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Hao Zhang

Publications and source records attributed to Hao Zhang.

At least 217 records · Page 12Linked to original sources
arXiv2026

Towards Photorealistic and Efficient Bokeh Rendering via Diffusion Framework

Existing mobile devices are constrained by compact optical designs, such as small apertures, which make it difficult to produce natural, optically realistic bokeh effects. Although recent learning-based methods have shown promising results, they still struggle with photos captured under high digital zoom levels, which often suffer from reduced resolution and loss of fine details. A naive solution is to enhance image quality before applying bokeh rendering, yet this two-stage pipeline reduces efficiency and introduces unnecessary error accumulation. To overcome these limitations, we propose MagicBokeh, a unified diffusion-based framework designed for high-quality and efficient bokeh rendering. Through an alternative training strategy and a focus-aware masked attention mechanism, our method jointly optimizes bokeh rendering and super-resolution, substantially improving both controllability and visual fidelity. Furthermore, we introduce degradation-aware depth module to enable more accurate depth estimation from low-quality inputs. Experimental results demonstrate that MagicBokeh efficiently produces photorealistic bokeh effects, particularly on real-world low-resolution images, paving the way for future advancements in bokeh rendering. Our code and models are available at https://github.com/vivoCameraResearch/MagicBokeh.

cs.CV↗
arXiv2026

Improving Temporal Action Segmentation via Constraint-Aware Decoding

Temporal action segmentation (TAS) divides untrimmed videos into labeled action segments. While fully supervised methods have advanced the field, challenges such as action variability, ambiguous boundaries, and high annotation costs remain, especially in new or low-resource domains. Grammar-based approaches improve segmentation with structural priors but rely on complex parsing limiting scalability. In this work, we propose a lightweight, constraint-based refinement framework that enhances TAS predictions by integrating statistical structural priors such as transition confidence, action boundary sets, and per-class duration, that can be directly extracted from annotated data. These constraints are integrated into a modified Viterbi decoding algorithm, allowing inference-time refinement without retraining or added model complexity. Our approach improves both fully and semi-supervised TAS models by correcting structural prediction errors while maintaining high efficiency. Code is available at https://github.com/LUNAProject22/CAD

cs.CV↗
arXiv2026

A Recursive Decomposition Framework for Causal Structure Learning in the Presence of Latent Variables

Constraint-based causal discovery is widely used for learning causal structures, but heavy reliance on conditional independence (CI) testing makes it computationally expensive in high-dimensional settings. To mitigate this limitation, many divide-and-conquer frameworks have been proposed, but most assume causal sufficiency, i.e., no latent variables. In this paper, we show that divide-and-conquer strategies can be theoretically generalized beyond causal sufficiency to settings with latent variables. Specifically, we propose a recursive decomposition framework, termed DiCoLa, that enables divide-and-conquer causal discovery in the presence of latent variables. It recursively decomposes the global learning task into smaller subproblems and integrates their solutions through a principled reconstruction step to recover the global structure. We theoretically establish the soundness and completeness of the proposed framework. Extensive experiments on synthetic data demonstrate that our approach significantly improves computational efficiency across a range of causal discovery algorithms, while experiments on a real-world dataset further illustrate its practical effectiveness.

cs.LG↗
arXiv2026

Correlation between nuclear isospin asymmetry and $α$-particle preformation probability for superheavy nuclei from a Bayesian inference

In the study of $α$ decay within the superheavy nuclear region ($Z \geq 90$ and $N \geq 140$), the $α$-particle preformation probability $P_α$ serves as a crucial physical quantity linking nuclear structure to decay observables. We introduce a phenomenological model incorporating the decay energy $Q_α$, mass number $A$, orbital angular momentum $l$, isospin asymmetry $I$, and unpaired nucleon effect. For the first time, a Bayesian inference method combined with Markov Chain Monte Carlo (MCMC) sampling has been employed to impose global constraints on the model parameters, enabling the systematic and high-precision calculation of $P_α$. The results reveal a significant suppressing effect of isospin asymmetry on $P_α$, a finding independently corroborated by random forest-based feature importance analysis, which identified $I$ as a dominant factor. Furthermore, calculations using the maximum a posteriori (MAP) parameters not only reproduce the shell effect at $N=152$ but also yield $α$ decay half-life predictions in excellent agreement with experimental ones, thereby validating this model universality. This work provides the first global analysis tool for probing the $α$ preformation mechanism in superheavy nuclei, underscores the potential of the Bayesian framework for inverting complex nuclear physics problems, and establishes a reliable theoretical benchmark for guiding future experimental exploration of superheavy nuclei.

nucl-th↗
arXiv2026

Future Validity is the Missing Statistic: From Impossibility to $Φ$-Estimation for Grammar-Faithful Speculative Decoding

Grammar-constrained generation is often combined with local vocabulary masking and speculative decoding, but the resulting sampling law is not the grammar-conditional distribution users usually intend. We show that any speculative decoder with local mask access, Leviathan rejection, and rollback soundness samples from the locally projected distribution $μ^{\mathrm{proj}}$ rather than the grammar-conditional distribution $μ^\star$. This extends the GAD impossibility result to speculative decoding; on Dyck grammars with Qwen3-8B, the total-variation gap can reach 0.996. We identify the future-validity function $Φ_t(y)=\Pr_p[\mathrm{valid\ completion}\mid y]$ as the missing correction statistic. The target distribution is a Doob transform of the base model with $h=Φ$, while local masking corresponds to setting $h$ to one. With exact $Φ$, our oracle decoder FVO-Spec samples exactly from $μ^\star$; with approximate $Φ$, we bound the resulting total-variation error. Because exact future validity is hard for general context-free grammars, we evaluate estimator hierarchies on tractable Dyck and finite JSON languages. OneStep reduces Dyck TV by 14% with under 1% throughput overhead, exact dynamic programming reduces it by 97%, and finite-language correction closes JSON gaps to numerical precision. All fidelity claims are scoped to enumerable grammars and token tries.

cs.LG↗
arXiv2026

Thermodynamics of stacking faults and phase stability in cobalt alloys: A combined computational and experimental study

Stacking fault energy dictates phase stability and deformation behavior in Co alloys and WC-Co cemented carbides, yet a quantitative assessment of alloying effects at finite temperatures remains poorly established. By integrating first-principles thermodynamics with microstructural characterization, we provide a rigorous evaluation of these influences across atomic and macroscopic scales. We show that stacking fault energetics at 0K for transition metal solutes are primarily governed by atomic misfit volume. While 4d and 5d elements follow a consistent linear trend, specific 3d solutes exhibit significant deviations due to non-negligible magnetic contributions. By incorporating phonon, electronic, longitudinal spin-fluctuation, and magnetic free-energy contributions, the model accurately captures the fcc-hcp transformation and quantifies how diverse solutes modulate the phase landscape. We demonstrate that V, Ni, Fe, Mo, and W lower the transformation temperature by stabilizing fcc phase, while Cr and C exhibit the opposite effect, consistent with experimental phase diagrams. Furthermore, microscopic analysis confirms that higher W content dissolved in the Co suppresses stacking-fault formation by elevating the stacking fault energy at finite temperatures. This work clarifies the physical mechanisms by which alloying regulates stacking fault energy and phase stability in Co-based systems, providing guidance for the design of Co-based alloys and WC-Co cemented carbides.

cond-mat.mtrl-sci↗
arXiv2026

Nature of magnetism in bilayer nickelate La3Ni2O7 single crystals

The recent discovery of high-temperature superconductivity in pressurized and thin film nickelates has generated intense interest, yet the nature of magnetism in their ambient-pressure parent phases remains poorly understood, despite its potentially crucial role in pairing. Here we use neutron scattering to resolve the spin order and dynamics of single-crystalline La3Ni2O7, an ambient-pressure parent of this class. Well defined spin excitations are observed at Q = (0, 0.5, 2.5), featuring a~5 meV spin gap and anisotropic in-plane dispersions, with zone-boundary softening along the transverse direction indicative of competing exchange interactions. The excitations exhibit pronounced out-of-plane modulations with bilayer periodicity, providing direct evidence for antiferromagnetic interlayer coupling. Their dispersion is well described by a bilayer Heisenberg Hamiltonian with strong interlayer exchange and competing in-plane couplings within a stripe-type magnetic order. Normalization of the spectra to absolute units reveals that, although the spin-wave bandwidth is only about 25% of that in cuprates, the local dynamic susceptibility at comparable energies is significantly enhanced, yielding a total fluctuating moment of comparable magnitude. These results highlight intense mid-energy spin excitations rooted in substantial electronic correlations as a defining feature of this family, establishing a magnetic framework distinct from cuprates and directly relevant to understanding superconductivity in this system.

cond-mat.str-el↗
arXiv2026

A Multimodal Data Fusion Attention-Empowered Generative Adversarial Network for Real Time 3D Underwater Sound Speed Field Construction

Sound speed profiles (SSPs) are crucial underwater parameters that determine the propagation patterns of acoustic signals, directly influencing the energy efficiency of underwater communication and the accuracy of positioning systems. Conventional techniques for obtaining SSPs, such as matched field processing (MFP), compressive sensing (CS), and deep learning (DL), typically depend on on-site sonar measurements, which impose stringent requirements on the deployment of underwater observation systems. To overcome this limitation and enable high-precision sound speed field reconstruction without the need for on-site underwater data collection, we propose a novel multimodal data-fusion generative adversarial network enhanced with residual attention blocks (MDF-RAGAN). This architecture integrates attention mechanisms to capture global spatial feature correlations effectively, while residual modules are employed to extract subtle perturbations in deep-ocean sound velocity distribution caused by sea surface temperature (SST) variations. Experimental results on a public real-world dataset demonstrate that the proposed model outperforms other state-of-the-art methods, achieving an estimation error of less than 0.3 m/s. Specifically, MDF-RAGAN reduces the root mean square error (RMSE) by nearly half compared to convolutional neural network (CNN) and spatial interpolation (SITP) methods, and attains a 65.8\% RMSE reduction relative to the mean profile method. These results highlight the effectiveness of multi-source fusion and cross-modal attention in enhancing the accuracy and robustness of sound speed profile reconstruction.

cs.SD↗
arXiv2026

Direct imaging of a Berry curvature nematic state in a spin-compensated magnet

Density waves conventionally describe the periodic modulation of charge or spin, yet the spatial modulation of electronic geometry has remained elusive. Here, we report subtle micrometer-scale spatial modulations of the magneto-optical Kerr signal in the noncollinear antiferromagnet Mn3NiN with compensated spins, consistent with a magnetic-field-induced Berry curvature density wave . These Berry curvature modulations exhibit orientations unpinned from the crystal lattice, forming a nematic state that spontaneously breaks rotational symmetry. We attribute this spatial instability to field-induced spatial variations of the spin texture driven by competing magnetic interactions. This discovery unveils a new class of collective order in spin-compensated magnets mediated by the geometric phase of the wavefunction itself. Its wavelength is controlled by chemical doping and its amplitude by magnetic field, providing concrete tuning knobs for antiferromagnetic and altermagnetic spintronics.

cond-mat.str-el↗
arXiv2026

EGAD: Entropy-Guided Adaptive Distillation for Token-Level Knowledge Transfer

Large language models (LLMs) have achieved remarkable performance across diverse domains, yet their enormous computational and memory requirements hinder deployment in resource-constrained environments. Knowledge distillation offers a promising solution by transferring knowledge from a large teacher model to a smaller student model. However, existing distillation methods typically treat all tokens equally, ignoring the fact that different tokens contribute unequally to model decisions. This can lead to inefficient knowledge transfer and reduced learning effectiveness. To address this limitation, we propose an entropy-based adaptive distillation strategy that dynamically adjusts the training process at the token level. Our method leverages the teacher's output entropy to guide three aspects of distillation. Specifically, we introduce a token-level curriculum by dynamically shifting focus from low- to high-entropy tokens during training. We further adjust the distillation temperature based on token entropy to better capture teacher confidence patterns. Moreover, we employ a dual-branch architecture for efficient logits-only distillation on easy tokens and deeper feature-based distillation on difficult tokens. Extensive experiments validate the soundness and effectiveness of our method.

cs.CL↗
arXiv2026

Tiny but Mighty: A Software-Hardware Co-Design Approach for Efficient Multimodal Inference on Battery-Powered Small Devices

Large Multimodal Models (LMMs) are inherently modular, comprising vision and audio encoders, a projector, and a language backbone. Yet existing systems execute them monolithically, underutilizing the heterogeneous accelerators (NPUs, GPUs, DSPs) on modern SoCs and inflating end-to-end latency. We present Nanomind, a hardware-software co-design inference framework that decomposes each LMM into modular "bricks"--vision, projector, language, and audio--and maps each brick to its best-suited compute units. A Token-Aware Buffer Manager (TABM) enables zero-copy embedding transfer across accelerators on unified-memory SoCs, bypassing CPU bottlenecks. Combined with customized hardware, a battery-aware scheduler, and fused low-bit GEMM kernels, Nanomind runs entirely on a compact, battery-powered prototype that operates fully offline. Nanomind reduces end-to-end energy by 42.3% against mainstream edge frameworks and devkits; in its on-demand low-power mode, the prototype runs LLaVA-OneVision-Qwen2-0.5B with a camera for nearly 18.8 hours on a single 2,000 mAh battery.

cs.DC↗
arXiv2026

Deeper detection limits in astronomical imaging using self-supervised spatiotemporal denoising

The detection limit of astronomical imaging observations is limited by several noise sources. Some of that noise is correlated between neighbouring image pixels and exposures, so in principle could be learned and corrected. We present an astronomical self-supervised transformer-based denoising algorithm (ASTERIS), that integrates spatiotemporal information across multiple exposures. Benchmarking on mock data indicates that ASTERIS improves detection limits by 1.0 magnitude at 90% completeness and purity, while preserving the point spread function and photometric accuracy. Observational validation using data from the James Webb Space Telescope (JWST) and Subaru telescope identifies previously undetectable features, including low-surface-brightness galaxy structures and gravitationally-lensed arcs. Applied to deep JWST images, ASTERIS identifies three times more redshift > 9 galaxy candidates, with rest-frame ultraviolet luminosity 1.0 magnitude fainter, than previous methods.

astro-ph.IM↗
arXiv2026

Spatially anisotropic Kondo resonance coupled with the superconducting gap in a kagome metal

The chromium-based kagome metal CsCr3Sb5 has garnered significant interest due to its strong electron correlations, intertwined orders and potential for unconventional superconductivity under high pressure. The evolution of magnetic and superconducting interactions as the more frequently studied CsV3Sb5 is doped to CsCr3Sb5 remains poorly understood. Here, we demonstrate the emergence of a spatially anisotropic Kondo resonance intertwined with the superconducting gap, enabled by introducing magnetic Cr impurities into the kagome superconductor CsV3Sb5. The addition of dilute Cr impurities not only weakens long range charge density wave order but also produces local magnetic moments that leads to Kondo resonances. We show that the Kondo resonance forms anisotropic, ripple like spatial patterns around individual Cr atoms, breaking all local mirror symmetries. We further reveal that with the emergence of Kondo screening, the coherence peak and depth of superconducting gap with finite zero-energy conductance are enhanced. This suggests that non superconducting carriers at the Fermi surface in the parent compound participate in the Kondo effect, simultaneously screening Cr magnetic moments and increasing the superfluid density. Our findings offer an opportunity to study the interplay between superconductivity and local magnetism in kagome materials.

cond-mat.supr-con↗
arXiv2026

Efficient-DLM: From Autoregressive to Diffusion Language Models, and Beyond in Speed

Diffusion language models (dLMs) have emerged as a promising paradigm that enables parallel, non-autoregressive generation, but their learning efficiency lags behind that of autoregressive (AR) language models when trained from scratch. To this end, we study AR-to-dLM conversion to transform pretrained AR models into efficient dLMs that excel in speed while preserving AR models' task accuracy. We achieve this by identifying limitations in the attention patterns and objectives of existing AR-to-dLM methods and then proposing principles and methodologies for more effective AR-to-dLM conversion. Specifically, we first systematically compare different attention patterns and find that maintaining pretrained AR weight distributions is critical for effective AR-to-dLM conversion. As such, we introduce a continuous pretraining scheme with a block-wise attention pattern, which remains causal across blocks while enabling bidirectional modeling within each block. We find that this approach can better preserve pretrained AR models' weight distributions than fully bidirectional modeling, in addition to its known benefit of enabling KV caching, and leads to a win-win in accuracy and efficiency. Second, to mitigate the training-test gap in mask token distributions (uniform vs. highly left-to-right), we propose a position-dependent token masking strategy that assigns higher masking probabilities to later tokens during training to better mimic test-time behavior. Leveraging this framework, we conduct extensive studies of dLMs' attention patterns, training dynamics, and other design choices, providing actionable insights into scalable AR-to-dLM conversion. These studies lead to the Efficient-DLM family, which outperforms state-of-the-art AR models and dLMs, e.g., our Efficient-DLM 8B achieves +5.4%/+2.7% higher accuracy with 4.5x/2.7x higher throughput compared to Dream 7B and Qwen3 4B, respectively.

cs.CL↗
arXiv2026

Precision extraction of the deuteron electric polarizability via the Baldin sum rule with full low-energy coverage

The photodisintegration cross sections of the deuteron have been systematically measured over the photon energy range of 2.33-19.65 MeV at the Shanghai Laser Electron Gamma Source (SLEGS). By applying the well-established Baldin sum rule to the newly obtained data, the sum of the electric and magnetic dipole polarizabilities of the deuteron is extracted for the first time based solely on a dense and continuous experimental dataset, yielding αE +\{beta}M = 0.719\pm0.009stat\pm0.014algo\pm0.023syst fm3 . With theoretical values of the magnetic polarizability \{beta}M calculated from the pionless effective field theory, a new value of the electric polarizability is obtained as αE = 0.637 \pm 0.009stat \pm 0.014algo \pm 0.023syst \pm 0.004theo fm3 , which is in excellent agreement with current theoretical predictions. This result resolves the previous discrepancy between experimental measurements from elastic scattering and theory, providing a high-precision benchmark for nuclear interaction models.

nucl-ex↗
arXiv2026

DepthMaster: Taming Diffusion Models for Monocular Depth Estimation

Monocular depth estimation within the diffusion-denoising paradigm demonstrates impressive generalization ability but suffers from low inference speed. Recent methods adopt a single-step deterministic paradigm to improve inference efficiency while maintaining comparable performance. However, they overlook the gap between generative and discriminative features, leading to suboptimal results. In this work, we propose DepthMaster, a single-step diffusion model designed to adapt generative features for the discriminative depth estimation task. First, to mitigate overfitting to texture details introduced by generative features, we propose a Feature Alignment module, which incorporates high-quality semantic features to enhance the denoising network's representation capability. Second, to address the lack of fine-grained details in the single-step deterministic framework, we propose a Fourier Enhancement module to adaptively balance low-frequency structure and high-frequency details. We adopt a two-stage training strategy to fully leverage the potential of the two modules. In the first stage, we focus on learning the global scene structure with the Feature Alignment module, while in the second stage, we exploit the Fourier Enhancement module to improve the visual quality. Through these efforts, our model achieves state-of-the-art performance in terms of generalization and detail preservation, outperforming other diffusion-based methods across various datasets. Our project page can be found at https://indu1ge.github.io/DepthMaster_page.

cs.CV↗
arXiv2026

Feedback-Driven Rate Control for Learned Video Compression

End-to-end learned video compression has achieved strong rate-distortion performance, but rate control remains underexplored, especially in target-bitrate-driven and budget-constrained scenarios. Existing methods mainly rely on explicit R-D-lambda modeling or feed-forward prediction, which may lack stable online adjustment when video content varies dynamically. We propose a feedback-driven rate control framework for learned video compression. First, we build a single-model multi-rate coding interface on top of a DCVC-style framework, enabling continuous bitrate control through the rate-distortion parameter lambda. Then, a log-domain PI/PID closed-loop controller updates lambda online according to the error between the target bitrate and the entropy-estimated bitrate, achieving stable target bitrate tracking. To further improve frame-level bit allocation under budget constraints, we introduce a dual-branch GRU-based adjustment controller that refines the base control signal using budget-state features and causal coding statistics. Experiments on UVG and HEVC show that the proposed PI/PID controller achieves average bitrate errors of 2.88% and 2.95% on DCVC and DCVC-TCM, respectively. With the proposed adjustment controller, the method further achieves average BD-rate reductions of 5.69% and 4.49%, while reducing the average bitrate errors to 2.13% and 2.24%. These results show that the proposed method provides a practical solution for learned video compression with both controllable bitrate and improved rate-distortion performance.

cs.MM↗
arXiv2026

Generalized Composed Alternating Relaxed Projection Algorithm for Two-Set Feasibility Problem

We study the two-set feasibility problem of finding a point in the intersection $X\cap Y$ of closed convex sets in a Hilbert space. We propose a generalized composed alternating relaxed projection algorithm (gCARPA) that blends Douglas-Rachford-type and projection-reflection-type dynamics via an outer averaging step $μ$ and an internal relaxation $(γ,θ,η)$. The algorithm contains several classical projection methods as special cases. We also introduce its non-stationary variant, in which $(γ_k,θ_k,η_k)$ vary over iterations, and establish its convergence. For the subspace feasibility model, we derive an explicit spectral characterization via principal-angle block decompositions, yielding computable subdominant-eigenvalue factors and a minimax parameter-selection recipe in a symmetric regime that targets critical damping on principal-angle planes. Numerical experiments illustrate that the generalized relaxation and its non-stationary tuning can improve or match baseline methods in problem-dependent regimes.

math.OC↗