Ineffective Controls on Capital Inflows Under Sophisticated Financial Markets: Brazil in the Nineties

We analyze the Brazilian experience in the 1990s to assess the effectiveness of controls on capital inflows in restricting financial inflows and changing their composition towards long term flows. Econometric exercises (VARs) showed that controls on capital inflows were effective in deterring financial inflows for only a brief period, from two to six months. The hypothesis to explain the ineffectiveness of the controls is that financial institutions performed several operations aimed at avoiding capital controls. To check this hypothesis, we conducted interviews with market players. We collected several examples of the financial strategies engineered to avoid the capital controls and invest in the Brazilian fixed income market. The main conclusion is that controls on capital inflows, while they may be desirable, are of very limited effectiveness under sophisticated financial markets.

Stabilization of vortex beams in Kerr media by nonlinear absorption

We elaborate a new solution for the problem of stable propagation of transversely localized vortex beams in homogeneous optical media with self-focusing Kerr nonlinearity. Stationary nonlinear Bessel-vortex states are stabilized against azimuthal breakup and collapse by multiphoton absorption, while the respective power loss is offset by the radial influx of the power from an intrinsic reservoir. A linear stability analysis and direct numerical simulations reveal a region of stability of these vortices. Beams with multiple vorticities have their stability regions too. These beams can then form robust tubular filaments in transparent dielectrics as common as air, water and optical glasses at sufficiently high intensities. We also show that the tubular, rotating and speckle-like filamentation regimes, previously observed in experiments with axicon-generated Bessel beams, can be explained as manifestations of the stability or instability of a specific nonlinear Bessel-vortex state, which is fully identified.Comment: Physical Review A, in press, 9 pages, 6 figure

Judging traffic differentiation as network neutrality violation according to internet regulation

Network Neutrality (NN) is a principle that establishes that traffic generated by Internet applications should be treated equally and it should not be affected by arbitrary interfer- ence, degradation, or interruption. Despite this common sense, NN has multiple defi- nitions spread across the academic literature, which differ primarily on what constitutes the proper equality level to consider the network as neutral. NN definitions may also be included in regulations that control activities on the Internet. However, the regulations are set by regulators whose acts are valid within a geographical area, named jurisdic- tion. Thus, both the academia and regulations provide multiple and heterogeneous NN definitions. In this thesis, the regulations are used as guidelines to detect NN violations, which are, by this approach, the adoption of traffic management practices prohibited by regulators. Thereafter, the solutions can provide helpful information for users to support claims against illegal traffic management practices. However, state-of-the-art solutions adopt strict academic definitions (e.g., all traffic must be treated equally) or adopt the regulatory definitions from one jurisdiction, which is not realistic or does not consider that multiple jurisdictions may be traversed in an end-to-end network path, respectively An impact analysis showed that, under certain circumstances, from 39% to 48% of the detected Traffic Differentiations (TDs) are not NN violations when the regulations are considered, exposing that the regulatory aspect must not be ignored. In this thesis, a Reg- ulation Assessment step is proposed to be performed after the TD detection. This step shall consider all NN definitions that may be found in an end-to-end network path and point out NN violation when they are violated. A service is proposed to perform this step for TD detection solutions, given the unfeasibility of every solution implementing the re- quired functionalities. A Proof-of-Concept (PoC) prototype was developed based on the requirements identified along with the impact analysis, which was evaluated using infor- mation about TDs detected by a state-of-the-art solution. The verdicts were inconclusive (the TD is an NN violation or not) for a quarter of the scenarios due to lack of information about the traversed network paths and the occurrence zones (where in the network path, the TD is suspected of being deployed). However, the literature already has proposals of approaches to obtain such information. These results should encourage TD detection solution proponents to collect this data and submit them for the Regulation Assessment.Neutralidade da rede (NR) é um princípio que estabelece que o tráfego de aplicações e serviços seja tratado igualitariamente e não deve ser afetado por interferência, degradação, ou interrupção arbitrária. Apesar deste senso comum, NR tem múltiplas definições na literatura acadêmica, que diferem principalmente no que constitui o nível de igualdade adequado para considerar a rede como neutra. As definições de NR também podem ser incluídas nas regulações que controlam as atividades na Internet. No entanto, tais regu- lações são definidas por reguladores cujos atos são válidos apenas dentro de uma área geográfica denominada jurisdição. Assim, tanto a academia quanto a regulação forne- cem definições múltiplas e heterogêneas de NR. Nesta tese, a regulação é utilizada como guia para detecção de violação da NR, que nesta abordagem, é a adoção de práticas de gerenciamento de tráfego proibidas pelos reguladores. No entanto, as soluções adotam definições estritas da academia (por exemplo, todo o tráfego deve ser tratado igualmente) ou adotam as definições regulatórias de uma jurisdição, o que pode não ser realista ou pode não considerar que várias jurisdições podem ser atravessadas em um caminho de rede, respectivamente. Nesta tese, é proposta uma etapa de Avaliação da Regulação após a detecção da Diferenciação de Tráfego (DT), que deve considerar todas as definições de NR que podem ser encontradas em um caminho de rede e sinalizar violações da NR quando elas forem violadas. Uma análise de impacto mostrou que, em determinadas cir- cunstâncias, de 39% a 48% das DTs detectadas não são violações quando a regulação é considerada. É proposto um serviço para realizar a etapa de Avaliação de Regulação, visto que seria inviável que todas as soluções tivessem que implementar tal etapa. Um protótipo foi desenvolvido e avaliado usando informações sobre DTs detectadas por uma solução do estado-da-arte. Os veredictos foram inconclusivos (a DT é uma violação ou não) para 75% dos cenários devido à falta de informações sobre os caminhos de rede percorridos e sobre onde a DT é suspeita de ser implantada. No entanto, existem propostas para realizar a coleta dessas informações e espera-se que os proponentes de soluções de detecção de DT passem a coletá-las e submetê-las para o serviço de Avaliação de Regulação

Ineffective controls on capital inflows under sophisticated financial markets: Brazil in the nineties

We analyze the Brazilian experience in the 1990s to access the effectiveness of controls on capital inflows in restricting financial inflows and changing their composition towards long term flows. Econometric exercises (VARs) lead us to conclude that controls on capital inflows were effective in deterring financial inflows for only a brief period, from two to six months. The hypothesis to explain the ineffectiveness of the controls is that financial institutions performed several operations aimed at avoiding capital controls. We then conducted interviews with market players in order to provide several examples of the financial strategies that were used in this period to invest in the Brazilian fixed income market while bypassing capital controls. The main conclusion is that controls on capital inflows, while they may be desirable, are of very limited effectiveness under sophisticated financial markets. Therefore, policy-makers should avoid spending the scarce resources of bank supervision trying to implement them and focus more in improving economic policy.

Extracting nuclear matter properties from neutron star matter EoS using deep neural networks

The extraction of the nuclear matter properties from neutron star observations is nowadays an important issue, in particular, the properties that characterize the symmetry energy which are essential to describe correctly asymmetric nuclear matter. We use deep neural networks (DNN) to map the relation between cold $\beta$-equilibrium neutron star matter and the nuclear matter properties. Assuming a quadratic dependence on the isospin asymmetry for the energy per particle of homogeneous nuclear matter and using a Taylor expansion up to fourth order in the iso-scalar and iso-vector contributions, we generate a dataset of different realizations of $\beta$-equilibrium NS matter and the corresponding nuclear matter properties. The DNN model was successfully trained, attaining great accuracy in the test set. Finally, a real case scenario was used to test the DNN model, where a set of 33 nuclear models, obtained within a relativistic mean field approach or a Skyrme force description, were fed into the DNN model and the corresponding nuclear matter parameters recovered with considerable accuracy, in particular, the standard deviations $\sigma(L_{\text{sym}})= 12.85$ MeV and $\sigma(K_{\text{sat}})= 41.02$ MeV were obtained, respectively, for the slope of the symmetry energy and the nuclear matter incompressibility at saturation.Comment: 10 pages, 5 figure

From NS observations to nuclear matter properties: a machine learning approach

This study is devoted to the inference problem of extracting the nuclear matter properties directly from a set of mass-radius observations. We employ Bayesian neural networks (BNNs), which is a probabilistic model capable of estimating the uncertainties associated with its predictions. To simulate different noise levels on the $M(R)$ observations, we create three different sets of mock data. Our results show BNNs as an accurate and reliable tool for predicting the nuclear matter properties whenever the true values are not completely outside the training dataset statistics, i.e., if the model is not heavily dependent on its extrapolating capacities. Using real mass-radius pulsar data, the model predicted, for instance, $L_{\text{sym}}=39.80\pm17.52$ MeV and $K_{\text{sym}}=-101.67\pm62.86$ MeV ($2\sigma$ interval). Our study provides a valuable inference framework when new NS data becomes available.Comment: 15 pages, 12 figure

IMPACTOS DA COVID-19 NO CONSUMO DE CARNE NO BRASIL

In den letzten Jahren hat sich das Fleischkonsumprofil der Brasilianer durch die Umstellung von Rindfleisch auf Hühnchen und Schweinefleisch auf der Suche nach niedrigeren Preisen und gesundem Konsum erheblich verändert. Es wurden neunhunderteinundneunzig Antworten aus den fünf brasilianischen Makroregionen analysiert, die die Präferenz und den Verzehr verschiedener Fleischsorten vor, während und nach der COVID-19-Pandemie umfassten. Die Teilnehmer wurden durch die nichthierarchische Clustering-Methode (Gruppierung) durch den K-Means-Algorithmus der Scikit-Learn-Bibliothek in Python-Sprache in 4 Gruppen namens „Cluster“ gruppiert. Die Analyse zeigt einen ähnlichen Konsum bei allen Gruppen (Clustern), vor der Pandemie mit überwiegendem Verzehr von Rindfleisch, während und nach der Pandemie jedoch mit umgekehrtem Konsum, wobei die Befragten mehr Hähnchenfleisch konsumierten. Die Gruppe mit der höchsten Beteiligung in der Region Nordosten (Cluster3), die nur von Frauen gebildet wird und auch die mit dem niedrigsten Einkommen (bis zu 1 Mindestlohn) hatte, hatte vor der Pandemie den höchsten Hähnchenkonsum (30,08 %) und auch hatte während der Pandemie den höchsten Proteinkonsum (75,43 %). Es wird geschlussfolgert, dass es in den 5 brasilianischen Regionen vor, während und nach der Pandemie eine relevante Veränderung im Verzehrprofil der Befragten gab, wobei das Hauptereignis der Austausch von Rindfleisch gegen Huhn und Schwein war.In recent years, the meat consumption profile of Brazilians has changed significantly with a switch from beef to chicken and pork, seeking lower prices and healthy consumption. Nine hundred and ninety-one responses from the 5 Brazilian macro-regions were analyzed, involving the preference and consumption of different meats, before, during and after the COVID-19 pandemic. Participants were grouped into 4 groups called “Clusters” through the Non-Hierarchical Clustering Method (grouping), by the K-Means algorithm of the Scikit-Learn library in Python language. The analysis reveals a similar consumption among all groups (clusters), before the pandemic with predominant consumption of beef, however, during and after the pandemic, consumption was reversed, with respondents consuming more chicken meat. The group with the highest participation in the Northeast region (Cluster3), formed only by women and also the one with the lowest income (Up to 1 minimum wage), had the highest consumption of chicken before the pandemic (30.08%), and also had the highest protein consumption during the pandemic (75.43%). It is concluded that there was a relevant change in the consumption profile of the interviewees in the pre, during and post pandemic periods in the 5 Brazilian regions, with the main occurrence being the exchange of beef for chicken and pork.El perfil de consumo de carne de los brasileños cambió significativamente, pasando de la carne de res a la de pollo y cerdo, buscando precios más bajos y un consumo saludable.. Se analizaron 991 respuestas de las 5 macrorregiones brasileñas, que involucran la preferencia y el consumo de diferentes carnes, antes, durante y después de la pandemia de COVID-19. Los participantes fueron agrupados en 4 grupos denominados “Clusters” a través del Método de Clustering No Jerárquico (agrupación), mediante el algoritmo K-Means de la librería Scikit-Learn en lenguaje Python. El análisis revela un consumo similar entre todos los grupos (clusters), antes de la pandemia con un consumo predominante de carne de res, sin embargo, durante y después de la pandemia, el consumo se invirtió, consumiendo más carne de pollo los encuestados. El grupo de mayor participación en la región Nordeste (Cluster3), formado solo por mujeres y también el de menores ingresos (Hasta 1 salario mínimo), tenía el mayor consumo de pollo antes de la pandemia (30,08%), y también tuvo el mayor consumo de proteínas durante la pandemia (75,43%). Se concluye que hubo un cambio relevante en el perfil de consumo de los entrevistados en los períodos pre, durante y post pandemia en las 5 regiones brasileñas, siendo el principal evento el intercambio de carne de res por pollo y cerdo.Ces dernières années, le profil de consommation de viande des Brésiliens a considérablement changé avec le passage du bœuf au poulet et au porc, à la recherche de prix plus bas et d'une consommation saine. Neuf cent quatre-vingt-onze réponses des 5 macro-régions brésiliennes ont été analysées, impliquant la préférence et la consommation de différentes viandes, avant, pendant et après la pandémie de COVID-19. Les participants ont été regroupés en 4 groupes appelés "Clusters" grâce à la méthode de clustering non hiérarchique (grouping), par l'algorithme K-Means de la bibliothèque Scikit-Learn en langage Python. L'analyse révèle une consommation similaire parmi tous les groupes (clusters), avant la pandémie avec une consommation prédominante de viande bovine, cependant, pendant et après la pandémie, la consommation s'est inversée, les répondants consommant plus de viande de poulet. Le groupe avec la plus forte participation dans la région du Nord-Est (Cluster3), formé uniquement par les femmes et aussi celui avec les revenus les plus bas (Jusqu'à 1 Smic), avait la plus forte consommation de poulet avant la pandémie (30,08%), et aussi avait la consommation de protéines la plus élevée pendant la pandémie (75,43%). Il est conclu qu'il y a eu un changement pertinent dans le profil de consommation des personnes interrogées dans les périodes pré, pendant et post-pandémie dans les 5 régions brésiliennes, le principal événement étant l'échange de bœuf contre du poulet et du porc.Negli ultimi anni, il profilo del consumo di carne dei brasiliani è cambiato in modo significativo, passando dal manzo al pollo e al maiale, alla ricerca di prezzi più bassi e di un consumo sano. Sono state analizzate novecentonovantuno risposte dalle 5 macroregioni brasiliane, riguardanti la preferenza e il consumo di carni diverse, prima, durante e dopo la pandemia di COVID-19. I partecipanti sono stati raggruppati in 4 gruppi chiamati “Clusters” attraverso il Non-Hierarchical Clustering Method (raggruppamento), dall'algoritmo K-Means della libreria Scikit-Learn in linguaggio Python. L'analisi rivela un consumo simile tra tutti i gruppi (cluster), prima della pandemia con il consumo predominante di carne bovina, tuttavia, durante e dopo la pandemia, il consumo si è invertito, con gli intervistati che consumano più carne di pollo. Il gruppo con la più alta partecipazione nella regione del Nordest (Cluster3), formato solo da donne e anche quello con il reddito più basso (Fino a 1 salario minimo), aveva il più alto consumo di pollo prima della pandemia (30,08%), e anche ha avuto il più alto consumo di proteine durante la pandemia (75,43%). Si conclude che c'è stato un cambiamento rilevante nel profilo di consumo degli intervistati nei periodi pre, durante e post pandemia nelle 5 regioni brasiliane, con l'evento principale che è stato lo scambio di carne bovina con pollo e maiale.Nos últimos anos, o perfil de consumo de carne do brasileiro mudou significativamente com a substituição da carne bovina pela de frango e suína, buscando preços mais baixos e consumo saudável. Foram analisadas 991 respostas das 5 macrorregiões brasileiras, envolvendo a preferência e consumo de diferentes carnes, antes, durante e após a pandemia de COVID-19. Os participantes foram agrupados em 4 grupos denominados “Clusters” através do Non-Hierarchical Clustering Method (agrupamento), pelo algoritmo K-Means da biblioteca Scikit-Learn em linguagem Python. A análise revela um consumo semelhante entre todos os grupos (clusters), antes da pandemia com consumo predominante de carne bovina, porém, durante e após a pandemia, o consumo se inverteu, com os entrevistados consumindo mais carne de frango. O grupo com maior participação na região Nordeste (Pluster3), formado apenas por mulheres e também o de menor renda (Até 1 salário mínimo), tinha o maior consumo de frango antes da pandemia (30,08%), e também teve o maior consumo de proteína durante a pandemia (75,43%). Conclui-se que houve mudança relevante no perfil de consumo dos entrevistados nos períodos pré, durante e pós pandemia nas 5 regiões brasileiras, sendo a principal ocorrência a troca da carne bovina por frango e suína