arXiv subjects

Tao Hong

Publications and source records attributed to Tao Hong.

At least 19 recordsLinked to original sources

arXiv2026

Magnetic order and excitations in the magnetically intercalated van der Waals material Cr$_{\frac{1}{4}}$NbSe$_2$

Cr$_{\frac{1}{4}}$NbSe$_2$ is a triangular lattice magnet in which magnetic Cr$^{3+}$ ions are intercalated to form triangular lattices between NbSe$_2$ van der Waals layers stacked along the c axis. By unpolarized and polarized neutron scattering experiments, we have revealed that the magnetic ground state of this system is a 120$^{\circ}$-type antiferromagnetic order characterized by the magnetic propagation wave vector of $q=(\frac{1}{3}, \frac{1}{3}, 0)$. We also performed inelastic neutron scattering measurements using co-aligned single crystals, and determined dispersion relations of magnetic excitations at low temperatures. Comparing the observed spectra with calculations based on the linear spin-wave theory, we revealed that the out-of-plane ferromagnetic interaction is fairly strong as compared to the in-plane nearest neighbor antiferromagnetic interaction. Although the crystal structure of this system is composed of two-dimensional van der Waals layers, the magnetic order has a three dimensional character, which would be attributed to long-range magnetic interactions mediated by conduction electrons.

cond-mat.str-el↗

arXiv2026

Accurate polarization calibration of FAST spectral data for measurements of Zeeman splittings of OH megamasers in IRAS 02524+2046

An accurate polarization calibration is essential for a spectral data analysis and Zeeman splitting measurements. Two anomalies challenge our understanding of OH megamasers in IRAS 02524+2046: an unexplained 1667/1665 MHz flux-ratio deviation, and complex Stokes V signatures. Well-calibrated sensitive polarization observations are required to understand them. We develop a polarization calibration solution for the L-band 19-beam receiver installed on the Five-hundred-meter aperture spherical radio telescope (FAST) to achieve a high calibration accuracy and thus enable accurate measurements of the OH megamaser properties in IRAS 02524+2046. We determined the Mueller matrix solution for spectral observations across the 1050-1450 MHz frequency range with an accuracy of about 0.01%-0.08% for circular polarization. We then applied it to FAST observational data of IRAS 02524+2046. Our results show narrower emission line components in the OH megamasers than previously reported, which are indistinguishable in the total power spectrum, but are detected in the circular polarization spectrum. The 1667 MHz OH megamaser emissions probably span a wide velocity range from ~54750 to ~53580 km/s, indicating greater complexity than previously recognized. Our fit of the total power and circular polarization spectra for IRAS 02524+2046 revealed ten line components with significant Zeeman splitting (>3sigma), indicating in situ magnetic fields with a strength of approximately -24.5 mG to +20.6 mG, most of which (8/10) have positive values.

astro-ph.GA↗

arXiv2025

Evidence of Phase Transitions in Small Transformer-Based Language Models

Phase transitions have been proposed as the origin of emergent abilities in large language models (LLMs), where new capabilities appear abruptly once models surpass critical thresholds of scale. Prior work, such as that of Wei et al., demonstrated these phenomena under model and data scaling, with transitions revealed after applying a log scale to training compute. In this work, we ask three complementary questions: (1) Are phase transitions unique to large models, or can they also be observed in small transformer-based language models? (2) Can such transitions be detected directly in linear training space, rather than only after log rescaling? and (3) Can these transitions emerge at early stages of training? To investigate, we train a small GPT-style transformer on a character-level corpus and analyze the evolution of vocabulary usage throughout training. We track the average word length, the number of correct versus incorrect words, and shifts in vocabulary diversity. Building on these measures, we apply Poisson and sub-Poisson statistics to quantify how words connect and reorganize. This combined analysis reveals a distinct transition point during training. Notably, these transitions are not apparent in standard loss or validation curves, but become visible through our vocabulary- and statistics-based probes. Our findings suggest that phase-transition reorganizations are a general feature of language model training, observable even in modest models, detectable directly in linear training space, and occurring surprisingly early as coherence emerges. This perspective provides new insight into the nonlinear dynamics of language model training and underscores the importance of tailored metrics for uncovering phase transition behaviors

cs.CL↗

arXiv2025

Hidden symmetry-breaking in a kagome Ising ferromagnet

Kagome metals can host unconventional electronic phenomena that emerge from their frustrated lattice geometry and associated band topology. Correlated electronic orders, such as charge-density waves and superconductivity, are observed to intertwine with subtle time-reversal symmetry breaking whose microscopic origin is not currently understood. Here, we provide evidence for such time-reversal symmetry breaking in the kagome metal TbV$_6$Sn$_6$ arising from staggered magnetic moments within the kagome layers. TbV$_6$Sn$_6$ consists of metallic V kagome layers separated by Tb triangular layers that host Ising ferromagnetic order. Deep in the ferromagnetic state, the Tb Ising doublet ground state should display a single, dispersionless spin-flip excitation. Instead, inelastic neutron scattering reveals two sharp excitations associated with inequivalent Tb sites, demonstrating that a symmetry-broken phase coexists with Ising ferromagnetism. No additional structural or magnetic phase transitions are detected, and first-principles calculations rule out lattice distortions as the origin of the splitting. We attribute this effect to time-reversal symmetry breaking encoded by small V moments that couple to the Tb sublattice and leave a measurable spectral fingerprint. Our results establish rare-earth local moment spectroscopy as a sensitive probe of subtle broken symmetries and highlight an unexpected interplay between kagome magnetism and rare-earth local moment magnetism.

cond-mat.str-el↗

arXiv2025

Magnetic dynamics in NiTiO3 honeycomb antiferromagnet using neutron scattering

The ilmenite NiTiO3 consists of a buckled honeycomb lattice, with the Ni spins aligned ferromagnetically in-plane and antiferromagnetically out-of-plane. Using neutron spectroscopy, the magnetic structure and the dynamics were investigated as a function of temperature. Dispersive acoustic bands and nearly dispersionless optical bands at ~3.7 meV are described by a highly anisotropy Heisenberg model with stronger antiferromagnetic (AFM) out-of-plane, weaker ferromagnetic (FM) in-plane interactions and an anisotropy gap of 0.95 meV. The order parameter yields a critical exponent between the Heisenberg and two-dimensional Ising models, consistent with highly anisotropic Heisenberg systems. The frustration parameter ~ 2 supports a weakly frustrated system.

cond-mat.mtrl-sci↗

arXiv2025

A Convergent Generalized Krylov Subspace Method for Compressed Sensing MRI Reconstruction with Gradient-Driven Denoisers

Model-based reconstruction plays a key role in compressed sensing (CS) MRI, as it incorporates effective image regularizers to improve the quality of reconstruction. The Plug-and-Play and Regularization-by-Denoising frameworks leverage advanced denoisers (e.g., convolutional neural network (CNN)-based denoisers) and have demonstrated strong empirical performance. However, their theoretical guarantees remain limited, as practical CNNs often violate key assumptions. In contrast, gradient-driven denoisers achieve competitive performance, and the required assumptions for theoretical analysis are easily satisfied. However, solving the associated optimization problem remains computationally demanding. To address this challenge, we propose a generalized Krylov subspace method (GKSM) to solve the optimization problem efficiently. Moreover, we also establish rigorous convergence guarantees for GKSM in nonconvex settings. Numerical experiments on CS MRI reconstruction with spiral and radial acquisitions validate both the computational efficiency of GKSM and the accuracy of the theoretical predictions. The proposed optimization method is applicable to any linear inverse problem.

eess.IV↗

arXiv2025

Convergent Complex Quasi-Newton Proximal Methods for Gradient-Driven Denoisers in Compressed Sensing MRI Reconstruction

In compressed sensing (CS) MRI, model-based methods are pivotal to achieving accurate reconstruction. One of the main challenges in model-based methods is finding an effective prior to describe the statistical distribution of the target image. Plug-and-Play (PnP) and REgularization by Denoising (RED) are two general frameworks that use denoisers as the prior. While PnP/RED methods with convolutional neural networks (CNNs) based denoisers outperform classical hand-crafted priors in CS MRI, their convergence theory relies on assumptions that do not hold for practical CNNs. The recently developed gradient-driven denoisers offer a framework that bridges the gap between practical performance and theoretical guarantees. However, the numerical solvers for the associated minimization problem remain slow for CS MRI reconstruction. This paper proposes a complex quasi-Newton proximal method that achieves faster convergence than existing approaches. To address the complex domain in CS MRI, we propose a modified Hessian estimation method that guarantees Hermitian positive definiteness. Furthermore, we provide a rigorous convergence analysis of the proposed method for nonconvex settings. Numerical experiments on both Cartesian and non-Cartesian sampling trajectories demonstrate the effectiveness and efficiency of our approach.

eess.IV↗

arXiv2025

Emergent Hidden Multipolar State in the Triangular Lattice Magnet TmZn2GaO5

TmZn2GaO5 is a newly synthesized triangular lattice magnet that exhibits a unique quantum phase characterized by strong Ising anisotropy, a pseudo-doublet crystal electric field ground state, and a low-energy gapped excitation at the K point. Unlike its well-known counterparts, TmMgGaO4 and YbMgGaO4, this material crystallizes in a distinct hexagonal structure, leading to a cleaner platform for investigating frustrated magnetism. Magnetic susceptibility, heat capacity, and inelastic neutron scattering measurements confirm the absence of long-range magnetic order down to 50 mK, placing TmZn2GaO5 in a distinct region of the transverse-field Ising model phase diagram. Theoretical calculations based on spin-wave theory and mean-field modeling reproduce key experimental observations, reinforcing the material's placement in a quantum disordered/multipolar state. These results highlight its potential for exploring quantum disordered states, anisotropic excitations, and exotic quantum phases in frustrated spin systems.

cond-mat.str-el↗

arXiv2025

Two-body strong decays of the hidden-charm tetraquark molecular states via the QCD sum rules

In this work, we extend our previous work on the $D^*\bar{D}^*$ molecular states with the $J^{PC}=0^{++}$, $1^{+-}$ and $2^{++}$ to investigate their two-body strong decays via the QCD sum rules based on rigorous quark-hadron duality. We obtain the partial decay widths therefore total widths of the ground states with the $J^{PC}=0^{++}$, $1^{+-}$ and $2^{++}$, which indicate that it is reasonable to assign the $X_2(4014)$ as the $D^*\bar{D}^*$ tetraquark molecular states with the $J^{PC}=2^{++}$.

hep-ph↗

arXiv2025

HOPS: High-order Polynomials with Self-supervised Dimension Reduction for Load Forecasting

Load forecasting is a fundamental task in smart grid. Many techniques have been applied to developing load forecasting models. Due to the challenges such as the Curse of Dimensionality, overfitting, and limited computing resources, multivariate higher-order polynomial models have received limited attention in load forecasting, despite their desirable mathematical foundations and optimization properties. In this paper, we propose low rank approximation and self-supervised dimension reduction to address the aforementioned issues. To further improve computational efficiency, we also utilize a fast Conjugate Gradient based algorithm for the proposed polynomial models. Based on the load datasets from the ISO New England, the proposed method high-order polynomials with self-supervised dimension reduction (HOPS) demonstrates higher forecasting accuracy over several competitive models. Additionally, experimental results indicate that our approach alleviates redundant variable construction, achieving better forecasts with fewer input variables.

cs.LG↗

arXiv2024

Dirac Magnon in Honeycomb Lattice Magnet NiTiO3

We performed inelastic neutron scattering experiments on single-crystal samples of the honeycomb lattice magnet, ilmenite NiTiO3. Below the Neel temperature of 22 K, spin wave excitations with a band energy of 3.7 meV were observed. The neutron energy spectra were well-reproduced by modeling the system as a ferromagnetic honeycomb lattice with antiferromagnetic interlayer coupling, using linear spin wave theory. Similar to another ilmenite CoTiO3, a crossing structure was observed at the K point, suggesting the resence of Dirac magnons in NiTiO3. Further calculations suggested the formation of Dirac nodal line.

cond-mat.str-el↗

arXiv2024

Using Randomized Nystr\"om Preconditioners to Accelerate Variational Image Reconstruction

Model-based iterative reconstruction plays a key role in solving inverse problems. However, the associated minimization problems are generally large-scale, nonsmooth, and sometimes even nonconvex, which present challenges in designing efficient iterative solvers. Preconditioning methods can significantly accelerate the convergence of iterative methods. In some applications, computing preconditioners on-the-fly is beneficial. Moreover, forward models in image reconstruction are typically represented as operators, and the corresponding explicit matrices are often unavailable, which brings additional challenges in designing preconditioners. Therefore, for practical use, computing and applying preconditioners should be computationally inexpensive. This paper adapts the randomized Nystr\"om approximation to compute effective preconditioners that accelerate image reconstruction without requiring an explicit matrix for the forward model. We leverage modern GPU computational platforms to compute the preconditioner on-the-fly. Moreover, we propose efficient approaches for applying the preconditioners to problems with classical nonsmooth regularizers, i.e., wavelet, total variation, and Hessian Schatten-norm. Our numerical results on image deblurring, super-resolution with impulsive noise, and 2D computed tomography reconstruction illustrate the efficiency and effectiveness of the proposed preconditioner.

eess.IV↗

arXiv2024

Provable Preconditioned Plug-and-Play Approach for Compressed Sensing MRI Reconstruction

Model-based methods play a key role in the reconstruction of compressed sensing (CS) MRI. Finding an effective prior to describe the statistical distribution of the image family of interest is crucial for model-based methods. Plug-and-play (PnP) is a general framework that uses denoising algorithms as the prior or regularizer. Recent work showed that PnP methods with denoisers based on pretrained convolutional neural networks outperform other classical regularizers in CS MRI reconstruction. However, the numerical solvers for PnP can be slow for CS MRI reconstruction. This paper proposes a preconditioned PnP (P^2nP) method to accelerate the convergence speed. Moreover, we provide proofs of the fixed-point convergence of the P^2nP iterates. Numerical experiments on CS MRI reconstruction with non-Cartesian sampling trajectories illustrate the effectiveness and efficiency of the P^2nP approach.

eess.IV↗

arXiv2024

Spin dynamics in linear magnetoelectric material Mn$_{3}$Ta$_{2}$O$_{8}$

We performed inelastic neutron scattering experiments on single crystal samples of a linear magnetoelectric material Mn$_{3}$Ta$_{2}$O$_{8}$, which exhibits a collinear antiferromagnetic order, to reveal the spin dynamics. Numerous modes observed in the neutron spectra were reasonably reproduced by linear spin-wave theory on the basis of the spin Hamiltonian including eight Heisenberg interactions and an easy-plane type single-ion anisotropy. The presence of strong frustration was found in the identified spin Hamiltonian.

cond-mat.str-el↗

arXiv2023

Static magnetic order with strong quantum fluctuations in spin-1/2 honeycomb magnet Na2Co2TeO6

Kitaev interactions, arising from the interplay of frustration and bond anisotropy, can lead to strong quantum fluctuations and, in an ideal case, to a quantum-spin-liquid state. However, in many nonideal materials, spurious non-Kitaev interactions typically promote a zigzag antiferromagnetic order in the d-orbital transition metal compounds. By combining neutron scattering with muon-spin rotation and relaxation techniques, we provide new insights into the exotic properties of Na2Co2TeO6, a candidate Kitaev material. Below TN, the zero-field muon-spin relaxation rate becomes almost constant (at 0.45 us-1). We attribute this temperature-independent muon-spin relaxation rate to the strong quantum fluctuations, as well as to the frustrated Kitaev interactions. As the magnetic field increases, neutron scattering data indicate a much broader spin-wave-excitation gap at the K-point. Therefore, quantum fluctuations seem not only robust, but are even enhanced by the applied magnetic field. Our findings provide valuable hints for understanding the onset of the quantum-spin-liquid state in Kitaev materials.

cond-mat.str-el↗

arXiv2023

A Mini-Batch Quasi-Newton Proximal Method for Constrained Total-Variation Nonlinear Image Reconstruction

Over the years, computational imaging with accurate nonlinear physical models has garnered considerable interest due to its ability to achieve high-quality reconstructions. However, using such nonlinear models for reconstruction is computationally demanding. A popular choice for solving the corresponding inverse problems is the accelerated stochastic proximal method (ASPM), with the caveat that each iteration is still expensive. To overcome this issue, we propose a mini-batch quasi-Newton proximal method (BQNPM) tailored to image reconstruction problems with constrained total variation regularization. Compared to ASPM, BQNPM requires fewer iterations to converge. Moreover, we propose an efficient approach to compute a weighted proximal mapping at a cost similar to that of the proximal mapping in ASPM. We also analyze the convergence of BQNPM in the nonconvex setting. We assess the performance of BQNPM on three-dimensional inverse-scattering problems with linear and nonlinear physical models. Our results on simulated and real data demonstrate the effectiveness and efficiency of BQNPM, while also validating our theoretical analysis.

math.OC↗

arXiv2023

Static and dynamical properties of the spin-5/2 nearly ideal triangular lattice antiferromagnet Ba3MnSb2O9

We study the ground state and spin excitations in Ba3MnSb2O9, an easy-plane S = 5/2 triangular lattice antiferromagnet. By combining single-crystal neutron scattering, electric spin resonance (ESR), and spin wave calculations, we determine the frustrated quasi-two-dimensional spin Hamiltonian parameters describing the material. While the material has a slight monoclinic structural distortion, which could allow for isosceles-triangular exchanges and biaxial anisotropy by symmetry, we observe no deviation from the behavior expected for spin waves in the in-plane 120o state. Even the easy-plane anisotropy is so small that it can only be detected by ESR in our study. In conjunction with the quasi-two-dimensionality, our study establishes that Ba3MnSb2O9 is a nearly ideal triangular lattice antiferromagnet with the quasi-classical spin S = 5/2, which suggests that it has the potential for an experimental study of Z- or Z2-vortex excitations.

cond-mat.str-el↗

arXiv2023

Coexistence of symmetry-protected topological order and Neel order in the spin-1/2 ladder antiferromagnet C9H18N2CuBr4

Topological phases of matter are beyond the paradigm of Landau's symmetry breaking and have challenged our understanding of condensed matter systems. Here we report a new type of symmetry-protected topological phase of matter in the spin-1/2 coupled two-leg ladder antiferromagnet C9H18N2CuBr4, DLCB for short. In this two-sublattice antiferromagnet with a weak easy-axis anisotropy, we find no evidence of a conventional spin-flop transition in the magnetization with the magnetic field applied parallel to the easy axis at T=0.4 K, well below TN=2.0 K. Moreover, the temperature dependence of the gapped transverse excitations across TN indicates that they are not the conventional S=1 magnons associated with explicit symmetry breaking. Instead, the thermal renormalization of the gap energy shows a remarkable agreement with a calculation for the three-dimensional O(3) nonlinear sigma model. Accordingly, the spin gap in DLCB is not due to the spin anisotropy but to the separation between a spin singlet state and a triplet excited state. Since an antiferromagnetic spin-1/2 ladder systems can be mapped onto the spin-1 chain, the notion of the Haldane gap is proposed to explain the opening of the spin gap in DLCB. Therefore, the ground state of DLCB is best described as a quantum superposition of a Haldane phase and a Neel-ordered phase, which resembles the quantum state of a qubit in quantum computing. Our results indicate the presence of a symmetry-protected topological order coexisting with an antiferromagnetic order in this material.

cond-mat.str-el↗