December 2025 arXiv papers — page 67

Roshan Kenia, Xiaoman Zhang, Pranav Rajpurkar

Autonomous coding agents built on large language models (LLMs) can now solve many general software and machine learning tasks, but they remain ineffective on complex, domain-specific scientific problems. Medical imaging is a particularly demanding domain, requiring long training cycles, high-dimensional data handling, and specialized preprocessing and valida

Matthieu Bonnivard, Igor Pažanin, Francisco J. Suárez-Grau

Inspired by the lubrication framework, in this paper we consider a micropolar fluid flow through a rough thin domain, whose thickness is considered as the small parameter $\varepsilon$ while the roughness at the bottom is defined by a periodical function with period of order $\varepsilon^{\ell}$ and amplitude $\varepsilon^{\delta}$, with $\delta>\ell>1$. Ass

Nicolas Ombredane, Eloi Flament, Charles Babin, Dominique Sugny

We develop a time-optimal approach to force sensing using a Bose-Einstein condensate in a shaken optical lattice. Optimal control protocols are derived from a Fisher information framework and yield optimal dynamics that spontaneously organize in intereferometer-like structures, where multiple interferences combine to maximize sensitivity. We analyse how meas

The Khai Nguyen, Ebrahim Bedeer

This paper proposes a novel preamble design and detection method for multiuser asynchronous massive MIMO LoRa networks. Unlike existing works, which only consider the preamble detection for a single end device (ED), we propose to simultaneously detect the preambles of multiple EDs that asynchronously transmit their uplink (UL) packets to a multiple-antenna g

Muhammad Osama Imran, Roshni Lulla, Rodney Sappington

We examine the conceptual and ethical gaps in current representations of Superintelligence misalignment. We find throughout Superintelligence discourse an absent human subject, and an under-developed theorization of an "AI unconscious" that together are potentiality laying the groundwork for anti-social harm. With the rise of AI Safety that has both thematic

Darja Nonaca, Jérémy Guichemerre, Reinhard Wiesmayr, Nihat Engin Tunali

Ultra-reliable low latency communication (URLLC) is a key part of 5G wireless systems. Achieving low latency necessitates codes with short blocklengths for which polar codes with successive cancellation list (SCL) decoding typically outperform message-passing (MP)-based decoding of low-density parity-check (LDPC) codes. However, SCL decoders are known to exh

Diego A. Mendoza, Areli J. Vega-Carmona, Arturo Camacho-Guardian, Miguel A. Bastarrachea-Magnani

Dissipative coupling refers to the effect where two systems interact with each other mediated by dissipation channels. Recent advances in controlling light-matter systems have opened new avenues to explore non-Hermitian effects arising from dissipative coupling, such as level attraction and anomalous dispersions. In this work, we perform a parametric study o

Coleridge Faraday, Ben Bert, Jack Brand, Werner Vogelsang

We present perturbative quantum chromodynamics (pQCD) predictions for the modification to the yield of high-momentum particles in very light ion collisions - ${}^{10}\mathrm{B} + {}^{10}\mathrm{B}$, ${}^{6}\mathrm{Li} + {}^{6}\mathrm{Li}$, ${}^{4}\mathrm{He} + {}^{4}\mathrm{He}$, and ${}^{3}\mathrm{He} + {}^{3}\mathrm{He}$ - both with and without medium-indu

Zixin Wang, Ishfaq Aziz, Mohamad Alipour

Accurate estimation of subsurface material properties, such as soil moisture, is critical for wildfire risk assessment and precision agriculture. Ground-penetrating radar (GPR) is a non-destructive geophysical technique widely used to characterize subsurface conditions. Data-driven parameter estimation methods typically require large amounts of labeled train

Paul P. Martin, Eric C. Rowell, Fiona Torzewska

We first motivate the study of a certain quotient of the loop braid category, both for the mathematics underpinning recent approaches to topological quantum computation; and as a key example in non-semisimple higher representation theory. For reasons that will become clear, we call this quotient the mixed doubles category, $MD$. Then our main result is a the

Igor Pažanin, Francisco J. Suárez-Grau

In this paper, we study the asymptotic behavior of the thermomicropolar fluid flow through a thin channel with rough boundary. The flow is governed by the prescribed pressure drop between the channel's ends and the heat exchange through the rough wall is allowed. Depending on the limit of the ratio between channel's thickness and the wavelength of the roughn

Christopher Eckner, Felipe Figueroa, Simon Metayer, Piotr Tourkine

We study graviton scattering amplitudes. Assuming they are UV completed by a theory of weakly coupled massive higher spins, we demonstrate that the UV completion must possess infinitely many Regge trajectories, and thus they are forced to have a stringy spectrum. We extend and simplify a previous proof by some of us for open-string like states to the case of

María Anguiano, Francisco J. Suárez-Grau

A relevant problem for applications is to model the behavior of Newtonian fluids through thin porous media, which is a domain with small thickness $\epsilon$ and perforated by periodically distributed cylinders of size and period $\epsilon^\delta$, with $\delta>0$. Depending on the relation between thickness and the size of the cylinders, it was introduced i

Rehan Akber, Adnan Khan

We propose and analyze a model for antibiotic resistance transfer in a bacterial biofilm and examine antibiotic dosing strategies that are effective in bacterial elimination. In particular, we consider a 1-D model of a biofilm with susceptible, persistor and resistant bacteria. Resistance can be transferred to the susceptible bacteria via horizontal gene tra

Heinz Pernegger, Emma Kate Anderson, Paula Bartulović, Ivan Berdalović

Dense tracking environments in experiments at CERN's High-Luminosity LHC and future FCC experiments call for an increased use of timing information in addition to the position measurement of pixel detectors. This adds one dimension to the information available, and is essential for pile-up mitigation at high luminosity. The CASSIA sensor project (CMOS Active

Shirshendu Ganguly, Milind Hegde, Lingfu Zhang

In classical percolation theory, the van den Berg-Kesten (BK) inequality is a fundamental tool that shows that disjoint events induce negative conditionings on each other. The inequality also holds in the context of last passage percolation (LPP), which is the zero temperature limit of polymer models and an important subclass in the Kardar-Parisi-Zhang (KPZ)

L. Sundberg, F. Candelier, N. Fintzi, G. Voth

Numerical simulation of particle motion in fluids at low particle Reynolds numbers is often based on empirical force and torque models obtained by fitting force and torque from ab-initio computations for simple particle shapes such as spheres, spheroids, or cylindrical disks and fibres. To do the same for more complex particles shapes, one needs to first kno

Kyle Pratt

Let $5 \leq k \leq 11$ and $0\leq i \leq k-1$ be integers. We determine all solutions to the equation \begin{align*} n(n+d)(n+2d)\cdots(n+(i-1)d)(n+(i+1)d) \cdots (n+(k-1)d) = y^3 \end{align*} in integers $n,d,y$ with $ny \neq 0$, $d\geq 1$, and $\text{gcd}(n,d) = 1$. Our method relies on the theory of elliptic curves, including elliptic curve Chabauty over

Liam Collins, Bhuvesh Kumar, Clark Mingxuan Ju, Tong Zhao

Modern Sequential Recommendation (SR) models commonly utilize modality features to represent items, motivated in large part by recent advancements in language and vision modeling. To do so, several works completely replace ID embeddings with modality embeddings, claiming that modality embeddings render ID embeddings unnecessary because they can match or even

Hannah Krahl, Katrin Hartwig, Ann-Kathrin Fischer, Theodora Nikolakopoulou

Mobile gaming apps are woven into children's daily lives. Given their ongoing cognitive and emotional development, children are especially vulnerable and depend on designs that safeguard their well-being. When apps feature manipulative interfaces or heavy advertising, they may exert undue influence on young users, contributing to prolonged screen time, disru

Philippe Dollfus, Jérôme Saint-Martin, Rémi Helleboid, Thibauld Cazimajou

Si- and Ge-based single-photon-avalanche-diodes (SPAD) are investigated by using self-consistent 3D Monte Carlo simulation, in a mixed-mode approach including the presence of a passive quenching circuit. This approach of transport allows us to capture all stochastic features of carrier transport and SPAD operation, not only for the avalanche triggering but a

Yue Li, Qi Ma, Runyi Yang, Mengjiao Ma

While 3DGS has emerged as a high-fidelity scene representation, encoding rich, general-purpose features directly from its primitives remains under-explored. We address this gap by introducing Chorus, a multi-teacher pretraining framework that learns a holistic feed-forward 3D Gaussian Splatting (3DGS) scene encoder by distilling complementary signals from 2D

Zhicheng Wang, Nathan C. Keim

Local rearrangements are the elements of plastic deformation in an amorphous solid. In oscillatory shear, they can switch reversibly between two distinct configurations. While these repeating relaxations are typically considered in the limit of slow driving, their dynamics is less well understood. We perform experiments on a colloidal amorphous solid at an o

Xinyan Zhao, Yi-Ching Tang, Rivaaj Monsia, Victor J. Cantu

Motivation: The clinical efficacy of antibody therapeutics critically depends on high-affinity target engagement, yet laboratory affinity-maturation campaigns are slow and costly. In computational settings, most protein language models (PLMs) are not trained to favor high-affinity antibodies, and existing preference optimization approaches introduce substant

Rolf Drechsler, Qian Liu

Test and verification are essential activities in hardware and system design, but their complexity grows significantly with increasing system sizes. While Behavior Driven Development (BDD) has proven effective in software engineering, it is not yet well established in hardware design, and its practical use remains limited. One contributing factor is the manu

Giulio Colombo, Alberto Farina, Marco Magliaro, Luciano Mari

We prove some rigidity and classification results for graphs with prescribed mean curvature and locally constant Dirichlet and Neumann data, for instance as they appear in capillarity problems. We consider domains in Riemannian manifolds, with emphasis on $\mathbb{R}^2$ and $\mathbb{R}^3$. We classify both the underlying domain and the resulting solution, pr

Franco De Palma, Elena Acinapura, Wonjin Jang, Fabian Oppliger

Ge/SiGe heterostructures represent a promising platform for hosting various quantum devices such as hole spin qubits and Andreev spin qubits. However, the compatibility of such heterostructures with high-quality-factor microwave superconducting cavities remains a challenge due to defects in the material stack. In this work, we present an approach to enhance

Adrien Hélias, Sarah C. Gallagher, Pauline Barmby

Active galactic nuclei (AGNs) exhibit variability in their luminosities with timescales that correlate with the mass of the black hole at the centre of the AGN. Presently, the empirical correlation lacks sufficient precision to confidently convert these timescales into black hole masses, especially at the low-mass end. To find more AGNs with timescale measur

Hrishidev Unni, Soumyajyoti Biswas, Anirban Chakraborti

We study opinion formation in a society where agents interact on a modular network generated using a stochastic block model (SBM). Opinion dynamics is modeled through the Biswas-Chatterjee-Sen (BChS) kinetic exchange model, in which agents undergo pairwise interactions that could be positive or negative. By tuning the relative strength of intra- and inter-gr

Anaelle Hertz, Mohammad Ahmadpoor, Oleksandr Dzhenzherov, Augusto Gerolin

Optimal transport between classical probability distributions has been proven useful in areas such as machine learning and random combinatorial optimization. Quantum optimal transport, and the quantum Wasserstein distance as the minimal cost associated with transforming one quantum state to another, is expected to have implications in quantum state discrimin

Antonia Höfert, Jonas Jalowy, Zakhar Kabluchko

We study the zero evolution of high powers of polynomials under the heat flow. For any fixed polynomial $P(z)$, we prove that the empirical zero distribution of its heat-evolved $n$-th power converges to a distribution on the complex plane as $n$ tends to infinity. We describe this limit distribution $μ_t$ as a function of the time parameter $t$ of the holom

Marcos Gil de Olivera, Malo Joly, Antonio Z. Khoury, Alberto Bramati

The Hawking effect -- the amplification of fluctuations at the horizon -- has been detected in quantum fluids through real-space density correlations. However, real-space observables integrate over frequency, mixing distinct scattering channels into a single interference pattern and obscuring the spectral and entanglement structure of the emission. Here, we

D. Dennstädt, J. Schaa, T. Berger

We address the problem of output reference tracking for unknown nonlinear multi-input, multi-output systems with relative degree two and bounded-input bounded-state (BIBS) stable internal dynamics. We propose a novel model-free adaptive controller that ensures the evolution of the tracking error within prescribed performance funnel boundaries. By applying an

Ben Adcock, Gregor Maier, Rahul Parhi

We develop a minimax theory for operator learning, where the goal is to estimate an unknown operator between separable Hilbert spaces from finitely many noisy input-output samples. For uniformly bounded Lipschitz operators, we prove information-theoretic lower bounds together with matching or near-matching upper bounds, covering both fixed and random designs

Arthur Couteau, Panayotis Dimopoulos Eggenschwiler, Patrick Jenny

We present computations of individual mode-to-mode energy transfers from direct numerical simulations of homogeneous isotropic turbulence. Unlike previous approaches based on shell-filtered velocity fields, this method distinguishes between the energy exchanged by each pair of modes within a triad. We introduce a potential function based on the energy conten

Gerard Marias Gonzalez, Alejandro Pena-Bello, Jérémy Dumoulin, Nicolas Wyrsch

Local Electricity Communities (communaut\'es \'electriques locales, CEL) will become operational in Switzerland in 2026, allowing prosumers, consumers, and storage operators within the same municipality and distribution system operator (DSO) area to exchange electricity over the public grid with reduced distribution tariffs. This report examines a rural Swis

Wang Bin, Ao Yang, Kedan Li, Aofan Liu

In the domain of software security testing, Directed Grey-Box Fuzzing (DGF) has garnered widespread attention for its efficient target localization and excellent detection performance. However, existing approaches measure only the physical distance between seed execution paths and target locations, overlooking logical relationships among code segments. This

Gurinder Singh, Thaddeus Pellegrini, Kenneth M. Merz,

Designing parameterized quantum circuits (PQCs) that are expressive, trainable, and robust to hardware noise is a central challenge for quantum machine learning (QML) on noisy intermediate-scale quantum (NISQ) devices. We present a Domain-Aware Quantum Circuit (DAQC) that leverages image priors to guide locality-preserving encoding and entanglement via non-o

Valeria Banica, Nicolas Burq

In this note we give a very short proof of the div-curl lemma in the limit conjugate case $\mathcal M-L^\infty$, where $\mathcal{M}$ is the set of Radon measures on $\mathbb{R}^d$. The proof follows the classical approach by defining here the product in the sense of distributions via a non unique microlocal Hodge's decomposition. The result is valid for many

Andrei Rasputnyi, Ilya Karuseichyk, Gerd Leuchs, Denis Seletskiy

Non-Gaussian states of light are a critical resource for fault-tolerant quantum computing and enhanced metrology, but are typically faint and often obtained via post-selection. Here, we demonstrate the deterministic generation of a bright non-Gaussian state by introducing a Kerr nonlinearity to a macroscopic state of light called bright squeezed vacuum (BSV)

Binh Vu

The unprecedented proliferation of digital data presents significant challenges in access, integration, and value creation across all data-intensive sectors. Valuable information is frequently encapsulated within disparate systems, unstructured documents, and heterogeneous formats, creating silos that impede efficient utilization and collaborative decision-m

Gautam Iyer, Raghavendra Venkatraman

Given $N$ i.i.d. samples from a probability measure $\mu$ on $\mathbf{R}^d$, we study the rate of convergence of the empirical measure $\mu_N \to \mu$ in the negative Sobolev space $W^{-\alpha, p}$. When $W^{-\alpha, p}$ contains point measures (i.e. when $\alpha p > (p-1)d$), we show $\mathbf{E} \| \mu_N - \mu \|_{W^{-\alpha, p}}^p \leq C_d / N^{p/2}$ for a

Paul Darius, Thomas Hoppe, Andrei Aleksandrov

In this study, we investigate system-level emergent risks of interacting AI agents. The core contribution of this work is an exploratory scenario-based identification of these risks as well as their categorization. We consider a multitude of systemic risk examples from existing literature and develop two scenarios demonstrating emergent risk patterns in doma

José E. Figueroa-López, Ruoting Gong

In the present paper, we study the near-maturity ($t\rightarrow T^{-}$) convergence rate of the optimal early-exercise price $b(t)$ of an American put under an exponential L\'{e}vy model with a {\it nonzero} Brownian component. Two important settings, not previous covered in the literature, are considered. In the case that the optimal exercise price converge

Jasmin Katz, Xiaopan Lian, Alexandru Malekshahian, Andrey Shapiro

Let $G$ be a graph and $\Gamma$ a finite abelian group. The zero-sum Ramsey number of $G$ over $\Gamma$, denoted by $R(G, \Gamma)$, is the smallest positive integer $t$ (if it exists) such that any edge-colouring $c:E(K_t)\to\Gamma$ contains a copy of $G$ with $\sum_{e\in E(G)}c(e)=0_\Gamma$. We prove a linear upper bound $R(G, \Gamma)\leq Cn$ that holds for

Alejandro Perez, Daniel Sudarsky

We analyze the expectation value of the energy-momentum tensor and its fluctuations in quantum field theory on curved spacetimes $\langle T_{ab} \rangle$. A generally accepeted condition for the conceptual consistency of semiclassical gravity, where $\langle T_{ab} \rangle$ represent the sources of the Einstein equations, is that the fluctuations of the ener

R. Ruffini, C. Sigismondi, Y. Wang, H. Quevedo

As the fiftieth anniversary of our common effort in the field of relativistic astrophysics is approaching, we offer a new look to some of our acquired knowledge in a more complete view, which evidence previous unnoticed connections. They are gaining due prominence in reaching a more complete picture evidencing the main results. We outline the history of GRB

Ruijing Tang, Nicola Franchini, Sebastian H. Völkel, Emanuele Berti

Exploring gravitational theories beyond general relativity (GR) with black hole (BH) spectroscopy requires accurate and flexible methods for computing their quasinormal mode (QNM) spectrum. A popular method of choice is the higher-order Wentzel-Kramers-Brillouin (WKB) approximation, mostly applied to nonrotating BHs. While previous studies demonstrated that

Xiangpeng Li, Junwei Ma, Samuel D Brody, Ali Mostafavi

Flash flood warnings are largely reactive, providing limited advance notice for evacuation planning and resource prepositioning. This study presents and validates an anticipatory, parametric framework that converts landscape vulnerability and precipitation into transparent, zone-aware threat levels at neighborhood scales. We first derive an inherent hazard l

Arya Chavoshi, Hassan Dashtian, Naveen Sudharsan, Dev Niyogi

Satellite-derived Land Surface Temperature (LST) products are central to surface urban heat island (SUHI) monitoring due to their consistent grid-based coverage over large metropolitan regions. However, cloud contamination frequently obscures LST observations, limiting their usability for continuous SUHI analysis. Most existing LST reconstruction methods rel

Yohanes Yudhi Adikusuma, Qixing Huang, Ying He

Computing geodesic distances on 3D surfaces is fundamental to many tasks in 3D vision and geometry processing, with deep connections to tasks such as shape correspondence. Recent learning-based methods achieve strong performance but rely on large 3D backbones, leading to high memory usage and latency, which limit their use in interactive or resource-constrai

Emil T. M. Pedersen, Freek Witteveen, Klaus Mølmer, Matthias Christandl

We present numerical calculations, and simulations performed on a Rydberg atom quantum simulator, of the adiabatic evolution of many-body quantum systems around a quantum phase transition. We demonstrate that the end-to-end transfer error, for a given process duration and dissipative losses, can be suppressed by adopting smooth initial and final scheduling f

T. Torres, J. Garnier, L. Zanaglia, M. Ferraro

We theoretically and experimentally investigate spontaneous self-organization in a conservative (Hamiltonian) turbulent wave system, operating far from thermodynamic equilibrium. Our system is governed by two coherently coupled nonlinear Schr\"odinger equations, describing the polarization evolution of light in a dispersive nonlinear optical fiber. The analy

Jiajia Li, Tian Guo, Fan Li, Huichao Ding

Working memory is a promising paradigm for assessing cognitive ergonomics of brain states in brain-computer interfaces(BCIs). This study decodes these states with a focus on environmental illumination effects via two distinct working memory tasks(Recall and Sequence) for mixed-recognition analysis. Leveraging nonlinear patterns in brain connectivity, we prop

Saikat Roy, Yannick Kirchhoff, Constantin Ulrich, Maximillian Rokuss

Large-scale supervised pretraining is rapidly reshaping 3D medical image segmentation. However, existing efforts focus primarily on increasing dataset size and overlook the question of whether the backbone network is an effective representation learner at scale. In this work, we address this gap by revisiting ConvNeXt-based architectures for volumetric segme

Simon Giebenhain, Tobias Kirschstein, Liam Schoneveld, Davide Davoli

Neural Parametric Head Models (NPHMs) are a recent advancement over mesh-based 3d morphable models (3DMMs) to facilitate high-fidelity geometric detail. However, fitting NPHMs to visual inputs is notoriously challenging due to the expressive nature of their underlying latent space. To this end, we propose Pix2NPHM, a vision transformer (ViT) network that dir

Charles Elbar, Alejandro Fernández-Jiménez, Filippo Santambrogio

We prove Li-Yau and Aronson-B\'enilan type estimates for the parabolic-elliptic Keller-Segel system with critical exponent $m=2-\frac 2d$, i.e. lower bounds on the Laplacian of a suitable notion of pressure in any dimension. We show that these estimates entail $L^{\infty}$ bounds on the density, depending on its initial mass, up to the critical mass case for

Dong Chen, Zhengqing Hu, Shixing Zhao, Yibo Guo

While the enormous parameter scale endows Large Models (LMs) with unparalleled performance, it also limits their adaptability across specific tasks. Parameter-Efficient Fine-Tuning (PEFT) has emerged as a critical approach for effectively adapting LMs to a diverse range of downstream tasks. However, existing PEFT methods face two primary challenges: (1) High

Axel Brandenburg, Oindrila Ghosh, Franco Vazza, Andrii Neronov

The exteriors of stellar and galactic dynamos are usually modeled as current-free potential fields. A more realistic description might instead be that of a force-free magnetic field. Here, we suggest that, in the absence of outflows, neither of these reflect the actual behavior when the magnetic field spreads diffusively into a more poorly conducting turbule

Tanjim Taharat Aurpa, Farzana Akter, Md. Mehedi Hasan, Shakil Ahmed

Medical Entity Recognition (MedER) is an essential NLP task for extracting meaningful entities from the medical corpus. Nowadays, MedER-based research outcomes can remarkably contribute to the development of automated systems in the medical sector, ultimately enhancing patient care and outcomes. While extensive research has been conducted on MedER in English

Vera Sosnovik, Caroline Violot, Mathias Humbert

YouTube has emerged as a major platform for political communication and news dissemination, particularly during high-stakes electoral periods. In the context of the 2024 European Parliament and French legislative elections, this study investigates how political actors and news media used YouTube to shape public discourse. We analyze over 100,000 video transc

Piotr Michał Bies

In the paper, we investigate the nonlinear thermoelasticity model in two- and three-dimensional convex and bounded domains. We propose new boundary conditions for the displacement. These conditions are not usual in thermoelasticity. Whereas, we posit the Neumann boundary condition for the temperature. We prove the existence of global, unique solutions for sm

Zhiwei Gao

In this manuscript, we investigate importance sampling methods for rare-event simulation in diffusion processes. We show, from a large-deviation perspective, that the resulting importance sampling estimator is log-efficient. This connection is established via a stochastic optimal control formulation, and the associated Hamilton--Jacobi--Bellman (HJB) equatio

G. Marchegiani, G. Catelani

The standard mean-field treatment of low-temperature superconductors leads to a square-root divergent density of states at the gap value. This feature can lead to unphysical logarithmic divergences in various quantities, such as currents and qubit transition rates. We revisit their possible regularization based on the proximity effect between two superconduc

Kangchen Lv, Mingrui Yu, Shihefeng Wang, Xiangyang Ji

Perception of deformable linear objects (DLOs), such as cables, ropes, and wires, is the cornerstone for successful downstream manipulation. Although vision-based methods have been extensively explored, they remain highly vulnerable to occlusions that commonly arise in constrained manipulation environments due to surrounding obstacles, large and varying defo

Luca Miglior, Matteo Tolloso, Alessio Gravina, Davide Bacciu

Effectively capturing long-range interactions remains a fundamental yet unresolved challenge in graph neural network (GNN) research, critical for applications across diverse fields of science. To systematically address this, we introduce ECHO (Evaluating Communication over long HOps), a novel benchmark specifically designed to rigorously assess the capabilit

Piotr Nowakowski

Given a nonincreasing sequence of positive numbers $(a_n)$ such that the series $\sum a_n$ is convergent, by $E(a_n)$ we denote the set of all subsums of the series $\sum a_n$ and call it the achievement set of $(a_n)$. It is well known that such a set can be a finite union of closed intervals, a Cantor set or a Cantorval. We give a new condition implying th

R. Abbasi, M. Ackermann, J. Adams, S. K. Agarwalla

The IceCube Neutrino Observatory has observed a diffuse flux of high-energy astrophysical neutrinos for more than a decade. A relevant background to the astrophysical flux is prompt atmospheric neutrinos, originating from the decay of charmed mesons produced in cosmic-ray-induced air showers. The production rate of charmed mesons in the very forward phase sp

Rahul Ravi, Ruizhe Li, Tarek Abdelfatah, Stephen Chan

Aim: This study investigates treatment response prediction to neoadjuvant chemotherapy (NACT) in breast cancer patients, using longitudinal contrast-enhanced magnetic resonance images (CE-MRI) and clinical data. The goal is to develop machine learning (ML) models to predict pathologic complete response (PCR binary classification) and 5-year relapse-free surv

Shubhang Krishnakant Trivedi, Phanish Suryanarayana

We present a spectral finite-element formulation of the optimized effective potential (OEP) method for atomic structure calculations in the random phase approximation (RPA). In particular, we develop a finite-element framework that employs a polynomial mesh with element nodes placed according to the Chebyshev-Gauss-Lobatto scheme, high-order $\mathcal{C}^0$-

Zhihan Zhou, Daqian Shi, Rui Song, Lida Shi

Comprehension of ancient texts plays an important role in archaeology and understanding of Chinese history and civilization. The rapid development of large language models needs benchmarks that can evaluate their comprehension of ancient characters. Existing Chinese benchmarks are mostly targeted at modern Chinese and transmitted documents in ancient Chinese

Lara Janiurek, Viv Kendon

We derive the continuous spacetime limit of the one dimensional lazy discrete time quantum walk, obtaining explicit macroscopic evolution equations for a three state model in the presence of decoherence. While continuum limits of two state quantum walks are well established, an explicit continuous spacetime formulation for the lazy three state walk, particul

Yun-Hua Chen

We perform a unified description of the experimental data of the $\pi^+\pi^-$ invariant mass spectra of $B_s^0 \to \psi(2S) \pi^+\pi^-$, the $\pi^+\pi^-$ and $K^+ K^-$ invariant mass spectra of $B_s^0 \to X(3872) \pi^+\pi^- (K^+ K^-)$, and the ratio of branch fractions $\mathcal{B}[B_s^0 \to X(3872)(K^+K^-)_{{\rm non-}\phi}]/ \mathcal{B}[B_s^0 \to X(3872)\pi

Jan Philip Wahle, Krishnapriya Vishnubhotla, Bela Gipp, Saif M. Mohammad

Work in Computational Affective Science and Computational Social Science explores a wide variety of research questions about people, emotions, behavior, and health. Such work often relies on language data that is first labeled with relevant information, such as the use of emotion words or the age of the speaker. Although many resources and algorithms exist t

Dimitrios Chatzis, John M. Marley, Daniel C. Thompson

We revisit the proposal that coupling two six-dimensional holomorphic Chern-Simons theories generates gaugings throughout the twistor-space diamond relating 6d hCS, 4d self-dual Yang-Mills, 4d Chern-Simons, and 2d integrable models. In previous work this mechanism was demonstrated only in a special case, leaving its general status unclear. By reformulating t

Mehmet Ali Erkan, Halil Eren Koçak

During the height of the hype in late 2021, the Metaverse drew more attention from around the world than ever before. It promised new ways to interact with people in three-dimensional digital spaces. This cross-sectional study investigates the attitudes, perceptions, and predictors of the willingness to engage with the Metaverse among 381 Turkish university

Shahab Mosallaie, Andrea Schiffauerova, Ashkan Ebadi

Artificial intelligence (AI) is transforming cancer diagnosis and treatment. The intricate nature of this disease necessitates the collaboration of diverse stakeholders with varied expertise to ensure the effectiveness of cancer research. Despite its importance, forming effective interdisciplinary research teams remains challenging. Understanding and predict

Aurelia Kusumastuti, Nikolay Tcholtchev, Philipp Lämmel, Sebastian Bock

The rise of quantum computing/technology potentially introduces significant security challenges to cloud computing, necessitating quantum-resistant encryption strategies as well as protection schemes and methods for cloud infrastructures offering quantum computing time and services (i.e. quantum clouds). This research explores various options for securing qu

Avigail Taylor, Micah P. Fletcher

Microplate-based 'omic studies of large clinical cohorts can massively accelerate biomedical research, but experimental power and veracity may be negatively impacted when plate positional effects confound clinical variables of interest. Plate designs must therefore deconvolve this technical and biological variation, and several computational approaches now e

Louigi Addario-Berry, Benoît Corsini, Neeladri Maitra, Meltem Ünel

Given $n \in \mathbb{N}$ and $\mu \in \mathbb{R}$, a $\textit{$\mu$-height-biased tree of size $n$}$ is a random plane tree $\mathbf{\mathbf{T}}_n$ with $n$ vertices with law given by $\mathbb{P}(\mathbf{T}=t) \propto e^{-\mu h(t)}$, where $t$ ranges over fixed plane trees with $n$ vertices, and $h(t)$ is the height of $t$. Fix a sequence $(\mu_n)_{n \ge 1}$

Kehinde Ogundipe, Bilguun Bayarsaikhan

We calculate the leading order amplitude and probability for the elastic scattering of an elementary meson and a kink in the $\phi^4$ double-well model. Classically, the kink is reflectionless, and so the leading contribution arises at one loop. At this order, the scattering amplitude exhibits a pole when the incoming meson energy is twice the shape mode ene

Adam R. Lamson, Mohammadhossein Firouznia, Michael J. Shelley

Cells regulate gene expression in part by forming DNA-protein condensates in the nucleus. While existing theories describe the equilibrium size and stability of such condensates, their dynamics remain less understood. Here, we use coarse-grained 3D Brownian-dynamics simulations to study how long, end-anchored biopolymers condense over time due to transient c

Tomás Foncea E., Sebastián Reyes-Carocca

In this article, we study orientably-regular maps of Euler characteristic $-2p^2$ and classify those that admit a group of orientation-preserving automorphisms of order $10p^2$, where $p$ is a prime number. Along the way, we classify all compact Riemann surfaces (or complex algebraic curves) of genus $1+p^2$ endowed with a group of conformal automorphisms of

Hallmann Óskar Gestsson, Alexandra Olaya-Castro

We investigate how optical second-order cross correlations witness the quantum features of a prototype donor-acceptor light-harvesting unit. By considering a pair of detuned two-level emitters electronically coupled and incoherently driven to a non-equilibrium steady-state, we gain insight into how electronic quantum properties such as exciton eigenstate del

Amaia Sagasti, Frederic Font

This study presents a use-case of a network of low-cost acoustic smart sensors deployed in the city of Pamplona to analyse changes in the urban soundscape during the San Fermin Festival. The sensors were installed in different areas of the city before, during, and after the event, capturing continuous acoustic data. Our analysis reveals a significant transfo

Johanna T. Malle, Christopher P. O. Reyer, Yael Amitai, Andrey L. D. Augustynczik

Climate impact assessments increasingly rely on high-resolution climate and forcing datasets, under the premise that finer detail enhances both the accuracy and policy relevance of projections. Yet systematic evaluations of when and where higher resolution actually improves impact model outcomes remain limited, and it is unclear whether increasing spatial re

Filip Tronarp

In this paper, a method for recursively computing approximate modal paths is developed. A recursive formulation of the modal path can be obtained either by backward or forward dynamic programming. By combining both methods, a ``two-filter'' formula is demonstrated. Both method involves a recursion over a so-called value function, which is intractable in gene

Davide Addona, Davide Bignamini, Carlo Orrieri, Luca Scarpa

Pathwise uniqueness for stochastic PDEs with drift in differential form is a main open problem in the recent literature on regularisation by noise. This paper establishes a self-contained theory in the framework of stochastic evolution equations on separable Hilbert spaces and provides a first result to address such an issue. The singularity of the drift all

Marta Russo, Antonella Maselli, Federico Maggiore, Giovanni Pezzulo

Accurate interception of moving objects, such as catching a ball, requires the nervous system to overcome sensory delays, noise, and environmental dynamics. One key challenge is predicting future object motion in the presence of sensory uncertainty and inherent neural processing latencies. Theoretical frameworks such as internal models and optimal control ha

Omar Faruq Shikdar, Fahad Ahammed, B. M. Shahria Alam, Golam Kibria

Tea is among the most widely consumed drinks globally. Tea production is a key industry for many countries. One of the main challenges in tea harvesting is tea leaf diseases. If the spread of tea leaf diseases is not stopped in time, it can lead to massive economic losses for farmers. Therefore, it is crucial to identify tea leaf diseases as soon as possible

Andrés Miniguano-Trujillo

We present an exact, closed-form expression for the Newtonian potential of the characteristic function associated with two overlapping discs in the plane. This setting naturally arises when discretising nonlocal interaction terms present in models of phase separation, aggregation dynamics, and quantum systems. We characterise the convolution integral as a pi

Jingmao Zhang, Zhiting Zhao, Yunqi Lin, Jianghong Ma

Beyond user-item modeling, item-to-item relationships are increasingly used to enhance recommendation. However, common methods largely rely on co-occurrence, making them prone to item popularity bias and user attributes, which degrades embedding quality and performance. Meanwhile, although diversity is acknowledged as a key aspect of recommendation quality,

Marinos Manolesos, Christos Ampatis, Dimitris Gkiolas, Konstantinos Rekoumis

Understanding and accurately reproducing gust-induced unsteady aerodynamics is essential for improving load prediction, aeroelastic analysis, and control strategies in aircraft, uninhabited aerial vehicles, and wind turbines, particularly in regimes where nonlinear flow phenomena dominate. In this work, a low-speed wind tunnel gust generator based on oscilla

Udisha Duhan, Arnab Pan, Ritesh Dubey, Samar Layek

Structure and morphology play a crucial role in enhancing the biomimetic oxidase activity of nanozymes. In this study, a facile \emph{in situ} chemical oxidation strategy was employed to synthesize MOF-derived MnO$_x$, utilizing the structural features of the parent MOF to enhance oxidase-mimicking activity. We systematically investigated the effects of phas

Yichen Jiang, Mohammed Talha Alam, Sohail Ahmed Khan, Duc-Tien Dang-Nguyen

Detectors of AI-generated images tend to inherit the biases of the data they are trained on: models fitted to GAN imagery learn to treat GAN-specific artifacts as the very definition of "fake" and consequently miss images produced by diffusion models and commercial generation tools. We study this generalization problem from two directions. First, we

Chengzhi Tang, Hong Yao

A conclusive experimental realization of 2D chiral topological superconductivity remains elusive. Here we present a theoretical demonstration that a topological $d+id$ fractionalized superconducting phase (SC*) can emerge in the weak-coupling limit of a Kondo lattice model, where conduction electrons interact with a Yao-Lee spin liquid on the honeycomb latti

Chan-in Sio, Alex Mann, Lingxi Fan, Andrew Cheung

The mental well-being of graduate students is an increasing concern, yet the adoption of scalable support remains uneven. Artificial intelligence-powered cognitive behavioral therapy chatbots (AI-CBT) offer low barrier help, but little is known about how Chinese postgraduates perceive and use them. This qualitative study explored perceptions and experiences

Kavin Rajasekaran, Niklas Sapountzoglou

In this contribution, we provide convergence rates for a finite volume scheme of a stochastic non-linear parabolic equation with multiplicative Lipschitz noise and homogeneous Neumann boundary conditions. More precisely, we give an error estimate for the $L^2$-norm of the space-time discretization by a semi-implicit Euler scheme with respect to time and a tw

Zdzisław Brzeźniak, Enrico Priola, Jianliang Zhai, Jiahui Zhu

We study the stochastic transport equation with globally $\beta$-H\"older continuous and bounded vector field driven by a non-degenerate pure-jump L\'evy noise of $\alpha$-stable type. Whereas the deterministic transport equation may lack uniqueness, we prove the existence and pathwise uniqueness of a weak solution in the presence of a multiplicative pure ju

Qian Zeng, Yihui Wang, Shu Yang, Yingxue Xu

Whole-slide images (WSIs) are an important data modality in computational pathology, yet their gigapixel resolution and lack of fine-grained annotations challenge conventional deep learning models. Multiple instance learning (MIL) offers a solution by treating each WSI as a bag of patch-level instances, but effectively modeling ultra-long sequences with rich