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%$p$ and 12.9%$p$ 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 $v_c$ was measured for atomic Bose-Einstein condensates (BECs) using a moving repulsive Gaussian potential and $v_c$ was minimized when the potential height $V_0$ of the obstacle was close to the condensate chemical potential $\mu$. Here we numerically investigate the evolution of the critical vortex shedding in a two-dimensional BEC with increasing $V_0$ and show that the minimum $v_c$ at the critical strength $V_{0c}\approx \mu$ 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 $v_c$ is examined. The particle density at the tip of the obstacle decreases as $V_0$ increases to $V_{c0}$ 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 $v_c$ exhibits power-law scaling with the obstacle size $\sigma$ as $v_c\sim \sigma^{-\gamma}$ with $\gamma\approx 1/2$.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