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Zhenyu Wang

Publications and source records attributed to Zhenyu Wang.

At least 37 records · Page 2Linked to original sources
arXiv2026

Supermoiré Chern mosaic in helical trilayer WSe2

Helically twisted multilayers offer access to moiré physics beyond the single-superlattice paradigm, yet their correlated and topological transport properties remain largely unexplored in semiconductor moiré materials. Here we report magnetotransport measurements of helical trilayer WSe2, in which two coupled moiré patterns relax into a supermoiré landscape composed of inequivalent local topological domains with distinct electronic structures and unequal spatial areas. By electrostatic tuning, we identify a trilayer-hybridized regime where interactions and real-space reconstruction combine to generate a plethora of magnetic and topological states absent in the twisted bilayers. At moiré filling factor $ν$ = -1, we observe a ferromagnetic insulating state that is robust against magnetic field and accompanied by a non-quantized anomalous Hall response ~-4 kOhms. This behaviour is consistent with a time-reversal-symmetry-breaking supermoiré Chern mosaic, in which the Hall response arises from the non-cancelling contributions of local domains with opposite Chern character arranged by the relaxed structure. Under strong magnetic fields, a symmetry-broken Chern insulating state (C = 1) emerges near $ν$ = -2/3, displaying a much larger positive Hall response together with strongly enhanced longitudinal resistance, suggestive of field-reconstructed topological minibands and domain-boundary scattering. These results establish relaxed supermoiré semiconductor trilayers as a platform for spatially organized magnetism and topology beyond the bilayer limit.

cond-mat.str-el↗
arXiv2026

SPAMoE: Spectrum-Aware Hybrid Operator Framework for Full-Waveform Inversion

Full-waveform inversion (FWI) is pivotal for reconstructing high-resolution subsurface velocity models but remains computationally intensive and ill-posed. While deep learning approaches promise efficiency, existing Convolutional Neural Networks (CNNs) and single-paradigm Neural Operators (NOs) struggle with one fundamental issue: frequency entanglement of multi-scale geological features. To address this challenge, we propose Spectral-Preserving Adaptive MoE (SPAMoE), a novel spectrum-aware framework for solving inverse problems with complex multi-scale structures. Our approach introduces a Spectral-Preserving DINO Encoder that enforces a lower bound on the high-to-low frequency energy ratio of the encoded representation, mitigating high-frequency collapse and stabilizing subsequent frequency-domain modeling. Furthermore, we design a novel Spectral Decomposition and Routing mechanism that dynamically assigns frequency bands to a Mixture-of-Experts (MoE) ensemble comprising FNO, MNO, and LNO. On the ten OpenFWI sub-datasets, experiments show that SPAMoE reduces the average MAE by 44.4% relative to the best officially reported OpenFWI baseline, thereby establishing a new architectural framework for learning-based full-waveform inversion. Our code and data are available at https://github.com/zhenyuwang12366/SPAMoE

cs.LG↗
arXiv2026

Discovery of d-orbital order in Tb2CoAl4Ge2

Orbital order describes a quantum state where occupied orbitals line up in a periodic pattern. While orbital physics plays a fundamental and universal role in strongly correlated electron systems, the existence and particularly the band structure fingerprint of orbital order remain a long-standing mystery. Here, we report the discovery of rare earth 5d-orbital order developed by the surface states of intermetallic compound Tb2CoAl4Ge2. Angle-resolved photoemission spectroscopy reveals characteristic nematic features like Fermi surface deformation and band split. These experimental observations can be described by a ferro-orbital order term in the mean-field Hamiltonian. The structural and magnetic origin of such order is excluded by systematic high-resolution neutron powder diffraction and scanning tunnelling microscopy measurements. Our results provide strong evidence for a pure surface orbital order scenario avoiding complications from structural distortion as in colossal magnetoresistance manganites, magnetic order as in iron-based superconductors, and charge transfer p-orbital order in cuprates.

cond-mat.str-el↗
arXiv2026

Spin-Hall-Like Magnon Transport in a Synthetic Antiferromagnetic Skyrmion Lattice

We investigate spin-Hall-like magnon edge transport in a synthetic antiferromagnetic skyrmion lattice composed of two antiferromagnetically coupled skyrmion lattice layers with opposite magnetic textures. Based on a relaxed bilayer texture from micromagnetic simulations, we construct the bosonic Bogoliubov-de Gennes Hamiltonian within linear spin-wave theory and calculate the bulk and strip magnon spectrum. We find counterpropagating in-gap edge modes with opposite layer polarization, whose layer-resolved propagation is further confirmed by dynamical micromagnetic simulations. A symmetry analysis shows that the fully coupled system lacks the pseudo-time-reversal symmetry required for a genuine bosonic Z2 topological phase. Thus, the observed edge modes are not Z2-protected helical magnon edge states, but layer-polarized, spin-Hall-like modes originating from the opposite Hall tendencies of the two skyrmion lattice layers. These results establish synthetic antiferromagnetic skyrmion lattices as a platform for spin-Hall-like magnon transport beyond a strict bosonic Z2 classification.

cond-mat.mes-hall↗
arXiv2026

Destructive interference of second harmonic generation in AA stacked MoTe$_2$/WSe$_2$

The stacking configuration of two-dimensional materials critically governs their optical and electronic responses. Monolayer transition-metal dichalcogenides (TMDC) lack inversion symmetry and exhibit exciton-enhanced second-harmonic generation (SHG). In TMDC bilayers, 60° (0°) stacking is conventionally expected to suppress (enhance) SHG owing to destructive (constructive) interference of the layer-resolved nonlinear polarizations. Here, we report an unconventional destructive SHG interference in nearly 0°-stacked (AA-stacked) MoTe2/WSe2 heterobilayers using two independent probes: atomic-resolution imaging and stacking-sensitive exciton hybridization measurements. Supported by ab initio GW and Bethe-Salpeter equation calculations, we show that distinct two-photon resonances associated with the WSe2 C exciton and the MoTe2 D exciton generate a nearly $π$ phase difference ($Δϕ$) in their second-order nonlinear susceptibilities $χ^{(2)}$, leading to the anomalous destructive interference. We further demonstrate that in small-angle twisted MoTe2/WSe2, the SHG polarization state is governed by the interplay between twist angle $α$ and phase difference $Δϕ$, and can be mapped onto trajectories on the Poincaré sphere. At excitation energies satisfying $Δϕ$ + 3$α$ = 180°, the SHG output becomes nearly circularly polarized (ellipticity ~ 0.91) and undergoes an abrupt 90° azimuthal rotation, corresponding to a geometric polarization singularity in the parameter space. Our findings open new routes for exciton-resonance engineered nonlinear photonics and stacking-resolved optical functionality in moiré materials.

cond-mat.mes-hall↗
arXiv2026

High-Pressure Crystal Structure Database

High-pressure research is a productive route to new structures and emergent properties. However, crucial high-pressure structural information remains highly fragmented across individual publications and heterogeneous computational repositories. This fragmentation creates a major bottleneck for data-driven materials design. To bridge this gap, we introduce the High-Pressure Crystal Structure Database (HPCSD), a traceable, pressure-resolved repository that integrates experimental and theoretical high-pressure structures. HPCSD is constructed from two complementary data streams: elemental high-pressure phases and a searchable configuration space of stable and metastable phases generated via CALYPSO crystal structure prediction. To ensure rigorous comparability, all retained structures underwent re-optimization under a unified density functional theory (DFT) framework , with continuous enthalpy curves systematically generated specifically for the elemental phases across their stability fields. The initial release encompasses 77,346 consistently evaluated structural entries spanning 89 elements. An analysis reveals that pressure-induced polymorphism is ubiquitous and exhibits pronounced family-dependent trends. Structural diversity is strongly influenced by an element's electronic adaptability , with the greatest structural complexity emerging at intermediate rather than highest pressures. By providing standardized, reusable, and rigorously evaluated high-pressure structural data, HPCSD establishes a robust infrastructure to accelerate experimental phase identification, facilitate cross-study thermodynamic comparisons, and support the development of machine-learning interatomic potentials and generative models for high-pressure systems.

cond-mat.mtrl-sci↗
arXiv2026

URMF: Uncertainty-aware Robust Multimodal Fusion for Multimodal Sarcasm Detection

Multimodal sarcasm detection (MSD) aims to identify sarcastic intent from semantic incongruity between text and image. Although recent methods have improved MSD through cross-modal interaction and incongruity reasoning, most still treat modalities as equally reliable. In real social media posts, however, text and images often differ in noise level and relevance, making deterministic fusion susceptible to noisy evidence and weakened incongruity cues. To address this issue, we propose Uncertainty-aware Robust Multimodal Fusion (URMF), a unified framework for robust MSD. URMF first injects visual evidence into textual representations through multi-head cross-attention, and then applies self-attention in the fused semantic space to enhance incongruity reasoning. It models textual, visual, and interaction-aware representations as learnable Gaussian posteriors to estimate modality-specific uncertainty. Based on the estimated uncertainty, URMF dynamically adjusts modality contributions during fusion to suppress unreliable evidence. We further optimize the model with a unified objective that combines information bottleneck regularization, modality prior regularization, cross-modal distribution alignment, and uncertainty-driven contrastive learning. Experiments on the public MSD and MMSD2 benchmarks show that URMF outperforms representative unimodal, multimodal, and MLLM-based baselines. The results demonstrate that explicit uncertainty modeling can improve both accuracy and robustness in multimodal sarcasm detection.

cs.CV↗
arXiv2026

AROMA: Augmented Reasoning Over a Multimodal Architecture for Virtual Cell Genetic Perturbation Modeling

Virtual cell modeling predicts molecular state changes under genetic perturbations in silico, which is essential for biological mechanism studies. However, existing approaches suffer from unconstrained reasoning, uninterpretable predictions, and retrieval signals that are weakly aligned with regulatory topology. To address these limitations, we propose AROMA, an Augmented Reasoning Over a Multimodal Architecture for virtual cell genetic perturbation modeling. AROMA integrates textual evidence, graph-topology information, and protein sequence features to model perturbation-target dependencies, and is trained with a two-stage optimization strategy to yield predictions that are both accurate and interpretable. We also construct two knowledge graphs and a perturbation reasoning dataset, PerturbReason, containing more than 498k samples, as reusable resources for the virtual cell domain. Experiments show that AROMA outperforms existing methods across multiple cell lines, and remains robust under zero-shot evaluation on an unseen cell line, as well as in knowledge-sparse, long-tail scenarios. Overall, AROMA demonstrates that combining knowledge-driven multimodal modeling with evidence retrieval provides a promising pathway toward more reliable and interpretable virtual cell perturbation prediction. Model weights are available at https://huggingface.co/blazerye/AROMA. Code is available at https://github.com/blazerye/AROMA.

q-bio.QM↗
arXiv2026

POLAR-PIC: A Holistic Framework for Matrixized PIC with Co-Designed Compute, Layout, and Communication

Particle-in-Cell (PIC) simulations are fundamental to plasma physics but often suffer from limited scalability due to particle-grid interaction bottlenecks and particle redistribution costs. Specifically, the particle-grid interaction computations have not taken full advantage of the emerging Matrix Processing Units (MPUs), the particle motion introduces irregular memory accesses, and the bulk-synchronous redistribution further destroys long-term data locality thereby limiting parallel efficiency. To address these inefficiencies, we present POLAR-PIC, a co-designed framework for large-scale PIC simulations that (i) reformulates Field Interpolation into an MPU-friendly outer-product form, (ii) maintains a physically ordered particle layout to preserve memory contiguity, and (iii) overlaps particle communication with Deposition to hide redistribution overhead. The evaluation on the pilot system of an Exascale supercomputer demonstrates that POLAR-PIC accelerates the entire particle-processing phase by up to 10.9x in uniform plasma and 4.4x in real-world laser-ion acceleration scenarios compared to the native WarpX reference pipeline on LX2. Ablation studies reveal that the speedups achieved by Interpolation and Deposition are 8.0x and 13.2x, respectively, and the asynchronous communication design sustains a 99.1% overlap ratio. In cross-platform comparisons, POLAR-PIC achieves 13.2% of theoretical peak efficiency on the CPU-based LS system, while WarpX reaches 9.6% on NVIDIA A800 GPUs. Notably, the scalability evaluation demonstrates that POLAR-PIC maintains 67.5% weak scaling efficiency on over 2 million cores under high-migration dynamic workloads, highlighting the importance of holistic co-design for future matrix-centric HPC systems.

cs.DC↗
arXiv2026

From Awareness to Intent: Mitigating Silent Driving System Failures through Prospective Situation Awareness Enhancing Interfaces

Silent automation failures, where a system fails to detect a hazard without warning, pose a critical safety challenge for partially automated vehicles. While research has mostly focused on takeover requests, how to support a driver in silent failure remains underexplored. We conducted a multi-modal driving simulator study with 48 participants to investigate how different Prospective Situation Awareness Enhancement (PSAE) interfaces, delivered via augmented reality head-up display, affect takeover performance. By integrating behavioral, subjective psychological, and physiological data, our analysis suggests that situational awareness (SA) serves as an important moderating factor through which PSAE interfaces improve takeover performance. Further, we found that providing perceptual cues was most effective in enhancing SA, while communicating system intent was superior for building trust. Finally, we identified a potential correlate of SA in the neuroactivity. Overall, this paper contributes to understanding how transparency-oriented interfaces may support drivers and provides design insights into HMI design for silent failures.

cs.HC↗
arXiv2026

Visualizing the interplay of dual electronic nematicities in kagome superconductors

Kagome superconductor AV$_3$Sb$_5$ (A stands for K, Rb, and Cs) hosts a wealth of intertwined electronic orders driven by geometric frustration and electron correlations. Among them, the breaking of rotational and/or time-reversal symmetry, observed within the triple-$Q$ charge density wave (CDW) phase yet exhibiting a more complex temperature dependence, remains a central puzzle. Here, by using scanning tunneling microscopy to study the electronic structures of CsV$_3$Sb$_5$ as a function of temperature and Ti doping, we disentangle the interrelation between two distinct nematic order parameters, one associated with the CDW and the other manifested as $C_2$ distortion of the V-$d_{x^{2}-y^{2}}$ Fermi pockets without breaking transition symmetry. The latter persists to high doping levels and high temperatures where the long-range CDW is fully suppressed. Moreover, its nematic director is oriented in a lattice direction distinct from that of the CDW-induced nematicity at intermediate doping, and eventually aligns with the strong nematic CDW order in the pristine compound where the quasiparticles of vanadium orbitals become coherent below a lower characteristic temperature. These observations, combined with Ginzburg-Landau analysis, reveal a rich interplay between two nematic orders that can be assigned to distinct kagome-lattice orbitals. Our results shed new light on the enigmatic intertwined orders in this family and establish a rare material platform in which dual nematic orders coexist and couple to give rise to unusual correlated phenomena.

cond-mat.supr-con↗
arXiv2026

Rethinking Exposure Correction for Spatially Non-uniform Degradation

Real-world exposure correction is fundamentally challenged by spatially non-uniform degradations, where diverse exposure errors frequently coexist within a single image. However, existing exposure correction methods are still largely developed under a predominantly uniform assumption. Architecturally, they typically rely on globally aggregated modulation signals that capture only the overall exposure trend. From the optimization perspective, conventional reconstruction losses are usually derived under a shared global scale, thus overlooking the spatially varying correction demands across regions. To address these limitations, we propose a new exposure correction paradigm explicitly designed for spatial non-uniformity. Specifically, we introduce a Spatial Signal Encoder to predict spatially adaptive modulation weights, which are used to guide multiple look-up tables for image transformation, together with an HSL-based compensation module for improved color fidelity. Beyond the architectural design, we propose an uncertainty-inspired non-uniform loss that dynamically allocates the optimization focus based on local restoration uncertainties, better matching the heterogeneous nature of real-world exposure errors. Extensive experiments demonstrate that our method achieves superior qualitative and quantitative performance compared with state-of-the-art methods. Code is available at https://github.com/FALALAS/rethinkingEC.

cs.CV↗
arXiv2026

Efficient Mixture-of-Expert for Video-based Driver State and Physiological Multi-task Estimation in Conditional Autonomous Driving

Road safety remains a critical challenge worldwide, with approximately 1.35 million fatalities annually attributed to traffic accidents, often due to human errors. As we advance towards higher levels of vehicle automation, challenges still exist, as driving with automation can cognitively over-demand drivers if they engage in non-driving-related tasks (NDRTs), or lead to drowsiness if driving was the sole task. This calls for the urgent need for an effective Driver Monitoring System (DMS) that can evaluate cognitive load and drowsiness in SAE Level-2/3 autonomous driving contexts. In this study, we propose a novel multi-task DMS, termed VDMoE, which leverages RGB video input to monitor driver states non-invasively. By utilizing key facial features to minimize computational load and integrating remote Photoplethysmography (rPPG) for physiological insights, our approach enhances detection accuracy while maintaining efficiency. Additionally, we optimize the Mixture-of-Experts (MoE) framework to accommodate multi-modal inputs and improve performance across different tasks. A novel prior-inclusive regularization method is introduced to align model outputs with statistical priors, thus accelerating convergence and mitigating overfitting risks. We validate our method with the creation of a new dataset (MCDD), which comprises RGB video and physiological indicators from 42 participants, and two public datasets. Our findings demonstrate the effectiveness of VDMoE in monitoring driver states, contributing to safer autonomous driving systems. The code and data will be released.

cs.CV↗
arXiv2026

SN 2023axu: A Type IIP Supernova Interacted with a Low-Density Stellar Wind

We present photometric and spectroscopic observations of Type IIP supernova SN 2023axu, spanning $\sim$400 d after the explosion. Its light curve is typical of normal SNe IIP, with a V-band peak of $-17.25 \pm 0.06$ mag and no early-time excess indicative of strong circumstellar interaction. The early spectra exhibit a distinctive broad "ledge" near 4600 Å. Through spectral modeling and comparison, we attribute this feature to a blend of C, N, and He lines excited by weak interaction between the ejecta and a low-density stellar wind. The late-time photometric evolution shows no discernible contribution from interaction, arguing against strong late-time circumstellar material engagement and supporting the low-density wind scenario. From modeling, this SN synthesized $\sim 0.055\,M_\odot$ of $^{56}$Ni, and nebular spectrum analysis indicates a progenitor mass near $15\,M_\odot$. SN 2023axu thus exemplifies weak ejecta-wind interaction and highlights the diversity of mass-loss histories and circumstellar environments of SNe II progenitors.

astro-ph.HE↗
arXiv2026

NeuroLoRA: Context-Aware Neuromodulation for Parameter-Efficient Multi-Task Adaptation

Parameter-Efficient Fine-Tuning (PEFT) techniques, particularly Low-Rank Adaptation (LoRA), have become essential for adapting Large Language Models (LLMs) to downstream tasks. While the recent FlyLoRA framework successfully leverages bio-inspired sparse random projections to mitigate parameter interference, it relies on a static, magnitude-based routing mechanism that is agnostic to input context. In this paper, we propose NeuroLoRA, a novel Mixture-of-Experts (MoE) based LoRA framework inspired by biological neuromodulation -- the dynamic regulation of neuronal excitability based on context. NeuroLoRA retains the computational efficiency of frozen random projections while introducing a lightweight, learnable neuromodulation gate that contextually rescales the projection space prior to expert selection. We further propose a Contrastive Orthogonality Loss to explicitly enforce separation between expert subspaces, enhancing both task decoupling and continual learning capacity. Extensive experiments on MMLU, GSM8K, and ScienceQA demonstrate that NeuroLoRA consistently outperforms FlyLoRA and other strong baselines across single-task adaptation, multi-task model merging, and sequential continual learning scenarios, while maintaining comparable parameter efficiency.

cs.LG↗
arXiv2026

Observation of Kondo hybridization wave in UTe2

Condensed matter systems with strong electronic correlations often manifest a variety of intertwined ordered phases of charge, spin, orbital and other degrees of freedom. As a prototypical strongly correlated electronic system, the Kondo lattice provides fertile soil for many fascinating quantum states, including quantum criticality, unconventional superconductivity, hidden order and topological Kondo insulator/semimetal. The foundation of Kondo physics lies in the hybridization between localized moments and itinerant electrons. Generally, the evolution of Kondo hybridization is characterized as a broad crossover rather than a phase transition. Thus far, an ordered hybridization phase has not been observed. Here, we use scanning tunneling microscopy (STM) to identify a translational-symmetry-breaking order of Kondo hybridization wave(KHW) for the first time on the surface of the spin-triplet heavy-fermion superconductor UTe2. The unprecedented phase of KHW manifests as a periodically modulated Fano lattice, accompanied by a commensurate charge density wave (CDW) and a pronounced energy gap opening near the Fermi level. This KHW-imprinted CDW has an intriguing real-space texture of complementary occupation of the heavy f and conduction charges, thereby forming a Kondo superlattice. The KHW is coexistent with superconductivity in UTe2, which may provide valuable insight into its controversial spin-triplet pairing symmetry and the underlying mechanism. Our first experimental evidence for an ordered hybridization state potentially sheds new light on the strong correlation physics of Kondo lattice system.

cond-mat.str-el↗
arXiv2026

StablePCA: Distributionally Robust Learning of Shared Representations from Multi-Source Data

When synthesizing multi-source high-dimensional data, a key objective is to extract low-dimensional representations that effectively approximate the original features across different sources. Such representations facilitate the discovery of transferable structures and help mitigate systematic biases such as batch effects. We introduce Stable Principal Component Analysis (StablePCA), a distributionally robust framework for constructing stable latent representations by maximizing the worst-case explained variance over multiple sources. A primary challenge in extending classical PCA to the multi-source setting lies in the nonconvex rank constraint, which renders the StablePCA formulation a nonconvex optimization problem. To overcome this challenge, we conduct a convex relaxation of StablePCA and develop an efficient Mirror-Prox algorithm to solve the relaxed problem, with global convergence guarantees. Since the relaxed problem generally differs from the original formulation, we further introduce a data-dependent certificate to assess how well the algorithm solves the original nonconvex problem and establish the condition under which the relaxation is tight. Finally, we explore alternative distributionally robust formulations of multi-source PCA based on different loss functions.

cs.LG↗
arXiv2026

Continuous-Flow Data-Rate-Aware CNN Inference on FPGA

Among hardware accelerators for deep-learning inference, data flow implementations offer low latency and high throughput capabilities. In these architectures, each neuron is mapped to a dedicated hardware unit, making them well-suited for field-programmable gate array (FPGA) implementation. Previous unrolled implementations mostly focus on fully connected networks because of their simplicity, although it is well known that convolutional neural networks (CNNs) require fewer computations for the same accuracy. When observing the data flow in CNNs, pooling layers and convolutional layers with a stride larger than one, the number of data at their output is reduced with respect to their input. This data reduction strongly affects the data rate in a fully parallel implementation, making hardware units heavily underutilized unless it is handled properly. This work addresses this issue by analyzing the data flow of CNNs and presents a novel approach to designing data-rate-aware, continuous-flow CNN architectures. The proposed approach ensures a high hardware utilization close to 100% by interleaving low data rate signals and sharing hardware units, as well as using the right parallelization to achieve the throughput of a fully parallel implementation. The results show that a significant amount of the arithmetic logic can be saved, which allows implementing complex CNNs like MobileNet on a single FPGA with high throughput.

cs.LG↗