December 2025 arXiv papers — page 50

Mingrui Wu, Zhaozhi Wang, Fangjinhua Wang, Jiaolong Yang

While Multimodal Large Language Models (MLLMs) have achieved impressive performance on semantic tasks, their spatial intelligence--crucial for robust and grounded AI systems--remains underdeveloped. Existing benchmarks fall short of diagnosing this limitation: they either focus on overly simplified qualitative reasoning or rely on domain-specific indoor data

Jiacheng Guo, Ling Yang, Peter Chen, Qixin Xiao

Training capable Large Language Model (LLM) agents is critically bottlenecked by the high cost and static nature of real-world interaction data. We address this by introducing GenEnv, a framework that establishes a difficulty-aligned co-evolutionary game between an agent and a scalable, generative environment simulator. Unlike traditional methods that evolve

Elizabeth B. Amona, Indranil Sahoo, David Chan, Marianne B. Lund

Despite the significant public health burden of common mental disorders (CMDs) among refugee populations, their underlying symptom structures remain underexplored. This study uses Gaussian graphical modeling to examine the symptom network of post-traumatic stress disorder (PTSD), depression, anxiety, and somatic distress among Eritrean refugees in the Greate

Xinyao Liao, Qiyuan He, Kai Xu, Xiaoye Qu

Autoregressive (AR) visual generation relies on tokenizers to map images to and from discrete sequences. However, tokenizers are trained to reconstruct clean images from ground-truth tokens, while AR generators are optimized only for token likelihood. This misalignment leads to generated token sequences that may decode into low-quality images, without direct

Farzam Tajdari, Georgios Papaioannou, Riender Happee

The security of Automated Vehicles (AVs) is an important emerging area of research in traffic safety. Methods have been published and evaluated in experimental vehicles to secure safe AV control in the presence of attacks, but human motion comfort is rarely investigated in such studies. In this paper, we present an innovative optimal-coupling-observer-based

Gerol Petruzella

The question of whether AI systems have morally relevant interests -- the 'model welfare' question -- depends in part on how we evaluate AI testimony about inner states. This paper develops what I call the inconsistency critique: independent of whether skepticism about AI testimony is ultimately justified, our actual epistemic practices regarding such testim

Hanyang Kong, Xingyi Yang, Xiaoxu Zheng, Xinchao Wang

Generating long-range, geometrically consistent video presents a fundamental dilemma: while consistency demands strict adherence to 3D geometry in pixel space, state-of-the-art generative models operate most effectively in a camera-conditioned latent space. This disconnect causes current methods to struggle with occluded areas and complex camera trajectories

Letizia Iannucci, Elisa Muratore, Antonis Matakos, Mikko Kivelä

In the era of widespread online content consumption, effective detection of coordinated efforts is crucial for mitigating potential threats arising from information manipulation. Despite advances in isolating inauthentic and automated actors, the actions of individual accounts involved in influence campaigns may not stand out as anomalous if analyzed indepen

Mojtaba Safari, Shansong Wang, Vanessa L Wildman, Mingzhe Hu

Background: High-resolution MRI is critical for diagnosis, but long acquisition times limit clinical use. Super-resolution (SR) can enhance resolution post-scan, yet existing deep learning methods face fidelity-efficiency trade-offs. Purpose: To develop a computationally efficient and accurate deep learning framework for MRI SR that preserves anatomical deta

Niclas Griesshaber, Jochen Streb

We leverage multimodal large language models (LLMs) to construct a dataset of 306,070 German patents (1877-1918) from 9,562 archival image scans using our LLM-based pipeline powered by Gemini-2.5-Pro and Gemini-2.5-Flash-Lite. Our benchmarking exercise provides tentative evidence that multimodal LLMs can create higher quality datasets than our research assis

Joseph A. M. Paddison, Lionel Desgranges, Gianguido Baldinozzi, Gerard H. Lander

Uranium dioxide, UO$_2$, is a canonical example of a magnetic material with strong spin-orbit coupling. Here, we present a study of the magnetic diffuse scattering measured on a polycrystalline sample of UO$_2$, which we interpret in terms of its magnetic interactions between U$^{4+}$ magnetic moments. By refining values of the magnetic interaction parameter

Arthur Blanc-Renaudie, Asaf Nachmias

We consider percolation on the discrete torus $\mathbb{Z}_n^d$ at $p_c(\mathbb{Z}^d)$, the critical value for percolation on the corresponding infinite lattice $\mathbb{Z}^d$, and within the scaling window around it. We assume that $d$ is a large enough constant for the nearest neighbor model, or any fixed $d>6$ for spread-out models. We prove that there exi

Lipeng Zu, Hansong Zhou, Yu Qian, Shayok Chakraborty

Real-world wireless data are expensive to collect and often lack sufficient expert demonstrations, causing existing offline RL methods to overfit suboptimal behaviors and exhibit unstable performance. To address this issue, we propose CORE, an offline RL framework specifically designed for wireless environments. CORE identifies latent expert trajectories fro

Dmitry S. Ageev, Yulia A. Ageeva

We develop a basis--covariant one--loop renormalization framework for two interacting real scalars in $D=4-\epsilon$ with the most general two--derivative Lorentz--violating quadratic form, allowing anisotropic spatial gradients and direction--dependent kinetic mixing, together with general cubic and quartic interactions forming RG complete set of operators

Jorge Becerra, Lukas Woike

The algebraic notion of a pivotal module category was developed by Schaumann and Shimizu and is central to the description of boundary conditions in conformal field theory according to a proposal by Fuchs and Schweigert. In this paper, we present a large class of examples of pivotal module categories of topological origin: For a unimodular finite ribbon cate

Matthias Raphael Stock, Hans Th. J. Steiger, Ulrike Fahrendholz, Luca Schweizer

Accurate single photoelectron (SPE) characterization of photosensors is essential for controlling systematic uncertainties in low-light neutrino and dark matter detectors. We present a compact laboratory setup for the characterization of photosensors under controlled, low-light conditions. Specifically, we demonstrate its use with photomultiplier tubes (PMTs

David Aguirre, Rubén Peña, Mikel Sanz

As quantum technologies continue to advance, the proliferation of hardware architectures with diverse capabilities and limitations has underscored the importance of benchmarking as a tool to compare performance across platforms. Achieving fair, scalable and consistent evaluations is a key open problem in quantum computing, particularly in the pre-fault-toler

Argha Kamal Samanta, Harshika Goyal, Vasudha Joshi, Tushar Mungle

Diabetic retinopathy (DR) is a leading cause of preventable blindness worldwide, demanding accurate automated diagnostic systems. While general-domain vision-language models like Contrastive Language-Image Pre-Training (CLIP) perform well on natural image tasks, they struggle in medical domain applications, particularly in cross-modal retrieval for ophthalmo

Austin Baldwin, Joshua D. Lothringer, Leonardo A. Dos Santos, David K. Sing

We present high-resolution NUV observations from Hubble Space Telescope's (HST) Space Telescope Imaging Spectrograph (STIS) data for the hottest known gas planet, KELT-9b. Observations were collected with STIS/E230M (2300-3000 $\r{A}$, R$\sim$ 30,000) and we de-correlate systematic effects from the telescope using jitter detrending. We show the clear presenc

Luchao Qi, Jiaye Wu, Jun Myeong Choi, Cary Phillips

In professional video compositing workflows, artists must manually create environmental interactions-such as shadows, reflections, dust, and splashes-between foreground subjects and background layers. Existing video generative models struggle to preserve the input video while adding such effects, and current video inpainting methods either require costly per

Constantin Shramov, Andrey Trepalin

It is known that any Mori fiber space birational to a minimal smooth del Pezzo surface $S$ of degree $4$ is either a del Pezzo surface of degree $4$ itself, or a smooth cubic surface with a structure of a relatively minimal conic bundle. We show that any del Pezzo surface of degree $4$ birational to $S$ is actually isomorphic to $S$. Also, we sketch an equiv

Matthew Bolan

We produce a family of bodies in $\mathbb R^3$ parameterized by $\varepsilon > 0$, each bounded by a smooth topological sphere with principal curvatures in $[-1, 1]$, and having volume arbitrarily close to $ 16 - 4\sqrt 3 + \left(10 \sqrt 3 - 14\right) \pi - \left(\frac{10}{3} - \sqrt 3\right) \pi^2 \approx 3.70.$ Thus, in contrast to the two-dimensional cas

Muhammad Erew, Moshe Goldstein

Magic states enable universal, fault-tolerant quantum computation within the stabilizer framework. Their non-stabilizerness supplies the resource needed to bypass the Eastin-Knill theorem while allowing fault-tolerant distillation. Although many measures of magic exist, not every nonzero-magic state is known to be distillable, and many of currently known dis

Aurélien Faucher

We prove the Kawamata-Morrison cone conjecture for Q-factorial terminal projective primitive symplectic varieties with second Betti number greater than five defined over a field of characteristic zero. As an application, we prove that the relative movable and the relative nef cone conjectures hold for fibrations whose very general fibre is a projective primi

Valdemar Melin, Paul Wiegmann, Konstantin Zarembo

We show that the semiclassical limit of thermodynamic Bethe Ansatz equations naturally reconstructs the algebro-geometric spectra of finite-gap periodic potentials. This correspondence is illustrated using the traveling-wave (snoidal) solution of the defocusing modified Korteweg--de Vries equation. In this framework, the Bethe-root distribution of the associ

Diptarka Chakraborty, Hendrik Fichtenberger, Bernhard Haeupler, Silvio Lattanzi

Designing efficient, effective, and consistent metric clustering algorithms is a significant challenge attracting growing attention. Traditional approaches focus on the stability of cluster centers; unfortunately, this neglects the real-world need for stable point labels, i.e., stable assignments of points to named sets (clusters). In this paper, we address

Zoltán Zimborás, Attila Portik, David Aguirre, Rubén Peña

As quantum processors continue to scale in size and complexity, the need for well-defined, reproducible, and technology-agnostic performance metrics becomes increasingly critical. Here we present a suite of scalable quantum computing benchmarks developed as key performance indicators (KPIs) within the EU Quantum Flagship. These proposed benchmarks are design

Serat Mahmud Saad, Yuan-Sen Ting

Wide-binary stars, separated by thousands of AU, reside in low-acceleration regimes where Modified Newtonian Dynamics (MOND) predicts deviation from Newtonian gravity. However, Gaia radial velocities (RVs) lack the precision to resolve the small velocity differences expected in these systems, limiting previous MOND analyses to two-dimensional kinematics. In

Filippos Ventirozos, Peter Appleby, Matthew Shardlow

Aspect-Category Sentiment Analysis (ACSA) provides granular insights by identifying specific themes within reviews and their associated sentiment. While supervised learning approaches dominate this field, the scarcity and high cost of annotated data for new domains present significant barriers. We argue that leveraging large language models (LLMs) in a zero-

Joe Davighi, Gino Isidori

We introduce a composite Higgs model in which a flavour deconstructed gauge group is embedded in the strong sector. The pattern of global symmetry breaking yields, as pseudo-Nambu-Goldstone (pNGB) bosons, both a Standard Model (SM)-like Higgs and the link field whose vacuum expectation value breaks the flavour non-universal gauge group down to the SM. Inevit

Aleksey Minabutdinov, Patrick Cheridito

We introduce a novel deep learning algorithm for computing convex conjugates of differentiable convex functions, a fundamental operation in convex analysis with various applications in different fields such as optimization, control theory, physics and economics. While traditional numerical methods suffer from the curse of dimensionality and become computatio

Arnold Caleb Asiimwe, Carl Vondrick

We reinterpret 4D Gaussian Splatting as a continuous-time dynamical system, where scene motion arises from integrating a learned neural dynamical field rather than applying per-frame deformations. This formulation, which we call EvoGS, treats the Gaussian representation as an evolving physical system whose state evolves continuously under a learned motion la

Hwai-Ray Tung, Sean D Lawley

Many physical processes depend on the time it takes a diffusing particle to find a target. Though this classical quantity is now well-understood in various scenarios, little is known if the diffusivity depends on the location of the particle. For such heterogeneous diffusion, an ambiguity arises in interpreting the stochastic process, which reflects the well

Martin Kreuzer, Anja Moldenhauer, Gerhard Rosenberger

Given a group $G = H_1 \ast_A H_2$ which is the free product of two finitely generated groups $H_1$ and $H_2$ with amalgamation over a cyclic subgroup $A$ which is malnormal in $G$, we study relations between the structure of its subgroups and the structure of the group $G$ itself. Firstly, we show that if $H_1$ and $H_2$ are 3-free products of cyclics of ra

Gabrielle O'Brien, Alexis Parker, Nasir Eisty, Jeffrey Carver

Programming is essential to modern scientific research, yet most scientists report inadequate training for the software development their work demands. Generative AI tools capable of code generation may support scientific programmers, but user studies indicate risks of over-reliance, particularly among inexperienced users. We surveyed 868 scientists who prog

John H. J. Einmahl, Denis Kojevnikov, Bas J. M. Werker

We wish to test whether a real-valued variable $Z$ has explanatory power, in addition to a multivariate variable $X$, for a binary variable $Y$. Thus, we are interested in testing the hypothesis $\mathbb{P}(Y=1\, | \, X,Z)=\mathbb{P}(Y=1\, | \, X)$, based on $n$ i.i.d.\ copies of $(X,Y,Z)$. In order to avoid the curse of dimensionality, we follow the common

Roberta Angius, Stefano Giaccari, Sarah M. Harrison, Roberto Volpato

We continue the study, initiated in [hep-th:2504.18619], of topological defect lines (TDLs) in the Conway module $V^{f \natural}$ and K3 non-linear sigma models (NLSMs). In the case of $V^{f \natural}$, we fully classify the potential $N=1$ (and $N=4$)--preserving duality defects for cyclic Tambara--Yamagami categories TY$(\mathbb{Z}_N)$, noting a curious re

Alejandro Ramírez-Arroyo, O. S. Peñaherrera-Pulla, Preben Mogensen

The emergence of commercial satellite communications networks, such as Starlink and OneWeb, has significantly transformed the communications landscape over the last years. As a complement to terrestrial cellular networks, non-terrestrial systems enable coverage extension and reliability enhancement beyond the limits of conventional infrastructure. Currently,

Zeev Dvir

We give a general lower bound on the rank of matrices of the form $\rho(h) - I$ with $\rho : G \rightarrow GL({\mathbb F}^n)$ an irreducible representation of a finite group $G$. The main tool in the proof is a (strengthening) of a reduction due to Efremenko from low rank matrices spanned by a few images of $\rho$ to Locally Decodable Codes (LDCs), which are

Carolina Gonçalves, Tiago D. Ferreira, Catarina S. Monteiro, Nuno A. Silva

Two-photon interference in a Hong-Ou-Mandel (HOM) interferometer can be used as a quantum sensing mechanism due to the sensitivity of the interference dip to perturbations of the photon indistinguishability. In particular, recent works have generalized this concept to microscopy setups, but the sensitivity to optical path differences constrains its applicati

T. T. Hieu, V. H. Binh, H. N. Long, H. T. Hung

We consider heavy neutral bosons in the 3-3-1 model with axionlike particles (331ALP), including the Higgs boson and the $Z^\prime$ boson which are outside the standard model (SM). Based on gluon-gluon fusion at the LHC, we investigate the signals of cross-sections in the parameter space region satisfying the current experimental limits of lepton flavor viol

Lyza Iamrache, Kamel Rekab, Majid Bani-Yagoub, Julia Pluta

Predicting relative risk (RR) of spatial clusters is a complex task in public health that can be achieved through various statistical and machine-learning methods for different time intervals. However, high-resolution longitudinal data is often unavailable to successfully apply such methods. The goal of the present study is to further develop and test a new

William Cvetko, Melissa Joseph, Gustavo Marques-Tavares

In this work we assess the implications of the Atacama Cosmology Telescope DR6 measurements for two interacting dark radiation scenarios previously shown to mitigate the Hubble tension. The first model, Wess-Zumino dark radiation (WZDR), features a mass threshold in the dark sector that induces a step-like reduction in the dark radiation abundance as the dar

Yuntao Gu, Louis Hector, Paolo Giusto, Matthew Titsworth

Quantum computing presents a promising alternative to classical computational methods for modeling strongly correlated materials with partially filled d orbitals. In this study, we perform a comprehensive quantum resource estimation using quantum phase estimation (QPE) and qubitization techniques for transition metal oxide molecules and a Pd zeolite catalyst

Da Tan, Michael Beck, Christopher P. Bidinosti, Robert H. Gulden

The success of agricultural artificial intelligence depends heavily on large, diverse, and high-quality plant image datasets, yet collecting such data in real field conditions is costly, labor intensive, and seasonally constrained. This paper investigates diffusion-based generative modeling to address these challenges through plant image synthesis, indoor-to

Amit Chakraborty, Shreecheta Chowdhury, Nilanjana Kumar, Vandana Sahdev

Beyond the Standard Model scenarios introduce additional scalar and fermion multiplets, which influence neutrino mass generation mechanisms and yield distinctive collider signatures. This work focuses on a particular scenario involving a fermion quintuplet and a scalar quadruplet. The study examines the production and decay of the scalar quadruplet component

Rolando Coto-Solano, Daisy Li, Manoela Teleginski Ferraz, Olivia Sasse

We present experiments on diacritic restoration, a form of text normalization essential for natural language processing (NLP) tasks. Our study focuses on two extremely under-resourced languages: Bribri, a Chibchan language spoken in Costa Rica, and Cook Islands M\=aori, a Polynesian language spoken in the Cook Islands. Specifically, this paper: (i) compares

Jiaqi Peng, Wenzhe Cai, Yuqiang Yang, Tai Wang

Trajectory planning in unstructured environments is a fundamental and challenging capability for mobile robots. Traditional modular pipelines suffer from latency and cascading errors across perception, localization, mapping, and planning modules. Recent end-to-end learning methods map raw visual observations directly to control signals or trajectories, promi

Akash Banerjee, Alamgir Hossain, Md. Nasim Akhtar

The quantization problem for random fractals presents unique challenges due to the lack of uniform geometric scaling inherent in deterministic systems. In this article, we establish the almost sure quantization dimension for a class of $1$-variable (homogeneously) random self-similar measures. Unlike the deterministic setting, where the dimension is derived

Elliot Fisher, Robin Smith

The hypothetical global delivery schedule of Santa Claus must follow strict rolling night-time windows that vary with the Earth's rotation and obey an energy budget that depends on payload size and cruising speed. To design this schedule, the Travelling-Santa Ant-Colony Optimisation framework (TSaP-ACO) was developed. This heuristic framework constructs pote

Sarah E. Kay, Ehsan Kourkchi, A. Molaeinezhad, H. G. Khosroshahi

We present a deep wide-field narrow-band imaging survey of the local rich and dynamically relaxed Coma cluster of galaxies, carried out with the Wide Field Camera at the Isaac Newton Telescope. The survey covers a region of about 2.5 sq. deg. extending from the core of the cluster out to the infall region over the south-west quadrant of the Coma cluster. The

Jherek Healy

This follow-up article analyzes the impact of foreign exchange option interpolation on the vanilla option implied volatilities. In particular different exact interpolations of broker quotes may lead to different implied volatilities at the 10$\Delta$ and 25$\Delta$ Puts and Calls.

Abdulaziz Abogoda, J. A. Sauls

We investigate isolated O-H and O-D pairs trapped in BCC Nb using a machine-learning interatomic potential (MLIP) trained to density-functional theory (DFT). The MLIP enables large-supercell analysis and identification of trapping sites within BCC Nb, as well as efficient mapping of three-dimensional (3D) potential-energy surfaces. In addition to the pair of

Jherek Healy

This article provides a list of counterexamples, where some of the popular fx option interpolations break down. Interpolation of FX option prices (or equivalently volatilities), is key to risk-manage not only vanilla FX option books, but also more exotic derivatives which are typically valued with local volatility or local stochastic volatilility models.

Zahra Sadeghi, Evangelos Milios, Frank Rudzicz

Summary assessment involves evaluating how well a generated summary reflects the key ideas and meaning of the source text, requiring a deep understanding of the content. Large Language Models (LLMs) have been used to automate this process, acting as judges to evaluate summaries with respect to the original text. While previous research investigated the align

Vasileios N. Oikonomou, Samuel Walsh

Vortex streets are periodic configurations of vortices propagating through an irrotational flow. In this paper, we study streets of hollow vortices, which are solutions to the free boundary $2$-d irrotational incompressible Euler equations. Each vortex core is a region of constant pressure in the complement of the fluid domain with a nonzero circulation arou

Filipe C. Mena, João M. Oliveira

We investigate energy bounds and the stability of stationary asymptotically flat spacetimes with an ergoregion and no future horizon in the context of Einstein-Maxwell-Scalar field models which naturally arise in Kaluza-Klein and String theories. In order to do that we consider scalar field perturbations non-minimally coupled to a background electromagnetic

Mara Kalicanin Dimitrov, Marko Dimitrov, Anatoliy Malyarenko, Ying Ni

Recently, an Almost-Exact Simulation (AES) scheme was introduced for the Heston stochastic volatility model and tested for European option pricing. This paper extends this scheme for pricing Bermudan and American options under both Heston and double Heston models. The AES improves Monte Carlo simulation efficiency by using the non-central chi-square distribu

Schuyler B. Nicholson, Luis Pedro García-Pintos

Chemical and biological networks can describe a wide variety of processes, from gene regulatory networks to biochemical oscillations. Modeled by chemical master equations, these processes are inherently stochastic, as fluctuations dominate deterministic order at mesoscopic scales. These classic many-body processes suffer from the so-called curse of high dime

Yannick Werner, Juan Miguel Morales, Salvador Pineda, Line Roald

The two-stage stochastic unit commitment problem has become an important tool to support decision-making under uncertainty in power systems. Representing the uncertainty by a large number of scenarios guarantees accurate results but challenges the solution process. One way to overcome this is by using scenario reduction methods, which aim at finding a distri

Robert V. Harlander, Robert H. Mason

We provide results for the vacuum expectation values of the flowed action density, the quark condensate, and the quark kinetic operator in the gradient-flow formalism. We work in $N_\text{F}$-flavor QCD, keeping the heaviest quark massive and all others massless. The vacuum expectation values of these operators are calculated numerically through next-to-next

Jherek Healy

The Heston stochastic volatility model is arguably, the most popular stochastic volatility model used to price and risk manage exotic derivatives. In spite of this, it is not necessarily easy to calibrate to the market and obtain stable exotic option prices with this model. This paper focuses on the vol-of-vol parameter and its relation with the volatility o

Artemis Panagopoulou, Aveek Purohit, Achin Kulshrestha, Soroosh Yazdani

While Multimodal Large Language Models have achieved human-like performance on many visual and textual reasoning tasks, their proficiency in fine-grained spatial understanding, such as route tracing on maps remains limited. Unlike humans, who can quickly learn to parse and navigate maps, current models often fail to respect fundamental path constraints, in p

Latévi M. Lawson, Prince K. Osei

We construct a higher lattice gauge theory based on the representation of 2-groups described by a category of crossed modules on a lattice model described by path 2-groupoids. Using these lattice gauge representations, an exactly solvable Hamiltonian for topological phases in 3+1 dimensions is constructed. We show that the ground states of this model are top

Youssef Aiache, Abderrahim El Allati, İlkay Demir, Khadija El Anouz

Accurate temperature estimation in the quantum and cryogenic regimes remains a fundamental challenge. Here, we investigate nonequilibrium quantum thermometry using a single-qubit probe coupled to a bosonic bath through noncommuting interaction operators, which unify pure dephasing and dissipative dynamics within a spin-boson model. We show that the interfere

George Karfakis, Lime Yao, Binglu Chen, Faraz Tahmasebi

RAPID-LLM is a unified performance modeling framework for distributed large language model (LLM) training and inference on GPU clusters, without relying on deployment-specific traces or expensive cycle-level simulation for exploration. From a workload and hardware specification, it builds hardware-aware operator-level execution models that capture tiling, me

Eric Zimmermann, Harley Wiltzer, Justin Szeto, David Alvarez-Melis

Recent breakthroughs in self-supervised Joint-Embedding Predictive Architectures (JEPAs) have established that regularizing Euclidean representations toward isotropic Gaussian priors yields provable gains in training stability and downstream generalization. We introduce a new, flexible family of KerJEPAs, self-supervised learning algorithms with kernel-based

Manas Khatore, Sumana Sridharan, Kevork Sulahian, Benjamin J. Smith

Automated answer matching, which leverages LLMs to evaluate free-text responses by comparing them to a reference answer, shows substantial promise as a scalable and aligned alternative to human evaluation. However, its reliability requires robustness against strategic attacks such as guesswork or verbosity that may artificially inflate scores without improvi

Mohammad Sarhil, Leonardo Andres Perez Ramirez, Max Jendrik Voss, Angela Madeo

Mechanical metamaterials exhibit size-effects when a few unit-cells are subjected to static loading because no clear micro-macro scale separation holds and the characteristic length of the deformation becomes comparable to the unit-cell size. These size-effects typically manifest themselves as a strengthening of the response in a form summarized as "smaller

Michael J. Baker, Joaquim Iguaz Juan, Aidan Symons, Andrea Thamm

The Hawking radiation from the explosion of a black hole would provide definitive information on the particle spectrum of nature. Here we quantify the potential of current and future gamma ray telescopes to probe new dark sectors. We improve on the analysis used in previous work by making careful use of the experimental response functions, deriving a more re

Sean Gomes, Lauri Oksanen

In this paper we study a Lorentzian version of the Calder\'{o}n problem, which is concerned with the determination of a connection and potential on a Hermitian vector bundle over a Lorentzian manifold from the Dirichlet-to-Neumann map of the associated connection wave operator. For a class of Lorentzian manifolds satisfying a curvature bound, including pertu

Rafael Miyazaki, Cosmin Pohoata, Michael Zheng

Around 10 years ago, Agol and Krushkal showed that the number of chromatic polynomials $P_{G}$ arising from graphs $G$ on $n$ vertices grows exponentially with $n$, by establishing that the (dual) flow polynomial $F_{G}\left(\frac{3+\sqrt{5}}{2}\right)$ already takes on exponentially many values, if one varies $G$ over all planar cubic graphs $G$ on $n$ vert

James L Hart, Andrew C Lang, Matthew T Hardy, Saikat Mukhopadhyay

ScN alloyed AlN (ScxAl1-xN, ScAlN) is a wurtzite semiconductor with attractive ferroelectric, dielectric, piezoelectric, and optical properties. Here, we show that ScAlN films (with x spanning 0.18 to 0.36) contain nanoscale Sc-rich clusters which maintain the wurtzite crystal structure. While both molecular beam epitaxy (MBE) and sputter deposited Sc0.3Al0.

Eric M. Rains

The {\em Prym} of a cyclic covering of smooth projective curves is the ``new'' part of the Jacobian: the quotient of the Jacobian of the covering curve by the Jacobians of the intermediate covers. Given a family of such coverings, the fundamental group of the base of the family acts on the Tate modules of the Pryms, and the image of this representation is a

I. I. Antokhin

V444 Cyg is a WN5+O6 V eclipsing binary system that exhibits a secular variation in its orbital period due to the loss of matter from the Wolf-Rayet star through its powerful stellar wind. This makes it possible to obtain a dynamical estimate of the WR star mass-loss rate with minimal modeling assumptions. Numerous studies have been published on this topic.

Sophie Mutzel, Antoine Tilloy

We develop a linear-programming method to extract dynamical information from static ground-state correlators in quantum field theory. We recast the K\"all\'en-Lehmann inversion as a convex optimization problem, in a spirit similar to the recent approach of Lawrence [arXiv:2408.11766]. This produces robust estimates of the smeared spectral density, the real-t

H. Göttling, L. Hoff, K. Hebeler, A. Schwenk

We study the order-by-order expansion of the energy per particle of asymmetric nuclear matter up to twice saturation density in chiral effective field theory (EFT) within a Bayesian framework. For this, we develop a two-dimensional Gaussian process (2D GP) that is trained using many-body perturbation theory results based on chiral two- and three-nucleon inte

XENON Collaboration, E. Aprile, J. Aalbers, K. Abe

We report on a search for sub-GeV dark matter upscattered via the solar reflection mechanism in the heavy mediator scenario. Under the Standard Halo Model, keV to MeV dark matter produces nuclear recoils with energies below the detection threshold of liquid xenon time projection chambers. We enhance sensitivity to low-mass dark matter by considering dark mat

Sana Begum Murtuja Shaikh, Priyanka Singh

Galaxy clusters represent some of the most extreme environments in the Universe. They are ideal locations to study the impact of an extreme environment on the evolution of the Stellar Mass Function (SMF), which describes the statistical distribution of galaxies as a function of their stellar masses. In this work, we examine how the SMF of galaxies depends on

Bernd Schroers, Martin Speight, Thomas Winyard

In planar chiral magnets, the competition of the positive definite Heisenberg exchange and Zeeman energies with the indefinite Dzyaloshinskii-Moriya interaction (DMI) energy allows for the possibility of negative energy ground states, and leads to an intricate dependence of the ground states on the parameters of the theory. In this paper, we consider arbitra

Charles Shaw

We introduce srvar-toolkit, an open-source Python package for Bayesian vector autoregression with shadow-rate constraints and stochastic volatility. The toolkit implements the methodology of Grammatikopoulos (2025, Journal of Forecasting) for forecasting macroeconomic variables when interest rates hit the effective lower bound. We provide conjugate Normal-In

Yaohui Lin

Valid uncertainty quantification after model selection remains challenging in high-dimensional linear regression, especially within the possibilistic inferential model (PIM) framework. We develop possibilistic inferential models for post-selection inference based on a regularized split possibilistic construction (RSPIM) that combines generic high-dimensional

Diego Marques, Pavel Trojovsky

Motivated by Erd\H{o}s' ternary conjecture and by recent work of Cui--Ma--Jiang [``Geometric progressions meet Cantor sets'', \textit{Chaos Solitons Fractals} \textbf{163} (2022), 112567.] on intersections between geometric progressions and Cantor-like sets in standard bases, we study the corresponding problem in the Zeckendorf numeration system. We prove th

Ignacio Iacobacci, Zhaozhi Qian, Faroq AL-Tam, Muhammad AL-Qurishi

Recently, a new wave of thinking-capable Large Language Models has emerged, demonstrating exceptional capabilities across a wide range of reasoning benchmarks. Early studies have begun to explore how the amount of compute in terms of the length of the reasoning process, the so-called thinking budget, impacts model performance. In this work, we propose a syst

Ziqian Huang, Boxiao Yu, Siqi Li, Savas Ozdemir

Dynamic PET enables the quantitative estimation of physiology-related parameters and is widely utilized in research and increasingly adopted in clinical settings. Parametric imaging in dynamic PET requires kinetic modeling to estimate voxel-wise physiological parameters based on specific kinetic models. However, parametric images estimated through kinetic mo

Yinhuai Wang, Runyi Yu, Hok Wai Tsui, Xiaoyi Lin

We present a system for learning generalizable hand-object tracking controllers purely from synthetic data, without requiring any human demonstrations. Our approach makes two key contributions: (1) HOP, a Hand-Object Planner, which can synthesize diverse hand-object trajectories; and (2) HOT, a Hand-Object Tracker that bridges synthetic-to-physical transfer

Tommaso Rainaldi, Victor Ale, Matt Grau, Dmitri Kharzeev

Hybrid qubit-qumode quantum computing platforms provide a natural setting for simulating interacting bosonic quantum field theories. However, existing continuous-variable gate constructions rely predominantly on polynomial functions of canonical quadratures. In this work, we introduce a complementary universality paradigm based on trigonometric continuous-va

Zhanybek Baitulenov, Maxim Olshanskii, Almas Temirbekov, Nurlan Temirbekov

This paper investigates a modification of the fictitious domain method with continuation in the lower-order coefficients for the unsteady Navier-Stokes equations governing the motion of an incompressible homogeneous fluid in a bounded 2D or 3D domain. The modification enables {a solution-dependent} choice of the critical parameter. Global-in-time existence a

Xiaozhe Hu, Francisco J. Gaspar, Carmen Rodrigo

An alternative to the fully implicit or monolithic methods used for the solution of the coupling of fluid flow and deformation in porous media is a sequential approach in which the fully coupled system is broken into subproblems (flow and mechanics problems) that are solved one after the other. This fully explicit coupling approach is a very simple scheme wh

Alex Edison, Matthew Lewandowski, Leonardo Senatore

We present a systematic construction of the non-local-in-time galaxy bias expansion in the Effective Field Theory of Large-Scale Structure. In order to fully capture time non-locality up to sixth order, we must take into account that every field can contribute non-locally from a separate time in the past. Starting from the general non-local-in-time expressio

Eric Guzman, Joel Meyers

The search for primordial gravitational waves is a central goal of cosmic microwave background (CMB) surveys. Isolating the characteristic $B$-mode polarization signal sourced by primordial gravitational waves is challenging for several reasons: the amplitude of the signal is inherently small; astrophysical foregrounds produce $B$-mode polarization contamina

Kirill Djebko, Tom Baumann, Erik Dilger, Frank Puppe

Attitude control is essential for many satellite missions. Classical controllers, however, are time-consuming to design and sensitive to model uncertainties and variations in operational boundary conditions. Deep Reinforcement Learning (DRL) offers a promising alternative by learning adaptive control strategies through autonomous interaction with a simulatio

Moxian Qian, Shiyang Chen

We combine reinforcement learning with variational autoregressive networks (VANs) to perform data-free training and sampling for the discrete Ising model and the continuous $\phi^4$ scalar field theory. We quantify the complexity of the target distribution via the KL divergence between the magnetization distribution and a reference Gaussian distribution, and

G. Luchini, G. B. Sant'Anna, U. Camara da Silva

We present a geometric extension of the Bogomolny-Prasad-Sommerfield (BPS) construction for scalar kinks in (1+1) dimensions embedded in static curved spacetimes. By introducing a nonminimal coupling between the scalar prepotential and the extrinsic curvature of the static foliation, the flat-space first-order Bogomolny equation remains exactly valid for arb

Rahim Vesal, Seyyed Mohammad Hasheminejad, Hervé Lissek

Double-wall cylindrical shells are widely used in applications where resistance to acoustic shock loading is critical. While the transient vibroacoustic behavior of single-walled shells has been extensively investigated, extending these analyses to double-wall cylindrical configurations introduces increased complexity due to multiple inter-shell acoustic ref

Rafael Benjumea Cejas, Juan Carlos García Vázquez

The aim of this article is to give an elementary proof of the fact that the Schwarz-Pick Lemma follows from the Ahlfors-Schwarz-Pick Lemma.

Angjelin Hila

We examine epistemological threats posed by human and LLM interaction. We develop collective epistemology as a theory of epistemic warrant distributed across human collectives, using bounded rationality and dual process theory as background. We distinguish internalist justification, defined as reflective understanding of why a proposition is true, from exter

Lapo Santarlasci, Armando Rungi, Loredana Fattorini, Nestor Maslej

Artificial intelligence has become a key arena of global technological competition and a central concern for Europe's quest for technological sovereignty. This paper analyzes global AI patenting from 2010 to 2023 to assess Europe's position in an increasingly bipolar innovation landscape dominated by the United States and China. Using linked patent, firm, ow

A. Valletta, G. Montani, M. G. Dainotti, E. Fazzari

In this work, two models of metric $f(R)$ gravity in the Jordan frame are investigated as a dynamical description of the late-time cosmic expansion using binned Type Ia Supernovae data. The aim is to provide an explanation for the effective running of the Hubble constant observed in both the binned Pantheon Sample and the Master Sample. To this end, the effe

Carlos Pérez-Ruiz, Joan Solà

This work introduces LIMOncello, a tightly coupled LiDAR-Inertial Odometry system that models 6-DoF motion on the $\mathrm{SGal}(3)$ manifold within an iterated error-state Kalman filter backend. Compared to state representations defined on $\mathrm{SO}(3)\times\mathbb{R}^6$, the use of $\mathrm{SGal}(3)$ provides a coherent and numerically stable discrete-t