arXiv subjects

Fuyang Liu

Publications and source records attributed to Fuyang Liu.

12 recordsLinked to original sources

arXiv2026

Superconductivity of Tellurium Polyhydride with Tc above 90K

We report experimental diacovery of superconductivity (SC) in tellurium (Te) polyhydride. The compound was synthesized at high pressure and high temperature conditions using a diamond anvil cell combined with a laser heating system. Subsequent in situ transport measurements at high pressures, performed as a function of temperature and applied magnetic field, revealed a superconducting transition with a critical temperature Tc about 91 K at 263 GPa. The superconducting phase is assigned to TeH4 with characterized face shared TeH12 cage forming quasi molecular H2 units based on synchrotron x-ray diffraction experiments. Analysis of the SC behavior at magnetic fields yielded a Ginzburg Landau (GL) coherence length of approximately 54 angstroms. Tellurium polyhydride thus becomes another chalcogen polyhydride superconductor in addition to the landmark discovery of the first polyhydride high Tc SC SH3.

cond-mat.supr-con↗

arXiv2026

Ground4D: Spatially-Grounded Feedforward 4D Reconstruction for Unstructured Off-Road Scenes

Feedforward Gaussian Splatting has recently emerged as an efficient paradigm for 4D reconstruction in autonomous driving. However, in unstructured off-road scenes, its performance degrades due to high-frequency geometry, ego-motion jitter, and increased non-rigid dynamics. These factors introduce conflicting Gaussian observations across timestamps, leading to either over-smoothed renderings or structural artifacts. To address this issue, we propose Ground4D, a spatially-grounded 4D feedforward framework for pose-free off-road reconstruction. The key idea is to resolve temporal conflicts through spatially localized conditioning. Specifically, we introduce voxel-grounded temporal Gaussian aggregation, which partitions the canonical Gaussian space into spatial voxels and performs query-conditioned temporal attention within each voxel. Intra-voxel softmax normalization ensures that temporal selectivity and spatial occupancy become mutually reinforcing rather than conflicting. We furthermore introduce surface normal cues as auxiliary geometric guidance to regularize the geometry of Gaussian primitives. Extensive experiments on ORAD-3D and RELLIS-3D demonstrate that Ground4D consistently outperforms existing feedforward methods in reconstruction quality and generalizes zero-shot to unseen off-road domains. Project page and code:https://github.com/wsnbws/Ground4D.

cs.CV↗

arXiv2025

Signatures of a Lifshitz transition in pressurized electron-doped cuprate

It is well known that the electronic structure of hole-doped cuprate superconductors is tunable through both chemical doping and external pressure, which frequently offer us new insights of understanding on the high-Tc superconducting mechanism. While, for electron-doped cuprate superconductors, although the chemical doping effects have been systematically and thoroughly investigated, there is still a notable lack of experimental evidence regarding the pressure-driven coevolution of Tc and electronic structure. In this study, we report the first observation on the signatures of pressure-induced Lifshitz transition in Pr0.87LaCe0.13CuO4+delta (PLCCO) single crystal, a typical electron-doped cuprate superconductor, through the comprehensive high-pressure measurements of electrical resistance, Hall coefficient (RH) and synchrotron X-ray diffraction (XRD). Our results reveal that, at 40 K, the ambient-pressure RH with a significantly negative value decreases with increasing pressure until it reaches zero at a critical pressure (Pc ~ 10 GPa). Meanwhile, the corresponding Tc exhibits a slight variation within this pressure range. As pressure is further increased beyond Pc, RH changes its sign from negative to positive and then shows a slight increase, while Tc displays a continuous decrease. Our XRD measurements at 40 K demonstrate that no crystal structure phase transition occurs across the Pc. These results reveal that applying pressure to PLCCO can induce a Lifshitz transition at Pc, manifesting the reconstruction of the Fermi surface (FS), which turns the superconductivity toward fading out. Our calculation further reinforces the Fermi surface reconstruction from electron-dominated to hole-dominated ones at around Pc. These findings provide new evidence that highlights the strong correlation between the superconductivity and the Fermi surface topology in the electron-doped cuprates.

cond-mat.supr-con↗

arXiv2025

Real-World Adverse Weather Image Restoration via Dual-Level Reinforcement Learning with High-Quality Cold Start

Adverse weather severely impairs real-world visual perception, while existing vision models trained on synthetic data with fixed parameters struggle to generalize to complex degradations. To address this, we first construct HFLS-Weather, a physics-driven, high-fidelity dataset that simulates diverse weather phenomena, and then design a dual-level reinforcement learning framework initialized with HFLS-Weather for cold-start training. Within this framework, at the local level, weather-specific restoration models are refined through perturbation-driven image quality optimization, enabling reward-based learning without paired supervision; at the global level, a meta-controller dynamically orchestrates model selection and execution order according to scene degradation. This framework enables continuous adaptation to real-world conditions and achieves state-of-the-art performance across a wide range of adverse weather scenarios. Code is available at https://github.com/xxclfy/AgentRL-Real-Weather

cs.CV↗

arXiv2025

Advancing Off-Road Autonomous Driving: The Large-Scale ORAD-3D Dataset and Comprehensive Benchmarks

A major bottleneck in off-road autonomous driving research lies in the scarcity of large-scale, high-quality datasets and benchmarks. To bridge this gap, we present ORAD-3D, which, to the best of our knowledge, is the largest dataset specifically curated for off-road autonomous driving. ORAD-3D covers a wide spectrum of terrains, including woodlands, farmlands, grasslands, riversides, gravel roads, cement roads, and rural areas, while capturing diverse environmental variations across weather conditions (sunny, rainy, foggy, and snowy) and illumination levels (bright daylight, daytime, twilight, and nighttime). Building upon this dataset, we establish a comprehensive suite of benchmark evaluations spanning five fundamental tasks: 2D free-space detection, 3D occupancy prediction, rough GPS-guided path planning, vision-language model-driven autonomous driving, and world model for off-road environments. Together, the dataset and benchmarks provide a unified and robust resource for advancing perception and planning in challenging off-road scenarios. The dataset and code will be made publicly available at https://github.com/chaytonmin/ORAD-3D.

cs.RO↗

arXiv2025

UNIV: Unified Foundation Model for Infrared and Visible Modalities

Joint RGB-infrared perception is essential for achieving robustness under diverse weather and illumination conditions. Although foundation models excel within single modalities, they suffer from substantial cross-modal degradation, an issue we attribute to a pattern shortcut, i.e., a modal bias that prioritizes superficial sensor patterns over underlying semantics. To address this problem, we introduce UNIV, a Unified foundation model for Infrared and Visible modalities. At the core of UNIV lies Patch Cross-modal Contrastive Learning (PCCL), a self-supervised contrastive learning strategy that constructs a unified cross-modal feature space. PCCL employs a frozen pre-trained model to sample pseudo patch pairs based on semantic similarity, and aligns infrared-visible representations by attracting semantically related pairs while repelling unrelated ones. This process simultaneously enhances cross-modal alignment and inter-class semantic separability, guiding the model to focus on semantic structure rather than falling into pattern shortcuts. To further enable cross-modal learning, we introduce MVIP, the most comprehensive visible-infrared benchmark to date, containing 98,992 precisely aligned image pairs across diverse scenes. Extensive experiments demonstrate UNIV's superior performance on infrared tasks (+1.7 mIoU for semantic segmentation and +0.7 mAP for detection), while maintaining competitive accuracy on RGB tasks.

cs.CV↗

arXiv2025

CORENet: Cross-Modal 4D Radar Denoising Network with LiDAR Supervision for Autonomous Driving

4D radar-based object detection has garnered great attention for its robustness in adverse weather conditions and capacity to deliver rich spatial information across diverse driving scenarios. Nevertheless, the sparse and noisy nature of 4D radar point clouds poses substantial challenges for effective perception. To address the limitation, we present CORENet, a novel cross-modal denoising framework that leverages LiDAR supervision to identify noise patterns and extract discriminative features from raw 4D radar data. Designed as a plug-and-play architecture, our solution enables seamless integration into voxel-based detection frameworks without modifying existing pipelines. Notably, the proposed method only utilizes LiDAR data for cross-modal supervision during training while maintaining full radar-only operation during inference. Extensive evaluation on the challenging Dual-Radar dataset, which is characterized by elevated noise level, demonstrates the effectiveness of our framework in enhancing detection robustness. Comprehensive experiments validate that CORENet achieves superior performance compared to existing mainstream approaches.

cs.CV↗

arXiv2025

MASTER: Multimodal Segmentation with Text Prompts

RGB-Thermal fusion is a potential solution for various weather and light conditions in challenging scenarios. However, plenty of studies focus on designing complex modules to fuse different modalities. With the widespread application of large language models (LLMs), valuable information can be more effectively extracted from natural language. Therefore, we aim to leverage the advantages of large language models to design a structurally simple and highly adaptable multimodal fusion model architecture. We proposed MultimodAl Segmentation with TExt PRompts (MASTER) architecture, which integrates LLM into the fusion of RGB-Thermal multimodal data and allows complex query text to participate in the fusion process. Our model utilizes a dual-path structure to extract information from different modalities of images. Additionally, we employ LLM as the core module for multimodal fusion, enabling the model to generate learnable codebook tokens from RGB, thermal images, and textual information. A lightweight image decoder is used to obtain semantic segmentation results. The proposed MASTER performs exceptionally well in benchmark tests across various automated driving scenarios, yielding promising results.

cs.CV↗

arXiv2024

PID: Physics-Informed Diffusion Model for Infrared Image Generation

Infrared imaging technology has gained significant attention for its reliable sensing ability in low visibility conditions, prompting many studies to convert the abundant RGB images to infrared images. However, most existing image translation methods treat infrared images as a stylistic variation, neglecting the underlying physical laws, which limits their practical application. To address these issues, we propose a Physics-Informed Diffusion (PID) model for translating RGB images to infrared images that adhere to physical laws. Our method leverages the iterative optimization of the diffusion model and incorporates strong physical constraints based on prior knowledge of infrared laws during training. This approach enhances the similarity between translated infrared images and the real infrared domain without increasing extra training parameters. Experimental results demonstrate that PID significantly outperforms existing state-of-the-art methods. Our code is available at https://github.com/fangyuanmao/PID.

cs.CV↗

arXiv2023

Structure responsible for the superconducting state in La3Ni2O7 at high pressure and low temperature conditions

Very recently, a new superconductor with Tc = 80 K was reported in nickelate (La3Ni2O7) at around 15 - 40 GPa conditions (Nature, 621, 493, 2023) [1], which is the second type of unconventional superconductor, beside the cuprates, with Tc above liquid nitrogen temperature. However, the phase diagram plotted in this report was mostly based on the transport measurement at low temperature and high pressure conditions, and the assumed corresponding X-ray diffraction (XRD) results was carried out at room temperature. This encouraged us to carry out in situ high pressure and low temperature synchrotron XRD experiments to determine which phase is responsible for the high Tc state. In addition to the phase transition from orthorhombic Amam structure to orthorhombic Fmmm structure, a tetragonal phase with space group of I4/mmm was discovered when the sample was compressed to 19 GPa at 40 K where the superconductivity takes palce in La3Ni2O7. The calculations based on this tetragonal structure reveal that the electronic states approached to the Fermi energy were mainly dominated by the eg orbitals (3dz2 and 3dx2-y2) of Ni atoms, which are located in the oxygen octahedral crystal field. The correlation between Tc and this structural evolution, especially Ni-O octahedra regularity and the in-plane Ni-O-Ni bonding angles, are analyzed. This work sheds new lights to identify what is the most likely phase responsible for superconductivity in the double layered nickelate.

cond-mat.supr-con↗

arXiv2023

Superconducting-insulating phase transition in pressurized Ba1-xKxBiO3

We report the first observation of a pressure-induced transition from a superconducting (SC) to an insulating (I) phase in single-crystal Ba1-xKxBiO3 (x = 0.4, 0.43, 0.52, and 0.58) superconductors. X-ray diffraction measurements conducted at 20 K reveal a direct relationship between this SC-I transition and a pressure-induced distortion of crystal structure. With increasing pressure, the lattice parameters a and c of the ambient-pressure superconducting tetragonal (T) phase are compressed continuously below a critical pressure (Pc1), wherein the pressure (P) dependence of superconducting transition temperature (Tc) displays a small variation. However, upon further compression, the lattice of the compressed T phase displays an anisotropic change, and Tc shows a monotonous decrease. When the pressure reaches Pc2 (Pc2 > Pc1), the compressed T phase collapses along the c axis, followed by the disappearance of superconductivity and the appearance of the insulating phase. This SC-I transition is fully reversible, with the critical pressure increasing alongside K doping concentration. These findings are strikingly similar to the SC-I transition observed in hole-doped high-Tc cuprate superconductors under pressure. Identifying their commonalities could deepen our understanding of the mechanisms that underlie high- Tc superconductivity in these two oxide superconductors with a perovskite structure.

cond-mat.supr-con↗

arXiv2022

On the structure of SbTeI

Antimony telluroiodide (SbTeI) is predicted to be a promising material in many technological applications based on theoretical simulations, however the bulk structure solution remains elusive. We consolidate SbTeI belonging to the base-centered monoclinic lattice with a space group C 2/m by combining single crystal X-ray diffraction and X-ray photoemission spectroscopy techniques. The atomic arrangement of the reported crystal structure is remarkable with one-dimensional double-chains forming two-dimensional blocks. In this structure, the Sb$^{3+}$ ion is surrounded by Te$^{2-}$ and I$^-$, which is distinguishable by an incomplete polyhedron resulting in the 5s$^2$ (Sb) lone pair electrons in the valence band. Manipulation of this material with pressure to induce novel structures and properties is highly anticipated.

cond-mat.mtrl-sci↗