November 2025 arXiv papers — page 36

Guoyao Li, Ran He, Shusen Jing, Kayhan Behdin

Large language models (LLMs) excel at capturing semantic nuances and therefore show impressive relevance ranking performance in modern recommendation and search systems. However, they suffer from high computational overhead under industrial latency and throughput requirements. In particular, cross-encoder ranking systems often create long context prefill-hea

Myroslava Hladchenko

This study aimed to explore the relationship between access models, authorship patterns, and citation impact in Ukrainian research output from 2020 to 2023. The focus was on scholars affiliated with the National Academy of Sciences of Ukraine (NASU) and universities. Findings highlight that open access (OA) articles constituted the majority of publications b

James P. Cossey, Mark L. Lewis, A. A. Schaeffer Fry, Hung P. Tong-Viet

Let $G$ be a finite group and $p$ a prime. We establish an upper bound for the derived length of a Sylow $p$-subgroup of $G$ in terms of the number of irreducible characters of $G$ whose degrees are divisible by $p$. We also prove that if $B$ is a $p$-block of a finite $p$-solvable group $G$ with defect group $D$, then the derived length of $D$ is at most on

Michael G. Scheer, Nisarg Chadha, Da-Chuan Lu, Eslam Khalaf

The recent development of bootstrap methods based on semidefinite relaxations of positivity constraints has enabled rigorous two-sided bounds on local observables directly in the thermodynamic limit. However, these bounds inevitably become loose in symmetry broken phases, where local constraints are insufficient to capture long-range order. In this work, we

Laslo Hunhold

In light of recent hardware advances, it is striking that real arithmetic in balanced ternary logic has received almost no attention in the literature. This is particularly surprising given ternary logic's promising properties, which could open new avenues for energy-efficient computing and offer novel strategies for overcoming the memory wall. This paper re

Euclid Collaboration, P. Monaco, M. Y. Elkhashab, B. R. Granett

We present the strategy used to identify and mitigate potential sources of angular systematics in the \textit{Euclid} spectroscopic galaxy survey, and we quantify their impact on galaxy clustering measurements and cosmological parameter estimation. We first surveyed the \textit{Euclid} processing pipeline to identify all evident, potential sources of systema

Mohammad R. Tavakol, Wenshan Cai

We propose and theoretically demonstrate nonreciprocal negative refraction enabled by time-varying photonic structures. By engineering temporal modulations at the interfaces of hyperbolic media, we achieve isolation between forward and backward beams while preserving the hallmark property of negative refraction. Two complementary approaches are developed: in

Sree Bhattacharyya, Yaman Kumar Singla, Sudhir Yarram, Somesh Kumar Singh

Visual content memorability has intrigued the scientific community for decades, with applications ranging widely, from understanding nuanced aspects of human memory to enhancing content design. A significant challenge in progressing the field lies in the expensive process of collecting memorability annotations from humans. This limits the diversity and scala

Ava K. Tse, Olivia M. Markowich, Trung V. Phan

A gravitational potential has the spherical property when the field outside any uniform spherical shell is indistinguishable from that of a point mass at the center. We present the general potentials that possess this property on constant curvature spaces, using the Euler-Poisson-Darboux identity for spherical means. Our results are consistent with known fin

A. Tsantiri, A. Spyrou, E. C. Good, K. Bosmpotinis

We provide the first experimental cross section of the $^{73}\text{As}(p,\gamma)^{74}\text{Se}$ reaction to constrain one of the main destruction mechanisms of the p nucleus $^{74}\text{Se}$ in explosive stellar environments. The measurement was done using a radioactive $^{73}\text{As}$ beam at effective center-of-mass energies of 2.9 and 2.3 MeV/nucleon. Al

Dong Dong, Weijie Su

We introduce a new tokenizer for language models that minimizes the average tokens per character, thereby reducing the number of tokens needed to represent text during training and to generate text during inference. Our method, which we refer to as the Length-MAX tokenizer, obtains its vocabulary by casting a length-weighted objective maximization as a graph

Tasha Kim, Yingke Wang, Hanvit Cho, Alex Hodges

Neural Signal Operated Intelligent Robots (NOIR) system is a versatile brain-robot interface that allows humans to control robots for daily tasks using their brain signals. This interface utilizes electroencephalography (EEG) to translate human intentions regarding specific objects and desired actions directly into commands that robots can execute. We presen

Joseph O'Leary, Andrew Melatos, Tom Kimpson, Dimitris M. Christodoulou

X-ray timing studies of the persistent, Galactic, accretion-powered pulsar 4U 1626$-$67 reveal torque reversals, during which the pulse frequency $\nu(t)$ alternates between multiyear episodes of secular acceleration and deceleration, separated by transitions lasting $\lesssim 150 \, \rm{days}$. Here an unscented Kalman filter is applied to track the $\nu(t)

Dina Sayed, Heiko Schuldt

Large Language Models (LLMs) have become one of the most transformative tools across many applications, as they have significantly boosted productivity and achieved impressive results in various domains such as finance, healthcare, education, telecommunications, and law, among others. Typically, state-of-the-art (SOTA) foundation models are developed by larg

Nathan X. Roth, Martin A. Cordiner, Dominique Bockelée-Morvan, Nicolas Biver

We report the detection of methanol (CH$_3$OH) toward interstellar comet 3I/ATLAS using the Atacama Compact Array of the Atacama Large Millimeter/Submillimeter Array (ALMA) on UT 2025 August 28, September 18 and 22, and October 1, and of hydrogen cyanide (HCN) on September 12 and 15. These observations spanned pre-perihelion heliocentric distances ($r_H$) of

David Szczecina, Nicholas Pellegrino, Paul Fieguth

Training deep networks with noisy labels leads to poor generalization and degraded accuracy due to overfitting to label noise. Existing approaches for learning with noisy labels often rely on the availability of a clean subset of data. By pre-training a feature extractor backbone without labels using self-supervised learning (SSL), followed by standard super

I. M. Ross, M. Karpenko

The home space for optimal control is a Sobolev space. The home space for pseudospectral theory is also a Sobolev space. It thus seems natural to combine pseudospectral theory with optimal control theory and construct ``pseudospectral optimal control theory,'' a term coined by Ross. In this paper, we review key theoretical results in pseudospectral optimal c

Narayan Budhathoki, Dongming Mei, Sanjay Bhattarai, Sunil Chhetri

Electrostatic effects can strongly constrain charge transport in thinned high-purity germanium (HPGe), with direct implications for radiation detectors and Ge-based electronic and quantum devices. We report a systematic experimental characterization of the thickness-dependent effective Hall mobility in bulk-grown, detector-grade HPGe at room temperature usin

Edmond Tong, Advaith Balaji, Anthony Opipari, Stanley Lewis

To manipulate objects in novel, unstructured environments, robots need task-oriented grasps that target object parts based on the given task. Geometry-based methods often struggle with visually defined parts, occlusions, and unseen objects. We introduce OVAL-Grasp, a zero-shot open-vocabulary approach to task-oriented, affordance based grasping that uses lar

M. A. Shishkin, E. S. Pikina

In this work, we investigate the elastic properties of deflated vesicles and their shape dynamics in uniaxial extensional flow. By analysing the Helfrich bending energy and viscous flow stresses in the limit of highly elongated shapes, we demonstrate that all stationary vesicle configurations are metastable. For vesicles with small reduced volume, we identif

Vladimer Khasia

We present Primal, a deterministic feature mapping framework that harnesses the number-theoretic independence of prime square roots to construct robust, tunable vector representations. Diverging from standard stochastic projections (e.g., Random Fourier Features), our method exploits the Besicovitch property to create irrational frequency modulations that gu

Amy K. Strong, Leila Bridgeman

Dissipativity is an input-output (IO) characterization of nonlinear systems that enables compositional robust control through Vidyasagar's Network Dissipativity Theorem (VDNT). However, determining the dissipativity of a system is an involved and, often, model-specific process. We present a general method to determine the local dissipativity properties of sm

Nicolas Baradel

In incomplete financial markets, pricing and hedging European options lack a unique no-arbitrage solution due to unhedgeable risks. This paper introduces a constrained deep learning approach to determine option prices and hedging strategies that minimize the Profit and Loss (P&L) distribution around zero. We employ a single neural network to represent the op

Asad Aali, Muhammad Ahmed Mohsin, Vasiliki Bikia, Arnav Singhvi

As language models (LMs) are increasingly adopted across domains, high-quality benchmarking frameworks are essential for guiding deployment decisions. In practice, however, frameworks such as Holistic Evaluation of Language Models (HELM) typically evaluate models under a single static prompt configuration, even though model behavior depends strongly on promp

Gao Wang, Yingying Huang, Lars Muckli, Daniele Faccio

Brain-computer interfaces (BCIs) are evolving from research prototypes into clinical, assistive, and performance enhancement technologies. Despite the rapid rise and promise of implantable technologies, there is a need for better and more capable wearable and non-invasive approaches whilst also minimising hardware requirements. We present a non-invasive BCI

Dionysios Adamopoulos, Anastasia Poulopoulou, Georgios Goumas, Christina Giannoula

Sparse Convolution (SpC) powers 3D point cloud networks widely used in autonomous driving and augmented/virtual reality. SpC builds a kernel map that stores mappings between input voxel coordinates, output coordinates, and weight offsets, then uses this map to compute feature vectors for output coordinates. Our work identifies three key properties of voxel c

Ruyi Liu, Joshua L. Warren, Yuki Ohnishi, Donna Spiegelman

In cluster-randomized trials (CRTs), entire clusters of individuals are randomized to treatment, and outcomes within a cluster are typically correlated. While frequentist approaches are standard practice for CRT analysis, Bayesian methods have emerged as a strong alternative. Previous work has investigated the use of Bayesian hierarchical models for continuo

Abdelkarim Kati, Florian Kerschbaum, Marina Blanton

Data imputation is an important data preparation task where the data analyst replaces missing or erroneous values to increase the expected accuracy of downstream analyses. The accuracy improvement of data imputation extends to private data analyses across distributed databases. However, existing data imputation methods violate the privacy of the data renderi

Khuram Naveed, Naveed ur Rehman

We propose a fully multivariate generalization of multifractal detrended fluctuation analysis (MFDFA) and leverage it to develop a fault diagnosis framework for multichannel machine vibration data. We introduce a novel covariance-weighted $L_{pq}$ matrix norm based on Mahalanobis distance to define a fully multivariate fluctuation function that uniquely capt

Reza Mansouri, Dustin Kempton, Pete Riley, Rafal Angryk

The solar wind, a continuous outflow of charged particles from the Sun's corona, shapes the heliosphere and impacts space systems near Earth. Accurate prediction of features such as high-speed streams and coronal mass ejections is critical for space weather forecasting, but traditional three-dimensional magnetohydrodynamic (MHD) models are computationally ex

Robert Schneider

In recent work by Botkin, Dawsey, Hemmer, Just and the present author, a deterministic model of prime number distribution is developed based on properties of integer partitions that gives almost exact estimates for $\pi(n)$, the number of primes less than or equal to positive integer $n$, up to $n=10{,}000$. In this follow-up paper, the author summarizes the

A. V. Belitsky

We construct the planar integrand of the six-leg amplitude of massive W-bosons on the special Coulomb branch of the maximally supersymmetric Yang-Mills theory to two-loop order. We use the six-dimensional supersymmetric spinor-helicity formalism and the generalized unitarity-cut sewing technique to perform this analysis. The thus-found expression corresponds

Atsushi Ito, Joaquín Moraga, Debaditya Raychaudhury, Wern Yeong

Let $X$ be a smooth projective variety of dimension $n\geq 3$, and let $L$ be an ample line bundle on $X$. In this article, we study the algebraic hyperbolicity of a very general section of the adjoint linear series $|K_X+mL|$ when the tangent bundle $T_X$ of $X$ has suitable positivity properties. As a consequence, we show that the linear system $|K_X+mL|$

Nicholas Pellegrino, David Szczecina, Paul W. Fieguth

Untrained large neural networks, just after random initialization, tend to favour a small subset of classes, assigning high predicted probabilities to these few classes and approximately zero probability to all others. This bias, termed Initial Guessing Bias, affects the early training dynamics, when the model is fitting to the coarse structure of the data.

Mingcheng Sheng

In this paper, we study the spectral orthogonality problem for special flows built over irrational rotations under two different types of roof functions: 1) the roof functions are real analytic. 2) the roof functions are piecewise $C^1$ with one discontinuity. These flows are also known as von-Neumann flows. We show that if $\{T^f_α\}$ is as in 1) and weak m

Nour G. Al Hassanieh, Alex H. Barnett, Leslie Greengard

We present a spectrally accurate fast algorithm for evaluating the solution to the scalar wave equation in free space driven by a large collection of point sources in a bounded domain. With $M$ sources temporally discretized by $N_t$ time steps of size $\Delta t$, a naive potential evaluation at $M$ targets on the same time grid requires $\mathcal O(M^2 N_t)

Roman Naeem, David Hagerman, Jennifer Alvén, Fredrik Kahl

Tubular tree structures such as blood vessels and lung airways are central to many clinical tasks, including diagnosis, treatment planning, and surgical navigation. Accurate centerline extraction with correct topology is essential, as missing small branches can lead to incomplete assessments or overlooked abnormalities. We propose RefTr, a 3D image-to-graph

Bienvenu Gnim Adewi, Isiaka Aremua

This work investigates a quantum system described by a Hamiltonian operator in a two dimensional noncommutative space. The system consists of an electron subjected to a perpendicular magnetic field $\mathbf{B}$, coupled to a harmonic potential and an external electric field $\mathbf{E}$, within the context of non-extensive statistical thermodynamics. The non

Samuele Dell'Erba, Andrew D. Bagdanov

Diffusion models have established the state-of-the-art in text-to-image generation, but their performance often relies on a diffusion prior network to translate text embeddings into the visual manifold for easier decoding. These priors are computationally expensive and require extensive training on massive datasets. In this work, we challenge the necessity o

Jiaojiao Han, Wujiang Xu, Mingyu Jin, Mengnan Du

Large language models (LLMs) have achieved remarkable progress, yet their internal mechanisms remain largely opaque, posing a significant challenge to their safe and reliable deployment. Sparse autoencoders (SAEs) have emerged as a promising tool for decomposing LLM representations into more interpretable features, but explaining the features captured by SAE

Cyril Geismar, Peter J. White, Anne Cori, Thibaut Jombar

Inferring who infected whom in an outbreak is essential for characterising transmission dynamics and guiding public health interventions. However, this task is challenging due to limited surveillance data and the complexity of immunological and social interactions. Instead of a single definitive transmission tree, epidemiologists often consider multiple plau

Luisa F. Zamudio-Ruvalcaba, Catherine C. Espaillat, Álvaro Ribas, Enrique Macías

Protoplanetary disks are an essential component of the planet-formation process. The amount of dust and gas in the disk constrains the number and size of planets that can form in a system. We analyze 178 T-Tauri stars, 18 in Serpens and 160 in L1641/L1647, and measure their disk dust masses using spectral energy distribution (SED) modeling and multiwavelengt

Thiebaut Schirmer, Theo Khouri, Wouter Vlemmings, Gunnar Nyman

Mass loss in oxygen-rich asymptotic giant branch (AGB) stars remains poorly understood, as the dust detected around them appears too transparent to drive winds through absorption alone. The current paradigm invokes outflows driven by photon scattering on relatively large grains ($\sim0.3\,\mu$m), but whether such grains exist in sufficient quantities remains

Md Tanvirul Alam, Saksham Aggarwal, Justin Yang Chae, Nidhi Rastogi

We present Sphinx, a synthetic environment for visual perception and reasoning that targets core cognitive primitives. Sphinx procedurally generates puzzles using motifs, tiles, charts, icons, and geometric primitives, each paired with verifiable ground-truth solutions, enabling both precise evaluation and large-scale dataset construction. The benchmark cove

Jiaju Qi, Lei Lei, Thorsteinn Jonsson, Dusit Niyato

The integration of Electric Buses (EBs) with renewable energy sources such as photovoltaic (PV) panels is a promising approach to promote sustainable and low-carbon public transportation. However, optimizing EB charging schedules to minimize operational costs while ensuring safe operation without battery depletion remains challenging - especially under real-

Chiara Fusar Bassini, Jacqueline Adelowo, Priya L. Donti, Lynn H. Kaack

In auction markets that are prone to market power abuse, preventive mitigation of bid prices can be applied through automated mitigation procedures (AMP). Despite the widespread application of AMP in US electricity markets, there exists scarce evidence on how firms strategically react to such price-cap-and-penalty regulation: when the price cap rarely leads

Aaron O. Feldman, D. Isaiah Harp, Joseph Duncan, Mac Schwager

We develop a data-driven approach for runtime safety monitoring in flight testing, where pilots perform maneuvers on aircraft with uncertain parameters. Because safety violations can arise unexpectedly as a result of these uncertainties, pilots need clear, preemptive criteria to abort the maneuver in advance of safety violation. To solve this problem, we use

T. Marshall Eubanks, Craig E. DeForest, Kevin J. Walsh, Simon Porter

In order to facilitate interplanetary spacecraft observationsof 3I/ATLAS, we have monitored and predicted the optical properties of its coma using both ground and space-based observations. Here, we describe how the data from space-based solar coronagraphs and the PUNCH mission enabled tracking of 3I/ATLAS's optical magnitude throughout its entire perihelion

Ozgur Kara, Yujia Chen, Ming-Hsuan Yang, James M. Rehg

We present Split-then-Merge (StM), a novel framework designed to enhance control in generative video composition and address its data scarcity problem. Unlike conventional methods relying on annotated datasets or handcrafted rules, StM splits a large corpus of unlabeled videos into dynamic foreground and background layers, then self-composes them to learn ho

Binghan Liu, Junwen Wang, Gary S. Grest, Shengfeng Cheng

Compact analytical forms are derived for the interactions involving thin disks in two dimensions using an integration approach. These include interactions between a disk and a material point, between two disks, and between a disk and a wall. Each object is treated as a continuous medium of materials points interacting by the Lennard-Jones 12-6 potential. By

Michael Imseis, Sruthi A. Narayanan, A. W. Peet

For a given conformal field theory (CFT), one can deform it via the addition of a marginal operator to the spectrum. In two dimensions, when the added operator has conformal weights $h=\bar{h}=1$, conformal symmetry is not broken and the resulting theory is a distinct CFT. Studying such marginal operators for celestial CFTs allows for a geometric understandi

Desmond Leitz, Ralph Morrison, Søren Newman-Taylor, Vincent X. Wang

We introduce and study the locus $\mathbb{M}_{g,d}^\textrm{nd}$ of genus $g$ tropical plane curves of gonality $d$ inside the moduli space $\mathbb{M}^{\textrm{nd}}_{g}$ of tropical plane curves of genus $g$. Each such tropical curve arises from a Newton polygon, and we conjecture that the gonality of the tropical curve is equal to an easily computed paramet

Kriti Ghosh, Devjyoti Chakraborty, Lakshmish Ramaswamy, Suchendra M. Bhandarkar

Neural Radiance Fields (NeRFs) have demonstrated remarkable capabilities in 3D reconstruction and novel view synthesis. However, most existing NeRF frameworks require complete retraining when new views are introduced incrementally, limiting their applicability in domains where data arrives sequentially. This limitation is particularly problematic in satellit

R. Louw, W. A. van Wijngaarden, W. Happer

The main determinants of Earth's absolute surface Temperature, T, are the solar constant, S, the Bond albedo, A, and the effective emissivity for thermal radiation, e. In this note we assume that the value of the effective emissivity, e = e(C), is determined by the atmospheric concentration C of CO2. We show that the solar constant is most important, the alb

Chandrasekhar Gokavarapu

This paper establishes the homological and geometric foundations of non-commutative n-ary Gamma-semirings, unifying two previously distinct directions in Gamma-algebra: the derived Gamma-geometry developed for the commutative ternary case and the structural and spectral theory for general non-commutative n-ary systems. We introduce categories of left, right,

Hossein Shokouhinejad, Griffin Higgins, Roozbeh Razavi-Far, Ali A. Ghorbani

Graph Neural Networks (GNNs) have become an effective tool for malware detection by capturing program execution through graph-structured representations. However, important challenges remain regarding scalability, interpretability, and the availability of reliable datasets. This paper brings together six related studies that collectively address these issues

Dario Morle, Reid Zaffino

Dynamic sampling mechanisms in deep learning architectures have demonstrated utility across many computer vision models, though the theoretical analysis of these structures has not yet been unified. In this paper we connect the various dynamic sampling methods by developing and analyzing a novel operator which generalizes existing methods, which we term "war

Trung Cuong Dang, David Mohaisen

Large language models, trained on massive corpora, are prone to verbatim memorization of training data, creating significant privacy and copyright risks. While previous works have proposed various definitions for memorization, many exhibit shortcomings in comprehensively capturing this phenomenon, especially in aligned models. To address this, we introduce a

Rio Alexa Fear, Payel Mukhopadhyay, Michael McCabe, Alberto Bietti

Recent advances in mechanistic interpretability have revealed that large language models (LLMs) develop internal representations corresponding not only to concrete entities but also distinct, human-understandable abstract concepts and behaviour. Moreover, these hidden features can be directly manipulated to steer model behaviour. However, it remains an open

Duy-Tung Pham, An The Nguyen, Viet-Hoang Tran, Nhan-Phu Chung

This paper investigates the dynamical properties of tokens in pre-trained Transformer models and explores their application to improving Transformers. To this end, we analyze the dynamical system governing the continuous-time limit of the pre-trained model and characterize the asymptotic behavior of its solutions. Specifically, we characterize when tokens mo

Pamela E. Harris, J. Carlos Martínez Mori, Alexander N. Wilson

We develop a circular-street argument, in the style of Pollak, to obtain a new proof that there are $C_n = \frac{1}{n+1}\binom{2n}{n}$ weakly increasing parking functions of length $n \geq 1$, where $C_n$ is the $n$th Catalan number.

Souradeep Dutta, Keshav Bulia, Neena S Nair

Facebook AI Research introduced KRISP [4], which integrates structured external knowledge into pipelines for vision-language reasoning. Despite its effectiveness, the original model has been developed for industrial-scale training, is computationally demanding, and is tightly connected to a large backbone. In this work, we reexamine KRISP from a different an

Tuan Pham, Alessandro Rinaldo

This short note contains a simple argument that allows us to go from fixed-time to any-time bounds for the concentration of matrix products. The result presented here is motivated by the analysis of Oja's algorithms.

Xiaojiao Xiao, Qinmin Vivian Hu, Tae Hyun Kim, Guanghui Wang

Liver tumor segmentation, dynamic enhancement regression, and classification are critical for clinical assessment and diagnosis. However, no prior work has attempted to achieve these tasks simultaneously in an end-to-end framework, primarily due to the lack of an effective framework that captures inter-task relevance for mutual improvement and the absence of

Kasra Rajabzadeh Dizaji, Leeseok Kim, Milad Marvian, Christian Arenz

The quantum Zeno effect typically refers to freezing the dynamics of a quantum system through frequent observations. In general, quantum Zeno dynamics is obtained with an error of order $\mathcal{O}(1/N)$, where $N$ is the number of projective measurements performed within a fixed evolution time. In this work, we develop higher-order Zeno sequences that achi

Kirsten Chapman, Garrett Smith, Kaitlyn Klabacka, Harrison Winslow

To understand how privacy incidents lead to harms, HCI researchers have historically leveraged legal frameworks. However, these frameworks expect acute, tangible harms and thus may not cover the full range of human experience relevant to modern-day digital privacy. To address this gap, our research builds upon these existing frameworks to develop a more comp

Stanislaw Kurdzialek

We introduce a superresolution technique that combines spatial mode demultiplexing (SPADE) with emitter blinking. We show that temporal fluctuations not only enhance the precision of SPADE imaging, but also drastically simplify the measurement required to recover full object information -- in the presence of fluctuations, SPADE can be replaced by the much si

Ivan Contreras, Nicolas Martinez-Alba, Rajan Amit Mehta

The AKSZ formalism is a construction of topological field theories where the target spaces are differential graded symplectic manifolds. In this paper, we describe an analogue of the AKSZ formalism where the target spaces are differential graded contact manifolds. We show that the space of fields inherits a weak contact structure, and we construct a solution

Luke Rimmo Lego, Samantha Gauthier, Denver Jn. Baptiste

Research increasingly relies on computational methods to analyze experimental data and predict molecular properties. Current approaches often require researchers to use a variety of tools for statistical analysis and machine learning, creating workflow inefficiencies. We present an integrated platform that combines classical statistical methods with Random F

Johan Carlström

Hund's coupling plays a decisive role in shaping electron correlations of multi-orbital systems, giving rise to a class of materials--Hund's metals--that combine local-moment physics with metallic transport. Here we derive an effective low-energy description of such a system near the Mott insulating regime, starting from the multi-orbital Hubbard-Kanamori Ha

Armando Martino, Motiejus Valiunas

Amenable groups are those admitting an invariant mean -- a finitely additive probability mean that assigns equal ``weight'' to any two translates of the same set. We introduce coset correct means (CCMs), a class of finitely additive means that, for any subgroup, assigns equal weight to all its cosets, weakening and therefore generalising the notion of an inv

Fabien Hoareau

We extend some results of Carderi and Le Ma\^itre on full groups in the probability context to the infinite measure one: there exists at most one Polish group topology (refining the weak topology and coarser than the uniform topology) on an ergodic full group, and the orbit full group of a locally compact group acting in a Borel manner can be endowed with a

Sindhuja Penchala, Gavin Money, Gabriel Marques, Samuel Wood

Understanding material surfaces from sparse visual cues is critical for applications in robotics, simulation, and material perception. However, most existing methods rely on dense or full-scene observations, limiting their effectiveness in constrained or partial view environment. To address this challenge, we introduce SMARC, a unified model for Surface MAte

Anderson E. Schwertner, Francisco N. C. Sobral

The Low Order-Value Optimization (LOVO) problem involves minimizing the minimum among a finite number of function values within a feasible set. LOVO has several practical applications such as robust parameter estimation, protein alignment, portfolio optimization, among others. In this work, we are interested in the constrained nonlinear optimization LOVO pro

Russel Arbore, Alvin Cheung, Max Willsey

Equality saturation is a program optimization technique based on non-destructive rewriting and a form of abstract interpretation called e-class analysis. Existing e-class analyses are pessimistic and therefore typically imprecise when analyzing cyclic programs, such as those in SSA form. We show that a straightforward optimistic variant of e-class analysis c

Niklas Wolf, Nico van der Vegt

When a drop of liquid comes into contact with a solid surface, it relaxes towards an equilibrium configuration, either wetting the surface or remaining in a droplet-like shape with a finite contact angle. The force driving the process towards equilibrium is the corresponding out-of-balance Young's force. However, the speed with which the liquid front advance

Alison Silva, Gustavo Callou

Cloud-based storage platforms are becoming more common in both academic and business settings due to their flexible access to data and support for collaborative functionalities. As reliability becomes a vital requirement, particularly for organizations looking for alternatives to public cloud services, assessing the dependability of these systems is crucial.

Thomas Norrenbrock, Timo Kaiser, Sovan Biswas, Neslihan Kose

Globally interpretable models are a promising approach for trustworthy AI in safety-critical domains. Alongside global explanations, detailed local explanations are a crucial complement to effectively support human experts during inference. This work proposes the Calibrated Hierarchical QPM (CHiQPM) which offers uniquely comprehensive global and local interp

Sara Motalebi

We present a unified framework incorporating both the Generalized and Extended Uncertainty Principles (GUP/EUP) in Anti-de Sitter space. This reveals a fundamental quantum gravity scale, the \textit{critical radius} $r_{\rm crit}=(\beta/\gamma)^{1/4}\sqrt{\ell_{P}L}$, which marks a phase transition where quantum gravitational ($\beta$) and AdS curvature ($\g

Hiroyuki Yamase, Luciano Zinni, Matías Bejas, Andrés Greco

Recently plasmon excitations in bilayer lattice systems were studied extensively in the weak-coupling regime. Unlike single-layer systems, these bilayers exhibit two distinct modes, $\omega_{\pm}$, which show characteristic dependences upon the momentum and hopping integrals along the $z$ direction. To apply them to cuprates, strong correlation effects shoul

Daniel Maschmann, Bradley C. Whitmore, David A. Thilker, Ivan Gerasimov

Dust production is a fundamental aspect of the baryonic cycle of star formation. It is known that dust is injected into the interstellar medium during early star formation by supernovae and later on by evolved stars. From individual objects, these mechanisms are well understood, but the overall dust production in star clusters at different evolutionary stage

Shibo Liu

It is known that the integral of the Jacobian determinant of a smooth map $f: \bar{\Omega} \rightarrow \mathbb{R}^n$ depends only on $f |_{\partial \Omega} $ and this result leads to an analytic proof of the Brouwer fixed point theorem. In this note we provide two new proofs of this result, one by classical analysis and one by differential forms and Stokes f

Samuel T. Elkin, Ghazi Khan, Ebrahim Forati, Brandon W. Langley

High-fidelity numerical methods that model the physical layout of a device are essential for the design of many technologies. For methods that characterize electromagnetic effects, these numerical methods are referred to as computational electromagnetics (CEM) methods. Although the CEM research field is mature, emerging applications can still stress the capa

Raghuveer Thirukovalluru, Xiaochuang Han, Bhuwan Dhingra, Emily Dinan

Image encoders, a fundamental component of vision-language models (VLMs), are typically pretrained independently before being aligned with a language model. This standard paradigm results in encoders that process images agnostically, without regard to the specific downstream task or text query. To address this limitation, we propose the Text-Guided Semantic

D. Armanno, O. Gingras, F. Goto, J. -M. Parent

To this day, high-temperature cuprate superconductors remain an unparalleled platform for studying the competition and coexistence of emergent, static and dynamic, quantum phases of matter exhibiting high transition temperature non-s-wave superconductivity, non-Fermi liquid transport and a still enigmatic pseudogap regime. However, how superconductivity emer

Tianshu Wang, Adam Burrows

In this work, we explore in a consistent fashion the effects of fast flavor conversion (FFC) in 1D and 2D core-collapse supernova (CCSN) simulations. In addition, we investigate the impact of various angular reconstruction methods and compare the ``3-species'' and ``4-species'' neutrino transport schemes. We find that the FFC effects are insensitive to the d

Karen Ullrich, Jingtong Su, Claudia Shi, Arjun Subramonian

Reliability is key to realizing the promise of autonomous UI-Agents, multimodal agents that directly interact with apps in the same manner as humans, as users must be able to trust an agent to complete a given task. Current evaluations rely on fixed environments, often clones of existing apps, which are limited in that they can only shed light on whether or

Sidharth Duthaluru, Kaiwen Zheng, Erik A. Henriksen, Kater W. Murch

Electron-on-neon (eNe) charge states coupled to superconducting circuits are a promising platform for quantum computing. Control over the formation of these charge states requires techniques to track and control the growth of solid Ne films on the circuit surface. We demonstrate a real-time Ne film-growth monitor using high-transition-temperature (high-$T_c$

Nicolas Leo Kaufmann, Thomas Pfeil, Sebastian Stammler, Anna Penzlin

TriPoDPy is a code simulating the dust evolution, including dust growth and dynamics in protoplanetary disks using the parametric dust model presented in (Pfeil et al., 2024). The simulation evolves a dust distribution in a one-dimensional grid in the radial direction. It's written in Python and the core routines are implemented in Fortran90. The code not on

Chi-kwan Chan, Hina Suzuki, David Forbes, Andrew Thomas West

We introduce a novel direct calibration algorithm to address phase delay, gain, and offset mismatches in Analog-to-Digital Converter (ADC) time interleaving systems. These mismatches, common in high-speed data acquisition, degrade system performance and signal integrity, particularly in applications such as radio astronomy and very long baseline interferomet

Karan Tiwana, Antoine Tilloy

Relativistic continuous matrix product states (RCMPS) are a powerful variational ansatz for quantum field theories of a single field. However, they inherit a property of their non-relativistic counterpart that makes them divergent for models with multiple fields, unless a regularity condition is satisfied. This has so far restricted the use of RCMPS to toy m

Djuna Croon, Benedict Crossey, Jose Maria Diego, Bradley J. Kavanagh

Caustic-crossing stars observed in giant arcs behind galaxy clusters provide a powerful probe of dark matter substructure. While previous work has focused on point-like lenses such as primordial black holes, we extend this framework to extended dark objects (EDOs), including ultracompact minihalos formed from the collapse of primordial overdensities. We deve

Raffaele Tito D'Agnolo, Alfredo Glioti, Gabriele Rigo, Alessandro Valenti

The phase space of hadron collider events spans hundreds of dimensions, generating an intricate geometry that we are just starting to explore. The number of possible new physics signals is exponential in the number of dimensions and detecting all of them is currently impossible for any human or artificial intelligence. In this work we introduce a method to s

Nicolas Dirnegger, Prineha Narang, Arpit Arora

Introducing new components and functionalities into quantum devices is critical in advancing state-of-the-art hardware. Here, we propose superconducting diodes (SDs) as a coherent nonreciprocal element in circuit quantum electrodynamics (cQED) architectures. In particular, we use an asymmetric SQUID as an SD controlled with a flux bias - nonreciprocal elemen

Anton Chudaykin, Mikhail M. Ivanov, Oliver H. E. Philcox

We carry out an independent re-analysis of the Dark Energy Spectroscopic Instrument (DESI) public dataset, focusing on extensions to the standard cosmological model, $\Lambda$CDM. Utilizing the dataset and Effective Field Theory (EFT)-based pipeline described in Paper 1, we constrain cosmological models with massive neutrinos ($\Lambda$CDM+$M_\nu$), spatial

Hina Suzuki, Yosuke Mizuno, Akhil Uniyal, Indu Kalpa Dihingia

We present a detailed study of higher-order photon rings of an accreting Kerr naked singularity (KNS) with dimensionless spin parameter $a=1.01$; i.e., a horizonless, overly spinning compact object. Motivated by horizon-scale very-long-baseline interferometry (VLBI) including Event Horizon Telescope (EHT) and future missions such as the Black Hole Explorer (

Aranya Bhattacharya, Lavish Chawla, Mario Flory, Mateusz Kulig

We explore the potential role of area metrics, a generalised notion of geometry, in the AdS/CFT correspondence. Guided by the Ryu-Takayanagi formula and the first law of entanglement, we derive both a holographic dictionary as well as a bulk equation of motion for area metric fluctuations linearised around a four dimensional AdS background. Furthermore, we e

Minju M. Lee, Georgios Magdis, Gabriel Brammer, Daizhong Liu

We present ECOology for Galaxies using ALMA archive and Legacy surveys (ECOGAL), an ALMA data mining project. Using the footprints of the James Webb Space Telescope (JWST) and the Hubble Space Telescope (HST), we query and uniformly reprocess ALMA data to produce continuum images and two complementary source catalogues: (i) a prior-based catalogue anchored t

Benchen Huang, Srinivas V. Mandyam, Weijie Wu, Bryce Kobrin

The integration of Nitrogen-Vacancy color centers into diamond anvil cells has opened the door to quantum sensing at megabar pressures. Despite a multitude of experimental demonstrations and applications ranging from quantum materials to geophysics, a detailed microscopic understanding of how stress affects the NV center remains lacking. In this work, using

Manuel Loparco, Grégoire Mathys, João Penedones, Jiaxin Qiao

We study bulk locality constraints in quantum field theories in AdS$_2$. The known derivation of locality sum rules in AdS$_{d+1}$ does not apply for $d=1$ due to the different singularity structure of the conformal blocks and the inequivalence of operator orderings on the boundary. Assuming unitarity and a mild growth condition, we establish power-law bound