March 2026 arXiv papers — page 128

Max Zimmer, Nico Pelleriti, Christophe Roux, Sebastian Pokutta

AI tools and agents are reshaping how researchers work, from proving theorems to training neural networks. Yet for many, it remains unclear how these tools fit into everyday research practice. This paper is a practical guide to AI-assisted research in mathematics and machine learning: We discuss how researchers can use modern AI systems productively, where t

Diana Shvydka, Victor Karpov, Nilendu Gupta

We consider physics behind the FLASH modality of cancer radiation treatment where extremely short treatment times are achieved with ultra high dose rates maintaining the conventional antitumor effectiveness and yet substantially reducing damage to normal tissues (sparing effect). The difference in responses between normal and tumor tissues is attributed here

Anchita Dey, Shubhendu Bhasin

This paper proposes an adaptive tube framework for model predictive control (MPC) of discrete-time linear time-invariant systems subject to parametric uncertainty and additive disturbances. In contrast to conventional tube-based MPC schemes that employ fixed tube geometry and constraint tightening designed for worst-case uncertainty, the proposed approach in

Claude Brisson

We introduce partial partial aggregates (PPA), a query optimization technique for distributed engines that pushes only the local compute phase of an aggregate operation through joins. A query that aggregates after a join involves two logical operations, each requiring a network shuffle. Pushing a full aggregate (COMPUTE$\rightarrow$DISTRIBUTE$\rightarrow$MER

Suzhen Zhong, Shayan Noei, Ying Zou, Bram Adams

Code review is a critical software engineering practice where developers review code changes before integration to ensure code quality, detect defects, and improve maintainability. In recent years, AI agents that can understand code context, plan review actions, and interact with development environments have been increasingly integrated into the code review

Lara Lee Russell-Lasalandra, Hudson Golino

This Monte Carlo simulation examines how prompt engineering strategies shape the quality of large language model (LLM)--generated personality assessment items within the AI-GENIE framework for generative psychometrics. Item pools targeting the Big Five traits were generated using multiple prompting designs (zero-shot, few-shot, persona-based, and adaptive),

Soham Desai, Dave Cade

Positioning using Global Navigation Satellite Systems (GNSS) typically requires several seconds of continuous signal reception from satellites in Medium Earth Orbit (MEO). This requirement poses challenges for applications where receivers can only capture signals intermittently or operate under constrained power and visibility conditions. In such scenarios,

Goutam Das, Michael Dorothy, Kyle Volle, Daigo Shishika

Game theory provides the gold standard for analyzing adversarial engagements, offering strong optimality guarantees. However, these guarantees often become brittle when assumptions such as perfect information are violated. Reinforcement learning (RL), by contrast, is adaptive but can be sample-inefficient in large, complex domains. This paper introduces a hy

Samayita Das, Alok Shukla

Graphene monolayer is a material with zero band gap, because of which its applications in optoelectronics are limited. The question arises, can we modify the optoelectronic properties of graphene by doping it with other atoms? Synthesis of 2D monolayer of graphene doped with hetero-atoms such as boron and nitrogen, and a few computational studies of their st

Philipp Bogdan

Existing audio-to-MIDI tools extract notes but discard the timbral characteristics that define an instrument's identity. We present Instrumental, a system that recovers continuous synthesizer parameters from audio by coupling a differentiable 28-parameter subtractive synthesizer with CMA-ES, a derivative-free evolutionary optimizer. We optimize a composite p

Claude Brisson

We present a method for estimating the number of distinct values (NDV) of a column in columnar file formats, using only existing file metadata--no extra storage, no data access. Two complementary signals are exploited: (1)~inverting the dictionary-encoded storage size equation yields accurate NDV estimates when distinct values are well-spread across row grou

Lukas Hauer, Katharina Sporbeck, Joseph F. McKenna, Dmytro Puchkov

Phase-separated biomolecular condensates with liquid-like properties play a key role in the organization and compartmentalization of the intracellular environment. Condensate-mediated capillary forces acting on membranes drive physiologically important reshaping of membrane-bound organelles, such as vacuoles and autophagosomes. Here, we explore condensate-me

Nathaniel Imel, Richard Futrell, Michael Franke, Noga Zaslavsky

Natural languages have been argued to evolve under pressure to efficiently compress meanings into words by optimizing the Information Bottleneck (IB) complexity-accuracy tradeoff. However, the underlying social dynamics that could drive the optimization of a language's vocabulary towards efficiency remain largely unknown. In parallel, evolutionary game theor

Haim Bar, Martin T. Wells

SEMMS (Scalable Empirical-Bayes Model for Marker Selection) is a variable-selection procedure for generalized linear models that uses a three-component normal mixture prior on regression coefficients. In its original form, SEMMS assumes that all observations are independent. Many real-world datasets, however, arise from repeated-measures or clustered designs

Tin Hoang

This dissertation investigates privacy-preserving federated learning for Alzheimer's disease classification using three-dimensional MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Existing methodologies often suffer from unrealistic data partitioning, inadequate privacy guarantees, and insufficient benchmarking, limiting their practical

Martin G. Frasch

Identifying physical laws from noisy observational data is a central challenge in scientific machine learning. We present Minimum-Action Learning (MAL), a framework that selects symbolic force laws from a pre-specified basis library by minimizing a Triple-Action functional combining trajectory reconstruction, architectural sparsity, and energy-conservation e

Roberto Morabito, Mallik Tatipamula

The Internet has evolved by progressively expanding what humanity connects: first computers, then people, and later billions of devices through the Internet of Things (IoT). While IoT succeeded in digitizing perception at scale, it also exposed fundamental limitations, including fragmentation, weak security, limited autonomy, and poor long-term sustainabilit

Azwad Anjum Islam, Tisa Islam Erana

We describe our system for SemEval-2026 Task 5, which requires rating the plausibility of given word senses of homonyms in short stories on a 5-point Likert scale. Systems are evaluated by the unweighted average of accuracy (within one standard deviation of mean human judgments) and Spearman Rank Correlation. We explore three prompting strategies using multi

Muhammad Hamza Iqbal, Alfonso Arturo Castrejón-Pita, José Rafael Castrejón-Pita, Miguel A. Quetzeri Santiago

Raindrop impact on the ocean has been proposed as a mechanism for microplastic transfer from seawater to the atmosphere, yet the interfacial dynamics governing particle ejection from floating microplastics remain largely unexplored. We investigate droplet impact onto microparticle monolayers (rafts) spanning a wide range of sizes, contrasting densities, and

Alexander M. Davies, Sara Pollock, Miriam E. Dennis, Anil V. Rao

An adaptive direct collocation method is developed for solving optimal control problems constrained by parabolic partial differential equations. The partial differential equation is first reformulated in a variational setting, where the spatial domain is discretized using the hp-Galerkin finite element method. To address nonlinearities in the variational for

Annie Z. Xia, Melody X. Lim, Jason Z. Kim, Bryan VanSaders

The complex behavior of many natural and engineered systems emerges from the interaction of a small number of effective degrees of freedom. Discovering the physical basis of the interactions between these degrees of freedom directly from experimental observations has been a longstanding challenge, particularly with respect to predicting the long-time dynamic

Vishnu Kumar, Bhargavi B. A., Saurabh A. Chandorkar

Phononic Crystals provide a versatile platform for controlling phonons in applications such as waveguiding, filtering, and sensing. To minimize dissipation, cavity resonators are often embedded within the bandgap of phononic crystals and integrated with suitable transduction techniques. Here, we demonstrate one-dimensional (1D) phononic transmission using el

Ritajit Dey, Iadh Ounis, Graham McDonald, Yashar Moshfeghi

Large Language Models (LLMs) often struggle with temporal fact conflicts due to outdated or evolving information in their training data. Two recent studies with accompanying datasets report opposite conclusions on whether external context can effectively resolve such conflicts. DYNAMICQA evaluates how effective external context is in shifting the model's out

Thiago S. Domingues, Matthew Luzum

We investigate a scaling property of transverse-momentum spectra in ultrarelativistic heavy-ion collisions obtained by removing the global scales of multiplicity and mean transverse momentum. The resulting dimensionless observable isolates the intrinsic shape of the spectrum and reveals an approximate universality across collision centralities, systems, and

Enrico Turato, Christelle N. Prinz, Jason P. Beech, Jonas. O Tegenfeldt

Mixing at the microfluidic scale is challenging due to the low Reynolds numbers and often high P\'eclet numbers. Without turbulence, mixing relies solely on diffusion, resulting in slow and inefficient mixing. We demonstrate enhanced mixing in a simple Y-shaped microfluidic channel using viscoelastic turbulence in fluids containing macromolecules, suchas DNA

Taulant Kerci, Angel Vaca, Andrew Groom, Julia Matevosyan

Frequency control in power systems is implemented in a hierarchical structure traditionally known as primary frequency control (PFC), secondary frequency control (SFC) and tertiary control reserve (TCR) and, some jurisdictions, include time error control (TEC) as well. This hierarchical structure has been designed around a century ago based on timescales sep

Andrea Tupini, Lars Liden, Reuben Tan, Yu Wang

With AsgardBench we aim to evaluate visually grounded, high-level action sequence generation and interactive planning, focusing specifically on plan adaptation during execution based on visual observations rather than navigation or low-level manipulation. In the landscape of embodied AI benchmarks, AsgardBench targets the capability category of interactive p

Ruchika Gupta, Illya Bakurov, Nathan Haut, Wolfgang Banzhaf

Traditional Image Quality Assessment (IQA) metrics typically fall into one of two extremes: rigid, hand-crafted mathematical models or "black-box" deep learning architectures that completely lack interpretability. To bridge this gap, we propose EvoIQA, a fully explainable symbolic regression framework based on Genetic Programming that Evolves explicit, human

Phillip Kingston, Nicholas Johnston

This paper introduces Galactic Bioware's Life Support System, a semi-closed-circuit breathing apparatus designed for integration into a positive-pressure firefighting suit and governed by an AI control system. The breathing loop incorporates a soda lime CO2 scrubber, a silica gel dehumidifier, and pure O2 replenishment with finite consumables. One-way exhaus

Puneet Sharma, Christer Henrik Pursiainen

Critical infrastructure increasingly incorporates embodied AI for monitoring, predictive maintenance, and decision support. However, AI systems designed to handle statistically representable uncertainty struggle with cascading failures and crisis dynamics that exceed their training assumptions. This paper argues that Embodied AIs resilience depends on bounde

Radoslav Ralev, Aditeya Baral, Iliya Zhechev, Jen Agarwal

Dense retrieval compresses texts into single embeddings ranked by cosine similarity. While efficient for recall, this interface is brittle for identity-level matching: minimal compositional edits (negation, role swaps) flip meaning yet retain high similarity. Motivated by geometric results for unit-sphere cosine spaces (Kang et al., 2025), we test this retri

Atri Dey, Tathagata Ghosh, Biswarup Mukhopadhyaya, Agnivo Sarkar

The Type-II seesaw model predicts doubly and singly charged scalars along with neutral Higgs states originating from an $SU(2)$ triplet. Current LHC searches by the ATLAS and CMS collaborations constrain these particles mainly under the assumption that the doubly charged scalar decays dominantly into same-sign dileptons or dibosons. However, when moderate ma

Maryam Salamatmoghadasi, Metin Ozturk, Halim Yanikomeroglu

This paper proposes a two phase framework to improve the sustainability in vertical heterogeneous networks that integrate various types of base stations~(BSs), including terrestrial macro BSs~(MBSs), small BSs~(SBSs), and a high altitude platform station super MBS (HAPS SMBS). In Phase I, we address the critical and often overlooked challenge of estimating t

Rena Mira Krishna, Ramya Sankar, Shadi Ghiasi

Electrodermal Activity (EDA) is a non-invasive physiological signal widely available in wearable devices and reflects sympathetic nervous system (SNS) activation. Prior multi-modal studies have demonstrated robust performance in distinguishing stress and exercise states when EDA is combined with complementary signals such as heart rate and accelerometry. How

Yang Shen, Yichen Fan, Weitao Yang

Delta self-consistent-field ($\Delta$SCF) theory is widely used for electronic excitation energy calculations. However, calculating the corresponding oscillator strengths is challenging. The corresponding many-electron wavefunctions are not directly accessible. Both the ground-state and the excited-state wave functions from $\Delta$SCF are described by refer

F. D. Amaro, R. Antonietti, E. Baracchini, L. Benussi

The CYGNO experiment employs an optical readout to image particle interactions in a gaseous Time Projection Chamber (TPC), combining cameras and photomultiplier tubes (PMTs) to achieve high spatial resolution and timing information. This approach enables detailed track reconstruction but poses significant challenges for data acquisition, particularly in view

Patrick Poissel

A classical theorem of M. Jodeit Jr. implies that if a compactly supported distribution on $\mathbf{R}^d$ is the symbol of an $L^p(\mathbf{R}^d)$-$L^q(\mathbf{R}^d)$ Fourier multiplier, then its pushforward by the canonical homomorphism from $\mathbf{R}^d$ to $\mathbf{T}^d$ is the symbol of an $\ell^p(\mathbf{Z}^d)$-$\ell^q(\mathbf{Z}^d)$ Fourier multiplier.

Adam Trybus, Karolina Rożko, Tomasz Skura

We describe a new method of finding interpolants for classical logic using certain refutation system as a starting point. Refutation can be thought of as an alternative approach to the analysis of formal systems: instead of focusing on which formulas provably belong to a given logic, it shows which formulas are to be rejected. Thus, it provides a mirror proo

Theresa C. Anderson, Evan M. O'Dorney

We study the Galois group $G_f$ of a random polynomial $f$ of height at most $H$ in the family of polynomials of degree $2n$ satisfying the twisted reciprocal relation $f(x) = x^{2n}/b^n \cdot f(b/x)$, which arise in a wide variety of applications. Our main result is a theorem of van der Waerden-Bhargava type: the probability that $G_f$ is not the full hyper

Vamshi Krishna Chillara, Wontak Kim

We present a semi-analytical geometrical acoustics method to numerically simulate ultrasonic signal characteristics pertinent to motion sensing applications in indoor environments. The proposed methodology treats motion sensing from the first-principles in the sense that the expressions for acoustic field from the source, that scattered by the target and the

Kaif Hilman, Maxime Ramzi

We construct multiplicative norms on equivariant nonconnective algebraic $K$-theory for finite groups $G$. We also construct a genuine equivariant version of THH equipped with a Dennis trace map from K-theory compatible with the multiplicative norms. To do so, we follow the general strategy of Blumberg-Gepner-Tabuada in the nonequivariant case by generalizin

Nicholas Moskovitz, Theodore Kareta, Samantha Hemmelgarn, Hannah Zigo

We present spectro-photometric griz colors for 189 near-Earth objects (NEOs) collected by the Mission Accessible Near-Earth Object Survey (MANOS). Data acquisition involved non-simultaneous multi-band exposures, thus particular attention was given to the influence of rotational lightcurves on the derived colors. We show that colors measured without accountin

Ezgi Korkmaz

Following the pivotal success of learning strategies to win at tasks, solely by interacting with an environment without any supervision, agents have gained the ability to make sequential decisions in complex MDPs. Yet, reinforcement learning policies face exponentially growing state spaces in high dimensional MDPs resulting in a dichotomy between computation

Evgueni Dinvay

We present a Riemannian optimization framework for Hartree-Fock theory formulated directly in the Sobolev space $H^1$. The orthonormality constraints are interpreted geometrically via infinite-dimensional Stiefel and Grassmann manifolds endowed with the embedded $H^1$ metric. Explicit expressions for Euclidean and Riemannian gradients, tangent-space projecti

J. Thainá-Batista, R. Cid Fernandes, R. M. González Delgado, J. E. Rodríguez-Martín

We characterize the spatially resolved stellar population and emission-line properties of galaxies in the M51 group using the same methodology previously applied to the M101 group, aiming to understand how environmental processes shape galaxy properties across different groups. Properties are derived by applying the \textsc{AlStar} spectral fitting code to m

Gianluca Audone, Francesco Marchetti, Emma Perracchione, Milvia Rossini

Classical Gaussian processes and Kriging models are commonly based on stationary kernels, whereby correlations between observations depend exclusively on the relative distance between scattered data. While this assumption ensures analytical tractability, it limits the ability of Gaussian processes to represent heterogeneous correlation structures. In this wo

Luis A. Escamilla, Berat Karadavut, Nihan Katırcı

We present updated observational constraints on the simple-gDE model, characterized by a constant inertial mass density (IMD) $\rho_{\rm DE}+p_{\rm DE}$,which belongs to the broader graduated dark energy family, and compare its cosmological implications with those of the $w$CDM and the $\Lambda$CDM models. This parametrization provides a physically motivated

Adriana Laurindo Monteiro, Jean-Michel Loubes

The growing use of Machine Learning (ML) tools comes with critical challenges, such as limited model explainability. We propose a global explainability framework that leverages Optimal Transport and Distributionally Robust Optimization to analyze how ML algorithms respond to constrained data perturbations. Our approach enforces constraints on feature-level s

Leire Larizgoitia

Coherent elastic neutrino-nucleus scattering (CE$\nu$NS) is a dominant low-energy neutrino interaction that remains experimentally challenging due to its weak-scale cross section and the small nuclear recoil energies involved. This thesis explores the scientific motivations and technical feasibility of CE$\nu$NS\ detection, emphasizing the use of diverse neu

Steven Nguyen, Jorge Cortés, Boris Kramer

We present an approach to approximate reachable sets for linear systems with bounded L-infinity controls in finite time. Our first approach investigates the boundaries of these sets and reveals an exact characterization for single-input, planar systems with real, distinct eigenvalues. The second approach leverages convergence of the Lp-norms to L-infinity an

Gabrielle Benabdallah

Explainable AI (XAI) interfaces seek to make large language models more transparent, yet explanation alone does not produce understanding. Explaining a system's behavior is not the same as being able to engage with it, to probe and interpret its operations through direct manipulation. This distinction matters for scientific disciplines in particular: scienti

Jakaria Rabbi, Nilanjan Ray, Dana Cobzas

Disentangling pathological changes from physiological aging in 3D medical shapes is crucial for developing interpretable biomarkers and patient stratification. However, this separation is challenging when diagnosis labels are limited or unavailable, since disease and aging often produce overlapping effects on shape changes, obscuring clinically relevant shap

Sergei Gladyshev, Connor Heimig, Adrià Canós Valero, Dmytro Gryb

Dielectric nanoparticles can be engineered to scatter light predominantly in the transverse direction, a phenomenon known as the transverse Kerker effect. Although complete cancelation of forward scattering from a single object is forbidden by the optical theorem, we show that a single photonic mode can nonetheless realize an ideal transverse Kerker effect.

Camilo Angulo

We study the structure of an LA-group identifying its underlying VB-group with a representation up to homotopy. We show that the Lie algebroid structure is determined by a complementary action up to homotopy of the Lie algebra of units. We identify the equations that the representation and the action need to verify in order to assemble into an LA-group, esta

Pranaya Jajoo, Harshit Sikchi, Siddhant Agarwal, Amy Zhang

Behavioral Foundation Models (BFMs) produce agents with the capability to adapt to any unknown reward or task. These methods, however, are only able to produce near-optimal policies for the reward functions that are in the span of some pre-existing state features, making the choice of state features crucial to the expressivity of the BFM. As a result, BFMs a

Zach Hunter, Matthew Kwan, Lisa Sauermann

Fix a finite field $\mathbb F_q$ and let $A\in \mathbb F_q^{n\times n}$ be a uniformly random $n\times n$ matrix over $\mathbb F_q$. The asymptotic distribution of the determinant $\det(A)$ is well-understood, but the asymptotic distribution of the permanent $\operatorname{per}(A)$ is still something of a mystery. In this paper we make a first step in this d

Dezhe Z. Jin

Neural networks are emerging as a powerful tool for determining the quantum states of interacting many-body fermionic systems. The standard approach trains a neural-network ansatz by minimizing the mean local energy estimated from Monte Carlo samples. However, this typically results in large sample-to-sample fluctuations in the estimated mean energy and thus

Chowdhury Mohammad Sakib Anwar, Konstantinos Georgalos

This paper investigates how natural language communication with an AI agent affects human cooperative behaviour in indefinitely repeated Prisoner's Dilemma games. We conduct a laboratory experiment (n = 126) with two between-subjects treatments varying whether human participants chat with an AI chatbot (GPT-5.2) before every round or only before the first ro

Mark L. Lewis, Andrew Summers

In this paper, an effort is made to classify which prime character degree graphs having eight vertices occur for some finite solvable group. To approach this, we compile known results and constructions from the literature which are used to develop a general algorithm to begin classifying graphs of any order. We then apply the algorithm to the graphs of order

Bishal Thapa, Phanish Suryanarayana, Igor I. Mazin

We assess the ability of the Thomas--Fermi--von Weizsacker (TFW) functional within orbital-free density functional theory (DFT) to describe itinerant magnetism. Magnetic stability is evaluated through the susceptibility obtained from the second derivative of the total energy with respect to the net magnetization. Calculations are performed for the paramagnet

Xiao Yue, Guangzhi Qu, Lige Gan

Graph-based Retrieval-Augmented Generation (RAG) has shown great potential for improving multi-level reasoning and structured evidence aggregation. However, existing graph-based RAG frameworks heavily rely on exploiting large language models (LLMs) during indexing and querying, leading to high token consumption, computational cost and latency overhead. In th

Owen Nyo Wei Yuan, Victor Tan Jia Xuan, Ong Jun Yao Fabian, Ryan Tan Jun Wei

The report presents with the development and optimisation of an enhanced algorithmic trading strategy through the use of historical S&P 500 market data and earnings call sentiment analysis. The proposed strategy integrates various technical indicators such as moving averages, momentum, volatility, and FinBERT-based sentiment analysis to improve overall trade

Eadom Dessalene, Botao He, Michael Maynord, Yonatan Tussa

We introduce FEEL (Force-Enhanced Egocentric Learning), the first large-scale dataset pairing force measurements gathered from custom piezoresistive gloves with egocentric video. Our gloves enable scalable data collection, and FEEL contains approximately 3 million force-synchronized frames of natural unscripted manipulation in kitchen environments, with 45%

Xuezhi Bian, Emily A. Carter

Achieving chemical accuracy for molecular simulations remains a central challenge in computational chemistry. Here, we present an embedded correlated wavefunction transfer learning (ECW-TL) framework for accurately simulating molecular dynamics in the condensed phase. ECW-TL incorporates high-level electron exchange and correlation effects in ECW theory whil

Alexander Dombowsky, Barbara E. Engelhardt, Aaditya Ramdas

Unnormalized probability distributions are frequently used in machine learning for modeling complex data generating processes. Though Markov chain Monte Carlo (MCMC) algorithms can approximately sample from unnormalized distributions, intractability of their normalizing constants renders likelihood ratio testing infeasible. We propose to use the parallel met

Saba Asaad, Chongjun Ouyang, Zhiguo Ding, Ali Bereyhi

Pinching antenna systems (PASSs) can dynamically adapt their transmit and receive arrays for sensing and communication in wireless systems. This work explores the potential of PASSs for integrated sensing and communication (ISAC) by proposing a novel PASS-aided ISAC design, in which pinching locations are adaptively adjusted to enable simultaneous sensing an

Nathan Bowler, Winfried Hochstättler, Stefan Kaspar

Is it possible to define cryptomorphic axiom systems for infinite oriented matroids by lifting some of the axiom systems for finite oriented matroids to the infinite setting while not losing duality in the process? We show that the answer to this question is a twofold "no". First, lifting the circuit axioms neither preserves duality nor inheritance of strong

Giulia Rodighiero, Andrea Ferrara, Michele Catone, Lorenzo Napolitano

Context. Galaxies discovered by JWST at z > 10 are predominantly characterized by extremely blue rest-frame UV slopes. Conversely, the existence of dust-reddened systems at such early epochs has remained largely unconfirmed spectroscopically. Aims. We present the spectroscopic confirmation of EGS-z11-R0 at z = 11.45, the most distant red galaxy identified to

Nazia Riasat

Large language models (LLMs) are increasingly used as decision-support tools in data-constrained scientific workflows, where correctness and validity are critical. However, evaluation practices often emphasize stability or reproducibility across repeated runs. While these properties are desirable, stability alone does not guar- antee agreement with statistic

Jongtaek Lee, Andrei Badescu, X. Sheldon Lin

In this paper, we propose a novel frequency-severity joint trip-level risk index that combines the frequency of abnormal driving patterns with a severity component reflecting how extreme such behavior is relative to a portfolio-level baseline. Severity is quantified through an inverse-probability penalty that increases with the rarity of observed tail extrem

Leah Jenks, Marc Kamionkowski

We study the effects of oscillating, ultralight scalar and pseudoscalar fields on the propagation of gravitational waves (GWs). We consider two potential couplings of the (pseudo)scalars to gravity; a parity-even Gauss-Bonnet coupling, and parity-odd Chern-Simons coupling. We find several effects at both the population and individual GW event level, characte

Amir Hossein Fahim Raouf, Ismail Guvenc

Mid-band spectrum between 2 and 8 GHz is a critical resource for sixth-generation (6G) systems as it uniquely balances favorable propagation characteristics with scalable bandwidth. Recent U.S. policy highlights candidate bands near 2.7, 4.4, and 7.1 GHz, all of which host substantial federal and non-federal incumbency, including high-power radiolocation and

Mushkan Sureka, Itay Ozer, Wenhua He, Michael R. Grace

Programmable linear optical interferometers are a core primitive in optical signal processing, quantum information processing, and photonic computing. Existing photonic-integrated implementations realize arbitrary $M$-mode unitaries using Mach--Zehnder-interferometer meshes whose footprint and accumulated loss scale with $O(M^2)$ optical components. Here we

Leandro M. Arancibia, Cristián G. Sánchez, Alejandro M. Lobos

We theoretically study the electronic and lattice properties of a trans-polyacetylene (tPA) molecule deposited on top of a metallic substrate at equilibrium. We describe the system using a modified Su-Schrieffer-Heeger (SSH) model generalized to incorporate the effects of a metallic environment, represented by independent one-dimensional semi-infinite chains

Francisco-Shu Kitaura, Francesco Sinigaglia

We introduce a spectral hierarchy of cosmic-web classifications obtained by applying simple scale-weighting kernels to the density field before performing a standard eigenvalue-based web classification. This unifies and extends several widely used web definitions within a single framework: the familiar potential/tidal web (large-scale, nonlocal), a curvature

Luis Cambelo, Ruben Heradio, Jose-Miguel Horcas, Dictino Chaos

The backbone of a Boolean formula is the set of literals that must be true in every assignment that satisfies the formula. This concept is fundamental to key operations on variability models, including propagating user configuration decisions to identify implied feature selections, detecting dead features and dead code blocks, and preprocessing formulas to a

Zi Yang Kang

This paper revisits the classic instrument choice problem in a setting with consumption externalities, through the lens of robust mechanism design. A regulator can implement any incentive-compatible policy but is uncertain about how individual demand is correlated with marginal externalities, and evaluates policies by worst-case welfare. The optimal policy i

Fabio Correa Xavier

The governance of frontier general-purpose artificial intelligence has become a public-sector problem of institutional design, not merely a technical issue of model performance. Recent evidence indicates that AI capabilities are advancing rapidly, though unevenly, while knowledge about harms, safeguards, and effective interventions remains partial and lagged

Sankalp Dubedy

Large language models (LLMs) are increasingly deployed as autonomous agents in uncertain, sequential decision-making contexts. Yet it remains poorly understood whether the behaviors they exhibit in such environments reflect principled cognitive patterns or simply surface-level prompt mimicry. This paper presents a controlled experiment in which GPT-4.1 was a

Robert Dougherty-Bliss, Sergi Elizalde

It is a well known that, for odd $n$, the number of subsets of $\{1,2,\dots,n\}$ the sum of whose elements is divisible by $n$ equals the number of binary necklaces of length $n$. In this paper generalize this result in two directions. On the one hand, we introduce a parameter $r$ so that requiring the subset sums to be congruent to $r$ modulo $n$ translates

Rishav Roshan, Indrajit Saha

We compute the gravitational wave spectrum from a dark sector phase transition driven by spontaneous $\ZDW$ breaking. If the transition is second-order, the only source of gravitational waves is the annihilation of domain walls (biased by quantum gravity). However, if the transition is first-order, this yields a twin-peaked signal from both the transition it

Mikael Mazur, Nicolas K. Fontaine, Roland Ryf, Martin Karrenbach

We demonstrate continuous distributed acoustic sensing over a 4400km long undersea cable. Bi-directional operation improves the strain signal-to-noise rate by >20dB, enabling 88000 50-m-spaced measurement points at a nominal telecom launch power.

Antonio Ambrosone, Marco Chianese, Carmelo Evoli

Quantum backreaction effects may quench Hawking evaporation through a ``memory burden'', allowing primordial black holes (PBHs) with formation masses well below $10^{15}~\mathrm{g}$ to survive to the present and contribute to the dark matter. We show that ultra-high-energy cosmic rays (UHECRs) provide a powerful and previously unexplored probe of thi

Juan Rached, Yixuan Jia, Kota Kondo, Jonathan P. How

Reliable dynamic object detection in cluttered environments remains a critical challenge for autonomous navigation. Purely geometric LiDAR pipelines that rely on clustering and heuristic filtering can miss dynamic obstacles when they move in close proximity to static structure or are only partially observed. Vision-augmented approaches can provide additional

Eunsuk Seo, Hyunmi Song, Lucia Guaita, Kyoung-Soo Lee

We investigated Lyman-continuum (LyC) emission from Lyman-$\alpha$ emitters (LAEs) at $z=4.5$, identified in the One-hundred-deg$^2$ DECam Imaging in Narrowbands (ODIN) survey. Of the 7,498 LAEs (4,101 in COSMOS and 3,397 in XMM-LSS), we excluded LAEs that are either likely low-z objects or contaminated by neighboring sources. Additional background modeling

Michaela Benk, Tim Miller

The HCI community commonly evaluates decision support systems based on whether they improve task performance or promote appropriate user reliance. In this work, we look beyond decision outcomes to examine the process through which users develop decision-making strategies. Through a web-based experiment (N = 290) comparing recommendation-driven and hypothesis

Renjie Liang, Yiling Ma, Yang Xing, Zhengkang Fan

Automated radiology report generation from 3D CT volumes often suffers from incomplete pathology coverage. We provide empirical evidence that this limitation stems from a representational bottleneck: contrastive 3D CT embeddings encode discriminative pathology signals, yet exhibit severe dimensional concentration, with as few as 2 effective dimensions out of

Thackshanaramana B

The assumption that prediction-equivalent models produce equivalent explanations underlies many practices in explainable AI, including model selection, auditing, and regulatory evaluation. In this work, we show that this assumption does not hold. Through a large-scale empirical study across 24 datasets and multiple model classes, we find that models with ide

Marek Gazdzicki, Mark Gorenstein, Anar Rustamov

An anomalous collision-energy dependence of proton number fluctuations is predicted as a consequence of the onset of deconfinement in heavy-ion collisions at the center of mass energy of the nucleon pair of about 10 GeV. The effect arises from changes in the effective degrees of freedom between confined and deconfined matter. This may provide a natural expla

Salah Eddine Bekhouche, Hichem Telli, Azeddine Benlamoudi, Salah Eddine Herrouz

Ambivalence and hesitancy (A/H) are subtle affective states where a person shows conflicting signals through different channels -- saying one thing while their face or voice tells another story. Recognising these states automatically is valuable in clinical settings, but it is hard for machines because the key evidence lives in the \emph{disagreements} betwe

Piero Zappi, Anna Ivagnes, Niccolò Tonicello, Gianluigi Rozza

Reynolds-Averaged Navier-Stokes (RANS) models are widely used for turbulent flow simulations due to their computational efficiency, but their accuracy strongly depends on the selected turbulence closure and may vary across the flow domain. Space-dependent model aggregation has been shown to improve RANS predictions by combining multiple turbulence models, al

Ebru Aktekin, Hicran Bakış, Volkan Bakış, Aytap Sezer

We present optical spectroscopic observations of the supernova remnants (SNRs) G107.7$-$5.1 and G150.3+4.5, each spanning nearly 3 degree. Both remnants were recently examined in the optical band through a deep H$\alpha$ and [OIII] emission-line imaging survey, which led to the discovery of G107.7$-$5.1. Using long-slit spectra obtained with the 1.5-m Russia

Banafsheh Karimian, Soufiane Belharbi, Alexis Guichemerre, Luke McCaffrey

Longitudinal mammography screening has become an important source of information for improving future breast cancer risk prediction. However, the performance of current longitudinal mammography models degrades when prior examinations are unavailable at inference, creating a structured privileged-information setting in which temporal context is available duri

Peter Müller

In 1878 Camille Jordan showed that every finite subgroup $G\le\text{GL}_n(\mathbb C)$ has an abelian normal subgroup $A$ such that $\lvert G/A\rvert$ is bounded in terms of $n$, but he did not give an explicit bound. An explicit bound was obtained by Blichfeldt in a series of papers beginning in 1904, using representation-theoretic methods. In 1911 Bieberbac

Aditya Iyer, Jack Roberts, Nora Ayanian

Multi-View Multi-Object Tracking (MV-MOT) aims to localize and maintain consistent identities of objects observed by multiple sensors. This task is challenging, as viewpoint changes and occlusion disrupt identity consistency across views and time. Recent end-to-end approaches address this by jointly learning 2D Bird's Eye View (BEV) representations and i

Malte Prinzler, Paulo Gotardo, Siyu Tang, Timo Bolkart

We present MATCH (Multi-view Avatars from Topologically Corresponding Heads), a multi-view Gaussian registration method for high-quality head avatar creation and editing. State-of-the-art multi-view head avatar methods require time-consuming head tracking followed by expensive avatar optimization, often resulting in a total creation time of more than one day

Holly L. Capelo, Jean-David Bodénan, Martin Jutzi, Jonas Kühn

Stability analysis of two-fluid protoplanetary disc models has enriched our understanding of how solids can grow into larger bodies called planetesimals. Dust particles entrained in a gas stream modify the flow, creating shear layers prone to instability. In such environments, drag occurs in the free-molecular (Epstein) regime. Recreating these two-phase flo

Samira Abedini, Sina Mavali, Lea Schönherr, Martin Pawelczyk

Large Language Model (LLM)-based Multi-Agent Systems (MASs) are increasingly deployed for agentic tasks, such as web automation, itinerary planning, and collaborative problem solving. Yet, their interactive nature introduces new security risks: malicious or compromised agents can exploit communication channels to propagate misinformation and manipulate colle

Charles Heaton, Jack W. D. Halliday, Taito Osaka, Ichiro Inoue

Axions are hypothetical particles, proposed to account for the invariance of CP symmetry in quantum chromodynamics. While axions and axion-like-particles are well-motivated by string theory and beyond-Standard-Model extensions, they have remained elusive to experimental searches even after significant effort over many decades. Building on a recent developmen

Kowe Kadoma, Priyal Shrivastava, Mor Naaman

Researchers have demonstrated that Automatic Speech Recognition (ASR) systems perform differently across demographic groups. In this work, we examined how subtitle errors affect evaluations of speakers and their content using a preregistered online experiment (N=207, U.S.-based crowdworkers). Participants watched speakers with various accents deliver a talk