November 2025 arXiv papers — page 26

Andrew Kiruluta

This report introduces a novel class of reasoning architectures, termed Quantum Circuit Reasoning Models (QCRM), which extend the concept of Variational Quantum Circuits (VQC) from energy minimization and classification tasks to structured logical inference and reasoning. We posit that fundamental quantum mechanical operations, superposition, entanglement, i

Siyu Heng, Yuan Huang, Hyunseung Kang

Difference-in-differences (DID) is one of the most widely used causal inference frameworks in observational studies. However, most existing DID methods are designed for binary treatments and cannot be readily applied to non-binary treatment settings. Although recent work has begun to extend DID to non-binary (e.g., continuous) treatments, these approaches ty

Kai Jaffarove, Tamar Ervin, Stuart D. Bale

Parker Solar Probe (PSP) observed a high speed stream near the Sun ($\sim 9.8 R_\odot$) in March 2025. As this stream was observed near the Sun, it allowed for an unparalleled opportunity to observe pristine, coronal hole wind with little evolution (expansion, stream interaction, etc) effects impacting its properties. Through an ensemble of magnetic connecti

Houbo He, Yizhou Xu, Lei Xia, Yaolong Hu

This paper presents a systematic study on developing multi-template machine learning (ML) surrogate models and applying them to the inverse design of transformers (XFMRs) in radio-frequency integrated circuits (RFICs). Our study starts with benchmarking four widely used ML architectures, including MLP-, CNN-, UNet-, and GT-based models, using the same datase

Matteo Bjornsson, Taylor Hardin, Taylor Heinecke, Marcin Furtak

Distributed ledger technologies (DLTs) rely on distributed consensus mechanisms to reach agreement over the order of transactions and to provide immutability and availability of transaction data. Distributed consensus suffers from performance limitations of network communication between participating nodes. BLOCKY ZipperChain guarantees immutability, agreeme

Zahra Ebrahim Nataj, Md. Sabbir Hossen Bijoy, Vladislav Korostelev, Dylan Wright

Magnetic order in van der Waals magnets can strongly influence their lattice dynamics, yet how this interaction manifests across different phonon length scales remains unclear. Optical phonons probe bond-scale exchange modulation and short-range spin correlations, whereas long-wavelength acoustic modes couple to uniform strain fields and are sensitive to the

Leonardo Colombo

We present a geometric and variational derivation of the Gorini--Kossakowski--Sudarshan--Lindblad equation from Euler--Poincar'e reduction on an adjoint--coupled semidirect product (ACSP). In this construction a Lie group $G$ acts on $V=\mathfrak{g}^{\oplus m}$ by the adjoint representation together with a second, adjointly compatible action whose failure to

Antonio García Muñoz, Dario De Fazio, David J. Wilson, Kevin France

Context. Neptune-sized exoplanets or exo-Neptunes are fundamental in the description of exoplanet diversity. Their evolution is sculpted by atmospheric escape, often traced by absorption in the H i Lyman-{\alpha} line at 1,216 {\AA} and the He i triplet line at 1.08 {\mu}m. On warm exo-Neptunes HAT-P-11 b, GJ 3470 b and GJ 436 b, H i Lyman-{\alpha} absorptio

Zsolt Benedek, Oscar Bulancea-Lindvall, Joel Davidsson, Viktor Ivády

Recently, density functional theory-based high-throughput screening of point defects in 4H-SiC revealed the positively charged chlorine-vacancy (ClV) defect to be a promising quantum bit candidate emitting at telecom wavelengths, with an electronic structure analogous to the well-known NV center in diamond. Furthermore, recent infrared photoluminescence (PL)

Hongyu Yang, Chunxi Wen, Jiyin Zhang, Nanfei Shen

Click-through rates prediction is critical in modern advertising systems, where ranking relevance and user engagement directly impact platform efficiency and business value. In this project, we explore how to model CTR more effectively using a large-scale Taobao dataset released by Alibaba. We start with supervised learning models, including logistic regress

Ingyu Jang, Leila J. Bridgeman

Communication-aware control is essential to reduce costs and complexity in large-scale networks. However, it is challenging to simultaneously determine a sparse communication topology and achieve high performance and robustness. This work achieves all three objectives through dissipativity-based, sparsity-promoting controller synthesis. The approach identifi

Branislav Sazdovic

This article is founded on two fundamental principles: the principle field equations introduced in Refs. \cite{S, S1, S2} and the Fock-Ivanenko covariant derivatives \cite{FI, F}. The former yields the equations of motion for free fields of arbitrary spin and helicity. In the massless case, it also dictates that Lorentz transformations for tensor fields acqu

Herbert Edelsbrunner, Michał Lipiński, Marian Mrozek, Manuel Soriano-Trigueros

The depth poset of a filtered Lefschetz complex reflects the dependencies between the cancellations of different shallow birth-death pairs. Using the fast algorithms for computing the depth poset in the present work and for updating the persistence diagram under transpositions (Vineyard persistence), we give a complete case analysis of how transpositions of

Chin-Wei Huang, Jaime Llorca, Antonia M. Tulino, Andreas F. Molisch

The proliferation of mission-critical latency-sensitive services has intensified the demand for next-generation cloud-integrated networks to guarantee both reliable and resilient service delivery. While reliability imposes timely-throughput requirements, i.e., percentage of packets to be delivered within a prescribed per-packet deadline, resilience relates t

Walid Houmaidi, Mohamed Hadadi, Youssef Sabiri, Yousra Chtouki

This paper presents a comprehensive comparative model analysis on a novel gastrointestinal medical imaging dataset, comprised of 4,000 endoscopic images spanning four critical disease classes: Diverticulosis, Neoplasm, Peritonitis, and Ureters. Leveraging state-of-the-art deep learning techniques, the study confronts common endoscopic challenges such as vari

Yiyan Zhai, Bintang Dwi Marthen, Sarath Balivada, Vamsi Sudhakar Bojji

Cache replacement algorithms are critical building blocks of storage systems. This paper examines the characteristics of metadata caches and argues that they inherently exhibit correlated references, even when the corresponding data accesses do not contain correlated references. The presence of correlated references reduces the effectiveness of cache replace

M. Polzin, M. Guzman

When modernizing a legacy application, it is easy to fall back on a like-for-like replica with new tools and updated design stylings, but this is an opportunity to explore making a more intuitive application that supports user tasks and efficiency. Rather than having a blank canvas-unburdened by legacy tech debt-to create a new application, you are working w

Dylan Meyer, Alexander Lind, William Groman, Hero Trent

Robust and portable optical clocks promise to bring sub-picosecond timing instability to smaller form factors, offering possible performance improvements and new scenarios for positioning and navigation, radar technologies, and experiments probing fundamental physics. However, there are currently limited methods suitable for broadly disseminating the sub-pic

Piotr Gruza, Mateusz Łełyk

The paper aims to establish a convenient formal framework for investigating the phenomenon of scheme definiteness, exemplified by first-order internal categoricity as studied by V\"a\"an\"anen, among others. To this end, we introduce the notion of $\Phi$-definiteness, thereby refining and extending the conceptual landscape that underlies various first-order

Yuezhu Xu, Mohamed Serry, Jun Liu, S. Sivaranjani

The design of tracking controllers that closely follow a reference trajectory while ensuring safety and robustness against disturbances is a challenging problem in the control of autonomous systems. In this work, we propose a neural network-based safe tracking control framework for nonlinear discrete-time systems with reach-avoid specifications in the presen

Krishna Khadka, Sunny Shree, Pujan Budhathoki, Yu Lei

Machine learning models are increasingly used in critical applications but are mostly "black boxes" due to their lack of transparency. Local explanation approaches, such as LIME, address this issue by approximating the behavior of complex models near a test instance using simple, interpretable models. However, these approaches often suffer from instability a

Yong Wang, Zhenghao Yin, Tobias Haug, Ciro Pentangelo

Photons are promising candidates for quantum information technology due to their high robustness and long coherence time at room temperature. Inspired by the prosperous development of photonic computing techniques, recent research has turned attention to performing quantum machine learning on photonic platforms. Although photons possess a high-dimensional qu

Ruoyuan Liu, Shao Liu, Tadahiro Oh

We study large $N$ limits of the hyperbolic $O(N)$ linear sigma model ($\text{HLSM}_N$) on the two-dimensional torus $\mathbb T^2$, namely, a system of $N$ interacting stochastic damped nonlinear wave equations (SdNLW) with coupled cubic nonlinearities. After establishing (pathwise) global well-posedness of $\text{HLSM}_N$ and the limiting equation, called t

Emilie Hertig, Antón Baleato Lizancos, Frank J. Qu, J. Richard Bond

Large-scale $B$-mode polarization of the cosmic microwave background (CMB) is a prime target for current and future experiments in search of primordial gravitational waves (PGW). With increasingly sensitive instruments being deployed, secondary $B$-modes induced by the weak gravitational lensing of CMB photons are becoming an important limitation and need to

Shilong Xiang, JangHyeon Lee, Min Namgung, Yao-Yi Chiang

Urban walkability is a cornerstone of public health, sustainability, and quality of life. Traditional walkability assessments rely on surveys and field audits, which are costly and difficult to scale. Recent studies have used satellite imagery, street view imagery, or population indicators to estimate walkability, but these single-source approaches capture o

Finlay G. C. Hudson, James A. D. Gardner, William A. P. Smith

Humans excel at constructing panoramic mental models of their surroundings, maintaining object permanence and inferring scene structure beyond visible regions. In contrast, current artificial vision systems struggle with persistent, panoramic understanding, often processing scenes egocentrically on a frame-by-frame basis. This limitation is pronounced in the

Bernard Parent, Felipe Martin Rodriguez Fuentes

Accurate prediction of electron temperature ($T_{\rm e}$) in non-equilibrium plasma flows is critical for applications ranging from hypersonic flight to plasma-assisted combustion. We recently proposed a thermodynamically consistent model for vibrational-electron (V-e) heating [Phys. Fluids 37, 096141 (2025)] which enforces convergence of $T_{\rm e}$ to the

Yi Wang, Shuhan Yang

In this paper, we study the stability of Minkowski inequality for nearly spherical domains that are $C^1$ close to the ball. We show the stability inequalities between the positive part of the $\sigma_k$ curvature integrals for $C^1$ perturbations of a ball; we also establish the stability inequalities for axially symmetric $C^1$ perturbations of a ball. Fin

Elisa Quintarelli, Fabio Alberto Schreiber, Kostas Stefanidis, Letizia Tanca

Ethics has become a major concern to the information management community, as both algorithms and data should satisfy ethical rules that guarantee not to generate dishonourable behaviours when they are used. However, these ethical rules may vary according to the situation-the context-in which the application programs must work. In this paper, after reviewing

Tanazza Khanam, Alessandra Corsi, Robert Coyne, Michael St. Pierre

The multi-messenger detection of the binary neutron star (NS) merger GW170817 has revolutionized the field of gravitational wave (GW) astronomy. However, several important questions remain to be answered. One of these is the nature of the compact remnant leftover by GW170817 (short- or long-lived NS versus black hole). A key goal going forward is to understa

Kiran Nair, Hubert Cecotti

Non-invasive Brain-Computer Interfaces (BCIs) based on Code-Modulated Visual Evoked Potentials (C-VEPs) require highly robust decoding methods to address temporal variability and session-dependent noise in EEG signals. This study proposes and evaluates several deep learning architectures, including convolutional neural networks (CNNs) for 63-bit m-sequence r

Suhas Suresh Bharadwaj, Murtaza Rangwala, Adarsh Ganesan

This paper proposes a method for generating phononic frequency combs (PFCs) using defect-localized modes in a two-dimensional hexagonal phononic crystal. Localized vibration modes from a singular point defect produce evenly spaced spectral lines corresponding to PFCs. Numerical modelling reveals robust energy transfer under a single-tone drive, generating sp

Ian Laga

Estimating the size of marginalized populations is a persistent challenge in survey statistics and public health, especially where stigma and legal restrictions exclude such groups from census and administrative data. Migrant domestic workers in Jordan represent one such population. We employ the Network Scale-up Method using the direct probe group method, e

Yupei Zhang, Yating Huang, Wanming Hu, Lequan Yu

Multimodal approaches that integrate histology and genomics hold strong potential for precision oncology. However, phenotypic and genotypic heterogeneity limits the quality of intra-modal representations and hinders effective inter-modal integration. Furthermore, most existing methods overlook real-world clinical scenarios where genomics may be partially mis

T. Rebolo, A. Grilo, C. Ribeiro

Unmanned Vehicles (UxVs) are increasingly used in modern military operations for reconnaissance, surveillance, and strike missions, enhancing situational awareness while reducing risk to personnel. Their affordability and rapid deployment have encouraged the adoption of commercial solutions. However, many rely on insecure protocols such as MAVLink, which lac

Natalie Collina, Eshwar Ram Arunachaleswaran, Meena Jagadeesan

AI agents are increasingly deployed in ecosystems where they repeatedly interact not only with each other but also with humans. In this work, we study these human-AI ecosystems from a theoretical perspective, focusing on the classical framework of repeated pricing games. In our stylized model, the AI agents play equilibrium strategies, and one or more humans

Tao Zhe, Huazhen Fang, Kunpeng Liu, Qian Lou

Feature transformation enhances downstream task performance by generating informative features through mathematical feature crossing. Despite the advancements in deep learning, feature transformation remains essential for structured data, where deep models often struggle to capture complex feature interactions. Prior literature on automated feature transform

Swathi Chandrasekhar, Shiva Raj Pokhrel, Swati Kumari, Navneet Singh

Escalating cyber threats and the high-dimensional complexity of IoT traffic have outpaced classical anomaly detection methods. While deep learning offers improvements, computational bottlenecks limit real-time deployment at scale. We present a quantum autoencoder (QAE) framework that compresses network traffic into discriminative latent representations and e

Henry Salgado, Meagan R. Kendall, Martine Ceberio

In this work, we propose a simple and computationally efficient framework for evaluating whether machine learning models align with the structure of the data they learn from; that is, whether the model says what the data says. Unlike existing interpretability methods that focus exclusively on explaining model behavior, our approach establishes a baseline der

Yuxin Li, Lorraine Xu, Meng Fan Wang

We propose a novel study on authorship attribution for Chinese lyrics, a domain where clean, public datasets are sorely lacking. Our contributions are twofold: (1) we create a new, balanced dataset of Chinese lyrics spanning multiple genres, and (2) we develop and fine-tune a domain-specific model, comparing its performance against zero-shot inference using

Enes Özeren, Matthias Aßenmacher

Chain-of-Thought (CoT) reasoning typically utilizes the discrete language space for thinking, which is inherently inefficient, as many generated tokens only enforce linguistic rules that are not required for reasoning. To bypass this, latent-space thinking allows models to think using the continuous embedding space. While existing methods for training those

Yifan Zhou, Sachin Grover, Mohamed El Mistiri, Kamalesh Kalirathnam

Reinforcement Learning (RL) traditionally relies on scalar reward signals, limiting its ability to leverage the rich semantic knowledge often available in real-world tasks. In contrast, humans learn efficiently by combining numerical feedback with language, prior knowledge, and common sense. We introduce Prompted Policy Search (ProPS), a novel RL method that

Alessandro Nordio, Alberto Tarable, Francisco J. Escribano

Intelligent reflecting surfaces (IRS) have become the subject of many current research efforts, as the ongoing wireless spectrum crunch has made the need to open higher frequency bands a priority. IRS are one of the alternatives proposed to overcome the problem of line-of-sight blocking in very high frequency wireless scenarios. The current state-of-the-art

Satrajit Chakrabarty, Ravi Soni

Foundation models, such as the Segment Anything Model (SAM), have heightened interest in promptable zero-shot segmentation. Although these models perform strongly on natural images, their behavior on medical data remains insufficiently characterized. While SAM 2 has been widely adopted for annotation in 3D medical workflows, the recently released SAM 3 intro

Alex Richardson, Jonathan Sprinkle

Digital twins of urban environments play a critical role in advancing autonomous vehicle (AV) research by enabling simulation, validation, and integration with emerging generative world models. While existing tools have demonstrated value, many publicly available solutions are tightly coupled to specific simulators, difficult to extend, or introduce signific

Lihui Liu

ChatGPT said: Text-attributed graphs, where nodes and edges contain rich textual information, are widely used across diverse domains. A central challenge in this setting is question answering, which requires jointly leveraging unstructured text and the structured relational signals within the graph. Although Large Language Models (LLMs) have made significant

Abhilasha Dave, James Russell, Mudit Mishra, Larry Ruckman

Achieving reliable real-time control of tokamak plasmas is essential for sustaining high-performance operation in next-generation fusion reactors. A major challenge is the accurate and timely prediction of edge-localized modes (ELMs), especially in high-confinement regimes such as wide-pedestal quiescent H-mode. We present a hardware-accelerated machine lear

Xinyu Liu, Xu Zhang, Can Chen, Ren Wang

Understanding how backdoor data influences neural network training dynamics remains a complex and underexplored challenge. In this paper, we present a rigorous analysis of the impact of backdoor data on the learning process, with a particular focus on the distinct behaviors between the target class and other clean classes. Leveraging the Information Bottlene

Bogdan A. Pastrav

Results of a case study of a sample of low-redshift galaxies are presented, to determine dust temperatures and emissivity indices through a less time-consuming method, and to connect both global and integrated galaxy properties with those of dust, ISM and star-formation. Dust temperatures ($T_{d}$) are determined based on the corresponding galaxy dust masses

Nicholas Frontiere, J. D. Emberson, Michael Buehlmann, Salman Habib

Self-consistently modeling baryonic effects in survey-scale cosmological simulations has become increasingly important as the diversity, precision, and statistical reach of modern observations continue to improve. The advent of exascale computing now enables a new generation of simulations that couple these physical processes across full-sky volumes with exc

Apu Kumar Chakroborti, Yi Ding, Lipeng Wan

As modern science becomes increasingly data-intensive, the ability to analyze and visualize large-scale, complex datasets is critical to accelerating discovery. However, many domain scientists lack the programming expertise required to develop custom data analysis workflows, creating barriers to timely and effective insight. Large language models (LLMs) offe

Francisco Sena, Aleksandr Politov, Corentin Moumard, Manuel Cáceres

Snarls and superbubbles are fundamental pangenome decompositions capturing variant sites. These bubble-like structures underpin key tasks in computational pangenomics, including structural-variant genotyping, distance indexing, haplotype sampling, and variant annotation. Snarls can be quadratically-many in the size of the graph, and since their introduction

Esmail Arasteh Rad, Somayeh Habibi

In this note we prove a motivic version of Leray-Hirsch theorem for pure Tate fibre bundles in the Grothendieck category of Chow motives. We then discuss some of its applications.

Luwei Bai, Yang Zeng, Baoyu Zhou

This work introduces a two-step stepsize schedule for stochastic gradient methods minimizing smooth strongly convex functions. We consider the setting where only stochastic gradient approximations, which are unbiased, of bounded variance, and supported on a finite set, are accessible. When the variance bound is relatively smaller than a ratio of the initial

Andrea Mejia, Peter Schweitzer

The energy-momentum tensor (EMT) form factor $D(t)$ is finite and negative in hadronic models and lattice QCD when only strong forces are included. However, when electromagnetic forces are considered, the $D(t)$ of charged hadrons undergoes a dramatic change: at small $t$, it changes sign and diverges like $1/\sqrt{-t}$ as shown for the proton in the classic

Aleksei G. Sorokin

Most scientific domains elicit the development of efficient algorithms and accessible scientific software. This thesis unifies our developments in three broad domains: Quasi-Monte Carlo (QMC) methods for efficient high-dimensional integration, Gaussian process (GP) regression for high-dimensional interpolation with built-in uncertainty quantification, and sc

Alexander A. Shashkin, Sergey V. Kravchenko

We review recent transport experiments that reveal two-threshold voltage-current characteristics, marked by a significant increase in noise between the two threshold voltages, at low electron densities in the insulating regime in two-dimensional (2D) electron systems, specifically in silicon metal-oxide-semiconductor field-effect transistors (MOSFETs) and Si

Sandra Mantovani, Mariano Messora

In this note, we propose a generalisation of G. Janelidze's notion of an ideally exact category beyond the Barr exact setting. We define an ideally regular category as a regular, Bourn protomodular category with finite coproducts in which the unique morphism 0 -> 1 is effective for descent. As in the ideally exact case, ideally regular categories support a n

Karin de Langis, William Walker, Khanh Chi Le, Dongyeop Kang

We propose an annotation approach that captures not only labels but also the reading process underlying annotators' decisions, e.g., what parts of the text they focus on, re-read or skim. Using this framework, we conduct a case study on the preference annotation task, creating a dataset PreferRead that contains fine-grained annotator reading behaviors obtain

Yurii Volkov, Oleksandr Volkov, Nataliia Voinalovych

The paper examines the construction and analysis of a new class of mixed exponential statistical structures that combine the properties of stochastic models and linear positive operators. The relevance of the topic is driven by the growing need to develop a unified theoretical framework capable of describing both continuous and discrete random structures tha

ATLAS Collaboration

A search for Standard Model (SM) Higgs bosons produced via vector-boson fusion at the Large Hadron Collider and decaying into a charm quark-antiquark pair ($H\rightarrow c\bar{c}$) is presented. The datasets used correspond to integrated luminosities of 37.5 fb$^{-1}$ and 51.5 fb$^{-1}$ and were collected by the ATLAS detector from proton-proton collisions a

Haoxuan Shan, Cong Guo, Chiyue Wei, Feng Cheng

The rapid scaling of large language models demands more efficient hardware. Quantization offers a promising trade-off between efficiency and performance. With ultra-low-bit quantization, there are abundant opportunities for results reuse, and thus it can be boosted with lookup tables (LUTs) based acceleration. However, existing LUT-based methods suffer from

Sofie Labat, Thomas Demeester, Véronique Hoste

Emotion-aware customer service needs in-domain conversational data, rich annotations, and predictive capabilities, but existing resources for emotion recognition are often out-of-domain, narrowly labeled, and focused on post-hoc detection. To address this, we conducted a controlled Wizard of Oz (WOZ) experiment to elicit interactions with targeted affective

Hamid Shamszare, Avishek Choudhury

Predicting human trust in AI systems is crucial for safe integration of AI-based decision support tools, especially in healthcare. This study proposes a multi-modal machine learning framework that combines image and galvanic skin response (GSR) data to predict early user trust in AI- or human-generated recommendations in a simulated ADHD mHealth context. Fac

Mikhail Cherdantsev, Elisa Davoli, Lorenza D'Elia, Samuele Riccò

We perform a simultaneous homogenization and linearization analysis for a magnetoelastic energy functional featuring a mixed Eulerian-Lagrangian structure. Neglecting Zeeman and anisotropic contributions, we characterize the asymptotic behavior in the sense of Gamma-convergence for the sum of a nonlinear magnetoelastic energy, a symmetric exchange term defin

Ying Wang, Yanlong Zhao, Ji-Feng Zhang, Karl Henrik Johansson

This paper focuses on the problem of quantized distributed estimation with event-triggered communication and packet loss, aiming to reduce the number of transmitted bits. The main challenge lies in the inability to differentiate between an untriggered event and a packet loss occurrence. This paper proposes an event-triggered distributed estimation algorithm

Danylo Radevych, Aidan Thorn, Manuel Engel, Aleksey N. Kolmogorov

We present an interface between the Vienna \textit{Ab initio} Simulation Package (VASP) and the EPW software for calculating materials properties governed by electron-phonon (e-ph) interactions. Computation of the e-ph matrix elements with the finite-difference supercell approach in VASP and their fine-grid interpolation in EPW enable accurate modeling of te

Wei Zhang, Yuzan Xiong, Jia-Mian Hu, Joseph Sklenar

The internal coupling of magnetic excitations (magnons) with themselves has created a new research sub-field in hybrid magnonics, i.e., magnon-magnon coupling, which focuses on materials discovery and engineering for probing and controlling magnons in a coherent manner. This is enabled by, one, the abundant mechanisms of introducing magnetic interactions, wi

Cecilia G. Morales, Fanurs Chi En Teh, Kai Li, Pushpak Agrawal

Remote photoplethysmography (rPPG) enables contactless vital sign monitoring using standard RGB cameras. However, existing methods rely on fixed parameters optimized for particular lighting conditions and camera setups, limiting adaptability to diverse deployment environments. This paper introduces the Projection-based Robust Signal Mixing (PRISM) algorithm,

Kunpeng Zhang, Hanwen Xu, Sheng Wang

Deciphering tumor microenvironment from Whole Slide Images (WSIs) is intriguing as it is key to cancer diagnosis, prognosis and treatment response. While these gigapixel images on one hand offer a comprehensive portrait of cancer, on the other hand, the extremely large size, as much as more than 10 billion pixels, make it challenging and time-consuming to na

Hernan Huwyler

The accelerating deployment of artificial intelligence systems across regulated sectors has exposed critical fragmentation in risk assessment methodologies. A significant "language barrier" currently separates technical security teams, who focus on algorithmic vulnerabilities (e.g., MITRE ATLAS), from legal and compliance professionals, who address regulator

Patricia Suriana, Joshua A. Rackers, Ewa M. Nowara, Pedro O. Pinheiro

Machine learning models for 3D molecular property prediction typically rely on atom-based representations, which may overlook subtle physical information. Electron density maps -- the direct output of X-ray crystallography and cryo-electron microscopy -- offer a continuous, physically grounded alternative. We compare three voxel-based input types for 3D conv

Dante Bonolis

In $2020$, Bhargava, Shankar, Taniguchi, Thorne, Tsimerman, and Zhao proved that for a finite extension $K/\mathbb{Q}$ of degree $n\geq 5$, the size of the $2$-torsion class group is bounded by $\# h_{2}(K)=O_{n,\varepsilon}(D_{K}^{\frac{1}{2}-\frac{1}{2n}+\varepsilon})$, where $D_{K}$ is the absolute discriminant of $K$. In the present paper, we improve the

Johannes Bertram, Luciano Dyballa, T. Anderson Keller, Savik Kinger

Foundation models have shown remarkable success in fitting biological visual systems; however, their black-box nature inherently limits their utility for understanding brain function. Here, we peek inside a SOTA foundation model of neural activity (Wang et al., 2025) as a physiologist might, characterizing each 'neuron' based on its temporal response propert

Devin R. Becker, Scott A. Hughes, Gaurav Khanna

Black hole binaries with small mass ratios will be important sources for the forthcoming Laser Interferometer Space Antenna (LISA) mission. Models of such binaries also serve as useful tools for understanding the dynamics of compact binary systems and the gravitational waves they emit. Using an eccentric Ori-Thorne procedure developed in previous work, we bu

Pouya Ahadi, Reza Marzban, Ali Adibi, Kamran Paynabar

Bayesian optimization is widely used for optimizing expensive black box functions, but most existing approaches focus on scalar responses. In many scientific and engineering settings the response is functional, varying smoothly over an index such as time or wavelength, which makes classical formulations inadequate. Existing methods often minimize integrated

Mahdi Aghaei, Saba Ebrahimi, Mohammad Saleh Arafati, Elham Cheshmikhani

Processing-in-Memory (PIM) has emerged as a promising computing paradigm to address the memory wall and the fundamental bottleneck of the von Neumann architecture by reducing costly data movement between memory and processing units. As with any engineering challenge, identifying the most effective solutions requires thorough exploration of diverse architectu

Tahia F. Dabash, Moataz H. Emam, Lukas Schoppner

We study the symmetries and conserved quantities in $f(R)$ gravity for the static, spherically symmetric Reissner--Nordstr\"om spacetime using two complementary frameworks: Noether symmetries and Mei symmetries. Starting from a canonical Lagrangian for radial metric functions and the curvature scalar $R$, we derive the associated Hamiltonian and show that th

R. Daniel Murphy, Elle Brinkman, Colter J. Richardson, Evan Semenak

We present the gravitational wave predictions from two-dimensional core collapse supernova (CCSN) simulations initiated from two nearly identical progenitors that have significantly different internal structures due to their late-stage stellar evolution. At the time of collapse, the 15.78 $M_{\odot}$ and 15.79 $M_{\odot}$ progenitors have compactness paramet

Oleg Gutik, Marko Serivka

We describe injective endomorphisms of the semigroup $\boldsymbol{B}_{\omega}^{\mathscr{F}^3}$ with a three-element family $\mathscr{F}^3$ of inductive non-empty subsets of $\omega$. In particular we find endomorphisms $\varpi_3$ and $\lambda$ of $\boldsymbol{B}_{\omega}^{\mathscr{F}^3}$ such that for every injective endomorphism $\varepsilon$ of the semigro

Fatemeh Akbarian, Anahita Baninajjar, Yingyi Zhang, Ananth Balashankar

Multi-modal foundation models align images, text, and other modalities in a shared embedding space but remain vulnerable to adversarial illusions [35], where imperceptible perturbations disrupt cross-modal alignment and mislead downstream tasks. To counteract the effects of adversarial illusions, we propose a task-agnostic mitigation mechanism that purifies

Eric Cochran

We show that all compact quasi-Einstein metrics of constant scalar curvature in dimension three are locally homogeneous. We accomplish this by using the equivalence of constant scalar curvature quasi-Einstein metrics $(M,g,X)$ and quasi-Einstein metrics with $X$ Killing in the compact case to make a connection to Sasakian geometry in dimension three. In high

Anjor Kanekar

We present GANDALF, a JAX-based spectral solver for Kinetic Reduced MHD (KRMHD) turbulence designed to lower infrastructure barriers to plasma turbulence research. Existing production codes require specialized HPC infrastructure and compilation expertise, limiting participation to well-resourced institutions. GANDALF addresses this barrier by leveraging JAX'

Dimitris Bertsimas, Caio de Prospero Iglesias, Nicholas A. G. Johnson

We study Sparse Multiple Kernel Learning (SMKL), which is the problem of selecting a sparse convex combination of prespecified kernels for support vector binary classification. Unlike prevailing l1 regularized approaches that approximate a sparsifying penalty, we formulate the problem by imposing an explicit cardinality constraint on the kernel weights and a

Regan Willis, Jason Bakos

Modern machine learning models often combine multiple input streams of data to more accurately capture the information that informs their decisions. In multimodal machine learning, choosing the strategy for fusing data together requires careful consideration of the application's accuracy and latency requirements, as fusing the data at earlier or later stages

Kyle Burke, Caroline Cashman, Alfie Davies, Kanae Yoshiwatari

We show that Mis\`ere Partizan Arc Kayles is PSPACE-complete on planar graphs via a reduction from Bounded Two-Player Constraint Logic. Furthermore, we show how to embed our gadgets onto the square and triangular grids. In order to clearly explain these results, we get into the details of Bounded Two-Player Constraint Logic and find three PSPACE-complete var

Bu Jin, Weize Li, Songen Gu, Yupeng Zheng

Articulated 3D objects play a vital role in realistic simulation and embodied robotics, yet manually constructing such assets remains costly and difficult to scale. In this paper, we present UniArt, a diffusion-based framework that directly synthesizes fully articulated 3D objects from a single image in an end-to-end manner. Unlike prior multi-stage techniqu

Batuhan Keskin, Cameron Afradi, Sylvain Lovis, Maurizio Palesi

Quantum computing platforms are evolving to a point where placing high numbers of qubits into a single core comes with certain difficulties such as fidelity, crosstalk, and high power consumption of dense classical electronics. Utilizing distributed cores, each hosting logical data qubits and logical ancillas connected via classical and quantum communication

Roger M. Hart, Lauren E. Messina, Eric A. Schenck, Samantha R. Kaplan

Introductory college Earth and space science courses offer rich opportunities for citizen science projects. One especially compelling context is Earth's geomagnetic field: a self-excited dynamo in the liquid outer core generates a global field that couples Earth's interior to solar forcing, providing a natural laboratory for space weather education. We teste

Tiffany Fan, Murray Cutforth, Marta D'Elia, Alexandre Cortiella

Extracting compact, physically interpretable representations from high-dimensional scientific data is a persistent challenge due to the complex, nonlinear structures inherent in physical systems. We propose a Gaussian Mixture Variational Autoencoder (GM-VAE) framework designed to address this by integrating an Expectation-Maximization (EM)-inspired training

Akbar Anbar Jafari, Gholamreza Anbarjafari

Contemporary autoregressive transformers operate in open loop: each hidden state is computed in a single forward pass and never revised, causing errors to propagate uncorrected through the sequence. We identify this open-loop bottleneck as a fundamental architectural limitation underlying well-documented failures in long-range reasoning, factual consistency,

Daniel Bilsborrow, Niall Jeffrey

We present a detection of the intrinsic galaxy alignments in the CAMELS suite of hydrodynamic simulations. We find that the alignment amplitude depends significantly on cosmological and supernova feedback parameters - specifically $\Omega_m$, $\sigma_8$, $A_{\text{SN1}}$, $A_{\text{SN2}}$- while no dependence on AGN feedback is observed (due to the limited s

A. Bahini, V. O. Nesterenko, P. von Neumann-Cosel, P. -G. Reinhard

Experimental data on $\alpha$-particle inelastic scattering for monopole excitations in $^{24}$Mg in the excitation-energy region $E_{\rm x}$$=$$9$$-$$25$ MeV, obtained at the iThemba Laboratory for Accelerator Based Sciences (iThemba LABS), have been analyzed within a fully self-consistent quasiparticle random-phase approximation (QRPA) framework using two

Ludovico Nista, Christoph D. K. Schumann, Temistocle Grenga, Jonathan F. MacArt

A dynamic mixed super-resolution model (DMSRM) for large-eddy simulations (LESs) is proposed, which combines the traditional dynamic mixed model (DMM) formulation with the generation of super-resolved velocity fields from which the subfilter-scale (SFS) stress tensor can be computed. A data-driven super-resolution generative adversarial network (SR-GAN) is e

Kaiyao Ke, Ali Reza Ibrahimzada, Rangeet Pan, Saurabh Sinha

Repository-level code translation aims to migrate entire repositories across programming languages while preserving functionality automatically. Despite advancements in repository-level code translation, validating the translations remains challenging. This paper proposes TRAM, which combines context-aware type resolution with mock-based in-isolation validat

Nenad Petrovic, Norbert Kroth, Axel Torschmied, Yinglei Song

This paper presents an event-chain-driven, LLM-empowered workflow for generating validated, automotive code from natural-language requirements. A Retrieval-Augmented Generation (RAG) layer retrieves relevant signals from large and evolving Vehicle Signal Specification (VSS) catalogs as code generation prompt context, reducing hallucinations and ensuring arch

Guy Blanc, William Pires, Toniann Pitassi

Differential privacy (DP) is the de facto notion of privacy both in theory and in practice. However, despite its popularity, DP imposes strict requirements which guard against strong worst-case scenarios. For example, it guards against seemingly unrealistic scenarios where an attacker has full information about all but one point in the data set, and still no

Hiroaki Chiba-Okabe, Joshua B. Plotkin

We study the behavior of for-profit institutions that broadcast reputations to foster trust among market participants. We develop a theoretical model in which buyers and sellers are matched on a platform to engage in transactions involving a moral hazard: sellers can either faithfully deliver goods after receiving payment, or not. Although the buyer does not

Qianxi Li, Ying-nan Mao, Kechen Wang

We propose a model-independent test of CP violation in the scalar sector. We consider a heavy neutral scalar $h_2$ with tree-level couplings at the $h_2 V V$ and $h_2 h_1 Z$ vertices (with $V=W^{\pm},Z$), alongside the 125~GeV SM-like Higgs boson $h_1$. At future muon colliders (MuC), we exploit vector-boson-fusion (VBF) production of $h_2$ followed by the d

A. L. Paredes

We examine the predictive power of a novel hybrid A3T-GCN architecture for forecasting closing stock prices of FTSE100 constituents. The dataset comprises 79 companies and 375,329 daily observations from 2007 to 2024, with node features including technical indicators (RSI, MACD), normalized and log returns, and annualized log returns over multiple windows (A