Convertible Codes: New Class of Codes for Efficient Conversion of Coded Data in Distributed Storage

Erasure codes are typically used in large-scale distributed storage systems to provide durability of data in the face of failures. In this setting, a set of k blocks to be stored is encoded using an [n, k] code to generate n blocks that are then stored on different storage nodes. A recent work by Kadekodi et al. [Kadekodi et al., 2019] shows that the failure rate of storage devices vary significantly over time, and that changing the rate of the code (via a change in the parameters n and k) in response to such variations provides significant reduction in storage space requirement. However, the resource overhead of realizing such a change in the code rate on already encoded data in traditional codes is prohibitively high. Motivated by this application, in this work we first present a new framework to formalize the notion of code conversion - the process of converting data encoded with an [n^I, k^I] code into data encoded with an [n^F, k^F] code while maintaining desired decodability properties, such as the maximum-distance-separable (MDS) property. We then introduce convertible codes, a new class of code pairs that allow for code conversions in a resource-efficient manner. For an important parameter regime (which we call the merge regime) along with the widely used linearity and MDS decodability constraint, we prove tight bounds on the number of nodes accessed during code conversion. In particular, our achievability result is an explicit construction of MDS convertible codes that are optimal for all parameter values in the merge regime albeit with a high field size. We then present explicit low-field-size constructions of optimal MDS convertible codes for a broad range of parameters in the merge regime. Our results thus show that it is indeed possible to achieve code conversions with significantly lesser resources as compared to the default approach of re-encoding

Endosperm sterol phenotype and germination in wheat

Free and conjugated sterols of endosperm, coats, scutellum, coleoptile and roots have been analysed at different germination stages in two wheat cultivars with different endosperm sterol phenotypes. It seems that sterol metabolism of the developing tissues, namely coleoptile and roots, is not affected by the sterol conjugation profile of the endosperm. Enough sterol is present in the mature embryo to supply the germinating axis during the observation period (144 hr at 16°). The data suggest that sterol is transferred from scutellum to coleoptile and roots during germinatio

Disorder-induced mechanism for positive exchange bias fields

We propose a mechanism to explain the phenomenon of positive exchange bias on magnetic bilayered systems. The mechanism is based on the formation of a domain wall at a disordered interface during field cooling (FC) which induces a symmetry breaking of the antiferromagnet, without relying on any ad hoc assumption about the coupling between the ferromagnetic (FM) and antiferromagnetic (AFM) layers. The domain wall is a result of the disorder at the interface between FM and AFM, which reduces the effective anisotropy in the region. We show that the proposed mechanism explains several known experimental facts within a single theoretical framework. This result is supported by Monte Carlo simulations on a microscopic Heisenberg model, by micromagnetic calculations at zero temperature and by mean field analysis of an effective Ising like phenomenological model.Comment: 5 pages, 4 figure

Crime prediction through urban metrics and statistical learning

Understanding the causes of crime is a longstanding issue in researcher's agenda. While it is a hard task to extract causality from data, several linear models have been proposed to predict crime through the existing correlations between crime and urban metrics. However, because of non-Gaussian distributions and multicollinearity in urban indicators, it is common to find controversial conclusions about the influence of some urban indicators on crime. Machine learning ensemble-based algorithms can handle well such problems. Here, we use a random forest regressor to predict crime and quantify the influence of urban indicators on homicides. Our approach can have up to 97% of accuracy on crime prediction, and the importance of urban indicators is ranked and clustered in groups of equal influence, which are robust under slightly changes in the data sample analyzed. Our results determine the rank of importance of urban indicators to predict crime, unveiling that unemployment and illiteracy are the most important variables for describing homicides in Brazilian cities. We further believe that our approach helps in producing more robust conclusions regarding the effects of urban indicators on crime, having potential applications for guiding public policies for crime control.Comment: Accepted for publication in Physica

Multivectorial strategy to interpret a resistive behaviour of loads in smart buildings

In Smart buildings, electric loads are affected by an important distortion in the current and voltage waveforms, caused by the increasing proliferation of non linear electronic devices. This paper presents an approach on non sinusoidal power theory based on Geometric Algebra that clearly improves traditional methods in the optimization of apparent power and power factor compensation. An example is included that demonstrates the superiority of this approach compared with traditional methods.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Decline in Telomere Length by Age and Effect Modification by Gender, Allostatic Load and Comorbidities in National Health and Nutrition Examination Survey (1999-2002)

Background: This study aims to assess the decline in telomere length (TL) with age and evaluate effect modification by gender, chronic stress, and comorbidity in a representative sample of the US population. Methods: Cross-sectional data on 7826 adults with a TL measurement, were included from the National Health and Nutrition Examination Survey, years 1999–2002. The population rate of decline in TL across 10-year age categories was estimated using crude and adjusted regression. Results: In an adjusted model, the population rate of decline in TL with age was consistent and linear for only three age categories: 20–29 (β = -0.0172, 95% CI: -0.0342, -0.0002), 50–59 (β = -0.0182, 95% CI: -0.0311, -0.0054) and 70–79 (β = -0.0170, 95% CI: -0.0329, -0.0011) years. The population rate of decline in TL with age was significantly greater for males and those with high allostatic load and a history of comorbidities. When the population rate of decline in TL was analyzed by gender in 10-year age bins, a fairly consistent yet statistically non-significant decline for males was observed; however, a trough in the rate was observed for females in the age categories 20–29 years (β = -0.0284, 95% CI: -0.0464, -0.0103) and 50–59 years (β = -0.0211, 95% CI: -0.0391, -0.0032). To further elucidate the gender difference observed in the primary analyses, secondary analyses were conducted with reproductive and hormonal status; a significant inverse association was found between TL and parity, menopause, and age at menopause. Conclusions: TL was shorter with increasing age and this decline was modified by gender, chronic stress and comorbidities; individuals with chronic morbidity and/or chronic stress and females in their twenties and fifties experienced greater decline. Female reproductive factors, i.e., parity and menopause, were associated with TL

Return to Tourist Destination. Is it Reputation, After All?

In this paper we study the hypothesis that the repeated purchases in the tourism markets could be considered as a consequence of asymmetrical information problems. We analyze this hypothesis with the case study of the Island of Tenerife by the estimation of a count data model. We obtain that the length of the stay and the information obtained from previous visits and/or relatives and friends might increase the return to a destination suggesting the presence of a reputation mechanism as proposed by Shapiro (1983). We also estimate the determinants of the willingness to return confirming the main results.reputation, tourism, count data, logit

Deim-based pgd for multi-parametric nonlinear model reduction

A new technique for efficiently solving parametric nonlinear reduced order models in the Proper Generalized Decomposition (PGD) framework is presented here. This technique is based on the Discrete Empirical Interpolation Method (DEIM)[1], and thus the nonlinear term is interpolated using the reduced basis instead of being fully evaluated. The DEIM has already been demonstrated to provide satisfactory results in terms of computational complexity decrease when combined with the Proper Orthogonal Decomposition (POD). However, in the POD case the reduced basis is a posteriori known as it comes from several pre-computed snapshots. On the contrary, the PGD is an a priori model reduction method. This makes the DEIM-PGD coupling rather delicate, because different choices are possible as it is analyzed in this work