Ultraviolet cutoffs for quantum fields in cosmological spacetimes

We analyze critically the renormalization of quantum fields in cosmological spacetimes, using non covariant ultraviolet cutoffs. We compute explicitly the counterterms necessary to renormalize the semiclassical Einstein equations, using comoving and physical ultraviolet cutoffs. In the first case, the divergences renormalize bare conserved fluids, while in the second case it is necessary to break the covariance of the bare theory. We point out that, in general, the renormalized equations differ from those obtained with covariant methods, even after absorbing the infinities and choosing the renormalized parameters to force the consistency of the renormalized theory. We repeat the analysis for the evolution equation for the mean value of an interacting scalar fieldComment: 19 pages. Minor changes. References adde

The role of natural selection in human evolution - insights from Latin America

A brief introduction considering Darwin's work, the evolutionary synthesis, and the scientific biological field around the 1970s and subsequently, with the molecular revolution, was followed by selected examples of recent investigations dealing with the selection-drift controversy. The studies surveyed included the comparison between essential genes in humans and mice, selection in Africa and Europe, and the possible reasons why females in humans remain healthy and productive after menopause, in contrast with what happens in the great apes. At the end, selected examples of investigations performed in Latin America, related to the action of selection for muscle performance, acetylation of xenobiotics, high altitude and tropical forest adaptations were considered. Despite dissenting views, the influence of positive selection in a considerable portion of the human genome cannot presently be dismissed

Learning Curves Prediction for a Transformers-Based Model

One of the main challenges when training or fine-tuning a machine learning model concerns the number of observations necessary to achieve satisfactory performance. While, in general, more training observations result in a better-performing model, collecting more data can be time-consuming, expensive, or even impossible. For this reason, investigating the relationship between the dataset's size and the performance of a machine learning model is fundamental to deciding, with a certain likelihood, the minimum number of observations that are necessary to ensure a satisfactory-performing model is obtained as a result of the training process. The learning curve represents the relationship between the dataset’s size and the performance of the model and is especially useful when choosing a model for a specific task or planning the annotation work of a dataset. Thus, the purpose of this paper is to find the functions that best fit the learning curves of a Transformers-based model (LayoutLM) when fine-tuned to extract information from invoices. Two new datasets of invoices are made available for such a task. Combined with a third dataset already available online, 22 sub-datasets are defined, and their learning curves are plotted based on cross-validation results. The functions are fit using a non-linear least squares technique. The results show that both a bi-asymptotic and a Morgan-Mercer-Flodin function fit the learning curves extremely well. Also, an empirical relation is presented to predict the learning curve from a single parameter that may be easily obtained in the early stage of the annotation process. Doi: 10.28991/ESJ-2023-07-05-03 Full Text: PD

Increasing returns to scale and international diffusion of technology: an empirical study for Brazil (1976-2000)

This article aims at exploring the empirical evidence regarding the effects of increasing returns to scale and international technological diffusion on the Brazilian manufacturing industry. Our departure point is a Kaldorian-type theoretical model that provides not only the positive effects of scale but also of diffusion on industrial performance. We use Vector Auto Regressive (VAR) for testing the model. VAR will estimate the coefficients related to industrial output, labor productivity, exports and the technological gap between the United States and Brazil. This technique also provides simulations for the short-term and long-term trajectories under exogenous shocks. The observations are on a three-month period basis and the sampling period runs from the second half of 1976 to the second half of 2000. The conclusion highlights both evidences of increasing returns on the Brazilian industry that faces, however, some structural constraints. Besides, the model also reveals Brazil's difficulties to catch uptechnological gap; increasing returns to scale; economic growth; Brazil

Interethnic admixture and the evolution of Latin American populations

A general introduction to the origins and history of Latin American populations is followed by a systematic review of the data from molecular autosomal assessments of the ethnic/continental (European, African, Amerindian) ancestries for 24 Latin American countries or territories. The data surveyed are of varying quality but provide a general picture of the present constitution of these populations. A brief discussion about the applications of these results (admixture mapping) is also provided. Latin American populations can be viewed as natural experiments for the investigation of unique anthropological and epidemiological issues