output
20152026
most citedPost-Lockdown Abatement of COVID-19 by Fast Periodic Switching

71 citations

12 papers

cs.LG2026

Approximation Algorithm for Constrained -Center Clustering: A Local Search Approach

Chaoqi Jia, Longkun Guo, Kewen Liao +3

Clustering is a long-standing research problem and a fundamental tool in AI and data analysis. The traditional k-center problem, a fundamental theoretical challenge in clustering,…

cond-mat.mtrl-sci2025★ 3 cited

Instability of oxide perovskite surfaces induced by vacancy formation

Ned Thaddeus Taylor, Michael Thomas Morgan, Steven Paul Hepplestone

This work presents a first principles study of the (001) surface energetics of nine oxide perovskites, with a focus on the role of surface vacancies in determining termination stab…

cs.AI2024★ 1 cited

Resource Constrained Pathfinding with Enhanced Bidirectional A* Search

Saman Ahmadi, Andrea Raith, Guido Tack +1

The classic Resource Constrained Shortest Path (RCSP) problem aims to find a cost optimal path between a pair of nodes in a network such that the resources used in the path are wit…

cond-mat.mtrl-sci2022

Nanoscale mapping of sub-gap electroluminescence from step-bunched, oxidized 4H-SiC surfaces

Natalia Alyabyeva, Jacques Ding, Mylène Sauty +7

Scanning tunneling luminescence microscopy (STLM) along with scanning tunneling spectroscopy (STS) is applied to a step-bunched, oxidized 4H-SiC surface prepared on the silicon fac…

cond-mat.mes-hall2022★ 15 cited

Imaging current control of magnetization in FeGeTe with a widefield nitrogen-vacancy microscope

Islay O. Robertson, Cheng Tan, Sam C. Scholten +6

Van der Waals (vdW) magnets are appealing candidates for realising spintronic devices that exploit current control of magnetization (e.g. switching or domain wall motion), but so f…

cs.LG2021★ 1 cited

Adapting to Reward Progressivity via Spectral Reinforcement Learning

Michael Dann, John Thangarajah

In this paper we consider reinforcement learning tasks with progressive rewards; that is, tasks where the rewards tend to increase in magnitude over time. We hypothesise that this…