Researcher profile

Allan Wang

Allan Wang contributes to research discovery and scholarly infrastructure.

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Published work

2 published item(s)

preprint2026arXiv

Xenon Anesthesia and Nuclear Spin Effects in Chiral Systems

A general mechanism for anesthetic function is not fully understood. Similarly, the mechanism by which xenon, a chemically inert noble gas, can produce anesthetic effects remains ambiguous. However, a previous study reported a surprisingly strong nuclear-spin-dependent variation in anesthetic potency in mice, although no rigorous molecular mechanism was proposed. This perspective examines that observation and explores a potential connection to the chiral-induced spin selectivity (CISS) effect, a phenomenon that can account for spin-dependent processes in chiral systems. Here we propose a mechanism that links spin-dependent charge organization with chiral molecular systems through a kinetic model that reproduces the reported nuclear spin dependence of xenon anesthesia. The model is based on the nuclear spin-dependent permeability of isotopes through homochiral media, which modulates biological function through ligand-receptor binding in analogy with the Hill-Langmuir equation. Unlike mechanisms that require long-range quantum coherence, our framework remains robust under physiological, room-temperature conditions because it relies on the intrinsic stability of the CISS effect in dissipative biological environments. Our analysis motivates further experimental investigation of spin-dependent processes, not limited to anesthesia, in complex living systems where chirality is pervasive.

preprint2022arXiv

Group-based Motion Prediction for Navigation in Crowded Environments

We focus on the problem of planning the motion of a robot in a dynamic multiagent environment such as a pedestrian scene. Enabling the robot to navigate safely and in a socially compliant fashion in such scenes requires a representation that accounts for the unfolding multiagent dynamics. Existing approaches to this problem tend to employ microscopic models of motion prediction that reason about the individual behavior of other agents. While such models may achieve high tracking accuracy in trajectory prediction benchmarks, they often lack an understanding of the group structures unfolding in crowded scenes. Inspired by the Gestalt theory from psychology, we build a Model Predictive Control framework (G-MPC) that leverages group-based prediction for robot motion planning. We conduct an extensive simulation study involving a series of challenging navigation tasks in scenes extracted from two real-world pedestrian datasets. We illustrate that G-MPC enables a robot to achieve statistically significantly higher safety and lower number of group intrusions than a series of baselines featuring individual pedestrian motion prediction models. Finally, we show that G-MPC can handle noisy lidar-scan estimates without significant performance losses.