Tootfinder

Optimizing Atomic Number Contrast in Multislice Electron Ptychography
Bridget R. Denzer, Colin Gilgenbach, James M. LeBeau
https://arxiv.org/abs/2506.09355

Optimizing Atomic Number Contrast in Multislice Electron Ptychography
Here we explore the atomic number ($Z$) dependence of multislice electron ptychography and approaches to optimize Z sensitivity. Specifically, we show that ptychography's $Z$-dependence is highly dependent on the integrated area of an atom column considered. A monotonic $Z$-dependence is found when the reconstructed projected atomic potentials are integrated over a small region. When increasing the integration area, $Z$-contrast changes significantly, becoming highly non-monotonic and following…

Quantum magnetotransport in monolayer $\mathrm{Pt_{2}HgSe_{3}}$
Muzamil Shah, Imtiaz Khan, Kashif Sabeeh, Muhammad Sabieh Anwar, Reza Asgari
https://arxiv.org/abs/2506.10697

Quantum magnetotransport in monolayer $\mathrm{Pt_{2}HgSe_{3}}$
We present a theoretical framework to investigate quantum magnetotransport in monolayer jacutingaite, focusing on its response to external electric fields and off-resonant circularly polarized laser irradiation. Our analysis reveals a sequence of topological phase transitions triggered by tuning these external parameters. We find that the zeroth LL exhibits spin- and valley-polarized splitting, leading to four distinct peaks in the DOSs for the $K$ and $K'$ valleys. Using the Kubo formalism, we…

Enhancing Automatic PT Tagging for MEDLINE Citations Using Transformer-Based Models
Victor H. Cid, James Mork
https://arxiv.org/abs/2506.03321 https://

Enhancing Automatic PT Tagging for MEDLINE Citations Using Transformer-Based Models
We investigated the feasibility of predicting Medical Subject Headings (MeSH) Publication Types (PTs) from MEDLINE citation metadata using pre-trained Transformer-based models BERT and DistilBERT. This study addresses limitations in the current automated indexing process, which relies on legacy NLP algorithms. We evaluated monolithic multi-label classifiers and binary classifier ensembles to enhance the retrieval of biomedical literature. Results demonstrate the potential of Transformer models …