236 research outputs found
Towards Best Practice of Interpreting Deep Learning Models for EEG-based Brain Computer Interfaces
As deep learning has achieved state-of-the-art performance for many tasks of
EEG-based BCI, many efforts have been made in recent years trying to understand
what have been learned by the models. This is commonly done by generating a
heatmap indicating to which extent each pixel of the input contributes to the
final classification for a trained model. Despite the wide use, it is not yet
understood to which extent the obtained interpretation results can be trusted
and how accurate they can reflect the model decisions. In order to fill this
research gap, we conduct a study to evaluate different deep interpretation
techniques quantitatively on EEG datasets. The results reveal the importance of
selecting a proper interpretation technique as the initial step. In addition,
we also find that the quality of the interpretation results is inconsistent for
individual samples despite when a method with an overall good performance is
used. Many factors, including model structure and dataset types, could
potentially affect the quality of the interpretation results. Based on the
observations, we propose a set of procedures that allow the interpretation
results to be presented in an understandable and trusted way. We illustrate the
usefulness of our method for EEG-based BCI with instances selected from
different scenarios
Control of free induction decay with quantum state preparation in a weakly coupled multi-spin system
Nuclear magnetic resonance (NMR) has been a widely used tool in various
scientific fields and practical applications, with quantum control emerging as
a promising strategy for synergistic advancements. In this paper, we propose a
novel approach that combines NMR and quantum state preparation techniques to
control free induction decay (FID) signals in weakly coupled spin systems,
specifically Trifluoroiodoethylene . We investigate the FID signal of
the three-spin system and compare the differences between the FID signals in
the thermal state and the pseudo-pure state (PPS), where the latter is
generated using quantum state preparation techniques. Our approach aims to
demonstrate a single exponentially decaying FID in weakly coupled spins, in
which oscillatory FID signals are often observed. We validate our findings
through numerical simulations and experimental measurements, and justify the
validity of the theory. Our method opens a door to advancing spin system
research and extending the capabilities of NMR with current quantum
technologies in various scientific and practical fields.Comment: 6 pages, 4 figures. Comments are welcom
Dosage effects of BDNF Val66Met polymorphism on cortical surface area and functional connectivity
The single nucleotide polymorphism (SNP) that leads to a valine-to-methionine substitution at codon 66 (Val66Met) in BDNF is correlated with differences in cognitive and memory functions, as well as with several neurological and psychiatric disorders.MRIstudies have already shown that this genetic variant contributes to changes in cortical thickness and volume, but whether the Val66Met polymorphism affects the cortical surface area of healthy subjects remains unclear. Here, we used multimodal MRI to study whether this polymorphism would affect the cortical morphology and resting-state functional connectivity of a large sample of healthy Han Chinese human subjects. An SNP-wise general linear model analysis revealed a "dosage effect" of the Met allele, specifically a stepwise increase in cortical surface area of the right anterior insular cortex with increasing numbers of the Met allele. Moreover, we found enhanced functional connectivity between the anterior insular and the dorsolateral prefrontal cortices that was linked with the dosage of the Met allele. In conclusion, these data demonstrated a "dosage effect" ofBDNFVal66Met on normal cortical structure and function, suggesting anewpath for exploring the mechanisms underlying the effects of genotype on cognition
Exploiting prompt learning with pre-trained language models for Alzheimer's Disease detection
Early diagnosis of Alzheimer's disease (AD) is crucial in facilitating
preventive care and to delay further progression. Speech based automatic AD
screening systems provide a non-intrusive and more scalable alternative to
other clinical screening techniques. Textual embedding features produced by
pre-trained language models (PLMs) such as BERT are widely used in such
systems. However, PLM domain fine-tuning is commonly based on the masked word
or sentence prediction costs that are inconsistent with the back-end AD
detection task. To this end, this paper investigates the use of prompt-based
fine-tuning of PLMs that consistently uses AD classification errors as the
training objective function. Disfluency features based on hesitation or pause
filler token frequencies are further incorporated into prompt phrases during
PLM fine-tuning. The decision voting based combination among systems using
different PLMs (BERT and RoBERTa) or systems with different fine-tuning
paradigms (conventional masked-language modelling fine-tuning and prompt-based
fine-tuning) is further applied. Mean, standard deviation and the maximum among
accuracy scores over 15 experiment runs are adopted as performance measurements
for the AD detection system. Mean detection accuracy of 84.20% (with std 2.09%,
best 87.5%) and 82.64% (with std 4.0%, best 89.58%) were obtained using manual
and ASR speech transcripts respectively on the ADReSS20 test set consisting of
48 elderly speakers.Comment: Accepted ICASSP 2023 (will update with IEEE vision later
Neural mechanisms of oxytocin receptor gene mediating anxiety-related temperament
A common variant (rs53576) of the OXTR gene has been implicated in a number of socio-emotional phenotypes, such as anxiety-related behavior. Previous studies have demonstrated that A-allele carriers have higher levels of physiological and dispositional stress reactivity and depressive symptomatology compared to those with the GG genotype, but the mediating neural mechanisms remain poorly understood. We combined voxel-based morphometry and resting-state functional connectivity analyses in a large cohort of healthy young Chinese Han individuals to test the hypothesis that the OXTR gene polymorphism influences an anxiety-related temperamental trait, as assessed by the harm avoidance subscale from the Tridimensional Personality Questionnaire via modulating the gray matter volume and resting-state functional connectivity of the brain, especially the limbic system. We revealed that female subjects with the AA genotype showed increased harm avoidance scores relative to G-carrier females. We also found that, compared to female individuals with the GG/GA genotype, female individuals with the AA genotype exhibited significantly smaller amygdala volumes bilaterally (especially the centromedial subregion), with a trend of allele-load-dependence. Compared to female individuals with the GG/GA genotype, female subjects with the AA genotype demonstrated reduced resting-state functional coupling between the prefrontal cortex and amygdala bilaterally, also with an allele-load-dependent trend. Furthermore, the magnitude of prefrontal-amygdala coupling in the left hemisphere was positively correlated with harm avoidance scores in female subjects. Our findings highlight a possible neural pathway by which a naturally occurring variation of the OXTR gene may affect an anxiety-related temperamental trait in female subjects by modulating prefrontal-amygdala functional connectivity
Enhancing Pre-trained ASR System Fine-tuning for Dysarthric Speech Recognition using Adversarial Data Augmentation
Automatic recognition of dysarthric speech remains a highly challenging task
to date. Neuro-motor conditions and co-occurring physical disabilities create
difficulty in large-scale data collection for ASR system development. Adapting
SSL pre-trained ASR models to limited dysarthric speech via data-intensive
parameter fine-tuning leads to poor generalization. To this end, this paper
presents an extensive comparative study of various data augmentation approaches
to improve the robustness of pre-trained ASR model fine-tuning to dysarthric
speech. These include: a) conventional speaker-independent perturbation of
impaired speech; b) speaker-dependent speed perturbation, or GAN-based
adversarial perturbation of normal, control speech based on their time
alignment against parallel dysarthric speech; c) novel Spectral basis GAN-based
adversarial data augmentation operating on non-parallel data. Experiments
conducted on the UASpeech corpus suggest GAN-based data augmentation
consistently outperforms fine-tuned Wav2vec2.0 and HuBERT models using no data
augmentation and speed perturbation across different data expansion operating
points by statistically significant word error rate (WER) reductions up to
2.01% and 0.96% absolute (9.03% and 4.63% relative) respectively on the
UASpeech test set of 16 dysarthric speakers. After cross-system outputs
rescoring, the best system produced the lowest published WER of 16.53% (46.47%
on very low intelligibility) on UASpeech.Comment: To appear at IEEE ICASSP 202
Bright nonblinking photoluminescence with blinking lifetime from a nanocavity-coupled quantum dot
Colloidal semiconductor quantum dots (QDs) are excellent luminescent
nanomaterials for a broad range of optoelectronic applications. Their
photoluminescence blinking, however, hinders their practical use in many
aspects. It has been shown that coupling QDs to plasmonic nanostructures may
provide a viable way to suppress blinking. Nevertheless, the underlying
mechanism of blinking suppression remains unclear and debated. Here, by
deterministically coupling a single QD to a plasmonic nanocavity, we clarify
the mechanism of blinking suppression, and demonstrate unprecedentedly bright
emission from a single colloidal QD. In particular, we report for the first
time that the coupled system exhibits nonblinking photoluminescence with
blinking lifetime, which shows that the elimination of photoluminescence
blinking originates from enhanced quantum yield of the charged states. We
identify that the radiative decay rate is boosted from (48 ns)-1 to (0.7 ns)-1,
which outcompetes Auger processes and enables similar quantum yields for
charged and neutral excitons. Moreover, we demonstrate ultrabright
photoluminescence of up to 17 million detected photons per second from a single
QD. This work sheds new light on the goal of achieving ultrabright nonblinking
QDs and may benefit a variety of QD-based applications.Comment: 17 pages; 3 figures
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