Generalized and Resource-Efficient VNet Embeddings with Migrations

This paper attends to the problem of embedding flexibly specified CloudNets, virtual networks connecting cloud resources (such as storage or computation). We attend to a scenario where customers can request CloudNets at short notice, and an infrastructure provider (or a potential itermediate broker or reseller) first embeds the CloudNet fast (e.g., using a simple heuristic). Later, however, long-lived CloudNets embeddings are optimized by migrating them to more suitable locations, whose precise definition depends on a given objective function. For instance, such migrations can be useful to reduce the peak resource loads in the network by spreading CloudNets across the infrastructure, to save energy by moving CloudNets together and switching off unused components, or for maintenance purposes. We present a very generic algorithm to compute optimal embeddings of CloudNets: It allows for different objective functions (such as load minimization or energy conservation), supports cost-aware migration, and can deal with all link types that arise in practice (e.g., full-duplex or even wireless or wired broadcast links with multiple endpoints). Our evaluation shows that such a rigorous optimization is even feasible in order to optimize a moderate-size CloudNet of full flexibility (e.g., a router site, a small physical infrastructure or virtual provider network)

Generation of folk song melodies using Bayes transforms

The paper introduces the `Bayes transform', a mathematical procedure for putting data into a hierarchical representation. Applicable to any type of data, the procedure yields interesting results when applied to sequences. In this case, the representation obtained implicitly models the repetition hierarchy of the source. There are then natural applications to music. Derivation of Bayes transforms can be the means of determining the repetition hierarchy of note sequences (melodies) in an empirical and domain-general way. The paper investigates application of this approach to Folk Song, examining the results that can be obtained by treating such transforms as generative models

Aspectos a observar na seleção de sistemas agroflorestais sucessionais para análise financeira.

Na construção de uma pesquisa em análise financeira de SAFs sucessionais é importante a atenção para determinados aspectos a serem observados para a seleção de SAFs sucessionais. Tal observação otimizará os esforços de pesquisa e gerará resultados mais consistentes. Buscaram-se áreas adequadas para a realização de análise financeira dos SAFs sucessionais e em cada SAF sucessional visitado foram averiguados quanto a adequação ou não para a realização da análise financeira, sendo os motivos das adequações e inadequações anotados a posteriormente analisados, agrupados e transformados em um roteiro de observações. Entre os aspectos, destacam-se verificar se o tamanho da área, a disposição do agricultor em colaborar, o acesso a informações básicas, a viabilidade logística para trabalhos de campo, a condução padronizada ao longo do tempo e a produção como objetivo principal do SAF sucessional

Seleção de sistemas agroflorestais multiestrata para análise financeira.

Este trabalho é focado nas características necessárias de um sistema agroflorestal (SAF) multiestrata para a realização de uma análise financeira, definidos a partir de um conjunto de planilhas eletrônicas usadas para SAFs. As características definidos foram:1 - Recomendações participativas, 2 - Área mínima, 3 - Disposição colaborativa, 4 - Acessibilidade a informações, 5 - Logística para análise, 6 - Fieza do agricultor no sistema, 7 - Objetivo produtivo, 8 - Planejamento prévio do SAF, 9 - Padrão temporal, 10 - Replicabilidade do sistema e 11 - Disponibilidade de parâmetros. Conclui-se que a presença destas características nas áreas selecionadas irá otimizar os esforços de pesquisa e gerar resultados mais consistentesEdição dos Resumos do 9º Congresso Brasileiro de Agroecologia, 2015, Belém

Potential determinants of obesity among children and adolescents in Germany: results from the cross-sectional KiGGS study

<p>Abstract</p> <p>Background</p> <p>Obesity among children and adolescents is a growing public health problem. The aim of the present paper is to identify potential determinants of obesity and risk groups among 3- to 17-year old children and adolescents to provide a basis for effective prevention strategies.</p> <p>Methods</p> <p>Data were collected in the German Health Interview and Examination Survey for Children and Adolescents (KiGGS), a nationally representative and comprehensive data set on health behaviour and health status of German children and adolescents. Body height and weight were measured and body mass index (BMI) was classified according to IOTF cut-off points. Statistical analyses were conducted on 13,450 non-underweight children and adolescents aged 3 to 17 years. The association between overweight, obesity and several potential determinants was analysed for this group as well as for three socio-economic status (SES) groups. A multiple logistic regression model with obesity as the dependent variable was also calculated.</p> <p>Results</p> <p>The strongest association with obesity was observed for parental overweight and for low SES. Furthermore, a positive association with both overweight (including obesity) and obesity was seen for maternal smoking during pregnancy, high weight gain during pregnancy (only for mothers of normal weight), high birth weight, and high media consumption. In addition, high intakes of meat and sausages, total beverages, water and tea, total food and beverages, as well as energy-providing food and beverages were significantly associated with overweight as well as with obesity. Long sleep time was negatively associated with obesity among 3- to 10-year olds. Determinants of obesity occurred more often among children and adolescents with low SES.</p> <p>Conclusion</p> <p>Parental overweight and a low SES are major potential determinants of obesity. Families with these characteristics should be focused on in obesity prevention.</p

Expectation in Melody: The Influence of Context and Learning

The Implication-Realization (IR) theory (Narmour, 1990) posits two cognitive systems involved in the generation of melodic expectations: The first consists of a limited number of symbolic rules that are held to be innate and universal; the second reflects the top-down influences of acquired stylistic knowledge. Aspects of both systems have been implemented as quantitative models in research which has yielded empirical support for both components of the theory (Cuddy & Lunny, 1995; Krumhansl, 1995a, 1995b; Schellenberg, 1996, 1997). However, there is also evidence that the implemented bottom-up rules constitute too inflexible a model to account for the influence of the musical experience of the listener and the melodic context in which expectations are elicited. A theory is presented, according to which both bottom-up and top-down descriptions of observed patterns of melodic expectation may be accounted for in terms of the induction of statistical regularities in existing musical repertoires. A computational model that embodies this theory is developed and used to reanalyze existing experimental data on melodic expectancy. The results of three experiments with increasingly complex melodic stimuli demonstrate that this model is capable of accounting for listeners’ expectations as well as or better than the two-factor model of Schellenberg (1997)