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Angelo A. Flores

Publications and source records attributed to Angelo A. Flores.

2 recordsLinked to original sources

Charting the life of Billboard hits through memory, turnover, and predictability

Rankings shape the visibility and success of cultural products, yet their temporal dynamics remain underexplored when comparing distinct ranked objects within the same domain. Here, we use nearly seven decades of Billboard Hot 100 songs and six decades of Billboard 200 albums to investigate how success emerges, persists, and differs between songs and albums. We find that albums exhibit a heavier-tailed permanence distribution and reenter the charts more often than songs, whereas songs typically have longer uninterrupted runs. Similarity between successive charts decays much faster for songs than for albums, suggesting that individual hits reflect shorter-lived collective attention, while albums retain longer cultural memory. Rank-turbulence divergence shows that consecutive charts are similar, but that top positions are dominated more by rank reshuffling than by turnover. Entropy-based analyses reveal high uncertainty in rank movements, with distinct historical patterns for songs and albums and a strong dependence on trajectory length. Clustering of trajectories shows that chart success is organized into a small number of typical pathways, including canonical rise-and-fall trajectories, high-end persistence, and monotonic decline. Together, these results show that musical charts are not merely records of popularity, but dynamic memory systems in which attention, turnover, and predictability interact differently for songs and albums.

physics.soc-ph

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