arXiv subjects

Leonardo G. J. M. Voltarelli

Publications and source records attributed to Leonardo G. J. M. Voltarelli.

4 recordsLinked to original sources

arXiv2026

Interpretable liquid crystal phase classification via two-by-two ordinal patterns

Liquid crystal textures encode rich structural information, yet mapping these images to mesophase identity remains challenging because visually similar patterns can arise from distinct structures. Here we present a simple, interpretable representation that maps textures to a 75-dimensional frequency vector of two-by-two ordinal patterns, grouped into eleven symmetry-based types to characterize a large-scale dataset spanning seven mesophases. Combined with a simple machine learning classifier, this lightweight representation yields near-perfect phase recognition, including the difficult distinction between smectic A and smectic B mesophases. Our approach generalizes to unseen compounds and accurately distinguishes between phase identity and material origin. Unlike deep learning methods, each ordinal pattern is readily interpretable, and model explanations augmented with network visualizations of pattern interactions reveal the specific types and pairwise dependencies that drive each mesophase decision, providing compact, physically meaningful summaries of texture determinants. These results establish two-by-two ordinal patterns as an interpretable and scalable tool for liquid crystal image analysis, with potential applications to other complex patterned systems in materials science.

cond-mat.soft↗

arXiv2025

Similarity networks of ordinal-pattern transitions classify falling paper trajectories

Paper fragments in free fall constitute a simple yet paradigmatic mechanical system exhibiting remarkably complex motions. Despite a long history of investigation, this system has defied comprehensive first-principles modeling, motivating the development of phenomenological and experimental approaches to classify the free-fall dynamics of small paper fragments. Here we apply the Bandt-Pompe symbolization method to extract high-dimensional features corresponding to ordinal-pattern transitions (so-called ordinal networks) from observed area time series of video-recorded falling papers shaped as circles, squares, hexagons, and crosses. We then represent each trajectory as a node in a weighted similarity network, with edges encoding pairwise dynamical similarity, and identify motion classes via community detection. Our method automatically clusters trajectories into tumbling and chaotic falls in excellent agreement with expert visual classification. Notably, it outperforms previous approaches based on classical physical features derived from complete three-dimensional trajectories -- especially for cross-shaped papers -- and requires no prior specification of the number of motion classes. We further find that trajectories diverging from expert classifications occupy more central positions in the similarity network, suggesting more complex and ambiguous dynamic behavior.

physics.data-an↗

arXiv2025

Nearest neighbor permutation entropy detects phase transitions in complex high-pressure systems

Understanding the high-pressure phase behavior of carbon dioxide-hydrocarbon mixtures is of considerable interest owing to their wide range of applications. Under certain conditions, these systems are not amenable to direct visual monitoring, and experimentalists often rely on spectrophotometric data to infer phase behavior. Consequently, developing computationally efficient and robust methods to leverage such data is crucial. Here, we combine nearest neighbor permutation entropy, computed directly from in situ near-infrared absorbance spectra acquired during depressurization trials of mixtures of carbon dioxide and a distilled petroleum fraction, with an anomaly detection approach to identify phase transitions. We show that changes in nearest neighbor entropy effectively signal transitions from initially homogeneous mixtures to two-phase equilibria, thereby enabling accurate out-of-sample online predictions of transition pressures. Our approach requires minimum data preprocessing, no specialized detection techniques or visual inspection of the spectra, and is sufficiently general to be adapted for studying phase behavior in other high-pressure systems monitored via spectrophotometry.

physics.chem-ph↗

arXiv2024

Characterizing unstructured data with the nearest neighbor permutation entropy

Permutation entropy and its associated frameworks are remarkable examples of physics-inspired techniques adept at processing complex and extensive datasets. Despite substantial progress in developing and applying these tools, their use has been predominantly limited to structured datasets such as time series or images. Here, we introduce the k-nearest neighbor permutation entropy, an innovative extension of the permutation entropy tailored for unstructured data, irrespective of their spatial or temporal configuration and dimensionality. Our approach builds upon nearest neighbor graphs to establish neighborhood relations and uses random walks to extract ordinal patterns and their distribution, thereby defining the k-nearest neighbor permutation entropy. This tool not only adeptly identifies variations in patterns of unstructured data, but also does so with a precision that significantly surpasses conventional measures such as spatial autocorrelation. Additionally, it provides a natural approach for incorporating amplitude information and time gaps when analyzing time series or images, thus significantly enhancing its noise resilience and predictive capabilities compared to the usual permutation entropy. Our research substantially expands the applicability of ordinal methods to more general data types, opening promising research avenues for extending the permutation entropy toolkit for unstructured data.

physics.data-an↗