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Yujie Feng

Publications and source records attributed to Yujie Feng.

23 records · Page 2Linked to original sources
arXiv2024

Microscopic study of deformation and orientation effects in heavy-ion reactions above Coulomb barrier using the Boltzmann-Uehling-Uhlenbeck model

Background: The understanding of the impact of initial deformation and collision orientation on quasi-fission and fusion-fission reactions remains incomplete. Purpose: This article aims to explore how the orientation of deformed nuclei influences quasi-fission and fusion-fission around 1.2 VB, employing a micro dynamical method in systems with diverse shapes, namely 24Mg + 178Hf, 34S + 168Er, and 48Ti + 154Sm. Method: Utilizing the Boltzmann-Uehling-Uhlenbeck model, this study investigates quasi-fission and fusion fission reactions. The model elucidates micro-dynamic processes and microscopic observables through the definition of the window and event-by-event simulations. Results: The findings reveal that the orientation of deformed nuclei significantly influences the nucleus-nucleus interaction potential, thereby impacting the competition between quasi-fission and fusion-fission. Particularly, the orientation of the deformed target nucleus emerges as the primary factor affecting this competition. Notably, a higher proportion of fusion-fission events is observed when the target nucleus is in the belly orientation compared to the tip. The study also observes that the configuration of the dinuclear system contributes to fluctuations and dissipation. Collisions with different orientations result in distinct dinuclear system configurations, with belly-oriented collisions leading to larger fluctuations between events, while tip-oriented collisions exhibit smaller fluctuations. Conclusions: Considering diverse orientations of nuclei with distinct initial deformations, this study concludes that the orientation of the target nucleus is the key factor influencing quasi-fission and fusion-fission reactions around 1.2 VB.

nucl-th↗
arXiv2024

A Multi-scale Generalized Shrinkage Threshold Network for Image Blind Deblurring in Remote Sensing

Remote sensing images are essential for many applications of the earth's sciences, but their quality can usually be degraded due to limitations in sensor technology and complex imaging environments. To address this, various remote sensing image deblurring methods have been developed to restore sharp and high-quality images from degraded observational data. However, most traditional model-based deblurring methods usually require predefined {hand-crafted} prior assumptions, which are difficult to handle in complex applications. On the other hand, deep learning-based deblurring methods are often considered as black boxes, lacking transparency and interpretability. In this work, we propose a new blind deblurring learning framework that utilizes alternating iterations of shrinkage thresholds. This framework involves updating blurring kernels and images, with a theoretical foundation in network design. Additionally, we propose a learnable blur kernel proximal mapping module to improve the accuracy of the blur kernel reconstruction. Furthermore, we propose a deep proximal mapping module in the image domain, which combines a generalized shrinkage threshold with a multi-scale prior feature extraction block. This module also incorporates an attention mechanism to learn adaptively the importance of prior information, improving the flexibility and robustness of prior terms, and avoiding limitations similar to hand-crafted image prior terms. Consequently, we design a novel multi-scale generalized shrinkage threshold network (MGSTNet) that focuses specifically on learning deep geometric prior features to enhance image restoration. Experimental results on real and synthetic remote sensing image datasets demonstrate the superiority of our MGSTNet framework compared to existing deblurring methods.

cs.CV↗
arXiv2023

Towards LLM-driven Dialogue State Tracking

Dialogue State Tracking (DST) is of paramount importance in ensuring accurate tracking of user goals and system actions within task-oriented dialogue systems. The emergence of large language models (LLMs) such as GPT3 and ChatGPT has sparked considerable interest in assessing their efficacy across diverse applications. In this study, we conduct an initial examination of ChatGPT's capabilities in DST. Our evaluation uncovers the exceptional performance of ChatGPT in this task, offering valuable insights to researchers regarding its capabilities and providing useful directions for designing and enhancing dialogue systems. Despite its impressive performance, ChatGPT has significant limitations including its closed-source nature, request restrictions, raising data privacy concerns, and lacking local deployment capabilities. To address these concerns, we present LDST, an LLM-driven DST framework based on smaller, open-source foundation models. By utilizing a novel domain-slot instruction tuning method, LDST achieves performance on par with ChatGPT. Comprehensive evaluations across three distinct experimental settings, we find that LDST exhibits remarkable performance improvements in both zero-shot and few-shot setting compared to previous SOTA methods. The source code is provided for reproducibility.

cs.CL↗
arXiv2023

Nest-DGIL: Nesterov-optimized Deep Geometric Incremental Learning for CS Image Reconstruction

Proximal gradient-based optimization is one of the most common strategies to solve inverse problem of images, and it is easy to implement. However, these techniques often generate heavy artifacts in image reconstruction. One of the most popular refinement methods is to fine-tune the regularization parameter to alleviate such artifacts, but it may not always be sufficient or applicable due to increased computational costs. In this work, we propose a deep geometric incremental learning framework based on the second Nesterov proximal gradient optimization. The proposed end-to-end network not only has the powerful learning ability for high-/low-frequency image features, but also can theoretically guarantee that geometric texture details will be reconstructed from preliminary linear reconstruction. Furthermore, it can avoid the risk of intermediate reconstruction results falling outside the geometric decomposition domains and achieve fast convergence. Our reconstruction framework is decomposed into four modules including general linear reconstruction, cascade geometric incremental restoration, Nesterov acceleration, and post-processing. In the image restoration step, a cascade geometric incremental learning module is designed to compensate for missing texture information from different geometric spectral decomposition domains. Inspired by the overlap-tile strategy, we also develop a post-processing module to remove the block effect in patch-wise-based natural image reconstruction. All parameters in the proposed model are learnable, an adaptive initialization technique of physical parameters is also employed to make model flexibility and ensure converging smoothly. We compare the reconstruction performance of the proposed method with existing state-of-the-art methods to demonstrate its superiority. Our source codes are available at https://github.com/fanxiaohong/Nest-DGIL.

eess.IV↗