November 2025 arXiv papers — page 66

E. J. Thompson

I construct and analyze a dark matter sector that is neutral under the unbroken Standard Model gauge group and couples only to the broken $\text{SU}(5)$ gauge directions, the leptoquark vectors $X,Y$. An exact $\mathbb Z_2$ renders the dark matter stable. I give a gauge-covariant definition of projectors onto the unbroken Standard Model and broken ($X,Y$) su

Xusheng Zhu, Kai-Kit Wong, Qingqing Wu, Hyundong Shin

Fluid antenna systems (FAS) have emerged as a revolutionary technology offering enhanced spatial diversity within a compact form factor. Concurrently, unmanned aerial vehicles (UAVs) are integral to future networks, necessitating channel models that capture both multipath fading and shadowing. This letter presents a novel performance analysis of a UAV-to-gro

Minshen Xu, Shiwei Lan, Lulu Kang

The performance of Gaussian Process (GP) regression is often hampered by the curse of dimensionality, which inflates computational cost and reduces predictive power in high-dimensional problems. Variable selection is thus crucial for building efficient and accurate GP models. Inspired by Bayesian bridge regression, we propose the Bayesian Bridge Gaussian Pro

Mariona Giner Mascarell, Anna-Christina Eilers, Kate Storey-Fisher

We measure the two-point correlation function of a uniformly selected, all-sky sample of $\sim$1.3 million quasars with magnitudes $G\leq20.5$ from the Gaia--unWISE Quasar Catalog (Quaia) over the redshift range $0 \leq z \leq 4$ to trace the evolution of the quasar clustering strength across cosmic time. We find a steady increase in the correlation length $

Faruk Abdulla, Anna Keselman, Daniel Podolsky

An external orbital magnetic field applied perpendicular to the separation vector of a pair of Weyl points can couple them and induce a gap in the electronic spectrum. In this work, we investigate the gap-opening behavior in the presence of a lattice, revealing rich phenomenology absent in the continuum picture. Specifically, we address the emergence of laye

Nikolay Nikolov, Giuliano Albanese, Sombit Dey, Aleksandar Yanev

Robotic Foundation Models (RFMs) hold great promise as generalist, end-to-end systems for robot control. Yet their ability to generalize across new environments, tasks, and embodiments remains limited. We argue that a major bottleneck lies in their foundations: most RFMs are built by fine-tuning internet-pretrained Vision-Language Models (VLMs). However, the

Hendrik Müller, Mingyu Hsieh, Sanjay Bhatnagar

In radio-interferometry, we recover an image from an incompletely sampled Fourier data. The de-facto standard algorithm, the Cotton-Schwab CLEAN, is iteratively switching between computing a deconvolution (minor loop) and subtracting the model from the visibilities (major loop). The next generation of radio interferometers is expected to deal with much highe

Carlos Hoyos, Niko Jokela, Andrea Olzi

We revisit the holographic description of heavy light mesons in the D3-D7 system at zero temperature, analyzing the dynamics of the coupled probe D7 branes through the non-Abelian Dirac-Born-Infeld action. Distinct quark masses are realized by separating the flavor branes, producing holographic flavor hierarchies. We refine the calculation made in previous w

Nathalie Kirch, Samuel Dower, Adrians Skapars, Helen Yannakoudakis

Probing has emerged as a promising method for monitoring large language models (LLMs), enabling cheap inference-time detection of concerning behaviours. However, natural examples of many behaviours are rare, forcing researchers to rely on synthetic or off-policy LLM responses for training probes. We systematically evaluate how off-policy data influences prob

H. Babaei-Aghbolagh, Komeil Babaei Velni, Song He, Fateme Isapour

We develop a unified framework for analyzing black hole thermodynamics and spacetime structure in Einstein gravity coupled to causal nonlinear electrodynamics (NED) in asymptotically anti-de Sitter backgrounds. The electromagnetic sector is governed by a Generalized Nonlinear Electrodynamics (GNED) Lagrangian obtained from a root-$T\bar T$ deformation constr

Dharma P. Permana, Mazena Mackoit Sinkevičienė, Julius Ruseckas, Hamid R. Hamedi

We investigate the propagation of an optical vector vortex weakly interacting with a coherently prepared atomic medium (phaseonium) in a three-level $\Lambda$ configuration. The vector beam consists of vortex pulse pairs with right- and left-circular polarizations, corresponding to opposite spin angular momentum (SAM), and carrying opposite orbital angular m

Yesheng Liu, Hao Li, Haiyu Xu, Baoqi Pei

Multiple-choice question answering (MCQA) has been a popular format for evaluating and reinforcement fine-tuning (RFT) of modern multimodal language models. Its constrained output format allows for simplified, deterministic automatic verification. However, we find that the options may leak exploitable signals, which makes the accuracy metrics unreliable for

Guilherme Coelho

Text-to-audio (TTA) systems are rapidly transforming music creation and distribution, with platforms like Udio and Suno generating thousands of tracks daily and integrating into mainstream music platforms and ecosystems. These systems, trained on vast and largely undisclosed datasets, are fundamentally reshaping how music is produced, reproduced and consumed

Javier Alonso Villegas Luis, Marco Antonio Sobrevilla Cabezudo

Linguistic features remain essential for interpretability and tasks that involve style, structure, and readability, but existing Spanish tools offer limited coverage. We present PUCP-Metrix, an open-source and comprehensive toolkit for linguistic analysis of Spanish texts. PUCP-Metrix includes 182 linguistic metrics spanning lexical diversity, syntactic and

Xiaoshan Zhou, Carol C. Menassa, Vineet R. Kamat

Non-invasive electroencephalography (EEG)-based brain-computer interfaces (BCIs) offer an intuitive means for individuals with severe motor impairments to independently operate assistive robotic wheelchairs and navigate built environments. Despite considerable progress in BCI research, most current motion control systems are limited to discrete commands, rat

Sukwon Yun, Heming Yao, Burkhard Hoeckendorf, David Richmond

Vision Transformers ($\text{ViTs}$) have become the backbone of vision foundation models, yet their optimization for multi-channel domains - such as cell painting or satellite imagery - remains underexplored. A key challenge in these domains is capturing interactions between channels, as each channel carries different information. While existing works have s

Wei-Kai Chang, Rajiv Khanna

As deep learning models continue to scale, the growing computational demands have amplified the need for effective coreset selection techniques. Coreset selection aims to accelerate training by identifying small, representative subsets of data that approximate the performance of the full dataset. Among various approaches, gradient based methods stand out due

Abhijeet Nayak, Sk Jamaluddin, Fan Wu, Emily Rapp

Kagome magnets represent a promising class of materials that exhibit intriguing electronic and magnetic properties, and they have recently garnered significant attention. While most kagome-lattice compounds are hexagonal, we report here single-crystal growth and physical property measurements of $R$Fe$_6$Ge$_6$ ($R$ = Y, Dy, Tb) compounds, which crystallize

Huangbiao Xu, Huanqi Wu, Xiao Ke, Junyi Wu

Multimodal Action Quality Assessment (AQA) has recently emerged as a promising paradigm. By leveraging complementary information across shared contextual cues, it enhances the discriminative evaluation of subtle intra-class variations in highly similar action sequences. However, partial modalities are frequently unavailable at the inference stage in reality.

Tomáš Domes, Pavel Veselý

Quantile summaries provide a scalable way to estimate the distribution of individual attributes in large datasets that are often distributed across multiple machines or generated by sensor networks. ReqSketch (arXiv:2004.01668) is currently the most space-efficient summary with two key properties: relative error guarantees, offering increasingly higher accur

Bhavesh Ramkorun

Solid nanoparticles which range in size from 1 to 500 nm can spontaneously grow from reactive gaseous precursors in nonthermal plasmas. This dissertation studies the particle size, and growth time with and without a background magnetic field in the plasma. Traditionally, studies have focused on the growth of either carbonaceous or silicate dust from either a

Jean-Pierre Delmas

This chapter presents a short overview of real elliptically symmetric (RES) distributions, complemented by circular complex elliptically symmetric (C-CES) and noncircular CES (NC-CES) distributions as complex representations of RES distributions. These distributions are both an extension of the multivariate Gaussian distribution and a multivariate extension

Georgia Baltsou, Ioannis Sarridis, Christos Koutlis, Symeon Papadopoulos

Face verification is a significant component of identity authentication in various applications including online banking and secure access to personal devices. The majority of the existing face image datasets often suffer from notable biases related to race, gender, and other demographic characteristics, limiting the effectiveness and fairness of face verifi

Thomas Groll, Sharyn O'Halloran

This chapter examines the link between delegation and lobbying, two themes central to political economy. Delegation models explore how legislatures manage uncertainty and control bureaucratic agents, while lobbying models analyze how organized interests influence policy through contributions, information, and advocacy. We review the growing body of research

E. T. Kokkinakis, I. Komis, K. G. Makris, E. N. Economou

The problem of a single Hermitian impurity has long served as a cornerstone in condensed matter physics, offering fundamental insights into the mechanisms of Anderson localization. Yet, despite the increased interest in the spectral and localization properties of non-Hermitian lattices with defects, the non-Hermitian extension of the single impurity problem

Małgorzata Nowak-Kępczyk

We investigate Frobenius-driven revivals in prime-modulus Laplacian cellular automata, a phenomenon in which long chaotic transients collapse into exact, multi-tile replicas of an initial seed at algebraically prescribed times $t=p^m$. The mechanism follows directly from the Frobenius identity $(I+B)^{p^m}=I+B^{p^m}$, which eliminates all mixed binomial term

Yusuf Baran Ates, Omer Morgul

Learning human-like, robust bipedal walking remains difficult due to hybrid dynamics and terrain variability. We propose a lightweight framework that combines a gait generator network learned from human motion with Proximal Policy Optimization (PPO) controller for torque control. Despite being trained only on flat or mildly sloped ground, the learned policie

Cequn Li, Le Yi, Kalana D. Halanayake, Jessica L. Thompson

The coupling of superconductivity to unconventional materials may lead to novel quantum states and potential applications. Controlling the quality of the superconductor-normal metal interface is of crucial importance to the understanding and engineering of the superconducting proximity effect. In many cases, conventional lithography-based deposition methods

Natalie A. Webb, Vincent Foustoul, Robbie Webbe, Matteo Bachetti

The regions around massive black holes can show X-ray variability on timescales from seconds to decades. Observing many black holes over different timescales can enhance our chances of detecting variability coming from (partial) tidal disruption events, massive black hole binaries, changing state AGN, blazar activity and much more. X-ray catalogues with hund

Keqin Peng, Guangxin Su, Qinshan Shi, Shuai Gao

Drug synergy is profoundly influenced by cellular context, as variations in protein interaction landscapes and pathway activities across cell types reshape how drugs act in combination. Most existing models overlook this heterogeneity, relying on static or bulk-level protein-protein interaction (PPI) networks that ignore cell-specific molecular wiring. The a

David Handelman

We begin with (densely-defined) fractional linear transformations (FLT) on (some) Banach algebras and their relatives. This leads to Wedderburn's continued fractions (recursively-defined noncommutative polynomials) for any ring. Along the way, we discover a one-parameter family of (noncommutative) polynomials \st if one of them is invertible, then read in th

Patricia Ribes-Metidieri, Ivan Agullo, Béatrice Bonga

Studies of quantum field entanglement in de Sitter space based on the von Neumann entropy of local patches have concluded that curvature enhances entanglement between regions and their complements. Similar conclusions about entanglement enhancement have been reached in analyses of Fourier modes in the cosmological patch of de Sitter space. We challenge this

Lan Wen, Aaron L. Sarvet, Jessica G. Young

In causal inference literature, potential outcomes are often indexed by the "elimination of all right-censoring events," leading to the perception that such a restriction is necessary for defining well-posed causal estimands. In this paper, we clarify that this restriction is not required: a well-defined estimand can be formulated without indexing on the eli

Wei-Kai Chang, Rajiv Khanna

Understanding the dynamics of optimization in deep learning is increasingly important as models scale. While stochastic gradient descent (SGD) and its variants reliably find solutions that generalize well, the mechanisms driving this generalization remain unclear. Notably, these algorithms often prefer flatter or simpler minima, particularly in overparameter

Huicong Xu, Shuang Liu, Xianyu Zhu, Qiyu Zhuang

Concurrent transaction processing is a fundamental capability of Relational Database Management Systems (RDBMSs), widely utilized in applications requiring high levels of parallel user interaction, such as banking systems, e-commerce platforms, and telecommunications infrastructure. Isolation levels offer a configurable mechanism to manage the interaction be

Anaïs Andrillon, Sandrine Micallef, Moreno Ursino, Pavel Mozgunov

With the development of novel therapies such as molecularly targeted agents and immunotherapy, the maximum tolerated dose paradigm that "more is better" does not necessarily hold anymore. In this context, doses and schedules of novel therapies may be inadequately characterized and oncology drug dose-finding approaches should be revised. This is increasingly

Benjamin R. Toaz, Quentin Goss, John Thompson, Seta Boğosyan

The vector cost bimatrix game is a method for multi-objective decision making that enables autonomous robotic systems to optimize for multiple goals at once while avoiding worst-case scenarios in neglected objectives. We expand this approach to arbitrary numbers of objectives and compare its performance to scalar weighted sum methods during competitive motio

Benjamin Przybocki, Guilherme V. Toledo, Yoni Zohar

We study properties that allow first-order theories to be disjointly combined, including stable infiniteness, shininess, strong politeness, and gentleness. Specifically, we describe a Galois connection between sets of decidable theories, which picks out the largest set of decidable theories that can be combined with a given set of decidable theories. Using t

Yixuan Pan, Ruoyi Qiao, Li Chen, Kashyap Chitta

Humanoid robots are envisioned to perform a wide range of tasks in human-centered environments, requiring controllers that combine agility with robust balance. Recent advances in locomotion and whole-body tracking have enabled impressive progress in either agile dynamic skills or stability-critical behaviors, but existing methods remain specialized, focusing

Emma Andrews, Prabhat Mishra

Classical autoencoders are widely used to learn features of input data. To improve the feature learning, classical masked autoencoders extend classical autoencoders to learn the features of the original input sample in the presence of masked-out data. While quantum autoencoders exist, there is no design and implementation of quantum masked autoencoders that

Emanuel Roth, Florent Schaffhauser

A decorated vector bundle is a vector bundle equipped with a reduction of structure group to a complex reductive subgroup $G \subseteq \mathbf{GL}(r,\mathbb{C})$. Examples include symplectic and special-orthogonal vector bundles, as well as vector bundles with trivial determinants. In this expository paper, we provide direct constructions of Harder-Narasimha

Felix Roser, Etienne M. Springer, Hans Peter Büchler, Nicolai Lang

We propose a scalable and noise-resilient protocol for the detection of the entanglement transition in a projective version of the transverse field Ising model. Entanglement transitions are experimentally difficult to observe due to the inherent randomness of projective measurements and noise in large-scale experimental settings. Our approach combines error

Eric L. Melin, Ahmed Musa Awon, Nasir U. Eisty, Neil A. Ernst

Developers often leave behind clues in their code, admitting where it falls short, known as Self-Admitted Technical Debt (SATD). In the world of Scientific Software (SSW), where innovation moves fast and collaboration is key, such debt is not just common but deeply impactful. As research relies on accurate and reproducible results, accumulating SATD can thre

Yankai Fu, Ning Chen, Junkai Zhao, Shaozhe Shan

Building a generalist robot that can perceive, reason, and act across diverse tasks remains an open challenge, especially for dexterous manipulation. A major bottleneck lies in the scarcity of large-scale, action-annotated data for dexterous skills, as teleoperation is difficult and costly. Human data, with its vast scale and diverse manipulation behaviors,

Evangelia Gazaki

We study the Chow group of zero-cycles $\text{CH}_0(S)$ of a bielliptic surface $S=(E_1\times E_2)/G$, where $E_1, E_2$ are elliptic curves and $G$ is a finite group acting on $E_1$ by translations and on $E_2$ by automorphisms such that $E_2/G\simeq\mathbb{P}^1$. We show that if $S$ is defined over an arbitrary field $k$ of characteristic not equal to $2,3$

Seunghun Oh, Jaesung Choe, Dongjae Lee, Daeun Lee

We extend the recently proposed sparse voxel rasterization paradigm to the task of high-fidelity surface reconstruction by integrating Signed Distance Function (SDF), named SVRecon. Unlike 3D Gaussians, sparse voxels are spatially disentangled from their neighbors and have sharp boundaries, which makes them prone to local minima during optimization. Although

Linxiang Su, András Balogh

Despite its remarkable success in zero-shot image-text matching, CLIP remains highly vulnerable to adversarial perturbations on images. As adversarial fine-tuning is prohibitively costly, recent works explore various test-time defense strategies; however, these approaches still exhibit limited robustness. In this work, we revisit this problem and propose a s

Amin Masoumi, Mert Korkali

Dynamic security control (DSC) is considered a pivotal step for the future power grid, which is increasingly penetrated by inverter-based resources. However, the efficiency of such practices, whether governed by automatic generation control or virtual inertia scheduling, can be intractable due to the complexity of the problem and the need to solve the differ

Seamie Hayes, Alexandre Boulch, Andrei Bursuc, Reenu Mohandas

Self-supervision for semantic occupancy estimation is appealing as it removes the labour-intensive manual annotation, thus allowing one to scale to larger autonomous driving datasets. Superquadrics offer an expressive shape family very suitable for this task, yet their deployment in a self-supervised setting has been hindered by the lack of efficient renderi

Lucas Warwaruk, Konstantinos Zinelis, Randy H. Ewoldt, Christopher W. Macosko

Dripping-onto-Substrate (DoS) rheometry is a well-established method for measuring the extensional rheology of low-viscosity liquids. However, clear guidelines on the capabilities and limitations of the technique are lacking. In the present work, we define operational limits for measuring a transient extensional viscosity directly from observation of the rat

Ruben Zeitoun

In this paper, we investigate the wave operator $\square_g$ on non-trapping (at all energies) even asymptotically de Sitter spaces. We construct a Feynman operator on the conformal extension of asymptotically de Sitter spaces and give a proof of uniform microlocal estimates for the Feynman operator in this setting. This enables the study of the Lorentzian "s

Daniil Ignatev, Ayman Santeer, Albert Gatt, Denis Paperno

We propose a zero-shot method for Natural Language Inference (NLI) that leverages multimodal representations by grounding language in visual contexts. Our approach generates visual representations of premises using text-to-image models and performs inference by comparing these representations with textual hypotheses. We evaluate two inference techniques: cos

Kirk Roffi

Dynamic friction models (DFMs) encode essential information for the simulation and control of systems with friction. Traditionally, DFMs have been published with conceptual block diagrams, promoting clarity and reproducibility in simulation. However, modern DFMs have grown increasingly complex and block diagrams are now rarely presented, limiting accessibili

Joseph Duthie

We study the gradient flow of Spin($7$)-structures and construct the first explicit solutions, in the homogeneous setting. As an intermediate step, we obtain formulae expressing the Spin($7$)-torsion tensor and gradient flow in terms of the Spin($7$)-torsion forms, which makes explicit computations more tractable. We use these formulae to find explicit solut

Taixi Chen, Jingyun Chen, Nancy Guo

Inspired by the recent success of the Mamba architecture in vision and language domains, we introduce a Unified Attention-Mamba (UAM) backbone. Unlike previous hybrid approaches that integrate Attention and Mamba modules in fixed proportions, our unified design flexibly combines their capabilities within a single cohesive architecture, eliminating the need f

Joanna Schroeder, Alan Wang, Kathryn Linehan, Joel Thurston

This article describes the use of metadata and standards in the Social Impact Data Commons to expose official statisticians to an innovative project built on actionable and evaluable metadata, which produces a FAIR data system. We begin by introducing the concept of the Data Commons, focusing on its features, and presenting an overview of current implementat

Xiaolong Qian, Qi Jiang, Lei Sun, Zongxi Yu

Beyond the commonly recognized optical aberrations, the imaging performance of simplified optical systems--including single-lens and metalens designs--is often further degraded by veiling glare caused by stray-light scattering from non-ideal optical surfaces and coatings, particularly in complex real-world environments. This compound degradation undermines t

Joël D. Beimler, William E. Olsen

We prove that the generic fibre of the Betti moduli space associated to any of the ten Painlev\'e equations coincides with the result of attaching Weinstein handles along the Stokes Legendrian, and provide Weinstein handlebody diagrams for each of them in the process. We moreover extend the procedure for obtaining presentations in Gompf normal form of Weinst

Ginés Carreto Picón, Peng Yuan Zhou, Qi Zhang, Alexandros Iosifidis

Transformer-based models have dramatically increased their size and parameter count to tackle increasingly complex tasks. At the same time, there is a growing demand for high performance, low-latency inference on devices with limited resources. In particular, stream data inference is typically performed over a sliding temporal window, leading to highly redun

Massimiliano Manenti, Andrea Iannelli

Hierarchical Reinforcement Learning promises, among other benefits, to efficiently capture and utilize the temporal structure of a decision-making problem and to enhance continual learning capabilities, but theoretical guarantees lag behind practice. In this paper, we propose a Feudal Q-learning scheme and investigate under which conditions its coupled updat

Christoph L. Schröder, Harvey B. Meyer

We develop elements of coordinate-space perturbation theory for massive quantum field theories in general $d$-dimensional Euclidean space. Using the expansion in Gegenbauer polynomials, we provide analytic expressions for several three-point correlation functions in theories with one massive and one massless field. To this end, a class of antiderivatives of

Sebastian Racedo, Brigitte Jaumard, Oscar Delgado, Meysam Masoudi

Open Radio Access Network (O RAN) disaggregates conventional RAN into interoperable components, enabling flexible resource allocation, energy savings, and agile architectural design. In legacy deployments, the binding between logical functions and physical locations is static, which leads to inefficiencies under time varying traffic and resource conditions.

Ilaria Brivio, Ken Mimasu, Peter Stangl, Anke Biekötter

We introduce the Polynomial Observable Prediction Exchange Format, POPxf, a structured, machine-readable data format for the publication and exchange of semi-analytical theoretical predictions in high energy physics. The format is designed to encode observables that can be expressed in terms of polynomials in model parameters, with particular emphasis on Eff

Chunyang Xu, Michel Mehrenberger, Chang Yang

The cascade remapping method, originally proposed by Nair et al. (2002) for atmospheric modeling, enables efficient and mass conservative semi Lagrangian (SL) transport through successive one dimensional remapping. While widely used in geophysical flows, its application to plasma kinetics remains limited. To exploit its potential advantages in conservation a

Hichem Sahbi

Skeleton-based action recognition is a hotspot in image processing. A key challenge of this task lies in its dependence on large, manually labeled datasets whose acquisition is costly and time-consuming. This paper devises a novel, label-efficient method for skeleton-based action recognition using graph convolutional networks (GCNs). The contribution of the

Markus Pobitzer, Chang Liu, Chenyi Zhuang, Teng Long

Step-by-step painting tutorials are vital for learning artistic techniques, but existing video resources (e.g., YouTube) lack interactivity and personalization. While recent generative models have advanced artistic image synthesis, they struggle to generalize across media and often show temporal or structural inconsistencies, hindering faithful reproduction

MicroBooNE collaboration, P. Abratenko, D. Andrade Aldana, L. Arellano

This work presents single-differential electron-neutrino charged-current cross sections on argon measured using the MicroBooNE detector at the Fermi National Accelerator Laboratory. The analysis uses data recorded when the Neutrinos at the Main Injector beam was operating in both neutrino and antineutrino modes, with exposures of $2 \times 10^{20}$ and $5 \t

N. V. Krishnendu, Tamara Evstafyeva, Aditya Vijaykumar, William E. East

A number of theoretical proposals have been made for horizonless compact objects with masses and spins similar to those of black holes. While gravitational wave signatures from their mergers can resemble those of black holes, features like the spin-induced quadrupole moment may reveal their distinct nature. Using the tight bounds on the spin-induced quadrupo

Yue Yin, Enze Tao, Dylan Campbell

Generative image models can produce convincingly real images, with plausible shapes, textures, layouts and lighting. However, one domain in which they perform notably poorly is in the synthesis of transparent objects, which exhibit refraction, reflection, absorption and scattering. Refraction is a particular challenge, because refracted pixel rays often inte

Yassir Bendou, Omar Ezzahir, Eduardo Fernandes Montesuma, Gabriel Mahuas

Prompt learning has emerged as an effective technique for fine-tuning large-scale foundation models for downstream tasks. However, conventional prompt learning methods are prone to overfitting and can struggle with out-of-distribution generalization. To address these limitations, Bayesian prompt learning has been proposed, which frames prompt optimization as

Paul A. Draghis, Jon M. Miller, Erin Kara, Elisa Costantini

We present the first high-resolution XRISM/Resolve view of the relativistically broadened Fe K line in Cygnus X-1. The data clearly separate the relativistic broad line from the underlying continuum and from narrow emission and absorption features in the Fe band. The unprecedented spectral resolution in the Fe K band clearly demonstrates that the flux excess

Xuan Fang, Tianyu Wang

Talagrand conjectured that if a family of sets $\mathcal{F}$ over $X = \{ 1,2,\cdots, N \}$ is of large measure, then constant times of unions of sets in $\mathcal{F}$ will cover a large portion of the power set of $X$. This conjecture is a central open problem at the intersection of combinatorics and probability theory, and was described by Talagrand as a p

Chiori Hori, Yoshiki Masuyama, Siddarth Jain, Radu Corcodel

Human-robot collaboration towards a shared goal requires robots to understand human action and interaction with the surrounding environment. This paper focuses on human-robot interaction (HRI) based on human-robot dialogue that relies on the robot action confirmation and action step generation using multimodal scene understanding. The state-of-the-art approa

Mary Ogborn, Michael Eracleous, Jessie C. Runnoe, Massimo Dotti

Variability studies of the broad emission lines of Active Galactic Nuclei (AGNs) and quasars show stochastic radial velocity variations (i.e., fluctuations in the centroid of the line), 'jitter', on timescales of weeks to months. This jitter may be intrinsic as the broad-line emitting region (BLR) reverberates from the AGN continuum. There are also coordinat

Lorenzo Alvisi, Victoria Popa, Guglielmo Cola, Serena Tardelli

Toxic and antisocial user behavior on social media platforms has received considerable scholarly attention due to its detrimental effects on society. This study takes a holistic perspective on the phenomenon of online toxicity by investigating the impact of local community norms on toxic expression. By using six large-scale datasets, comprising over 500 mill

Virginia Dignum, Frank Dignum

Agentic AI seeks to endow systems with sustained autonomy, reasoning, and interaction capabilities. To realize this vision, its assumptions about agency must be complemented by explicit models of cognition, cooperation, and governance. This paper argues that the conceptual tools developed within the Autonomous Agents and Multi-Agent Systems (AAMAS) community

Sydney Reis

According to the theory of International Political Economy (IPE), states are often incentivized to rely on rather than constrain powerful corporations. For this reason, IPE provides a useful lens to explain why efforts to govern Artificial Intelligence (AI) at the international and national levels have thus far been developed, applied, and enforced unevenly.

Haoxin Tu, Huan Zhao, Yahui Song, Mehtab Zafar

Automatically generated code is gaining traction recently, owing to the prevalence of Large Language Models (LLMs). Further, the AlphaProof initiative has demonstrated the possibility of using AI for general mathematical reasoning. Reasoning about computer programs (software) can be accomplished via general mathematical reasoning; however, it tends to be mor

Nathaniel Levy, Lucien Heurtier

When primordial black holes (PBHs) evaporate, they deposit energy in the surrounding plasma, leading to temperature gradients, or hot spots, that evolve during the evaporation process. Motivated by recent studies suggesting that a memory burden may slow down PBH evaporation, we explore how a suppression of the evaporation rate affects the morphology of such

Alan Dyson

We rigorously prove the existence and uniqueness of fast traveling pulse solutions to the singularly perturbed neural field system with linear feedback and Heaviside nonlinearity structure within a spatial convolution. Although a long-standing open problem, the pulse is well-accepted to often exist based on its original singular construction, closed form whe

Philipp Lohmann, Daniel Wendland, Francesco Lenzini, Wolfram H. P. Pernice

Integrated photonic platforms can greatly enhance the efficiency of nonlinear frequency conversion processes by tightly confining light on a sub-micron scale. However, this advantage is often reduced by large fiber-to-chip coupling losses which drastically reduce the overall performance. Here we demonstrate a highly efficient thin-film lithium niobate freque

Hendrik Fichtenberger, Michael Kapralov, Ekaterina Kochetkova, Silvio Lattanzi

In the graph clustering problem with a planted solution, the input is a graph on $n$ vertices partitioned into $k$ clusters, and the task is to infer the clusters from graph structure. A standard assumption is that clusters induce well-connected subgraphs (i.e. $\Omega(1)$-expanders), and form $\epsilon$-sparse cuts. Such a graph defines the clustering uniqu

Yahong Guo, Congming Li, Yugao Ouyang

In this paper, we study the boundary H\"older regularity for solutions to the fractional Dirichlet problem in unbounded domains with boundary \begin{equation*} \begin{cases} (-\Delta)^s u(x) = g(x),&\text{in } \Omega, u(x)=0, &\text{in } \Omega^c. \end{cases} \end{equation*} Existing results rely on the global $L^{\infty}$ norm of solutions to control their

Christoph Vogl, Tommaso Comellato, Konstantin Gusev, Brennan Hackett

Commercial argon contains cosmogenic $^{42}$Ar whose progeny $^{42}$K is a critical background component for the Large Enriched Germanium Experiment for Neutrinoless $\beta \beta$ Decay (LEGEND). LEGEND operates High-Purity Germanium (HPGe) detectors bare in liquid argon. $^{42}$K is attracted by the HPGe detectors' electric fields, and drifts toward the ger

Callie C. Liao, Duoduo Liao, Ellie L. Zhang

Recent advances in generative AI have made music generation a prominent research focus. However, many neural-based models rely on large datasets, raising concerns about copyright infringement and high-performance costs. In contrast, we propose MusicAIR, an innovative multimodal AI music generation framework powered by a novel algorithm-driven symbolic music

Dongbo Shi, Shen Cao, Bojian Wu, Jinhui Guo

In this paper, we introduce NoPe-NeRF++, a novel local-to-global optimization algorithm for training Neural Radiance Fields (NeRF) without requiring pose priors. Existing methods, particularly NoPe-NeRF, which focus solely on the local relationships within images, often struggle to recover accurate camera poses in complex scenarios. To overcome the challenge

Osamah N. Neamah

QDNA-ID is a trust-chain framework that links physical quantum behavior to digitally verified records. The system first executes standard quantum circuits with random shot patterns across different devices to generate entropy profiles and measurement data that reveal device-specific behavior. A Bell or CHSH test is then used to confirm that correlations orig

Nezhla Aghaei, Reinier Kramer, Nicolas Orantin, Kento Osuga

We investigate a supersymmetric generalisation of topological recursion from two perspectives: algebraic and geometric. The algebraic side concerns a recursive structure encoded in modules of a super Virasoro algebra, and the geometric counterpart is what we call superconformal topological recursion defined on a super Riemann surface. Superconformal topologi

Qipan Wang, Tianxiang Zhu, Tianyu Jia, Yibo Lin

Rising demand in AI and automotive applications is accelerating 2.5D IC adoption, with multiple chiplets tightly placed to enable high-speed interconnects and heterogeneous integration. As chiplet counts grow, traditional placement tools, limited by poor scalability and reliance on slow simulations, must evolve beyond wirelength minimization to address therm

Mikael Lundbäck, Erik Wallin, Carola Häggström, Mattias Nyström

We present FORWARD, a high-resolution multimodal dataset of a cut-to-length forwarder operating in rough terrain on two harvest sites in the middle part of Sweden. The forwarder is a large Komatsu model equipped with vehicle telematics sensors, including global positioning via satellite navigation, movement sensors, accelerometers, and engine sensors. The fo

Karthik Subramaniam Eswaran, Ali Emami Kopaei, Krzysztof Sacha

We develop a geometric description of quantum light in photonic time crystals on the SU(1,1) coherent-state manifold. In a projective picture, the evolution of each mode appears as a M\"obius isometry on the Poincar\'e disk, where topologies of trajectories distinguish stable, unstable, and critical regimes. The geometric phase is related to the hyperbolic a

Mateusz Jacniacki, Martí Carmona Serrat

Conversational agents built on large language models (LLMs) are becoming increasingly prevalent, yet most systems are designed for one-on-one, turn-based exchanges rather than natural, asynchronous group chats. As AI assistants become widespread throughout digital platforms, from virtual assistants to customer service, developing natural and humanlike intera

Fedele Lizzi

I will discuss how the concept of basho, introduced by Nishida Kitaro nearly a century ago, can give an interesting insight to understand the concept of a point in modern quantum gravity. A quantum spacetime, necessary for the quantization of gravity, requires a whole rethinking of geometry, starting from the primitive concepts, like that of a point. I argue

Carl Jolly, David Kelliher, Jean-Baptiste Lagrange, Alan Letchford

Beam stacking allows a Fixed Field alternating gradient Accelerator (FFA) to increase the extracted beam current whilst also allowing for a flexible time structure making FFAs a promising candidate for future spallation neutron sources and high beam intensity applications. For successful beam stacking, beam loss caused by RF knockout must be avoided. RF knoc

Yi Zhang, Soumya Shreeram, Gabriele Ponti, Johan Comparat

Recent observations with SRG/eROSITA have revealed the average X-ray surface brightness profile of the X-ray-emitting circumgalactic medium (CGM) around Milky Way (MW)-mass galaxies, offering valuable insights into the baryon mass in these systems. However, the estimation of the baryon mass depends critically on several assumptions regarding the gas density

Israt Jahan Tulin, Sebastian Starke, Dominic Windisch, André Bieberle

Ultrafast electron beam X-ray computed tomography produces noisy data due to short measurement times, causing reconstruction artifacts and limiting overall image quality. To counteract these issues, two self-supervised deep learning methods for denoising of raw detector data were investigated and compared against a non-learning based denoising method. We fou

Anjitha John William, Maciej Bilicki, Wojciech A. Hellwing, Szymon J. Nakoneczny

We investigate the angular clustering and effective bias of photometrically selected quasars in the Kilo-Degree Survey Data Release 4 (KiDS DR4). We update the previous photometric redshifts (photo-$z$s) of the KiDS quasars using Hybrid-z, a deep learning framework combining four-band KiDS images and nine-band KiDS+VIKING magnitudes. Hybrid-z is trained on t

Neil J. Calkin, Eliza Gallagher, Ben Gobler

We study the relationship between three combinatorial objects -- a taffy pulling machine, the Calkin-Wilf tree of all fractions, and Conway's rational tangles. After introducing these objects, we develop a taffy analogue for Conway's characterization of rational tangles, and we give a direct geometric connection between rational tangles and taffy pulls.

CMS Collaboration

A search for new physics in events featuring a single photon and missing transverse momentum is presented, using proton-proton $\sqrt{s}$ = 13 TeV collision data corresponding to an integrated luminosity of 101 fb$^{-1}$ collected by the CMS experiment at the CERN LHC between 2017 and 2018. This analysis, combined with a previous study of 36 fb$^{-1}$ of 201

David Nordström, Johan Edstedt, Fredrik Kahl, Georg Bökman

Self-supervised learning on images seeks to extract meaningful visual representations from unlabeled data. When scaled to large datasets, this paradigm has achieved state-of-the-art performance and the resulting trained models such as DINOv3 have seen widespread adoption. However, most prior efforts are optimized for semantic understanding rather than geomet