Radiative Corrections to the Neutrino Counting Process e+e−→ννγ

The standard model weak corrections to the process e+e- -» vvy which are important from the point o f view o f consistent renormalization are calculated. The calculations o f the hard bremsstrahlung corrections to the process are done and they are found to be in good agreement with the former results

Relações de poder na favela carioca: um breve esforço analítico

A favela no Rio de Janeiro constitui um fenômeno que, no espaço das últimas décadas, evoluiu, criando vários mecanismos sociais ausentes em outras partes da cidade. O artigo foca a análise das relações de poder na favela a partir dos trabalhos de Hannah Arendt e Erich Fromm, apontando na direção de soluções sistêmicas típicas de sistemas totalistas, o que traz consigo várias consequências para a formação humana do indivíduo.Le bidonville carioca constitue un phénomène qu’a augmenté dans les dernières décennies, en créant plusiers mécanismes sociaux qui n’existent pas dans les autres espaces de la ville. L’article souligne l’analyse des rapports de pouvoir dans le bidonville carioca à partir des travaux de Hannah Arendt et Erich Fromm, en indiquant des solutions systémiques propres aux systèmes « totalistes », ce qui apporte plusiers effets sur la formation humaine de l’individu.The slum of Rio de Janeiro is a phenomenon which, during the time of decades of evolution, has created several social mechanisms absent in other parts of the city. The article is focused on analyzing the relations of power in the slum, from the theoretical concepts of Hannah Arendt and Erich Fromm. It shows the evolution of the relations of power in the slum into direction of systematic solutions characteristic for totalistic systems, which brings with itself various consequences for human individual development.La favela del Rio de Janeiro constituyó un fenómeno, cual en espacio de las décadas del desarrollo ha creado varios mecanismos sociales ausentes en las otras partes de la cuidad. El artículo concentra-se en una reseña de las relaciones del poder en la favela, desde los conceptos teoréticos de Hannah Arendt y Erich Fromm. El artículo apunta a la evolución de las relaciones del poder en la favela en la dirección de las soluciones sistemáticas particulares para las sistemas totalistas, lo que lleva consigo varias consecuencias para desarrollo individual del sujeto

Mixing Predictions for Online Metric Algorithms

A major technique in learning-augmented online algorithms is combining multiple algorithms or predictors. Since the performance of each predictor may vary over time, it is desirable to use not the single best predictor as a benchmark, but rather a dynamic combination which follows different predictors at different times. We design algorithms that combine predictions and are competitive against such dynamic combinations for a wide class of online problems, namely, metrical task systems. Against the best (in hindsight) unconstrained combination of ℓ predictors, we obtain a competitive ratio of O(ℓ2), and show that this is best possible. However, for a benchmark with slightly constrained number of switches between different predictors, we can get a (1 + ε)- competitive algorithm. Moreover, our algorithms can be adapted to access predictors in a banditlike fashion, querying only one predictor at a time. An unexpected implication of one of our lower bounds is a new structural insight about covering formulations for the k-server problem

Mixing predictions for online metric algorithms

A major technique in learning-augmented online algorithms is combining multiple algorithms or predictors. Since the performance of each predictor may vary over time, it is desirable to use not the single best predictor as a benchmark, but rather a dynamic combination which follows different predictors at different times. We design algorithms that combine predictions and are competitive against such dynamic combinations for a wide class of online problems, namely, metrical task systems. Against the best (in hindsight) unconstrained combination of $\ell$ predictors, we obtain a competitive ratio of $O(\ell^2)$, and show that this is best possible. However, for a benchmark with slightly constrained number of switches between different predictors, we can get a $(1+\epsilon)$-competitive algorithm. Moreover, our algorithms can be adapted to access predictors in a bandit-like fashion, querying only one predictor at a time. An unexpected implication of one of our lower bounds is a new structural insight about covering formulations for the $k$-server problem

Mixing predictions for online metric algorithms

A major technique in learning-augmented online algorithms is combining multiple algorithms or predictors. Since the performance of each predictor may vary over time, it is desirable to use not the single best predictor as a benchmark, but rather a dynamic combination which follows different predictors at different times. We design algorithms that combine predictions and are competitive against such dynamic combinations for a wide class of online problems, namely, metrical task systems. Against the best (in hindsight) unconstrained combination of ℓ predictors, we obtain a competitive ratio of (ℓ2), and show that this is best possible. However, for a benchmark with slightly constrained number of switches between different predictors, we can get a (1+)-competitive algorithm. Moreover, our algorithms can be adapted to access predictors in a bandit-like fashion, querying only one predictor at a time. An unexpected implication of one of our lower bounds is a new structural insight about covering formulations for the -server problem

Mixing Predictions or Online Metric Algorithms

A major technique in learning-augmented online algorithms is combining multiple algorithms or predictors. Since the performance of each predictor may vary over time, it is desirable to use not the single best predictor as a benchmark, but rather a dynamic combination which follows different predictors at different times. We design algorithms that combine predictions and are competitive against such dynamic combinations for a wide class of online problems, namely, metrical task systems. Against the best (in hindsight) unconstrained combination of ℓ predictors, we obtain a competitive ratio of O(ℓ2), and show that this is best possible. However, for a benchmark with slightly constrained number of switches between different predictors, we can get a (1+ϵ)-competitive algorithm. Moreover, our algorithms can be adapted to access predictors in a bandit-like fashion, querying only one predictor at a time. An unexpected implication of one of our lower bounds is a new structural insight about covering formulations for the k-server problem

Learning-augmented dynamic power management with multiple states via new ski rental bounds

No abstract availabl

Policy-based SLA storage management model for distributed data storage services

There is  high demand for storage related services supporting scientists in their research activities. Those services are expected to provide not only capacity but also features allowing for more flexible and cost efficient usage. Such features include easy multiplatform data access, long term data retention, support for performance and cost differentiating of SLA restricted data access. The paper presents a policy-based SLA storage management model for distributed data storage services. The model allows for automated management of distributed data aimed at QoS provisioning with no strict resource reservation. The problem of providing  users with the required QoS requirements is complex, and therefore the model implements heuristic approach  for solving it. The corresponding system architecture, metrics and methods for SLA focused storage management are developed and tested in a real, nationwide environment