Search for: All records

The eastern Puuc Region of the northern Yucatán was first settled by agriculturalists near the beginning of the Middle Preclassic Period with small villages developing in the hilly Bolonchen District starting around 1000 – 900 BCE. Recent excavations at one of these small sites, Xanab Chak, uncovered a small hand-made figurine from a Middle Preclassic context. Figurines dating to this period have been recovered from a wide range of archaeological sites across central and eastern Mesoamerica, with the notable exception of the northern Yucatan Peninsula. The Xanab Chak figurine’s features overlap with certain aspects of figurines from other areas, including the northern Petén and Gulf Coast regions, but its combined suite of characteristics is distinctive. The well-preserved, androgynous figure sits cross-legged and holds a large, spherical tecomate in its lap. This paper provides an in-depth description of the figurine, compares it with contemporaneous figurine-making traditions, and contextualizes its use and interment within the cultural landscape of the Middle Preclassic Puuc Region. 

n this paper, we focus on the problem of shrinking-horizon Model Predictive Control (MPC) in uncertain dynamic environments. We consider controlling a deterministic autonomous system that interacts with uncontrollable stochastic agents during its mission. Employing tools from conformal prediction, existing works derive high-confidence prediction regions for the unknown agent trajectories, and integrate these regions in the design of suitable safety constraints for MPC. Despite guaranteeing probabilistic safety of the closed-loop trajectories, these constraints do not ensure feasibility of the respective MPC schemes for the entire duration of the mission. We propose a shrinking-horizon MPC that guarantees recursive feasibility via a gradual relaxation of the safety constraints as new prediction regions become available online. This relaxation enforces the safety constraints to hold over the least restrictive prediction region from the set of all available prediction regions. In a comparative case study with the state of the art, we empirically show that our approach results in tighter prediction regions and verify recursive feasibility of our MPC scheme.