arXiv subjects

Jinyu Hu

Publications and source records attributed to Jinyu Hu.

11 recordsLinked to original sources

arXiv2026

\texorpdfstring{$\alpha$}{}-decay for superheavy nucleus: The alpha decay energy to the one-fourth power

Recently, Sobhani and Luo \cite{sobhani2025unified} proposed a new empirical formula for $\alpha$ decay based on the $Q_\alpha^{-1/4}$ energy dependence, incorporating the proton number $Z$, neutron number $N$, and relative neutron excess $I = (N - Z)/(N + Z)$ as primary parameters. In this work, we extend this model by explicitly including the angular momentum of the emitted $\alpha$ particle and the quadrupole deformation of the daughter nucleus. Using this improved formula to evaluate the $\alpha$-decay half-lives of 400 nuclei yields a root-mean-square (RMS) deviation of 0.97 relative to experimental data. Furthermore, we employ support vector regression (SVR)-taking $Q_\alpha^{-1/4}$, $N$, $Z$, angular momentum, and daughter-nucleus deformation as input features-which further reduces the RMS deviation to 0.56. Finally, we apply both the extended formula and the SVR model to predict the $\alpha$-decay half-lives of even-even nuclei with $Z = 120$ and $Z = 122$. The predicted half-lives show good consistency with those from the Sobhani and Poenaru formulas, and both approaches strongly support $N = 184$ as the next neutron magic number.

nucl-th↗

arXiv2026

Nonlocality Effect in the Alpha decay half-lives of superheavy nuclei with XGBRegressor

Building on the work of E. L. Medeiros [1] and our previous study [2], we generalize the alpha-nucleus nonlocality effect to odd-A and odd-odd nuclei within the two-potential approach (TPA) framework. The coordinate-dependent parameters introduced by this nonlocality are optimized using an advanced gradient boosting regression model. This improved TPA is applied to calculate the $\alpha$-decay half-lives of 599 nuclei with $Z = 52-118$, yielding a root-mean-square (RMS) deviation that is 74.8$\%$ lower than that of the original TPA. Subsequently, we employ the improved TPA, along with the DZR [3] and MUDL [4]models, to predict alpha-decay half-lives for 142 superheavy nuclei with $Z = 117-120$. The predictions from all three models are in close agreement, with the results from our improved TPA and the DZR model being nearly identical.

nucl-th↗

arXiv2026

Selecting Optimal Stellar Calibration Fields for the CSST Imaging Survey

The Chinese Space Station Survey Telescope (CSST) will perform a decade-long high-precision wide-field imaging survey that relies on rigorous on-orbit calibration. This necessitates stable celestial benchmark fields to maintain photometric and astrometric consistency throughout the mission lifetime. We establish comprehensive selection criteria including observational visibility, stellar number density, bright-star contamination, and interstellar dust extinction. Using the CSST Observation Strategy Analysis Tool (COSAT) and all-sky dust maps from Planck and SFD, we constrain eligible regions to the ranges of ecliptic latitude $ |\beta| > 50^\circ$ and galactic latitude $|b| > 15^\circ$. From an initial sample of 29 candidate clusters meeting these spatial constraints, six globular clusters (M13, M92, NGC 104, NGC 362, NGC 1261, and NGC 1851) are identified as optimal calibration fields, fulfilling all the critical criteria. These selected clusters are recommended as optimal calibration field candidates for CSST's on-orbit calibration program, and are fundamental to achieving unprecedented photometric precision in CSST's space-based survey.

astro-ph.SR↗

arXiv2025

$\alpha$-decay systematics for superheavy nucleus: the effect of deformation of daughter nucleus

Recently, V.Yu. Denisov proposed a new empirical formula incorporating the deformation of the daughter nucleus, which has significantly improved the description of $\alpha$-decay half-lives for even-even nuclei compared to formulas neglecting the deformation of the daughter nucleus. In this work, we generalize the deformation of the daughter nucleus proposed by V.Yu. Denisov to the DUR model, the AKRA model, the New Geiger-Nuttall law, generating three improved versions of these models. We then employ both the original and modified the DUR model, the AKRA model, and the New Geiger-Nuttall law to investigate the $\alpha$-decay half-lives of 400 isotopes. Results show that among the six models, the modified AKRA model provides the closest agreement with experimental $\alpha$-decay half-life data. For comparative analysis, we use the new empirical formulas developed for the DUR model (DUR+D), the AKRA model (AKRA+D) and a new empirical (ND) proposed by V.Yu. Denisov to predict the $\alpha$-decay properties of 71 even-even nuclei with Z = 118, 120, 122, and 124. The predictions from the DUR+D model, the AKRA+D model, and ND are largely consistent overall. Notably, when the neutron number $N>190$, the predictions from the DUR+D model and the AKRA+D model exceed those of ND, which may be attributed to additional physical contributions (e.g., the hexadecapole and the hexacontatetrapole deformation of the deformed daughter nucleus) incorporated in the DUR+D model and the AKRA+D model but not in ND.

nucl-th↗

arXiv2025

Nonlocality Effect in the Tunneling of Alpha Radioactivity with the Aid of Machine Learning

Recently, building upon the research findings of E. L. Medeiros, we have extended the alpha-particle non-locality effect to the two-potential approach (TPA). This extension demonstrates that the integration of the alpha-particle nonlocality effect into TPA yields relatively favorable results. In the present work, we employ machine learning methods to further optimize the aforementioned approach, specifically utilizing three classical machine learning models: decision tree regression, random forest regression, and XGBRegressor. Among these models, both the decision tree regression and XGBRegressor models exhibit the highest degree of agreement with the reference data, whereas the random forest regression model shows inferior performance. In terms of standard deviation, the results derived from the decision tree regression and XGBRegressor models represent improvements of 54.5% and 53.7%, respectively, compared to the TPA that does not account for the coordinate-dependent effective mass of alpha particles. Furthermore, we extend the decision tree regression and XGBRegressor models to predict the alpha-decay half-lives of 20 even-even nuclei with atomic numbers Z=118 and Z=120. Subsequently, the superheavy nucleus half-life predictions generated by our proposed models are compared with those from two established benchmarks: the improved eight-parameter Deng-Zhang-Royer (DZR) model and the new empirical expression (denoted as "New+D") proposed by V. Yu. Denisov, which explicitly incorporates nuclear deformation effects. Overall, the predictions from these models and formulas are generally consistent. Notably, the predictions of the decision tree regression model show a high level of consistency with those of the New+D expression, while the XGBRegressor model exhibits deviations from the other two comparative models.

nucl-th↗

arXiv2025

Nonlocality effect in $\alpha$ decay half-lives for even-even nuclei within a two potential approach

In this paper, we carefully look at the $\alpha$ -decay half-lives of 196 even-even nuclei using a two-potential approach that is made better by taking into account an alpha particle's effective mass that changes with coordinates. The result shows that the accuracy of this model has been improved after considering effective mass for the alpha particle. Furthermore, considering $\alpha$ decay energies derived from three mass models, namely the Weizsacker-Skyrme-4 (WS4) mass model, the relativistic continuum Hartree-Bogoliubov theory mass model, and the FRDM (2012), we extend this model to predict the $\alpha$ -decay half-lives of Z = 118 and 120 isotopes. Finally, we carefully study the predicted $\alpha$ decay energies and half-lives of Z = 118 and 120 isotopes and discuss the shell structure of superheavy nuclei. We found that the shell effect is obvious at N = 178 and at N = 184 in the WS4 mass model and FRDM(2012), while the shell effect is only obvious at N = 184 in the relativistic continuum Hartree-Bogoliubov theory mass model.

nucl-th↗

arXiv2025

CL-CaGAN: Capsule differential adversarial continuous learning for cross-domain hyperspectral anomaly detection

Anomaly detection (AD) has attracted remarkable attention in hyperspectral image (HSI) processing fields, and most existing deep learning (DL)-based algorithms indicate dramatic potential for detecting anomaly samples through specific training process under current scenario. However, the limited prior information and the catastrophic forgetting problem indicate crucial challenges for existing DL structure in open scenarios cross-domain detection. In order to improve the detection performance, a novel continual learning-based capsule differential generative adversarial network (CL-CaGAN) is proposed to elevate the cross-scenario learning performance for facilitating the real application of DL-based structure in hyperspectral AD (HAD) task. First, a modified capsule structure with adversarial learning network is constructed to estimate the background distribution for surmounting the deficiency of prior information. To mitigate the catastrophic forgetting phenomenon, clustering-based sample replay strategy and a designed extra self-distillation regularization are integrated for merging the history and future knowledge in continual AD task, while the discriminative learning ability from previous detection scenario to current scenario is retained by the elaborately designed structure with continual learning (CL) strategy. In addition, the differentiable enhancement is enforced to augment the generation performance of the training data. This further stabilizes the training process with better convergence and efficiently consolidates the reconstruction ability of background samples. To verify the effectiveness of our proposed CL-CaGAN, we conduct experiments on several real HSIs, and the results indicate that the proposed CL-CaGAN demonstrates higher detection performance and continuous learning capacity for mitigating the catastrophic forgetting under cross-domain scenarios.

cs.CV↗

arXiv2025

The Role of Hydrogen and Oxygen Interstitial Defects in Crystalline Si cells: Mechanism of Device Degradation in Humid Environment

The efficiency of silicon solar cells gradually decreases in various environments, with humidity being a key factor contributing to this decline through moisture-induced degradation (MID) involving multiple mechanisms including encapsulant hydrolysis and metal ion migration. Among these mechanisms, the role of water-derived hydrogen and oxygen interstitial defects represents an underexplored yet fundamental degradation pathway. This study employs density functional theory and quantum transport theory to investigate hydrogen and oxygen interstitial defects as a novel perspective for understanding MID mechanisms. Results reveal that neutral hydrogen interstitials at bond-center sites exhibit low diffusion barriers (0.96 eV) and act as deep-level recombination centers, while oxygen interstitials face higher diffusion barriers (2.2 eV) with limited trapping capability. Device simulations demonstrate that hydrogen defects cause substantially more pronounced photovoltaic current degradation through enhanced non-radiative recombination. Critically, under humid conditions, hydrogen from water molecules readily penetrates silicon lattices forming active recombination centers, while oxygen incorporation remains kinetically limited with negligible impact. This interstitial defect perspective provides novel understanding of MID mechanisms, explaining why moisture exposure primarily degrades silicon solar cells through hydrogen rather than oxygen incorporation, offering fundamental insights for developing targeted mitigation strategies.

cond-mat.mtrl-sci↗

arXiv2024

MAP-UOT: A Memory-Efficient Approach to Unbalanced Optimal Transport Implementation

Unbalanced optimal transport (UOT) has been widely used as a fundamental tool in many application domains, where it often dominates the application running time. While many researchers have proposed various optimizations for UOT, few have attempted to optimize it from a computer architecture's perspective. In this paper, we first study the performance bottlenecks of UOT through a series of experiments, which reveals that UOT is heavily memory-bound. Guided by these findings, we propose MAP-UOT, a Memory-efficient APproach to the implementation and optimization of UOT on CPU and GPU platforms. Our experimental evaluations show that the proposed strategy consistently and significantly outperforms the state-of-the-art (SOTA) implementations. Specifically, it provides single-threaded performance improvement over POT/COFFEE by up to 2.9X/2.4X, with an average of 1.9X/1.6X. At the same time, it provides parallelized performance improvement over POT/COFFEE by up to 2.4X/1.9X, with an average of 2.2X/1.8X, on Intel Core i9-12900K; and over POT by up to 3.5X, with an average of 1.6X, on Nvidia GeForce RTX 3090 Ti. MAP-UOT also shows great performance improvement on the Tianhe-1 supercomputer.

cs.DC↗

arXiv2023

Evaluating multiple large language models in pediatric ophthalmology

IMPORTANCE The response effectiveness of different large language models (LLMs) and various individuals, including medical students, graduate students, and practicing physicians, in pediatric ophthalmology consultations, has not been clearly established yet. OBJECTIVE Design a 100-question exam based on pediatric ophthalmology to evaluate the performance of LLMs in highly specialized scenarios and compare them with the performance of medical students and physicians at different levels. DESIGN, SETTING, AND PARTICIPANTS This survey study assessed three LLMs, namely ChatGPT (GPT-3.5), GPT-4, and PaLM2, were assessed alongside three human cohorts: medical students, postgraduate students, and attending physicians, in their ability to answer questions related to pediatric ophthalmology. It was conducted by administering questionnaires in the form of test papers through the LLM network interface, with the valuable participation of volunteers. MAIN OUTCOMES AND MEASURES Mean scores of LLM and humans on 100 multiple-choice questions, as well as the answer stability, correlation, and response confidence of each LLM. RESULTS GPT-4 performed comparably to attending physicians, while ChatGPT (GPT-3.5) and PaLM2 outperformed medical students but slightly trailed behind postgraduate students. Furthermore, GPT-4 exhibited greater stability and confidence when responding to inquiries compared to ChatGPT (GPT-3.5) and PaLM2. CONCLUSIONS AND RELEVANCE Our results underscore the potential for LLMs to provide medical assistance in pediatric ophthalmology and suggest significant capacity to guide the education of medical students.

cs.CL↗

arXiv2023

Evaluating Large Language Models in Ophthalmology

Purpose: The performance of three different large language models (LLMS) (GPT-3.5, GPT-4, and PaLM2) in answering ophthalmology professional questions was evaluated and compared with that of three different professional populations (medical undergraduates, medical masters, and attending physicians). Methods: A 100-item ophthalmology single-choice test was administered to three different LLMs (GPT-3.5, GPT-4, and PaLM2) and three different professional levels (medical undergraduates, medical masters, and attending physicians), respectively. The performance of LLM was comprehensively evaluated and compared with the human group in terms of average score, stability, and confidence. Results: Each LLM outperformed undergraduates in general, with GPT-3.5 and PaLM2 being slightly below the master's level, while GPT-4 showed a level comparable to that of attending physicians. In addition, GPT-4 showed significantly higher answer stability and confidence than GPT-3.5 and PaLM2. Conclusion: Our study shows that LLM represented by GPT-4 performs better in the field of ophthalmology. With further improvements, LLM will bring unexpected benefits in medical education and clinical decision making in the near future.

cs.CL↗