October 2025 arXiv papers — page 93

Sanaz Zarei

Broadband switching of terahertz waves at room temperature is demonstrated using a reconfigurable subwavelength metallic hole coupled disk array. The interaction between a metallic membrane featuring periodically arranged circular holes and a substrate bearing a correspondingly periodic array of metallic disks - precisely aligned at their centers - significa

Henrik Hellström, Jiwon Jeong, Ayfer Özgür, Viktoria Fodor

We consider the problem of non-coherent over-the-air computation (AirComp), where $n$ devices carry high-dimensional data vectors $\mathbf{x}_i\in\mathbb{R}^d$ of sparsity $\lVert\mathbf{x}_i\rVert_0\leq k$ whose sum has to be computed at a receiver. Previous results on non-coherent AirComp require more than $d$ channel uses to compute functions of $\mathbf{

Ayrton Almada, Laurent Pagnier, Igal Goldshtein, Saif R. Kazi

Power system operators need tools for rapid, real-time counterfactual assessments of grid security under fast-changing conditions. Traditional N-1 contingency analysis lacks dynamic evaluation, especially of frequency swings from common faults. This paper introduces a real-time dashboard framework to screen dynamic contingencies. It assumes: (a) the grid sta

Ryota Ikeda, Daisuke Iono, Ken-ichi Tadaki, Maximilien Franco

We present an analysis of rest-frame optical and far-infrared continuum emission in three luminous submillimeter galaxies (SMGs) at $3.0\lesssim z\lesssim4.5$. The SMGs are spatially resolved down to 400-500 pc (0.05'') resolution by James Webb Space telescope (JWST) and Atacama Large Millimeter/submillimeter Array (ALMA) observations. Despite simila

Mohammad Haidar, Hugo D. Nogueira, J. -Ph. Karr

We present high-precision quantum computing simulations of three-body atoms (He, H$^-$) and molecules (H$_2^+$, HD$^+$), the latter being studied beyond the Born-Oppenheimer approximation. The Non-Iterative Disentangled Unitary Coupled Cluster Variational Quantum Eigensolver (NI-DUCC-VQE) [M. Haidar et al., Quantum Sci. Technol. 10, 025031 (2025)] is used. B

Francis Ndikum Nji, Vandana Janeja, Jianwu Wang

Deep subspace clustering models are vital for applications such as snowmelt detection, sea ice tracking, crop health monitoring, infectious disease modeling, network load prediction, and land-use planning, where multivariate spatiotemporal data exhibit complex temporal dependencies and reside on multiple nonlinear manifolds beyond the capability of tradition

Junli Ren, Junfeng Long, Tao Huang, Huayi Wang

We present a reinforcement learning framework for autonomous goalkeeping with humanoid robots in real-world scenarios. While prior work has demonstrated similar capabilities on quadrupedal platforms, humanoid goalkeeping introduces two critical challenges: (1) generating natural, human-like whole-body motions, and (2) covering a wider guarding range with an

J. C. Bellizotti Souza, C. J. O. Reichhardt, C. Reichhardt, A. Saxena

We show that skyrmions can exhibit what we call a magnetic diode effect, where there is a nonreciprocal response in the transport when the magnetic field is reversed. This effect can be achieved for skyrmions moving in a channel with a sawtooth potential on one side and a reversed sawtooth potential on the other side. We consider the cases of both spin-trans

Justin Kalloor, Lucas Kovalsky, Mathias Weiden, John Kubiatowicz

Compilation and optimization of quantum circuits are critical components in the execution of algorithms on quantum computers. These components must successfully balance two competing priorities: minimizing the number of expensive resources, such as two-qubit gates or arbitrary angle single-qubit rotations, and minimizing the approximation error of the compil

Ghazal Danaee, Marc Niethammer, Jarrett Rushmore, Sylvain Bouix

Deep-learning-based segmentation algorithms have substantially advanced the field of medical image analysis, particularly in structural delineations in MRIs. However, an important consideration is the intrinsic bias in the data. Concerns about unfairness, such as performance disparities based on sensitive attributes like race and sex, are increasingly urgent

Nishant Subramani, Alfredo Gomez, Mona Diab

Modern language models are evaluated on large benchmarks, which are difficult to make sense of, especially for model selection. Looking at the raw evaluation numbers themselves using a model-centric lens, we propose SimBA, a three phase framework to Simplify Benchmark Analysis. The three phases of SimBA are: stalk, where we conduct dataset & model comparison

Nico Benincasa

In this letter, we provide a simple algorithm, anyPUB, to systematically derive the $2 \rightarrow 2$ scattering matrix in the high-energy limit for any kind of models, irrespective of their gauge group or their field representation. After computing the eigenvalues analytically and/or numerically from this matrix, we impose perturbative unitarity bounds on t

Yifei Luo, Joseph Wick, Alexie Leauthaud, Andrew Wetzel

Hydrodynamic simulations have proposed that stellar feedback and bursty star-formation can produce dark matter cores in low-mass galaxies. A key prediction is that feedback-driven gas outflow and inflow cycles can lead to ``breathing modes'' (rapid fluctuations in the global gravitational potential) which drive correlated variations in galaxy size, kinematic

Abhigya Verma, Seganrasan Subramanian, Nandhakumar Kandasamy, Naman Gupta

Large language models (LLMs) are increasingly deployed as agents, expected to decompose goals, invoke tools, and verify results in dynamic environments. Realizing these capabilities requires access to agentic data-structured interaction records that couple user intents with tool specifications, argument-grounded calls, and verifiable execution traces. Howeve

Eduardo Jacobo-Villegas, Josué Manik Nava-Sedeño, Bibiana Obregón-Quintana

In this paper we present an analytical and numerical study of a generalized model of two-actor cooperative-competitive conflict of the Continuous Opinions and Discrete Actions (CODA) type. Theoretically, we note that the in troduction of a new parameter allows generalizing feedback as strong and weak. Furthermore, we show that for positive-positive and negat

Katarzyna Ryszewska, Rico Zacher

In this paper we establish the Harnack inequality for globally positive local solutions to a general class of nonlocal in time subdiffusion equations in one space dimension, which includes time-fractional diffusion equations with time order less than one. It is already known that for these equations the classical Harnack inequality does not hold if the space

Steven Ferrante, Lillian Luo, Maxim Perelstein, Taewook Youn

We study collider constraints on the near-continuum dark matter model, in which the dark sector consists of a tower of closely spaced states with weak-scale masses coupled to the Standard Model through a $Z$-portal. To capture this structure in a model-agnostic way, we introduce a minimal parameterization that encodes the dominant geometric information with

Ivan N. Burenev

We study the first-passage properties of a jump process with constant drift where jump amplitudes and inter-arrival times follow arbitrary light-tailed distributions with smooth densities. Using a mapping to an effective discrete-time random walk, we identify three regimes determined by the drift strength: survival (weak drift), absorption (strong drift), an

Cristiano Ugolini

After the third LIGO--Virgo--KAGRA observing run, the number of detected binary black hole (BBH) mergers became sufficient to identify statistical features of the population. We explore how different prescriptions for the final fate of massive stars and key binary-evolution processes shape isolated binaries and their remnants. Using \textsc{sevn}, we evolved

Andrew M. Buchan, Pier-Emmanuel Tremblay, Antoine Bédard, Evan B. Bauer

Many white dwarfs have accreted material from their own planetary systems. These objects can be used to infer the composition of exoplanetary material and identify evidence for key geological processes. However, the white dwarf atmospheric physics distorts the inferred material composition away from the true composition, mainly through differential atomic di

Aritra Bal, Markus Klute, Benedikt Maier, Melik Oughton

Classical deep neural networks can learn rich multi-particle correlations in collider data, but their inductive biases are rarely anchored in physics structure. We propose quantum-informed neural networks (QINNs), a general framework that brings quantum information concepts and quantum observables into purely classical models. While the framework is broad, i

Tarik Anowar, Ripan Saha

This paper introduces Hom-type analogues of affine algebraic structures, termed Hom-affgebras. Extending Brzezi\'nski's theory of affgebras and the Hom-algebra framework developed by Hartwig-Larsson-Silvestrov, we define and study Hom-associative, Hom-pre-Lie, and Hom-Lie affgebras, where the classical identities are twisted by an affine self-map. We show ho

Samudra Sur, Thierry Giamarchi

Persistent current is a hallmark of quantum phase coherence. We study the fate of the persistent current in a non-equilibrium setting, where a tight-binding ring is subjected to stochastic disorder as well as a fermionic reservoir attached to each site. We evaluate the current using Keldysh technique and find that it exhibits non-monotonic behavior, suggesti

Maria Axenovich, Wouter Cames van Batenburg, Oliver Janzer, Lukas Michel

We show that the $k$-colour Ramsey number of an odd cycle of length $2 \ell + 1$ is at most $(4 \ell)^k \cdot k^{k/\ell}$. This proves a conjecture of Fox and is the first improvement in the exponent that goes beyond an absolute constant factor since the work of Bondy and Erd\H{o}s from 1973.

Patricio Escalona, João Paulo Pinheiro, Vinícius Oliveira, A. Doff

Flavor-changing neutral current (FCNC) processes play a prominent role in the search for physics beyond the Standard Model (SM) due to their sensitivity to new physics at the TeV scale. Meson-antimeson transitions and rare meson decays provide stringent constraints on new physics through precision measurements of observables such as mass differences, CP asym

Vladyslav Bohun, Andrij Kuzmak, Maciej Koch-Janusz

Quantum computing promises exponential improvements in solving large systems of partial differential equations (PDE), which forms a bottleneck in high-resolution computational fluid dynamics (CFD) simulations, in, among others, aerospace applications and weather forecasting. One approach is via mapping classical CFD problems to a quantum Hamiltonian evolutio

J. Gabriel Richardson, Prajit Dhara, Abhishek Bhatt, Saikat Guha

While the last few decades have seen a proliferation of experimental demonstrations of entanglement sources, practicality of deployment has been a secondary concern. Recently, the ZALM source was introduced, as a well-engineered functional device, easily integrated within a complete networking system. It addresses numerous concerns which make typical academi

Michael Unger

Accurate measurements of cosmic-ray fragmentation cross sections are essential for maximizing the physics potential of precise measurements of secondary and primary cosmic-ray fluxes from current balloon and space-borne experiments. NA61/SHINE, operating at the CERN SPS H2 beamline, is uniquely suited to studying these interactions at energies above 10 GeV/c

Fabio Apruzzi, Noppadol Mekareeya, Brandon Robinson, Alessandro Tomasiello

Six-dimensional superconformal field theories (SCFTs) give rise to four-dimensional (4d) ones when compactified on Riemann surfaces. In the $\mathcal{N}=(2,0)$ case, this yields the famous class S family. For $\mathcal{N}=(1,0)$ theories that arise from linear unitary quivers, the holographic duals of the 4d theories are known in massive IIA supergravity, bu

Sezen Sekmen

The Run 2 data-taking period of the CERN Large Hadron Collider during years 2015-2018 provided about 140 fb$^{-1}$ of proton-proton collisions at 13 TeV, offering an unprecedented opportunity to explore supersymmetry (SUSY) across a wide range of experimental signatures. CMS responded with a broad and diverse search program, carrying out dozens of analyses t

G. Brunelli, G. Ponti, H. Zhang, E. de Oña Wilhelmi

1LHAASO J1740+0948u is a very-high-energy (VHE) source reported by LHAASO, with no counterpart at other wavelengths. It is located at 0.2{\deg} from PSR J1740+1000, a radio and gamma-ray pulsar placed well above the Galactic plane, which displays an X-ray tail. Despite the offset, the association between the two sources is likely. We aim to study the diffuse

Lukasz Fidkowski, Cenke Xu, Carolyn Zhang

In this work we realize the 3 + 1 dimensional non-invertible ${\mathbb{Z}}_N$ chiral symmetry generator as an operator in a many body lattice Hilbert space. A crucial ingredient in our construction is the use of infinite dimensional $U(1)$ rotor site Hilbert spaces. Specifically, our Hilbert space is that of a $U(1)$ lattice gauge theory coupled to a charge

Aritra Kundu, Robyn Sanderson, Adam Lidz, Pratik J. Gandhi

The `near-far' approach to studying reionization leverages the star formation histories of the Milky Way (MW) or Local Group (LG) galaxies, derived from resolved photometry, to infer the low-mass/faint-end of the stellar mass functions (SMFs) or the ultraviolet luminosity functions (UVLFs) of high-redshift galaxies ($z \gtrsim 6$), beyond the current JWST de

Maryum Sayeed, Andrew R. Casey, Benjamin T. Montet, Melissa K. Ness

Lithium-rich red giants have been a long-standing mystery in stellar astrophysics. A leading theory to explain these chemically peculiar and rare objects is interactions with a close companion. To investigate their companion fraction, we collected high-resolution spectra of 33 Li-rich red giants using ESPRESSO, and used The Joker constrain their orbital para

Matthias Fabry, Andrej Prša

The evolution of low-mass contact binaries is influenced by angular-momentum loss, mass and energy transfer, and the nuclear evolution of the components. They have periods shorter than one day, and we expect their period evolution to be dominated by magnetic braking. Evidence for saturated magnetic braking was presented by studying the period distribution of

Pablo A. Cano, Marina David, Guido van der Velde

We show that perturbatively-small higher-derivative corrections to the Einstein-Hilbert action can lead to order-one modifications of the quasinormal mode spectrum of near-extremal Kerr black holes. The spectrum of such black holes contains zero-damping modes (ZDMs) and damped modes (DMs), with the latter only existing when the ratio $\mu=m/(l+1/2)$ is below

Stefan Gieseke, Felix Kahlhoefer, Henry Seebach

We investigate the decay modes of a CP-even scalar boson $\phi$ that mixes with the Standard Model Higgs boson, focusing on the mass range between 2 GeV and $2 m_\tau$. Starting from a higher-order perturbative calculation of the inclusive decays $\phi \to gg$ and $\phi \to s\bar{s}$, we employ a hadronisation model to obtain predictions for individual hadro

Liam Parker, Francois Lanusse, Jeff Shen, Ollie Liu

While foundation models have shown promise across a variety of fields, astronomy still lacks a unified framework for joint modeling across its highly diverse data modalities. In this paper, we present AION-1, a family of large-scale multimodal foundation models for astronomy. AION-1 integrates heterogeneous imaging, spectroscopic, and scalar data using a two

Jeff Shen, Francois Lanusse, Liam Holden Parker, Ollie Liu

Sequential scientific data span many resolutions and domains, and unifying them into a common representation is a key step toward developing foundation models for the sciences. Astronomical spectra exemplify this challenge: massive surveys have collected millions of spectra across a wide range of wavelengths and resolutions, yet analyses remain fragmented ac

Matthias Fabry, Andrej Prša

Contact binaries are very short-period systems that are continuously interacting by transferring mass and energy. Obtaining large, statistical samples of contact binaries from photometric surveys can put valuable constraints on the various processes involved in their evolution. Modeling those systems however present some challenges. In some contact-binary li

Yarin Meir Shani, Na'ama Hallakoun, Sagi Ben-Ami, Sahar Shahaf

We analyze the orbital period distribution of post-common-envelope white-dwarf-main-sequence (WDMS) binaries by cross-matching the new spectroscopic Gaia DR3 WDMS catalog with TESS light curves, and applying a uniform periodicity search and vetting pipeline. We identify 107 periodic systems, including 74 eclipsing binaries (32 new) and 33 binaries exhibiting

Kassidy E. Kollmann, James W. Nightingale, Mariangela Lisanti, Andrew Robertson

The inner region of a subhalo's density distribution is particularly sensitive to dark matter microphysics, with alternative dark matter models leading to both cored and steeply-rising inner density profiles. This work investigates how the lensing signature and detectability of dark matter subhalos in mock HST-, Euclid-, and JWST-like strong lensing observat

Ilay Kamai, Hagai B. Perets, Jakob Stegmann, Evgeni Grishin

Gas-giant planets are thought to require conditions beyond the water snow line to build solid cores efficiently. In close binary star systems, the companion's gravity additionally limits the region of stable orbits, potentially excluding the zone where giants should form.} We aim to identify binary systems in which gas giants exist despite the snow line lyin

Adrien Kuntz

We use an approximation of the Regge-Wheeler-Zerilli potential, known as P\"{o}schl-Teller, to exactly compute the time-domain Green function of black hole perturbations in this simplified model, taking into account all causality conditions. We find the existence of an additional early times piece in the Green function, contributing to new exponentially grow

Andrew D. Santarelli, Ebraheem Farag, Earl P. Bellinger, Priyamvada Natarajan

JWST has revealed a population of red, compact, high-redshift (${z\sim3-10}$) objects referred to as ``Little Red Dots'' (LRDs). These objects exhibit unusual spectral features reminiscent of stellar spectra with blackbody-like SEDs, large hydrogen Balmer breaks, Balmer line absorption, and classical stellar absorption features such as calcium H&K and the ca

Josef Seitz, Erez Y. Urbach

We discuss the correspondence between highly excited strings and black holes in the presence of angular momentum. At fixed imaginary angular velocity $\nu$, we show that free strings exhibit a Hagedorn instability due to a thermal-winding mode turning tachyonic. This allows us to determine the exact Hagedorn temperature $\beta_H(\nu)$ for bosonic, type II, a

Leindert A. Boogaard, Fabian Walter, Axel Weiss, Luis Colina

We present high-resolution (0.13"-0.23") NOEMA observations of the dust continuum emission at 1.1 mm (rest-frame 220 micron) and JWST/NIRCam and MIRI imaging of the z=4.055 starburst galaxy GN20. The sensitive NOEMA imaging at 1.6 kpc resolution reveals extended dust emission, ~14 kpc in diameter (r_e~2.5 kpc, b/a=0.5), that is centrally asymmetric and clump

Zixin Yin, Ling-Hao Chen, Lionel Ni, Xili Dai

Recent advances in training-free attention control methods have enabled flexible and efficient text-guided editing capabilities for existing generation models. However, current approaches struggle to simultaneously deliver strong editing strength while preserving consistency with the source. This limitation becomes particularly critical in multi-round and vi

Rui Pan, Yang Luo, Yuxing Liu, Yang You

Memory-efficient optimization is critical for training increasingly large language models (LLMs). A popular strategy involves gradient low-rank projection, storing only the projected optimizer states, with GaLore being a representative example. However, a significant drawback of many such methods is their lack of convergence guarantees, as various low-rank p

Adina Yakefu, Bin Xie, Chongyang Xu, Enwen Zhang

Testing on real machines is indispensable for robotic control algorithms. In the context of learning-based algorithms, especially VLA models, demand for large-scale evaluation, i.e. testing a large number of models on a large number of tasks, is becoming increasingly urgent. However, doing this right is highly non-trivial, especially when scalability and rep

Jiale Cheng, Yusen Liu, Xinyu Zhang, Yulin Fei

Large language models (LLMs) increasingly rely on long-context modeling for tasks such as document understanding, code analysis, and multi-step reasoning. However, scaling context windows to the million-token level brings prohibitive computational and memory costs, limiting the practicality of long-context LLMs. In this work, we take a different perspective-

Debarati Das, Jacob Gilbert, MohammadTaghi Hajiaghayi, Tomasz Kociumaka

We present the first dynamic algorithms for Dyck and tree edit distances with subpolynomial update times. Dyck edit distance measures how far a parenthesis string is from a well-parenthesized expression, while tree edit distance quantifies the minimum number of node insertions, deletions, and substitutions required to transform one rooted, ordered, labeled t

Samuel Talkington, Cameron Khanpour, Rahul K. Gupta, Sergio A. Dorado-Rojas

This paper presents conservative probabilistic bounds for the spectrum of the admittance matrix and classical linear power flow models under uncertain network parameters; for example, probabilistic line contingencies. Our proposed approach imports tools from probability theory, such as concentration inequalities for random matrices. This provides a theoretic

Alexius Wadell, Anoushka Bhutani, Victor Azumah, Austin R. Ellis-Mohr

Accurate prediction of atomistic, thermodynamic, and kinetic properties from molecular structures underpins materials innovation. Existing computational and experimental approaches lack the scalability required to navigate chemical space efficiently. Scientific foundation models trained on large unlabelled datasets offer a path towards navigating chemical sp

Akshara Prabhakar, Roshan Ram, Zixiang Chen, Silvio Savarese

As information grows exponentially, enterprises face increasing pressure to transform unstructured data into coherent, actionable insights. While autonomous agents show promise, they often struggle with domain-specific nuances, intent alignment, and enterprise integration. We present Enterprise Deep Research (EDR), a multi-agent system that integrates (1) a

Meng Han

A recent preprint by Ardana-Lamas et al. (Ref. [1], arXiv:2510.04086) re-analyzes the data set of the attosecond streaking experiment on krypton atoms originally reported in Phys Rev X 7, 041030 (2017) [2], and claims the characterization of a 19.2 attosecond light pulse with an overall photon flux of 4.8*10^10 photons per second. In this comment, we highlig

Francesco Verdiani, Lea Harscouet, Matteo Zennaro, David Alonso

We study the projected clustering of photometric luminous red galaxies from the DESI Legacy Survey, combining their angular power spectrum, bispectrum, and cross-correlation with maps of the CMB lensing convergence from the Planck satellite. We employ a perturbative bias expansion in Eulerian space to describe the clustering of galaxies, modelling the power

Yujie Luo, Zhuoyun Yu, Xuehai Wang, Yuqi Zhu

Replicating AI research is a crucial yet challenging task for large language model (LLM) agents. Existing approaches often struggle to generate executable code, primarily due to insufficient background knowledge and the limitations of retrieval-augmented generation (RAG) methods, which fail to capture latent technical details hidden in referenced papers. Fur

Omer Haq

Modern probabilistic regressors often remain overconfident under distribution shift. Functional Distribution Networks (FDN) place input-conditioned distributions over network weights, producing predictive mixtures whose dispersion adapts to the input; we train them with a Monte Carlo beta-ELBO objective. We pair FDN with an evaluation protocol that separates

Austin Xu, Xuan-Phi Nguyen, Yilun Zhou, Chien-Sheng Wu

Finetuning specialized generative evaluators has emerged as a popular paradigm to meet the increasing demand for scalable evaluation during both training and test-time. However, recent work has largely focused on applying new methodology, such as reinforcement learning (RL), to training evaluators, shying away from large-scale, data-driven development. In th

Gabriel B. Margolis, Michelle Wang, Nolan Fey, Pulkit Agrawal

We introduce SoftMimic, a framework for learning compliant whole-body control policies for humanoid robots from example motions. Imitating human motions with reinforcement learning allows humanoids to quickly learn new skills, but existing methods incentivize stiff control that aggressively corrects deviations from a reference motion, leading to brittle and

Roberto Hernandez

We compute the rational points on certain members of the following family of hyperelliptic curves \[C_a \colon y^2 = x^8 + (4-4a^4) x^6 + (8a^4 + 6)x^4 + (4-4a^4)x^2 + 1\] via the method first developed by Dem'yanenko \cite{dem1966rational} and then further generalized by Manin \cite{manin1969p}. In particular, we show that the method of Chabauty--Coleman, w

Tung Nguyen, Tuan Pham, Troy Arcomano, Veerabhadra Kotamarthi

Accurate weather forecasting across time scales is critical for anticipating and mitigating the impacts of climate change. Recent data-driven methods based on deep learning have achieved significant success in the medium range, but struggle at longer subseasonal-to-seasonal (S2S) horizons due to error accumulation in their autoregressive approach. In this wo

Gaetano Lambiase, Shinji Mukohyama, Tanmay Kumar Poddar, Anna Chiara Rescigno

General Relativity (GR) is an effective field theory valid in the infrared regime. Quadratic curvature extensions intended to probe ultraviolet physics generically propagate a massive spin-$2$ ghost and are therefore non-unitary. One route to remove ghost is by enlarging the geometric sector (torsion, non-metricity). We investigate the infrared phenomenology

Kyung Yun Lee, Nils Meyer-Kahlen, Karolina Prawda, Vesa Välimäki

We address the problem of estimating room impulse responses (RIRs) in noisy, uncontrolled environments where non-stationary sounds such as speech or footsteps corrupt conventional deconvolution. We propose AnyRIR, a non-intrusive method that uses music as the excitation signal instead of a dedicated test signal, and formulate RIR estimation as an L1-norm reg

Adam Stecklov, Noah El Rimawi-Fine, Mathieu Blanchette

Allocating extra computation at inference time has recently improved sample quality in large language models and diffusion-based image generation. In parallel, Flow Matching (FM) has gained traction in language, vision, and scientific domains, but inference-time scaling methods for it remain under-explored. Concurrently, Kim et al., 2025 approach this proble

Michał Wichrowski

I propose a vertex patch smoother where local problems are solved inexactly by a nested, matrix-free p-multigrid, creating a multigrid-within-multigrid framework. A single iteration of the local solver can be evaluated with $\mathcal{O}(p^{d+1})$ operations, and the approach is applicable to non-separable problems on unstructured meshes. Numerical experiment

Christopher A McClurg, Alan R Wagner

We advance the understanding of robotic intervention in high-risk scenarios by examining their potential to distract and impede a school shooter. To evaluate this concept, we conducted a virtual reality study with 150 university participants role-playing as a school shooter. Within the simulation, an autonomous robot predicted the shooter's movements and pos

Simeon Adebola, Chung Min Kim, Justin Kerr, Shuangyu Xie

Commercial plant phenotyping systems using fixed cameras cannot perceive many plant details due to leaf occlusion. In this paper, we present Botany-Bot, a system for building detailed "annotated digital twins" of living plants using two stereo cameras, a digital turntable inside a lightbox, an industrial robot arm, and 3D segmentated Gaussian Splat models. W

Sergio Sánchez Cruz

The top quark plays an important role in a number of new physics models, some of which introduce violations to some of the accidental symmetries of the SM, such as the lepton number conservation or introduce additional sources of others already broken, such as the CP symmetry. A set of measurements is presented that probe violation of these symmetries in pro

Hua Sun, Syed A. Jafar

A quantum message is encoded into $N$ storage nodes (quantum systems $Q_1\dots Q_N$) with assistance from $N_B$ maximally entangled bi-partite quantum systems $A_1B_1, \dots, A_{N_B}B_{N_B}$, that are prepared in advance such that $B_1\dots B_{N_B}$ are stored separately as entanglement assistance (EA) nodes, while $A_1\dots A_{N_B}$ are made available to th

Simon Brendle, Raphael Tsiamis, Yipeng Wang

We consider fill-ins of spin manifolds with scalar curvature bounded by $-n(n-1)$. Gromov proposed a conjecture relating the infimum of the mean curvature of such a fill-in to the hyperspherical radius. We observe that the inequality conjectured by Gromov follows by combining an inequality of Hijazi-Montiel-Rold\'an for the first Dirac eigenvalue with a rece

Mark A. Hughes, Huan Liu, Yaping Dan

Er implanted Si (Er:Si) is a promising candidate for scalable planar quantum memory (QM) applications. Er has a preference to coordinate with O impurities, and multiple types of Er center are typically formed after a post implant anneal. Float zone Si was implanted with 1018 cm-3 Er and separate samples were annealed using a rapid quench annealing technique

Frédéric Déglise

These notes, written version of a Bourbaki talk, survey Morel-Voevodsky's motivic homotopy theory over a field, with a focus on computations of motivic homotopy sheaves, both stable and unstable. We also describe Isaksen-Wang-Xu's applications to the determination of stable stems through the motivic Adams spectral sequence, which also paved the way toward sy

Samir Khaki, Junxian Guo, Jiaming Tang, Shang Yang

Vision Language Models (VLMs) have rapidly advanced in integrating visual and textual reasoning, powering applications across high-resolution image understanding, long-video analysis, and multi-turn conversation. However, their scalability remains limited by the growing number of visual tokens that dominate inference latency. We present SparseVILA, a new par

Jackson Harmon, Andreas Hochlehnert, Matthias Bethge, Ameya Prabhu

Scaled post-training now drives many of the largest capability gains in language models (LMs), yet its effect on pretrained knowledge remains poorly understood. Not all forgetting is equal: Forgetting one fact (e.g., a U.S. president or an API call) does not "average out" by recalling another. Hence, we propose a sample-wise paradigm to measure what is forgo

Kweku Abraham, Amnon Balanov, Tamir Bendory, Carlos Esteve-Yagüe

Motivated by single-particle cryo-electron microscopy, we study the sample complexity of the multi-target detection (MTD) problem, in which an unknown signal appears multiple times at unknown locations within a long, noisy observation. We propose a patching scheme that reduces MTD to a non-i.i.d. multi-reference alignment (MRA) model. In the one-dimensional

Sanjan C. Muchandimath, Joaquim R. R. A. Martins, Alex A. Gorodetsky

Mathematical models in computational physics contain uncertain parameters that impact prediction accuracy. In turbulence modeling, this challenge is especially significant: Reynolds averaged Navier-Stokes (RANS) models, such as the Spalart-Allmaras (SA) model, are widely used for their speed and robustness but often suffer from inaccuracies and associated un

Md. Enamul Atiq, Shaikh Anowarul Fattah

Skin cancer is a life-threatening disease where early detection significantly improves patient outcomes. Automated diagnosis from dermoscopic images is challenging due to high intra-class variability and subtle inter-class differences. Many deep learning models operate as "black boxes," limiting clinical trust. In this work, we propose a dual-encoder attenti

Ryan A. Robinett, Sophia A. Madejski, Kyle Ruark, Samantha J. Riesenfeld

Despite the popularity of the manifold hypothesis, current manifold-learning methods do not support machine learning directly on the latent $d$-dimensional data manifold, as they primarily aim to perform dimensionality reduction into $\mathbb{R}^D$, losing key manifold features when the embedding dimension $D$ approaches $d$. On the other hand, methods that

Zhining Liu, Ziyi Chen, Hui Liu, Chen Luo

Vision-Language Models (VLMs) achieve strong results on multimodal tasks such as visual question answering, yet they can still fail even when the correct visual evidence is present. In this work, we systematically investigate whether these failures arise from not perceiving the evidence or from not leveraging it effectively. By examining layer-wise attention

Zichen Hua, Federico Lelli, Enrico Di Teodoro, Stacy McGaugh

The mass-size relations of galaxies are generally studied considering only stars or only gas separately. Here we study the baryonic mass-size relation of galaxies from the SPARC database, using the total baryonic mass ($M_{\rm bar}$) and the baryonic half-mass radius ($R_{\rm 50, bar}$). We find that SPARC galaxies define two distinct sequences in the $M_{\r

Michael Nestor, Jiaxin Wang, Ning Zhang, Fei Teng

The increasing penetration of inverter-based resources into the power grid, with often only black-box models available, challenges long-standing frequency control methods. Most recent works take a decentralized approach without online device coordination via communication. This paper considers both dynamic behavior and communication within secondary frequenc

Jennifer M. Laing, Christine D. Wilson

Work by the Physics at High Angular resolution in Nearby GalaxieS (PHANGS) collaboration found higher molecular gas surface densities and velocity dispersions in the centres of barred galaxies compared to unbarred galaxies. We explore central molecular gas using published high resolution (150 pc) measurements of CO$(2-1)$ from the PHANGS-ALMA survey and a ne

Danilo A. Arturo Rodriguez, Rebecca G. Martin

Exoplanet observations show that close-in super-Earths are more common around M-dwarfs than around solar mass stars. Since the snow line in a protoplanetary disc plays a crucial role in determining the amount of solid material available for planet formation, we explore the icy regions of protoplanetary discs around stars with masses 0.1, 0.5 and 1 $\rm M_\od

Nikolaos Kidonakis, Chris Foster

We present calculations of higher-order QCD and electroweak (EW) corrections to the associated production of a top-antitop quark pair and a $Z$ boson, i.e. $t{\bar t}Z$ production, in proton collisions. We find that the contributions from soft-gluon corrections are numerically dominant and large. We present approximate NNLO (aNNLO) and approximate N$^3$LO (a

Sudip Halder, Supriya Pan, Paulo M. Sá, Tapan Saha

In this article, we investigate the existence of accelerating scaling solutions in coupled phantom cosmology without assuming any specific potential for the phantom scalar field. The coupling between phantom dark energy and dark matter is motivated by the warm inflationary paradigm, with the dissipation coefficient assumed to be either constant or variable.

Xiao Ye, Jacob Dineen, Zhaonan Li, Zhikun Xu

Medical Large language models achieve strong scores on standard benchmarks; however, the transfer of those results to safe and reliable performance in clinical workflows remains a challenge. This survey reframes evaluation through a levels-of-autonomy lens (L0-L3), spanning informational tools, information transformation and aggregation, decision support, an

Marouane Tliba, Mohamed Amine Kerkouri, Yassine Nasser, Nour Aburaed

Knee osteoarthritis (KOA) diagnosis from radiographs remains challenging due to the subtle morphological details that standard deep learning models struggle to capture effectively. We propose a novel multimodal framework that combines anatomical structure with radiographic features by integrating a morphological graph representation - derived from Segment An

Yongming Li

We establish the full asymptotic stability of solitary wave solutions for the 1D focusing cubic Schr\"odinger equation on the line under small perturbations in weighted Sobolev spaces, building upon our results in [58]. The proof integrates the space-time resonances approach, based on the distorted Fourier transform, with modulation techniques to show modifi

Alexandra E. Ballentine, Raghvendra V. Cowlagi

We apply a physics-informed neural network (PINN) to solve the two-point boundary value problem (BVP) arising from the necessary conditions postulated by Pontryagin's Minimum Principle for optimal control. Such BVPs are known to be numerically difficult to solve by traditional shooting methods due to extremely high sensitivity to initial guesses. In the ligh

Debabrata Adak, J. A. Rubiño-Martín, R. T. Génova-Santos, M. Remazeilles

We introduce a novel approach to estimate the spectral index, $\beta_s$, of polarised synchrotron emission, combining the moment expansion of CMB and the constrained-ILC. We reconstructed the maps of the first two synchrotron moments, combining multi-frequency data, and applied the `T-T plot' technique between two moment maps to estimate the synchrotron spec

Sandro Andric

We prove an exact equality between the minimal quadratic control energy and the squared normal-quantile gap for terminal halfspaces in linear-Gaussian systems with additive control and quadratic effort $E(u) = \tfrac12\!\int u^\top M u\,dt$ where $M = B^\top\Sigma^{-1}B$. For terminal halfspace events, the minimal energy equals the squared normal-quantile ga

Flavio Salizzoni, Luca Sodomaco, Julian Weigert

Rayleigh quotient minimization deals with optimizing a quadratic homogeneous function over a sphere. Its critical points correspond to the normalized eigenvectors of the symmetric matrix associated with the quadratic form. In this paper, we consider a homogeneous polynomial objective function $f$ over a sphere, a projective algebraic variety $X$, and we stud

Alex NieMiera, William Good, Huey-Wen Lin, Fei Yao

We present the first lattice-QCD determination of the nucleon gluon parton distribution function (PDF) within the large-momentum effective theory (LaMET) framework, employing the hybrid scheme with self renormalization, in the continuum limit. High statistics calculations with boost momentum $P_z \approx 2.0$-$2.2$~GeV are performed at lattice spacings of $a

César Barilla

A decision-maker periodically acquires information about a changing state, controlling both the timing and content of updates. I characterize optimal policies using a decomposition of the dynamic problem into optimal stopping and static information acquisition. Eventually, information acquisition either stops or follows a simple cycle in which updates occur

Younghyun Koo, Maryam Rahnemoonfar

As an increasing amount of remote sensing data becomes available in the Arctic Ocean, data-driven machine learning (ML) techniques are becoming widely used to predict sea ice velocity (SIV) and sea ice concentration (SIC). However, fully data-driven ML models have limitations in generalizability and physical consistency due to their excessive reliance on the

Brenden W. Hamilton, Alexander R. Muñoz, Travis E. Jones, Benjamin T. Nebgen

The cerium iso-structural phase transition (gamma to alpha) is dominated by f-electron localization changes that results in a magnetic ordering change and a volume collapse. Generally, these physics are difficult to capture with ab initio and first principles methods. However, previous works have shown various methods to be successful in predicting at least

Nicolas Harmand, Julien Dervaux, Christophe Poulard, Sylvie Hénon

We measured the thickness of MDCK epithelia grown on substrates with a sinusoidal profile. We show that while at long wavelength the profile of the epithelium follows that of the substrate, at short wavelengths cells are thicker in valleys than on ridges. This is reminiscent of the so-called {\guillemotleft} healing length {\guillemotright} in the case of a

Celeste Riley, Omar Al-Refai, Yadira Colunga Reyes, Eman Hammad

As stories of human-AI interactions continue to be highlighted in the news and research platforms, the challenges are becoming more pronounced, including potential risks of overreliance, cognitive offloading, social and emotional manipulation, and the nuanced degradation of human agency and judgment. This paper surveys recent research on these issues through