Instituto Tecnológico Autónomo de México

Abstract. Floods are a major source of losses from natural hazards, yet modelling their occurrence and severity is challenging due to complex spatial dependencies and non-stationary behaviour over time, both of which are increasingly affected by climate change. In this study, we characterise spatial and temporal non-stationarity in flood risk and quantify the dependence of flood occurrences under minimal modelling assumptions. We analyse a unique dataset of flood records from 27 Alpine lakes spanning 1,450 years, applying penalised additive mixed models to capture the empirical spatio-temporal dependence structure of flood events. Our results reveal pronounced regional and temporal variations in flood risk and highlight periods of elevated susceptibility. The model further allows extrapolation of flood occurrence probabilities to unobserved locations across the European Alps, providing a robust tool for hazard assessment under changing climatic conditions.

Stabilizing the Unstable: How Long-History PCA Sharpens Portfolio Risk Forecasts Investors focus on market volatility, but a more subtle challenge is “second-order risk”—the discrepancy between a portfolio’s predicted risk and its actual performance. Standard practice relies on short snapshots of data (typically one year) to estimate the covariance structure under the assumption that older data are irrelevant in fast-changing markets. However, this “short-memory” approach often mistakes random noise for real market signals, leading to optimized portfolios that are riskier than they appear. In “Long-History PCA in a Dynamic Factor Model with Weak Loadings,” Anderson, Kim, and Ryu challenge this norm. They introduce Long-History Principal Component Analysis (LH-PCA), demonstrating that extending the data history to six years acts as a crucial stabilizer. Theoretically, the authors prove PCA remains a consistent estimator even in dynamic markets with “weak” loadings, provided the historical timeline is sufficiently large. Both simulation and empirical tests on U.S. and European markets confirm that looking back six years significantly reduces estimation bias.

In the biology field of botany, leaf shape recognition is an important task. One way of characterising the leaf shape is through the centroid contour distances (CCD). Each CCD path might have different resolution, so normalisation is done by associating each contour to a circular density. Densities are rotated by subtracting the mean or mode preferred direction. Distance measures between densities are used to produce a hierarchical clustering method to cluster the leaves. We illustrate our approach with a motivating small dataset as well as a larger dataset.

Abstract Modelling multiple network data is crucial for addressing a wide range of applied research questions. However, there are many challenges, both theoretical and computational, to address. Network cycles are often of particular interest in many applications; for example in ecology a largely unexplored area has been how to incorporate network cycles within the inferential framework in an explicit way. The recently developed Spherical Network Family of models (SNF) offers a flexible formulation for modelling multiple network data that permits any type of metric. This has opened up the possibility to formulate network models that focus on network properties hitherto not possible or practical to consider. In this article we propose a novel network distance metric that measures similarities between networks with respect to their cycles, and incorporates this within the SNF model to allow inferences that explicitly capture information on cycles. These network motifs are of particular interest in ecological studies aimed at understanding competitive and hierarchical interactions. We further propose a novel computational framework to allow posterior inferences from the intractable SNF model for moderate-sized networks. Lastly, we apply the resulting methodology to a set of ecological network data studying aggressive interactions between species of fish. We show our model is able to make cogent inferences concerning the cycle behaviour amongst the species, and beyond those possible from a model that does not consider this network motif.

El texto que ahora reseñamos se enfoca en el yo, un tema central y recurrente en el pensamiento occidental, al lado de la naturaleza de la realidad y la esencia del conocimiento. El yo, como la entidad que posee conciencia de sí misma, es el punto de partida desde el cual los seres humanos experimentan el mundo; también toca aspectos ontológicos, epistemológicos, psicológicos y éticos, haciendo de su estudio un desafío complejo y multifacético, que el autor confronta desde la conciencia que tiene de sí mismo, pero que no consiste en ser en sí mismo sin más, sino que existe de manera fracturada, en lucha con el sí y el no de su ser.

Previous research explores the relationship between the cognitive status of people with dementia (PwD) and their performance in complex, domain-specific interaction tasks. However, these activities and their sophisticated instructions are challenging for PwD, potentially biasing performance metrics. This study aims to answer whether the cognitive status of PwD is related to their performance in simple touch and tangible tasks disassociated from a domain. This study involved 7 formal caregivers and data from 21 PwD corresponding to completion times of interaction tasks. Relationships between completion times and the cognitive status of PwD were explored using Spearman's rank correlation, mutual information, gini importance, permutation importance, and a principal component analysis (PCA) visualization. Completion times of drag & drop, grasp, and tap interaction tasks have a strong negative monotonic association with the MMSE score (Bonferroni-corrected p-value < 0.05). The most relevant gestures were drag & drop, tap, and grasp. The PCA visualization allowed formal caregivers to detect relationships between patients' performance and their MMSE scores (p-value < 0.05). The cognitive status of PwD is related to their performance in simple interaction tasks disassociated from a domain. As the MMSE score decreases, task completion times increase. In addition, the PCA visualization was considered useful to inform decision-making. This work provides the foundation for technology-enhanced cognitive activities supported by touch and tangible gestures that can be used to determine the cognitive status of PwD.