arXiv subjects

Hao Jin

Publications and source records attributed to Hao Jin.

At least 19 recordsLinked to original sources

arXiv2026

Coupled Continuous-Discrete Generation for Scene Text Image Super-Resolution

Scene text image super-resolution (STISR) aims to recover visually plausible appearance while preserving character semantics from degraded inputs. Existing STISR systems often rely on externally generated priors or separate image and text models, resulting in error propagation and costly multi-stage inference. We present DualTSR, a unified framework that formulates STISR as coupled continuous-discrete generation. Conditional flow matching restores continuous image latents, while absorbing-state discrete diffusion reconstructs text tokens. Both processes share a multimodal transformer backbone, allowing the evolving image and text states to interact throughout generation without an external OCR prior at inference. On CTR-TSR, DualTSR achieves the best FID, LPIPS, ACC, and NED among the compared methods at both X2 and X4. On an aligned RealCE subset, it obtains the best FID, ACC, and NED with competitive LPIPS. Compared with DiffTSR at X4, DualTSR improves ACC by 12.78 percentage points while reducing the parameter count from 1.23B to 203M and end-to-end latency from 13.3s to 132ms. These results establish DualTSR as an accurate and efficient method for STISR.

cs.CV↗

arXiv2026

Machine-learning-identified two-dimensional van der Waals multiferroics for four-state nonvolatile memory

Two-dimensional (2D) van der Waals (vdW) multiferroics offer an attractive platform for four-state nonvolatile memory by combining switchable ferroelectric polarization and magnetization within a single material system. However, their development is hindered by the scarcity of synthesizable candidates and the lack of non-destructive readout schemes. Here, we combine machine-learning screening with first-principles calculations to explore the 2D vdW ABC$_2$X$_6$ family and identify a set of high-confidence multiferroic candidates. Among them, AuCrP$_2$S$_6$ monolayer emerges as a representative system with a ferromagnetic ground state, a sizable out-of-plane polarization of 7.46 pC/m, and a moderate ferroelectric switching barrier of $\sim$130 meV/f.u. Moreover, the nonlinear optical response mediated by the bulk photovoltaic effect (BPVE) in AuCrP$_2$S$_6$ provides a dual-channel probe of the ferroic orders, in which the polarization direction governs the photocurrent sign while the magnetic order selects the spin channel via robust exchange splitting. This intrinsic coupling enables the non-destructive readout of four logic states within a single atomic layer, thereby providing a practical blueprint for next-generation multistate optoelectronic memory.

cond-mat.mtrl-sci↗

arXiv2026

Third-order intrinsic anomalous Hall effect as a transport fingerprint of altermagnets

The intrinsic anomalous Hall effect (IAHE) provides a powerful transport fingerprint of quantum magnets, with its linear and second-order responses distinguishing ferromagnets and $\mathcal{P}\mathcal{T}$-symmetric antiferromagnets, respectively. Altermagnets, as an emergent class of quantum magnets, have recently been shown to host a third-order extrinsic anomalous Hall effect, raising a question of whether an \textit{intrinsic} counterpart can serve as a diagnostic of altermagnetic order. Based on spin-group symmetry analysis, we demonstrate that the third-order IAHE is generically allowed in the ten spin Laue groups relevant to altermagnets when spin-orbit coupling (SOC) is taken into account. By combining these symmetry constraints with the anomalous velocity induced by the second-order Berry curvature, we uncover a resonant third-order IAHE arising near the altermagnetic band crossings at generic momenta in both the Lieb-lattice altermagnet and the experimentally realized altermagnet V$_2$Se$_2$O. Notably, we identify the Berry curvature quadrupole, encoded in the second-order Berry curvature and activated by finite SOC, as the microscopic quantum geometric origin of this resonance. Our results establish the third-order IAHE as an intrinsic quantum geometric transport fingerprint of altermagnets and extend the hierarchy of IAHE across collinear quantum magnets.

cond-mat.mtrl-sci↗

arXiv2026

Surface-enhanced Raman scattering and density functional theory study of selected-lanthanide-citrate complexes (lanthanide: Tb, Dy, Ho, Er, Tm, Yb and Lu)

In this study, surface-enhanced Raman scattering (SERS) and density functional theory (DFT) calculations were combined to investigate the SERS spectra of Ln-citrate complexes (Ln: Tb, Dy, Ho, Er, Tm, Yb, and Lu) under 488 and 532 nm excitation. Peak assignment was supported by simulated SERS spectra calculated with an optimized DFT method using large-core effective core potentials. The main bands near 935, 1060, 1315, and 1485 cm-1 were assigned to (C-COO-) + (CH2), (CH2) + (C-O -- Ln), sym(COO-) + (CH2), and asym(COO-) + (CH2), respectively. Relative peak intensities were evaluated by normalizing the bands near 935, 1060, and 1485 cm-1 to that near 1315 cm-1. The ratios I_935/I_1315 and I_1485/I_1315 generally increased from Dy-citrate to Lu-citrate, whereas the I_1060/I_1315 ratio decreased. These trends were observed under both excitation wavelengths. The decrease in relative SERS peak intensity of the 1060 cm-1 band is attributed to stronger Ln-O interaction and reduced polarizability change, whereas the increases of the 935 and 1485 cm-1 bands are likely related to changes in local electronic distribution and effective symmetry sensitivity.

cond-mat.mtrl-sci↗

arXiv2026

DualTSR: Unified Dual-Diffusion Transformer for Scene Text Image Super-Resolution

Scene Text Image Super-Resolution (STISR) aims to restore high-resolution details in low-resolution text images, which is crucial for both human readability and machine recognition. Existing methods, however, often depend on external Optical Character Recognition (OCR) models for textual priors or rely on complex multi-component architectures that are difficult to train and reproduce. In this paper, we introduce DualTSR, a unified end-to-end framework that addresses both issues. DualTSR employs a single multimodal transformer backbone trained with a dual diffusion objective. It simultaneously models the continuous distribution of high-resolution images via Conditional Flow Matching and the discrete distribution of textual content via discrete diffusion. This shared design enables visual and textual information to interact at every layer, allowing the model to infer text priors internally instead of relying on an external OCR module. Compared with prior multi-branch diffusion systems, DualTSR offers a simpler end-to-end formulation with fewer hand-crafted components. Experiments on synthetic Chinese benchmarks and a curated real-world evaluation protocol show that DualTSR achieves strong perceptual quality and text fidelity.

cs.CV↗

arXiv2026

Phenomenological energy exchange of diatomic gases: Comparison of Pullin and Borgnakke-Larsen models in direct simulation Monte Carlo method

In hypersonic rarefied flows, insufficient intermolecular collisions cause significant deviations between translational and rotational temperatures, leading to strong thermal nonequilibrium. For diatomic gases such as nitrogen and oxygen, the direct simulation Monte Carlo (DSMC) method commonly employs the Borgnakke-Larsen (BL) model to simulate translational-rotational energy exchange (relaxation) processes. Although widely used, the BL model lacks a rigorous theoretical foundation and assumes that only a fraction of collisions lead to rotational relaxation. To address these shortcomings, Pullin introduced a kinetically consistent relaxation model into the gas kinetic theory. By employing the Beta function for energy partitioning, a concrete collision cross section that satisfies the detailed balance condition is constructed. In this study, a comparative investigation of the BL and Pullin models is performed within the DSMC framework, where both original and simplified equations are considered and parameterized by physical accommodated coefficient in the Beta function. A series of test cases--including zero-dimensional rotational relaxation of nitrogen, one-dimensional planar Couette flow and normal shock wave, two-dimensional hypersonic flow past a cylinder, and three-dimensional hypersonic flow around an X38-like vehicle--are performed to assess the accuracy and efficiency of these models. The results confirm the consistency between the Pullin and BL models. Owing to its rigorous theoretical foundation and accurate physical representation, the Pullin model is expected to provide substantial support for the extension of subsequent theoretical studies and numerical simulations. Moreover, in the highly rarefied flow regime (Knudsen number greater than 1, or altitudes above 100 km), the simplified Pullin model exhibits performance comparable to that of the BL model.

physics.flu-dyn↗

arXiv2026

The extended gas-kinetic theory from Pullin equation: the relaxation rates, transport coefficients and model equation

The Borgnakke-Larsen model, widely used in rarefied flow predictions, serves as the mainstream energy-exchange kernel for polyatomic gases. However, it lacks integrability and does not guarantee detailed balance, limiting theoretical foundations for near-continuum relaxation mechanisms, transport coefficients, and relaxation model equations. In this work, we adopt the Pullin equation, which possesses an integrable collision kernel and satisfies detailed balance, to analyze near-continuum relaxation. Considering only translational and rotational degrees, we obtain explicit analytical expressions for the relaxation rates of macroscopic variables including stress, temperatures, and heat fluxes by approximating the distribution function in mixed Hermite and Laguerre spaces. Based on the same elementary moments, we derive transport coefficients via Chapman-Enskog expansion, rigorously confirming a long-standing speculation that thermal conductivity depends on the degree of thermal non-equilibrium; under equilibrium, the results reduce to those of Mason and Monchick. Using the correct relaxation rates, we propose a novel Rykov-type relaxation model that captures the coupled relaxation of translational and rotational heat fluxes, a mechanism ignored in the widely used Rykov equation. The model is validated against benchmark test cases.

physics.comp-ph↗

arXiv2026

Flat-band Ferromagnetism of SU$(N)$ Hubbard Model on the Kagome Lattices

The kagome lattice, a well known example of the geometrically frustrated system, hosts a dispersionless flat band that offers a unique platform for studying correlation-driven quantum phenomena. At appropriate particle concentrations, the existence of a flat band allows a representation of percolation with nontrivial weights. In this work, we investigate the paramagnetic-ferromagnetic transition in the repulsive SU($N$) Hubbard model on the kagome lattice within this percolation framework. In this representation, the model can be rigorously mapped to a classical $N$-state site-percolation problem on a triangular lattice, with the SU($N$) symmetry reflected in the nontrivial weights. By large-scale Monte Carlo simulations for SU($3$), SU($4$), and SU($10$) symmetries, we demonstrate that the critical particle concentration for ferromagnetism exceeds the standard percolation threshold and increases with $N$, indicating a strengthening of the effective entropic repulsion.

cond-mat.str-el↗

arXiv2025

Integrating LLM and Diffusion-Based Agents for Social Simulation

Large language models (LLMs) offer strong semantic reasoning capabilities for user modeling, but applying LLM-based simulation to an entire social network is computationally expensive and often unreliable for users with sparse behavioral histories. Meanwhile, conventional information diffusion models efficiently exploit historical propagation patterns and social structures, but provide limited understanding of item content and user-item semantic compatibility. We propose HySID, a hybrid framework for individual-level information adoption prediction that combines semantic reasoning with structural diffusion. HySID first analyzes the historical user-relation graph to adaptively select a small set of structurally informative core users. It then applies LLM-based simulation to estimate the engagement of these users and converts the judgments into a diffusion-compatible seed. Finally, a plug-in diffusion backbone propagates this seed through historical interaction structures to rank potential adopters across the full user population. This design enables LLMs to focus on users for whom semantic reasoning is most informative while allowing neural diffusion models to generalize the evidence to users that are not explicitly simulated. Experiments on three real-world datasets from Weibo, Zhihu, and KuaiRand show that HySID consistently improves four diffusion backbones in Recall and NDCG, while outperforming full-population LLM simulation baselines. At the same time, selective simulation reduces LLM inference cost by approximately 83\% to 99.9\%, demonstrating that HySID provides an effective and computationally practical approach to scalable information adoption prediction.

cs.CY↗

arXiv2025

Generalizing the composite fermion theory for fractional Chern insulators

We propose a generalized composite fermion (CF) theory for fractional Chern insulators (FCIs) by adapting the quantum mechanics approach of CFs. The theoretical framework naturally produces an effective CF Hamiltonian and a wavefunction ansatz, and the Bloch band characteristics of FCIs determine effective scalar and vector potentials experienced by CFs. Our analysis clarifies the construction of CF wavefunctions and state counting in CF phase space, which is subject to a density-of-states correction for filling factors $|\nu| \neq 1/2$. We apply the theory to study the $\nu=-2/3$ FCI state of the twisted bilayer MoTe$_2$ system, modeling it as either a $1/3$-filled electron band or a $2/3$-filled hole band. While both CF models exhibit trends and features consistent with exact diagonalization results, the electron-based model shows better agreement. Furthermore, we find that the FCI phase transition coincides with a topological phase transition in unoccupied CF $\Lambda$-bands.

cond-mat.str-el↗

arXiv2025

Sketch-based Fluid Video Generation Using Motion-Guided Diffusion Models in Still Landscape Images

Integrating motion into static images not only enhances visual expressiveness but also creates a sense of immersion and temporal depth, establishing it as a longstanding and impactful theme in artistic expression. Fluid elements such as waterfall, river, and oceans are common features in landscape, but their complex dynamic characteristics pose significant challenges in modeling and controlling their motion within visual computing. Physics-based methods are often used in fluid animation to track particle movement. However, they are easily affected by boundary conditions. Recently, latent diffusion models have been applied to video generation tasks, demonstrating impressive capabilities in producing high-quality and temporally coherent results. However, it is challenging for the existing methods to animate fluid smooth and temporally consistent motion. To solve these issues, this paper introduces a framework for generating landscape videos by animating fluid in still images under the guidance of motion sketches. We propose a finetuned conditional latent diffusion model for generating motion field from user-provided sketches, which are subsequently integrated into a latent video diffusion model via a motion adapter to precisely control the fluid movement.

cs.GR↗

arXiv2025

Neural-Driven Image Editing

Traditional image editing typically relies on manual prompting, making it labor-intensive and inaccessible to individuals with limited motor control or language abilities. Leveraging recent advances in brain-computer interfaces (BCIs) and generative models, we propose LoongX, a hands-free image editing approach driven by multimodal neurophysiological signals. LoongX utilizes state-of-the-art diffusion models trained on a comprehensive dataset of 23,928 image editing pairs, each paired with synchronized electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), photoplethysmography (PPG), and head motion signals that capture user intent. To effectively address the heterogeneity of these signals, LoongX integrates two key modules. The cross-scale state space (CS3) module encodes informative modality-specific features. The dynamic gated fusion (DGF) module further aggregates these features into a unified latent space, which is then aligned with edit semantics via fine-tuning on a diffusion transformer (DiT). Additionally, we pre-train the encoders using contrastive learning to align cognitive states with semantic intentions from embedded natural language. Extensive experiments demonstrate that LoongX achieves performance comparable to text-driven methods (CLIP-I: 0.6605 vs. 0.6558; DINO: 0.4812 vs. 0.4636) and outperforms them when neural signals are combined with speech (CLIP-T: 0.2588 vs. 0.2549). These results highlight the promise of neural-driven generative models in enabling accessible, intuitive image editing and open new directions for cognitive-driven creative technologies. The code and dataset are released on the project website: https://loongx1.github.io.

cs.CV↗

arXiv2025

A hybrid numerical algorithm based on the stochastic particle Shakhov and DSMC method

The Direct Simulation Monte Carlo (DSMC) method is widely employed for simulating rarefied nonequilibrium gas flows. With advances in aerospace engineering and micro/nano-scale technologies, gas flows exhibit the coexistence of rarefied and continuum/near-continuum regimes, which calls for larger time steps and coarser spatial grids for efficient numerical simulation. However, the mesh sizes and time steps in DSMC are constrained by the single-scale nature of the Boltzmann equation and the explicit treatment of collision term following operator splitting. To overcome the resulting computational inefficiency, the Time-Relaxed Monte Carlo (TRMC) method introduces a suitable time discretization of the Boltzmann equation, allowing for significantly larger time steps. Besides, domain decomposition methods leverage the complementary strengths of continuum and particle-based approaches, facilitating the efficient simulation of multi-scale gas flows. However, in TRMC method, the physically accurate high-order terms are truncated and approximated through convergence to a local Maxwellian distribution. Meanwhile, the continuum breakdown criteria employed in hybrid methods are either empirical or semi-empirical. Recently, a timescale-based decomposition of the Boltzmann equation has been proposed to enable a more rational coupling between DSMC and Navier-Stokes. Inspired by this strategy, a novel hybrid particle method is proposed to couple the stochastic particle Shakhov with DSMC, in which the collision operator is decomposed into two sub-steps based on local observation timescale and the relaxation time. The validity and accuracy of the proposed method are demonstrated through a series of benchmark cases, including 1-D sod shock tube, 2-D hypersonic flow around cylinder and jet expansion into the vacuum, 3-D hypersonic flows around sphere and X-38 like vehicle in near-continuum flow regimes.

physics.comp-ph↗

arXiv2025

Surface-enhanced Raman scattering and density functional theory study of selected-lanthanide-citrate complexes (lanthanide: La, Ce, Pr, Nd, Sm, Eu, and Gd)

In this study, we combined the surface-enhanced Raman scattering (SERS) with density functional theory (DFT) calculations to investigate the SERS spectra of lanthanide (Ln)-citrate complexes (Ln = La, Ce, Pr, Nd, Sm, Eu, and Gd) under 488, 532, and 660 nm laser excitations. Detailed vibrational analysis and peak assignments were performed based on SERS spectra simulated using an optimized DFT setting, in which small-core effective core potentials (ECPs) in the def2-tzvpd basis set were replaced by large-core ECPs. Characteristic SERS peaks appeared at 1065, 1315, and 1485 cm-1 were assigned to the {\gamma}(CH2)+v(C-O{\ldots}Ln), vsym(COO-)+{\gamma}(CH2), and vasym(COO-)+{\gamma}(CH2) vibrational bands, respectively. SERS intensity ratios were obtained by normalizing the peak intensity I near 1065 or 1485 cm-1 to that near 1315 cm-1. I1065/I1315 depended solely on the type of Ln3+ ion and was independent of the excitation wavelength. In contrast, I1485/I1315 increased with decreasing excitation wavelength, indicating additional enhancement by charge-transfer. Additionally, as the number of unpaired 4f electrons increased, Ln3+ in the coordination region attracted oxygen negative charges more strongly, reducing the electric dipole moment of the C-O bond and altering its symmetry.

physics.chem-ph↗

arXiv2025

EVA-MED: An Enhanced Valence-Arousal Multimodal Emotion Dataset for Emotion Recognition

We introduce a novel multimodal emotion recognition dataset that enhances the precision of Valence-Arousal Model while accounting for individual differences. This dataset includes electroencephalography (EEG), electrocardiography (ECG), and pulse interval (PI) from 64 participants. Data collection employed two emotion induction paradigms: video stimuli that targeted different valence levels (positive, neutral, and negative) and the Mannheim Multicomponent Stress Test (MMST), which induced high arousal through cognitive, emotional, and social stressors. To enrich the dataset, participants' personality traits, anxiety, depression, and emotional states were assessed using validated questionnaires. By capturing a broad spectrum of affective responses while accounting for individual differences, this dataset provides a robust resource for precise emotion modeling. The integration of multimodal physiological data with psychological assessments lays a strong foundation for personalized emotion recognition. We anticipate this resource will support the development of more accurate, adaptive, and individualized emotion recognition systems across diverse applications.

cs.HC↗

arXiv2025

From Architectural Sketch to Conceptual Representation: Using Structure-Aware Diffusion Model to Generate Renderings of School Buildings

Generative Artificial Intelligence (AI) has advanced rapidly, enabling the generation of renderings from architectural sketches. This progress has significantly improved the efficiency of communication and conceptual expression during the early stage of architectural design. However, generated images often lack the structural details from architects' sketches. While sketches typically emphasize the overall structure, crucial components such as windows and doors are often represented by simple lines or omitted entirely. For school buildings, it is essential to control architectural components, such as the shape and proportion of windows, as these factors directly influence the accuracy of the generated images in reflecting the architect's design intentions. To address this issue, we propose a structure-aware diffusion model for architectural image generation to refine expressing design intentions through retrieval augmentation. Our framework utilizes architectural components to enhance the generation process, addressing the details that may be lacking in the sketches. These components provide clear spatial and structural details, improving the model's ability to interpret and generate architectural details. The refined sketches, combined with text prompts, are fed into the proposed structure-aware diffusion model to generate detailed and realistic school building images. The experiment results demonstrate the effectiveness of our framework in generating architectural designs.

cs.GR↗

arXiv2025

Quantum Geometric Engineering of Dual Hall Effects in 2D Antiferromagnetic Bilayers via Interlayer Magnetic Coupling

The interplay between quantum geometry and magnetic order offers a novel strategy for designing next-generation nanodevices. Here, we demonstrate that interlayer magnetic coupling in two-dimensional (2D) CoPSe3 bilayers enables precise control over quantum geometric mechanisms, unlocking dual intrinsic Hall effects. Our first-principles calculations reveal that the altermagnetic (AM) phase exhibits a giant anisotropic anomalous Hall effect (AHE) ($\sigma_{xy}$ is approximately 46 S/cm) driven by Berry curvature localized at generic k-points, while the PT-symmetric antiferromagnetic (AFM) phase hosts an intrinsic second-order nonlinear anomalous Hall effect (NAHE) ($\chi_{xyy}$ is approximately 160 ${\mu}$S/V) originating from quantum metric accumulation at high-symmetry k-points. By tuning interlayer magnetic couplings, we achieve reversible switching between these phases, leveraging their distinct band structures and symmetry constraints. The Neel-vector-dependent AHE in the AM phase and the symmetry-protected NAHE in the AFM phase highlight quantum geometry as a versatile tool for manipulating transport properties. Our work establishes 2D antiferromagnets as a promising platform for multifunctional device architectures, bridging linear and nonlinear magnetoelectric responses through tailored quantum geometric engineering.

cond-mat.mes-hall↗

arXiv2025

Towards Routing and Edge Computing in Satellite-Terrestrial Networks: A Column Generation Approach

Edge computing that enables satellites to process raw data locally is expected to bring further timeliness and flexibility to satellite-terrestrial networks (STNs). In this letter, we propose a three-layer edge computing protocol, where raw data collected by the satellites can be processed locally, or transmitted to other satellites or the ground station via multi-hop routing for further processing. The overall computing capacity of the proposed framework is maximized by determining the offloading strategy and routing formation, subject to channel capacity and hop constraints. Given that the problem scale grows exponentially with the number of satellites and maximum-allowed hops, the column generation approach is employed to obtain the global optimal solution by activating only a subset of variables. Numerical results reveal that the proposed three-layer computing protocol, when tolerating a 5-hop routing latency, achieves a 60% improvement in computation capacity compared to the single-layer local computing configuration.

eess.SY↗