Exchange Interactions in a Dinuclear Manganese (II) Complex with Cyanopyridine-N-oxide Bridging Ligands

The structure and magnetic properties of oligonuclear manganese complex [Mn(hfa)2cpo]2 (where hfa is hexafluoroacetylacetonate dehydrate and cpo is 4-cyanopyridine-N-oxide) are presented. In the unit cell, the dinuclear molecules are well isolated from each other. The non-monotonous dependence of magnetic susceptibility is explained in terms of the hierarchy of exchange parameters by using exact diagonalization. The thermodynamic behavior of pure cpo and [Mn(hfa)2(cpo)]2 is simulated numerically in a dimer approximation by an extrapolation to spin S = 5/2. The Mn-Mn exchange integral is evaluated.Comment: Final corrected version, pdf, 12 page

Binge drinking among adolescents in Russia: prevalence, risk and protective factors.

Despite evidence that alcohol misuse has been having an increasingly detrimental effect on adolescent wellbeing in Russia in recent years this phenomenon has been little researched. Using data from 2112 children from the Arkhangelsk Social and Health Assessment (SAHA) 2003, this study examined which factors acted as 'risk' or 'protective' factors for adolescent binge drinking within three domains we termed the 'family environment', the 'alcohol environment' and 'deviant behaviour'. The results showed that in the presence of comparatively moderate levels of binge drinking among both boys and girls, being able to access alcohol easily, being unaware of the risks of binge drinking and having peers who consumed alcohol increased the risk of adolescent binge drinking - as did playing truant, smoking and marijuana use, while parental warmth was protective against binge drinking for girls. Our finding that risk and protective factors occur across domains suggests that any interventions targeted against adolescent binge drinking may need to simultaneously focus on risk behaviours in different domains, while at the same time, broader social policy should act to limit the availability of alcohol to adolescents in Russia more generally

Linking working memory and long-term memory: A computational model of the learning of new words

The nonword repetition (NWR) test has been shown to be a good predictor of children’s vocabulary size. NWR performance has been explained using phonological working memory, which is seen as a critical component in the learning of new words. However, no detailed specification of the link between phonological working memory and long-term memory (LTM) has been proposed. In this paper, we present a computational model of children’s vocabulary acquisition (EPAM-VOC) that specifies how phonological working memory and LTM interact. The model learns phoneme sequences, which are stored in LTM and mediate how much information can be held in working memory. The model’s behaviour is compared with that of children in a new study of NWR, conducted in order to ensure the same nonword stimuli and methodology across ages. EPAM-VOC shows a pattern of results similar to that of children: performance is better for shorter nonwords and for wordlike nonwords, and performance improves with age. EPAM-VOC also simulates the superior performance for single consonant nonwords over clustered consonant nonwords found in previous NWR studies. EPAM-VOC provides a simple and elegant computational account of some of the key processes involved in the learning of new words: it specifies how phonological working memory and LTM interact; makes testable predictions; and suggests that developmental changes in NWR performance may reflect differences in the amount of information that has been encoded in LTM rather than developmental changes in working memory capacity. Keywords: EPAM, working memory, long-term memory, nonword repetition, vocabulary acquisition, developmental change

Explaining semantic short-term memory deficits:evidence for the critical role of semantic control

Patients with apparently selective short-term memory (STM) deficits for semantic information have played an important role in developing multi-store theories of STM and challenge the idea that verbal STM is supported by maintaining activation in the language system. We propose that semantic STM deficits are not as selective as previously thought and can occur as a result of mild disruption to semantic control processes, i.e., mechanisms that bias semantic processing towards task-relevant aspects of knowledge and away from irrelevant information. We tested three semantic STM patients with tasks that tapped four aspects of semantic control: (i) resolving ambiguity between word meanings, (ii) sensitivity to cues, (iii) ignoring irrelevant information and (iv) detecting weak semantic associations. All were impaired in conditions requiring more semantic control, irrespective of the STM demands of the task, suggesting a mild, but task-general, deficit in regulating semantic knowledge. This mild deficit has a disproportionate effect on STM tasks because they have high intrinsic control demands: in STM tasks, control is required to keep information active when it is no longer available in the environment and to manage competition between items held in memory simultaneously. By re-interpreting the core deficit in semantic STM patients in this way, we are able to explain their apparently selective impairment without the need for a specialised STM store. Instead, we argue that semantic STM patients occupy the mildest end of spectrum of semantic control disorders

Exposure to Community Violence, Psychopathology, and Personality Traits in Russian Youth

Previous research with the US inner-city youth demonstrated the hazardous effects of community violence exposure. It remains unclear, however, whether these findings are generalizable to other cultures and populations. Furthermore, the role of factors influencing the processing of traumatic events such as personality has not been investigated. Two groups of Russian adolescents (community youth (N = 546) and male delinquents (N = 352)) completed questionnaires assessing their exposure to community violence, conduct problems, internalizing psychopathology and personality. The study demonstrates that the relationships between exposure to violence and psychopathology are similar across different populations within the same culture (community youth and juvenile delinquents), suggesting similar mechanisms behind this phenomenon. The patterns of these relationships were also similar for boys and girls, suggesting similarities in the mechanisms across gender. Hence, the effects of community violence exposure are generalizable to other cultures outside the US. The associations between personality traits and specific types of behaviors also tend to be similar across different populations. Higher levels of novelty seeking were related to more severe problem behaviors and to higher levels of witnessing and victimization, whereas higher levels of harm avoidance were related to higher levels of depression and posttraumatic stress

How Safe Am I Given What I See? Calibrated Prediction of Safety Chances for Image-Controlled Autonomy

End-to-end learning has emerged as a major paradigm for developing autonomous systems. Unfortunately, with its performance and convenience comes an even greater challenge of safety assurance. A key factor of this challenge is the absence of the notion of a low-dimensional and interpretable dynamical state, around which traditional assurance methods revolve. Focusing on the online safety prediction problem, this paper proposes a configurable family of learning pipelines based on generative world models, which do not require low-dimensional states. To implement these pipelines, we overcome the challenges of learning safety-informed latent representations and missing safety labels under prediction-induced distribution shift. These pipelines come with statistical calibration guarantees on their safety chance predictions based on conformal prediction. We perform an extensive evaluation of the proposed learning pipelines on two case studies of image-controlled systems: a racing car and a cartpole.Comment: This is supplementary material to the paper: How Safe Am I Given What I See? Calibrated Prediction of Safety Chances for Image-Controlled Autonomy in 6th Annual Learning for Dynamics & Control Conference (L4DC 2024

Conservative Safety Monitors of Stochastic Dynamical Systems

Generating accurate runtime safety estimates for autonomous systems is vital to ensuring their continued proliferation. However, exhaustive reasoning about future behaviors is generally too complex to do at runtime. To provide scalable and formal safety estimates, we propose a method for leveraging design-time model checking results at runtime. Specifically, we model the system as a probabilistic automaton (PA) and compute bounded-time reachability probabilities over the states of the PA at design time. At runtime, we combine distributions of state estimates with the model checking results to produce a bounded time safety estimate. We argue that our approach produces well-calibrated safety probabilities, assuming the estimated state distributions are well-calibrated. We evaluate our approach on simulated water tanks

Curating Naturally Adversarial Datasets for Trustworthy AI in Healthcare

Deep learning models have shown promising predictive accuracy for time-series healthcare applications. However, ensuring the robustness of these models is vital for building trustworthy AI systems. Existing research predominantly focuses on robustness to synthetic adversarial examples, crafted by adding imperceptible perturbations to clean input data. However, these synthetic adversarial examples do not accurately reflect the most challenging real-world scenarios, especially in the context of healthcare data. Consequently, robustness to synthetic adversarial examples may not necessarily translate to robustness against naturally occurring adversarial examples, which is highly desirable for trustworthy AI. We propose a method to curate datasets comprised of natural adversarial examples to evaluate model robustness. The method relies on probabilistic labels obtained from automated weakly-supervised labeling that combines noisy and cheap-to-obtain labeling heuristics. Based on these labels, our method adversarially orders the input data and uses this ordering to construct a sequence of increasingly adversarial datasets. Our evaluation on six medical case studies and three non-medical case studies demonstrates the efficacy and statistical validity of our approach to generating naturally adversarial dataset