November 2025 arXiv papers — page 25

Mohammad Ali Gorji, Susmita Jana, Pavel Petrov

We investigate black hole solutions in the mimetic extension of the Einstein-Yang-Mills system, in which the Yang-Mills term is constrained to be constant. In the Abelian U(1) case, we find a static spherically symmetric solution that includes the Schwarzschild and Reissner-Nordstrom black holes as special cases. Moreover, we identify a stealth Schwarzschild

Boyao Peng, Linkun Liu

As autonomous driving technology evolves, ensuring the stability and safety of Autonomous Driving Systems (ADS) through alignment with human values becomes increasingly crucial. While existing research emphasizes the adherence of AI to honest ethical principles, it overlooks the potential benefits of benevolent deception, which maximize overall payoffs. This

Shuyue Feng, Zijian Gan, Camryn J. Gloor, Wei You

Two-dimensional organic-inorganic hybrid perovskite (2D-OIHP) quantum wells are emerging as promising light sources for quantum communication technologies, owing to their ability to generate polarization-encoded optical signals. In this work, we explore how nonlinear optical phenomena can be exploited for quantum information applications, demonstrating the v

Shailendra K. Rathor, Lina Jaurigue, Martin Ziegler, Jörg Schumacher

Reservoir computing (RC) is a powerful framework for predicting nonlinear dynamical systems, yet the role of reservoir topology$-$particularly symmetry in connectivity and weights$-$remains not adequately understood. This work investigates how the structure of the network influences the performance of RC in four systems of increasing complexity: the Mackey-G

Maxim R. Burke, Nikolaos D. Macheras, Werner Strauss

We investigate the properties of linear primitive liftings $\rho\colon \mathcal{L}^p(\mu)\to \mathcal{L}^p(\mu)$ for probability spaces $(X,\Sigma,\mu)$, which are linear maps selecting a representative from each class for almost everywhere equality. We call them vector liftings. They have the advantage over liftings or linear liftings that they exist for al

Zi-Han Zhang, Tan Liu, Shenghua Yu, Zong-Kuan Guo

The mass transfer process is prevalent during the inspiral phase of compact binary systems. Detection of gravitational waves from the inspiral phase of binaries with white dwarfs will allow us to measure the mass transfer rate. Mass transfer effects provide additional contributions to the phase of gravitational waves, which can break the degeneracy between b

Xiaohan Zhang, Kan Liu, Yangle Liu, Fengze Li

In the Virtual Reality (VR) gaming industry, maintaining immersion during real-world interruptions remains a challenge, particularly during transitions along the reality-virtuality continuum (RVC). Existing methods tend to rely on digital replicas or simple visual transitions, neglecting to address the aesthetic discontinuities between real and virtual envir

Jing Hao, Yuci Liang, Lizhuo Lin, Yuxuan Fan

Multimodal Large Language Models (MLLMs) have exhibited immense potential across numerous medical specialties; yet, dentistry remains underexplored, in part due to limited domain-specific data, scarce dental expert annotations, insufficient modality-specific modeling, and challenges in reliability. In this paper, we present OralGPT-Omni, the first dental-spe

Ze-Hao Wang, Tong-Tian Weng, Long-Kun Shan, Xiang-Dong Chen

Structured illumination microscopy (SIM) is a wide-field super-resolution technique normally limited to roughly twice the diffraction-limited resolution ($\approx 100$--$200$~nm). Surpassing this bound is a classic ill-posed inverse problem: recovering high-frequency structure from band-limited raw data. We introduce SIMFormer, a fully blind SIM reconstructi

Jing-Yi Shi, Ming-Fei Li, Ling-An Wu

Low-light image enhancement is an essential computer vision task to improve image contrast and to decrease the effects of color bias and noise. Many existing interpretable deep-learning algorithms exploit the Retinex theory as the basis of model design. However, previous Retinex-based algorithms, that consider reflected objects as ideal Lambertian ignore spe

Minghui Zhao

Lusztig introduced the geometric realizations of quantum groups associated to finite quivers and defined their canonical bases. Sala and Schiffmann introduced the Ringel-Hall algebra of line and realized it as the direct limit of Ringel-Hall algebras of finite quivers of type $A$. In this paper, we shall give geometric realizations of Ringel-Hall algebras of

Yingjue Bian, Tianye Wang, Shiming Tang, Tai Sing Lee

Area V4 is a mid-level stage of the macaque ventral visual stream, known to encode intermediate visual features such as color, curvature, corners, texture, three-dimensional (3D) solids, and local form. Classical neurophysiological studies have typically examined these dimensions in isolation, contrasting V4 selectivity for shape versus texture, 3D solid sur

Joshua Richland, Tuomo Kiiskinen, William Wang, Sophia Lu

We present a scalable framework for computing polygenic risk scores (PRS) in high-dimensional genomic settings using the recently introduced Univariate-Guided Sparse Regression (uniLasso). UniLasso is a two-stage penalized regression procedure that leverages univariate coefficients and magnitudes to stabilize feature selection and enhance interpretability. B

Junoh Kang, Donghun Ryou, Bohyung Han

Real world image super-resolution (Real-ISR) often leverages the powerful generative priors of text-to-image diffusion models by regularizing the output to lie on their learned manifold. However, existing methods often overlook the importance of the regularizing manifold, typically defaulting to a text-conditioned manifold. This approach suffers from two key

Richard J. Young

Large Language Model (LLM) safety guardrail models have emerged as a primary defense mechanism against harmful content generation, yet their robustness against sophisticated adversarial attacks remains poorly characterized. This study evaluated ten publicly available guardrail models from Meta, Google, IBM, NVIDIA, Alibaba, and Allen AI across 1,445 test pro

Tong Li, Xu Yan, Bo Wu, Cheng Luo

Due to the limited permissions for upgrading dualside (i.e., server-side and client-side) loss tolerance schemes from the perspective of CDN vendors in a multi-supplier market, modern large-scale live streaming services are still using the automatic-repeat-request (ARQ) based paradigm for loss recovery, which only requires server-side modifications. In this

Nathan S. Prins, Timur V. Tscherbul

We extend the rigorous adiabatic coupled-channel formalism to ultracold nonreactive atom-molecule collisions in the presence of an external magnetic field. The wavefunction of the collision complex is expanded in adiabatic basis states obtained by solving the eigenvalue problem for the adiabatic Hamiltonian (the total Hamiltonian of the collision complex min

Tianyu Zhang, Zihang Xi, Jingyu Hua, Sheng Zhong

In the realm of black-box jailbreak attacks on large language models (LLMs), the feasibility of constructing a narrow safety proxy, a lightweight model designed to predict the attack success rate (ASR) of adversarial prompts, remains underexplored. This work investigates the distillability of an LLM's core security logic. We propose a novel framework that in

Xuchen Liu, Ruocheng Li, Bin Xin, Weijia Yao

Although quadrotor navigation has achieved high performance in trajectory planning and control, real-time adaptability in unknown complex environments remains a core challenge. This difficulty mainly arises because most existing planning frameworks operate in an open-loop manner, making it hard to cope with environmental uncertainties such as wind disturbanc

Lei Li, Jiale Gong, Ziyang Li, Hong Wang

Conventional subtractive manufacturing inevitably involves material loss during geometric realization, while additive manufacturing still suffers from limitations in surface quality, process continuity, and productivity when fabricating complex geometries. To address these challenges, this paper proposes a volume-consistent kneading-based forming method for

Thomas Choi, Yuning Zhang, Issei Kanno, Masaaki Ito

Accurate path loss (PL) modeling is essential for evaluating and optimizing cell-free massive MIMO systems, especially in dense urban environments where traditional models fail to capture the complexity of real-world propagation. This paper introduces CUNEC (Cell-free massive MIMO for Urban Non-stationary Environments with Correlations, a novel PL model that

Guanghui Wang, Jinze Yu, Xing Zhang, Dayuan Jiang

Large Language Models (LLMs) are increasingly deployed for structured data generation, yet output consistency remains critical for production applications. We introduce a comprehensive framework for evaluating and improving consistency in LLM-generated structured outputs. Our approach combines: (1) STED (Semantic Tree Edit Distance), a novel similarity metri

Jiayuan Du, Yiming Zhao, Zhenglong Guo, Yong Pan

This paper introduces a novel architecture for trajectory-conditioned forecasting of future 3D scene occupancy. In contrast to methods that rely on variational autoencoders (VAEs) to generate discrete occupancy tokens, which inherently limit representational capacity, our approach predicts multi-frame future occupancy in an end-to-end manner directly from ra

Rochana Chaturvedi, Yue Zhou, Andrew D. Boyd, Brian T. Layden

Clinical notes in Electronic Health Records (EHRs) capture rich temporal information on events, clinician reasoning, and lifestyle factors often missing from structured data. Leveraging them for predictive modeling can be impactful for timely identification of chronic diseases. However, they present core natural language processing (NLP) challenges: long tex

Zhifeng Wu, Yuelin Han, Shaolei Ren

The intense computational demands of AI, especially large foundation models, are driving a global boom in data centers. These facilities bring both tangible benefits and potential environmental burdens to local communities. However, the planning processes for data centers often fail to proactively integrate local public opinion in advance, largely because tr

Jingjun Xu, Chongshan Lin, Haofei Yu, Tao Feng

Academic research generates diverse data sources, and as researchers increasingly use machine learning to assist research tasks, a crucial question arises: Can we build a unified data interface to support the development of machine learning models for various academic tasks? Models trained on such a unified interface can better support human researchers thro

Batin Kurt, Umut Orguner

We propose analytical mean square error (MSE) expressions for the Kalman filter (KF) and the Kalman smoother (KS) for benchmark studies, where the true system dynamics are unknown or unavailable to the estimator. In such cases, as in benchmark evaluations for target tracking, the analysis relies on deterministic state trajectories. This setting introduces a

Chunzheng Zhu, Yangfang Lin, Jialin Shao, Jianxin Lin

Diabetic retinopathy (DR) grading plays a critical role in early clinical intervention and vision preservation. Recent explorations predominantly focus on visual lesion feature extraction through data processing and domain decoupling strategies. However, they generally overlook domain-invariant pathological patterns and underutilize the rich contextual knowl

Yejia Liu, Zhifeng Wu, Pengfei Li, Shaolei Ren

The electric power sector is a leading source of air pollutant emissions, impacting the public health of nearly every community. Although regulatory measures have reduced air pollutants, fossil fuels remain a significant component of the energy supply, highlighting the need for more advanced demand-side approaches to reduce the public health impacts. To enab

Geun-Deok Jang, Dong-Kyun Han, Seo-Hyeon Park, Seong-Whan Lee

Drowsy driving is a growing cause of traffic accidents, prompting recent exploration of electroencephalography (EEG)-based drowsiness detection systems. However, the inherent variability of EEG signals due to psychological and physical factors necessitates a cumbersome calibration process. In particular, the inter-subject variability of EEG signals leads to

Shuchen Du, Shuo Lei, Feiran Li, Jiacheng Li

Unsupervised domain adaptation (UDA) greatly facilitates the deployment of neural networks across diverse environments. However, most state-of-the-art approaches are overly complex, relying on challenging adversarial training strategies, or on elaborate architectural designs with auxiliary models for feature distillation and pseudo-label generation. In this

Zhao Liu, Xiang Wang, Yin Yang, Pengcheng Ma

The discovery of superconducting electrides, characterized by interstitial anionic electrons (IAEs) residing in lattice cavities, has established a distinctive platform for investigating superconductors. Yet the superconducting origin and the fundamental role of IAEs in Cooper pairing formation remain poorly understood due to the challenges in directly obser

Xinquan Hu, Jun Zhang

In the housing market model introduced by Shapley and Scarf (1974), we propose a new axiom, local unanimity, that extends the unanimity condition widely used in social choice theory. It applies the unanimity condition to any subset of agents in the model who unanimously agree on the best exchange of their endowments. Building on this axiom, we provide severa

Brandon Beltz, Po-Yu Chen, James Doty, Yvonne Fonken

This paper introduces GAMBiT (Guarding Against Malicious Biased Threats), a cognitive-informed cyber defense framework that leverages deviations from human rationality as a new defensive surface. Conventional cyber defenses assume rational, utility-maximizing attackers, yet real-world adversaries exhibit cognitive constraints and biases that shape their inte

Yiguo Zhang

I analyzed ice water content (IWC) data from the Aura Microwave Limb Sounder (MLS) and sea surface temperature (SST) data from NOAA's Optimum Interpolation SST (OISST) product from 2004 to 2024. Using these data, I derived monthly infrared (IR) leakage over the tropics and computed derivatives of both the IR leakage and tropical SST time series from 2005 to

Joel Alberto Santos, Zongwei Wu, Xavier Alameda-Pineda, Radu Timofte

Understanding human instructions is essential for enabling smooth human-robot interaction. In this work, we focus on object grounding, i.e., localizing an object of interest in a visual scene (e.g., an image) based on verbal human instructions. Despite recent progress, a dominant research trend relies on using text as an intermediate representation. These ap

Elon Litman

We liberate Equilibrium Propagation (EP) from the limit of infinitesimal perturbations by establishing a finite-nudge foundation for local credit assignment. By modeling network states as Gibbs-Boltzmann distributions rather than deterministic points, we prove that the gradient of the difference in Helmholtz free energy between a nudged and free phase is exa

Zipeng Chen, Zhaoyang Yin

This paper focuses on the $d$-dimensional ($d\geq2$) Boussinesq equation with fractional dissipation $(-\Delta)^{\alpha}$ on the torus. We show that the uniqueness property breaks down within the function space $L^p_tL^\infty_x$ for any $p<\frac{2\alpha}{2\alpha-1}$ when $1\leq\alpha<\frac{d+1}{2}$ and the function space $L^\frac{2\alpha}{2\alpha-1}_tL^q_x$

Yuri Lima, Davi Obata, Mauricio Poletti

We prove that if a geodesic flow on a closed orientable $C^\infty$ surface is transitive and has positive topological entropy, then it has a unique measure of maximal entropy. This covers all previous results of the literature on the uniqueness of the measure of maximal entropy in this context, as well as it applies to new examples such as the ones construct

Amador Cruz-Fuentes

We present a complete classification of normal toric surfaces that are resolved by a single normalized Nash blowup. Likewise, we obtain a complete classification of those resolved by a single Nash blowup. In both cases, the classification is expressed in terms of the continued fraction associated with the normal toric surface.

Zhenxiang Lin, Maryam Haghighat, Will Browne, Dimity Miller

Vision-language models (VLMs), such as CLIP, have gained popularity for their strong open vocabulary classification performance, but they are prone to assigning high confidence scores to misclassifications, limiting their reliability in safety-critical applications. We introduce a training-free, post-hoc uncertainty estimation method for contrastive VLMs tha

Chunzheng Zhu, Yangfang Lin, Shen Chen, Yijun Wang

Accurate medical diagnosis often involves progressive visual focusing and iterative reasoning, characteristics commonly observed in clinical workflows. While recent vision-language models demonstrate promising chain-of-thought (CoT) reasoning capabilities via reinforcement learning with verifiable rewards (RLVR), their purely on-policy learning paradigm tend

Ishaan Kunwar, Henry Cantor, Tyler Rizzo, Ayaan Qayyum

The goal of LocaGen is to improve the localization performance of audio signals in the 2-D beam localization problem. LocaGen reduces sampling quantization errors through machine learning models trained on realistic synthetic data generated by a simulation. The system increases the accuracy of both direction-of-arrival (DOA) and precise location estimation o

Yann Le Beux, Oluchi Audu, Oche D. Ankeli, Dhananjay Balakrishnan

Existing AI bias evaluation benchmarks largely reflect Western perspectives, leaving African contexts underrepresented and enabling harmful stereotypes in applications across various domains. To address this gap, we introduce AfriStereo, the first open-source African stereotype dataset and evaluation framework grounded in local socio-cultural contexts. Throu

Kaoru Sano

Fix a strong rectangulation pattern $P$ of size $L$. We show that the growth constant of the class of strong rectangulations avoiding $P$ is strictly smaller than $\Lambda =27/2$, the growth constant for all strong rectangulations. More precisely, forbidding any such $P$ yields a pattern-uniform exponential drop of at least $\Lambda - 1/\Lambda^{3L-1}$. Cons

Shaun Fallat, Kamyar Khodamoradi, David Kirkpatrick, Valerii Maliuk

We study the problem of learning hypergraphs with shortest-path queries (SP-queries), and present the first provably optimal online algorithm for a broad and natural class of hypertrees that we call orderly hypertrees. Our online algorithm can be transformed into a provably optimal offline algorithm. Orderly hypertrees can be positioned within the Fagin hier

Huai-Dong Cao, Fengjiang Li, James Siene

In this paper, we classify $n$-dimensional ($n\geq 5$) quasi-Einstein manifolds with harmonic Weyl curvature, thus extending the work of Shin \cite{Shin} in dimension four for quasi-Einstein manifolds and refining the work of He-Petersen-Wylie \cite{HPW}. As a consequence, we provide new examples of quasi-Einstein manifolds which are neither locally conforma

Aarshana R. Parekh, Rui Gao, Rajeev K. Jaiman

Cantilevered elastic foils can undergo self-induced, large-amplitude flapping when subject to fluid flow, a widely observed phenomenon of fluid-structure interaction, from fluttering leaves or the movement of fish fins. When harnessed in steady currents, these oscillations enable the extraction of kinetic energy from the flow. However, accurately predicting

Lei Yang, Jingjing Hu, Tianxiang Liu

With the advancement of modern applications, an increasing number of composite optimization problems arise whose smooth component does not possess a globally Lipschitz continuous gradient. This setting prevents the direct use of the proximal gradient (PG) method and its variants, and has motivated a growing body of research on new PG-type methods and their c

Provakar Mondal, Eli Tilevich

BACKGROUND: Modern distributed systems replicate data across multiple execution sites. Business requirements and resource constraints often necessitate mixing different languages across replica sites. To facilitate the management of replicated data, modern software engineering practices integrate special-purpose replicated data libraries (RDLs) that provide

Sen Fang, Hongbin Zhong, Yalin Feng, Yanxin Zhang

New technologies such as Rectified Flow and Flow Matching have significantly improved the performance of generative models in the past two years, especially in terms of control accuracy, generation quality, and generation efficiency. However, due to some differences in its theory, design, and existing diffusion models, the existing acceleration methods canno

I. Komis, E. T. Kokkinakis, K. G. Makris, E. N. Economou

The impact of disorder on wave transport has been extensively studied in Hermitian systems, where static randomness gives rise to Anderson localization. In non-Hermitian lattices, static disorder can lead to peculiar transport features, including jumpy wave evolution. By contrast, much less is known about how transport is modified when the on-site disorder e

Sthavishtha R. Bhopalam, Ruben Juanes, Hector Gomez

We study the mobilization of an oil droplet in a deformable, actuated constricted tube subjected to two different actuation mechanisms: hydrodynamic actuation (oscillatory body force in the fluid) and dynamic wall actuation (oscillatory traction on the tube walls). Using high-resolution fluid-structure interaction simulations, we analyze the effects of actua

Louis Follet, Jordan Goldstein, Christopher L. Panuski, Ian Christen

High-speed thermal imaging in the long-wave infrared (LWIR) is critical for applications from autonomous navigation to medical screening, yet existing uncooled detectors are fundamentally constrained. Resistive bolometers are limited by electronic noise and the parasitic thermal load of wired readouts, while state-of-the-art nanomechanical resonators typical

Mathieu Fourment, Jiansi Gao, Marc A Suchard, Frederick A Matsen

Fixed tree topologies are widely used in phylodynamic analyses to reduce computational burden, yet the consequences of this assumption remain insufficiently understood. Here, we systematically assess the impact of various fixed-topology strategies on phylogenetic and phylodynamic parameter estimates across a diverse set of viral datasets. We compare fully Ba

Eliot Wong-Toi, Alex Boyd, Vincent Fortuin, Stephan Mandt

Uncertainty quantification is vital for decision-making and risk assessment in machine learning. Mean-variance regression models, which predict both a mean and residual noise for each data point, provide a simple approach to uncertainty quantification. However, overparameterized mean-variance models struggle with signal-to-noise ambiguity, deciding whether p

Yuanzhe Ma, Yian Huang, Hongseok Namkoong

Limited overlap between treated and control groups is a key challenge in observational analysis. Standard approaches like trimming importance weights can reduce variance but introduce a fundamental bias. We propose a sensitivity framework for contextualizing findings under limited overlap, where we assess how irregular the outcome function has to be in order

Boyan Duan, Xiao Liang, Shuai Lu, Yaoxiang Wang

Automated theorem proving in Euclidean geometry, particularly for International Mathematical Olympiad (IMO) level problems, remains a major challenge and an important research focus in Artificial Intelligence. In this paper, we present a highly efficient method for geometry theorem proving that runs entirely on CPUs without relying on neural network-based in

Muxian Xu, Siyu Cheng, Andrea Capa Salinas, Ganesh Pokharel

Kagome superconductors $A$V$_3$Sb$_5$ ($A$ = Cs, K, Rb) have developed into an exciting playground for realizing and exploring exotic solid state phenomena. Abundant experimental evidence suggests that electronic structure breaks rotational symmetry of the lattice, but whether this may be a simple consequence of the symmetry of the underlying 2 $\times$ 2 ch

David Isztl, Tahm Spitznagel, Gabor Mark Somfai, Rui Santos

Large vision foundation models have been widely adopted for retinal disease classification without systematic evidence justifying their parameter requirements. In the present work we address two critical questions: First, are large domain-specific foundation models essential, or do compact general-purpose architectures suffice? Second, does specialized retin

Michele Aldé, Michael Feischl, Dirk Praetorius

We consider the Landau-Lifshitz-Gilbert equation (LLG) that models time-dependent micromagnetic phenomena. We propose a full discretization that employs first-order finite elements in space and a BDF2-type two-step method in time. In each time step, only one linear system of equations has to be solved. We employ linear interpolation in time to reconstruct th

Cyrill Krähenbühl, Nico Hauser, Christelle Gloor, Juan Angel García-Pardo

Although our lives are increasingly transitioning into the digital world, many digital assets still relate to objects or places in the physical world, e.g., websites of stores or restaurants, digital documents claiming property ownership, or digital identifiers encoded in QR codes for mobile payments in shops. Currently, users cannot securely associate digit

Apratim Bhattacharyya, Bicheng Xu, Sanjay Haresh, Reza Pourreza

Multi-modal Large Language Models (LLM) have advanced conversational abilities but struggle with providing live, interactive step-by-step guidance, a key capability for future AI assistants. Effective guidance requires not only delivering instructions but also detecting their successful execution, as well as identifying and alerting users to mistakes, all of

Ranjeet K. Tiwari, Daniel Sgarioto, Peter Graham, Alexei Skvortsov

Real-time sea state estimation is vital for applications like shipbuilding and maritime safety. Traditional methods rely on accurate wave-vessel transfer functions to estimate wave spectra from onboard sensors. In contrast, our approach jointly estimates sea state and vessel parameters without needing prior transfer function knowledge, which may be unavailab

Jin Zhang, Xiaowei Liu

This work develops a convergence theory for H(div)-conforming finite element methods applied to the steady Oseen problem, focusing on cases where the exact finite element complex holds while the commuting diagram property may fail. The proposed method incorporates vorticity stabilization to ensure optimal-order convergence of the velocity error, especially f

Zhen-Yan Lu, Shu-Peng Wang, Qi Lu, Bo-Nan Zhang

In compact stellar environments, the stability of dense QCD matter requires the simultaneous fulfillment of charge neutrality and beta equilibrium. In this work, we study how temperature and finite chemical potential affect QCD topology and axion properties within this medium, analyzing both cases with and without the charge neutrality condition. Our results

Yuchen Lu, Megan Zheng, Will Crichton, Akshay Narayan

Computational notebooks are convenient for programmers, but can easily become confusing and inconsistent due to the ability to incrementally edit a program that is running. Recent reactive notebook systems, such as Ipyflow, Marimo and Observable, strive to keep notebook state in sync with the current cell code by re-executing a minimal set of cells upon modi

Changjie Chen

We study the relationship between the lengths of closed geodesics on hyperbolic surfaces and their topological complexity, measured by the self-intersection number. In particular, we provide explicit upper bounds for the length $s_k(X)$ of a shortest closed geodesic with exactly $k$ self-intersections in terms of the length $L_\textswab{8}(X)$ of a shortest

Zhe Chen, Xinran Li, Michael O. Harhay, Bo Zhang

Two binary instrumental variables (IVs) are nested if individuals who comply under one binary IV also comply under the other. This situation often arises when the two IVs represent different intensities of encouragement or discouragement to take the treatment, with one stronger than the other. In a nested IV structure, treatment effects can be identified for

Julio Carlos Bertua Marasca, Homer Davila Gutierrez, Oswaldo Alberto Martinez Osorio, Josue Ismael Mosquera Hadatty

We present an explicit numerical implementation of the Friedmann equations to model the expansion of the Universe in spatially flat, homogeneous and isotropic Friedmann-Lemaitre-Robertson-Walker (FLRW) cosmologies. Using cosmological parameters from the Planck 2018 results for the concordance LCDM model, we compute the evolution of the scale factor a(t) by i

Shaona Ghosh, Barnaby Simkin, Kyriacos Shiarlis, Soumili Nandi

This paper introduces a dynamic and actionable framework for securing agentic AI systems in enterprise deployment. We contend that safety and security are not merely fixed attributes of individual models but also emergent properties arising from the dynamic interactions among models, orchestrators, tools, and data within their operating environments. We prop

Nachiket Subbaraman, Jaskinder Sarai, Aniruddh Nath, Lichan Hong

Large Language Models (LLMs) have demonstrated remarkable capabilities in reasoning, generalization, and simulating human-like behavior across a wide range of tasks. These strengths present new opportunities to enhance traditional recommendation systems (RS), especially in the cold-start item scenario where newly introduced items lack interactions. Existing

Grigory Franguridi, Hyungsik Roger Moon

We consider a generalized method of moments framework in which a part of the data vector is missing for some units in a completely unrestricted, potentially endogenous way. In this setup, the parameters of interest are usually only partially identified. We characterize the identified set for such parameters using the support function of the convex set of mom

B. Courtney-Barrer, X. Haubois, P. Wood, D. Dionese

Long secondary periods (LSPs) occur in about one-third of evolved stars, yet their origin remains unclear. The leading explanations are oscillatory convective modes and a binary companion embedded in dust. We investigate the LSP of the red giant RT Pav using multi-wavelength VLTI interferometry (PIONIER, GRAVITY, MATISSE; 1.5-5.0 microns), obtained near the

José Carmona Tapia, Antonio J. Martínez Aparicio, Pedro J. Martínez-Aparicio

In this article, we investigate the existence and multiplicity of solutions to the Robin problem \begin{equation*} \begin{cases} -Δu = λf(u) & \text{in } Ω, \frac{\partial u}{\partial ν} + γu=0 & \text{on } \partialΩ, \end{cases} \end{equation*} where $Ω\subset \mathbb{R}^N$ ($N\geq 1$) is a smooth bounded domain, and $λ, γ>0$. Our main assumption is that $f

Romina Abarca-Ramírez, Diego Román-Cortés, Maxim Mazanov, Vlad Simonyan

Photonic molecules support the excitation of higher-order states, which are otherwise hard to access at individual waveguides. In this work, we demonstrate the resonant excitation of photonic molecular states which evanescently couple to single-mode waveguides. We implement the experiments on femtosecond laser written photonic structures and demonstrate an e

Alfio M. Bonanno, S. R. Haridev, Gaurav Narain

In this work we studies the long-range interactions in non-gravitational field theories and their behaviour in the deep infrared. To model such effects, we consider a nonlocal scalar theory obtained by adding a $ϕ\Box^{-1}ϕ$ term to the local action. Using the functional renormalisation group, we analyse its infrared fixed-point structure. Within the LPA, we

Denise Reustlen, Sebastian Sailler, Davina U. Schmidt, Richard Schlitz

The spin Hall magnetoresistance (SMR) is widely used to study the interplay between charge and spin currents in bilayers of a magnetic insulator and a normal metal. However, not much is known about the spatial variation of the SMR across the surface of one and the same sample. In this work, we investigate the statistical distribution of the SMR in hundreds o

Kiril Hristov, Saurish Khandelwal, Yi Pang, Gabriele Tartaglino-Mazzucchelli

We develop a consistent partially off-shell framework for evaluating higher-derivative actions of five-dimensional $\cal{N}=1$ gauged supergravity with abelian vector multiplets on AdS$_5$. Using the superconformal formalism, we show that the resulting holographic expression reproduces the trial $a$-anomaly coefficient of the dual conformal field theory, ide

Connor Heimig, Jonas Biechteler, Cristina Cruciano, Armando Genco

Two-dimensional semiconductors, such as monolayer transition metal dichalcogenides (TMDC), exhibit strong excitonic transitions at room temperature and offer a unique platform for exploring light-matter interactions in nanoscale photonic systems. In this work, we demonstrate a compact and polarization-invariant photonic metasurface, fabricated from hexagonal

P. N. Fedorov, A. M. Dmytrenko, V. S. Akhmetov, A. B. Velichko

In this paper, we construct a detailed circular velocity curve of the Milky Way out to 20 kpc based on the radial component of the Jeans equation in cylindrical coordinates, assuming an axisymmetric gravitational potential, and show its dependence on azimuth. We use only Gaia DR3 data and aim to minimize the use of model data and various assumptions. To buil

Hardik Bohra, Allic Sivaramakrishnan

We study how to recover timelike worldlines in AdS from CFT data as a toy model for holographically reconstructing realistic observers. We give a bulk extremization procedure that determines composite timelike-spacelike geodesics that connect timelike-separated boundary points. The total geodesic length matches the length extracted from CFT correlators at th

Amirhossein Alizad, Mostafa Milani

Shapley-like values, including the Shapley and Banzhaf values, provide a principled way to quantify how individual tuples contribute to a query result. Their exact computation, however, is intractable because it requires aggregating marginal contributions over exponentially many permutations or subsets. While sampling-based estimators have been studied in co

Aiyao Zhang, Xiaodong Lee, Zhixian Zhuang, Jiuqi Wei

Access control is a security mechanism designed to ensure that only authorized users can access specific resources. Cross-domain access control involves access to resources across different organizations, institutions, or applications. Traditional access control, however, which handles authentication and authorization separately in centralized environments,

Alejandro Peñuela Diaz

Area-constrained critical surfaces for the Hawking quasi-local energy (&#34;Hawking surfaces&#34;) provide a natural setting for that energy: they enjoy positivity and rigidity properties. We construct large-scale foliations at infinity by Hawking surfaces in asymptotically Schwarzschild initial data sets. Using a Lyapunov-Schmidt reduction within a Willmore

Giovanni Canepa, Michele Schiavina

We extend the cohomological setting developed by Batalin, Fradkin and Vilkovisky (BFV), which produces a resolution of coisotropic reduction in terms of hamiltonian dg manifolds, to the case of nested coisotropic embeddings $C\hookrightarrow C_\circ \hookrightarrow F$ inside a symplectic manifold $F$. To this, we naturally assign $\underline{C}$ and $\underl

Alessandro Andretta, Lorenzo Notaro

Generalizing a result of Törnquist and Weiss, we study the connection between the existence of $\varSigma_2^1$ Sierpiński&#39;s coverings of $\mathbb{R}^n$, and a cardinal invariant of the upper semi-lattice of constructibility degrees known as breadth.

Elba Garcia-Failde, Paolo Gregori, Kento Osuga

We investigate volumes of moduli spaces of bordered Klein surfaces, which include non-orientable surfaces. On these moduli spaces, the top form introduced by Norbury diverges as the lengths of 1-sided geodesics approach zero. However, when integrated over Gendulphe&#39;s regularised moduli space, on which the systole of 1-sided geodesics is bounded below by

Alif Ilham Madani, Riska A. Kuswati, Alex M. Lechner, Muhamad Risqi U. Saputra

Digital Elevation Models (DEMs) are vital datasets for geospatial applications such as hydrological modeling and environmental monitoring. However, conventional methods to generate DEM, such as using LiDAR and photogrammetry, require specific types of data that are often inaccessible in resource-constrained settings. To alleviate this problem, this study pro

Xuchen Li, Hengrui Gu, Mohan Zhang, Qin Liu

Text-prompted foundation models for medical image segmentation offer an intuitive way to delineate anatomical structures from natural language queries, but their predictions often lack spatial precision and degrade under domain shift. In contrast, visual-prompted models achieve strong segmentation performance across diverse modalities by leveraging spatial c

Nishchay Suri, Zhihui Wang, Tanay Roy, Davide Venturelli

We present a quantum sensing protocol for coupled qubit-oscillator systems that surpasses the standard quantum limit (SQL) by exploiting a geometrical phase. The signal is encoded in the geometrical phase that is proportional to the area enclosed in oscillator phase space. This area is amplified through squeezing, enabling sensitivities beyond the SQL. Our m

Leren Qian, Mohammad Khishe, Yiqian Huang, Seyedali Mirjalili

The chimp optimization algorithm (ChOA) is a nature-inspired algorithm that imitates chimpanzees' individual intelligence and hunting behaviors. In this algorithm, the hunting process consists of four steps: driving, blocking, chasing, and attacking. Because of the novelty of ChOA, the steps of the hunting process have been modeled in a simple way, leading t

Futian Wang, Chaoliu Weng, Xiao Wang, Zhen Chen

The precise reading recognition of pointer meters plays a key role in smart power systems, but existing approaches remain fragile due to challenges like reflections, occlusions, dynamic viewing angles, and overly between thin pointers and scale markings. Up to now, this area still lacks large-scale datasets to support the development of robust algorithms. To

Kyla de Villa, Xiaoyu Wang, Eva Zurek, Burkkhard Militzer

Polyhydrides have been shown to form novel structures at high pressure, which may be found in the interiors of giant planets. With density functional molecular dynamics simulations we studied the behavior of ammonium polyhydride compounds with stoichiometries of NH$_7$, NH$_9$, NH$_{10}$, NH$_{11}$, NH$_{14}$, NH$_{20}$, and NH$_{24}$ which were predicted wi

Maalvladédon Ganet Somé, Edward Korveh

In this paper, we investigate a mean-field singular stochastic optimal control problem for systems governed by mean-field regime-switching singular stochastic differential equations. The state process is assumed to depend on both a regular and a singular control, and the coefficient associated with the singular component is allowed to be regime dependent. We

Guying Lin, Kemeng Huang, Michael Liu, Ruihan Gao

We introduce PAT3D, the first physics-augmented text-to-3D scene generation framework that integrates vision-language models with physics-based simulation to produce physically plausible, simulation-ready, and intersection-free 3D scenes. Given a text prompt, PAT3D generates 3D objects, infers their spatial relations, and organizes them into a hierarchical s

Brantly Callaway, Derek Dyal, Pedro H. C. Sant'Anna, Emmanuel S. Tsyawo

In this paper, we propose a new approach to causal inference with panel data. Instead of using panel data to adjust for differences in the distribution of unobserved heterogeneity between the treated and comparison groups, we instead use panel data to search for "close comparison groups" -- groups that are similar to the treated group in terms of pre-treatme

Matthew Thomas, Zhisong Qu, Matthew Hole

The shear Alfv\'en spectrum is computed in the presence of symmetry breaking perturbations that introduce chaotic magnetic field trajectories. Quadratic flux minimised surfaces allow the creation of pseudo straight field line coordinates in the chaotic region. With these coordinates, the reduced ideal MHD equations are cast into an eigenvalue problem and sol

Hernan Huwyler

Organizations investing in artificial intelligence face a fundamental challenge: traditional return on investment calculations fail to capture the dual nature of AI implementations, which simultaneously reduce certain operational risks while introducing novel exposures related to algorithmic malfunction, adversarial attacks, and regulatory liability. This re

Pamela D. Rivière, Sean Trott

Despite an in-principle understanding of self-attention matrix operations in Transformer language models (LMs), it remains unclear precisely how these operations map onto interpretable computations or functions--and how or when individual attention heads develop specialized attention patterns. Here, we present a pipeline to systematically probe attention mec