佩皮尼昂大学

Inbreeding depression (ID)-the reduction in fitness with increasing parental relatedness-is classically attributed to the expression of recessive deleterious mutations in homozygous individuals. Yet, the assumption that ID can only arise from changes in genetic heterozygosity has rarely, if ever, been directly tested. To test this, we produced highly inbred lines (F = 0.99999997) of the freshwater snail Physa acuta and generated offspring that differed in parental relatedness (self-fertilization, sib or cousin matings) while their parents, produced by crossing two inbred lines, all shared an identical genome. Several fitness traits showed significant declines with increasing parental relatedness. These traits included juvenile survival, body size, and self-fertility, and the magnitude of their decline was equivalent to a substantial fraction of the ID observed in natural, genetically polymorphic populations of P. acuta. Individual-based simulations demonstrated that spontaneous mutation rates compatible with natural levels of ID are far too low to account for the magnitude of ID observed here. These findings suggest that non-genetic mechanisms, most plausibly involving heritable epigenetic changes, can generate ID even in genetically uniform populations. This challenges the long-standing view that ID arises exclusively from genetic homozygosity and highlights the need to investigate epigenetic contributions to ID.

The accelerating infusion of advanced computational methods into geopolitical analysis has created new opportunities to anticipate unrest, economic shocks and diplomatic shifts. Traditional machine learning pipelines can extract statistical patterns from large event corpora, but they often struggle to incorporate real-time contextual information or explain their predictions in language accessible to decision-makers. This study proposes a comprehensive framework, LLM4Geopolitics, that couples a domain-adapted large language model with a retrieval-augmented generation mechanism grounded in a structured knowledge graph. The forecasting component employs a transformer architecture tailored to sparse, irregular event streams, while the generative component translates model outputs into dialogue-ready assessments enriched with up-to-date economic and peace-index indicators. Experiments conducted on the Gdelt dataset demonstrate that the integrated approach improves event-severity prediction and generates fact-consistent narratives compared with baseline time series and text-only models. These findings highlight the potential of combining specialised sequence models, on-demand knowledge retrieval and generative reasoning to deliver timely and interpretable insights for geopolitical forecasting.

I stumbled by chance on a nice musical interpretation of an esoteric theorem due to Pierre Beauguitte that more clearly demonstrates its perceptual consequences. It is a translation of a statement on Fourier coefficients in terms of the $ \operatorname {IFunc} $ IFunc between two generated pc-sets, and it characterizes the non-periodic maximally even sets, those generalizing pentatonic or diatonic scales. As a simple example, a chromatic pentachord always has three notes in common with any diatonic scale, except for one case when there are only two; and the diatonic and pentatonic scales are the only pc-sets harbouring that relationship with these chromatic spans. A very slightly different and simpler condition characterizes periodic pc-sets.

Generating truncated multivariate normal distributions is widely used in Bayesian constrained statistical modeling. This technique is applied in various fields, including ecology, economics, physics, computer science, biology, geosciences, and machine learning. In this article, we extend the approach proposed by Ray, Pati, and Bhattacharya (2020) and further developed in Souris, Bhattacharya, and Pati (2019). Their main idea is to incorporate a smooth relaxation of the complex constraints appearing in the constrained density function into the likelihood and to employ a highly efficient Markov Chain Monte Carlo (MCMC) sampler. Our main contribution is the extension of this approach to address general linear and nonlinear inequality constraints, thereby enhancing its applicability to a wider range of problems. In light of this, we propose updating the approximation parameter in the likelihood at each MCMC iteration to enhance stability and ensure convergence of the algorithm. The flexibility, efficiency, and accuracy of the proposed approach are demonstrated through several numerical examples and a real-world application.