On the equivalence of the self-dual and Maxwell-Chern-Simons models coupled to Fermions

We study the exact equivalence between the self-dual model minimally coupled with a Dirac field and the Maxwell-Chern-Simons model with non-minimal magnetic coupling to fermions. We show that the fermion sectors of the models are equivalent only if a Thirring like interaction is included. Using functional methods we verify that, up to renormalizations, the equivalence persists at the quantum level.Comment: 8 pages, revte

Polynomial Time Construction for Spatially Balanced Latin Squares

In this paper we propose a construction that generates spatially balanced Latin squares (SBLSs) in polynomial time. These structures are central to the design of agronomic experiments, as they avoid biases that are otherwise unintentionally introduced due to spatial auto-correlation. Previous approaches were able to generate SBLSs of order up to 35 and required about two weeks of computation. Our algorithm runs in O(n2) and generates SBLSs of arbitrary order n where 2n + 1 is prime. For example, this algorithm generates a SBLS of order 999 in a fraction of a second.National Science Foundation (NSF Expeditions in Computing award for Computational Sustainability, grant 0832782; NSF IIS award, grant 0514429), Intelligent Information Systems Institute, Cornell University (Air Force O ce of Scienti c Research, AFOSR, grant FA9550-04-1-0151), Natural Sciences and Engineering Research Council of Canada (NSERC

A stochastic perturbation of inviscid flows

We prove existence and regularity of the stochastic flows used in the stochastic Lagrangian formulation of the incompressible Navier-Stokes equations (with periodic boundary conditions), and consequently obtain a \holderspace{k}{\alpha} local existence result for the Navier-Stokes equations. Our estimates are independent of viscosity, allowing us to consider the inviscid limit. We show that as $\nu \to 0$, solutions of the stochastic Lagrangian formulation (with periodic boundary conditions) converge to solutions of the Euler equations at the rate of $O(\sqrt{\nu t})$.Comment: 13 pages, no figures

Computationally efficient rule-based classification for continuous streaming data

Advances in hardware and software technologies allow to capture streaming data. The area of Data Stream Mining (DSM) is concerned with the analysis of these vast amounts of data as it is generated in real-time. Data stream classification is one of the most important DSM techniques allowing to classify previously unseen data instances. Different to traditional classifiers for static data, data stream classifiers need to adapt to concept changes (concept drift) in the stream in real-time in order to reflect the most recent concept in the data as accurately as possible. A recent addition to the data stream classifier toolbox is eRules which induces and updates a set of expressive rules that can easily be interpreted by humans. However, like most rule-based data stream classifiers, eRules exhibits a poor computational performance when confronted with continuous attributes. In this work, we propose an approach to deal with continuous data effectively and accurately in rule-based classifiers by using the Gaussian distribution as heuristic for building rule terms on continuous attributes. We show on the example of eRules that incorporating our method for continuous attributes indeed speeds up the real-time rule induction process while maintaining a similar level of accuracy compared with the original eRules classifier. We termed this new version of eRules with our approach G-eRules

Do monitoramento autônomo à pesquisa colaborativa virtual: parceria com o movimento indígena do Nordeste durante a pandemia da covid-19 como apoio ao controle social

Nos primeiros meses da pandemia de covid-19, em 2020, os movimentos e organizações indígenas da região Nordeste do Brasil estabeleceram uma extensa rede de apoio e parcerias com grupos de pesquisadores e entidades da sociedade civil para a organização de campanhas de solidariedade aos povos indígenas. A produção de informações gerais e dados empíricos sobre como a doença atingiu os territórios e populações indígenas constituiu uma das principais estratégias de ação. Essa mobilização foi a base para a constituição de redes colaborativas que investigaram como ocorreu o enfrentamento dos povos diante da pandemia, por meio de um viés antropológico e aplicando métodos que poderíamos definir como uma pesquisa colaborativa virtual. Este artigo, portanto, discute o potencial desse tipo de parceria para a reflexão sobre o Subsistema de Atenção à Saúde Indígena, argumentando que esse modelo pode constituir uma forma de apoio ao controle social exercido por parte das comunidades.In the first months of the covid-19 pandemic, in 2020, indigenous movements and organizations in the Northeast region of Brazil established an extensive network of support and partnerships with groups of researchers and civil society entities to organize campaigns of solidarity with the indigenous peoples. The production of general information and empirical data on how the disease reached indigenous territories and populations constituted one of the main strategies for action. This mobilization was the basis for establishing collaborative networks that investigated how the indigenous peoples faced the pandemic, from an anthropological bias and applying methods that we could define as virtual collaborative research. The article, thus, discusses the potential of this type of partnership for reflection on the Indigenous Health Care Subsystem, arguing that this model can constitute a kind of support for social control exercised by the communities

Prebiotic Homochirality as a Critical Phenomenon

The development of prebiotic homochirality on early-Earth or another planetary platform may be viewed as a critical phenomenon. It is shown, in the context of spatio-temporal polymerization reaction networks, that environmental effects -- be them temperature surges or other external disruptions -- may destroy any net chirality previously produced. In order to understand the emergence of prebiotic homochirality it is important to model the coupling of polymerization reaction networks to different planetary environments.Comment: 6 Pages, 1 Figure, In Press: Origins of Life and Evolution of Biosphere

A Survey on Semi-Supervised Learning for Delayed Partially Labelled Data Streams

Unlabelled data appear in many domains and are particularly relevant to streaming applications, where even though data is abundant, labelled data is rare. To address the learning problems associated with such data, one can ignore the unlabelled data and focus only on the labelled data (supervised learning); use the labelled data and attempt to leverage the unlabelled data (semi-supervised learning); or assume some labels will be available on request (active learning). The first approach is the simplest, yet the amount of labelled data available will limit the predictive performance. The second relies on finding and exploiting the underlying characteristics of the data distribution. The third depends on an external agent to provide the required labels in a timely fashion. This survey pays special attention to methods that leverage unlabelled data in a semi-supervised setting. We also discuss the delayed labelling issue, which impacts both fully supervised and semi-supervised methods. We propose a unified problem setting, discuss the learning guarantees and existing methods, explain the differences between related problem settings. Finally, we review the current benchmarking practices and propose adaptations to enhance them