arXiv subjects

Xiaofeng Xu

Publications and source records attributed to Xiaofeng Xu.

At least 37 records · Page 2Linked to original sources

arXiv2026

Multigap nodeless superconductivity in Dirac semimetal PdTe

PdTe has recently been reported to be a type-II Dirac semimetal while a bulk nodal and surface nodeless superconductivity (SC) has been claimed to coexist. In this work, we applied point-contact spectroscopy (PCS) method to systematically study the superconducting gap in PdTe single crystals with a SC transition temperature $T_{c}=4.3$ K. The obtained differential conductance curves show a common deviation from a single-gap superconducting behavior and can be better fitted by a two-gap Blonder-Tinkham-Klapwijk model, suggesting the larger gap $Δ_{L}$ with $2Δ_{L}$=3.7 $k_{B}T_{c}$ and the smaller gap $Δ_S$ yielding $2Δ_{S}$=1.1-2.2 $k_{B}T_{c}$ with a weak interband scattering. The variations of conductance spectra among different contacts are proposed to be caused by the anisotropy of Fermi surface topology associated with different gaps.

cond-mat.supr-con↗

arXiv2025

Cluster algebras for cosmological correlators

In this paper, we explore the cluster algebras for symbol letters or singularities of cosmological correlators in a conformally coupled scalar field theory. We show that the symbol letters for tree-level n-site ladder cosmological correlators are governed by A_{2(n-1)} cluster algebras. Additionally, we demonstrate that the symbol letters for one-loop bubble cosmological correlator are an union of two A_3 cluster algebras. The algebras relations of letters will provide an important tool to bootstrap analytic cosmological correlators.

hep-th↗

arXiv2025

Large Anomalous and Topological Hall Effect and Nernst Effect in a Dirac Kagome Magnet Fe3Ge

The search for kagome magnets with unconventional magnetic and electronic properties has gained significant attention in recent years. We report the magnetic, electronic, and thermoelectric properties of Fe3Ge single crystals, where the Fe atoms form a slightly distorted kagome lattice. Fe3Ge exhibits a large anomalous Hall effect and anomalous Nernst effect. The anomalous transverse thermoelectric conductivity reaches about 4.6 A m^-1 K^-1, exceeding values reported for conventional ferromagnets and most topological ferromagnets. First-principles calculations indicate that these transport responses are primarily governed by intrinsic mechanisms, highlighting the dominant role of Berry curvature arising from massive Dirac gaps in momentum space. In addition, we observe a topological Hall resistivity of about 0.9 microOhm cm and a topological Nernst coefficient of 1.2 microvolt K^-1, which are attributed to the Berry phase associated with field-induced scalar spin chirality. These findings demonstrate the combined influence of Berry phases in both momentum and real space, establishing Fe3Ge as a promising candidate for room-temperature transverse thermoelectric applications.

cond-mat.mtrl-sci↗

arXiv2025

Differential equations for tree--level cosmological correlators with massive states

We study mathematical aspects concerning two site tree-level cosmological correlators with massive internal and external states in a de Sitter universe. We employ integration by parts identities, (relative) twisted cohomology and the method of differential equations. We explicitly express the internally massive, externally conformally coupled correlator as a power series with respect to a small mass parameter, where the various terms in the series are given by multiple polylogarithms.

hep-th↗

arXiv2025

In-Plane Magnetic Anisotropy and Large topological Hall Effect in Self-Intercalated Ferromagnet Cr1.61Te2

Self-intercalated chromium tellurides Cr1+xTe2 have garnered growing attention due to their high-temperature ferromagnetism, tunable spin structures and air stability, all of which are vital for versatile applications in next-generation memory and information technology. Here, we report strong magnetic anisotropy and a large topological Hall effect (THE) in self-intercalated Cr1.61Te2 single crystals, which are both highly desirable properties for future spintronic applications. Our results demonstrate that Cr1.61Te2 is a soft ferromagnet with strong in-plane magnetic anisotropy. Remarkably, distinct THE behaviors are observed in different temperature regimes, reflecting the intricate spin structures and competing exchange interactions. More interestingly, a large topological Hall resistivity, induced by microscopic non-coplanar spin structures, emerges in the temperature range 70-240 K, reaching a maximum value of 0.93 μΩ cm at 150 K. Moreover, a sign-reversed and weak THE is observed at low temperatures below ~70 K, indicating the emergence of an additional topological spin structure with opposite topological charges. This work not only offers valuable insights into the correlation between magnetocrystalline anisotropy and topological phenomena in Cr1+xTe2 systems, but also provides a robust platform for engineering the evolution of complex spin textures that can be leveraged in diverse spintronic device applications.

cond-mat.mtrl-sci↗

arXiv2025

Self-dualities and Galois symmetries in Feynman integrals

It is well-known that all Feynman integrals within a given family can be expressed as a finite linear combination of master integrals. The master integrals naturally group into sectors. Starting from two loops, there can exist sectors made up of more than one master integral. In this paper we show that such sectors may have additional symmetries. First of all, self-duality, which was first observed in Feynman integrals related to Calabi--Yau geometries, often carries over to non-Calabi--Yau Feynman integrals. Secondly, we show that in addition there can exist Galois symmetries relating integrals. In the simplest case of two master integrals within a sector, whose definition involves a square root $r$, we may choose a basis $(I_1,I_2)$ such that $I_2$ is obtained from $I_1$ by the substitution $r \rightarrow -r$. This pattern also persists in sectors, which a priori are not related to any square root with dependence on the kinematic variables. We show in several examples that in such cases a suitable redefinition of the integrals introduces constant square roots like $\sqrt{3}$. The new master integrals are then again related by a Galois symmetry, for example the substitution $\sqrt{3} \rightarrow -\sqrt{3}$. To handle the case where the argument of a square root would be a perfect square we introduce a limit Galois symmetry. Both self-duality and Galois symmetries constrain the differential equation.

hep-th↗

arXiv2025

Symbol letters of Feynman integrals from Gram determinants

Symbol letters are crucial for analytically calculating Feynman integrals in terms of iterated integrals. We present a novel method to construct the symbol letters for a given integral family without prior knowledge of the canonical differential equations. We provide a program package implementing our algorithm, and demonstrate its effectiveness in solving non-trivial problems with multiple loops and legs. Using our method, we successfully bootstrap the canonical differential equations for a two-loop five-point family with two external masses and for a three-loop four-point family with two external masses, which were previously unknown in the literature. We anticipate that our method can be applied to a wide range of cutting-edge calculations in the future.

hep-ph↗

arXiv2025

Randomized Greedy Algorithms for Neural Network Optimization

Greedy algorithms have been successfully analyzed and applied in training neural networks for solving variational problems, ensuring guaranteed convergence orders. In this paper, we extend the analysis of the orthogonal greedy algorithm (OGA) to convex optimization problems, establishing its optimal convergence rate. This result broadens the applicability of OGA by generalizing its optimal convergence rate from function approximation to convex optimization problems. In addition, we also address the issue regarding practical applicability of greedy algorithms, which is due to significant computational costs from the subproblems that involve an exhaustive search over a discrete dictionary. We propose to use a more practical approach of randomly discretizing the dictionary at each iteration of the greedy algorithm. We quantify the required size of the randomized discrete dictionary and prove that, with high probability, the proposed algorithm realizes a weak greedy algorithm, achieving optimal convergence orders. Through numerous numerical experiments on function approximation, linear and nonlinear elliptic partial differential equations, we validate our analysis on the optimal convergence rate and demonstrate the advantage of using randomized discrete dictionaries over a deterministic one by showing orders of magnitude reductions in the size of the discrete dictionary, particularly in higher dimensions.

math.NA↗

arXiv2025

Anisotropic superconductivity in the quasi-one-dimensional superconductor V$_2$Ga$_5$

The intermetallic quasi-one-dimensional binary superconductor V$_2$Ga$_5$ was recently found to exhibit a topologically nontrivial normal state, making it a natural candidate for a topological superconductor (TSC). By combining dc-magnetization, nuclear magnetic resonance (NMR), and muon-spin rotation ({$μ$SR) measurements on high-quality V$_2$Ga$_5$ single crystals, we investigate the electronic properties of its normal- and superconducting (SC) ground states. NMR measurements in the normal state indicate a strong anisotropy in both the line shifts and the relaxation rates. Such anisotropy persists also in the superconducting state, as shown by the magnetization- and $μ$SR-spectroscopy results. In the latter case, data collected at different temperatures, pressures, and directions of the magnetic field evidence a fully-gapped, strongly anisotropic superconductivity. At the same time, hydrostatic pressure is shown to only lower the $T_c$ value, but not to change the superfluid density nor its temperature dependence. Lastly, we discuss the search for topological signatures in the normal state of V$_2$Ga$_5$, as well as a peak splitting in the FFT of the $μ$SR spectrum, possibly related to an unconventional vortex lattice. Our results suggest that V$_2$Ga$_5$ is a novel system, whose anisotropy plays a key role in determining its unusual electronic properties.

cond-mat.supr-con↗

arXiv2025

Group Preference Alignment: Customized LLM Response Generation from In-Situ Conversations

LLMs often fail to meet the specialized needs of distinct user groups due to their one-size-fits-all training paradigm \cite{lucy-etal-2024-one} and there is limited research on what personalization aspects each group expect. To address these limitations, we propose a group-aware personalization framework, Group Preference Alignment (GPA), that identifies context-specific variations in conversational preferences across user groups and then steers LLMs to address those preferences. Our approach consists of two steps: (1) Group-Aware Preference Extraction, where maximally divergent user-group preferences are extracted from real-world conversation logs and distilled into interpretable rubrics, and (2) Tailored Response Generation, which leverages these rubrics through two methods: a) Context-Tuned Inference (GAP-CT), that dynamically adjusts responses via context-dependent prompt instructions, and b) Rubric-Finetuning Inference (GPA-FT), which uses the rubrics to generate contrastive synthetic data for personalization of group-specific models via alignment. Experiments demonstrate that our framework significantly improves alignment of the output with respect to user preferences and outperforms baseline methods, while maintaining robust performance on standard benchmarks.

cs.CL↗

arXiv2025

Pressure-driven superconductivity in the topological insulator GeBi4Te7

The van der Waals, pseudo-binary chalcogenides (ACh)m(Pn2Ch3)n (A = Ge, Mn, Pb, etc.; Pn = Sb or Bi; Ch = Te, Se) have recently been reported to host a vast landscape of topological phases of matter, including the quantum anomalous Hall state and topological axion state with quantized magnetoelectric effect. A subgroup in this series, like MnSb4Te7 and GeSb4Te7, can be driven to a superconducting state by applying a physical pressure, making them viable candidates to realize so-called topological superconductivity. However, the role of magnetic fluctuations in this pressure-induced superconductivity remains unclear. Here, we report the pressure-induced multiple superconducting phases in the nonmagnetic GeBi4Te7, accompanied by corresponding structural transitions evidenced from the high-pressure Raman scattering. In comparison with other members in this family, we find the superconducting transition temperature of the nonmagnetic subgroup is significantly higher than their magnetic homologues, possibly hinting at the detrimental role played by the magnetic fluctuations in the superconductivity formation, at least in this pseudo-binary chalcogenide family.

cond-mat.supr-con↗

arXiv2025

GenTool: Enhancing Tool Generalization in Language Models through Zero-to-One and Weak-to-Strong Simulation

Large Language Models (LLMs) can enhance their capabilities as AI assistants by integrating external tools, allowing them to access a wider range of information. While recent LLMs are typically fine-tuned with tool usage examples during supervised fine-tuning (SFT), questions remain about their ability to develop robust tool-usage skills and can effectively generalize to unseen queries and tools. In this work, we present GenTool, a novel training framework that prepares LLMs for diverse generalization challenges in tool utilization. Our approach addresses two fundamental dimensions critical for real-world applications: Zero-to-One Generalization, enabling the model to address queries initially lacking a suitable tool by adopting and utilizing one when it becomes available, and Weak-to-Strong Generalization, allowing models to leverage enhanced versions of existing tools to solve queries. To achieve this, we develop synthetic training data simulating these two dimensions of tool usage and introduce a two-stage fine-tuning approach: optimizing tool ranking, then refining tool selection. Through extensive experiments across four generalization scenarios, we demonstrate that our method significantly enhances the tool-usage capabilities of LLMs ranging from 1B to 8B parameters, achieving performance that surpasses GPT-4o. Furthermore, our analysis also provides valuable insights into the challenges LLMs encounter in tool generalization.

cs.CL↗

arXiv2025

Abnormal Normal State and Pressure-driven Reentrant Superconductivity in the Heavy $d$-electron Superconductor Rh$_{17}$S$_{15}$

Superconductivity beyond the conventional Bardeen-Cooper-Schrieffer (BCS) framework often emerges out of a normal state that is accompanied by exotic magnetism and thereby displays many exceptional transport and thermodynamic properties. Here we report that the normal state of the heavy $d$-electron superconductor Rh$_{17}$S$_{15}$ is characterized by a weak \textit{ferromagnetism} that persists up to room temperature. We show that the broad hump in its resistivity likely results from the Kondo interaction of the conduction electrons with this novel magnetism. By applying pressure, superconductivity is fully suppressed first. In the high-pressure regime, however, we observe a second dome of superconductivity with its maximum $T_c$ greater than the ambient pressure value, highlighting the possible \textit{unconventional} superconductivity in this heavy $d$-electron sulfide.

cond-mat.supr-con↗

arXiv2025

Effects of disorder on the quantum transport properties in topologically nontrivial metal PbTaSe$_{2}$

Weak antilocalization (WAL), an increase in the electrical conductivity at low temperatures associated with the suppression of electron localization due to quantum interference effects, is often observed in topological materials. In this study, we report the observation of WAL in topologically nontrivial metal PbTaSe$_{2}$ at low temperatures. In the pristine sample, we identified the presence of WAL, which is attributed to the topologically protected backscattering. In order to investigate the influence of disorder on the WAL, we successively introduced controlled amounts of disorder by H$^{+}$-irradiation. As disorder increases, the dip-like magnetoresistance caused by WAL changes to a linear magnetoresistance(MR), and eventually to a quadratic MR as the electronic system becomes highly localized. This research unveils the significance of disorder in shaping the quantum transport characteristics of topological materials.

cond-mat.mes-hall↗

arXiv2024

A High Energy-Efficiency Multi-core Neuromorphic Architecture for Deep SNN Training

There is a growing necessity for edge training to adapt to dynamically changing environment. Neuromorphic computing represents a significant pathway for high-efficiency intelligent computation in energy-constrained edges, but existing neuromorphic architectures lack the ability of directly training spiking neural networks (SNNs) based on backpropagation. We develop a multi-core neuromorphic architecture with Feedforward-Propagation, Back-Propagation, and Weight-Gradient engines in each core, supporting high efficient parallel computing at both the engine and core levels. It combines various data flows and sparse computation optimization by fully leveraging the sparsity in SNN training, obtaining a high energy efficiency of 1.05TFLOPS/W@ FP16 @ 28nm, 55 ~ 85% reduction of DRAM access compared to A100 GPU in SNN trainings, and a 20-core deep SNN training and a 5-worker federated learning on FPGAs. Our study develops the first multi-core neuromorphic architecture supporting the direct SNN training, facilitating the neuromorphic computing in edge-learnable applications.

cs.AR↗

arXiv2024

Intrinsic pinning of FeSe$_1$$_-$$_x$S$_x$ single crystals probed by torque magnetometry

Intrinsic pinning is caused by natural pinning centers that occur because of the modulation of the order parameter or weak superconducting layers. Early work has shown that intrinsic pinning generates a high pinning force and critical current density in some layered oxide superconductors. Studying the intrinsic pinning of superconductors is crucial for both fundamental studies and potential applications. Herein, we use torque magnetometry to study angle-resolved in-plane and out-of-plane magnetic torque for a series of high-quality FeSe$_1$$_-$$_x$S$_x$ single crystals. A fourfold torque signal was observed when the magnetic field was within the \textit{ab} plane. We interpret that this fourfold in-plane irreversible torque is from the intrinsic pinning due to combined effects of gap nodes/minimum and twin domains. Additionally, we attributed the observed out-of-plane torque peaks to intrinsic pinning due to the layered structure.

cond-mat.supr-con↗

arXiv2024

Advancing global aerosol forecasting with artificial intelligence

Aerosol forecasting is essential for air quality warnings, health risk assessment, and climate change mitigation. However, it is more complex than weather forecasting due to the intricate interactions between aerosol physicochemical processes and atmospheric dynamics, resulting in significant uncertainty and high computational costs. Here, we develop an artificial intelligence-driven global aerosol-meteorology forecasting system (AI-GAMFS), which provides reliable 5-day, 3-hourly forecasts of aerosol optical components and surface concentrations at a 0.5° x 0.625° resolution. AI-GAMFS combines Vision Transformer and U-Net in a backbone network, robustly capturing the complex aerosol-meteorology interactions via global attention and spatiotemporal encoding. Trained on 42 years of advanced aerosol reanalysis data and initialized with GEOS Forward Processing (GEOS-FP) analyses, AI-GAMFS delivers operational 5-day forecasts in one minute. It outperforms the Copernicus Atmosphere Monitoring Service (CAMS) global forecasting system, GEOS-FP forecasts, and several regional dust forecasting systems in forecasting most aerosol variables including aerosol optical depth and dust components. Our results mark a significant step forward in leveraging AI to refine physics-based aerosol forecasting, facilitating more accurate global warnings for aerosol pollution events, such as dust storms and wildfires.

physics.ao-ph↗

arXiv2024

Unveiling the anisotropy of linear and nonlinear charge-spin conversion in Weyl semimetal TaIrTe4

In Weyl semimetals, the nonlinear planar Hall effect (NPHE) and spin-orbit torque (SOT) are prominent manifestations of nonlinear and linear charge-spin conversion, respectively. However, simultaneous investigations of these phenomena within a single material system are scarce, limiting our understanding of their intrinsic connection and underlying mechanisms. Here, we report the first simultaneous observation of NPHE and SOT in a TaIrTe4/Py heterostructure. By employing harmonic Hall measurements and developing a magnetic field-dependent method, we successfully separated the contributions from NPHE, field-like SOT, and damping-like SOT, enabling accurate characterization of both linear and nonlinear charge-spin conversion properties. Our experiments revealed significant anisotropy along the [100] and [010] crystallographic directions of TaIrTe4, with stronger nonlinear responses and field-like SOT along the [100] direction, and larger damping-like SOT along the [010] direction. The distinct directional dependence of these phenomena provides new insights into the interplay between surface and bulk contributions to charge-spin conversion in Weyl semimetals. These findings enhance our understanding of anisotropic charge-spin conversion mechanisms in Weyl semimetals, which may inform future research and development of spintronic devices based on topological materials.

cond-mat.mtrl-sci↗