Showing 1–50 of 358 results for author: Fu, G

Asymmetric Aerosol Distribution on the Terminators of the Warm Saturn WASP-69 b Revealed by JWST NIRISS/SOSS

Authors: Le-Chris Wang, Sagnick Mukherjee, Stephen P. Schmidt, Kevin B. Stevenson, Mei Ting Mak, Patrick McCreery, Harry Baskett, Carlos Gascón, David K. Sing, Katharine A. Bennett, Duncan A. Christie, Guangwei Fu, Mercedes López-Morales, Joshua D. Lothringer, Nathan J. Mayne, Lakeisha M. Ramos Rosado, Zafar Rustamkulov, Kevin C. Schlaufman, Kristin S. Sotzen

Abstract: How aerosols form, are transported, and cycle between condensation and evaporation across exoplanet temperature regimes remains poorly understood. Recent models and observations suggest that warm giant planets near $800$--$1000$ K may span a transition between homogeneous and longitudinally heterogeneous aerosol distributions. We present a robust detection of aerosol asymmetry in a giant planet wi… ▽ More How aerosols form, are transported, and cycle between condensation and evaporation across exoplanet temperature regimes remains poorly understood. Recent models and observations suggest that warm giant planets near $800$--$1000$ K may span a transition between homogeneous and longitudinally heterogeneous aerosol distributions. We present a robust detection of aerosol asymmetry in a giant planet with $T_{\rm eq}\lesssim1000$ K, using the $0.86$--$2.82~μ$m JWST NIRISS/SOSS transmission spectrum of WASP-69 b. The evening limb shows prominent 1.4 $μ$m H$_2$O absorption ($Δ\mathrm{BIC}_{\rm H_2O}=+22.7$), whereas H$_2$O is not detected on the morning limb ($Δ\mathrm{BIC}_{\rm H_2O}=-8.7$). Atmospheric retrievals reveal significant aerosol opacity on both limbs, with high-altitude, optically thick clouds muting molecular features on the morning limb and lower cloud opacity allowing H$_2$O to emerge on the evening limb. The evening terminator is hotter by $304^{+62}_{-91}$ K, consistent with morning-limb condensates partially evaporating during transport toward the evening limb. This mechanism is independently verified with 3D general circulation models. Stellar contamination or aerosols dominated by photochemical haze do not readily explain the asymmetry. From a limb-resolved analysis, we infer a stellar-to-superstellar atmospheric metallicity, with $\rm[M/H]=0.11^{+0.40}_{-0.46}$ from the equilibrium retrieval and [O/H]$=1.38^{+0.44}_{-0.79}$ from the free retrieval. We also detect an escaping metastable-helium tail extending to $3.08^{+0.50}_{-0.45}\,R_p$. WASP-69 b anchors the cooler edge of the emerging population of planets with asymmetric aerosol distributions and suggests that substantial aerosol opacity may persist on both limbs across this transition. △ Less

Submitted 20 August, 2026; originally announced August 2026.

Comments: 24 pages, 11 figures, 6 tables, submitted to ApJL

Probing Dark Matter with Gravitational Waves: Spin-Modulated Dephasing from Black Holes in Halos

Authors: Guoyang Fu, Yunqi Liu, Jian-Pin Wu, Bin Wang, Rui-Hong Yue

Abstract: We develop a novel analytical framework for constructing axisymmetric black hole spacetimes sourced by dark matter (DM) halos. Applying this to extreme mass ratio inspirals (EMRIs), we find that the DM induces a detectable gravitational-wave dephasing, scaling monotonically with the halo's compactness. Notably, BH spin significantly suppresses this dephasing, indicating that analyses neglecting ro… ▽ More We develop a novel analytical framework for constructing axisymmetric black hole spacetimes sourced by dark matter (DM) halos. Applying this to extreme mass ratio inspirals (EMRIs), we find that the DM induces a detectable gravitational-wave dephasing, scaling monotonically with the halo's compactness. Notably, BH spin significantly suppresses this dephasing, indicating that analyses neglecting rotation would overestimate DM signatures. Faithfulness calculations confirm that future space-borne detectors can robustly distinguish such DM environments, establishing EMRIs as a novel probe for galactic DM distributions. △ Less

Submitted 15 August, 2026; originally announced August 2026.

No Helium Detected in LHS 1140 b from Four JWST NIRISS/SOSS Transits

Authors: Katherine A. Bennett, Carlos Gascón, Jacob Lustig-Yaeger, Guangwei Fu, David K. Sing, Kevin B. Stevenson, Jonathan Brande, Munazza K. Alam, Jeff A. Valenti, Mercedes López-Morales, Sten J. Vermeiren, Megan Weiner Mansfield, Sarah E. Moran, Kristin S. Sotzen, Jegug Ih, Sarah Peacock

Abstract: In the effort to determine which low-mass exoplanets have atmospheres, LHS 1140 b remains one of the most favorable targets. Its large size (5.6 $\rm M_{\oplus}$ and 1.7 $\rm R_{\oplus}$) and relatively long orbital period (24.7 days) imply an atmosphere may be likely, and notably, recent interior models favor either a hydrogen-dominated "mini-Neptune" or a "water world" over a true terrestrial pl… ▽ More In the effort to determine which low-mass exoplanets have atmospheres, LHS 1140 b remains one of the most favorable targets. Its large size (5.6 $\rm M_{\oplus}$ and 1.7 $\rm R_{\oplus}$) and relatively long orbital period (24.7 days) imply an atmosphere may be likely, and notably, recent interior models favor either a hydrogen-dominated "mini-Neptune" or a "water world" over a true terrestrial planet. Another possibility is that it has a helium-rich atmosphere. This hypothesis is supported by recent ground-based observations that detected the metastable helium triplet during transit. These observations indicated there may be current helium escape from the planet's upper atmosphere, yet the signal was not detected during a subsequent observation, suggesting time-variable escape. Here we present four observations of LHS 1140 b with JWST NIRISS/SOSS, which covers the metastable helium triplet, obtained between 2023 and 2026. These observations span the epoch of the ground-based measurements, and although none were contemporaneous with the ground-based transits, all four are sensitive to helium absorption at the previously reported level. However, we detect no helium absorption in any visit. We reject the best-fit ground-based model at $>3σ$ in each visit, and find no clear trend in mass-loss with time. Our results suggest the reported ground-based detection may be spurious, although variability cannot be excluded if detectable helium absorption occurs in $\lesssim50\%$ of transits. The nature of LHS 1140 b thus remains a mystery until future transmission and emission analyses are complete. △ Less

Submitted 13 August, 2026; originally announced August 2026.

Comments: 13 pages, 4 figures. Submitted to AAS journals. Comments welcome

PartMat: Material-Aware 3D Part Decomposition with a Single Global Latent

Authors: Guangming Fu, Jin Song, Yiyun Fei, Guoqiu Li, Ruigao Yang, Jianan Jiang

Abstract: Part-level 3D generation has recently attracted increasing attention for producing structured and editable 3D assets. However, existing methods typically decompose objects according to functional semantics rather than the editable material boundaries (e.g., fabric, wood, metal) required in practical 3D applications such as interior design. Additionally, current methods often generate parts indepen… ▽ More Part-level 3D generation has recently attracted increasing attention for producing structured and editable 3D assets. However, existing methods typically decompose objects according to functional semantics rather than the editable material boundaries (e.g., fabric, wood, metal) required in practical 3D applications such as interior design. Additionally, current methods often generate parts independently, causing computational costs to scale linearly with the part count. To address these limitations, we present PartMat, an efficient material-aware 3D part decomposition pipeline that represents multi-part geometry with a single global latent. Given a reference image and a single whole-object geometry, PartMat decomposes the object into parts that follow material boundaries. First, we propose PartVAE to learn such a unified representation and decode all material parts in a single forward pass, thereby decoupling inference cost from the number of parts. Second, with this representation, a diffusion model is trained for part generation and refined via reinforcement learning for accurate material assignment and overlap suppression. Finally, to recover fine-grained geometric details, we introduce a sparse-voxel flow-matching model with part attention for geometry post-processing. Extensive experiments demonstrate that PartMat significantly outperforms existing baselines in material-aware decomposition accuracy and achieves comparable geometric quality, while maintaining efficient inference. △ Less

Submitted 3 August, 2026; originally announced August 2026.

Inter-Residue Geometry Attention for Antibody-Specific Epitope Prediction

Authors: Chuanliu Fan, Nan Yu, Junjie Wu, Guohong Fu

Abstract: Antibody-specific epitope prediction aims to identify which antigen residues are recognized by a given antibody, a task that depends on the three-dimensional complementarity between antibody CDRs and the antigen surface. Existing methods usually leverage PLM embeddings and inject structure through additional graph, surface, or point-cloud encoders, where the positional mechanism inside attention r… ▽ More Antibody-specific epitope prediction aims to identify which antigen residues are recognized by a given antibody, a task that depends on the three-dimensional complementarity between antibody CDRs and the antigen surface. Existing methods usually leverage PLM embeddings and inject structure through additional graph, surface, or point-cloud encoders, where the positional mechanism inside attention remains largely tied to one-dimensional sequence order. For proteins, the analogue of a token offset is not only sequence separation, but also the three-dimensional displacement between residues after folding. This raises a question, can folded residue geometry serve as the positional mechanism of attention itself? We propose Local-Frame 3D Rotary Position Encoding (LF3DRoPE), which expresses inter-residue displacements in backbone-defined local frames and injects them directly into rotary attention. This design preserves continuous directional geometry while ensuring invariance to global $\mathrm{SE}(3)$ transformations. On the AsEP benchmark, LF3DRoPE achieves state-of-the-art $\mathrm{MCC}$ on both ratio and epitope-group splits. Ablations and rigid transformation tests show that local three-dimensional geometry provides information beyond sequence-order attention while preserving invariance to arbitrary global coordinate systems. Mutation ranking results further indicate that LF3DRoPE captures antigen-specific structural compatibility. △ Less

Submitted 2 August, 2026; originally announced August 2026.

Multi-Asset Liquidation in Dark Pools with Adverse Selection

Authors: Guanxing Fu, Johannes Ruf, Xiaomin Shi, Zuo Quan Xu

Abstract: We study the optimal liquidation of a multi-asset portfolio using both a traditional exchange and dark pools in the presence of quadratic adverse-selection costs. The problem leads to a matrix-valued backward stochastic differential equation with jumps and a singular terminal condition. We establish existence and uniqueness of its solution and use it to characterize the value function and the opti… ▽ More We study the optimal liquidation of a multi-asset portfolio using both a traditional exchange and dark pools in the presence of quadratic adverse-selection costs. The problem leads to a matrix-valued backward stochastic differential equation with jumps and a singular terminal condition. We establish existence and uniqueness of its solution and use it to characterize the value function and the optimal liquidation strategy. The uniqueness result is the main mathematical contribution and strengthens the existing theory even in simpler special cases; the existence result is also new. For a two-asset model, we distinguish the roles of asset correlation, own-asset adverse selection, and cross-asset spillover in adverse-selection costs. Under diagonal temporary impact and in the absence of cross-asset spillover, an initially well-diversified portfolio remains well diversified during optimal liquidation and, for a fixed sign of the correlation, its liquidation cost is strictly decreasing in the magnitude of the correlation. By contrast, under the same diagonal-impact specification, under explicit conditions and sufficiently close to the liquidation horizon, cross-asset spillover makes a well-diversified portfolio more costly to liquidate than its poorly diversified sign-reversed counterpart and causes sufficiently unbalanced well-diversified portfolios to become poorly diversified with positive probability. Separately, without requiring diagonal temporary impact, we show that, in the absence of cross-asset spillover, own-asset adverse selection introduces an explicit shrinkage factor in the optimal dark-pool order relative to the order minimizing the post-execution continuation value. Finally, we derive an explicit condition under which a dark-pool execution transforms a poorly diversified portfolio into a well-diversified one. △ Less

Submitted 10 August, 2026; v1 submitted 29 July, 2026; originally announced July 2026.

Comments: 39 pages

Echoes and quasinormal modes for static loop quantum black bounces

Authors: Huajie Gong, Guoyang Fu, Shulan Li, Jian-Pin Wu, Qin Tan, Qiyuan Pan

Abstract: We investigate scalar perturbations of the static loop quantum black bounce (LQBB) spacetime with multipole index $l=1$, focusing on time-domain signals and fundamental quasinormal frequencies (QNFs). The LQBB model provides a unified description of regular black holes (RBHs) and traversable wormholes, governed by the quantum parameter $α$ and the bounce parameter $r_b$. Using the finite differenc… ▽ More We investigate scalar perturbations of the static loop quantum black bounce (LQBB) spacetime with multipole index $l=1$, focusing on time-domain signals and fundamental quasinormal frequencies (QNFs). The LQBB model provides a unified description of regular black holes (RBHs) and traversable wormholes, governed by the quantum parameter $α$ and the bounce parameter $r_b$. Using the finite difference method, we find no echoes for the displayed RBH configurations with a single-barrier effective potential, whereas clear echoes are produced by the potential well structure in selected traversable wormhole configurations. The QNFs obtained from the Prony method and the direct integration method are in good agreement. In the RBH case, increasing $r_b$ or $α$ leads to a slower decay. In the wormhole case, the QNFs depend non-monotonically on the model parameters, and the emergence of echoes is closely tied to the effective potential profile. These results show that the LQBB spacetime provides a useful framework for studying wave dynamics in RBHs and traversable wormholes, and for clarifying how horizon and throat structures affect ringdown and echoes. △ Less

Submitted 28 July, 2026; originally announced July 2026.

Comments: 23 pages, 7 figures, 3 tables

An Integrated Deep Learning and Statistical Framework for Whole-Network Gene--Environment Association with Leaf Vascular Architecture

Authors: Geran Zhao, Yangsheng Wang, Xiaotian Dai, Guifang Fu

Abstract: Leaf veins exhibit remarkable diversity in architecture and patterning, yet existing gene--environment association studies have primarily quantified leaf venation using a small collection of low-dimensional summary traits, thereby discarding most of the structural information contained in the original images. We propose an integrated deep learning and statistical framework. The proposed framework… ▽ More Leaf veins exhibit remarkable diversity in architecture and patterning, yet existing gene--environment association studies have primarily quantified leaf venation using a small collection of low-dimensional summary traits, thereby discarding most of the structural information contained in the original images. We propose an integrated deep learning and statistical framework. The proposed framework achieves four methodological advances. First, it represents the complete leaf vascular architecture as a whole-network image phenotype. Second, it fine-tunes the deep learning-based Edge Detection with Transformers (EDTER) model to accurately extract whole-network leaf vascular architecture from RGB images by jointly learning local and global contextual features. Third, it constructs a new annotated leaf image database by integrating edge maps generated by DiffusionEdge with the Berkeley Segmentation Database (BSDS500). Fourth, it applies Semiparametric Sparse Canonical Correlation Analysis (SSCCA) to perform variable selection and model associations between repeatedly measured high-dimensional Bivariate image responses and high-dimensional predictors while simultaneously accommodating sparse, zero-inflated data represented by edge maps through a truncated latent Gaussian copula model. Two simulation studies demonstrate the performance of the proposed framework under increasing levels of complexity. Application to a real \emph{Populus} dataset identifies three significant gene--geography interactions associated with leaf vascular architecture, providing new biological insights and establishing a broadly applicable methodological framework for high-dimensional complex image phenotypes. △ Less

Submitted 23 July, 2026; originally announced July 2026.

A Consistent Feature Screening Approach for Tensor Responses with Applications to Genome-Wide Facial Shape Association

Authors: Shaofei Zhao, Zuofeng Shang, Seth M. Weinberg, Peter Claes, John R. Shaffer, Guifang Fu

Abstract: As data collecting technologies advance, data structures are getting more and more complex, from single vectors to multi-dimensional tensors. This article is motivated by a variable selection problem to detect important genes from an ultrahigh dimensional pool that are associated with human facial shape variations. We propose a data-driven trimmed feature screening method based on a tensor ridge r… ▽ More As data collecting technologies advance, data structures are getting more and more complex, from single vectors to multi-dimensional tensors. This article is motivated by a variable selection problem to detect important genes from an ultrahigh dimensional pool that are associated with human facial shape variations. We propose a data-driven trimmed feature screening method based on a tensor ridge regression model (TrimTenRidge) through setting thresholds on the tensor coefficients to perform a feature screening procedure. Unlike existing approaches, the TrimTenRidge does not require any sparse structures. In addition, it not only detects important predictors but also locates specific regions/components of the tensor response that are associated with each of the selected predictors. We prove the theoretical selection consistency and also assess its empirical performance through various simulation settings. The approach copes with ultra-high dimensional predictors and tensor responses simultaneously and contributes to the literature from theoretical, methodological, and five applicational aspects. We further apply the TrimTenRidge approach to genome-wide human facial shape data, from which the entire facial shapes form a $2,342\times 7,160\times 3$ tensor, and we successfully detect several novel genetic loci and also confirm some existing findings that are associated to facial shape. △ Less

Submitted 24 July, 2026; originally announced July 2026.

Theoretical Properties of Multivariate Random Forest in Feature Selection and its Application to Facial Morphology-Gene Detection

Authors: Yangsheng Wang, Samruddhi Thakar, Anton Schick, Guifang Fu

Abstract: This work establishes a theoretical foundation for joint feature selection with multivariate outcomes, positioning the permutation-based variable importance measure (PVIM) of multivariate random forests (MRF) as a principled tool for high-dimensional feature selection. We establish the first consistency guaranty for MRF, showing that it retains all truly influential features with probability tendi… ▽ More This work establishes a theoretical foundation for joint feature selection with multivariate outcomes, positioning the permutation-based variable importance measure (PVIM) of multivariate random forests (MRF) as a principled tool for high-dimensional feature selection. We establish the first consistency guaranty for MRF, showing that it retains all truly influential features with probability tending to one as the sample size grows to infinity under mild regularity conditions. Incomplete U-statistics is employed to incorporate three layers of randomness: subsampling of subjects for training each tree, subsampling of features at each split, and permutation of each feature for the out-of-bag (OOB) samples. Unlike independence-based screening that evaluates each feature in isolation, PVIM is a joint screening approach that accounts for multicollinearity, nonlinear, high-order interactions, and subject heterogeneity via ensemble aggregation. Moreover, we demonstrate the practical utility of MRF through a genome-wide association study (GWAS) of human facial morphology (with 2,342 subjects and 453,273 SNPs), where MRF identifies several novel loci and interaction hubs that extend prior findings. Extensive simulations show that MRF accurately identifies truly influential signals while producing parsimonious feature sets with well controlled false selection rates, outperforming canonical correlation analysis (CCA) and several other independence multivariate screening approaches. In addition, we also propose a novel simulation framework, including image outcomes, that more closely mimic the intricate nature of real-world data and provide rigorous testbeds for machine learning research. △ Less

Submitted 23 July, 2026; originally announced July 2026.

Toward High-Fidelity 3D Point-Cloud Learning for Brain Folding Morphology Prediction Using Trans-Unet

Authors: Geran Zhao, Xiaotian Li, Poorya Chavoshnejad, Mir Jalil Razavi, Akbar Solhtalab, Lijun Yin, Guifang Fu

Abstract: Learning high-fidelity point-cloud features in the 3D space poses significant challenges, including permutation invariance, lack of local context, difficulty in fine-grained surface reconstruction, and high computational cost. In this article, we propose Trans-Unet, a novel framework that addresses these issues by first tansforming 3D point-cloud data into a 2D grid domain and then employing a U-s… ▽ More Learning high-fidelity point-cloud features in the 3D space poses significant challenges, including permutation invariance, lack of local context, difficulty in fine-grained surface reconstruction, and high computational cost. In this article, we propose Trans-Unet, a novel framework that addresses these issues by first tansforming 3D point-cloud data into a 2D grid domain and then employing a U-shaped hybrid model that integrates Convolutional Neural Networks, and self-attention mechanisms. The proposed Trans-Unet effectively learns and reconstructs precise features from high-resolution 3D point-cloud data (with 40,401 points in surface and 2,382 points in fiber) derived from a predefined finite element brain patch growth model, enabling accurate prediction of brain folding patterns. By combining multiple techniques, Trans-Unet leverages the complementary strengths: the 3D-to-2D transformation preserves fine-grained structural information while significantly reducing computational cost and the curse of dimensionality; convolutional blocks capture hierarchical, low-level local representations; and the self-attention mechanism models global, high-level semantics and long-range dependencies. The dataset consists of 3D point-clouds containing both brain surface patches and fiber information generated by a large-scale finite element model. Trans-Unet is applied to predict brain surface folding from the initial state (state 0 or states 0-2) to the final state (state 3). Experimental results demonstrate that Trans-Unet achieves high-resolution predictions of brain patch growth, surpassing existing methods in both fidelity and accuracy. △ Less

Submitted 23 July, 2026; originally announced July 2026.

Reconstruction of Enhanced Causal Omnidirectional Network (RECON)

Authors: Praveen Niranda, Peter T. McKenney, Guifang Fu

Abstract: Learning a dynamical system and reconstructing the underlying regulatory network from $p$ discretely observed state trajectories remain challenging problems. Existing approaches often produced a large number of spurious edges and suffered from several methodological limitations. We propose a new approach, Reconstruction of Enhanced Causal Omnidirectional Network (RECON), that leverages an integral… ▽ More Learning a dynamical system and reconstructing the underlying regulatory network from $p$ discretely observed state trajectories remain challenging problems. Existing approaches often produced a large number of spurious edges and suffered from several methodological limitations. We propose a new approach, Reconstruction of Enhanced Causal Omnidirectional Network (RECON), that leverages an integral-based additive nonparametric ODE model to reconstruct regulatory networks from $p$ time-course data. RECON incorporates five methodological advances. First, it incorporates a new data-driven edge selection procedure that substantially reduces spurious edges while preserving true regulatory edges. Second, it reconstructs an omnidirectional network that captures causal regulatory relationships rather than merely statistical associations or noise artifacts. Third, it substantially broadens the applicability of standard ODE-based approaches by accommodating both dense regular and sparse irregular longitudinal sampling scenarios. Fourth, it models both node trajectories and edge regulatory effects as time-varying functions, emphasizing a dynamic regulatory network. Fifth, it reconstructs a signed and weighted regulatory network and provides comprehensive network interpretation through two-way direction, activatory/inhibitory indicator, and strength, together with keystone node identification and topological structure. Across five simulation studies, RECON consistently outperforms GRADE by removing nearly all spurious edges while retaining nearly all true regulatory edges, resulting in highly accurate network reconstruction. In the most challenging scenario, the number of spurious edges is reduced from 239 to 0. △ Less

Submitted 23 July, 2026; originally announced July 2026.

Longitudinal Random Forests for Sparse and Irregular Response Trajectories

Authors: Yangsheng Wang, Xiaotian Dai, Haoda Fu, Guifang Fu

Abstract: Longitudinal studies often collect data at sparse, irregular, and unequally spaced time points. Such heterogeneity is often driven by subject-specific covariates, yet existing methods have been restricted to a scalar endpoint value, completely neglecting the underlying response trajectories. We propose a novel Longitudinal Random Forest (LRF) framework that leverages tree-based ensemble machine le… ▽ More Longitudinal studies often collect data at sparse, irregular, and unequally spaced time points. Such heterogeneity is often driven by subject-specific covariates, yet existing methods have been restricted to a scalar endpoint value, completely neglecting the underlying response trajectories. We propose a novel Longitudinal Random Forest (LRF) framework that leverages tree-based ensemble machine learning with adaptive node-wise longitudinal trajectory estimation. The LRF framework makes five methodological contributions. it captures each subject's individual response trajectory while simultaneously accommodating within-node correlation, between-node heterogeneity, and nonlinear and interactive covariate effects. It introduces a novel trajectory-based splitting criterion that maximizes trajectory separation while incorporating a size-weighted penalty; it provides two variants, Principal Analysis by Conditional Expectation (LRF-PACE) and adaptive linear mixed-effects models (LRF-adaptiveLMM), which employ nonparametric and semiparametric node-wise smoothers, respectively, while learning covariate effects in a data-driven manner. It provides a comprehensive interpretation of covariates using both the classical trajectory-based permutation variable importance measure (PVIM) and a newly proposed finite-way interaction frequency count, and it not only predicts entire trajectories for new subjects but also forecasts future trajectories for existing subjects. Extensive simulation studies demonstrate that LRF achieves superior performance over several competing methods, even under severe sparsity. The practical significance of the LRF framework lies in its ability to address five important clinical questions. △ Less

Submitted 23 July, 2026; originally announced July 2026.

Phase-Field Models, Sharp Interface Limits, and Numerical Schemes for Contact Line Dynamics

Authors: Guosheng Fu, Yuan Gao, Jian-Guo Liu

Abstract: We study phase-field and sharp-interface models for contact line dynamics of a liquid droplet on a solid substrate within a unified variational framework. The motion of the contact line, where liquid, gas, and solid phases meet, poses a fundamental difficulty in continuum modeling due to the classical stress singularity of no-slip hydrodynamics. Phase-field models regularize this singularity by in… ▽ More We study phase-field and sharp-interface models for contact line dynamics of a liquid droplet on a solid substrate within a unified variational framework. The motion of the contact line, where liquid, gas, and solid phases meet, poses a fundamental difficulty in continuum modeling due to the classical stress singularity of no-slip hydrodynamics. Phase-field models regularize this singularity by introducing a thin transition layer of thickness and encoding interfacial effects through a Ginzburg-Landau free energy augmented by a wall energy on the substrate. Starting from the total free energy $E = E_b + E_w$, we analyze two phase-field models: the Allen-Cahn equation and the Cahn-Hilliard equation. Using matched asymptotic expansions as $δ\to 0$, we recover their corresponding sharp interface limits. In the Allen-Cahn case, the limit yields motion by mean curvature with a contact line law driven by deviations of the dynamic contact angle from Young's angle. In the Cahn-Hilliard case, the limit leads to a Mullins-Sekerka problem with the same form of contact line dynamics. A central result of this work is the identification of consistent gradient-flow structures across both models. The Allen-Cahn dynamics correspond to an $L^2$-gradient flow, while the Cahn-Hilliard dynamics correspond to an $H^{-1}$-gradient flow, and both converge to sharp-interface evolutions that preserve the same energy-dissipation structure. This provides a unified interpretation of contact line motion as a consequence of a single variational principle. Finally, we develop energy-stable numerical schemes based on the minimizing movement principle and establish discrete energy dissipation and well-posedness of the fully discrete problem. Numerical examples confirm that both schemes relax toward the same stationary sharp interface solution while their dynamics reflect the different dissipation mechanisms. △ Less

Submitted 19 July, 2026; originally announced July 2026.

Comments: 35 pages

Toward Federated Multimodal Graph Foundation Models: A Topology-Aware Multimodal Alignment Framework

Authors: Xunkai Li, Guohao Fu, Yuming Ai, Zhengyu Wu, Hongchao Qin, Rong-Hua Li, Guoren Wang

Abstract: Multimodal-attributed graphs (MAGs), whose nodes carry modalities such as images and text alongside topological structure, now pervade applications including social platforms, e-commerce, and biomedical networks, offering richer semantic signals than single-modality graphs. In practice, such graphs are fragmented across privacy-restricted silos owned by different platforms and institutions, so lea… ▽ More Multimodal-attributed graphs (MAGs), whose nodes carry modalities such as images and text alongside topological structure, now pervade applications including social platforms, e-commerce, and biomedical networks, offering richer semantic signals than single-modality graphs. In practice, such graphs are fragmented across privacy-restricted silos owned by different platforms and institutions, so learning a broadly transferable model over them demands collaborative training that never exposes raw data. This places the task at the intersection of multimodal graph learning and federated learning, yet existing methods cover only one side of it. To address the challenges from these two perspectives, we propose FedGAMMA, casting federated multimodal graph foundation learning as a two-stage semantic-structural alignment problem of federated pre-training and prompt-based fine-tuning. During pre-training, a shared-private semantic enhancer disentangles cross-modal commonality from modality-specific information, aligning it through optimal transport, a topology-aware graph fusion module decouples semantic and structural views via semantic residual graphs and dual positional encodings, and a dual-channel affinity-aware aggregation mechanism estimates client similarity from feature and graph centroids without exposing raw data. During fine-tuning, FedGAMMA adapts the pretrained encoder through lightweight graph-aware prompts, a shared prompt pool with controlled exploration, and channel-wise prompt synchronization. Experiments on twelve multimodal graph datasets show FedGAMMA consistently surpassing a broad range of baselines across downstream tasks, with gains of up to 12.96%. FedGAMMA further outperforms competitive baselines accross multi-domain datasets on multiple tasks with up to 5.71% under few-shot learning scenario. △ Less

Submitted 17 July, 2026; originally announced July 2026.

Comments: Under Review

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields

Authors: Qiang Liu, Feng Ma, Wei Zhu, Xiyu Jia, Jianmin Xue, Jun Wen, Gaojun Fu

Abstract: Compressible physical fields are widely present in the real physical world, but current artificial intelligence lacks an understanding mechanism for the non-differentiable features in compressible physical fields. Addressing the limitations of existing deep learning architectures in handling global non-differentiable features, we propose the Inverse Low-Dimensional Manifold reconstruction framewor… ▽ More Compressible physical fields are widely present in the real physical world, but current artificial intelligence lacks an understanding mechanism for the non-differentiable features in compressible physical fields. Addressing the limitations of existing deep learning architectures in handling global non-differentiable features, we propose the Inverse Low-Dimensional Manifold reconstruction framework (ILDM). This framework couples the Non-differentiable Approximation Function (NAF) for capturing non-differentiable features in compressible flows with the Smooth Fluid Reconstruction (SFR) module tailored for smooth fluid regions. Extensive evaluations across 1D and 2D benchmarks, including Riemann problems and double Mach reflection, demonstrate that ILDM significantly outperforms cPINN and R-adaptive DeepONet. Specifically, ILDM achieves superior localization of non-differentiable interfaces and maintains robust super-resolution performance even with low-resolution inputs, establishing a physically consistent and scalable paradigm for data-driven fluid dynamics. △ Less

Submitted 8 July, 2026; originally announced July 2026.

Comments: 25 pages with 6 figures

Mitigating Charge Migration in JWST NIRISS Reveals That KELT-7 b is a Metal-enriched Ultra-hot Jupiter Orbiting a Young Metal-rich Star

Authors: Stephen P. Schmidt, Erin M. May, Joshua D. Lothringer, Patrick McCreery, Mei Ting Mak, Myles Pope, Harry Baskett, Sagnick Mukherjee, David K. Sing, Katherine A. Bennett, Arika Egan, Guangwei Fu, Daniel P. Thorngren, Duncan A. Christie, Carlos Gascón, Le-Chris Wang, Lakeisha M. Ramos Rosado, Nathan J. Mayne, Natalie H. Allen, Zafar Rustamkulov, Mercedes López-Morales, Kevin C. Schlaufman

Abstract: We present the first panchromatic JWST transmission spectrum of an ultra-hot Jupiter, combining NIRISS and NIRSpec observations to constrain KELT-7\,b's atmospheric properties. We show evidence for charge migration in our NIRISS SOSS observation between 1--1.5~$μ$m, a wavelength range crucial to test for enhanced H$^-$ previously inferred from HST WFC3/IR G141 observations. We mitigate charge migr… ▽ More We present the first panchromatic JWST transmission spectrum of an ultra-hot Jupiter, combining NIRISS and NIRSpec observations to constrain KELT-7\,b's atmospheric properties. We show evidence for charge migration in our NIRISS SOSS observation between 1--1.5~$μ$m, a wavelength range crucial to test for enhanced H$^-$ previously inferred from HST WFC3/IR G141 observations. We mitigate charge migration by fitting the ramp after extracting 1D stellar spectra at the group level. This ``late-ramp-fit'' method accurately calculates KELT-7\,b's transmission spectrum between 1--1.5~$μ$m at higher signal-to-noise. Using the transit-derived stellar mean density during stellar property inference reveals that KELT-7 is a $640\pm100$ Myr-old, $[\text{Fe}/\text{H}]=0.46\pm0.02$ star. Combined with NIRSpec and re-reduced WFC3/UVIS G280 data, our free retrieval analysis shows strong evidence for H$_2$O, CO$_2$, and TiO among high-temperature species, but not H$^-$ or clouds. Unaccounted-for systematics may therefore bias longer-wavelength WFC3/IR G141 transit depths shallower. Our free retrieval, two equilibrium retrievals, and self-consistent grid fit all prefer a high metallicity but find discrepant C/O ratios. Agglomerated together, we constrain a super-stellar $\text{M/H}=92^{+24}_{-23}\times$~Solar and C/O~$\leq0.9$, suggesting enhanced metal accretion in the later stages of KELT-7\,b's formation. Our GCMs explain the observed lack of limb asymmetry with superrotating jet-driven efficient horizontal mixing. The stark contrast between our panchromatic analysis and prior analyses on subsets of these data demonstrates the value of broad wavelength coverage for the comprehensive study of exoplanet atmospheres. △ Less

Submitted 7 July, 2026; originally announced July 2026.

Comments: 38 pages, 20 figures, 7 tables. Submitted to AAS Journals - comments welcome!

Photochemical Production of CS2 in Temperate-to-Warm Gas Giant Exoplanet Atmospheres

Authors: Jeehyun Yang, Vighnesh Nagpal, Michael Zhang, Qiao Xue, Eliza M. -R. Kempton, Jacob L. Bean, Michael R. Line, Jonathan J. Fortney, Peter Gao, Matthew C. Nixon, Caroline Piaulet-Ghorayeb, Kevin B. Stevenson, Madison Brady, Joost P. Wardenier, Luis Welbanks, Jean-Michel Désert, Guangwei Fu, Vivien Parmentier

Abstract: Sulfur chemistry has emerged as an important probe of exoplanet atmospheres in the JWST era, although observational constraints have thus far been largely limited to SO2 and H2S in warm and hot exoplanets. Recent JWST observations have revealed CS2 in several cooler gas-giant exoplanets, yielding a new tracer of sulfur chemistry. However, the detailed chemical pathways responsible for the formatio… ▽ More Sulfur chemistry has emerged as an important probe of exoplanet atmospheres in the JWST era, although observational constraints have thus far been largely limited to SO2 and H2S in warm and hot exoplanets. Recent JWST observations have revealed CS2 in several cooler gas-giant exoplanets, yielding a new tracer of sulfur chemistry. However, the detailed chemical pathways responsible for the formation of CS2 remain poorly understood. Here, we use TOI-6894 b, a temperate gas giant with evidence for CS2, as a test case for one-dimensional photochemical kinetic-transport modeling and sensitivity analyses of CS2 chemistry. We show that CS2 is produced through coupled thermochemical and photochemical processes involving CH4 and H2S as the primary carbon and sulfur reservoirs, with S2 photolysis driving disequilibrium sulfur chemistry. Our models provide a self-consistent explanation for the observed CS2 feature in TOI-6894 b. Extending our analysis to gas giant exoplanets spanning a wide range of Teq, we find that CS2 abundance peaks in temperate to warm atmospheres (Teq ~ 500 - 700 K), and declines toward both lower and higher temperatures. This temperature dependence provides a unified framework for interpreting current CS2 observations, accounting for reported detections in temperate to warm planets and the lack of detections in colder and hotter giant exoplanets. Our results establish CS2 as a complementary probe of sulfur inventories and atmospheric metallicity in cool gas giant exoplanets △ Less

Submitted 22 June, 2026; originally announced June 2026.

Comments: 15 pages, 5 figures, Companion paper to Zhang et al. (2026), submitted concurrently to ApJL

C, N, O, S, and photochemistry in a temperate giant planet orbiting a late M dwarf

Authors: Michael Zhang, Qiao Xue, Jeehyun Yang, Vighnesh Nagpal, Michael R. Line, Guangwei Fu, Matthew C. Nixon, Jacob L. Bean, Peter Gao, Eliza M. -R. Kempton, Luis Welbanks, Edward M. Bryant, Daniel Bayliss, Madison Brady, Jean-Michel Désert, Vincent Van Eylen, Jonathan J. Fortney, Andrés Jordán, Vivien Parmentier, Caroline Piaulet-Ghorayeb, Elyar Sedaghati, Kevin B. Stevenson, Amaury H. M. J. Triaud

Abstract: We report the JWST NIRSpec/PRISM transit spectrum of TOI-6894b, an exceptional 420 K sub-Saturn that is the only known giant planet transiting a late M dwarf. Remarkably, both the light curve and the transit spectrum exhibit almost no stellar contamination. The spectrum is dominated by prominent absorption features from CH$_4$ and the photochemical product CS$_2$. For the first time in a transit s… ▽ More We report the JWST NIRSpec/PRISM transit spectrum of TOI-6894b, an exceptional 420 K sub-Saturn that is the only known giant planet transiting a late M dwarf. Remarkably, both the light curve and the transit spectrum exhibit almost no stellar contamination. The spectrum is dominated by prominent absorption features from CH$_4$ and the photochemical product CS$_2$. For the first time in a transit spectrum, NH$_3$ is visually evident, while subtler features from H$_2$O, and CO$_2$ can also be seen. We significantly improve upon state-of-the-art photochemical reaction networks, and use our new network to run radiative-convective photochemical models at different metallicities. These models show that the spectrum--in particular the size of the NH$_3$ and CO$_2$ features relative to the CH$_4$ and H$_2$O features--is most consistent with a metallicity of 3--10$\times$ solar. Using a semi-free retrieval framework that perturbs the self-consistent model's abundance and temperature profiles to fit the data, we find that the planet's C/O, N/O, and S/O ratios are broadly consistent with solar values. A grid retrieval on 1D radiative-convective photochemical equilibrium (RCPE) models reveals a similar result: $[M/H]=0.46 \pm 0.08$ and C/O=$0.69 \pm 0.06$. The planet's atmospheric metallicity, abundance ratios, and bulk metal fraction are all strikingly similar to that of Jupiter, Saturn, and other gas giant exoplanets, despite orbiting a very low-mass star. △ Less

Submitted 25 June, 2026; v1 submitted 22 June, 2026; originally announced June 2026.

Comments: Updating with far more realistic error inflation scheme in semi-free retrievals, which has the side benefit of greatly shrinking N/O, C/N, S/N, M/H posteriors

Atmospheric asymmetries in WASP-121 b revealed by rotational transits detected with JWST

Authors: Cyril Gapp, Aurélien Falco, Thomas M. Evans-Soma, David K. Sing, Shashank Dholakia, Vivien Parmentier, Jérémy Leconte, Eva-Maria Ahrer, Guangwei Fu

Abstract: Close-in exoplanets are tidally locked to their host star and thus exhibit extreme atmospheric temperature gradients. It has been theorized that the fraction of star light absorbed by such planets during transit changes as a function of orbital phase as progressively hotter or colder atmospheric gas rotates into view, but this effect has not been observed so far. Here, we show that two transits of… ▽ More Close-in exoplanets are tidally locked to their host star and thus exhibit extreme atmospheric temperature gradients. It has been theorized that the fraction of star light absorbed by such planets during transit changes as a function of orbital phase as progressively hotter or colder atmospheric gas rotates into view, but this effect has not been observed so far. Here, we show that two transits of the ultra-hot Jupiter WASP-121 b, acquired with JWST/NIRSpec and NIRISS, exhibit asymmetric transit light curves caused by the planet's rotation during transit. We observe increasing CO absorption and slightly decreasing H$_2$O absorption in the transmission spectrum, as the planet rotates. These results are indicative of a stronger longitudinal temperature gradient across the evening than across the morning terminator, consistent with higher temperatures in the eastern half than in the western half of the dayside. The observed changes of the transmission spectrum with orbital phase are in line with the temperature increase causing thermal dissociation of H$_2$O, while CO remains abundant. The observation of longitudinal gradients of atmospheric temperature and chemistry from the planet's rotational transit provides a new probe for constraining atmospheric heterogeneity using JWST beyond differences between morning and evening terminators from limb asymmetries. △ Less

Submitted 17 June, 2026; originally announced June 2026.

Comments: 33 pages, 10 figures, 2 tables, including Extended Data. Published in Nature Astronomy. Algorithms for fitting light curves used in this work are available at https://doi.org/10.5281/zenodo.20734079

F-RNG: Feed-Forward Relightable Neural Gaussians

Authors: Guangming Fu, Jiahui Fan, Jian Yang, Miloš Hašan, Beibei Wang

Abstract: Capturing relightable 3D assets from real-world objects is a widely researched problem. Several per-scene optimization-based methods, based on 3D Gaussian splatting (3DGS), support relighting; however, they usually require dense input views, and their overfitting nature makes it difficult to generalize across scenes. Unlike per-scene optimization methods, generalized feed-forward models can direct… ▽ More Capturing relightable 3D assets from real-world objects is a widely researched problem. Several per-scene optimization-based methods, based on 3D Gaussian splatting (3DGS), support relighting; however, they usually require dense input views, and their overfitting nature makes it difficult to generalize across scenes. Unlike per-scene optimization methods, generalized feed-forward models can directly reconstruct Gaussians from sparse input views. However, the resulting assets have baked-in illumination and cannot be easily used for relighting. In this paper, we present F-RNG, a feed-forward framework that directly generates relightable 3DGS assets from sparse-view inputs. Training such a model from scratch can require massive data and computing resources, and it is especially challenging to generate relightable assets in a feed-forward manner with acceptable cost. We develop F-RNG upon an existing large reconstruction model (LRM) to extract relightable representations, while also utilizing priors from an intrinsic decomposition model (IDM). Specifically, we first introduce a latent-interpolated fine-grained geometry synthesis to enhance the LRM's geometry representation. Second, we propose a prior-guided relightable appearance distillation to extract relightable neural representations by incorporating IDM priors. Finally, a universal neural renderer enables flexible and high-fidelity relighting. F-RNG requires neither re-training nor fine-tuning of the underlying LRMs, thus can automatically benefit from better LRMs and IDMs in the future. With only small networks that can be trained with affordable data and computational resources, F-RNG avoids the repetitive inference of large models under different light conditions. By comparison to the state-of-the-art LRM-based relighting method, F-RNG achieves ~25x faster relighting, as well as superior quality (~+2.0 dB). △ Less

Submitted 28 May, 2026; v1 submitted 25 May, 2026; originally announced May 2026.

End-to-End Unmixing with Material Prompts for Hyperspectral Object Tracking

Authors: Xu Han, Mohammad Aminul Islam, Lei Wang, Zekun Long, Guanmanyi Fu, Wangshu Cai, Kuldip K. Paliwal, Jun Zhou

Abstract: Hyperspectral imagery encodes rich material properties that can improve tracking robustness under appearance ambiguity, illumination change, and background clutter. However, due to the limited availability of hyperspectral video data, many existing methods adapt pretrained RGB trackers via spatial or channel fusion strategies, largely neglecting the intrinsic material information in hyperspectral… ▽ More Hyperspectral imagery encodes rich material properties that can improve tracking robustness under appearance ambiguity, illumination change, and background clutter. However, due to the limited availability of hyperspectral video data, many existing methods adapt pretrained RGB trackers via spatial or channel fusion strategies, largely neglecting the intrinsic material information in hyperspectral imagery. Moreover, the few material-aware approaches typically rely on external spectral unmixing pipelines that are decoupled from the tracking objective, limiting effective optimization of material representations for target localization. To address these limitations, we formulate hyperspectral object tracking as a joint optimization problem of material decomposition and target localization, coupling the two tasks via a weighted target-oriented unmixing loss that explicitly aligns material representations with localization accuracy. Specifically, we propose a material representation decomposition module for deep learning-based spectral unmixing with adaptive frequency decomposition. Building on the decomposed material representations, we further introduce a dual-branch wavelet-enhanced material prompt module that learns low- and high-frequency material prompts through efficient spatial-material interactions in the frequency domain. The framework is model-agnostic and can be seamlessly generalized to different unmixing backbones. Extensive experiments on standard hyperspectral tracking benchmarks demonstrate state-of-the-art performance and validate the effectiveness of the proposed end-to-end material-aware tracking framework. Code is available at https://github.com/han030927/E2EMPT. △ Less

Submitted 19 May, 2026; originally announced May 2026.

HyperVision: A Channel-Adaptive Ground-Based Hyperspectral Vision Pre-trained Backbone

Authors: Guanyiman Fu, Jingtao Li, Zihang Cheng, Zhuanfeng Li, Diqi Chen, Yan Xu, Xiangyu Liu, Fengchao Xiong, Jianfeng Lu, Chengrong Chen, Jun Zhou

Abstract: While hyperspectral imaging provides rich spatial-spectral information across hundreds of narrow wavelength bands for precise material identification, ground-based hyperspectral pre-trained backbones remain absent, constrained by varying spectral configurations across sensors, the scarcity and inconsistency of labels, and the limited scale and scene diversity of existing datasets. To address these… ▽ More While hyperspectral imaging provides rich spatial-spectral information across hundreds of narrow wavelength bands for precise material identification, ground-based hyperspectral pre-trained backbones remain absent, constrained by varying spectral configurations across sensors, the scarcity and inconsistency of labels, and the limited scale and scene diversity of existing datasets. To address these challenges and enable universal perception, we propose HyperVision, the first ground-based hyperspectral pre-trained backbone. First, to handle varying spectral configurations, HyperVision adopts a channel-adaptive dynamic embedding mechanism to map heterogeneous inputs into a unified token space. Second, we develop an unsupervised representation learning framework. Specifically, to address label scarcity and inconsistency, a multi-source pseudo-labeling method is introduced to fuse spatial structures from SAM2 and fine-grained spectral material information from HyperFree. Furthermore, to enrich scene diversity and compensate for limited dataset scale, a cross-modal knowledge distillation mechanism is utilized to transfer rich semantic representations from a pre-trained RGB vision model to our backbone. Pre-trained on a collection of 15k images from 26 diverse ground-based datasets, HyperVision demonstrates exceptional generalization. Requiring only efficient head-only adaptation without adjusting backbone parameters, it achieves state-of-the-art performance compared to task-specific methods across three downstream tasks under varying sensor configurations, yielding up to a 16.3% relative improvement in hyperspectral semantic segmentation $\mathrm{Acc}_{\mathrm{M}}$, a 2.1% relative gain in object tracking AUC, and a 35.5% reduction in salient object detection MAE. The source code and pre-trained model will be publicly available on https://github.com/lronkitty/HyperVision . △ Less

Submitted 28 May, 2026; v1 submitted 17 May, 2026; originally announced May 2026.

Intrinsic Wasserstein Rates for Score-Based Generative Models on Smooth Manifolds

Authors: Guoji Fu, Taiji Suzuki, Wee Sun Lee, Atsushi Nitanda

Abstract: Score-based generative models are trained in high-dimensional ambient spaces, yet many data distributions are supported on low-dimensional nonlinear structures. We prove that, for compact $d$-dimensional smooth manifolds $\mathcal{M} \subset [0,1]^D$ with $d > 2$ and $β$-Hölder densities strictly positive on $\mathcal{M}$, a variance-preserving SGM estimator attains the intrinsic Wasserstein--1 sa… ▽ More Score-based generative models are trained in high-dimensional ambient spaces, yet many data distributions are supported on low-dimensional nonlinear structures. We prove that, for compact $d$-dimensional smooth manifolds $\mathcal{M} \subset [0,1]^D$ with $d > 2$ and $β$-Hölder densities strictly positive on $\mathcal{M}$, a variance-preserving SGM estimator attains the intrinsic Wasserstein--1 sample exponent $\tilde{\mathcal{O}}(D^{\mathcal{O}_β(d)}n^{-(β+1)/(d+2β)})$, up to logarithmic factors and explicit geometry and density factors. The full nonasymptotic bound explicitly isolates the finite-order geometry envelope, Hölder radius, density lower bound, ambient dependence, and finite-order correction terms. The analysis separates score approximation into a large-noise tangent-cell regime and a small-noise projection-centered, de-Gaussianized Laplace regime. The key technical ingredient is a ReLU implementation of nearest-projection coordinates via finite intrinsic anchors and Gauss--Newton iterations, rather than approximating the manifold projection as a black-box high-dimensional smooth map. Consequently, for families with polynomially controlled geometry and density lower bounds, the constructed score-network parameters have polynomial ambient dependence. △ Less

Submitted 15 May, 2026; originally announced May 2026.

Implicit spatial-frequency fusion of hyperspectral and lidar data via kolmogorov-arnold networks

Authors: Zekun Long, Judy X. Yang, Jing Wang, Ali Zia, Guanyiman Fu, Jun Zhou

Abstract: Hyperspectral image (HSI) classification is challenging in complex scenes due to spectral ambiguity, spatial heterogeneity, and the strong coupling between material properties and geometric structures. Although LiDAR provides complementary elevation information, most HSI-LiDAR fusion methods rely on CNNs or MLPs with fixed activation functions and linear weights. These methods struggle to model st… ▽ More Hyperspectral image (HSI) classification is challenging in complex scenes due to spectral ambiguity, spatial heterogeneity, and the strong coupling between material properties and geometric structures. Although LiDAR provides complementary elevation information, most HSI-LiDAR fusion methods rely on CNNs or MLPs with fixed activation functions and linear weights. These methods struggle to model structural discontinuities in LiDAR data, intricate spectral features of HSI, and their interactions. In addition, fusion of the two modalities in both spatial and frequency domains with LiDAR guidance remains underexplored. To address these issues, we propose the Implicit Frequency-Geometry Fusion Network (IFGNet), which leverages Kolmogorov-Arnold Networks (KANs) with learnable spline-based functions to adaptively capture highly nonlinear relationships between hyperspectral and LiDAR features. Furthermore, IFGNet introduces a LiDAR-guided implicit aggregation module in both spatial and frequency domains, enhancing geometry-aware spatial representations while capturing global structural patterns. Experiments on the Houston 2013 and MUUFL benchmarks demonstrate that IFGNet consistently outperforms existing fusion methods in overall accuracy, average accuracy, and Cohen's Kappa, while maintaining an efficient architecture. △ Less

Submitted 13 May, 2026; originally announced May 2026.

Comments: 6 pages, 1 figure, conference

SI-Diff: A Framework for Learning Search and High-Precision Insertion with a Force-Domain Diffusion Policy

Authors: Yibo Liu, Stanko Oparnica, Simon Shewchun-Jakaitis, Guoyi Fu, Jie Wang, Jun Yang, Anand Jagannathan, Tony Hong-Yau Lo

Abstract: Contact-rich assembly is fundamental in robotics but poses significant challenges due to uncertainties in relative poses, such as misalignments and small clearances in peg-in-hole tasks. Existing approaches typically address search and high-precision insertion separately, because these tasks involve distinct action patterns. However, supporting both tasks within a single model, without switching m… ▽ More Contact-rich assembly is fundamental in robotics but poses significant challenges due to uncertainties in relative poses, such as misalignments and small clearances in peg-in-hole tasks. Existing approaches typically address search and high-precision insertion separately, because these tasks involve distinct action patterns. However, supporting both tasks within a single model, without switching models or weights, is desirable for intelligent assembly systems. In this work, we propose SI-Diff, a framework that learns both search and high-precision insertion through a force-domain diffusion policy. To this end, we introduce a new mode-conditioning mechanism that enables the policy to capture distinct action behaviors under a single framework. Moreover, we develop a new search teacher policy that can generate diverse trajectories. By training on successful and efficient demonstrations provided by the teacher policy, the model learns the mapping from tactile and end-effector velocity observations to effective action behaviors. We conduct thorough experiments to show that SI-Diff extends the tolerance to x-y misalignments from 2 mm to 5 mm compared to the state-of-the-art baseline, TacDiffusion, while also demonstrating strong zero-shot transferability to unseen shapes. △ Less

Submitted 12 May, 2026; originally announced May 2026.

Comments: 9 pages, 8 figures

Assessing EMRI Detectability of the Rotating Quantum Oppenheimer-Snyder Black Hole

Authors: Dan Zhang, Shulan Li, Guoyang Fu, Jian-Pin Wu

Abstract: This letter presents an assessment of quantum gravity effects on extreme-mass-ratio inspirals (EMRIs) for the rotating quantum Oppenheimer-Snyder (qOS) black hole. Employing the adiabatic evolution, we compute the gravitational wave (GW) dephasing, which quantifies the cumulative phase shift induced by the quantum correction α . We further generate the augmented analytic kludge (AAK) waveform and… ▽ More This letter presents an assessment of quantum gravity effects on extreme-mass-ratio inspirals (EMRIs) for the rotating quantum Oppenheimer-Snyder (qOS) black hole. Employing the adiabatic evolution, we compute the gravitational wave (GW) dephasing, which quantifies the cumulative phase shift induced by the quantum correction α . We further generate the augmented analytic kludge (AAK) waveform and investigate the faithfulness between the waveforms with and without the quantum parameter α for different values of a. Our results reveal that the quantum gravity effect induces detectable imprints in LISA, while the presence of rotation suppresses these signatures. This suggests that rotational degrees of freedom must be carefully accounted for when probing quantum gravity with EMRI observations. △ Less

Submitted 25 April, 2026; originally announced April 2026.

Positivity-Preserving and Entropy-Stable Oscillation-Eliminating DGSEM for the Compressible Euler Equations on Curvilinear Meshes with Adaptive Mesh Refinement

Authors: Jieling Yang, Guosheng Fu

Abstract: We extend the entropy-stable oscillation-eliminating discontinuous Galerkin spectral element method (ES-OEDG) on curvilinear meshes to adaptive mesh refinement (AMR) grids with nonconforming interfaces. The formulation targets two-dimensional curvilinear quadrilateral meshes under a 2:1 refinement constraint, allowing a single level of hanging nodes. Elementwise volume discretization and geometric… ▽ More We extend the entropy-stable oscillation-eliminating discontinuous Galerkin spectral element method (ES-OEDG) on curvilinear meshes to adaptive mesh refinement (AMR) grids with nonconforming interfaces. The formulation targets two-dimensional curvilinear quadrilateral meshes under a 2:1 refinement constraint, allowing a single level of hanging nodes. Elementwise volume discretization and geometric mapping are retained, while oscillation elimination and interface coupling are adapted for nonconforming interfaces. A central contribution is the design and analysis of numerical fluxes for such interfaces. We construct an entropy-stable flux that ensures global conservation and a semi-discrete entropy inequality. However, for polynomial degree N >= 2, negative entries in nonconforming interpolation operators lead to loss of formal high-order consistency. To address this, we propose a mortar-based flux that preserves high-order accuracy by interpolating at the solution level and evaluating standard two-point fluxes on fine-side mortars, at the cost of losing provable entropy stability. We also extend the Zhang--Shu positivity-preserving framework to curvilinear AMR meshes. Under forward Euler time stepping and a suitable CFL condition, the scheme using either flux preserves positivity of cell-average density and pressure. Combined with the Zhang--Shu limiter, this yields a fully discrete scheme maintaining admissibility at all nodal points. We further incorporate shock-indicator-based AMR and a conservative, positivity-preserving data transfer procedure between successive meshes, resulting in a robust and efficient algorithm. Numerical experiments on Cartesian and curvilinear AMR grids confirm high-order accuracy and robustness. △ Less

Submitted 23 April, 2026; originally announced April 2026.

A Differentiable Physical Framework for Goal-Driven Spin-State Engineering in Magnetic Resonance Spectroscopy

Authors: Gaocheng Fu, Shiji Zhang, Kai Huang, Xue Yang, Huilin Zhang, Daxiu Wei, Ye-Feng Yao

Abstract: Magnetic Resonance Spectroscopy (MRS) offers a unique non-invasive window into metabolic processes, yet its potential remains strictly constrained by severe spectral congestion and intrinsic insensitivity. Traditional pulse sequence design, tethered to human intuition, predominantly targets simple quantum states, thereby overlooking the vast majority of the exponentially scaling operator space whi… ▽ More Magnetic Resonance Spectroscopy (MRS) offers a unique non-invasive window into metabolic processes, yet its potential remains strictly constrained by severe spectral congestion and intrinsic insensitivity. Traditional pulse sequence design, tethered to human intuition, predominantly targets simple quantum states, thereby overlooking the vast majority of the exponentially scaling operator space which consists of complex spin superpositions. Here, we introduce a spectrum-driven, end-to-end differentiable physical framework that transcends these heuristic limitations. By integrating physical laws with automatic differentiation algorithm, our approach directly navigates the high-dimensional spin dynamics space, bypassing the intractable inverse problem of state preparation. This enables the discovery of non-intuitive, complex mixed states that simultaneously satisfy the dual objectives of selective excitation and interferometric signal enhancement. We validate this paradigm by achieving the robust separation of Glutamate and Glutamine, which is a longstanding neuroimaging challenge, in the human brain at 3T, demonstrating spectral fidelity superior to conventional methods. By unlocking the "dark" informational content of nuclear spin ensembles, our work establishes a generalizable paradigm for goal-driven quantum state engineering in magnetic resonance and beyond. △ Less

Submitted 2 April, 2026; originally announced April 2026.

From molecular dynamics to kinetic models: data-driven generalized collision operators in 1D3V plasmas

Authors: Yue Zhao, Guosheng Fu, Huan Lei

Abstract: We present a data-driven approach for constructing generalized collisional kinetic models for inhomogeneous plasmas in one-dimensional physical space and three-dimensional velocity space (1D-3V). The collision operator is directly learned from micro-scale molecular dynamics (MD) and accurately accounts for the unresolved particle interactions over a broad range of plasma conditions. Unlike the sta… ▽ More We present a data-driven approach for constructing generalized collisional kinetic models for inhomogeneous plasmas in one-dimensional physical space and three-dimensional velocity space (1D-3V). The collision operator is directly learned from micro-scale molecular dynamics (MD) and accurately accounts for the unresolved particle interactions over a broad range of plasma conditions. Unlike the standard Landau operator, the present operator takes an anisotropic, non-stationary form that captures the heterogeneous collisional energy transfer arising from the many-body interactions, which is crucial for plasma kinetics beyond the weakly coupled regime. Efficient numerical evaluation is achieved through a low-rank tensor representation with $O(N \log N)$ computational complexity. The constructed kinetic equation strictly preserves conservation laws and physical constraints and therefore, enables us to develop an explicit second-order, energy-conserving scheme that ensures fully discrete conservation of mass and total energy. Numerical results demonstrate that the present model accurately predicts both transport coefficients and several 1D-3V kinetic processes compared with MD simulations across a broad range of densities and temperatures in spatially inhomogeneous settings. This work provides a systematic pathway for bridging micro-scale MD and inhomogeneous plasma kinetic descriptions where empirical models show limitation. △ Less

Submitted 29 March, 2026; originally announced March 2026.

Sharp Exponent of Stable Standing Waves for the Perturbated Hartree Equation

Authors: Guoyi Fu, Shanshan Fu, Xiaoguang Li, Jian Zhang, Shihui Zhu

Abstract: This paper is concerned with the stability of standing waves for the mass-critical Hartree equation with a focusing perturbation by the variational method. The profile decomposition theory is employed to prove the attainability of the cross constrained variational problem, and then the comparison of two cross constrained variational problems is derived. The sharp criteria of blowup, the orbital st… ▽ More This paper is concerned with the stability of standing waves for the mass-critical Hartree equation with a focusing perturbation by the variational method. The profile decomposition theory is employed to prove the attainability of the cross constrained variational problem, and then the comparison of two cross constrained variational problems is derived. The sharp criteria of blowup, the orbital stability, and strong instability of standing waves without any frequency constraint are obtained. This improves the cross constrained variational argument proposed by Zhang (2005). △ Less

Submitted 25 March, 2026; originally announced March 2026.

MSC Class: 35Q55

Measurement of the cosmic muon flux at the Stawell Underground Physics Laboratory

Authors: G. Fu, M. Mews, F. Scutti, P. Urquijo, E. Barberio, V. Bashu, L. J. Bignell, I. Bolognino, A. Cools, F. Dastgiri, A. R. Duffy, L. Einfalt, M. Froehlich, T. Fruth, M. Gerathy, M. Hancock, R. James, S. Kapoor, S. Krishnan, G. J. Lane, K. T. Leaver, D. Marcantonio, P. McGee, J. McKenzie, L. McKie , et al. (13 additional authors not shown)

Abstract: We report the first measurement of the underground cosmic muon flux at the Stawell Underground Physics Laboratory. The measurement uses eight EJ200 plastic scintillator panels, equipped with Hamamatsu R13089 PMT pairs at the ends, which are the primary components of the muon veto system for the upcoming SABRE South experiment. The measured muon flux is f = (6.33 +/- 0.04_stat +/- 0.35_sys) x 10^{-… ▽ More We report the first measurement of the underground cosmic muon flux at the Stawell Underground Physics Laboratory. The measurement uses eight EJ200 plastic scintillator panels, equipped with Hamamatsu R13089 PMT pairs at the ends, which are the primary components of the muon veto system for the upcoming SABRE South experiment. The measured muon flux is f = (6.33 +/- 0.04_stat +/- 0.35_sys) x 10^{-8} [s^{-1} cm^{-2}]. This measurement is in excellent agreement with simulations, with a relative uncertainty an order of magnitude smaller than the modelling uncertainty. △ Less

Submitted 12 March, 2026; originally announced March 2026.

Comments: 25 pages, 21 figures

WS-Net: Weak-Signal Representation Learning and Gated Abundance Reconstruction for Hyperspectral Unmixing via State-Space and Weak Signal Attention Fusion

Authors: Zekun Long, Ali Zia, Guanyiman Fu, Vivien Rolland, Jun Zhou

Abstract: Weak spectral responses in hyperspectral images are often obscured by dominant endmembers and sensor noise, resulting in inaccurate abundance estimation. This paper introduces WS-Net, a deep unmixing framework specifically designed to address weak-signal collapse through state-space modelling and Weak Signal Attention fusion. The network features a multi-resolution wavelet-fused encoder that captu… ▽ More Weak spectral responses in hyperspectral images are often obscured by dominant endmembers and sensor noise, resulting in inaccurate abundance estimation. This paper introduces WS-Net, a deep unmixing framework specifically designed to address weak-signal collapse through state-space modelling and Weak Signal Attention fusion. The network features a multi-resolution wavelet-fused encoder that captures both high-frequency discontinuities and smooth spectral variations with a hybrid backbone that integrates a Mamba state-space branch for efficient long-range dependency modelling. It also incorporates a Weak Signal Attention branch that selectively enhances low-similarity spectral cues. A learnable gating mechanism adaptively fuses both representations, while the decoder leverages KL-divergence-based regularisation to enforce separability between dominant and weak endmembers. Experiments on one simulated and two real datasets (synthetic dataset, Samson, and Apex) demonstrate consistent improvements over six state-of-the-art baselines, achieving up to 55% and 63% reductions in RMSE and SAD, respectively. The framework maintains stable accuracy under low-SNR conditions, particularly for weak endmembers, establishing WS-Net as a robust and computationally efficient benchmark for weak-signal hyperspectral unmixing. △ Less

Submitted 9 March, 2026; originally announced March 2026.

RelaxFlow: Text-Driven Amodal 3D Generation

Authors: Jiayin Zhu, Guoji Fu, Xiaolu Liu, Qiyuan He, Yicong Li, Angela Yao

Abstract: Image-to-3D generation faces inherent semantic ambiguity under occlusion, where partial observation alone is often insufficient to determine object category. In this work, we formalize text-driven amodal 3D generation, where text prompts steer the completion of unseen regions while strictly preserving input observation. Crucially, we identify that these objectives demand distinct control granulari… ▽ More Image-to-3D generation faces inherent semantic ambiguity under occlusion, where partial observation alone is often insufficient to determine object category. In this work, we formalize text-driven amodal 3D generation, where text prompts steer the completion of unseen regions while strictly preserving input observation. Crucially, we identify that these objectives demand distinct control granularities: rigid control for the observation versus relaxed structural control for the prompt. To this end, we propose RelaxFlow, a training-free dual-branch framework that decouples control granularity via a Multi-Prior Consensus Module and a Relaxation Mechanism. Theoretically, we prove that our relaxation is equivalent to applying a low-pass filter on the generative vector field, which suppresses high-frequency instance details to isolate geometric structure that accommodates the observation. To facilitate evaluation, we introduce two diagnostic benchmarks, ExtremeOcc-3D and AmbiSem-3D. Extensive experiments demonstrate that RelaxFlow successfully steers the generation of unseen regions to match the prompt intent without compromising visual fidelity. △ Less

Submitted 27 May, 2026; v1 submitted 5 March, 2026; originally announced March 2026.

Comments: Accepted as a spotlight presentation at ICML 2026. Code: https://github.com/viridityzhu/RelaxFlow

Dielectric Barrier Corona Discharge Anomaly by Ionic Wind under Unipolar Voltage Excitation

Authors: Gan Fu

Abstract: An anomalous back discharge movement phenomenon is induced by a set of dielectric barrier corona discharges (DBCD) at unipolar half-sine voltage waveforms, where the back discharge has a time delay that relates to the applied voltage level. An ionic wind model is employed to analyze the physical behavior. Theoretical explanation and quantitative analysis are presented in this study based on abunda… ▽ More An anomalous back discharge movement phenomenon is induced by a set of dielectric barrier corona discharges (DBCD) at unipolar half-sine voltage waveforms, where the back discharge has a time delay that relates to the applied voltage level. An ionic wind model is employed to analyze the physical behavior. Theoretical explanation and quantitative analysis are presented in this study based on abundant experimental results of 5 typical insulating materials and a FEP insulating cable. A numerical model is derived, which indicates that the back discharge can be activated under a relatively low potential voltage level in this study. The results highlight that the back discharge movement phenomenon behaves distinctly under half-sine voltage with negative polarity, yielding a significantly different partial discharge (PD) pattern with positive polarity. Besides, PD amplitude dependent on dielectric thickness is demonstrated by plotting in phase resolved partial discharge (PRPD) pattern. Furthermore, comparative experiments are conducted with respect to the variation of air gap length and dielectric geometry, manifesting different influences on PD amplitude. △ Less

Submitted 4 March, 2026; originally announced March 2026.

M-Gaussian: An Magnetic Gaussian Framework for Efficient Multi-Stack MRI Reconstruction

Authors: Kangyuan Zheng, Xuan Cai, Jiangqi Wang, Guixing Fu, Zhuoshuo Li, Yazhou Chen, Xinting Ge, Liangqiong Qu, Mengting Liu

Abstract: Magnetic Resonance Imaging (MRI) is a crucial non-invasive imaging modality. In routine clinical practice, multi-stack thick-slice acquisitions are widely used to reduce scan time and motion sensitivity, particularly in challenging scenarios such as fetal brain imaging. However, the resulting severe through-plane anisotropy compromises volumetric analysis and downstream quantitative assessment, ne… ▽ More Magnetic Resonance Imaging (MRI) is a crucial non-invasive imaging modality. In routine clinical practice, multi-stack thick-slice acquisitions are widely used to reduce scan time and motion sensitivity, particularly in challenging scenarios such as fetal brain imaging. However, the resulting severe through-plane anisotropy compromises volumetric analysis and downstream quantitative assessment, necessitating robust reconstruction of isotropic high-resolution volumes. Implicit neural representation methods, while achieving high quality, suffer from computational inefficiency due to complex network structures. We present M-Gaussian, adapting 3D Gaussian Splatting to MRI reconstruction. Our contributions include: (1) Magnetic Gaussian primitives with physics-consistent volumetric rendering, (2) neural residual field for high-frequency detail refinement, and (3) multi-resolution progressive training. Our method achieves an optimal balance between quality and speed. On the FeTA dataset, M-Gaussian achieves 40.31 dB PSNR while being 14 times faster, representing the first successful adaptation of 3D Gaussian Splatting to multi-stack MRI reconstruction. △ Less

Submitted 24 February, 2026; originally announced March 2026.

Comments: 15 pages, 9 figures

SceneTransporter: Optimal Transport-Guided Compositional Latent Diffusion for Single-Image Structured 3D Scene Generation

Authors: Ling Wang, Hao-Xiang Guo, Xinzhou Wang, Fuchun Sun, Kai Sun, Pengkun Liu, Hang Xiao, Zhong Wang, Guangyuan Fu, Eric Li, Yang Liu, Yikai Wang

Abstract: We introduce SceneTransporter, an end-to-end framework for structured 3D scene generation from a single image. While existing methods generate part-level 3D objects, they often fail to organize these parts into distinct instances in open-world scenes. Through a debiased clustering probe, we reveal a critical insight: this failure stems from the lack of structural constraints within the model's int… ▽ More We introduce SceneTransporter, an end-to-end framework for structured 3D scene generation from a single image. While existing methods generate part-level 3D objects, they often fail to organize these parts into distinct instances in open-world scenes. Through a debiased clustering probe, we reveal a critical insight: this failure stems from the lack of structural constraints within the model's internal assignment mechanism. Based on this finding, we reframe the task of structured 3D scene generation as a global correlation assignment problem. To solve this, SceneTransporter formulates and solves an entropic Optimal Transport (OT) objective within the denoising loop of the compositional DiT model. This formulation imposes two powerful structural constraints. First, the resulting transport plan gates cross-attention to enforce an exclusive, one-to-one routing of image patches to part-level 3D latents, preventing entanglement. Second, the competitive nature of the transport encourages the grouping of similar patches, a process that is further regularized by an edge-based cost, to form coherent objects and prevent fragmentation. Extensive experiments show that SceneTransporter outperforms existing methods on open-world scene generation, significantly improving instance-level coherence and geometric fidelity. Code and models will be publicly available at https://2019epwl.github.io/SceneTransporter/. △ Less

Submitted 26 February, 2026; originally announced February 2026.

Comments: published at iclr 2026

Stochastic Control Problems with Infinite Horizon and Regime Switching Arising in Optimal Liquidation with Semimartingale Strategies

Authors: Xinman Cheng, Guanxing Fu, Xiaonyu Xia

Abstract: We study an optimal control problem on infinite time horizon with semimartingale strategies, random coefficients and regime switching. The value function and the optimal strategy can be characterized in terms of three systems of backward stochastic differential equations (BSDEs) with infinite horizon. One of them is a system of linear BSDEs with unbounded coefficients and infinite horizon, which s… ▽ More We study an optimal control problem on infinite time horizon with semimartingale strategies, random coefficients and regime switching. The value function and the optimal strategy can be characterized in terms of three systems of backward stochastic differential equations (BSDEs) with infinite horizon. One of them is a system of linear BSDEs with unbounded coefficients and infinite horizon, which seems to be new in literature. We establish the existence of the solutions to these BSDEs by BMO analysis and comparison theorem for multi-dimensional BSDEs. Next, we establish that the optimal control problem is well posed, in the sense that the value function is finite and the optimal strategy-when it exists-is unique. This is achieved by reformulating the cost functional as the sum of a quadratic functional and the candidate value function. The reformulation crucially relies on the well-established well-posedness results for systems of BSDEs. Finally, under additional assumptions, we obtain the unique optimal strategy. △ Less

Submitted 26 February, 2026; v1 submitted 24 February, 2026; originally announced February 2026.

MagHeart: Exploring Playful Avatar Co-Creation and Shared Heartbeats for Icebreaking in Hybrid Meetings

Authors: Black Sun, Haiyang Xu, Ge Kacy Fu, Liyue Da, Eve Hoggan

Abstract: Hybrid meetings often begin with social awkwardness and asymmetric participation, particularly for remote attendees who lack access to informal, co-present interaction. We present MagHeart, a multimodal system that explores symmetric icebreaking in hybrid meetings through playful LEGO-based avatar co-creation and a tangible magnetic device that represents a remote participant's heartbeat as an amb… ▽ More Hybrid meetings often begin with social awkwardness and asymmetric participation, particularly for remote attendees who lack access to informal, co-present interaction. We present MagHeart, a multimodal system that explores symmetric icebreaking in hybrid meetings through playful LEGO-based avatar co-creation and a tangible magnetic device that represents a remote participant's heartbeat as an ambient presence cue. By combining creative co-creation with abstract bio-feedback, MagHeart rethinks how remote participants can become materially and perceptually present during meeting openings. We report findings from a scenario-based exploratory study combining quantitative and qualitative data, examining participants' anticipated engagement, perceived social presence, and future-use intentions from both co-located and remote perspectives. Our results highlight opportunities for playful, embodied icebreakers to support early hybrid interaction, while also surfacing tensions around privacy, distraction, and contextual appropriateness. This work contributes design insights and open questions for future hybrid meeting tools that balance playfulness, embodiment, and social sensitivity. △ Less

Submitted 25 February, 2026; v1 submitted 20 February, 2026; originally announced February 2026.

An entropy-stable oscillation-eliminating dgsem for the euler equations on curvilinear meshes

Authors: Jielin Yang, Guosheng Fu

Abstract: We develop an entropy-stable high-order numerical method for the two-dimensional compressible Euler equations on general curvilinear meshes. The proposed approach is based on a nodal discontinuous Galerkin spectral element method (DGSEM) that satisfies the summation-by-parts (SBP) property. At the semidiscrete level, entropy stability is established through the SBP structure and the discrete metri… ▽ More We develop an entropy-stable high-order numerical method for the two-dimensional compressible Euler equations on general curvilinear meshes. The proposed approach is based on a nodal discontinuous Galerkin spectral element method (DGSEM) that satisfies the summation-by-parts (SBP) property. At the semidiscrete level, entropy stability is established through the SBP structure and the discrete metric identities associated with curvilinear coordinate mappings. By incorporating entropy-stable numerical fluxes at element interfaces, a global discrete entropy inequality is obtained. To further control nonphysical oscillations near strong discontinuities, the entropy-stable DG formulation is combined with a modified oscillation-eliminating discontinuous Galerkin (OEDG) method, which was originally proposed in [59]. We observe that the zero-order damping coefficient in the original OEDG method naturally serves as an effective shock indicator, which enables localization of the oscillation control mechanism and significantly reduces computational cost. Moreover, while the original OEDG formulation relies on local orthogonal modal bases and is primarily restricted to simplicial meshes, we reformulate the OE procedure using projection operators, allowing for a systematic extension to general curvilinear meshes. The resulting method preserves conservation and entropy stability while effectively suppressing spurious oscillations. A series of challenging numerical experiments is presented to demonstrate the accuracy, robustness, and effectiveness of the proposed entropy-stable OEDG method on both Cartesian and curvilinear meshes. △ Less

Submitted 17 February, 2026; originally announced February 2026.

The proximal Galerkin method for non-symmetric variational inequalities

Authors: Guosheng Fu, Brendan Keith, Dohyun Kim, Rami Masri, Will Pazner

Abstract: We introduce the proximal Galerkin (PG) method for non-symmetric variational inequalities. The proposed approach is asymptotically mesh-independent and yields constraint-preserving approximations. We present both a conforming PG formulation and a hybrid mixed first-order system variant (FOSPG). We establish optimal a priori error estimates for each variant, which are verified numerically. We concl… ▽ More We introduce the proximal Galerkin (PG) method for non-symmetric variational inequalities. The proposed approach is asymptotically mesh-independent and yields constraint-preserving approximations. We present both a conforming PG formulation and a hybrid mixed first-order system variant (FOSPG). We establish optimal a priori error estimates for each variant, which are verified numerically. We conclude by applying the method to American option pricing, free boundary problems in porous media, advection-diffusion with a semipermeable boundary, and the enforcement of discrete maximum principles. △ Less

Submitted 16 February, 2026; originally announced February 2026.

MSC Class: 35J86; 35R35; 49J40; 65K15; 65N30

Quantum criticality and mixed-state entanglement in holographic superconductor--insulator transitions

Authors: Zhe Yang, Fang-Jing Cheng, Guoyang Fu, Yi Ling, Peng Liu, Jian-Pin Wu

Abstract: We study quantum criticality in a holographic Einstein--Maxwell--Dilaton--Axion (EMDA) p-wave superconductor exhibiting a superconductor--insulator transition (SIT). By tracking the superconducting energy gap, we show that approaching the quantum critical point (QCP) closes the gap and induces incipient insulating features, indicating that enhanced quantum fluctuations suppress superconducting ord… ▽ More We study quantum criticality in a holographic Einstein--Maxwell--Dilaton--Axion (EMDA) p-wave superconductor exhibiting a superconductor--insulator transition (SIT). By tracking the superconducting energy gap, we show that approaching the quantum critical point (QCP) closes the gap and induces incipient insulating features, indicating that enhanced quantum fluctuations suppress superconducting order and trigger the SIT. We suggest that this behavior occurs only when the condensate orientation is aligned with the direction of translational symmetry breaking. To probe the transition, we employ two holographic indicators: holographic entanglement entropy (HEE) and the entanglement wedge cross-section (EWCS), the latter being a mixed-state entanglement measure. In contrast to HEE, which for sufficiently large configuration is dominated by the thermal entropy and is therefore largely insensitive to entanglement along the temperature direction, EWCS displays pronounced critical scaling and provides a robust diagnostic of the quantum phase transition (QPT). We attribute this contrast to the fact that HEE at large scales is controlled by the infrared (IR) geometry, whereas EWCS is governed by deformations of the entire bulk. Our results establish EWCS as a robust probe of holographic quantum criticality in mixed states. △ Less

Submitted 15 May, 2026; v1 submitted 15 February, 2026; originally announced February 2026.

Probing Quantum Gravity effects with Extreme Mass Ratio Inspirals around Rotating Hayward Black Holes

Authors: Dan Zhang, Chao Zhang, Qiyuan Pan, Guoyang Fu, Jian-Pin Wu

Abstract: We investigate extreme mass-ratio inspirals (EMRIs) around a rotating Hayward black hole to assess the detectability of signatures arising from quantum gravity.The quantum parameter $α_0$, which encodes deviations from general relativity (GR), introduces extra correction terms in both the orbital frequency and the fluxes. Our results show that after one year of accumulated observation, these corre… ▽ More We investigate extreme mass-ratio inspirals (EMRIs) around a rotating Hayward black hole to assess the detectability of signatures arising from quantum gravity.The quantum parameter $α_0$, which encodes deviations from general relativity (GR), introduces extra correction terms in both the orbital frequency and the fluxes. Our results show that after one year of accumulated observation, these corrections induce a detectable dephasing in the EMRI waveform. Using the modified orbital evolution driven by $α_0$, we generate waveforms via the augmented analytic kludge (AAK) model implemented in the \texttt{FastEMRIWaveforms} package. Furthermore, we utilize the time-delay interferometry (TDI) to suppress the laser noise and phase fluctuations induced by spacecraft motion, and then employ the Fisher information matrix (FIM) to test the sensitivity of LISA in detecting deviations from GR. Our results demonstrate the potential of LISA to probe quantum-gravity effects through high-precision observations of EMRIs. △ Less

Submitted 7 February, 2026; originally announced February 2026.

Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation

Authors: Jiahao Nie, Guanqiao Fu, Wenbin An, Yap-Peng Tan, Alex C. Kot, Shijian Lu

Abstract: Cross-Domain Few-Shot Segmentation aims to segment categories in data-scarce domains conditioned on a few exemplars. Typical methods first establish few-shot capability in a large-scale source domain and then adapt it to target domains. However, due to the limited quantity and diversity of target samples, existing methods still exhibit constrained performance. Moreover, the source-trained model's… ▽ More Cross-Domain Few-Shot Segmentation aims to segment categories in data-scarce domains conditioned on a few exemplars. Typical methods first establish few-shot capability in a large-scale source domain and then adapt it to target domains. However, due to the limited quantity and diversity of target samples, existing methods still exhibit constrained performance. Moreover, the source-trained model's initially weak few-shot capability in target domains, coupled with substantial domain gaps, severely hinders the effective utilization of target samples and further impedes adaptation. To this end, we propose Multi-view Progressive Adaptation, which progressively adapts few-shot capability to target domains from both data and strategy perspectives. (i) From the data perspective, we introduce Hybrid Progressive Augmentation, which progressively generates more diverse and complex views through cumulative strong augmentations, thereby creating increasingly challenging learning scenarios. (ii) From the strategy perspective, we design Dual-chain Multi-view Prediction, which fully leverages these progressively complex views through sequential and parallel learning paths under extensive supervision. By jointly enforcing prediction consistency across diverse and complex views, MPA achieves both robust and accurate adaptation to target domains. Extensive experiments demonstrate that MPA effectively adapts few-shot capability to target domains, outperforming state-of-the-art methods by a large margin (+7.0%). △ Less

Submitted 31 May, 2026; v1 submitted 4 February, 2026; originally announced February 2026.

Comments: Accepted by CVPR 2026

Accelerating Scientific Research with Gemini: Case Studies and Common Techniques

Authors: David P. Woodruff, Vincent Cohen-Addad, Lalit Jain, Jieming Mao, Song Zuo, MohammadHossein Bateni, Simina Branzei, Michael P. Brenner, Lin Chen, Ying Feng, Lance Fortnow, Gang Fu, Ziyi Guan, Zahra Hadizadeh, Mohammad T. Hajiaghayi, Mahdi JafariRaviz, Adel Javanmard, Karthik C. S., Ken-ichi Kawarabayashi, Ravi Kumar, Silvio Lattanzi, Euiwoong Lee, Yi Li, Ioannis Panageas, Dimitris Paparas , et al. (11 additional authors not shown)

Abstract: Recent advances in large language models (LLMs) have opened new avenues for accelerating scientific research. While models are increasingly capable of assisting with routine tasks, their ability to contribute to novel, expert-level mathematical discovery is less understood. We present a collection of case studies demonstrating how researchers have successfully collaborated with advanced AI models,… ▽ More Recent advances in large language models (LLMs) have opened new avenues for accelerating scientific research. While models are increasingly capable of assisting with routine tasks, their ability to contribute to novel, expert-level mathematical discovery is less understood. We present a collection of case studies demonstrating how researchers have successfully collaborated with advanced AI models, specifically Google's Gemini-based models (in particular Gemini Deep Think and its advanced variants), to solve open problems, refute conjectures, and generate new proofs across diverse areas in theoretical computer science, as well as other areas such as economics, optimization, and physics. Based on these experiences, we extract common techniques for effective human-AI collaboration in theoretical research, such as iterative refinement, problem decomposition, and cross-disciplinary knowledge transfer. While the majority of our results stem from this interactive, conversational methodology, we also highlight specific instances that push beyond standard chat interfaces. These include deploying the model as a rigorous adversarial reviewer to detect subtle flaws in existing proofs, and embedding it within a "neuro-symbolic" loop that autonomously writes and executes code to verify complex derivations. Together, these examples highlight the potential of AI not just as a tool for automation, but as a versatile, genuine partner in the creative process of scientific discovery. △ Less

Submitted 6 March, 2026; v1 submitted 3 February, 2026; originally announced February 2026.

Comments: The changes over version 2 are that we cleaned up the last paragraph on color-coding at the end of section 2. Also, for section 6.1 we added a reference to followup work of the authors, and other minor edits in that section

Kimi K2.5: Visual Agentic Intelligence

Authors: Kimi Team, Tongtong Bai, Yifan Bai, Yiping Bao, S. H. Cai, Yuan Cao, Ziwei Chai, Y. Charles, H. S. Che, Cheng Chen, Guanduo Chen, Huarong Chen, Jia Chen, Jianlong Chen, Jun Chen, Kefan Chen, Liang Chen, Ruijue Chen, Xinhao Chen, Yanru Chen, Yanxu Chen, Yicun Chen, Yimin Chen, Yingjiang Chen, Yuankun Chen , et al. (312 additional authors not shown)

Abstract: We introduce Kimi K2.5, an open-source multimodal agentic model designed to advance general agentic intelligence. K2.5 emphasizes the joint optimization of text and vision so that two modalities enhance each other. This includes a series of techniques such as joint text-vision pre-training, zero-vision SFT, and joint text-vision reinforcement learning. Building on this multimodal foundation, K2.5… ▽ More We introduce Kimi K2.5, an open-source multimodal agentic model designed to advance general agentic intelligence. K2.5 emphasizes the joint optimization of text and vision so that two modalities enhance each other. This includes a series of techniques such as joint text-vision pre-training, zero-vision SFT, and joint text-vision reinforcement learning. Building on this multimodal foundation, K2.5 introduces Agent Swarm, a self-directed parallel agent orchestration framework that dynamically decomposes complex tasks into heterogeneous sub-problems and executes them concurrently. Extensive evaluations show that Kimi K2.5 achieves state-of-the-art results across various domains including coding, vision, reasoning, and agentic tasks. Agent Swarm also reduces latency by up to $4.5\times$ over single-agent baselines. We release the post-trained Kimi K2.5 model checkpoint to facilitate future research and real-world applications of agentic intelligence. △ Less

Submitted 7 August, 2026; v1 submitted 2 February, 2026; originally announced February 2026.

Comments: Kimi K2.5 tech report

MM-OpenFGL: A Comprehensive Benchmark for Multimodal Federated Graph Learning

Authors: Xunkai Li, Yuming Ai, Yinlin Zhu, Haodong Lu, Yi Zhang, Guohao Fu, Bowen Fan, Qiangqiang Dai, Rong-Hua Li, Guoren Wang

Abstract: Multimodal-attributed graphs (MMAGs) provide a unified framework for modeling complex relational data by integrating heterogeneous modalities with graph structures. While centralized learning has shown promising performance, MMAGs in real-world applications are often distributed across isolated platforms and cannot be shared due to privacy concerns or commercial constraints. Federated graph learni… ▽ More Multimodal-attributed graphs (MMAGs) provide a unified framework for modeling complex relational data by integrating heterogeneous modalities with graph structures. While centralized learning has shown promising performance, MMAGs in real-world applications are often distributed across isolated platforms and cannot be shared due to privacy concerns or commercial constraints. Federated graph learning (FGL) offers a natural solution for collaborative training under such settings; however, existing studies largely focus on single-modality graphs and do not adequately address the challenges unique to multimodal federated graph learning (MMFGL). To bridge this gap, we present MM-OpenFGL, the first comprehensive benchmark that systematically formalizes the MMFGL paradigm and enables rigorous evaluation. MM-OpenFGL comprises 19 multimodal datasets spanning 7 application domains, 8 simulation strategies capturing modality and topology variations, 6 downstream tasks, and 57 state-of-the-art methods implemented through a modular API. Extensive experiments investigate MMFGL from the perspectives of necessity, effectiveness, robustness, and efficiency, offering valuable insights for future research on MMFGL. △ Less

Submitted 29 January, 2026; originally announced January 2026.

Comments: Under Review

The Third Option: Color Phase Curves to Characterize the Atmospheres of Temperate Rocky Exoplanets

Authors: Drake Deming, Andrew Lincowski, Laura Kreidberg, Miles Currie, Jean-Michel Desert, Guangwei Fu, Jacob Lustig-Yaeger, Victoria Meadows, Ignas Snellen

Abstract: Detecting and characterizing the atmospheres of rocky exoplanets has proven to be challenging for JWST. Transit spectroscopy of the TRAPPIST-1 planets has been impacted by the effects of spots and faculae on the host star. Secondary eclipses have detected hot rocks, but evidence for atmospheres has been difficult to obtain. However, there is a third option that we call color phase curves. This met… ▽ More Detecting and characterizing the atmospheres of rocky exoplanets has proven to be challenging for JWST. Transit spectroscopy of the TRAPPIST-1 planets has been impacted by the effects of spots and faculae on the host star. Secondary eclipses have detected hot rocks, but evidence for atmospheres has been difficult to obtain. However, there is a third option that we call color phase curves. This method will apply to synchronously rotating non-transiting planets as well as transiting planets. A color phase curve uses photometry at a long-IR wavelength near the peak of the planetary thermal emission (e.g., 21 microns) divided by photometry at a shorter wavelength where the star dominates more strongly (e.g., 12 microns). We avoid wavelengths having potentially strong molecular absorption (e.g., 15 microns) to minimize degeneracies in the color phase curve, and we aim to detect and characterize the planetary atmosphere via its longitudinal heat transfer. The ratio of two wavelengths observed nearly simultaneously is designed to isolate thermal emission from the planet, discriminate against the star, and largely cancel instrumental systematic effects. Moreover, we show that invoking mass-radius relations, and using self-consistent physical models, will permit the longitudinal heat transfer to be measured independent of the orbital inclination. Radial velocity surveys are detecting many new exoplanets, including temperate rocky worlds with Earth-like masses. Most of those planets will not transit, but color phase curves have the potential to detect and characterize their atmospheres. △ Less

Submitted 28 January, 2026; originally announced January 2026.

Comments: 14 pages, 6 figures, submission to The Open Journal of Astrophysics

Interleaved Tool-Call Reasoning for Protein Function Understanding

Authors: Chuanliu Fan, Zicheng Ma, Huanran Meng, Aijia Zhang, Wenjie Du, Jun Zhang, Yi Qin Gao, Ziqiang Cao, Guohong Fu

Abstract: Recent advances in large language models (LLMs) have highlighted the effectiveness of chain-of-thought reasoning in symbolic domains such as mathematics and programming. However, our study shows that directly transferring such text-based reasoning paradigms to protein function understanding is ineffective: reinforcement learning mainly amplifies superficial keyword patterns while failing to introd… ▽ More Recent advances in large language models (LLMs) have highlighted the effectiveness of chain-of-thought reasoning in symbolic domains such as mathematics and programming. However, our study shows that directly transferring such text-based reasoning paradigms to protein function understanding is ineffective: reinforcement learning mainly amplifies superficial keyword patterns while failing to introduce new biological knowledge, resulting in limited generalization. We argue that protein function prediction is a knowledge-intensive scientific task that fundamentally relies on external biological priors and computational tools rather than purely internal reasoning. To address this gap, we propose PFUA, a tool-augmented protein reasoning agent that unifies problem decomposition, tool invocation, and grounded answer generation. Instead of relying on long unconstrained reasoning traces, PFUA integrates domain-specific tools to produce verifiable intermediate evidence. Experiments on four benchmarks demonstrate that PFUA consistently outperforms text-only reasoning models with an average performance improvement of 103%. △ Less

Submitted 4 March, 2026; v1 submitted 7 January, 2026; originally announced January 2026.

A Schrödinger-Based Dispersive Regularization Approach for Numerical Simulation of One-Dimensional Shallow Water Equations

Authors: Guosheng Fu, Chun Liu

Abstract: We propose a novel dispersive regularization framework for the numerical simulation of the one-dimensional shallow water equations (SWE). The classical hyperbolic system is regularized by a third-order dispersive term in the momentum equation, which renders the system equivalent, via the Madelung transform, to a defocusing cubic nonlinear Schrödinger equation with a drift term induced by bottom to… ▽ More We propose a novel dispersive regularization framework for the numerical simulation of the one-dimensional shallow water equations (SWE). The classical hyperbolic system is regularized by a third-order dispersive term in the momentum equation, which renders the system equivalent, via the Madelung transform, to a defocusing cubic nonlinear Schrödinger equation with a drift term induced by bottom topography. Instead of solving the shallow water equations directly, we solve the associated Schrödinger equation and recover the hydrodynamic variables through a simple postprocessing procedure. This approach transforms the original nonlinear hyperbolic system into a semilinear complex-valued equation, which can be efficiently approximated using a Strang time-splitting method combined with a spectral element discretization in space. Numerical experiments demonstrate that, in subcritical regimes without shock formation, the Schrödinger regularization provides an $O(\varepsilon)$ approximation to the classical shallow water solution, where $\varepsilon$ denotes the regularization parameter. Importantly, we observe that this convergence behavior persists even in the presence of moving wetting--drying interfaces, where vacuum states emerge and standard shallow water solvers often encounter difficulties. These results suggest that the Schrödinger-based formulation offers a robust and promising alternative framework for the numerical simulation of shallow water flows with dry states. △ Less

Submitted 5 January, 2026; originally announced January 2026.

Comments: 15 pages