arXiv preprints from January 1, 2026 through September 30, 2026 — 05:16:49 EST
The rapid growth of AI for Science (AI4S) has underscored the significance of scientific datasets, leading to the establishment of numerous national scientific data centers and sharing platforms. Despite this progress, efficiently promoting dataset sharing and utilization for scientific research remains challenging. Scientific...
Understanding how educators interact and form peer networks in online professional development contexts has become increasingly important as MOOCs for educators (MOOC-Eds) proliferate. This study examines peer discussion network formation and evolution in 'The Digital Learning Transition in K-12 Schools', a MOOC-Ed offered to U.S. and...
The increasing deployment of artificial intelligence (AI) in clinical settings challenges foundational assumptions underlying traditional frameworks of medical evidence. Classical statistical approaches, centered on randomized controlled trials, frequentist hypothesis testing, and static confidence intervals, were designed for fixed...
Aligning large language models (LLMs) with human preferences has become essential for safe and beneficial AI deployment. While Reinforcement Learning from Human Feedback (RLHF) established the dominant paradigm, a proliferation of alternatives -- Direct Preference Optimization (DPO), Identity Preference Optimization (IPO),...
The orbital Hall effect (OHE) in light transition metals offers a promising route to generate orbital torques for efficient magnetization control, providing an alternative to conventional spin Hall effect approaches that rely on heavy metals. We demonstrate perpendicular magnetization switching in [Co/Pt]3 multilayers driven by the...
Galaxy clusters provide an ideal laboratory for investigating the chemical enrichment history of the universe because they host the hot intracluster medium (ICM), which contains various chemical elements. The X-ray observations have constituted a unique way to measure the element abundance and composition of the ICM due to their...
In this work, the potential parameters of the independent quark model are systematically reduced using previously determined inputs from a broad range of baryons. The reduced-parameter formulation, developed within the relativistic Dirac formalism and employing a Martin-like potential, is then applied to the spectroscopy of the singly...
We introduce EmoLoom-2B, a lightweight and reproducible pipeline that turns small language models under 2B parameters into fast screening candidates for joint emotion classification and Valence-Arousal-Dominance prediction. To ensure protocol-faithful and fair evaluation, we unify data loading, training, and inference under a single...
A theorem of Meyer and Reisner characterizes ellipsoids by the collinearity of centroids of parallel sections: if $Ω\subset\mathbb{R}^{n+1}$ is a convex body such that for every $n$-dimensional subspace $M\subset\mathbb{R}^{n+1}$ the centroids of the sections $(x+M)\cap Ω$ are collinear, then $Ω$ is an ellipsoid. We study natural...
This paper studies phase and norm retrieval by subspaces. We first investigate norm retrieval by hyperplanes. We show that if $N$ hyperplanes $\{\varphi_i^\perp\}_{i=1}^N\subset \mathbb{R}^N$ allow norm retrieval and the vectors $\{\varphi_i\}_{i=1}^N$ are linearly independent, then these vectors must be an orthonormal basis for...
Tidal structures around globular clusters provide valuable insights into cluster evolution and the hierarchical assembly of the Milky Way. Using wide-field imaging data from the DESI Legacy Survey combined with a color-magnitude matched-filter technique, we perform a systematic analysis of extra-tidal features in 28 Galactic globular...
The strategy of combining diffusion-based generative models with classifiers continues to demonstrate state-of-the-art performance on adversarial robustness benchmarks. Known as adversarial purification, this exploits a diffusion model's capability of identifying high density regions in data distributions to purify adversarial...
Grain boundary (GB) migration is a pivotal process that significantly impacts the development of microstructures, thereby influencing the practical performance of polycrystalline materials. Recent advances in 3D experimental techniques have revealed conflicts between observed GB migration behaviors and classical theoretical models....
Accurate aerodynamic and aerothermodynamic predictions are crucial for numerous hypersonic applications. This paper proposes a gas-kinetic scheme (GKS) coupled with a two-temperature kinetic model, which distinguishes between the translational-rotational and vibrational modes of temperature. Compared with one-temperature model and the...
Autonomous object recovery in the hadal zone is challenging due to extreme hydrostatic pressure, limited visibility and currents, and the need for precise manipulation at full ocean depth. Field experimentation in such environments is costly, high-risk, and constrained by limited vehicle availability, making early validation of...
This study examines long-term CO$_2$ emission trajectories across five major economies: Nigeria, the United States, China, Brazil, and Russia, by integrating national energy-mix characteristics with time-series forecasting models. Annual emissions from 2000-2023 were analyzed alongside energy production data to classify countries into...
This paper investigates the performance of the emerging non-variational Quantum Walk-based Optimisation Algorithm (NV-QWOA) for solving small instances of the Quadratic Assignment Problem (QAP). NV-QWOA is benchmarked against classical heuristics, the MaxMin Ant System (MMAS) and Greedy Local Search (GLS), as well as the Grover...
We present HAQAGen, a unified generative model for resolution-invariant NIR-to-RGB colorization that balances chromatic realism with structural fidelity. The proposed model introduces (i) a combined loss term aligning the global color statistics through differentiable histogram matching, perceptual image quality measure, and feature...
We discuss the low energy resolvent estimates for the Schrödinger operator with slowly decaying attractive potential. The main results are Rellich's theorem, the limiting absorption principle and Sommerfeld's uniqueness theorem. For the proofs we employ an elementary commutator method due to Ito--Skibsted, for which neither of...
Despite the rise of data-driven software systems in the modern digital landscape, data governance under a legal framework remains a critical challenge. In India, the Digital Personal Data Protection (DPDP) Act mandates rigorous data privacy and compliance requirements, necessitating software frameworks that are both ethical and...
Driving non-superconducting materials into a superconducting state through specific modulation is a key focus in the field of superconductivity. Pressure is a powerful method that can switch a three-dimensional (3D) material between non-superconducting and superconducting states. In the two-dimensional (2D) case, strain engineering...
This paper presents a comparative study of a custom convolutional neural network (CNN) architecture against widely used pretrained and transfer learning CNN models across five real-world image datasets. The datasets span binary classification, fine-grained multiclass recognition, and object detection scenarios. We analyze how...
In this paper, we study normalized solutions for the following critical Schrödinger-Bopp-Podolsky system: $$-Δu + q(x)φu = λu + |u|^{p-2}u + \bigl(I_α* |u|^{3+α}\bigr)|u|^{1+α}u,\quad \text{in } Ω_r,$$ $$-Δφ+ Δ^2φ= q(x)u^2, \ \qquad\qquad\qquad\qquad\qquad\qquad\qquad\ \text{ in } Ω_r,$$ where $Ω_r \subset \mathbb R^3$ is a smooth...
Recent works have demonstrated promising performances of neural networks on hyperbolic spaces and symmetric positive definite (SPD) manifolds. These spaces belong to a family of Riemannian manifolds referred to as symmetric spaces of noncompact type. In this paper, we propose a novel approach for developing neural networks on such...
Excitonic insulators host the condensates of bound electron-hole pairs, offering a platform for studying correlated bosonic quantum states. Yet, how macroscopic coherence emerges from locally collapsed pairing remains elusive. Here, using scanning tunnelling spectroscopy, we report the impurity-induced pair breaking in an excitonic...