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    Language Lessons on the Prehistory of South America
    (Oxford University Press, 2026-05-21)
    Linguistics holds rich potential to inform our understanding of South American prehistory. We set out the various dimensions of the linguistic panorama: patterns in language diversity, the expansions and divergence of great language families, and contact and convergence between languages of independent origins. We survey the methodological toolkit by which language comparisons on each level can shed light on their speakers' pasts. We assess also how best to link up this linguistic record with archaeology and genetics, through correlations in the where, when, and why of prehistory. We look at geographical distributions and seek the homelands out of which each major language family began its expansion. We look at chronology and the different time-depths of those families. And we look at what causation might best explain how each great family came to exist at all, and spread so spectacularly, while other regions harbor acute language diversity. We assess big-picture patterns and contrasts between the Andean highlands and the eastern lowlands, particularly Amazonia. Amid a host of smaller families and unique language isolates, we briefly survey the origins and prehistories of the main individual language families within each macroregion: Quechua, Aymara, Chibchan, and Mapudungun; Arawak, Tupí, Carib, and Jê. Separately, we consider the various forms, intensities, and outcomes of contact and convergence among the speakers of these languages over time. Throughout, we assess what all this means for prehistory: how to read these linguistic signals in concert with archaeology and genetics for a more holistic prehistory of Indigenous South America.
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    Improving adaptive large neighborhood search: an evaluation of parallel approaches with deep learning integration
    (Springer Science+Business Media, 2026-02-01)
    This paper introduces a hybrid optimization framework that enhances vehicle routing by integrating Parallel Adaptive Large Neighborhood Search (PALNS) with deep generative modeling. Our method uses Variational Autoencoders (VAEs) to extract latent representations of routing patterns, which dynamically guide neighborhood selection during the search. Unlike conventional heuristics that rely on handcrafted rules, our approach learns from historical solution data to balance exploration and exploitation. We conduct extensive computational experiments on benchmark CVRP instances and real routing data, showing significant improvements in solution quality and steeper convergence curves compared to standalone ALNS and other metaheuristics. While there is modest runtime overhead, the latency is consistent and within practical bounds. The results also highlight interpretability of latent features and their contribution to dynamic search behavior. This work bridges machine learning and large-scale combinatorial optimization, with practical implications for logistics and supply chain applications.
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