We explore the interplay between $\omega $-categoricity and pseudofiniteness for groups, and we conjecture that $\omega $-categorical pseudofinite groups are finite-by-abelian-by-finite. We show that the conjecture reduces to nilpotent p-groups of class 2, and give a proof that several of the known examples of $\omega $-categorical p-groups satisfy the conjecture. In particular, we show by a direct counting argument that for any odd prime p the ($\omega $-categorical) model companion of the theory of nilpotent class 2 exponent p groups, constructed by Saracino and Wood, is not pseudofinite, and that an $\omega $-categorical group constructed by Baudisch with supersimple rank 1 theory is not pseudofinite. We also survey some scattered literature on $\omega $-categorical groups over 50 years.

Do people like financial nudges? To answer that question we conducted a pre-registered survey presenting people with 36 hypothetical scenarios describing financial interventions. We varied levels of transparency (i.e., explaining how the interventions worked), framing (interventions framed in terms of spending, or saving), and ‘System’ (interventions could target either System 1 or System 2). Participants were a random sample of 2,100 people drawn from a representative Australian population. All financial interventions were tested across six dependent variables: approval, benefit, ethics, manipulation, the likelihood of use, as well as the likelihood of use if the intervention were to be proposed by a bank. Results indicate that people generally approve of financial interventions, rating them as neutral to positive across all dependent variables (except for manipulation, which was reverse coded). We find effects of framing and System. People have strong and significant preferences for System 2 interventions, and interventions framed in terms of savings. Transparency was not found to have a significant impact on how people rate financial interventions. Financial interventions continue to be rated positive, regardless of the messenger. Looking at demographics, we find that participants who were female, younger, living in metro areas and earning higher incomes were most likely to favor financial interventions, and this effect is especially strong for those aged under 45. We discuss the implications for these results as applied to the financial sector.

Parameter learning is a crucial task in the field of Statistical Relational Artificial Intelligence: given a probabilistic logic program and a set of observations in the form of interpretations, the goal is to learn the probabilities of the facts in the program such that the probabilities of the interpretations are maximized. In this paper, we propose two algorithms to solve such a task within the formalism of Probabilistic Answer Set Programming, both based on the extraction of symbolic equations representing the probabilities of the interpretations. The first solves the task using an off-the-shelf constrained optimization solver while the second is based on an implementation of the Expectation Maximization algorithm. Empirical results show that our proposals often outperform existing approaches based on projected answer set enumeration in terms of quality of the solution and in terms of execution time.

Prior work has yielded mixed findings regarding the relationship between language control and domain-general inhibitory control. Here, we tested the possibility that omnibus language ability would moderate the relationship between language control and inhibitory control in bilingual children. We tested 43 Spanish-English bilingual children (ages 4–5.92 years; 25 females). Children engaged in play-based interactions with their parent, and rates of cross-speaker switches (using a language different from one used by parent) indexed language control. Inhibitory control was measured via a non-verbal flanker task. Analyses revealed that higher frequency of cross-speaker code-switches was associated with better inhibitory control only for children with higher levels of language ability. For children with lower language skills, there was no association between switches and inhibitory control. These findings align with the literature linking cognitive control and language control in bilinguals and extend it to indicate that the strength of the language system constrains this link.

This paper describes a semantics for pure Prolog programs with negation that provides meaning to metaprograms. Metaprograms are programs that construct and use data structures as programs. In Prolog a primary mataprogramming construct is the use of a variable as a literal in the body of a clause. The traditional Prolog 3-line metainterpreter is another example of a metaprogram. The account given here also supplies a meaning for clauses that have a variable as head, even though most Prolog systems do not support such clauses. This semantics naturally includes such programs, giving them their intuitive meaning. Ideas from Denecker and his colleagues form the basis of this approach. The key idea is to notice that if we give meanings to all propositional programs and treat Prolog rules with variables as the set of their ground instances, then we can give meanings to all programs. We must treat Prolog rules (which may be metarules) as templates for generating ground propositional rules, and not as first-order formulas, which they may not be. We use parameterized inductive definitions to give propositional models to Prolog programs, in which the propositions are expressions. Then the set of expressions of a propositional model determine a first-order Herbrand Model, providing a first-order logical semantics for all (pure) Prolog programs, including metaprograms. We give examples to show the applicability of this theory. We also demonstrate how this theory makes proofs of some important properties of metaprograms very straightforward.

In the quest for “identity dominance” over the enemy, armed forces are increasingly leveraging biometrics for a variety of purposes. This paper focuses on the combat employment of one of them – facial recognition, which, unlike other biometrics, does not appear to have been widely utilized for targeting purposes yet. With the purchasing patterns of advanced militaries suggesting that such a development is around the corner, this paper assesses the compliance with international humanitarian law (IHL) of the use of facial recognition technologies for targeting purposes. It peruses the applicable legal framework to demonstrate that IHL is neutral towards the use of new technologies and that the right to privacy under international human rights law does not preclude the use of biometrics in hostilities. The analysis zooms in on two specific use cases in which facial recognition is likely to be employed on the battlefield, namely (1) targeted killings against combatants and (2) targeted killings against civilians directly participating in hostilities. The paper closes with an acknowledgment that while facial recognition does have obvious operational benefits, it also has the potential to exacerbate targeting practices that stretch the limits of IHL.