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This study facilitates the understanding of evolutionary transitions in individuality (ETIs) through a novel artificial life framework, named VitaNova, that intricately merges self-organization and natural selection to simulate the emergence of complex, reproductive groups. By dynamically modelling individual agents within an environment that challenges them with predators and spatial constraints, VitaNova elucidates the mechanisms by which simple agents evolve into cohesive units exhibiting collective reproduction. The findings underscore the synergy between self-organized behaviours and adaptive evolutionary strategies as fundamental drivers of ETIs. This approach not only contributes to a deeper understanding of higher-order biological individuality but also sets a new precedent in the empirical investigation of ETIs, challenging and extending current theoretical frameworks., Comment: 9 pages, 10 figures, conference

Heterogeneity and comorbidity are two interwoven challenges associated with various healthcare problems that greatly hampered research on developing effective treatment and understanding of the underlying neurobiological mechanism. Very few studies have been conducted to investigate heterogeneous causal effects (HCEs) in graphical contexts due to the lack of statistical methods. To characterize this heterogeneity, we first conceptualize heterogeneous causal graphs (HCGs) by generalizing the causal graphical model with confounder-based interactions and multiple mediators. Such confounders with an interaction with the treatment are known as moderators. This allows us to flexibly produce HCGs given different moderators and explicitly characterize HCEs from the treatment or potential mediators on the outcome. We establish the theoretical forms of HCEs and derive their properties at the individual level in both linear and nonlinear models. An interactive structural learning is developed to estimate the complex HCGs and HCEs with confidence intervals provided. Our method is empirically justified by extensive simulations and its practical usefulness is illustrated by exploring causality among psychiatric disorders for trauma survivors., Comment: In Proceedings of the 40th International Conference on Machine Learning (ICML) Code implementing the proposed algorithm is open-source and publicly available at: https://github.com/richard-watson/ISL

The youngest Galactic supernova remnant G1.9+0.3, probably the result of a Type Ia supernova, shows surprising anomalies in the distribution of its ejecta in space and velocity. In particular, high-velocity shocked iron is seen in several locations far from the remnant center, in some cases beyond prominent silicon and sulfur emission. These asymmetries strongly suggest a highly asymmetric explosion. We present high-resolution hydrodynamic simulations in two and three dimensions of the evolution from ages of 100 seconds to hundreds of years of two asymmetric Type Ia models, expanding into a uniform medium. At the age of G1.9+0.3 (about 100 years), our 2D model shows almost no iron shocked to become visible in X-rays. Only in a much higher-density environment could significant iron be shocked, at which time the model's expansion speed is completely inconsistent with the observations of G1.9+0.3. Our 3D model, evolving the most asymmetric of a suite of Type Ia SN models from Seitenzahl et al.~(2013), shows some features resembling G1.9+0.3. We characterize its evolution with images of composition in three classes: C and O, intermediate-mass elements (IMEs), and iron-group elements (IGEs). From ages of 13 to 1800 years, we follow the evolution of the highly asymmetric initial remnant as the explosion asymmetries decrease in relative strength to be replaced by asymmetries due to evolutionary hydrodynamic instabilities. At an age of about 100 years, our 3D model has comparable shocked masses of C+O, IMEs, and IGEs, with about 0.03 $M_\odot$ each. Evolutionary changes appear to be rapid enough that continued monitoring with the Chandra X-ray Observatory may show significant variations., Comment: 30 pages, 23 figures. Accepted for publication in ApJS. Animations will be available in online published version

Automated Compliance Checking (ACC) systems aim to semantically parse building regulations to a set of rules. However, semantic parsing is known to be hard and requires large amounts of training data. The complexity of creating such training data has led to research that focuses on small sub-tasks, such as shallow parsing or the extraction of a limited subset of rules. This study introduces a shallow parsing task for which training data is relatively cheap to create, with the aim of learning a lexicon for ACC. We annotate a small domain-specific dataset of 200 sentences, SPaR.txt, and train a sequence tagger that achieves 79,93 F1-score on the test set. We then show through manual evaluation that the model identifies most (89,84%) defined terms in a set of building regulation documents, and that both contiguous and discontiguous Multi-Word Expressions (MWE) are discovered with reasonable accuracy (70,3%)., Comment: To be published in the NLLP workshop at EMNLP 2021, 9 pages (15 including reference and appendices). For the ScotReg corpus, SPaR.txt dataset and code see: http://github.com/rubenkruiper/SPaR.txt