arXiv subjects

Han Ma

Publications and source records attributed to Han Ma.

At least 19 recordsLinked to original sources

arXiv2026

Exchange-frustrated quadrupoles on the honeycomb lattice: Flavor-wave spectra, classical degeneracies and parton constructions

We study the quadrupolar Kitaev model, an $S=1$ honeycomb-lattice model with frustrated bond-dependent quadrupolar interactions. Using complementary methods and expanding around controlled limits, we uncover several intertwined structures. First, a semiclassical variational analysis based on $\mathrm{SU}(3)$ flavor theory reveals an extensively degenerate manifold of classical mean-field ground states, suggesting that quantum fluctuations may stabilize a quantum-disordered phase. Second, in the bond-anisotropic limit, perturbation theory is used to derive effective low-energy Hamiltonians, which crucially depend on the presence (or absence) of a residual symmetry $\mathcal{M}$ of combined lattice reflection and discrete spin rotation. A Majorana parton construction uncovers an exact $\mathbb Z_2$ gauge structure and motivates possible confined and deconfined phases driven by gauge-charge condensation, consistent with the effective theories obtained in anisotropic limit. Further, within the same parton formalism, different Majorana mean-field ans\"atze produce both gapless and gapped candidate quantum-disordered states, distinguished by linear versus projective implementations of $\mathcal M$. Our results highlight frustrated quadrupolar interactions as a route to quantum-disordered phases, relevant to $S \geq 1$ Kitaev materials and Rydberg-array quantum simulators.

cond-mat.str-el↗

arXiv2025

UniTS: Unified Spatio-Temporal Generative Model for Remote Sensing

One of the primary objectives of satellite remote sensing is to capture the complex dynamics of the Earth environment, which encompasses tasks such as reconstructing continuous cloud-free image sequences, detecting land cover changes, and forecasting future surface evolution. However, existing methods typically require specialized models tailored to different tasks, and lack a general framework that can address these multi-level tasks from a unified perspective. In this paper, we propose a Unified Spatio-Temporal Generative Model (UniTS), which integrates several long-separated core tasks, including time series reconstruction, time series cloud removal, time series semantic change detection, and time series forecasting. Based on the flow matching generative paradigm, UniTS constructs a deterministic evolution path from noise to targets under the guidance of task-specific conditions, achieving unified modeling of spatiotemporal representations for multi-level tasks. The UniTS architecture consists of a diffusion transformer with spatiotemporal blocks, where we design an Adaptive Condition Injector (ACor) to enhance the model's conditional perception of multimodal inputs, enabling high-quality controllable generation. Additionally, we design a Spatiotemporal-aware Modulator (STM) to improve the ability of spatiotemporal blocks to capture complex spatiotemporal dependencies. It substantially outperforms existing specialized models, particularly under challenging conditions such as severe cloud contamination, modality absence, and forecasting complex phenological variations.

cs.CV↗

arXiv2025

Fine-grained Hierarchical Crop Type Classification from Integrated Hyperspectral EnMAP Data and Multispectral Sentinel-2 Time Series: A Large-scale Dataset and Dual-stream Transformer Method

Fine-grained crop type classification serves as the fundamental basis for large-scale crop mapping and plays a vital role in ensuring food security. It requires simultaneous capture of both phenological dynamics (obtained from multi-temporal satellite data like Sentinel-2) and subtle spectral variations (demanding nanometer-scale spectral resolution from hyperspectral imagery). Research combining these two modalities remains scarce currently due to challenges in hyperspectral data acquisition and crop types annotation costs. To address these issues, we construct a hierarchical hyperspectral crop dataset (H2Crop) by integrating 30m-resolution EnMAP hyperspectral data with Sentinel-2 time series. With over one million annotated field parcels organized in a four-tier crop taxonomy, H2Crop establishes a vital benchmark for fine-grained agricultural crop classification and hyperspectral image processing. We propose a dual-stream Transformer architecture that synergistically processes these modalities. It coordinates two specialized pathways: a spectral-spatial Transformer extracts fine-grained signatures from hyperspectral EnMAP data, while a temporal Swin Transformer extracts crop growth patterns from Sentinel-2 time series. The designed hierarchical classification head with hierarchical fusion then simultaneously delivers multi-level crop type classification across all taxonomic tiers. Experiments demonstrate that adding hyperspectral EnMAP data to Sentinel-2 time series yields a 4.2% average F1-scores improvement (peaking at 6.3%). Extensive comparisons also confirm our method's higher accuracy over existing deep learning approaches for crop type classification and the consistent benefits of hyperspectral data across varying temporal windows and crop change scenarios. Codes and dataset are available at https://github.com/flyakon/H2Crop.

cs.CV↗

arXiv2025

AgriFM: A Multi-source Temporal Remote Sensing Foundation Model for Agriculture Mapping

Accurate crop mapping fundamentally relies on modeling multi-scale spatiotemporal patterns, where spatial scales range from individual field textures to landscape-level context, and temporal scales capture both short-term phenological transitions and full growing-season dynamics. Transformer-based remote sensing foundation models (RSFMs) offer promising potential for crop mapping due to their innate ability for unified spatiotemporal processing. However, current RSFMs remain suboptimal for crop mapping: they either employ fixed spatiotemporal windows that ignore the multi-scale nature of crop systems or completely disregard temporal information by focusing solely on spatial patterns. To bridge these gaps, we present AgriFM, a multi-source remote sensing foundation model specifically designed for agricultural crop mapping. Our approach begins by establishing the necessity of simultaneous hierarchical spatiotemporal feature extraction, leading to the development of a modified Video Swin Transformer architecture where temporal down-sampling is synchronized with spatial scaling operations. This modified backbone enables efficient unified processing of long time-series satellite inputs. AgriFM leverages temporally rich data streams from three satellite sources including MODIS, Landsat-8/9 and Sentinel-2, and is pre-trained on a global representative dataset comprising over 25 million image samples supervised by land cover products. The resulting framework incorporates a versatile decoder architecture that dynamically fuses these learned spatiotemporal representations, supporting diverse downstream tasks. Comprehensive evaluations demonstrate AgriFM's superior performance over conventional deep learning approaches and state-of-the-art general-purpose RSFMs across all downstream tasks. Codes will be available at https://github.com/flyakon/AgriFM.

cs.CV↗

arXiv2025

Fixed Point Stability Switches from Attractive to Repulsive at 2d Pomeranchuk/Stoner Instabilities via Field-Theoretical RG

We study an interacting two-flavor fermionic system via field-theoretical functional renormalization group (RG). Each flavor, labeled by $\pm$, has a dispersion of $E^{\pm}=c k^{2\alpha}-\mu^\pm$ with tunable real exponent $\alpha>0$. The effective theory is parametrized by intra-flavor and inter-flavor interactions, preserving global U(1) $\times$ U(1) symmetry, which can be enhanced to U(2). The U(2) symmetric system has a Fermi liquid phase and two possible instabilities, leading to spontaneous spatial rotational or flavor symmetry breaking, known as the Pomeranchuk and Stoner instabilities, respectively. The key discovery of this work is the following. The Stoner instability possesses an RG fixed point that preserves the U(2) symmetry. For $\alpha<1$, this fixed point is attractive, indicating a continuous transition. Conversely, for $\alpha>1$, the fixed point becomes repulsive, and without fine-tuning, there is runaway RG flow, resulting in a discontinuous transition. The U(1) $\times$ U(1) symmetric system, with $\mu^+\neq \mu^-$, exhibits richer physics. This system have two Pomeranchuk instabilities. At one of them, a non-trivial RG fixed point switches its nature from attractive to repulsive as $\alpha$ increases across $1$. Notably, the runaway flow at $\alpha>1$ results in the depletion of a Fermi surface at the transition. Collective modes in these Fermi liquids are also investigated. A universal Fermi surface deformation ratio $\delta\mu^+/\delta\mu^-$ is predicted for $\alpha<1$ at the instability as a continuous transition, which can be observed experimentally.

cond-mat.str-el↗

arXiv2024

Online Time-Informed Kinodynamic Motion Planning of Nonlinear Systems

Sampling-based kinodynamic motion planners (SKMPs) are powerful in finding collision-free trajectories for high-dimensional systems under differential constraints. Time-informed set (TIS) can provide the heuristic search domain to accelerate their convergence to the time-optimal solution. However, existing TIS approximation methods suffer from the curse of dimensionality, computational burden, and limited system applicable scope, e.g., linear and polynomial nonlinear systems. To overcome these problems, we propose a method by leveraging deep learning technology, Koopman operator theory, and random set theory. Specifically, we propose a Deep Invertible Koopman operator with control U model named DIKU to predict states forward and backward over a long horizon by modifying the auxiliary network with an invertible neural network. A sampling-based approach, ASKU, performing reachability analysis for the DIKU is developed to approximate the TIS of nonlinear control systems online. Furthermore, we design an online time-informed SKMP using a direct sampling technique to draw uniform random samples in the TIS. Simulation experiment results demonstrate that our method outperforms other existing works, approximating TIS in near real-time and achieving superior planning performance in several time-optimal kinodynamic motion planning problems.

cs.RO↗

arXiv2024

Reclaiming the Lost Conformality in a non-Hermitian Quantum 5-state Potts Model

Conformal symmetry, emerging at critical points, can be lost when renormalization group fixed points collide. Recently, it was proposed that after collisions, real fixed points transition into the complex plane, becoming complex fixed points described by complex conformal field theories (CFTs). Although this idea is compelling, directly demonstrating such complex conformal fixed points in microscopic models remains highly demanding. Furthermore, these concrete models are instrumental in unraveling the mysteries of complex CFTs and illuminating a variety of intriguing physical problems, including weakly first-order transitions in statistical mechanics and the conformal window of gauge theories. In this work, we have successfully addressed this complex challenge for the (1+1)-dimensional quantum $5$-state Potts model, whose phase transition has long been known to be weakly first-order. By adding an additional non-Hermitian interaction, we successfully identify two conjugate critical points located in the complex parameter space, where the lost conformality is restored in a complex manner. Specifically, we unambiguously demonstrate the radial quantization of the complex CFTs and compute the operator spectrum, as well as new operator product expansion coefficients that were previously unknown.

cond-mat.stat-mech↗

arXiv2024

LLM-Detector: Improving AI-Generated Chinese Text Detection with Open-Source LLM Instruction Tuning

ChatGPT and other general large language models (LLMs) have achieved remarkable success, but they have also raised concerns about the misuse of AI-generated texts. Existing AI-generated text detection models, such as based on BERT and RoBERTa, are prone to in-domain over-fitting, leading to poor out-of-domain (OOD) detection performance. In this paper, we first collected Chinese text responses generated by human experts and 9 types of LLMs, for which to multiple domains questions, and further created a dataset that mixed human-written sentences and sentences polished by LLMs. We then proposed LLM-Detector, a novel method for both document-level and sentence-level text detection through Instruction Tuning of LLMs. Our method leverages the wealth of knowledge LLMs acquire during pre-training, enabling them to detect the text they generate. Instruction tuning aligns the model's responses with the user's expected text detection tasks. Experimental results show that previous methods struggle with sentence-level AI-generated text detection and OOD detection. In contrast, our proposed method not only significantly outperforms baseline methods in both sentence-level and document-level text detection but also demonstrates strong generalization capabilities. Furthermore, since LLM-Detector is trained based on open-source LLMs, it is easy to customize for deployment.

cs.CL↗

arXiv2023

Aurora:Activating Chinese chat capability for Mixtral-8x7B sparse Mixture-of-Experts through Instruction-Tuning

Existing research has demonstrated that refining large language models (LLMs) through the utilization of machine-generated instruction-following data empowers these models to exhibit impressive zero-shot capabilities for novel tasks, without requiring human-authored instructions. In this paper, we systematically investigate, preprocess, and integrate three Chinese instruction-following datasets with the aim of enhancing the Chinese conversational capabilities of Mixtral-8x7B sparse Mixture-of-Experts model. Through instruction fine-tuning on this carefully processed dataset, we successfully construct the Mixtral-8x7B sparse Mixture-of-Experts model named "Aurora." To assess the performance of Aurora, we utilize three widely recognized benchmark tests: C-Eval, MMLU, and CMMLU. Empirical studies validate the effectiveness of instruction fine-tuning applied to Mixtral-8x7B sparse Mixture-of-Experts model. This work is pioneering in the execution of instruction fine-tuning on a sparse expert-mixed model, marking a significant breakthrough in enhancing the capabilities of this model architecture. Our code, data and model are publicly available at https://github.com/WangRongsheng/Aurora

cs.CL↗

arXiv2023

Symmetries and anomalies of Kitaev spin-$S$ models: Identifying symmetry-enforced exotic quantum matter

We analyze the internal symmetries and their anomalies in the Kitaev spin-$S$ models. Importantly, these models have a lattice version of a $\mathbb{Z}_2$ 1-form symmetry, denoted by $\mathbb{Z}_2^{[1]}$. There is also an ordinary 0-form $\mathbb{Z}_2^{(x)}\times\mathbb{Z}_2^{(y)}\times\mathbb{Z}_2^T$ symmetry, where $\mathbb{Z}_2^{(x)}\times\mathbb{Z}_2^{(y)}$ are $\pi$ spin rotations around two orthogonal axes, and $\mathbb{Z}_2^T$ is the time reversal symmetry. The anomalies associated with the full $\mathbb{Z}_2^{(x)}\times\mathbb{Z}_2^{(y)}\times\mathbb{Z}_2^T\times\mathbb{Z}_2^{[1]}$ symmetry are classified by $\mathbb{Z}_2^{17}$. We find that for $S\in\mathbb{Z}$ the model is anomaly-free, while for $S\in\mathbb{Z}+\frac{1}{2}$ there is an anomaly purely associated with the 1-form symmetry, but there is no anomaly purely associated with the ordinary symmetry or mixed anomaly between the 0-form and 1-form symmetries. The consequences of these symmetries and anomalies apply to not only the Kitaev spin-$S$ models, but also any of their perturbed versions, assuming that the perturbations are local and respect the symmetries. If these local perturbations are weak, generically these consequences still apply even if the perturbations break the 1-form symmetry. A notable consequence is that there should generically be a deconfined fermionic excitation carrying no fractional quantum number under the $\mathbb{Z}_2^{(x)}\times\mathbb{Z}_2^{(y)}\times\mathbb{Z}_2^T$ symmetry if $S\in\mathbb{Z}+\frac{1}{2}$, which implies symmetry-enforced exotic quantum matter. We also discuss the consequences for $S\in\mathbb{Z}$.

cond-mat.str-el↗

arXiv2023

Anomalous quasiparticle lifetime in geometric quantum critical metals

Metals can undergo geometric quantum phase transitions where the local curvature of the Fermi surface changes sign without a change in symmetry or topology. At the inflection points on the Fermi surface, the local curvature vanishes, leading to an anomalous dynamics of quasiparticles. In this paper, we study geometric quantum critical metals that support inflection points in two dimensions, and show that the decay rate of quasiparticles goes as $E^{\alpha}$ with $1<\alpha<2$ as a function of quasiparticle energy $E$ at the inflection points.

cond-mat.str-el↗

arXiv2023

Discrepancy-based Active Learning for Weakly Supervised Bleeding Segmentation in Wireless Capsule Endoscopy Images

Weakly supervised methods, such as class activation maps (CAM) based, have been applied to achieve bleeding segmentation with low annotation efforts in Wireless Capsule Endoscopy (WCE) images. However, the CAM labels tend to be extremely noisy, and there is an irreparable gap between CAM labels and ground truths for medical images. This paper proposes a new Discrepancy-basEd Active Learning (DEAL) approach to bridge the gap between CAMs and ground truths with a few annotations. Specifically, to liberate labor, we design a novel discrepancy decoder model and a CAMPUS (CAM, Pseudo-label and groUnd-truth Selection) criterion to replace the noisy CAMs with accurate model predictions and a few human labels. The discrepancy decoder model is trained with a unique scheme to generate standard, coarse and fine predictions. And the CAMPUS criterion is proposed to predict the gaps between CAMs and ground truths based on model divergence and CAM divergence. We evaluate our method on the WCE dataset and results show that our method outperforms the state-of-the-art active learning methods and reaches comparable performance to those trained with full annotated datasets with only 10% of the training data labeled.

cs.CV↗

arXiv2023

Hidden Critical Points in the Two-Dimensional $O(n>2)$ model: Exact Numerical Study of a Complex Conformal Field Theory

The presence of nearby conformal field theories (CFTs) hidden in the complex plane of the tuning parameter was recently proposed as an elegant explanation for the ubiquity of "weakly first-order" transitions in condensed matter and high-energy systems. In this work, we perform an exact microscopic study of such a complex CFT (CCFT) in the two-dimensional $O(n)$ loop model. The well-known absence of symmetry-breaking of the $O(n>2)$ model is understood as arising from the displacement of the non-trivial fixed points into the complex temperature plane. Thanks to a numerical finite-size study of the transfer matrix, we confirm the presence of a CCFT in the complex plane and extract the real and imaginary parts of the central charge and scaling dimensions. By comparing those with the analytic continuation of predictions from Coulomb gas techniques, we determine the range of validity of the analytic continuation to extend up to $n_g \approx 12.34$, beyond which the CCFT gives way to a gapped state. Finally, we propose a beta function which reproduces the main features of the phase diagram and which suggests an interpretation of the CCFT as a liquid-gas critical point at the end of a first-order transition line.

cond-mat.stat-mech↗

arXiv2023

Fermi liquids beyond the forward scattering limit: the role of non-forward scatterings for scale invariance and instabilities

Landau Fermi liquid theory is a fixed point theory of metals that includes the forward scattering amplitudes as exact marginal couplings. However, the fixed point theory that only includes the strict forward scatterings is non-local in real space. In this paper, we revisit the Fermi liquid theory using the field-theoretic functional renormalization group formalism and show how the scale invariant fixed point emerges as a local theory, which includes not only the forward scatterings but also non-forward scatterings with small but non-zero momentum transfers. In the low-energy limit, the non-forward scattering amplitude takes a scale invariant form. If the bare coupling is attractive beyond a critical strength, the coupling function exhibits a run-away flow drived by non-forward scattering amplitudes, signifying potential instabilities in particle-hole channels. The pairing interaction also obeys a scaling relation if the center of mass momentum of Cooper pairs is comparable with energy. The coupling functions fully capture the universal low-energy dynamics of the collective modes and instabilities of Fermi liquids. The divergence of the cocupling function in the particle-hole channel beyond a critical interaction suggests an instability toward an ordered phase with a momentum that depends on the interaction strength. At the critical interaction, the instability corresponds to the uniform Pomeranchuk or Stoner instability, but the momentum of the leading instability becomes non-zero for stronger attractive interaction. In the particle-particle channel, the coupling function reveals the dynamics of the unstable mode associated with the BCS instability. When an unstable normal metal evolves into the superconducting state, there exists a period in which a superconducting state with spatially non-uniform phase appears due to the presence of unstable Cooperon modes with non-zero momenta.

cond-mat.str-el↗

arXiv2022

$\mathbb Z_2$ spin liquids in the higher spin-$S$ Kitaev honeycomb model: An exact deconfined $\mathbb Z_2$ gauge structure in a non-integrable model

The higher spin Kitaev model prominently features the extensive locally conserved quantities the same as the spin-$1/2$ Kitaev honeycomb model, although it is not exactly solvable. It remains an open question regarding the physical meaning of these conserved quantities in the higher spin model. In this Letter, by introducing a Majorana parton construction for a general spin-$S$ we uncover that these conserved quantities are exactly the $\mathbb Z_2$ gauge fluxes in the general spin-$S$ model, including the case of spin-$1/2$. Particularly, we find an even-odd effect that the $\mathbb Z_2$ gauge charges are fermions in the half integer spin model, but are bosons in the integer spin model. We further prove that the fermionic $\mathbb Z_2$ gauge charges are always deconfined; hence the half integer spin Kitaev model would have non-trivial spin liquid ground states regardless of interaction strengths in the Hamiltonian. The bosonic $\mathbb Z_2$ gauge charges of the integer spin model, on the other hand, could condense, leading to a trivial product state, and this is indeed the case at the anisotropic limit of the model.

cond-mat.str-el↗

arXiv2022

Helical superfluid in a frustrated honeycomb Bose-Hubbard model

We study a "helical" superfluid, a nonzero-momentum condensate in a frustrated bosonic model. At mean-field Bogoliubov level, such a novel state exhibits "smectic" fluctuation that are qualitatively stronger than that of a conventional superfluid. We develop a phase diagram and compute a variety of its physical properties, including the spectrum, structure factor, condensate depletion, momentum distribution, all of which are qualitatively distinct from that of a conventional superfluid. Interplay of fluctuations, interaction and lattice effects gives rise to the phenomenon of order-by-disorder, leading to a crossover from the smectic superfluid regime to the anisotropic XY superfluid phase. We complement the microscopic lattice analysis with a field theoretic description for such a helical superfluid, which we derive from microscopics and justify on general symmetry grounds, reassuringly finding full consistency. Possible experimental realizations are discussed.

cond-mat.quant-gas↗

arXiv2022

Quenched random mass disorder in the large N theory of vector bosons

We study the critical bosonic O(N) vector model with quenched random mass disorder in the large N limit. Due to the replicated action which is sometimes not bounded from below, we avoid the replica trick and adopt a traditional approach to directly compute the disorder averaged physical observables. At $N=\infty$, we can exactly solve the disordered model. The resulting low energy behavior can be described by two scale invariant theories, one of which has an intrinsic scale. At finite $N$, we find that the previously proposed attractive disordered fixed point at $d=2$ continues to exist at $d=2+\epsilon$ spatial dimensions. We also studied the system in the $3<d<4$ spatial dimensions where the disorder is relevant at the Gaussian fixed point. However, no physical attractive fixed point is found right below four spatial dimensions. Nevertheless, the stable fixed point at $2+\epsilon$ dimensions can still survive at $d=3$ where the system has experimental realizations. Some critical exponents are predicted in order to be checked by future numerics and experiments.

cond-mat.str-el↗

arXiv2022

BiAIT*: Symmetrical Bidirectional Optimal Path Planning with Adaptive Heuristic

Adaptively Informed Trees (AIT*) is an algorithm that uses the problem-specific heuristic to avoid unnecessary searches, which significantly improves its performance, especially when collision checking is expensive. However, the heuristic estimation in AIT* consumes lots of computational resources, and its asymmetric bidirectional searching strategy cannot fully exploit the potential of the bidirectional method. In this article, we propose an extension of AIT* called BiAIT*. Unlike AIT*, BiAIT* uses symmetrical bidirectional search for both the heuristic and space searching. The proposed method allows BiAIT* to find the initial solution faster than AIT*, and update the heuristic with less computation when a collision occurs. We evaluated the performance of BiAIT* through simulations and experiments, and the results show that BiAIT* can find the solution faster than state-of-the-art methods. We also analyze the reasons for the different performances between BiAIT* and AIT*. Furthermore, we discuss two simple but effective modifications to fully exploit the potential of the adaptively heuristic method.

cs.RO↗