arXiv subjects

Fangyuan Liu

Publications and source records attributed to Fangyuan Liu.

4 recordsLinked to original sources

arXiv2026

Encapsulation epitaxy of air-stable monolayer superconducting films for quantum circuits and qubits

Two-dimensional (2D) superconductors are an emerging platform for strongly correlated physics and quantum information science. Their reduced dimensionality, atomically flat interfaces, and high crystallinity are attractive for realizing compact lumped-element devices in superconducting circuits. However, synthesizing large-area, monolayer 2D superconductors remains challenging because of their susceptibility to oxidation. Here, we report an "encapsulation epitaxy" mechanism that enables the growth of large-area, air-stable, monolayer superconducting NbSe2 films and explore their use in superconducting quantum circuits. A 2D encapsulation layer, such as graphene or hexagonal boron nitride (hBN), pre-deposited on a 3D substrate (e.g., SiO2 or Si3N4), serves both as a template for epitaxial growth of monolayer NbSe2 (1L-NbSe2) underneath it and as a protective cover. This approach produces uniform, large-area (>1-inch) 1L-NbSe2 with greatly enhanced ambient stability, enabling device fabrication in air. The resulting 1L-graphene/NbSe2 heterostructures exhibit robust superconductivity (Tc ~ 1 K) and enhanced charge density wave order (TCDW ~ 177 K), indicative of high material quality. We further integrate 1L-NbSe2 into superconducting circuits using oxidation-free transfer and superconducting edge-contact techniques. The 1L-NbSe2 exhibits a measured kinetic inductance LK ~ 0.7 nH/square, making it suitable for quantum circuits requiring high-kinetic-inductance elements. Encapsulation epitaxy thus provides a route to air-stable 2D superconductors and van der Waals heterostructures, with potential for wafer-scale, monolithic fabrication of superconducting quantum circuitry.

cond-mat.supr-con↗

arXiv2026

EviDep: Uncertainty-Aware Multimodal Depression Estimation via Disentangled Evidential Learning

Audio--visual recordings provide complementary cues for estimating depression severity, but their informativeness varies across time and modalities. Point predictions alone do not express the uncertainty associated with these estimates. We present EviDep, a multimodal evidential regression framework that integrates multi-scale temporal modeling and shared--private representation learning for uncertainty-aware depression estimation. Frequency-aware Feature Extraction decomposes behavioral feature sequences into multiple frequency bands and refines them with scale-specific experts. Disentangled Evidential Learning encourages the disentanglement of cross-modal shared and modality-specific information in the refined features. Multi-branch Evidential Regression maps the resulting shared and private representations to three Normal-Inverse-Gamma (NIG) outputs and uses evidence-weighted aggregation to estimate depression severity and quantify aleatoric and epistemic uncertainty. Experiments on AVEC 2013, AVEC 2014, DAIC-WOZ, and E-DAIC show competitive prediction accuracy, with ablation studies supporting the contributions of frequency-aware refinement and shared--private disentanglement. Further analyses show that estimated epistemic uncertainty helps identify higher-error predictions, while both uncertainty estimates generally increase under controlled feature degradation.

cs.LG↗

arXiv2024

A Finger on the Pulse of Cardiovascular Health: Estimating Blood Pressure with Smartphone Photoplethysmography-Based Pulse Waveform Analysis

Utilizing mobile phone cameras for continuous blood pressure (BP) monitoring presents a cost-effective and accessible approach, yet it is challenged by limitations in accuracy and interpretability. This study introduces four innovative strategies to enhance smartphone-based photoplethysmography for BP estimation (SPW-BP), addressing the interpretability-accuracy dilemma. First, we employ often-neglected data-quality improvement techniques, such as height normalization, corrupt data removal, and boundary signal reconstruction. Second, we conduct a comprehensive analysis of thirty waveform indicators across three categories to identify the most predictive features. Third, we use SHapley Additive exPlanations (SHAP) analysis to ensure the transparency and explainability of machine learning outcomes. Fourth, we utilize Bland-Altman analysis alongside AAMI and BHS standards for comparative evaluation. Data from 127 participants demonstrated a significant correlation between smartphone-captured waveform features and those from standard BP monitoring devices. Employing multiple linear regression within a cross-validation framework, waveform variables predicted systolic blood pressure (SBP) with a mean absolute error (MAE) of 3.08-16.64 mmHg and diastolic blood pressure (DBP) with an MAE of 2.86-13.16 mmHg. Further application of Random Forest models significantly improved the prediction MAE for SBP to 2.61-15.21 mmHg and for DBP to 2.14-11.22 mmHg, indicating enhanced predictive accuracy. Correlation and SHAP analysis identified key features for improving BP estimation. However, Bland-Altman analysis revealed systematic biases, and MAE analysis showed that the results did not meet AAMI and BHS accuracy standards. Our findings highlight the potential of SPW-BP, yet suggest that smartphone PPG technology is not yet a viable alternative to traditional medical devices for BP measurement.

eess.SP↗

arXiv2024

Your blush gives you away: detecting hidden mental states with remote photoplethysmography and thermal imaging

Multimodal emotion recognition techniques are increasingly essential for assessing mental states. Image-based methods, however, tend to focus predominantly on overt visual cues and often overlook subtler mental state changes. Psychophysiological research has demonstrated that HR and skin temperature are effective in detecting ANS activities, thereby revealing these subtle changes. However, traditional HR tools are generally more costly and less portable, while skin temperature analysis usually necessitates extensive manual processing. Advances in remote-PPG and automatic thermal ROI detection algorithms have been developed to address these issues, yet their accuracy in practical applications remains limited. This study aims to bridge this gap by integrating r-PPG with thermal imaging to enhance prediction performance. Ninety participants completed a 20-minute questionnaire to induce cognitive stress, followed by watching a film aimed at eliciting moral elevation. The results demonstrate that the combination of r-PPG and thermal imaging effectively detects emotional shifts. Using r-PPG alone, the prediction accuracy was 77% for cognitive stress and 61% for moral elevation, as determined by SVM. Thermal imaging alone achieved 79% accuracy for cognitive stress and 78% for moral elevation, utilizing a RF algorithm. An early fusion strategy of these modalities significantly improved accuracies, achieving 87% for cognitive stress and 83% for moral elevation using RF. Further analysis, which utilized statistical metrics and explainable machine learning methods including SHAP, highlighted key features and clarified the relationship between cardiac responses and facial temperature variations. Notably, it was observed that cardiovascular features derived from r-PPG models had a more pronounced influence in data fusion, despite thermal imaging's higher predictive accuracy in unimodal analysis.

cs.CY↗