REVIEW 4 cited by
Boundary critical behavior of the three-dimensional Heisenberg universality class
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
We study the boundary critical behavior of the three-dimensional Heisenberg universality class, in the presence of a bidimensional surface. By means of high-precision Monte Carlo simulations of an improved lattice model, where leading bulk scaling corrections are suppressed, we prove the existence of a special phase transition, with unusual exponents, and of an extraordinary phase with logarithmically decaying correlations. These findings contrast with na\"ive arguments on the bulk-surface phase diagram, and allow us to explain some recent puzzling results on the boundary critical behavior of quantum spin models.
Forward citations
Cited by 4 Pith papers
-
Long-Range Order in a Strictly Short-Range Quasi-2D XY Model: When Critical Fluctuations Matter
In a strictly short-range XY model made of a plane intersected by parallel planes, true long-range order appears along the intersection lines when the parallel planes enter a Berezinskii-Kosterlitz-Thouless critical phase.
-
Accurate boundary bootstrap for the three-dimensional O($N$) normal universality class
High-truncation eta-minimization bootstrap yields accurate boundary critical amplitudes for the 3d O(N) normal universality class, resolving prior Monte Carlo discrepancies and giving new Ising boundary data.
-
Frequency-Decorrelated Temporal Ensembles for EEG--fNIRS Imagined-Handwriting Decoding
Frequency-diverse EEG ensemble plus balanced-block decoding reaches 0.7952 overall accuracy (fourth place) in the EEG-fNIRS imagined-handwriting challenge, where fNIRS alone is at chance.
-
Subject Specific Deep Learning Model for Motor Imagery Direction Decoding
An EEGNet variant with squeeze-and-excitation electrode and filter ranking reaches about 58.8% accuracy in online decoding of left versus right imagined hand movements.
Discussion (0). Continue with ORCID to comment.