A first-order structure $\mathfrak {A}$ is called monadically stable iff every expansion of $\mathfrak {A}$ by unary predicates is stable. In this paper we give a classification of the class $\mathcal {M}$ of $\omega $ -categorical monadically stable structure in terms of their automorphism groups. We prove in turn that $\mathcal {M}$ is the smallest class of structures which contains the one-element pure set, is closed under isomorphisms, and is closed under taking finite disjoint unions, infinite copies, and finite index (...) first-order reducts. Using our classification we show that every structure in $\mathcal {M}$ is first-order interdefinable with a finitely bounded homogeneous structure. We also prove that every structure in $\mathcal {M}$ has finitely many reducts up to interdefinability, thereby confirming Thomas’ conjecture for the class $\mathcal {M}$. (shrink)

The prediction of legal judgments is one of the most recognized fields in Natural Language Processing, Artificial Intelligence, and Law combined. By legal prediction, we mean intelligent systems capable of predicting specific judicial characteristics such as the judicial outcome, the judicial class, and the prediction of a particular case. In this study, we used an artificial intelligence classifier to predict the decisions of Brazilian courts. To this end, we developed a text crawler to extract data from official Brazilian electronic legal (...) systems, consisting of two datasets of cases of second-degree murder and active corruption. We applied various classifiers, such as Support Vector Machines, Neural Networks, and others, to predict judicial outcomes by analyzing text features from the dataset. Our research demonstrated that Regression Trees, Gated Recurring Units, and Hierarchical Attention Networks tended to have higher metrics across our datasets. As the final goal, we searched the weights of one of the algorithms, Hierarchical Attention Networks, to find samples of the words that might be used to acquit or convict defendants based on their relevance to the algorithm. (shrink)

Novelty is central to the study of memory, but the wide range of experimental manipulations aimed to reveal its effects on learning produced inconsistent results. The novelty/encoding hypothesis suggests that novel information undergoes enhanced encoding and thus leads to benefits in memory, especially in recognition performance; however, recent studies cast doubts on this assumption. On the other hand, data from animal studies provided evidence on the robust effects of novelty manipulations on the neurophysiological correlates of memory processes. Conceptualizations and operationalizations (...) of novelty are remarkably variable, and were categorized into different subtypes, such as stimulus, context, associative or spatial novelty. Here, we summarize previous findings about the effects of novelty on memory and suggest that predictive coding theories provide a framework that could shed light on the differential influence of novelty manipulations on memory performance. In line with predictive coding theories, we emphasize the role of unexpectedness as a crucial property mediating the behavioral and neural effects of novelty manipulations. (shrink)