November 2025 arXiv papers — page 82

D. A. Cook, A. Olimpieri, I. P. R. Baranov, H. A. Borges

Polymer models are effective in describing quantum gravity effects around the initial singularity, leading to its replacement by bouncing surfaces on which the curvature and densities are finite. Their properties depend on the space-time symmetry and on the particular polymerisation scheme adopted. In this article we investigate anisotropic models under the

Eli Morhayim, Michael T. Ziemba, J. Lim, B. E. Sauer

We consider stimulated Raman adiabatic passage (STIRAP) when the final state is a superposition of two non-degenerate states. The system consists of four states coupled by two light fields. We find the relative phase of the final superposition depends on relative amplitude, width and timing of the adiabatic transfer pulses. We discuss these results in the co

Fredrick Olness

We extend the QCD Parton Model analysis by employing a factorized nuclear structure model that explicitly accounts for both individual nucleons and correlated nucleon pairs. This novel framework establishes a paradigm that directly links the nuclear physics description of matter (in terms of protons and neutrons) to the particle physics schema (in terms of q

Naomi Simumba, Nils Lehmann, Paolo Fraccaro, Hamed Alemohammad

Geospatial Foundation Models (GeoFMs) are transforming Earth Observation (EO), but evaluation lacks standardized protocols. GEO-Bench-2 addresses this with a comprehensive framework spanning classification, segmentation, regression, object detection, and instance segmentation across 19 permissively-licensed datasets. We introduce ''capability'' groups to ran

Lucas Kotz, Aurore Courtoy, Pavel Nadolsky, Fredrick Olness

We systematically explore the parametrization dependence of the Parton Distribution Functions (PDFs) to better quantify the true uncertainty from global QCD analyses. To achieve this, we employ a novel technique that automates the generation of polynomial parametrizations for PDFs using B\'ezier curves. This technique is implemented in a C++ module, named Fa

Yue Li, Qing Xu, Yixuan Zhang, Xiangjian He

The Segment Anything Model 2 (SAM2) demonstrates remarkable universal segmentation capabilities on natural images. However, its performance on ultrasound images is significantly degraded due to domain disparities. This limitation raises two critical challenges: how to efficiently adapt SAM2 to ultrasound imaging while maintaining parameter efficiency, and ho

Di Zhang

The JUNO experiment has recently released its first measurement results based on 59.1 days of data, achieving unprecedented precision in measuring the lepton mixing angle $\theta_{12}$. This significant improvement places stringent constraints on certain neutrino mass models and flavor mixing patterns. In this work, we examine the impact of the latest JUNO r

Sivakumar Sudarsanan, Manikandan Raghunathan, Shwetha Viswesh, R. I. Sujith

Spiky oscillations are characterized by slow-fast dynamics and are observed in excitable media such as neuronal membranes and cardiac cells. In a turbulent reactive flow system, we observe that the heat release rate exhibits self-sustained periodic, spiky oscillations in synchrony with the sinusoidal periodic acoustic pressure oscillations. These self-sustai

Kim N. Nolle, Ivana Dusparic, Rhodri Cusack, Vinny Cahill

Continual learning (CL) is a branch of machine learning that aims to enable agents to adapt and generalise previously learned abilities so that these can be reapplied to new tasks or environments. This is particularly useful in multi-task settings or in non-stationary environments, where the dynamics can change over time. This is particularly relevant in cyb

Jared N. Lakhani

Arnold and Arvanitis (2020) introduced a novel bivariate conditionally specified distribution, a distribution in which dependence between two random variables is established by defining the distribution of one variable conditional on the other. This novel conditioning regime was achieved through the use of survival functions, and the approach was termed the

Kristian Cvecek, Markus Döring, Alexander Romboy, Johannes Heberle

We investigate ablation experiments performed by laser-plasma-filament guided electrical discharges at high-voltages of up to 145 kV. The guiding was accomplished via fs-laser-generated plasma filaments across a gap of 201 mm of air onto steel 1.3343 samples. This method combines remote material processing and enables the steering and deflection of high volt

Janice Mak, Joyce Nakatumba-Nabende, Tony Clear, Alison Clear

Generative AI (GenAI) presents societal and ethical challenges related to equity, academic integrity, bias, and data provenance. In this paper, we outline the goals, methodology and deliverables of their collaborative research, considering the ethical and societal impacts of GenAI in higher computing education. A systematic literature review that addresses a

M. Rodriguez Zarate

We present a categorical formulation of the Hamiltonian renormalisation programme for quantum field theories, establishing a systematic bridge between functional and lattice renormalisation. To this end, we introduce two categories, $Seq$ and $Func$, whose objects correspond to resolution spaces at different ultraviolet scales, and whose morphisms encode emb

Chao-Wei Shen, Yong-Hui Lin, Hao-Jie Jing

The mass spectrum of $\Lambda_c$ baryon family is investigated within the $DN$-$D^*N$ coupled-channel framework using two phenomenological approaches for the low-energy $D^{(*)} N$ interactions: the heavy quark effective theory and the flavor-symmetry-constrained effective Lagrangian method. It is shown that the LHCb pentaquark states have direct analogs in

Théo André, Szymon Cygan, Anna Marciniak-Czochra, Finn Münnich

This paper investigates pattern formation in reaction--diffusion systems with both diffusive and nondiffusive components, providing necessary and sufficient conditions for diffusion-driven instability (DDI) and establishing the existence of far-from-equilibrium patterns. While previous work has linked DDI to instability in the purely nondiffusive subsystem -

Gabriel Flath

We revisit the ergodic theorem for the frontier of branching Brownian motion (BBM). Motivated by the proof of Arguin, Bovier, and Kistler \cite{arguin2012ergodic}, we provide a shorter and more direct argument. It relies on two observations: pairs of extremal particles observed at well-separated times must have branched early, and pairs of early-branching ex

Gabriel Frohaug, Konstantin Maslov, Veronica Dexheimer, Joaquin Grefa

We present a new hadronic EoS with hyperons built within the relativistic mean-field (RMF) formalism with baryon-density- and isospin-density-dependent couplings. Motivated by microscopic calculations showing density- and isospin-asymmetry-dependence of self-energies, we implement a new form for the baryon-meson couplings. The parameters for the couplings ar

Samih Fadli

Large language model safety is usually assessed with static benchmarks, but key failures are dynamic: value drift under distribution shift, jailbreak attacks, and slow degradation of alignment in deployment. Building on a recent Second Law of Intelligence that treats ethical entropy as a state variable which tends to increase unless countered by alignment wo

Valentina Scotti, Antonio Anastasio, Alfonso Boiano, Francesco Cafagna

The POEMMA-Balloon with Radio (PBR) mission incorporates an advanced data processing system (DP) to enable the detection and characterization of ultra-high-energy cosmic rays and astrophysical neutrinos. The data acquisition (DAQ) system integrates inputs from the Cherenkov Camera, the Fluorescence Camera, the Radio Instrument and the X-Gamma detectors, ensu

Leonardo N. Ferreira, Haroon Mumtaz, Gabor Pinter

We study macroeconomic fluctuations in the United Kingdom over seven centuries (1271--2022) using a time-varying VAR with stochastic volatility. We identify business cycle shocks as innovations explaining the largest share of future output variance. Before 1900, these shocks display a stagflationary, supply-driven pattern, while post-1900 shocks become deman

C. Nico Barati, Arie Croitoru, Ross Gore, Michael Jarret

Quantum computing promises transformative advances, but remains constrained by recurring misconceptions and methodological pitfalls. This paper demonstrates a fundamental incompatibility between traditional agent-based modeling (ABM) implementations and quantum optimization frameworks like Quadratic Unconstrained Binary Optimization (QUBO). Using Schelling's

Bardia Nadimi, Khashayar Filom, Deming Chen, Hao Zheng

With the rapid advancement of Large Language Models (LLMs), there is growing interest in applying them to hardware design and verification. Among these stages, design verification remains the most time-consuming and resource-intensive phase, where generating effective stimuli for the design under test (DUT) is both critical and labor-intensive. We present {\

Robert Davies, Christopher B. Smiet, Charlotte Batzdorf, J. Geiger

This work analyses vacuum magnetic field topology in Wendelstein 7-X (W7-X) with respect to changes in the current in the superconducting coils. We develop a fast automated scheme to locate fixed points (such as X- and O-points) and calculate the trace of the Jacobian of the field line map for them (Tr(M)), which represents several important properties of th

Shourov Joarder, Tushar Talukder Showrav, Md. Kamrul Hasan

Ultrasound Strain Elastography (USE) is a powerful non-invasive imaging technique for assessing tissue mechanical properties, offering crucial diagnostic value across diverse clinical applications. However, its clinical application remains limited by tissue decorrelation noise, scarcity of ground truth, and inconsistent strain estimation under different defo

Mark T. Lusk

An analytical expression is derived for the rate of gravitational Faraday rotation measured by Eulerian observers. The reference frame is a Fermi-Walker triad aligned with the spatial wave vector. Attention is restricted to the ADM split of Kerr spacetime and geometric optics. Our exact, closed-form GFR formula is implemented and verified to be consistent wi

Yang Cui, Denis Anikiev, Umair Bin Waheed, Yangkang Chen

Supervised deep learning methods typically require large datasets and high-quality labels to achieve reliable predictions. However, their performance often degrades when trained on imperfect labels. To address this challenge, we propose a physics-aware loss function that serves as a penalty term to mitigate label imperfections during training. In addition, w

Ankush Thakur, Renu Bala, H. S. Nataraj

The present work reports the spectroscopic parameters and molecular properties for the ground electronic state, $^1\Sigma^+$, of CdH$^+$, HgH$^{+}$, and YbH$^{+}$ molecular ions. We have used the state-of-the-art relativistic coupled cluster method together with the relativistic core-valence triple- and quadruple zeta quality basis sets for the calculation o

Valentina Scotti, Antonio Anastasio, Mario Bertaina, Alfonso Boiano

The POEMMA-Balloon with Radio (PBR) mission is a NASA super-pressure balloon experiment designed to advance the detection of ultra-high-energy cosmic rays, high-altitude horizontal air showers, and astrophysical neutrinos. A key instrument of PBR is the Cherenkov Camera (CC), which utilizes a 2048-pixel SiPM camera to detect the optical Cherenkov emission fr

Maleeha Khawaja, Samir Siksek

Let $C$ be a smooth projective absolutely irreducible curve of genus at least 2, defined over the rationals. For a number field $L$, we define the set of $L$-new points on $C$ to be $C(L)_{new} = \{P \in C(L) : \mathbb{Q}(P)=L\}$; this is the set of points on $C$ defined over $L$ but not any strictly smaller field. Let $n$ be at least 2. We conjecture that $

Benjamin Dupuis, Mert Gürbüzbalaban, Umut Şimşekli, Jian Wang

Characterizing the differential privacy (DP) of learning algorithms has become a major challenge in recent years. In parallel, many studies suggested investigating the behavior of stochastic gradient descent (SGD) with heavy-tailed noise, both as a model for modern deep learning models and to improve their performance. However, most DP bounds focus on light-

Tao Hu, Lan Li, Zhen-Hao Xie, Da-Wei Zhou

Class-Incremental Learning (CIL) enables models to learn new classes continually while preserving past knowledge. Recently, vision-language models like CLIP offer transferable features via multi-modal pre-training, making them well-suited for CIL. However, real-world visual and linguistic concepts are inherently hierarchical: a textual concept like "dog" sub

Petrus E. O. G. B. Abreu, Gabriela M. M. Paixão, Jiawei Li, Paulo R. Gomes

Data-driven methods for electrocardiogram (ECG) interpretation are rapidly progressing. Large datasets have enabled advances in artificial intelligence (AI) based ECG analysis, yet limitations in annotation quality, size, and scope remain major challenges. Here we present CODE-II, a large-scale real-world dataset of 2,735,269 12-lead ECGs from 2,093,807 adul

Giuseppe Maria Coclite, Nicola De Nitti, Kuang Huang

We consider a class of nonlocal conservation laws modeling traffic flows, given by $ \partial_t u_\varepsilon + \partial_x(V(u_\varepsilon \ast \gamma_\varepsilon) u_\varepsilon) = 0$, with a rescaled convolution kernel $\gamma_\varepsilon(\cdot) := \varepsilon^{-1}\gamma(\cdot/\varepsilon)$. We establish the strong $\mathrm L^1_{\mathrm{loc}}$-convergence o

Thomas Lee, Andy Sun

We develop a GPU-accelerated dynamic programming (DP) method for valuing, operating, and bidding energy storage under multistage stochastic electricity prices. Motivated by computational limitations in existing models, we formulate DP backward induction entirely in tensor-based algebraic operations that map naturally onto massively parallel GPU hardware. Our

Julio C. Facelli

Microproteins are a newly recognized and rapidly growing class of small proteins, typically encoded by fewer than 100 to 150 codons and translated from small open reading frames (smORFs). Although research has shown that smORFs and their corresponding microproteins constitute a significant portion of the genome and proteome, there is still limited informatio

Chris Culliton, Amber Roberts, Bryan DeMarcy, Sowgat Muzahid

We searched the Hubble Space Telescope Cosmic Origins Spectrograph archive for ultraviolet spectra of 428 AGN to identify intrinsic NV absorption systems. We filtered out Type 2 AGN, blazars, and spectra that do not cover at least part of the velocity window from 5000 km/s blueward to 5000 km/s redward (hereafter, the ``associated'' region) of the NV emissio

M. Sapkas, A. Triossi, M. Zanetti

This work explores the use of the AMD Xilinx Versal Adaptable Intelligent Engine (AIE) to accelerate Gated Recurrent Unit (GRU) inference for latency constrained applications. We present a custom workload distribution framework across the AIE's vector processors and propose a hybrid AIE - Programmable Logic (PL) design to optimize computational efficiency. O

Pu-Ting Yu

Let $A\colon H\rightarrow H$ be a normal operator on an infinite-dimensional separable Hilbert space $H$ and let $S\subseteq H$ be a finite subset such that $\{A^nx\}_{n\geq 0,\,x\in S}$ can be rescaled to form a frame for $H$. That is, there exist some subsets $J_x\subseteq \mathbb{N}\cup\{0\}$ and some set of nonzero scalars $(c_{n,x})_{n\in J_x,\,x\in S}$

Omar Mahmood, Pedro O. Pinheiro, Richard Bonneau, Saeed Saremi

Ligand-based drug discovery (LBDD) relies on making use of known binders to a protein target to find structurally diverse molecules similarly likely to bind. This process typically involves a brute force search of the known binder (query) against a molecular library using some metric of molecular similarity. One popular approach overlays the pharmacophore-sh

Eren Tekeler, Xiangru Zhong, Huan Zhang, Samuel Chevalier

Security-Constrained DC Optimal Power Flow (SC DCOPF) is an important tool for transmission system operators, enabling economically efficient and physically secure dispatch decisions. Although CPU-based commercial solvers (e.g., Gurobi) can efficiently solve SC-DCOPF problems with a reasonable number of security constraints, their performance degrades rapidl

Dante Francisco Wasmuht, Otto Brookes, Maximillian Schall, Pablo Palencia

Automated video analysis is critical for wildlife conservation. A foundational task in this domain is multi-animal tracking (MAT), which underpins applications such as individual re-identification and behavior recognition. However, existing datasets are limited in scale, constrained to a few species, or lack sufficient temporal and geographical diversity - l

Mohit Sharma, Robbe Van Rompaey, Wouter Lanneer, Marc Moonen

This paper addresses the challenges in short-time Fourier transform (STFT) domain subband adaptive filtering, in particular, subband system identification. Previous studies in this area have primarily focused on setups with subband filtering at a downsampled rate, implemented using the weighted overlap-add (WOLA) filter bank, popular in audio and speech-proc

Nils Wildt, Daniel M. Tartakovsky, Sergey Oladyshkin, Wolfgang Nowak

Ordinary differential equations (ODEs) are a conventional way to describe the observed dynamics of physical systems. Scientists typically hypothesize about dynamical behavior, propose a mathematical model, and compare its predictions to data. However, modern computing and algorithmic advances now enable purely data-driven learning of governing dynamics direc

Tingrui Shen, Yiheng Zhang, Chen Tang, Chuan Ping

Autoregressive models can generate high-quality 3D meshes by sequentially producing vertices and faces, but their token-by-token decoding results in slow inference, limiting practical use in interactive and large-scale applications. We present FlashMesh, a fast and high-fidelity mesh generation framework that rethinks autoregressive decoding through a predic

Aravind Asok, Jean Fasel, Samuel Lerbet

Suppose $X$ is a smooth affine real variety and $\mathscr{E}$ is a vector bundle over $X$. We analyze the problem of splitting off a free rank one summand from $\mathscr{E}$ in corank $0$ and $1$. The problem in corank $0$ can be viewed as the search for a real analog of Murthy's celebrating splitting theorem in the algebraically closed case: to wit, beyond

Sai Puppala, Ismail Hossain, Jahangir Alam, Sajedul Talukder

The integration of advanced robotics in nuclear power plants (NPPs) presents a transformative opportunity to enhance safety, efficiency, and environmental monitoring in high-stakes environments. Our paper introduces the Optimus-Q robot, a sophisticated system designed to autonomously monitor air quality and detect contamination while leveraging adaptive lear

Jing Bi, Filippos Bellos, Junjia Guo, Yayuan Li

Test-time thinking (that is, generating explicit intermediate reasoning chains) is known to boost performance in large language models and has recently shown strong gains for large vision language models (LVLMs). However, despite these promising results, there is still no systematic analysis of how thinking actually affects visual reasoning. We provide the f

Sunder Ram Krishnan

A Cartan-geometric, jet bundle formulation of curvature-aware variance bounds in parametric statistical estimation is developed. Building on our earlier extrinsic Hilbert space approach to the Cram\'er-Rao and Bhattacharyya-type inequalities, we show that the curvature corrections induced by the square root embedding of a statistical model admit a canonical

Narasimha Chary Bonala, S Senthamarai Kannan, Santosha Pattanayak

We consider the action of the one-parameter subgroup of the special linear group corresponding to a simple root on Grassmannians and describe the structure of the associated Geometric Invariant Theory (GIT) quotients with respect to Pl\"ucker line bundle. Using the combinatorics of Weyl group elements, we explicitly describe the semistable loci and identify

Xian-Yu Wang, Songhu Wang, J. M. Joel Ong

The stellar obliquity transition, defined by a $\textit{T}_{\rm eff}$ cut separating aligned from misaligned hot Jupiter systems, has long been assumed to coincide with the rotational Kraft break. Yet the commonly quoted obliquity transition (6100 or 6250 K) sits a few hundred kelvin cooler than the rotational break (~6500 K), posing a fundamental inconsiste

Rodrigo Alonso, Christoph Englert, Wrishik Naskar, Shakeel Ur Rahaman

Truncations of effective field theory expansions are technically necessary but inherently intertwined with the redundancies of general field redefinitions. This can be viewed as a juxtaposition of power-counting and theoretical uncertainties, which seek to estimate neglected higher-dimensional interactions through approaches based on community consensus. One

Jiong Mei, Shao-Hang Shi, Ping Xu, Ziyan Chen

In this work, we revisit the electron-hole asymmetry of antiferromagnetism in cuprates by studying the three-band Emery model. Using parameters relevant to La$_2$CuO$_4$, we benchmark the anti-ferromagnetic response for a large range of dopings with variational Monte Carlo, determinant quantum Monte Carlo, constrained-path auxiliary-field quantum Monte Carlo

Senyu Fei, Siyin Wang, Li Ji, Ao Li

Vision-Language-Action (VLA) models excel in robotic manipulation but are constrained by their heavy reliance on expert demonstrations, leading to demonstration bias and limiting performance. Reinforcement learning (RL) is a vital post-training strategy to overcome these limits, yet current VLA-RL methods, including group-based optimization approaches, are c

Mark Ebert, Raphael Rouquier

We construct a model for the tensor product of the regular 2-representation of the enveloping algebra of $\mathfrak{sl}_2^+$ with the vector 2-representation, based on the $\infty$-categorical definition of the second author. Our model contains McMillan's minimal one. Our use of an infinite family of generators provides a simpler model that we prove is equiv

Bin Xie, Gady Agam

Medical image segmentation typically adopts a point-wise convolutional segmentation head to predict dense labels, where each output channel is heuristically tied to a specific class. This rigid design limits both feature sharing and semantic generalization. In this work, we propose a unified decoupled segmentation head that separates multi-class prediction i

Midhuna V Ajith, Peter J Cameron, Mainak Ghosh, Aparna Lakshmanan S

Let $G$ be a group. The directed endomorphism graph, $\dend(G)$ of $G$ is a directed graph with vertex set $G$ and there is a directed edge from the vertex $a$ to the vertex $b$ if $a \neq b$ and there exists an endomorphism on $G$ mapping $a$ to $b$. The endomorphism graph, $\uend(G)$ is the corresponding undirected simple graph. The automorphism graph of $

Giorgos Michailidis, Efthalia Traianou, Nicola Marchili, Giorgos Filippos Paraschos

The $\gamma$-ray-loud blazar TXS 2013+370, a powerful multiwavelength emitter at $z = 0.859$, underwent an exceptional GeV outburst in late 2020-early 2021. In this work, we present full-polarization VLBI imaging at 22, 43, and 86 GHz together with contemporaneous single-dish monitoring (radio and $\gamma$-rays) to localize the high-energy dissipation site a

Miruna-Alexandra Gafencu, Yordanka Velikova, Nassir Navab, Mohammad Farid Azampour

Ultrasound offers a radiation-free, cost-effective solution for real-time visualization of spinal landmarks, paraspinal soft tissues and neurovascular structures, making it valuable for intraoperative guidance during spinal procedures. However, ultrasound suffers from inherent limitations in visualizing complete vertebral anatomy, in particular vertebral bod

Tommaso Lorenzi, Horacio Tettamanti, Mattia Zanella

We present a new class of models for assessing the cell dynamics characterising muscular dystrophies. The proposed approach comprises a system of integro-differential equations for the statistical distributions, over a large patient cohort, of the densities of muscle fibers and immune cells implicated in muscle inflammation, degeneration, and regeneration, w

Haoran Wu, Xuwen Zhu

We analyze a 1-parameter family of heart shape and a 3-parameter family obtained by gluing three footballs, both of which are examples of reducible spherical conical metrics. For these examples we verify the structure theorem given in [15] and show that such metrics naturally arise from Abelian differentials of the third kind. We then obtain the geometric de

Xufei Wang, Junqiao Zhao, Siyue Tao, Qiwen Gu

LiDAR place recognition plays a crucial role in SLAM, robot navigation, and autonomous driving. However, existing LiDAR place recognition methods often struggle to adapt to new environments without forgetting previously learned knowledge, a challenge widely known as catastrophic forgetting. To address this issue, we propose KDF+, a novel continual learning f

Bhishan Jacelon

Quantum metric Choquet simplices are special kinds of compact quantum metric spaces designed for distance measurement in and around the category of stably finite Elliott-classifiable $\mathrm{C}^*$-algebras. The primary objective of this article is to introduce versions of these structures for which the associated tracial metrics need not be induced by Lipsc

Christoph Aistleitner, Lorenz Fruehwirth, Joscha Prochno

A classical observation in analysis asserts that lacunary systems of dilated functions show many properties which are also typical for systems of independent random variables. For example, if $(n_k)_{k \ge 1}$ is a sequence of integers satisfying the Hadamard gap condition $n_{k+1}/n_k\ge q > 1,~k \ge 1$, then the normalized sums $\sum_{n=1}^N \cos(2\pi n_k

Koen Scheres, Rodolphe Sepulchre

Excitable neuromorphic circuits are physical models of event behaviors: their continuous-time trajectories consist of sequences of discrete events. This paper explores the possibility of extracting a discrete-event model out of the physical continuous-time model. We discuss the potential of this methodology for analysis and design of neuromorphic control sys

Alexis Audran-Reiss, Jordi Armengol-Estapé, Karen Hambardzumyan, Amar Budhiraja

AI research agents offer the promise to accelerate scientific progress by automating the design, implementation, and training of machine learning models. However, the field is still in its infancy, and the key factors driving the success or failure of agent trajectories are not fully understood. We examine the role that ideation diversity plays in agent perf

Emil R. Hellebek, Anders S. Sørensen

Long distance entanglement generation at a high rate is a major quantum technological goal yet to be fully realized, with the promise of many interesting applications, such as secure quantum computing on remote servers and quantum cryptography. One possible implementation is using a variant of the DLCZ-scheme by combining atomic-ensemble memories and linear

Qian Zhu, Yuxuan Liu, Ziyuan Zhu, Shangqing Liu

Extended Berkeley Packet Filter (eBPF) allows developers to extend Linux kernel functionality without modifying its source code. To ensure system safety, an in-kernel safety checker, the verifier, enforces strict safety constraints (for example, a limited program size) on eBPF programs loaded into the kernel. These constraints, combined with eBPF's performan

Gage MacLin, Venanzio Cichella, Andrew Patterson, Irene Gregory

In this paper, we propose a Transformer-based framework for approximating solutions to infinite-dimensional optimization problems: calculus of variations problems and optimal control problems. Our approach leverages offline training on data generated by solving a sample of infinite- dimensional optimization problems using composite Bernstein collocation. Onc

Aaron Ferguson, Ahmed A. A. Osman, Berta Bescos, Carsten Stoll

We present MHR, a parametric human body model that combines the decoupled skeleton/shape paradigm of ATLAS with a flexible, modern rig and pose corrective system inspired by the Momentum library. Our model enables expressive, anatomically plausible human animation, supporting non-linear pose correctives, and is designed for robust integration in AR/VR and gr

Eugene Wu, Yiru Chen, Haneen Mohammed, Zezhou Huang

Human-data interaction (HDI) presents fundamentally different challenges from traditional data management. HDI systems must meet latency, correctness, and consistency needs that stem from usability rather than query semantics; failing to meet these expectations breaks the user experience. Moreover, interfaces and systems are tightly coupled; neither can easi

Rafaela F. S. Penacchio, Siham Mohamed, Sérgio L. Morelhão, Sergey L. Bud'ko

Here, we provide a detailed study of the crystal structure and physical properties of the recently discovered vdW ferromagnet FePd$_2$Te$_2$. We find this compound has a relatively wide width of formation, and grow single crystals with compositions Fe$_x$Pd$_{y}$Te$_2$ where $x$ ranges from 0.9 to 1.1 and $y$ from 1.8 to 2.5, respectively. Temperature-depend

Alberto Bucci, Yuji Nakatsukasa, Taejun Park

The Nystr\"om method is a widely used technique for improving the scalability of kernel-based algorithms, including kernel ridge regression, spectral clustering, and Gaussian processes. Despite its popularity, the numerical stability of the method has remained largely an unresolved problem. In particular, the pseudo-inversion of the submatrix involved in the

O. Moreno Segura, Y. Pavlyukh, R. Tuovinen

Predicting real-time dynamics in correlated systems is demanding: exact two-time Green's function methods are accurate but often too costly, while the Generalized Kadanoff-Baym Ansatz (GKBA) offers time-linear propagation at the risk of uncontrolled behavior. We examine when and why GKBA fails in a minimal yet informative setting, the Holstein dimer that des

Kayo Tei, Haruto Mishina, Naoki Yamamoto, Kazunori Ueda

Systematic discovery of optimization paths in quantum circuit simplification remains a challenge. Today, ZX-calculus, a computing model for quantum circuit transformation, is attracting attention for its highly abstract graph-based approach. Whereas existing tools such as PyZX and Quantomatic offer domain-specific support for quantum circuit optimization, vi

Sifan Zhou, Yichao Cao, Jiahao Nie, Yuqian Fu

3D single object tracking (SOT) in LiDAR point clouds is a critical task in computer vision and autonomous driving. Despite great success having been achieved, the inherent sparsity of point clouds introduces a dual-redundancy challenge that limits existing trackers: (1) vast spatial redundancy from background noise impairs accuracy, and (2) informational re

Francesco Pecora, William H. Matthaeus, Antonella Greco, Pablo Dmitruk

Spatiotemporal correlation of magnetic field fluctuations is investigated using the Magnetospheric Multiscale mission in the terrestrial magnetosheath. The first observation of the turbulence propagator in space emerges through analysis of more than a thousand intervals. Results show clear features of spatial and spectral anisotropy, leading to a distinct be

Urjitkumar Patel, Fang-Chun Yeh, Chinmay Gondhalekar

With the increasing prevalence of video content, effectively understanding and answering questions about long form videos has become essential for numerous applications. Although large vision language models (LVLMs) have enhanced performance, they often face challenges with nuanced queries that demand both a comprehensive understanding and detailed analysis.

Pietro Capovilla

We present an explicit construction of closed oriented aspherical smooth 4-manifolds with $\chi = \sigma = n$ for every positive integer $n$. This proves a conjecture of Edmonds by providing a closed oriented aspherical 4-manifold with Euler characteristic 1, and it shows that the real analogue of the Bogomolov-Miyaoka-Yau inequality fails for aspherical 4-m

Kyriaki Chatzikyriakidou, Kristi L. Hall, Edward F. Redish, Todd J. Cooke

The epistemology of science students, i.e., their beliefs about the nature of the knowledge they are learning, about what they have to do to learn it, and about how they will use that knowledge, often plays a powerful role in what they learn in their science courses. This perspective paper provides a broad overview of the theoretical frameworks, designs, and

Qihao Yang, Xuelin Wang, Jiale Chen, Xuelian Dong

Language acquisition is vital to revealing the nature of human language intelligence and has recently emerged as a promising perspective for improving the interpretability of large language models (LLMs). However, it is ethically and practically infeasible to conduct experiments that require controlling human learners' language inputs. This poses challenges

Vikram K Suresh

In this study, we evaluate the persona fidelity of frontier LLMs, GPT-5, Claude Sonnet 4.5 and Gemini 2.5 Flash when assigned distinct socioeconomic personas performing scholastic assessment test (SAT) mathematics items and affective preference tasks. Across 15 distinct role conditions and three testing scenarios, GPT-5 exhibited complete contextual collapse

Weiheng Zhu, Gang Cao, Jing Liu, Lifang Yu

Recent AI-generated image (AIGI) detectors achieve impressive accuracy under clean condition. In view of antiforensics, it is significant to develop advanced adversarial attacks for evaluating the security of such detectors, which remains unexplored sufficiently. This letter proposes a Dual-domain Feature Importance Attack (DuFIA) scheme to invalidate AIGI d

Zachary T. Jerzyk, David R. Smith, Matthew Otten

The design and performance of future fusion power plants will depend on accurate atomic data for plasma-facing material and plasma impurity species. A leading candidate for the plasma-facing material is tungsten due to its high melting point, however, the energy levels and wavefunctions of high-Z atoms with many electrons (e.g. 30 or more), including tungste

ATLAS Collaboration

This article presents measurements of $Z$ boson pair production cross-sections at a center-of-mass energy of $\sqrt{s} = 13$ TeV, using 140 fb$^{-1}$ of proton-proton data collected with the ATLAS detector at the Large Hadron Collider. The analysis includes both inclusive $ZZ$ production and $ZZ$ production in association with two jets ($ZZjj$), where one $Z

René Pfitscher

We establish sharp algebraic criteria for the $L^{p}$-integrability, for $p = 1, 2, \infty$, of a natural generalization of the Siegel transform to the setting of rational representations of semisimple algebraic $\mathbb{Q}$-groups, extending Siegel's analytic work in the geometry of numbers. As an application, we derive an effective asymptotic formula for t

Kevin Qinghong Lin, Siyuan Hu, Linjie Li, Zhengyuan Yang

Computer-Use Agents (CUA) are becoming increasingly capable of autonomously operating digital environments through Graphical User Interfaces (GUI). Yet, most GUI remain designed primarily for humans--prioritizing aesthetics and usability--forcing agents to adopt human-oriented behaviors that are unnecessary for efficient task execution. At the same time, rap

Matteo Vandelli, Francesco Ferrari, Daniele Dragoni

Quantum algorithms for combinatorial optimization typically encode constraints as soft penalties within the objective function, which can reduce efficiency and scalability compared to state-of-the-art classical methods that instead exploit constraints to guide the search toward high-quality solutions. Although solving this issue for an arbitrary problem is i

Daniel Bermuth, Alexander Poeppel, Wolfgang Reif

Human pose forecasting predicts future poses based on past observations, and has many significant applications in areas such as action recognition, autonomous driving or human-robot interaction. This paper evaluates a wide range of pose forecasting algorithms in the task of absolute pose forecasting, revealing many reproducibility issues, and provides a unif

Paul Scheffler, Thomas Benz, Tim Fischer, Lorenzo Leone

We present a roadmap for open-source chiplet-based RISC-V systems targeting high-performance computing and artificial intelligence, aiming to close the performance gap to proprietary designs. Starting with Occamy, the first open, silicon-proven dual-chiplet RISC-V manycore in 12nm FinFET, we scale to Ramora, a mesh-NoC-based dual-chiplet system, and to Ogopo

Shehbaz Tariq, Symeon Chatzinotas

As quantum networks evolve toward a full quantum Internet, reliable transmission in quantum multiple-input multiple-output (QuMIMO) settings becomes essential, yet remains difficult due to noise, crosstalk, and the mixing of quantum information across subchannels. To improve reliability in such settings, we study an adaptive diversity strategy for discrete-v

Haripriya Bangaru, Giovanni Calderini, Dominik Dannheim, Rui De Oliveira

Fine-pitch hybridisation processes are essential for next-generation pixel detectors and high-density microelectronic assemblies. Conventional bump-bonding techniques, although reliable, remain costly and difficult to implement for single-die applications. In this work, we present flip-chip hybridisation results combining Electroless Nickel Immersion Gold (E

Seth Lloyd

Consider a population of organisms that harvest free energy from their environment to reproduce. This paper shows that if the organisms' reproductive rates are proportional to the amount of physical free energy that they can convert into reproductive work, then the implicit probabilities that the organisms assign to environmental states are updated according

Louison Bocquet-Nouaille, Jérôme Morio, Benjamin Bobbia

The estimation of the Extreme Value Index (EVI) is fundamental in extreme value analysis but suffers from high variance due to reliance on only a few extreme observations. We propose a control variates based transfer learning approach in a semi-supervised framework, where a small set of coupled target and source observations is combined with abundant unpaire

Zi-Ang Wang, Bin Hu, Xianghe Han, Hui Chen

A pair density wave (PDW) is a superconducting state characterized by an order parameter with finite center-of-mass momentum in the absence of an external magnetic field, thereby breaking the conventional translational symmetry in homogeneous superconductors. It is proposed that PDW emerges from magnetic interactions, strong electron-electron correlations, a

Gajanan D Harale, Surajit Paul

Galaxy clusters are among the most massive gravitationally bound systems in the Universe and are considered major reservoirs of high-energy cosmic rays, yet no conclusive $\gamma$-ray detection from them has been achieved. This non-detection may stem from limited sensitivity and source localization of current $\gamma$-ray instruments, as well as strong inter

Harun Basmaci, Hasan Ozgur Cildiroglu

We demonstrate that Dirac fermions in 2+1 dimensions, coupled to Abelian gauge fields in multiply-connected regions, exhibit a parity anomaly that directly manifests as Aharonov-Bohm (AB) type topological phases. Using the Fujikawa method, we show that this anomaly reproduces both the AB phase and the spin-dependent Aharonov-Casher (AC) phase. We explicitly

Mariam El Harras, My Abdelouhab Salahddine

Regulatory technology (RegTech) is transforming financial compliance by integrating advanced information technologies to strengthen anti money laundering and countering the financing of terrorism (AML CFT) frameworks. Recent literature suggests that such technologies represent more than just an efficiency tool; they mark a paradigm shift in regulation and th

Selim Furkan Tekin, Rajesh Bordawekar

Storing and processing of embedding vectors by specialized Vector databases (VDBs) has become the linchpin in building modern AI pipelines. Most current VDBs employ variants of a graph-based ap- proximate nearest-neighbor (ANN) index algorithm, HNSW, to an- swer semantic queries over stored vectors. Inspite of its wide-spread use, the HNSW algorithm suffers

Jonah P. Sengupta, Mohammad Imran Vakil, Thanh M. Dang, Ian Pardee

Event-based Sensing (EBS) hardware is quickly proliferating while finding foothold in many commercial, industrial, and defense applications. At present, there are a handful of technologically mature systems which produce data streams with diverse output formats. In the near future it is anticipated there will be vendors who offer new sensor hardware which co

Yuxin Wang, Yuankai He, Weisong Shi

Autonomous vehicles (AVs) are evolving into mobile computing platforms, equipped with powerful processors and diverse sensors that generate massive heterogeneous data, for example 14 TB per day. Supporting emerging third-party applications calls for a general-purpose, queryable onboard storage system. Yet today's data loggers and storage stacks in vehicles f