arXiv subjects

Zixuan Sun

Publications and source records attributed to Zixuan Sun.

6 recordsLinked to original sources

arXiv2026

CRFT: Consistent-Recurrent Feature Flow Transformer for Cross-Modal Image Registration

We present Consistent-Recurrent Feature Flow Transformer (CRFT), a unified coarse-to-fine framework based on feature flow learning for robust cross-modal image registration. CRFT learns a modality-independent feature flow representation within a transformer-based architecture that jointly performs feature alignment and flow estimation. The coarse stage establishes global correspondences through multi-scale feature correlation, while the fine stage refines local details via hierarchical feature fusion and adaptive spatial reasoning. To enhance geometric adaptability, an iterative discrepancy-guided attention mechanism with a Spatial Geometric Transform (SGT) recurrently refines the flow field, progressively capturing subtle spatial inconsistencies and enforcing feature-level consistency. This design enables accurate alignment under large affine and scale variations while maintaining structural coherence across modalities. Extensive experiments on diverse cross-modal datasets demonstrate that CRFT consistently outperforms state-of-the-art registration methods in both accuracy and robustness. Beyond registration, CRFT provides a generalizable paradigm for multimodal spatial correspondence, offering broad applicability to remote sensing, autonomous navigation, and medical imaging. Code and datasets are publicly available at https://github.com/NEU-Liuxuecong/CRFT.

cs.CV↗

arXiv2025

Accelerating Multi-modal LLM Gaming Performance via Input Prediction and Mishit Correction

Real-time sequential control agents are often bottlenecked by inference latency. Even modest per-step planning delays can destabilize control and degrade overall performance. We propose a speculation-and-correction framework that adapts the predict-then-verify philosophy of speculative execution to model-based control with TD-MPC2. At each step, a pretrained world model and latent-space MPC planner generate a short-horizon action queue together with predicted latent rollouts, allowing the agent to execute multiple planned actions without immediate replanning. When a new observation arrives, the system measures the mismatch between the encoded real latent state and the queued predicted latent. For small to moderate mismatch, a lightweight learned corrector applies a residual update to the speculative action, distilled offline from a replanning teacher. For large mismatch, the agent safely falls back to full replanning and clears stale action queues. We study both a gated two-tower MLP corrector and a temporal Transformer corrector to address local errors and systematic drift. Experiments on the DMC Humanoid-Walk task show that our method reduces the number of planning inferences from 500 to 282, improves end-to-end step latency by 25 percent, and maintains strong control performance with only a 7.1 percent return reduction. Ablation results demonstrate that speculative execution without correction is unreliable over longer horizons, highlighting the necessity of mismatch-aware correction for robust latency reduction.

cs.AI↗

arXiv2025

GDROS: A Geometry-Guided Dense Registration Framework for Optical-SAR Images under Large Geometric Transformations

Registration of optical and synthetic aperture radar (SAR) remote sensing images serves as a critical foundation for image fusion and visual navigation tasks. This task is particularly challenging because of their modal discrepancy, primarily manifested as severe nonlinear radiometric differences (NRD), geometric distortions, and noise variations. Under large geometric transformations, existing classical template-based and sparse keypoint-based strategies struggle to achieve reliable registration results for optical-SAR image pairs. To address these limitations, we propose GDROS, a geometry-guided dense registration framework leveraging global cross-modal image interactions. First, we extract cross-modal deep features from optical and SAR images through a CNN-Transformer hybrid feature extraction module, upon which a multi-scale 4D correlation volume is constructed and iteratively refined to establish pixel-wise dense correspondences. Subsequently, we implement a least squares regression (LSR) module to geometrically constrain the predicted dense optical flow field. Such geometry guidance mitigates prediction divergence by directly imposing an estimated affine transformation on the final flow predictions. Extensive experiments have been conducted on three representative datasets WHU-Opt-SAR dataset, OS dataset, and UBCv2 dataset with different spatial resolutions, demonstrating robust performance of our proposed method across different imaging resolutions. Qualitative and quantitative results show that GDROS significantly outperforms current state-of-the-art methods in all metrics. Our source code will be released at: https://github.com/Zi-Xuan-Sun/GDROS.

eess.IV↗

arXiv2025

Experimental Observation of Extremely Strong Defect-Phonon Scatterings in Semiconductor Single Crystals

The role of doping in tailoring thermal transport in semiconductors is critical for efficient thermal management in electronic devices. While the effects of doping have been extensively studied to tune electrical properties, its impact on thermal transport has not yet been thoroughly explored, particularly with respect to experimental investigations into exceptionally strong non-Rayleigh defect-phonon scattering phenomena. Herein, by combining the high-quality growth and advanced characterizations of cubic silicon carbide single crystals with well controlled boron doping, we experimentally observe anomalous strong defect-phonon scatterings, among the strongest reported in common semiconductors, that exceeds the predictions of the classic mass difference model by tens of times in magnitude. The measured thermal conductivity of doped 3C SiC match excellently with those predicted by first principle calculations in which resonant scattering of low frequency phonon is considered. Our findings not only shed light on the fundamental understanding of defect-phonon interactions and will also impact applications such as thermal management of electronics.

cond-mat.mtrl-sci↗

arXiv2024

Thermal Conductivity of Cubic Silicon Carbide Single Crystals Heavily Doped by Nitrogen

High-purity cubic silicon carbide possesses the second-highest thermal conductivity among large-scale crystals, surpassed only by diamond, making it crucial for practical applications of thermal management. Recent theoretical studies predict that heavy doping reduces the thermal conductivity of 3C-SiC via phonon-defect and phonon-electron scattering. However, experimental evidence has been limited. In this work, we report the thermal conductivity of heavily nitrogen doped 3C SiC single crystals, grown using the top-seeded solution growth method, measured via time domain thermoreflectance. Our results show that a significant reduction (up to 30%) in thermal conductivity is observed with nitrogen doping concentrations around 1020 cm-3. A comparison with theoretical calculations indicates less intensive scatterings are observed in the measured thermal conductivity. We speculate that the electron-phonon scattering may have a smaller impact than previously anticipated or the distribution of defects are nonuniform which leads to less intensive scatterings. These findings shed light on understanding the doping effects on thermal transport in semiconductors and support further exploration of 3C SiC for thermal management in electronics.

cond-mat.mtrl-sci↗

arXiv2021

Understanding Hot Carrier Reliability in FinFET Technology from Trap-based Approach

In this paper, the recent advances of our studies on hot carrier degradation (HCD) are presented from trap-based approach. The microscopic speculation of interface trap generation is carried out by time-dependent DFT (TDDFT) simulation in "real-time". Two types of oxide traps contributing to HCD are identified from experiments. Combining the contributions of interface and oxide traps, a unified compact model has been proposed which can accurately predict hot carrier degradation and variation in full Vgs/Vds bias. The trap locations, degradation contributions and temperature dependence are studied in detail. In addition, the mixed mode reliability of HCD-BTI coupling through self-heating and under off-state stress are discussed.

cond-mat.mes-hall↗