October 2025 arXiv papers — page 15

Thanasis Bouganis, Rafail Psyroukis

We consider a Dirichlet series $D(F,G;s)$ attached to two automorphic forms $F$ and $G$ of an orthogonal group of real signature $(2,4)$, involving their Fourier--Jacobi coefficients. When $F$ is a Hecke eigenform and $G$ a lift of a Jacobi-Poincar\'e series, our main result gives that $D(F,G;s)$ is equal to the standard $L$-function attached to $F$, up to a

Vikrant Sahu, Gagan Raj Gupta, Raghav Borikar, Nitin Mane

The rapid growth of programming education has outpaced traditional assessment tools, leaving faculty with limited means to provide meaningful, scalable feedback. Conventional autograders, while efficient, act as black-box systems that simply return pass/fail results, offering little insight into student thinking or learning needs. Autograder+ is designed to

Joshua Rooijakkers, Leiv Rønneberg, François-Xavier Briol, Jeremias Knoblauch

Multi-output Gaussian process (MOGP) regression allows modelling dependencies among multiple correlated response variables. Similarly to standard Gaussian processes, MOGPs are sensitive to model misspecification and outliers, which can distort predictions within individual outputs. This situation can be further exacerbated by multiple anomalous response vari

Jonathan Bennett, Arnaud Dumont, Andrew J. Morris

It is well-known that solvability of the $\mathrm{L}^{p}$-Dirichlet problem for elliptic equations $Lu:=-\mathrm{div}(A\nabla u)=0$ with real-valued, bounded and measurable coefficients $A$ on Lipschitz domains $\Omega\subset\mathbb{R}^{1+n}$ is characterised by a quantitative absolute continuity of the associated $L$-harmonic measure. We prove that this loc

Gabriella G. Damas, Ciro Micheletti Diniz, Norton G. de Almeida, Celso J. Villas-Bôas

We present a practical design framework for high-fidelity quantum control in coupled Kerr-nonlinear oscillators, directly addressing the challenge of spectral crowding. We show that systematic spectral degeneracies, which hinder selective addressing, are a direct consequence of rational Kerr-nonlinearity ratios ($K_1/K_2$). Our solution is a universal archit

Gabriele Centurelli, Joachim Peinke, Bughsin' Djath, Johannes Schulz-Stellenfleth

Large offshore wind farm wakes in shallow atmospheric boundary layers (ABL) exhibit often an asymmetric behaviour when observed through Synthetic-Aperture-Radar or simulated through Large-Eddy Simulations (LES). In previous LES of wind farms in the northern hemisphere, the asymmetry manifests as a streak at the left side of the wake, looking downstream, wher

Ziliang Lyu, Yiguang Hong, Lihua Xie, Miroslav Krstic

We investigate the gain margin of a general nonlinear system under an inverse optimal input-to-state safe (ISSf) controller of the form u=u0(x)+u*(x,u0), where u0 is the nominal control and u* is the inverse optimal safety filter that minimally modifies the nominal controller's unsafe actions over the infinite horizon. By first establishing a converse ISSf-B

Joel Z. Leibo, Alexander Sasha Vezhnevets, William A. Cunningham, Stanley M. Bileschi

The emergence of agentic Artificial Intelligence (AI) is set to trigger a "Cambrian explosion" of new kinds of personhood. This paper proposes a pragmatic framework for navigating this diversification by treating personhood not as a metaphysical property to be discovered, but as a flexible bundle of obligations (rights and responsibilities) that societies co

Xianjue Zhao, Peiyun Ge, Li You, Biao Wu

A quadratic speedup of the quantum adiabatic algorithm (QAA) for finding independent sets (ISs) in a graph is proven analytically. In comparison to the best classical algorithm with $O(n^2)$ scaling, where $n$ is the number of vertexes, our quantum algorithm achieves a time complexity of $O(n^2)$ for finding a large IS, which reduces to $O(n)$ for identifyin

Ameneh Deljouifar, Anita Yadav, Nataša Stojić, H. Rahimpour Soleimani

The emergence of hidden spin polarization in centrosymmetric nonmagnetic crystals due to local symmetry breaking has created new opportunities for potential spintronic applications and for enhancing our understanding of mechanisms to electrically manipulate spin-related phenomena. In this work, we investigate within density functional theory the properties o

Juanjo Zulaika, Ibone Oleaga, Anne Sanz, Naia Presno

The manufacturing sector is moving from rigid, hardware-dependent systems toward flexible, software-driven environments. This transformation is shaped by the convergence of several Software-Defined technologies: Software-Defined Automation virtualizes industrial control, replacing proprietary PLCs with containerized, programmable solutions that enable scalab

Fatemeh Bazikar, Hossein Moosaei, Atefeh Hemmati, Panos M. Pardalos

Multi-task learning (MTL) enables simultaneous training across related tasks, leveraging shared information to improve generalization, efficiency, and robustness, especially in data-scarce or high-dimensional scenarios. While deep learning dominates recent MTL research, Support Vector Machines (SVMs) and Twin SVMs (TWSVMs) remain relevant due to their interp

Igor Abramov, Ilya Makarov

Existing EEG-driven image reconstruction methods often overlook spatial attention mechanisms, limiting fidelity and semantic coherence. To address this, we propose a dual-conditioning framework that combines EEG embeddings with spatial saliency maps to enhance image generation. Our approach leverages the Adaptive Thinking Mapper (ATM) for EEG feature extract

Xizhi Tian, Changjun Zhou, Yulin. Yang

Multi-organ segmentation is a critical task in computer-aided diagnosis. While recent deep learning methods have achieved remarkable success in image segmentation, huge variations in organ size and shape challenge their effectiveness in multi-organ segmentation. To address these challenges, we propose a Spatial Prior-Guided Cross Dual Encoder Network (SPG-CD

Wenchang Duan, Yaoliang Yu, Jiwan He, Yi Shi

Recently, deep multi-agent reinforcement learning (MARL) has demonstrated promising performance for solving challenging tasks, such as long-term dependencies and non-Markovian environments. Its success is partly attributed to conditioning policies on large fixed context length. However, such large fixed context lengths may lead to limited exploration efficie

Machiel Kolstein, Mokhtar Chmeissani, Andreu Pacheco

When acquiring PET images, body motions are unavoidable, given that the acquisition time could last 10-20 minutes or more. These motions can seriously deteriorate the quality of the final image at the level of image reconstruction and attenuation corrections. Movements can have rhythmic patterns, related to respiratory or cardiac motions, or they can be abru

Konstantin von Beringe, Mark Whitmeyer

We study welfare analysis for policy changes when supply and demand behavior are only partially known. We augment the robust approach pioneered by Kang and Vasserman (2025) by incorporating the supply side. We posit intervals of feasible pass-through and conduct (market-power) parameters, then apply them to two equilibrium snapshots to characterize the extre

Diana I. Nikolova, Rachel Tuffin, Mengfan Guo, Neil D. Mathur

The electrocaloric (EC) effect offers a promising energy-efficient and clean cooling technology. We present the first direct measurements of EC temperature change in a new family of EC fluids, ferroelectric nematic liquid crystals (FNLCs), demonstrating in two such materials temperature jumps of $|{\Delta}T_j|$ ~ 0.2 K for field changes as low as ${\Delta}E$

M. Deka, M. Marconi, R. Molinaro, G. De Somma

A major challenge in modeling classical Cepheids is the treatment of convection, particularly its complex interplay with pulsation. This inherently three-dimensional process is typically approximated in one-dimensional hydrocodes using dimensionless turbulent convection (TC) free parameters. Calibrating these parameters is essential for reproducing key obser

Kunal Kumar Pant, Remya Ajai A S, Nithin Nagaraj

Forest fires are among the most dangerous and unpredictable natural disasters worldwide. Forest fire can be instigated by natural causes or by humans. They are devastating overall, and thus, many research efforts have been carried out to predict whether a fire can occur in an area given certain environmental variables. Many research works employ Machine Lear

Yingdong Yin

This paper investigates the point convergence of accelerated gradient methods for multiobjective optimization, in both continuous and discrete settings. We address the open problems of whether the solution trajectory of the multiobjective inertial gradient-like dynamical system (MAVD) with asymptotic vanishing damping converges when $\alpha = 3$, and whether

Cheng Lin, Ge Xiong

For the intersection body operator of lower order $I_iK$ of a star body $K$ in $\mathbb{R}^n$, $i\in\{1, 2,\ldots, n-2\}$, we prove that $I_i^2K = cK$ iff $K$ is an origin-symmetric ball, and hence $I_iK = cK$ iff $K$ is an origin-symmetric ball. Combining the recent breakthrough (case $i = n-1$) of Milman, Shabelman and Yehudayoff (Invent. Math., 241 (2025)

Yuanhang Liu, Beichen Wang, Peng Li, Yang Liu

Artificial intelligence (AI) has demonstrated impressive progress in mathematical reasoning, yet its integration into the practice of mathematical research remains limited. In this study, we investigate how the AI Mathematician (AIM) system can operate as a research partner rather than a mere problem solver. Focusing on a challenging problem in homogenizatio

Shaojun Wu, Shan Jin, Abolfazl Bayat, Xiaoting Wang

Variational quantum algorithms (VQAs) face an inherent trade-off between expressivity and trainability: deeper circuits can represent richer states but suffer from noise accumulation and barren plateaus, while shallow circuits remain trainable and implementable but lack expressive power. Here, we propose a general framework to address this challenge by enhan

María Pía Piccirilli, Gabriel León, Rosa-Laura Lechuga-Solis, Daniel Sudarsky

We revisit an inflationary scenario in which primordial inhomogeneities arise from a quantum collapse, a stochastic mechanism described in the context of quantum collapse theories in its continuous version and within semiclassical gravity. The predictions of the model show a non-conventional scalar spectrum governed by two new parameters in the collapse rate

Sayedus Salehin, Syed Shaheer Uddin Ahmed, Uzay Tefek, Laura Weirauch

Microplastics are increasingly recognized as a global environmental health threat, yet their detection and characterization remain constrained by the cost, form factor, and throughput of existing analytical tools. Portable micro/nanotechnology-based sensors are emerging to address this need, but most rely on the assumption of spherical particle geometry in t

Ningning Tao, Fei Xie, Baoxiang Pan, Hongyu Wang

Sudden Stratospheric Warmings (SSWs) are key sources of subseasonal predictability and major drivers of extreme weather in winter. Accurate and efficient probabilistic forecasting of these events remains a persistent challenge for Numerical Weather Prediction (NWP) systems due to computational bottlenecks and limitations in physical representation. While dat

Darith Hun, Nicolas Moës, Heiner Olbermann

We consider the minimization of integral functionals in one dimension and their approximation by $r$-adaptive finite elements. Including the grid of the FEM approximation as a variable in the minimization, we are able to show that the optimal grid configurations have a well-defined limit when the number of nodes in the grid is being sent to infinity. This is

Qianli Shen, Daoyuan Chen, Yilun Huang, Zhenqing Ling

Reinforcement finetuning (RFT) is a key technique for aligning Large Language Models (LLMs) with human preferences and enhancing reasoning, yet its effectiveness is highly sensitive to which tasks are explored during training. Uniform task sampling is inefficient, wasting computation on tasks that are either trivial or unsolvable, while existing task selecti

Juan David Álvarez-Cuartas, John H. Reina

Quantum batteries (QBs) exploit collective quantum resources to surpass the limits of classical energy storage and power delivery. We analyze $N$-qubit cavity-coupled QBs governed by Dicke and Tavis--Cummings models under Gaussian driving and open-system dynamics. Finite-size scaling laws $\mathcal{O}(N)\!\sim\!N^{\alpha}$ demonstrate an optimal region of re

Chengwei Liu, Haoyin Yan, Shaofei Xue, Xiaotao Liang

Generative modeling has recently achieved remarkable success across text, image, and audio domains, demonstrating powerful capabilities for unified representation learning. However, audio generation models still face challenges in terms of audio quality and generalization ability across tasks. This fragmentation results in redundant development efforts, inco

Anton Chernev, Corina Cîrstea, Helle Hvid Hansen, Clemens Kupke

Automata admitting at most one accepting run per structure, known as unambiguous automata, find applications in verification of reactive systems as they extend the class of deterministic automata whilst maintaining some of their desirable properties. In this paper, we generalise a classical construction of unambiguous automata from thin trees to thin coalgeb

Xiliang Yang, Tetsuo Martynowicz, Allard Katan, Kenji Watanabe

Optically accessible, scalable planar nanofluidic channels are attractive for studying transport and localization under confinement. Two dimensional (2D) materials provide large area, atomically flat interfaces for generating such platforms, yet achieving long range one-dimensional (1D) confinement with top-down nanofabrication remains challenging because it

Mustafa Mohammed Mustafa

We propose a command-filter backstepping controller that integrates a disturbance observer and a high-gain observer (HGO) to handle unknown internal and external disturbances acting on a quadrotor. To build the controller, we first define tracking errors between the measured and desired quadrotor outputs, which allow the system to be rewritten in a new set o

Yutaka Sakamura

We discuss the cancellation of the ultraviolet cutoff scale $\Lambda_{\rm cut}$ in the calculation of the expectation value of the five-dimensional (5D) energy-momentum tensor $\langle T_{MN}\rangle$ ($M,N=0,1,\cdots,4$). Since 5D fields feel the background geometry differently depending on their spins, the bosonic and the fermionic contributions to the $\La

Vishnu A Pai, Titus K Mathew

We present a novel extended version of the $\Lambda$CDM model that provides analytical solution for Hubble parameter uniting all epochs of cosmic evolution starting from inflation to late-acceleration, with intermediate radiation and matter-dominated epochs. This is achieved by relaxing the perfect fluid assumption in the standard model and considering a gen

Qirui Zhou, Jiebao Sun, Yi Ran, Boying Wu

In this paper, local H\"older regularization is incorporated into a physics-informed neural networks (PINNs) framework for solving elliptic partial differential equations (PDEs). Motivated by the interior regularity properties of linear elliptic PDEs, a modified loss function is constructed by introducing local H\"older regularization term. To approximate th

Ilya Kurinov, Miroslav Ivanov, Grzegorz Orzechowski, Aki Mikkola

Forestry forwarders play a central role in mechanized timber harvesting by picking up and moving logs from the felling site to a processing area or a secondary transport vehicle. Forwarder operation is challenging and physically and mentally exhausting for the operator who must control the machine in remote areas for prolonged periods of time. Therefore, eve

Tobias Löw, Cem Bilaloglu, Sylvain Calinon

Many tasks in human environments require collaborative behavior between multiple kinematic chains, either to provide additional support for carrying big and bulky objects or to enable the dexterity that is required for in-hand manipulation. Since these complex systems often have a very high number of degrees of freedom coordinating their movements is notorio

Steven R. Costenoble, Thomas Hudson

In this, the first of three papers about $C_2$-equivariant complex quadrics, we calculate the equivariant ordinary cohomology of smooth antisymmetric quadrics. One of these quadrics coincides with a $C_2$-equivariant Grassmannian, and we use this calculation to prove an equivariant refinement of the result that there are 27 lines on a cubic surface in $\math

Zhi-Yuan Li, Zheng-Cheng Liang, Cong-mao Zhang, Jian-dong Zhang

Future space-based gravitational-wave detectors such as TianQin, LISA, and Taiji are expected to conduct joint observations. Such a multi-detector network will provide complementary viewing angles for the anisotropic stochastic gravitational-wave background (SGWB), thereby significantly enhancing the capability to reconstruct and localize its spatial distrib

Mirko Usuelli, David Rapado-Rincon, Gert Kootstra, Matteo Matteucci

Autonomous robots in orchards require real-time 3D scene understanding despite repetitive row geometry, seasonal appearance changes, and wind-driven foliage motion. We present AgriGS-SLAM, a Visual--LiDAR SLAM framework that couples direct LiDAR odometry and loop closures with multi-camera 3D Gaussian Splatting (3DGS) rendering. Batch rasterization across co

Alex W. Brown, Sami C. Al-Izzi, Jack L. Parker, Sophie Hertel

The protective capsid encasing the genetic material of Human Immunodeficiency Virus (HIV) has been shown to traverse the nuclear pore complex (NPC) intact, despite exceeding the passive diffusion threshold by over three orders of magnitude. This remarkable feat is attributed to the properties of the capsid surface, which confer solubility within the NPC's ph

Minseok Lee, Tal Lifshitz, Young Ki Lee, Geon Kim

Conventional bright-field (BF) cytology of thyroid fine-needle aspiration biopsy (FNAB) suffers from staining variability and limited subcellular contrast. Here, we present a refractive index-correlated pseudocoloring (RICP) framework that integrates quantitative refractive index (RI) maps obtained by holotomography (HT) with color BF images to enhance diagn

Stephen Wan, Wei Liu, Michael Strube

With the increasing use of generative Artificial Intelligence (AI) methods to support science workflows, we are interested in the use of discourse-level information to find supporting evidence for AI generated scientific claims. A first step towards this objective is to examine the task of inferring discourse structure in scientific writing. In this work, we

Albi Isufaj, Pablo Mollá, Helmut Prendinger

Financial forecasting increasingly uses large neural network models, but their opacity raises challenges for trust and regulatory compliance. We present several approaches to explainable and reliable AI in finance. \emph{First}, we describe how Time-LLM, a time series foundation model, uses a prompt to avoid a wrong directional forecast. \emph{Second}, we sh

Kotaro Furuya, Yuichi Kitagawa

While a multi-agent approach based on large language models (LLMs) represents a promising strategy to surpass the capabilities of single models, its success is critically dependent on synergistic team composition. However, forming optimal teams is a significant challenge, as the inherent opacity of most models obscures the internal characteristics necessary

Nargis Sultana, Siddharth Seetharaman, Rejish Nath

We examine the quantum dynamics of both a single spin-J particle and a pair of spin-J particles in the presence of static and rotating magnetic fields, which can be important for qudit-based quantum technologies. Notably, we find resonant, periodic oscillations between two maximally stretched states, irrespective of the value of J. Additionally, we observe p

Furkan Pala, Islem Rekik

Federated learning (FL) has emerged as a key paradigm for collaborative model training across multiple clients without sharing raw data, enabling privacy-preserving applications in areas such as radiology and pathology. However, works on collaborative training across clients with fundamentally different neural architectures and non-identically distributed da

Faisal Alam, Jan Lukas Bosse, Ieva Čepaitė, Adrian Chapman

Simulating the time-dynamics of quantum many-body systems was the original use of quantum computers proposed by Feynman, motivated by the critical role of quantum interactions between electrons in the properties of materials and molecules. Accurately simulating such systems remains one of the most promising applications of general-purpose digital quantum com

Juheon Lee, Yeon Wook Kim, Hwanseok Chang, Herve Hugonnet

Biaxial anisotropy, arising from distinct optical responses along three principal directions, underlies the complex structure of many crystalline, polymeric, and biological materials. However, existing techniques such as X-ray diffraction and electron microscopy require specialized facilities or destructive preparation and cannot provide full three-dimension

Sebastian Zieglmeier, Niklas Erdmann, Narada D. Warakagoda

Reinforcement learning (RL) algorithms are designed to optimize problem-solving by learning actions that maximize rewards, a task that becomes particularly challenging in random and nonstationary environments. Even advanced RL algorithms are often limited in their ability to solve problems in these conditions. In applications such as searching for underwater

Robin Schmöcker, Alexander Dockhorn, Bodo Rosenhahn

One approach to enhance Monte Carlo Tree Search (MCTS) is to improve its sample efficiency by grouping/abstracting states or state-action pairs and sharing statistics within a group. Though state-action pair abstractions are mostly easy to find in algorithms such as On the Go Abstractions in Upper Confidence bounds applied to Trees (OGA-UCT), nearly no state

Mykhailo Poliakov, Nadiya Shvai

Health-related misinformation is very prevalent and potentially harmful. It is difficult to identify, especially when claims distort or misinterpret scientific findings. We investigate the impact of synthetic data generation and lightweight fine-tuning techniques on the ability of large language models (LLMs) to recognize fallacious arguments using the MISSC

Bingyan Xie, Jihong Park, Yongpeng Wu, Wenjun Zhang

Existing semantic communication schemes primarily focus on single-hop scenarios, overlooking the challenges of multi-hop wireless image transmission. As semantic communication is inherently lossy, distortion accumulates over multiple hops, leading to significant performance degradation. To address this, we propose the multi-hop parallel image semantic commun

Xiaoyuan Cheng, Yiming Yang, Wei Jiang, Chenyang Yuan

This paper studies how to achieve accurate modeling and effective control in stochastic nonlinear dynamics with multiple interacting objects. However, non-uniform interactions and random topologies make this task challenging. We address these challenges by proposing \textit{Graph Controllable Embeddings} (GCE), a general framework to learn stochastic multi-o

Nicolas Arancibia Robert

For a real group $G$, it is known from the work of Kostant and Vogan that the L-packet associated with an L-parameter $\varphi$ of $G$ contains a \emph{generic} representation if and only if the ${}^{\vee}G$-orbit in the variety of geometric parameters corresponding to $\varphi$ is open. In these notes, we generalize this result slightly by proving that the

Zhigao Guo, Feng Dong

Incorporating causal knowledge and mechanisms is essential for refining causal models and improving downstream tasks such as designing new treatments. In this paper, we introduce a novel concept in causal discovery, termed interventional constraints, which differs fundamentally from interventional data. While interventional data require direct perturbations

C. Clontz, A. C. Seth, Z. Wang, M. Haeberle

An understanding of the assembly history of the complex star cluster Omega Centauri has long been sought after, with many studies separating the stars on the color-magnitude diagram into multiple groupings across small magnitude ranges. Utilizing the oMEGACat combined astro-photometric and spectroscopic dataset we parse 14 subpopulations from the upper red-g

Mingyu Sung, Seungjae Ham, Kangwoo Kim, Yeokyoung Yoon

Image super-resolution(SR) is fundamental to many vision system-from surveillance and autonomy to document analysis and retail analytics-because recovering high-frequency details, especially scene-text, enables reliable downstream perception. Scene-text, i.e., text embedded in natural images such as signs, product labels, and storefronts, often carries the m

Z. M. McIntyre, A. Kasman, R. Milson

Using the formalism of Maya diagrams and ladder operators, we describe the algebra of annihilating operators for the class of rational extensions of the harmonic oscillator. This allows us to construct the corresponding coherent state in the sense of Barut and Girardello. The resulting time-dependent function is an exact solution of the time-dependent Schr\"

Yann Portella, Petr Steindl, Juan Rafael Álvarez, Tim Hebenstreit

Quantum key distribution is a key application of quantum mechanics, shaping the future of privacy and secure communications. Many protocols require single photons, often approximated by strongly attenuated laser pulses. Here, we harness the emission of a quantum dot embedded in a micropillar and explore a hybrid approach where the information is encoded on a

Nishit Neema, Srinjoy Mukherjee, Sapan Shah, Gokul Ramakrishnan

Large Language Models (LLMs) excel at general tasks but underperform in specialized domains like economics and psychology, which require deep, principled understanding. To address this, we introduce ACER (Automated Curriculum-Enhanced Regimen) that transforms generalist models into domain experts without sacrificing their broad capabilities. ACER first synth

Julian M. Bopp, Lucca Valerius, Tim Schröder

Waveguide-coupled photonic crystal cavities with a triangular cross section fabricated by angled etching are suitable to interface embedded color centers with flying photonic qubits in quantum information applications. Moreover, their fabrication requires fewer processing steps compared to nanostructures produced by quasi-isotropic undercutting. As an altern

Thiago Carvalho Corso, Gaspard Kemlin, Christof Melcher, Benjamin Stamm

This paper deals with the numerical simulation of the 2D magnetic time-dependent Ginzburg-Landau (TDGL) equations in the regime of small but finite (inverse) Ginzburg-Landau parameter $\epsilon$ and constant (order $1$ in $\epsilon$) applied magnetic field. In this regime, a well-known feature of the TDGL equation is the appearance of quantized vortices with

P. Scicluna, S. Zeegers, J. P. Marshall, F. Kemper

As astronomy advances and data becomes more complex, models and inference also become more expensive and complex. In this paper we present {\sc ampere}, which aims to solve this problem using modern inference techniques such as flexible likelihood functions and likelihood-free inference. {\sc ampere}\ can be used to do Bayesian inference even with very expen

Ignacio M. Jiménez

In this work, we extend Howard's construction of compatible families of Heegner points to the setting of towers of Gross curves and Shimura curves over totally real fields. Following the strategy of Longo and Vigni, our approach simultaneously treats totally definite and indefinite quaternion algebras. We then extend their interpolation methods to define big

Guowei Dai, Yingxin Sun

We investigate the following Robin eigenvalue problem \begin{equation*} \left\{ \begin{array}{ll} -\Delta u=\mu u\,\, &\text{in}\,\, B,\\ \partial_\texttt{n} u+\alpha u=0 &\text{on}\,\, \partial B \end{array} \right. \end{equation*} on the unit ball of $\mathbb{R}^N$. We obtain the complete spectral structure of this problem. In particular, for $\alpha>0$, t

Jean-François Marckert, Ludovic Morin

Pick $N$ random points $U_1,\cdots,U_{N}$ independently and uniformly in a triangle ABC with area 1, and take the convex hull of the set $\{A,B,U_1,\cdots,U_{N}\}$. The boundary of this convex hull is a convex chain $V_0=B,V_1,\cdots,$ $V_{\mathbf{n}(N)}$, $V_{\mathbf{n}(N)+1}=A$ with random size $\mathbf{n}(N)$. The first aim of this paper is to study the a

B. Bilata-Woldeyes, J. D. Perea, J. M. Solanes

We present a suite of 100 cosmologically motivated, controlled N-body simulations designed to advance the understanding of the role of purely gravitational dynamics in the early formation of low-mass galaxy groups (~ 1-5 x 10^13 M_sun). In this work, we investigate the temporal evolution of key indicators of dynamical relaxation, with particular emphasis on

David Schmotz, Sahar Abdelnabi, Maksym Andriushchenko

Enabling continual learning in LLMs remains a key unresolved research challenge. In a recent announcement, a frontier LLM company made a step towards this by introducing Agent Skills, a framework that equips agents with new knowledge based on instructions stored in simple markdown files. Although Agent Skills can be a very useful tool, we show that they are

Shangyu Lou

Urban Artificial Intelligence (Urban AI) has advanced human-centered urban tasks such as perception prediction and human dynamics. Large Language Models (LLMs) can integrate multimodal inputs to address heterogeneous data in complex urban systems but often underperform on domain-specific tasks. Urban-MAS, an LLM-based Multi-Agent System (MAS) framework, is i

Leilei Shi, Cheng Zhang, Da-jun Zhang

The Nijhoff-Quispel-Capel (NQC) equation is a general lattice quadrilateral equation presented in terms of a function $S(a,b)$ where $a$ and $b$ serve as extra parameters. It can be viewed as the counterpart of Q3 equation which is the second top equation in the Adler-Bobenko-Suris list. In this paper, we review some known formulations of the NQC variable $S

Gergely Bunth, József Pitrik, Tamás Titkos, Dániel Virosztek

We prove Kantorovich duality for a linearized version of a recently proposed non-quadratic quantum optimal transport problem, where quantum channels realize the transport. As an application, we determine optimal solutions of both the primal and the dual problem using this duality in the case of quantum bits and distinguished cost operators, with certain rest

Zhiyang Xun, Shivam Gupta, Eric Price

Given a noisy linear measurement $y = Ax + \xi$ of a distribution $p(x)$, and a good approximation to the prior $p(x)$, when can we sample from the posterior $p(x \mid y)$? Posterior sampling provides an accurate and fair framework for tasks such as inpainting, deblurring, and MRI reconstruction, and several heuristics attempt to approximate it. Unfortunatel

Sascha Mücke

Training Support Vector Machines (SVMs) can be formulated as a QUBO problem, enabling the use of quantum annealing for model optimization. In this work, we study how the number of qubits - linked to the discretization level of dual weights - affects predictive performance across datasets. We compare QUBO-based SVM training to the classical LIBSVM solver and

Fares Fawzi, Vinitra Swamy, Dominik Glandorf, Tanya Nazaretsky

Language models can be used to provide interactive, personalized student feedback in educational settings. However, real-world deployment faces three key challenges: privacy concerns, limited computational resources, and the need for pedagogically valid responses. These constraints require small, open-source models that can run locally and reliably ground th

Oscar Veenema, Niranjan Thatte, Dimitra Rigopoulou, Ismael García-Bernete

We present combined JWST NIRSpec and MIRI/MRS integral field spectroscopy data of the nuclear and circumnuclear regions of the highly dust obscured Seyfert 2 galaxy NGC 7582, which is part of the sample of AGN in the Galaxy Activity, Torus and Outflow Survey (GATOS). Spatially resolved analysis of the pure rotational H$_2$ lines (S(1)-S(7)) reveals a charact

Jiaming Liu, Dingwei Fan, Junyong Zhao, Chunlin Li

The anatomical structure segmentation of the spine and adjacent structures from computed tomography (CT) images is a key step for spinal disease diagnosis and treatment. However, the segmentation of CT images is impeded by low contrast and complex vertebral boundaries. Although advanced models such as the Segment Anything Model (SAM) have shown promise in va

Syed M. Arslan, Syed Shahmir, Noureldin Mohammad, Saif Al-Kuwari

Twin-Field Quantum Key Distribution (TF-QKD) has emerged as a potential protocol for long distance secure communication, overcoming the rate-distance limitations of conventional quantum key distribution without requiring trusted repeaters. By having two parties transmit phase encoded weak coherent pulses (WCP) to an untrusted central node, the TF-QKD exploit

Hanbing Fang, Yu Li

In this paper, we study the singular set $\mathcal{S}$ of a noncollapsed Ricci flow limit space, arising as the pointed Gromov--Hausdorff limit of a sequence of closed Ricci flows with uniformly bounded entropy. The singular set $\mathcal{S}$ admits a natural stratification: \begin{equation*} \mathcal S^0 \subset \mathcal S^1 \subset \cdots \subset \mathcal

C. Bozza, A. Calivà, A. De Caro, D. De Gruttola

The detailed simulation of extensive air showers, produced by primary cosmic rays interacting in the atmosphere, is a task that is traditionally undertaken by means of Monte Carlo methods. These processes are computationally intensive, accounting for a major fraction of the computational resources used in the large-scale simulations required by current and f

Junlai Qiu, Yunzhu Chen, Hao Zheng, Yawen Huang

Diabetic retinopathy (DR) is a leading cause of vision loss among middle-aged and elderly people, which significantly impacts their daily lives and mental health. To improve the efficiency of clinical screening and enable the early detection of DR, a variety of automated DR diagnosis systems have been recently established based on convolutional neural networ

Alex Sampson, Peter J. Baker, Lucas Wilkins, John M. Wilkinson

One of the key challenges in performing muon experiments is knowing which temperatures and applied fields to measure at, and how many muon decays should be measured at each temperature/field combination to get the most useful dataset. We have developed a technique using Fisher information which, for a given muon asymmetry function, can analytically calculate

Stein Andreas Bethuelsen, Christian Mönch

We consider independent long-range percolation models on locally finite vertex-transitive graphs. Using coupling ideas we prove strict monotonicity of the critical points with respect to local perturbations in the connection function, thereby improving upon previous results obtained via the classical essential enhancement method of Aizenman and Grimmett in s

M. A. Clark, A. Hanlon, D. Howarth, B. Joo

The vast majority of visible matter in our universe comes from protons and neutrons (the nucleons). Nucleon interactions are fundamental to how the universe developed after the Big Bang and govern all nuclear phenomena. The subtle balance in how two nucleons interact shapes the universe's hydrogen content that is central to our existence. Our objective is to

Suman Kumar Panja, Mathias Boström

We present a short review of an unusual but important application for a high-temperature charged plasma. The unorthodox proposition was made by Ninham concerning a contribution from Casimir forces across high-temperature electron-positron plasma in nuclear interactions. The key message in the current work is how high temperatures ($\sim10^{11}$ \,K) pop out

Ruilin Tong, Haodong Lu, Yuhang Liu, Dong Gong

Continual learning (CL) aims to incrementally train a model on a sequence of tasks while retaining performance on prior ones. However, storing and replaying data is often infeasible due to privacy or security constraints and impractical for arbitrary pre-trained models. Data-free CL seeks to update models without access to previous data. Beyond regularizatio

Elisa Alos, Frido Rolloos, Kenichiro Shiraya

The covariance between the return of an asset and its realized volatility can be approximated as the difference between two specific implied volatilities. In this paper it is proved that in the small time-to-maturity limit the approximation error tends to zero. In addition a direct relation between the short time-to-maturity covariance and slope of the at-th

Jiseong Chung, Ronny Ko, Wonchul Yoo, Makoto Onizuka

Compliance at web scale poses practical challenges: each request may require a regulatory assessment. Regulatory texts (e.g., the General Data Protection Regulation, GDPR) are cross-referential and normative, while runtime contexts are expressed in unstructured natural language. This setting motivates us to align semantic information in unstructured text wit

R. Maggiolo, F. Dhooghe, G. Gronoff, J. de Keyser

Spectral observations of 3I/ATLAS (C/2025 N1) with JWST/NIRSpec and SPHEREx reveal an extreme CO2 enrichment (CO2/H2O = 7.6+-0.3) that is 4.5 sigma above solar system comet trends and among the highest ever recorded. This unprecedented composition, combined with substantial absolute CO levels (CO/H2O = 1.65+-0.09) and red spectral slopes, provides direct evi

Sophie Rosu, Luca Sciarini, Sylvia Ekström, Raphaël Hirschi

Unveiling massive stars' internal structure and the physical origin and efficiency of the internal mixing processes? It is now possible using the apsidal motion rate in close eccentric binaries! The apsidal motion rate depends on the tidal interactions occurring between the stars and is proportional to k2, a measure of the star's inner density profile. Confr

Hao Fu, Francesco Shankar, Feng Yuan, Daniel Roberts

The interplay between the accretion of supermassive black holes (SMBHs) and the stellar mass growth of the host galaxies is still a matter of hot debate. The accretion of the SMBHs is expected to release energy under the form of AGNs. This energy is believed to impact the star formation activity and contribute to the quenching of galaxies. Here, we address t

Jelizaveta Jankowska, Bożena Kostek, Fernando Alonso-Fernandez, Prayag Tiwari

The subject of this work is to check how different types of music affect human emotions. While listening to music, a subjective survey and brain activity measurements were carried out using an EEG helmet. The aim is to demonstrate the impact of different music genres on emotions. The research involved a diverse group of participants of different gender and m

Beomhan Baek, Minhak Song, Chulhee Yun

Adam [Kingma & Ba, 2015] is the de facto optimizer in deep learning, yet its theoretical understanding remains limited. Prior analyses show that Adam favors solutions aligned with $\ell_\infty$-geometry, but these results are restricted to the full-batch regime. In this work, we study the implicit bias of incremental Adam (using one sample per step) for logi

Ziliang Chen, Tianang Xiao, Jusheng Zhang, Yongsen Zheng

Contrastive Language-Image Pre-training (CLIP) delivers strong cross modal generalization by aligning images and texts in a shared embedding space, yet it persistently fails at compositional reasoning over objects, attributes, and relations often behaving like a bag-of-words matcher. Prior causal accounts typically model text as a single vector, obscuring to

Jingyuan Liu, Fatemeh Ghaffari, Xuchuang Wang, Xutong Liu

Preference learning from pairwise feedback is a widely adopted framework in applications such as reinforcement learning with human feedback and recommendations. In many practical settings, however, user interactions are limited or costly, making offline preference learning necessary. Moreover, real-world preference learning often involves users with differen

Faisal Alam, Jan Lukas Bosse, Ieva Čepaitė, Adrian Chapman

Simulation of the time-dynamics of fermionic many-body systems has long been predicted to be one of the key applications of quantum computers. Such simulations -- for which classical methods are often inaccurate -- are critical to advancing our knowledge and understanding of quantum chemistry and materials, underpinning a wide range of fields, from biochemis

Dominik Schwarz

As Large Language Models (LLMs) are increasingly integrated into automated, multi-stage pipelines, risk patterns that arise from unvalidated trust between processing stages become a practical concern. This paper presents a mechanism-centered taxonomy of 41 recurring risk patterns in commercial LLMs. The analysis shows that inputs are often interpreted non-ne

Sofiene Kammoun, Xavier Alameda-Pineda, Simon Leglaive

Neural audio codecs (NACs) provide compact latent speech representations in the form of sequences of continuous vectors or discrete tokens. In this work, we investigate how these two types of speech representations compare when used as training targets for supervised speech enhancement. We consider both autoregressive and non-autoregressive speech enhancemen