March 2026 arXiv papers — page 101

Nikolaos D. Tantaroudas

This paper addresses the fundamental question of determining the minimum number of distinct control laws required for global controllability of nonlinear systems that exhibit singularities in their feedback linearising controllers. We introduce and rigorously prove the (k+1)-Controller Lemma, which establishes that for an nth order single-input single-output

Dingzu Wang, Hao Zhu, Guo-Feng Zhang, Dario Poletti

Spectral statistics probe integrability versus chaos and have recently been extended to Markovian open quantum systems described by Lindbladians, whose quantum-trajectory unraveling decomposes the evolution into no-jump dynamics generated by an effective non-Hermitian Hamiltonian and recycling jumps. In this work, we perform spectrum-statistics diagnostics f

Kenneth Joseph, Kim Williams, David Lazer

NLP+CSS work has operationalized ideology almost exclusively on a left/right partisan axis. This approach obscures the fact that people hold interpretations of many different complex and more specific ideologies on issues like race, climate, and gender. We introduce a framework that understands ideology as an attributed, multi-level socio-cognitive concept n

Can Huang, Michela Ottobre, Gideon Simpson

We study numerical schemes for Stochastic Partial Differential Equations (SPDEs). We introduce a general method of proof of non-asymptotic uniform in time error bounds on numerical integrators for SPDEs, ensuring the schemes capture both the transient and the long term dynamics faithfully. We then consider SPDEs with non-globally Lipshitz nonlinearities, whi

Marco Flaim, Erik Hupp, Karl-Theodor Sturm

We present a synthetic notion of scalar curvature (and its integral) for Riemannian manifolds and metric measure spaces, defined in terms of the initial slope of a Gaussian (double) integral. We explicitly calculate the integral scalar curvature for Lipschitz gluings of smooth Riemannian manifolds and for cones. In dimension 2, the former coincides with the

Bruna Alves, Armando J. Pinho, Sónia Gouveia

The topic of Multivariate Time Series Anomaly Detection (MTSAD) has grown rapidly over the past years, with a steady rise in publications and Deep Learning (DL) models becoming the dominant paradigm. To address the lack of systematization in the field, this study introduces a novel and unified taxonomy with eleven dimensions over three parts (Input, Output a

Xinghao Zhao

Understanding uncertainty in chain-of-thought reasoning is critical for reliable deployment of large language models. In this work, we propose a simple yet effective diagnostic approach based on trajectory shape rather than scalar magnitude. We show that this signal is practical, interpretable, and inexpensive to obtain in black-box settings, while remaining

Nilotpola Sarma, Vaishali Ghanshyam Chaudhuri, Chandan Karfa

Masking is a countermeasure against Power Side Channel Attacks (PSCAs) in both software and hardware implementations of cryptographic algorithms. Compared to software masking, implementing masked hardware is time consuming and error prone. Recent approaches, therefore, rely on High Level Synthesis (HLS) tools to automatically generate masked Register Transfe

Sakshi Arya, Satarupa Bhattacharjee, Bharath K. Sriperumbudur

We study contextual bandits with finitely many actions in which the reward of each arm follows a single-index model with an arm-specific index parameter and an unknown nonparametric link function. We consider a regime in which arms correspond to stable decision options and covariates evolve adaptively under the bandit policy. This setting creates significant

Zhengyao Huang

In this paper, we study Monge's problem on Riemannian manifolds $(M, g)$ with positive sectional curvature. Assuming that the source and target measures are absolutely continuous with respect to the Riemannian volume measure, we generalize a variational method from the Euclidean setting to establish the existence of a transport density and an explicit disint

Chun Zhou, Yanyang Zhou, Ping Wang, Tan Li

Low-altitude drones can serve as dynamic nodes apparently mitigating terrain-induced impacts for quantum networks. However, it is extremely hard to establish a sable quantum link in a drone-based dynamic platform, which requires centimeter-level positioning techniques and high-precision time synchronization technologies. In this paper, we develop a single-en

Cyril Barlasov

Based on the work of Dundas, Lindenstrauss and Richter we compute the topological Andr\'e-Quillen homology with reduced coefficients for Eilenberg-MacLane spectra such as $H\mathbb{Z}$ and $H\mathbb{Z}/p^n$. The case of $H\mathbb{F}_p$ was settled in an unpublished work of Basterra and Mandell, which was refined later by Brantner and Mathew in the context of

J. Griff-McMahon, V. Valenzuela-Villaseca, C. A. Walsh, S. Malko

Self-generated magnetic fields are commonly produced in high-power laser-plasma interactions. These fields can inhibit plasma heat-flow which makes them important in inertial fusion and controlled laboratory astrophysics experiments. In this work, we characterize the time evolution of self-generated magnetic fields using multi-view proton tomography at two t

Junyi Zhu, Huan Liu

The well-posedness for the Dbar problem associated with the AKNS spectral problem is considered. In general, the relevant Dbar equation with normalization condition is quivalent to an integral equation, where the kernel involves exponents $\mathrm{e}^{\pm2ikx}$ with physical variable $x$ as a parameter. We develop a decomposition technique to control the con

Sunil Arya, David M. Mount

We prove polarity duality for covering problems in Hilbert geometry. Let $G$ and $K$ be convex bodies in $\mathbb{R}^d$ where $G \subset \operatorname{int}(K)$ and $\operatorname{int}(G)$ contains the origin. Let $N^H_K(G,\alpha)$ and $S^H_K(G,\alpha)$ denote, respectively, the minimum numbers of radius-$\alpha$ Hilbert balls in the geometry induced by $K$ n

Tommaso Costa

Sensitivity analysis for unmeasured confounding in observational studies is commonly based on threshold quantities, such as the Cornfield condition or the E-value, which quantify how strong a confounder must be to explain away an observed association. However, these approaches do not address a fundamental inferential question: how plausible is it that such a

Ezekiel Nii Noye Nortey, Jones Asante-Koranteng, Marcellin Atemkeng, Theophilus Ansah-Narh

Accurate prediction of loan defaults is a central challenge in credit risk management, particularly in modern financial datasets characterised by nonlinear relationships, class imbalance, and evolving borrower behaviour. Traditional statistical models and static ensemble methods often struggle to maintain reliable performance under such conditions. This stud

Yanhan Guo, Cheng Li

We present a statistical analysis for the interaction-induced central concentration of HI gas distributions and its connection with interaction-induced central star formation enhancement, using a large sample of $\sim 10^4$ galaxies from the ALFALFA and SDSS surveys. By adopting the HI profile parameter $K$, an indicator of gas concentration inferred from th

A. A. Saoulis, T. -S. Pham, A. M. G. Ferreira

Bayesian inference represents a principled way to incorporate Earth structure uncertainty in full-waveform moment tensor inversions, but traditional approaches generally require significant approximations that risk biasing the resulting solutions. We introduce a robust method for handling theory errors using simulation-based inference (SBI), a machine learni

Feifan Luo, Hongyang Chen

Estimating correspondences between pairs of non-rigid deformable 3D shapes remains a significant challenge in computer vision and graphics. While deep functional map methods have become the go-to solution for addressing this problem, they primarily focus on optimizing pointwise and functional maps either individually or jointly, rather than directly enhancin

Sean Jordan, Shang-Min Tsai, Paul B. Rimmer, Oliver Shorttle

The organosulfur biosignature gases dimethylsulfide (DMS) and dimethlydisulfide (DMDS) have recently been claimed to be present in the atmosphere of sub-Neptune exoplanet K2-18b, leading to the suggestion of possible extraterrestrial life. Abiotic formation pathways for DMS and DMDS in reducing atmospheres have also been proposed, raising concern over the us

Akira Endo, Yoshiaki Hashimoto

Quantum Hall systems having Corbino geometry are expected to have a large Peltier coefficient $\Pi_{rr}$ in the quantum Hall plateau region. We present an analytic formula for $\Pi_{rr}$ calculated employing the spectral conductivity obtained based on the self-consistent Born approximation. The coefficient $\Pi_{rr}$ is shown to have a large negative (positi

Peter Stadler, Alexander Meinert, Niklas Baldauf, Alen Turnwald

This work introduces two lightweight model predictive control (MPC) approaches for attitude tracking with reaction wheels during spacecraft rendezvous synchronization. Both approaches are based on a novel attitude deviation formulation, which enables the use of inherently linear constraints on angular velocity. We develop a single-loop and a dual-loop MPC; t

Christoph Siemroth

A German ministry recently proposed a limit of at most one price increase per day for petrol stations. At what time should the price reset be allowed in order to lower price levels the most throughout the day? To answer this question, I infer the share of price-sensitive consumers for every hour of the day from German petrol station price data, based on a si

Chinonso Onah, Agneev Guin, Carsten Othmer, J. A. Montañez-Barrera

Aerodynamic drag reduction on highways through vehicle platooning is a well-known concept, but it has not yet seen systematic uptake, arguably because of significant technological and legislative obstacles. As a low-tech entry point to real multi-vehicle platooning, "Windbreaking-as-a-Service" (WaaS) was introduced recently. Here we use a QUBO formulation to

Parvin Ghaffarzadeh, Debarati Chakraborty, Koorosh Aslansefat, Ali Dostan

This Data Descriptor presents a fully open, multi-modal dataset for estimating vertical ground reaction force (vGRF) from consumer-grade Apple Watch sensors with laboratory force plate ground truth. Ten healthy adults aged 26--41 years performed five activities: walking, jogging, running, heel drops, and step drops, while wearing two Apple Watches positioned

Michael Wassermair, Gerhard Kahl, Roland Roth, Andrew J. Archer

Colloidal fluids can exhibit complex phase behavior and determining phase diagrams via experiments or computer simulations can be laborious. We demonstrate that the dispersion relation $\omega(k)$, obtained from dynamical density functional theory for the uniform density system, is a highly versatile tool for {\it predicting} where in the phase diagram compl

Shi-Ji Cao, Jing-Juan Qi, Yi-Fan Zhao, Chao Wang

We investigate the direct $CP$ violation in the decay $D^\pm \to \pi^\pm \pi^+ \pi^-$ incorporating the $a_0^0(980)$-$f_0(980)$ mixing mechanism. The integrated mixing intensities $\overline \xi_{fa}$ and $\overline \xi_{af}$ are calculated using meson masses and coupling constants extracted from various theoretical models and experimental data, yielding val

Diego Calvanese, Angelo Casciani, Giuseppe De Giacomo, Marlon Dumas

This paper presents a manifesto that articulates the conceptual foundations of Agentic Business Process Management (APM), an extension of Business Process Management (BPM) for governing autonomous agents executing processes in organizations. From a management perspective, APM represents a paradigm shift from the traditional process view of the business proce

Yufei Chang, Yangyang Cheng, Zhilan Wang, Shuo Wei

Oriented graph discrepancy problems focus on finding specific subgraphs within a given oriented graph $G$ that contain a significant number of edges in one direction. This concept was first introduced by Gishboliner, Krivelevich, and Michaeli, and has since been further investigated by Freschi and Lo [J. Combin. Theory, Ser. B 169 (2024)], who gave a tight l

Shiliang Zhang, Sabita Maharjan

The rapid proliferation of artificial intelligence (AI) technologies has led to a dynamic regulatory landscape, where legislative frameworks strive to keep pace with technical advancements. As AI paradigms shift towards greater autonomy, specifically in the form of agentic AI, it becomes increasingly challenging to precisely articulate regulatory stipulation

Alejandro Sevilla

Turbulence remains one of the central open problems in classical physics, largely due to the absence of a closed dynamical description of the Reynolds stress. Existing approaches typically rely either on local constitutive assumptions or on high-dimensional statistical representations, without identifying a minimal set of dynamical variables governing the ca

Ahmed Tawfik Aboukhadra, Marcel Rogge, Nadia Robertini, Abdalla Arafa

Understanding realistic hand-object interactions from monocular RGB videos is essential for AR/VR, robotics, and embodied AI. Existing methods rely on category-specific templates or heavy computation, yet still produce physically inconsistent hand-object alignment in 3D. We introduce GHOST (Gaussian Hand-Object Splatting), a fast, category-agnostic framework

Vedant Pandya

Knowledge-grounded dialogue systems aim to generate informative, contextually relevant responses by conditioning on external knowledge sources. However, most existing approaches focus exclusively on English, lack explicit citation mechanisms for verifying factual claims, and offer limited transparency into model decision-making. We present XKD-Dial, a progre

Alexander Meinert, Niklas Baldauf, Peter Stadler, Alen Turnwald

This paper presents a safety-guaranteed, runtime-efficient imitation learning framework for spacecraft close proximity control. We leverage Control Barrier Functions (CBFs) for safety certificates and Control Lyapunov Functions (CLFs) for stability as unified design principles across data generation, training, and deployment. First, a nonlinear Model Predict

Andriani Vervelaki, Boris Gross, Daniel Jetter, Katharina Kress

The quantum anomalous Hall effect has been observed in several magnetically doped topological insulators, where its robustness and macroscopic magnetization properties have been taken to suggest the presence of long-range ferromagnetic order. However, experiments in such systems have found evidence for both long- and short-range order, leaving the precise na

Daniele Moretto, Andrea Franceschini, Massimiliano Ferronato

Accurate numerical simulation of fault and fracture mechanics is critical for the performance and safety assessment of many subsurface systems. The discretized representation of discontinuity surfaces and the robust simulation of their frictional contact behavior still represent major challenges. In this work, we use the mortar method to enforce the contact

ALICE Collaboration

This article reports the measurement of the transverse-momentum ($p_{\rm T}$) differential production cross section of B$^0$ mesons in proton-proton collisions at a centre-of-mass energy of $\sqrt{s}=13.6$ TeV with the ALICE detector at the CERN LHC. For the first time, the B$^0$ production cross section is measured at midrapidity ($|y|<0.5$) down to $p_{\rm

Simone Baldassarri, Maike C. de Jongh

In this paper, we develop a Markov decision process (MDP) formulation for the low--temperature metastable Ising model evolving according to Kawasaki dynamics in a finite box of the two--dimensional square lattice. We analyze how an external controller can guide the system to the all--occupied state by appropriately adding and moving particles at specified mo

Hatim Ennayar, Jeanette Hussong

A two-color laser-induced fluorescence (2C-LIF) technique is presented for investigating droplet impact on thin liquid films, enabling simultaneous, spatially and temporally resolved measurements of film thickness and scalar concentration. The method is applied to water droplets impacting thin liquid films over a range of Reynolds numbers, Weber numbers and

Carla M. Canelo, Dinalva A. Sales, Vitor Avelaneda, Alexander G. G. M. Tielens

Polycyclic aromatic hydrocarbons (PAHs) are fundamental to understanding the interstellar medium (ISM) of several astrophysical objects. Normally present in Starburst (SB) galaxies, they have also been more frequently detected in active galaxy nuclei (AGNs), suggesting an inner dusty torus that can shield the radiation from the central black role. In this wo

Francisco Guillen-Gonzalez, Exequiel Mallea-Zepeda, Maria A. Rodriguez-Bellido, Elder J. Villamizar-Roa

In this paper we study the following three-dimensional parabolic-parabolic chemo-repulsion model with potential production, logistic reaction and bilinear control, defined in $Q=(0,T)\times\Omega$: \begin{equation*}\label{eq0} \left\{ \begin{array}{rcl} \partial_tu-\Delta u&=&\nabla\cdot(u\nabla v)+r\,u-\mu\, u^p,\\ \partial_tv-\Delta v+v&=&u^p+f\,v\, 1_{\Om

Steffen Dereich, Thang Do, Arnulf Jentzen

The adaptive moment estimation (Adam) optimizer proposed by Kingma & Ba (2014) is presumably the most popular stochastic gradient descent (SGD) optimization method for the training of deep neural networks (DNNs) in artificial intelligence (AI) systems. Despite its groundbreaking success in the training of AI systems, it still remains an open research problem

Anagani Bhanusree, Sai Divya Vissamsetty, K VenkataKrishna Rao, Rimjhim

Large Language Models (LLMs) have been progressively exhibiting there capabilities in various areas of research. The performance of the LLMs in acute maternal healthcare area, predominantly in low resource languages like Telugu, Hindi, Tamil, Urdu etc are still unstudied. This study presents how ChatGPT-4o, GeminiAI, and Perplexity AI respond to pregnancy re

Yitong Li, Igor Yakushev, Dennis M. Hedderich, Christian Wachinger

Positron emission tomography (PET) is a widely recognized technique for diagnosing neurodegenerative diseases, offering critical functional insights. However, its high costs and radiation exposure hinder its widespread use. In contrast, magnetic resonance imaging (MRI) does not involve such limitations. While MRI also detects neurodegenerative changes, it is

Min Hun Lee

Artificial intelligence (AI) systems are deployed as collaborators in human decision-making. Yet, evaluation practices focus primarily on model accuracy rather than whether human-AI teams are prepared to collaborate safely and effectively. Empirical evidence shows that many failures arise from miscalibrated reliance, including overuse when AI is wrong and un

Vedanta S P, Ponnurangam Kumaraguru

Large language models are increasingly proposed as autonomous agents for high-stakes public workflows, yet we lack systematic evidence about whether they would follow institutional rules when granted authority. We present evidence that integrity in institutional AI should be treated as a pre-deployment requirement rather than a post-deployment assumption. We

Nicolas Martorell, Bruno Bianchi

Tracking the internal states of large language models across conversations is important for safety, interpretability, and model welfare, yet current methods are limited. Linear probes and other white-box methods compress high-dimensional representations imperfectly and are harder to apply with increasing model size. Taking inspiration from human psychology,

Youngwan Lee, Soojin Jang, Yoorhim Cho, Seunghwan Lee

Spatial reasoning is foundational for Vision-Language Models (VLMs), particularly when deployed as Vision-Language-Action (VLA) agents in physical environments. However, existing benchmarks predominantly focus on elementary, single-hop relations, neglecting the multi-hop compositional reasoning and precise visual grounding essential for real-world scenarios.

Tianci Luo, Jinpeng Wang, Shiyu Qin, Niu Lian

Visual In-Context Learning (VICL) aims to complete vision tasks by imitating pixel demonstrations. Recent work pioneered prompt fusion that combines the advantages of various demonstrations, which shows a promising way to extend VICL. Unfortunately, the patch-wise fusion framework and model-agnostic supervision hinder the exploitation of informative cues, th

D. Bennis, A. Bouziri, S. D. Kumar, T. Singh

In this paper, we introduce and study the $S$-versions of several fundamental elements in commutative rings. Specifically, for a commutative ring $R$ with identity and a multiplicative subset $S$, we define and investigate the notions of $S$-invertible, $S$-idempotent, $S$-von Neumann regular, and $S$-$\pi$-regular elements. We establish their basic properti

F. Guillen-Gonzalez, F. Palmero-Ramos, M. A. Rodriguez-Bellido, G. Tierra

In this work we introduce a new optimal control algorithm for the Keller-Segel chemo-attraction system, where both boundary and distributed controls are considered and both are associated with introducing/removing the amount of chemical substances in the system. The key idea of our approach is to design the optimal control algorithm after discretizing the st

Marcy Palejova

Hierarchical predictive processing explains adaptive behaviour through precision-weighted inference. Explicit belief revision often fails to produce corresponding changes in stress reactivity or autonomic regulation. This asymmetry suggests the framework leaves under-specified a governance-level constraint concerning which identity-level hypotheses regulate

Zainab Malik

Using an expansion beyond the eikonal regime, we derive relatively compact and accurate analytic expressions for the gravitational quasinormal modes of an asymptotically flat black hole supported by a Dehnen-type dark-matter halo. The spacetime admits a simple analytic metric describing a supermassive black hole embedded in a galactic environment, with the l

Pranjal Aggarwal, Marjan Ghazvininejad, Seungone Kim, Ilia Kulikov

The ability to precisely derive mathematical objects is a core requirement for downstream STEM applications, including mathematics, physics, and chemistry, where reasoning must culminate in formally structured expressions. Yet, current LM evaluations of mathematical and scientific reasoning rely heavily on simplified answer formats such as numerical values o

Pouria Emtenani, Marta Loletti, Felix Nippert, Eduardo Bede Barros

Ultrawide-bandgap rutile GeO2 is emerging as a promising semiconductor for power electronics, where efficient heat dissipation is essential to suppress self-heating and ensure device reliability. However, the temperature dependence and microscopic origin of its anisotropic heat transport have remained experimentally unresolved. Here, temperature-dependent ti

Zhihe Zhang, Bin Yue, Yidong Xu, Yin-Zhe Ma

Astrophysical luminous objects such as the first stars have not yet formed in the Dark Ages. However, primordial black holes (PBHs) always exist throughout cosmic history since the inflation epoch. During the Dark Ages, PBHs may accrete the ambient gas and release radiation like astrophysical luminous objects, change the cosmic radiation field, the thermal s

Jonathan Braine, Edvige Corbelli

Large high-quality samples of HII regions and their parent Giant Molecular Clouds (GMC) are now available for local galaxies. It is therefore possible to investigate links between the CO and H$\alpha$ luminosity functions and whether massive stars form in GMCs of all masses. The CO luminosity functions (LF), representing the distribution of GMC masses, are c

Jake Ineson, Aleksander Dubas, Alex Skillen

Rayleigh-B\'enard convection under an imposed inductionless magnetic field is analysed statistically from the perspective of single-point and multi-scale energy budgets. The data is obtained from direct numerical simulations with a Rayleigh number of $10^6$, a Prandtl number of $1$ and Hartmann numbers of $0$, $20$, $40$ and $80$. Wall-parallel and wall-norm

Krzysztof Janowicz, Gengchen Mai, Rui Zhu, Song Gao

Understanding how AI will represent and reason about geography should be a key concern for all of us, as the broader public increasingly interacts with spaces and places through these systems. Similarly, in line with the nature of foundation models, our own research often relies on pre-trained models. Hence, understanding what world AI systems construct is a

Lourdes Moreno, Paloma Martínez

Plain Language and Easy-to-Read formats in text simplification are essential for cognitive accessibility. Yet current automatic simplification and evaluation pipelines remain largely automated, metric-driven, and fail to reflect user comprehension or normative standards. This paper introduces a hybrid framework that explicitly integrates human participation

S. Mohammad Moosavi Nejad

It is known that about $60\%$ of all Higgses produced at the CERN-LHC decay into a pair of bottom quarks. Bottoms quickly hadronize, in most cases, into bottom-flavored (B) hadrons before they decay. Therefore, the study of scaled-energy distribution of B-mesons in the decay process $H\to B+Jets$ can be considered as a channel to search for the Higgs charact

Zhi-Guo He, Guanghui Li, Yu-Jie Tian, Xin-Kai Wen

The color-octet (CO) mechanism is a cornerstone of non-relativistic QCD, yet its long-distance matrix elements remain limited, preventing stringent tests of the theory. We demonstrate that the Artru-Collins asymmetry in hadronic decays of the $P$-wave bottomonium state $\chi_{b2}$ provides a direct probe of CO dynamics. The asymmetry arises exclusively from

Carlos Rafael Catalan, Patricia Nicole Monderin, Lheane Marie Dizon, Gap Estrella

Popular language learning applications such as Duolingo use large language models (LLMs) to generate lessons for its users. Most lessons focus on general real-world scenarios such as greetings, ordering food, or asking directions, with limited support for profession-specific contexts. This gap can hinder learners from achieving professional-level fluency, wh

Yizhou Han, Di Wu, Blesson Varghese

In real-world Federated Learning (FL) deployments, data distributions on devices that participate in training evolve over time. This leads to asynchronous data drift, where different devices shift at different times and toward different distributions. Mitigating such drift is challenging: frequent retraining incurs high computational cost on resource-constra

Gaoxiang Cao, Wenke Yuan, Huasen He, Yunpeng Hou

Urban Vehicular Ad-Hoc Networks (VANETs) can become fragmented because buildings obstruct wireless links and vehicle mobility continuously changes the network topology. Unmanned Aerial Vehicles (UAVs) can serve as mobile relays, but Deep Reinforcement Learning (DRL)-based deployment often suffers from inefficient exploration because it lacks road-topology gu

Claudia Noack, Tomasz Olma, Christoph Rothe

Clustered sampling is prevalent in empirical regression discontinuity (RD) designs, but it has not received much attention in the theoretical literature. In this paper, we introduce a general model-based framework for such settings and derive high-level conditions under which the standard local linear RD estimator is asymptotically normal. We verify that our

Beatriz Dias, Jan Lukas Bosse, James R. Seddon

Fermionic Gaussian circuits can be simulated efficiently on a classical computer, but become universal when supplemented with non-Gaussian operations. Similar to stabilizer circuits augmented with non-stabilizer resources, these non-Gaussian circuits can be simulated classically using rank- or extent-based methods. These methods decompose non-Gaussian states

Nelson Navajas Fernández, Jeffrey T. Hancock, Maurice Jakesch

AI-based tools that mediate, enhance or generate parts of video communication may interfere with how people evaluate trustworthiness and credibility. In two preregistered online experiments (N = 2,000), we examined whether AI-mediated video retouching, background replacement and avatars affect interpersonal trust, people's ability to detect lies and confiden

Michael S. Floater

We derive an identity that relates a class of multiple integrals involving Vandermonde polynomials to divided differences. Alternatively the identity can be viewed as an integral formula for divided differences. As part of the derivation we show that both sums of pure partial derivatives and mixed partial derivatives of Vandermonde polynomials are zero, whic

Xuemian Wu, Shizhe Zhao, Zhongqiang Ren

Multi-Agent Path Finding (MAPF) seeks collision-free paths for multiple agents from their respective start locations to their respective goal locations while minimizing path costs. Most existing MAPF algorithms rely on a common assumption of synchronized actions, where the actions of all agents start at the same time and always take a time unit, which may li

Xiucheng Wang, Zixuan Guo, Nan Cheng

Radio maps (RMs) provide spatially continuous propagation characterizations essential for 6G network planning, but high-fidelity RM construction remains challenging. Rigorous electromagnetic solvers incur prohibitive computational latency, while data-driven models demand massive labeled datasets and generalize poorly from simplified simulations to complex mu

Samuel Del Fré, Gilberto A. Alou Angulo, Maurice Monnerville, Alejandro Rivero Santamaría

Accurate atomistic simulations of gas-surface scattering require potential energy surfaces that remain reliable over broad configurational and energetic ranges while retaining the efficiency needed for extensive trajectory sampling. Here, we develop a data-driven workflow for constructing a machine-learning interatomic potential (MLIP) tailored to gas-surfac

Yana Veitsman, Yihong Liu, Hinrich Schütze

Better cross-lingual alignment is often assumed to yield better cross-lingual transfer. However, explicit alignment techniques -- despite increasing embedding similarity -- frequently fail to improve token-level downstream performance. In this work, we show that this mismatch arises because alignment and downstream task objectives are largely orthogonal, and

A. J. Smith, K. Godbey, C. Hebborn, W. Nazarewicz

Understanding how nuclear size evolves with the number of protons and neutrons tests our models of strongly interacting matter. The nuclear charge (and proton) radii accessible through electromagnetic probes carry fundamental information on the saturation density and nuclear correlations. The radii of the neutron distribution are more difficult to measure, b

Damyon Kim, Yuichi Honjo, Tatsuya Iizuka, Naomi Okubo

Air-dispersed sensor networks deployed from aerial robotic systems (e.g., UAVs) provide a low-cost approach to wide-area environmental monitoring. However, existing methods often rely on active actuators for mid-air shape or trajectory control, increasing both power consumption and system cost. Here, we introduce a passive elastic-folding hinge mechanism tha

Marcel Weichel, Martin Reder, Gerit Mühlberg, David Burger

Sodium-ion batteries employing hard carbon electrodes are considered a drop-in technology for lithium-ion batteries. Electrode drying is a critical manufacturing step, as binder migration during pore emptying impacts the mechanical integrity and electrical performance of the electrode. Existing modeling approaches predominantly rely on the film shrinkage pha

Delaram Moradi, Pierre Popoli, Jeffrey Shallit, Ingrid Vukusic

The Fibonacci infinite word ${\bf f} = (f_i)_{i \geq 0} = 01001010\cdots$ is one of the most celebrated objects in combinatorics on words. There is a simple $5$-state automaton that, given $i$ in lsd-first Zeckendorf representation, computes its $i$'th term $f_i$, and a $2$-state automaton for msd-first. In this paper we consider the state complexity of the

Zacharias Roupas

Proto-stellar clusters, likely progenitors of globular clusters, are compact with typical mass $\sim 10^6\,{\rm M}_\odot$ and size $\sim 1\,{\rm pc}$, as revealed recently by JWST observations at $z\sim 10$. Sufficiently high compactness can provide a time window for early-formed stellar black holes (BHs) to accrete primordial gas. We develop a model to dete

Bishoy Galoaa, Shayda Moezzi, Xiangyu Bai, Sarah Ostadabbas

Recent video reasoning models increasingly produce spatio-temporal evidence chains that localize objects at specific timestamps. While these traces improve interpretability by grounding \emph{where} and \emph{when} evidence appears, they often leave the motion connecting observations, the \textit{how}, implicit. This makes dynamic and trajectory-dependent cl

Xiucheng Wang, Yue Zhang, Nan Cheng

Integrating large language models (LLMs) into wireless communication optimization is a promising yet challenging direction. Existing approaches either use LLMs as black-box solvers or code generators, tightly coupling them with numerical computation. However, LLMs lack the precision required for physical-layer optimization, and the scarcity of wireless train

Robert Lemke, Matthias Rössle, Holger Lange, Andreas Knorr

In this paper we introduce momentum-resolved metal Boltzmann-Bloch equations (MBBE) for the combined description of electronic intra- and interband processes in noble metals. This microscopic framework incorporates a full treatment of many-body electron-electron and electron-phonon interactions, relevant for relaxation and dephasing processes after optical e

Xiucheng Wang, Zhenye Chen, Nan Cheng, Zhisheng Yin

Autonomous aerial vehicles (AAVs) enable data collection for sixth-generation Internet-of-Things networks, but their trajectories couple nonlinear wireless rates with long-horizon service progress. This paper views the evolution of AAV kinematics, channel state, and user backlog as a structured differentiable world model and develops Learn for Variation (L4V

Marcel Marburger, Christoph Möller, Andrew MacFarlane, Max Schneider

This study introduces a novel experimental configuration using OH-PLIF imaging to directly determine the stretch factor ($I_0$) in laminar premixed hydrogen flames transitioning from a quasi-stable to a thermodiffusively unstable regime. A rod-anchored V-flame is stabilised in a laminar premixed reactant flow. Near the anchoring rod, the mildly strained flam

Chongda Huang, Yue Xiao, Qianzhen Zhang, Lilin Dan

Simultaneous wireless information and power transfer (SWIPT) critically depends on waveform design, which governs both reliable data delivery and efficient energy harvesting. Among waveform characteristics, the peak-to-average power ratio (PAPR) plays a pivotal role: low-PAPR signals improve power amplifier (PA) efficiency, while high-PAPR signals exploit re

Xiangyu Bai, Bishoy Galoaa, Sarah Ostadabbas

Video question answering (VQA) with vision-language models (VLMs) depends critically on which frames are selected from the input video, yet most systems rely on uniform or heuristic sampling that cannot be optimized for downstream answering quality. We introduce \textbf{HORNet}, a lightweight frame selection policy trained with Group Relative Policy Optimiza

Halvor Herlyng, Shawn C. Shadden

The dynamic flow of cerebrospinal fluid (CSF) in brain ventricles exhibits flow features on several scales, both spatially and temporally. Most analysis of this complex flow and the accompanying transport has used instantaneous (Eulerian) flow variables. Such analysis makes understanding of unsteady transport challenging. Here, we analyze brain ventricular C

Michael Wiebusch

We present a minimal-component bidirectional digital interconnect that enables simultaneous transmission and reception of baseband logic signals over a single coaxial cable. The circuit consists of a passive resistive hybrid providing matched line termination and directional separation, a single CMOS logic gate as driver, and a commercial LVDS receiver used

Lukas Lüchtrath, Christian Mönch

We prove a Sidorenko-type inequality for directed trees: for every oriented tree $T$ on $k$ vertices and every finite directed graph $G$, the homomorphism count hom$(T,G)$ is bounded above by the maximum of the two pure star counts hom$(S_{0,k-1},G)$ and hom$(S_{k-1,0},G)$. In other words, among all directed trees on $k$ vertices, the pure in- and out-stars

Adrian Seyboldt, Eliot L. Carlson, Bob Carpenter

Although Hamiltonian Monte Carlo (HMC) scales as O(d^(1/4)) in dimension, there is a large constant factor determined by the curvature of the target density. This constant factor can be reduced in most cases through preconditioning, the state of the art for which uses diagonal or dense penalized maximum likelihood estimation of (co)variance based on a sample

S. V. Bolokhov

We study quasinormal modes and grey-body factors of scalar, electromagnetic and Dirac test fields for a black hole surrounded by matter distributed according to the Einasto density profile. The quasinormal spectrum is calculated using the high-order WKB method with Pad\'e approximants and checked by the time-domain integration. For small values of the Einast

Chao Min, Junyi Cui, Stanisław Migórski, Yonglan Xie

Drilling investment is pivotal to operational planning in oil and gas (O\&G) exploration. Conventional deployment relies heavily on fragmented expert assessments of geological and economic factors, with limited integration ability of information. As the tool of portfolio show strong potential for mitigating uncertainty and selecting superior drilling plans,

Wei Jia

The geometry of Fermi sea hosts a unique form of quantum topology that governs the conductance quantization of metal and is characterized by the Euler characteristic $\chi_F$, offering a new perspective in the study of topological quantum matter. Here, we discover that characterizing Fermi sea topology solely by $\chi_F$ is insufficient: Fermi seas with iden

Benedetta Facciotti, Marta Mazzocco, Nikita Nikolaev

The focus of this paper is the study of the moduli space of representations of fundamental groupoids of surfaces $\Sigma$ with boundaries with values in $G:=GL_n(\mathbb C)$. In absence of marked points on the boundary, this moduli space is realized in many equivalent ways: as the moduli space of linear local systems on $\Sigma$, as the moduli space of repre

Meysam Masoudi, Milad Ganjalizadeh, Tahar Zanouda, Pal Frenger

Energy consumption is a significant concern for mobile network operators, and to enable further network energy improvements it is also an important target when developing the emerging 6G standard. In this paper we show that, despite the existence of many energy-saving features in 5G new radio (NR) networks, activating them in isolation yields only suboptimal

Xuejun Cheng, Qian Zhang, Yunnuo Xu, Zheng Dong

Reconfigurable intelligent surface (RIS)-aided rate-splitting multiple access (RSMA) systems have demonstrated remarkable potential in enhancing spectral efficiency. However, most existing works rely on ideal hardware, which is unrealistic.In practical deployments, RIS elements suffer from amplitude-phase coupling, where transceivers are subject to hardware

Pierre Chantelot, Samuel Croquette, Fabrice Lemoult

Identifying a universal material constitutive law, that describes the mechanical response of rubber-like solids for all deformation fields and achievable extensions, is an outstanding challenge. Here, we propose to exploit the propagation of elastic waves and demonstrate that monitoring incremental guided wave propagation in an elastomer plate undergoing uni

Zhouting Zhao, Tin Lok James Ng

Striking an optimal balance between predictive performance and fairness continues to be a fundamental challenge in machine learning. In this work, we propose a post-processing framework that facilitates fairness-aware prediction by leveraging model ensembling. Designed to operate independently of any specific model internals, our approach is widely applicabl

Rahul Halder, Arash Hajisharifi, Kabir Bakhshaei, Gianluigi Rozza

We investigate model order reduction (MOR) strategies for simulating unsteady hemodynamics within cerebrovascular systems, contrasting a physics-based intrusive approach with a data-driven non-intrusive framework. High-fidelity 3D Computational Fluid Dynamics (CFD) snapshots of an idealised basilar artery bifurcation are first compressed into a low-dimension

Sulbha Jain, Shivani Tripathi, Shi Qiao, Alekh Jindal

Business users need to search enterprise databases using natural language, just as they now search the web using ChatGPT or Perplexity. However, existing benchmarks -- designed for open-domain QA or text-to-SQL -- do not evaluate the end-to-end quality of such a search experience. We present an evaluation framework for structured database search that generat