November 2025 arXiv papers — page 148

Sameia Zaman, Joel Î-j. Wang, Thomas Werkmeister, Miuko Tanaka

Van der Waals (vdW) superconductors remain superconducting down to the monolayer limit, enabling the exploration of emergent physical phenomena and functionality driven by reduced dimensionality. Here, we report the characterization of the kinetic inductance of atomically thin NbSe$_2$, a two-dimensional van der Waals superconductor, using superconducting co

Siddharth Sahay

This paper presents a comprehensive methodology and comparative performance analysis for the automated classification and object detection of peripheral blood cells (PBCs) in microscopic images. Addressing the critical challenge of data scarcity and heterogeneity, robust data pipeline was first developed to standardize and merge four public datasets (PBC, BC

Anh Mai Vu, Tuan L. Vo, Ngoc Lam Quang Bui, Nam Nguyen Le Binh

Interpretability is essential in Whole Slide Image (WSI) analysis for computational pathology, where understanding model predictions helps build trust in AI-assisted diagnostics. While Integrated Gradients (IG) and related attribution methods have shown promise, applying them directly to WSIs introduces challenges due to their high-resolution nature. These m

Erika Palmerio

Space weather predictions of the solar wind impacting Earth are usually first based on remote-sensing observations of the solar disc and corona, and eventually validated and/or refined with in-situ measurements taken at the Sun$-$Earth Lagrange L1 point, where real-time monitoring probes are located. However, this pipeline provides, on average, only a few te

Claire Wang, Ziyang Li, Saikat Dutta, Mayur Naik

Static analysis tools provide a powerful means to detect security vulnerabilities by specifying queries that encode vulnerable code patterns. However, writing such queries is challenging and requires diverse expertise in security and program analysis. To address this challenge, we present QLCoder - an agentic framework that automatically synthesizes queries

Maria Couto Teixeira, Marisa Tschopp, Anna Jobin

As Artificial Intelligence (AI) is increasingly promoted and used in qualitative research, it also raises profound methodological issues. This position paper critically interrogates the role of generative AI (genAI) in the context of qualitative coding methodologies. Despite widespread hype and claims of efficiency, we propose that genAI is not methodologica

Hu Xirui

We study an inverse source problem for a semilinear parabolic equation in a bounded domain, where the nonlinearity depends on the unknown function and its gradient through a quadratic reaction term and a Burgers-type convection term. From partial boundary observation of the time derivative and its spatial gradient on an open portion of the boundary, together

Lorenzo Giacomelli, Michał Łasica, Salvador Moll

We consider the functional of total variation of maps from an interval into a Riemannian submanifold of $\mathbb R^N$. We define a notion of strong solution to the system of equations corresponding to the $L^2$-gradient flow of this functional. We prove global existence of strong solutions for initial data of bounded variation. We show that the solutions sat

Sanchita Chakraborty, Jeremy Hoskins, Alan E. Lindsay

We present a high order numerical method for the solution of the Neumann Green's function in two dimensions. For a general closed planar curve, our computational method resolves both the interior and exterior Green's functions with the source placed either in the bulk or on the surface -- yielding four distinct functions. Our method exactly represent

Qian Wang, Suhaib Ardah, Tom Reddyhoff, Daniele Dini

Soft lubricated contacts exhibit complex interfacial behaviours governed by the coupled effects of multiscale surface roughness and non-linear fluid-solid interactions. Accurately capturing this interplay across thin-film flows is challenging due to the strong synergy between contact mechanics and hydrodynamic flow, spanning over various spatiotemporal scale

Fazlay Ahmed, Qiang Wu, Sushant G Ghosh, Tao Zhu

We study gravitational wave emission from periodic orbits of a test particle around a noncommutative-inspired black hole surrounded by quintessence. Using the zoom-whirl taxonomy, which is characterized by three topological numbers $(z, w, v)$, we classify these orbits and calculate several representative gravitational waveforms for certain periodic orbits.

Shiyan Zheng, Herun Wan, Minnan Luo, Junhang Huang

While existing social bot detectors perform well on benchmarks, their robustness across diverse real-world scenarios remains limited due to unclear ground truth and varied misleading cues. In particular, the impact of shortcut learning, where models rely on spurious correlations instead of capturing causal task-relevant features, has received limited attenti

Tianyu Jia, Xingchen Yang, Ciaran McGeady, Yifeng Li

Brain-computer interfaces (BCIs) promise to extend human movement capabilities by enabling direct neural control of supernumerary effectors, yet integrating augmented commands with multiple degrees of freedom without disrupting natural movement remains a key challenge. Here, we propose a tactile-encoded BCI that leverages sensory afferents through a novel ta

Ziv Epstein, Farnaz Jahanbakhsh, Tiziano Piccardi, Isabel Gallegos

The value alignment of sociotechnical systems has become a central debate, but progress depends on how human values are perceived in the content these systems surface and how such perceptions can be measured at scale. Social media platforms are a prominent class of sociotechnical systems where algorithmic curation shapes exposure to value-laden content at sc

Max Hörmann, Anja Langheld, Jonas Leibig, Andreas Schellenberger

Recently, Mendon\c{c}a et al. [arXiv:2503.04961] investigated the Dicke-XXZ model and the Dicke-Ising model. For the latter model, their calculated quantum phase diagram contradicts claims about the existence of an intermediate phase with superradiant and antiferromagnetic order and the change in order of some phase transition lines, observed in other studie

Luis S. Yagüe Bosch, Sandro Wimberger

A shortcut-to-adiabaticity is compared with a numerically optimized protocol for implementing a high-fidelity quantum gate on Rydberg atoms. The counterdiabatic method offers an analytical framework for accelerating high-fidelity gates by mimicking the time evolution of a counterdiabatic Hamiltonian using fast-oscillating fields. This approach is contrasted

Tianyi Wu, Yiyang Zhang, Zhu Fang, Shuting Lei

All-solid-state sodium batteries represent a promising next-generation energy storage technology, owing to cost-effectiveness and enhanced safety. Among solid electrolytes for solid-state sodium batteries, NASICON-structured Na3Zr2Si2PO12 has emerged as a predominant candidate. However, its widespread implementation remains limited by suboptimal ionic conduc

Fabiano Feleppa, Welmoed Marit de Graaf, Philippe Brax, Gaetano Lambiase

We test screened dark energy with near-Earth, space-based measurements. In a post-Newtonian framework, we compute leading corrections to geodetic precession (Gravity Probe B), LAGEOS-2 pericenter advance, and the Sagnac delay in a prospective orbital configuration, yielding bounds on chameleon, symmetron, and dilaton models. LAGEOS-2 sets the strongest Earth

Pietro Facchini, Eva K. Grebel, Anna Pasquali, Elena Sabbi

$\textit{Context.}$ There is considerable debate on how massive stars form, including whether a high-mass star must always form with a population of low-mass stars or whether it can also form in isolation. Massive stars found in the field are often considered to be runaways from star clusters or OB associations. However, there is evidence in the Milky Way an

Emmanuel Gnandi, Raymond A. Hounnonkpe

The question of whether a closed, orientable manifold can admit a nontrivial vector field that is parallel with respect to some Riemannian metric is a classical problem in Differential Geometry, first posed by S. S. Chern [11]. In this work, we provide a complete answer to Chern's question in dimension three. Specifically, we show that a closed, orientable 3

Xiang Li, You Li, Yazhou Zhang

EEG-based emotion recognition is hampered by profound dataset heterogeneity (channel/subject variability), hindering generalizable models. Existing approaches struggle to transfer knowledge effectively. We propose 'One Model for All', a universal pre-training framework for EEG analysis across disparate datasets. Our paradigm decouples learning into t

Gideon Geier, Pariya Hajipour, Jan Reineke

Hardware-software leakage contracts have emerged as a formalism for specifying side-channel security guarantees of modern processors, yet verifying that a complex hardware design complies with its contract remains a major challenge. While verification provides strong guarantees, current verification approaches struggle to scale to industrial-sized designs. C

Elizabeth J. Schaefer, Andrew J. Schaefer

We give two graph-theoretic models and a mixed-integer program to calculate the maximum achievable score in the popular board game "Ticket to Ride." In Ticket to Ride, players compete to claim railway routes on a map, with points awarded based on the length of each route and the successful completion of destination tickets connecting specific city pairs. Eac

Mehryar Mohri, Jon Schneider, Yifan Wu

Self-improvement is a critical capability for large language models and other intelligent systems, enabling them to refine their behavior and internal consistency without external supervision. Despite its importance, prior approaches largely rely on empirical heuristics and lack formal guarantees. In this paper, we propose a principled framework for self-imp

Alireza Abbaspour, Tejaskumar Balgonda Patil, B Ravi Kiran, Russel Mohr

Dataset integrity is fundamental to the safety and reliability of AI systems, especially in autonomous driving. This paper presents a structured framework for developing safe datasets aligned with ISO/PAS 8800 guidelines. Using AI-based perception systems as the primary use case, it introduces the AI Data Flywheel and the dataset lifecycle, covering data col

Shivam Pandey, Christopher C. Lovell, Chirag Modi, Benjamin D. Wandelt

Connecting the formation and evolution of galaxies to the large-scale structure is crucial for interpreting cosmological observations. While hydrodynamical simulations accurately model the correlated properties of galaxies, they are computationally prohibitive to run over volumes that match modern surveys. We address this by developing a framework to rapidly

P. R. McCullough, Joel D. Green

In this starter guide, we provide a high-level overview of analysis of WFC3/IR data available from the Mikulski Archive for Space Telescopes (MAST). We intend this guide as a starting point for users examining WFC3/IR data for the first time, or for those refreshing their memory on WFC3/IR data analysis. Therefore, we focus on the analysis of archival data,

Matus Bojko, Maros Kollar, Marek Jakab, Wanda Benesova

Semi-supervised learning (SSL) enables training of powerful models with the assumption of limited, carefully labelled data and a large amount of unlabeled data to support the learning. In this paper, we propose a hybrid consistency learning approach to effectively exploit unlabeled data for semi-supervised medical image segmentation by leveraging Cross-Pyram

Dennis Huber, Steffen J. Glaser

The Fourier transform (FT) represents a key tool in modern spectroscopy which drastically reduces measurement times and helps to improve the signal-to-noise ratio in spectra. Fourier transforming exponentially decaying time domain signals gives Lorentzian line shapes which can be manipulated by apodization methods. The underlying transitions of spectral line

Jingyi Cao, Dongchen Li, Virginia R. Young, Bin Zou

We revisit the optimal dividend problem of de Finetti by adding a variance term to the usual criterion of maximizing the expected discounted dividends paid until ruin, in a singular control framework. Investors do not like variability in their dividend distribution, and the mean-variance (MV) criterion balances the desire for large expected dividend payments

Benjamin Bordais, Daniel Neider

Learning finite automata from positive examples has recently gained attention as a powerful approach for understanding, explaining, analyzing, and verifying black-box systems. The motivation for focusing solely on positive examples arises from the practical limitation that we can only observe what a system is capable of (positive examples) but not what it ca

Peng Liu, Steven Vanduffel, Yi Xia

We establish sharp upper and lower bounds for distortion risk metrics under distributional uncertainty. The uncertainty sets are characterized by four key features of the underlying distribution: mean, variance, unimodality, and Wasserstein distance to a reference distribution. We first examine very general distortion risk metrics, assuming only finite varia

Yang Yang, Tanya Tian, Brian Uzzi, Benjamin Jones

Communication of scientific knowledge beyond the walls of science is key to science's societal impact. Media channels play sizable roles in disseminating new scientific ideas about human health, economic welfare, and government policy as well as responses to emergent challenges such as climate change. Indeed, effectively communicating science to the public h

Sophie Chemla, Fabio Gavarini, Niels Kowalzig

We study the effect of linear duality on action bialgebroids (also known as smash product or scalar extension bialgebroids) and, for those bearing a quantisation nature, the effect of Drinfeld functors underlying the quantum duality principle. By means of various categorical equivalences, it is shown that any braided commutative Yetter-Drinfeld algebra over

Marino Badiale, Isabella Cravero

These notes are a supplementary file to the paper Hopf bifurcations for HANDY-type models (M. Badiale and I. Cravero, under submission), providing full details of the computations developed in Section 4.2. The purpose of this supplement is to derive explicitly the first Lyapunov coefficient associated with a Hopf bifurcation, following the framework of Yu. A

Linda-Sophie Schneider, Yipeng Sun, Chengze Ye, Markus Michen

Deep learning has brought significant advancements to X-ray Computed Tomography (CT) reconstruction, offering solutions to challenges arising from modern imaging technologies. These developments benefit from methods that combine classical reconstruction techniques with data-driven approaches. Differentiable operators play a key role in this integration by en

Varun Rajeev Pavizhakumari, Thomas Olsen

Magnetic anisotropy is crucial for sustaining long range magnetic order in two-dimensional materials (2D) and must be taken into account by any approximate scheme for calculating critical temperatures. While 2D ferromagnets have received significant attention with regard to predicting Curie temperatures, the treatment of 2D anti-ferromagnetism has largely be

Zeyang Li, Kaveh Alim, Navid Azizan

Diffusion and flow-matching have emerged as powerful methodologies for generative modeling, with remarkable success in capturing complex data distributions and enabling flexible guidance at inference time. Many downstream applications, however, demand enforcing hard constraints on generated samples (for example, robot trajectories must avoid obstacles), a re

Evgeniya Kabliman, Gabriel Kronberger

Process-structure-property relationships are fundamental in materials science and engineering and are key to the development of new and improved materials. Symbolic regression serves as a powerful tool for uncovering mathematical models that describe these relationships. It can automatically generate equations to predict material behaviour under specific man

Saeed Tafazolian, Jaap Top

We construct explicit families of hyperelliptic curves over $\QQ$ whose Jacobians admit complex multiplication (CM). Each curve in these families is defined by \[ v^2 = (u+2)\,\varphi_d(u), \quad d = 2^e \text{ or } d=p \geq 3 \text{ prime}, \] where $\varphi_d(x)$ is the Chebyshev polynomial of degree $d$. We prove that the Jacobians are simple and determin

Débora A. F. Albanez, Maicon José Benvenutti, Jing Tian

In this study, we develop a continuous data assimilation algorithm to recover the parameter $\alpha$ in the simplified Bardina model. Our method utilizes the observations of finitely many Fourier modes by using a nudging framework that involves recursive parameter updates. We provide a rigorous convergence analysis, showing that the approximate parameter app

Talitha Nauta, Richard Pates

The Scaled Relative Graph (SRG) is a promising tool for stability and robustness analysis of multi-input multi-output systems. In this paper, we provide tools for exact and computable constructions of the SRG for closed linear operators, based on maximum and minimum gain computations. The results are suitable for bounded and unbounded operators, and we speci

Saber Omidi, Marek Petrik, Se Young Yoon, Momotaz Begum

Safety in stochastic control systems, which are subject to random noise with a known probability distribution, aims to compute policies that satisfy predefined operational constraints with high confidence throughout the uncertain evolution of the state variables. The unpredictable evolution of state variables poses a significant challenge for meeting predefi

Hannah Lydon, Milad Kazemi, Martin Bishop, Nicola Paoletti

Accurately simulating systems governed by PDEs, such as voltage fields in cardiac electrophysiology (EP) modelling, remains a significant modelling challenge. Traditional numerical solvers are computationally expensive and sensitive to discretisation, while canonical deep learning methods are data-hungry and struggle with chaotic dynamics and long-term predi

Xiyuan Wei, Chih-Jen Lin, Tianbao Yang

Accurately estimating the normalization term (also known as the partition function) in the contrastive loss is a central challenge for training Contrastive Language-Image Pre-training (CLIP) models. Conventional methods rely on large batches for approximation, demanding substantial computational resources. To mitigate this issue, prior works introduced per-s

Hai-Long Qin, Jincheng Dai, Guo Lu, Shuo Shao

Semantic communications mark a paradigm shift from bit-accurate transmission toward meaning-centric communication, essential as wireless systems approach theoretical capacity limits. The emergence of generative AI has catalyzed generative semantic communications, where receivers reconstruct content from minimal semantic cues by leveraging learned priors. Amo

Deshui Cao, Lina Zhang, Songbai Chen, Qiyuan Pan

We employ the Poincar\'{e} section, fast Lyapunov indicator, recurrence analysis, bifurcation diagram and basins of attraction to investigate the dynamical behaviors of the motion of particles around a new dyonic Kerr-Newman black hole immersed in the Melvin-swirling universe presented in [A. Di Pinto, S. Klemm, and A. Vigan\`o, J. High Energy Phys. {\bf 06}

Simon Widmann, Johannes Düreth, Siddhartha Dam, Christian G. Mayer

Topological phases in exciton-polaritons and other metamaterial platforms have attracted significant attention due to their flexibility as Hamiltonian simulators. In previous works, signatures of topology have mainly been investigated from the perspective of edge states - strongly localised modes with exponentially decaying intensity into the bulk. While the

H. Benziadi, A. López Almorox, C. Tejero Prieto

We study harmonic mappings from a Riemannian manifold $N$ into a principal $G$-bundle $P$ endowed with a $G$-invariant Riemannian metric (i.e. a Kaluza-Klein metric). These morphisms are called Kaluza-Klein harmonic maps and naturally lead to the notion of generalized magnetic maps for an arbitrary gauge group $G$, which are just their projections onto the b

Ruochuan Shi, Runyu Lu, Yuanheng Zhu, Dongbin Zhao

In graph-structured multi-agent reinforcement learning (MARL) adversarial tasks such as pursuit and confrontation, agents must coordinate under highly dynamic interactions, where sparse rewards hinder efficient policy learning. We propose Adaptive Regularized Multi-Agent Soft Actor-Critic (ARAC), which integrates an attention-based graph neural network (GNN)

Perry Beamer, Zixuan Cang

Spatial transcriptomics (ST) measures gene expression at a set of spatial locations in a tissue. Communities of nearby cells that express similar genes form \textit{spatial domains}. Specialized ST clustering algorithms have been developed to identify these spatial domains. These methods often identify spatial domains at a single morphological scale, and int

Chao Liu, Ankur Dhar, Ronald Agustsson, Diego Amirari

RF pulse modulation techniques are widely applied to shape RF pulses for various types of RF stations of particle accelerators. The amplitude and phase modulations are typically implemented with additional RF components that require drive or control electronics. For the RF system-on-chip (RFSoC) based next generation LLRF (NG-LLRF) platform, which we have de

Junxian Li, Xinyue Xu, Sai Ma, Di Zhang

Multimodal Large Language Models (MLLMs) frequently suffer from unfaithfulness, generating reasoning chains that drift from visual evidence or contradict final predictions. We propose Faithful-First Reasoning, Planning, and Acting (RPA) framework in which FaithEvi provides step-wise and chain-level supervision by evaluating the faithfulness of intermediate r

Apimuk Sornsaeng, Itai Arad, Dario Poletti

Tensor networks (TNs) are one of the best available tools to study many-body quantum systems. TNs are particularly suitable for one-dimensional local Hamiltonians, while their performance for generic geometries is mainly limited by two aspects: the limitation in expressive power and the approximate extraction of information. Here we investigate the performan

Siddhesh Pimpale, Sagar Mahadik

The multi-phase inverter has become more complicated, particularly in an Electric Vehicle (EV)'s power train, which requires a robust fault protection system. The proposed active short circuit and safe discharge mechanisms are also included in this work, dedicated to multi-phase converters in failure conditions. With silicon carbide (SiC) power modules incre

S. Iudin, M. Veshchezerova, K. Tsarova, G. Tadumadze

Liquefied Natural Gas (LNG) transportation is a critical component of the energy industry. It enables the efficient and large-scale movement of natural gas across vast distances by converting it into a liquid form, thereby addressing global demand and connecting suppliers with consumers. In this study, we present the Multistart Large Neighborhood Search heur

Lucian Trestioreanu, Wazen Shbair, Flaviene Scheidt de Cristo, Radu State

Recent technologies such as inter-ledger payments, non-fungible tokens, and smart contracts are all fruited from the ongoing development of Distributed Ledger Technologies. The foreseen trend is that they will play an increasingly visible role in daily life, which will have to be backed by appropriate operational resources. For example, due to increasing dem

Difei Gu, Yunhe Gao, Mu Zhou, Dimitris Metaxas

Accurate disease interpretation from radiology remains challenging due to imaging heterogeneity. Achieving expert-level diagnostic decisions requires integration of subtle image features with clinical knowledge. Yet major vision-language models (VLMs) treat images as holistic entities and overlook fine-grained image details that are vital for disease diagnos

C. Evans Hedges

We prove that training a source model optimally for its own task is generically suboptimal when the objective is downstream transfer. We study the source-side optimization problem in L2-SP ridge regression and show a fundamental mismatch between the source-optimal and transfer-optimal source regularization: outside of a measure-zero set, $\tau_0^* \neq \tau_

K. I. A Derouiche

Mathematicians have long been fascinated by the resolution of algebraic and Diophantine equations in search of integer or rational solutions. This article presents a list of thirty-three open problems in number theory, posed in the 13th century by Ibn al-Khaww\=am al-Baghd\=ad\=i (Abdall\=ah ibn Muhammad ibn Muhammad al-Khaww\=am), extracted from his arithme

Hua Ye, Hang Ding, Siyuan Chen, Yiyang Jiang

Most multimodal models treat every negative pair alike, ignoring the ambiguous negatives that differ from the positive by only a small detail. We propose Boundary-Aware Curriculum with Local Attention (BACL), a lightweight add-on that turns these borderline cases into a curriculum signal. A Boundary-aware Negative Sampler gradually raises difficulty, while a

Mymuna Monem, Ian L. Dryden, Florence George

The method of Principal Nested Spheres (PNS) is a non-linear dimension reduction technique for spherical data. The method is a backwards fitting procedure, starting with fitting a high-dimensional sphere and then successively reducing dimension at each stage. After reviewing the PNS method in detail, we introduce some new methods for model selection at each

Margaret R. Martin, Soha Hassoun

Despite growing reference libraries and advanced computational tools, progress in the field of metabolomics remains constrained by low rates of annotating measured spectra. The recent developments of large language models (LLMs) have led to strong performance across a wide range of generation and reasoning tasks, spurring increased interest in LLMs' applicat

Jonas Hirsch

In the Euclidean setting, the well-known Alexandrov theorem states that convex functions are twice differentiable almost everywhere. In this note, we extend this theorem to rank-one convex functions. Our approach is novel in that it draws more from viscosity techniques developed in the context of fully nonlinear elliptic equations. As a byproduct, the origin

Zhiwei Zhang, Xinyi Du, Xuanchi Guo, Weihao Wang

Multivariate time series forecasting is crucial across a wide range of domains. While presenting notable progress for the Transformer architecture, iTransformer still lags behind the latest MLP-based models. We attribute this performance gap to unstable inter-channel relationships. To bridge this gap, we propose EMAformer, a simple yet effective model that e

Xingyu Liu, Jiawei Liang, Yipu Zhang, Linfeng Du

We propose a hardware-efficient RBD accelerator based on FPGA, introducing three key innovations. First, we propose a precision-aware quantization framework that reduces DSP demand while preserving motion accuracy. This is also the first study to systematically evaluate quantization impact on robot control and motion for hardware acceleration. Second, we lev

Sian Gooding, Edward Grefenstette

The alignment of Large Language Models (LLMs) for multi-turn conversations typically relies on reward signals derived from the content of the text. This approach, however, overlooks a rich, complementary source of signal: the dynamics of the interaction itself. This paper introduces TRACE (Trajectory-based Reward for Agent Collaboration Estimation), a novel

Max Engelstein, Daniel Restrepo, Zihui Zhao

In this article we study the structure of solutions to the one-phase Bernoulli problem that are modeled either infinitesimally or at infinity by one-homogeneous solutions with an isolated singularity. In particular, we prove a uniqueness of blowups result under a natural symmetry condition on the one-homogeneous solution (\`a la Allard--Almgren) and we prove

Tangrui Li, Pei Wang, Hongzheng Wang Christian Hahm, Matteo Spatola

Large Language Models (LLMs) often exhibit limited logical coherence, mapping premises to conclusions without adherence to explicit inference rules. We propose Proof-Carrying Reasoning with LLMs (PCRLLM), a framework that constrains reasoning to single-step inferences while preserving natural language formulations. Each output explicitly specifies premises,

Fernando Ardana-Lamas, Seth L. Cousin, Juliette Lignieres, Jens Biegert

We recently reported a refine analysis of a previously conducted soft X-ray (SXR) attosecond streaking measurement [1], employing the Variational Phase Gradient Temporal Analysis (VPGTA) retreival algorithm [2]. This re-evaluation, prompted by new metrological insights, revealed a 19.2 attosecond pulse - consistent with the expectations and estimates of our

Ahmed Farag Ali

We develop a fully expectation--value formulation of the GUP/Bekenstein--bound (BEB) correspondence, building on \cite{Ali:2024tbd,Ali:2022ckm,Ali:2022ulp}. Using Dirac's commutator--Poisson equivalence, the BEB supplies an information backreaction on the GUP--deformed bracket; at saturation the residual uncertainty in a sector cancels, enabling \emph{operat

Vishal Bhatoy, Colin Ingalls, Félix LaRoche, Ravali Nookala

We compute the Hochschild cohomology and the Kodaira spencer map for known families of Koszul Artin-Schelter regular algebras of dimension four. We show that when the Kodaira Spencer map at a point is a surjection, the image of the family is a component of the moduli stack of such algebras, and when the Kodaira Spencer map is a bijection, the map to the modu

Yi-Jen Shih, David Harwath

Speech Foundation Models have gained significant attention recently. Prior works have shown that the fusion of representations from multiple layers of the same model or the fusion of multiple models can improve performance on downstream tasks. We unify these two fusion strategies by proposing an interface module that enables fusion across multiple upstream s

Stefania Vassiliadis

We give a solution to the Poincar\'e Problem, in the formulation of Cerveau and Lins Neto. We obtain a bound on the degree of general leaves of foliations of general type, which is linear in $g$. To achieve this we study the birational geometry of foliations within the framework of the Minimal Model Program (MMP). Extending the approach of Spicer--Svaldi and

Sorachi Kato, Ryoma Yataka, Pu Perry Wang, Pedro Miraldo

Radar-based indoor 3D human pose estimation typically relied on fine-grained 3D keypoint labels, which are costly to obtain especially in complex indoor settings involving clutter, occlusions, or multiple people. In this paper, we propose \textbf{RAPTR} (RAdar Pose esTimation using tRansformer) under weak supervision, using only 3D BBox and 2D keypoint label

Markus Kirchweger, Tomáš Peitl, Bernardo Subercaseaux, Stefan Szeider

Norin (2008) conjectured that any $2$-edge-coloring of the hypercube $Q_n$ in which antipodal edges receive different colors must contain a monochromatic path between some pair of antipodal vertices. While the general conjecture remains elusive, progress thus far has been made on two fronts: finite cases and asymptotic relaxations. The best finite results ar

Vance Faber

For the Kautz digraph $K(d,D)$, let $\rho_k(d,D)$ be the number of oriented edges whose shortest directed cycle has length $k+1$, and define $\Delta_k(d,D) = \rho_k(d,D) - \rho_k(d,D-1)$. We give an exact, finite-dimensional matrix product that computes $\Delta_k(d,D)$ directly, without first computing $\rho$. In particular, $\Delta_k(d,D)=0$ for $k < D/2+2$

Lucy Brissenden, Konstantinos Dimopoulos, Eemeli Tomberg

Cosmic inflation is the leading theory to explain early Universe history and structure formation. Non-oscillatory inflation is a class of models which can naturally introduce a post-inflationary stiff period of the Universe's evolution which boosts the signal of primordial gravitational waves (GWs), making it possible to observe them in forthcoming GW experi

Dhrumil Bhatt

The coexistence of heterogeneous service classes in 5G Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communication (URLLC), and Massive Machine-Type Communication (mMTC) poses major challenges for meeting diverse Quality-of-Service (QoS) requirements under limited spectrum and power resources. Existing radio access network (RAN) slicing scheme

Sven Bachmann, Alan Getz, Pieter Naaijkens, Naomi Wray

We study the superselection sectors of two quantum lattice systems stacked onto each other in the operator algebraic framework. We show in particular that all irreducible sectors of a stacked system are unitarily equivalent to a product of irreducible sectors of the factors. This naturally leads to a faithful functor between the categories for each system an

Tangrui Li, Justin Y. Shi, Matteo Spatola, Hongzheng Wang

This paper reports three computational experiments for a von Neumann inspired reconfigurable fault tolerant multiprocessor for neural network (NN) training workflows. The experiments are intended to prove the feasibility of the proposed reconfigurable multiprocessor architecture for non-regular workflows on robustness of adaptability. A potential integration

Bao-Jun Cai, Bao-An Li, Yu-Gang Ma

First-order phase transitions (FOPTs) in cold neutron stars (NSs) have been extensively studied and have provided valuable insights into the behavior of the densest matter visible in our Universe, although a strong consensus has yet to emerge. Revisiting the possibility of a hadron-quark FOPT from a new perspective, we examine the interplay between the coupl

Giorgio Piras, Raffaele Mura, Fabio Brau, Luca Oneto

Refusal refers to the functional behavior enabling safety-aligned language models to reject harmful or unethical prompts. Following the growing scientific interest in mechanistic interpretability, recent work encoded refusal behavior as a single direction in the model's latent space; e.g., computed as the difference between the centroids of harmful and harml

Michael Bowman, Xiaoli Zhang

Intent inferencing in teleoperation has been instrumental in aligning operator goals and coordinating actions with robotic partners. However, current intent inference methods often ignore subtle motion that can be strong indicators for a sudden change in intent. Specifically, we aim to tackle 1) if we can detect sudden jumps in operator trajectories, 2) how

Özay Ezerceli, Gizem Gümüşçekiçci, Tuğba Erkoç, Berke Özenç

This paper introduces TurkEmbed, a novel Turkish language embedding model designed to outperform existing models, particularly in Natural Language Inference (NLI) and Semantic Textual Similarity (STS) tasks. Current Turkish embedding models often rely on machine-translated datasets, potentially limiting their accuracy and semantic understanding. TurkEmbed ut

Darius Saif, Ashraf Matrawy

QUIC is an advanced transport layer protocol whose ubiquity on the Internet is now very apparent. Importantly, QUIC fuels the next generation of web browsing: HTTP/3. QUIC is a stateful and connection oriented protocol which offers similar features (and more) to the combination of TCP and TLS. There are several difficulties which readers may encounter when l

Ashis Tamang, Nishal Rai, Karl Landsteiner, Eugenio Megias

We study the transport properties of relativistic fluids induced by quantum anomalies in presence of explicit symmetry breaking. To this end we consider a holographic Einstein-Maxwell model in 5 dimensions with pure gauge and a mixed gauge-gravitational Chern-Simons terms, coupled with a scalar field. To study the chiral vortical effects and the energy trans

Henrik Daniel Christensen, Saverio Giallorenzo, Jacopo Mauro

Distributed applications employ Kubernetes for scalable, fault-tolerant deployments over computer clusters, where application components run in groups of containers called pods. The scheduler, at the heart of Kubernetes' architecture, determines the placement of pods given their priority and resource requirements on cluster nodes. To quickly allocate pods, t

Bernd J. Kröger

This paper describes the current implementation of the dynamic articulatory model DYNARTmo, which generates continuous articulator movements based on the concept of speech gestures and a corresponding gesture score. The model provides a neurobiologically inspired computational framework for simulating the hierarchical control of speech production from lingui

Soham Basu, Frank Hutter, Danny Stoll

While Deep Learning (DL) experts often have prior knowledge about which hyperparameter settings yield strong performance, only few Hyperparameter Optimization (HPO) algorithms can leverage such prior knowledge and none incorporate priors over multiple objectives. As DL practitioners often need to optimize not just one but many objectives, this is a blind spo

David Woller, Viktor Kozák, Miroslav Kulich, Libor Přeučil

The Electric Vehicle Routing Problem (EVRP) extends the classical Vehicle Routing Problem (VRP) to reflect the growing use of electric and hybrid vehicles in logistics. Due to the variety of constraints considered in the literature, comparing approaches across different problem variants remains challenging. A minimalistic variant of the EVRP, known as the Ca

Xinyu Zhou, Yu Wu, Jiayao Ma, Wenhao Wang

This work introduces Text-based Aerial-Ground Person Retrieval (TAG-PR), which aims to retrieve person images from heterogeneous aerial and ground views with textual descriptions. Unlike traditional Text-based Person Retrieval (T-PR), which focuses solely on ground-view images, TAG-PR introduces greater practical significance and presents unique challenges d

Chase van de Geijn, Timo Lüddecke, Polina Turishcheva, Alexander S. Ecker

Rotary Positional Encodings (RoPE) have emerged as a highly effective technique for one-dimensional sequences in Natural Language Processing spurring recent progress towards generalizing RoPE to higher-dimensional data such as images and videos. The success of RoPE has been thought to be due to its positional equivariance, i.e. its status as a relative posit

Yuxuan Zhou, Yuzhao Peng, Yang Bai, Kuofeng Gao

Large Vision-Language Models (VLMs) are susceptible to jailbreak attacks: researchers have developed various attack strategies that bypass the safety mechanisms of VLMs. Among these approaches, jailbreak methods based on the Out-of-Distribution (OOD) strategy have garnered widespread attention due to their simplicity and effectiveness. This paper further adv

Miguel Silva, Daniela Pinto, João Vitorino, Eva Maia

The integration of Artificial Intelligence (AI) in Network Intrusion Detection Systems (NIDS) is a promising approach to tackle the increasing sophistication of cyberattacks. However, since Machine Learning (ML) and Deep Learning (DL) models rely heavily on the quality of their training data, the lack of diverse and up-to-date datasets hinders their generali

Qi Wang, Veronika Ecker, Marcel Früh, Sergios Gatidis

Physiological motion can affect the diagnostic quality of magnetic resonance imaging (MRI). While various retrospective motion correction methods exist, many struggle to generalize across different motion types and body regions. In particular, machine learning (ML)-based corrections are often tailored to specific applications and datasets. We hypothesize tha

Xinyi Wang, Yiping Song, Zhiliang Tian, Bo Liu

In multi-hop question answering (MHQA) tasks, Chain of Thought (CoT) improves the quality of generation by guiding large language models (LLMs) through multi-step reasoning, and Knowledge Graphs (KGs) reduce hallucinations via semantic matching. Outcome Reward Models (ORMs) provide feedback after generating the final answers but fail to evaluate the process

Srihari R, Pallavi M, Tejaswini S, Vaishnavi R C

An AI-powered data visualization platform that automates the entire data analysis process, from uploading a dataset to generating an interactive visualization. Advanced machine learning algorithms are employed to clean and preprocess the data, analyse its features, and automatically select appropriate visualizations. The system establishes the process of aut

Gordon Ma, Dimitris G. Angelakis

Qubit-efficient optimization studies how large combinatorial problems can be addressed with quantum circuits whose width is far smaller than the number of logical variables. In quadratic unconstrained binary optimization (QUBO), objective values depend only on one- and two-body statistics, yet standard variational algorithms explore exponentially large Hilbe

Helena Monke, Benjamin Sae-Chew, Benjamin Fresz, Marco F. Huber

The complexity and opacity of neural networks (NNs) pose significant challenges, particularly in high-stakes fields such as healthcare, finance, and law, where understanding decision-making processes is crucial. To address these issues, the field of explainable artificial intelligence (XAI) has developed various methods aimed at clarifying AI decision-making