arXiv preprints from January 1, 2026 through September 30, 2026 — 01:49:45 EST
In this paper, a derivative for functions $f : G \to H$, where $G$ is any metric divisible group and $H$ is a metric Abelian group with a group metric, is defined. Basic differentiation theorems are stated and demonstrated. In particular, we obtain the Chain Rule
This proceedings paper briefly reviews the status of direct experimental probes into the neutrino-mass scale, with an emphasis on recent results from the KATRIN experiment.
Partially ionized plasma physics has attracted a lot of attention recently due to numerous technological applications made possible by the increased sophistication of computer modelling, the depth of the theoretical analysis, and the technological applications to a vast field of the manufacturing for computer components. The partially...
In this paper we consider a class of impulsive nonlinear differential equations with adaptive state-dependent delays. We discuss the existence and uniqueness of solutions of the initial value problem using a Picard-Lindelöf type argument where we define approximate solutions with the help of equations with piecewise-constant arguments...
How many workers displaced by automation can realistically transition to safer jobs? We answer this using a validated knowledge graph of 9,978 Egyptian job postings, 19,766 skill activities, and 84,346 job-skill relationships (0.74% error rate). While 20.9% of jobs face high automation risk, we find that only 24.4% of at-risk workers...
We present a spectro-polarimetric re-analysis of the first IXPE observation of Cygnus X-2 which we determine to be mainly in the normal branch, from quasi-simultaneous observations with NuSTAR, NICER, and INTEGRAL. We measure the hard X-ray polarization angle and find it to be consistent with the previously measured position angle of...
Clinicians routinely navigate fragmented electronic health record (EHR) interfaces to assemble a coherent picture of a patient's problems, medications, recent encounters, and longitudinal trends. This manuscript describes EHRSummarizer, a privacy-aware, FHIR-native reference architecture for structured EHR summarization. The...
Photoacoustic tomography leverages ultrasound's deep tissue penetration to retrieve optical absorption contrast well beyond the optical diffusion limit. Conventional photoacoustic systems rely on externally delivered light and are therefore constrained by optical attenuation, limiting imaging depths to several centimeters. Here, we...
It is widely thought that the quantum theory of de Sitter space requires the existence of a physical observer in the static patch. What exactly is meant by an observer is unclear; it could be anything from a few photons with energy just above the Gibbons-Hawking temperature to a gravitationally bound cluster of galaxies. In a recent...
Deep reinforcement learning (DRL) has shown great promise in addressing multi-objective combinatorial optimization problems (MOCOPs). Nevertheless, the robustness of these learning-based solvers has remained insufficiently explored, especially across diverse and complex problem distributions. In this paper, we propose a unified...
Consider a collection of m competing machine learning algorithms. Given their performance on a benchmark of datasets, we would like to identify the best performing algorithm. Specifically, which algorithm is most likely to ``win'' (rank highest) on a future, unseen dataset. The standard maximum likelihood approach suggests counting...
We study generative modeling of \emph{variable-length trajectories} -- sequences of visited locations/items with associated timestamps -- for downstream simulation and counterfactual analysis. A recurring practical issue is that standard mini-batch training can be unstable when trajectory lengths are highly heterogeneous, which in...
Complex data features, such as unmodelled censored event times and variables with time-dependent effects, are common in cancer recurrence studies and pose challenges for Bayesian survival modelling. Current methodologies for predictive model checking and comparison often fail to adequately address these features. This paper bridges...
In this work, we propose a novel partial electromagnetic duality and construct its consistent realization using an extended gauge group $\mathrm{U}(1)_{\mathrm{q}}^{}\otimes\mathrm{U}(1)_{\mathrm{d}}^{}$ over a localized space region, where $\mathrm{U}(1)_{\mathrm{q}}^{}$ is the conventional gauge group of QED and...
We introduce a unified framework that combines nonlinear dynamics, perceptual psychophysics and high frequency haptic rendering to enhance realism in surgical simulation. The interaction of the surgical device with soft tissue is elevated to an augmented state space with a Koopman operator formulation, allowing linear prediction and...
We present a method for consistent lighting and shadows when animated 3D Gaussian Splatting (3DGS) avatars interact with 3DGS scenes or with dynamic objects inserted into otherwise static scenes. Our key contribution is Deep Gaussian Shadow Maps (DGSM), a modern analogue of the classical shadow mapping algorithm tailored to the...
The excess energy of the one-component plasma fluid is calculated using the variational approach combined with different variants of the excess entropy of the hard-sphere fluid, which is used as a reference system. Our comparison with recent Monte Carlo results for the excess energy of the one-component plasma identifies the...
Tactile sensing is essential for robotic manipulation, prosthetics and assistive technologies, yet neuromorphic tactile datasets remain limited compared to their visual counterparts. We introduce STEMNIST, a large-scale neuromorphic tactile dataset extending ST-MNIST from 10 digits to 35 alphanumeric classes (uppercase letters A--Z...
Upscaling is central to offshore wind's cost-reduction strategy, with increasingly large rotors and nacelles requiring taller and stronger towers. In Floating Offshore Wind Turbines (FOWTs), this trend amplifies fatigue loads due to coupled wind-wave dynamics and platform motion. Conventional fatigue evaluation requires millions of...
We extend the previously developed [1] generally covariant formalism to include diffusion of conserved charges, and comment on the seming difference between the chemica potential term and the diffusion term
Accurate crop yield prediction relies on diverse data streams, including satellite, meteorological, soil, and topographic information. However, despite rapid advances in machine learning, existing approaches remain crop- or region-specific and require data engineering efforts. This limits scalability, reproducibility, and operational...
Fix a prime $p > 3$. Working over $\mathbb{Z}_p$, we show that the quantum connection of any closed Calabi-Yau threefold gives rise to a Fontaine-Laffaile module when restricted to the even degree and torsion-free part of $p$-adic quantum cohomology, whose associated Frobenius endomorphism has leading order term prescribed by the...
In the face of adverse motives, it is indispensable to achieve a consensus. Elections have been the canonical way by which modern democracy has operated since the 17th century. Nowadays, they regulate markets, provide an engine for modern recommender systems or peer-to-peer networks, and remain the main approach to represent...
Building on recent advances in reduced density matrix theory, we develop a geometric framework for describing strongly correlated lattice bosons. We first establish that translational symmetry, together with a fixed pair interaction, enables an exact functional formulation expressed solely in terms of momentum occupation numbers....
This work presents DemoBot, a learning framework that enables a dual-arm, multi-finger robotic system to acquire complex manipulation skills from a single unannotated RGB-D video demonstration. The method extracts structured motion trajectories of both hands and objects from raw video data. These trajectories serve as motion priors...