October 2025 arXiv papers — page 132

Yannis Voet, Espen Sande

While interpolatory bases such as the Lagrange basis form the cornerstone of classical finite element methods, they have been replaced in the more general finite element setting of isogeometric analysis in favor of other desirable properties. Yet, interpolation is a key property for devising accurate mass lumping strategies that are ubiquitous in explicit dy

A. Fumagalli, M. Costanzi, T. Castro, A. Saro

This study explores the impact of observational and modelling systematic effects on cluster number counts and cluster clustering and provides model prescriptions for their joint analysis, in the context of the \Euclid survey. Using 1000 \Euclid-like cluster catalogues, we investigate the effect of systematic uncertainties on cluster summary statistics and th

Congying Liu, Xingyuan Wei, Peipei Liu, Yiqing Shen

Biomedical queries often rely on a deep understanding of specialized knowledge such as gene regulatory mechanisms and pathological processes of diseases. They require detailed analysis of complex physiological processes and effective integration of information from multiple data sources to support accurate retrieval and reasoning. Although large language mod

Yifan Hu, Ruihuan Mao, Guozhen Shen

A set $A$ is dually Dedekind finite if every surjection from $A$ onto $A$ is injective; otherwise, $A$ is dually Dedekind infinite. An amorphous set is an infinite set that cannot be partitioned into two infinite subsets. A strictly amorphous set is an amorphous set in which every partition has only finitely many non-singleton blocks. It is proved consistent

Haitao Li, Jiusi Yu, Jiayu Fan, Shijie Kang

Dirac vortices, originally studied in quantum field theories to predict localized zero-energy modes, were recently realized in photonics, leading to Dirac vortex cavities. With topological protection, Dirac vortex cavities offer robust single-mode large-area localized modes appealing for high-performance micro-lasers and other applications. As a spectrally-i

Cyril Alispach, Matthieu Heller, Teresa Montaruli

Current optimization of ground-based Cherenkov telescopes arrays, also called Imaging Air Cherenkov Telescope (IACT) arrays, relies on brute-force human-driven approaches based on large simulations requiring both high amount of storage and long computation time. To explore the full phase space of telescope positioning of a given array even more simulations w

Lanpeng Ni, Junhao Liu, Zheng Qi, Chao Feng

Attosecond pulse trains (APTs) are indispensable for probing electron dynamics at their intrinsic timescales. High harmonic generation (HHG) has long been a successful and widely used technique in producing extreme ultraviolet APTs. While in the soft X-ray regime, HHG suffers from low conversion efficiency and lacking flexibility in the waveform and spectrum

Ko Aoki

Stefanich generalized the notion of (locally) presentable $(\infty, 1)$-category to the notion of presentable $(\infty, n)$-category. We give a new description based on the new notion of $\kappa$-compactly generated $(\infty, n)$-category, which avoids universe enlargement. Using the new definition, we prove the underlying functor of a morphism between prese

Christopher Carilli, Bojan Nikolic, Laura Torino, N. Thyagarajan

We present a new method for aperture masking interferometric (AMI) imaging at near-IR wavelengths using radio astronomical techniques. The method starts with derivation of interferometric visibilities from a Fourier transform of the interferograms. An iterative joint optimization process is then employed, using self-calibration of the interferometric element

He Du, Bowen Li, Chengxing Xie, Chang Gao

Reward models can significantly enhance the reasoning capabilities of large language models (LLMs), but they typically require extensive curated data and costly training. To mitigate these challenges, training-free approaches such as LLM-as-a-Judge leverage the intrinsic reasoning abilities of LLMs to evaluate responses, achieving promising results. Recent w

Shujun Xia, Haokun Lin, Yichen Wu, Yinan Zhou

LLMs hold great promise for healthcare applications, but the rapid evolution of medical knowledge and errors in training data often cause them to generate outdated or inaccurate information, limiting their applicability in high-stakes clinical practice. Model editing has emerged as a potential remedy without full retraining. While parameter-based editing oft

Xiaozhe Li, TianYi Lyu, Siyi Yang, Yuxi Gong

Understanding human intent is a complex, high-level task for large language models (LLMs), requiring analytical reasoning, contextual interpretation, dynamic information aggregation, and decision-making under uncertainty. Real-world public discussions, such as consumer product discussions, are rarely linear or involve a single user. Instead, they are charact

Zexin Wang, Lin Shi, Haoyu Wu, Junru Luo

Epilepsy is a prevalent neurological disorder marked by sudden, brief episodes of excessive neuronal activity caused by abnormal electrical discharges, which may lead to some mental disorders. Most existing deep learning methods for epilepsy detection rely solely on unimodal EEG signals, neglecting the potential benefits of multimodal information. To address

Jürgen Dölz, Michael Multerer

Analysis and processing of data is a vital part of our modern society and requires vast amounts of computational resources. To reduce the computational burden, compressing and approximating data has become a central topic. We consider the approximation of labeled data samples, mathematically described as site-to-value maps between finite metric spaces. Withi

Dexin Kong, Diana Pamela Moya Osorio, Erik G. Larsson

Recent advancements in polymer microwave fiber (PMF) technology have created significant opportunities for robust, low-cost, and high-speed sub-terahertz (THz) radio-over-fiber communications. Recognizing these potential benefits, this paper explores a novel radio-over-fiber (RoF) structure that interconnects multiple radio units (RUs) in cascade via fiber,

Tommaso Bonomo, Luca Gioffré, Roberto Navigli

Question Answering (QA) on narrative text poses a unique challenge to current systems, requiring a deep understanding of long, complex documents. However, the reliability of NarrativeQA, the most widely used benchmark in this domain, is hindered by noisy documents and flawed QA pairs. In this work, we introduce LiteraryQA, a high-quality subset of NarrativeQ

Deeptimaan Banerjee, Prateek Gothwal, Ashis Kumer Biswas

In many domains, including online education, healthcare, security, and human-computer interaction, facial emotion recognition (FER) is essential. Real-world FER is still difficult despite its significance because of some factors such as variable head positions, occlusions, illumination shifts, and demographic diversity. Engagement detection, which is essenti

Xuxu Xiang, Jianren Long

The existence of entire solutions to quadratic trinomial Fermat type differential-difference equations and \(q\)-difference differential equations involving second-order derivatives is studied by using Nevanlinna theory, and the exact form of entire solutions of the equations mentioned above is founded. Furthermore, some examples are given to show these resu

Allen Bao, Anunoy Chakraborty, David L. Duncan, Jordan Larson

We consider the family of Torelli homeomorphisms on a genus-three surface given by powers of a fixed bounding pair map. For each such homeomorphism $\phi$ we determine the number of connected components of the fixed point set of the induced map on the representation variety of the surface, as well as the number of connected components of the representation v

Ali Douaki, Shukun Weng, Silvia Dante, Nako Nakatsuka

In nanopore technology, the development of multiplexed detection and release platforms with high spatial and temporal resolution remains a significant challenge due to the difficulty in distinguishing signals originating from different nanopores in a single chip. In this work, we present a solid-state nanopore system functionalized with molecularly imprinted

Qin Zhang, Zi-chen Zhang, Yi-jia Yang, Zheng Liu

A quantum thermal diode, similar to an electronic diode, allows for unidirectional heat transmission. In this paper, we study a quantum thermal diode composed of two two-level atoms coupled to auxiliary two-level atoms. We find that the excited auxiliary atoms can weaken heat current and enhance the rectification effect, but the ground-state auxiliary atoms

Maximilian Stasica, Arne Bick, Nico Bohlinger, Omid Mohseni

Legged robots, particularly quadrupeds, excel at navigating rough terrains, yet their performance under vertical ground perturbations, such as those from oscillating surfaces, remains underexplored. This study introduces a novel approach to enhance quadruped locomotion robustness by training the Unitree Go2 robot on an oscillating bridge - a 13.24-meter stee

Ignacio Bono Parisi, Antonio J. Durán, Ignacio N. Zurrián

We introduce a couple of methods to construct exceptional matrix polynomials. One of them uses what we have called quasi-Darboux transformations. This seems to be a more powerful method to deal with the non-commutativity problems that appear when matrix-valued polynomials are considered. The other method does not use any transformation of Darboux type. Using

Dominik Koll, Sebastian Fichter, Michael Hotchkis, Martin Martschini

The detection of interstellar radionuclides in geological archives provides insights into nucleosynthesis in stars and stellar explosions as well as interstellar medium dynamics in the Local Bubble and the Local Interstellar Cloud. In this work, current projects to detect interstellar radionuclides with accelerator mass spectrometry will be reviewed. These p

Akash Kulkarni, Rajshekhar V Bhat

In 6G systems, extremely large-scale antenna arrays operating at terahertz frequencies extend the near-field region to typical user distances from the base station, enabling near-field communication (NFC) with fine spatial resolution through beamfocusing. Existing multiuser NFC systems predominantly employ linear precoding techniques such as zero-forcing (ZF

Gonca Ayık, Hayrullah Ayık, Ilinka Dimitrova, Jörg Koppitz

Let $\mathcal{PORD}_{n}$ be the semigroup consisting of all oriented and order-decreasing partial transformations on the finite chain $X_{n}=\{ 1<\cdots<n \}$. Let $\mathcal{IORD}_{n}$ be the subsemigroup of $\mathcal{PORD}_{n}$ consisting of all injective transformations of $\mathcal{PORD}_{n}$. For $2\leq r\leq n$, let $\mathcal{PORD}(n,r) =\{ \alpha\in \m

Jonathan M. Fraser, Firdavs Rakhmonov

The discrete Fourier transform has proven to be an essential tool in many geometric and combinatorial problems in vector spaces over finite fields. In general, sets with good uniform bounds for the Fourier transform appear more `random' and are easier to analyze. However, there is a trade-off: in many cases, obtaining good uniform bounds is not possible, eve

M. A. Baistrukov, E. A. Bekhtenev, A. A. Krasnov, D. A. Nikiforov

Construction of the new fourth generation synchrotron radiation facility (SRF) "SKIF" near Novosibirsk is nearing completion. "SKIF" uniqueness is ultra-low 75 pm natural emittance at 400 mA beam current and 3 GeV beam energy. Collective effects (beam interaction with the vacuum chamber) are the main obstacles of achieving such emittance and beam current. To

Daniel Adu Worae, Spyridon Mastorakis

Internet of Things (IoT) networks generate diverse and high-volume traffic that reflects both normal activity and potential threats. Deriving meaningful insight from such telemetry requires cross-layer interpretation of behaviors, protocols, and context rather than isolated detection. This work presents an LLM-powered AI agent framework that converts raw pac

Areej AlOtaibi, Lina Alyahya, Raghad Alshabanah, Shahad Alfawzan

Large Language Models (LLMs) have significantly advanced the field of natural language processing, enhancing capabilities in both language understanding and generation across diverse domains. However, developing LLMs for Arabic presents unique challenges. This paper explores these challenges by focusing on critical aspects such as data curation, tokenizer de

Rushna Quddus, Kent Kirshenbaum, David G. Grier

This study introduces a Holographic Agglutination Assay for quantifying levels of the immunoglobulin protein IgA in biological samples. This is the first example of a label-free and bead-free assay that quantifies protein agglutinates by direct detection using Total Holographic Characterization. A proof-of-concept assay for human serum immunoglobulins is dem

Abdul Wahab Jbara

Pluto and Charon are a dwarf binary system with a high mass ratio $\mu$, preventing Trojan companions. This instability creates ideal intersections for low-energy pathways that spacecraft can traverse and serves as an important test case for stability at Lagrange points for high $\mu$ binaries. This study models the Pluto-Charon system in the planar Circular

Wenxin Deng, Hengsong Yue, Xiaoyan Liu, Jianhong Liang

High-bandwidth germanium (Ge) photodetectors are crucial for silicon photonic integrated circuits. However, their bandwidth is restricted by carrier transit time and parasitic parameters. In this work, we propose an equalization photodetector (EqPD) utilizing the frequency response of a high-bandwidth photodetector PDA to subtract the frequency response of a

Mingqi Sun, Kai Liao, Xi-Long Fan

The stochastic gravitational-wave background (SGWB), arising from the incoherent superposition of numerous compact binary coalescences, serves as a powerful probe of both astrophysical populations and fundamental physics. In this work, we investigate the influence of gravitational lensing on the SGWB, focusing on primordial black holes (PBHs) as potential le

Victor Boone, Adrienne Tuynman

Although average gain optimality is a commonly adopted performance measure in Markov Decision Processes (MDPs), it is often too asymptotic. Further incorporating measures of immediate losses leads to the hierarchy of bias optimalities, all the way up to Blackwell optimality. In this paper, we investigate the problem of identifying policies of such optimality

Zhongxuan Hou, Stefano Berti, Teodor Burghelea, Francesco Romanò

Elastic turbulence is a spatially and temporally disordered flow state appearing in viscoelastic fluids at vanishing fluid inertia and large elasticity. The resulting flows have broad technological interest, particularly to enhance mixing and heat transfer in microdevices. Although its experimental characterization is now well established in different setups

Pradeep Bisht, Punita Batra

This paper explores the irreducible uniformly bounded weight modules of map extended Special Lie algebras and map extended Hamiltonian Lie algebras under some condition on the action of the Laurent polynomial ring A_{N}.

Shehbaz Tariq, Muhammad Talha, Symeon Chatzinotas, Hyundong Shin

Quantum reservoir computing (QRC) leverages the high-dimensional, nonlinear dynamics inherent in quantum many-body systems for extracting spatiotemporal patterns in sequential and time-series data with minimal training overhead. Although QRC inherits the expressive capabilities associated with quantum encodings, recent studies indicate that quantum classifie

Yongkang Wan, Zhonghao Liang, Qunying Liao

Since the $\mathrm{Fibonacci}$ sequence has good properties, it's important in theory and applications, such as in combinatorics, cryptography, and so on. In this paper, for the generalized Fibonacci sequence $\left\{W_n\left(a,b,p,q\right)\right\}$, by using elementary methods and techniques, we respectively give the asymptotic estimation values of $\left(\

Shouvik Datta Choudhury

We formalize the ``metric bundle'' viewpoint by defining, for any smooth $n$--manifold $M$, the open fiberwise cones $\mathcal{G}^{p,q}\subset S^2\Tstar M$ of nondegenerate symmetric bilinear forms with fixed signature $(p,q)$, and we package \emph{multi\-metric} (``polymetric'') geometries as sections of finite products of such cones. This framework subsume

Parissa Sadat Alavi, Guillaume Anciaux, Jean-François Molinari, Loris Rocchi

Accurately predicting friction in sliding interfaces that contain third body wear particles is critical for engineering applications such as sliding movement in pistons, bearings, or metal forming. We present a hierarchical multiscale framework that links particle scale mechanics to macroscopic friction in a strip draw friction test. At the macroscale, a one

S. A Katre, Deepa Krishnamurthi

It is known that every matrix of order n over the maximal order in an algebraic number eld is a sum of k-th powers in various cases if a discriminant condition is satis ed. It has been proved by Wadikar and Katre that for every matrix of size 2 over maximal orders in rational quaternion division algebras is a sum of squares and cubes. In this paper we consid

Stefan Kulk, Frederik Zuiderveen Borgesius

In this chapter we discuss the relation between privacy and freedom of expression in Europe. In principle, the two rights have equal weight in Europe - which right prevails depends on the circumstances of a case. We use the Google Spain judgment of the Court of Justice of the European Union, sometimes called the 'right to be forgotten' judgment, to illustrat

Enhan Li, Hongyang Du, Kaibin Huang

Large Language Models (LLMs) remain static in functionality after training, and extending their capabilities requires integration with external data, computation, and services. The Model Context Protocol (MCP) has emerged as a standard interface for such extensions, but current implementations rely solely on semantic matching between users' requests and serv

Natali Helberger, Frederik Zuiderveen Borgesius, Agustin Reyna

In modern markets, many companies offer so-called 'free' services and monetize consumer data they collect through those services. This paper argues that consumer law and data protection law can usefully complement each other. Data protection law can also inform the interpretation of consumer law. Using consumer rights, consumers should be able to challenge e

Frederik Zuiderveen Borgesius

Artificial intelligence (AI) has a huge impact on our personal lives and also on our democratic society as a whole. While AI offers vast opportunities for the benefit of people, its potential to embed and perpetuate bias and discrimination remains one of the most pressing challenges deriving from its increasing use. This new study, which was prepared by Prof

Emily Miller, Michael Milford, Muhammad Burhan Hafez, SD Ramchurn

Visual Place Recognition (VPR) enables robots and autonomous vehicles to identify previously visited locations by matching current observations against a database of known places. However, VPR systems face significant challenges when deployed across varying visual environments, lighting conditions, seasonal changes, and viewpoints changes. Failure-critical V

Pablo Villacorta-Aylagas, Manuel Rodríguez-Cayetano, Carlos Castillo-Passi, Pablo Irarrazaval

MRI sequence prototyping increasingly relies on graphical design environments and numerical simulators to accelerate development and validation. While several platforms support interactive sequence construction, fully web-based solutions that combine integrated phantom management, high-fidelity Bloch simulation, and scalable multi-user deployment remain limi

Dejun Luo, Feifan Teng

We consider stochastic 2D Euler equations with $L^2$-initial vorticity and driven by L\'evy transport noise in the Marcus sense. Under a suitable scaling limit of the noises, we prove that the weak solutions converge weakly to the unique solution of the deterministic 2D Navier-Stokes equation. This shows that small scale jump noises generate eddy viscosity,

Xin Zhao, Xiaojun Chen, Bingshan Liu, Haoyu Gao

Large language models (LLMs) with Mixture-of-Experts (MoE) architectures achieve impressive performance and efficiency by dynamically routing inputs to specialized subnetworks, known as experts. However, this sparse routing mechanism inherently exhibits task preferences due to expert specialization, introducing a new and underexplored vulnerability to backdo

Yangye Jiang, Jiachen Wang, Daofei Li

Accurate prediction of vehicle collision dynamics is crucial for advanced safety systems and post-impact control applications, yet existing methods face inherent trade-offs among computational efficiency, prediction accuracy, and data requirements. This paper proposes a dual Physics-Informed Neural Network framework addressing these challenges through two co

Ishrat Jahan Ansari, Vikas Jadhav, Devendra Shirolkar

In this paper we determine the congruence of Jacobi sums $J(1, 1)_{49}$ of order 49 over a field $\mathbb{F}_p$. We also show that simpler congruences hold for $J(1, 1)_{49}$ in the case of artiad and hyperartiad primes.

James Coe, Martin Hairer, Leonardo Tolomeo

We study the qualitative properties of solutions to the 2D stochastic Navier-Stokes equations with forcing that is white in time and coloured in space. Our main result shows that the unique invariant measure of this system is equivalent to that of the corresponding Ornstein-Uhlenbeck process. Our method relies on a generalization of the "time-shifted Girsano

Timothy Wong, Tom Freeman, Joseph Feehily

Effective assessment of mobile network coverage and the precise identification of service weak spots are paramount for network operators striving to enhance user Quality of Experience (QoE). This paper presents a novel framework for mobile coverage and weak spot analysis utilising crowdsourced QoE data. The core of our methodology involves coverage analysis

Matteo Della Rossa, Lorenzo Freddi, Mattia Pinatto

We study a generalized version of Zermelo&#39;s navigation problem where the set of admissible velocities is a general compact convex set, replacing the classical Euclidean ball. After establishing existence results under the natural assumption of weak currents, we derive necessary optimality conditions via Pontryagin&#39;s maximum principle and convex analy

Ruijing Fang, Jie Zhang, Zhichao Zhou, Xiao Li

Valley Hall effect is fundamental to valleytronics and provides a promising avenue for advancing information technology. While conventional valley Hall effect requires the inversion symmetry breaking, the recently proposed nonlinear valley Hall (NVH) effect removes the symmetry constraint, and broaden material choices. However, existing studies are limited t

Hao Du, Yiman Gao, Wenqiao Li, Ziming Li

A complete reduction $\phi$ for derivatives in a differential field is a linear operator on the field over its constant subfield. The reduction enables us to decompose an element $f$ as the sum of a derivative and the remainder $\phi(f)$. A direct application of $\phi$ is that $f$ is in-field integrable if and only if $\phi(f) = 0.$ In this paper, we present

Sofia Bordoni

This work provides a curve-based approach to Ulrich bundles on surfaces, establishing a correspondence that characterizes their existence, with a focus on applications to surfaces in $\mathbb{P}^3$.

Tian-Chi Ma, Xiang-Yu Wang, Hai-Qing Zhang

In this work, we study the time evolution of radial kinks in the background of boson stars. In particular, we consider two types of boson stars: the massive boson star and the solitonic boson star. For each boson star, we study the dynamics of the kinks with four different compactnesses. We observe that the greater the compactness is, the slower the kinks mo

Hyojun Go, Dominik Narnhofer, Goutam Bhat, Prune Truong

The rapid progress of large, pretrained models for both visual content generation and 3D reconstruction opens up new possibilities for text-to-3D generation. Intuitively, one could obtain a formidable 3D scene generator if one were able to combine the power of a modern latent text-to-video model as "generator" with the geometric abilities of a recent (feedfo

Jetin E Thomas, Ramandeep S. Johal

We study non-equilibrium models comprising of beads connected by springs. The system is coupled to two thermal baths kept at different temperatures. We derive the steady state probability distributions of positions of the bead for the one-bead system in the underdamped case. We employ the recently proposed technique of an effective temperature, along with nu

Ole-Christian Galbo Engstrøm

This thesis investigates the application of near-infrared hyperspectral imaging (NIR-HSI) for food quality analysis. The investigation is conducted through four studies operating with five research hypotheses. For several analyses, the studies compare models based on convolutional neural networks (CNNs) and partial least squares (PLS). Generally, joint spati

Li Bai, Qingqing Ye, Xinwei Zhang, Sen Zhang

Machine learning models are often vulnerable to inference attacks that expose sensitive information from their training data. Shadow model technique is commonly employed in such attacks, such as membership inference. However, the need for a large number of shadow models leads to high computational costs, limiting their practical applicability. Such inefficie

Masahiro Fujisawa, Futoshi Futami

Calibration of predicted probabilities is critical for reliable machine learning, yet it is poorly understood how standard training procedures yield well-calibrated models. This work provides the first theoretical proof that canonical $L_{2}$-regularized empirical risk minimization directly controls the smooth calibration error (smCE) without post-hoc correc

Nikolaj Rønne, Tejs Vegge, Arghya Bhowmik

We introduce GO-Diff, a diffusion-based method for global structure optimization that learns to directly sample low-energy atomic configurations without requiring prior data or explicit relaxation. GO-Diff is trained from scratch using a Boltzmann-weighted score-matching loss, leveraging only the known energy function to guide generation toward thermodynamic

Julian Legler, Sebastian Werner, Maria C. Borges, Stefan Tai

Microservice architectures have become the dominant paradigm for cloud-native systems, offering flexibility and scalability. However, this shift has also led to increased demand for cloud resources, contributing to higher energy consumption and carbon emissions. While existing research has focused on measuring fine-grained energy usage of CPU and memory at t

Mohammad Mansoori, Amira Soliman, Farzaneh Etminani

Clinical notes contain unstructured text provided by clinicians during patient encounters. These notes are usually accompanied by a sequence of diagnostic codes following the International Classification of Diseases (ICD). Correctly assigning and ordering ICD codes is essential for medical diagnosis and reimbursement. However, automating this task remains ch

Santiago Mazuelas, Veronica Alvarez

Boosting methods often achieve excellent classification accuracy, but can experience notable performance degradation in the presence of label noise. Existing robust methods for boosting provide theoretical robustness guarantees for certain types of label noise, and can exhibit only moderate performance degradation. However, previous theoretical results do no

Nico Pelleriti, Christoph Spiegel, Shiwei Liu, David Martínez-Rubio

Certifying nonnegativity of polynomials is a well-known NP-hard problem with direct applications spanning non-convex optimization, control, robotics, and beyond. A sufficient condition for nonnegativity is the Sum of Squares (SOS) property, i.e., it can be written as a sum of squares of other polynomials. In practice, however, certifying the SOS criterion re

Mojtaba Mollahossein, Gholamreza Vossoughi, Mohammad Hossein Rohban

Electromyography (EMG) signals are widely used for predicting body joint angles through machine learning (ML) and deep learning (DL) methods. However, these approaches often face challenges such as limited real-time applicability, non-representative test conditions, and the need for large datasets to achieve optimal performance. This paper presents a transfe

Lorenz Mohr, Marc Miranda, Sebastian Semper, Julia Beuster

Measured impulse responses obtained from a dynamic unmanned aerial vehicle (UAV) channel sounding system exhibit effects attributable to time-varying carrier frequency offset (CFO) and sampling frequency offset (SFO). To correct the recorded data in post-processing, we extend existing geometry-based drift compensation algorithms by an explicit line-of-sight

George Webber, Alexander Hammers, Andrew P. King, Andrew J. Reader

Diffusion models have recently enabled state-of-the-art reconstruction of positron emission tomography (PET) images while requiring only image training data. However, domain shift remains a key concern for clinical adoption: priors trained on images from one anatomy, acquisition protocol or pathology may produce artefacts on out-of-distribution data. We prop

Shreya G. Sarkar, Kuneh Parag Shah, Brahmananda Chakraborty

Two-dimensional (2D) nitride materials such as hexagonal boron nitride (h-BN), graphitic carbon nitride (g-C$_3$N$_4$), and beryllonitrene (BeN$_4$) have emerged as promising candidates for next generation electronic, optoelectronic, and energy applications due to their unique structural and electronic properties. This study presents a systematic investigati

Jiaxing Deng, Junbiao Pang, Zhicheng Wang, Haitao Yu

Parking spots are essential components, providing vital mobile resources for residents in a city. Accurate Global Positioning System (GPS) points of parking spots are the core data for subsequent applications,e.g., parking management, parking policy, and urban development. However, high-rise buildings tend to cause GPS points to drift from the actual locatio

Pierre Glaser, Kevin Han Huang, Arthur Gretton

We perform a non-asymptotic analysis of the contrastive divergence (CD) algorithm, a training method for unnormalized models. While prior work has established that (for exponential family distributions) the CD iterates asymptotically converge at an $O(n^{-1 / 3})$ rate to the true parameter of the data distribution, we show, under some regularity assumptions

Ashish Bhatia, Renato Cordeiro de Amorim, Vito De Feo

Regression analysis is employed to examine and quantify the relationships between input variables and a dependent and continuous output variable. It is widely used for predictive modelling in fields such as finance, healthcare, and engineering. However, traditional methods often struggle with real-world data complexities, including uncertainty and ambiguity.

Yan-Hong Yao, Yi-Hao Shen, Tian-Nuo Li, Guo-Hong Du

In this work, we propose a non-standard dark matter (NSDM) model in which the equation of state (EoS) of dark matter (DM) is parameterized as $w_{\rm dm} = w_2 a^2$, and this DM model is motivated by the idea that DM must become cold dark matter (CDM) in the neighborhood of the scale factor $a = 0$, which implies that both the EoS of DM, $w_{\rm dm}$, and it

Jingtian Zhao, Beatriz Noheda, Martin F. Sarott

Highly-crystalline ferroelectric oxides integrated on Si hold great promise for energy-efficient memory and logic technologies. Exploiting epitaxial strain engineering in these materials is, however, severely hampered on Si, where the large structural mismatch often results in an inferior interfacial quality and causes a degradation of the ferroelectric swit

Pavithra Elumalai, Mohammad Bashiri, Goirik Chakrabarty, Suhas Shrinivasan

Visual perception relies on inference of 3D scene properties such as shape, pose, and lighting. To understand how visual sensory neurons enable robust perception, it is crucial to characterize their selectivity to such physically interpretable factors. However, current approaches mainly operate on 2D pixels, making it difficult to isolate selectivity for phy

Yushan Han, Hui Zhang, Honglei Zhang, Chuntao Ding

Collaborative perception has been proven to improve individual perception in autonomous driving through multi-agent interaction. Nevertheless, most methods often assume identical encoders for all agents, which does not hold true when these models are deployed in real-world applications. To realize collaborative perception in actual heterogeneous scenarios, e

Kayode Olumoyin, Katarzyna Rejniak

Physics-informed neural networks (PINNs) are neural networks that embed the laws of dynamical systems modeled by differential equations into their loss function as constraints. In this work, we present a PINN framework applied to oncology. Here, we seek to learn time-varying interactions due to a combination therapy in a tumor microenvironment. In oncology,

Ibrahim Sheikh Mohamed, Abdullah Yahya Abdullah Omaisan

Infrastructure in smart cities is increasingly monitored by networks of closed circuit television (CCTV) cameras. Roads, bridges and tunnels develop cracks, potholes, and fluid leaks that threaten public safety and require timely repair. Manual inspection is costly and hazardous, and existing automatic systems typically address individual defect types or pro

Kazuo Fujikawa

It is customary to identify $\psi_{+}=\nu_{R} + C\overline{\nu_{R}}^{T}$ with a Majorana fermion on the basis of chirality changing charge conjugation $\tilde{C}: \nu_{R}\rightarrow C\overline{\nu_{R}}^{T}$ and parity $\tilde{P}: \nu_{R}\rightarrow i\gamma^{0}\nu_{R}$. The theorem on the absence of a Majorana-Weyl fermion in $d=4$ states $\tilde{C}\gamma_{5}

Ahmed Alzubaidi, Shaikha Alsuwaidi, Basma El Amel Boussaha, Leen AlQadi

This survey provides the first systematic review of Arabic LLM benchmarks, analyzing 40+ evaluation benchmarks across NLP tasks, knowledge domains, cultural understanding, and specialized capabilities. We propose a taxonomy organizing benchmarks into four categories: Knowledge, NLP Tasks, Culture and Dialects, and Target-Specific evaluations. Our analysis re

Shuyu Sun, Zhangchengrui Wang, Lei Zhang, Jijing Zhao

We propose a domain-decomposition pore-network method (DD-PNM) for modeling single-phase Stokes flow in porous media. The method combines the accuracy of finite-element discretizations on body-fitted meshes within pore subdomains with a sparse global coupling enforced through interface unknowns. Local Dirichlet-to-Neumann operators are precomputed from finit

Andrew R. Siegel

We present a verification challenge based on the fractional cascading (FC) technique for accelerating repeated searches across a collection of sorted arrays. The specific context is nuclear cross section lookup in a simulation code, where a material consists of many nuclides, each with its own sorted energy grid. A naive search performs a binary search in ea

Junchao Zhang

Sparse matrix vector multiplication (SpMV) is a fundamental kernel in scientific codes that rely on iterative solvers. In this first part of our work, we present both a sequential and a basic MPI parallel implementations of SpMV, aiming to provide a challenge problem for the scientific software verification community. The implementations are described in the

Sehyeok Park, Santosh Nagarakatte

This paper describes our experience developing polynomial approximations for trigonometric functions that produce correctly rounded results for multiple representations and rounding modes using the RLIBM approach. A key challenge with trigonometric functions concerns range reduction with "pi", which reduces a given input in the domain of a 32-bit float to a

Alper Altuntas, Allison H. Baker, John Baugh, Ganesh Gopalakrishnan

Earth System Models (ESMs) are critical for understanding past climates and projecting future scenarios. However, the complexity of these models, which include large code bases, a wide community of developers, and diverse computational platforms, poses significant challenges for software quality assurance. The increasing adoption of GPUs and heterogeneous ar

Alexander C. Wilton

Scientific software is, by its very nature, complex. It is mathematical and highly optimized which makes it prone to subtle bugs not as easily detected by traditional testing. We outline how symbolic execution can be used to write tests similar to traditional unit tests while providing stronger verification guarantees and apply this methodology to a sparse m

Matthew Sottile, Mohit Tekriwal, John Sarracino

Correctness in scientific computing (SC) is gaining increasing attention in the formal methods (FM) and programming languages (PL) community. Existing PL/FM verification techniques struggle with the complexities of realistic SC applications. Part of the problem is a lack of a common understanding between the SC and PL/FM communities of machine-verifiable cor

Jiangyuan Guo, Wei Chen, Yuxuan Sun, Bo Ai

Deep joint source-channel coding (DJSCC) has emerged as a robust alternative to traditional separate coding for communications through wireless channels. Existing DJSCC approaches focus primarily on point-to-point wireless communication scenarios, while neglecting end-to-end communication efficiency in hybrid wireless-wired networks such as 5G and 6G communi

Charly Lassalle, Patrick Bonnay, Frédéric Bouly, Marco Di Giacomo

SPIRAL2 is a state-of-the-art superconducting linear accelerator for heavy ions. The radiofrequency operation of the linac can be disrupted by anomalies that affect its reliability. This work leverages fast, multivariate time series post-mortem data from the Low-Level Radio Frequency (LLRF) systems to differentiate anomaly groups. However, interpreting these

Jianhui Zhang, Sheng Cheng, Qirui Sun, Jia Liu

In this work, we present Patch-Adapter, an effective framework for high-resolution text-guided image inpainting. Unlike existing methods limited to lower resolutions, our approach achieves 4K+ resolution while maintaining precise content consistency and prompt alignment, two critical challenges in image inpainting that intensify with increasing resolution an

Yifu Luo, Xinhao Hu, Keyu Fan, Haoyuan Sun

Reinforcement learning (RL) has garnered increasing attention in text-to-image (T2I) generation. However, most existing RL approaches are tailored to either diffusion models or autoregressive models, overlooking an important alternative: masked generative models. In this work, we propose Mask-GRPO, the first method to incorporate Group Relative Policy Optimi

Liesbeth Allein, Nataly Pineda-Castañeda, Andrea Rocci, Marie-Francine Moens

How does a cause lead to an effect, and which intermediate causal steps explain their connection? This work scrutinizes the mechanistic causal reasoning capabilities of large language models (LLMs) to answer these questions through the task of implicit causal chain discovery. In a diagnostic evaluation framework, we instruct nine LLMs to generate all possibl

Debjyoti Majumdar

We study the effect of the spatial distribution of active force dipoles on the folding pathways and mechanical stability of rigid-elastic networks using Langevin dynamics simulations. While it has been shown in Majumdar et al., J. Chem. Phys. 163, 114902 (2025) that a sharp collapse transition is evident in triangular (elastic) bead-spring networks under the

Luis Crespo, Álvaro Pelayo

The coupled angular momentum is an integrable system with two degrees of freedom which is fundamental in physics and the theory of integrable systems. It is obtained by coupling two angular momenta. We construct a $p$-adic analog of this system for any prime number $p$ and describe its symplectic normal forms at the critical points. This analog has a rich si

Max Fan, Ariel E. Kellison, Samuel D. Pollard

We mechanize the fundamental properties of a rounding error model for floating-point arithmetic based on relative precision, a measure of error proposed as a substitute for relative error in rounding error analysis. A key property of relative precision is that it forms a true metric, providing a well-defined measure of distance between exact results and thei

Paul D. Hovland

We describe a challenge problem for verification based on the MPICH implementation of MPI. The MPICH implementation includes several algorithms for allreduce, all of which should be functionally equivalent to reduce followed by broadcast. We created standalone versions of three algorithms and verified two of them using CIVL.