May 2025 arXiv papers — page 116

Carlos Hernández, Roberto Santana

Multi-objective reinforcement learning (MORL) addresses the challenge of simultaneously optimizing multiple, often conflicting, rewards, moving beyond the single-reward focus of conventional reinforcement learning (RL). This approach is essential for applications where agents must balance trade-offs between diverse goals, such as speed, energy efficiency, or

Yu Guo, Dong Jin, Shenghao Ye, Shuangwu Chen

Large Language models (LLMs) have demonstrated significant potential in text-to-SQL reasoning tasks, yet a substantial performance gap persists between existing open-source models and their closed-source counterparts. In this paper, we introduce SQLForge, a novel approach for synthesizing reliable and diverse data to enhance text-to-SQL reasoning in LLMs. We

S. Jaster-Merz, R. W. Assmann, J. Beinortaitė, J. Björklund Svensson

Detailed knowledge of particle-beam properties is of great importance to understand and push the performance of existing and next-generation particle accelerators. We recently proposed a new phase-space tomography method to reconstruct the five-dimensional (5D) phase space, i.e., the charge density distribution in all three spatial directions and the two tra

Pratik Rathore, Zachary Frangella, Sachin Garg, Shaghayegh Fazliani

Gaussian processes (GPs) play an essential role in biostatistics, scientific machine learning, and Bayesian optimization for their ability to provide probabilistic predictions and model uncertainty. However, GP inference struggles to scale to large datasets (which are common in modern applications), since it requires the solution of a linear system whose siz

Xavier O'Keefe, Katy McCutchan, Alexis Muniz, Jack Burns

Robotic systems that can traverse planetary or lunar surfaces to collect environmental data and perform physical manipulation tasks, such as assembling equipment or conducting mining operations, are envisioned to form the backbone of future human activities in space. However, the environmental conditions in which these robots, or "rovers," operate present ch

Jianwei Li, Jung-Eun Kim

Recent studies on the safety alignment of large language models (LLMs) have revealed that existing approaches often operate superficially, leaving models vulnerable to various adversarial attacks. Despite their significance, these studies generally fail to offer actionable solutions beyond data augmentation for achieving more robust safety mechanisms. This p

Preetisha Goswami, Marta Gilaberte Basset, Jorge Fuenzalida, Markus Gräfe

We present a frequency-correlated non-degenerate photon-pair source consisting of a second-order nonlinear crystal that generates ultraviolet UV-A and infrared light via spontaneous parametric down-conversion. Quantum imaging and sensing techniques like quantum imaging with undetected light and quantum ghost imaging leverage on wavelength correlations betwee

Guangle Du, Bing Miao, David S. Dean

Stochastic density functional theory (SDFT) has been widely used to study the out of equilibrium properties of electrolyte solutions. Examples include investigations of electrical conductivity -- both within and beyond linear response -- and modifications of thermal van der Waals interactions in driven electrolytes. Within the approximation scheme derived fr

Jacob M. Aguirre, Diego Cifuentes, Vincent Guigues, Renato D. C. Monteiro

This paper introduces cuHALLaR, a GPU-accelerated implementation of the HALLaR method proposed in Monteiro et al. 2024 for solving large-scale semidefinite programming (SDP) problems. We demonstrate how our Julia-based implementation efficiently uses GPU parallelism through optimization of simple, but key, operations, including linear maps, adjoints, and gra

Safal Shrestha, Minwu Kim, Aadim Nepal, Anubhav Shrestha

Designing effective reasoning-capable LLMs typically requires training using Reinforcement Learning with Verifiable Rewards (RLVR) or distillation with carefully curated Long Chain of Thoughts (CoT), both of which depend heavily on extensive training data. This creates a major challenge when the amount of quality training data is scarce. We propose a sample-

Davide Cugini, Giacomo Guarnieri, Mario Motta, Dario Gerace

Quantum state preparation lies at the heart of quantum computation and quantum simulations, enabling the investigation of complex manybody systems across physics, chemistry, and data science. While existing methods such as Variational Quantum Algorithms (VQAs) and Adiabatic Preparation (AP) offer viable pathways, both face substantial limitations. Here we in

Alexander Lange, Robin H. D. Corbet, Joel B. Coley, Guillaume Dubus

We present the results of multi-wavelength observations of the High-Mass Gamma-Ray Binary 4FGL J1405.1-6119. A pair of joint XMM-Newton and NuSTAR observations taken in 2019 (sampling the gamma-ray maximum and X-ray maximum) characterize the emission of soft and hard X-rays. We find variability of the hydrogen column density along our line of sight, $N_{\rm

Tianze Wang, Christian Hubicki

Collision-free planning is essential for bipedal robots operating within unstructured environments. This paper presents a real-time Model Predictive Control (MPC) framework that addresses both body and foot avoidance for dynamic bipedal robots. Our contribution is two-fold: we introduce (1) a novel formulation for adjusting step timing to facilitate faster b

Laura Flagg, Aleks Scholz, V. Almendros-Abad, Ray Jayawardhana

We present the 0.6--12-micron spectrum of Cha\,1107-7626, a 6-10 Jupiter-mass free-floating object in the $\sim$2\,Myr-old Chamaeleon-I star-forming region, from observations with the NIRSpec and MIRI instruments onboard the James Webb Space Telescope. We confirm that Cha\,1107-7626 is one of the lowest-mass objects known to harbor a dusty disk with infrared

V. Heesen, M. Stein, N. Pourjafari, M. Brüggen

Context. Edge-on spiral galaxies give us an outsiders' view of the radio halo, which envelops these galaxies. The radio halos are caused by extra-planar cosmic-ray electrons that emit synchrotron emission in magnetic fields. Aims. We aim to study the origin of radio halos around galaxies and infer the role of cosmic-rays in supporting the gaseous discs. We w

Matteo Baggioli, Maxim N. Chernodub, Karl Landsteiner, Alessandro Principi

The dynamics of Dirac semimetals is modeled at low energies by the massless Dirac Hamiltonian with the Fermi velocity replacing the velocity of light. The classical action is scale invariant. In 3D materials, Coulomb interactions induce a conformal anomaly associated to the charge renormalization already known in quantum field theory. In this work, we descri

Maxime Van de Moortel

The $r^p$ method, first introduced in [DR10], has become a robust strategy to prove decay for wave equations in the context of black holes and beyond. In this note, we propose an extension of this method, which is particularly suitable for proving decay for a general class of wave equations featuring a scale-critical time-dependent potential and/or first-ord

Noam Avidan, Rotem Arnon

Computational entropies provide a framework for quantifying uncertainty and randomness under computational constraints. They play a central role in classical cryptography, underpinning the analysis and construction of primitives such as pseudo-random generators, leakage-resilient cryptography, and randomness extractors. In the quantum setting, however, compu

Reed Bender, Karina Kofman, Blaise Agüera y Arcas, Michael Levin

The question of "what is life?" has challenged scientists and philosophers for centuries, producing an array of definitions that reflect both the mystery of its emergence and the diversity of disciplinary perspectives brought to bear on the question. Despite significant progress in our understanding of biological systems, psychology, computation, and informa

Jiayu Chen, Le Xu, Aravind Venugopal, Jeff Schneider

Offline reinforcement learning (RL) offers a powerful paradigm for data-driven control. Compared to model-free approaches, offline model-based RL (MBRL) explicitly learns a world model from a static dataset and uses it as a surrogate simulator, improving data efficiency and enabling potential generalization beyond the dataset support. However, most existing

Jane Lange, Arsen Vasilyan

We say that a classifier is \emph{adversarially robust} to perturbations of norm $r$ if, with high probability over a point $x$ drawn from the input distribution, there is no point within distance $\le r$ from $x$ that is classified differently. The \emph{boundary volume} is the probability that a point falls within distance $r$ of a point with a different l

Nathan London, Mohammad R. Momeni

Developing new methods for the accurate and efficient calculations of real-time quantum correlation functions is deemed one of the most challenging problems of modern condensed matter theory. Many popular methods, such as centroid molecular dynamics (CMD), make use of Feynman path integrals (PIs) to efficiently introduce nuclear quantum effects into classica

Julia Jose, Rachel Greenstadt

Propagandists use rhetorical devices that rely on logical fallacies and emotional appeals to advance their agendas. Recognizing these techniques is key to making informed decisions. Recent advances in Natural Language Processing (NLP) have enabled the development of systems capable of detecting manipulative content. In this study, we look at several Large La

Jayatee Kanwar, Peter Woitke, Inga Kamp, Paul Rimmer

The inner regions of planet-forming disks are warm and dense. The chemical networks used for disk modelling so far were developed for a cold and dilute medium and do not include a complete set of pressure-dependent reactions. The chemical networks developed for planetary atmospheres include such reactions along with the inverse reactions related to the Gibb'

W. David Wick

In the 1980's and 90's, A. O Barut and colleagues developed a nonperturbative approach to electrodynamics eschewing so-called ``second-quantization". Based on incorporation of self-energy terms, the resulting nonlinear and nonlocal theory explained many well-known phenomena of atomic and radiation physics. In 2017, this author introduced a nonlinear, nonloca

Charles A. Johnson, Keon Ho, Park, Enoch Yeung

Cell sensory transcription networks are the intracellular computation structure that regulates and drives cellular activity. Activity in these networks determines the the cell's ability to adapt to changes in its environment. Resilient cells successfully identify (classify) and appropriately respond to environmental shifts. We present a model for identificat

Mariana A. Fazio, Manel Martinez-Ramon, Salvador Sosa Güitron, Marcus Babzien

MeV ultrafast electron diffraction (MUED) is a pump-probe technique used to study the dynamic structural evolution of materials. An ultrashort laser pulse triggers structural changes, which are then probed by an ultrashort relativistic electron beam. To overcome low signal-to-noise ratios, diffraction patterns are averaged over thousands of shots. However, s

Jake Rule, Andrew Mummery, Steven Balbus, James Stone

A set of analytic solutions for the plunging region thermodynamics have been developed recently under the assumption that the fluid undergoes a gravity-dominated geodesic plunge into the black hole. We test this model against a dedicated 3D global GRMHD simulation of a thin accretion disc around a Schwarzschild black hole using the code AthenaK. Provided tha

D. Concha, C. Arcos, D. Turis-Gallo, T. B. Souza

The Be star $\pi$ Aquarii shows peculiar $\gamma$ Cas-type X-ray emission, likely caused by its outer disk interacting with a low-mass companion, probably a white dwarf. We study the long-term variability of its optical spectra to derive stellar and disk parameters during major changes. We identify and analyze Balmer, helium, silicon, and iron emission lines

Ryan Budney

In this paper we describe what should perhaps be called a `type-2' Vassiliev invariant of knots S^2 -> S^4. We give a formula for an invariant of 2-knots, taking values in Z_2 that can be computed in terms of the double-point diagram of the knot. The double-point diagram is a collection of curves and diffeomorphisms of curves, in the domain S^2, that describ

Yota Maeda, Kazuma Ohara

Classical results on the classification of reflections in an arithmetic subgroup $\Gamma$ imply that if the graded algebra of modular forms $M_*(\Gamma)$ is freely generated, then $\Gamma$ must be an arithmetic subgroup of either the orthogonal group $\operatorname{O}^+(2,n)$ or the unitary group $\operatorname{U}(1,n)$. Vinberg and Schwarzman showed that in

Soumya Rani Samineni, Durgesh Kalwar, Karthik Valmeekam, Kaya Stechly

Reinforcement learning based post-training of large language models (LLMs) has recently gained attention, particularly following the release of DeepSeek R1, which applied GRPO for fine-tuning. Amid the growing claims around improved reasoning abilities attributed to RL post-training, we critically examine the formulation and assumptions underlying these meth

Zizhan He, Maxime Daigle, Pouya Bashivan

Many animals possess a remarkable capacity to rapidly construct flexible cognitive maps of their environments. These maps are crucial for ethologically relevant behaviors such as navigation, exploration, and planning. Existing computational models typically require long sequential trajectories to build accurate maps, but neuroscience evidence suggests maps c

Valeria Cimini, Mandar M. Sohoni, Federico Presutti, Benjamin K. Malia

A Gaussian boson sampler (GBS) is a special-purpose quantum computer that can be practically realized at large scale in optics. Here we report on experiments in which we used a frequency-multiplexed GBS with $>400$ modes as the reservoir in the quantum-machine-learning approach of quantum reservoir computing. We evaluated the accuracy of our GBS-based reserv

Clint Fuchs, John D. Hastings

In response to the rising frequency and complexity of data breaches and evolving global privacy regulations, this study presents a comprehensive examination of academic literature on the classification of privacy breaches and violations between 2010-2024. Through a systematic literature review, a corpus of screened studies was assembled and analyzed to ident

Hiya Bhatt, Shaunak Biswas, Srinivasan Rakhunathan, Karthik Vaidhyanathan

Machine Learning Enabled Systems (MLS) are becoming integral to real-world applications, but ensuring their sustainable performance over time remains a significant challenge. These systems operate in dynamic environments and face runtime uncertainties like data drift and model degradation, which affect the sustainability of MLS across multiple dimensions: te

Lori Huseby, Sarah E. Moran, Neil Pearson, Tiffany Kataria

Temperate sub-Neptune exoplanets could contain large inventories of water in various phases, such as water-worlds with water-rich atmospheres or even oceans. Both space-based and ground-based observations have shown that many exoplanets likely also contain photochemically-generated hazes. Haze particles are a key source of organic matter and may impact the e

Eliot Finch, Isaac Legred, Katerina Chatziioannou, Reed Essick

Perturbative quantum chromodynamics (pQCD), while valid only at densities exceeding those found in the cores of neutron stars, could provide constraints on the dense-matter equation of state (EOS). In this work, we examine the impact of pQCD information on the inference of the EOS using a nonparametric framework based on Gaussian processes (GPs). We examine

Long Meng, Paola Terolli, Xiaogang Hu

Functional electrical stimulation (FES) is a prevalent technique commonly used to activate muscles in individuals with neurological disorders. Traditional FES strategies predominantly utilize low-frequency (LF) stimulation, which evokes synchronous action potentials, leading to rapid muscle fatigue. To address these limitations, we introduced a subthreshold

Paul Glendinning, Siyuan Ma, James Montaldi

Criteria for piecewise linear circle homeomorphisms to be conjugate to a rigid rotation, $x\to x+\omega~({\rm mod}~1)$, with rational rotation number $\omega$ are given. The consequences of the existence of such maps in families of maps is considered and the results are illustrated using two examples: Herman's classic family of piecewise linear maps with two

Seongsil Heo, Calvin Murdock, Michael Proulx, Christi Miller

Turn-taking prediction is crucial for seamless interactions. This study introduces a novel, lightweight framework for accurate turn-taking prediction in triadic conversations without relying on computationally intensive methods. Unlike prior approaches that either disregard gaze or treat it as a passive signal, our model integrates gaze with speaker localiza

Siddharth Prasad, Maria-Florina Balcan, Tuomas Sandholm

We derive the revenue-optimal efficient (welfare-maximizing) mechanism in a general multidimensional mechanism design setting when type spaces -- that is, the underlying domains from which agents' values come from -- can capture arbitrarily complex informational constraints about the agents. Type spaces can encode information about agents representing, for e

V V Sreedhar, Ankit Yadav

A quantum mechanical system of two coupled rotors (particles constrained to move on a circle) is studied from an open quantum systems point of view. One of the rotors is integrated out and the reduced density operator of the other rotor is studied. It's eigenvalues are worked out explicitly using the properties of Mathieu functions, and the von Neumann entro

Anayet Ullah Siddique, Roseanne Warren

The formation of gaseous, spherical cap-shaped domains (so-called "surface nanobubbles") at the solid-liquid interface is a topic of fundamental interest due to the possible effects of nanobubbles on surface cleaning, wetting, and nanoscale patterning. This work investigates the spontaneous formation of surface nanobubbles on oxygen plasma-treated Si by Peak

Hamid Abban, Ivan Cheltsov, Takashi Kishimoto, Frederic Mangolte

A smooth variety is said to satisfy Condition (A) if every finite abelian subgroup of its automorphism group has a fixed point. We classify smooth Fano 3-folds that satisfy Condition (A).

Zihan Chen, Jiakang Li, Minghao Guo, Henry Chen

This paper introduces Genesis, the first compiler designed to support Hamiltonian Simulation on hybrid continuous-variable (CV) and discrete-variable (DV) quantum computing systems. Genesis is a two-level compilation system. At the first level, it decomposes an input Hamiltonian into basis gates using the native instruction set of the target hybrid CV-DV qua

Aitor Fernández, César D. Fosco, Guillermo Hansen

We apply the result of Kenneth and I. Klich, Phys. Rev. Lett.97, 160401 (2006) to derive a theorem for two objects coupled to a Dirac field. We demonstrate that a sufficient condition for the Casimir interaction to be repulsive, is for their geometric configuration to be odd under the same reflection operation considered in that work.

Matheus Capela, Kaumudibikash Goswami

Quantum processes can exhibit scenarios beyond a fixed order of events. We propose information inequalities that, when violated, constitute sufficient conditions to certify quantum processes without a fixed causal order -- causally separable or indefinite causal ordered processes. The inequalities hold valid for a vast class of information measures. Neverthe

Siddharth Prasad, Maria-Florina Balcan, Tuomas Sandholm

Core-selecting combinatorial auctions are popular auction designs that constrain prices to eliminate the incentive for any group of bidders -- with the seller -- to renegotiate for a better deal. They help overcome the low-revenue issues of classical combinatorial auctions. We introduce a new class of core-selecting combinatorial auctions that leverage bidde

Heather Leitch, Alastair Kay

The discovery of the family of balanced product codes was pivotal in the subsequent development of 'good' low density quantum error correcting codes that have optimal scaling of the key parameters of distance and storage density. We review this family, giving a completely different presentation to the original, minimising the abstraction and technicalities w

Alastair Hamilton

We integrate the notion of an effective field theory, as described by Costello, with the framework of noncommutative symplectic geometry introduced by Kontsevich; providing a definition for the renormalization group flow in noncommutative geometry that is defined through the use of ribbon graphs. As in the commutative case, the resulting noncommutative effec

Yuchao Yi, Yang Zhang

We consider the restricted Dirichlet-to-Neumann map $\Lambda^{U,V}_{g,A,q}$ for the wave equation with magnetic potential $A$ and scalar potential $q$, on an admissible Lorentzian manifold $(M, g)$ of dimension $n \geq 3$ with boundary. Here $U$ and $V$ are disjoint open subsets of $\partial M$, where we impose the Dirichlet data on $U$ and measure the Neuma

Evelyn A. Escudero Bruna, Federico Romá, Fernando Meneses, Paula. G. Bercoff

Magnetic nanowires are critical components in fields such as data storage and spintronics, where precise control of their magnetic properties is essential for device optimization. In particular, the behavior of isolated nanowires is often different from that of an ensemble, offering an opportunity to explore the role that dipolar and magnetoelastic interacti

Olivier Lamarre, Jonathan Kelly

In robotic planetary surface exploration, strategic mobility planning is an important task that involves finding candidate long-distance routes on orbital maps and identifying segments with uncertain traversability. Then, expert human operators establish safe, adaptive traverse plans based on the actual navigation difficulties encountered in these uncertain

Sacha Alanoca, Shira Gur-Arieh, Tom Zick, Kevin Klyman

AI governance has transitioned from soft law, such as national AI strategies and voluntary guidelines, to binding regulation at an unprecedented pace. This evolution has produced a complex legislative landscape: blurred definitions of "AI regulation" mislead the public and create a false sense of safety; divergent regulatory frameworks risk fragmenti

Giannis Chatziveroglou

Inference-time scaling has emerged as a powerful alternative to parameter scaling for improving language model performance on complex reasoning tasks. While existing methods have shown strong performance gains under fixed compute budgets, there has been little focus on optimally utilizing that budget during inference. In this work, we introduce A*-decoding,

Denis A. Leahy, S. Ranasinghe, J. Hansen, M. D. Filipović

We develop a multipole analysis method for images with a circular boundary, then apply it to supernova remnant (SNR) images. The morphology of SNR images is related to several factors, including the inhomogeneities of the supernova ejecta and of the circumstellar medium in which the ejecta and shock wave travel. The current multipole method corrects some err

Joan Vendrell Gallart, Alan Kuhnle, Solmaz Kia

This paper introduces Rewired Sequential Greedy (ResQue Greedy), an enhanced approach for submodular maximization under cardinality constraints. By integrating a novel set curvature metric within a lattice-based framework, ResQue Greedy identifies and corrects suboptimal decisions made by the standard sequential greedy algorithm. Specifically, a curvature-aw

Barkin Dagda, Muhammad Awais, Saber Fallah

Cross-view geo-localisation identifies coarse geographical position of an automated vehicle by matching a ground-level image to a geo-tagged satellite image from a database. Despite the advancements in Cross-view geo-localisation, significant challenges still persist such as similar looking scenes which makes it challenging to find the correct match as the t

Mahmood Hegazy, Aaron Rodrigues, Azzam Naeem

Modern consumer banking applications require accurate and efficient retrieval of information in response to user queries. Mapping user utterances to the most relevant Frequently Asked Questions (FAQs) is a crucial component of these systems. Traditional approaches often rely on a single model or technique, which may not capture the nuances of diverse user in

Michal Jablonowski

We resolve an open problem by showing that the Yoshikawa's fifth oriented move in his list cannot be reproduced by any finite sequence of the other nine moves and planar isotopies. Our proof introduces a link-type semi-invariant that remains unchanged under all moves except the fifth, highlighting its necessity in generating the full move set. Second, we ext

Omer F. Erdem, David P. Broughton, Josef Svoboda, Chengkun Huang

In multi-objective design tasks, the computational cost increases rapidly when high-fidelity simulations are used to evaluate objective functions. Surrogate models help mitigate this cost by approximating the simulation output, simplifying the design process. However, under high uncertainty, surrogate models trained on noisy data can produce inaccurate predi

Haolei Tong, Yuezhe Zhang, Sophie Lueth, Georgia Chalvatzaki

Coordinated multi-arm manipulation requires satisfying multiple simultaneous geometric constraints across high-dimensional configuration spaces, which poses a significant challenge for traditional planning and control methods. In this work, we propose Adaptive Diffusion Constrained Sampling (ADCS), a generative framework that flexibly integrates both equalit

Simon Portegies Zwart, Shuo Huang

The Sun was born in a clustered environment with 10,000 other stars. Being an isolated star today, the Sun must have left the nest. We do not directly know when that happened, how violent the ejection was, or how far the Solar siblings have drifted apart. The mass of the fragile outer Opic-Oort cloud, (between $r_{\rm inner} \sim 30,000$\,au and $200\,000$au

Caio Machado Fernandes, Aila O. Santos, Vanessa S. Antonin, Joao Paulo C. Moura

We investigated cerium oxide nanoparticles of various morphologies (nanosheets, nanocubes, and nanoparticles) supported on carbon Vulcan XC-72 for the two-electron oxygen reduction reaction (ORR). It was used a continuous magnetic field (2000 Oe) for the first time in the literature. The best results were for 5% (w/w) CeO2 for all three different morphologie

Ming Ding, Rasmus Kyng, Federico Solda, Weixuan Yuan

As large language models (LLMs) advance, their role in higher education, particularly in free-response problem-solving, requires careful examination. This study assesses the performance of GPT-4o and o1-preview under realistic educational conditions in an undergraduate algorithms course. Anonymous GPT-generated solutions to take-home exams were graded by tea

Nima Bahmani, Dariush Salami, Hüseyin Yiğitler, Juhapekka Hietala

Cardiovascular diseases remain a leading cause of mortality and disability. The convenient measurement of cardiovascular health using smart systems is therefore a key enabler to foster accurate and early detection and diagnosis of cardiovascular diseases and it require accessing a correct pulse morphology similar to arterial pressure wave. This paper investi

Jacob Imola, Fabrizio Boninsegna, Hannah Keller, Anders Aamand

In the approximate quantiles problem, the goal is to output $m$ quantile estimates, the ranks of which are as close as possible to $m$ given quantiles $0 \leq q_1 \leq\dots \leq q_m \leq 1$. We present a mechanism for approximate quantiles that satisfies $\varepsilon$-differential privacy for a dataset of $n$ real numbers where the ratio between the distance

Jakub Valdhans, Petr Klenovský

We have theoretically studied the G-center in bulk silicon material using the empirical tight-binding model for calculations of unfolded band structures with configuration interaction correction for the exciton at $\Gamma$ point of the Brillouin zone. The G-center in B configuration (emissive) being a candidate structure as the telecom single- and entangled-

Kaheon Kim, Bohan Zhou, Changbo Zhu, Xiaohui Chen

This paper introduces a new constraint-free concave dual formulation for the Wasserstein barycenter. Tailoring the vanilla dual gradient ascent algorithm to the Sobolev geometry, we derive a scalable Sobolev gradient ascent (SGA) algorithm to compute the barycenter for input distributions discretized over a regular grid. Despite the algorithmic simplicity, w

Varsha Kumari, Julia Bauer, Alexandru B. Georgescu

Correlated electron materials with molecular orbital states extending over transition metal clusters can host multiferroicity, spin frustration, and unconventional insulating phases. However, the fundamental criteria that govern cluster formation and stability remain unclear. Here, we identify a symmetry, correlation, and electron filling driven criteria tha

A. Donís Vela, C. W. J. Beenakker

The massless two-dimensional Dirac equation in a perpendicular magnetic field B supports a B-independent "zeroth Landau level", a dispersionless zero-energy-mode protected by chiral symmetry. On a lattice the zero-mode becomes doubly degenerate with states of opposite chirality, which removes the protection and allows for a broadening when the magnetic field

Charles J. Torres, Richard Futrell

Orthographic transparency -- how directly spelling is related to sound -- lacks a unified, script-agnostic metric. Using ideas from algorithmic information theory, we quantify orthographic transparency in terms of the mutual compressibility between orthographic and phonological strings. Our measure provides a principled way to combine two factors that decrea

Sabah Al-Fedaghi

To handle the complexity of our world, the carving metaphor has been used to build a conceptual system of reality. In such an endeavor, we can choose various joints to carve at; that is, we can conceptualize various aspects of reality. Conceptual modeling concerns carving (e.g., categorization) and specifying a conceptual picture of a subject domain. This pa

Jiahao Xu, Rui Hu, Olivera Kotevska

Federated Learning with client-level differential privacy (DP) provides a promising framework for collaboratively training models while rigorously protecting clients' privacy. However, classic approaches like DP-FedAvg struggle when clients have heterogeneous privacy requirements, as they must uniformly enforce the strictest privacy level across all clients,

Joshua Owotogbe, Indika Kumara, Dario Di Nucci, Damian Andrew Tamburri

Chaos engineering aims to improve the resilience of software systems by intentionally injecting faults to identify and address system weaknesses that cause outages in production environments. Although many tools for chaos engineering exist, their practical adoption is not yet explored. This study examines 971 GitHub repositories that incorporate 10 popular c

S. K. H. Bahr, A. V. Mosenkov

Galaxies with polar structures -- of which polar-ring galaxies (PRGs) are a prominent subclass -- contain components that are kinematically decoupled and highly inclined relative to the host galaxy's major axis. Modern deep optical surveys provide a powerful means of detecting low surface brightness (LSB) features around galaxies, offering critical insights

Karina Zainullina, Alexander Golubev, Maria Trofimova, Sergei Polezhaev

Large language models (LLMs) have recently achieved remarkable results in complex multi-step tasks, such as mathematical reasoning and agentic software engineering. However, they often struggle to maintain consistent performance across multiple solution attempts. One effective approach to narrow the gap between average-case and best-case performance is guide

Jiahao Xu, Rui Hu, Olivera Kotevska, Zikai Zhang

Due to the distributed nature of Federated Learning (FL) systems, each local client has access to the global model, which poses a critical risk of model leakage. Existing works have explored injecting watermarks into local models to enable intellectual property protection. However, these methods either focus on non-traceable watermarks or traceable but white

Chou-Ying Hsieh, Chun-Fu Jang, Cheng-En Hsieh, Qian-Hui Chen

Graphs serve as versatile data structures in numerous real-world domains-including social networks, molecular biology, and knowledge graphs-by capturing intricate relational information among entities. Among graph-based learning techniques, Graph Contrastive Learning (GCL) has gained significant attention for its ability to derive robust, self-supervised gra

Ayrton da Cruz Pereira do Nascimento, Henrique Boschi-Filho, Jorge Noronha

We study the proton structure functions $F_1$ and $F_2$ in the context of holography. We develop a general framework that extends previous holographic calculations of $F_1$ and $F_2$ to the case where the bulk geometry stems from bottom-up Einstein-Dilaton models, which are commonly used in the literature to describe some properties of QCD in the strong coup

V. C. Storey, R. Lukyanenko, A. Castellanos

Conceptual modeling is an important part of information systems development and use that involves identifying and representing relevant aspects of reality. Although the past decades have experienced continuous digitalization of services and products that impact business and society, conceptual modeling efforts are still required to support new technologies a

A. Taherifar

For $a\in R$, let $P_a$ denote the intersection of all minimal prime ideals of $R$ containing $a$. An ideal $I$ of a ring $R$ is called a $z^{\circ}$-ideal if $P_a\subseteq I$ for all $a\in I$. In this paper, we first investigate the class of $z^{\circ}$-ideals in non-commutative rings. We provide characterizations of $z^{\circ}$-ideals in 2-by-2 generalized

Renata Kallosh, Andrei Linde

We give a brief review of the basic principles of inflationary theory and discuss the present status of the simplest inflationary models that can describe Planck/BICEP/Keck observational data by choice of a single model parameter. In particular, we discuss the Starobinsky model, Higgs inflation, and $\alpha$-attractors, including the recently developed $\alp

Matthew R. Argall, Li-Jen Chen, Noé Lugaz, Norberto Romanelli

An interplanetary shock driven by a coronal mass ejection (CME) containing an interval of sub-Alfv\'enic flow impacted Earth on April 23, 2024. In this article, we analyze the turbulence in the sheath region between the shock and CME to determine how it evolves from L1 (as observed by Wind) to Earth (as observed by MMS, upstream of the bow shock). Wind and M

Felix Dangel, Tim Siebert, Marius Zeinhofer, Andrea Walther

Computing partial differential equation (PDE) operators via nested backpropagation is expensive, yet popular, and severely restricts their utility for scientific machine learning. Recent advances, like the forward Laplacian and randomizing Taylor mode automatic differentiation (AD), propose forward schemes to address this. We introduce an optimization techni

Rakibul Hasan Rajib, Md Akil Raihan Iftee, Mir Sazzat Hossain, A. K. M. Mahbubur Rahman

Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, making it ideal for privacy-sensitive applications. However, FL models often suffer performance degradation due to distribution shifts between training and deployment. Test-Time Adaptation (TTA) offers a promising solution by allowing models to a

Diodato Ferraioli, Giovanna Varricchio

In this work, we consider the design of Non-Obviously Manipulable (NOM) mechanisms, mechanisms that bounded rational agents may fail to recognize as manipulable, for two relevant classes of succinctly representable Hedonic Games: Additively Separable and Fractional Hedonic Games. In these classes, agents have cardinal scores towards other agents, and their p

Jukka Ruohonen, Eun-Young Kang, Qusai Ramadan

The paper presents an alignment evaluation between the mitigations present in the MITRE's ATT&CK framework and the essential cyber security requirements of the recently introduced Cyber Resilience Act (CRA) in the European Union. In overall, the two align well with each other. With respect to the CRA, there are notable gaps only in terms of data minimization

Alexandre Blondin Massé, Alain Goupil, Ralphael L'Heureux, Louis Marin

Let d be an integer between 0 and 4, and W be a 2-dimensional word of dimensions h x w on the binary alphabet {0, 1}, where h, w in Z > 0. Assume that each occurrence of the letter 1 in W is adjacent to at most d letters 1. We provide an exact formula for the maximum number of letters 1 that can occur in W for fixed (h, w). As a byproduct, we deduce an upper

J. Recker, R. Lukyanenko, M. A. Jabbari, B. M. Samuel

The role of information systems (IS) as representations of real-world systems is changing in an increasingly digitalized world, suggesting that conceptual modeling is losing its relevance to the IS field. We argue the opposite: Conceptual modeling research is more relevant to the IS field than ever, but it requires an update with current theory. We develop a

Massimo Fioravanti, Giovanni Agosta

Large Language Models (LLMs) have demonstrated strong performance on tasks with short time frames, but struggle with tasks requiring longer durations. While datasets covering extended-duration tasks, such as software engineering tasks or video games, do exist, there are currently few implementations of complex board games specifically designed for reinforcem

Krystian Nowakowski, Hitesh Agarwal, Sergey Slizovskiy, Robin Smeyers

Detecting individual light quanta is essential for quantum information, space exploration, advanced machine vision, and fundamental science. Here, we introduce a novel single photon detection mechanism using highly photosensitive non-equilibrium electron phases in moir\'e materials. Using tunable bands in bilayer graphene/hexagonal-boron nitride superlattice

Baiting Chen, Tong Zhu, Jiale Han, Lexin Li

Large Language Models (LLMs) have demonstrated strong generative capabilities but remain prone to inconsistencies and hallucinations. We introduce Peer Elicitation Games (PEG), a training-free, game-theoretic framework for aligning LLMs through a peer elicitation mechanism involving a generator and multiple discriminators instantiated from distinct base mode

David Alemán-Espinosa, Nikhil Kumar

We consider the problem of multicommodity flows in outerplanar graphs. Okamura and Seymour showed that the cut-condition is sufficient for routing demands in outerplanar graphs. We consider the unsplittable version of the problem and prove that if the cut-condition is satisfied, then we can route each demand along a single path by exceeding the capacity of a

Luca Previdi, Francesco Albarelli, Matteo G. A. Paris

The hydroxyl radical, hereafter referred to as the OH molecule (OHM), carries both electric and magnetic dipole moments and, as a diatomic molecule, admits a comparatively simple and accurate model. This makes it a natural quantum probe for the joint estimation of electric and magnetic fields. Here we study simultaneous estimation of both fields using the to

Wentao Song, He Huang, Youqiang Sun, Fang Qu

Advanced plant phenotyping technologies play a crucial role in targeted trait improvement and accelerating intelligent breeding. Due to the species diversity of plants, existing methods heavily rely on large-scale high-precision manually annotated data. For self-occluded objects at the grain level, unsupervised methods often prove ineffective. This study pro

Hui Huang, Jethro Warnett

Recently, the paper [12] introduces a derivative-free consensus-based particle method that finds the Nash equilibrium of non-convex multiplayer games, where it proves the global exponential convergence in the sense of mean-field law. This paper aims to address theoretical gaps in [12], specifically by providing a quantitative estimate of the mean-field limit

Andrei Manolache, Luiz F. O. Chamon, Mathias Niepert

Equivariant neural networks are designed to respect symmetries through their architecture, boosting generalization and sample efficiency when those symmetries are present in the data distribution. Real-world data, however, often departs from perfect symmetry because of noise, structural variation, measurement bias, or other symmetry-breaking effects. Strictl

Moses Charikar, Prasanna Ramakrishnan, Zihan Tan, Kangning Wang

In the well-studied metric distortion problem in social choice, we have voters and candidates located in a shared metric space, and the objective is to design a voting rule that selects a candidate with minimal total distance to the voters. However, the voting rule has limited information about the distances in the metric, such as each voter's ordinal rankin