arXiv subjects

Kun Han

Publications and source records attributed to Kun Han.

At least 19 recordsLinked to original sources

arXiv2026

Field-induced topological Hall effect and butterfly-shaped magnetoresistance in the centrosymmetric antiferromagnet EuAuAs

The coupling between magnetic and electronic degrees of freedom gives rise to a variety of intriguing transport phenomena. Among them, the topological Hall effect, originating from the real-space Berry phase associated with nontrivial magnetic textures, has attracted considerable attention. Here, we systematically investigate the magnetic and transport properties of antiferromagnet EuAuAs. Magnetic characterizations reveal antiferromagnetic transition at 5.7 K and 6.3 K for $H \parallel ab$ and $H \parallel c$, accompanied by metamagnetic transition and small hysteresis for $H \parallel ab$. Electrical transport measurements reveal a pronounced topological Hall effct in the antiferromagnetic state with $H \parallel ab$ and $I \parallel c$, which may be attributed to finite scalar spin chirality. Furthermore, the magnetoresistance exhibits butterfly-shaped hysteresis and strong angular dependence, which are likely associated with spin-dependent electron scattering, magnetic-domain evolution, and domain-wall pinning. Our results suggest that field-induced spin textures play an important role in the magnetotransport properties and provide insights into the interplay between magnetic textures and electronic transport in centrosymmetric antiferromagnets.

cond-mat.mtrl-sci↗

arXiv2026

Dynamic E-unification

We present an E-unification procedure for a set of non-ground (dis)equations, along with a dynamic set of ground (dis)equations, and prove its completeness. The ground part is dynamic in the sense that it continually changes. The algorithm saturates the non-ground equations using Superposition modulo the ground theory. We also have an Instantiation rule that matches the left hand side of non-ground (dis)equations with ground terms, creating new ground (dis)equations, which changes the ground theory. This algorithm can be used in quantified SMT problems, where the dynamic ground theory represents the evolving model. We develop an ordering to compare terms modulo a ground theory, which is used to orient non-ground equations. We prove properties of this ordering, using a weak form of monotonicity and subterm property. We finally present a set of inference rules for our ordering, which allows us to properly orient equations in theories of some finite data structures, such as a theory of finite lists with length and append.

cs.LO↗

arXiv2026

Rotation Equivariant Convolutions in Deformable Registration of Brain MRI

Image registration is a fundamental task that aligns anatomical structures between images. While CNNs perform well, they lack rotation equivariance - a rotated input does not produce a correspondingly rotated output. This hinders performance by failing to exploit the rotational symmetries inherent in anatomical structures, particularly in brain MRI. In this work, we integrate rotation-equivariant convolutions into deformable brain MRI registration networks. We evaluate this approach by replacing standard encoders with equivariant ones in three baseline architectures, testing on multiple public brain MRI datasets. Our experiments demonstrate that equivariant encoders have three key advantages: 1) They achieve higher registration accuracy while reducing network parameters, confirming the benefit of this anatomical inductive bias. 2) They outperform baselines on rotated input pairs, demonstrating robustness to orientation variations common in clinical practice. 3) They show improved performance with less training data, indicating greater sample efficiency. Our results demonstrate that incorporating geometric priors is a critical step toward building more robust, accurate, and efficient registration models.

cs.CV↗

arXiv2025

Interfacial Strain Modulated Correlated Plasmons in La1.85Sr0.15CuO4 and Their Role in High-temperature Superconductivity

High-temperature superconductivity in cuprate materials remains a major challenge in physics due to the complexity of their strongly correlated electronic states. Interfacial strain is a powerful lever for tuning electronic correlations in complex oxides, offering new pathways to control emergent quantum phases. Here, we report the discovery of interfacial strain modulated correlated plasmons observed exclusively in superconducting La1.85Sr0.15CuO4 (LSCO) through spectroscopic ellipsometry. This form of plasmons is absent in the non-superconducting LSCO counterparts. Detailed analysis reveals that these correlated plasmons, arising from the collective excitations within Mott-correlated bands, are driven by long-range electronic correlations in the Cu-O planes. Furthermore, long-range electronic correlations, intricately modulated by interfacial strain, may play a crucial role in the emergence of superconductivity and in tuning the transition temperature. Dynamical cluster approximation (DCA) with quantum Monte Carlo (QMC) calculations of the extended Hubbard model suggest that long-range Coulomb interactions play an important role in LSCO, showing good agreement with our experimental findings. The collective evidence from both the experimental results and theoretical findings provides new insights into the nature of collective excitations and their pivotal role in the emergence of high-temperature superconductivity.

cond-mat.str-el↗

arXiv2025

Checkerboard-type Zhang-Rice States in Overdoped Cuprate Superconductors

Cuprate superconductors remain central to condensed matter physics due to their technological relevance and unconventional, incompletely understood electronic behavior. While the canonical phase diagram and low-energy models have been shaped largely by studies of underdoped and moderately doped cuprates, the overdoped regime has received comparatively limited attention.Here, we track the evolution of the electronic structure from optimal to heavy overdoping in La2-xSrxCuO4(LSCO) using broadband optical spectroscopy across x=0.15-0.60. The measured spectral changes--including the redistribution of Zhang-Rice-related spectral weigh--are in qualitative agreement with determinant quantum Monte Carlo simulations of the three-orbital Emery model, which together indicate a pronounced reconstruction of the electronic structure beyond hole concentrations x>0.2. Guided by these observations, we propose a spontaneous checkerboard-type Zhang-Rice electronic configuration that captures the coexistence of itinerant and localized carriers characteristic of the heavily overdoped state. Our results refine the doping-dependent Zhang-Rice-based framework for cuprates, illuminate how correlations persist deep into the overdoped regime, and provide new constraints on microscopic mechanisms of high-temperature superconductivity, with broader implications for correlated transition-metal oxides.

cond-mat.supr-con↗

arXiv2025

Modulation of Quantum Transport in Complex Oxide Heterostructures with Proton Implantation

The interfacial electronic properties of complex oxides are governed by a delicate balance between charge transfer, lattice distortions, and electronic correlations, posing a key challenge for controlled tunability in materials research. Here, we demonstrate that proton implantation serves as a precise tool for modulating interfacial transport in SrTiO3-based heterostructures. By introducing protons into the SrTiO3 substrate beneath an amorphous (La,Sr)(Al,Ta)O3 capping layer, we uncover a competition between disorder and charge doping induced by implantation. At low implantation fluences below 1x1015 protons/cm2 (1E15), charge doping dominates, leading to an increase in carrier density and mobility, analogous to electrostatic gating effect. This enables the emergence of quantum transport oscillations at low temperature. Conversely, at higher fluences (above 1E15), disorder scattering prevails, suppressing carrier mobility and inducing an insulating state. The nonmonotonic evolution of transport with implantation fluence underscores the critical interplay between electronic correlations and disorder, offering a new paradigm for the controlled engineering of interfacial quantum states in SrTiO3-based oxide heterostructures.

cond-mat.mtrl-sci↗

arXiv2025

MoME: Mixture of Visual Language Medical Experts for Medical Imaging Segmentation

In this study, we propose MoME, a Mixture of Visual Language Medical Experts, for Medical Image Segmentation. MoME adapts the successful Mixture of Experts (MoE) paradigm, widely used in Large Language Models (LLMs), for medical vision-language tasks. The architecture enables dynamic expert selection by effectively utilizing multi-scale visual features tailored to the intricacies of medical imagery, enriched with textual embeddings. This work explores a novel integration of vision-language models for this domain. Utilizing an assembly of 10 datasets, encompassing 3,410 CT scans, MoME demonstrates strong performance on a comprehensive medical imaging segmentation benchmark. Our approach explores the integration of foundation models for medical imaging, benefiting from the established efficacy of MoE in boosting model performance by incorporating textual information. Demonstrating competitive precision across multiple datasets, MoME explores a novel architecture for achieving robust results in medical image analysis.

cs.CV↗

arXiv2025

XOCT: Enhancing OCT to OCTA Translation via Cross-Dimensional Supervised Multi-Scale Feature Learning

Optical Coherence Tomography Angiography (OCTA) and its derived en-face projections provide high-resolution visualization of the retinal and choroidal vasculature, which is critical for the rapid and accurate diagnosis of retinal diseases. However, acquiring high-quality OCTA images is challenging due to motion sensitivity and the high costs associated with software modifications for conventional OCT devices. Moreover, current deep learning methods for OCT-to-OCTA translation often overlook the vascular differences across retinal layers and struggle to reconstruct the intricate, dense vascular details necessary for reliable diagnosis. To overcome these limitations, we propose XOCT, a novel deep learning framework that integrates Cross-Dimensional Supervision (CDS) with a Multi-Scale Feature Fusion (MSFF) network for layer-aware vascular reconstruction. Our CDS module leverages 2D layer-wise en-face projections, generated via segmentation-weighted z-axis averaging, as supervisory signals to compel the network to learn distinct representations for each retinal layer through fine-grained, targeted guidance. Meanwhile, the MSFF module enhances vessel delineation through multi-scale feature extraction combined with a channel reweighting strategy, effectively capturing vascular details at multiple spatial scales. Our experiments on the OCTA-500 dataset demonstrate XOCT's improvements, especially for the en-face projections which are significant for clinical evaluation of retinal pathologies, underscoring its potential to enhance OCTA accessibility, reliability, and diagnostic value for ophthalmic disease detection and monitoring. The code is available at https://github.com/uci-cbcl/XOCT.

cs.CV↗

arXiv2025

Electrostatic gate-controlled quantum interference in a high-mobility two-dimensional electron gas at the (La$_{0.3}$Sr$_{0.7}$)(Al$_{0.65}$Ta$_{0.35}$)O$_3$/SrTiO$_3$ interface

We report quantum oscillations in magnetoresistance that are periodic in magnetic field ($B$), observed at the interface between (La$_{0.3}$Sr$_{0.7}$)(Al$_{0.65}$Ta$_{0.35}$)O$_3$ and SrTiO$_3$. Unlike Shubnikov-de Haas oscillations, which appear at magnetic fields $ > 7$ T and diminish quickly as the temperature rises, these $B$-periodic oscillations emerge at low fields and persist up to 10 K. Their amplitude decays exponentially with both temperature and field, specifying dephasing of quantum interference. Increasing the carrier density through electrostatic gating results in a systematic reduction in both the amplitude and frequency of the oscillations, with complete suppression beyond a certain gate voltage. We attribute these oscillations to the Altshuler-Aronov-Spivak effect, likely arising from naturally formed closed-loop paths due to the interconnected quasi-one-dimensional conduction channels along SrTiO$_3$ domain walls. The relatively long phase coherence length ($\sim$ 1.8 $\mu$m at 0.1 K), estimated from the oscillation amplitude, highlights the potential of complex oxide interfaces as a promising platform for exploring quantum interference effects and advancing device concepts in quantum technologies, such as mesoscopic interferometers and quantum sensors.

cond-mat.mes-hall↗

arXiv2024

GameIR: A Large-Scale Synthesized Ground-Truth Dataset for Image Restoration over Gaming Content

Image restoration methods like super-resolution and image synthesis have been successfully used in commercial cloud gaming products like NVIDIA's DLSS. However, restoration over gaming content is not well studied by the general public. The discrepancy is mainly caused by the lack of ground-truth gaming training data that match the test cases. Due to the unique characteristics of gaming content, the common approach of generating pseudo training data by degrading the original HR images results in inferior restoration performance. In this work, we develop GameIR, a large-scale high-quality computer-synthesized ground-truth dataset to fill in the blanks, targeting at two different applications. The first is super-resolution with deferred rendering, to support the gaming solution of rendering and transferring LR images only and restoring HR images on the client side. We provide 19200 LR-HR paired ground-truth frames coming from 640 videos rendered at 720p and 1440p for this task. The second is novel view synthesis (NVS), to support the multiview gaming solution of rendering and transferring part of the multiview frames and generating the remaining frames on the client side. This task has 57,600 HR frames from 960 videos of 160 scenes with 6 camera views. In addition to the RGB frames, the GBuffers during the deferred rendering stage are also provided, which can be used to help restoration. Furthermore, we evaluate several SOTA super-resolution algorithms and NeRF-based NVS algorithms over our dataset, which demonstrates the effectiveness of our ground-truth GameIR data in improving restoration performance for gaming content. Also, we test the method of incorporating the GBuffers as additional input information for helping super-resolution and NVS. We release our dataset and models to the general public to facilitate research on restoration methods over gaming content.

cs.CV↗

arXiv2024

Frequency-Reactive Power Optimization Strategy of Grid-forming Offshore Wind Farm Using DRU-HVDC Transmission

The diode rectifier unit-based high voltage direct current (DRU-HVDC) transmission with grid-forming (GFM) wind turbine is becoming a promising scheme for offshore wind farm(OWF) integration due to its high reliability and low cost. In this scheme, the AC network of the OWF and the DRU has completely different synchronization mechanisms and power flow characteristics from the traditional power system. To optimize the power flow and reduce the net loss, this paper carries out the power flow modeling and optimization analysis for the DRU-HVDC transmission system with grid-forming OWFs. The influence of the DRU and the GFM wind turbines on the power flow of the system is analyzed. On this basis, improved constraint conditions are proposed and an optimal power flow (OPF) method is established. This method can minimize the power loss by adjusting the reactive power output of each wind turbine and internal network frequency. Finally, based on MATLAB, this paper uses YALMIP toolkit and CPLEX mathematical solver to realize the programming solution of the OPF model proposed in this paper. The results show that the proposed optimization strategy can effectively reduce the power loss of the entire OWF and the transmission system with an optimization ratio of network losses exceeding 25.3%.

math.OC↗

arXiv2024

Local Manipulation of Skyrmion Lattice in Fe3GaTe2 at Room Temperature

Motivated by advances in spintronic devices, an extensive exploration is underway to uncover materials that host topologically protected spin textures, exemplified by skyrmions. One critical challenge involved in the potential application of skyrmions in van der Waals (vdW) materials is the attainment and manipulation of skyrmions at room temperature. In this study, we report the creation of intrinsic skyrmion state in van der Waals ferromagnet Fe3GaTe2. By employing variable temperature magnetic force microscopy, the skyrmion lattice can be locally manipulated on Fe3GaTe2 flake. The ordering of skyrmion state is further analyzed. Our result suggest Fe3GaTe2 emerges as a highly promising contender for the realization of skyrmion-based layered spintronic memory devices.

cond-mat.mtrl-sci↗

arXiv2024

Orion-14B: Open-source Multilingual Large Language Models

In this study, we introduce Orion-14B, a collection of multilingual large language models with 14 billion parameters. We utilize a data scheduling approach to train a foundational model on a diverse corpus of 2.5 trillion tokens, sourced from texts in English, Chinese, Japanese, Korean, and other languages. Additionally, we fine-tuned a series of models tailored for conversational applications and other specific use cases. Our evaluation results demonstrate that Orion-14B achieves state-of-the-art performance across a broad spectrum of tasks. We make the Orion-14B model family and its associated code publicly accessible https://github.com/OrionStarAI/Orion, aiming to inspire future research and practical applications in the field.

cs.CL↗

arXiv2023

Small polarons mediated near-room-temperature metal-insulator transition in vanadium dioxide and their hopping dynamics

Researchers pursuing advanced photoelectric devices have discovered near room-temperature metal-insulator transitions (MIT) in non-volatile VO2. Despite theoretical investigations suggesting that polaron dynamics mediate the MIT, direct experimental evidence remains scarce. In this study, we present direct evidence of the polaron state in insulating VO2 through high-resolution spectroscopic ellipsometry measurements and first-principles calculations. We illustrate the complementary role of polaron dynamics in facilitating Peierls and Mott transitions, thereby contributing to the MIT processes. Furthermore, our observations and characterizations of conventional metallic and correlated plasmons in the respective phases of the VO2 film offer valuable insights into their electron structures. This investigation enhances comprehension of the MIT mechanism in correlated systems and underscores the roles of polarons, lattice distortions, and electron correlations in facilitating phase transition processes in strongly-correlated systems. Additionally, the detailed detection of small polarons and plasmons serves as inspiration for the development of new device functionalities.

physics.app-ph↗

arXiv2023

CVTHead: One-shot Controllable Head Avatar with Vertex-feature Transformer

Reconstructing personalized animatable head avatars has significant implications in the fields of AR/VR. Existing methods for achieving explicit face control of 3D Morphable Models (3DMM) typically rely on multi-view images or videos of a single subject, making the reconstruction process complex. Additionally, the traditional rendering pipeline is time-consuming, limiting real-time animation possibilities. In this paper, we introduce CVTHead, a novel approach that generates controllable neural head avatars from a single reference image using point-based neural rendering. CVTHead considers the sparse vertices of mesh as the point set and employs the proposed Vertex-feature Transformer to learn local feature descriptors for each vertex. This enables the modeling of long-range dependencies among all the vertices. Experimental results on the VoxCeleb dataset demonstrate that CVTHead achieves comparable performance to state-of-the-art graphics-based methods. Moreover, it enables efficient rendering of novel human heads with various expressions, head poses, and camera views. These attributes can be explicitly controlled using the coefficients of 3DMMs, facilitating versatile and realistic animation in real-time scenarios.

cs.CV↗

arXiv2023

Multifunctional magnetic oxide-MoS$_2$ heterostructures on silicon

Correlated oxides and related heterostructures are intriguing for developing future multifunctional devices by exploiting their exotic properties, but their integration with other materials, especially on Si-based platforms, is challenging. Here, van der Waals heterostructures of La$_{0.7}$Sr$_{0.3}$MnO$_3$ (LSMO), a correlated manganite perovskite, and MoS$_2$ are demonstrated on Si substrates with multiple functions. To overcome the problems due to the incompatible growth process, technologies involving freestanding LSMO membranes and van der Waals force-mediated transfer are used to fabricate the LSMO-MoS$_2$ heterostructures. The LSMO-MoS$_2$ heterostructures exhibit a gate-tunable rectifying behavior, based on which metal-semiconductor field-effect transistors (MESFETs) with on-off ratios of over 104 can be achieved. The LSMO-MoS$_2$ heterostructures can function as photodiodes displaying considerable open-circuit voltages and photocurrents. In addition, the colossal magnetoresistance of LSMO endows the LSMO-MoS$_2$ heterostructures with an electrically tunable magnetoresponse at room temperature. This work not only proves the applicability of the LSMO-MoS$_2$ heterostructure devices on Si-based platform but also demonstrates a paradigm to create multifunctional heterostructures from materials with disparate properties.

cond-mat.str-el↗

arXiv2023

Competition of electronic correlation and reconstruction in La1-xSrxTiO3/SrTiO3 heterostructures

Electronic correlation and reconstruction are two important factors that play a critical role in shaping the magnetic and electronic properties of correlated low-dimensional systems. Here, we report a competition between the electronic correlation and structural reconstruction in La1-xSrxTiO3/SrTiO3 heterostructures by modulating material polarity and interfacial strain, respectively. The heterostructures exhibit a critical thickness (tc) at which a metal-to-insulator transition (MIT) abruptly occurs at certain thickness, accompanied by the coexistence of two- and three-dimensional (2D and 3D) carriers. Intriguingly, the tc exhibits a V-shaped dependence on the doping concentration of Sr, with the smallest tc value at x = 0.5. We attribute this V-shaped dependence to the competition between the electronic reconstruction (modulated by the polarity) and the electronic correlation (modulated by strain), which are borne out by the experimental results, including strain-dependent electronic properties and the evolution of 2D and 3D carriers. Our findings underscore the significance of the interplay between electronic reconstruction and correlation in the realization and utilization of emergent electronic functionalities in low-dimensional correlated systems.

cond-mat.str-el↗

arXiv2023

Light Field Diffusion for Single-View Novel View Synthesis

Single-view novel view synthesis (NVS), the task of generating images from new viewpoints based on a single reference image, is important but challenging in computer vision. Recent advancements in NVS have leveraged Denoising Diffusion Probabilistic Models (DDPMs) for their exceptional ability to produce high-fidelity images. However, current diffusion-based methods typically utilize camera pose matrices to globally and implicitly enforce 3D constraints, which can lead to inconsistencies in images generated from varying viewpoints, particularly in regions with complex textures and structures. To address these limitations, we present Light Field Diffusion (LFD), a novel conditional diffusion-based approach that transcends the conventional reliance on camera pose matrices. Starting from the camera pose matrices, LFD transforms them into light field encoding, with the same shape as the reference image, to describe the direction of each ray. By integrating light field encoding with the reference image, our method imposes local pixel-wise constraints within the diffusion process, fostering enhanced view consistency. Our approach not only involves training image LFD on the ShapeNet Car dataset but also includes fine-tuning a pre-trained latent diffusion model on the Objaverse dataset. This enables our latent LFD model to exhibit remarkable zero-shot generalization capabilities across out-of-distribution datasets like RTMV as well as in-the-wild images. Experiments demonstrate that LFD not only produces high-fidelity images but also achieves superior 3D consistency in complex regions, outperforming existing novel view synthesis methods.

cs.CV↗