2,153 research outputs found
Robust face anti-spoofing framework with Convolutional Vision Transformer
Owing to the advances in image processing technology and large-scale
datasets, companies have implemented facial authentication processes, thereby
stimulating increased focus on face anti-spoofing (FAS) against realistic
presentation attacks. Recently, various attempts have been made to improve face
recognition performance using both global and local learning on face images;
however, to the best of our knowledge, this is the first study to investigate
whether the robustness of FAS against domain shifts is improved by considering
global information and local cues in face images captured using self-attention
and convolutional layers. This study proposes a convolutional vision
transformer-based framework that achieves robust performance for various unseen
domain data. Our model resulted in 7.3% and 12.9% increases in FAS
performance compared to models using only a convolutional neural network or
vision transformer, respectively. It also shows the highest average rank in
sub-protocols of cross-dataset setting over the other nine benchmark models for
domain generalization.Comment: ICIP 202
Minimum critical velocity of a Gaussian obstacle in a Bose-Einstein condensate
When a superfluid flows past an obstacle, quantized vortices can be created
in the wake above a certain critical velocity. In the experiment by W. J. Kwon
et al. [Phys. Rev. A 91, 053615 (2015)], the critical velocity was
measured for atomic Bose-Einstein condensates (BECs) using a moving repulsive
Gaussian potential and was minimized when the potential height of
the obstacle was close to the condensate chemical potential . Here we
numerically investigate the evolution of the critical vortex shedding in a
two-dimensional BEC with increasing and show that the minimum at
the critical strength results from the local density
reduction and vortex pinning effect of the repulsive obstacle. The spatial
distribution of the superflow around the moving obstacle just below is
examined. The particle density at the tip of the obstacle decreases as
increases to and at the critical strength, a vortex dipole is suddenly
formed and dragged by the moving obstacle, indicating the onset of vortex
pinning. The minimum exhibits power-law scaling with the obstacle size
as with .Comment: 8 pages, 5 figure
Changes in Power and Information Flow in Resting-state EEG by Working Memory Process
Many studies have analyzed working memory (WM) from electroencephalogram
(EEG). However, little is known about changes in the brain neurodynamics among
resting-state (RS) according to the WM process. Here, we identified
frequency-specific power and information flow patterns among three RS EEG
before and after WM encoding and WM retrieval. Our results demonstrated the
difference in power and information flow among RS EEG in delta (1-3.5 Hz),
alpha (8-13.5 Hz), and beta (14-29.5 Hz) bands. In particular, there was a
marked increase in the alpha band after WM retrieval. In addition, we
calculated the association between significant characteristics of RS EEG and WM
performance, and interestingly, correlations were found only in the alpha band.
These results suggest that RS EEG according to the WM process has a significant
impact on the variability and WM performance of brain mechanisms in relation to
cognitive function.Comment: Submitted to 2023 11th IEEE International Winter Conference on
Brain-Computer Interfac
Siamese Sleep Transformer For Robust Sleep Stage Scoring With Self-knowledge Distillation and Selective Batch Sampling
In this paper, we propose a Siamese sleep transformer (SST) that effectively
extracts features from single-channel raw electroencephalogram signals for
robust sleep stage scoring. Despite the significant advances in sleep stage
scoring in the last few years, most of them mainly focused on the increment of
model performance. However, other problems still exist: the bias of labels in
datasets and the instability of model performance by repetitive training. To
alleviate these problems, we propose the SST, a novel sleep stage scoring model
with a selective batch sampling strategy and self-knowledge distillation. To
evaluate how robust the model was to the bias of labels, we used different
datasets for training and testing: the sleep heart health study and the
Sleep-EDF datasets. In this condition, the SST showed competitive performance
in sleep stage scoring. In addition, we demonstrated the effectiveness of the
selective batch sampling strategy with a reduction of the standard deviation of
performance by repetitive training. These results could show that SST extracted
effective learning features against the bias of labels in datasets, and the
selective batch sampling strategy worked for the model robustness in training.Comment: Submitted to 2023 11th IEEE International Winter Conference on
Brain-Computer Interfac
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