March 2026 arXiv papers — page 92

Yijia Guo, Junqing Zhang, Yao-Win Peter Hong, Stefano Tomasin

The rise of wireless technologies has made the Internet of Things (IoT) ubiquitous, but the broadcast nature of wireless communications exposes IoT to authentication risks. Physical layer authentication (PLA) offers a promising solution by leveraging unique characteristics of wireless channels. As a common approach in PLA, hypothesis testing yields a theoret

Yirong Wang, Isaac Khor, Peter Desnoyers

We introduce 2DIO, a microbenchmark creating cache-accurate, stressful I/O traces. While existing tools are limited to generating traces with well-behaved, concave hit ratio curves, 2DIO produces ones with tunable complex cache behaviors, particularly performance cliffs and plateaus. Our framework encodes a workload as a compact parameter triplet, capturing

Shaoshuai Du, Joze M. Rozanec, Andy Pimentel, Ana-Lucia Varbanescu

Although recent generative models can produce time series with close marginal distributions, they often face a fundamental tension between preserving global temporal structure and modeling stochastic local variations, particularly for highly volatile signals with weak or irregular periodicity. Direct distribution matching in such settings can amplify noise o

Leon Lufkin, Tomás Figliolia, Beren Millidge, Kamesh Krishnamurthy

Recurrent neural networks (RNNs) and self-attention are both widely used sequence-mixing layers that maintain an internal memory. However, this memory is constructed using two orthogonal mechanisms: RNNs compress the entire past into a fixed-size state, whereas self-attention's state stores every past time step growing its state (the KV cache) linearly with

Anabel Ovide, Andreu Angles-Castillo, Carmen G. Almudever

Trapped-ion Quantum Charge-Coupled Device (QCCD) architectures promise scalability through interconnected trap zones and dynamic ion transport; however, this transport capability creates a complex compilation challenge: how to move qubits efficiently without degrading fidelity. We introduce a routing strategy that turns this challenge into an advantage by ex

William T. Redman

Reinforcement learning (RL) models have shown the capability of learning complex behaviors, but quantitatively assessing those behaviors - which is critical for safety assurance and the discovery of novel strategies - is challenging. By viewing RL models as control systems, we hypothesize that data-driven approximations of their associated Koopman operators

Anrafel Fernandes Pereira, Tatiane Ornelas, Allysson Allex Araujo, Marcos Kalinowski

[Background] Well-formulated Software Engineering (SE) research problems are essential for bridging the gap between industry-academia. Lean Research Inception (LRI) aims to support this activity. [Goal] Apply LRI to support SE students in formulating practice-aligned research problems. [Method] We conducted a case study with 60 students and 7 faculty advisor

Amir Atef Habel, Roohan Ahmed Khan, Fawad Mehboob, Clement Fortin

Wind disturbances remain a key barrier to reliable autonomous navigation for lightweight quadrotors, where the rapidly varying airflow can destabilize both planning and tracking. This paper introduces GustPilot, a hierarchical wind-resilient navigation stack in which a deep reinforcement learning (DRL) policy generates inertial-frame velocity reference for g

Rudra Prakash, S. Janardhanan, Shaunak Sen

This paper analyzes the computational complexity of validated interval methods for uncertain nonlinear systems and steady-state enclosure. Interval analysis produces guaranteed enclosures that account for uncertainty and round-off, but its adoption is often limited by computational cost in high dimensions. We develop an algorithm-level worst-case framework t

Yijia Guo, Junqing Zhang, Yao-Win Peter Hong

Wireless networks are highly vulnerable to spoofing attacks, especially when attackers transmit consecutive spoofing packets. Conventional physical layer authentication (PLA) methods have mostly focused on single-packet spoofing attack. However, under consecutive spoofing attacks, they become ineffective due to channel evolution caused by device mobility and

Nassim Ali Ousalah, Peyman Rostami, Vincent Gaudillière, Emmanuel Koumandakis

In this paper, we address the problem of 6-DoF object pose estimation from a single RGB image. Indirect methods that typically predict intermediate 2D keypoints, followed by a Perspective-n-Point solver, have shown great performance. Direct approaches, which regress the pose in an end-to-end manner, are usually computationally more efficient but less accurat

Teseo San Jose, Yasumichi Aoki, Matteo Di Carlo, Felix Erben

We report our ongoing lattice QCD study of radiative leptonic decays of the charged pseudoscalar mesons $D$, $D_s$, $B$, and $B_c \to \ell \nu_\ell \gamma$. We carry out our analysis on a single JLQCD ensemble with lattice spacing $a=0.044~\text{fm}$. This work is a step towards a complete QCD+QED lattice calculation of these modes, aimed at reducing theoret

Mark F. Adams

We extend the semi-Lagrangian discontinuous Galerkin (SLDG) method of Einkemmer to velocity grids with adaptive mesh refinement (AMR) and to three-dimensional velocity space. The original SLDG formulation assumes uniform cell widths, which permits the overlap matrices to be precomputed once per fractional shift and reused for every cell. On an adaptively ref

Vlaho-Josip Štironja, Luka Petrović, Juraj Peršić, Ivan Marković

Radar-Inertial Odometry (RIO) has emerged as a robust alternative to vision- and LiDAR-based odometry in challenging conditions such as low light, fog, featureless environments, or in adverse weather. However, many existing RIO approaches assume known radar-IMU extrinsic calibration or rely on sufficient motion excitation for online extrinsic estimation, whi

Jinrui Huang, Yinghui Wang, Huanyao Wen, Ruizhao Zi

The present note is to make minor correction on the assumption of Theorem 1.2 and its proof in our paper [arXiv:2111.02059, Jinrui Huang, Yinghui Wang, Huanyao Wen and Rizhao Zi, {\it J. Differential Equations}, 306(2022), 456--491].

Akshay Tewari, Navid Qureshi, Rolf Heid, Andrea Piovano

We investigated the lattice dynamics of the unconventional superconductor LiFeAs using inelastic neutron scattering experiments and density-functional theory (DFT) calculations. By comparing the neutron scattering intensities with lattice-dynamics simulations we can identify the polarization symmetry of all modes along the main-symmetry directions yielding a

Joshua Pickard, Xin Mao, Can Chen

Controlling real-world networked systems, including ecological, biomedical, and engineered networks that exhibit higher-order interactions, remains challenging due to inherent nonlinearities and large system scales. Despite extensive studies on graph controllability, the controllability properties of hypergraphs remain largely underdeveloped. Existing result

Melwin Xavier, Vaisakh M A, Melveena Jolly, Midhun Xavier

Agent frameworks increasingly encode tool-using behavior as explicit workflow graphs, yet safety enforcement remains a runtime concern. These frameworks expose analyzable graph structure through their APIs, enabling pre-deployment static verification of safety properties that runtime guardrails can only check reactively. This paper presents Agentproof, a sys

Alessandro Bruno, Silvia Dalla

Ground Level Enhancements (GLEs) probe the earliest, highest-energy solar energetic particles and thus provide a unique window onto particle release and transport from the low corona to 1 AU. We present a uniform, event-resolved analysis of the early anisotropy for ten well-observed GLEs, combining consistently reconstructed neutron-monitor pitch-angle distr

Patrick Malcolm, Klaus Bogenberger

In this paper, the potential capacity and spatial efficiency of future autonomous lane-free traffic in urban environments are explored using a combination of analytical and simulation-based approaches. For lane-free roadways, a simple analytical approach is employed, which shows not only that lane-free traffic offers a higher capacity than lane-based traffic

Zhengbang Zhou, Chengkang Zhou, Menghan Song, Yong Baek Kim

Quantum Fisher information (QFI) is a measure of multipartite quantum entanglement that can be obtained from inelastic neutron scattering data on quantum magnets. In this work, we demonstrate that the QFI can distinguish an unconventional quantum critical point (QCP) with fractionalization and emergent gauge structure from conventional ones within the Landau

Yuchen Wu, Shinya Sugiura

This paper proposes low-overhead and low-complexity channel estimation (CE) of frequency-domain equalization aided faster-than-Nyquist (FTN) signaling. In the proposed CE scheme, the concept of pilot superimposition is employed, where the FTN block is designed to superimpose pilot symbols with information symbols, and thus, no dedicated time and frequency re

Harish Karthikeyan, Antigoni Polychroniadou

Privacy-preserving aggregation is a cornerstone for AI systems that learn from distributed data without exposing individual records, especially in federated learning and telemetry. Existing two-server protocols (e.g., Prio and successors) set a practical baseline by validating inputs while preventing any single party from learning users' values, but they imp

Jyotirmaya Ijaradar, Ning Xie, Lei Wei, Sebastian Pape

The unexpected collapse of the Carola Bridge in Dresden, Germany, provides a rare opportunity to characterise how urban network traffic adapts to an unexpected infrastructure disruption. This study develops a data-driven analytical framework using traffic data from the Dresden traffic management system to assess the short-term impacts of the disruption. By c

Jean Abou Samra, David Alexander Madore

The Arthur-Nimue-Merlin degrees are a generalization of the Turing degrees introduced by Kihara as a tangible description of the partially ordered set of Lawvere-Tierney topologies on the effective topos (equivalently, subtoposes of the effective topos). They are defined in terms of a three-player game that introduces both angelic and demonic non-determinism

Allen B. Downey

Five-year cancer survival rates are widely reported and often interpreted to mean that early detection saves lives, that a late fatal diagnosis would have been prevented by earlier detection, and that increasing survival over time proves better treatment. This expository article explains why such inferences are not supported by survival statistics alone. A s

Ricardo Crisostomo, Diana Mykhalyuk

This paper investigates whether large language models (LLMs) can generate reliable stock market predictions. We evaluate four state-of-the-art models - ChatGPT, Gemini, DeepSeek, and Perplexity - across three prompting strategies: a naive query, a structured approach, and chain-of-thought reasoning. Our results show that LLM-generated recommendations are hin

Xiankang Tang, Yixuan Zhang, Juri Barthel, Chun-Lin Jia

Quantitative high-resolution transmission electron microscopy (HRTEM) provides an indispensable means to understand the structure-property relationships of a material in atomic dimensions. Successful quantification requires reliable retrieval of essential atomic structural information despite artifacts arising from unwanted but practically unavoidable imagin

Nabeel Ahmad Saidd

Applying reinforcement learning (RL) to foreign exchange (Forex) trading remains challenging because realistic environments, well-defined reward functions, and expressive action spaces must be satisfied simultaneously, yet many prior studies rely on simplified simulators, single scalar rewards, and restricted action representations, limiting both interpretab

Yao Zhang, Lanyi Cao, Zhen Sun, Jihan Zhou

Atomic electron tomography (AET) determines the three-dimensional (3D) coordinates and chemical identities of individual atoms from a series of scanning transmission electron microscopy images taken at different tilt angles. However, under the low dose conditions required to mitigate beam damage, the reduced signal-to-noise ratio forces a trade off among acc

Qiao Liu, Xincheng Shi

In this paper, we prove that a weak solution of the Cauchy problem for 3D unsteady flows of a generalized Newtonian fluid becomes a strong solution for $\frac{5}{3} <p<\frac{11}{5} $ provided that the gradient of velocity $\nabla \boldsymbol{u}$ belongs to the critical space $L^{\frac{2}{2-(3-p)a}}(0,T;\dot{B}^{-a}_{\infty,\infty}(\mathbb{R}^3))$, where $a\i

Angelo Maria Sabatini

This study presents a hybrid topic modelling framework for computational literary analysis that integrates Latent Dirichlet Allocation (LDA) with sparse Partial Least Squares Discriminant Analysis (sPLS-DA) to model thematic structure and longitudinal dynamics in narrative poetry. As a case study, we analyse Evgenij Onegin-Aleksandr S. Pushkin's novel in ver

Àlex R. Atrio, Antonio Lopez, Jino Rohit, Yassine El Ouahidi

We introduce Earth Virtual Expert (EVE), the first open-source, end-to-end initiative for developing and deploying domain-specialized LLMs for Earth Intelligence. At its core is EVE-Instruct, a domain-adapted 24B model built on Mistral Small 3.2 and optimized for reasoning and question answering. On newly constructed Earth Observation and Earth Sciences benc

Haodong He, Yuan Gao, Weizhong Zhang, Gui-Song Xia

Diffusion Probabilistic Models (DPMs) have achieved great success in image generation but suffer from high inference latency due to their iterative denoising nature. Motivated by the evolving feature dynamics across the denoising trajectory, we propose a novel framework to optimize the computational graph of pre-trained DPMs on a per-timestep basis. By learn

Ashis Kumar Panigrahi, Alok Kumar, Babulu Pradhan, Priyanka Sahu

In this work, we demonstrate interfacial charge transfer-driven transport enhancement in few-layer graphene monolayer MoS2 vertical heterostructure field-effect transistor. Raman scattering and Raman intensity mapping results confirm the successful stacking of FL graphene on ML MoS2. Pronounced photoluminescence (PL) quenching of MoS2 and spectral redshift i

Ji-il Park, Inwook Shim

LiDAR sensors provide high-resolution 3D perception and long-range detection, making them indispensable for autonomous driving and robotics. However, their performance significantly degrades under adverse weather conditions such as snow, rain, and fog, where spurious noise points dominate the point cloud and lead to false perception. To address this problem,

Nikola Jovišić, Milica Škipina, Vanja Švenda

Data scarcity and weak supervision continue to limit the performance of machine learning models in many real-world applications, such as mammography, where Multiple Instance Learning (MIL) often offers the best formulation. While recent foundation models provide strong semantic representations out of the box, effective augmentation of such representations of

Hinrich Rahlfs, Markus Hüllebrand, Sebastian Schmitter, Jonathan Andrae

Atherosclerosis of the carotid artery increases stroke risk. Atherosclerosis assessment with MRI requires multimodal and multidimensional segmentation of the carotid artery, reproducible extraction of biomarkers, and the visualization of segmentations and biomarkers. We developed CaroTo, a tool that allows for standardized carotid atherosclerosis assessment.

Luiz C. Borro, Luiz A. B. Macarini, Gordon Tindall, Michael Montero

As large language models (LLMs) evolve into autonomous agents, persistent memory at the API layer is essential for enabling context-aware behavior across LLMs and multi-session interactions. Existing approaches force vendor lock-in and rely on injecting large volumes of raw conversation into prompts, leading to high token costs and degraded performance. We i

Hsin-Hui Huang, Meguya Ryu, Shuji Kamegaki, Haoran Mu

Determination of orientation in the imaged sample/scene has a large application potential when the anisotropy of properties is analysed, usually, under a linearly polarised illumination. This study combined several improvements of microscopy imaging: use of an incoherent white illumination source (a lamp) with a spectral filter to define a spectral window, a

Martina G. Vilas, Timothy Schaumlöffel, Gemma Roig

How much scene context a single object carries is a well-studied question in human scene perception, yet how this capacity is organized in vision-language models (VLMs) remains poorly understood, with direct implications for the robustness of these models. We investigate this question through a systematic behavioral and mechanistic analysis of contextual inf

Anandaroop Ray, Andrew McPherson, Ross C. Brodie, Alan Yusen Ley-Cooper

Time domain airborne electromagnetic (AEM) surveying is a mature geophysical tool for imaging the Earth's shallow subsurface. It produces images of the electromagnetic conductivity structure of the earth, down to depths of a few hundred metres. The AEM method is fast, with aircraft acquiring data at speeds of 100-300 km/hr, making it an ideal near-surface re

Zhixiang Lu, Chong Zhang, Yulong Li, Angelos Stefanidis

The vision of an inclusive World Wide Web is impeded by a severe linguistic divide, particularly for communities in low-resource regions of Southeast Asia. While large language models (LLMs) offer a potential solution for translation, their deployment in data-poor contexts faces a dual challenge: the scarcity of high-quality, culturally relevant data and the

Anouar Nechi, Rainer Buchty, Mladen Berekovic, Saleh Mulhem

Millimeter-wave (mmWave) and terahertz (THz) massive MIMO systems often rely on predefined beamforming codebooks, which are usually suboptimal in Non-Line-of-Sight (NLoS) conditions and for hardware-limited transceivers. Reinforcement Learning (RL) enables adaptive, data-driven codebook design without explicit Channel State Information (CSI), but the robustn

Sen Jia, Ning Zhu, Jinqin Zhong, Jiale Zhou

RAM incorporates a motion-aware semantic tracker with adaptive Kalman filtering to achieve robust identity association under severe occlusions and dynamic interactions. A memory-augmented Temporal HMR module further enhances human motion reconstruction by injecting spatio-temporal priors for consistent and smooth motion estimation. Moreover, a lightweight Pr

Hridya Dilip, Clarissa Astuto, Armando Coco, Giovanni Russo

We develop a new numerical technique for approximating solutions of the Navier-Stokes equations on moving domains. The method aims at simulating an incompressible fluid past an object whose motion is assigned a priori using a level-set function. The proposed approach relies on a space discretization based on the ghost finite element method (ghost-FEM), which

Jinyuan Qu, Hongyang Li, Lei Zhang

3D instance segmentation methods typically rely on high-quality point clouds or posed RGB-D scans, requiring complex multi-stage processing pipelines, and are highly sensitive to reconstruction noise. While recent feed-forward transformers have revolutionized multi-view 3D reconstruction, they remain decoupled from high-level semantic understanding. In this

Antoine Taroni, Ludovic Moncla, Frederique Laforest

Efficient communication requires balancing informativity and simplicity when encoding meanings. The Information Bottleneck (IB) framework captures this trade-off formally, predicting that natural language systems cluster near an optimal accuracy-complexity frontier. While supported in visual domains such as colour and motion, linguistic stimuli such as words

Emanuele Di Bella, Willem A. de Graaf, Andrea Santi

This paper is a contribution to the supersymmetry gap problem for supergravity backgrounds $(M,g,F)$ in $11$ dimensions. We study restrictions on the curvature of $(M,g,F)$ and, using the bijective correspondence between the space of certain filtered deformations of Lie superalgebras and the space of highly supersymmetric supergravity backgrounds, we establi

Kristina I. Popova, Georgiy A. Solomakha, Zicheng Wen, Mikhail M. Popov

This research focuses on the design and evaluation of an ultrathin cylindrical metasurface for improving the transmit efficiency of traveling-wave magnetic resonance imaging (MRI) of the human brain. To improve efficiency, we matched a travelling waveguide mode to an electrically large, lossy dielectric load using a thin cylindrical metasurface, which occurs

Stefano Perrella, Eric Morales Agostinho, Hugo Zaragoza

Machine Translation (MT) and automatic MT evaluation have improved dramatically in recent years, enabling numerous novel applications. Automatic evaluation techniques have evolved from producing scalar quality scores to precisely locating translation errors and assigning them error categories and severity levels. However, it remains unclear how to reliably m

Tuna Gürbüz, Ege Özsoy, Tony Danjun Wang, Nassir Navab

Operating rooms (ORs) are cluttered, dynamic, highly occluded environments, where reliable spatial understanding is essential for situational awareness during complex surgical workflows. Achieving spatial understanding for panoptic segmentation from sparse multiview images poses a fundamental challenge, as limited visibility in a subset of views often leads

M. Buccheri, S. P. Cosentino, M. L. Pumo

Hydrogen-rich supernovae can efficiently accelerate particles when the expanding ejecta interact with the surrounding circumstellar medium (CSM), producing high-energy (TeV--PeV) neutrinos. In this work we investigate the nearby SN~2024ggi, whose proximity and clear signatures of ejecta--CSM interaction make it a promising candidate for studying high-energy

Jizhou Han, Chenhao Ding, Yuhang He, Qiang Wang

Generalized Category Discovery (GCD) seeks to uncover novel categories in unlabeled data while preserving recognition of known categories, yet prevailing visual-only pipelines and the loose coupling between supervised learning and discovery often yield brittle boundaries on fine-grained, look-alike categories. We introduce the Analogical Textual Concept Gene

Diego Arcis, Jesús Juyumaya

Party-Hecke algebras are introduced as a two-parameter deformation of party algebras, where one parameter deforms the party generators and the other deforms the elementary transpositions. We construct a basis for this algebra and show that it can be realized as a quotient of the algebra of braids and ties. Furthermore, we study the party monoid and its relat

Ivo Bartoň, Jan Aubrecht, Bara Švejkarová, Jan Pokorný

Structured-core thulium-doped fibers were developed to reduce heat load, enable shorter-wavelength operation, and achieve a pedestal-free design. In a proof-of-principle experiment, laser slope efficiencies of 52% at 1907 nm and 54% at 1940 nm were achieved with respect to absorbed power.

Piotr Lebiedowicz, Antoni Szczurek

We present predictions for the ultraperipheral heavy-ion reaction ${\rm Pb} {\rm Pb} \to {\rm Pb} {\rm Pb} D \bar{D}$, where $D$ refers to either $D^0$ or $D^+$, limiting to low invariant mass of $D \bar{D}$ and at energies available at the LHC. The calculation of the $\gamma \gamma \to D \bar{D}$ subprocess is done including the continuum mechanisms and the

Manuel Scheibl, Julian Leichert, Sinem Görmez, Britta Wrede

Physiological signals are increasingly relevant to estimate the mental states of users in human-robot interaction (HRI), yet ROS 2-based HRI frameworks still lack reusable support to integrate such data streams in a standardized way. Therefore, we propose Sense4HRI, an adapted framework for human-robot interaction in ROS 2 that integrates physiological measu

Guglielmo Faggioli, Alessandra Buonanno, Maarten van de Meent, Gaurav Khanna

We characterize and phenomenologically model the merger-ringdown of gravitational waves emitted by a small compact object that plunges and merges into a Kerr black hole from equatorial-eccentric inspirals. The waveforms are generated employing a time-domain Teukolsky code sourced with trajectories computed using the effective-one-body framework. We span valu

Alessio Fallani, Pi A. B. Haase, Julianne F. F. Eckert, Luukas Nikkanen

Transition-metal complexes (TMCs) are promising photosensitizers for Type~I photodynamic therapy (PDT), where electron-transfer processes can generate reactive oxygen species under hypoxic conditions. Yet identifying candidates with the required ground- and excited-state redox energetics remains challenging across the vast chemical space of TMCs. Here, we de

Rémi Delaporte-Mathurin, Ross MacDonald, James Dark, Milan Rother

In this work, we present a multi-fidelity, physics-informed framework for tritium fuel cycle modelling based on the open-source PathSim/PathView platform. Three complementary modelling approaches are demonstrated within a unified dynamic simulation environment. First, a zero-dimensional residence time model is used to reproduce the fuel cycle behaviour of an

Zixin Huang, Ludovico Lami, Vishal Singh, Mark M. Wilde

Discriminating between noisy quantum processes is a central primitive for quantum communication, metrology, and computing. While discrimination limits for finite-dimensional channels are well understood, the continuous-variable setting, particularly under experimentally relevant energy constraints, remains significantly less developed. In this work, we estab

Yuxuan He, Chaiming Huang, Yifan Wu, Hongjun Wang

A short video succeeds not simply because of what it shows, but because of how it schedules attention -- yet current multimodal models lack the structural grammar to parse or produce this organization. Existing models can describe scenes, answer event-centric questions, and read on-screen text, but they are far less reliable at identifying timeline-grounded

Boxun Song, Min Gao, Jiawei Cheng

Group recommendation systems play a pivotal role in supporting collective decisions across various contexts, from leisure activities to organizational team-building. Existing group recommendation approaches typically use either handcrafted aggregation rules (e.g. mean, least misery, weighted sum) or neural aggregation models (e.g. attention-based deep learni

Kewei Wang, Georg Stadler

The spherical-radial decomposition (SRD) is an efficient method for estimating probabilistic functions and their gradients defined over finite-dimensional elliptical distributions. In this work, we generalize the SRD to infinite stochastic dimensions by combining subspace SRD with standard Monte Carlo methods. The resulting method, which we call hybrid infin

Huaijin Zhang, Zhang-Qi Yin

Solid-spin defects in diamond provide long coherence times and room-temperature optical initialization and readout, making them an attractive platform for compact solid-state quantum gyroscopes. A central challenge for NV-based gyroscopes is that the rotation-induced signal is weak, while near-resonant operation, although enhancing the response, can induce n

Preeti Meena, Himanshu Kumar, Sandeep Yadav

A Scene, represented visually using different formats such as RGB-D, LiDAR scan, keypoints, rectangular, spherical, multi-views, etc., contains information implicitly embedded relevant to applications such as scene indexing, vision-based navigation. Thus, these representations may not be efficient for such applications. This paper proposes a novel 360{\deg}

Lukas Pichlmann, Samuel Studer, Aurel R. Arnoldt, Paul Oberhauser

Differential scanning calorimetry (DSC) is a standard tool for studying precipitation and phase transformations in aluminum alloys, yet its relation to mechanical performance has so far remained mostly indirect. Here, we demonstrate that DSC curves themselves act as fingerprints that directly encode mechanical properties. Four representative 6xxx series allo

Yong Ma, Xuesong Zhang, Xuedong Zhang, Natalia Bartłomiejczyk

Voice assistants (VAs) are typically evaluated through task performance metrics and self-report questionnaires, but people's voices themselves carry rich paralinguistic cues that reveal affect, effort, and interaction breakdowns. We present a within-subjects study (N=49) that systematically compared three VA personas across three usage scenarios to investiga

Unnikrishnan Kunnath Ganesan, Sai Subramanyam Thoota, Erik G. Larsson

Phase synchronization of access points (APs) in a distributed multiple-input multiple-output (D-MIMO) system is critical to leverage the performance benefits of D-MIMO. Existing over-the-air phase synchronization methods assume that APs can communicate directly to perform necessary measurements. However, this assumption might not hold in scenarios where inte

Andrea Alessandrelli, Fabrizio Durante, Andrea Ladiana, Andrea Lepre

Federated learning enables collaborative training without sharing raw data, but struggles under client heterogeneity and streaming distribution shifts, where drift and novel data can impair convergence and cause forgetting. We propose a federated associative-memory framework that learns shared archetypes in heterogeneous, continual settings, where client dat

Gerard Anglès Munné, Felix Huber

A fundamental problem in quantum coding theory is to determine the maximum size of quantum codes of given block length and distance. A recent work introduced bounds based on semidefinite programming, strengthening the well-known quantum linear programming bounds. However, floating-point inaccuracies prevent the extraction of rigorous non-existence proofs fro

Michael S. Floater

We derive upper and lower bounds on the determinant of an exponential matrix. They can be transformed into corresponding bounds for the determinant of a univariate Gaussian matrix.

Hao Wang, Licheng Pan, Qingsong Wen, Jialin Yu

Autocorrelation is a defining characteristic of time-series data, where each observation is statistically dependent on its predecessors. In the context of deep time-series forecasting, autocorrelation arises in both the input history and the label sequences, presenting two central research challenges: (1) designing neural architectures that model autocorrela

Yulong Ye, Hongyuan Liang, Chao Jiang, Miqing Li

Configuration tuning for better performance is crucial in quality assurance. Yet, there has long been a mystery on tuners' effectiveness, due to the black-box nature of configurable systems. Prior efforts predominantly adopt static domain analysis (e.g., static taint analysis), which often lacks generalizability, or dynamic data analysis (e.g., benchmarking

Xiaoming Yu, Shize Tang, Guanghua Yu, Linchuan Xie

We introduce Delta-Aware Quantization (DAQ), a data-free post-training quantization framework that preserves the knowledge acquired during post-training. Standard quantization objectives minimize reconstruction error but are agnostic to the base model, allowing quantization noise to disproportionately corrupt the small-magnitude parameter deltas ($\Delta W$)

Boyan Liu, Gongming Zhao, Hongli Xu

Tool-using large language model (LLM) agents often face a fundamental tension between answer quality and execution cost. Fixed workflows are stable but inflexible, while free-form multi-step reasoning methods such as ReAct may improve task performance at the expense of excessive tool calls, longer trajectories, higher token consumption, and increased latency

Matthew Middlehurst, Aiden Rushbrooke, Ali Ismail-Fawaz, Maxime Devanne

Time series machine learning (TSML) is a growing research field that spans a wide range of tasks. The popularity of established tasks such as classification, clustering, and extrinsic regression has, in part, been driven by the availability of benchmark datasets. An archive of 30 multivariate time series classification datasets, introduced in 2018 and common

Marcel Guardia, Vadim Kaloshin, Pau Martín, Pablo Roldan

One of the most remarkable instability zones in the Solar system are Kirkwood gaps in the asteroid belt. In this paper we analyze instabilities in the famous Kirkwood gap $3:1$ in the regime of small eccentricity of Jupiter. Mathematically speaking, we study the evolution of asteroids under the influence of the Sun and Jupiter using the restricted planar ell

Chenhan Wang, Zhengyi Bao, Huipin Lin, Jiahao Nie

Accurately predicting the state-of-health (SOH) and remaining useful life (RUL) of lithium-ion batteries is crucial for ensuring the safe and efficient operation of electric vehicles while minimizing associated risks. However, current deep learning methods are limited in their ability to selectively extract features and model time dependencies for these two

Marcel Guardia, Vadim Kaloshin, Pau Martín, Pablo Roldan

In this paper we discuss the existence of a normally hyperbolic invariant lamination (NHIL) at the Kirkwood gap $3:1$ for the Restricted Planar Elliptic 3 Body Problem. This problem models the Sun-Jupiter-Asteroid dynamics. We also show that the induced dynamics on the NHIL is a partially hyperbolic skew-shift which is of the form \[ f:(\omega,I,\theta)\to (

Yizhe Zhao, Yongjian Fu, Zihao Feng, Hao Pan

Mobile advertising dominates app monetization but introduces risks ranging from intrusive user experience to malware delivery. Existing detection methods rely either on static analysis, which misses runtime behaviors, or on heuristic UI exploration, which struggles with sparse and obfuscated ads. In this paper, we present MANA, the first agentic multimodal r

Manuel Pavon Valderrama

In nuclear matter, for interparticle separations larger than the healing distance (a characteristic long-distance scale of finite-density fermionic systems), the in-medium two-body wave function is essentially a free wave function. In terms of the renormalization group (RG), this implies that the running of the effective field theory (EFT) couplings freezes

K. Azizi, Y. Sarac, H. Sundu

Recent progress in experimental facilities, together with larger data samples and more refined analysis strategies has enabled the observation of many exotic hadronic states, adding new members to the hadron spectrum. Each newly reported signal encourages further experimental searches and simultaneously motivates theoretical studies aimed at uncovering addit

Rajasmita Sahoo

In this work, we investigate the equilibrium structure of white dwarfs within the covariant formulation of symmetric teleparallel $f(Q)$ gravity, in which gravity is described by the nonmetricity scalar $Q$ instead of spacetime curvature. We consider static and spherically symmetric stellar configurations composed of cold, fully degenerate electron matter an

Sören Budig, Frank Schaarschmidt, Max Menssen

In pharmaceutical and toxicological research, historical control data are increasingly used to validate concurrent control groups, typically via the construction of historical control limits. While methods have been described for continuous and dichotomous endpoints, approaches for overdispersed multinomial data, common in developmental and reproductive toxi

Eleni Zapridou, Anastasia Ailamaki

Mission-critical applications often run "forever" and process large data volumes in real time while demanding low latency. To handle the large state of these applications, modern streaming engines rely on key-value stores and store state on local storage or remotely, but accessing such state inflates latency. As today's engines tightly couple the data path w

K. Abe, S. Abe, A. Abhishek, F. Acero

BL Lacertae (BL Lac), the archetypal blazar of its subclass and one of the most studied blazars in the last decades, has undergone a series of major multi-wavelength outbursts since 2020, resulting in its highest recorded $\gamma$-ray flare to date between September and November 2022 together with those from August 2021 and October 2024. We characterised the

Antonis Klironomos, Ioannis Dasoulas, Francesco Periti, Mohamed Gad-Elrab

The vast collection of machine learning records available on the web presents a significant opportunity for meta-learning, where past experiments are leveraged to improve performance. Two crucial meta-learning tasks are pipeline performance estimation (PPE), which predicts pipeline performance on target datasets, and dataset performance-based similarity esti

Fardin Afdideh, Mehdi Astaraki, Fernando Seoane, Farhad Abtahi

Machine learning systems deployed in medical devices require governance frameworks that ensure safety while enabling continuous improvement. Regulatory bodies including the FDA and European Union have introduced mechanisms such as the Predetermined Change Control Plan (PCCP) and Post-Market Surveillance (PMS) to manage iterative model updates without repeate

Jiaxing Xu, Jingying Ma, Xin Lin, Yuxiao Liu

Brain network analysis provides an interpretable framework for characterizing brain organization and has been widely used for neurological disorder identification. Recent advances in self-supervised learning have motivated the development of brain network foundation models. However, existing approaches are often limited by atlas dependency, insufficient expl

José A. Almanza-Marrero, Édgar Roldán, Gonzalo Manzano

Thermal machines are physical systems that, when fueled by input energy, perform output tasks such as heat pumping or the production of work. Their performance is characterized with several, often competing quantities, such as power, efficiency, energy waste, and resilience to environmental noise. Multi-objective optimization provides a key tool to investiga

Mikael Berggren, Bryan Bliewert, Jenny List, Dimitris Ntounis

The Higgs mechanism is essential for the success of the Standard Model (SM) and can be experimentally verified with the determination of the Higgs self-coupling. As the simplest model of a Higgs potential, the SM provides a clear prediction of the Higgs self-coupling in terms of the Higgs boson mass and the vacuum expectation value. Any deviations would indi

Piyus Kedia

Low-level C programs remain highly vulnerable to out-of-bounds memory corruption. State-of-the-art precise defenses either introduce severe runtime overhead due to metadata memory lookups, or break standard C semantics by disallowing partial structs or the creation of an object's end address (EA), a legal operation ubiquitous in real-world C code. Conversely

Wenyu Xin, Xiaokun Hou, Xianfei Zhang, Shaolan Bi

Gravitational-wave observations of binary black hole (BH) mergers provide a novel avenue for testing massive-star evolution and the resulting BH mass spectrum. Recent population analyses under the hierarchical-merger hypothesis have offered evidence for the BH mass gap and inferred its lower edge to $\sim 44 - 68$ M$_\odot$. Motivated by these findings, we c

Mattia Eluchans, Giovanni Pezzulo

Planning entails identifying sequences of actions to reach a goal, but how much of a future plan is constructed before movement and how visual exploration supports this process remain poorly understood. Here, we investigated how visual exploration before and during action relates to the executed path and how this relationship is shaped by task difficulty and

Federico Toschi, Nicolò Brunello, Andrea Sassella, Vincenzo Scotti

The recent advancements introduced by Large Language Models (LLMs) have transformed how Artificial Intelligence (AI) can support complex, real world tasks, pushing research outside the text boundaries towards multi modal contexts and leading to Multimodal Large Language Models (MLMs). Given the current adoption of LLM based assistants in solving technical or

Dong Yan, Jian Liang, Yanbo Wang, Shuo Lu

Test-Time Reinforcement Learning (TTRL) enables Large Language Models (LLMs) to enhance reasoning capabilities on unlabeled test streams by deriving pseudo-rewards from majority voting consensus. However, existing TTRL methods rely exclusively on positive pseudo-labeling strategies. Such reliance becomes vulnerable under challenging scenarios where answer di

Tijmen Kuijpers, Karolin Winter, Remco Dijkman

Synchronizing decisions between running cases in business processes facilitates fair and efficient use of resources, helps prioritize the most valuable cases, and prevents unnecessary waiting. Consequently, decision synchronization patterns are regularly built into processes, in the form of mechanisms that temporarily delay one case to favor another. These d

Paul Romatschke

In this work, I consider scalar field theory with negative quartic self-interaction, corresponding to an upside-down classical potential. Despite not possessing a classically stable ground state, such potentials are known to behave properly when treated quantum mechanically, leading to stable and unitary time evolution. Using two different saddle-point expan