arXiv subjects

Tianyuan Chen

Publications and source records attributed to Tianyuan Chen.

5 recordsLinked to original sources

arXiv2026

Quasiperiodic Slipping Motion of Flare Ribbon Fine Structures Anchored in a Sunspot Light Bridge

We used high-resolution observations from the New Vacuum Solar Telescope and the Solar Dynamics Observatory to carry out a detailed multiwavelength analysis of the fine structures in the flare ribbon of a C3.9-class flare on 22 April 2021. A segment of the flare ribbon was rooted in a sunspot light bridge and exhibited discrete substructures, which we term "burrs", with equivalent diameters of 233-895 km and inter-core separations of 1129-1739 km. These structures are characterized by discrete redshifted cores accompanied by "tails" with lengths of 700-1370 km and widths of 310-600 km that show faint blueshifts. The burrs display systematic slipping motions along the ribbon, with apparent velocities decreasing from about 40 to 21 km/s, and show a distinct quasi-periodicity of about 6 minutes in H-alpha and EUV passbands. Differential emission measure analysis indicates that the emitting plasma is multi-thermal and dominated by temperatures of 1-2 MK. The observed morphology and kinematics are consistent with impulsive energy deposition by precipitating plasmoids, or oblique flux ropes, produced by tearing-mode fragmentation in the coronal current sheet. The close spatiotemporal association between the tails and blueshifts supports the interpretation that these features are related to untwisting magnetic flux ropes. The approximately 6-minute periodicity further suggests that the reconnection process may be modulated by photospheric p-mode oscillations coupled with tearing-mode instability. These results provide observational evidence that light-bridge-anchored fine structures can act as elementary units of flare energy release.

astro-ph.SR↗

arXiv2025

Preference-Guided Reinforcement Learning for Efficient Exploration

In this paper, we investigate preference-based reinforcement learning (PbRL), which enables reinforcement learning (RL) agents to learn from human feedback. This is particularly valuable when defining a fine-grain reward function is not feasible. However, this approach is inefficient and impractical for promoting deep exploration in hard-exploration tasks with long horizons and sparse rewards. To tackle this issue, we introduce LOPE: \textbf{L}earning \textbf{O}nline with trajectory \textbf{P}reference guidanc\textbf{E}, an end-to-end preference-guided RL framework that enhances exploration efficiency in hard-exploration tasks. Our intuition is that LOPE directly adjusts the focus of online exploration by considering human feedback as guidance, thereby avoiding the need to learn a separate reward model from preferences. Specifically, LOPE includes a two-step sequential policy optimization technique consisting of trust-region-based policy improvement and preference guidance steps. We reformulate preference guidance as a trajectory-wise state marginal matching problem that minimizes the maximum mean discrepancy distance between the preferred trajectories and the learned policy. Furthermore, we provide a theoretical analysis to characterize the performance improvement bound and evaluate the effectiveness of the LOPE. When assessed in various challenging hard-exploration environments, LOPE outperforms several state-of-the-art methods in terms of convergence rate and overall performance.The code used in this study is available at https://github.com/buaawgj/LOPE.

cs.LG↗

arXiv2025

Offline RL with Smooth OOD Generalization in Convex Hull and its Neighborhood

Offline Reinforcement Learning (RL) struggles with distributional shifts, leading to the $Q$-value overestimation for out-of-distribution (OOD) actions. Existing methods address this issue by imposing constraints; however, they often become overly conservative when evaluating OOD regions, which constrains the $Q$-function generalization. This over-constraint issue results in poor $Q$-value estimation and hinders policy improvement. In this paper, we introduce a novel approach to achieve better $Q$-value estimation by enhancing $Q$-function generalization in OOD regions within Convex Hull and its Neighborhood (CHN). Under the safety generalization guarantees of the CHN, we propose the Smooth Bellman Operator (SBO), which updates OOD $Q$-values by smoothing them with neighboring in-sample $Q$-values. We theoretically show that SBO approximates true $Q$-values for both in-sample and OOD actions within the CHN. Our practical algorithm, Smooth Q-function OOD Generalization (SQOG), empirically alleviates the over-constraint issue, achieving near-accurate $Q$-value estimation. On the D4RL benchmarks, SQOG outperforms existing state-of-the-art methods in both performance and computational efficiency.

cs.LG↗

arXiv2024

Policy Optimization with Smooth Guidance Learned from State-Only Demonstrations

The sparsity of reward feedback remains a challenging problem in online deep reinforcement learning (DRL). Previous approaches have utilized offline demonstrations to achieve impressive results in multiple hard tasks. However, these approaches place high demands on demonstration quality, and obtaining expert-like actions is often costly and unrealistic. To tackle these problems, we propose a simple and efficient algorithm called Policy Optimization with Smooth Guidance (POSG), which leverages a small set of state-only demonstrations (where expert action information is not included in demonstrations) to indirectly make approximate and feasible long-term credit assignments and facilitate exploration. Specifically, we first design a trajectory-importance evaluation mechanism to determine the quality of the current trajectory against demonstrations. Then, we introduce a guidance reward computation technology based on trajectory importance to measure the impact of each state-action pair, fusing the demonstrator's state distribution with reward information into the guidance reward. We theoretically analyze the performance improvement caused by smooth guidance rewards and derive a new worst-case lower bound on the performance improvement. Extensive results demonstrate POSG's significant advantages in control performance and convergence speed in four sparse-reward environments, including the grid-world maze, Hopper-v4, HalfCheetah-v4, and Ant maze. Notably, the specific metrics and quantifiable results are investigated to demonstrate the superiority of POSG.

cs.LG↗

arXiv2020

Diverse R-PPG: Camera-Based Heart Rate Estimation for Diverse Subject Skin-Tones and Scenes

Heart rate (HR) is an essential clinical measure for the assessment of cardiorespiratory instability. Since communities of color are disproportionately affected by both COVID-19 and cardiovascular disease, there is a pressing need to deploy contactless HR sensing solutions for high-quality telemedicine evaluations. Existing computer vision methods that estimate HR from facial videos exhibit biased performance against dark skin tones. We present a novel physics-driven algorithm that boosts performance on darker skin tones in our reported data. We assess the performance of our method through the creation of the first telemedicine-focused remote vital signs dataset, the VITAL dataset. 432 videos (~864 minutes) of 54 subjects with diverse skin tones are recorded under realistic scene conditions with corresponding vital sign data. Our method reduces errors due to lighting changes, shadows, and specular highlights and imparts unbiased performance gains across skin tones, setting the stage for making medically inclusive non-contact HR sensing technologies a viable reality for patients of all skin tones.

eess.IV↗