arXiv subjects

AnaMaria Perez

Publications and source records attributed to AnaMaria Perez.

3 recordsLinked to original sources

arXiv2023

Controlling Formal Fibers of Countably Many Principal Prime Ideals

Let $T$ be a complete local (Noetherian) ring. For each $i \in \mathbb{N}$, let $C_i$ be a nonempty countable set of nonmaximal pairwise incomparable prime ideals of $T$, and suppose that if $i \neq j$, then either $C_i = C_j$ or no element of $C_i$ is contained in an element of $C_j$. We provide necessary and sufficient conditions for $T$ to be the completion of a local integral domain $A$ satisfying the condition that, for all $i \in \mathbb{N}$, there is a nonzero prime element $p_i$ of $A$, such that $C_i$ is exactly the set of maximal elements of the formal fiber of $A$ at $p_iA$. We then prove related results where the domain $A$ is required to be countable and/or excellent.

math.AC↗

arXiv2023

Completions of Quasi-excellent Domains

Let $T$ be a complete local (Noetherian) ring of characteristic zero. We find necessary and sufficient conditions for $T$ to be the completion of a quasi-excellent local domain. In the case that $T$ contains the rationals, we provide necessary and sufficient conditions for $T$ to be the completion of a countable quasi-excellent local domain. We also prove results regarding the possible lengths of maximal saturated chains of prime ideals of these quasi-excellent local domains, and we show that these results lead to interesting examples of noncatenary quasi-excellent local domains.

math.AC↗

arXiv2022

Characterizing Within-Driver Variability in Driving Dynamics During Obstacle Avoidance Maneuvers

Variability in human response creates non-trivial challenges for modeling and control of human-automation systems. As autonomy becomes pervasive, methods that can accommodate human variability will become paramount, to ensure efficiency, safety, and high levels of performance. We propose an easily computable modeling framework which takes advantage of a metric to assess variability in individual human response in a dynamic task that subjects repeat over several trials. Our approach is based in a transformation of observed trajectories to a reproducing kernel Hilbert space, which captures variability in human response as a distribution embedded within the Hilbert space. We evaluate the similarity across responses via the maximum mean discrepancy, which measures the distance between distributions within the Hilbert space. We apply this metric to a difficult driving task designed to elucidate differences across subjects. We conducted a pilot study with 6 subjects in an advanced driving simulator, in which subjects were tasked with collision avoidance of an obstacle in the middle of the road, around a blind corner, in a nighttime scenario, while steering only with the non-dominant hand.

eess.SY↗