arXiv subjects

Rui Jing

Publications and source records attributed to Rui Jing.

3 recordsLinked to original sources

arXiv2026

Comparison of several model averaging methods in nuclear charge radius predictions

The performance of five model averaging methods, including the arithmetic mean (AM), weighted mean (WM), naive Bayesian model averaging (NBMA), principal component analysis (PCA), and power-moderated mean (PMM) methods, in nuclear charge radius predictions is investigated. Five commonly used nuclear charge radius models are adopted as inputs for the averaging procedures. The charge radius differences between the experimental data and the original nuclear models are analyzed and the results after considering the model averaging methods are also discussed. The calculations show that the NBMA method can provide the best root-mean-square (rms) deviation among these five model averaging methods. The PCA method can extract useful physical information and not only helps to interpret the model differences but also offers a feasible way to construct improved empirical models by recombining the principal components. In contrast to the other methods, whose results worsen upon including a new model with a larger rms deviation, the rms deviation of the PCA method remains almost unaffected. The PMM method is capable of integrating the strengths of various nuclear models and delivering reasonable uncertainty estimates not only in known regions but also in unknown ones. This method can automatically adjust data uncertainties to achieve consistency, and it can provide a tool for a smooth transition of the nuclear charge radius prediction from the WM to the AM. The extrapolation ability of these model averaging methods is checked by 66 newly observed data after year 2021. The calculations show that model averaging offers a reliable strategy for nuclear charge radius predictions, combining high accuracy on known data with robust extrapolation to new measurements. The charge radii and the odd-even staggering in calcium isotopes are also discussed.

nucl-th↗

arXiv2026

CDRL: A Reinforcement Learning Framework Inspired by Cerebellar Circuits and Dendritic Computational Strategies

Reinforcement learning (RL) has achieved notable performance in high-dimensional sequential decision-making tasks, yet remains limited by low sample efficiency, sensitivity to noise, and weak generalization under partial observability. Most existing approaches address these issues primarily through optimization strategies, while the role of architectural priors in shaping representation learning and decision dynamics is less explored. Inspired by structural principles of the cerebellum, we propose a biologically grounded RL architecture that incorporate large expansion, sparse connectivity, sparse activation, and dendritic-level modulation. Experiments on noisy, high-dimensional RL benchmarks show that both the cerebellar architecture and dendritic modulation consistently improve sample efficiency, robustness, and generalization compared to conventional designs. Sensitivity analysis of architectural parameters suggests that cerebellum-inspired structures can offer optimized performance for RL with constrained model parameters. Overall, our work underscores the value of cerebellar structural priors as effective inductive biases for RL.

cs.LG↗

arXiv2024

FedDTPT: Federated Discrete and Transferable Prompt Tuning for Black-Box Large Language Models

In recent years, large language models (LLMs) have significantly advanced the field of natural language processing (NLP). By fine-tuning LLMs with data from specific scenarios, these foundation models can better adapt to various downstream tasks. However, the fine-tuning process poses privacy leakage risks, particularly in centralized data processing scenarios. To address user privacy concerns, federated learning (FL) has been introduced to mitigate the risks associated with centralized data collection from multiple sources. Nevertheless, the privacy of LLMs themselves is equally critical, as potential malicious attacks challenge their security, an issue that has received limited attention in current research. Consequently, establishing a trusted multi-party model fine-tuning environment is essential. Additionally, the local deployment of large LLMs incurs significant storage costs and high computational demands. To address these challenges, we propose for the first time a federated discrete and transferable prompt tuning, namely FedDTPT, for black-box large language models. In the client optimization phase, we adopt a token-level discrete prompt optimization method that leverages a feedback loop based on prediction accuracy to drive gradient-free prompt optimization through the MLM API. For server optimization, we employ an attention mechanism based on semantic similarity to filter all local prompt tokens, along with an embedding distance elbow detection and DBSCAN clustering strategy to enhance the filtering process. Experimental results demonstrate that, compared to state-of-the-art methods, our approach achieves higher accuracy, reduced communication overhead, and robustness to non-iid data in a black-box setting. Moreover, the optimized prompts are transferable.

cs.CL↗