arXiv subjects

Xiaofeng Xu

Publications and source records attributed to Xiaofeng Xu.

At least 19 recordsLinked to original sources

arXiv2026

Enhanced anomalous Nernst effect in Pr-doped kagome and honeycomb magnet LaCo$_5$

In this work, we report the successful synthesis of La$_{1-x}$Pr$_x$Co$_5$ ($x = 0.09, 0.21, 0.45$) single crystals. Magnetic field-dependent magnetization measurements reveal that Pr substitution induces negligible changes in the magnetic properties of LaCo$_5$, with the ferromagnetic ordering predominantly governed by the Co sublattices. Remarkably, the doped systems exhibit significant enhancements in the anomalous Nernst effect. At 300~K, the anomalous Nernst thermopower $S^A_{yx}$ reaches 6.5~$\mathrm{μV/K}$ in La$_{0.55}$Pr$_{0.45}$Co$_5$, corresponding to a $\sim$ 40 \% enhancement compared to the parent compound. This significant improvement can be predominantly attributed to Pr-doping-induced Fermi level modification, which directly leads to a redistribution of Berry curvature across the Fermi surface. This work highlights the effectiveness of Pr doping in boosting the anomalous Nernst effect of La$_{1-x}$Pr$_x$Co$_5$, offering a practical strategy to design advanced materials for room-temperature energy-harvesting technologies and high-efficiency thermal sensing devices.

cond-mat.mtrl-sci↗

arXiv2026

Emergent magnetism, heavy electrons and pressure-induced reentrant superconductivity in the iron substituted 4d transition-metal sulfides

Superconductivity emerging from or in the vicinity of magnetic states is generally considered to be mediated by spin fluctuations and thus lies beyond the scope of conventional electron-phonon coupled BCS framework. Here we report the emergence of novel ferromagnetism in the d-electron rhodium sulfide Rh17S15 superconductor, characterized by an enhanced Sommerfeld coefficient γ arising from the flat topological band and many-body correlations. We further demonstrate that the ferromagnetism can be tuned via Fe substitution at the Rh sites, leading to a spin glass ground state induced by the competing ferromagnetic and antiferromagnetic exchange interactions. Fe doping results in a further enhancement of both the γ and electron effective masses. At a doping level of x = 0.67 in Rh17-xFexS15, γ reaches 312 mJ mol-1K-2, second only to the well-documented d-electron heavy-fermion material LiV2O4. Furthermore, upon applying pressure, superconductivity is first suppressed; under high pressures, however, we observe the reentrant superconductivity in both pristine and Fe-doped samples. Our results not only demonstrate the unusual magnetic states and possible heavy-fermion features in these frustration-free, d-based superconductors, but also suggest that the superconductivity in this system is likely mediated by the intrinsic spin fluctuations and may thus be unconventional.

cond-mat.supr-con↗

arXiv2026

Observation of Magnetic-Anisotropy Crossover and High-Temperature Skyrmions in the Dirac Magnet Fe3Ge with a Distorted Kagome Lattice

Topological materials that simultaneously host robust high-temperature skyrmions and nontrivial electronic band structures have attracted tremendous interest owing to their distinctive advantages for both fundamental research and prospective technological applications. Here, we report the observation of robust skyrmions in the Dirac kagome magnet Fe3Ge, which exhibits a high Curie temperature of ~ 650 K. At room temperature, Fe3Ge shows a large intrinsic anomalous Hall conductivity of ~ 380 Ω-1cm-1, originating from its nontrivial electronic band topology. Systematic magnetization measurements reveal a spin reorientation transition at ~ 375 K, indicating a crossover from easy-plane to easy-axis magnetic anisotropy. Below the spin reorientation temperature, a large topological Hall effect is observed, arising from microscopic noncoplanar spin structures. Lorentz transmission electron microscopy shows that mesoscopic skyrmions are stabilized in the easy-axis magnetic anisotropy regime and persist over an exceptionally wide temperature window of 375-650 K, far exceeding that of most previously reported skyrmion-hosting materials. These results establish Fe3Ge as a promising platform for exploring diverse topological properties, with strong potential for advancing future high-temperature spintronic applications, ranging from next-generation information storage to logic computing devices.

cond-mat.mtrl-sci↗

arXiv2026

Intersection matrices associated to geometric-ordered bases of Feynman integrals

In integration-by-parts reduction of Feynman integrals, the order relation in the Laporta algorithm determines a set of master integrals. In this paper we investigate the intersection matrices of the integrands of the master integrals that are obtained from a geometric order relation. With an appropriate definition of integrands and their duals, we find that the intersection matrices are simpler than expected: For a filtration-compatible basis, the entries of the intersection matrix are Laurent polynomials in the dimensional regularisation parameter $\varepsilon$. For an $\varepsilon$-factorised basis, the entries are instead integers, up to an overall power of $\varepsilon$, if the boundary values for the auxiliary functions of the rotation are chosen appropriately. This has practical consequences: We can systematically eliminate certain auxiliary transcendental functions, introduced in going from a filtration-compatible basis to an $\varepsilon$-factorised basis. We provide an algorithm that performs this elimination while minimising the number of required calculations.

hep-th↗

arXiv2026

Neural operator-based digital twins for modeling amyloid-$β$ and tau propagation and treatment optimization in Alzheimer's disease

Accurately predicting the spatiotemporal evolution of amyloid-$β$ and tau proteins at the individual level is critical for improving the diagnosis and treatment of Alzheimer's disease. We consider the problem of constructing patient-specific digital twins that model the propagation of these biomarkers on the cortical surface using reaction--diffusion dynamics. A major challenge is that the underlying nonlinear aggregation mechanisms are unknown and must be inferred from sparse, noisy, and heterogeneous longitudinal PET imaging data. To address this, we develop a data-driven framework that learns biomarker dynamics directly from clinical observations. The approach combines operator learning with reduced-order representations to infer governing equations of disease progression from data. Using this framework, we achieve predictive accuracies of 87\% for amyloid-$β$ and 81\% for tau. Building on the learned dynamics, we further formulate a PDE-constrained optimal control problem to design personalized therapeutic strategies that regulate pathological protein propagation. By integrating data-driven dynamical modeling with treatment optimization, the proposed digital twin framework provides an interpretable and predictive platform for understanding disease progression and enabling precision interventions in neurodegenerative disorders.

cs.LG↗

arXiv2026

LineageMark: Multi-user White-box Watermarking for Contribution Tracing in Model Derivation Chains

In open large language model (LLM) ecosystems, models are frequently adapted across multiple domains and applications, forming multi-stage derivation chains. Consequently, tracking and verifying historical contributions is essential for model provenance and intellectual property protection. However, existing watermarking methods are mainly designed for single-user, one-time embeddings, often fail under repeated model derivation and incremental updates. To address this problem, we propose LineageMark, a multi-user white-box watermarking framework for model derivation chains. The framework encodes watermarks in model parameters using a projection-based approach. Stable carriers are first selected to reduce sensitivity to model changes, each watermark bit is then represented as a projection statistic over these carriers. Additional watermark insertions introduce only bounded perturbations in the projection space, and margin constraints are used to maintain signal integrity. We evaluate the effectiveness of LineageMark in multi-stage model derivation chains. Experimental results show that LineageMark preserves contributor watermarks across multi-stage derivation and supports incremental multi-user watermark insertion. Furthermore, it exhibits robustness against perturbations such as re-watermarking, fine-tuning, quantization, and pruning.

cs.CR↗

arXiv2026

Superconductivity in the pressure-amorphized topological insulator CrP$_4$

The interplay among superconductivity, magnetism, and nontrivial band topology represents one of the most compelling frontiers in condensed matter physics. The exploration of novel superconductivity in 3d transition-metal compounds, particularly the rare Cr-based systems containing strongly magnetic Cr ions, has long attracted attention owing to their unconventional pairing mechanisms that challenge conventional wisdom. Yet, Cr-based superconductors remain scarce, especially those possessing nontrivial topological character, underscoring the urgent need to uncover new members. Here we report the observation of superconductivity in pressure-amorphized Cr-based topological insulator CrP$_4$. Upon compression, CrP$_4$ undergoes an anomalous quantum phase transition from a metallic to a semiconducting-like state at around 15 GPa, driven by significant changes in the electronic structure. At approximately 70 GPa, re-metallization with superconductivity occurs alongside an irreversible amorphization. The superconducting transition temperature Tc increases monotonically with pressure, reaching 4.8 K at 141.3 GPa. Furthermore, theoretical calculations predict multiple topological phase transitions from a strong topological insulator to a trivial state and finally back to a strong topological state under pressure. Our study not only establishes CrP$_4$ as the first Cr-based amorphous superconductor but also opens a new paradigm for exploring superconducting and topological properties in amorphous materials.

cond-mat.supr-con↗

arXiv2026

New algorithms for Feynman integral reduction and $\varepsilon$-factorised differential equations

In this paper, we give a detailed account of the algorithm outlined in [1] for Feynman integral reduction and $\varepsilon$-factorised differential equations. The algorithm consists of two steps. In the first step, we use a new geometric order relation in the integration-by-parts reduction to obtain a basis of master integrals, whose differential equations on the maximal cut are of a Laurent polynomial form in the regularisation parameter $\varepsilon$ and compatible with a filtration. This step works entirely with rational functions. In a second step, we provide a method to $\varepsilon$-factorise the aforementioned Laurent differential equations. The second step may introduce algebraic and transcendental functions. We illustrate the versatility of the algorithm by applying it to different examples with a wide range of complexity.

hep-th↗

arXiv2026

Giant anomalous Hall conductivity in frustrated magnet EuCo2Al9

The interaction between conduction electrons and localized magnetic moments profoundly influences the electrical and magnetic properties of materials, giving rise to a variety of fascinating physical phenomena and quantum effects. Here, we discover a giant anomalous Hall effect (AHE) in a frustrated Eu-based magnet, exhibiting a giant anomalous Hall conductivity (AHC) of 31000 Ω-1cm-1 and a remarkable anomalous Hall angle (AHA, tanθH) of 12 %--surpassing conventional mechanisms (either intrinsic or extrinsic) by two orders of magnitude. Combining magnetotransport, quantum oscillations, neutron diffraction and ab initio calculations, we establish that the giant AHC originates from fluctuating spin chirality skew scattering, generated by indirect Ruderman-Kittel-Kasuya-Yosida (RKKY) interactions of Eu-4f moments. Simultaneously, Hund's coupling of itinerant electrons and localized Eu-4f spins triggers giant exchange splitting, evidenced by temperature-dependent Fermi surface reconstruction. This work establishes a frustrated magnetic platform for engineering the AHE and elucidates the governing role of exchange interactions and spin textures in quantum transport, while also providing a framework for designing unconventional spintronic systems that harness emergent spin-texture dynamics.

cond-mat.str-el↗

arXiv2026

WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback

As large language models (LLMs) continue to advance, aligning these models with human preferences has emerged as a critical challenge. Traditional alignment methods, relying on human or LLM annotated datasets, are limited by their resource-intensive nature, inherent subjectivity, misalignment with real-world user preferences, and the risk of feedback loops that amplify model biases. To overcome these limitations, we introduce WildFeedback, a novel framework that leverages in-situ user feedback during conversations with LLMs to create preference datasets automatically. Given a corpus of multi-turn user-LLM conversation, WildFeedback identifies and classifies user feedback to LLM responses between conversation turns. The user feedback is then used to create examples of preferred and dispreferred responses according to users' preference. Our experiments demonstrate that LLMs fine-tuned on WildFeedback dataset exhibit significantly improved alignment with user preferences, as evidenced by both traditional benchmarks and our proposed checklist-guided evaluation. By incorporating in-situ feedback from actual users, WildFeedback addresses the scalability, subjectivity, and bias challenges that plague existing approaches, marking a significant step toward developing LLMs that are more responsive to the diverse and evolving needs of their users.

cs.CL↗

arXiv2026

A time grating approach to ultrahigh-Q guided mode resonance

Guided mode resonance (GMR), the resonant coupling of free-space light into leaky waveguide modes, is traditionally achieved with periodic patterned structures. However, this approach makes its key properties such as quality factor (Q-factor) fabrication-dependent and non-tunable. Here, we introduce a time grating platform, i.e., a homogeneous waveguide whose refractive index is modulated periodically in time, that allows tunable GMRs through temporal modulation engineering rather than spatial structural redesign. We show that the Q-factors of these GMRs diverge as the modulation depth vanishes. Furthermore, unconstrained by energy conservation, the resonances exhibit near-unity reflection for fundamental harmonics and values exceeding 40 for first-order harmonics. Our findings not only apply to yield a giant Goos-Hänchen shift over 103 times wavelength without sacrificing the reflection magnitude, but also open new avenues for related phenomena such as bound states in the continuum, unidirectional GMRs and beyond.

physics.optics↗

arXiv2026

The geometric bookkeeping guide to Feynman integral reduction and $\varepsilon$-factorised differential equations

We report on three improvements in the context of Feynman integral reduction and $\varepsilon$-factorised differential equations: Firstly, we show that with a specific choice of prefactors, we trivialise the $\varepsilon$-dependence of the integration-by-parts identities. Secondly, we observe that with a specific choice of order relation in the Laporta algorithm, we directly obtain a basis of master integrals, whose differential equation on the maximal cut is in Laurent polynomial form with respect to $\varepsilon$ and compatible with a particular filtration. Thirdly, we prove that such a differential equation can always be transformed to an $\varepsilon$-factorised form. This provides a systematic algorithm to obtain an $\varepsilon$-factorised differential equation for any Feynman integral. Furthermore, the choices for the prefactors and the order relation significantly improve the efficiency of the reduction algorithm.

hep-th↗

arXiv2026

Enhancement of metallicity by Na doping in La$_3$Ni$_2$O$_{7+δ}$

The observation of high-$T_c$ superconductivity in bilayer nickelate La$_3$Ni$_2$O$_7$ under high pressure provides a new venue for exploring novel unconventional superconductors and elucidating the mechanism of high-$T_c$ superconductivity. Subsequently, numerous chemical substitution studies have been reported, aiming to stabilize superconductivity at ambient pressure, or significantly reduce the pressure threshold required for its occurrence. Here, we report the comprehensive study on sodium (Na) doping in the Ruddlesden-Popper nickelate La$_3$Ni$_2$O$_{7+δ}$, where Na$^+$ substitutes for La$^{3+}$ at the A-site with varying doping concentrations. The structural, thermal, magnetic, and electronic transport properties of as-synthesized polycrystalline samples were systematically investigated. X-ray diffraction (XRD) analysis reveals that Na doping induces a structural transition from the '327' Amam phase to the '4310' Bmab phase when $x\geq0.075$, which is further corroborated by thermogravimetric analysis (TGA) measurements. Substitution of La$^{3+}$ with Na$^+$ gives rise to a gradual expansion of the '327' phase lattice. Meanwhile, resistivity measurements indicate that the density wave (DW) transition is marginally suppressed and metallicity is significantly enhanced. Upon the application of pressure, DW transition can be further suppressed, whereas the low-$T$ insulating behaviors remain insensitive to pressure. These results offer critical insights into the roles of elemental substitution and charge carrier doping in steering the competing electronic phases in layered nickelates.

cond-mat.supr-con↗

arXiv2026

An algorithm towards $\varepsilon$-factorising Feynman Integrals

In this talk, we use several examples to elaborate on how a recently proposed algorithm can turn non-trivial Feynman integrals into an $\varepsilon $-factorised manner, regardless of their hidden geometric essence. In particular, some extra details about three-loop banana integrals with unequal-mass configuration are provided.

hep-th↗

arXiv2026

Pressure-induced reentrant superconductivity in a misfit layered compound $\mathrm{(SnS)_{1.15}(TaS_2)}$

Misfit layered compounds are natural van der Waals heterostructures in which electronically active transition-metal dichalcogenide layers are decoupled by incommensurate blocking layers, enabling bulk realization of quasi-two-dimensional quantum states. Here we investigate the superconducting, transport,and structural properties of the misfit compound $\mathrm{(SnS)_{1.15}(TaS_2)}$ under pressures up to 150 GPa. The low-pressure superconducting phase is gradually suppressed and disappears near 14.7 GPa,accompanied by increasing residual resistance. Remarkably, a distinct superconducting phase reemerges above 80 GPa and persists to the highest pressures achieved. This reentrant superconductivity follows a pressure-induced sign reversal of the Hall coefficient near 60 GPa and a nonmonotonic evolution of the normal-state resistance, indicating an electronic reconstruction. No structural phase transition is detected over the entire pressure range. Our results demonstrate a pressure-driven electronic reconstruction leading to reentrant superconductivity in a misfit layered compound, establishing pressure as an effective route to engineer superconductivity and electronic states in natural van der Waals heterostructures.

cond-mat.supr-con↗

arXiv2026

Improving integration-by-parts and differential equations

In this talk, we discuss how ideas from geometry help to improve Feynman integral reduction and the construction of $\varepsilon$-factorised differential equations. In particular, we outline a systematic procedure to obtain an $\varepsilon$-factorised differential equation for any Feynman integral.

hep-th↗

arXiv2026

Ferroelectricity in Atomically Thin Metallic TaNiTe$_5$ with Ultrahigh Carrier Density

Ferroelectric metals, characterized by the coexistence of ferroelectricity and metallic conductivity, present a fundamental challenge due to the screening effect of free charge carriers on the long-range electric dipole order. Existing strategies to circumvent this obstacle include employing two-dimensional (2D) crystals, where reduced dimensionality and low carrier densities suppress screening, or designing materials of van der Waals (vdW) superlattice with spatially separated and decoupled conductive and nearly insulating ferroelectric layers. Here, we report an alternative paradigm in TaNiTe5, where an ultrahigh carrier density coexists with an out-of-plane ferroelectric order within the same surface monolayer. Using piezoresponse force microscopy (PFM), we observed robust ferroelectric behavior in TaNiTe5 down to single-unit-cell thickness (~1.3 nm) at room temperature. Scanning transmission electron microscopy (STEM) gives structural evidence that the ferroelectricity might originate from the vertical displacement of outmost Te atoms on the surface, breaking the inversion symmetry. Concurrently, electrical transport measurements reveal a metallic state with a carrier density on the order of 10$^{15}$ cm$^{-2}$ (or 10$^{22}$ cm$^{-3}$) -- comparable to that of Copper (Cu). Our findings establish a unique platform for exploring the interplay between ferroelectricity and an ultrahigh density of mobile carriers in the 2D limit.

cond-mat.mtrl-sci↗

arXiv2026

Solving High-Dimensional PDEs Using Linearized Neural Networks

Linearized shallow neural networks that are constructed by fixing the hidden-layer parameters have recently shown strong performance in solving partial differential equations (PDEs). Such models, widely used in the random feature method (RFM) and extreme learning machines (ELM), transform network training into a linear least-squares problem. In this paper, we conduct a numerical study of the variational (Galerkin) and collocation formulations for these linearized networks. Our numerical results reveal that, in the variational formulation, the associated linear systems are severely ill-conditioned, forming the primary computational bottleneck in scaling the neural network size, even when direct solvers are employed. In contrast, collocation methods combined with robust least-squares solvers exhibit better numerical stability and achieve higher accuracy as we increase neuron numbers. This behavior is consistently observed for both ReLU$^k$ and $\tanh$ activations, with $\tanh$ networks exhibiting even worse conditioning. Furthermore, we demonstrate that random sampling of the hidden layer parameters, commonly used in RFM and ELM, is not necessary for achieving high accuracy. For ReLU$^k$ activations, this follows from existing theory and is verified numerically in this paper, while for $\tanh$ activations, we introduce two deterministic schemes that achieve comparable accuracy.

math.NA↗