arXiv subjects

Hongxing Liu

Publications and source records attributed to Hongxing Liu.

6 recordsLinked to original sources

arXiv2026

Tuning superconductivity and charge density wave order by next-nearest-neighbor hopping integral in honeycomb Holstein model

By using unbiased determinant quantum Monte Carlo simulations, we investigate the interplay between superconductivity and charge density wave order in the Holstein model on a honeycomb lattice with next-nearest-neighbor hopping $t^{\prime}$. We find that a finite negative $t^{\prime}$ enhances $s$-wave superconducting pairing susceptibility near the van Hove fillings in the weak electron-phonon coupling regime, while it suppresses superconductivity and promotes charge density wave order at intermediate electron-phonon coupling strengths. The effect of $t^{\prime}$ on a charge density wave is filling-dependent: It suppresses the charge density wave at half filling but enhances it near the van Hove singularities. A spectral analysis reveals the opening of a gap at low temperatures, highlighting the competitive relationship between superconducting and charge density wave orders mediated by electron-phonon coupling and tuned by $t^{\prime}$.

cond-mat.supr-con↗

arXiv2026

Strain-enhanced edge ferromagnetism and bipolar magnetic semiconducting behavior in Janus graphene nanoribbons

Using first-principles density functional theory and determinant quantum Monte Carlo methods, we show that Janus graphene nanoribbons with topological defect arrays ($m=2$) exhibit robust intrinsic ferromagnetism across widths $W=2-6$, with bandgaps exceeding 200 $meV$ and stable ferromagnetic ground states. Notably, uniaxial tensile strain significantly enhances their ferromagnetic properties: at 25\% strain, the Curie temperature increases to $222K$, a fivefold improvement over unstrained systems and the highest reported for graphene-based nanoribbons. Strain also induces a reversible transition to a bipolar magnetic semiconductor, with spin-flipped valence and conduction band edges beyond 10\% strain. This dual functionality, strain-enhanced ferromagnetism and strain-induced spin flip, stems from strain-modulated $p_{z}$ orbital hybridization and strong direct exchange interaction. Among these, $W=5$ Janus graphene nanoribbons emerge as potential candidates for room-temperature spintronic devices and strain-programmable quantum transport systems.

cond-mat.mtrl-sci↗

arXiv2025

LaneDiffusion: Improving Centerline Graph Learning via Prior Injected BEV Feature Generation

Centerline graphs, crucial for path planning in autonomous driving, are traditionally learned using deterministic methods. However, these methods often lack spatial reasoning and struggle with occluded or invisible centerlines. Generative approaches, despite their potential, remain underexplored in this domain. We introduce LaneDiffusion, a novel generative paradigm for centerline graph learning. LaneDiffusion innovatively employs diffusion models to generate lane centerline priors at the Bird's Eye View (BEV) feature level, instead of directly predicting vectorized centerlines. Our method integrates a Lane Prior Injection Module (LPIM) and a Lane Prior Diffusion Module (LPDM) to effectively construct diffusion targets and manage the diffusion process. Furthermore, vectorized centerlines and topologies are then decoded from these prior-injected BEV features. Extensive evaluations on the nuScenes and Argoverse2 datasets demonstrate that LaneDiffusion significantly outperforms existing methods, achieving improvements of 4.2%, 4.6%, 4.7%, 6.4% and 1.8% on fine-grained point-level metrics (GEO F1, TOPO F1, JTOPO F1, APLS and SDA) and 2.3%, 6.4%, 6.8% and 2.1% on segment-level metrics (IoU, mAP_cf, DET_l and TOP_ll). These results establish state-of-the-art performance in centerline graph learning, offering new insights into generative models for this task.

cs.CV↗

arXiv2024

Comparison of superconducting pairing in doped cuprates and nickelates within an extended Hubbard model

Within a Hubbard model, we investigate the superconducting pairing behavior of infinite-layer nickelate $\mathrm{NdNiO_2}$ and cuprate superconductors by using the determinant quantum Monte Carlo method. Our focus is on comparing their dominant pairing symmetries. The results indicate that the $d_{x^2-y^2}$ pairing interaction is significantly enhanced at low temperatures in both doped nickelates and cuprates, whereas other typical pairing symmetries are effectively suppressed, highlighting the dominance of the $d_{x^2-y^2}$ pairing form. Additionally, we find that the effective pairing interaction for $d_{x^2-y^2}$ pairing in doped nickelates is slightly lower than that in doped cuprates, which may be attributed to the different degrees of Fermi surface warping caused by the third-nearest hopping $t''$. Further studies show that the hole doping and interaction strength have significant effects on the $d_{x^2-y^2}$ pairing interaction within the selected parameter range. The $d_{x^2-y^2}$ pairing interaction is notably weakened when the hole doping increases, whereas it is significantly enhanced with increasing Coulomb interaction strength $U$. This comparative analysis reveals the similarities and differences in the pairing behaviors of doped nickelates and cuprates, which may provide further insights into understanding the superconducting properties of these two classes of materials.

cond-mat.str-el↗

arXiv2023

BrainZ-BP: A Non-invasive Cuff-less Blood Pressure Estimation Approach Leveraging Brain Bio-impedance and Electrocardiogram

Accurate and continuous blood pressure (BP) monitoring is essential to the early prevention of cardiovascular diseases. Non-invasive and cuff-less BP estimation algorithm has gained much attention in recent years. Previous studies have demonstrated that brain bio-impedance (BIOZ) is a promising technique for non-invasive intracranial pressure (ICP) monitoring. Clinically, treatment for patients with traumatic brain injuries (TBI) requires monitoring the ICP and BP of patients simultaneously. Estimating BP by brain BIOZ directly can reduce the number of sensors attached to the patients, thus improving their comfort. To address the issues, in this study, we explore the feasibility of leveraging brain BIOZ for BP estimation and propose a novel cuff-less BP estimation approach called BrainZ-BP. Two electrodes are placed on the forehead and occipital bone of the head in the anterior-posterior direction for brain BIOZ measurement. Various features including pulse transit time and morphological features of brain BIOZ are extracted and fed into four regression models for BP estimation. Results show that the mean absolute error, root mean square error, and correlation coefficient of random forest regression model are 2.17 mmHg, 3.91 mmHg, and 0.90 for systolic pressure estimation, and are 1.71 mmHg, 3.02 mmHg, and 0.89 for diastolic pressure estimation. The presented BrainZ-BP can be applied in the brain BIOZ-based ICP monitoring scenario to monitor BP simultaneously.

cs.LG↗

arXiv2022

Motif-topology and Reward-learning improved Spiking Neural Network for Efficient Multi-sensory Integration

Network architectures and learning principles are key in forming complex functions in artificial neural networks (ANNs) and spiking neural networks (SNNs). SNNs are considered the new-generation artificial networks by incorporating more biological features than ANNs, including dynamic spiking neurons, functionally specified architectures, and efficient learning paradigms. In this paper, we propose a Motif-topology and Reward-learning improved SNN (MR-SNN) for efficient multi-sensory integration. MR-SNN contains 13 types of 3-node Motif topologies which are first extracted from independent single-sensory learning paradigms and then integrated for multi-sensory classification. The experimental results showed higher accuracy and stronger robustness of the proposed MR-SNN than other conventional SNNs without using Motifs. Furthermore, the proposed reward learning paradigm was biologically plausible and can better explain the cognitive McGurk effect caused by incongruent visual and auditory sensory signals.

cs.NE↗