arXiv subjects

Shihao Zhang

Publications and source records attributed to Shihao Zhang.

At least 37 records · Page 2Linked to original sources

arXiv2026

Third-order optical response in d-wave altermagnets: Analytical and numerical results from microscopic model

Altermagnets represent a novel category of magnetic materials characterized by zero net magnetization yet featuring spin-split band structures, and they demonstrate distinctive orbital-spin locking phenomena. Commencing from the minimal multi-orbital tight-binding Hamiltonian of d-wave altermagnets, we conduct an analysis of the general formulas for the third-order injection and shift currents. These currents are solely determined by the quantum metric and quantum connection, being free from Berry curvature contamination. In the ideal scenario where the $δ$-bond hopping $V_δ$ approaches zero ($V_δ= 0$), we derive closed-form analytical solutions for the third-order photoconductivities. For the general situation with a finite value of $V_δ$, we present a perturbative analytical solution within the limit of $V_δ\ll V_π$, and this solution is verified through numerical calculations. Our research establishes a comprehensive theoretical description of the third-order optospintronic responses in d-wave altermagnets based on a microscopic model. Moreover, it offers a viable approach for the experimental observation of pure quantum geometric effects.

cond-mat.mes-hall↗

arXiv2026

Efficient Matrix Implementation for Rotary Position Embedding

Rotary Position Embedding (RoPE) has become a core component of modern Transformer architectures across language, vision, and 3D domains. However, existing implementations rely on vector-level split and merge operations that introduce non-negligible computational overhead, often overlooked in attention optimization. The problem is further amplified in multi-dimensional settings (e.g., 2D and 3D RoPE), where additional vector operations and uneven feature partitions degrade hardware utilization. To overcome these limitations, we propose RoME (Rotary Matrix position Embedding), a mathematically equivalent yet computationally efficient reformulation of RoPE that replaces vector operations with unified matrix transformations. RoME eliminates dimension-specific operations, simplifies implementation, and enables fused parallel execution across Cube and Vector units on modern NPUs. Experiments show that RoME delivers substantial acceleration at both the operator and full-model levels. The implementation is available at https://gitcode.com/cann/ops-transformer/blob/master/experimental/posembedding/rope_matrix/README.md.

cs.LG↗

arXiv2026

Provable Post-Training Quantization: Theoretical Analysis of OPTQ and Qronos

Post-training quantization (PTQ) has become a crucial tool for reducing the memory and compute costs of modern deep neural networks, including large language models (LLMs). Among PTQ algorithms, the OPTQ framework-also known as GPTQ-has emerged as a leading method due to its computational efficiency and strong empirical performance. Despite its widespread adoption, however, OPTQ lacks rigorous quantitative theoretical guarantees. This paper presents the first quantitative error bounds for both deterministic and stochastic variants of OPTQ, as well as for Qronos, a recent related state-of-the-art PTQ algorithm. We analyze how OPTQ's iterative procedure induces quantization error and derive non-asymptotic 2-norm error bounds that depend explicitly on the calibration data and a regularization parameter that OPTQ uses. Our analysis provides theoretical justification for several practical design choices, including the widely used heuristic of ordering features by decreasing norm, as well as guidance for selecting the regularization parameter. For the stochastic variant, we establish stronger infinity-norm error bounds, which enable control over the required quantization alphabet and are particularly useful for downstream layers and nonlinearities. Finally, we extend our analysis to Qronos, providing new theoretical bounds, for both its deterministic and stochastic variants, that help explain its empirical advantages.

cs.LG↗

arXiv2026

Qronos: Correcting the Past by Shaping the Future... in Post-Training Quantization

We introduce Qronos -- a new state-of-the-art post-training quantization algorithm that sequentially rounds and updates neural network weights. Qronos not only explicitly corrects errors due to both weight and activation quantization, but also errors resulting from quantizing previous layers. Our iterative algorithm is based on an interpretable and disciplined optimization framework that subsumes and surpasses existing data-driven approaches. At each step, Qronos alternates between error correction and diffusion via optimal update rules. Importantly, we prove that Qronos admits an efficient implementation that uses the Cholesky decomposition for solving least-squares problems. We also demonstrate that Qronos is compatible with existing transformation techniques such as Hadamard-based incoherence processing and weight-activation scaling equalization, among others. We evaluate Qronos using recent autoregressive language generation models in the Llama3 family; Qronos consistently outperforms previous state-of-the-art adaptive rounding methods when quantizing the weights, activations, and/or KV caches.

cs.LG↗

arXiv2025

Multicolor groups for molecules and solids

The local magnetic moments of atoms in a molecule or solid can be designated by different colors. Magnetic groups, or 2-color groups, or black-and-white groups have been applied in crystallography to classify different magnets. Despite its successes in the past decades, the recent advents of altermagnets and p-wave magnets raise new challenges to this long-standing framework, which urges for a new and unified one. Here we develop a multicolor group classification framework to classify all kinds of molecules and solids, including nonmagnetic materials and magnets with collinear or non-collinear magnetism, and with or without spin-orbit couplings (SOC). This new scheme can unify the classifications of matters into a single framework, including the recently identified altermagnets and p-wave magnets. Especially, altermagnetic topological matters and p-wave magnets with SOC, can also be diagnosed with multicolor groups, a task which can not be accomplished by magnetic space groups and spin space groups. Moreover, insufficiencies and misconceptions of conventional magnetic group classification can be supplemented through this new framework. Multicolor group will serve as a new stage in the symmetry classification of matters.

cond-mat.mtrl-sci↗

arXiv2025

A Simple and Effective Random Forest Modelling for Nonlinear Time Series Data

In this paper, we propose Random Forests by Random Weights (RF-RW), a theoretically grounded and practically effective alternative RF modelling for nonlinear time series data, where existing RF-based approaches struggle to adequately capture temporal dependence. RF-RW reconciles the strengths of classic RF with the temporal dependence inherent in time series forecasting. Specifically, it avoids the bootstrap resampling procedure, therefore preserves the serial dependence structure, whilst incorporates independent random weights to reduce correlations among trees. We establish non-asymptotic concentration bounds and asymptotic uniform consistency guarantees, for both fixed- and high-dimensional feature spaces, which extend beyond existing theoretical analyses of RF. Extensive simulation studies demonstrate that RF-RW outperforms existing RF-based approaches and other benchmarks such as SVM and LSTM. It also achieves the lowest error among competitors in our real-data example of predicting UK COVID-19 daily cases.

stat.ME↗

arXiv2025

Three-dimensional flat bands and possible interlayer triplet pairing superconductivity in the alternating twisted NbSe$_2$ moiré bulk

Moiré superlattices hosting flat bands and correlated states have emerged as a focal topic in condensed matter research. Through first-principles calculations, we investigate three-dimensional flat bands in alternating twisted NbSe$_2$ moiré bulk structures. These structures exhibit enhanced interlayer interactions compared to twisted bilayer configurations. Our results demonstrate that moiré bulks undergo spontaneous large-scale structural relaxation, resulting in the formation of remarkably flat energy bands at twist angles $\leq$ 7.31°. The $k_z$-dependent dispersion of flat bands across different moiré bulks reveals their intrinsic three-dimensional character. The presence of out-of-plane mirror symmetry in these moiré bulk structures suggests possible interlayer triplet superconducting pairing mechanisms that differ from those in twisted bilayer systems. Our work paves the way for exploring potential three-dimensional flat bands in other moiré bulk systems.

cond-mat.supr-con↗

arXiv2025

Local-Antisymmetric Flat Band and Coexisting Correlated stripe charge orders in WSe2-Modulated Twisted Bilayer Graphene

Insulating, atomically flat transition metal dichalcogenides (TMDs) like WSe2 are ideal substrates for probing intrinsic graphene properties. Conventionally, their influence on graphene's band structure is assumed negligible, particularly when small moire patterns form. Combining scanning tunneling microscopy/spectroscopy and theoretical analysis, we reveal that the atomic registry in graphene/WSe2 heterostructures profoundly modulates the electronic structure of magic-angle twisted bilayer graphene (MATBG). At special graphene/WSe2 twist angles, an incommensurate moire superlattice hosts three distinct atomic stacking configurations (A, B, X types). These induce position-dependent potentials that asymmetrically shift MATBG's flat bands, transforming them from hole-side to electron-side asymmetric within a single AA-stacked region. This symmetry breaking enables the unprecedented coexistence of orthogonal stripe charge orders in the correlated regime-a phenomenon previously considered mutually exclusive due to Coulomb repulsion. This band modulation arises from the synergistic effects of the graphene/WSe2 interfacial atomic registry and heterostrain within the MATBG, exhibiting multi-field tunability. Our work establishes interfacial atomic registry as a critical, previously overlooked tuning parameter for flat-band physics, opening avenues to engineer correlated quantum states in van der Waals heterostructures.

cond-mat.mes-hall↗

arXiv2025

Beacon: Post-Training Quantization with Integrated Grid Selection

Quantization is a widely used compression technique for reducing the memory and computation costs of large pre-trained models. A key challenge in per-channel post-training quantization (PTQ) is selecting appropriate scaling factors to replace weight values with values from a scaled integer grid. Existing methods typically fix the scale at the outset via heuristic tuning or grid search. We propose Beacon, a simple and effective algorithm that eliminates the need for such manual tuning. Beacon performs per-channel PTQ directly using an unscaled grid and automatically determines the optimal scaling factors by exploiting the geometry of scalar quantization. It does not rely on back-propagation or large calibration sets. Despite its simplicity and tuning-free nature, Beacon achieves competitive performance compared to state-of-the-art methods, making it a practical solution for efficient model deployment.

cs.LG↗

arXiv2025

Sliding-induced ferrovalley polarization and possible antiferromagnetic half-metal in bilayer altermagnets

Altermagnets, a newly discovered class of materials, exhibit zero net magnetization while hosting spin-split electronic bands. However, monolayer altermagnets maintain degenerate band gaps at the high-symmetry X and Y points in the Brillouin zone, manifesting a paravalley phase characterized by unpolarized valley states. In this work, we demonstrate that spontaneously broken valley degeneracy can be achieved through interlayer sliding in engineered M$_2$A$_2$B and M$_2$AA$'$B bilayer altermagnets by first-principles calculations and minimal microscopic model. We propose a promising route to achieve antiferromagnetic half-metal driven by sliding and emergent ferrovalley phase without applied electric field, which is realized in the V$_2$SSeO engineered bilayer. Our calculations also reveal that Mo$_2$O$_2$O exhibits the largest valley splitting gap of ~0.31 eV, making it a promising candidate for valley-spin valve devices. Furthermore, band structure calculations on Mo$_2$AA$'$O materials demonstrate that increasing the difference in atomic number ($Δ$Z) between A and A$'$ site atoms effectively enhances valley polarization. This work establishes a novel platform for discovering and controlling ferrovalley states in altermagnetic systems.

cond-mat.mtrl-sci↗

arXiv2025

Giant plateau-like topological Hall effect controlled by tailoring the magnetic exchange stiffness in a kagome magnet

The ferrimagnet TbMn6Sn6 has attracted vast attention, because its pristine Mn kagome lattice with strong spin-orbit coupling and out-of-plane Tb-Mn exchange supports quantum-limit Chern topological magnetism which can be described by the simple spinless Haldane model. We unveil herein that engineering the kagome lattice through partial substitution of Mn with nonmagnetic Cr induces a striking structural reorganization-Cr preferentially concentrates within a single Mn layer per unit cell, reducing the crystal symmetry from the D6h point group to the C2. This tailored structure configuration gives rise to a plateau-like topological Hall effect (THE), achieving a record-breaking resistivity of 19.1 ohm cm among bulk systems. Complementary magnetic force microscopy measurements unveil a magnetic domain transition near 1 T at 180 K, aligning with the field-dependent phase diagram of the THE. Our direct visualization of the magnetic domain structure underscores the critical role of broken kagome lattice symmetry in generating distinct exchange stiffness between the two Mn layers. These findings establish a new paradigm for exploring exotic states in kagome topological magnets and provide a proof-of-principle strategy for unraveling the interplay between magnetism and emergent topological properties in kagome systems.

cond-mat.str-el↗

arXiv2025

On the initial-boundary value problem for the 2D partially dissipative Oldroyd-B model: global well-posedness and large time stability

This paper establishes the global well-posedness of solutions to the Oldroyd-B model with purely horizontal viscosity and arbitrarily large initial data in two-dimensional settings, including the full space $\mathbb{R}^2$, the partially periodic domain $\mathcal{T}\times\mathbb{R}$ and the fully periodic torus $\mathcal{T}^2$, where $\mathcal{T}$ represents the one-dimensional periodic torus. Our analysis relies on energy methods to derive key {\it a priori} estimates that capture the anisotropic regularization induced by horizontal viscosity. Furthermore, for the cases of spatial domains $\mathcal{T}\times\mathbb{R}$ and $\mathcal{T}^2$, we further investigate the long-time behavior of solutions with small initial data. The compactness along the horizontal direction plays a pivotal role in constructing uniform-in-time estimates, ultimately leading to exponential decay of solutions as $t\to\infty$. This decay mechanism reveals how geometric constraints enhance the dissipation in viscoelastic flows.

math.AP↗

arXiv2025

CLIP-Optimized Multimodal Image Enhancement via ISP-CNN Fusion for Coal Mine IoVT under Uneven Illumination

Clear monitoring images are crucial for the safe operation of coal mine Internet of Video Things (IoVT) systems. However, low illumination and uneven brightness in underground environments significantly degrade image quality, posing challenges for enhancement methods that often rely on difficult-to-obtain paired reference images. Additionally, there is a trade-off between enhancement performance and computational efficiency on edge devices within IoVT systems.To address these issues, we propose a multimodal image enhancement method tailored for coal mine IoVT, utilizing an ISP-CNN fusion architecture optimized for uneven illumination. This two-stage strategy combines global enhancement with detail optimization, effectively improving image quality, especially in poorly lit areas. A CLIP-based multimodal iterative optimization allows for unsupervised training of the enhancement algorithm. By integrating traditional image signal processing (ISP) with convolutional neural networks (CNN), our approach reduces computational complexity while maintaining high performance, making it suitable for real-time deployment on edge devices.Experimental results demonstrate that our method effectively mitigates uneven brightness and enhances key image quality metrics, with PSNR improvements of 2.9%-4.9%, SSIM by 4.3%-11.4%, and VIF by 4.9%-17.8% compared to seven state-of-the-art algorithms. Simulated coal mine monitoring scenarios validate our method's ability to balance performance and computational demands, facilitating real-time enhancement and supporting safer mining operations.

cs.CV↗

arXiv2025

Improving Deep Regression with Tightness

For deep regression, preserving the ordinality of the targets with respect to the feature representation improves performance across various tasks. However, a theoretical explanation for the benefits of ordinality is still lacking. This work reveals that preserving ordinality reduces the conditional entropy $H(Z|Y)$ of representation $Z$ conditional on the target $Y$. However, our findings reveal that typical regression losses do little to reduce $H(Z|Y)$, even though it is vital for generalization performance. With this motivation, we introduce an optimal transport-based regularizer to preserve the similarity relationships of targets in the feature space to reduce $H(Z|Y)$. Additionally, we introduce a simple yet efficient strategy of duplicating the regressor targets, also with the aim of reducing $H(Z|Y)$. Experiments on three real-world regression tasks verify the effectiveness of our strategies to improve deep regression. Code: https://github.com/needylove/Regression_tightness.

cs.LG↗

arXiv2025

Theoretical Guarantees for Low-Rank Compression of Deep Neural Networks

Deep neural networks have achieved state-of-the-art performance across numerous applications, but their high memory and computational demands present significant challenges, particularly in resource-constrained environments. Model compression techniques, such as low-rank approximation, offer a promising solution by reducing the size and complexity of these networks while only minimally sacrificing accuracy. In this paper, we develop an analytical framework for data-driven post-training low-rank compression. We prove three recovery theorems under progressively weaker assumptions about the approximate low-rank structure of activations, modeling deviations via noise. Our results represent a step toward explaining why data-driven low-rank compression methods outperform data-agnostic approaches and towards theoretically grounded compression algorithms that reduce inference costs while maintaining performance.

cs.LG↗

arXiv2025

Dirac fermions in the altermagnet Ce4Sb3

Altermagnetism exhibits zero net magnetization and spin-splitting electronic structure. The interplay between altermagnetism and topological physics becomes an important topic in the condensed matter physics. In this letter, we propose that the altermagnet Ce4Sb3 hosts Dirac fermions through first-principles calculations and tight-binding model analysis. When subjected to external magnetic field, the altermagnet Ce4Sb3 change from altermagnetic state to ferromagnetic state. Accompanied with magnetic order transition, the four-fold degenerate Dirac points are transited into sextuple degenerate points. Especially, spin-orbital coupling effect has little influence about the Dirac fermions. The topological phases in the altermagnetic state may hold the exotic spin-splitter torque and remarkable nonlinear transport effect. Our work paves the way for exploring the interplay between altermagnetism and non-trivial topological phase.

cond-mat.mtrl-sci↗

arXiv2024

Efficient Detection Framework Adaptation for Edge Computing: A Plug-and-play Neural Network Toolbox Enabling Edge Deployment

Edge computing has emerged as a key paradigm for deploying deep learning-based object detection in time-sensitive scenarios. However, existing edge detection methods face challenges: 1) difficulty balancing detection precision with lightweight models, 2) limited adaptability of generalized deployment designs, and 3) insufficient real-world validation. To address these issues, we propose the Edge Detection Toolbox (ED-TOOLBOX), which utilizes generalizable plug-and-play components to adapt object detection models for edge environments. Specifically, we introduce a lightweight Reparameterized Dynamic Convolutional Network (Rep-DConvNet) featuring weighted multi-shape convolutional branches to enhance detection performance. Additionally, we design a Sparse Cross-Attention (SC-A) network with a localized-mapping-assisted self-attention mechanism, enabling a well-crafted joint module for adaptive feature transfer. For real-world applications, we incorporate an Efficient Head into the YOLO framework to accelerate edge model optimization. To demonstrate practical impact, we identify a gap in helmet detection -- overlooking band fastening, a critical safety factor -- and create the Helmet Band Detection Dataset (HBDD). Using ED-TOOLBOX-optimized models, we address this real-world task. Extensive experiments validate the effectiveness of ED-TOOLBOX, with edge detection models outperforming six state-of-the-art methods in visual surveillance simulations, achieving real-time and accurate performance. These results highlight ED-TOOLBOX as a superior solution for edge object detection.

cs.CV↗

arXiv2024

Strong Inter-valley Electron-Phonon Coupling in Magic-Angle Twisted Bilayer Graphene

The unusual properties of superconductivity in magic-angle twisted bilayer graphene (MATBG) have sparked enormous research interest. However, despite the dedication of intensive experimental efforts and the proposal of several possible pairing mechanisms, the origin of its superconductivity remains elusive. Here, utilizing angle-resolved photoemission spectroscopy with micrometer spatial resolution, we have revealed flat band replicas in superconducting MATBG, where MATBG is unaligned with its hexagonal boron nitride (hBN) substrate11. These replicas exhibit uniform energy spacing, approximately 150 +- 15 meV apart, indicative of strong electron-boson coupling. Strikingly, these replicas are absent in non-superconducting twisted bilayer graphene (TBG) systems, either when MATBG is aligned to hBN or when TBG deviates from the magic angle. Calculations suggest that the formation of these flat band replicas in superconducting MATBG are attributed to the strong coupling between flat band electrons and an optical phonon mode at the graphene K point, facilitated by inter-valley scattering. These findings, although do not necessarily put electron phonon coupling as the main driving force for the superconductivity in MATBG, unravel the unique electronic structure inherent in superconducting MATBG, thereby providing crucial information for understanding the unusual electronic landscape from which the superconductivity is derived.

cond-mat.mes-hall↗