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Qing Zhang

Publications and source records attributed to Qing Zhang.

At least 19 recordsLinked to original sources
arXiv2026

Finite-Temperature Magnetism and Longitudinal Spin Transport in Compensated Half-Metal Ferrimagnets with Near-Zero Magnetization

Fully compensated ferrimagnets, characterized by nearly vanishing magnetic moments at zero temperature, perfect spin-polarized electronic structures, and ultrafast spin dynamics, represent a highly promising materials platform for next-generation spintronic devices. By minimizing stray magnetic fields and reducing switching currents, FCFs enable a viable path toward high-speed, low-power spintronic devices. In this work, we present a systematic investigation of the electronic structure, finite-temperature magnetism, and spin-dependent transport properties of the Heusler-type fully compensated half-metallic ferrimagnet Mn3Al in magnetic tunnel junctions. We demonstrate that Mn3Al is a half-metallic FCF with 100% spin polarization and fully compensated magnetization at 0 K. Combining density functional theory with Monte Carlo simulations, we characterize its finite-temperature magnetic properties and show that Mn3Al exhibits a net magnetization below 11.6 emu/cc at 300 K, confirming the robustness of its nearly compensated ferrimagnetic order at room temperature. Furthermore, we predict that the Mn3Al/MgO/Mn3Al MTJ yields a giant tunneling magnetoresistance effect at 0 K, which remains as high as 232% at 300 K. Our results establish Mn3Al and related half-metallic FCFs as excellent candidates for magnetoresistance-based spintronic applications.

cond-mat.mtrl-sci↗
arXiv2026

AC-JEPA: Anchored Cross-Modal Joint Embedding Predictive Architecture for Visual-to-Symbolic Translation

Cross-modal translation requires models to capture shared semantics across modalities while preserving the fine-grained structure required by the target modality. Yet in real-world scientific domains, paired data are often imperfect, while obtaining clean pairs at scale is costly. Supervised fine-tuning (SFT) directly optimizes cross-modal translation through categorical token-level supervision, but this objective is sensitive to errors in the target sequences. JEPA offers a complementary representation-space objective, but most existing formulations focus on prediction within a modality rather than translation across modalities. We introduce Anchored Cross-Modal JEPA (AC-JEPA), an SFT-compatible plug-in that predicts masked intermediate states in both cross-modal directions using paired data. The joint-embedding objective targets cross-modal structure predictable from context, while the causal SFT path retains direct token-level supervision for the target sequence. During self-training, AC-JEPA learns from continuous latent targets derived from imperfect pseudo-labeled pairs, offering intrinsic tolerance to local data errors without promoting pseudo-tokens to hard categorical supervision. Using optical chemical structure recognition as a testbed, AC-JEPA trained on clean pairs achieves 76.07% accuracy on the Simple set of the WildMol benchmark, compared with 67.40% for SFT alone. Using the same pseudo-labeled data for both approaches, AC-JEPA-based self-training further raises accuracy to 80.23%, whereas accuracy under direct SFT drops from 67.40% to 63.47%. These results support SFT-anchored joint-embedding prediction as an effective strategy for cross-modal translation under both curated and imperfect supervision.

cs.AI↗
arXiv2026

Inferring Nuclear Radial Parameters from Initial-State Information in Heavy-Ion Collisions Using Deep Neural Networks

The geometric structure of atomic nuclei plays an important role in shaping the initial conditions of high-energy nuclear collisions and the subsequent evolution of the quark--gluon plasma. In this work, we develop a multimodal deep-learning framework to infer the Woods--Saxon radius ($R_0$) and diffuseness parameter ($a$) from initial-state information. Using the TRENTo model for relativistic $^{238}$U+$^{238}$U collisions, we construct a dataset covering multiple combinations of $R_0$ and $a$, and perform parameter regression by combining two-dimensional entropy-density distributions with event-level scalar observables, including multiplicity and eccentricities $\varepsilon_2$--$\varepsilon_5$. The results show that the proposed framework can effectively recover the Woods--Saxon parameters from initial-state information. Joint multi-event inputs suppress statistical fluctuations and improve regression performance by enhancing the extraction of collective information. The inversion accuracy exhibits a pronounced centrality dependence: $R_0$ is best constrained in central collisions, whereas $a$ shows the strongest sensitivity in the mid-peripheral region and becomes less well constrained in ultra-central and peripheral collisions. These findings indicate that the initial-state geometry retains useful information about nuclear radial structure and provide a baseline for future studies that incorporate transport evolution and experimental final-state observables.

nucl-th↗
arXiv2026

OmniInteract: Towards Unified Whole-Body Interaction Control for Dual Humanoids

Physical interaction is a key capability for humanoid collaboration, requiring coordinated whole-body motion and precise contact. However, learning diverse dual-humanoid interactions faces two major challenges: (1) human-robot morphological differences disrupt contact interfaces and spatial relationships during retargeting, and (2) jointly learning motor skills and partner coordination makes it difficult to retain control fidelity as interaction diversity grows. In this paper, we propose OmniInteract to address these challenges through surface-aware modeling and scalable residual policy learning. First, we introduce Surface Contact-Aware Retargeting, which leverages dense human-robot surface correspondences to jointly retarget both humanoids, preserving explicit interaction contact interfaces and their near-field geometric context. Second, we propose a Scalable Flow-based Interaction Policy for partner-conditioned residual generation. It factorizes dual-humanoid control into a reusable motor foundation and interaction-specific adaptations: partner-aware experts learn corrections over a frozen whole-body motion prior, while conditional flow matching unifies them in a shared residual action space. This enables scalable skill consolidation while retaining pretrained motor capabilities. Extensive experiments demonstrate that OmniInteract improves retargeting fidelity and dual-humanoid coordination while better retaining interaction performance as skill diversity grows. Real-world deployment further demonstrates successful contact establishment, stable whole-body coordination, and recovery from external perturbations.

cs.RO↗
arXiv2026

Plausible Future, Wrong Choice: Steering World-Action Models toward Intended Future

World-Action Models (WAMs) have emerged as a promising paradigm for embodied generalist policies by jointly modeling future world evolution and robot actions across diverse manipulation tasks. However, representing diverse futures is only the first step: a capable WAM must also generate futures that match the requirements of the current task and execution progress. In long-horizon manipulation where similar observation-action segments occur across different tasks and stages, existing approaches may generate locally plausible yet misdirected rollouts to drift toward wrong task stage, i.e., a phenomenon we term Stage Confusion. To address this problem, we propose TaskWAM, a closed-loop, stage-aware framework that represents fine-grained task requirements with Mode Tokens and tracks execution progress with State Tokens, forming a structured task state to steer world--action generation toward intended future. We further introduce StageBench, a controlled multi-stage manipulation benchmark with parameterized stage variations and automated evaluation.Extensive experiments on both simulation benchmarks and real-world tasks demonstrate that TaskWAM effectively reduces confusion and achieves competitive performance with state-of-the-art methods. Moreover, TaskWAM provides strong plug-and-play transfer across heterogeneous WAM architectures and robot embodiments, including single-arm robots, dual-arm grippers, dexterous hands, and humanoid robots.

cs.RO↗
arXiv2026

Beyond Activation: Gaze Invocation with Visible Status for an Embodied AR Assistant in Co-Located Collaboration

Wake words face an inherent trade-off: higher sensitivity reduces missed commands but increases accidental activations. In co-located augmented reality (AR), the system must also determine whether the user is speaking to the assistant or to a nearby person. We introduce a new perspective on this problem: combining gaze-based address with an embodied assistant, visible listening status, and turn management across activation, continued interaction, and release. In our implementation, users look at an assistant anchored in the scene and see activation progress and listening status on its body. Sustained gaze opens a local interaction channel; speech and playback keep it open when attention returns to the task; and inactivity closes it. We evaluated this design with 25 participants. After selecting the assistant's placement and dwell duration, each participant worked with a partner to plan a trip while using the assistant. The study recorded 500 interaction outcomes, including 496 assistant-directed utterance attempts. Gaze acquisition completed before speech for 490 of these attempts (98.8\%): 467 began while the channel remained open, whereas 23 began after it had been released. The other 6 attempts began before acquisition completed. Among 102 reviewed attempts in which gaze left after acquisition but before speech, the configured retention policy kept the channel open for 79 and released it before 23. The results show that an embodied target with visible status can support clear entry into an assistant interaction while revealing a different problem at release: after users look back to their work, they may not see that the assistant has stopped listening. We contribute the implemented gaze-invocation design and design implications for communicating assistant state after visual attention moves elsewhere.

cs.HC↗
arXiv2026

TACD: Distilling Efficient Text-to-Motion Models via Terminal Amplification Control

Recent text-to-motion models have improved motion quality and instruction following, yet many-step denoising and large model components make deployment slow and memory-intensive. We present Terminal-Amplification-Controlled Distillation (TACD), an on-policy approach for training efficient motion generators from text prompts and pretrained teachers, without real-motion training data. Building on segmented on-policy flow distillation, we supervise clean-motion predictions along student-generated trajectories. We identify a failure mode in which velocity matching on a fixed supervision grid repeatedly overweights errors near the denoising endpoint, degrading few-step generation. TACD ties the latest teacher query to the student's step size, bounding the effective loss weights in clean-motion space without changing inference. Experiments on HumanML3D and KIT-ML demonstrate improved few-step generation, including a 58% reduction in eight-step HY-Motion student FID relative to distillation without this bound. For diffusion teachers, the endpoint-matching form of TACD yields four-step students with lower FID and matched or improved text-motion retrieval relative to their 50-step teachers on HumanML3D. On HY-Motion and Kimodo, eight-step students with compact components achieve 7.7-11.9x end-to-end speedups and reduce peak GPU memory by 3.8-6.7x relative to their teachers. Project page: https://vkgo.github.io/TACD/

cs.AI↗
arXiv2026

Tensor-Engineered Van der Waals NbOCl2 Resonant Metasurface for Polarization-entangled Bell State Generation

Polarization-entangled photon pairs are essential resources for quantum information technologies, yet realizing compact sources with intrinsically controllable entanglement remains challenging. Van der Waals (vdW) nonlinear materials such as NbOCl2 provide atomically thin platforms for quantum light generation, yet their native crystalline anisotropies as natural materials impose limitations on accessible quantum states. Here, we develop a resonant vdW nonlinear metasurface based on NbOCl2 that exploits its intrinsic optical anisotropy to engineer polarization-dependent nonlinear responses. The anisotropic optical dispersion enables selective manipulation of resonant modes, resulting in a three-order-of-magnitude enhancement of the nonlinear response along the c axis. By further tailoring these resonant modes, we redistribute the effective second-order nonlinear susceptibility tensor between orthogonal polarization channels, balancing the spontaneous parametric down-conversion pathways along the b and c axes. This enables polarization-entangled photon generation with a measured fidelity of up to 92%. Our work establishes metasurface-enabled nonlinear optical engineering as a strategy for enhancing and controlling quantum light generation in vdW materials, providing a pathway toward scalable quantum photonic platforms.

physics.optics↗
arXiv2026

Spectral Evolution of Ceres' Surface and Implications for Space Weathering

The surface of Ceres exposed to the space environment will be gradually altered, but the evolution of its spectral properties remains poorly understood. Here we analyze visible and infrared spectra acquired by the Dawn mission to investigate spectral variations across different surface units. We identify local shifts of a few nm toward shorter wavelengths in the 2.7 $μ$m band center, predominantly associated with freshly exposed slumping and impact materials. Our analysis suggests that compositional variations in Ceres' regolith are unlikely the explanation for the band center shift, and therefore it may represent a spectral evolution within 10$^6$ years. Combining previous studies about the spectral reddening on Ceres, we propose a two-stage spectral evolution on Ceres. The blue-shifted regions are interpreted as the youngest materials. Within 10$^6$ years after exposure, these materials become spectrally bluer, the 2.7 $μ$m band weakens, and its center shifts toward longer wavelengths. Then the evolution is characterized by spectral reddening and strengthening of the 2.7 $μ$m band without the band center shift. Based on the previous laboratory work and observations on other C-complex asteroids, we suggest that the observed spectral variations may be attributed to the chemical and/or physical changes of the regolith, possibly associated with space weathering.

astro-ph.EP↗
arXiv2026

Representations of $p$-adic groups and orbits with smooth closure in a variety of Langlands parameters

Let $G$ be a reductive $p$-adic group for which the local Langlands correspondence is known, and $λ$ an infinitesimal parameter of $G$. In this paper, we prove that if the $p$-adic Kazhdan-Lusztig hypothesis holds for $λ$, then for a Langlands parameter $ϕ$ with infinitesimal parameter $λ$, if $Π_ϕ(G)$ contains a generic representation, then $L(s, ϕ, \Ad)$ is regular at $s=1$. We then prove an analogous statement for ABV-packets, which together with Vogan's conjecture on ABV-packets implies that if Arthur's conjectures for $G$ are known, then one direction of Shahidi's enhanced genericity conjecture holds: If an Arthur packet $Π_ψ(G)$ contains a generic representation, then $ϕ_ψ$ is tempered. We also offer some speculation about the relationship between Arthur type representations and singularities in varieties of Langlands parameters defined by Vogan.

math.RT↗
arXiv2026

Beyond "Made with AI": Visualizing Provenance Density to Mitigate the Transparency Penalty

As generative AI makes polished prose cheap to produce, users can no longer rely on fluency as a proxy for truth. We call this failure mode the Fluency Trap: users trust fluent hallucinations while also discounting accurate content once it is disclosed as AI-generated. Binary ``Made with AI'' labels respond with authorship disclosure, but they do not show what supports a claim. We propose Provenance Density, an evidence-visualization interface that shows the density of verified claims in a text. In a user study with 81 participants, an idealized Provenance Density interface produced a large discernment gap between truth and fabrication ($+4.15$ points, $d=1.82$), whereas participants given no signal showed no detectable discrimination. A technical audit with 200 samples shows that retrieval density alone is insufficient; unexpectedly, the Consistency Veto carries most of the discriminative signal on dynamic queries. As AI-generated content becomes indistinguishable from human writing, effective transparency must move from authorship disclosure toward evidence visualization.

cs.AI↗
arXiv2026

TrapVLA: Trapping Vision-Language-Action Models in Configured Failure Modes

This work introduces Configured Failure Trapping, a novel backdoor attack task against Vision-Language-Action (VLA) models, which aims to activate attacks through stealthy textual triggers and induce configured failure modes. Unlike prior backdoor attacks that treat any task failure as a successful attack, Configured Failure Trapping requires the attacker to control how the robot fails (e.g., causing the robot to grasp with a specified positional offset), making it substantially more challenging and hard to detect. To support the new task, we propose an effective data engine for synthesizing high-quality target trajectories and an automated suite for measuring configured-failure fidelity. Then, based on this foundation, we construct two new benchmarks, namely Trap-LIBERO and Trap-RoboTwin, that instantiate Configured Failure Trapping across four representative failure modes. To address this task, we identify sparse action deviation as a critical challenge and accordingly propose a novel method named TrapVLA, which explicitly learns trigger-induced action residuals to steer the policy toward the configured failure behavior. Extensive experiments across simulation benchmarks and real-world robotic settings show that TrapVLA effectively injects configured failure modes into VLA models while largely preserving performance on clean data. Project page: https://john-liua.github.io/TrapVLA/

cs.RO↗
arXiv2026

Product of Rankin-Selberg convolutions and a new proof of Jacquet's local converse conjecture

In this article, we construct a family of integrals which represent the product of Rankin-Selberg $L$-functions of $\mathrm{GL}_{l}\times \mathrm{GL}_m$ and of $\mathrm{GL}_{l}\times \mathrm{GL}_n $ when $m+n<l$. When $n=0$, these integrals are those defined by Jacquet--Piatetski-Shapiro--Shalika up to a shift. In this sense, these new integrals generalize Jacquet--Piatetski-Shapiro--Shalika's Rankin-Selberg convolution integrals. We study basic properties of these integrals. In particular, we define local gamma factors using this new family of integrals. As an application, we obtain a new proof of Jacquet's local converse conjecture using these new integrals.

math.RT↗
arXiv2026

Spin-group theory on Edelstein effect and spin-orbit torque in Collinear Ferromagnets

Current-induced spin-orbit torques (SOTs) are central to the electrical manipulation of magnetic order in spintronic devices. In transition-metal/collinear ferromagnet bilayers, field-like and damping-like torques have been described only phenomenologically via the spin or orbital Hall effect, lacking a rigorous symmetry-based foundation. The precise role of spin-orbit coupling (SOC) in both the Edelstein effect and SOTs has remained unresolved. Here we develop a spin-group symmetry theory for the Edelstein effect and SOTs in collinear ferromagnets, treating SOC as a symmetry-breaking perturbation. For 4mm (C4v) point group symmetry, we derive the full forms of field-like and damping-like torques, which arise predominantly from first- and second-order SOC. We further show that SOTs in both orbital-Hall-dominated Ti/Ni and spin-Hall-dominated Pt/CoFe bilayers originate at first-order SOC. Taking the 3m (C3v) torque as a paradigmatic example, we elucidate the role of second- and higher-order SOC torques in field-free switching of perpendicular magnetic anisotropy. Remarkably, in PtMnSb, we demonstrate that SOTs under certain point group symmetries deviate from the conventional form: zeroth- and first-order SOC contributions vanish identically, with the leading SOT emerging at second order. All symmetry-based predictions from spin-group theory are in excellent quantitative agreement with first-principles calculations. Our work establishes a unified symmetry framework for the microscopic understanding of the Edelstein effect and current-induced spin torques in ferromagnetic systems.

cond-mat.mtrl-sci↗
arXiv2026

Near-Field Velocity Estimation and Doppler-Aware Localization in OFDM Massive MIMO

In Orthogonal Frequency Division Multiplexing (OFDM)-based massive Multiple-Input Multiple-Output (MIMO) near-field (NF) sensing, target motion induces an antenna-dependent bistatic Doppler variation across the array aperture. Ignoring this spatial Doppler variation leads to a model mismatch that degrades NF localization. In this paper, we propose a low-complexity recursive framework for joint radial/transverse velocity estimation and Doppler-aware localization. Initialized by a constant-Doppler coarse localization, the method alternates between closed-form Least Squares Estimator (LSE)-based velocity estimation and antenna-dependent Doppler-aware localization refinement. Simulation and measurement results demonstrate the effectiveness of the proposed framework against two benchmark methods. Compared with a low-complexity constant-Doppler baseline method, the proposed algorithm improves range, angle, and radial velocity estimation results, while also enabling transverse velocity estimation. In the measurement results, the overall localization error decreases from 0.268 m to 0.064 m. The radial and transverse velocity estimation errors are 0.032 m/s and 0.069 m/s, respectively. Compared with a high-complexity exhaustive four-dimensional (4D) Maximum Likelihood Estimator (MLE), the proposed method achieves comparable velocity estimation results while yielding a more accurate localization result when the 4D MLE has a practical finite search grid.

eess.SP↗
arXiv2026

Towards Reliable Stain Transfer: An Iterative Data-Model Co-Optimization Framework Based on Multimodal Expert-Guided Assessment

Histopathological examination primarily relies on hematoxylin and eosin (H&E) and immunohistochemistry (IHC) staining. Although IHC provides critical molecular information, it is costly and requires specialized expertise. Stain transfer provides an efficient alternative by computationally generating IHC from H&E images, but remains challenged by unified and interpretable modeling for heterogeneous biomarkers under pixel-unaligned supervision. We propose DMCoStain, a novel Data-Model Co-optimization framework for Stain transfer. It iteratively co-refines training data and model capability, improving staining accuracy and interpretability in both pathological and structural consistency. To refine training data in a clinically meaningful manner, it incorporates the Multimodal Expert-Guided Finer Selection (MEGFS) strategy, built upon a pioneering IHC-positive-expression (IPE) vision-language model (VLM) that emulates pathologist reasoning. To support MEGFS, we construct ImmunoInstruction, the first large-scale IPE instruction-following dataset with 150K VQA samples. Extensive experiments on multiple tissues and biomarkers demonstrate that DMCoStain achieves state-of-the-art (SOTA) accuracy. This paradigm offers strong practical value, and MEGFS also functions as a specialized evaluation tool for future model development. Dataset, code, and more details are in https://github.com/SikangSHU/DMCoStain.

cs.CV↗
arXiv2026

Spin Hall Effect in Collinear Ferromagnets from Spin-Group Symmetry

Magnetic materials support both time-reversal-even (T-even) and time-reversal-odd (T-odd) spin Hall currents, yet their underlying microscopic origins remain elusive. Here, we elucidate the spin Hall effect (SHE) in collinear ferromagnets by treating spin-orbit coupling (SOC) as a perturbation that breaks spin-group symmetry, thereby revealing how magnetic order activates distinct spin Hall response. To first order in SOC, we identify two dominant T-even SHE mechanisms: a magnetization-independent conventional contribution and a magnetization-dependent channel associated with anomalous Hall charge transport. At the same order, the leading T-odd magnetic spin Hall effect (MSHE) originates from the exchange interaction between the conventional spin current and the local magnetization. At second order in SOC, we further uncover a distinct T-odd planar spin Hall mechanism. Our spin-symmetry analysis is corroborated by first-principles calculations, which reveal a pronounced anisotropic magnetic spin Hall effect whose magnitude can be comparable to the T-even spin Hall conductivity (SHC) when the magnetic moment is tilted away from the principal crystallographic axes. These findings clarify the microscopic origins of the SHC in collinear ferromagnets and pave the way for ferromagnet-based spin current sources with versatile properties in spintronic applications.

cond-mat.mes-hall↗
arXiv2026

On the complementary Arthur representations and unitary dual for p-adic classical groups

In [HJLLZ24], we proposed a new conjecture on the structure of the unitary dual of connected reductive groups over non-Archimedean local fields of characteristic zero based on their Arthur representations and verified it for all the known cases on the unitary dual problem. One step towards this conjecture involves the question whether certain complementary Arthur representations are unitary. In this paper, we give an explicit characterization of the complementary Arthur representations for symplectic and split odd special orthogonal groups. As applications, we obtain interesting constraints on local components of irreducible self-dual cuspidal automorphic representations of $\mathrm{GL}_N$, especially when $N=2,3$.

math.RT↗