December 2025 arXiv papers — page 121

Marc Scibelli, Krystelle Gonzalez Papaux, Julia Valenti, Srishti Kush

The rise of generative and autonomous agents marks a fundamental shift in computing, demanding a rethinking of how humans collaborate with probabilistic, partially autonomous systems. We present the Human-AI-Experience (HAX) framework, a comprehensive, three-phase approach that establishes design foundations for trustworthy, transparent, and collaborative ag

Gastão Almeida Braga, Antônio Marcos da Silva, Jussara de Matos Moreira

Despite of the fact that the Damped Wave and the Heat equations describe phenomena of distinct nature, it is amazing that their solutions are related in the limit as $t \to \infty$. The aim of this note is to explain to undergraduate students, with a good calculus background, how the relation between these solutions is established. We follow a ``do it yourse

Suman Chatterjee, Mario G. Santos, Kristof Rozgonyi, Keith Grainge

The MeerKAT Large Area Synoptic Survey (MeerKLASS) is designed to map large areas of the Southern sky for cosmology using the single-dish HI intensity mapping (IM) technique, while simultaneously delivering a wide, high angular-resolution interferometric survey. We present the design and first results of the MeerKLASS On-the-Fly (OTF) continuum data, which e

Sushmita Nath

Predictive maintenance is an important sector in modern industries which improves fault detection and cost reduction processes. By using machine learning algorithms in the whole process, the defects detection process can be implemented smoothly. Semiconductor is a sensitive maintenance field that requires predictability in work. While convolutional neural ne

Anastasiia Zbandut, Carolina Goldstein

This paper maps the emerging market for decentralized credit in which ERC 4626 vaults and third-party curators, rather than monolithic lending protocols alone, increasingly determine underwriting and leverage decisions. We show that modular vaults differ in capital utilization, cross-chain and cross asset concentration, and liquidity risk structure. Further,

Noam Ness, Barry E. Cimring

The optical dump at the LUXE experiment has the potential to create a large flux of $O(GeV)$ photons that can be used to look for new physics when directed at a solid material dump. The LUXE-NPOD extension of LUXE, which focuses the hard photons onto a slab of tungsten, offers two interaction points (laser-electron and photon-tungsten), making it well suited

Gianluca Teza, Attilio L. Stella, Trevor GrandPre

Detecting broken time-reversibility at micro- and nanoscale is often difficult when experiments offer limited state resolution. We introduce a lumping method that builds an effective semi-Markov model able to reproduce exactly the full entropy-production statistics of the microscopic dynamics. The mean entropy production stays accurate even when hidden curre

Cicero X. Lu, Isabel Rebollido, Sean Brittain, Tracy Beck

We have obtained a high-resolution, JWST NIRSpec $2.87$ -- $5.14$ $\mu$m spectrum of the debris disk around HD 131488. We discover CO fundamental emission indicating the presence of warm fluorescent gas within $\sim10$ AU of the star. The large discrepancy in CO's vibrational and rotational temperature indicates that CO is out of thermal equilibrium and is e

Jonathan Gomez Barrientos, Heather A. Knutson, Morgan Saidel, Michael Greklek-McKeon

Earth-sized planets transiting M dwarf stars present one of the best opportunities with current facilities for studying the atmospheric and bulk compositions of terrestrial worlds. Here, we statistically validate five new transiting Earth and super-Earth sized planets orbiting M dwarf stars using a combination of light curves from the Transiting Exoplanet Su

Marta Florido-Llinàs, Álvaro M. Alhambra, David Pérez-García, J. Ignacio Cirac

Canonical forms are central to the analytical understanding of tensor network states, underpinning key results such as the complete classification of one-dimensional symmetry-protected topological phases within the matrix product state (MPS) framework. Yet, the established theory applies only to uniform MPS with periodic boundary conditions, leaving many phy

Kaito Kobayashi, Benjamin Sappler, Frank Pollmann

Tensor network methods, most prominently matrix product states (MPS), have become fundamental tools in modern quantum many-body physics. While MPS and extensions like the multiscale entanglement renormalization ansatz (MERA) and tree tensor networks (TTN) efficiently capture area-law entanglement and its logarithmic violations, they inherently struggle to re

Sunghoon Jung, Sungjung Kim, Jiwoo Park, Seokhyeon Song

Gravitationally prepared states are quantum field theoretic states prepared by gravitational path integrals with spatial boundaries that have fixed boundary conditions for gravity but not for matter fields. They can be interpreted as quantum field theoretic states of closed universes encoding quantum gravitational effects of the past. We propose a method of

Pradip Kattel, Abay Zhakenov, Natan Andrei

The traditional multichannel Kondo effect takes place when several gapless metallic electronic channels interact with a localized spin-$S$ impurity, with the number of channels $n$ exceeding the size of the impurity spin, $n>2S$, leading to the emergence of non-Fermi liquid impurity behavior at low temperatures. Here, we show that the effect can be realized

Sourabh Paul, Keith Grainge, Mario G. Santos, Suman Chatterjee

We present the first public data release (DR1) from the interferometric component of the MeerKAT Large Area Synoptic Survey (MeerKLASS) UHF survey, a legacy program demonstrating a novel on-the-fly (OTF) mapping technique. This release is based on 12 hours of early science observations covering approximately 800 deg$^2$ of the southern sky. We describe the d

Matthew S. Mitchell, David Poland

We perform a systematic classification of (2+1)d Gross--Neveu--Yukawa-like models built out of one or more 4-component Dirac fermions and $M$ scalar fields, which preserve an O($M$) symmetry rotating the scalars. We then identify the perturbative fixed points of these models in the $4-\epsilon$ expansion. Our classification highlights several targets for the

Vivek Kumar Jha, Anshul Kumar Sharma, Madhu Sudan, Hum Chand

We investigate optical variability across multiple timescales in a sample of radio-loud narrow-line Seyfert~1 (RL-NLSy1) galaxies, including $\gamma$-ray detected, $\gamma$-ray undetected, and non-jetted systems along with a comparison set of highly polarised core-dominated quasars (HPQs). Using Zwicky Transient Facility light curves, we measure fractional v

Nabeel Rehemtulla, Michael W. Coughlin, Adam A. Miller, Theophile Jegou du Laz

Robotic wide-field time-domain surveys, such as the Zwicky Transient Facility and the Asteroid Terrestrial-impact Last Alert System, capture dozens of transients each night. The workflows for discovering and classifying transients in survey data streams have become increasingly automated over decades of development. The recent integration of machine learning

Nabeel Rehemtulla, Adam A. Miller, Mike Walmsley, Ved G. Shah

Modern wide-field time-domain surveys facilitate the study of transient, variable and moving phenomena by conducting image differencing and relaying alerts to their communities. Machine learning tools have been used on data from these surveys and their precursors for more than a decade, and convolutional neural networks (CNNs), which make predictions directl

Keming Zhang

Sun-like stars are known to host a paucity of brown dwarf companions at close separations. Direct imaging surveys of intermediate-mass stars have suggested that the brown dwarf desert may be fundamentally a feature in the mass ratio. Microlensing surveys provide a unique opportunity to investigate the nature of this desert around low mass stars, as microlens

Francesco Calisto, Clifford Cheung, Grant N. Remmen, Francesco Sciotti

We prove that symmetry in the presence of gravity implies a version of the completeness hypothesis. For a broad class of theories, we demonstrate that the existence of finitely many charged particles logically necessitates the existence of infinitely many charged particles populating the entire charge lattice. Our conclusions follow from the consistency of p

Alexander J. Dittmann, Geoffrey Ryan, Luciano Combi

Circumbinary disks crucially affect the orbital and electromagnetic properties of binary systems across the universe, from stars in our galactic neighborhood to supermassive black hole binaries formed as the result of tumultuous galactic mergers. Previous simulations have focused nearly exclusively on thick accretion disks, appropriate for studying stellar b

Weiyao Ke, Bianka Mecaj, Ivan Vitev

We present a first-principles analysis of the renormalization group (RG) evolution of the two-point energy-energy correlator (EEC) in light-quark and gluon jets propagating through nuclear matter. Our work focuses on the analytic structure of the RG equations in the thin-medium regime, highlighting how collinear emissions in the presence of a dense QCD mediu

João Barata, Matvey V. Kuzmin, Xoán Mayo López, Andrey V. Sadofyev

We consider the propagation of heavy quarks through a dense, hydrodynamically flowing QCD medium, representative of the quark-gluon plasma formed in ultrarelativistic heavy-ion collisions. Working in the high-energy limit, we identify two novel mass-dependent effects arising from the heavy quark coupling to the local medium flow. The first is the emergence o

Max Jaarsma, Yibei Li, Ian Moult, Wouter J. Waalewijn

Correlations in the distribution of energy produced in collider experiments provide a snapshot of the microscopic dynamics of QCD, and its evolution from asymptotically free quarks and gluons, to confined hadrons. There has recently been considerable progress in the interpretation and precision calculation of these correlations, using a specific class of obs

Jan U. Müller, Robin Tim Landsgesell, Leif Van Holland, Patrick Stotko

The recent success of 3D Gaussian Splatting (3DGS) has reshaped novel view synthesis by enabling fast optimization and real-time rendering of high-quality radiance fields. However, it relies on simplified, order-dependent alpha blending and coarse approximations of the density integral within the rasterizer, thereby limiting its ability to render complex, ov

Ye Fang, Tong Wu, Valentin Deschaintre, Duygu Ceylan

Large-scale video generation models have shown remarkable potential in modeling photorealistic appearance and lighting interactions in real-world scenes. However, a closed-loop framework that jointly understands intrinsic scene properties (e.g., albedo, normal, material, and irradiance), leverages them for video synthesis, and supports editable intrinsic rep

Ruining Li, Yuxin Yao, Chuanxia Zheng, Christian Rupprecht

We introduce Particulate, a feed-forward model that, given a 3D mesh of an object, infers its articulations, including its 3D parts, their kinematic structure, and the motion constraints. The model is based on a transformer network, the Part Articulation Transformer, which predicts all these parameters for all joints. We train the network end-to-end on a div

Saurabh V. Kadam, Aahiri Naskar, Indrakshi Raychowdhury, Jesse R. Stryker

This work is the second installment of a series on the loop-string-hadron (LSH) approach to SU(3) lattice Yang-Mills theory. Here, we present the infinite-dimensional matrix representation for arbitrary gauge-invariant operators at a trivalent vertex, which results in a standalone framework for computations that supersedes the underlying Schwinger-boson fram

Nicolas Cerardi, Emma Tolley, Ashutosh Mishra

Cold dark matter (CDM) evolves as a collisionless fluid under the Vlasov-Poisson equations, but N-body simulations approximate this evolution by discretising the distribution function into particles, introducing discreteness effects at small scales. We present a physics-informed neural network approach that evolves CDM fields without any use of N-body data o

Ahmad Shamail, Claire McWhite

Many systems exhibit complex interactions between their components: some features or actions amplify each other's effects, others provide redundant information, and some contribute independently. We present a simple geometric method for discovering interactions and redundancies: when elements are added in random sequential orders and their contributions plot

Yang Fei, George Stoica, Jingyuan Liu, Qifeng Chen

Reality is a dance between rigid constraints and deformable structures. For video models, that means generating motion that preserves fidelity as well as structure. Despite progress in diffusion models, producing realistic structure-preserving motion remains challenging, especially for articulated and deformable objects such as humans and animals. Scaling tr

Seiji Fujimoto, Yoshihisa Asada, Rohan P. Naidu, John Chisholm

We present deep JWST/NIRSpec G395M spectroscopy of GLIMPSE-16043, a promising $z\sim6$ Pop III candidate originally identified through NIRCam photometry as having weak [OIII]$\lambda\lambda4959,5007$ emission. Our follow-up reveals clear [OIII] emission, ruling out a genuine zero-metallicity nature. However, the combination of the measured line fluxes and ph

Jaime E. Munoz Rivera, Maria Grazia Naso, Bruna T. Silva Sozzo

We study the Euler-Bernoulli beam model with singularities at the points $x=\xi_1$, $x=\xi_2$ and with localized viscoelastic dissipation of Kelvin-Voigt type. We assume that the beam is composed by two materials; one is an elastic material and the other one is a viscoelastic material of Kelvin-Voigt type. Our main result is that the corresponding semigroup

Ayush Asthana

Quantum Krylov subspace methods can extract ground and excited states by diagonalizing the Hamiltonian in a compact variational space. In practice, these spaces are almost always generated by real or imaginary time evolution, forcing a timestep trade-off between dynamical accuracy and basis collapse and often producing ill-conditioned overlap matrices that s

Justin Lemmon, Jaroslav Trnka

In this note we study on-shell tree-level gravity amplitudes in the infinite momentum limit. In the case of the two-line BCFW shift, we have a famous improved behavior at infinity that allows for the amplitude to be reconstructed from the pole factorization. For other shifts, the poles at infinity are present and need to be considered, however general princi

Hannes Homburger, Bastian Jäckl, Stefan Wirtensohn, Christian Stopp

The maritime sector is undergoing a disruptive technological change driven by three main factors: autonomy, decarbonization, and digital transformation. Addressing these factors necessitates a reassessment of inland vessel operations. This paper presents the design and development of a decision support system for ferry operations based on a shrinking-horizon

Shujing Chen, Dmitriy Kunisky

We consider the distribution of the top eigenvector $\widehat{v}$ of a spiked matrix model of the form $H = θvv^* + W$, in the supercritical regime where $H$ has an outlier eigenvalue of comparable magnitude to $\|W\|$. We show that, if $v$ is sufficiently delocalized, then the distribution of the individual entries of the projector $\widehat{v}\widehat{v}^*

Junchi Lu, Xinke Li, Yuheng Liu, Qi Alfred Chen

The increasing use of generative models such as diffusion models for synthetic data augmentation has greatly reduced the cost of data collection and labeling in downstream perception tasks. However, this new data source paradigm may introduce important security concerns. Publicly available generative models are often reused without verification, raising a fu

Andrew Adiletta, Kathryn Adiletta, Kemal Derya, Berk Sunar

The rapid deployment of Large Language Models (LLMs) has created an urgent need for enhanced security and privacy measures in Machine Learning (ML). LLMs are increasingly being used to process untrusted text inputs and even generate executable code, often while having access to sensitive system controls. To address these security concerns, several companies

Peiqing Yang, Shangchen Zhou, Kai Hao, Qingyi Tao

Video matting remains limited by the scale and realism of existing datasets. While leveraging segmentation data can enhance semantic stability, the lack of effective boundary supervision often leads to segmentation-like mattes lacking fine details. To this end, we introduce a learned Matting Quality Evaluator (MQE) that assesses semantic and boundary quality

Vineet Pasumarti, Lorenzo Bianchi, Antonio Loquercio

Through multi-agent competition and the sparse high-level objective of winning a race, we find that both agile flight (e.g., high-speed motion pushing the platform to its physical limits) and strategy (e.g., overtaking or blocking) emerge from agents trained with reinforcement learning. We provide evidence in both simulation and the real world that this appr

Max McGuinness, Alex Serrano, Luke Bailey, Scott Emmons

Activation monitoring, which probes a model's internal states using lightweight classifiers, is an emerging tool for AI safety. However, its worst-case robustness under a misalignment threat model--where a model might learn to actively conceal its internal states--remains untested. Focusing on this threat model, we ask: could a model learn to evade previousl

Matvei S. Kniazev, Nikolai A. Stepanov, Mikhail A. Skvortsov

We investigate the statistical properties of the vortex pinning potential in a thin superconducting film. Modeling intrinsic inhomogeneities by a random-temperature Ginzburg-Landau functional with short-range Gaussian disorder, we derive the pinning landscape $E(\mathbf{R})$ by determining how the vortex core adapts to randomness. Within the hard-core approx

Alessio D'Alì

Strongly Koszul algebras were introduced by Herzog, Hibi and Restuccia in 2000. The goal of the present paper is to provide an in-depth study of such algebras and to investigate how strong Koszulness interacts with the existence of a quadratic Gr\"obner basis for the defining ideal. Firstly, we prove that the existence of a quadratic revlex-universal Gr\"obn

Sinyoung Park, Matthew Nunes, Sandipan Roy

Spectral clustering is a popular tool in network data analysis, with applications in a variety of scientific application areas. However, many studies have shown that classical spectral clustering does not perform well on certain network structures, particularly core-periphery networks. To improve clustering performance in core-periphery structures, Adjacency

Vladimer Khasia

Coordinate-based neural networks have emerged as a powerful tool for representing continuous physical fields, yet they face two fundamental pathologies: spectral bias, which hinders the learning of high-frequency dynamics, and the curse of dimensionality, which causes parameter explosion in discrete feature grids. We propose the Adaptive Vekua Cascade (AVC),

Dmytro Sukhov

Tunnel Geometry and Proliferation Logic were developed as independent attempts to describe structure without assuming an underlying continuum of points. Although their languages differ, both frameworks encode the same underlying idea: that locality is not primitive but emerges from stable patterns of refinement. This paper shows that each theory admits a rep

Aditya Garg, Marissa Morales, Aashini Shah, Daniel Kim

Volatile organic compounds (VOCs) are valuable health indicators, with synthetic breath biomarkers offering rapid and disease specific diagnostics. However, their <100 ppb level exhalation requires mass spectrometry, limiting clinical integration. Surface-enhanced Raman spectroscopy (SERS) offers a portable, cost-effective alternative. Yet, detecting synthet

Britton Jordan, Jordan Thompson, Jesse F. d'Almeida, Hao Li

Monocular depth estimation (MDE) provides a useful tool for robotic perception, but its predictions are often uncertain and inaccurate in challenging environments such as surgical scenes where textureless surfaces, specular reflections, and occlusions are common. To address this, we propose ProbeMDE, a cost-aware active sensing framework that combines RGB im

Pedro Luis Grande, Raul Carlos Fadanelli, Maarten Vos

Quantum mechanics is the most successful theory to describe microscopic phenomena. It was derived in different ways over the past 100 years by Heisenberg, Schr\"{o}dinger, and Feynman. At the same time, other interpretations have been suggested, including the Bohm-De Broglie interpretation and the so-called Bohmian mechanics. Here, we show that Bohmian mecha

Kai Yao, Marc Juarez

Model fingerprint detection has shown promise to trace the provenance of AI-generated images in forensic applications. However, despite the inherent adversarial nature of these applications, existing evaluations rarely consider adversarial settings. We present the first systematic security evaluation of these techniques, formalizing threat models that encomp

S. O. Kara

We develop a unified theoretical framework that embeds a light leptophilic vector boson into nuclear energy density functional (EDF) theory. Starting from an underlying leptophilic gauge interaction, the mediator is integrated out in the static limit, yielding an effective current--current interaction that couples proton and lepton densities. This interactio

Xiaoyu Ma, Zhengqing Yuan, Zheyuan Zhang, Kaiwen Shi

Vision-language-action (VLA) models enable impressive zero shot manipulation, but their inference stacks are often too heavy for responsive web demos or high frequency robot control on commodity GPUs. We present BLURR, a lightweight inference wrapper that can be plugged into existing VLA controllers without retraining or changing model checkpoints. Instantia

Felix St-Amour, H. Cynthia Chiang, Jamie Cox, Eamon Egan

Surface ablation measurements of glaciers are critical for understanding mass change over time. Mass-balance stakes are commonly used for localized measurements, with the exposed length typically measured manually at infrequent intervals. This paper presents the design and validation of new instrumentation that automates mass-balance stake readings, thus ena

Xavier R. Advincula, Christoph Schran, Angelos Michaelides

Water behaves very differently at surfaces and under extreme confinement, but the boundary between these two regimes has remained unclear. Despite evidence that interfacial effects persist under sub-nanometre confinement, the molecular-scale behaviour and its evolution with slit width remain unclear. Here, we use machine-learning molecular dynamics with firs

Marcel Nutz, Alessandro Prosperi

We study the high-frequency limit of an $n$-trader optimal execution game in discrete time. Traders face transient price impact of Obizhaeva--Wang type in addition to quadratic instantaneous trading costs $\theta(\Delta X_t)^2$ on each transaction $\Delta X_t$. There is a unique Nash equilibrium in which traders choose liquidation strategies minimizing expec

Giorgio Arcadi, David Cabo-Almeida, Florian Goertz, Maya Hager

In theories with extended scalar sectors the lightest new scalar degree of freedom might be accessible at colliders. Going beyond simplified models, such a theory can be described in a gauge-invariant and agnostic way via an EFT with a non-linearly realized electroweak symmetry. In this extended HEFT, depending on the $SU(2)$ nature of the new scalar in the

Mohammad Dehghanmanshadi, Wallapak Tavanapong

Generating realistic synthetic microscopy images is critical for training deep learning models in label-scarce environments, such as cell counting with many cells per image. However, traditional domain adaptation methods often struggle to bridge the domain gap when synthetic images lack the complex textures and visual patterns of real samples. In this work,

Jack Liell-Cock, Zev Shirazi, Sam Staton

Relative monads provide a controlled view of computation. We generalise the monadic metalanguage to a relative setting and give a complete semantics with strong relative monads. Adopting this perspective, we generalise two existing program calculi from the literature. We provide a linear-non-linear language for graded monads, LNL-RMM, along with a semantic p

Jamal Al-Karaki, Muhammad Al-Zafar Khan, Rand Derar Mohammad Al Athamneh

The scarcity of cyberattack data hinders the development of robust intrusion detection systems. This paper introduces PHANTOM, a novel adversarial variational framework for generating high-fidelity synthetic attack data. Its innovations include progressive training, a dual-path VAE-GAN architecture, and domain-specific feature matching to preserve the semant

Trisha Dawn, Jesús Arroyo

Data integration is essential across diverse domains, from historical records to biomedical research, facilitating joint statistical inference. A crucial initial step in this process involves merging multiple data sources based on matching individual records, often in the absence of unique identifiers. When the datasets are networks, this problem is typicall

Aditya Tripathi, Karan Sharma, Rahul Mishra, Tapas Kumar Maiti

Federated Learning (FL) distributes model training across clients who retain their data locally, but this architecture exposes a fundamental vulnerability: Byzantine clients can inject arbitrarily corrupted updates that degrade or subvert the global model. While robust aggregation methods (including Krum, Bulyan, and coordinate-wise defenses) offer theoretic

D. M. Skowron, M. L. Fouesneau, R. Drimmel, S. Khanna

The Wesenheit function is widely used to reduce the effects of interstellar reddening in distance measurements. Its construction, however, relies on the assumption of a universal extinction curve and on fixed values of the total-to-selective extinction ratio, Rv. Recent studies have shown that Rv varies significantly across the Milky Way and between differen

Stefano Bruni, Enrico Prati

Calculating the molecular ground-state energy is a central challenge in computational chemistry. Conventional methods such as the Complete Active Space Configuration Interaction scale exponentially with molecular size, limiting their applicability to large molecules. Quantum computing offers a promising alternative by mapping molecular Hamiltonians by qubits

Valeria Díaz Moreno, Nikola D. Dimitrov, Vladimir E. Manucharyan, Maxim G. Vavilov

High-fidelity two-qubit gates have been demonstrated in systems of two fluxonium qubits; however, the realization of scalable quantum processors requires maintaining low error rates in substantially larger architectures. In this work, we analyze a system of four inductively coupled fluxonium qubits to determine the impact of spectator qubits on the performan

Yuming Feng, Xinrui Jiang

Online product reviews contain rich but noisy signals that overwhelm users and hinder effective decision-making. Existing LLM-based summarizers remain generic and fail to account for individual preferences, limiting their practical utility. We propose SUMFORU, a steerable review summarization framework that aligns outputs with explicit user personas to suppo

Abhirup Bhattacharya, Onkar Parrikar

We study entanglement entropy for ball-shaped regions in excited states of holographic conformal field theories. The excited states are prepared by the Euclidean path integral in the CFT with a source turned on for some double-trace operator, with a small, $O(1)$ amplitude $\lambda$. On the gravity side, the double-trace operator deforms the bulk geometry as

Agustin Cabrera, David Ziegler, Markus Schläpfer

Cities are increasingly challenged by extreme heat events, which pose serious risks to public health and urban livability. Micromobility users, whose numbers have increased rapidly in recent years, are particularly vulnerable to outdoor heat exposure. Yet, their exposure patterns and the effectiveness of mitigation measures remain poorly understood. Here, we

Joseph N. Headley, Edgar W. Lyons, Mathew Q. Giso, Emily P. Kuwaye

Softer means stickier for solid adhesives, because material compliance facilitates close contact between non-conformal surfaces. Recent discoveries have revealed that soft materials can exhibit a rich array of new physics arising from competing effects of continuum elasticity, fluid-like surface mechanics, and internal poroelastic flows, all of which can dir

Andy A. Shen, Eli Ben-Michael, Avi Feller, Luke Keele

While balancing covariates between groups is central for observational causal inference, selecting which features to balance remains a challenging problem. Kernel balancing is a promising approach that first estimates a kernel that captures similarity across units and then balances a (possibly low-dimensional) summary of that kernel, indirectly learning impo

Ernesto Casablanca, Oliver Schön, Paolo Zuliani, Sadegh Soudjani

Ensuring the safety of AI-enabled systems, particularly in high-stakes domains such as autonomous driving and healthcare, has become increasingly critical. Traditional formal verification tools fall short when faced with systems that embed both opaque, black-box AI components and complex stochastic dynamics. To address these challenges, we introduce LUCID (L

Minglei Shi, Haolin Wang, Borui Zhang, Wenzhao Zheng

Visual generation grounded in Visual Foundation Model (VFM) representations offers a highly promising unified pathway for integrating visual understanding, perception, and generation. Despite this potential, training large-scale text-to-image diffusion models entirely within the VFM representation space remains largely unexplored. To bridge this gap, we scal

Mohammed El Fallaki Idrissi, Jad Mounayer, Sebastian Rodriguez, Fodil Meraghni

This paper presents a novel paradigm in simulation-based engineering sciences by introducing a new framework called Generative Parametric Design (GPD). The GPD framework enables the generation of new designs along with their corresponding parametric solutions given as a reduced basis. To achieve this, two Rank Reduction Autoencoders (RRAEs) are employed, one

Hana Kopecka, Jose Such

A robot's appearance is a known factor influencing user's mental model and human-robot interaction, that has not been studied in the context of its influence in expected robot explanations. In this study, we investigate whether and to what extent the human-like appearance of robots elicits anthropomorphism, which is conceptualised as an attribution of mental

Liqiang Huang, Rachel W. Mills, Saikiran Mandula, Lin Bai

Whole-slide multiplex imaging of brain tissue generates massive information-dense images that are challenging to analyze and require custom software. We present an alternative query-driven programming-free strategy using a multiplex visual search engine (mViSE) that learns the multifaceted brain tissue chemoarchitecture, cytoarchitecture, and myeloarchitectu

Eric F. Bell, Benjamin Harmsen, Matthew Cosby, Paul A. Price

A merger origin has been suggested for M83's massive, metal-rich extended HI disk and nuclear starburst. We observe M83's stellar halo to test this idea. We train nearest-neighbor star-galaxy separation on wide-area Subaru imaging with Hubble Space Telescope data to map M83's halo in resolved stars. We find that M83 has an extended, very low density smooth s

Yongsheng Huang, Peibo Duan, Yujie Wu, Kai Sun

Spiking neural networks (SNNs), regarded as the third generation of artificial neural networks, are expected to bridge the gap between artificial intelligence and computational neuroscience. However, most mainstream SNN research directly adopts the rigid, chain-like hierarchical architecture of traditional artificial neural networks (ANNs), ignoring key stru

M. Gomes, A. C. Lehum, A. J. da Silva

We revisit the renormalization of the gauge coupling in massless QED coupled to a scaleless quadratic theory of gravity. We compare two alternative prescriptions for the running of the electric charge: (i) the conventional $\mu$-running in minimal subtraction, and (ii) a ''physical'' running extracted from the logarithmic dependence of amplitudes on a hard s

Kyle Fridberg

If most of the pixels in an $n \times m$ digital image are the same color, must the image contain a large connected component? How densely can a given set of connected components pack in $\mathbb{Z}^2$ without touching? We answer these two closely related questions for both 4-connected and 8-connected components. In particular, we use structural arguments to

Amit Levy, S. Matthew Weinberg, Chenghan Zhou

We model the ultimate price paid by users of a decentralized ledger as resulting from a two-stage game where Miners (/Proposers/etc.) first purchase blockspace via a Tullock contest, and then price that space to users. When analyzing our distributed ledger model, we find: - A characterization of all possible pure equilibria (although pure equilibria are not

Chengzhi Xia, Jianwei Chen, Yixuan Jiang, Qi Yan

Multimodal MRI offers complementary multi-scale information to characterize the brain structure. However, it remains challenging to effectively integrate multimodal MRI while achieving neuroscience interpretability. Here we propose to use Laplacian harmonics and spectral graph theory for multimodal alignment and multiscale integration. Based on the cortical

Charles M. Elliott, Achilleas Mavrakis

We analyze two fully time-discrete numerical schemes for the incompressible Navier-Stokes equations posed on evolving surfaces in $\mathbb{R}^3$ with prescribed normal velocity using the evolving surface finite element method (ESFEM). We employ generalized Taylor-Hood finite elements $\mathrm{\mathbf{P}}_{k_u}$-- $\mathrm{P}_{k_{pr}}$-- $\mathrm{P}_{k_\lambd

Yuntao Dai

This paper introduces a geometric theory of model error, treating true and model dynamics as geodesic flows generated by distinct affine connections on a smooth manifold. When these connections differ, the resulting trajectory discrepancy--termed the Latent Error Dynamic Response (LEDR)--acquires an intrinsic dynamical structure governed by curvature. We sho

Sirui Ning

This thesis primarily dives into investigating the details of four-dimensional vacua within String Theory using the AdS/CFT correspondence. In the first and second part of the thesis, we study the fibred Calabi-Yau and M-theory moduli stabilization scenario. We consider both flux-stabilized models and non-perturbative stabilization methods. We perform a holo

Adewale Akinfaderin, Shreyas Subramanian

Financial AI systems suffer from a critical blind spot: while Retrieval-Augmented Generation (RAG) excels at finding relevant documents, language models still generate calculation errors and regulatory violations during reasoning, even with perfect retrieval. This paper introduces VERAFI (Verified Agentic Financial Intelligence), an agentic framework with ne

Trisha Dawn, Yang Ni

Spatial transcriptomics technologies enable the measurement of gene expression with spatial context, providing opportunities to understand how gene regulatory networks vary across tissue regions. However, existing graphical models focus primarily on undirected graphs or directed acyclic graphs, limiting their ability to capture feedback loops that are preval

Andrea Conti, Giacomo Morelli

The estimation of the Risk Neutral Density (RND) implicit in option prices is challenging, especially in illiquid markets. We introduce the Deep Log-Sum-Exp Neural Network, an architecture that leverages Deep and Transfer learning to address RND estimation in the presence of irregular and illiquid strikes. We prove key statistical properties of the model and

E. Iancu, D. N. Triantafyllopoulos, S. Y. Wei, F. Yuan

Using the Colour Glass Condensate description of electron-nucleus collisions at high energy, we study the diffractive production of a pair of jets with transverse momenta much larger than the nuclear saturation momentum $Q_s$. At leading order in the QCD coupling, the di-jet cross-section exhibits transverse-momentum dependent (TMD) factorisation, with a glu

Diego Tancara, Herbert Díaz-Moraga, Vicente Sepúlveda-Trivelli, Dardo Goyeneche

While variational quantum algorithms are among the most promising approaches for the noisy intermediate-scale quantum (NISQ) era, their scalability is often hindered by the barren plateau problem. Among the proposals that have demonstrated robustness against this issue, the ADAPT-VQE algorithm stands out for ground state estimation, primarily due to its iter

Emily E. Whitehead, Raphael Hirschi, Vishnu Varma, Bernhard Mueller

While convection has been known to play a key role in stars for many decades, its implementation in one-dimensional stellar evolution codes still represents a major uncertainty today. The purpose of this work is to investigate the impact of initial mass dependent convective boundary mixing (CBM), often referred to as overshooting, on the frequency and type o

Ghenadie N. Mardari

The concept of correlation appears straightforward: measurement outcomes coincide, and patterns emerge. For any record of events, the coefficients are uniquely determined. Thus, if correlations change spontaneously, as seen in quantum monogamy, then individual behavior must have changed first. Surprisingly, this is not always true. When two observables are m

Yuze He, Ferdi Kossmann, Srinivasan Seshan, Peter Steenkiste

Recent advances in video analytics address real-time data drift by continuously retraining specialized, lightweight DNN models for individual cameras. However, the current practice of retraining a separate model for each camera suffers from high compute and communication costs, making it unscalable. We present ECCO, a new video analytics framework designed f

Ahana Ghoshal, Carlos de Gois, Kiara Hansenne, Otfried Gühne

Simulating many-body fermionic systems in conventional qubit-based quantum computers poses significant challenges due to the overheads associated with the encoding of fermionic statistics in qubits, leading to the proposal of native fermionic simulators as an alternative. While allowing for fermionic problems to be simulated efficiently, this class of fermio

Carl Feghali, Hoang-Oanh Le, Van Bang Le

This paper continues the study of a new variant of graph coloring with a connectivity constraint recently introduced by Hsieh et al. [COCOON 2024]. A path in a vertex-colored graph is called conflict-free if there is a color that appears exactly once on its vertices. A connected graph is said to be strongly conflict-free vertex-connection $k$-colorable if it

Tittaya Mairittha, Tanakon Sawanglok, Panuwit Raden, Jirapast Buntub

While voice-based AI systems have achieved remarkable generative capabilities, their interactions often feel conversationally broken. This paper examines the interactional friction that emerges in modular Speech-to-Speech Retrieval-Augmented Generation (S2S-RAG) pipelines. By analyzing a representative production system, we move beyond simple latency metrics

B. S. Hu, A. Ekström, C. Forssén, G. Hagen

Interactions derived from effective field theories of quantum chromodynamics have thus far failed to bind calcium nuclei beyond neutron number $N=40$, while nuclear density functionals typically place the neutron dripline near $^{70}$Ca, at $N=50$. We present the chiral interaction N$^3$LO$_{\rm Texas}$, a combination of two- and three-nucleon potentials at

Lin Bai, Xiaoyang Li, Liqiang Huang, Quynh Nguyen

We present a weak to strong generalization methodology for fully automated training of a multi-head extension of the Mask-RCNN method with efficient channel attention for reliable segmentation of overlapping cell nuclei in multiplex cyclic immunofluorescent (IF) whole-slide images (WSI), and present evidence for pseudo-label correction and coverage expansion

Raffaele Folino, César A. Hernández Melo, Luis F. López Ríos, Ramón G. Plaza

This paper establishes the spectral stability of monotone, stationary front solutions for reaction-diffusion equations where the reaction function is of Nagumo (or bistable) type and with diffusion coefficients which are density dependent and degenerate at zero (one of the equilibrium points of the reaction). These stationary profiles connect the non-degener

Yan Zhang, Han Zou, Lincong Feng, Cong Xie

Recent pose-to-video models can translate 2D pose sequences into photorealistic, identity-preserving dance videos, so the key challenge is to generate temporally coherent, rhythm-aligned 2D poses from music, especially under complex, high-variance in-the-wild distributions. We address this by reframing music-to-dance generation as a music-token-conditioned m

Yilmaz Korkmaz, Jay N. Paranjape, Celso M. de Melo, Vishal M. Patel

Change detection in remote sensing imagery is essential for applications such as urban planning, environmental monitoring, and disaster management. Traditional change detection methods typically identify all changes between two temporal images without distinguishing the types of transitions, which can lead to results that may not align with specific user nee