Macroeconomic Volatility Trade-off and Monetary Policy Regime in the Euro Area

This research uncovers a well-defined monetary policy regime starting in 1986 in the aggregate Euro Area. Both alternative solution-estimation methods employed - optimal control cum GMM, and dynamic programming cum FIML - identify a regime of strict inflation targeting with interest rate smoothing. The unemployment gap, properly estimated as quasi real-time information, is a relevant element in the information set of the monetary authority, despite not being included in its preferences. The emergence of the regime relates to the improvement of the volatility trade-off between inflation and unemployment gap since the mid-80s. Additional improving factors have been milder supply shocks and better ability of policymakers to set the interest rate closer to optimum.Monetary Policy Regime, Euro Area, Optimal Control, Dynamic Programming, GMM, FIML.

Testing for Asymmetries in the Preferences of the Euro-Area Monetary Policymaker

This paper tests for asymmetries in the preferences of the Euro-Area monetary policymaker with 1995:I-2004:III data from the last update of the ECB's Area-wide database. Following the relevant literature, we distinguish between three types of asymmetry: precautionary demand for expansions, precautionary demand for price stability and interest rate smoothing asymmetry. Based on the joint GMM estimation of the Euler equation of optimal policy and the AS-AD structure of the macroeconomy, we find evidence of precautionary demand for price stability in the preferences revealed by the monetary policymaker. This type of asymmetry is consistent with the ECB’s definition of price stability and with the priority of credibility-building by a recently created monetary authority.Central Bank Preferences, Asymmetry, Euro Area, Optimal Control, GMM.

Universality of political corruption networks

Corruption crimes demand highly coordinated actions among criminal agents to succeed. But research dedicated to corruption networks is still in its infancy and indeed little is known about the properties of these networks. Here we present a comprehensive investigation of corruption networks related to political scandals in Spain and Brazil over nearly three decades. We show that corruption networks of both countries share universal structural and dynamical properties, including similar degree distributions, clustering and assortativity coefcients, modular structure, and a growth process that is marked by the coalescence of network components due to a few recidivist criminals. We propose a simple model that not only reproduces these empirical properties but reveals also that corruption networks operate near a critical recidivism rate below which the network is entirely fragmented and above which it is overly connected. Our research thus indicates that actions focused on decreasing corruption recidivism may substantially mitigate this type of organized crime

Machine learning partners in criminal networks

Recent research has shown that criminal networks have complex organizational structures, but whether this can be used to predict static and dynamic properties of criminal networks remains little explored. Here, by combining graph representation learning and machine learning methods, we show that structural properties of political corruption, police intelligence, and money laundering networks can be used to recover missing criminal partnerships, distinguish among diferent types of criminal and legal associations, as well as predict the total amount of money exchanged among criminal agents, all with outstanding accuracy. We also show that our approach can anticipate future criminal associations during the dynamic growth of corruption networks with signifcant accuracy. Thus, similar to evidence found at crime scenes, we conclude that structural patterns of criminal networks carry crucial information about illegal activities, which allows machine learning methods to predict missing information and even anticipate future criminal behavior

Deep Learning Criminal Networks

Recent advances in deep learning methods have enabled researchers to develop and apply algorithms for the analysis and modeling of complex networks. These advances have sparked a surge of interest at the interface between network science and machine learning. Despite this, the use of machine learning methods to investigate criminal networks remains surprisingly scarce. Here, we explore the potential of graph convolutional networks to learn patterns among networked criminals and to predict various properties of criminal networks. Using empirical data from political corruption, criminal police intelligence, and criminal financial networks, we develop a series of deep learning models based on the GraphSAGE framework that are capable to recover missing criminal partnerships, distinguish among types of associations, predict the amount of money exchanged among criminal agents, and even anticipate partnerships and recidivism of criminals during the growth dynamics of corruption networks, all with impressive accuracy. Our deep learning models significantly outperform previous shallow learning approaches and produce high-quality embeddings for node and edge properties. Moreover, these models inherit all the advantages of the GraphSAGE framework, including the generalization to unseen nodes and scaling up to large graph structures.Comment: 14 two-column pages, 5 figure

Rarity of monodominance in hyperdiverse Amazonian forests.

Tropical forests are known for their high diversity. Yet, forest patches do occur in the tropics where a single tree species is dominant. Such "monodominant" forests are known from all of the main tropical regions. For Amazonia, we sampled the occurrence of monodominance in a massive, basin-wide database of forest-inventory plots from the Amazon Tree Diversity Network (ATDN). Utilizing a simple defining metric of at least half of the trees ≥ 10 cm diameter belonging to one species, we found only a few occurrences of monodominance in Amazonia, and the phenomenon was not significantly linked to previously hypothesized life history traits such wood density, seed mass, ectomycorrhizal associations, or Rhizobium nodulation. In our analysis, coppicing (the formation of sprouts at the base of the tree or on roots) was the only trait significantly linked to monodominance. While at specific locales coppicing or ectomycorrhizal associations may confer a considerable advantage to a tree species and lead to its monodominance, very few species have these traits. Mining of the ATDN dataset suggests that monodominance is quite rare in Amazonia, and may be linked primarily to edaphic factors

The Brazilian Developments on the Regional Atmospheric Modeling System (BRAMS 5.2): An Integrated Environmental Model Tuned for Tropical Areas

We present a new version of the Brazilian developments on the Regional Atmospheric Modeling System where different previous versions for weather, chemistry and carbon cycle were unified in a single integrated software system. The new version also has a new set of state-of-the-art physical parameterizations and greater computational parallel and memory usage efficiency. Together with the description of the main features are examples of the quality of the transport scheme for scalars, radiative fluxes on surface and model simulation of rainfall systems over South America in different spatial resolutions using a scale-aware convective parameterization. Besides, the simulation of the diurnal cycle of the convection and carbon dioxide concentration over the Amazon Basin, as well as carbon dioxide fluxes from biogenic processes over a large portion of South America are shown. Atmospheric chemistry examples present model performance in simulating near-surface carbon monoxide and ozone in Amazon Basin and Rio de Janeiro megacity. For tracer transport and dispersion, it is demonstrated the model capabilities to simulate the volcanic ash 3-d redistribution associated with the eruption of a Chilean volcano. Then, the gain of computational efficiency is described with some details. BRAMS has been applied for research and operational forecasting mainly in South America. Model results from the operational weather forecast of BRAMS on 5 km grid spacing in the Center for Weather Forecasting and Climate Studies, INPE/Brazil, since 2013 are used to quantify the model skill of near surface variables and rainfall. The scores show the reliability of BRAMS for the tropical and subtropical areas of South America. Requirements for keeping this modeling system competitive regarding on its functionalities and skills are discussed. At last, we highlight the relevant contribution of this work on the building up of a South American community of model developers

The Brazilian developments on the Regional Atmospheric Modeling System (BRAMS 5.2): an integrated environmental model tuned for tropical areas

We present a new version of the Brazilian developments on the Regional Atmospheric Modeling System (BRAMS), in which different previous versions for weather, chemistry, and carbon cycle were unified in a single integrated modeling system software. This new version also has a new set of state-of-the-art physical parameterizations and greater computational parallel and memory usage efficiency. The description of the main model features includes several examples illustrating the quality of the transport scheme for scalars, radiative fluxes on surface, and model simulation of rainfall systems over South America at different spatial resolutions using a scale aware convective parameterization. Additionally, the simulation of the diurnal cycle of the convection and carbon dioxide concentration over the Amazon Basin, as well as carbon dioxide fluxes from biogenic processes over a large portion of South America, are shown. Atmospheric chemistry examples show the model performance in simulating near-surface carbon monoxide and ozone in the Amazon Basin and the megacity of Rio de Janeiro. For tracer transport and dispersion, the model capabilities to simulate the volcanic ash 3-D redistribution associated with the eruption of a Chilean volcano are demonstrated. The gain of computational efficiency is described in some detail. BRAMS has been applied for research and operational forecasting mainly in South America. Model results from the operational weather forecast of BRAMS on 5 km grid spacing in the Center for Weather Forecasting and Climate Studies, INPE/Brazil, since 2013 are used to quantify the model skill of near-surface variables and rainfall. The scores show the reliability of BRAMS for the tropical and subtropical areas of South America. Requirements for keeping this modeling system competitive regarding both its functionalities and skills are discussed. Finally, we highlight the relevant contribution of this work to building a South American community of model developers.CNPqFAPESPEarth System Research Laboratory at the National Oceanic and Atmospheric Administration (ESRL/NOAA), Boulder, USAInst Nacl Pesquisas Espaciais, Ctr Previsao Tempo & Estudos Climat, Cachoeira Paulista, SP, BrazilDiv Ciência da Computação, Instituto Tecnológico de Aeronáutica, São José dos Campos, SP, BrazilUniv Estadual Paulista Unesp, Fac Ciencias, Bauru, SP, BrazilCtr Meteorol Bauru IPMet, Bauru, SP, BrazilUniv Fed Sao Paulo, Dept Ciencias Ambientais, Diadema, SP, BrazilUniv Sao Paulo, Inst Astron Geofis & Ciencias Atmosfer, Sao Paulo, SP, BrazilUniv Fed Campina Grande, Dept Ciencias Atmosfer, Campina Grande, PB, BrazilEmbrapa Informat Agr, Campinas, SP, BrazilUniv Fed Sao Paulo, Inst Ciencia & Tecnol, Sao Jose Dos Campos, SP, BrazilUniv Fed Rio Grande do Norte, Dept Ciencias Atmosfer & Climat, Programa Pos Grad Ciencias Climat, Natal, RN, BrazilInst Nacl Pesquisas Espaciais, Ctr Ciencias Sistema, Sao Jose Dos Campos, SP, BrazilUniv Fed Sao Joao Del Rei, Dept Geociencias, Sao Joao Del Rei, MG, BrazilInst Nacl Pesquisas Espaciais, Lab Associado Computacao & Matemat Aplica, Sao Jose Dos Campos, BrazilUniv Evora, Inst Ciencias Agr & Ambientais Mediterr, Evora, PortugalUniv Lusofona Humanidades & Tecnol, Ctr Interdisciplinar Desenvolvimento Ambient Gest, Lisbon, PortugalUniv Fed Pelotas, Fac Meteorol, Pelotas, RS, BrazilUnive Tecnol Fed Parana, Londrina, PR, BrazilNASA, Goddard Space Flight Ctr, Univ Space Res Assoc, Goddard Earth Sci Technol & Res Global Modeling &, Greenbelt, MD USAUniv Fed Sao Paulo, Inst Ciencia & Tecnol, Sao Jose Dos Campos, SP, BrazilUniv Fed Sao Paulo, Inst Ciencia & Tecnol, Sao Jose Dos Campos, SP, BrazilCNPq: 306340/2011-9FAPESP: 2014/01563-1FAPESP: 2015/10206-0FAPESP: 2014/01564-8Web of Scienc

The genomes of two key bumblebee species with primitive eusocial organization

Background: The shift from solitary to social behavior is one of the major evolutionary transitions. Primitively eusocial bumblebees are uniquely placed to illuminate the evolution of highly eusocial insect societies. Bumblebees are also invaluable natural and agricultural pollinators, and there is widespread concern over recent population declines in some species. High-quality genomic data will inform key aspects of bumblebee biology, including susceptibility to implicated population viability threats. Results: We report the high quality draft genome sequences of Bombus terrestris and Bombus impatiens, two ecologically dominant bumblebees and widely utilized study species. Comparing these new genomes to those of the highly eusocial honeybee Apis mellifera and other Hymenoptera, we identify deeply conserved similarities, as well as novelties key to the biology of these organisms. Some honeybee genome features thought to underpin advanced eusociality are also present in bumblebees, indicating an earlier evolution in the bee lineage. Xenobiotic detoxification and immune genes are similarly depauperate in bumblebees and honeybees, and multiple categories of genes linked to social organization, including development and behavior, show high conservation. Key differences identified include a bias in bumblebee chemoreception towards gustation from olfaction, and striking differences in microRNAs, potentially responsible for gene regulation underlying social and other traits. Conclusions: These two bumblebee genomes provide a foundation for post-genomic research on these key pollinators and insect societies. Overall, gene repertoires suggest that the route to advanced eusociality in bees was mediated by many small changes in many genes and processes, and not by notable expansion or depauperation