Numerical Analysis
Autoencoders vs. Numerical Analysis–Informed Manifold Learning for Navier–Stokes Flows
Alessandro Della Pia, Lucia Russo, Ioannis Kevrekidis et al. (+1 more)
Autoencoders (AEs) have become a dominant approach to nonlinear latent-space construction in data-driven reduced-order modelling (ROM), with their decoders lifting latent representations back to the ambient state space. Their prominence, however, has overshadowed an established alternative:
manifold-learning methods grounded in classical numerical analysis. We revisit this alternative using Parsimonious
Diffusion Maps (PDMs), benchmarking them against Proper Orthogonal Decomposition (POD)-based ROMs and several convolutional AE architectures for the two-dimensional incompressible flow past a rotating cylinder —a bifurcating Navier-Stokes (NS) system organized by a codimension-2 Bogdanov-Takens point and its associated
Hopf, saddle-node, and homoclinic bifurcations. Our approach uses PDMs to identify a parsimonious and interpretable set of intrinsic latent coordinates and to estimate their dimension directly from data. Gaussian process regression then learns the latent dynamics, while convex K-nearest-neighbor (K-NN) interpolation in PDMs space constructs the pre-image map, for which we establish pointwise consistency. The resulting nonlinear ROM substantially outperforms POD-based ROMs and achieves reconstruction and prediction accuracy comparable to —and, in some bifurcating regimes, better than— that of AE-based ROMs. At the same time, latent-variable learning with PDMs requires orders of magnitude less computational time than AE training.
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All authors Alessandro Della Pia, Lucia Russo, Ioannis Kevrekidis, Constantinos Siettos
Fluid Dynamics
Impact of a resonator on vortex induced vibrations of a wind turbine airfoil
Sergio Horcas, David Roca, Enrique Ortega et al. (+1 more)
The aeroelastic instability known as
vortex induced vibrations (VIV) has its origin in the coupling of bluff body shedding and the structure dynamics, and can have adverse effects on several industrial fields. In this work the use of locally resonant metamaterials (LRM) is explored as a wind turbine blade VIV mitigation strategy. The problem is cast into a classical airfoil in transverse oscillation, elastically mounted. A fluid structure interaction model is then generated and validated for the main system. This is achieved using an open source finite volume code with arbitrary Lagrangian Eulerian capabilities and a built-in rigid body motion solver. Subsequently, the capabilities of the code are extended by coupling the dynamics of a single resonator into the main system through a tailored development. The performance of the resonator was evaluated for different designs, which were parameterized mainly by the resonator frequency and its relative mass with respect to the airfoil. Analysis of the results identified a frequency band where the resonator effectively influenced vibration amplitudes. However, the amplitude attenuation was very low when considering realistic relative resonator masses. In particular, achieving a 50% amplitude reduction required a mass ratio larger than 0.25. Studying the mechanisms uncovered the cause of these beneficial effects, which relied on the aeroelastic frequency bifurcation introduced by the resonator. These fundamental findings open the door to future applications of alternative technologies for VIV suppression.
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All authors Sergio Horcas, David Roca, Enrique Ortega, Juan Cante
Computer Vision
AlignMorph: Tuning-Free Diffusion Image Morphing via Explicit Semantic Transport
Wuyi Liu, Xu Han, Yuren Chen et al. (+3 more)
Image morphing aims to produce a smooth and semantically consistent transition between two input images. Existing
diffusion-based morphing methods either require expensive per-pair optimization or rely on implicit spatial
alignment, which easily fails under large layout discrepancies. To address these limitations, we propose AlignMorph, a novel tuning-free
diffusion framework guided by the principle of transport-then-denoise. We explicitly decouple
geometric alignment from generative denoising to avoid structural
entanglement. Our framework consists of two core components. (1) Global Semantic Transport, which achieves
diffusion-compatible semantic
alignment via entropic optimal transport and reliability-aware latent warping; and (2) Coordinate-Aligned Generation, which uses a symmetric bi-phase
attention handoff to maintain consistent spatial coordinates throughout denoising. Without any tuning, AlignMorph effectively eliminates ghosting and achieves superior structural coherence and temporal smoothness on morphing
benchmarks. Code is available at https://github.com/51xOne/Alignmorph.
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All authors Wuyi Liu, Xu Han, Yuren Chen, Yige Mao, Zishuo Peng, Xianzhi Li
Earth and Planetary Astrophysics
Impact of accretion variability on the CO emission of a disk around a very-low-mass star with JWST
C. Bergez-Casalou, B. Tabone, Y. Aoyama et al. (+14 more)
Studying the structure of very-low-mass
stars ($M_\star<0.2M_\odot$) protoplanetary disk is crucial to understand the formation of Earth-like
planets. In this paper, we characterize the innermost regions of the disk around a known very-low-mass
star, 2MASS J16053215-1933159 (J1605 hereafter), by analyzing new JWST/NIRSpec observations taken in 2024, 2 years after a first observation with JWST/MIRI. We find that between the two epochs (2022 & 2024), the CO flux is reduced by a factor $\sim 3$, the flux of the HI 10-6 line seen by both NIRSpec and MIRI is reduced by a factor $\sim13$, and the total continuum density flux is reduced by $\sim 15 \%$. The
accretion rate derived from individual HI lines is consistent with a decrease of a factor $\sim13$ in 2 years. After correcting the spectrum from the CO absorption present in J1605
stellar photosphere, we find that the decrease of the disk's CO emission is consistent with a CO gas of same column density and temperature at each epoch, but originating from a smaller emitting area when the
accretion rate is low. While warm hydrocarbons ($\simeq 500~$K) are detected with MIRI-MRS with extremely high column densities of $\rm C_2H_2$, no hydrocarbons features are seen with NIRSpec. We propose that non-LTE effects quench the near-IR emission of hydrocarbons in the warm reservoir. The variation of the
accretion luminosity of J1605 correlates with the variation of its fundamental CO luminosity in a similar way as in T-Tauri
stars, supporting the idea that very-low-mass
stars could be seen as scaled-down versions of T-Tauri
stars. The detection of the emission of the fundamental band of CO in disks around cold objects is challenging because of the presence of CO absorption in their photosphere. Future observations (e.g., with ELT/METIS) looking for such emission will require robust ways to constrain the photosphere emission.
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All authors C. Bergez-Casalou, B. Tabone, Y. Aoyama, G. Herczeg, A. Caratti o Garatti, E. Habart, E. F. van Dishoeck, I. Chemerynska, T. Henning, S. L. Grant, M. Guedel, A. M. Arabhavi, J. Kanwar, G. Olofsson, G. Perotti, L. Tychoniec, L. B. F. M. Waters
Machine Learning
A Distributional Optimisation Perspective on Combining Models in Deep Learning
Congye Wang, Yan Lin, Zheyang Shen et al. (+2 more)
Combining predictions from different models can improve performance at machine learning tasks, but the training of the individual models and the rule used to combine them are typically chosen separately, and by ad hoc means. Recent advances in distributional optimisation (i.e. where the optimisation occurs over the set of probability distributions) offer an opportunity for principled joint training, viewing the collection of models as a discrete distribution whose support points are to be optimised, but the potential of these methods is not well-understood. In this paper we (1) cast two standard combination strategies - ensembles and low-rank adapter averaging - as
entropy-regularised distributional optimisation, observing that the resulting objective is convex in the ensemble case but not in the adapter-averaging case, so that existing convergence guarantees for mean field Langevin dynamics transfer only to the former; (2) assess existing and novel
algorithms for this task, including a functional variant of variational gradient descent; and (3) report an empirical study spanning synthetic classification tasks and fine-tuning of large
language models on a commonsense
reasoning benchmark.
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All authors Congye Wang, Yan Lin, Zheyang Shen, Matthew A. Fisher, Chris. J. Oates
Optics
Non-Hermitian photonic time quasicrystal
Shuhang Chen, Zhi Zheng and Qi Zhu
Photonic time
crystals amplify waves through temporal modulation. We study a temporal analogue of the non-Hermitian Aubry-André-Harper model. By continuing an incommensurate temporal modulation into the complex phase plane, we find that the bulk amplification rate as a function of the complexification coordinate is organized into distinct integer phases, with its slope locked to an integer within each phase. This integer is Avila's acceleration, connecting a global structure of quasiperiodic operator theory with a directly observable optical propagation response. The quantized response persists in the presence of defects in the time-dependent permittivity modulation that preserve the underlying analytic phase structure. As a wave-level consequence, two input frequencies belonging to different integer phases acquire exponentially separated amplification, producing spectral selection and reshaping the temporal beat pattern. Our results establish that a global integer of quasiperiodic operator theory can govern a tunable, quantized growth response in a non-Hermitian photonic time quasicrystal.
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Materials Science
Amorphous insulating MoS$_x$ films deposited by magnetron sputtering as a low-index optical coating
S.G. Martanov, E.V. Tarkaeva, A.V. Muratov et al. (+4 more)
While much effort are now concentrated on exploration of crystalline form of MoS$_2$ as a two-dimensional material, we show that room-temperature DC magnetron sputtering from pressurized MoS$_2$ powder target produces amorphous MoS$_x$ films with optical properties fundamentally different from those of crystalline MoS$_2$. The films were found to have a sulfur content of approximately $x=1.8$, indicating sulfur deficiency. They are optically isotropic and exhibit rather low optical losses, with a refractive index of about 2 over the visible and near-infrared spectral range. Their compatibility with lift-off lithography and dry etching makes amorphous MoS$_x$ a promising material for integrated photonic applications.
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All authors S.G. Martanov, E.V. Tarkaeva, A.V. Muratov, A.V. Lubenchenko, O.N. Pavlov, A.G. Vitukhnovsky, A.Yu. Kuntsevich
Category Theory
Observations on the variety of equationally linear Heyting semilattices
Xabier García-Martínez, James R. A. Gray, Michael A. Hoefnagel et al. (+2 more)
In previous work, we analysed a number of categorical properties, of interest in the context of Janelidze-Márki-Tholen semi-abelian categories, for the variety $\mathsf{HSLat}$ of Heyting semilattices. In this paper, we focus on the subvariety $\mathsf{ELHSLat}$ of equationally linear Heyting semilattices. Our main objective is to show that, unlike $\mathsf{HSLat}$, this category is algebraically coherent. We furthermore prove that it is neither locally algebraically cartesian closed nor cosmash associative.
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All authors Xabier García-Martínez, James R. A. Gray, Michael A. Hoefnagel, Tim Van der Linden, Corentin Vienne
Artificial Intelligence
Brain-Token Learning: Microstate-Based Tokenization and Multi-Scale Interaction for Long-Horizon EEG Sequence Modeling
Weishan Ye, Yue Pan, Li Zhang et al. (+2 more)
Electroencephalography (
EEG) provides a non-invasive window into dynamic
brain activity, yet modeling long-horizon
EEG sequences remains challenging due to their high temporal complexity, substantial variability across subjects, and the lack of biologically meaningful sequence representations. Existing tokenization strategies, such as fixed-window and patch-based representations, discretize
EEG signals according to artificial temporal boundaries, which may disrupt intrinsic
brain-state dynamics. In this work, we propose
Brain-
Token Learning, a neuroscience-inspired framework that introduces
Brain Tokenization for long-horizon
EEG sequence modeling. Instead of partitioning
EEG signals into predefined temporal segments,
Brain Tokenization represents
EEG as sequences of recurrent microstate-derived
brain tokens, where each
token corresponds to a quasi-stable large-scale
brain state with variable temporal duration. Based on these biologically grounded
tokens, we further develop a multi-scale
token interaction module consisting of Latent State Aggregation and State Transition Modeling to jointly capture global
brain-state context and local microstate transitions. We evaluate
Brain-
Token on five heterogeneous
EEG datasets, including the newly collected long-horizon NeuroLong
dataset and four affective or clinical
EEG datasets (SEED, DEAP, MDD, and NSSI). Extensive experiments demonstrate that
Brain-
Token consistently outperforms conventional CNN/LSTM architectures,
Transformer-based models, and domain adaptation methods across diverse
EEG scenarios. Further analysis verifies the effectiveness of microstate-based tokenization and multi-scale interaction for learning robust and interpretable
EEG representations. These results establish
Brain-
Token as a biologically grounded tokenization paradigm for long-horizon
EEG sequence modeling.
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All authors Weishan Ye, Yue Pan, Li Zhang, Gan Huang, Zhen Liang
Rings and Algebras
Derivation Algebras of Three-Dimensional Leibniz Algebras: A Complete Description
Leonid A. Kurdachenko, Oleksandr O. Pypka and Mykola M. Semko
Derivation
algebras are natural structural invariants of Leibniz
algebras. In a number of earlier papers, derivations were described for several classes of one-generated, nilpotent, non-nilpotent, and low-dimensional Leibniz
algebras. In the present paper we complete the description for three-dimensional non-Lie left Leibniz
algebras over arbitrary fields. We use a refined organization of the three-dimensional classification into sixteen isomorphism types, including parameterized families. Previously known cases are incorporated by reference, with changes of basis recorded when necessary. For the remaining cases we determine the derivations by direct coefficient calculations. Special
attention is paid to characteristic two, where several derivation
algebras increase in dimension. We also record simple decompositions of the resulting Lie
algebras into natural ideals and subalgebras. A final table gives the derivation
algebra of every type in the classification.
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Machine Learning
The Undetected Damage of Quantization on Retrieval and How to Fix It
Luca Zhou, Alessandro Zirilli, Daniele Solombrino et al. (+2 more)
We show that a quantized model that keeps its classification accuracy still changes $14$ to $46\%$ of its top-1
retrieval results, and that aggregate ranking metrics reveal only part of this damage. We tie this failure to the gap between the two highest model scores and use that gap to decide when to trust a quantized answer and where additional precision should be spent. We show that the top-1 result is guaranteed to survive quantization only when this gap exceeds twice the largest rounding error. In classification, the scores are logits, and training compares the correct class against every other class, which encourages this gap. In
retrieval, the scores are query-document similarities, and training compares each positive only against sampled negatives, so nothing separates the top-1 item from the second. This gap can be measured without labels. Before deployment, it predicts which models will break under quantization, and at deployment time it tells, per input, whether the quantized answer still matches the full-precision answer. Most classification inputs have a gap wide enough to trust the quantized answer, but few
retrieval queries do. That gap motivates a different fix in each task. In
retrieval, spending extra bit-width on the layers whose quantization moves the gap most recovers up to three-quarters of an extra bit's benefit for half its cost. In classification, routing the few low-gap inputs to full precision recovers most of the lost accuracy at a fraction of the cost.
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All authors Luca Zhou, Alessandro Zirilli, Daniele Solombrino, Roberto Dessì, Emanuele Rodolà
Solar and Stellar Astrophysics
Companion masses of Gaia DR3 AstroSpectroSB1 binaries: a Bayesian forward-modelling framework
Prasid Sandave and Raghuveer Garani
Gaia Data Release 3 (DR3) has substantially expanded the study of unresolved binaries by providing combined astrometric and spectroscopic orbital solutions for a large sample of non-single
stars. We present a
Bayesian forward-modelling framework for estimating companion masses in
Gaia DR3 AstroSpectroSB1 systems. The method samples the physical parameters of each binary, predicts the corresponding astrometric Thiele-Innes constants and spectroscopic orbital coefficients, and evaluates their likelihood using the full covariance
matrix published by
Gaia. An external prior on the primary mass is incorporated to resolve the remaining degeneracy between the component masses and orbital inclination in single-lined systems. We apply this framework to 1035 AstroSpectroSB1 systems with primary-mass estimates drawn from the
Gaia DR3 astrophysical\_parameters table. Of these, 760 systems also have
Gaia-reported estimates for both component masses and are used as a validation sample. The remaining 275 systems constitute an inference sample for which the mass of the fainter, unresolved companion is estimated. For systems with well converged posteriors, the inferred parameters reproduce the
Gaia orbital observables, while the companion masses recovered for the validation sample are consistent with the
Gaia estimates. In the inference sample we find 113 well converged systems for which the companion masses are meaningfully inferred. We find that the population is dominated by ordinary
stars, together with a smaller high-mass tail that may contain massive white
dwarfs or other compact-object candidates. Our framework provides a scalable approach for converting
Gaia AstroSpectroSB1 solutions into companion-mass posterior distributions, identifying promising systems for targeted follow-up, and preparing for the larger and more precise orbital samples anticipated with
Gaia DR4.
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Rings and Algebras
Automorphism Groups of Three-Dimensional Leibniz Algebras: A Complete Description
Leonid A. Kurdachenko, Oleksandr O. Pypka and Mykola M. Semko
Automorphism groups are among the most natural structural invariants of an
algebra, and their explicit determination is a basic problem in the structure theory of Leibniz
algebras. In a series of earlier papers,
automorphism groups were obtained for several classes of low-dimensional, one-generated, nilpotent, and non-nilpotent Leibniz
algebras. In the present paper we complete this picture for three-dimensional non-Lie left Leibniz
algebras over arbitrary fields. We use a refined organization of the three-dimensional classification into sixteen isomorphism types, including parameterized families. Previously known cases are not recomputed; instead, they are incorporated by reference, with changes of basis recorded when necessary. For the remaining types we determine the
automorphism groups explicitly through a direct analysis of the
automorphism conditions. Particular
attention is paid to exceptional parameter values and to characteristic two. A final table summarizes the
automorphism group of every type in the classification.
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Astrophysics of Galaxies
Gravity-driven Emergence of Multi-fractal Density Structure in the Orion A Integral Shaped Filament
Mengke Zhao, Guang-Xing Li, Keping Qiu et al. (+1 more)
Molecular clouds are often described as self-similar structures, although spatially averaged measures do not retain local variations in density scaling. We use the density exponent $\kappa_\rho$ ($\rho \propto r^{\kappa_\rho}$) to characterize the density structure of the Integral Shaped Filament (ISF) in Orion\,A. Applying the Multiscale Decomposition Reconstruction method to the Herschel column-density map, we find distinct density–scale relations across the connected filament. Their slopes steepen from $\kappa_\rho \approx -1.7$ to $-1.9$ in the quiescent OMC-4/5 regions, through $\approx -2.1$ in the
star-forming OMC-2/3, to $\approx -2.3$ in OMC-1, which hosts massive
star formation. The ISF therefore does not follow a single local density-scaling exponent but exhibits multi-fractal density scaling. The pixel-level distributions show the same progression toward higher volume density and more negative $\kappa_\rho$. Since $\kappa_\rho$ measures the concentration of gas toward smaller scales, we interpret this sequence as
gravity-driven differential collapse: denser regions have shorter free-fall times and develop steeper density profiles. Longitudinal gas motions toward OMC-1 may limit the mass supply available for large-scale growth in the outer sub-regions and help maintain the observed range of local exponents. These results link local density scaling to gravitational concentration within a single connected filamentary system.
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All authors Mengke Zhao, Guang-Xing Li, Keping Qiu, Guangya Zeng
Instrumentation and Methods for Astrophysics
Fast and accurate astronomical source deblending with Density-Peak Clustering
M. D. Lepinzan, F. Tomba, E. Romelli et al. (+2 more)
Source deblending is a fundamental challenge for current and forthcoming astronomical surveys, where increasing source density and image depth lead to a growing number of overlapping detections. Accurate deblending is essential for reliable measurements of source morphology and photometry, as well as for cosmological analyses. We present a redesign of the Advanced Density Peak (ADP) clustering
algorithm, tailored to the identification and separation of blended astronomical sources within detection regions. We develop a validation framework combining realistic image simulations, automatically generated ground-truth
segmentation, and label-invariant metrics. ADP is assessed against ASTErIsM, an established density-based astronomical deblender, using pairwise simulations, synthetic multi-source images, and Euclid Q1 public data. In pairwise simulations, the methods show comparable performance across a broad range of source separations and flux ratios, with photometric differences typically below 1% and reaching 5-7% in the most challenging cases, without systematic bias. In multi-source simulations, ADP recovers approximately 8% more ground-truth sources, while the positions of sources identified by both methods agree at the sub-pixel level. On Euclid Q1 public data, the methods show strong agreement in
segmentation area, ellipticity, position angle, and photometry, with the largest differences for the smallest and faintest sources. ADP also provides a substantial computational advantage: end-to-end
benchmarks on 19200 x 19200 pixel Euclid images require approximately 16-200 s, corresponding to speedups of 12-156x relative to ASTErIsM, with median and mean improvements of 36x and 54x, respectively. These results show that ADP provides scientifically competitive deblending at substantially lower computational cost, making it a promising approach for large-scale astronomical imaging surveys.
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All authors M. D. Lepinzan, F. Tomba, E. Romelli, A. Rodriguez, L. Tornatore
Robotics
Performance-Preserving Online Adaptation in Social Navigation via Diffusion Steering
Haruto Nagahisa, Kohei Matsumoto, Yuki Hyodo et al. (+1 more)
In social
navigation, modeling the complex interactions between humans and
robots is difficult, and deep reinforcement learning has therefore been actively studied. However, because simulation alone cannot fully reproduce diverse scenarios,
robot dynamics, and the social conventions that vary across deployment environments, fine-tuning in the deployment environment is promising. In doing so, learning that preserves the base model's performance is required, so as not to compromise the primary objective of
navigation, namely avoiding pedestrians and reaching the destination. In this study, we propose a method that applies
diffusion steering via reinforcement learning (DSRL), which trains only the noise policy while keeping the
diffusion policy fixed, thereby achieving learning that preserves performance. Furthermore, we integrate
diffusion-based RL policies trained with multiple seeds to construct the base policy, improving learning performance. Our evaluation shows that, compared with other methods, the proposed method enables efficient learning while preserving performance, and we confirm flexible behavior control through adaptation to social conventions, as well as its effectiveness on a physical
robot through hardware-in-the-loop simulation.
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All authors Haruto Nagahisa, Kohei Matsumoto, Yuki Hyodo, Ryo Kurazume
Quantum Physics
A Sharp Noise Threshold for Shor's Quantum Factoring and Discrete Log Algorithms
Jin-Yi Cai and Ben Young
We study the asymptotic behavior of Shor's
quantum factoring and discrete log
algorithms when noise affects the precise controlled rotation gates in their
quantum Fourier transforms. Improving the results of Cai (2024) and Cai and Young (2025), we identify a sharp and vanishingly small noise threshold. If the noise level lies below this threshold, then the two
algorithms succeed in expected
polynomial time. If the noise level exceeds this threshold, then the
algorithms provably fail to solve their respective problems in expected
polynomial time when the underlying
primes belong to a set of positive density.
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Strongly Correlated Electrons
Slave-Rotor and Auxiliary Chain Analysis of the Mott Transition in a Modified Periodic Anderson Model
Ankur Majumder and Sudeshna Sen
The Mott-Hubbard transition is a paradigmatic phenomenon driven by strong electronic correlations. We investigate this transition in a three-orbital modified periodic Anderson model using slave-rotor mean-field theory. A minimal two-site cluster extension is introduced to understand the minimal effects of short-range spatial correlations. In comparison to the single-site solution, short-range spatial correlations reveal a shift of the transition point. Going beyond the slave-rotor analysis, we also evaluate the dynamical self-energy and map it onto the boundary Green's function of a non-interacting semi-infinite auxiliary chain. For the metallic phase, the resulting hopping pattern corresponds to a topologically trivial generalized Su–Schrieffer–Heeger chain. This construction provides a framework for exploring the possible topological character of the Mott transition in this model.
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Algebraic Geometry
Trifocal Tensors in Three-View Geometry
Yiran Xu and Changqing Xu
Matrices and tensors are ubiquitous throughout computer
vision. The trifocal tensor $\caT$ is a $3\times 3\times 3$ tensor that plays a vital role in three-view geometry. However, this tensor, though displayed in a third-order form, is manipulated and operated primarily in a
matrix style in standard literature. This article establishes the conformal tensor
algebra by replacing ad-hoc
matrix collections with well-defined multilinear operators. We treat all three views symmetrically by covariant subscript indexing ($T_{ijk}$). To make precise what is new beyond notation: the classical point, line, and mixed correspondence constraints are here re-derived, but uniformly, each as a single contraction in one shared
algebra; the results that are genuinely new include the exact rank characterization of the trifocal tensor — all mode-$k$ unfoldings $\caT[k]$ in $\RR^{3 \times 9}$ satisfy $\rank(\caT[k])=3$ for non-degenerate setups, and rank deficiency of any unfolding certifies degeneracy of the camera configuration, which yields a near-zero-cost degeneracy alarm and a validation criterion for estimated tensors; the outer-product representation $\caT = A^{\top}\times \bfb_{4} - B^{\top}\times_{2} \bfa_{4}$; and direct epipole extraction via double contractive traces. The coordinate-free multilinear operators seamlessly map to tensor auto-differentiation frameworks (e.g., PyTorch, TensorFlow); a concrete PyTorch experiment demonstrates that the induced $24$-parameter bilinear parametrization acts as a structural prior that eliminates the accuracy drift exhibited by an unstructured $27$-entry autodiff refinement.
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Computer Vision
NeuIDO: Neural Intrinsic Dynamics Operator for Physics-Informed 4D World Models
Jiajing Lin, Xin Zhang and Jianhua Sun
World models aim to capture environmental dynamics and predict future trajectories, showing growing potential for
embodied intelligence. Physics-informed 4D generation integrates physical simulation to predict 3D object interactions, offering a promising pathway toward world models. However, this paradigm relies on manually imposed dynamical assumptions rather than internalizing world dynamics, and thus still leaves a gap toward a true world model. To bridge this gap, we propose NeuIDO, a novel world dynamics modeling framework that learns a unified intrinsic dynamics representation from visual observations, advancing physics-informed 4D generation toward a world model. Specifically, we formulate world modeling as a neural operator learning problem and introduce a two-stage training strategy to learn a generalizable mapping from the visual observation distribution to the intrinsic dynamics distribution. Building on this observation-dynamics mapping, NeuIDO enables zero-shot dynamics inference directly from
videos and can be further aligned with complex real-world dynamics via few-shot adaptation. Extensive experiments demonstrate that NeuIDO effectively unifies the intrinsic dynamics underlying diverse visual observations into a shared representation and rapidly infers dynamics in novel scenes.
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Computer Vision
AnalogDepth: Multi-view Geometry from FPV drones under Analog Video Transmission
André Amorim and Pedro F. Proença
Analog
video transmission (VTX) remains widespread in FPV drones due to low latency, weight and low cost. However analog VTX suffers from complex spatially structured image degradation which differ fundamentally from digital image corruption (e.g. AWGN) used in standard training augmentation. This work shows that this type of noise severely degrades the accuracy of Depth Anything 3 (DA3), a state-of-the-art feed forward visual geometry foundation model. To address this gap, we present AnalogDepth, a parameter-efficient training pipeline that adapts DA3 to analog FPV imagery using student-teacher knowledge
distillation with Low-Rank Adaptation (LoRA) injected into the DINOv2 backbone. Rather than synthesizing noise analytically, we build a noise bank from static FPV recordings under diverse conditions and compare real-noise injection against PSD-matched Gaussian synthesis and AWGN as baselines. Experiments on six real FPV flight sequences across three indoor scenes show that training with our noise bank consistently reduces per-frame depth RMSE and 3D reconstruction Chamfer distance compared to the pretrained DA3 baseline and both Gaussian noise variants. These results demonstrate that replicating the spatial structure of real analog transmission noise is critical for effective adaptation.
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Superconductivity
Coexistence and Interconversion of Multiple-Order Majorana Modes in Topological Superconductor Films with Varying Thickness
Zhizhong Ding, Chuanbao Zhang, Shunhong Zhang et al. (+4 more)
We theoretically investigate the thickness-dependent
evolution of Majorana modes in $C_{2h}$-symmetric topological
superconductor films (such as the recently discovered 2M-WS$_2$) proximity coupled with
magnetic insulators. For sufficiently thick films, two Majorana bound states coexist as end modes at the surface and interface along a
vortex line, with the interfacial mode evolving into a
chiral Majorana edge mode upon increasing the proximity-induced exchange field. The intrinsic $C_{2h}$ crystalline
symmetry selects two
chiral Majorana modes circulating along the hinges on two of the four side surfaces of the film. When the penetration depth of the exchange field is sufficiently shallow, the two circulating modes are localized near the interface, but with qualitatively different subsequent evolutions. One of them further collapses to form two Majorana corner modes, while the other merges with the
chiral Majorana mode circulating around the interface. Importantly, the corner modes are well decoupled from the interfacial
chiral mode, thereby enabling an unprecedented coexistence of first-, second-, and third-order Majorana modes within a single material platform. We further show that such coexistence persists even in the ultrathin-film limit, where the electric-field-controlled two-dimensional $Z_2$
topology offers an extra advantage to readily interconvert the multiple-order Majorana modes. These findings highlight the pivotal role of the proper crystalline
symmetry in enabling emergence, manipulation, and potential braiding of Majorana modes for demonstrating non-Abelian statistics and fault-tolerant
quantum computation.
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All authors Zhizhong Ding, Chuanbao Zhang, Shunhong Zhang, Xiaoyu Zhu, Wei Qin, Ping Cui, Zhenyu Zhang
Audio and Speech Processing
Morpho-VITS: Variational Inference with Morphological Modeling for End-to-End Speech Synthesis of a Tonal Bantu Language
Antoine Nzeyimana
Text-to-
speech models for Bantu tonal
languages are challenged by a tonal system that is rooted in both the lexis (i.e., the inventory of words, stems, and affixes) and the grammar (i.e., morpho-syntax). To complicate matters, the standard writing systems of these
languages often omit tone markings and syllable duration information, which must be disambiguated by the reader based on context. Motivated by linguistic descriptions of Bantu
language tone systems, we propose an end-to-end text-to-
speech model that augments the text encoding mechanism with a morpho-syntactic prior. We replace the standard phoneme encoder in the VITS architecture with a morpheme sequence encoder and a phoneme-to-morpheme
attention network. We posit that, by using this explicit morphological modeling, we can capture the information required to produce the correct tone. Experiments conducted on the Kinyarwanda
language, a tonal and morphologically complex Bantu
language, reveal substantial TTS improvement from this morphological modeling. Specifically, the proposed method significantly improves the naturalness, intonation, and intelligibility of the produced synthetic voices.
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Number Theory
On the abundance of rigid meromorphic cocycles for quadratic forms in four variables
Lennart Gehrmann and Sören Sprehe
Let $(V,q)$ be an anisotropic quadratic space of dimension $4$ over the rationals and let $p$ be a
prime such that the local quadratic space $V\otimes \mathbb{Q}_p$ is the orthogonal direct sum of two
hyperbolic planes. We show that the group of $p$-adic rigid meromorphic cocycles attached to $V$ has infinite rank. A key ingredient in the proof is the computation of the group of invertible rigid analytic functions on products of certain rigid analytic subspaces of the projective line such as the $p$-adic upper half-plane.
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Computer Vision
HappyWorld-Bench
Zhiqi Bai, Junai Cai, Yixin Chen et al. (+33 more)
Evaluating world models requires assessing both the quality of the worlds they generate and their consistency and responsiveness under exploration, interaction, and modification. We introduce HappyWorld-Bench, a comprehensive
benchmark that evaluates whether generated worlds remain reliable as
agents interact with them. Our design is built on a hierarchical capability framework of six world capabilities (W1-W6), from generative construction to unified world modeling, instantiated across three independent evaluation tracks:
video world models, spatial world models, and
embodied world models. HappyWorld-Bench comprises 1,138
video prompts, 300 spatial scenes, and 254
embodied test cases. Across all three tracks, we build and operate HappyWorld-Arena to organize human A/B comparisons and derive model-level Elo ratings, which complement newly designed automated metrics that capture behavioral correctness. We evaluate 14
video world models, 9 spatial systems, and 8
embodied candidates under this unified framework. Results reveal remaining reliability gaps across all three tracks:
video models exhibit reduced consistency during extended rollouts and revisits, spatial models achieve at best 70.14% placement accuracy and 73.33% edit execution, and
embodied models struggle to preserve state across multi-step actions and respond precisely to altered action conditions and physical rules. These findings highlight the need to evaluate world models not only by visual quality, but also by state consistency and the correctness of their responses to actions and interventions.
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All authors Zhiqi Bai, Junai Cai, Yixin Chen, Jingrun Du, Tao Feng, Wei Gong, Siyuan Huang, Xiao Lin, Jiaheng Liu, Jun Luo, Yongzhe Lyu, Liya Ma, Zenan Meng, Lin Qu, Wenbo Su, Jiaming Wang, Qinghe Wang, Shaofei Wang, Yanghai Wang, Zequn Wang, Ziming Wang, Hu Wei, Jiangtao Wu, Ruiqi Wu, Jiaxin Xie, Yuchi Xu, Ze Xu, Chengting Yu, Liangyu Yuan, Gang Zeng, Yawen Zeng, Xingyao Zhang, Zizheng Zhang, Bo Zheng, Jiancheng Zhu, Song-Chun Zhu
Classical Analysis and ODEs
Uniqueness sets in weighted $\ell^2$-spaces via simultaneous approximation
Linus Bergqvist, Adem Limani and Bartosz Malman
We consider a family of uniqueness problems in the setting of weighted $\ell^2$
Fourier spaces on the unit-circle, from the perspective of simultaneous approximation. We develop a general potential theoretical framework, which enables us to extend and unify several classical results by Ahlfors, Beurling, Frostman and Khrushchev, previously known in the more classical regimes. In addition, we prove a surprisingly sharp result that illustrates a deep discrepancy between unilateral and bilateral uniqueness sets.
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High Energy Physics (Experiment)
Jevons' Paradox and Fast Generative Simulation for HEP: Why Realistic Benchmarking is Essential
Thorsten Buss, Henry Day-Hall, Frank Gaede et al. (+7 more)
Simulation is a major computational expense in HEP, and calorimeter simulation in particular drives the overall energy cost of our physics analyses. Future detectors will contain more finely grained calorimeters than ever, and their data analyses will demand unprecedented simulated statistics. Fast generative models redefine what is possible, producing simulations 100 times more efficiently. This article addresses Jevons' paradox in our field and considers the importance of realistic metrics in achieving "true" efficiency.
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All authors Thorsten Buss, Henry Day-Hall, Frank Gaede, Gregor Kasieczka, Katja Krüger, Anatolii Korol, Thomas Madlener, Peter McKeown, Martina Mozzanica, Lorenzo Valente
Statistics Applications
Transporting summary measures of relative effects from randomised trials to the treated patient population: an application to breast cancer endocrine therapy
Bonnie E. Shook-Sa, Paul N. Zivich, Carolyn Taylor et al. (+4 more)
Randomised trials often report relative treatment effects, such as risk ratios and hazard ratios, for trial populations. Clinical decision-making, however, often benefits from estimates of absolute treatment effects in the population eligible for treatment. Trial participants may not represent this target population well, and restrictions on access to individual participant trial data can further complicate absolute effect estimation. Routine care data are often representative of the target population but may be subject to uncontrolled confounding. We consider estimation of the average treatment effect on the treated (ATT), an absolute measure, by combining a representative sample of treated routine care patients with summary measures (i.e., estimated risk or hazard ratios) from either a randomised trial or a meta-analysis of trials. Under marginal or conditional transportability assumptions, the ATT is shown to be identifiable. The implications of collapsibility of the effect measure on transportability are discussed, and plug-in
estimators of the ATT are presented. Simulation studies are used to assess finite sample performance of the
estimators in a range of settings. The proposed methods are applied to estimate the ATT of endocrine therapy on 15-year breast cancer mortality using results from a meta-analysis of randomised trials and England's National Disease Registration Service.
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All authors Bonnie E. Shook-Sa, Paul N. Zivich, Carolyn Taylor, David Dodwell, Jake Probert, Sarah C Darby, Paul McGale
Instrumentation and Methods for Astrophysics
Scale-Vector Alignment: A Scale-Aware Framework for Spatially Resolved Morphological Similarity in Astronomical Images
Mengke Zhao, Guang-Xing Li, Keping Qiu et al. (+1 more)
Astronomical maps made with different tracers are not expected to have identical morphology. Excitation, optical depth, chemistry, radiation, and ISM phase alter the response of a tracer, and the resulting differences can depend on both position and spatial scale. We propose scale-vector
alignment, a scale-aware method based on Constrained
Diffusion Decomposition (CDD). CDD decomposes an image into localized scale components; at each position, their amplitudes define a scale vector that describes how the measured intensity is distributed over spatial scale. We define the pixel-wise similarity $\Spix(x,y)$ as the normalized
alignment of two local scale vectors. The normalization removes the overall amplitude, so $\Spix$ compares relative scale composition rather than absolute flux. We also define the scale-wise similarity $\Sscale(l)$ by comparing the two CDD component maps at each spatial scale. Spatial shifts are used to construct an empirical shifted reference distribution for $\Spix$. In Orion~A, the tracer with the highest similarity to the
dust-derived column-density map changes from $^{12}$CO to $^{13}$CO to C$^{18}$O toward higher column density. In NGC~6334I(N), the line–continuum similarity decreases locally around the brightest compact structures, where radiative-transfer effects can alter the observed line morphology. In NGC~3627, CO is most similar to 21~$\mu$m emission, and $\Sscale$ reaches its maximum at an intermediate sub-kpc scale. The method measures where two tracers have similar multiscale structure and at which scales their spatial distributions agree. The implementation is publicly available at https://github.com/meng-ke/Scale-Vector-
Alignment.
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All authors Mengke Zhao, Guang-Xing Li, Keping Qiu, Shanghuo Li
Machine Learning
SupportCal: Label-Free Calibration of Post-Trained LLMs via Reference Support and Corroboration
Linhan Luo, Lequan Lin, Dai Shi et al. (+3 more)
Post-training often improves task performance but can degrade confidence calibration, leaving post-trained
language models (PoLMs) more overconfident than their corresponding pretrained
language models (PLMs). Because task-specific labeled calibration data can be costly or unavailable, the corresponding PLM provides a natural label-free reference for post-hoc calibration. Prior agreement-gated PLM-referenced calibration fits a scalar temperature using only examples on which the PoLM and its PLM reference agree, excluding disagreement examples because direct
alignment can drive the fitted temperature excessively high and induce under-confidence. We revisit this binary treatment. A controlled reintroduction diagnostic reveals a non monotonic aggregate effect: admitting a moderate fraction of disagreement examples can improve calibration, whereas the benefit diminishes as unit weight inclusion approaches the full disagreement set. We introduce SupportCal, a label-free post-hoc method that retains agreement examples at unit weight and assigns disagreement examples continuous weights based on the own-base PLM's relative support and corroboration from pretrained references selected from a size-compatible candidate pool. We further characterize when the resulting weighted objective admits a finite optimal temperature. Across MedMCQA and MathQA, SupportCal yields lower mean ECE than the agreement-only baseline for nearly all evaluated target-model configurations; supplementary TweetEval Sentiment results show the same pattern on a fixed-label classification task.
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All authors Linhan Luo, Lequan Lin, Dai Shi, Feng Chen, José Miguel Hernández-Lobato, Junbin Gao