March 2025 arXiv papers — page 89

Antonis Vasileiou, Stefanie Jegelka, Ron Levie, Christopher Morris

Message-passing graph neural networks (MPNNs) have emerged as the leading approach for machine learning on graphs, attracting significant attention in recent years. While a large set of works explored the expressivity of MPNNs, i.e., their ability to separate graphs and approximate functions over them, comparatively less attention has been directed toward in

Abdon Atangana

In this paper, we present a new hypercomplex number system, Trinition, that has an unusual structure of commutativity, noncommutativity, nonassociativity, and deformability.

Ragendhu Sp, Tony Thomas, Sabu Emmanuel

Cancelable biometric schemes are designed to extract an identity-preserving, non-invertible as well as revocable pseudo-identifier from biometric data. Recognition systems need to store only this pseudo-identifier, to avoid tampering and/or stealing of original biometric data during the recognition process. State-of-the-art cancelable schemes generate pseudo

Jumanh Atoum, Garrison L. H. Johnston, Nabil Simaan, Jie Ying Wu

Recognizing surgical gestures in real-time is a stepping stone towards automated activity recognition, skill assessment, intra-operative assistance, and eventually surgical automation. The current robotic surgical systems provide us with rich multi-modal data such as video and kinematics. While some recent works in multi-modal neural networks learn the relat

Barreto Joaquim Reizi

We establish a categorical framework relating two canonical model constructions in first-order logic: the Henkin construction and compactness-based constructions via ultraproducts or saturation. By introducing a globally fixed set of Henkin witness constants, we define two functors from the category of consistent first-order theories to the category of model

Torsten Tiltack

This paper introduces AIJIM, the Artificial Intelligence Journalism Integration Model -- a novel framework for integrating real-time AI into environmental journalism. AIJIM combines Vision Transformer-based hazard detection, crowdsourced validation with 252 validators, and automated reporting within a scalable, modular architecture. A dual-layer explainabili

Henning Bahl, Johannes Braathen, Martin Gabelmann, Sebastian Paßehr

Reconstructing the shape of the Higgs potential realised in Nature is a central part of the physics programme at the LHC and future colliders. In this context, accurate theoretical predictions for trilinear and quartic Higgs couplings are becoming increasingly important. In this paper, we present results that enable significant progress in the automation of

Yifan Tang, Gian Marcello Andolina, Alica Cuzzocrea, Matěj Mezera

Recent years have witnessed a surge of experimental and theoretical interest in controlling the properties of matter, such as its chemical reactivity, by confining it in optical cavities, where the enhancement of the light-matter coupling strength leads to the creation of hybrid light-matter states known as polaritons. However, ab initio calculations that ac

Maxim Dzero, Alex Kamenev

Collective modes in superconductors provided the first realization of the Higgs mechanism. The transverse Goldstone mode acquires a gap (i.e. a mass) when it hybridizes with the electromagnetic gauge field. The longitudinal Schmid-Higgs mode, on the other hand, is always massive. In conventional BCS theory, its gap is exactly $2\Delta$, coinciding with the e

Fatemeh Bibak, Carlo Cepollaro, Nicolás Medina Sánchez, Borivoje Dakić

Understanding how classical physics emerges from quantum mechanics remains a central problem in the foundations of physics. Here we derive a classical limit from finite-resolution measurements, modeled by continuous coarse-grained POVMs. When the resolved phase-space area is large compared with Planck's constant, the accessible statistics of any quantum stat

Anna Cecilie Åsland, Alv Johan Skarpeid, Matthias Hartl, Marte Stalsberg

Surface alloying can alter surface electronic and magnetic properties, which are key parameters when developing new materials tailored for specific applications. A magnetic surface alloy was formed by depositing Sb on Ni(111) at elevated temperatures, yielding new electronic states at the Fermi level and modifying the Ni-derived bandstructure. In particular,

Márton Mestyán, Balázs Pozsgay

It has been observed recently that various spin chain Hamiltonians admit special zero energy "crosscap" eigenstates. These states are made up of maximally entangled Bell pairs prepared on antipodal sites of a periodic chain. We generalize the states by allowing the antipodal pairs to have non-maximal, tunable entanglement. We give sufficient conditions for s

Kaitlin Gili, Kyle Heuton, Astha Shah, David Hammer

Advances in machine learning (ML) offer new possibilities for science education research. We report on early progress in the design of an ML-based tool to analyze students' mechanistic sensemaking, working from a coding scheme that is aligned with previous work in physics education research (PER) and amenable to recently developed ML classification strategie

Maria A. Larrazabal, Zhiyuan Wang, Mark Rucker, Emma R. Toner

Mobile sensing is ubiquitous and offers opportunities to gain insight into state mental health functioning. Detecting state elevations in social anxiety would be especially useful given this phenomenon is highly prevalent and impairing, but often not disclosed. Although anxiety is highly dynamic, fluctuating rapidly over the course of minutes, most work to d

Carlos E. Arreche, Hari P. Sitaula

A rational function $f(x)$ is rationally summable if there exists a rational function $g(x)$ such that $f(x)=g(x+1)-g(x)$. Detecting whether a given rational function is summable is an important and basic computational subproblem that arises in algorithms to study diverse aspects of shift difference equations. The discrete residues introduced by Chen and Sin

Euclid Collaboration, Abdurro'uf, C. Tortora, M. Baes

The European Space Agency's Euclid mission will observe approximately 14,000 $\rm{deg}^{2}$ of the extragalactic sky and deliver high-quality imaging for many galaxies. The depth and high spatial resolution of the data will enable a detailed analysis of stellar population properties of local galaxies. In this study, we test our pipeline for spatially resolve

Nathanael Jo, Kathleen Creel, Ashia Wilson, Manish Raghavan

Firms' algorithm development practices are often homogeneous. Whether firms train algorithms on similar data, aim at similar benchmarks, or rely on similar pre-trained models, the result is correlated predictions. We model the impact of correlated algorithms on competition in the context of personalized pricing. Our analysis reveals that (1) higher correlati

Amélie Royer, Moritz Böhle, Gabriel de Marmiesse, Laurent Mazaré

The recent successes of Vision-Language models raise the question of how to equivalently imbue a pretrained speech model with vision understanding, an important milestone towards building a multimodal speech model able to freely converse about images. Building such a conversational Vision-Speech model brings its unique challenges: (i) paired image-speech dat

Tarik Akan, Mehmet Ali Olpak, Altug Ozpineci

In this study, we utilize light-cone QCD sum rules at twist-3 accuracy to compute the coupling parameters of the $\chi_{c1}(2P)$ state with $D$ and $D^*$ mesons. The analysis reveals that the observed $\chi_{c1}(3872)$ meson incorporates significant amounts of both charmonium and molecular components. The interplay between these components highlights the exo

Xuanhe Li, Brendan Unikewicz, S. Chockalingam, Hudson Borja da Rocha

Understanding and mitigating the failure of reinforced elastomers has been a long-standing challenge in many industrial applications. In an early attempt to shed light on the fundamental mechanisms of failure, Gent and Park presented a systematic experimental study examining the field that develops near rigid beads that are embedded in the material and descr

Rogelio Cruz, Jonatan Contreras, Francisco Guerrero, Ezequiel Rodriguez

In this paper, we propose a novel prompting approach aimed at enhancing the ability of Large Language Models (LLMs) to generate accurate Python code. Specifically, we introduce a prompt template designed to improve the quality and correctness of generated code snippets, enabling them to pass tests and produce reliable results. Through experiments conducted o

Damiano F. G. Fiorillo, Tetyana Pitik, Edoardo Vitagliano

We revisit the production of axion-like particles (ALPs) coupled to electrons at tree-level in a relativistic plasma. We explicitly demonstrate the equivalence between pseudoscalar and derivative couplings, incorporate previously neglected processes for the first time-namely, semi-Compton production ($\gamma e^-\rightarrow a e^-$) and pair annihilation ($e^+

Luc McCutcheon, Bahman Gharesifard, Saber Fallah

Control Lyapunov functions are traditionally used to design a controller which ensures convergence to a desired state, yet deriving these functions for nonlinear systems remains a complex challenge. This paper presents a novel, sample-efficient method for neural approximation of nonlinear Lyapunov functions, leveraging self-supervised Reinforcement Learning

Pedro Soubelet, Yao Tong, Asier Astaburuaga Hernandez, Peirui Ji

We investigate the confinement of neutral excitons in a one-dimensional (1D) potential, engineered by proximizing hBN-encapsulated monolayer MoSe$_2$ to ferroelectric domain walls (DW) in periodically poled LiNbO$_3$. Our device exploits the nanometer scale in-plane electric field gradient at the DW to induce the dipolar exciton confinement via the Stark eff

Isabella Lenz, Yu Rong, Daniel Bliss, Julie Liss

Millimeter Wave (mmWave) radar has emerged as a promising modality for speech sensing, offering advantages over traditional microphones. Prior works have demonstrated that radar captures motion signals related to vocal vibrations, but there is a gap in the understanding of the analytical connection between radar-measured vibrations and acoustic speech signal

Dylan Léveillé, Jason Jaskolka

Selecting the combination of security controls that will most effectively protect a system's assets is a difficult task. If the wrong controls are selected, the system may be left vulnerable to cyber-attacks that can impact the confidentiality, integrity, and availability of critical data and services. In practical settings, as standardized control catalogue

Matthew Massey, Nusrat Munia, Abdullah-Al-Zubaer Imran

Surficial geologic (SG) maps are essential for understanding surface processes and supporting infrastructure planning, but current workflows are labor-intensive and difficult to scale. We introduce EarthScape, an AI-ready multimodal dataset for SG mapping that integrates digital elevation models, aerial imagery, multi-scale terrain features, and hydrologic a

K. V. Lezhnin, S. R. Totorica, J. Griff-McMahon, M. Medvedev

Understanding plasma self-magnetization is one of the fundamental challenges in both laboratory and astrophysical plasmas. Self-magnetization can modify the plasma transport properties, altering the dynamical evolution of plasmas. Multiple high-energy-density (HED) experiments have observed the formation of ion-scale magnetic filaments of megagauss strength,

Mustafa E. Ismagambetov, Aleksey V. Lunkin, Pavel M. Ostrovsky

We study localization effects in Josephson junctions with two superconductors connected by a strongly disordered metallic wire of length $L$. The conventional description of the Josephson effect in such systems, based on the quasiclassical Usadel equation, neglects electron interference and is only applicable when $L$ is shorter than the localization length

Alessandra Parziale, Gianmario Voria, Giammaria Giordano, Gemma Catolino

As machine learning (ML) systems become central to critical decision-making, concerns over fairness and potential biases have increased. To address this, the software engineering (SE) field has introduced bias mitigation techniques aimed at enhancing fairness in ML models at various stages. Additionally, recent research suggests that standard ML engineering

Federico Cocchi, Nicholas Moratelli, Davide Caffagni, Sara Sarto

Recent progress in Multimodal Large Language Models (MLLMs) has highlighted the critical roles of both the visual backbone and the underlying language model. While prior work has primarily focused on scaling these components to billions of parameters, the trade-offs between model size, architecture, and performance remain underexplored. Additionally, inconsi

Austin Xu, Srijan Bansal, Yifei Ming, Semih Yavuz

The large language model (LLM)-as-judge paradigm has been used to meet the demand for a cheap, reliable, and fast evaluation of model outputs during AI system development and post-deployment monitoring. While judge models -- LLMs finetuned to specialize in assessing and critiquing model outputs -- have been touted as general purpose evaluators, they are typi

N. G. Karaçaylı, C. M. Hirata

The highly anisotropic nature of the Lyman-alpha (Ly$\alpha$) forest data introduces a complex survey window function that complicates the measurement of the three-dimensional power spectrum ($P_{\mathrm{3D}}$). In this paper, we present the first fully optimal estimator for $P_{\mathrm{3D}}$, which exactly deconvolves the survey window function and marginal

Gabriel M. C. Neves, Hugerles S. Silva, Higo T. P. Silva, Wamberto J. L. Queiroz

This paper investigates the physical layer security (PLS) performance of $\alpha$-$\mathcal{F}$ fading channels with pointing errors under passive and active eavesdropping scenarios. Novel analytical expressions are derived for key PLS metrics, including the probability of strictly positive secrecy capacity, the average secrecy capacity, and the secure outag

Masud Ahmed, Zahid Hasan, Syed Arefinul Haque, Abu Zaher Md Faridee

Traditional transformer-based semantic segmentation relies on quantized embeddings. However, our analysis reveals that autoencoder accuracy on segmentation mask using quantized embeddings (e.g. VQ-VAE) is 8% lower than continuous-valued embeddings (e.g. KL-VAE). Motivated by this, we propose a continuous-valued embedding framework for semantic segmentation.

Sebastiano Francesco Albergo, Alessandro Braghieri, Alexander Burdyko, Yuchen Cai

We present the results of a test beam campaign on a capillary-tube fibre-based dual-readout calorimeter, designed for precise hadronic and electromagnetic energy measurements in future collider experiments. The calorimeter prototype consists of nine modules, each composed of brass capillary tubes housing scintillating and Cherenkov optical fibres, read out u

Joshua McClellan, Greyson Brothers, Furong Huang, Pratap Tokekar

Equivariant Graph Neural Networks (EGNNs) have emerged as a promising approach in Multi-Agent Reinforcement Learning (MARL), leveraging symmetry guarantees to greatly improve sample efficiency and generalization. However, real-world environments often exhibit inherent asymmetries arising from factors such as external forces, measurement inaccuracies, or intr

Sorin Dascalescu, Constantin Nastasescu, Laura Nastasescu

We consider certain quotient algebras of tensor algebras of bimodules $M$ over a finite-dimensional algebra $R$, and we investigate Frobenius type properties of such algebras. Our main interest is in the case where $M=R^*$, the linear dual of $R$. We obtain a large class of Frobenius or symmetric algebras, which are also equipped with a finite grading.

Gavin P. Lamb, Thomas Baxter, Conor M. B. Omand, Dimple

The merger origin long GRB 211211A was a class (re-)defining event. A precursor was identified with a $\sim 1$ s separation from the main burst, as well as a claimed candidate quasi-periodic oscillation (QPO) with a frequency $\sim20$ Hz. Here, we explore the implications of the precursor, assuming the quasi-periodicity is real. The precursor variability tim

Joseph Schindler, Philipp Strasberg, Niklas Galke, Andreas Winter

We introduce a definition of coarse-grained entropy that unifies measurement-based (observational entropy) and max-entropy-based (Jaynes) approaches to coarse-graining, by identifying physical constraints with information theoretic priors. The definition is shown to include as special cases most other entropies of interest in physics. We then consider second

Jemin Park, Junmo Jeon, SungBin Lee

Cold atom arrays in optical lattices offer a highly tunable platform for exploring complex quantum phenomena that are difficult to realize in conventional materials. Here, we investigate the emergence of controllable long-range quantum correlations in a simulated twisted bilayer structure with fermionic cold atoms. By exploiting the incommensurate nature of

Denys Bulavka, Russ Woodroofe

We show that if a simplicial complex is a near-cone of sufficiently high depth, then the only maximum families of small pairwise intersecting faces are those with a common intersection. Thus, near-cones of sufficiently high depth satisfy the strict Erd\H{o}s-Ko-Rado property conjectured by Holroyd and Talbot and by Borg. One consequence is a strict Erd\H{o}s

Kevin Nguyen, Jakob Salzer

Carrollian conformal field theory offers an alternative description of massless scattering amplitudes, that is holographic in nature. In an effort to build a framework that is both predictive and constraining, we construct operator product expansions (OPE) that are compatible with carrollian symmetries. In this way, we unify and extend preliminary works on t

Dongjun Li, Pratik Wagle, Yanbei Chen, Nicolás Yunes

Gravitational waves offer new ways to test general relativity (GR) in the strong-field regime, including tests involving the ringdown phase of binary black hole mergers, characterized by oscillating and quickly decaying quasinormal modes (QNMs). Recent advances have extended QNM calculations to several theories beyond GR through the development of the modifi

Mauro Sereno

In the standard theory of growth of the nonbaryonic dark matter, cosmic structures form hierarchically and self-similarly from smaller clumps. The assembly merger tree goes from the linear perturbations in the early universe to highly non linear structures at late times. Gravity is the driving force and self-similarity should inform cosmic haloes. However, i

Emma W. Nielsen, Charles L. Steinhardt, Mathieux Harper, Conor McPartland

The quenching mechanisms of galaxies are not yet fully understood, but post-starburst galaxies provide one explanation for the rapid transition between star-forming and quiescent galaxies. At low redshift, it is generally thought that the starburst initiating the post-starburst phase is merger-driven, however, not all post-starburst galaxies show evidence of

Henry Bloss, Brandon Kriesten, T. J. Hobbs

Deeply inelastic scattering (DIS) is a powerful probe for investigating the QCD structure of hadronic matter and testing the standard model (SM). DIS can be described through QCD factorization theorems which separate contributions to the scattering interaction arising from disparate scales - e.g., with nonperturbative matrix elements associated with long dis

Sarah H. Taft, Robert D. Gehrz, Charles E. Woodward, Nathan Smith

RY Scuti, thought to be a Wolf-Rayet (WR) progenitor, is a massive, post-main-sequence, binary star system undergoing Roche lobe overflow (RLOF). SOFIA (+FORCAST) spectroscopy of the inner, ionized region of RY Scuti's double ringed toroidal nebula affirms the previous detection of the well-studied 12.81 $\mu$m Ne II forbidden transition and reveals four dis

Thomas W. Grimm, David Prieto, Mick van Vliet

Quantum gravity is expected to impose constraints on the moduli spaces of massless fields that can arise in effective quantum field theories. A recent proposal asserts that the asymptotic volume growth of these spaces is severely restricted, and related to the existence of duality symmetries. In this work we link this proposal to a tameness criterion, by sug

Robin Eappen, Pavel Kroupa

We investigate the shape and morphology of early-type galaxies (ETGs) within the framework of Modified Newtonian Dynamics (MOND). Building on our previous studies, which demonstrated that the monolithic collapse of primordial gas clouds in MOND produces galaxies (noted throughout as 'model relics' in the context of this work) with short star formation timesc

Maryum Sayeed, Daniel Huber, Ashley Chontos, Yaguang Li

We present a homogeneous catalog of global asteroseismic parameters and derived stellar parameters for 765 Kepler main-sequence and subgiant stars. The catalog was produced by re-analyzing all available Kepler DR25 short-cadence data using pySYD, an automated pipeline to extract global asteroseismic parameters. We find 50 new detections, seven of which are a

Jaroslav Haas, Pavel Kroupa, Ladislav Šubr, Myank Singhal

Various past theoretical considerations and observational efforts suggest the presence of a population of stellar-mass black holes in the innermost parsec of the Galactic centre. In this Letter, we investigate the impact of these black holes on the composition of the embedding stellar population through their direct collisions with the individual stars. Base

Yoshinobu Fudamoto, Jakob M. Helton, Xiaojing Lin, Fengwu Sun

We report the discovery of a galaxy proto-cluster candidate (dubbed MACS0416-OD-z8p5) at a spectroscopic redshift of $z\sim8.47$, dating back to $\sim550$Myr after the Big Bang. The observations are part of the JWST Cycle-3 treasury program, Slitless Areal Pure-Parallel HIgh-Redshift Emission Survey (SAPPHIRES) with NIRCam-grism. Using wide field slitless sp

M. Głowacki, I. Soszyński, A. Udalski, M. K. Szymański

We present an updated collection of eclipsing and ellipsoidal binary systems in the Large and Small Magellanic Clouds (LMC and SMC), as observed by the Optical Gravitational Lensing Experiment (OGLE) survey. The catalog comprises a total of 75 400 binary systems, including 63 252 in the LMC and 12 148 in the SMC. The sample is categorized into 67 971 eclipsi

Nabendu Kumar Khan, Anupam Ray, Girish Kulkarni, Basudeb Dasgupta

Primordial black holes (PBHs) have been explored as potential dark matter candidates, with various astrophysical observations placing upper limits on the fraction $f_\mathrm{PBH}$ of dark matter in the form of PBHs. However, a largely underutilized probe of PBH abundance is the temperature of the intergalactic medium (IGM), inferred from the thermal broadeni

Pablo G. Pérez-González, Göran Östlin, Luca Costantin, Jens Melinder

We present a sample of six F200W and three F277W dropout sources identified as $16<z<25$ galaxy candidates using the deepest JWST/NIRCam data to date (5$\sigma$ depths $\sim31.5$ mag at $\geq2$ $\mu$m), provided by the MIRI Deep Imaging Survey (MIDIS) and the Next Generation Deep Extragalactic Exploratory Public survey (NGDEEP). We estimate ultraviolet (UV)

Jacob L. Bourjaily, Song He, Canxin Shi, Yichao Tang

We determine the 4-point correlation function and amplitude in planar, maximally supersymmetric Yang-Mills theory to 12 loops. We find that the recently-introduced 'double-triangle' rule in fact implies the previously described square and pentagon rules; and when applied to 12 loops, it fully determines the 11-loop correlator and fixes all but 3 of the (22,0

Adam He, Mikhail M. Ivanov, Simeon Bird, Rui An

We present the first search for evidence of neutrino self-interaction with two new, state-of-the-art likelihoods for eBOSS Lyman-$\alpha$ data. These are an effective field theory (EFT) likelihood with priors from the Sherwood simulation suite, and a compressed likelihood derived from an emulator built using the PRIYA simulation suite. Previous analyses that

Hee-Cheol Kim, Minsung Kim, Sung-Soo Kim, Kimyeong Lee

We investigate codimension-2 defect partition functions and quantum Seiberg-Witten curves in 5d rank-1 supersymmetric QFTs, including non-Lagrangian and Kaluza-Klein theories. Using generalized blowup equations, we compute defect partition functions in the $\Omega$-background and show that, in the Nekrasov-Shatashvili limit, they satisfy certain difference e

Francesco D'Eugenio, Jakob M. Helton, Kevin Hainline, Fengwu Sun

We report the discovery of a remarkably large and luminous line-emitting nebula extending on either side of the Balmer-break galaxy JADES-GS-518794 at z=5.89, detected with JADES JWST/NIRCam imaging in [O III]$\lambda\lambda$4959,5007 and H$\alpha$ and spectroscopically confirmed with NIRCam/WFSS thanks to the pure-parallel SAPPHIRES programme. The end-to-en

Connor Hainje, Oren Slone, Mariangela Lisanti, Denis Erkal

This work explores how assumptions regarding the particle-physics nature of dark matter can alter the evolution of the Sagittarius (Sgr) dwarf spheroidal galaxy and its expansive stellar stream. We run a large suite of $N$-body simulations to model the infall of a Sgr-like dwarf, exploring how the presence of dark matter self interactions impacts its evoluti

Jeet Shah, Gautam Nambiar, Alexey V. Gorshkov, Victor Galitski

We define a quantum monomer-dimer model in the space of maximal dimer coverings of quasicrystalline Penrose tilings. Since Penrose tilings do not admit perfect dimer coverings, as shown by F. Flicker et al., PRX 10, 011005 (2020), monomers are necessarily present in our model. The model features a frustration-free Rokhsar-Kivelson (RK) point where the ground

Fengwu Sun, Yoshinobu Fudamoto, Xiaojing Lin, Jakob M. Helton

We present the early data release (EDR) of SAPPHIRES, a JWST Cycle-3 Treasury imaging and spectroscopic survey using the powerful NIRCam wide-field slitless spectroscopic (WFSS) mode in pure parallel. SAPPHIRES will obtain NIRCam imaging and WFSS data in many cosmological deep fields totaling a telescope charged time of 709 hours (557-hour exposures). In thi

Esther Whang, Skyler Thomas, Ji Yi, Adam S. Charles

Advances in neural imaging have enabled neuroscientists to study how large neural populations conspire to produce perception, behavior and cognition. Despite many advances in optical methods, there exists a fundamental tradeoff between imaging speed, field of view, and resolution that limits the scope of neural imaging, especially for the raster-scanning mul

Kushagra Gupta, Ross Allen, David Fridovich-Keil, Ufuk Topcu

Non-cooperative dynamic game theory provides a principled approach to modeling sequential decision-making among multiple noncommunicative agents. A key focus has been on finding Nash equilibria in two-agent zero-sum dynamic games under various information structures. A well-known result states that in linear-quadratic games, unique Nash equilibria under feed

Zineng Tang, Long Lian, Seun Eisape, XuDong Wang

Despite the recent success of image-text contrastive models like CLIP and SigLIP, these models often struggle with vision-centric tasks that demand high-fidelity image understanding, such as counting, depth estimation, and fine-grained object recognition. These models, by performing language alignment, tend to prioritize high-level semantics over visual unde

Taylor Sorensen, Pushkar Mishra, Roma Patel, Michael Henry Tessler

Modelling human variation in rating tasks is crucial for personalization, pluralistic model alignment, and computational social science. We propose representing individuals using natural language value profiles -- descriptions of underlying values compressed from in-context demonstrations -- along with a steerable decoder model that estimates individual rati

Gaurav Gyawali, Henry Shackleton, Zhu-Xi Luo, Michael Lawler

A central challenge in quantum error correction is identifying powerful quantum codes tailored to specific hardware and determining their error thresholds above which quantum information is unprotected. This problem is hard because we cannot determine the noise models for our devices. Inspired by the quantum capacity theorem, we seek an optimal quantum sourc

Richard Barney, Djamil Lakhdar-Hamina, Victor Galitski

We propose a natural quantization of a standard neural network, where the neurons correspond to qubits and the activation functions are implemented via quantum gates and measurements. The simplest quantized neural network corresponds to applying single-qubit rotations, with the rotation angles being dependent on the weights and measurement outcomes of the pr

Yves-Simon Zeulner, Simon Crämer, Sandeep Selvaraj, Roberto Calandra

Towards the grand challenge of achieving human-level manipulation in robots, playing piano is a compelling testbed that requires strategic, precise, and flowing movements. Over the years, several works demonstrated hand-designed controllers on real world piano playing, while other works evaluated robot learning approaches on simulated piano playing. In this

Ricardo Freire, Thyago S. R. Santos

We investigate models of dispersive long internal waves with rotational effects, specifically the Benjamin-Ono (BO) and intermediate long wave (ILW) equations modified by the presence of the nonlocal operator $\partial_x^{-1}$, which mathematically accounts for rotational influences. We establish a local and global well-posedness theory while ensuring the un

Klaus Paschek, Thomas K. Henning, Karan Molaverdikhani, Yoshinori Miyazaki

Understanding what environmental conditions prevailed on early Earth during the Hadean eon, and how this set the stage for the origins of life, remains a challenge. Geologic processes such as serpentinization and bombardment by chondritic material during the late veneer might have been very active, shaping an atmospheric composition reducing enough to allow

Yifei Zhou, Song Jiang, Yuandong Tian, Jason Weston

Large language model (LLM) agents need to perform multi-turn interactions in real-world tasks. However, existing multi-turn RL algorithms for optimizing LLM agents fail to perform effective credit assignment over multiple turns while leveraging the generalization capabilities of LLMs and it remains unclear how to develop such algorithms. To study this, we fi

Noam Razin, Zixuan Wang, Hubert Strauss, Stanley Wei

The success of Reinforcement Learning from Human Feedback (RLHF) critically depends on the quality of the reward model. However, while this quality is primarily evaluated through accuracy, it remains unclear whether accuracy fully captures what makes a reward model an effective teacher. We address this question from an optimization perspective. First, we pro

Suchismita Das, Raghunath Chelakkot

We investigate the wetting transitions displayed by the collection of active Brownian particles (ABPs) confined within rigid, impenetrable, flat walls. In our computational study using Brownian dynamics simulations, the wall-particle interactions are implemented with a short-range repulsive potential. An enhanced rotational diffusion at the walls is used as

Foundation AI Team, Kiran Bhat, Nishchaie Khanna, Karun Channa

Foundation models trained on vast amounts of data have demonstrated remarkable reasoning and generation capabilities in the domains of text, images, audio and video. Our goal at Roblox is to build such a foundation model for 3D intelligence, a model that can support developers in producing all aspects of a Roblox experience, from generating 3D objects and sc

Maciej Ziaja, Pawel Kowaleczko, Daniel Kostrzewa, Nicolas Longépé

Super-resolution is aimed at reconstructing high-resolution images from low-resolution observations. State-of-the-art approaches underpinned with deep learning allow for obtaining outstanding results, generating images of high perceptual quality. However, it often remains unclear whether the reconstructed details are close to the actual ground-truth informat

Ka-Wa Yip, Kübra Yeter-Aydeniz, Sijia S. Dong

We introduce a variational quantum annealing (VarQA) algorithm for electronic structure theory, in which we use the quantum annealer as a sampler and prepare an ansatz state through its statistics. We also introduce a strategy called the "digitizer" for searching the space of variational parameters efficiently. We demonstrate the effectiveness of VarQA by ev

Brian Keith, Fausto German, Eric Krokos, Sarah Joseph

As narrative extraction systems grow in complexity, establishing user trust through interpretable and explainable outputs becomes increasingly critical. This paper presents an evaluation of an Explainable Artificial Intelligence (XAI) system for narrative map extraction that provides meaningful explanations across multiple levels of abstraction. Our system i

Zeqi Gu, Difan Liu, Timothy Langlois, Matthew Fisher

Recent diffusion-based methods have achieved impressive results on animating images of human subjects. However, most of that success has built on human-specific body pose representations and extensive training with labeled real videos. In this work, we extend the ability of such models to animate images of characters with more diverse skeletal topologies. Gi

Joshua D. Lothringer, Katherine A. Bennett, David K. Sing, Brian Kehoe-Seamons

The atmospheres of ultra-hot Jupiters are unique compared to other planets because of the presence of both refractory and volatile gaseous species, enabling a new lens to constrain a planet's composition, chemistry, and formation. WASP-178b is one such ultra-hot Jupiter that was recently found to exhibit enormous NUV absorption between 0.2 and 0.4 $\mu$m fro

Maine Christos, Pietro M. Bonetti, Mathias S. Scheurer

Inspired by the recent experimental discovery of superconductivity emerging from a time-reversal symmetry-breaking normal state in tetralayer rhombohedral graphene, we here investigate superconducting instabilities in this system. We classify the possible pairing instabilities, including states with commensurate and incommensurate center of mass momenta. As

Boshen Xu, Yuting Mei, Xinbi Liu, Sipeng Zheng

Egocentric video-language pretraining has significantly advanced video representation learning. Humans perceive and interact with a fully 3D world, developing spatial awareness that extends beyond text-based understanding. However, most previous works learn from 1D text or 2D visual cues, such as bounding boxes, which inherently lack 3D understanding. To bri

Dong Xu, Mengyao Liao, Zhenglin Lai, Xueliang Li

Text classification assigns text to predefined categories. Traditional methods struggle with complex structures and long-range dependencies. Deep learning with recurrent neural networks and Transformer models has improved feature extraction and context awareness. However, these models still trade off interpretability, efficiency and contextual range. We prop

Bernanda Telalovic, Mauricio Bustamante

Discovering Lorentz-invariance violation (LIV) would upend the foundations of modern physics. Because LIV effects grow with energy, high-energy astrophysical neutrinos provide the most sensitive tests of Lorentz invariance in the neutrino sector. We examine an understudied yet phenomenologically rich LIV signature: compass asymmetries, where neutrinos of dif

Zdenek Sekanina

Until the second half of the 19th century, two or more brief appearances of bright comets, such as the ones in 1668 and 1702, alike in aspect and motion, seen with a tail near the Sun, were almost universally believed to be periodic returns of a single object. It is likely that the exceptional story of Halley's comet was the compelling precedent for this sch

Brice Coffer, Matthew Parker, Michael Coniglio, Cameron Homeyer

Storm-relative helicity (SRH) is an important ingredient in supercell development, as well as mesocyclone intensity, and is linked to tornadogenesis and tornado potential. Derived from the storm-relative wind profile, SRH is composed of both the vertical wind shear and storm-relative flow. Recent studies have come to conflicting findings regarding whether sh

Ruichen Chen, Keith G. Mills, Di Niu

Diffusion Models (DM) have revolutionized the text-to-image visual generation process. However, the large computational cost and model footprint of DMs hinders practical deployment, especially on edge devices. Post-training quantization (PTQ) is a lightweight method to alleviate these burdens without the need for training or fine-tuning. While recent DM PTQ

Alexander Held, Sam Albin, Garhan Attebury, Kenneth Bloom

The IRIS-HEP software institute, as a contributor to the broader HEP Python ecosystem, is developing scalable analysis infrastructure and software tools to address the upcoming HL-LHC computing challenges with new approaches and paradigms, driven by our vision of what HL-LHC analysis will require. The institute uses a "Grand Challenge" format, constructing a

Jia-Nan Li, Jian Guan, Songhao Wu, Wei Wu

Large language models (LLMs) have traditionally been aligned through one-size-fits-all approaches that assume uniform human preferences, fundamentally overlooking the diversity in user values and needs. This paper introduces a comprehensive framework for scalable personalized alignment of LLMs. We establish a systematic preference space characterizing psycho

Sarah Pak, Muhammed A. Dada, Niranjan Govind, Daniel R. Nascimento

Spectroscopic techniques based on core-level excitations provide powerful tools for probing molecular and electronic structures with high spatial resolution. However, accurately calculating spectral features at the L or M edges is challenging due to the significant influence of spin-orbit and multiplet effects. While scalar-relativistic effects can be incorp

L. Farina, M. Piccoli, S. Iandolo, A. Solida

The deployment of cooperative-intelligent transport systems (C-ITS) has started, and standardization and research activities are moving forward to improve road safety and vehicular efficiency. An aspect that is still felt as a limitation by the research groups active in the field, is the difficulty to validate the solutions with real hardware and software, b

Lachlan P Lindoy, Daniel Rodrigo-Albert, Yannic Rath, Ivan Rungger

We present the Python Tree Tensor Network package (pyTTN) for the evaluation of dynamical properties of closed and open quantum systems that makes use of Tree Tensor Network (TTN), or equivalently the multi-layer multiconfiguration time-dependent Hartree (ML-MCTDH), based representations of wavefunctions. This package includes several features allowing for e

William D. Cohen

We find a condition on the acylindrical action of a finitely presented group on a simplicial tree which guarantees that this action will be dominated by an acylindrical action with finitely generated edge stabilisers, and find the first example of an action of a finitely presented group where there is no such dominating action. As a consequence, we show that

Gediminas Juska, Simone Varo, Nicola Maraviglia, John O'Hara

This work presents a foundational approach for fabricating arrays of self-aligned micro- and nanopillar structures incorporating individual site-controlled quantum dots (QDs) for enhanced light extraction. This method leverages the non-planar surface morphology of pyramidal QD samples to define dielectric masks self - aligned to the QD positions. The mask si

Yuanzhi Zhu, Xi Wang, Stéphane Lathuilière, Vicky Kalogeiton

Masked Diffusion Models (MDMs) have emerged as a powerful generative modeling technique. Despite their remarkable results, they typically suffer from slow inference with several steps. In this paper, we propose Di$\mathtt{[M]}$O, a novel approach that distills masked diffusion models into a one-step generator. Di$\mathtt{[M]}$O addresses two key challenges:

Aayam Bansal, Keertan Balaji, Zeus Lalani

In contemporary power systems, energy consumption prediction plays a crucial role in maintaining grid stability and resource allocation enabling power companies to minimize energy waste and avoid overloading the grid. While there are several research works on energy optimization, they often fail to address the complexities of real-time fluctuations and the c

Lara Maleyeff, Shirin Golchi, Erica E. M. Moodie, R. John Kimoff

Precision medicine tailors treatments to individual patient characteristics, which is especially valuable for conditions like obstructive sleep apnea (OSA), where treatment responses vary widely. Traditional trials often overlook subgroup differences, leading to suboptimal recommendations. Current approaches rely on pre-specified thresholds with inherent unc

Yuelyu Ji, Hang Zhang, Yanshan Wang

Medical Question Answering systems based on Retrieval Augmented Generation is promising for clinical decision support because they can integrate external knowledge, thus reducing inaccuracies inherent in standalone large language models (LLMs). However, these systems may unintentionally propagate or amplify biases associated with sensitive demographic attrib

Pablo Romero

A two-terminal graph is a graph equipped with two distinguished vertices, called terminals. Let $T_{n,m}$ be the set of all nonisomorphic connected simple two-terminal graphs on $n$ vertices and $m$ edges. Let $G$ be any two-terminal graph in $T_{n,m}$. For every number $p$ in $[0,1]$ we let each of the edges in $G$ be independently deleted with probability