arXiv subjects

Xiaolong Liu

Publications and source records attributed to Xiaolong Liu.

At least 37 records · Page 2Linked to original sources

arXiv2026

Full-channel wavefront manipulation of surface waves with chirality-assisted geometric-phase metasurface

Owing to their localized field enhancement and subwavelength resolution, surface waves (SWs) offer broad application potential in communications, sensing, and photonics via on-chip wavefront manipulation. This makes multi-channel SW wavefront manipulation highly desirable. However, conventional metasurfaces for SW wavefront shaping, relying on geometric and propagation phase mechanisms, typically exhibit similar functionalities for co- or cross-polarized output channels under different circularly polarized (CP) incidences, thereby limiting the development of high-capacity on-chip integrated devices. Here, by introducing the chirality-assisted phase as an additional phase control mechanism, we effectively decouple both co- and cross-polarized output channels, enabling independent SW wavefront shaping in four distinct channels. We numerically and experimentally demonstrate two metasurfaces in the microwave range: a four-channel SW meta-deflector and a four-channel SW metadevice that simultaneously produces a focused SW beam, a SW Bessel beam, and two deflected SW beams in different directions. Therefore, chirality-assisted geometric-phase metasurfaces provide a versatile platform for multi-channel SW wavefront engineering, offering significant potential for high-capacity on-chip communication and integrated photonic systems.

physics.optics↗

arXiv2026

Quasiparticle Interference Kernel Extraction with Variational Autoencoders via Latent Alignment

Quasiparticle interference (QPI) imaging is a powerful tool for probing electronic structures in quantum materials, but extracting the single-scatterer QPI pattern (i.e., the kernel) from a multi-scatterer image remains a fundamentally ill-posed inverse problem, because many different kernels can combine to produce almost the same observed image, and noise or overlaps further obscure the true signal. Existing solutions to this extraction problem rely on manually zooming into small local regions with isolated single-scatterers. This is infeasible for real cases where scattering conditions are too complex. In this work, we propose the first AI-based framework for QPI kernel extraction, which models the space of physically valid kernels and uses this knowledge to guide the inverse mapping. We introduce a two-step learning strategy that decouples kernel representation learning from observation-to-kernel inference. In the first step, we train a variational autoencoder to learn a compact latent space of scattering kernels. In the second step, we align the latent representation of QPI observations with those of the pre-learned kernels using a dedicated encoder. This design enables the model to infer kernels robustly under complex, entangled scattering conditions. We construct a diverse and physically realistic QPI dataset comprising 100 unique kernels and evaluate our method against a direct one-step baseline. Experimental results demonstrate that our approach achieves significantly higher extraction accuracy, improved generalization to unseen kernels. To further validate its effectiveness, we also apply the method to real QPI data from Ag and FeSe samples, where it reliably extracts meaningful kernels under complex scattering conditions.

cs.LG↗

arXiv2025

DTCCL: Disengagement-Triggered Contrastive Continual Learning for Autonomous Bus Planners

Autonomous buses run on fixed routes but must operate in open, dynamic urban environments. Disengagement events on these routes are often geographically concentrated and typically arise from planner failures in highly interactive regions. Such policy-level failures are difficult to correct using conventional imitation learning, which easily overfits to sparse disengagement data. To address this issue, this paper presents a Disengagement-Triggered Contrastive Continual Learning (DTCCL) framework that enables autonomous buses to improve planning policies through real-world operation. Each disengagement triggers cloud-based data augmentation that generates positive and negative samples by perturbing surrounding agents while preserving route context. Contrastive learning refines policy representations to better distinguish safe and unsafe behaviors, and continual updates are applied in a cloud-edge loop without human supervision. Experiments on urban bus routes demonstrate that DTCCL improves overall planning performance by 48.6 percent compared with direct retraining, validating its effectiveness for scalable, closed-loop policy improvement in autonomous public transport.

cs.RO↗

arXiv2025

Visualizing Pair-breaking Scattering Interference in Bulk FeSe

Spatially periodic modulations of the superconducting gap have been recently reported in diverse materials and are often attributed to pair density wave order. An alternative mechanism, termed pair-breaking scattering interference (PBSI), was proposed to produce gap modulations without finite-momentum pairing. Here we investigate signatures of PBSI in bulk FeSe using scanning tunneling microscopy with superconductive tips, enabling enhanced energy resolution and Josephson tunneling. Subsurface magnetic scatterers with Yu-Shiba-Rusinov states are identified in FeSe, around which we observe particle-hole symmetric gap modulations accompanied by spatial modulation of the Josephson current. Those modulations have wavevectors consistent with intra-pocket PBSI. We further demonstrate that phase-referenced quasiparticle interference imaging offers an independent and direct probe of PBSI beyond gap mapping. These results establish PBSI as a viable origin of gap modulations in superconductors lacking preexisting charge/spin density wave orders, and motivate further investigation of the intriguing gap modulation phenomenology.

cond-mat.supr-con↗

arXiv2025

Locomotion in CAVE: Enhancing Immersion through Full-Body Motion

Cave Automatic Virtual Environment (CAVE) is one of the virtual reality (VR) immersive devices currently used to present virtual environments. However, the locomotion methods in the CAVE are limited by unnatural interaction methods, severely hindering the user experience and immersion in the CAVE. We proposed a locomotion framework for CAVE environments aimed at enhancing the immersive locomotion experience through optimized human motion recognition technology. Firstly, we construct a four-sided display CAVE system, then through the dynamic method based on Perspective-n-Point to calibrate the camera, using the obtained camera intrinsics and extrinsic parameters, and an action recognition architecture to get the action category. At last, transform the action category to a graphical workstation that renders display effects on the screen. We designed a user study to validate the effectiveness of our method. Compared to the traditional methods, our method has significant improvements in realness and self-presence in the virtual environment, effectively reducing motion sickness.

cs.GR↗

arXiv2025

NIR-II Fluorescence Project Technology for Augmented Reality Surgical Navigation

NIR-II fluorescence imaging provides superior tissue penetration and clarity, yet its clinical use in surgical navigation is hindered by a critical workflow issue. Surgeons must divert their attention between the operative field and external monitors, increasing cognitive load and disrupting procedures. Current strategies have failed to resolve this fundamental problem. Here, we developed a co-axial NIR-II fluorescence projection navigation system to enable real-time, in situ visualization. This system creates an intraoperative augmented reality by directly projecting high-precision, pseudocolored fluorescence images onto the surgical field, spatially integrating functional signals with patient anatomy. Validated through in vitro, in vivo, and clinical patient studies, our system eliminates visual field switching, reduces intraoperative distraction, and preserves natural stereoscopic vision. This approach represents a paradigm shift toward a more coherent, efficient, and ergonomically optimized optical imaging modality for surgical navigation.

physics.optics↗

arXiv2025

Hierarchical LoRA MoE for Efficient CTR Model Scaling

Deep models have driven significant advances in click-through rate (CTR) prediction. While vertical scaling via layer stacking improves model expressiveness, the layer-by-layer sequential computation poses challenges to efficient scaling. Conversely, horizontal scaling through Mixture of Experts (MoE) achieves efficient scaling by activating a small subset of experts in parallel, but flat MoE layers may struggle to capture the hierarchical structure inherent in recommendation tasks. To push the Return-On-Investment (ROI) boundary, we explore the complementary strengths of both directions and propose HiLoMoE, a hierarchical LoRA MoE framework that enables holistic scaling in a parameter-efficient manner. Specifically, HiLoMoE employs lightweight rank-1 experts for parameter-efficient horizontal scaling, and stacks multiple MoE layers with hierarchical routing to enable combinatorially diverse expert compositions. Unlike conventional stacking, HiLoMoE routes based on prior layer scores rather than outputs, allowing all layers to execute in parallel. A principled three-stage training framework ensures stable optimization and expert diversity. Experiments on four public datasets show that HiLoMoE achieving better performance-efficiency tradeoff, achieving an average AUC improvement of 0.20\% in AUC and 18.5\% reduction in FLOPs compared to the non-MoE baseline.

cs.LG↗

arXiv2025

Monolithic Expandable-FOV Metalens Enabled by Radially Gradient-Tilted Meta-Atoms

Metalens, as the most promising and applicable emerging optical device, has long been constrained by the limited field of view (FOV). Recent studies employing phase engineering or multi-layer strategies have made some progress, but they all rely on upright meta-atoms. This leads us to consider whether tilted meta-atoms could represent a promising yet underexplored approach for enhancing the field of view? In this work, we introduce the control of the tilt angle of meta-atoms as a new degree of freedom into the design of metalenses and propose a wide-field-of-view (WFOV) metalens design framework utilizing radially gradient-tilted meta-atoms. Based on the proposed method, we designed two WFOV metalenses with distinct tilt configurations to meet specific application requirements of efficiency or precision. Simulation results demonstrate that both designs, exhibiting distinct performance characteristics, achieve diffraction-limited focusing within a 120 degree FOV. Additionally, the FOV can be expanded with this method by tuning the tilt angle configurations of meta-atoms and the metalens diameter. These findings will establish a promising pathway towards compact optical systems capable of combining ultra-wide angular coverage with high-resolution imaging.

physics.optics↗

arXiv2025

InterFormer: Effective Heterogeneous Interaction Learning for Click-Through Rate Prediction

Click-through rate (CTR) prediction, which predicts the probability of a user clicking an ad, is a fundamental task in recommender systems. The emergence of heterogeneous information, such as user profile and behavior sequences, depicts user interests from different aspects. A mutually beneficial integration of heterogeneous information is the cornerstone towards the success of CTR prediction. However, most of the existing methods suffer from two fundamental limitations, including (1) insufficient inter-mode interaction due to the unidirectional information flow between modes, and (2) aggressive information aggregation caused by early summarization, resulting in excessive information loss. To address the above limitations, we propose a novel module named InterFormer to learn heterogeneous information interaction in an interleaving style. To achieve better interaction learning, InterFormer enables bidirectional information flow for mutually beneficial learning across different modes. To avoid aggressive information aggregation, we retain complete information in each data mode and use a separate bridging arch for effective information selection and summarization. Our proposed InterFormer achieves state-of-the-art performance on three public datasets and a large-scale industrial dataset.

cs.IR↗

arXiv2025

g-Factor Enhanced Upper Critical Field in Superconducting PdTe2 due to Quantum Confinement

The Pauli limiting field of superconductors determines the maximal possible value of magnetic field at which superconductivity remains possible. For weak-coupling superconductors, it is determined by an established relation that can be found by setting the condensation energy equal to the magnetization free energy. The latter is a function of the carrier g-factor. Here, we demonstrate in a van der Waals superconductor PdTe2, that quantum confinement can tune the effective g-factor causing the Pauli limit to become thickness dependent. We experimentally probe the in-plane upper critical field (Hc2||) of PdTe2 at intermediate thicknesses down to 20mK. Hc2|| is enhanced by more than an order of magnitude as the thickness is varied from 50nm down to 19nm. We model its temperature and thickness dependence, revealing that both orbital and spin Zeeman depairing mechanisms impact its value. While the variation of the orbital interaction is expected, our findings reveal how the Zeeman interaction impacts superconductivity in thin films. They aid in the search for mixed and odd pairing superconductivity where an enhancement of Hc2|| can be occasionally associated with those unconventional pairing symmetries.

cond-mat.supr-con↗

arXiv2025

Atomic-scale Frustrated Josephson Coupling and Multi-condensate Visualization in FeSe

In a Josephson junction involving multi-band superconductors, competition between inter-band and inter-junction Josephson coupling gives rise to frustration and spatial disjunction of superfluid densities among superconducting condensates. Such frustrated coupling manifests as quantum interference of Josephson currents from different tunneling channels and becomes tunable if channel transparency can be varied. To explore these unconventional effects in the prototypical $s^\pm$-wave superconductor FeSe, we use atomic resolution scanned Josephson tunneling microscopy SJTM for condensate resolved imaging and junction tuning -- capabilities unattainable in macroscopic Josephson devices with fixed characteristics. We quantitatively demonstrate frustrated Josephson tunneling by examining two tunneling inequalities. The relative transparency of two parallel tunneling pathways is found tunable, revealing a tendency towards a 0-pi transition with decreasing SJTM junction resistance. Simultaneous visualization of both superconducting condensates reveals anti correlated superfluid modulations, highlighting the role of inter-band scattering. Our study establishes SJTM as a powerful tool enabling new research frontiers of multi condensate superconductivity.

cond-mat.supr-con↗

arXiv2025

Odd-Parity Quasiparticle Interference in the Superconductive Surface State of UTe2

Although no known material exhibits intrinsic topological superconductivity, wherein spin-triplet odd-parity electron pairing occurs, UTe2 is now the leading representative of this class. Conventionally, the parity of the superconducting order parameter may be established by using Bogoliubov quasiparticle interference (QPI) imaging. However, odd-parity superconductors should support a topological quasiparticle surface band (QSB) at energies within the maximum superconducting energy gap. QPI would then be dominated by the electronic structure of the QSB and only reveal the characteristics of the bulk order parameter excursively. Here, we visualize quasiparticle interference patterns of UTe2 and find that, at the (0-11) cleave surface, a new band of Bogoliubov quasiparticles appears only in the superconducting state. QPI visualization then allows study of dispersion of states within this QSB, which we demonstrate exists only within the range of Fermi momenta projected onto the (0-11) surface. Finally, we develop a theoretical framework to predict the QPI signatures of such a QSB at the (0-11) surface of UTe2. Its predictions are most consistent with the experimental results if the bulk superconducting gap function exhibits time-reversal conserving, odd-parity, a-axis nodal, B3u symmetry.

cond-mat.supr-con↗

arXiv2025

Observation of Persistent Zero Modes and Superconducting Vortex Doublets in UTe$_2$

Superconducting vortices can reveal electron pairing details and nucleate topologically protected states. Yet, vortices of bulk spin-triplet superconductors have never been visualized. Recently, UTe$_2$ has emerged as a nominative spin-triplet superconductor, but its superconducting order parameter is elusive, and whether time-reversal symmetry is broken remains unsettled. Here, we visualize superconducting vortices on the (011) surface of ultra-clean UTe$_2$ single crystals ($T_c=2.1 K$) using scanning tunneling microscopy (STM). We introduce $\frac{d^2 I}{dV^2}$ imaging as an effective method for vortex visualization in superconductors with substantial residual zero-energy density of states (DOS), as in UTe$_2$. Anisotropic single-flux-quantum vortices, with coherence lengths of $\sim$ 12 nm (4 nm) parallel (perpendicular) to the a-axis, form a triangular vortex lattice (VL) under small out-of-plane magnetic fields. The vortex size decreases at higher fields, suggesting multi-band superconductivity. The robustness of vortex structures and VL against magnetic field polarity and cooling history strongly supports time-reversal invariant superconductivity. Spatially non-splitting and spectroscopically narrow zero-bias peaks (ZBPs) of differential conductance at vortex cores (VCs) persist up $8T$, well beyond the Pauli limit ($3.9T$), consistent with Majorana zero modes (MZMs) in a topological vortex line protected by a combination of mirror, time-reversal, and particle-hole symmetries. Close examination of vortex structures reveals a mirror-asymmetric doublet $--$ one with a singular core and another with an enhanced energy gap. Such unconventional vortices in a non-chiral $p$-wave superconductor could originate from the induction of a subdominant order parameter close to the VCs.

cond-mat.supr-con↗

arXiv2025

JoyType: A Robust Design for Multilingual Visual Text Creation

Generating images with accurately represented text, especially in non-Latin languages, poses a significant challenge for diffusion models. Existing approaches, such as the integration of hint condition diagrams via auxiliary networks (e.g., ControlNet), have made strides towards addressing this issue. However, diffusion models often fall short in tasks requiring controlled text generation, such as specifying particular fonts or producing text in small fonts. In this paper, we introduce a novel approach for multilingual visual text creation, named JoyType, designed to maintain the font style of text during the image generation process. Our methodology begins with assembling a training dataset, JoyType-1M, comprising 1 million pairs of data. Each pair includes an image, its description, and glyph instructions corresponding to the font style within the image. We then developed a text control network, Font ControlNet, tasked with extracting font style information to steer the image generation. To further enhance our model's ability to maintain font style, notably in generating small-font text, we incorporated a multi-layer OCR-aware loss into the diffusion process. This enhancement allows JoyType to direct text rendering using low-level descriptors. Our evaluations, based on both visual and accuracy metrics, demonstrate that JoyType significantly outperforms existing state-of-the-art methods. Additionally, JoyType can function as a plugin, facilitating the creation of varied image styles in conjunction with other stable diffusion models on HuggingFace and CivitAI. Our project is open-sourced on https://jdh-algo.github.io/JoyType/.

cs.CV↗

arXiv2025

KGARevion: An AI Agent for Knowledge-Intensive Biomedical QA

Biomedical reasoning integrates structured, codified knowledge with tacit, experience-driven insights. Depending on the context, quantity, and nature of available evidence, researchers and clinicians use diverse strategies, including rule-based, prototype-based, and case-based reasoning. Effective medical AI models must handle this complexity while ensuring reliability and adaptability. We introduce KGARevion, a knowledge graph-based agent that answers knowledge-intensive questions. Upon receiving a query, KGARevion generates relevant triplets by leveraging the latent knowledge embedded in a large language model. It then verifies these triplets against a grounded knowledge graph, filtering out errors and retaining only accurate, contextually relevant information for the final answer. This multi-step process strengthens reasoning, adapts to different models of medical inference, and outperforms retrieval-augmented generation-based approaches that lack effective verification mechanisms. Evaluations on medical QA benchmarks show that KGARevion improves accuracy by over 5.2% over 15 models in handling complex medical queries. To further assess its effectiveness, we curated three new medical QA datasets with varying levels of semantic complexity, where KGARevion improved accuracy by 10.4%. The agent integrates with different LLMs and biomedical knowledge graphs for broad applicability across knowledge-intensive tasks. We evaluated KGARevion on AfriMed-QA, a newly introduced dataset focused on African healthcare, demonstrating its strong zero-shot generalization to underrepresented medical contexts.

cs.AI↗

arXiv2025

Training Large Recommendation Models via Graph-Language Token Alignment

Recommender systems (RS) have become essential tools for helping users efficiently navigate the overwhelming amount of information on e-commerce and social platforms. However, traditional RS relying on Collaborative Filtering (CF) struggles to integrate the rich semantic information from textual data. Meanwhile, large language models (LLMs) have shown promising results in natural language processing, but directly using LLMs for recommendation introduces challenges, such as ambiguity in generating item predictions and inefficiencies in scalability. In this paper, we propose a novel framework to train Large Recommendation models via Graph-Language Token Alignment. By aligning item and user nodes from the interaction graph with pretrained LLM tokens, GLTA effectively leverages the reasoning abilities of LLMs. Furthermore, we introduce Graph-Language Logits Matching (GLLM) to optimize token alignment for end-to-end item prediction, eliminating ambiguity in the free-form text as recommendation results. Extensive experiments on three benchmark datasets demonstrate the effectiveness of GLTA, with ablation studies validating each component.

cs.IR↗

arXiv2025

Pair Wavefunction Symmetry in UTe2 from Zero-Energy Surface State Visualization

Although nodal spin-triplet topological superconductivity appears probable in UTe2, its superconductive order-parameter $Δ_k$ remains unestablished. In theory, a distinctive identifier would be the existence of a superconductive topological surface band (TSB), which could facilitate zero-energy Andreev tunneling to an s-wave superconductor, and also distinguish a chiral from non-chiral $Δ_k$ via enhanced s-wave proximity. Here we employ s-wave superconductive scan-tips and detect intense zero-energy Andreev conductance at the UTe2 (0-11) termination surface. Imaging reveals sub-gap quasiparticle scattering interference signatures with a-axis orientation. The observed zero-energy Andreev peak splitting with enhanced s-wave proximity, signifies that $Δ_k$ of UTe2 is a non-chiral state: B1u, B2u or B3u. However, if the quasiparticle scattering along the a-axis is internodal, then a non-chiral B3u state is the most consistent for UTe2.

cond-mat.supr-con↗

arXiv2025

Robust density estimation over star-shaped density classes

We establish a novel criterion for comparing the performance of two densities, $g_1$ and $g_2$, within the context of corrupted data. Utilizing this criterion, we propose an algorithm to construct a density estimator within a star-shaped density class, $\mathcal{F}$, under conditions of data corruption. We proceed to derive the minimax upper and lower bounds for density estimation across this star-shaped density class, characterized by densities that are uniformly bounded above and below (in the sup norm), in the presence of adversarially corrupted data. Specifically, we assume that a fraction $ε\leq \frac{1}{3}$ of the $N$ observations are arbitrarily corrupted. We obtain the minimax upper bound $\max\{ τ_{\overline{J}}^2, ε\} \wedge d^2$. Under certain conditions, we obtain the minimax risk, up to proportionality constants, under the squared $L_2$ loss as $$ \max\left\{ τ^{*2} \wedge d^2, ε\wedge d^2 \right\}, $$ where $τ^* := \sup\left\{ τ: Nτ^2 \leq \log \mathcal{M}_{\mathcal{F}}^{\text{loc}}(τ, c) \right\}$ for a sufficiently large constant $c$. Here, $\mathcal{M}_{\mathcal{F}}^{\text{loc}}(τ, c)$ denotes the local entropy of the set $\mathcal{F}$, and $d$ is the $L_2$ diameter of $\mathcal{F}$.

math.ST↗