Generalized Metropolis dynamics with a generalized master equation: An approach for time-independent and time-dependent Monte Carlo simulations of generalized spin systems

The extension of Boltzmann-Gibbs thermostatistics, proposed by Tsallis, introduces an additional parameter $q$ to the inverse temperature $\beta$. Here, we show that a previously introduced generalized Metropolis dynamics to evolve spin models is not local and does not obey the detailed energy balance. In this dynamics, locality is only retrieved for $q=1$, which corresponds to the standard Metropolis algorithm. Non-locality implies in very time consuming computer calculations, since the energy of the whole system must be reevaluated, when a single spin is flipped. To circumvent this costly calculation, we propose a generalized master equation, which gives rise to a local generalized Metropolis dynamics that obeys the detailed energy balance. To compare the different critical values obtained with other generalized dynamics, we perform Monte Carlo simulations in equilibrium for Ising model. By using the short time non-equilibrium numerical simulations, we also calculate for this model: the critical temperature, the static and dynamical critical exponents as function of $q$. Even for $q\neq 1$, we show that suitable time evolving power laws can be found for each initial condition. Our numerical experiments corroborate the literature results, when we use non-local dynamics, showing that short time parameter determination works also in this case. However, the dynamics governed by the new master equation leads to different results for critical temperatures and also the critical exponents affecting universality classes. We further propose a simple algorithm to optimize modeling the time evolution with a power law considering in a log-log plot two successive refinements.Comment: 10 pages, 5 figures and 5 table

Counseling Brazilian Farmers on Their Management Activities

The study identified some reasons that explain the limited use of scientific management tools by Brazilian farmers. A matrix of management activities was built to classify these reasons. Primary data used in the study were collected during two phases. During the first, 8 focus group manned by cash crop farmers, beef cattle farmers, and extension agents and counselors, were used to produce qualitative information. During the second phase, quantitative informations were collected via a survey with 494individual questionnaires applied to the same public covered by phase one. For the statistical tests performed 95% of significance was required. Some conclusions of the study are: (1) farmers differ significantly from extension agents or counselors on farm management subjects; (2) farmers do not alter their strategic production plans in response to price changes or other signals perceived as short or mid term movements due to costs of changes in their production processes; (3) in organizing their human resources structure farms tend to concentrate into their hands amounts of responsibilities larger than they can handle; (4) in organizing their financial flows there is a large gap between the desired level of details and their abilities to collect the data; and (5) the major difficulties faced in the function of controlling are linked with problems of collecting data. This is due to low levels of formal education that characterize the farm-hired labor.Brazilian farm management, counseling farmers, matrix of management activities., Farm Management, Teaching/Communication/Extension/Profession,

A Unified multilingual semantic representation of concepts

Semantic representation lies at the core of several applications in Natural Language Processing. However, most existing semantic representation techniques cannot be used effectively for the representation of individual word senses. We put forward a novel multilingual concept representation, called MUFFIN , which not only enables accurate representation of word senses in different languages, but also provides multiple advantages over existing approaches. MUFFIN represents a given concept in a unified semantic space irrespective of the language of interest, enabling cross-lingual comparison of different concepts. We evaluate our approach in two different evaluation benchmarks, semantic similarity and Word Sense Disambiguation, reporting state-of-the-art performance on several standard datasets