November 2025 arXiv papers — page 146

Rustam Galimullin, Munyque Mittelmann, Laurent Perrussel

In diffusion auctions, sellers can leverage an underlying social network to broaden participation, thereby increasing their potential revenue. Specifically, sellers can incentivise participants in their auction to diffuse information about the auction through the network. While numerous variants of such auctions have been recently studied in the literature,

Abhimanyu Borthakur, Jack Eden Hirschman, Sergio Carbajo

Ultrashort laser pulses enable attosecond-scale measurements and drive breakthroughs across science and technology, but their routine use hinges on reliable pulse characterization. Frequency-Resolved Optical Gating (FROG) is a leading solution, forming a spectrogram by scanning the delay between two pulse replicas and recording the nonlinear signal spectrum.

Jerry M. Huang, Stefan T. Radev

Immersive rooms are increasingly popular augmented reality systems that support multi-agent interactions within a virtual world. However, despite extensive content creation and technological developments, insights about perceptually-driven social dynamics, such as the complex movement patterns during virtual world navigation, remain largely underexplored. Co

Abdulkadir Bilge, Eren Akyol, Murat Kuscu

Diffusion-based Molecular Communication (MC) is inherently challenged by severe inter-symbol interference (ISI). This is significantly amplified in mobile scenarios, where the channel impulse response (CIR) becomes time-varying and stochastic. Obtaining accurate Channel State Information (CSI) for traditional model-based detection is intractable in such dyna

Benjamin M. Fregoso

We compute the average density of a three-dimensional multiband crystal of arbitrary symmetry, metal or insulator, to first and second order in a weak homogeneous magnetic field. To linear order and for insulators, the density follows the well-known Streda formula, but for metals there is an extra contribution from the orbital magnetic moments at the Fermi s

Audrey Fovelle, Juan B. Seoane-Sepúlveda

In their papers, Leonetti, Russo, Somaglia, Menet and Papathanasiou posed the question of whether there exists an infinite dimensional vector space of sequences in $\ell_\infty$ having (except for the zero sequence) an even amount of accumulation points. Here we answer this question in the negative, by showing that this previous set of sequences is not even

Haoxiang Wan, Linhan Fang, Xingpeng Li

Data centers are facilities housing computing infrastructure for processing and storing digital information. The rapid expansion of artificial intelligence is driving unprecedented growth in data center capacity, with global electricity demand from data centers projected to double by 2026. This growth creates substantial challenges for power transmission net

Alex Rose

We prove finite-field analogs of Bourgain's projection theorem in higher dimensions. In particular, for a certain range of parameters we improve on an exceptional set estimate by Chen in all dimensions and codimensions.

Al Nahian Mugdho, Md. Ibrahim, A. S. M. Badrudduza, Md. Abdur Rakib

In the context of emerging sixth-generation (6G) wireless networks, reconfigurable intelligent surfaces (RISs) are gaining prominence for their ability to intelligently control electromagnetic wave propagation and enhance backhaul communication performance. In this paper, we propose a novel dual-hop wireless network, where the first hop consists of a hybrid

Alexandra C Salem, Mohammad Shokri, Johanna Devaney

We evaluate five Transformer-based strategies for chord-conditioned melody and bass generation using a set of music theory-motivated metrics capturing pitch content, pitch interval size, and chord tone usage. The evaluated models include (1) no chord conditioning, (2) independent line chord-conditioned generation, (3) bass-first chord-conditioned generation,

Konrad Mickiewicz, Valentin Link, Walter T. Strunz

We present an approach for efficiently simulating strongly damped quantum systems subjected to periodic driving, employing a periodic matrix product operator representation of the influence functional. This representation enables the construction of a numerically exact Floquet propagator that captures the non-Markovian open system dynamics, thus providing a

Rad Haghi, Bipin Gaikwad, Abani Patra

Fourier Neural Operators (FNOs) have emerged as promising surrogates for partial differential equation solvers. In this work, we extensively tested FNOs on a variety of systems with non-linear and non-stationary properties, using a wide range of forcing functions to isolate failure mechanisms. FNOs stand out in modeling linear systems, regardless of complexi

Akshita Gupta, Arna Bhardwaj, Yashwanth Kumar Nakka, Changrak Choi

This work presents a global-to-local, task-aware fault detection and identification (FDI) framework for multi-spacecraft systems conducting collaborative inspection missions in low Earth orbit. The inspection task is represented by a global information-driven cost functional that integrates the sensor model, spacecraft poses, and mission-level information-ga

Yakov Shlapentokh-Rothman, Mihai Tohaneanu

On the full range of sub-extremal Kerr exterior spacetimes we give a new proof of energy boundedness for high-frequency projections of solutions to the wave equation onto trapped frequencies. A key feature of the new estimate is that it circumvents the use of an integrated local energy decay (ILED) statement. As an illustration of the robustness of the estim

Jesus Silva-Rodriguez, Xingpeng Li

The alternating direction method of multipliers (ADMM) is a powerful algorithm for solving decentralized optimization problems including networked microgrid energy management (NetMEM). However, its performance is highly sensitive to the selection of its penalty parameter \r{ho}, which can lead to slow convergence, suboptimal solutions, or even algorithm dive

Mehrdad Zakershahrak

Reinforcement learning has traditionally focused on a singular objective: learning policies that select actions to maximize reward. We challenge this paradigm by asking: what if we explicitly architected RL systems as inference engines that can answer diverse queries about their environment? In deterministic settings, trained agents implicitly encode rich kn

Estefania Talavera, Deblina Bhattacharjee, Himangi Mittal, Mengwei Ren

The Women in Computer Vision Workshop (WiCV@CVPR 2025) was held in conjunction with the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2025) in Nashville, Tennessee, United States. This report presents an overview of the workshop program, participation statistics, mentorship outcomes, and historical trends from previous WiCV editions. T

Isaac Joffe, Chris Eliasmith

The Abstraction and Reasoning Corpus for Artificial General Intelligence (ARC-AGI) is a generative, few-shot fluid intelligence benchmark. Although humans effortlessly solve ARC-AGI, it remains extremely difficult for even the most advanced artificial intelligence systems. Inspired by methods for modelling human intelligence spanning neuroscience to psycholo

Gabriel Wong

The standard, gapped entanglement boundary condition in Chern Simons theory breaks the topological invariance of the theory by introducing a complex structure on the entangling surface. This produces an infinite dimensional subregion Hilbert space, a non-trivial modular Hamiltonion, and a UV-divergent entanglement entropy that is a universal feature of local

Jinlei Zhou, Sukang Peng, Yongchuan Yu, Dong Wu

A unified Smoothed Particle Hydrodynamics (SPH) simulation framework for coronary stent implantation is developed, which unifies weakly compressible hemodynamics, Neo-Hookean solids, and stent-artery contacts, based on a multi-resolution particle discretization. Prior to application, feasibility and accuracy are established via three baseline validations: (i

Amandine Aftalion, Rémy Rodiac

Motivated by recent experiments on fermionic rings, we study the asymptotic behaviour of minimizers of the Ginzburg-Landau (GL) energy in an annulus with a Dirichlet data which depends on the GL parameter on the outer boundary. We show that there is a critical degree of order $|\ln \varepsilon|$ under which the ground state displays a giant vortex and above

Zengyi Qin, Jinyuan Chen, Yunze Man, Shengcao Cao

Training computer use agents requires full-featured OS sandboxes with GUI environments, which consume substantial hardware resources as the number of sandboxes scales. Stochastic errors arising from diverse software execution within these sandboxes further demand robust infrastructure design and reliable error recovery. We present OSGym, a scalable OS enviro

Johannes Hagel

We study the nonlinear oscillator z'' + omega^2 z + g(t) z^2 = 0 with a time-dependent coefficient g(t). We show that this equation admits an exact quadratic invariant I(z,p,t) provided that g(t) = alpha2(t)^(-5/2) and that alpha2(t) satisfies a nonlinear third-order differential equation. The resulting invariant constrains the dynamics to a smooth two-dimen

Jeremy van der Heijden, Erik Verlinde, Jiuci Xu

The emergence of the quantum $R$-matrix in the double-scaled SYK model points to an underlying quantum group structure. In this work, we identify the quantum group $\mathcal{U}_q(\mathfrak{su}(1,1))$ as a subalgebra of the chord algebra. Specifically, we construct the generators of $\mathcal{U}_q(\mathfrak{s} \mathfrak{u}(1,1))$ from combinations of operator

Miguel A. Durán-Olivencia

Bridging the gap between individual agent behavior and macroscopic societal patterns is a central challenge in the social sciences. In this work, we propose a solution to this problem via a kinetic theory formulation. We demonstrate that complex, empirically-observed phenomena, such as the concentration of populations in cities and the emergence of power-law

Kai S. Yun, Navid Azizan

Ensuring the safety of real-world systems is challenging, especially when they rely on learned perception modules to infer the system state from high-dimensional sensor data. These perception modules are vulnerable to epistemic uncertainty, often failing when encountering out-of-distribution (OoD) measurements not seen during training. To address this gap, w

Deepesh Bhamre, J. P. B. C. de Melo

We investigate the Ward-Takahashi identity at one-loop in the light-front (LF) formalism for a bound state of fermions. We consider a spinless bound state made up of two fermions in which the Ward-Takahashi identity is satisfied in the covariant formulation. Considering the same system in the light-front formalism, we investigate the proof of Ward-Takahashi

Chiara Paulsen

A classical theorem of Szeg\H{o} states that for any probability measure $\mu=w\frac{\mathrm{d}\theta}{2\pi}+\mu_s$ on the unit circle the polynomials are dense in $L^2(\mathbb{T},\mu)$ if and only if $\log(w)\notin L^1(\mathbb{T})$. A related question asks whether the monomials with exponents in some subset $\Lambda\subseteq \mathbb{N}_0$ already span $L^2(

Will Stephenson, Nan Cheng, Kai Sun, Xiaoming Mao

Using a mapping between spatial disorder and temporal stochasticity, we develop a new framework using Lyapunov exponents to explain exotic wave localization and mobility phenomena in disordered one-dimensional (1D) mechanical systems that can be constructed via a spatial analog of a Markov process, which we call ``mechanical Markov systems.'' We show that di

Bernardo Rivas, Kaito Iwasaki, William Kalies, Anthony Bloch

The analysis of global dynamics, particularly the identification and characterization of attractors and their regions of attraction, is essential for complex nonlinear and hybrid systems. Combinatorial methods based on Conley's index theory have provided a rigorous framework for this analysis. However, the computation relies on rigorous outer approximations

Ryan Ent, Golbon Zakeri, Tongxin Zheng, Jinye Zhao

Energy imbalance reserve (EIR) product is introduced into the Independent System Operator (ISO) of New England's day-ahead wholesale electricity market to provide a better fuel procurement incentive for generating resources. Different from existing forward reserve products, EIR is a novel real option product, which is settled against real-time energy price r

Hasib Uddin Molla, Matthew Backhouse, Ankit Banarjee, Jinniao Qiu

In this work, we extend deep learning-based numerical methods to fully coupled forward-backward stochastic differential equations (FBSDEs) within a non-Markovian framework. Error estimates and convergence are provided. In contrast to the existing literature, our approach not only analyzes the non-Markovian framework but also addresses fully coupled settings,

Mingjia He, Zhiyu He, Jan Ghadamian, Florian Dörfler

Mobility systems are complex socio-technical environments influenced by multiple stakeholders with hierarchically interdependent decisions, rendering effective control and policy design inherently challenging. We bridge hierarchical game-theoretic modeling with online feedback optimization by casting urban mobility as a tri-level Stackelberg game (travelers,

Carlos A. Taveras, Santiago Segarra, César A. Uribe

We study the problem of graph coarsening within the Gromov-Wasserstein geometry. Specifically, we propose two algorithms that leverage a novel representation of the distortion induced by merging pairs of nodes. The first method, termed Greedy Pair Coarsening (GPC), iteratively merges pairs of nodes that locally minimize a measure of distortion until the desi

Marta Lagomarsino, Elena Merlo, Andrea Pupa, Timo Birr

Remarkable capabilities have been achieved by robotics and AI, mastering complex tasks and environments. Yet, humans often remain passive observers, fascinated but uncertain how to engage. Robots, in turn, cannot reach their full potential in human-populated environments without effectively modeling human states and intentions and adapting their behavior. To

Gabriele Cozzo, Paola Testa, Juan Martinez-Sykora, Paolo Pagano

Recent observations of fast and bursty ``nanojets'' suggest novel diagnostics of nanoflare heating in the solar corona. The aim of this work is to investigate the presence and properties of reconnection outflows, similar to observed nanojets, in numerical simulations, and explore their relationship with the nanoflare properties. This work explores their pote

Sacha-Élie Ayoun, Opale Sjöstedt, Azalea Raad

Symbolic execution (SE) tools often rely on intermediate languages (ILs) to support multiple programming languages, promising reusability and efficiency. In practice, this approach introduces trade-offs between performance, accuracy, and language feature support. We argue that building SE engines \emph{directly} for each source language is both simpler and m

Tiago Machado, Maysa Malfiza Garcia de Macedo, Rogerio Abreu de Paula, Marcelo Carpinette Grave

This work aims to investigate how different Large Language Models (LLMs) alignment methods affect the models' responses to prompt attacks. We selected open source models based on the most common alignment methods, namely, Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Reinforcement Learning with Human Feedback (RLHF). We conducted a

Annett Thøgersen, Georg Muntingh, Lasse Vines, Øystein Prytz

High-resolution mapping of electronic properties at oxide heterointerfaces remains challenging due to probe delocalization and overlapping signals. In this work, we employ monochromated, probe-corrected scanning transmission electron microscopy combined with electron energy-loss spectroscopy (STEM-EELS) to resolve band gap variations across $\kappa$-Ga$_2$O$

Alina Deriyeva, Benjamin Paassen

Learning Analytics Dashboards can be a powerful tool to support self-regulated learning in Digital Learning Environments and promote development of meta-cognitive skills, such as reflection. However, their effectiveness can be affected by the interpretability of the data they provide. To assist in the interpretation, we employ a large language model to gener

Simon Hettler, Raul Arenal

In-situ transmission electron microscopy (TEM) has become an important technique to study dynamic processes at highest spatial resolution and one branch is the investigation of phenomena related with electrical currents. Here, we present experimental results obtained from a peculiar in-situ TEM device, which was prepared with the aim to analyze the relations

Gabriele Cozzo, Paola Testa, Juan Martinez-Sykora, Fabio Reale

The nature and detailed properties of the heating of the million-degree solar corona are important issues that are still largely unresolved. Nanoflare heating might be dominant in active regions and quiet Sun, although direct signatures of such small-scale events are difficult to observe in the highly conducting, faint corona. The aim of this work is to test

Katy Aruachan, Sanoj Raj, Yamil J. Colón, Daniel Aravena

Solid-state molecular qubits with open-shell ground states have great potential for addressability, scalability, and tunability, but understanding the fundamental limits of quantum coherence in these systems is challenging due to the complexity of the qubit environment. To address this, we develop a random Hamiltonian approach where the molecular $g$-tensor

Andrew Hassell, Qiuye Jia, Ethan Sussman, Andras Vasy

This is the less technical half of a two-part work in which we introduce a robust microlocal framework for analyzing the non-relativistic limit of relativistic wave equations with time-dependent coefficients, focusing on the Klein--Gordon equation. Two asymptotic regimes in phase space are relevant to the non-relativistic limit: one corresponding to what phy

Shu-wen Yang, Ming Tu, Andy T. Liu, Xinghua Qu

Speech-to-Speech (S2S) models have shown promising dialogue capabilities, but their ability to handle paralinguistic cues - such as emotion, tone, and speaker attributes - and to respond appropriately in both content and style remains under-explored. Progress is further hindered by the scarcity of high-quality and expressive demonstrations. To address this,

Mohammed Ali El Adlouni, Ling Jin, Xiaodan Xu, C. Anna Spurlock

Urban congestions cause inefficient movement of vehicles and exacerbate greenhouse gas emissions and urban air pollution. Macroscopic emission fundamental diagram (eMFD)captures an orderly relationship among emission and aggregated traffic variables at the network level, allowing for real-time monitoring of region-wide emissions and optimal allocation of tra

Andreas Einwiller, Kanishka Ghosh Dastidar, Artur Romazanov, Annette Hautli-Janisz

In behavioral sciences, experiments such as the ultimatum game are conducted to assess preferences for fairness or self-interest of study participants. In the dictator game, a simplified version of the ultimatum game where only one of two players makes a single decision, the dictator unilaterally decides how to split a fixed sum of money between themselves a

Saba Asaad, Chongjun Ouyang, Ali Bereyhi, Zhiguo Ding

We study the energy efficiency of pinching-antenna systems (PASSs) by developing a consistent formulation for power distribution in these systems. The per-antenna power distribution in PASSs is not controlled explicitly by a power allocation policy, but rather implicitly through tuning of pinching couplings and locations. Both these factors are tunable: (i)

Rachel T Gonzalez, Madeline R Abbott, Brahmajee Nallamothu, Scott Hummel

The ubiquitous nature of mobile health (mHealth) technology has expanded opportunities for the integration of reinforcement learning into traditional clinical trial designs, allowing researchers to learn individualized treatment policies during the study. LowSalt4Life 2 (LS4L2) is a recent trial aimed at reducing sodium intake among hypertensive individuals

Matthäus Siebenhofer, Pjotrs Žguns, Bilge Yildiz

Ion migration in WO$_3$ is a critical process for various technological applications, such as in batteries, electrochromic devices and energy-efficient brain-inspired computing devices. In this study, we investigate the migration mechanisms of H$^+$, Li$^+$, and Mg$^{2+}$ ions in monoclinic WO$_3$, and how energy barriers are affected by the presence of elec

Yuxin Bai, Aranyak Acharyya, Ashwin De Silva, Zeyu Shen

Optimal control of the future is the next frontier for AI. Current approaches to this problem are typically rooted in reinforcement learning (RL). RL is mathematically distinct from supervised learning, which has been the main workhorse for the recent achievements in AI. Moreover, RL typically operates in a stationary environment with episodic resets, limiti

Ce Sui, Yi Mao, Xiaosheng Zhao, Tao Jing

The advent of increasingly advanced surveys and cosmic tracers has motivated the development of new inference techniques and novel approaches to extracting information from cosmic fields. A central challenge in this endeavor is to quantify the information content carried by these summary statistics in cosmic fields. In particular, how should we assess which

Connar Hite, Sean Saud, Raef Taha, Nayim Rahman

Answer Set Programming (ASP) is a declarative programming paradigm based on logic programming and non-monotonic reasoning. It is a tremendously powerful tool for describing and solving combinatorial problems. Like any other language, ASP requires users to learn how it works and the syntax involved. It is becoming increasingly required for those unfamiliar wi

Gabriel Rodriguez-Canal, David Katz, Nick Brown

With the slowing of Moore's Law, heterogeneous computing platforms such as Field Programmable Gate Arrays (FPGAs) have gained increasing interest for accelerating HPC workloads. In this work we present, to the best of our knowledge, the first implementation of selective code offloading to FPGAs via the OpenMP target directive within MLIR. Our approach combin

Maciej Rzeszut

The classical Davis inequality $\mathbb{E} Mf\simeq \mathbb{E} Sf$, where $(Sf)^2=\sum_{k}\left|f_{k}-f_{k-1}\right|^2$ is the square function and $Mf= \sup_n \left|f_n\right|$ is the maximal function, is true with a universal constant for any martingale $f$ on any filtration. A natural analog in the setting of (F4) doubly indexed filtrations, i.e. $\left(\m

Abhipsa Basu, Aviral Gupta, Abhijnya Bhat, R. Venkatesh Babu

Image classification systems often inherit biases from uneven group representation in training data. For example, in face datasets for hair color classification, blond hair may be disproportionately associated with females, reinforcing stereotypes. A recent approach leverages the Stable Diffusion model to generate balanced training data, but these models oft

Romain Cosentino, Sarath Shekkizhar, Adam Earle

We develop and analyze a theoretical framework for agent-to-agent interactions in a simplified in-context linear regression setting. In our model, each agent is instantiated as a single-layer transformer with linear self-attention (LSA) trained to implement gradient-descent-like updates on a quadratic regression objective from in-context examples. We then st

Roman R. Rafikov

The dynamics of systems of multiple gravitationally interacting bodies is often studied in a frame attached to one of the objects (e.g. a central star in a planetary system). As this frame is generally non-inertial, indirect forces appear in the equations describing the motion of bodies relative to the reference object. According to the convention adopted in

Hyunho Kook, Byeongho Yu, Jeong Min Oh, Eunhyeok Park

Recent advancements in the direct training of Spiking Neural Networks (SNNs) have demonstrated high-quality outputs even at early timesteps, paving the way for novel energy-efficient AI paradigms. However, the inherent non-linearity and temporal dependencies in SNNs introduce persistent challenges, such as temporal covariate shift (TCS) and unstable gradient

Zhuojun Tian, Mehdi Bennis

In this letter, we formulate a compositional distributed learning framework for multi-view perception by leveraging the maximal coding rate reduction principle combined with subspace basis fusion. In the proposed algorithm, each agent conducts a periodic singular value decomposition on its learned subspaces and exchanges truncated basis matrices, based on wh

Yaroslav Popryho, Debjit Pal, Inna Partin-Vaisband

Industrial Systems-on-Chips (SoCs) often comprise hundreds of thousands to millions of nets and millions to tens of millions of connectivity edges, making empirical evaluation of hardware-Trojan (HT) detectors on realistic designs both necessary and difficult. Public benchmarks remain significantly smaller and hand-crafted, while releasing truly malicious RT

David Sanchez, Holly Lopez, Michelle Buraczyk, Anantaa Kotal

As machine learning systems move from theory to practice, they are increasingly tasked with decisions that affect healthcare access, financial opportunities, hiring, and public services. In these contexts, accuracy is only one piece of the puzzle - models must also be fair to different groups, protect individual privacy, and remain accountable to stakeholder

Bo Wen, Chen Wang, Erhan Bilal

ARC-AGI and ARC-AGI-2 measure generalization-through-composition on small color-quantized grids, and their prize competitions make progress on these harder held-out tasks a meaningful proxy for systematic generalization. Recent instruction-first systems translate grids into concise natural-language or DSL rules executed in generate-execute-select loops, yet

S. E. Chorfi, F. Et-tahri, L. Maniar, M. Yamamoto

We study some inverse problems for time-fractional Schr\"odinger equations involving the Caputo derivative of fractional order $\alpha \in (0,1)$. We prove refined uniqueness results from sets of positive Lebesgue measure for various problems by weakening the regularity of initial data.

Abraham Itzhak Weinberg

As Multi Agent Reinforcement Learning systems are used in safety critical applications. Understanding why agents make decisions and how they achieve collective behavior is crucial. Existing explainable AI methods struggle in multi agent settings. They fail to attribute collective outcomes to individuals, quantify emergent behaviors, or capture complex intera

Shi-Xian Wu, Peng-Nan Sun, Xiao-Ting Huang, Yu-Xiang Peng

This paper proposes a novel consistent {\delta}+- Updated Lagrangian Particle Hydrodynamics (ULPH) model. Although the Smoothed Particle Hydrodynamics (SPH) model has gained recognized achievements, it is afflicted by excessive numerical dissipation when the neighboring particles are insufficient. The present proposed consistent {\delta}+-ULPH model has adva

Nishita Jadoo, J. David Brown, Charles R. Evans

We model the general relativistic interaction of a small hyperelastic sphere with a Schwarzschild black hole as it follows an initially marginally-bound orbit through a close encounter. While the interaction reveals effects that are encoded by the Mathisson-Papapetrou-Dixon (MPD) multipolar equations through quadrupole order, the calculation is made using an

Christopher R. Mankovich, Marzia Parisi, Damon F. Landau, Janosz W. Dewberry

Close orbits by a Uranus Orbiter and Probe (UOP) could be used to deduce Uranus's multipolar gravity field to higher precision and angular degree than the J2 and J4 currently measured from ground-based ring occultations and the Voyager 2 flyby. We examine Jn sensitivity limits obtained from simulations of candidate UOP trajectories, pairing these with Uranus

Can Yang, Zhenzhong Wang, Junyuan Liu, Yunpeng Gong

Accurate and efficient simulations of physical phenomena governed by partial differential equations (PDEs) are important for scientific and engineering progress. While traditional numerical solvers are powerful, they are often computationally expensive. Recently, data-driven methods have emerged as alternatives, but they frequently suffer from error accumula

Xuke Qiu, Jinge Guo, Jiahe Cui, Runchen Zhang

Determining the amplitude, phase, and polarization profile of light is essential for both fundamental scientific discovery and applications spanning optical metrology, microscopy, astronomy, and optical communication/computing technologies. However, most modern measurement approaches are unable to retrieve such parameters readily, often relying on bulky and

Maria J. Rodriguez, Luis Fernando Temoche

We revisit black hole perturbations through Heun differential equations, focusing on Frobenius power-series solutions near regular singularities and their connection formulas. Central to our approach is the notion of a cline in the complex plane, which organizes singular points of the differential equations and remain invariant under M\"obius transformations

Leonard Jung, Alan Papalia, Kevin Doherty, Michael Everett

Long-term state estimation over graphs remains challenging as current graph estimation methods scale poorly on large, long-term graphs. To address this, our work advances a current state-of-the-art graph sparsification algorithm, maximizing algebraic connectivity (MAC). MAC is a sparsification method that preserves estimation performance by maximizing the al

S. Andreon, M. Radovich

This paper, the third in a series, investigates the scaling relations between optical richness, weak-lensing mass, and Compton $Y$ for a sample of galaxy clusters selected purely by the effect of their gravitational potential on the shapes of background galaxies. This selection method is uncommon, as most cluster samples in the literature are selected based

Enis Kaya, Stephen McKean, Sam Streeter, H. Uppal

We classify the number of $k$-rational lines and conic fibrations on del Pezzo surfaces over a field $k$ in terms of relatively minimal surfaces and establish rational curve analogues of the inverse Galois problem for del Pezzo surfaces. We completely solve these problems in all degrees over all global, local and finite fields and provide new solutions of th

Pavel E. Mancera Piña, Enrico M. Di Teodoro, S. Michael Fall, Antonino Marasco

We derive the Tully-Fisher (TFR, $M_\ast-V_{\rm circ,f}$) and Fall (FR, $j_\ast-M_\ast$) relations at redshift $z = 0.9$ using a sample of 43 main-sequence disc galaxies with H$\alpha$ IFU data and JWST/HST imaging. The strength of our analysis lies in the use of state-of-the-art 3D kinematic models to infer galaxy rotation curves, the inclusion and morpholo

Antonio J. Porras-Valverde, Priyamvada Natarajan, Angelo Ricarte, Kung-Yi Su

The accretion and feedback processes governing supermassive black hole (SMBH) growth span an enormous range of spatial scales, from the Event Horizon to the circumgalactic medium. Recent general relativistic magnetohydrodynamic (GRMHD) simulations demonstrate that strong magnetic fields can substantially suppress Bondi accretion by creating magnetically arre

Matthew Kirk, Danny van Dyk

In light of ongoing issues with the first-row unitarity test of the CKM matrix, we showcase a global fit to measurements of $K\to \ell \nu$, $K\to \pi\ell \nu$, $\tau \to K\nu$, and $\tau\to K\pi \nu$ decays for the first time. Fitting the semileptonic and 3-body $\tau$ decay data simultaneously becomes computationally feasible because we employ a simple for

Wolfgang Altmannshofer, Joachim Brod, Patipan Uttayarat, Daniil Volkov

We present the first complete two-loop calculation of the electric and chromo-electric dipole moments of the light quarks and the gluon, as well as contributions to CP-violating lepton-quark interactions, in the unconstrained two-Higgs doublet model. We include the most general Yukawa interactions of the Higgs doublets with the Standard Model fermions up to

Gilberto Colangelo, Martina Cottini, Jacobo Ruiz de Elvira

This is the first of a series of papers devoted to a detailed analysis of isospin-breaking effects in the $\pi \pi$ scattering amplitude and the vector form factor of the pion. Isospin breaking originates from the mass difference between up and down quarks and from electromagnetic effects. The latter can be further split into effects due to the pion-mass dif

Heather Samonski, Samir Salim, John Salzer

The currently known compact extreme emission-line galaxies (the "Green Peas", GPs) in SDSS are rare and were mostly found among serendipitous spectroscopic targets, thus leaving open the possibility that a substantial population of GPs is missed. A significantly larger number of identified GPs in the Local Universe might provide a better characterization of

Zhong-Zhi Xianyu, Jiaju Zang

The particle model building of cosmological collider physics often involves boost-breaking bilinear mixing between a heavy particle and the nearly massless inflaton mode. In cosmological correlators, such a mixing is obtained by taking a folded limit of a generic tree graph, which is a special case of degenerate kinematics. In this work, we continue our expl

Dusmanta Patra, Gopal-Krishna, Ravi Joshi

We report evidence for in-situ acceleration/re-acceleration of relativistic particles in 11 radio wings out of a total of 68 wings sufficiently well-resolved for spectral mapping, which belong to our sample of 40 X-shaped radio galaxies (XRGs). This representative XRG sample includes 15 XRGs newly reported here, which we selected from the LOTSS-DR2 survey, f

J. I. Villaseñor, H. Sana, J. Bodensteiner, N. Britavskiy

We present an overview of our recent results from the BLOeM campaign in the Small Magellanic Cloud ($Z=0.2\,{\rm Z}_{\odot}$). Using nine-epoch VLT/FLAMES spectroscopy, we investigated the multiplicity of 929 massive stars. Our findings reveal contrasting binary properties across evolutionary stages: O-type stars show an intrinsic close-binary fraction of $7

Gabriella De Lucia, Lizhi Xie, Michaela Hirschmann, Fabio Fontanot

We investigate the environments of massive quiescent galaxies at 3 < z < 5 using the GAlaxy Evolution and Assembly (GAEA) theoretical model. We select galaxies with stellar mass ~10^10.8 Msun and specific star formation rate below 0.3x t_Hubble, yielding in a sample of about 5,000 galaxies within a simulated volume of ~685 Mpc. These galaxies have formation

Si-Rui Ge, Nikko J. Cleri, Joel Leja, Antonello Calabro

Rest-frame optical emission line diagnostics are often used to help classify ionizing sources within galaxies. However, rest-frame optical tracers can miss sources with high dust attenuation, leading to misclassification of the dominant ionizing source. Longer wavelength tracers, such as those in the near-infrared, carry the power to diagnose ionizing source

Joshua Lin, Bruno Scheihing-Hitschfeld, Thomas Steingasser

Recent developments in the understanding of real-time path integrals led to the development of the ``steadyon picture'' for the semi-classical calculation of quantum tunneling rates. We discuss tunneling out of a generic localized initial state in this picture and present its application for the important example of a resonance state in a one-dimensional poi

Joshua Lin, Bruno Scheihing-Hitschfeld, Thomas Steingasser

When tunneling occurs out of generic initial states, a significant fraction of probability is lost at early times during which the dynamics is governed by excited resonance states. However, first-principles analyses based on path integrals have only captured the leading asymptotic behavior during which the tunneling rate is dominated by the false vacuum cont

Kung-Yi Su, Angelo Ricarte, Priyamvada Natarajan, Antonio J. Porras-Valverde

The co-evolution of supermassive black holes (SMBHs) and their host galaxies remains one of the central open questions in cosmology, rooted in the coupling between accretion, feedback, and the multi-scale physics that links the event horizon to the circumgalactic medium. Here we bridge these scales by embedding a first-principles, GRMHD-informed prescription

H. P. Ojeda Collado, Ludwig Mathey

Parametric amplification is a key ingredient of a wide range of phenomena, from the classical to the quantum domain. Although such phenomena have been demonstrated in non-equilibrium settings, their use for fluctuation engineering has been put forth in Raman-cavity hybrids only recently. In this work, we generalize fluctuation engineering to a multi-mode sce

Jingtong Yue, Ziqi Huang, Zhaoxi Chen, Xintao Wang

The landscape of video generation is shifting, from a focus on generating visually appealing clips to building virtual environments that support interaction and maintain physical plausibility. These developments point toward the emergence of video foundation models that function not only as visual generators but also as implicit world models, models that sim

Oriana K. Diessel, Subir Sachdev, Pietro M. Bonetti

We investigate the phase diagram of a relativistic, parametrically driven O($N$)-symmetric theory coupled to a Markovian thermal bath. Our analysis reveals a rich variety of phases, including both uniform and spatially modulated symmetry-broken states, some of which feature an order parameter oscillating at half the drive frequency. When coupled to a backgro

Michal P. Heller, Alexandre Serantes, Michał Spaliński, Benjamin Withers

We provide a systematic framework for solving the initial value problem for relativistic hydrodynamics formulated as a gradient expansion. Secular growth is handled by a suitable covariant resummation scheme, which reorganises the degrees of freedom at each order in the expansion while preserving the sum. Our scheme can be applied to any order in the gradien

Ying Jiao, Rodrigo Castellano Ontiveros, Luc De Raedt, Marco Gori

Neurosymbolic (NeSy) AI aims to combine the strengths of neural architectures and symbolic reasoning to improve the accuracy, interpretability, and generalization capability of AI models. While logic inference on top of subsymbolic modules has been shown to effectively guarantee these properties, this often comes at the cost of reduced scalability, which can

Drishti Sharma, Purba Mukherjee, Anjan A Sen, Suhail Dhawan

We investigate the impact of instrumental and astrophysical systematics on dark energy (DE) constraints from Type Ia supernova (SN-Ia) observations. Using simulated datasets consistent with current SN-Ia measurements, we examine how photometric calibration, intergalactic dust, progenitor evolution in luminosity and light-curve stretch, intrinsic color scatte

Ahmed Farag Ali

In our previous work \cite{FaragAli:2024jpo} we outlined an exploratory framework in which the 24-cell acts both as the quantum of spacetime and as a geometric representation of elementary particles. In this paper we provide comprehensive mathematical and phenomenological evidence that deepens and refines this primary model. The symmetry of the 24-cell yield

Léo Grinsztajn, Klemens Flöge, Oscar Key, Felix Birkel

The first tabular foundation model, TabPFN, and its successor TabPFNv2 have impacted tabular AI substantially, with dozens of methods building on it and hundreds of applications across different use cases. This report introduces TabPFN-2.5, the next generation of our tabular foundation model, built for datasets with up to 50,000 data points and 2,000 feature

Belinda Z. Li, Zifan Carl Guo, Vincent Huang, Jacob Steinhardt

Can language models (LMs) learn to faithfully describe their internal computations? Are they better able to describe themselves than other models? We study the extent to which LMs' privileged access to their own internals can be leveraged to produce new techniques for explaining their behavior. Using existing interpretability techniques as a source of ground

Katherine Freese, George M. Fuller, Sohan Ghodla, Cosmin Ilie

We show that dark stars, which are dark-matter-powered stars in the early universe, can grow by accretion to masses in the range $\mathscr{O}\left ({10}^4\right )-\mathscr{O}\left ({10}^7\right)\,{M_\odot}$ before the general-relativistic Feynman-Chandrasekhar instability causes their dynamical collapse to black holes. These accreting dark star configuration

Joseph Fioresi, Ishan Rajendrakumar Dave, Mubarak Shah

We introduce a novel formulation of visual privacy preservation for video foundation models that operates entirely in the latent space. While spatio-temporal features learned by foundation models have deepened general understanding of video content, sharing or storing these extracted visual features for downstream tasks inadvertently reveals sensitive person

Francesco Sala, Olivier Schiffmann, Parth Shimpi

We establish, for each orbifold crepantly resolving a Kleinian singularity, the existence of the cohomological Hall algebra (COHA) of coherent sheaves supported on the exceptional locus and explicitly compute this COHA as a completion of some positive half of the associated affine Yangian. Tracking these categories under derived autoequivalences and the McKa