March 2026 arXiv papers — page 70

Lorenzo Carlucci, Andrea Volpi, Konrad Zdanowski

We calibrate the reverse mathematical strength of a family of extensions of Ramsey's theorem to finite colorings of certain subsets of the natural numbers of unbounded finite dimension. Specifically, we analyze the principles $\mathsf{RT}^{!\alpha}_k$ asserting that every $k$-coloring of the exactly $\alpha$-large subsets of an infinite $X \subseteq \mathbb{

James Hugglestone, Samuel Jacob Chacko, Dawson Stoller, Ryan Schmidt

Large Language Models (LLMs) have demonstrated potential in code generation, yet they struggle with the multi-step, stateful reasoning required for offensive cybersecurity operations. Existing research often relies on static benchmarks that fail to capture the dynamic nature of real-world vulnerabilities. In this work, we introduce STRIATUM-CTF (A Search-bas

Giovana Kerche Bonás, Roseval Malaquias Junior, Marcos Piau, Thiago Laitz

We introduce CAPITU, a benchmark for evaluating instruction-following capabilities of Large Language Models (LLMs) in Brazilian Portuguese. Unlike existing benchmarks that focus on English or use generic prompts, CAPITU contextualizes all tasks within eight canonical works of Brazilian literature, combining verifiable instruction constraints with culturally-

Rohit Rana, Eric R. Heller, Antonios M. Alvertis, Jeffrey B. Neaton

We construct a many-body model Hamiltonian to capture how phonons renormalize exciton binding as a function of temperature. By using the GW approximation and density functional perturbation theory, we are able to parameterize this Hamiltonian completely from first principles. To capture static quasiparticle properties non-perturbatively, we evolve this Hamil

Fin Amin, Nathaniel Dennler, Andreea Bobu

Robots learn reward functions from user demonstrations, but these rewards often fail to generalize to new environments. This failure occurs because learned rewards latch onto spurious correlations in training data rather than the underlying human intent that demonstrations represent. Existing methods leverage visual or semantic similarity to improve robustne

Lucas Vogels, Reza Mohammadi, Marit Schoonhoven, Sinan Yildirim

This article considers Bayesian model inference on binary model spaces. Binary model spaces are used by a large class of models, including graphical models, variable selection, mixture distributions, and decision trees. Traditional strategies in this field, such as reversible jump or birth-death MCMC algorithms, are still popular, despite suffering from a sl

Yalda Foroutan, Ipek Oztas, Daniel Rebain, Aysegul Dundar

Radiance fields have emerged as powerful tools for 3D scene reconstruction. However, casual capture remains challenging due to the narrow field of view of perspective cameras, which limits viewpoint coverage and feature correspondences necessary for reliable camera calibration and reconstruction. While commercially available 360$^\circ$ cameras offer signifi

Zhiyuan Li, Ruiwen Xie, Hongbin Zhang

Based on detailed first-principles calculations, we investigate the tetragonal-to-hexagonal phase transition in Fe-doped BaTiO$_3$. Free energy calculations confirm a crossover from the tetragonal to hexagonal phases at 2.7--6\% Fe on cooling from the sintering temperature, in agreement with experimental observations, where comparative calculations show that

Tenghan Zhong

We introduce a proxy-reliance-controlled conformal recalibration framework for one-sided Value-at-Risk (VaR), and study a question that existing state-aware methods do not usually isolate: how strongly should the recalibration adjustment depend on an imperfect volatility proxy? We formalize this through a proxy-reliance parameter that continuously interpolat

Minghan Li, Rachel Gonsalves, Weiyue Li, Sunghoon Yoon

Large language models (LLMs) are increasingly deployed as autonomous agents in financial trading. However, they often exhibit a hazardous behavioral bias that we term uniform trust, whereby retrieved information is implicitly assumed to be factual and heterogeneous sources are treated as equally informative. This assumption stands in sharp contrast to human

Aria Pessianzadeh, Alex H. Poole, Rezvaneh Rezapour

The 2022 U.S. Supreme Court decision in Dobbs v. Jackson Women's Health Organization reshaped the reproductive rights landscape, introducing new uncertainty and barriers to abortion access. We present a large-scale computational analysis of abortion discourse on Reddit, examining how barriers to access are articulated across information-seeking and informati

Danai Deligeorgaki, Krishna Menon

We study a new class of palindromic descent polynomials. Given a Dyck path $d$ of semilength $n$ and a permutation $\sigma$ of size $n$, one can label the up-steps and down-steps of $d$ with the elements of $\sigma$. The labeled Dyck path determines a multiset permutation called a canon (or nonnesting) permutation. Such permutations arise as linear extension

Xingzhi Sun, João Felipe Rocha, Brett Phelan, Dhananjay Bhaskar

Understanding cellular trajectories via time-resolved single-cell transcriptomics is vital for studying development, regeneration, and disease. A key challenge is inferring continuous trajectories from discrete snapshots. Biological complexity stems from stochastic cell fate decisions, temporal proliferation changes, and spatial environmental influences. Cur

Young Hyun Cho, Will Wei Sun

Preference-based fine-tuning has become an important component in training large language models, and the data used at this stage may contain sensitive user information. A central question is how to design a differentially private pipeline that is well suited to the distinct structure of reinforcement learning from human feedback. We propose a privacy-preser

A. Faggionato, C. Tagliaferri

We consider the Voronoi tessellation associated to a stationary simple point process on $\mathbb{R}^d$ with finite and positive intensity. We introduce the Delaunay triangulation as its dual graph, i.e.~the graph with vertex set given by the point process and with edges between vertices whose Voronoi cells share a $(d-1)$-dimensional face. We also attach to

Laurence Anthony

This paper asks whether a bounded neural architecture can exhibit a meaningful division of labor between intuition and deliberation on a classic 64-item syllogistic reasoning benchmark. More broadly, the benchmark is relevant to ongoing debates about world models and multi-stage reasoning in AI. It provides a controlled setting for testing whether a learned

Naomi Oke, Aja M. Carter, Ben Gu, Steven Man

Scaling the design of robots up or down remains a fundamental challenge. While biological systems follow well-established isometric and allometric scaling laws relating mass, stride frequency, velocity, and torque, it is unclear how these relationships translate to robotic systems. In this paper, we generate similar allometric scaling laws for bipedal robots

Eric R. Bittner

Classical thermodynamics contains familiar geometric relations associated with cyclic processes, most notably the identification of mechanical work with the area enclosed by a trajectory in the $(P,V)$ plane. We show that the area laws for work and reversible heat arise as projections of a single canonical two--form defined on the equilibrium thermodynamic m

François Pachet, Jean-Daniel Zucker

Generating synthetic populations from aggregate statistics is a core component of microsimulation, agent-based modeling, policy analysis, and privacy-preserving data release. Beyond classical census marginals, many applications require matching heterogeneous unary, binary, and ternary constraints derived from surveys, expert knowledge, or automatically extra

ICARUS Collaboration

We present a search for muon neutrino disappearance in the Booster Neutrino Beam (BNB) at Fermilab using the ICARUS detector. Neutrino interactions identified as muon neutrinos interacting with argon nuclei via the charged current interaction and having only a muon and at least one proton in the final state (1$\mu$Np) have been selected from data collected i

Eva Erickson, Eric E. Handy-Cardenas, Joel W. Newbolt, Christin Murphy

We report on experiments designed to characterize the vortex-induced vibration (VIV) and wake-induced vibration (WIV) experienced by bluff bodies immersed in both steady and unsteady flows. Using a real-time Cyber-Physical System (CPS) we systematically prescribe the virtual mass, spring constant, and damping of elastically mounted models. This allows us to

R. Alfaro, C. Alvarez, A. Andrés, E. Anita-Rangel

The last five years have shown us that ultra-high-energy (UHE; $>$100 TeV) gamma-ray sources are ubiquitous, but the nature of these sources remain highly uncertain. UHE gamma rays can be produced via either leptonic (Inverse compton) or hadronic (pion decay) emission mechanisms. To decisively determine the emission mechanisms, multimessenger searches are es

Anna Stuhlmacher, Panupong Srisuthankul, Johanna L. Mathieu, Peter Seiler

Agrivoltaic systems--photovoltaic (PV) panels installed above agricultural land--have emerged as a promising dual-use solution to address competing land demands for food and energy production. In this paper, we propose a model predictive control (MPC) approach to dual-axis agrivoltaic panel tracking control that dynamically adjusts panel positions in real ti

El Hassane Lahrar, Mathieu Salanne, Rudolf Weeber, Céline Merlet

Simulations of electrochemical double layer capacitors based on porous carbon electrodes, energy storage systems which accumulate and release energy through reversible ion adsorption at electrode/electrolyte interfaces, are often performed at the microscopic scale, using molecular dynamics. Such simulations provide crucial information to understand the adsor

Anushka Tonapi, Dana Paquin

In this paper, we introduce dynamic coprime labeling (DCL), a novel extension of coprime labeling for time-sensitive networks. In particular, we explore whether there exists a graph labeling scheme that maintains relative coprimality among adjacent vertices as the graph evolves over time. We extend the definition of coprime labeling to include an injective l

Niyati Desai, Garreth Ruane, Susan Redmond, Dimitri Mawet

Achieving the Habitable Worlds Observatory (HWO) goal of 10^-10 contrast at a separation of 3 $\lambda$/D across a 20% bandwidth requires coronagraph focal plane masks with both broadband high contrast performance and high planet throughput. Scalar vortex coronagraphs (SVCs) offer a promising alternative to polarization-sensitive vector vortex designs but fa

Abhinav Jangir, Anees Ahmed

We investigate the enhanced dynamically assisted electron-positron pair production in differently polarized electric fields with frequency chirps within the real-time Dirac-Heisenberg-Wigner formalism. The combined influence of the chirp strength and the field polarization on the momentum distribution and the total number density of the created pairs is stud

Zhiyi Zhou, Ján Drgoňa, Yury Dvorkin

The adversarial subproblem in two-stage adaptive robust optimization (ARO), which identifies the worst-case uncertainty realization, is a major computational bottleneck. This difficulty is exacerbated when the recourse value function is non-concave and the uncertainty set shifts across applications. Existing approaches typically exploit specific structural a

V. V. Desai, N. P. Armitage

We introduce Biphoton Entanglement Light Spectroscopy (BELS), a quantum spectroscopic technique that employs polarization entangled Bell pairs and two photon interference to probe material properties. In BELS, the measured signal arises not from single photon intensities but from changes in the joint polarization and path correlations of biphoton Bell pairs

Fedor B. Lyudogovskiy

We study the partition graph $G_n$, whose vertices are the partitions of $n$ and whose edges correspond to elementary unit transfers between parts. We define the self-conjugate axis, its distance neighborhoods, and the thin spine, a first off-axis layer built from common neighbors of distinct axial vertices. We prove that distinct self-conjugate vertices are

Anirvan Shukla

The NA61/SHINE spectrometer at the CERN Super Proton Synchrotron (SPS) scans particle production in collisions of nuclei with various sizes at a set of energies covering the SPS energy range towards various physics goals. This paper presents the first differential production measurements of deuterons at energies relevant for cosmic-ray studies, produced in i

Finnley Goss, Kelly McKinnie

A lattice point $\vec x=(x_1,\dots,x_n)\in\mathbb Z^{n}$ is said to be visible if the line segment between $\vec x$ and the origin contains no other lattice point. In this paper, we compute the asymptotic density of visible lattice points on hyperplanes and their intersections. In particular, we show that the hyperplane $\vec a \cdot \vec x = b$ in $\mathbb

Tam Cheetham-West, Khanh Le

We give conditions on a Haken hyperbolic rational homology three sphere that imply that any other 3-manifold with profinitely equivalent fundamental group must also be Haken. In the appendix, we show that a regular finite-sheeted cover of an aspherical integral homology three-sphere with positive first Betti number must have first Betti number at least four.

Albert Reuther, William Arndt, Johannes Blaschke, Christian Boehme

When we think of how we use smartphones, e-commerce, collaboration platforms, LLMs, etc., most of our interactions with computers are interactive and often urgent. Similar trends of interactivity and urgency are coming to HPC, with applications from simulations to data analysis and machine learning requiring more parallel computational capability and more in

Isaac Meilijson

On the $Z^2$ lattice, vertices are assigned random weights $W(i,j)$. The point-to-point last passage percolation (LPP) time $S_{M,N+1-M}$ between $(1,1)$ and $(M,N+1-M)$ is the maximum total weight among all upward/right-oriented paths connecting the two. Point-to-line LPP time $R_N$ is the maximum of these maximal total weights over $M$. Asymptotic distribu

Yuanzhen Yue, Stella Self, Yichao Wu, Jiajia Zhang

Quantile regression is useful for characterizing the conditional distribution of a response variable and understanding heterogeneity in the covariate effects at different quantiles. The rise of high-dimensional physiological data in biomedical research through wearable and sensor devices underscores the need for effective variable selection methods for inter

Lazar Supic, Alec Mullen, E. Paxon Frady

In visual scene understanding tasks, it is essential to capture both invariant and equivariant structure. While neural networks are frequently trained to achieve invariance to transformations such as translation, this often comes at the cost of losing access to equivariant information - e.g., the precise location of an object. Moreover, invariance is not nat

Prateek Agrawal, Nathaniel Craig, Amalia Madden, Iñigo Valenzuela Lombera

We present the FERMIACC, a scaffolded reasoning model built on OpenAI agents designed to autonomously generate and quantitatively validate theory hypotheses for high energy physics data at scale.

Niyati Desai, Garreth Ruane, Daniel Shanks, Lorenzo König

The Habitable Worlds Observatory (HWO) mission will require coronagraphs capable of suppressing starlight at the $\sim 10^{-10}$ contrast level to directly image exo-Earths. High contrast achromatic coronagraphic masks are the missing critical component to achieving this. Vortex coronagraphs, particularly scalar vortex designs with an achromatic focal plane

Luz Martinez-Lucas, Pravin Mote, Abinay Reddy Naini, Mohammed Abdelwahab

Affective computing aims to understand and model human emotions for computational systems. Within this field, speech emotion recognition (SER) focuses on predicting emotions conveyed through speech. While early SER systems relied on limited datasets and traditional machine learning models, recent deep learning approaches demand largescale, naturalistic emoti

Jingtian Dang, Ritik Raj, Changhai Man, Jianming Tong

Cycle-accurate simulators are widely used to study systolic accelerators, yet their accuracy and usability are often limited by weak validation against real hardware and poor integration with modern ML compiler stacks. This paper presents SCALE-Sim TPU, a validated and extended version of SCALE-Sim v3 for TPU-style accelerators. Specifically, we make three c

Victoria Luongo Fisher, Jessica C. Franklin, Leo G. Rebholz

This paper shows how continuous data assimilation (CDA) can be used to provably enable and accelerate convergence of a (efficient at each iteration due to a physics-splitting, but generally slowly converging and not robust) nonlinear solver for incompressible Navier-Stokes equations (NSE). Herein we develop, analyze and test an Uzawa-based nonlinear solver f

Be'eri Greenfeld, George King, Xiaoxuan Li, Sam Tacheny

Given a size-$k$ subset $S$ of a group $G$, how large can the product set $S^n$ be? We study this question, at several layers of refinement, for the infinite dihedral group. First, we give an explicit formula for the maximum size of $S^n$ among all size-$k$ subsets with a prescribed number of reflections. We then determine the optimal number of reflections t

Mark Webster, Abraham Jacob, Oscar Higgott

The distance of a classical or quantum code is a key figure of merit which reflects its capacity to detect errors. Quantum LDPC code families have considerable promise in reducing the overhead required for fault-tolerant quantum computation, but calculating their distance is challenging with existing methods. We generally assess the performance of a quantum

Kaizhen Tan, Fan Zhang

Sidewalk width is an important indicator of pedestrian accessibility, comfort, and network quality, yet large-scale width data remain scarce in most cities. Existing approaches typically rely on costly field surveys, high-resolution overhead imagery, or simplified geometric assumptions that limit scalability or introduce systematic error. To address this gap

Shoubin Yu, Lei Shu, Antoine Yang, Yao Fu

Multimodal AI agents are increasingly automating complex real-world workflows that involve online web execution. However, current web-agent benchmarks suffer from a critical limitation: they focus entirely on web-based interaction and perception, lacking grounding in the user's real-world physical surroundings. This limitation prevents evaluation in crucial

Achmad Anggawirya Alimin, Artur M. Schweidtmann

Large Language Models (LLMs) combined with Retrieval-Augmented Generation (RAG) and knowledge graphs offer new opportunities for interacting with engineering diagrams such as Piping and Instrumentation Diagrams (P&IDs). However, directly processing raw images or smart P&ID files with LLMs is often costly, inefficient, and prone to hallucinations. This work i

Honglin He, Yukai Ma, Brad Squicciarini, Wayne Wu

Sidewalk micromobility is a promising solution for last-mile transportation, but current learning-based control methods struggle in complex urban environments. Imitation learning (IL) learns policies from human demonstrations, yet its reliance on fixed offline data often leads to compounding errors, limited robustness, and poor generalization. To address the

Amir Jafari

Magnetic flux freezing states that, in ideal magnetohydrodynamics, magnetic flux is transported by the flow and magnetic field lines remain frozen into the plasma. In turbulent plasmas, however, the velocity and magnetic fields are spatially rough, invalidating the regularity assumptions underlying the classical theorem. Previous work has shown that Lagrangi

Samrendra Roy, Kazuma Kobayashi, Souvik Chakraborty, Rizwan-uddin

Operator learning models are rapidly emerging as the predictive core of digital twins for nuclear and energy systems, promising real-time field reconstruction from sparse sensor measurements. Yet their robustness to adversarial perturbations remains uncharacterized, a critical gap for deployment in safety-critical systems. Here we show that neural operators

David Kalaj, Jian-Feng Zhu

We prove a local contraction property for holomorphic functions that are nearly constant, relating weighted Bergman spaces $A^p_α(\B_n)$ and $A^q_β(\B_n)$. Our approach converts geometric information on weighted superlevel sets into analytic deficit inequalities and rests crucially on a quantitative stability result (of Fuglede type) for the isoperimetric in

Sofia Tapias Montana, Ronnie de Souza Santos

This paper investigates how software professionals perceive the economic implications of diversity in software engineering teams. Motivated by a gap in software engineering research, which has largely emphasized socio-technical and process-related outcomes, we adopted a qualitative interview approach to capture practitioners' reasoning about diversity in rel

Youcef Kehal, Khireddine Nouicer

We study neutron star configurations in a teleparallel gravity model featuring a scalar field coupled to both matter and torsion. In the Einstein frame, the theory includes a derivative coupling between the scalar field and the torsion vector, together with a conformal matter coupling \(A(\phi)=\exp(\beta\phi^{2}/2)\). Static and slowly rotating neutron-star

M. Cruz-Sánchez, S. Blazquez, C. Vega, V. M. Trejos

Perchloric acid (HClO$_4$) is widely used to prepare perchlorate salts with applications in propellants, industry, environmental chemistry, and biology. In this work, we used the intermolecular parameters from the extended Madrid-2019 force field for the perchlorate anion (ClO$_4^-$) and the oxonium cation (H$_3$O$^+$) together with TIP4P/2005 water to model

Oliver Zbinden, Sharun Parayil Shaji, Wolfgang Tress

Carbon-electrode-based PSC devices are stressed under 1 Sun equivalent illumination in a stability setup, and different scan-speed dependent current-voltage (J-V) curves are measured during aging. The collected data is used to estimate several physical parameters that contain information about charge transport and recombination using Machine Learning (ML), w

Michael Hind, Basel Shbita, Bo Wu, Farhan Ahmed

Textual Large Language Models (LLMs) provide a simple and familiar interface: a string of text is used for both input and output. However, the information conveyed to an LLM often has a richer structure and semantics, which is not conveyed in a string. For example, most prompts contain both instructions ("Summarize this paper into a paragraph") and data (the

Azizbek Nuriddinov, Ebrahim Ahmadisharaf, Mohammad Reza Alizadeh

Validation of flood models, used to support risk mitigation strategies, remains challenging due to limited observations during extreme events. High-frequency, high-resolution optical imagery (~3 m), such as PlanetScope, offers new opportunities for flood mapping, although applications remain limited by cloud cover and the lack of labeled training data during

K. K. Ernazarov

We consider the scalar-tensor theory witn non-minimal coupling in the Jordan frame. The action of the model contains a potential term $U(\varphi)$, a coupling function $f(\varphi)$. We explore a reconstruction procedure for a generic static spherically symmetric metric written in the Buchdahl parametrization: $ds^2 = \left(A(u)\right)^{-1}du^2 - A(u)dt^2 + C

Christopher Ewasiuk, Stefano Profumo

We calculate the present-day and cosmological volumetric rate of primordial black hole (PBH) quantum tunneling events to white holes, incorporating the competition between Hawking evaporation and tunneling, cosmological depletion, realistic mass-dependent abundance constraints, extended mass functions, and the alternative memory-burden scenario. The burst ra

Sifat Munim, Aditya Ramamoorthy

Large-scale distributed learning aims at minimizing a loss function $L$ that depends on a training dataset with respect to a $d$-length parameter vector. The distributed cluster typically consists of a parameter server (PS) and multiple workers. Gradient coding is a technique that makes the learning process resilient to straggling workers. It introduces redu

Yingqiang Gao, Veton Matoshi, Luca Rolshoven, Tilia Ellendorff

Public procurement refers to the process by which public sector institutions, such as governments, municipalities, and publicly funded bodies, acquire goods and services. Swiss law requires the integration of ecological, social, and economic sustainability requirements into tender evaluations in the format of criteria that have to be fulfilled by a bidder. H

Alexander Dittrich, Fuda van Diggelen, Dario Floreano

Biological neural networks continuously adapt and modify themselves in response to experiences throughout their lifetime - a capability largely absent in artificial neural networks. Hebbian plasticity offers a promising path toward rapid adaptation in changing environments. Here, we introduce Hebbian Attractor Networks (HAN), a class of plastic neural networ

Carlos Jimeno Miguel, Mikel Izal

Capture The Flag (CTF) competitions have established themselves as a highly effective pedagogical tool in cybersecurity education, offering students hands-on experience in realistic attack and defense scenarios. However, organizing and hosting these events requires considerable infrastructure effort, which frequently limits their adoption in academic setting

Delin An, Chaoli Wang

Diffusion probabilistic models have demonstrated significant potential in generating high-quality, realistic medical images, providing a promising solution to the persistent challenge of data scarcity in the medical field. Nevertheless, producing 3D medical volumes with anatomically consistent structures under multimodal conditions remains a complex and unre

Amna Irshad, Emil Björnson, Alva Kosasih, Vitaly Petrov

Future wireless networks are expected to support increasingly high data rates and user densities, motivating advanced multi-antenna architectures capable of adapting to dynamic propagation environments. Movable antenna (MA) arrays have recently emerged as an extension of massive MIMO, enabling physical repositioning of antenna elements to better exploit spat

Marco Lozano, Manish Kumar, Pavel Jelinek, Diego Soler-Polo

Recent advances in on-surface chemistry, combined with scanning probe microscopy, have enabled the synthesis of correlated molecules on surfaces and the characterization of their chemical and electronic properties with unprecedented spatial resolution. Low-energy magnetic excitations of individual molecules are frequently investigated by scanning tunneling s

Juan P. Aguilera, Thibaut Kouptchinsky, Keita Yokoyama

A classical theorem of Lusin states that all analytic sets are Lebesgue-measurable. In this article we established the reverse mathematical strength of Lusin's theorem, which depends on how precisely it is formalized. By doing so, we answer to a question of Simpson. Our main proof is motivated towards proving a specific version of that result, namely that an

Sandeep Zachariah, Francisco Yandun, Sachet Korada, Abhisesh Silwal

Monitoring and controlling invasive tree species across large forests, parks, and trail networks is challenging due to limited accessibility, reliance on manual scouting, and degraded under-canopy GNSS. We present MapForest, a modular field robotics system that transforms multi-modal sensor data into GIS-ready invasive-species maps. Our system features: (i)

Oliver Chubet, Niyathi Kukkapalli, Anvi Kudaraya, Donald R. Sheehy

Given a metric space, a standard metric range search, given a query (q, r), finds all points within distance r of the point q. Suppose now we have two different metrics d1 and d2. A product range query (q, r1, r2) is a point q and two radii $r1$ and $r2$. The output is all points within distance $r1$ of q with respect to d1 and all points within $r2$ of q wi

Jeffrey Flynt

Synthetic insider threat benchmarks face a consistency problem: corpora generated without an external factual constraint cannot rule out cross-artifact contradictions. The CERT dataset -- the field's canonical benchmark -- is also static, lacks cross-surface correlation scenarios, and predates the LLM era. We present OrgForge-IT, a verifiable synthetic bench

J. Ilnytskyi, T. Patsahan

We consider the SEIRS epidemiology model with such features of the COVID-19 outbreak as: abundance of unidentified infected individuals, limited time of immunity and a possibility of vaccination. The control of the pandemic dynamics is possible by restricting the transmission rate, increasing identification and isolation rate of infected individuals, and via

Niyati Bafna, Ryan Soh-Eun Shim, Barbara Plank, David Yarowsky

Where there is growing interest in in-context language learning (ICLL) for unseen languages with large language models, such languages usually suffer from the lack of NLP tools, data resources, and researcher expertise. This means that progress is difficult to assess, the field does not allow for cheap large-scale experimentation, and findings on ICLL are of

Nikunj Khetan, Jerome Mertz

We present a compressive beamforming method for coherent plane-wave compounding (CPWC) ultrasound imaging based on a far-field decomposition of the received radiofrequency (RF) data into virtual plane waves. This decomposition recasts the imaging operation entirely in the spatial frequency domain ($k$-space), allowing direct and flexible control over $k$-spa

David A. Bader

In the 1980s, high-performance computing (HPC) became another tool for research in the open (non-defense) science and engineering research communities. However, HPC came with a high price tag; the first Cray-2 machines, released in 1985, cost between \$12 million and \$17 million, according to the Computer History Museum, and were largely available only at g

J. Dutta, P. M. Ferreira, S. Heinemeyer

In generalizations of the Standard Models with extended scalar sectors, pseudoscalar particles will contribute to associated production of the Higgs boson discovered at the LHC, $h$, and the $Z$ boson. The pseudoscalar can be produced via gluon-gluon fusion, and as such may give contributions to $Zh$ production comparable to the value predicted by the Standa

Jan Li, Owidiusz Makuta, Evert van Nieuwenburg, Jordi Tura

As Hermitian operators, many-body Bell operators can naturally be identified as many-body Hamiltonians. An important subclass of such Hamiltonians is the stoquastic class, characterized by having nonpositive off-diagonal matrix elements in a given basis. Interestingly, this property is shared by the permutationally invariant (PI) Bell operators underlying th

Davide Bucciarelli, Evelyn Turri, Lorenzo Baraldi, Marcella Cornia

Inference-time scaling has emerged as an effective way to improve generative models at test time by using a verifier to score and select candidate outputs. A common choice is to employ Multimodal Large Language Models (MLLMs) as verifiers, which can improve performance but introduce substantial inference-time cost. Indeed, diffusion pipelines operate in an a

Gregory J. Cooke, Nikku Madhusudhan, Emily G. Mitchell

New observations are opening the possibility of characterising habitable environments in exoplanetary systems, with the recent example of the candidate hycean world K2-18 b. This motivates an exploration of the possible ecological conditions on such planets to better interpret biosignatures as well as understand the nature of potential life. On Earth, the Lo

Yaiza Cano, Jose Manuel Alarcón

In this paper, we consider three compact objects (HESS J1731-347, PSR J1231-1411, XTE J1814-338) with anomalous mass-radius relation to analyze the possibility of being dark matter admixed neutron stars. We try to infer the dark matter particle properties, under the assumption of behaving as a free Fermi gas. The main novelty relies on the use of a baryonic

Charoes Huang, Xin Huang, Ngoc Phu Tran, Amin Milani Fard

The Model Context Protocol (MCP) has rapidly emerged as a universal standard for connecting AI assistants to external tools and data sources. While MCP simplifies integration between AI applications and various services, it introduces significant security vulnerabilities, particularly on the client side. In this work we conduct threat modelings of MCP implem

Muhammad Awais Jadoon, Sebastian Robitzsch

Current architecture proposals within standards development organizations such as ETSI and 3GPP enable sensing capabilities in mobile networks; however, they do not include a repository for storing sensing data. Such a repository can be used for AI model training and to complement ongoing sensing service provisioning by improving efficiency and accuracy. One

Hsian-Hua Tseng

We study Virasoro constraints for Gromov-Witten theory of a product variety when one factor has semi-simple quantum cohomology.

Gregory B. Cook, Xiyue Wang

The spin-weighted spheroidal functions are the eigenfunctions of the angular Teukolsky equation. They are a generalization of the widely used spin-weighted spherical functions, and are extremely important in the area of black-hole perturbation theory. Like other special functions, they have an inherent phase ambiguity and need to be phase fixed to be uniquel

J. M. F. Castillo, W. H. G. Corrêa

We show: 1) The existence of the first twisted Hilbert space that is not isomorphic to its dual; this solves a problem posed by Cabello in [Nonlinear centralizers in homology, Math. Ann. 358 (2014), no. 3-4, 779-798]. 2) The existence of a large coneable family of relatively incomparable such examples, improving the coneable family obtained in [W.H. Corr\^{e

M. S. Grbić, I. Jakovac, I. Kupčić, H. Tanaka

We investigate the microscopic properties of the kagome compound Cs$_2$Cu$_3$SnF$_{12}$ using $^{63,65}$Cu nuclear quadrupolar resonance (NQR). Analysis of the local hyperfine fields below the Néel temperature $T_N = 20$ K indicates a spin structure consistent with $P2_1/n$ symmetry of negative vector chirality. Measurements of the spin-lattice relaxation ra

Hasini Sumalee Perera, Zadia Codabux, Fabio Palomba

Serverless computing is a cloud execution model where developers run code, and the server management is handled by the cloud provider. Serverless computing is increasingly gaining popularity as more systems adopt it to enhance scalability and reduce operational costs. While it has numerous benefits, it also embodies unique challenges inherent to serverless c

Shakil Mia, Umme Habiba, Urmi Akter, SK Rezwana Quadir Raisa

Early and precise identification of plant diseases, especially in potato crops is important to ensure the health of the crops and ensure the maximum yield . Potato leaf diseases, such as Early Blight and Late Blight, pose significant challenges to farmers, often resulting in yield losses and increased pesticide use. Traditional methods of detection are not o

Clément Hongler, Franck Gabriel, Valentin Hartmann, Arthur Renard

Defining a constructive process to build general capabilities for language models in an automatic manner is considered an open problem in artificial intelligence. Towards this, we consider the problem of building a curriculum of tasks that grows a model via relevant skill discovery. We provide a concrete framework for this task, using a family of tasks calle

ATLAS Collaboration

Differential cross-section measurements are presented for the production of a $W$ boson in association with a photon. The analysis is performed using proton--proton collision data collected by the ATLAS experiment at $\sqrt{s}= 13$ TeV, corresponding to an integrated luminosity of 140~fb$^{-1}$. The differential cross sections are measured in the $W\gamma \r

Sean Cottrell, Guo-Wei Wei, Longxiu Huang

Spatial transcriptomics data analysis integrates cellular transcriptional activity with spatial coordinates to identify spatial domains, infer cell-type dynamics, and characterize gene expression patterns within tissues. Despite recent advances, significant challenges remain, including the treatment of batch effects, the handling of mixed cell-type signals,

Euclid Collaboration, S. Joudaki, V. Pettorino, L. Blot

We provide a description of the code implementation and structure of Cosmology Likelihood for Observables in Euclid (CLOE), developed by members of the Euclid Consortium. CLOE is a modular Python code for computing the theoretical predictions of cosmological observables and evaluating them against state-of-the-art data from galaxy surveys such as Euclid in a

Lohith Senthilkumar, Tim Menzies

Software analytics often builds from labeled data. Labeling can be slow, error prone, and expensive. When human expertise is scarce, SE researchers sometimes ask large language models (LLMs) for the missing labels. While this has been successful in some domains, recent results show that LLM-based labeling has blind spots. Specifically, their labeling is not

Luca Vendruscolo, Eduardo Sebastián, Amanda Prorok, Ajay Shankar

Autonomous aerial and aquatic robots that attain mobility by perturbing their medium, such as multicopters and torpedoes, produce wake effects that act as disturbances for adjacent robots. Wake effects are hard to model and predict due to the chaotic spatio-temporal dynamics of the fluid, entangled with the physical geometry of the robots and their complex m

Jin-Xin Tan, Ting-Jia Miao, Mu-Hua Zhang, Xiang-He Pang

We employ {PhysMaster}, an autonomous agentic AI system integrating theoretical reasoning, numerical computation, and exploitation strategies towards ultra-long horizon automation, to tackle long-standing challenges in non-perturbative lattice analyzes, including low signal-to-noise ratio at large transverse separation, complex systematic uncertainties, and

Danilo Saccani, Luca Furieri, Giancarlo Ferrari-Trecate

The growing complexity of modern control tasks calls for controllers that can react online as objectives and disturbances change, while preserving closed-loop stability. Recent approaches for improving the performance of nonlinear systems while preserving closed-loop stability rely on time-invariant recurrent neural-network controllers, but offer no principl

Enric Alberola-Boloix, Ioar Casado-Telletxea

We derive posterior contraction rates (PCRs) and finite-sample Bernstein von Mises (BvM) results for non-parametric Bayesian models by extending the diffusion-based framework of Mou et al. (2024) to the infinite-dimensional setting. The posterior is represented as the invariant measure of a Langevin stochastic partial differential equation (SPDE) on a separa

Malte Schulze, Sebastiano Bernuzzi, Piero Rettegno, Joan Fontbuté

Using state-of-the-art scattering results in post-Minkowskian (PM) gravity, we improve the tidal sector of four different flavors of the effective-one-body (EOB) formalism. We notably explore both adiabatic and post-adiabatic gravitoelectric and gravitomagnetic quadrupolar tidal effects at the next-to-next-to-leading PM-order. When comparing the predictions

Weitong Cai, Hang Zhang, Yukai Huang, Shitong Sun

Always-on sensing is essential for next-generation edge/wearable AI systems, yet continuous high-fidelity RGB video capture remains prohibitively expensive for resource-constrained mobile and edge platforms. We present a new paradigm for efficient streaming video understanding: grayscale-always, color-on-demand. Through preliminary studies, we discover that

Emmanouil M. Athanasakos

Federated Learning (FL) is constrained by the communication and energy limitations of decentralized edge devices. While gradient sparsification via Top-K magnitude pruning effectively reduces the communication payload, it remains inherently energy-agnostic. It assumes all parameter updates incur identical downstream transmission and memory-update costs, igno

Alexander Chamolly, Takuji Ishikawa

Microswimmers in suspension exhibit collective swimming behaviour, forming various self-organised structures including ordered, aggregated, and turbulent-like structures. When mixed with passive particles phase-separation is known to occur, but due to the difficulty of accurately handling many-body hydrodynamic interactions, the formation of self-organised s