March 2026 arXiv papers — page 45

Amrutha N Madhusuthanan, Madhuparna Karmakar

We study finite-momentum superconductivity in a two-dimensional $d$-wave altermagnetic superconductor using a non-perturbative Monte Carlo approach beyond mean-field theory. We show that altermagnetism stabilizes a pair density wave (PDW) state without external magnetic fields and enables its survival at finite temperatures with robust phase coherence. Our r

Fan Zhang

Motivated by a one-cycle cosmological scenario where the big bang marks the egress from a Euclidean metric signature regime, we investigate the possibility of black holes (BH) hosting the mirroring entryways. Analogous to the inflationary stage following the exit end, the entry portals must be enveloped by de Sitter cores inside BHs, in order to satisfy regu

Jingyi Wang, Haowei Wang, Nai-Yuan Chiang, Juliane Mueller

Efficient global optimization (EGO) is one of the most widely used noise-free Bayesian optimization algorithms.It comprises the Gaussian process (GP) surrogate model and expected improvement (EI) acquisition function. In practice, when EGO is applied, a scalar matrix of a small positive value (also called a nugget or jitter) is usually added to the covarianc

Younes Salmi, Hanna Bogucka

Deep learning (DL) has been widely studied for assisting applications of modern wireless communications. One of the applications is automatic modulation classification (AMC). However, DL models are found to be vulnerable to adversarial machine learning (AML) threats. One of the most persistent and stealthy threats is the backdoor (Trojan) attack. Nevertheles

Kusal Darshana

Current Large Language Models (LLMs) mostly use BPE (Byte Pair Encoding) based tokenizers, which are very effective for simple structured Latin scripts such as English. However, standard BPE tokenizers struggle to process complex Abugida scripts due to their structural complexity. The problem is that these tokenizers break complex conjuncts, which are multi-

Paolo Guida, Didier Barradas-Bautista

Multiphase flows frequently occur naturally and in manufactured devices. Controlling such phenomena is extremely challenging due to the strongly non-linear dynamics, rapid phase transitions, and the limited spatial and temporal resolution of available sensors, which can lead to significant inaccuracies in predicting and managing these flows. In most cases, n

Zoltán Sóstai

We construct an encoding of finite strings over a fixed finite alphabet as natural numbers, based on a block partition of the Fibonacci sequence. Each position in the string selects one Fibonacci number from a dedicated block, with unused indices between blocks guaranteeing non-adjacency. The encoded number is the sum of the selected Fibonacci numbers, and Z

Sergio Andrés Vallejo-Peña, Antonio Enea Romano, Jonathan Gair

The ratio between the gravitational waves (GW) and electromagnetic waves (EMW) luminosity distance ratio is a key observable that allows to test the nature of gravity, using gravitational waves emitted from compact binary coalescences. We develop a new non parametric method for constraining the GW-EMW distance ratio, in order to perform model independent ana

Younes Salmi, Hanna Bogucka

Deep Learning (DL) has become a key technology that assists radio frequency (RF) signal classification applications, such as modulation classification. However, the DL models are vulnerable to adversarial machine learning threats, such as data manipulation attacks. We study a physical backdoor (Trojan) attack that targets a DL-based modulation classifier. In

Younes Ben Mazziane, Cleque-Marlain Mboulou Moutoubi, Eitan Altman, Francesco De Pellegrini

The Kelly or proportional allocation mechanism is a simple and efficient auction-based scheme that distributes an infinitely divisible resource proportionally to the agents bids. When agents are aware of the allocation rule, their interactions form a game extensively studied in the literature. This paper examines the less explored repeated Kelly game, focusi

Salim Chouaki, Savaiz Nazir, Sandra Siby

YouTube has today become the primary news source for many users, which raises concerns about the role its recommendation algorithm can play in the spread of misinformation and political polarization. Prior work in this area has mainly analyzed how recommendations evolve based on users' watch history within the platform. Nevertheless, recommendations can also

Susanna R. Rozental, Denis A. Kislov, Ilia M. Fradkin, Nikita S. Babich

Optical tweezers confine a particle in an intensity-defined potential well by engaging its local multipoles. In this picture, eliminating far-field scattering from the particle should cancel the optical force, as the multipole moments underpinning the conventional optomechanical response vanish. We show that certain resonant states, such as, e.g., the hybrid

Shota Fujishima

This paper examines the latent functional block structure of Japan's production network using interregional input-output data. To isolate non-trivial production linkages, we first estimate a structural gravity model to account for spatial frictions and economic scale, and then apply a weighted stochastic blockmodel (SBM) to the resulting residual network. Be

Yiyang Peng, Binggui Zhou, Yutong Zheng, Danilo Mandic

Beyond-diagonal reconfigurable intelligent surface (BD-RIS) generalizes the conventional diagonal RIS (D-RIS) by introducing tunable inter-element connections, offering enhanced wave manipulation capabilities. However, realizing the advantages of BD-RIS requires accurate channel state information (CSI), whose acquisition becomes significantly more challengin

Suhyun Jeon, Yumin Lim, Woo-Jeong Baek, Hyeonseo Kim

The objective of constrained motion planning is to connect start and goal configurations while satisfying task-specific constraints. Motion planning becomes inefficient or infeasible when the configurations lie in disconnected regions, known as essentially mutually disconnected (EMD) components. Constraints further restrict feasible space to a lower-dimensio

Lin Jiang, Yu Wang, Yukun Yang, Xuanbing Qiu

Radiative association processes leading to the formation of AgH in cold astrophysical environments are investigated for the first time using full quantum scattering theory. High accuracy potential energy curves and transition dipole moments for the low-lying electronic states (X$^1\Sigma^+$, A$^1\Sigma^+$, $1^1\Pi$, $3^1\Sigma^+$, $2^1\Pi$) are computed empl

Hongru Han, Tingrui Guo, Liming Zhang, Yan Su

Low-light image enhancement (LLIE) has traditionally been formulated as a deterministic mapping. However, this paradigm often struggles to account for the ill-posed nature of the task, where unknown ambient conditions and sensor parameters create a multimodal solution space. Consequently, state-of-the-art methods frequently encounter luminance discrepancies

Simon Kuberski, Alessandro Conigli, Patrick Fritzsch, Antoine Gérardin

We present a determination of the charm- and bottom-quark masses using the heavy-quark step-scaling strategy. Renormalization is performed in small volumes where relativistic bottom quarks can be simulated directly. A sequence of finite-volume simulations connects this calculation to large-volume CLS ensembles, where simulations at physical light and strange

Yoshimichi Ueda

We investigate the rate functions that emerge in our previous works towards large deviation principle for the matrix liberation process driven by the unitary Brownian motion as well as the unitary Brownian motion itself. Our approach is grounded in the viewpoint of the martingale problem. Specifically, we formulate and solve a "free martingale problem" withi

Shu Wan, Saketh Vishnubhatla, Iskander Kushbay, Tom Heffernan

Directed Acyclic Graphs (DAGs) are widely used to represent structured knowledge in scientific and technical domains. However, datasets for real-world DAGs remain scarce because constructing them typically requires expert interpretation of domain documents. We study Doc2SemDAG construction: recovering a preferred semantic DAG from a document together with th

Manuel Hauke

The seminal work of Kurzweil (1955) provides for any fixed badly approximable $\alpha$ and monotonically decreasing $\psi$ a Khintchine-type statement on the set of the inhomogeneous real parameters $\gamma$ for which $\lVert n \alpha + \gamma\rVert \leq \psi(n)$ has infinitely many integer solutions, and further shows that the assumption of $\alpha$ being b

Martiño Rivera-Dourado, Rubén Pérez-Jove, Alejandro Pazos, Jose Vázquez-Naya

Passkeys have recently emerged as a passwordless authentication mechanism, yet their usability in captive portals remains unexplored. This paper presents an empirical, comparative usability study of passkeys and passwords in a Wi-Fi hotspot using a captive portal. We conducted a controlled laboratory experiment with 50 participants following a split-plot des

Fabian Stricker, David Bermbach, Christian Zirpins

Federated Learning (FL) is a paradigm for training machine learning (ML) models in collaborative settings while preserving participants' privacy by keeping raw data local. A key requirement for the use of FL in production is reliability, as insufficient reliability can compromise the validity, stability, and reproducibility of learning outcomes. FL inherentl

Bingfang Li, Songhao Yang, Guosong Wang, Yiwen Hu

In scenarios where synchronous generators (SGs) and grid-following renewable energy sources (GFLR) are co-located, existing research, which mainly focuses on the first-swing stability of SGs, often overlooks ongoing dynamic interactions between GFLRs and SGs throughout the entire rotor swing period. To address this gap, this study first reveals that the angl

Chris J Mitchell, Peter R Wild

Negative avoiding sequences of span $n$ are periodic sequences of elements from $\mathbb{Z}_k$ for some $k$ with the property that no $n$-tuple occurs more than once in a period and if an $n$-tuple does occur then its negative does not. They are a special type of cut-down de Bruijn sequence with potential position-location applications. We establish a simple

Demetrio Lacava

This paper examines how trade policy uncertainty influences the correlation between U.S. stock indices and short-term government bonds. The objective is to assess whether policy-related shocks, especially those linked to trade tensions, alter the traditional stock-T bill relationship and its implications for investors. We extend the Dynamic Conditional Corre

Shigeng Wang, Chao Li, Yangyuxuan Kang, Jiawei Fan

In this paper, we address post-training quantization (PTQ) for large language models (LLMs) from an overlooked perspective: given a pre-trained high-precision LLM, the predominant sequential quantization framework treats different layers equally, but this may be not optimal in challenging bit-width settings. We empirically study the quantization impact of di

Adam Gabet, Sarah Kohn, Guy Lutsker, Shira Gelman

Gait is increasingly recognized as a vital sign, yet current approaches treat it as a symptom of specific pathologies rather than a systemic biomarker. We developed a gait foundation model for 3D skeletal motion from 3,414 deeply phenotyped adults, recorded via a depth camera during five motor tasks. Learned embeddings outperformed engineered features, predi

Xin Liu, Ziqing Xie, Yongjun Yuan, Yong Zhang

This paper investigates the dynamics of spin-2 Bose-Einstein condensates (BECs) with rotation and spin-orbit coupling (SOC). In order to better simulate the dynamics, we present an efficient high-order compact splitting Fourier spectral method. This method splits the Hamiltonian into a linear part, which consists of the Laplace, rotation and SOC terms, and a

Nikola Zlatanov, Amin Gohari, Farzad Shahrivari, Mikhail Rudakov

Classical estimation outputs a single point estimate of an unknown $d$-dimensional vector from an observation. In this paper, we study \emph{$k$-list estimation}, in which a single observation is used to produce a list of $k$ candidate estimates and performance is measured by the expected squared distance from the true vector to the closest candidate. We com

Stefan Frei, Tobias Knoke, Marc C. Steinbach, Anne-Kathrin Wenske

This work develops and analyzes a variational-monolithic unfitted finite element formulation of a linear fluid-structure interaction problem in Eulerian coordinates with a fixed interface. The overall discretization is based on a backward Euler scheme in time and finite elements in space. For the spatial discretization we employ a cut finite element method o

Shikha Rathi, Ulrich D. Jentschura, Paul Indelicato, Ben Ohayon

We compute the ground-state binding energy of muonic $^9$Be in two ways: first, the fully perturbative treatment of the nuclear-size effect often employed in light systems, and second, an approach that accounts for the finite-nuclear-size to all orders (and is inspired by calculations otherwise employed for heavy muonic ions). The results are compared term b

Mudit K. Srivastava, Arijit Maiti, Vipin Kumar, Bhaveshkumar Mistry

ProtoPol is a medium-resolution echelle spectro-polarimeter developed for Physical Research Laboratory (PRL) 1.2m and 2.5m telescopes, Mt. Abu, India. Though initially conceived to evaluate the development methodology of the echelle spectro-polarimeter, it was subsequently elevated to the level of a full-fledged back-end instrument for PRL telescopes. ProtoP

Iasson Karafyllis, Dionysios Theodosis, Miroslav Krstic

In this paper we study the stability properties of the equilibrium point for an age-structured chemostat model with renewal boundary condition and coupled substrate dynamics under constant dilution rate. This is a complex infinite-dimensional feedback system. It has two feedback loops, both nonlinear. A positive static loop due to reproduction at the age-zer

Weijia Li, Haoen Xiang, Tianxu Wang, Shuaibing Wu

Modern autonomous vehicle perception systems are often constrained by occlusions, blind spots, and limited sensing range. While existing cooperative perception paradigms, such as Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I), have demonstrated their effectiveness in mitigating these challenges, they remain limited to ground-level collaboration

Zhuofan Zhang, Herbert Wiklicky

The probabilistic abstract interpretation framework of neural network analysis analyzes a neural network by analyzing its density distribution flow of all possible inputs. The grids approximation is one of abstract domains the framework uses which abstracts concrete space into grids. In this paper, we introduce two novel approximation methods: distribution a

Saskia Kern

We introduce the crisp topology for schemes as a refinement of the fpqc topology. This Grothendieck topology uses the new notion of crisp morphisms, which generalise universal injectivity from ring homomorphisms to arbitrary morphisms of schemes. We study basic properties and demonstrate that this topology is well-behaved.

S. Chattopadhyay, L. Thomarat, C. S. Ong, K. Kargeti

We investigate the pressure-induced evolution of crystal structure and magnetism in the kagome ferromagnet Fe$_3$Sn$_2$ by combining X-ray diffraction, X-ray Emission Spectroscopy, X-ray Magnetic Circular Dichroism, and spin-polarized density functional theory calculations. X-ray diffraction reveals a structural phase transition above $\sim$20~GPa, which coi

Jaehyeok Huh, Chanyong Park

Applying a rule-based holographic method, we investigate the reconstruction of dual gravity theories from the quantum field theory (QFT) data, specifically entanglement entropy. We first derive a three-dimensional black hole geometry from the entanglement entropy of a two-dimensional thermal system. Using the reconstructed solution, we extract various thermo

Nicolás Benjamín Ocampo, Tommaso Caselli, Davide Ceolin

Hateful content online is often expressed using fact-like, not necessarily correct information, especially in coordinated online harassment campaigns and extremist propaganda. Failing to jointly address hate speech (HS) and misinformation can deepen prejudice, reinforce harmful stereotypes, and expose bystanders to psychological distress, while polluting pub

Abhijnan Nath, Hannah VanderHoeven, Nikhil Krishnaswamy

We introduce CRAFT, a multi-agent benchmark for evaluating pragmatic communication in large language models under strict partial information. In this setting, multiple agents with complementary but incomplete views must coordinate through natural language to construct a shared 3D structure that no single agent can fully observe. We formalize this problem as

Yuhan Chen, Pengwen Dai, Chuan Wang, Dayan Wu

Text-video retrieval tasks have seen significant improvements due to the recent development of large-scale vision-language pre-trained models. Traditional methods primarily focus on video representations or cross-modal alignment, while recent works shift toward enriching text expressiveness to better match the rich semantics in videos. However, these methods

Zhuofan Zhang, Herbert Wiklicky

Probabilistic abstract interpretation is a theory used to extract particular properties of a computer program when it is infeasible to test every single inputs. In this paper we apply the theory on neural networks for the same purpose: to analyse density distribution flow of all possible inputs of a neural network when a network has uncountably many or count

Yuanning Chen, Xinxin Yang, Simon Gröblacher

Quantum state transfer between distant nodes is essential for distributed quantum information processing. Existing protocols are typically optimized for specific coupling regimes, such as adiabatic dark-state transfer in the single-mode limit and pitch-and-catch schemes in the multimode regime, leaving the crossover between them without a simple and unified

Gang Hu, Qun Zhang, Jingyao Luo, Yile Jiang

Publicly traded companies must disclose financial information under regulations of the Securities and Exchange Commission (SEC) and the Generally Accepted Accounting Principles (GAAP). The eXtensible Business Reporting Language (XBRL), as an XML-based financial language, enables standardized and machine-readable reporting, but accurate tag selection from lar

M. Gatto, V. Ripepi, M. Bellazzini, C. Tortora

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will produce unprecedentedly deep and wide photometric catalogs, enabling transformative studies of faint stellar systems such as the research of ultra-faint dwarf galaxies (UFDs). A critical challenge for these studies is reliable star-galaxy separation at faint magnitudes, where compact b

R. F. Offer, E. Klantsataya, A. P. Hilton, A. Strathearn

The highest performance atomic clocks are based on interrogation of ultra-narrow optical transitions. There is now significant interest in developing these systems as a source of GNSS-independent time in deployed, dynamic environments. We report on the development and field trial of a portable optical atomic clock interrogating the 10mHz wide $^1$S$_0\righta

Davide Tebaldi, Niccolò Paradisi, Fabio Pini, Luigi Biagiotti

Research on mobile manipulation systems that physically interact with humans has expanded rapidly in recent years, opening the way to tasks which could not be performed using fixed-base manipulators. Within this context, developing suitable control methodologies is essential since mobile manipulators introduce additional degrees of freedom, making the design

André Pscherer, Jannes Liersch, Patrick Abgrall, Andrew D. Beyer

We introduce a multilayer superconducting microwave resonator with sub-Ohm impedance optimized for high coupling strength to single electron spins. The design minimizes the magnetic far-field and therefore achieves a Purcell factor $F_P > 10^{15}$. We show several ways to fabricate this type of resonator and present resonators with an intrinsic $Q$-factor ex

Younes Salmi, Hanna Bogucka

This paper investigates the susceptibility to model integrity attacks that overload virtual machines assigned by the k-means algorithm used for resource provisioning in fog networks. The considered k-means algorithm runs two phases iteratively: offline clustering to form clusters of requested workload and online classification of new incoming requests into o

Md Awsafur Rahman, Chandrakanth Gudavalli, Hardik Prajapati, B. S. Manjunath

Trajectory anomaly detection underpins applications from fraud detection to urban mobility analysis. Dense GPS methods preserve fine-grained evidence such as abnormal speeds and short-duration events, but their quadratic cost makes multi-month analysis intractable; consequently, no existing approach detects anomalies over multi-month dense GPS trajectories.

Alice L. L. Gao, Ya-Xing Li, Yun Li

The inverse Kazhdan--Lusztig polynomial of a matroid was introduced by Gao and Xie, and the inverse $Z$-polynomial of a matroid was introduced by Ferroni, Matherne, Stevens, and Vecchi. In this paper, we study these two polynomials for fan matroids, a family of graphic matroids associated with fan graphs. We first derive the generating functions for the inve

Taolin Han, Shuang Wu, Jinghang Wang, Yuhao Zhou

Large language models (LLMs) hold considerable potential for advancing scientific discovery, yet systematic assessment of their dynamic reasoning in real-world research remains limited. Current scientific evaluation benchmarks predominantly rely on static, single-turn Question Answering (QA) formats, which are inadequate for measuring model performance in co

Jacopo Bilotto, Martin Trulsson, Jean-François Molinari

Athermal elongated particles are well-known to follow Jeffery orbits when sheared in viscous fluids. It is less clear if similar orbits appear in dense granular flows. We show that when sheared for long enough, sufficiently elongated frictionless granular rods, rather than following noisy Jeffery-like orbits, exist in a quasi-equilibrium state, whose orienta

Gregor Baer, Chao Zhang, Isel Grau, Pieter Van Gorp

Explainable AI (XAI) methods are commonly evaluated using functional correctness metrics, sometimes termed faithfulness or fidelity, which estimate how closely an explanation reflects the model's reasoning. Higher correctness is assumed to produce better human understanding, but this link has not been tested with controlled levels. We conducted a user st

Yabin Zhang, Maya Varma, Yunhe Gao, Jean-Benoit Delbrouck

Out-of-distribution (OOD) detection aims to identify samples that deviate from in-distribution (ID). One popular pipeline addresses this by introducing negative labels distant from ID classes and detecting OOD based on their distance to these labels. However, such labels may present poor activation on OOD samples, failing to capture the OOD characteristics.

Qingfeng Li, Haoxian Zhang, Xu He, Songlin Tang

Visual tokenizers play a central role in latent image generation by bridging high-dimensional images and tractable generative modeling. However, most existing tokenizers are still trained with reconstruction-dominated objectives, which often yield latent representations that are only weakly grounded in high-level semantics. Recent approaches improve semantic

Raj Nath Patel, Sourav Dutta

Vector embeddings from pre-trained language models form a core component in Neural Information Retrieval systems across a multitude of knowledge extraction tasks. The paradigm of late interaction, introduced in ColBERT, demonstrates high accuracy along with runtime efficiency. However, the current formulation fails to take into account the attention weights

Taejin Jeong, Joohyeok Kim, Jinyeong Kim, Chanyoung Kim

Spatial Transcriptomics (ST) provides spatially-resolved gene expression, offering crucial insights into tissue architecture and complex diseases. However, its prohibitive cost limits widespread adoption, leading to significant attention on inferring spatial gene expression from readily available whole slide images. While graph neural networks have been prop

Armin Pirastehzad, Bart Besselink

We propose a methodology that exploits the contract formalism to characterize the continuous-time safety control problem, which is often difficult to address, in terms of a discrete-time one, for which numerous efficient solution scheme exist. We construct contracts as pairs of assumptions and guarantees which are set-valued mappings that describe the safe b

Ying Liang, Yi-Da Chu, Shi-Jie Hu, Xue-Feng Zhang

Scanning tunneling microscopy (STM) serves as a powerful pictorial tool for visualizing the local density of states (LDOS) of an individual stripe, which strongly intertwines with superconductivity in the underdoped cuprates. The exotic LDOS map patterns thus appear as the key to uncovering the mystery of the underlying microscopic mechanisms. With the quant

Jiaming Liang, Chi-Man Pun

Despite their great success, deep neural networks rely on high-dimensional, non-robust representations, making them vulnerable to imperceptible perturbations, even in transfer scenarios. To address this, both training-time defenses (e.g., adversarial training and robust architecture design) and post-attack defenses (e.g., input purification and adversarial d

Zhengrui Chen, Zixuan Song, Yu Li, Qi Sun

Large language models and autonomous agents are increasingly explored for EDA automation, but many existing integrations still rely on script-level or request-level interactions, which makes it difficult to preserve tool state and support iterative optimization in real production-oriented environments. In this work, we present FluxEDA, a unified and stateful

M. M. Malamud, H. Neidhardt, V. V. Peller

Recently the authors solved a long-standing problem and showed that for an arbitrary pair of contractions on Hilbert space with trace class difference has an integrable spectral shift function on the unit circle ${\Bbb T}$ and an analogue of the Lifshits--Krein trace formula holds. It is also known that it may happen that there is no real-values integrable s

Hironori Ohigashi, Shinichiro Hamada

Combinatorial optimization problems like the Traveling Salesman Problem are critical in industry yet NP-hard. Neural Combinatorial Optimization has shown promise, but its reliance on online reinforcement learning (RL) hampers deployment and underutilizes decades of algorithmic knowledge. We address these limitations by applying the offline RL framework, Deci

Han Zhang, Guo-Hua Yuan, Chaohao Yuan, Tingyang Xu

Modeling cellular states and predicting their responses to perturbations are central challenges in computational biology and the development of virtual cells. Existing foundation models for single-cell transcriptomics provide powerful static representations, but they do not explicitly model the distribution of cellular states for generative simulation. Here,

Don Yin

What substrate features allow life? We exhaustively classify all 262,144 outer-totalistic binary cellular automata rules with Moore neighbourhood for self-replication and produce phase diagrams in the $(\lambda, F)$ plane, where $\lambda$ is Langton's rule density and $F$ is a background-stability parameter. Of these rules, 20,152 (7.69%) support pattern pro

Guoqian Sun, Xinze Lyu, Bruno Clerckx

Most Rate-Splitting Multiple Access (RSMA) implementations rely on successive interference cancellation (SIC) at the receiver, whose performance is inherently limited by error propagation during common-stream decoding. This paper addresses this issue by developing a SIC-free RSMA receiver based on joint demapping (JD), which directly evaluates bit vectors ov

Tamer Tlas

We demonstrate that the pushforward of the product of Haar measures by the lattice Yang-Mills action concentrates as a Gaussian. It is also sketched how, using this fact, one can recover the strong-coupling expansion.

Pralay Paul, Kusal M. Abeywickrama, Nisha Geng, Mritunjaya Parashar

In highly purified host, the coherence of quantum emitters is ultimately limited by hyperfine interactions between the emitter and lattice nuclei possessing non-zero nuclear magnetic moments. This limitation can only be mitigated through isotopic purification. In this work, we investigate CeO2 as a host composed entirely of nuclei with zero nuclear moment. H

Valentin Bonnet Gibet, Nicola Tosi

Earth's long-lived geodynamo is difficult to reconcile with recent high estimates of the core thermal conductivity, a problem known as the new core paradox. At the same time, the long-term thermal evolution of the mantle remains uncertain, largely due to the poorly constrained onset of modern-style plate tectonics, which marks the transition to efficient

Andrea Buffoni, Giovanni Cupini, Ermanno Lanconelli

We introduce a new flatness index for the boundary of an open subset $\Omega$ of $\mathbb{R}^n$, $n\ge 2$. This index provides a necessary condition for $\partial\Omega$ to be a harmonic pseudosphere and sufficient conditions for a harmonic pseudosphere to be a Euclidean sphere. These conditions will follow from a stability inequality formulated in terms of

Jiaming Liang, Chi-Man Pun

Transformation-based adversarial attacks (TAAs) demonstrate strong transferability when deceiving classification models. However, existing TAAs often perform unsatisfactorily or even fail when applied to structured tasks such as semantic segmentation and object detection. Encouragingly, recent studies that categorize transformations into non-spatial and spat

Sazzad Hossain, Saiful Islam, Muhammad Ibrahim, Md. Rasel Ahmed

Skin diseases are a major public health concern worldwide, and their detection is often challenging without access to dermatological expertise. In countries like Bangladesh, which is highly populated, the number of qualified skin specialists and diagnostic instruments is insufficient to meet the demand. Due to the lack of proper detection and treatment of sk

Jonas Hein, Lilian Calvet, Matthias Seibold, Siyu Tang

Purpose: Accurate detection and 6D pose estimation of surgical instruments are crucial for many computer-assisted interventions. However, supervised methods lack flexibility for new or unseen tools and require extensive annotated data. This work introduces a training-free pipeline for accurate multi-view 6D pose estimation of unseen surgical instruments, whi

Giuseppe Samo, Paola Merlo

This study compares the impact of natural and synthetic data on training and evaluating large language models (LLMs), using the case of passive verb alternation in French and Italian. We use Blackbird Language Matrices (BLMs), structured datasets designed to probe linguistic knowledge of underlying patterns across sentence sets. We compare structured templat

Fanheng Kong, Jingyuan Zhang, Yang Yue, Chenxi Sun

The emergence of Large Language Models (LLMs) has catalyzed a paradigm shift in programming, giving rise to "vibe coding", where users can build complete projects and even control computers using natural language instructions. This paradigm has driven automated webpage development, but it introduces a new requirement about how to automatically verify whether

Ling Wen, Yan-Tao Yang, Qing-Dong Cai

This paper presents a novel and straightforward compact reconstruction procedure for the high-order finite volume method on unstructured grids. In this procedure, we constructed a linear approximation relationship between the mean values and the function values, as well as the derivative values. Compared with the classical compact schemes, which employ a Tay

Arthur Charpentier, Christophe Denis, Romuald Elie, Mohamed Hebiri

Demographic parity (DP) is a widely used group fairness criterion requiring predictive distributions to be invariant across sensitive groups. While natural in classification, full distributional DP is often overly restrictive in regression and can lead to substantial accuracy loss. We propose a relaxation of DP tailored to regression, enforcing parity only a

Qijia Chen, Andrea Bellucci, Giulio Jacucci

Newcomers are crucial for the growth of online communities, yet their successful integration into these spaces requires overcoming significant initial hurdles. Social Virtual Reality (VR) platforms are novel avenues that offer unprecedented online interaction experiences. Unlike well-studied two-dimensional online environments, the pathways to successful new

Danlu Chen, Ka Sing He, Jiahe Tian, Chenghao Xiao

The landscape of extremely low-resource machine translation (MT) is characterized by perplexing variability in reported performance, often making results across different language pairs difficult to contextualize. For researchers focused on specific language groups -- such as ancient languages -- it is nearly impossible to determine if breakthroughs reported

Tan-Hau Nguyen, Thu-Le Tran, Kien Trung Nguyen

Robust Support Vector Machines (R-SVMs) address feature noise by adopting a worst-case robust formulation that explicitly incorporates uncertainty sets into training. While this robustness improves reliability, it also leads to increased computational cost. In this work, we develop safe sample screening rules for R-SVMs that reduce the training complexity wi

Moiz Sadiq Awan, Muhammad Haris Noor, Muhammad Salman Munaf

Automated benchmarks dominate the evaluation of large language models, yet no systematic study has compared user satisfaction, adoption motivations, and frustrations across competing platforms using a consistent instrument. We address this gap with a cross-platform survey of 388 active AI chat users, comparing satisfaction, adoption drivers, use case perform

Faustine Cantalloube, Camille Noûs, A. Jolly, Julien Milli

In 2025, the French Society for Astronomy \& Astrophysics (SF2A), gave the environmental transition commission the opportunity to share their considerations during a plenary session at the annual SF2A conference. This year, the presentation focused on some of the main results obtained from the survey entitled 'French astronomy and astrophysics research activ

Nathan Claudet

It was once conjectured that two graph states are local unitary (LU) equivalent if and only if they are local Clifford (LC) equivalent. This so-called LU-LC conjecture was disproved in 2007, as a pair of 27-qubit graph states that are LU-equivalent, but not LC-equivalent, was discovered. We prove that this counterexample to the LU-LC conjecture is minimal. I

Pengyu Chen, Haotian Sa, Yiwei Hu, Yuhan Cheng

Detecting small unmanned aerial vehicles (UAVs) from a ground-to-air (G2A) perspective presents significant challenges, including extremely low pixel occupancy, cluttered aerial backgrounds, and strict real-time constraints. Existing YOLO-based detectors are primarily optimized for general object detection and often lack adequate feature resolution for sub-p

Demetrio Lacava

This paper introduces a new extension of the Conditional Autoregressive Value at Risk (CAViaR) model aimed at improving tail risk forecasting across assets. The proposed component-based model, CAViaR with Spillover Effects (CAViaR-SE), decomposes the conditional Value at Risk into a proper-risk component and a spillover component driven by a linear combinati

Ziqi Chen, Yi Ren, Yixuan Huang, Qi Sun

Integrating AI into the physical layer is a cornerstone of 6G networks. However, current data-driven approaches struggle to generalize across dynamic environments because they lack an intrinsic understanding of electromagnetic wave propagation. We introduce the Wireless World Model (WWM), a multi-modal foundation framework predicting the spatiotemporal evolu

Christine Tasson, Aymeric Walch

The differential $\lambda$-calculus studies how the quantitative aspects of programs correspond to differentiation and to Taylor expansion inside models of linear logic. Recent work has generalized the axioms of Taylor expansion so they apply to many models that only feature partial sums. However, that work does not cover the classic web based models of K{\"

Dhiman Biswas, Junyeob Song, Francisco Guzman, Levi Brown

Thin films of functional inorganic materials, particularly oxides, play a vital role in optoelectronics, enabling applications that range from active optical components to MEMS-based architectures. Achieving high aspect ratio patterning of these functional materials remains a significant challenge, as many of their constituent elements do not readily form vo

Dağhan Erdönmez, H. Birkan Yilmaz

Transmitter localization in vessel-like molecular communication channels is a fundamental problem with potential applications in healthcare. Existing analytical solutions either assume knowledge of emission time or require multiple closely spaced receivers, which limits their applicability in realistic scenarios. In this letter, we propose a simple closed-fo

P. Yang, A. V. Malyshev, E. A. Prokhorchuk, I. I. Tupitsyn

Calculations of the self-energy corrections to ionization energies of the $3s$, $3p_{1/2}$, and $3p_{3/2}$ states in sodium-like ions with nuclear-charge numbers $Z=30$, $50$, $70$, and $92$ are presented. The calculations are performed using two approaches: the rigorous bound-state QED formalism and the model-QED-operator method. Within the first method, th

Jurrien Keulen, Bayu Jayawardhana, Arjan van der Schaft

This paper presents a port-Hamiltonian formulation of hysteretic energy storage elements. First, we revisit the passivity property of backlash-driven storage elements by presenting a family of storage functions associated to the dissipativity property of such elements. We explicitly derive the corresponding available storage and required supply functions `a

Maria Kaselimi, Anna Belehaki

Coupling constitutes a foundational mechanism in the Earth system, regulating the interconnected physical, chemical, and biological processes that link its spheres. This review examines how emerging artificial intelligence (AI) methods create new opportunities to enhance Earth system coupling and address long-standing limitations in multi-component models. R

Javokhir Sharipov, Pankaj Sheoran, Sanjar Shaymatov

We investigate the optical and radiative signatures of an accretion disk around a Schwarzschild black hole (BH) immersed in a uniform magnetic field. The spacetime geometry is described by the Schwarzschild-Bertotti-Robinson (SBR) metric, which represents the non-rotating sector of the recently discovered Kerr-Bertotti-Robinson exact solution to the Einstein

Jiahao Tian, Chenxi Song, Wei Cheng, Chi Zhang

Generating long videos using pre-trained video diffusion models, which are typically trained on short clips, presents a significant challenge. Directly applying these models for long-video inference often leads to a notable degradation in visual quality. This paper identifies that this issue primarily stems from two out-of-distribution (O.O.D) problems: fram

Zixu Li, Simon Lloyd

We study the random rotation number for random circle homeomorphisms. We introduce two new definitions of the random rotation number that can be stated without reference to any choice of lift of the dynamics to the real line, and prove that they are equivalent to the standard random rotation number. We then prove that the mean random rotation number may be a

Elena Sabdy Martinez, Alexander D. Kaiser, Alexander K. Reed, Sascha W. Stocker

Patient-specific computational modeling has emerged as a powerful tool for surgical planning in complex congenital heart disease. One promising application is complex biventricular repair, which often requires construction of a custom intraventricular baffle to establish a physiologic left ventricle-to-aorta outflow pathway. In current practice, baffle geome

Xuan Wu, Sheng Di, Tripti Agarwal, Kai Zhao

Error-controlled lossy compressors have been widely used in scientific applications to reduce the unprecedented size of scientific data while keeping data distortion within a user-specified threshold. While they significantly mitigate the pressure for data storage and transmission, they prolong the time to access the data because decompression is required to

Suliang Si

In this article, we improve the classical Bukhgeim-Klibanov method presented in [1],which can be used to prove the conditional stability of inverse source problem for a hyperbolic equation from the measurement on the subboundary. A major ingredient of our proof is a novel Carleman estimate. This inequality eliminates the need to extend the solution in time,