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Punishment regulates selfish behaviors and maintains cooperation. However, because punishment imposes costs on another person, it could also harm relationships. The current work asked how punishment shapes 5- to 10-year-olds' (Study 1; n=128) and adults' (Study 2; n=159) attitudes toward punishers and those who receive punishment as well as their inferences about relationships between punishers and targets. We reasoned that the motives underlying punishment might shape evaluations; punishments motivated by prosocial desires may elicit more positive responses than punishments motivated by antisocial desires. We tested both motives that were external to the punisher (the behavior that elicited the punishment) as well as internal motives (the desire to harm versus rehabilitate transgressors). The main result is that we found negative social relationships among punishers, targets, and observers. Both children and adults preferred punishers who inflicted punishment for behaviors that violated (versus did not violate) norms, preferred targets of punishment who had not (versus had) violated norms, and expected punishers and targets to dislike each other. External motives, but not internal motives, consistently influenced participants’ own social preferences. In contrast, neither external nor internal motives consistently shaped participants' inferences about social relationships between punishers and their targets. Our work contributes to social cognitive development by clarifying how motives shape children's and adults' understanding of social relationships. 

Public transit systems are crucial to mobility and access in cities throughout the world. This article addresses the importance of these transit systems in San Antonio, Texas. We show how transit systems exacerbate race and class inequality and the accessibility of city spaces with a focus on San Antonio’s buses. Using mixed methods (surveys, interviews, ethnography, and document analysis) we illustrate that poor and working-class Latinx communities experience reduced access to resource rich areas of the city when they are dependent upon the city’s public transportation. To better describe this experience we use the concepts, enclaves of exclusion and enclaves of inaccessibility. Our findings show that mobility through San Antonio for poor and working class Latinxs is limited especially for people in these communities who rely on public transit. This experience with these public transit systems often renders them as individuals who do not belong in certain neighborhoods, and ultimately reinforces the longstanding histories of race and class segregation in San Antonio. 

The burgeoning sophistication of Artificial Intelligence (AI) has catalyzed the rapid proliferation of Large Language Models (LLMs) within software development. These models are increasingly employed to automate the generation of functionally correct code, address complex computational problems, and facilitate the debugging of existing software systems. However, LLM-generated code often faces challenges due to inherent inefficiencies, including redundant logical structures, factually inconsistent content (hallucinations), and programming errors. To address this issue, our research rigorously evaluated the computational efficiency of Python code generated by three prominent LLMs: GPT-4o-Mini, GPT-3.5-Turbo, and GPT-4-Turbo. The evaluation metrics encompass execution time, memory utilization, and peak memory consumption, while maintaining the functional correctness of the generated code. Leveraging the EffiBench benchmark datasets within the Google Vertex AI Workbench environment, across a spectrum of machine configurations, the study implemented a consistent seed parameter to ensure experimental reproducibility. Furthermore, we investigated the impact of two distinct optimization strategies: Chain-of-Thought (CoT) prompting and model fine-tuning. Our findings reveal a significant enhancement in efficiency metrics for GPT-4o-Mini and GPT-3.5-Turbo when employing CoT prompting; however, this trend was not observed for GPT-4-Turbo. Based on its promising performance with CoT prompting, we selected the GPT-4o-Mini model for subsequent fine-tuning, aiming to further enhance both its computational efficiency and accuracy. However, contrary to our expectations, fine-tuning the GPT-4o-Mini model led to a discernible degradation in both its accuracy and computational efficiency. In conclusion, this study provides empirical evidence suggesting that the deployment of high-CPU machine configurations, in synergy with the utilization of the GPT-4o-Mini model and CoT prompting techniques, yields demonstrably more efficient and accurate LLM-generated Python code, particularly within computationally intensive application scenarios. 

The halide perovskite heterostructure (CuCl4)2(MTPA)4Cu3Cl6 (Cu_Cu; MTPA = 3-(methylthio)-propylammonium) forms from solution as single crystals consisting of alternating layers of 2D CuII–Cl perovskite and 1D CuII–Cl diamond–chain intergrowth. Using magnetometry, heat capacity, and electron paramagnetic resonance measurements, we interrogate the magnetic ordering of the 2D perovskite and 1D intergrowth layers at temperatures down to 0.055 K. As with other Cu‒Cl perovskites, the perovskite-layer spins order ferromagnetically at 10 K. Magnetization data of Cu_Cu feature a multi–component curve, consistent with magnetization of the perovskite layers and one of the three additional CuII sites in the intergrowth layer, suggesting antiferromagnetic coupling of the remaining two intergrowth-layer spins. A broad feature in AC susceptibility measurements at 6 K and an anomalous heat capacity feature at 0.3 K suggest that local ordering events occur at dramatically different energy scales with decreasing temperature. EPR spectra indicate that these local orderings occur within the 1D chains. Notably, no long–range magnetic ordering event in the intergrowth is evident down to 0.055 K, suggesting that the geometric constraints imposed by the perovskite framework and the steric bulk of the MTPA ligands physically separate and magnetically isolate the diamond chains. In contrast, well–studied diamond-spin-chain materials such as azurite show long-range magnetic order at low-temperatures due to interchain interactions. Thus, Cu_Cu provides an ideal platform for studying isolated, anisotropic spin chains. More generally, this study illustrates the capability of halide perovskite heterostructures to serve as vehicles for the scalable synthesis of complex magnetic materials. 

Abstract We study the formation of stars with varying amounts of heavy elements synthesized by the rapid neutron-capture process (r-process) based on our detailed cosmological zoom-in simulation of a Milky Way–like galaxy with anN-body/smoothed particle hydrodynamics code,asura. Most stars with no overabundance inr-process elements, as well as the stronglyr-process-enhanced (RPE)r-II stars ([Eu/Fe] > +0.7), are formed in dwarf galaxies accreted by the Milky Way within the 6 Gyr after the Big Bang. In contrast, over half of the moderately enhancedr-I stars (+0.3 < [Eu/Fe] ≤ +0.7) are formed in the main in situ disk after 6 Gyr. Our results suggest that the fraction ofr-I andr-II stars formed in disrupted dwarf galaxies is larger the higher their [Eu/Fe] is. Accordingly, the most strongly enhancedr-III stars ([Eu/Fe] > +2.0) are formed in accreted components. These results suggest that non-r-process-enhanced stars andr-II stars are mainly formed in low-mass dwarf galaxies that hosted either none or a single neutron star merger, while ther-I stars tend to form in the well-mixed in situ disk. We compare our findings with high-resolution spectroscopic observations of RPE metal-poor stars in the halo and dwarf galaxies, including those collected by theR-Process Alliance. We conclude that observed [Eu/Fe] and [Eu/Mg] ratios can be employed in chemical tagging of the Milky Way’s accretion history. 

Punishment is a key mechanism to regulate selfish behaviors and maintain cooperation in a society. However, children often show mixed evaluations about third-party punishment. The current work asked how punishment severity might shape children's social judgments. In two studies, 5- to 10-year-old children heard about a punisher who took different numbers of items from a transgressor and evaluated the punisher's behavior and moral character. In Study 1 (n=68), when the transgression was relatively mild (i.e., unfair sharing), children across ages evaluated taking no items from the unfair sharer ("no punishment") most positively, while evaluating taking three items ("harshest punishment") most negatively. In Study 2 (n=68), when the transgression was more serious (i.e., stealing), younger children evaluated taking two items ("equality-establishing punishment") more positively than older children, while evaluating taking none most negatively. However, children became more likely to evaluate equality-establishing punishment negatively with age. Overall, the current results show that punishment severity is a key factor underlying children's third-party punishment judgments. The current research extends work on moral development by showing how children conceptualize the severity of punishment. 

Abstract We introduce a new concept of the local flux conservation and investigate its role in the coupled flow and transports. We demonstrate how the proposed concept of the locally conservative flux can play a crucial role in obtaining the$$L^2$$ $L^2$norm stability of the discontinuous Galerkin finite element scheme for the transport in the coupled system with flow. In particular, the lowest order discontinuous Galerkin finite element for the transport is shown to inherit the positivity and maximum principle when the locally conservative flux is used, which has been elusive for many years in literature. The theoretical results established in this paper are based on the equivalence between Lesaint-Raviart discontinuous Galerkin scheme and Brezzi-Marini-Süli discontinuous Galerkin scheme for the linear hyperbolic system as well as the relationship between the Lesaint-Raviart discontinuous Galerkin scheme and the characteristic method along the streamline. Sample numerical experiments have then been performed to justify our theoretical findings. 

Abstract We consider additive Schwarz methods for boundary value problems involving the $$p$$-Laplacian. While existing theoretical estimates suggest a sublinear convergence rate for these methods, empirical evidence from numerical experiments demonstrates a linear convergence rate. In this paper we narrow the gap between these theoretical and empirical results by presenting a novel convergence analysis. First, we present a new convergence theory for additive Schwarz methods written in terms of a quasi-norm. This quasi-norm exhibits behaviour akin to the Bregman distance of the convex energy functional associated with the problem. Secondly, we provide a quasi-norm version of the Poincaré–Friedrichs inequality, which plays a crucial role in deriving a quasi-norm stable decomposition for a two-level domain decomposition setting. By utilizing these key elements we establish the asymptotic linear convergence of additive Schwarz methods for the $$p$$-Laplacian.