arXiv subjects

Yingjie Huang

Publications and source records attributed to Yingjie Huang.

6 recordsLinked to original sources

arXiv2026

Rise Time and Charge Collection Efficiency of Graphene-Optimized 4H-SiC p-i-n Detector

Silicon carbide detectors exhibit good detection performance and are being considered for detection applications. However, the presence of surface electrode of detector limits the application of low-penetration particle detectors, photodetectors and heavy-ion detection. A graphene-optimized 4H-SiC detector has been fabricated to expand the application of SiC detectors.Its electrical properties and the charge collection performance of α particles are reported. The effective doping concentration of lightly doped 4H-SiC epitaxial layer is about 4.5\times10^{13}cm^{-3}, approaching the limit of the lowest doping level by the SiC epitaxial growth technique. The rise time of the graphene-optimized ring electrode detector is reduced by 24% at 200 V, compared to ring electrode detector. The charge collection efficiency (CCE) of graphene-optimized 4H-SiC PIN is 99.22%. When the irradiation dose is 2\times10^{11} n_{eq}/cm^2, the irradiation has no significant impact on the rise time and uniformity of the rise time for the graphene-optimized 4H-SiC detectors. This study proves that graphene has a certain radiation resistance. Graphene-optimized 4H-SiC detectors can not only reduce the signal rise time, but also improve uniformity of signal rise time and stability of charge collection. This research will expand the application of graphene-based 4H-SiC detectors in fields such as low energy ions, X-ray, UV light detection, particle physics, medical dosimetry and heavy-ion detection.

physics.ins-det↗

arXiv2026

Stability of Charge Collection Efficiency in a Novel Graphene-Optimized Silicon Carbide Detector Under 160 keV X-Ray Irradiation

A novel graphene-optimized silicon carbide PIN detector was fabricated. Its electrical properties, charge collection performance and signal rise time were evaluated under non-irradiated conditions and under X-ray irradiation with an energy of 160 keV at doses of 0.1 MGy and 1 MGy. The leakage currents of the detectors under non-irradiated, 0.1 MGy, and 1 MGy irradiation conditions are approximately 1.45e-10 A, 1.51e-10 A, and 1.57e-10 A, respectively. The effective doping concentration of the detector is approximately 8.08e13 cm^-3 before and after irradiation, with no significant change. The rise times of the signals from alpha particles signal detected by the detector under unirradiated, 0.1 MGy, and 1 MGy X-ray irradiation conditions are 336 ps, 368 ps, and 387 ps, respectively. The rise times of the beta particles signal detected by the detector under unirradiated, 0.1 MGy, and 1 MGy X-ray irradiation conditions are 342 ps, 375 ps, and 398 ps, respectively. After 0.1 MGy and 1 MGy X-ray irradiation, the charge collection efficiencies (CCEs) of the detector for alpha particles are 97.2% and 90.0%, respectively; for beta particles, they are 100.0% and 97.0%, respectively. Experiments confirm that 160 keV X-ray irradiation may not cause significant displacement damage in the 4H-SiC, and the minor performance degradation may be attributed to ionization induced changes in the graphene electrode. The detector exhibits excellent charge collection performance and fast time response. These results demonstrate stable performance under extreme X-ray exposure, highlighting the detector's potential for radiation-hard applications in high-energy physics, space missions, and nuclear reactor monitoring.

physics.ins-det↗

arXiv2026

Stability of Charge Collection Efficiency and Time Resolution in a Novel Ultra-fast Graphene-Optimized Silicon Carbide Detector Under X-ray Irradiation

A graphene-optimized silicon carbide PIN detector was fabricated and its radiation tolerance under X-ray irradiation of 160 keV was evaluated. Its electrical properties, charge collection performance and time resolution of beta-particles (90Sr) are reported. After 1 MGy irradiation, the detector maintains an ultralow leakage current of approximately 2.2e-10 A @ 300 V and the C-V characteristics are basically consistent with full depletion at 120V. The time resolution of the graphene-optimized silicon carbide detector is 58.0 ps. The time resolution is comparable to that of state-of-the-art 4H-SiC low-gain avalanche detectors (LGADs). The G/RE 4H-SiC PIN detector exhibits outstanding time resolution performance. Compared with the time resolution of the RE 4H-SiC PIN detector, the time resolution of the G/RE 4H-SiC PIN detector has decreased by 39.6%. This demonstrates the significance of the graphene electrode design. The graphene detector exhibits a charge collection efficiency (CCE) of 99.24% after X-ray irradiation, along with excellent stability. The graphene-optimized silicon carbide detector maintains good timing resolution: 58.0ps before and 64.0ps after X-ray irradiation. Experimental results indicate that the CCE and time resolution performance exhibit good stability before and after irradiation. These results demonstrate stable performance under extreme X-ray exposure, highlighting the detectors potential for radiation-hard applications in high-energy physics, space missions, and nuclear reactor monitoring.

physics.ins-det↗

arXiv2025

Post-TIPS Prediction via Multimodal Interaction: A Multi-Center Dataset and Framework for Survival, Complication, and Portal Pressure Assessment

Transjugular intrahepatic portosystemic shunt (TIPS) is an established procedure for portal hypertension, but provides variable survival outcomes and frequent overt hepatic encephalopathy (OHE), indicating the necessity of accurate preoperative prognostic modeling. Current studies typically build machine learning models from preoperative CT images or clinical characteristics, but face three key challenges: (1) labor-intensive region-of-interest (ROI) annotation, (2) poor reliability and generalizability of unimodal methods, and (3) incomplete assessment from single-endpoint prediction. Moreover, the lack of publicly accessible datasets constrains research in this field. Therefore, we present MultiTIPS, the first public multi-center dataset for TIPS prognosis, and propose a novel multimodal prognostic framework based on it. The framework comprises three core modules: (1) dual-option segmentation, which integrates semi-supervised and foundation model-based pipelines to achieve robust ROI segmentation with limited annotations and facilitate subsequent feature extraction; (2) multimodal interaction, where three techniques, multi-grained radiomics attention (MGRA), progressive orthogonal disentanglement (POD), and clinically guided prognostic enhancement (CGPE), are introduced to enable cross-modal feature interaction and complementary representation integration, thus improving model accuracy and robustness; and (3) multi-task prediction, where a staged training strategy is used to perform stable optimization of survival, portal pressure gradient (PPG), and OHE prediction for comprehensive prognostic assessment. Extensive experiments on MultiTIPS demonstrate the superiority of the proposed method over state-of-the-art approaches, along with strong cross-domain generalization and interpretability, indicating its promise for clinical application. The dataset and code are available.

cs.CV↗

arXiv2025

ESCT3D: Efficient and Selectively Controllable Text-Driven 3D Content Generation with Gaussian Splatting

In recent years, significant advancements have been made in text-driven 3D content generation. However, several challenges remain. In practical applications, users often provide extremely simple text inputs while expecting high-quality 3D content. Generating optimal results from such minimal text is a difficult task due to the strong dependency of text-to-3D models on the quality of input prompts. Moreover, the generation process exhibits high variability, making it difficult to control. Consequently, multiple iterations are typically required to produce content that meets user expectations, reducing generation efficiency. To address this issue, we propose GPT-4V for self-optimization, which significantly enhances the efficiency of generating satisfactory content in a single attempt. Furthermore, the controllability of text-to-3D generation methods has not been fully explored. Our approach enables users to not only provide textual descriptions but also specify additional conditions, such as style, edges, scribbles, poses, or combinations of multiple conditions, allowing for more precise control over the generated 3D content. Additionally, during training, we effectively integrate multi-view information, including multi-view depth, masks, features, and images, to address the common Janus problem in 3D content generation. Extensive experiments demonstrate that our method achieves robust generalization, facilitating the efficient and controllable generation of high-quality 3D content.

cs.CV↗

arXiv2025

Sparse Bayesian Factor Models with Mass-Nonlocal Factor Scores

Bayesian factor models are widely used for dimensionality reduction and pattern discovery in high-dimensional datasets across diverse fields. These models typically focus on imposing priors on factor loading to induce sparsity and improve interpretability. However, factor scores, which play a critical role in individual-level associations with factors, have received less attention and are assumed to follow a standard normal distribution. This assumption oversimplifies the heterogeneity often observed in real-world applications. We propose the sparse Bayesian Factor model with MAss-Nonlocal factor scores (BFMAN), a novel framework that addresses these limitations by introducing a mass-nonlocal prior on factor scores. This prior allows for both exact zeros and flexible, nonlocal behavior, capturing individual-level sparsity and heterogeneity. The sparsity in the score matrix enables a robust and novel approach to determine the optimal number of factors. Model parameters are estimated via a fast and efficient Gibbs sampler. Extensive simulations demonstrate that BFMAN outperforms standard Bayesian factor models in factor recovery, sparsity detection, score estimation, and selection of the optimal number of factors. We apply BFMAN to the Hispanic Community Health Study/Study of Latinos, identifying meaningful dietary patterns and their associations with cardiovascular disease, showcasing the model's ability to uncover insights into complex nutritional data.

stat.ME↗