arXiv preprints from January 1, 2026 through October 1, 2026 — 04:40:16 EST
Reconstructing dynamic 3D scenes from monocular videos requires simultaneously capturing high-frequency appearance details and temporally continuous motion. Existing methods using single Gaussian primitives are limited by their low-pass filtering nature, while standard Gabor functions introduce energy instability. Moreover, lack of...
We present the first XRISM/Resolve observations of the active galactic nucleus, NGC 1365, obtained in 2024 February and July. NGC 1365 is known for rapid transitions between Compton-thick and Compton-thin states, along with strong absorption from a highly ionized wind. During our observations, the source is found in a persistent...
Left ventricle (LV) segmentation is critical for clinical quantification and diagnosis of cardiac images. In this work, we propose two novel deep learning architectures called LNU-Net and IBU-Net for left ventricle segmentation from short-axis cine MRI images. LNU-Net is derived from layer normalization (LN) U-Net architecture, while...
We study the topological stability of Voronoi percolation in higher dimensions. We show that slightly increasing p allows a discretization that preserves increasing topological properties with high probability. This strengthens a theorem of Bollobás and Riordan and generalizes it to higher dimensions. As a consequence, we prove a...
Spontaneous symmetry breaking leads to diverse phenomena across the natural sciences, from the Higgs mechanism in particle physics to superconductors and collective animal behavior. In photonic systems, the symmetry of light states can be broken when two optical fields interact through the Kerr nonlinearity, as shown in early...
Verifying whether a language model is genuinely reasoning or pattern-matching remains an open problem: learned verifiers are expensive, and output-based heuristics are brittle. We show that valid mathematical reasoning induces a measurable, training-free spectral signature in transformer attention. By treating each attention matrix as...
Right-handed neutrinos are naturally induced by dark extra dimension models and play an essential role in neutrino oscillations. The model parameters can be examined by the long-baseline neutrino oscillation experiments. In this work, we compute the predicted neutrino oscillation spectra within/without extra dimension models and...
In this work, we attempted to unleash the potential of self-supervised learning as an auxiliary task that can optimise the primary task of generalised deepfake detection. To explore this, we examined different combinations of the training schemes for these tasks that can be most effective. Our findings reveal that fusing the feature...
The Architecture, Engineering, and Construction (AEC) industry is undergoing rapid digital transformation, producing diverse digital assets such as datasets, computational models, use cases, and educational materials across the built environment lifecycle. However, these resources are often fragmented across repositories and...
Population-based cancer registries depend on pathology reports as their primary diagnostic source, yet manual abstraction is resource-intensive and contributes to delays in cancer data. While transformer-based NLP systems have improved registry workflows, their ability to generalize across jurisdictions with differing reporting...
Generative AI models, particularly Text-to-Video (T2V) systems, offer a promising avenue for transforming science education by automating the creation of engaging and intuitive visual explanations. In this work, we take a first step toward evaluating their potential in physics education by introducing a dedicated benchmark for...
The Galilean moons of Io, Europa, and Ganymede exhibit a 4:2:1 commensurability in their mean motions, a configuration known as the Laplace resonance. The prevailing view for the origin of this three-body resonance involves the convergent migration of the moons, resulting from gas-driven torques in the circum-Jovian disk wherein they...
Federated data sharing promises utility without centralizing raw data, yet existing embedding-level generators struggle under non-IID client heterogeneity and provide limited formal protection against gradient leakage. We propose FedHypeVAE, a differentially private, hypernetwork-driven framework for synthesizing embedding-level data...
The standard approach to characterizing topological matter, computing topological invariants, fails when the symmetry protecting the topological phase is preserved only on average in a disordered system. Because topological invariants rely on enforcing the symmetry exactly, they can overcount phases by incorrectly identifying certain...
Previous work on home router security has shown that using system calls to train a transformer-based language model built on a BERT-style encoder using contrastive learning is effective in detecting several types of malware, but the performance remains limited at low false positive rates. In this work, we demonstrate that using a...
In this article, we study Chow polynomials of weakly ranked posets and prove the existence of Gorenstein algebras with the Kähler package such that their Hilbert--Poincaré series agrees with the Chow polynomial. Our statement provides evidence in support of a conjecture by Ferroni, Matherne and the second author about the existence of...
Learning models with categorical variables requires optimizing expectations over discrete distributions, a setting in which stochastic gradient-based optimization is challenging due to the non-differentiability of categorical sampling. A common workaround is to replace the discrete distribution with a continuous relaxation, yielding a...
This study considers a point-to-point wireless link, in which both the transmitter and receiver are equipped with multiple antennas. In addition, two reconfigurable metasurfaces are deployed, one in the immediate vicinity of the transmit antenna array, and one in the immediate vicinity of the receive antenna array. The resulting...
In this paper we study a rigorous setting for the numerical approximation via deep neural networks of the generalized Korteweg-de Vries (gKdV) model in one dimension, for subcritical and critical nonlinearities, and assuming that the domain is the unbounded real line. The fact that the model is posed on the real line makes the problem...
Extraordinary acoustic transmission is commonly associated with periodic or multi-aperture structures. In this work, we show that a single subwavelength slit can support strongly enhanced transmission when its boundary response is described by an effective impedance. Using a reduced analytical model together with numerical...
While Vision-Language Models (VLMs) and Multimodal Large Language Models (MLLMs) have shown strong generalisation in detecting image and video deepfakes, their use for audio deepfake detection remains largely unexplored. In this work, we aim to explore the potential of MLLMs for audio deepfake detection. Combining audio inputs with a...
How much do worker skills, firm pay policies, and their interaction contribute to wage inequality? Standard approaches rely on latent fixed effects identified through worker mobility, but sparse networks inflate variance estimates, additivity assumptions rule out complementarities, and the resulting decompositions lack...
Atmospheric blocking events are quasi-stationary high-pressure systems that disrupt the typical paths of polar and subtropical air currents, often producing prolonged extreme weather events such as summer heat waves or winter cold spells. Despite their critical role in shaping mid-latitude weather, accurately modeling and analyzing...
The increasing prevalence of sparse Mixture-of-Experts (MoE) architectures in large language models raises important questions regarding their reliability under stochastic decoding. While conditional computation enables substantial gains in computational efficiency, it remains unclear whether the interaction between sparse routing and...
In this work, we analyze the evolution of PMFs within a perturbed Friedmann-Lemaître-Robertson-Walker (FLRW) spacetime using the formalisms of Numerical Relativity (NR). We apply the 3+1 decomposition to first-order cosmological perturbations to derive the cosmological dynamo equation under the kinematic-dynamo approximation. Our...