November 2025 arXiv papers — page 89

David L. Cole, Jordan Jalving, Jonah Langlieb, Jesse D. Jenkins

We present a general, flexible modeling abstraction for building and working with distributed optimization problems called a RemoteOptiGraph. This abstraction extends the OptiGraph model in Plasmo$.$jl, where optimization problems are represented as hypergraphs with nodes that define modular subproblems (variables, constraints, and objectives) and edges that

Emdadul Haque Chowdhury, Masoud Aryanpour, Yun Kyung Shin, Bladimir Ramos-Alvarado

Supercritical CO2 (scCO2) plays a crucial role as a solvent in separation processes, advanced power cycles, and materials processing. Nonetheless, the atomistic comprehension of how the dense scCO2 matrix influences the fundamental reaction of carbon monoxide (CO) is still insufficiently explored. Experimental studies and molecular dynamics (MD) simulations

Heather J. Alexander, Jonathan A. Simon, Frédéric Pinard

The law draws a sharp distinction between objects and persons, and between two kinds of persons, the ''fictional'' kind (i.e. corporations), and the ''non-fictional'' kind (individual or ''natural'' persons). This paper will assess whether we maximize overall long-term legal coherence by (A) maintaining an object classification for all future AI systems, (B)

Adrian Shuai Li, Elisa Bertino

Machine learning (ML)-based malware detectors degrade over time as concept drift introduces new and evolving families unseen during training. Retraining is limited by the cost and time of manual labeling or sandbox analysis. Existing approaches mitigate this via drift detection and selective labeling, but fully label-free adaptation remains largely unexplore

Haizhou Wen, He Li, Zhen Li

Reconstruction of 3D erythrocyte or red blood cell (RBC) morphology from partial observations, such as microscope images, is essential for understanding the physiology of RBC aging and the pathology of various RBC disorders. In this study, we propose a multi-fidelity neural network (MFNN) approach to fuse high-fidelity cross-sections of an RBC, with a morpho

Artur A. Oliveira, Mateus Espadoto, Roberto M. Cesar, Roberto Hirata

We introduce Graph Memory (GM), a structured non-parametric framework that represents an embedding space through a compact graph of reliability-annotated prototype regions. GM encodes local geometry and regional ambiguity through prototype relations and performs inference by diffusing query evidence across this structure, unifying instance retrieval, prototy

Swagata Acharya, Brooks Tellekamp, Jerome Jackson, Dimitar Pashov

Rare-earth nickelates RNiO3 (R=rare-earth element) exhibit three kinds of phase transitions with decreasing temperature: a structural transition from a pseudo-cubic to a monoclinic phase, a metal- insulator transition (MIT), and a magnetic transition from a paramagnetic state to an ordered one. The first two occur at the same temperature, which has led to a

Joaquín Moraga, Juan Pablo Zúñiga

We study degenerations of cluster type varieties and pairs. Our first theorem proves that degenerations of toric pairs are finite quotients of toric pairs. In a similar vein, under some mild conditions, we prove that degenerations of cluster type pairs are finite quotients of cluster type pairs. Then, we focus on degenerations of cluster type surfaces. We gi

Alessa I. Wiggins, Jamie R. Quinn, Micah Oeur, Sarah R. Loebman

Open clusters (OCs) act as key probes that can be leveraged to constrain the formation and evolution of the Milky Way (MW)'s disk, as each has a unique chemical fingerprint and well-constrained age. Significant Galactic dynamic interactions can leave imprints on the orbital properties of OCs, allowing us to use the present day properties of long-lived OCs to

Helmut Repp

The method described in the first part for frequency-selectively measuring the transfer function and the noise power spectral density of the superimposed noise at the output of a disturbed, real system with nonlinearities using windowing was limited to time-invariant systems with stationary and zero-mean processes. Here, we investigate how this measurement m

Suzie Kim

Conventional reinforcement learning (RL) approaches often struggle to learn effective policies under sparse reward conditions, necessitating the manual design of complex, task-specific reward functions. To address this limitation, reinforcement learning from human feedback (RLHF) has emerged as a promising strategy that complements hand-crafted rewards with

Siegfried Kaidisch, Amir Kleiner, Sivan Refaely-Abramson, Peter Puschnig

The momentum-space signatures of excitons can be experimentally accessed through time-resolved (pump-probe) photoelectron spectroscopy. In this work, we develop a computational framework for exciton photoemission orbital tomography (exPOT) in periodic systems, enabling the simulation and interpretation of experimental observables within many-body perturbatio

Ryan R. Curtin, Fred Lu, Edward Raff, Priyanka Ranade

The number of n-gram features grows exponentially in n, making it computationally demanding to compute the most frequent n-grams even for n as small as 3. Motivated by our production machine learning system built on n-gram features, we ask: is it possible to accurately, deterministically, and quickly recover the top-k most frequent n-grams? We devise a multi

Sam Chow, Qing-Long Zhou

Consider a sequence of integral matrices $\mathcal{A}=(A_n)_{n\in\N}$, and a $d$-tuple function ${\bf r}=(r_1,\ldots,r_d)\colon \N\to (0,\frac{1}{2})$. For a fixed vector ${\bm \alpha},$ we are interested in the set $\mathcal{T}_{{\bm \alpha}}(\mathcal{A}, {\bf r})$ of vectors ${\bm \beta}\in[0,1)^{d}$ for which $A_n{\bm \alpha}~~\!\!\!\!\!\pmod{1}$ infinite

Kartheek Bondugula, Santiago Mazuelas, Aritz Pérez

Supervised learning with large-scale data usually leads to complex optimization problems, especially for classification tasks with multiple classes. Stochastic subgradient methods can enable efficient learning with a large number of samples for classification techniques that minimize the average loss over the training samples. However, recent techniques, suc

Mohamed Abdallah Salem, Hamdy Ahmed Ashour, Ahmed Elshenawy

This research addresses the significant challenges of energy consumption and environmental impact in laser cutting by proposing novel deep learning (DL) methodologies to achieve energy reduction. Recognizing the current lack of adaptive control and the open-loop nature of CO2 laser suction pumps, this study utilizes closed-loop configurations that dynamicall

Sam Fisher, Dmitry Lesnik, Tobias Schäfer

Estimating racial disparities in loan-approval probabilities when race is unobserved is routinely required for fair lending compliance. In such cases, race probabilities-typically from Bayesian Improved Surname Geocoding (BISG)-stand in for true race. Prior work shows that common heuristic approaches, including the Threshold and Weighting estimators, are inc

Simon K. Yung, C. M. Yung, Lorcán O. Conlon, Syed M. Assad

Optimal measurements for quantum multiparameter estimation are complicated by the uncertainty principle. Generally, there is a trade-off between the precision with which different parameters can be simultaneously estimated. The task of determining the minimum achievable estimation error is a central task of multiparameter quantum metrology. For estimating pa

Atsushi Higuchi, Vasileios A. Letsios

It is commonly believed that a unitary supersymmetric quantum field theory (QFT) involving graviton and gravitino fields on fixed 4-dimensional de Sitter spacetime ($dS_4$) cannot exist due to known challenges associated with supersymmetry (SUSY) on spaces with positive cosmological constant. In this talk, we contradict this expectation by presenting a new u

Jaro Meyer, Frédéric Giraud, Joschua Wüthrich, Marc Pollefeys

Accurate spatiotemporal alignment of multi-view video streams is essential for a wide range of dynamic-scene applications such as multi-view 3D reconstruction, pose estimation, and scene understanding. However, synchronizing multiple cameras remains a significant challenge, especially in heterogeneous setups combining professional and consumer-grade devices,

K. Khelifa-Kerfa

We determine the structure of both Abelian and non-Abelian non-global logarithms up to four loops for $e^+e^-$ processes in perturbative QCD, where final-state jets are defined using the Cambridge--Aachen (C/A) clustering algorithm. The calculations are performed within the soft (eikonal) approximation using strong-energy ordering of the final-state partons

Qiu-Yi Chen, Feng Qiao, Zu-Jian Ying

Quantum Rabi model (QRM) is a fundamental model for light-matter interactions, the finite-component quantum phase transition (QPT) in the QRM has established a paradigmatic application for critical quantum metrology (CQM). However, such a paradigmatic application is restricted to a local regime of the QPT which has only a single critical point. In this work

Fan Yang, Quanting Xie, Atsunori Moteki, Shoichi Masui

Periodic human activities with implicit workflows are common in manufacturing, sports, and daily life. While short-term periodic activities -- characterized by simple structures and high-contrast patterns -- have been widely studied, long-term periodic workflows with low-contrast patterns remain largely underexplored. To bridge this gap, we introduce the fir

Jashan Bal

We study projectivity in the category of $G$-flows and affine $G$-flows for Polish groups $G$. We also introduce the notion of \emph{proximally irreducible} extensions between affine $G$-flows. Using this we provide a characterization of extreme amenability, strong amenability, and amenability for closed subgroups $H \leq G$ in terms of certain ``dynamical i

Thomas Karabela, Wenjun Niu

The vacuum manifold $\mathcal{M}$ of a topological twist of a 3d $\mathcal{N}=4$ gauge theory is a hyper-K\"ahler variety; deformations and quantizations of $\mathcal{M}$ can be constructed in the framework of 3 dimensional topological quantum field theories. In particular, based on physics arguments, turning on an omega-background results in the quantizatio

Ilia Binder, Adi Glücksam

We investigate the fine properties of harmonic measure and boundary rotation. By focusing on quasidisks and, in particular, on connected Jordan Repellers arising from conformal expanding dynamical systems, we explore those using the deep interplay between geometric function theory and dynamical systems as a unified framework. While some of the results presen

Pau Bosch-Cabot, Alexandra J. Tetarenko, Erik Rosolowsky, Francesco Carotenuto

We present Atacama Large Millimeter/Submillimeter Array (ALMA) observations aimed at identifying potential jet-ISM interaction sites in the vicinity of the transient black hole X-ray binaries MAXI J1348-630 and MAXI J1820+070, both of which have recently undergone an outburst, and displayed powerful large scale jets. Using this dataset, we construct molecula

Florian Pabst, Harald Forbert, Dominik Marx

The lowest-frequency Raman mode of water, observed through depolarized light scattering or optical Kerr effect techniques, is routinely used to track dynamic changes in water molecules near ions or biomolecules. Yet, the microscopic origin of this mode and its relation to dielectric relaxation still remains debated for pure water with conflicting interpretat

Daniel Oliveira de Brito, Letícia Gabriella de Souza, Marcelo Matheus Gauy, Marcelo Finger

This technical report investigates the performance of pre-trained audio models on COVID-19 detection tasks using established benchmark datasets. We fine-tuned Audio-MAE and three PANN architectures (CNN6, CNN10, CNN14) on the Coswara and COUGHVID datasets, evaluating both intra-dataset and cross-dataset generalization. We implemented a strict demographic str

M. Zeeshan Haider, Tayyaba Noreen, M. D. Assuncao, Kaiwen Zhang

Sharding has emerged as a key technique to address blockchain scalability by partitioning the ledger into multiple shards that process transactions in parallel. Although this approach improves throughput, static or heuristic shard allocation often leads to workload skew, congestion, and excessive cross-shard communication diminishing the scalability benefits

Jack D. Collard, Tamar Ervin, Ryan M. Dewey, Yeimy J. Rivera

While the fast solar wind has well-established origins in coronal holes, the source of the slow solar wind remains uncertain. Compositional metrics, such as heavy ion charge state ratios are set in the lower corona, providing insights into solar wind source regions. However, prior to the launch of Solar Orbiter, in situ measurements of heavy ion charge state

Niloofar Mireshghallah, Neal Mangaokar, Narine Kokhlikyan, Arman Zharmagambetov

Large Language Models (LLMs) increasingly use persistent memory from past interactions to enhance personalization and task performance. However, this memory introduces critical risks when sensitive information is revealed in inappropriate contexts. We present CIMemories, a benchmark for evaluating whether LLMs appropriately control information flow from memo

Mathieu Dufour, Andrew Duncan

Large language models trained on clinical text risk exposing sensitive patient information, yet differential privacy (DP) methods often severely degrade the diagnostic accuracy needed for deployment. Despite rapid progress in DP optimisation and text generation, it remains unclear which privacy-preserving strategy actually works best for clinical language ta

Lidia Caros, Witold J. F. Koning, Takayuki Nagata, Keisuke Asai

This study compares aerodynamic performance of the CLF5605 rotor airfoil -- which flew on Ingenuity from 2021 to 2024 -- with that of a new optimized roamx-0201 airfoil designed for Martian conditions at NASA Ames. Specifically, performance is studied at a Reynolds number of 20,000 and a Mach number of 0.60, across a range of angles of attack, using three in

Lars Forberger, Jacob T. Baillie, Zhaojie Feng, Rachel E. Gariepy

The hexagonal $\beta$-phase of sodium yttrium fluoride (NaYF) is a leading host material for lanthanide upconversion and anti-Stokes fluorescence laser refrigeration based on its low phonon energies and high upconversion efficiency. Recently experiments have been proposed to use this material as an optically-levitated sensor of high-frequency gravitational w

Nicholas Corso, Dong Lai

Plasma-mediated interaction between astrophysical objects can play an important role and produce electromagnetic radiation in various binary systems, ranging from planet-moon and star-planet systems to binary compact objects. We perform 3D magnetohydrodynamic numerical simulations to study an ideal magnetized plasma flowing past an unmagnetized conducting sp

Sanjib K C, Viacheslav M Sadykov, Dustin Kempton

Radiation exposure at aviation altitudes presents significant health risks to aircrews due to the cumulative effects of ionizing radiation. Physics-based models estimate radiation levels based on geophysical and atmospheric parameters, but often struggle to capture the highly dynamic and complex nature of the radiation environment, limiting their real-time p

Kumar Utkarsh, Daniel M. Abrams

Information criteria such as Akaike's (AIC) and Bayes' (BIC) are widely used for model selection in physics and beyond, quantifying the tradeoff between model complexity and goodness-of-fit to enforce parsimony. However, their derivation assumes uncorrelated samples, an assumption systematically violated by dynamical systems data. Here, through analysis of s

Robert Kubinec, Aseem Mahajan

In this paper, we provide a novel measure for greenwashing -- i.e., climate-related misinformation -- that shows how polluting companies can use social media advertising related to climate change to redirect criticism. To do so, we identify greenwashing content in 11 million social-political ads in Meta's Ad Targeting Datset with a measurement technique that

Ahmed E. Seleit

We present a new adaptive collocation scheme for solving partial differential equations based on Local Coupled Multiquadrics (LCMQs) within a covers-and-nodes framework. The method, referred to as the Adaptive Ch Method, automatically prioritizes adjusting the local cover size C then refines local nodal spacing h to achieve a prescribed tolerance. Numerical

Akshay K. Rao, Fletcher T. Chapin, Erin Musabandesu, Adhithyan Sakthivelu

Load shifting by commercial and industrial power consumers reduces costs and Scope 2 emissions for the consumer and the grid. Incentivizing this behavior requires tools for valuing flexibility amidst the heterogeneity in load characteristics across diverse sectors and the spatiotemporal variation in electricity prices and emissions factors. This work present

Kaiyuan Hu, Yili Jin, Junhua Liu, Xize Duan

Volumetric video enables immersive and interactive visual experiences by supporting free viewpoint exploration and realistic motion parallax. However, existing volumetric representations from explicit point clouds to implicit neural fields, remain costly in capture, computation, and rendering, which limits their scalability for on-demand video and reduces th

Alfredo R. R. Narváez, Jeinny Peralta, M. A. C. Candezano

This paper studies the Linear Quadratic Regulator (LQR) problem for continuous-time Markov Jump Linear Systems (MJLS) governed by general finite-state Markov chains that may include transient, absorbing, or non-communicating states. The problem, posed over a finite time horizon, is reformulated deterministically by expressing the cost functional in terms of

Changhong Mou, Yeyu Zhang, Xuewen Zhu, Qiao Zhuang

Nonlinear physical phenomena often show complex multiscale interactions; motivated by the principles of multiscale modeling in scientific computing, we propose PAS-Net, a physics-informed Adaptive-Scale Deep Operator Network for learning solution operators of nonlinear and singularly perturbed evolution PDEs with small parameters and localized features. Spec

Ian George, Karen Yeats

For any finite poset we define a generating polynomial counting upsets, downsets, and their intersection. We investigate the behaviour of this polynomial with respect to poset operations, show that it distinguishes series-parallel posets, and comment on connections to the causal set approach to quantum gravity.

Tom Dodd, Javier Martínez-Cifuentes, Oliver Thomson Brown, Nicolás Quesada

If classical algorithms have been successful in reproducing the estimation of expectation values of observables of some quantum circuits using off-the-shelf computing resources, matching the performance of the most advanced quantum devices on sampling problems usually requires extreme cost in terms of memory and computing operations, making them accessible t

Pranay Kumar Peddi, Dhrubajyoti Ghosh

Deep graph learning has advanced Alzheimer's (AD) disease classification from MRI, but most models remain correlational, confounding demographic and genetic factors with disease specific features. We present Causal-GCN, an interventional graph convolutional framework that integrates do-calculus-based back-door adjustment to identify brain regions exerting st

Mohamed Rouili, Yang Xiao, Sihang Liu, Raouf Boutaba

The disaggregation and virtualization of 5G Open RAN (O-RAN) introduces new vulnerabilities in the control plane that can greatly impact the quality of service (QoS) of latency-sensitive 5G applications and services. One critical issue is Random Access (RA) signaling storms where, a burst of illegitimate or misbehaving user equipments (UEs) send Radio Resour

Zhengyang Shen, Hua Tu, Mayue Shi

Neural networks exhibit severe brittleness to semantically irrelevant transformations. A mere 75ms electrocardiogram (ECG) phase shift degrades latent cosine similarity from 1.0 to 0.2, while sensor rotations collapse activity recognition performance with inertial measurement units (IMUs). We identify the root cause as "laissez-faire" representation learning

Sebastian Dingler, Hannes Burrichter

Odometry with lidar sensors is a state-of-the-art method to estimate the ego pose of a moving vehicle. Many implementations of lidar odometry use variants of the Iterative Closest Point (ICP) algorithm. Real-world effects such as dynamic objects, non-overlapping areas, and sensor noise diminish the accuracy of ICP. We build on a recently proposed method that

Zefan Yang, Ge Wang, James Hendler, Mannudeep K. Kalra

Chest X-ray radiography (CXR) is an essential medical imaging technique for disease diagnosis. However, as 2D projectional images, CXRs are limited by structural superposition and hence fail to capture 3D anatomies. This limitation makes representation learning and disease diagnosis challenging. To address this challenge, we propose a novel CXR world model n

Aysha Aamer, Matt Nicholl, Charlotte Angus, Shubham Srivastav

Superluminous supernovae (SLSNe) are some of the brightest explosions in the Universe representing the extremes of stellar deaths. At the upper end of their distribution is SN\,2023taz, one of the most luminous SLSNe discovered to date with a peak absolute magnitude of $M_{g,\rm{peak}}=-22.75 \pm 0.03$ and a lower limit for energy radiated of $E=2.9 \times 1

Paarth Jain, Artur F. Izmaylov, Erik R. Kjellgren

Preserving spin symmetry in variational quantum algorithms is essential for producing physically meaningful electronic wavefunctions. Implementing spin-adapted transformations on quantum hardware, however, is challenging because the corresponding fermionic generators translate into noncommuting Pauli operators. In this work, we introduce an exact and computa

Yuntao Lu, Yunxin Zhang

The burst approximation is a widely used technique to simplify stochastic gene expression models. However, the dynamics and analytical properties of the protein number distribution in gene expression models under the burst approximation are barely studied. In this study, we propose and systematically analyze surrogate models with multiple gene states and arb

Frank C. van den Bosch, Shashank Dattathri

Self-interacting dark matter (SIDM) is an intriguing alternative to the standard cold dark matter (CDM) paradigm, which predicts that dark matter halos typically have large, isothermal cores. Numerical simulations have shown that dynamical friction ceases to operate in cores of (roughly) constant density, a phenomenon known as core stalling. In addition, suc

Brenna Mockler, Erica Hammerstein, Eric R. Coughlin, Matt Nicholl

Stars that orbit too close to a black hole can be ripped apart by strong tides, producing a type of luminous transient event called a ``tidal disruption event" (TDE). Tidal disruption events of stars by supermassive black holes (SMBHs) provide windows into the nuclei of galaxies at size scales that are difficult to observe directly outside our own galactic n

Adeline Guéret

Electrifying passenger cars will impact future power systems. To understand the challenges and opportunities that arise, it is necessary to reflect "sector coupling" in the modeling space. This paper focuses on a specific modeling approach that includes dozens of individual BEV profiles rather than one aggregated BEV profile. Although including additional BE

Yassine Ibork, Myounggyu Won, Lokesh Das

Ramp merging is a critical and challenging task for autonomous vehicles (AVs), particularly in mixed traffic environments with human-driven vehicles (HVs). Existing approaches typically rely on either lane-changing or inter-vehicle gap creation strategies based solely on local or neighboring information, often leading to suboptimal performance in terms of sa

Amit Lavon

Simulated microbial communities are used in benchmarking microbial abundance estimators and other bioinformatic utilities. To match current data scales, large simulated samples are needed, and many. The speed of current implementations might create bottlenecks for scientists testing new innovations. Here, a new implementation is introduced, based on existing

Gefté Almeida, Marcio Pohlmann, Alex Severo, Diego Kreutz

In this study, we evaluate open-source models for security incident classification, comparing them with proprietary models. We utilize a dataset of anonymized real incidents, categorized according to the NIST SP 800-61r3 taxonomy and processed using five prompt-engineering techniques (PHP, SHP, HTP, PRP, and ZSL). The results indicate that, although propriet

Yasser Al Eryani

This article travels one century into the future--from 2025 to 2125--through the analytical lens of the Information--Curvature Efficiency Law (ICEL). It contends that wireless evolution will not proceed through incremental generations such as 6G or 7G, but through a curvature-managed integration of electromagnetics, biology, thermodynamics, and cognition. Th

Hyun Sang Park, Timothy Long, Michael Wall, Alexander deJong

The increasing importance of artificial intelligence and machine learning in materials research has created demand for automated, high-throughput characterization techniques capable of rapidly generating large data sets. We describe here a new instrument for simultaneous X-ray diffraction and X-ray fluorescence spectroscopy, optimized for high-throughput stu

Leonid V. Pourovskii

Anisotropic intersite exchange interactions in frustrated rare-earth magnets are difficult to assess both theoretically and experimentally. Here, we propose an ab initio force-theorem framework combining the quasi-atomic Hubbard-I approach to 4f correlations with a static mean-field treatment of the on-site intershell Coulomb interaction between rare-earth 4

Beatriz Machado, Douglas Lautert, Cristhian Kapelinski, Diego Kreutz

This paper proposes an automated LLM-based method to extract and structure vulnerabilities from OpenVAS and Tenable WAS scanner reports, converting unstructured data into a standardized format for risk management. In an evaluation using a report with 34 vulnerabilities, GPT-4.1 and DeepSeek achieved the highest similarity to the baseline (ROUGE-L greater tha

Ruixin Zhang, Jon Donnelly, Zhicheng Guo, Ghazal Khalighinejad

Large language models (LLMs) have exhibited remarkable capabilities across various domains. The ability to call external tools further expands their capability to handle real-world tasks. However, LLMs often follow an opaque reasoning process, which limits their usefulness in high-stakes domains where solutions need to be trustworthy to end users. LLMs can c

Eduard Koller, Swetlana Swarup, Johannes Knolle, Natalia B. Perkins

Recent Raman experiments on the Kitaev material $\alpha$-RuCl$_3$ have reported a finite Raman circular dichroism (RCD), revealing chiral phonon behaviour not expected from lattice symmetry alone. To explain this observation, we develop a diagrammatic framework for the spin-phonon coupled Kitaev model. We demonstrate that bare phonons contribute no RCD, but

Zhenshi Li, Weikang Yu, Dilxat Muhtar, Xueliang Zhang

As CLIP's global alignment limits its ability to capture fine-grained details, recent efforts have focused on enhancing its region-text alignment. However, current remote sensing (RS)-specific CLIP variants still inherit this limited spatial awareness. We identify two key limitations behind this: (1) current RS image-text datasets generate global captions fr

Daniel Gilo, Or Litany

We address the task of multi-view image editing from sparse input views, where the inputs can be seen as a mix of images capturing the scene from different viewpoints. The goal is to modify the scene according to a textual instruction while preserving consistency across all views. Existing methods, based on per-scene neural fields or temporal attention mecha

Dmitri Finkelshtein, Eugene Lytvynov, Maria Joao Oliveira

Let $\Phi$ be an (LB)-space over $\mathbb F=\mathbb R$ or $\mathbb C$, and let $\Phi'$ be the dual space of~$\Phi$. We study the set $\mathbb S(\Phi)$ of Sheffer operators acting in polynomials on $\Phi'$. We prove that $\mathbb S(\Phi)$ is a group for the usual product of operators. We equip $\mathbb S(\Phi)$ with a natural topology which makes $\mathbb S(\

Thijs Lenssen, Aleksandr Talitckii, Matthew Peet, Amritam Das

We develop a $\mu$-analysis and synthesis framework for infinite-dimensional systems that leverages the Integral Quadratic Constraints (IQCs) to compute the structured singular value's upper bound. The methodology formulates robust stability and performance conditions jointly as Linear Partial Integral Inequalities within the Partial Integral Equation framew

Cristhian Kapelinski, Douglas Lautert, Beatriz Machado, Diego Kreutz

This work presents AnonLFI 2.0, a modular pseudonymization framework for CSIRTs that uses HMAC SHA256 to generate strong and reversible pseudonyms, preserves XML and JSON structures, and integrates OCR and technical recognizers for PII and security artifacts. In two case studies involving OCR applied to PDF documents and an OpenVAS XML report, the system ach

Mohammad Cheraghinia, Eli De Poorter, Jaron Fontaine, Kwang Soon Kim

While machine learning is widely used to optimize wireless networks, training a separate model for each task in communication and localization is becoming increasingly unsustainable due to the significant costs associated with training and deployment. Foundation models offer a more scalable alternative by enabling a single model to be adapted across multiple

S. Joshi, D. Steinkamp

The Fermilab Acceleraor Division, Beam Instrumentation Department, is always adopting modern and current software methodologies for complex DAQ architectures. This paper highlights the Redis Adapter (RA) as the key software component enabling high performance, modular communication between digitizers and distributed control systems by leveraging Redis and co

Changjun Li, Heather Allore, Michael O. Harhay, Fan Li

Detecting heterogeneity in treatment response enriches the interpretation of gerontologic trials. In aging research, estimating the effect of the intervention on clinically meaningful outcomes faces analytical challenges when it is truncated by death. For example, in the Whole Systems Demonstrator trial, a large cluster-randomized study evaluating telecare a

Pedro Resende, João Paulo Santos

We prove some facts about locales $L$ equipped with the Scott topology $\Omega(L)$, in particular studying a canonical frame homomorphism $\phi:\Omega(L)\to L$ which is motivated by an application to cognitive science. Such a topological locale $L$ is called a Scott locale if the inclusion of primes $p:\Sigma(L)\to L$ is continuous. We prove that the spectru

Jamie M. Karthein, Volker Koch, Claudia Ratti

In addition to signals for the critical point, evidence for a first order phase transition would indicate a nontrivial structure within the QCD phase diagram. Moreover, while not a direct measurement of the critical point, the presence of a first order transition would imply its existence. This motivates the need to understand signatures of this first order

Helmut Repp

There is already a method known from the literature with which it is possible to measure both the transfer function and the noise power spectral density of the superimposed noise at the output of a disturbed, time-invariant, real system with nonlinearities in one measurement. By using a suitable window, the frequency selectivity of the measurement of the pow

Grace Kim, Filip Svoboda, Nicholas Lane

As Low Earth Orbit (LEO) satellite constellations rapidly expand to hundreds and thousands of spacecraft, the need for distributed on-board machine learning becomes critical to address downlink bandwidth limitations. Federated learning (FL) offers a promising framework to conduct collaborative model training across satellite networks. Realizing its benefits

Divya Sahani, Sunit Das, Kenji Watanabe, Takashi Taniguchi

Nonuniform strain in graphene acts as a valley-dependent gauge field, generating pseudomagnetic fields (PMFs) that mimic real magnetic fields but preserve global time-reversal symmetry. While local probes have visualized such fields, their quantitative detection via macroscopic transport has remained elusive. Here, we demonstrate that high-mobility graphene

Nathan M. Roberts, Xiaosong Du

The rapid advancement of electric vertical takeoff and landing (eVTOL) aircraft offers a promising opportunity to alleviate urban traffic congestion but is still limited by excessive power demands, especially during the takeoff phase. Thus, developing optimal takeoff trajectories for minimum energy consumption becomes essential for broader eVTOL aircraft app

Paola Perion, Clara Magnin, Fulvia Arfelli, Bertrand Faure

Modulation-based imaging (MoBI) is an X-ray phase-contrast technique that uses an intensity modulator (or membrane) in the beam. Although MoBI can be performed in a single shot, multiple exposures are typically needed to improve the quality of the result. The membrane is typically moved using a regular stepping pattern for convenience; however, the impact of

Eleonora Cinti, Enzo Maria Merlino, Berardo Ruffini

We show a strong version of the fractional quantitative isoperimetric inequality, in which the isoperimetric deficit controls not only the Fraenkel asymmetry but also a sort of oscillation of the boundary. This generalizes the local result by Fusco and Julin in \cite{FJ}. The proof follows a regularization process as in \cite{FJ} but it is quite different in

Antonio Ruiz, Tao Wu, Andrew Melnik, Qing Cheng

Methods that synthesize indoor 3D scenes from text prompts have wide-ranging applications in film production, interior design, video games, virtual reality, and synthetic data generation for training embodied agents. Existing approaches typically either train generative models from scratch or leverage vision-language models (VLMs). While VLMs achieve strong

Bernhard Aigner, Marcus Waurick

We extend a contraction mapping argument for ordinary state-dependent delay differential equations to evolutionary partial differential equations in the sense of R. Picard, that is, to equations of the form $\bigl(\partial_{t} M(\partial_{t}) + A\bigr) u(t) = F\bigl(t,u_{(t)}\bigr)$, where $A$ is an $\mathrm{m}$-accretive (unbounded) linear operator and $M$

Michael T. Goodrich, Songyu Liu, Ioannis Panageas

In this paper, we study the exact learning problem for weighted graphs, where we are given the vertex set, $V$, of a weighted graph, $G=(V,E,w)$, but we are not given $E$. The problem, which is also known as graph reconstruction, is to determine all the edges of $E$, including their weights, by asking queries about $G$ from an oracle. As we observe, using si

Bi Xue, Hong Wu, Lei Chen, Chao Yang

Serving deep learning based recommendation models (DLRM) at scale is challenging. Existing approaches rely on dedicated ANN indexing and filtering services on CPUs, suffering from non-negligible costs and missing co-design opportunities. Such inefficiency makes them difficult to support complex model architectures, such as learned similarities and multi-task

Michał Kowalczyk, Javier Monreal

In this paper we consider an $SU(2)$ Yang-Mills field propagating in the $4+1$ dimensional wormhole spacetime. Assuming the spherically symmetric magnetic ansatz the problem reduces to a one dimensional non linear wave equation. This equation posses a degree one solution (instanton) which is odd in space. We consider small, odd perturbations of the instanton

Houssem-Eddine Gueziri, Abicumaran Uthamacumaran, Widad Safih, Abdulrahman Almansouri

Background. Bimanual psychomotor proficiency is fundamental to neurosurgical procedures, yet it remains difficult for trainees to acquire and for educators to objectively evaluate performance. In this study, we investigate the feasibility of a neurosurgical simulation platform that integrates an anatomically realistic brain model with surgical instrument tra

Sashikanta Mohapatra, Sanjay Moudgalya, Ajit C. Balram

Quantum many-body scars (QMBS) represent a mechanism for weak ergodicity breaking, characterized by the coexistence of atypical non-thermal eigenstates within an otherwise thermalizing many-body spectrum. In this work, we revisit the spin-$1$ $XY$ model on a periodic chain and construct several new families of exact scar eigenstates embedded within its exten

Patricia Alonso Ruiz, Valentia Fragkiadaki

This paper studies a dyadic version of fractional Sobolev spaces in $\mathbb{R}^n$ for $n\geq 1$. It provides new proofs of the corresponding fractional Sobolev embedding as well as the algebra property of the spaces, which rely solely on dyadic techniques and in particular bypass the Fourier transform. Specific counterexamples are constructed to verify the

Tonguç Ünlüyurt

This review aims to provide a comprehensive update on the progress made on the Sequential Testing problem (STP) in the last 20 years after the review, [1] was published. Many studies have provided new theoretical results, extensions of the problem, and new applications. In this review, we pinpoint the main results and discuss the relations between the proble

Henry Wong, Clement Fung, Weiran Lin, Karen Li

To autonomously control vehicles, driving agents use outputs from a combination of machine-learning (ML) models, controller logic, and custom modules. Although numerous prior works have shown that adversarial examples can mislead ML models used in autonomous driving contexts, it remains unclear if these attacks are effective at producing harmful driving acti

Vsevolod Chestnov, Giulio Crisanti

We introduce SP$\mathbb{Q}$R, a new Mathematica package for the division and elimination of variables from polynomial systems. SP$\mathbb{Q}$R works by sampling and reconstructing results over finite fields, in an analogous manner to many state of the art Integration by Parts algorithms for Feynman integrals. This allows SP$\mathbb{Q}$R to effectively overco

Carsten Gundlach, Laetitia Martel

We consider line elements of the form $-2G\,du\,(dx+B\,du) + R^2(...)$, where $(...)$ does not contain $dx$. Surfaces of constant $u$ are then null surfaces, and their affinely parameterised generators have tangent vector $G^{-1}\partial_x$. Considering $u$ as the time coordinate, we can evolve either $R$ or $G$, with the other one found by solving the Raych

Ryshard-Pavel Kostecki

We review the theory of Va\u{i}nberg--Br\`{e}gman relative entropies and quasinonexpansive operators on reflexive Banach spaces, and obtain several new results. We also develop an extension of this theory to nonreflexive Banach spaces, which is a joint generalisation of the reflexive Banach space approach and the finite-dimensional information geometric appr

Saeed Shaebani

A (not necessarily proper) vertex coloring of a graph $G$ with color classes $V_1$, $V_2$, $\dots$, $V_k$, is said to be a {\it Fair And Tolerant vertex coloring of $G$ with $k$ colors}, whenever $V_1$, $V_2$, $\dots$, $V_k$ are nonempty and there exist two real numbers $\alpha$ and $\beta$ such that $\alpha \in [0,1]$ and $\beta \in [0,1]$ and the following

Fuyang Zhang, Pradeep Kumar Jayaraman, Xiang Xu, Yasutaka Furukawa

This paper presents a novel geometric representation for CAD Boundary Representation (B-Rep) based on volumetric distance functions, dubbed B-Rep Distance Functions (BR-DF). BR-DF encodes the surface mesh geometry of a CAD model as signed distance function (SDF). B-Rep vertices, edges, faces and their topology information are encoded as per-face unsigned dis

Xueying Ding, Xingyue Huang, Mingxuan Ju, Liam Collins

Large language models produce powerful text embeddings, but their causal attention mechanism restricts the flow of information from later to earlier tokens, degrading representation quality. While recent methods attempt to solve this by prepending a single summary token, they over-compress information, hence harming performance on long documents. We propose

Sayan Gupta, Kaushik Majumder

The Ramsey number for the pair of graphs $\mathbb{K}_{1,n}$ (star) versus $W_{m}$ (wheel) has been extensively studied. In contrast, the Ramsey number of $\mathbb{K}_{2,n}$ versus the wheel is not yet explored due to the bit more structural complexity of $\mathbb{K}_{2,n}$ compared to the star. In this article, we have established an exact value of $\mathbb{

Igor L. C. Lima, M. V. Milošević, F. M. Peeters, Andrey Chaves

We theoretically investigate the binding energy and electron-hole (e-h) overlap of excitonic states confined at the interface between two-dimensional materials with type-II band alignment, i.e., with lowest conduction and highest valence band edges placed in different materials, arranged in a side-by-side planar heterostructure. We propose a variational proc