arXiv preprints from January 1, 2026 through September 30, 2026 — 16:45:43 EST
WASP-107~b is an extremely low-density super-puff exoplanet whose inflated radius and evidence of strong internal heating make it a key target for understanding planetary structure and evolution. Its orbital eccentricity is a critical parameter for testing mechanisms such as tidal heating and high-eccentricity migration, yet previous...
The concept of gravitons and their masses, clear in the case of Minkowski spacetime, remains ambiguous for de Sitter spacetime. Here, we used a two-fluid approach to de Sitter thermodynamics and found a collective mode that is analogous to second sound in the two-fluid dynamics of the de Sitter state. This mode is massless and...
This paper establishes a rigorous mathematical framework for the Multi-Scale Negative Coupled System (MNCS), a dynamical model describing hierarchical state spaces with directed, sign-structured interactions. We address the stabilization of reaction-diffusion systems on bounded domains $Ω\subset \mathbb{R}^d$ ($d \le 3$) subject to...
Aerosol-cloud interactions represent the largest uncertainty in climate-change assessment, and while cloud turbulence is considered crucial for droplet growth, its precise role remains unclear. Our laboratory-controlled studies show that turbulence does not always enhance collision and coalescence; instead, its influence emerges only...
X-ray selected surveys of clusters of galaxies have been reported to contain more regular cool core clusters compared to samples selected using the Sunyaev-Zel'dovich (SZ) effect. Morphology population studies on X-ray selected clusters will be biased without taking into account selection, as cool cores are more easily detected at low...
Integrating Artificial Intelligence into Software Engineering (SE) requires having a curated collection of models suited to SE tasks. With millions of models hosted on Hugging Face (HF) and new ones continuously being created, it is infeasible to identify SE models without a dedicated catalogue. To address this gap, we present SEMODS:...
In economics, construction of perfect models in a way that would be comparable to the standards customary in physical sciences is generally not feasible. In particular, the observed value for an economic equilibrium may deviate significantly from its model-based a priori expected value. Mathematically, the a posteriori observed...
Real-time log analysis is the cornerstone of observability for modern infrastructure. However, existing online parsers are architecturally unsuited for the dynamism of production environments. Built on fundamentally static template models, they are dangerously brittle: minor schema drifts silently break parsing pipelines, leading to...
Motivated by variational inference methods, we propose a zeroth-order algorithm for solving optimization problems in the space of Gaussian probability measures. The algorithm is based on an interacting system of Gaussian particles that stochastically explore the search space and self-organize around global minima via a consensus-based...
We prove an inequality featuring three well-known functions from analysis, namely the cotangent, the Euler-Riemann zeta function, and the digamma function. Aside from a simple proof of our result, we give a conjectured strengthening. We offer various remarks about the origins of this problem.
We present a reference-free holographic telepresence system that directly captures and replays complex optical wavefronts from a single intensity speckle measurement. Using a pre-characterized geometric phase diffuser, the incident field self-interferes to form a speckle pattern, from which the wavefront is recovered via a...
Automatic speech recognition (ASR) for conversational code-switching speech remains challenging due to the scarcity of realistic, high-quality labeled speech data. This paper explores multilingual text-to-speech (TTS) models as an effective data augmentation technique to address this shortage. Specifically, we fine-tune the...
A critical review of artificial intelligence and deep machine learning (AI/ML) applied to downscaling of global climate model simulations provides some words of caution, based on past experiences and well-established principles. Recent papers tend to ignore more subtle successes with statistics and mathematical based downscaling, and...
Accurate hindcasting of sea state events is a cornerstone of coastal engineering, risk assessment, and climate-related studies, yet it remains limited by uncertainties in physical parameterizations and model structure. This study introduces an automated calibration framework based on Bayesian Optimization (BO) using the...
Intelligent Connected Vehicles (ICVs) are a core component of modern transportation systems, and their security is crucial as it directly relates to user safety. Despite prior research, most existing studies focus only on specific sub-components of ICVs due to their inherent complexity. As a result, there is a lack of systematic...
Preoperative prognosis of Ependymoma is critical for treatment planning but challenging due to the lack of semantic insights in MRI compared to post-operative surgical reports. Existing multimodal methods fail to leverage this privileged text data when it is unavailable during inference. To bridge this gap, we propose HyperPriv-EPN, a...
We propose a real-time 3D human pose estimation and motion analysis method termed RePose for rehabilitation training. It is capable of real-time monitoring and evaluation of patients'motion during rehabilitation, providing immediate feedback and guidance to assist patients in executing rehabilitation exercises correctly. Firstly, we...
Large Language Models (LLMs) such as ChatGPT, Claude, and Gemini increasingly act as general-purpose copilots, yet they often respond with unnecessary length on simple requests, adding redundant explanations, hedging, or boilerplate that increases cognitive load and inflates token-based inference cost. Prior work suggests that...
Direct Preference Optimization (DPO) has shown strong potential for mitigating hallucinations in Multimodal Large Language Models (MLLMs). However, existing multimodal DPO approaches often suffer from overfitting due to the difficulty imbalance in preference data. Our analysis shows that MLLMs tend to overemphasize easily...
Electromagnetically induced transparency (EIT) in $Λ$-type atomic systems underpins quantum technologies such as high-fidelity memory and nonlinear optics, but conventional setups face intrinsic limitations. Standard geometries of one-dimensional atomic chains coupled to waveguides allow only a single bright superradiant channel,...
For a graph $G$, the spectral radius $ρ(G)$ of $G$ is the largest eigenvalue of its adjacency matrix. In this paper, we give three lammas on $ρ(G)$ when $G$ contains a spanning complete bipartite graph. Using these lemmas and typical spectral method, we characterized the unique extremal graph with the maximum spectral radius among all...
A graph is reconstructible if it is determined up to isomorphism from the collection of all its one-vertex-deleted subgraphs, known as the deck of G. The Reconstruction Conjecture (RC) posits that every finite simple graph with at least three vertices is reconstructible. In this paper, we prove that the class of graphs with domination...
Cobalt-free high-entropy alloys (HEAs) have garnered interest for nuclear structural applications due to their good mechanical performance, thermal stability, and resistance to radiation-induced degradation, while avoiding long-lived Co radioisotopes. This study presents an experimental and computational investigation of the plastic...
We have previously shown that Good-Turing statistics can be applied to molecular dynamics trajectories to estimate the probability of observing completely new (thus far unobserved) biomolecular structures, and showed that the method is stable, dependable and its predictions verifiable. The major problem with that initial algorithm was...
Despite significant advances in generic object detection, a persistent performance gap remains for tiny objects compared to normal-scale objects. We demonstrate that tiny objects are highly sensitive to annotation noise, where optimizing strict localization objectives risks noise overfitting. To address this, we propose Tiny Object...