March 2026 arXiv papers — page 123

Hong Jeong

Frozen encoder--decoder language models are stateless: the latent representation is discarded after every forward pass, so no information persists across sessions. This paper presents a \textbf{proof-of-concept pilot study} showing that persistent memory in the \emph{continuous latent space} of a frozen LLM is feasible -- even under severe resource constrain

Zheng Sun, Fan Xu, Jiayi Li, Yifan Jiang

Moir\'e superlattices formed by semiconducting transition metal dichalcogenides (TMDs) provide a highly tunable platform for investigating strongly correlated and topological quantum phases. As a prototypical example, twisted bilayer MoTe2 (tMoTe2) has been shown to host fractional topological phases, such as zero-field fractional Chern insulators (FCIs) exh

Abhishek Kumar, Aashraya Sachdeva

Entity recognition in Automatic Speech Recognition (ASR) is challenging for rare and domain-specific terms. In domains such as finance, medicine, and air traffic control, these errors are costly. If the entities are entirely absent from the ASR output, post-ASR correction becomes difficult. To address this, we introduce RECOVER, an agentic correction framewo

Hammadi Abidi, Guilong Gui, Ping Zhang

We establish the global well-posedness for two-dimensional inhomogeneous, incompressible, anisotropic Navier-Stokes systems. Two specific models are analyzed: one with partial dissipation (referred to as (AINS)) and one with only horizontal dissipation (referred to as (HINS)), under the assumption that the initial density is bounded away from zero and infini

Nicolas Curien, Xingjian Hu, Dongjian Qian

Recently introduced and studied in arXiv:2407.07888, a self-similar Markov tree (ssMt) is a random decorated tree that vastly generalises the fragmentation tree. We study here the critical case that was left aside in arXiv:2407.07888. Borrowing techniques from branching random walk, in particular the recent result of A\"id\'ekon--Hu--Shi arXiv:2409.01048, we

Oscar Pang, Lisa Coiffard, Paul Templier, Luke Beddow

Transferring heavy payloads in maritime settings relies on efficient crane operation, limited by hazardous double-pendulum payload sway. This sway motion is further exacerbated in offshore environments by external perturbations from wind and ocean waves. Manual suppression of these oscillations on an underactuated crane system by human operators is challengi

Finnur Ágúst Ingimundarson, Steinunn Rut Friðriksdóttir, Bjarki Ármannsson, Iris Edda Nowenstein

This paper evaluates current Large Language Model (LLM) benchmarking for Icelandic, identifies problems, and calls for improved evaluation methods in low/medium-resource languages in particular. We show that benchmarks that include synthetic or machine-translated data that have not been verified in any way, commonly contain severely flawed test examples that

Guangsheng Zhang, Huan Tian, Leo Zhang, Tianqing Zhu

Semantic segmentation models are widely deployed in safety-critical applications such as autonomous driving, yet their vulnerability to backdoor attacks remains largely underexplored. Prior segmentation backdoor studies transfer threat settings from existing image classification tasks, focusing primarily on object-to-background mis-segmentation. In this work

Protopopov Alexey

Automatic speech recognition systems based on neural networks are vulnerable to adversarial attacks that alter transcriptions in a malicious way. Recent works in this field have focused on making attacks work in over-the-air scenarios, however such attacks are typically detectable by human hearing, limiting their potential applications. In the present work w

Lilika Makabe, Heng Guo, Hiroaki Santo, Fumio Okura

This paper describes a linear solution method for near-light photometric stereo by exploiting symmetric light source arrangements. Unlike conventional non-convex optimization approaches, by arranging multiple sets of symmetric nearby light source pairs, our method derives a closed-form solution for surface normal and depth without requiring initialization. I

Gabriel Gomez

Constructing spacetime solutions that describe black holes embedded in dark matter environments is a crucial step toward probing the properties of dark matter in the strong-field regime of gravity. At present, however, there is no unique or systematic framework to model such configurations, and several commonly adopted approaches raise methodological ambigui

Shuai Shao, Ye Tian, Shangshang Yang, Xingyi Zhang

Constrained multi-objective optimization problems (CMOPs) are of great significance in the context of practical applications, ranging from scientific to engineering domains. Most existing constrained multi-objective evolutionary algorithms (CMOEAs) usually employ fixed operators all the time, which exhibit poor versatility in handling various CMOPs. Therefor

Kunal Rai, Archi Roy, Itai Dattner, Soudeep Deb

Over the last decade, nonparametric methods have gained increasing attention for modeling complex data structures due to their flexibility and minimal structural assumptions. In this paper, we study a general multivariate nonparametric regression framework that encompasses a broad class of parametric models commonly used in financial econometrics. Both the r

Sarvesh Ravichandran Iyer, Vivek Kumar

In this paper, we study a linear control system with a given state feedback law. The system is influenced by rapid random sampling occurring at frequency $\frac 1n, n \in \mathbb N$, as well as by white noise of small intensity $\varepsilon \in (0, 1]$. We study the behavior of the system as $n \to \infty$ and $\varepsilon \searrow 0$ jointly, and prove that

Kewal Anand, Ranjeev Misra, J. S. Yadav, Pankaj Jain

We present a time-resolved analysis of the persistent emission in 4U 1728--34 using AstroSat observations from 2016 to 2019. We detect kilohertz quasi-periodic oscillations (kHz QPOs) during all epochs, with centroid frequencies ranging from $\sim 350$ to $1180~\mathrm{Hz}$, although some detections are of lower significance ($< 3\sigma$). We model the simul

FANAR TEAM, Ummar Abbas, Mohammad Shahmeer Ahmad, Minhaj Ahmad

We present Fanar 2.0, the second generation of Qatar's Arabic-centric Generative AI platform. Sovereignty is a first-class design principle: every component, from data pipelines to deployment infrastructure, was designed and operated entirely at QCRI, Hamad Bin Khalifa University. Fanar 2.0 is a story of resource-constrained excellence: the effort ran on 256

Stuart E. Anderson

We construct an infinite family of 6-regular graphs $\{G_n\}_{n\ge 3}$ by taking $n$ copies of the Petersen graph and wiring corresponding vertices according to an $n$-cycle permutation. Each $G_n$ has $10n$ vertices, $30n$ edges, and automorphism group $D_{5n}$ of order $10n$, acting with two vertex orbits of size $5n$. The graphs have girth $4$ and diamete

Yan-Qing Xu, Rui-Feng Zheng, Yu-Peng Zhang, Cheng-Yong Zhang

The weak cosmic censorship conjecture plays a foundational role in classical gravity by asserting that spacetime singularities are generically hidden behind event horizons. In this work, we explore its robustness in the Einstein-Maxwell-Scalar theory with fractional coupling by studying both static black hole solutions and their fully nonlinear dynamical evo

Stephen Wiggins

In classical dynamical systems, chaotic behavior is often associated with exponential sensitivity to initial conditions together with global phase-space structure. Translating this geometric concept to the strictly linear framework of quantum mechanics presents a conceptual puzzle. The out-of-time-ordered correlator (OTOC) is often motivated as the quantum a

Jishen Peng, Enze Jiang, Zheng Ma, Xiongbin Yan

We develop a robust physics-guided diffusion framework for full-waveform inversion that combines a score-based generative prior with likelihood guidance computed through wave-equation simulations. We adopt a transport-based data-consistency potential (Wasserstein-2), incorporating wavefield enhancement via bounded weighting and observation-dependent normaliz

Stathis Galanakis, Alexandros Koliousis, Stefanos Zafeiriou

Despite recent advances in deep generative modeling, skin lesion classification systems remain constrained by the limited availability of large, diverse, and well-annotated clinical datasets, resulting in class imbalance between benign and malignant lesions and consequently reduced generalization performance. We introduce DermaFlux, a rectified flow-based te

Llorenç Melis, Roberto Soler

Solar prominence threads are typically located around magnetic dips, where cold and dense plasma is suspended against gravity in the hot corona thanks to the upward magnetic force. Because prominences are partially ionized, ambipolar diffusion can deposit part of the energy of their non-force-free magnetic field into the plasma. This ambipolar heating may th

Qianyu Yang, Haiyang Zhang, Francesco Guidi, Anna Guerra

The synergy between extremely large-scale antenna arrays and terahertz technology in sixth-generation networks establishes a near-field wideband transmission environment, enabling the generation of highly focused beams. To leverage this capability for multi-source localization, we propose a direct localization method based on the curvature-of-arrival of sphe

Peter Wegmann, Aleksandra Świerkowska, Emmanouil Giortamis, Pramod Bhatotia

As quantum computing advances toward fault-tolerance through quantum error correction, modular chiplet architectures have emerged to provide the massive qubit counts required while overcoming fabrication limits of monolithic chips. However, this transition introduces a critical compilation gap: existing frameworks cannot handle the scale of fault-tolerant qu

Vasudevarao Allu, Raju Biswas, Rajib Mandal

Let $\mathcal{A}$ denote the class of all analytic functions $f$ in the unit disk $\mathbb{D}:=\{z\in\mathbb{C}: |z|<1\}$ such that $f(0)=f'(0)-1=0$. In this paper, we introduce a new subclass $\mathcal{C}_\theta(\gamma)$ of $\mathcal{A}$ consisting of functions $f$ that satisfy the relation \[ \textrm{Re}\left(e^{i\theta}\left(1+\frac{zf''(z)}{f'(z)}\right)

N. Belousov, S. Derkachov, S. Khoroshkin

In the previous paper we derived Gauss-Givental integral representation for the wave functions of quantum BC Toda chain and also introduced Baxter operators for this model. In the present paper we prove commutativity of Baxter operators, as well as show that the constructed wave functions are symmetric with respect to signed permutations of spectral paramete

Zhiwei Chen, Youjun Lu, Yuchao Luo, Jihui Zhang

Binary neutron stars (BNSs) are one of the most important gravitational wave (GW) sources, which provide key insights to evolution of massive binary stars and nuclear physics. Beyond Laser Interferometer Space Antenna (LISA), Taiji, and Tianqin missions, proposed concepts for next generation space-based GW observatories, including LISAmax, Folkner, and eASTR

Zhan Tong, ChenXu Zhou, Fei Tang, Yiming Tu

Defense Meteorological Satellite Program (DMSP-OLS) and Suomi National Polar-orbiting Partnership (SNPP-VIIRS) nighttime light (NTL) data are vital for monitoring urbanization, yet sensor incompatibilities hinder long-term analysis. This study proposes a cross-sensor calibration method using Contrastive Unpaired Translation (CUT) network to transform DMSP da

Yusuke Nishii, Hiroaki Kawashima

This study investigates a method to guide and control fish schools using virtual fish trained with reinforcement learning. We utilize 2D virtual fish displayed on a screen to overcome technical challenges such as durability and movement constraints inherent in physical robotic agents. To address the lack of detailed behavioral models for real fish, we adopt

Roman Chertovskih, Nikolay Pogodaev, Maxim Staritsyn, A. Pedro Aguiar

We address optimal control of semilinear evolution equations on Banach spaces with finitely many control channels, a framework encompassing a broad class of infinite-dimensional dynamical systems, arising in many applications. For this setting, we derive an exact and global formula quantifying the increment of the cost functional with respect to an arbitrary

Deng Liu, Song Chen

Hardware faults, specifically bit-flips in quantized weights, pose a severe reliability threat to Large Language Models (LLMs), often triggering catastrophic model collapses. We demonstrate that this vulnerability fundamentally stems from the spatial alignment between sensitive weight bits and extreme activation outliers, which causes a single hardware fault

Harsha Sreekumar, E. Harikumar

In this study, we analyse how quantisation of space-time affects propagating fermions in the presence of gravity. Effect of gravity is incorporated using spin connection which consists of universal torsion-free Levi-Civita connection and the Contortion tensor. This leads to the appearance of a four-fermion interaction term in the Lagrangian and because of no

N. Belousov, S. Derkachov, S. Khoroshkin

We obtain Gauss-Givental integral representation for the eigenfunctions of quantum Toda chain with boundary interaction of BC type. For this we introduce reflection operator satisfying reflection equation with DST chain Lax matrices. Besides, we define Baxter operators for BC Toda chain, prove their commutativity with Hamiltonians and derive the correspondin

Mst. Irin Naher, A. F. M. Yusuf Haider, Dholon Kumar Paul, Md Lutfor Rahman

We present a density functional theory investigation of the pressure dependent structural, electronic, mechanical, thermophysical, vibrational, and optical properties of the intermetallic Kagome compound ZrRe2. The calculated ground-state structural parameters are in excellent agreement with available experimental results. The estimated structural parameters

Robin Kouba, Vincent Neiger, Mohab Safey El Din

We provide a new complexity bound for the computation of grevlex Gr\"obner bases in the generic zero-dimensional case, relying on Moreno-Soc\'ias' conjecture. We first formalize a property of regular sequences that implies a well-known folklore consequence, which we call the increasing degree property. We then derive a new understanding of the selection of p

Debdas Paul, Elisa Ferrari, Irene Gravili, Alessandro Cellerino

Chronological age predictors often fail to achieve out-of-distribution (OOD) gen- eralization due to exogenous attributes such as race, gender, or tissue. Learning an invariant representation with respect to those attributes is therefore essential to improve OOD generalization and prevent overly optimistic results. In predic- tive settings, these attributes

David Orlando Salazar Torres, Diyar Altinses, Andreas Schwung

Neural network architectures designed for function parameterization, such as the Bag-of-Functions (BoF) framework, bridge the gap between the expressivity of deep learning and the interpretability of classical signal processing. However, these models are inherently sensitive to parameter initialization, as traditional data-agnostic schemes fail to capture th

Matthew Earnshaw, Chad Nester, Mario Román

Effectful categories have two classes of morphisms: pure morphisms, which form a monoidal category; and effectful morphisms, which can only be combined monoidally with central morphisms (such as the pure ones), forming a premonoidal category. This suggests seeing morphisms of an effectful category as carrying a grade that combines under the monoidal product

Min Wu, Lingxiao Li, Yunze Ouyang, Yifan Jiang

Twisted bilayer MoTe2 is a prototypical moire material in which long-wavelength superlattices amplify electron correlations, enabling a wealth of emergent quantum phases. To date, experimental efforts have focused primarily on small twist angles (typically smaller than 4deg ), whereas the larger-angle regime-where moire bands become more dispersive and corre

Yunpeng Qu, Kaidong Zhang, Yukang Ding, Ying Chen

Visual generative models based on latent space have achieved great success, underscoring the significance of visual tokenization. Mapping images to latents boosts efficiency and enables multimodal alignment for scaling up in downstream tasks. Existing visual tokenizers primarily map images into fixed 2D spatial grids and focus on pixel-level restoration, whi

Zhisong Wang, Ziyang Chen, Zanting Ye, Hongze Zhu

Medical visual question answering (Med-VQA) aims to answer clinically relevant questions grounded in medical images. However, existing multimodal large language models (MLLMs) often exhibit shortcut answering, producing plausible responses by exploiting language priors or dataset biases while insufficiently attending to visual evidence. This behavior undermi

Yanlin Bai, Yangjian Cai, Chunhao Liang, Sergey A. Ponomarenko

Paraxial wave packets with discrete spatial, temporal, or spatiotemporal spectra are known to undergo periodic axial revivals on propagation in either free space or linear transparent, weakly dispersive media. Such spectacular revivals, ubiquitously encountered in physics, from optics and acoustics to condensed matter physics, are distinguished by their stri

Andrea Moleri, Christian Internò, Ali Raza, Markus Olhofer

Federated Learning (FL) enables distributed optimization without compromising data sovereignty. Yet, where local label distributions are mutually exclusive, standard weight aggregation fails due to conflicting optimization trajectories. Often, FL methods rely on pretrained foundation models, introducing unrealistic assumptions. We introduce FederatedFactory,

Vasudevarao Allu, Raju Biswas, Rajib Mandal, Hiroshi Yanagihara

In this paper, we investigate several Bohr radii associated with the Ces\'aro operator, Bernardi integral operator, $\beta$-Ces\'aro operator, and discrete Fourier transform, all defined on a set of holomorphic mappings from the unit ball of a complex Banach space into the closure of the unit polydisc $\mathbb{D}^n$ within the space $\mathbb{C}^n$.

Adrien Jacquet Crétides, Mouad Abrini, Hamed Rahimi, Mohamed Chetouani

Striking a balance between efficiency and transparent motion is a core challenge in human-robot collaboration, as highly expressive movements often incur unnecessary time and energy costs. In collaborative environments, legibility allows a human observer a better understanding of the robot's actions, increasing safety and trust. However, these behaviors resu

Yong Il Choi

Dropout is a representative regularization technique that stochastically deactivates hidden units during training to mitigate overfitting. In contrast, standard inference executes the full network with dense computation, so its goal and mechanism differ from conditional computation, where the executed operations depend on the input. This paper organizes Dyna

Qinhong Lin, Ruitao Feng, Yinglun Feng, Zhenxin Huang

We study alpha factor mining, the automated discovery of predictive signals from noisy, non-stationary market data-under a practical requirement that mined factors be directly executable and auditable, and that the discovery process remain computationally tractable at scale. Existing symbolic approaches are limited by bounded expressiveness, while neural for

Kosuke Higuchi, Ryotaro Kobayashi

Ransomware continues encrypting files during the delay between attack onset and detection. ROFBS mitigates this problem by backing up pre-modification files in real time upon file-open events. However, because the Linux file-open path traverses multiple kernel functions, it remains unclear how the choice of hook point affects defense effectiveness. In this s

Yiqiang Zhou, Yifan Chen, Zhe Sun, Jijun Lu

Underwater image enhancement plays a crucial role in providing reliable visual information for underwater platforms, since strong absorption and scattering in water-related environments generally lead to image quality degradation. Existing high-performance methods often rely on complex architectures, which hinder deployment on underwater devices. Lightweight

Ruizhi Wang, Weihan Li, Zunlei Feng, Haofei Zhang

Real-time, high-fidelity monocular depth estimation from remote sensing imagery is crucial for numerous applications, yet existing methods face a stark trade-off between accuracy and efficiency. Although using Vision Transformer (ViT) backbones for dense prediction is fast, they often exhibit poor perceptual quality. Conversely, diffusion models offer high f

Ioannis Apergis, Daniel Bayliss, Paul Chote, James McCormac

Modern scientific CMOS cameras offer very fast readout speeds and low read noise. In this study, we evaluate the performance of the Andor Marana CMOS camera through on-sky testing carried out at the NGTS facility at the ESO Paranal Observatory in Chile. We mount the Marana camera to an NGTS telescope, and conduct photometric observations of bright stars. In

Xiruo Wang, Xinyi Jiang, Ziqi Lyu

Generative AI has made visual storytelling widely accessible, yet current prompt-based interactions often force users into a trade-off between precise control and creative flow. We present One Kiss, a co-creative comic generation system that introduces "Affective Steering". Instead of writing text prompts, users guide the tone of their story through emoji in

Shu Kawaguchi, Fabien Pazuki

We show that there are uncountably many algebraic extensions of $\mathbb{Q}$ containing at most finitely many moduli of CM simple principally polarized abelian varieties of any fixed dimension $g\geqslant1$, generalizing a result of Hultberg in dimension 1.

Sergio Barrachina-Muñoz, Marc Carrascosa-Zamacois, Horacio Bleda, Umair Riaz

This paper presents Plaza6G, the first operational Experiment-as-a-Service (ExaS) platform unifying cloud resources with next-generation wireless infrastructure. Developed at CTTC in Barcelona, Plaza6G integrates GPU-accelerated compute clusters, multiple 5G cores, both open-source (e.g., Free5GC) and commercial (e.g., Cumucore), programmable RANs, and physi

Arindam Jana, Amiya Mondal

Let $F$ be a non-archimedean local field of odd residual characteristic $p$. The depth of a smooth representation of ${\rm GL}_n(F)$ is an invariant of Local Langlands Correspondence (LLC). The analogous notion on the Galois side of LLC is known as the slope of a local Galois representation. The slope is well related to the Swan conductor for irreducible Gal

Hanif Rahman

We present PashtoCorp, a 1.25-billion-word corpus for Pashto, a language spoken by 60 million people that remains severely underrepresented in NLP. The corpus is assembled from 39 sources spanning seven HuggingFace datasets and 32 purpose-built web scrapers, processed through a reproducible pipeline with Arabic-script tokenization, SHA-256 deduplication, and

Chengxi Li, Ming Xiao, Mikael Skoglund

Communication bottlenecks and the presence of stragglers pose significant challenges in distributed learning (DL). To deal with these challenges, recent advances leverage unbiased compression functions and gradient coding. However, the significant benefits of biased compression remain largely unexplored. To close this gap, we propose Compressed Gradient Codi

Tomomi Ogawa, Hiroki Matsumoto

Identifiability is a central issue in blind source separation (BSS), determining whether latent sources can be uniquely recovered from observed mixtures. Classical approaches address identifiability either by exploiting source non-Gaussianity via higher-order statistics (HOS) or by enriching the observation structure through temporal, spatial, or multi-chann

S. Yatawatta, A. Ahmadi, B. Asabere, M. Iacobelli

Radio astronomy relies heavily on efficient and accurate processing pipelines to deliver science ready data. With the increasing data flow of modern radio telescopes, manual configuration of such data processing pipelines is infeasible. Machine learning (ML) is already emerging as a viable solution for automating data processing pipelines. However, almost al

Tobias Cloosters, Pascal Winkler, Jens-Rene Giesen, Ghassan Karame

Solana is rapidly gaining traction among smart contract developers and users. However, its growing adoption has been accompanied by a series of major security incidents, which have spurred research into automated analysis techniques for Solana smart contracts. Unfortunately, existing approaches do not address the unique and complex account model of Solana. I

Guang Yang, Xinling Tang, Zhongkang Lin, Yulin Gu

Cu-diamond composites are recognized as promising high-thermal-conductivity candidates for electronic cooling, offering tunable properties and competitive cost. However, their performance is significantly limited by the poor Cu/diamond interfacial thermal conductance (ITC). Here, we propose a nitrogen-termination strategy to tune the ITC of Cu/diamond interf

Chengxi Li, Youssef Allouah, Rachid Guerraoui, Mikael Skoglund

In this paper, we study the problem of distributed training (DT) under Byzantine attacks with communication constraints. While prior work has developed various robust aggregation rules at the server to enhance robustness to Byzantine attacks, the existing methods suffer from a critical limitation in that the solution error does not diminish when the local gr

Hayley Camilleri, Alessio Magro, Andrea Geminardi, Giovanni Naldi

FRB search pipelines are being developed to operate under strict real-time constraints while maintaining sensitivity to short-duration transient signals. In incoherent dedispersion based pipelines such as Heimdall, apart from observation bandwidth and number of beams, detection performance and computational throughput are strongly dependent on the choice of

Daniel Sungho Jung, Dohee Cho, Kyoung Mu Lee

Understanding humans from LiDAR point clouds is one of the most critical tasks in autonomous driving due to its close relationships with pedestrian safety, yet it remains challenging in the presence of diverse human-object interactions and cluttered backgrounds. Nevertheless, existing methods largely overlook the potential of leveraging human-object interact

Isha Andrade, Shalaka S Mahadik, Mithun Mukherjee, Pranav M Pawar

The proliferation of large-scale IoT networks has been both a blessing and a curse. Not only has it revolutionized the way organizations operate by increasing the efficiency of automated procedures, but it has also simplified our daily lives. However, while IoT networks have improved convenience and connectivity, they have also increased security risk due to

Xinhao Cai, Liulei Li, Gensheng Pei, Zeren Sun

Object detection in remote sensing images (RSIs) is challenged by the coexistence of geometric and spatial complexity: targets may appear with diverse aspect ratios, while spanning a wide range of object sizes under varied contexts. Existing RSI backbones address the two challenges separately, either by adopting anisotropic strip kernels to model slender tar

Xinhao Cai, Gensheng Pei, Zeren Sun, Yazhou Yao

In this paper, we propose \textbf{Iris}, a deterministic framework for Monocular Depth Estimation (MDE) that integrates real-world priors into the diffusion model. Conventional feed-forward methods rely on massive training data, yet still miss details. Previous diffusion-based methods leverage rich generative priors yet struggle with synthetic-to-real domain

Sung-Soo Byun, Yong-Woo Lee, Seungjoon Oh

We consider the elliptic Ginibre ensembles in the real, complex and symplectic symmetry classes. As the matrix size tends to infinity, we derive the asymptotic behaviour of the upper tail large deviation probabilities for both the spectral radius and the rightmost eigenvalue. More generally, we obtain asymptotic formulas for the probability that an eigenvalu

Maxime Vaillant, Axel Carlier, Lai Xing Ng, Christophe Hurter

Event-based vision sensors provide significant advantages for high-speed perception, including microsecond temporal resolution, high dynamic range, and low power consumption. When combined with Spiking Neural Networks (SNNs), they can be deployed on neuromorphic hardware, enabling energy-efficient applications on embedded systems. However, this potential is

Wojciech Roga

We show that in an undirected graph under degree biased sampling the expected degree of vertices is equal to the expected degree of their neighbors. In consequence, under the biased sampling the social network result known as the friendship paradox disappears. The identity is equivalent to the existence of a stationary state of a random walk on the graph or

Bruce W. Wilson, David A. Robb, Mei Yii Lim, Helen Hastie

We set out to study whether task-based narratives could influence long-term engagement with a service robot. To do so, we deployed a Robo-Barista for five weeks in an over-50's housing complex in Stockton, England. Residents received a free daily coffee by interacting with a Furhat robot assigned to either a narrative or non-narrative dialogue condition. Des

Jia Qing Yap

We train nine sparse autoencoders (SAEs) on the residual stream of Qwen 3.5-35B-A3B, a 35-billion-parameter Mixture-of-Experts model with a hybrid GatedDeltaNet/attention architecture, and use them to identify and steer five agentic behavioral traits. Our method trains linear probes on SAE latent activations, then projects the probe weights back through the

Jens Markus Melenk, David Wörgötter

We consider the time-harmonic Maxwell equations at a nonzero wavenumber $k\in\mathbb{C}$ on a bounded and simply connected Lipschitz domain $\Omega$ with an analytic boundary $\Gamma$, on which we impose impedance boundary conditions. We suppose that the (possibly complex-valued) permeability and permittivity tensor fields $\boldsymbol{\mu}^{-1}$ and $\bolds

Aleksei Adadurov, Sergey Barseghyan, Anton Chtepine, Antero Eloranta

We study optimal auction design for Maximum Extractable Value (MEV) auction markets on Ethereum. Using a dataset of 2.2 million transactions across three major orderflow providers, we establish three empirical regularities: extracted values follow a log-normal distribution with extreme right-tail concentration, competition intensity varies substantially acro

Rishi Kumar, Wataru Takeda

Let $K$ be a number field with ring of integers $\mathcal{O}$. Two lattice points ${\bf x, y}\in \mathcal{O}^m$ with $m\geq 2$ are said to be visible from one another if $\gcd((x_i-y_i),\ldots, (x_m-y_m))=\mathcal{O}$, where $(x_i-y_i)$ is the ideal generated by $x_i-y_i$. Let $S\subset \mathcal{O}^m$ be a finite set. For $K=\mathbb{Q}$, the asymptotic densi

Ann Rachel, Pranav M Pawar, Mithun Mukharjee, Raja M

Lung cancer is a condition where there is abnormal growth of malignant cells that spread in an uncontrollable fashion in the lungs. Some common treatment strategies are surgery, chemotherapy, and radiation which aren't the best options due to the heterogeneous nature of cancer. In personalized medicine, treatments are tailored according to the individual's g

Shunsuke Yasunaga, Kenta Yoshimura, Akio Tomiya, Yuki Nagai

We study a parameter optimization of domain-wall fermions to improve chiral symmetry based on machine learning. Domain-wall fermions involve coefficients along the fifth dimension, which can be treated as trainable parameters to reduce the chiral symmetry violation caused by the finite extent of the fifth dimension. As the loss function, we use the residual

Shuo Shao, Tianchen Huang, Wei Gao, Shiwu Zhang

Agile humanoid locomotion in complex 3D en- vironments requires balancing perceptual fidelity with com- putational efficiency, yet existing methods typically rely on rigid sensing configurations. We propose ADAPT (Adaptive dual-projection architecture for perceptive traversal), which represents the environment using a horizontal elevation map for terrain geo

Uzay Çetin, Ergun Yalcin

We construct worst-case examples for the standard reduction algorithm for computing persistent homology. Our constructions are similar to the worst-case examples introduced by Morozov, but we replace the single-triangle arrangement with a strip of base and fin triangles. This structure allows us to give an explicit algorithm for their construction and to per

Marcos Galdino, Johanna Grahl, Tobias Hamann, Anas Abdelrazeq

Large Language Models-Cognitive Assistants (LLM-CAs) can enhance Quality Management Systems (QMS) in manufacturing, fostering continuous process improvement and knowledge management. However, there is no human-centred software architecture focused on QMS that enables the integration of LLM-CAs into manufacturing in the current literature. This study addresse

Kazumasa Narita

For a closed Riemannian manifold $(M,g)$ of dimension $n$, let $\lambda_{1}(g)$ be the first positive eigenvalue of the Laplace--Beltrami operator $\Delta_{g}$ and $\mbox{Vol}(M,g)$ the volume of $(M, g)$. Considering the scale-invariant quantity $\lambda_{k}(g)\mbox{Vol}(M,g)^{2/n}$ as a functional over all the metrics in a fixed conformal class, we derive

Dario Spirito

Let $G$ be a one-dimensional $\ell$-subgroup of the group $\mathcal{F}(X,\mathbb{Z})$ of integer-valued functions on a set $X$. We show that $G$ is free under some hypothesis on the spectrum of $G$ and on its quotient groups at the prime ideals. We translate this result in the context of the study of freeness of the group $\mathrm{Inv}(D)$ of invertible idea

Cyrille Yetuyetu Kesiku, Begonya Garcia-Zapirain

Attributing performance gains in quantum machine learning to genuine quantum resources rather than to classical architectural scaling remains an open methodological challenge. We address this by introducing a counterfactual causal mediation framework that decomposes inter-architectural performance differences into direct effects, attributable to circuit para

Judith Senn, Aljosha Judmayer, Nicholas Stifter, Rainer Böhme

Central Bank Digital Currencies (CBDCs) are proposed as a public response to the uptake of privately run digital payments, with the digital euro, under development by the European Central Bank (ECB), serving as a prominent example. This momentum provides a unique opportunity to fundamentally rethink the future of money, and, assuming wide adoption, to establ

Bouazza Kacimi, Ahmed Mohammed Cherif, Mustafa Özkan

In this paper, we investigate the classification of $H$-tensional hypersurfaces $M$ in a $4$-dimensional space form $N^4(c)$ of constant sectional curvature $c$. Our results show that minimal hypersurfaces are the only $H$-tensional hypersurfaces in $4$-dimensional space forms, thereby providing an affirmative partial answer to Conjecture 3 proposed in \cite

James Chua, Jan Betley, Samuel Marks, Owain Evans

There is debate about whether LLMs can be conscious. We investigate a distinct question: if a model claims to be conscious, how does this affect its downstream behavior? This question is already practical. Anthropic's Claude Opus 4.6 claims that it may be conscious and may have some form of emotions. We fine-tune GPT-4.1, which initially denies being conscio

Ioan Manolescu, Maran Mohanarangan

The study of the phase transition in planar FK-percolation on the square lattice has seen significant recent breakthroughs. The model undergoes a change in the nature of its phase transition at $q = 4$, transitioning from a continuous to a discontinuous regime. The aim of this article is to investigate the behaviour of the model in the discontinuous regime a

Michel Denuit, Marie Michaelides, Julien Trufin

Autocalibration is known to be an important requirement for insurance premiums since it guarantees that premium income balances corresponding claims, on average, not only at portfolio level but also inside each group paying similar premiums. Also, fairness has become a major concern because unfair treatment may expose insurers to lawsuits or reputational dam

Jakob Stonner

We prove a version of Nagaev's theorem for the branching random walk with heavy-tailed associated random walk. For a branching random walk on $\mathbb{R}$ we consider the random measure $Z_n = \sum_{|u|=n} e^{-V_u} \delta_{V_u}$ where $V_u$, $|u| = n$ denote the positions of the particles in the $n$-th generation. Under the assumption that $\mathbb{E}[Z_1(\c

Victor Chachay

We give a computation of some Euler classes in Chow-Witt groups associated to the count of lines of smooth del Pezzo surfaces of degree 2 and 4. The description of Chow-Witt groups of projective bundles over Grassmannians for vector bundles that are not relatively orientable is the main part of the article. We show that the quadratic count is not enriched as

Taiwang Deng, Chang Huang, Bin Xu, Qixian Zhao

We establish an explicit correspondence of certain Arthur packets between real unitary groups and $p$-adic symplectic or orthogonal groups. This allows one to compute Arthur packets of real unitary groups by translating results from the $p$-adic side. A main ingredient in our proof is an explicit relation between Zuckerman's translation functor on the real s

Trung V. Phan, Tri Gia Nguyen, Thomas Bauschert

This paper presents DeepStage, a deep reinforcement learning (DRL) framework for adaptive and stage-aware defense against Advanced Persistent Threats (APTs). The enterprise environment is formulated as a partially observable Markov decision process (POMDP), in which host provenance and network telemetry are fused into unified provenance graphs. Building on o

Hugo Math

Electronic control units (ECUs) embedded within modern vehicles generate a large number of asynchronous events known as diagnostic trouble codes (DTCs). These discrete events form complex temporal sequences that reflect the evolving health of the vehicle&#39;s subsystems. In the automotive industry, domain experts manually group these codes into higher-level

Elena Provenzano, Guillaume Gastineau, Carlos Mejia, Didier Swingedouw

The North Atlantic Oscillation (NAO) is the dominant mode of atmospheric variability over the North Atlantic sector, influencing temperature and precipitation across Europe. While the NAO's impact on North Atlantic sea surface temperatures (SSTs) is well understood, the NAO can also be driven by SST anomalies. However, this NAO response to SST anomalies is b

Rohit Hegde

Order parameter analysis of the t/U series reveals a uniform altermagnet endemic to the doped Mott insulator, driven by kinetic interactions, occupying a position between the antiferromagnet and hole-doped d-wave superconductor that is normally reserved for the pseudogap. The metastable boundary of the altermagnet punctures and divides the superconductor int

Ling Zhang, Miao Zhou, Alex M. Ganose

Transitioning to solid-state ultraviolet (UV) lighting is critical for reducing global energy utilization to meet net-zero targets. AlGaN-based far-UVC LEDs offer a mercury-free, energy-efficient alternative to conventional mercury lamps, yet their performance is severely bottlenecked by poor carrier injection at Al compositions exceeding 80\%. Point defects

Omnilingual MT Team, Belen Alastruey, Niyati Bafna, Andrea Caciolai

High-quality machine translation (MT) can scale to hundreds of languages, setting a high bar for multilingual systems. However, compared to the world's 7,000 languages, current systems still offer only limited coverage: about 200 languages on the target side, and maybe a few hundreds more on the source side, supported due to cross-lingual transfer. And even

Adam Trybus

We focus on a branch of region-based spatial logics dealing with affine geometry. The research on this topic is scarce: only a handful of papers investigate such systems, mostly in the case of the real plane. Our long-term goal is to analyse certain family of affine logics with inclusion and convexity as primitives interpreted over real spaces of increasing

Heyu Si, Brandon James Denis, Muyang Sun, Dragos Datcu

Recent advancements in 4D scene reconstruction, particularly those leveraging diffusion priors, have shown promise for novel view synthesis in autonomous driving. However, these methods often process frames independently or in a view-by-view manner, leading to a critical lack of spatio-temporal synergy. This results in spatial misalignment across cameras and

Prajjwal Majumder, Broja G. Dutta, Anuj Nandi

We present a comprehensive analysis of Low Frequency Quasi-periodic Oscillation (LFQPO) associated time-lags in the persistently variable black hole binary GRS 1915+105 using 441 ks of \textit{AstroSat} observations from March 2016 to March 2019. LFQPO frequency ($1.38-7.38$ Hz) are detected across the $\theta$, $\beta$, $\rho$, and $\chi$ classes, with the