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Although Machine Learning algorithms are solving tasks of ever-increasing complexity, gathering data and building training sets remains an error prone, costly, and difficult problem. However, reusing knowledge from related previously-solved tasks enables reducing the amount of data required to learn a new task. We here propose a method for reusing a tree-based model learned in a source task with abundant data in a target task with scarce data. We perform an empirical evaluation showing that our method is useful, especially in scenarios where the labels are unavailable in the target task.

Student’s grade retention is a key issue faced by many education systems, especially those in developing countries. In this paper, we seek to gauge the relevance of students’ personality traits in predicting grade retention in Brazil. For that, we used data collected in 2012 and 2017, in the city of Sertaozinho, countryside of the state of Sao Paulo, Brazil. The surveys taken in Sertaozinho included several socioeconomic questions, standardized tests, and a personality test. Moreover, students were in grades 4, 5, and 6 in 2012. Our approach was based on training machine learning models on the surveys’ data to predict grade retention between 2012 and 2017 using information from 2012 or before, and then using some strategies to quantify personality traits’ predictive power. We concluded that, besides proving to be fairly better than a random classifier when isolated, personality traits contribute to prediction even when using socioeconomic variables and standardized tests results.

Although Machine Learning algorithms are solving tasks of ever-increasing complexity, gathering data and building training sets remains an error prone, costly, and difficult problem. However, reusing knowledge from related previously-solved tasks enables reducing the amount of data required to learn a new task. We here propose a method for learning a mapping model that maps data from a source task with labeled data to a related target task with only unlabeled data. We perform an empirical evaluation showing that our method achieves performance comparable to a model learned directly in the target task.

Student’s grade retention is a key issue faced by many education systems, especially those in developing countries. In this paper, we seek to gauge the relevance of students’ personality traits in predicting grade retention in Brazil. For that, we used data collected in 2012 and 2017, in the city of Sertaozinho, countryside of the state of Sao Paulo, Brazil. The surveys taken in Sertaozinho included several socioeconomic questions, standardized tests, and a personality test. Moreover, students were in grades 4, 5, and 6 in 2012. Our approach was based on training machine learning models on the surveys’ data to predict grade retention between 2012 and 2017 using information from 2012 or before, and then using some strategies to quantify personality traits’ predictive power. We concluded that, besides proving to be fairly better than a random classifier when isolated, personality traits contribute to prediction even when using socioeconomic variables and standardized tests results.

Uma das características que nos distinguem de outros seres vivos é nossa cultura. Entretanto, como comportamentos não fossilizam, é difícil reconstruir o passado para gerar insights sobre por que nos tornamos o que somos hoje. Juntamente com dados etnográficos e experimentais, os modelos matemáticos têm sido utilizados para abordar a questão sobre como nossos comportamentos foram moldados pela evolução. Esta tese está dividida em duas partes. Na primeira parte, discutiremos sobre seleção multinível e sobre como o framework matemático chamado Two-level Fisher Wright (TLFW) pode nos ajudar a entender a evolução da cooperação em populações humanas. Após descrevermos o problema da cooperação através do uso de ferramentas de teoria dos jogos, revisamos algumas das teorias atuais sobre por que a cooperação evoluiu. Em seguida, empregamos o framework TLFW ao problema da emergência de altruísmo em populações de caçadores-coletores, considerando uma situação em que o conflito entre grupos direciona a seleção. Na segunda parte, abordamos o tópico de diversidade linguística e apresentamos a importância de se estudar a competição entre línguas para ajudar a preservá-las. Traçando um paralelo entre a evolução das línguas e a evolução de normas sociais, introduzimos um modelo para analisar a persistência de dialetos, quando existe competição com uma língua padrão nacional. One of the features that distinguish human beings from other living species is our culture. However, since behaviors do not fossilize, it is difficult to reconstruct the past to get insights about why we are who we are. Along with ethnographic and experimental data, mathematical models have been used to address the question of how our behaviors were shaped by evolution. This thesis is divided into two parts. In the first part, we will discuss multilevel selection and how the mathematical framework Two-Level Fisher-Wright (TLFW) can help us to understand the evolution of cooperation in human populations. After describing the problem of cooperation by using game theory, we review some of the present theories about why cooperation has evolved. Then, we apply the TLFW framework to the problem of the evolution of altruism in populations of hunter-gatherers, considering a situation in which group conflict drives selection. In the second part, we discuss language diversity and present the importance of studying the competition between languages for helping to preserve them. By drawing a parallel between the evolution of language and social norms, we introduce a mathematical model to analyze the persistence of dialects competing against a national standard language.

Background Osteoporosis is an underdiagnosed condition, and its seriousness is not considered until severe complications arise. This study aimed to evaluate general dentists’ knowledge about osteoporosis and their ability to identify patients with this disease by assessing mandibular cortical width (MCW) and mandibular cortical index (MCI) on panoramic dental radiographs using a visual method. Methods In this cross-sectional study, an email questionnaire regarding the diagnosis and prevention of osteoporosis was sent to 20,773 dentists in 2016. Those who completed the questionnaire were invited to participate in radiomorphometric training and then to analyze the MCI and MCW of 114 panoramic radiographs of postmenopausal women who underwent both panoramic radiography and bone densitometry. Based on the radiomorphometric indices and while blinded to the densitometry results, the dentists determined whether they would indicate densitometry for these patients. Results The response rate was 2.3%: 485 dentists completed the questionnaire, and 50 evaluated panoramic radiographs using the MCW and MCI. All of them reported some knowledge about osteoporosis, but 41.6% demonstrated a misleading conceptualization of the disease. Approximately 90% reported minimal access to this information during graduation, and only 27.0% were exposed to the topic during their postgraduate studies. Interest in osteoporosis prevention was expressed by 70.7% of the respondents, and interest in learning the radiomorphometric indices was expressed by 99.0%. The sensitivity in the detection of low bone mineral density through the MCW and MCI was 52.9%, and the specificity was 64%. Conclusions Brazilian dentists demonstrated insufficient knowledge about osteoporosis and a low ability to detect osteopenia or osteoporosis by applying radiomorphometric indices.

In supervised learning, training and test datasets are often sampled from distinct distributions. Domain adaptation techniques are thus required. Covariate shift adaptation yields good generalization performance when domains differ only by the marginal distribution of features. Covariate shift adaptation is usually implemented using importance weighting, which may fail, according to common wisdom, due to small effective sample sizes (ESS). Previous research argues this scenario is more common in high-dimensional settings. However, how effective sample size, dimensionality, and model performance/generalization are formally related in supervised learning, considering the context of covariate shift adaptation, is still somewhat obscure in the literature. Thus, a main challenge is presenting a unified theory connecting those points. Hence, in this paper, we focus on building a unified view connecting the ESS, data dimensionality, and generalization in the context of covariate shift adaptation. Moreover, we also demonstrate how dimensionality reduction or feature selection can increase the ESS, and argue that our results support dimensionality reduction before covariate shift adaptation as a good practice., Comment: Neural Comput & Applic (2022)

Energy fraud is a critical economical burden for electric power organizations in Brazil. In this paper we present the application of cutting-edge Machine Learning algorithms, namely XGBoost and Isolation Forest, for prediction of irregularities in electrical energy consumption. By using a Logistic Regression model as a benchmark, we show that the use of XGBoost results in a significant improvement in the F1-score for fraud predictions in two different scenarios: with and without inspection history features. Moreover, we also propose the use of the Isolation Forest algorithm for detection of anomalies in electrical energy consumption. We show that this approach may be useful in the case of lack of inspection history features, surpassing dummy classifiers.

Nesta tese desenvolvemos modelos de apreçamento de ativos financeiros baseados no conceito de influência social, analisamos também algumas das consequências destes modelos e comparamos com os modelos correspondentes clássicos. Por meio das funções de utilidade generalizadas exponencial e quadrática, deduzimos o CAPM com influência social. Obtivemos que o coeficiente beta da fórmula do CAPM depende de uma aversão ao risco efetiva do mercado que depende da distribuição de riqueza dos agentes do mercado. Supondo que distribuição de riqueza dos agentes do mercado segue uma distribuição de Pareto, fomos capazes de conectar, aversão ao risco média efetiva do mercado, volatilidade e distribuição de riqueza dos agentes, estabelecendo a previsão empírica de que a volatilidade aumenta com a concentração da distribuição de riqueza dos agentes do mercado, a qual foi corroborada por meio de análise estatística. Através da função generalizada tipo potência são feitas algumas considerações sobre alguns \"puzzles\" econômicos bem conhecidos (o \"Equity Premium Puzzle\" e o \"Riskfree Rate Puzzle\") que mostram que a modelagem da influência social pode ter impacto no esclarecimento destes \"puzzles\". In this thesis we develop pricing models for financial assets based in the concept of social influence, we analyze too some of consequences of this models and we compare with the corresponding classical models. By means of the exponential and quadratic generalized utility functions, we deduce the CAPM with social influence. We obtained that the coefficient beta from the formula of the CAPM depends of a market effective risk aversion that depends of the wealth distribution of the market agents. Supposing that the wealth distribution of the market agents follows a Pareto distribution, we were able to connect, market effective average risk aversion, volatility and wealth distribution of the agents, establishing the empirical forecasting that the volatility grows with the concentration of the wealth distribution of the market agents, which was corroborated by means of statistical analysis. Through the generalized power function are made some considerations about some economic puzzles well-known (the Equity Premium Puzzle and the Riskfree Rate Puzzle) that show us that the modeling of the social influence can to have impact in the clarification these puzzles.

The moral foundations theory supports that people, across cultures, tend to consider a small number of dimensions when classifying issues on a moral basis. The data also show that the statistics of weights attributed to each moral dimension is related to self-declared political affiliation, which in turn has been connected to cognitive learning styles by the recent literature in neuroscience and psychology. Inspired by these data, we propose a simple statistical mechanics model with interacting neural networks classifying vectors and learning from members of their social neighbourhood about their average opinion on a large set of issues. The purpose of learning is to reduce dissension among agents when disagreeing. We consider a family of learning algorithms parametrized by δ , that represents the importance given to corroborating (same sign) opinions. We define an order parameter that quantifies the diversity of opinions in a group with homogeneous learning style. Using Monte Carlo simulations and a mean field approximation we find the relation between the order parameter and the learning parameter δ at a temperature we associate with the importance of social influence in a given group. In concordance with data, groups that rely more strongly on corroborating evidence sustain less opinion diversity. We discuss predictions of the model and propose possible experimental tests.

We study the dynamics of the adoption of new products by agents with continuous opinions and discrete actions (CODA). The model is such that the refusal in adopting a new idea or product is increasingly weighted by neighbor agents as evidence against the product. Under these rules, we study the distribution of adoption times and the final proportion of adopters in the population. We compare the cases where initial adopters are clustered to the case where they are randomly scattered around the social network and investigate small world effects on the final proportion of adopters. The model predicts a fat tailed distribution for late adopters which is verified by empirical data., Comment: 14 pages, 8 figures, revised text, a new section with empirical evidence has been added

Within an agent-based model where moral classifications are socially learned, we ask if a population of agents behaves in a way that may be compared with conservative or liberal positions in the real political spectrum. We assume that agents first experience a formative period, in which they adjust their learning style acting as supervised Bayesian adaptive learners. The formative phase is followed by a period of social influence by reinforcement learning. By comparing data generated by the agents with data from a sample of 15000 Moral Foundation questionnaires we found the following. 1. The number of information exchanges in the formative phase correlates positively with statistics identifying liberals in the social influence phase. This is consistent with recent evidence that connects the dopamine receptor D4-7R gene, political orientation and early age social clique size. 2. The learning algorithms that result from the formative phase vary in the way they treat novelty and corroborative information with more conservative-like agents treating it more equally than liberal-like agents. This is consistent with the correlation between political affiliation and the Openness personality trait reported in the literature. 3. Under the increase of a model parameter interpreted as an external pressure, the statistics of liberal agents resemble more those of conservative agents, consistent with reports on the consequences of external threats on measures of conservatism. We also show that in the social influence phase liberal-like agents readapt much faster than conservative-like agents when subjected to changes on the relevant set of moral issues. This suggests a verifiable dynamical criterium for attaching liberal or conservative labels to groups., 31 pages, 5 figures

In this work, we propose a Bayesian online reconstruction algorithm for sparse signals based on Compressed Sensing and inspired by L1-regularization schemes. A previous work has introduced a mean-field approximation for the Bayesian online algorithm and has shown that it is possible to saturate the offline performance in the presence of Gaussian measurement noise when the signal generating distribution is known. Here, we build on these results and show that reconstruction is possible even if prior knowledge about the generation of the signal is limited, by introduction of a Laplace prior and of an extra Kullback–Leibler divergence minimization step for hyper-parameter learning.

We study evolution of cooperation in a population structured in a large number of groups of variable size, connected by random migration at rate $m$. Social interactions, including cooperation and competition occur only inside the groups. Assuming that groups are large, we define a parameter $\lambda$ that measures the strength of the local regulation, i.e., the rigidity of group sizes. Individuals are of two possible genotypes, one typically assumed to produce a non-cooperative phenotype and the other a phenotype that is cooperative with all members of its own group. Gene action may be additive, producing fitness functions that are linear in the number of cooperators in a group, or not. Assuming weak selection, we obtain the following two contrasting conclusions. (1) ``Hamilton regime'': If $\lambda << m$, then cooperative behavior can spread under a certain condition, which in the additive, i.e., linear, case is precisely Hamilton's rule. The general version of this condition is also relatively easy to apply and is based on Wright's classical beta distribution for the frequency of alleles in infinite island models. We call it the ``beta version of Hamilton's rule''. (2) ``Taylor regime'': If $m << \lambda$, then cooperation that is costly to the actor is eliminated by selection.

We observe that the Taylor-Frank method for making kin selection models when fitness $w$ is a nonlinear function of a continuous actor's phenotype $y$ and the average phenotype $z$ in its social environment requires $w(y,z)$ to be differentiable (as a function of two variables, i.e., jointly in $y$ and $z$). This means that even if $w(y,z)$ is non-linear globally, locally it must be close to linear, meaning that its graph must be well approximated by a plane. When more than two individuals interact, this assumption is only satisfied when the marginal fitness of the actor is a linear function of the fraction of individuals in its social environment that share its phenotype. This assumption sometimes fails for biologically important fitness functions, for instance in microbial data and the theory of repeated n-person games. In these cases, the Taylor-Frank methodology cannot be used, and a more general form of direct fitness must replace it, to decide when a social mutant allele can invade a monomorphic population.

article i nfo Article history: Initial receipt 5 June 2013 Final revision received 2 February 2014 Unlike other vertebrates, humans cooperate in large groups with unrelated individuals. Many authors have argued that the evolution of such cooperation has resulted from reciprocity and other forms of contingent cooperation. This argument is not well supported by existing theory. The theory of contingent cooperation in pairs is well developed: reciprocating strategies are stable when common, and can increase when rare as long as population structure leads to modest levels of relatedness. In larger groups, however, it is not clear whether contingent cooperation can increase when rare. Existing work suggests that contingent strategies cannot increase unless relatedness is high, but depends on unrealistic assumptions about the effects of population structure. Here we develop and analyze a model incorporating a two level population structure that captures important features of human hunter-gatherer societies. This model suggests that previous work underestimates the range of conditions under which contingent cooperation can evolve, but also predicts that cooperation will not evolve unless (1) social groups are small, and (2) the relatedness within ethnolinguistic groups is at the high end of the range of empirical estimates.

Low-density parity-check codes with irregular constructions have been recently shown to outperform the most advanced error-correcting codes to date. In this paper we apply methods of statistical physics to study the typical properties of simple irregular codes. We use the replica method to find a phase transition which coincides with Shannon's coding bound when appropriate parameters are chosen. The decoding by belief propagation is also studied using statistical physics arguments; the theoretical solutions obtained are in good agreement with simulations. We compare the performance of irregular with that of regular codes and discuss the factors that contribute to the improvement in performance., Comment: 20 pages, 9 figures, revised version submitted to JPA

A variation of Gallager error-correcting codes is investigated using statistical mechanics. In codes of this type, a given message is encoded into a codeword which comprises Boolean sums of message bits selected by two randomly constructed sparse matrices. The similarity of these codes to Ising spin systems with random interaction makes it possible to assess their typical performance by analytical methods developed in the study of disordered systems. The typical case solutions obtained via the replica method are consistent with those obtained in simulations using belief propagation (BP) decoding. We discuss the practical implications of the results obtained and suggest a computationally efficient construction for one of the more practical configurations., 35 pages, 4 figures

We review the application of statistical mechanics methods to the study of online learning of a drifting concept in the limit of large systems. The model where a feed-forward network learns from examples generated by a time dependent teacher of the same architecture is analyzed. The best possible generalization ability is determined exactly, through the use of a variational method. The constructive variational method also suggests a learning algorithm. It depends, however, on some unavailable quantities, such as the present performance of the student. The construction of estimators for these quantities permits the implementation of a very effective, highly adaptive algorithm. Several other algorithms are also studied for comparison with the optimal bound and the adaptive algorithm, for different types of time evolution of the rule.

We discuss the connection between information and copula theories by showing that a copula can be employed to decompose the information content of a multivariate distribution into marginal and dependence components, with the latter quantified by the mutual information. We define the information excess as a measure of deviation from a maximum entropy distribution. The idea of marginal invariant dependence measures is also discussed and used to show that empirical linear correlation underestimates the amplitude of the actual correlation in the case of non-Gaussian marginals. The mutual information is shown to provide an upper bound for the asymptotic empirical log-likelihood of a copula. An analytical expression for the information excess of T-copulas is provided, allowing for simple model identification within this family. We illustrate the framework in a financial data set., Comment: to appear in Europhysics Letters

We study opinion dynamics in a population of interacting adaptive agents voting on a set of complex multidimensional issues. We consider agents which can classify issues into for or against. The agents arrive at the opinions about each issue in question using an adaptive algorithm. Adaptation comes from learning and the information for the learning process comes from interacting with other neighboring agents and trying to change the internal state in order to concur with their opinions. The change in the internal state is driven by the information contained in the issue and in the opinion of the other agent. We present results in a simple yet rich context where each agent uses a Boolean Perceptron to state its opinion. If there is no internal clock, so the update occurs with asynchronously exchanged information among pairs of agents, then the typical case, if the number of issues is kept small, is the evolution into a society thorn by the emergence of factions with extreme opposite beliefs. This occurs even when seeking consensus with agents with opposite opinions. The curious result is that it is learning from those that hold the same opinions that drives the emergence of factions. This results follows from the fact that factions are prevented by not learning at all from those agents that hold the same opinion. If the number of issues is large, the dynamics becomes trapped and the society does not evolve into factions and a distribution of moderate opinions is observed. We also study the less realistic, but technically simpler synchronous case showing that global consensus is a fixed point. However, the approach to this consensus is glassy in the limit of large societies if agents adapt even in the case of agreement., 16 pages, 10 figures, revised version

We employ the Bayesian framework to define a cointegration measure aimed to represent long term relationships between time series. For visualization of these relationships we introduce a dissimilarity matrix and a map based on the Sorting Points Into Neighborhoods (SPIN) technique, which has been previously used to analyze large data sets from DNA arrays. We exemplify the technique in three data sets: US interest rates, monthly inflation rates and gross domestic product growth rates., 13 pages, 8 figures, extended version submitted to Physica A

We investigate the Heston model with stochastic volatility and exponential tails as a model for the typical price fluctuations of the Brazilian S\~ao Paulo Stock Exchange Index (IBOVESPA). Raw prices are first corrected for inflation and a period spanning 15 years characterized by memoryless returns is chosen for the analysis. Model parameters are estimated by observing volatility scaling and correlation properties. We show that the Heston model with at least two time scales for the volatility mean reverting dynamics satisfactorily describes price fluctuations ranging from time scales larger than 20 minutes to 160 days. At time scales shorter than 20 minutes we observe autocorrelated returns and power law tails incompatible with the Heston model. Despite major regulatory changes, hyperinflation and currency crises experienced by the Brazilian market in the period studied, the general success of the description provided may be regarded as an evidence for a general underlying dynamics of price fluctuations at intermediate mesoeconomic time scales well approximated by the Heston model. We also notice that the connection between the Heston model and Ehrenfest urn models could be exploited for bringing new insights into the microeconomic market mechanics., Comment: 20 pages, 9 figures, to appear in Physica A

We employ the methods presented in the previous chapter for decoding corrupted codewords, encoded using sparse parity check error correcting codes. We show the similarity between the equations derived from the TAP approach and those obtained from belief propagation, and examine their performance as practical decoding methods.