This paper proposes a new methodology for early validation of high-level requirements on cyber-physical systems with the aim of improving their quality and, thus, lowering chances of specification errors propagating into later stages of development where it is much more expensive to fix them. The paper presents a transformation of a real-world requirements specification of a medical device—the Patient-Controlled Analgesia (PCA) Pump—into an Event Calculus model that is then evaluated using Answer Set Programming and the s(CASP) system. The evaluation under s(CASP) allowed deductive as well as abductive reasoning about the specified functionality of the PCA pump on the conceptual level with minimal implementation or design dependent influences and led to fully automatically detected nuanced violations of critical safety properties. Further, the paper discusses scalability and non-termination challenges that had to be faced in the evaluation and techniques proposed to (partially) solve them. Finally, ideas for improving s(CASP) to overcome its evaluation limitations that still persist as well as to increase its expressiveness are presented.

We study the geometric particle-in-cell methods for an electrostatic hybrid plasma model. In this model, ions are described by the fully kinetic equations, electron density is determined by the Boltzmann relation and space-charge effects are incorporated through the Poisson equation. By discretizing the action integral or the Poisson bracket of the hybrid model, we obtain a finite dimensional Hamiltonian system, for which the Hamiltonian splitting methods or the discrete gradient methods can be used to preserve the geometric structure or energy. The global neutrality condition is conserved under suitable boundary conditions. Moreover, the results are further developed for an electromagnetic hybrid model proposed by Vu (J. Comput. Phys., vol. 124, issue 2, 1996, pp. 417–430). Numerical experiments of finite grid instability, Landau damping and resonantly excited nonlinear ion waves illustrate the behaviour of the numerical methods constructed.

Making informed clinical decisions based on individualised outcome predictions is the cornerstone of precision psychiatry. Prediction models currently employed in psychiatry rely on algorithms that map a statistical relationship between clinical features (predictors/risk factors) and subsequent clinical outcomes. They rely on associations that overlook the underlying causal structures within the data, including the presence of latent variables, and the evolution of predictors and outcomes over time. As a result, predictions from sparse associative models from routinely collected data are rarely actionable at an individual level. To be actionable, prediction models should address these shortcomings. We provide a brief overview of a general framework for the rationale for implementing causal and actionable predictions using counterfactual explanations to advance predictive modelling studies, which has translational implications. We have included an extensive glossary of terminology used in this paper and the literature (Supplementary Box 1) and provide a concrete example to demonstrate this conceptually, and a reading list for those interested in this field (Supplementary Box 2).

The dominating set reconfiguration problem is defined as determining, for a given dominating set problem and two among its feasible solutions, whether one is reachable from the other via a sequence of feasible solutions subject to a certain adjacency relation. This problem is PSPACE-complete in general. The concept of the dominating set is known to be quite useful for analyzing wireless networks, social networks, and sensor networks. We develop an approach to solve the dominating set reconfiguration problem based on answer set programming (ASP). Our declarative approach relies on a high-level ASP encoding, and both the grounding and solving tasks are delegated to an ASP-based combinatorial reconfiguration solver. To evaluate the effectiveness of our approach, we conduct experiments on a newly created benchmark set.

Patient involvement in psychiatry education struggles to be representative of the patients that doctors will treat once qualified. The issues of mental health stigma, cultural perspectives of mental health and the unique role of teaching, required exploring to establish the barriers and facilitators to increasing the diversity of patients involved in psychiatry education. To explore the causes of this lack of representation, a roundtable event with 34 delegates composed of people with lived experience of mental health issues, people from underserved communities, academics, mental health professionals and charity representatives met to discuss the barriers to involvement in psychiatry education and possible solutions. Themes were further developed in a context expert focus group. Notes from the roundtable and focus group were analysed and developed into recommendations for medical schools and mental health professional teaching departments.

At least since Aristotle defined human beings as “political animals,” politics in the Western tradition has largely been defined in anthropocentric terms. Politics was a realm of distinctively human endeavors, while nonhuman nature remained outside. Nature might impinge on or set limits to political action, but was conceived as constitutively outside of politics. However else nonhuman entities might engage with humans or each other, these relations or engagements were not understood as political. Until quite recently, Western political theory was decidedly anthropocentric. The rise of environmental problematics, and particularly the political salience of the global climate crisis, however, have made the idea of a constitutive separation between (nonhuman) nature and (human) politics less tenable. Not only the material manipulation of the nonhuman world, but also its conceptual framing, are increasingly understood as political projects.1 At the same time, Western political thought has become increasingly open to non-Western cosmologies that do not posit a rigid divide between human and non- (or more-than-) human worlds. Environmental (or green) political theory has become an increasingly robust subdiscipline,2 and political theory, like a number of other humanities disciplines, has undergone an “animal turn.”3 Three of the four recent books under consideration form part of this latter animal turn while Sharon Krause’s Eco-Emancipation is firmly situated in the field of environmental political theory.

This is the expanded written version of the James Madison Lecture delivered on September 6, 2024, at the APSA Annual Meeting in Philadelphia, PA. I grapple with the pressing question before us as social scientists and as citizens: How and why have US politics and governance arrived at the present juncture where long-standing constitutional practices and democratically responsive governance are very much at stake? My answer focuses on what I see as the prime driver of the current crisis: the recent radicalization of the Republican Party and its allies, as they have pursued two forms and phases of antidemocratic politics. The first version involves maximum use of legal hardball steps that stretch existing laws and rules to disadvantage partisan opponents (I also call this approach “McConnellism” in honor of its chief practitioner, outgoing GOP Senate Leader Mitch McConnell of Kentucky). The second approach targets political competitors and government operations with extralegal harassment, threats of violence, and even actual violence. Drawing on my own research with many collaborators, as well as from many excellent studies by colleagues in political science and beyond, I will dissect the elite and popular roots of recent Republican embrace of both forms of antidemocratic politics.

Recent efforts in interpreting convolutional neural networks (CNNs) focus on translating the activation of CNN filters into a stratified Answer Set Program (ASP) rule-sets. The CNN filters are known to capture high-level image concepts, thus the predicates in the rule-set are mapped to the concept that their corresponding filter represents. Hence, the rule-set exemplifies the decision-making process of the CNN w.r.t the concepts that it learns for any image classification task. These rule-sets help understand the biases in CNNs, although correcting the biases remains a challenge. We introduce a neurosymbolic framework called NeSyBiCor for bias correction in a trained CNN. Given symbolic concepts, as ASP constraints, that the CNN is biased toward, we convert the concepts to their corresponding vector representations. Then, the CNN is retrained using our novel semantic similarity loss that pushes the filters away from (or toward) learning the desired/undesired concepts. The final ASP rule-set obtained after retraining, satisfies the constraints to a high degree, thus showing the revision in the knowledge of the CNN. We demonstrate that our NeSyBiCor framework successfully corrects the biases of CNNs trained with subsets of classes from the Places dataset while sacrificing minimal accuracy and improving interpretability.

Antarctica is populated by a diverse array of terrestrial fauna that have successfully adapted to its extreme environmental conditions. The origins and diversity of the taxa have been of continuous interest to ecologists since their discovery. Early theory considered contemporary populations as descendants of recent arrivals; however, mounting molecular evidence points to firmly established indigenous taxa far earlier than the Last Glacial Maximum, thus indicating more ancient origins. Here we present insights into Antarctica's terrestrial invertebrates by synthesizing available phylogeographic studies. Molecular dating supports ancient origins for most indigenous taxa, including Acari (up to 100 million years ago; Ma), Collembola (21–11 Ma), Nematoda (~30 Ma), Tardigrada (> 1 Ma) and Chironomidae (> 49 Ma), while Rotifera appear to be more recent colonizers (~130 Ka). Subsequent population bottlenecks and rapid speciation have occurred with limited gene transfer between Continental and Maritime Antarctica, while repeated wind- or water-borne dispersal and colonization of contiguous regions during interglacial periods shaped current distributions. Greater knowledge of Antarctica's fauna will focus conservation efforts to ensure their persistence.

The development of large language models (LLMs), such as GPT, has enabled the construction of several socialbots, like ChatGPT, that are receiving a lot of attention for their ability to simulate a human conversation. However, the conversation is not guided by a goal and is hard to control. In addition, because LLMs rely more on pattern recognition than deductive reasoning, they can give confusing answers and have difficulty integrating multiple topics into a cohesive response. These limitations often lead the LLM to deviate from the main topic to keep the conversation interesting. We propose AutoCompanion, a socialbot that uses an LLM model to translate natural language into predicates (and vice versa) and employs commonsense reasoning based on answer set programming (ASP) to hold a social conversation with a human. In particular, we rely on s(CASP), a goal-directed implementation of ASP as the backend. This paper presents the framework design and how an LLM is used to parse user messages and generate a response from the s(CASP) engine output. To validate our proposal, we describe (real) conversations in which the chatbot’s goal is to keep the user entertained by talking about movies and books, and s(CASP) ensures (i) correctness of answers, (ii) coherence (and precision) during the conversation—which it dynamically regulates to achieve its specific purpose—and (iii) no deviation from the main topic.

We examine a common pool resource (CPR) where appropriations deteriorate the quality of the resource and, thus, its impact on the exploitation of the CPR. We focus on two settings: (i) firms use the CPR without abatement efforts, and (ii) abatement is allowed. We provide comparisons between these two settings and identify socially optimal appropriation levels. We find that (i) higher quality of the CPR could induce firms to overuse the resource, and (ii) first-period appropriations with abatement decrease in the regeneration rate. However, abatement induces an overuse of the resource when the quality of the CPR improves.

Given a number field $\mathbb {K} \subset \mathbb {C}$ that is not contained in $\mathbb {R}$, we prove the existence of a dense set (with respect to the topology of local uniform convergence) of entire maps $f \colon \mathbb {C} \rightarrow \mathbb {C}$ whose preperiodic points and multipliers all lie in $\mathbb {K}$. This contrasts with the case of rational maps. In addition, we show that there exists an escaping quadratic-like map that is not conjugate to an affine escaping quadratic-like map and whose multipliers all lie in $\mathbb {Q}$.

When we want to compute the probability of a query from a probabilistic answer set program, some parts of a program may not influence the probability of a query, but they impact on the size of the grounding. Identifying and removing them is crucial to speed up the computation. Algorithms for SLG resolution offer the possibility of returning the residual program which can be used for computing answer sets for normal programs that do have a total well-founded model. The residual program does not contain the parts of the program that do not influence the probability. In this paper, we propose to exploit the residual program for performing inference. Empirical results on graph datasets show that the approach leads to significantly faster inference. The paper has been accepted at the ICLP2024 conference and under consideration in Theory and Practice of Logic Programming (TPLP).

This cross-sectional ecological study described fruit and vegetable (F&V) intake variability across 144 cities in 8 Latin American countries and by city-level contextual variables. Data sources came from health surveys and census data (Argentina, Brazil, Chile, Colombia, El Salvador, Guatemala, Mexico, and Peru). Self-reported frequency of F&V intake was harmonised across surveys. Daily F&V intake was considered as consumption 7 d of the week. Using a mixed-effects model, we estimated age and sex-standardised city prevalences of daily F&V intake. Through Kruskal–Wallis tests, we compared city F&V daily intake prevalence by tertiles of city variables related to women’s empowerment, socio-economics, and climate zones. The median prevalence for daily F&V intake was 55.7% across all cities (22.1% to 85.4%). Compared to the least favourable tertile of city conditions, F&V daily intake prevalence was higher for cities within the most favourable tertile of per capita GDP (median = 65.7% vs. 53.0%), labour force participation (median = 68.7% vs. 49.4%), women achievement-labour force score (median = 63.9% vs. 45.7%), and gender inequality index (median = 58.6% vs. 48.6%). Also, prevalences were higher for temperate climate zones than arid climate zones (median = 65.9% vs. 50.6%). No patterns were found by city level of educational attainment, city size, or population density. This study provides evidence that the prevalence of daily F&V intake varies across Latin American cities and may be favoured by higher socio-economic development, women’s empowerment, and temperate weather. Interventions to improve F&V intake in Latin America should consider the behaviour disparities related to underlying local social, economic, and climate zone characteristics.