when the grain undergoes sonication process. As the staple food for an estimated 3 billion people worldwide, fortification of rice grains through sonication and adsorption allows vitamins to be delivered into the body regularly through the diet, which can be a potential approach towards a massive food fortification programs to address severe nutrient deficiencies in the population

Extending the SACOC algorithm through the Nystrom method for dense manifold data analysis

Data analysis has become an important field over the last decades. The growing amount of data demands new analytical methodologies in order to extract relevant knowledge. Clustering is one of the most competitive techniques in this context.Using a dataset as a starting point, these techniques aim to blindly group the data by similarity. Among the different areas, manifold identification is currently gaining importance. Spectral-based methods, which are the mostly used methodologies in this area, are however sensitive to metric parameters and noise. In order to solve these problems, new bio-inspired techniques have been combined with different heuristics to perform the clustering solutions and stability, specially for dense datasets. Ant Colony Optimization (ACO) is one of these new bio-inspired methodologies. This paper presents an extension of a previous algorithm named Spectral-based ACO Clustering (SACOC). SACOC is a spectral-based clustering methodology used for manifold identification. This work is focused on improving this algorithm through the Nystrom extension. The new algorithm, named SACON, is able to deal with Dense Data problems.We have evaluated the performance of this new approach comparing it with online clustering algorithms and the Nystrom extension of the Spectral Clustering algorithm using several datasets

Medoid-based clustering using ant colony optimization

The application of ACO-based algorithms in data mining has been growing over the last few years, and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works about unsupervised learning have focused on clustering, showing the potential of ACO-based techniques. However, there are still clustering areas that are almost unexplored using these techniques, such as medoid-based clustering. Medoid-based clustering methods are helpful—compared to classical centroid-based techniques—when centroids cannot be easily defined. This paper proposes two medoid-based ACO clustering algorithms, where the only information needed is the distance between data: one algorithm that uses an ACO procedure to determine an optimal medoid set (METACOC algorithm) and another algorithm that uses an automatic selection of the number of clusters (METACOC-K algorithm). The proposed algorithms are compared against classical clustering approaches using synthetic and real-world datasets

MOCDroid: multi-objective evolutionary classifier for Android malware detection

Malware threats are growing, while at the same time, concealment strategies are being used to make them undetectable for current commercial Anti-Virus. Android is one of the target architectures where these problems are specially alarming, due to the wide extension of the platform in different everyday devices.The detection is specially relevant for Android markets in order to ensure that all the software they offer is clean, however, obfuscation has proven to be effective at evading the detection process. In this paper we leverage third-party calls to bypass the effects of these concealment strategies, since they cannot be obfuscated. We combine clustering and multi-objective optimisation to generate a classifier based on specific behaviours defined by 3rd party calls groups. The optimiser ensures that these groups are related to malicious or benign behaviours cleaning any non-discriminative pattern. This tool, named MOCDroid, achieves an ac-curacy of 94.6% in test with 2.12% of false positives with real apps extracted from the wild, overcoming all commercial Anti-Virus engines from VirusTotal

A genetic graph-based approach for partitional clustering

Clustering is one of the most versatile tools for data analysis. In the recent years, clustering that seeks the continuity of data (in opposition to classical centroid-based approaches) has attracted an increasing research interest. It is a challenging problem with a remarkable practical interest. The most popular continuity clustering method is the spectral clustering (SC) algorithm, which is based on graph cut: It initially generates a similarity graph using a distance measure and then studies its graph spectrum to find the best cut. This approach is sensitive to the parameters of the metric, and a correct parameter choice is critical to the quality of the cluster. This work proposes a new algorithm, inspired by SC, that reduces the parameter dependency while maintaining the quality of the solution. The new algorithm, named genetic graph-based clustering (GGC), takes an evolutionary approach introducing a genetic algorithm (GA) to cluster the similarity graph. The experimental validation shows that GGC increases robustness of SC and has competitive performance in comparison with classical clustering methods, at least, in the synthetic and real dataset used in the experiments

From Collapse to Freezing in Random Heteropolymers

We consider a two-letter self-avoiding (square) lattice heteropolymer model of N_H (out ofN) attracting sites. At zero temperature, permanent links are formed leading to collapse structures for any fraction rho_H=N_H/N. The average chain size scales as R = N^{1/d}F(rho_H) (d is space dimension). As rho_H --> 0, F(rho_H) ~ rho_H^z with z={1/d-nu}=-1/4 for d=2. Moreover, for 0 < rho_H < 1, entropy approaches zero as N --> infty (being finite for a homopolymer). An abrupt decrease in entropy occurs at the phase boundary between the swollen (R ~ N^nu) and collapsed region. Scaling arguments predict different regimes depending on the ensemble of crosslinks. Some implications to the protein folding problem are discussed.Comment: 4 pages, Revtex, figs upon request. New interpretation and emphasis. Submitted to Europhys.Let

Some Properties of the Speciation Model for Food-Web Structure - Mechanisms for Degree Distributions and Intervality

We present a mathematical analysis of the speciation model for food-web structure, which had in previous work been shown to yield a good description of empirical data of food-web topology. The degree distributions of the network are derived. Properties of the speciation model are compared to those of other models that successfully describe empirical data. It is argued that the speciation model unifies the underlying ideas of previous theories. In particular, it offers a mechanistic explanation for the success of the niche model of Williams and Martinez and the frequent observation of intervality in empirical food webs.Comment: 23 pages, 6 figures, minor rewrite

Analysing temporal performance profiles of UAV operators using time series clustering

The continuing growth in the use of Unmanned Aerial Vehicles (UAVs) is causing an important social step forward in the performance of many sensitive tasks, reducing both human and economical risks. The work of UAV operators is a key aspect to guarantee the success of this kind of tasks, and thus UAV operations are studied in many research fields, ranging from human factors to data analysis and machine learning. The present work aims to describe the behaviour of operators over time using a profile-based model where the evolution of the operator performance during a mission is the main unit of measure. In order to compare how different operators act throughout a mission, we describe a methodology based of multivariate-time series clustering to define and analyse a set of representative temporal performance profiles. The proposed methodology is applied in a multi-UAV simulation environment with inexperienced operators, obtaining a fair description of the temporal behavioural patterns followed during the course of the simulation