arXiv subjects

Jiaxuan Fan

Publications and source records attributed to Jiaxuan Fan.

6 recordsLinked to original sources

arXiv2026

Quadratic piezoelectricity from stacking-engineered interference in multilayer sliding ferroelectrics

Designing nonlinear piezoelectricity requires suppressing the linear piezoelectric coefficient without extinguishing higher-order electromechanical response, yet a general and reconfigurable route remains lacking. Here we introduce stacking-engineered piezoelectric interference as such a mechanism in multilayer sliding ferroelectrics. Combining first-principles calculations with a generalized Ginzburg--Landau framework, we show that each interlayer gap acts as a local piezoelectric channel whose sign and magnitude are determined by stacking. Constructive interference between same-signed channels produces a linear-dominated response, whereas destructive interference between oppositely signed channels suppresses the linear coefficient while preserving a finite quadratic response. Representative MoS$_2$ and NiTe$_2$ multilayers approach the parabolic limit, with BAAC-stacked MoS$_2$ reducing the linear-to-quadratic crossover strain by a factor of 25 relative to CBA-stacked MoS$_2$. Experimentally accessible tetralayer MoS$_2$ sliding pathways further connect linear-dominated, quadratic-dominated and sign-inverted states. Here, we identify stacking-engineered interference as a design principle for programmable nonlinear electromechanics in layered materials.

cond-mat.mtrl-sci↗

arXiv2026

Iterative construction of Hermitian-Einstein metrics on stable bundles

Let $E$ be a stable holomorphic vector bundle over a compact Kähler (or Gauduchon) manifold $(M,ω_g)$. We show that for any real number $μ>0$ and any initial Hermitian metric $h_0$ on $E$, there exists a unique iteration sequence $\{h_m\}$ satisfying $$ Λ_{ω_g}\left(\sqrt{-1}R^{h_{m+1}}\right) =(λ_E-μ)h_{m+1}+μh_m, $$ and $\{h_m\}$ converges smoothly to a Hermitian-Einstein metric $h_\infty$ on $E$ satisfying $$ Λ_{ω_g}\left(\sqrt{-1}R^{h_{\infty}}\right) =λ_Eh_\infty, $$ where $λ_E\in \mathbb R$ is the stability constant. A key feature of this proof is that it is independent of Donaldson's variational framework and applies to non-Kähler manifolds.

math.DG↗

arXiv2026

Existence of Hermitian metrics with prescribed Hermitian-Yang-Mills tensors II

In this paper, we solve the prescribed Hermitian-Yang-Mills tensor problem for Higgs bundles over compact complex manifolds. Let $ (E,θ) $ be a Higgs bundle over a compact Hermitian manifold $(M,ω_g) $. Suppose that there exists a smooth Hermitian metric $ h_0 $ on $E$ such that the Hermitian-Yang-Mills tensor $ Λ_{ω_g}\left(\sqrt{-1} R^{D^{h_0}}\right) $ of the Higgs connection is positive definite. Then for any Hermitian positive definite tensor $ P\in Γ\left(M,E^*\otimes \bar E^*\right) $, there exists a unique smooth Hermitian metric $ h $ on $E$ such that $$Λ_{ω_g} \left(\sqrt{-1} R^{D^h}\right)=P.$$ We also establish quantitative Chern number inequalities for Higgs bundles.

math.DG↗

arXiv2026

Visual-Informed Speech Enhancement Using Attention-Based Beamforming

Recent studies have demonstrated that incorporating auxiliary information, such as speaker voiceprint or visual cues, can substantially improve Speech Enhancement (SE) performance. However, single-channel methods often yield suboptimal results in low signal-to-noise ratio (SNR) conditions, when there is high reverberation, or in complex scenarios involving dynamic speakers, overlapping speech, or non-stationary noise. To address these issues, we propose a novel Visual-Informed Neural Beamforming Network (VI-NBFNet), which integrates microphone array signal processing and deep neural networks (DNNs) using multimodal input features. The proposed network leverages a pretrained visual speech recognition model to extract lip movements as input features, which serve for voice activity detection (VAD) and target speaker identification. The system is intended to handle both static and moving speakers by introducing a supervised end-to-end beamforming framework equipped with an attention mechanism. The experimental results demonstrated that the proposed audiovisual system has achieved better SE performance and robustness for both stationary and dynamic speaker scenarios, compared to several baseline methods.

eess.AS↗

arXiv2025

Two-dimensional Dual-Switchable Ferroelectric Altermagnets: Altering Electrons and Magnons

Ferroelectric altermagnets (FEAMs) offer unique magnetoelectric coupling properties by combining the characteristics of both antiferromagnets and ferromagnets, yet their multifunctional electric control remains largely unexplored. Here, we introduce and investigate a scenario for the simultaneous electrical switching of electronic spin and magnonic chirality splitting in two-dimensional FEAMs. Based on the C2DB database, employing symmetry analysis and first-principles calculations, we study prototypical candidates CrPS$_3$ and V$_2$I$_2$O$_2$BrCl. We identify the mechanism: ferroelectricity arises from asymmetric displacements (P along $z$ in CrPS$_3$, V along the $xy$-direction in V$_2$I$_2$O$_2$BrCl), which inherently couples electric polarization to both electronic and magnonic degrees of freedom by retaining [C$_2$$||$M] symmetry. Our calculations explicitly demonstrate that reversing the ferroelectric polarization concurrently switches the sign of the electronic spin splitting and chirality of magnonic modes. This shows these materials as dual-switchable FEAMs, enabling unified electrical manipulation of electron and magnon properties. A potentially experimentally detectable method via the magneto-optical Kerr effect was derived. This work provides a materials-specific realization and theoretical basis for designing novel electrically controlled multifunctional spintronic, spin caloritronic, and magnonic devices.

cond-mat.mtrl-sci↗

arXiv2025

LeForecast: Enterprise Hybrid Forecast by Time Series Intelligence

Demand is spiking in industrial fields for multidisciplinary forecasting, where a broad spectrum of sectors needs planning and forecasts to streamline intelligent business management, such as demand forecasting, product planning, inventory optimization, etc. Specifically, these tasks expecting intelligent approaches to learn from sequentially collected historical data and then foresee most possible trend, i.e. time series forecasting. Challenge of it lies in interpreting complex business contexts and the efficiency and generalisation of modelling. With aspirations of pre-trained foundational models for such purpose, given their remarkable success of large foundation model across legions of tasks, we disseminate \leforecast{}, an enterprise intelligence platform tailored for time series tasks. It integrates advanced interpretations of time series data and multi-source information, and a three-pillar modelling engine combining a large foundation model (Le-TSFM), multimodal model and hybrid model to derive insights, predict or infer futures, and then drive optimisation across multiple sectors in enterprise operations. The framework is composed by a model pool, model profiling module, and two different fusion approaches regarding original model architectures. Experimental results verify the efficiency of our trail fusion concepts: router-based fusion network and coordination of large and small models, resulting in high costs for redundant development and maintenance of models. This work reviews deployment of LeForecast and its performance in three industrial use cases. Our comprehensive experiments indicate that LeForecast is a profound and practical platform for efficient and competitive performance. And we do hope that this work can enlighten the research and grounding of time series techniques in accelerating enterprise.

cs.LG↗