October 2025 arXiv papers — page 5

Xuan Gong, Senmiao Wang, Hanbo Huang, Ruoyu Sun

Supervised fine-tuning (SFT) on long chain-of-thought (CoT) trajectories has emerged as a crucial technique for enhancing the reasoning abilities of large language models (LLMs). However, the standard cross-entropy loss treats all tokens equally, ignoring their heterogeneous contributions across a reasoning trajectory. This uniform treatment leads to misallo

Mohammed Marzuk T M, Vijayasarathy R, Madona Mathew

The act of posting a person's private photos or videos without their consent is known as revenge porn, and it is usually done to extort money or seek revenge. According to a 2010 cybercrime survey, about 18.3% of women were unaware that they were victims of revenge porn. In densely populated countries like India, such incidents are more likely, yet there is

Luc Molinet, Tomoyuki Tanaka

We study the Cauchy problem for one-dimensional dispersive equations posed on $\mathbb{R} $, under the hypotheses that the dispersive operator behaves, for high frequencies, as a Fourier multiplier by $ i |\xi|^\alpha \xi $ with $ 1 \le \alpha\le 2 $, and that the nonlinear term is of the form $ \partial_x f(u) $ where $f $ is a real analytic function satisf

Casper Fibaek, Abi Riley, Kelsey Doerksen, Do-Hyung Kim

This paper presents a multi-tiered human-in-the-loop framework for interactive school mapping designed to improve the accuracy and completeness of educational facility records, particularly in developing regions where such data may be scarce and infrequently updated. The first tier involves a machine learning based analysis of population density, land cover,

N. A. Makda, S. L. Blyth, R. E. Skelton

We perform a systematic statistical study of ultra-diffuse galaxy analogues (NUDGEs) in a large sample of galaxy clusters to investigate their properties with respect to the host clusters. We used data from the Hyper Suprime-Cam Subaru Strategic Program wide field survey and find a total of 5057 NUDGEs exceeding the background counts in 51 out of 66 galaxy c

I. N. Khoroshiy, S. A. Skorobogatov, S. E. Nikitin, I. A. Gudim

The multiferroic ferroborate GdFe$_3$(BO$_3$)$_4$ with huntite-type structure exhibits magnetic ordering below T$_N$ = 38 K and contains two magnetic subsystems associated with Gd and Fe ions. Competing anisotropies of these subsystems drive a spin reorientation transition at T$_{SR}$ = 10.7 K, switching the ground state from easy-axis to easy-plane. Using a

Georg Wieland, Axel Maas

Gauge invariance requires physical states to be composite, even in the weak sector of the Standard Model (SM). The Fr\"ohlich-Morchio-Strocchi (FMS) mechanism resolves this subtlety and predicts additional Higgs contributions in SM processes. While this has been supported by theoretical investigations in the bosonic sector, its impact on fermionic observable

John Bagiliko, David Stern, Francis Feehi Torgbor, Danny Parsons

In data-sparse regions, satellite and reanalysis rainfall estimates (SREs) are vital but limited by inherent biases. This study evaluates bias correction (BC) methods, including traditional statistical (LOCI, QM) and machine learning (SVR, GPR), applied to seven SREs across 38 stations in Ghana and Zambia. We introduce a constrained LOCI method to prevent th

Prosenjit Roy, Itai Shafrir

The aim of this article is to analyze the asymptotic behaviour of the eigenvalues of elliptic operators in divergence form with mixed boundary type conditions for domains that become unbounded in several directions, while they stay bounded in some directions (cylindrical domains). The limiting behavior of such eigenvalues is shown to depend on an ensemble of

Vitalii Vertogradov, Maksim Grigorev

The evaporation of black holes with two horizons presents a rich thermodynamic landscape that departs fundamentally from the Schwarzschild paradigm. In this work, we analyze the Hawking temperature dynamics of the Kiselev black hole under varying mass $M$ and anisotropic fluid parameter $N$, explicitly connecting temperature behavior to phase transitions and

Bernold Fiedler

For real $\mathbf{b}$, consider quadratic heat equations like \begin{equation*} \mathbf{w}_t=\mathbf{w}_{\boldsymbol{\xi}\boldsymbol{\xi}} + \mathbf{b}(\boldsymbol{\xi})\,\mathbf{w}^2 \end{equation*} on $\boldsymbol{\xi}\in(0,\pi)$ with Neumann boundary conditions. For $\mathbf{b}$=1, pioneering work by Ky\^uya Masuda in the 1980s aimed to circumvent PDE blo

Jianwen Sun, Fanrui Zhang, Yukang Feng, Chuanhao Li

Scientific illustrations demand both high information density and post-editability. However, current generative models have two major limitations: Frist, image generation models output rasterized images lacking semantic structure, making it impossible to access, edit, or rearrange independent visual components in the images. Second, code-based generation met

Karol Bołbotowski

We introduce a new non-linear optimal transport formulation for a pair of probability measures on $\mathbb{R}^d$ sharing a common barycentre, in which admissible transference plans satisfy two martingale-type constraints. This bi-martingale framework underlies and interconnects several variational problems on the space of probability measures. For the quadra

Kaushal Gupta, Theophilus Gera, Amit Sharma, Ashok Ji Gupta

We introduce the notion of pure extending modules, a refinement of classical extending modules in which only pure submodules are required to be essential in direct summands. Fundamental properties and characterizations are established, showing that pure extending and extending modules coincide over von Neumann regular rings. As an application, we prove that

A. Aliane, A. Daami, T. Card, R. Gwoziecki

A huge interesting progress in the field of organic electronic materials and devices has been observed in the last decade. However, the understanding of these materials is still a challenge to overcome. Most studies in literature focus on active devices such as OTFTs, OLEDs and OPVs. Nevertheless, a complete technology has to have also passive devices in ord

Yuhao Zhang, Dingxin Hu, Tinghao Yu, Hao Liu

Multi-modal Large Language Models (MLLMs) have gained significant attention in both academia and industry for their capabilities in handling multi-modal tasks. However, these models face challenges in mathematical geometric reasoning due to the scarcity of high-quality geometric data. To address this issue, synthetic geometric data has become an essential st

Gopika P B, Peter Bastian, Nagaiah Chamakuri

The numerical tools to simulate the bidomain model in cardiac electrophysiology are constantly developing due to the great clinical interest and scientific advances in mathematical models and computational power. The bidomain model consists of an elliptic partial differential equation (PDE) and a non-linear parabolic PDE of reaction-diffusion type, where the

Kanka Ghosh, Andreas M. Menzel

When a hole is introduced into an elastic material, it will usually act to reduce the overall mechanical stiffness. A general ambition is to investigate whether a stiff shell around the hole can act to maintain the overall mechanical properties. We consider this effect from a macroscopic continuum perspective down to atomistic scales. For this purpose, we fo

Adriano Da Silva, Eyüp Kizil, Victor Ayala

In this article, we study linear control systems on a 4-dimensional solvable Lie group. Our motivation stems from the model introduced in \cite{baspinar}, which presents a precise geometric framework in which the primary visual cortex $V1$ is interpreted as a fiber bundle over the retinal plane $M$ (identified with $\mathbb{R}^{2}$), with orientation $\theta

Meenu Ravi, Shailik Sarkar, Yanshen Sun, Vaishnavi Singh

Understanding post-wildfire vegetation loss is critical for developing effective ecological recovery strategies and is often challenging due to the extended time and effort required to capture the evolving ecosystem features. Recent works in this area have not fully explored all the contributing factors, their modalities, and interactions with each other. Fu

Irina A. Bilenko

Based on data obtained from Wilcox Solar Observatiry the solar polar magnetic fields reversals in cycles 21\,--\,25 were considered. The results indicate that the polarity reversal occurs at the maximum of sunspot activity of each cycle, but the beginning, end, and duration of the reversals did not demonstrate any association with the Wolf numbers, which are

Filip Novotný, Marek Talíř, Emil Varga, Ladislav Skrbek

Horizontally ($\mathbf{\Omega} \perp \mathbf{v}_{\rm{ns}}$) and axially ($\mathbf{\Omega} \parallel \mathbf{v}_{\rm{ns}}$) rotating counterflow of superfluid $^4$He (He~II) generated thermally in a square channel is studied using the second sound attenuation technique, detecting statistically steady state and temporal decay of the density of quantized vortex

Prashantkumar G. Patel

In this paper, we introduce a new family of Szasz-Mirakyan-Durrmeyer operators defined on the half-line [0,\infty), constructed using Laguerre-type kernels. We discuss in detail the algebraic structure and analytical properties of these operators. thoroughly investigated. Explicit closed-form expressions for the moments are derived, along with a differential

Yanlong Yang, Guanxiong Luo

Optical imaging systems are inherently imperfect due to diffraction limits, lens manufacturing tolerances, assembly misalignment, and other physical constraints. In addition, unavoidable camera shake and object motion further introduce non-ideal degradations during acquisition. These aberrations and motion-induced variations are typically unknown, difficult

Muhammad Garba, Alwell Nwachukwu, Jamel Ali, Theo Siegrist

We present a numerical investigation of the magnetophoresis of metal ions in porous media under static, nonuniform magnetic fields. The multiphysics simulations couple momentum transport, mass diffusion, and magnetic field equations, with the porous medium modeled using two distinct approaches: a Stokes-based formulation incorporating effective diffusivity,

Rebecca Taylor, Antoine Chancé, Dario Augusto Giove, Natalia Milas

This document is comprised of a collection of consolidated parameters for the key parts of the muon collider. These consolidated parameters follow on from the October 2024 Preliminary Parameters Report. Attention has been given to a high-level consistent set of baseline parameters throughout all systems of the complex, following a 10 TeV center-of-mass desig

Tomoko Yonezawa, Hirotake Yamazoe, Atsuo Fujino, Daigo Suhara

Human-robot interaction frequently involves physical proximity or contact. In human-human settings, people flexibly accept, reject, or tolerate such approaches depending on the relationship and context. We explore the design of a robot's rejective internal state and corresponding avoidance behaviors, such as withdrawing or pushing away, when a person approac

Łukasz Mazurkiewicz, Marcin Michalski, Szymon Żeberski

In this paper, we study the translations into the Baire space of several well-known $\sigma$-ideals and families originally defined on the Cantor space, using their combinatorial characterizations. These include the ideals of null sets, small sets, those generated by closed measure-zero sets, and the meager sets, leading to their "fake" analogues in the Bair

Pengfei Sun, Jascha Achterberg, Zhe Su, Dan F. M. Goodman

Neural networks rely on learning synaptic weights. However, this overlooks other neural parameters that can also be learned and may be utilized by the brain. One such parameter is the delay: the brain exhibits complex temporal dynamics with heterogeneous delays, where signals are transmitted asynchronously between neurons. It has been theorized that this del

Hetvi Jadav, Sadhana Matth, Himanshu Pandey

Metal-ion batteries are in huge demand to cope with the increasing need for renewable energy, especially in automobiles. In this work, we apply first-principle calculations to examine two-dimensional beryllium carbide (2D-Be2C) as a possible anode material for metal-ion (Na and K) batteries. 2D-Be2C is a semiconductor and becomes metallic by adsorbing metal

WonJun Moon, MinSeok Jung, Gilhan Park, Tae-Young Kim

Partially Relevant Video Retrieval (PRVR) seeks videos where only part of the content matches a text query. Existing methods treat every annotated text-video pair as a positive and all others as negatives, ignoring the rich semantic variation both within a single video and across different videos. Consequently, embeddings of both queries and their correspond

Emanuele Greco

Magnetic turbulence plays a crucial role in confining charged particles near the shock front of Supernova Remnants, enabling them to reach energies up to hundreds of TeV through a process known as Diffusive Shock Acceleration (DSA). These high-energy electrons spiral along magnetic field lines, emitting X-ray synchrotron radiation. The launch of the Imaging

Binhong Li, Benshuai Lyu

In this paper, we develop an analytical model to investigate the sound generated by the shock-instability interactions (SII) in supersonic round jets, extending our previous two-dimensional planar study to circular configurations. The jet is represented by a vortex sheet, with its motion modeled by the Euler equations. Shock and instability waves are modeled

Hetvi Jadav, Sadhana Matth, Himanshu Pandey

Metal-ion batteries (MIBs) are essential for transitioning to a cleaner and more sustainable energy future. By employing the density functional formalism, we have investigated the hexagonal (h) monolayer of BeS and BeTe as electrode materials for alkali (Li and Na) MIBs. The structural and thermodynamic stability, adsorption of Li/Na atoms, density of states

Noa Izsak

In the aftermath of COVID-19, many universities implemented supplementary "reinforcement" roles to support students in demanding courses. Although the name for such roles may differ between institutions, the underlying idea of providing structured supplementary support is common. However, these roles were often poorly defined, lacking structured materials, p

Hehui Zheng, Bhavya Sukhija, Chenhao Li, Klemens Iten

Soft robots offer unmatched adaptability and safety in unstructured environments, yet their compliant, high-dimensional, and nonlinear dynamics make modeling for control notoriously difficult. Existing data-driven approaches often fail to generalize, constrained by narrowly focused task demonstrations or inefficient random exploration. We introduce SoftAE, a

V. A. Yerokhin, B. Ohayon

We demonstrate that recent advances in QED theory of Li-like ions [V. A. Yerokhin et al., Phys. Rev. A 112, 042801 (2025)] enable determinations of absolute nuclear charge radii for heavy elements. By incorporating constraints derived from electron-scattering data, we obtain radii that are independent of the assumed model of the nuclear charge distribution.

Hugo Parlier

This article gives a short proof that all ideal polygons admit a short orthogeodesic decomposition. Specifically, all $n$-gons admit an orthogeodesic decomposition with orthogeodesics all of length at most $\sim 2 \log(n)$, and this is roughly optimal.

Bernard Pire, Kirill Semenov-Tian-Shansky, Paweł Sznajder, Lech Szymanowski

To study cross sections and polarization asymmetries for the processes $e p \to e n \pi^+$ and $e p \to e p \pi^0$ in the backward region, we develop a flexible phenomenological model for nucleon-to-pion transition distribution amplitudes ($\pi N$ TDAs), which are used in the QCD collinear factorization description of the scattering amplitudes. Our model is

Monu Sharma

Workday's compliance with global standards -- such as GDPR, SOC 2, HIPAA, ISO 27001, and FedRAMP -- shows its ability to best protect critical financial, healthcare, and government data.Automated compliance attributes like audit trails, behavioral analytics, and continuous reporting improve automation of the process and cut down on the manual effort to audit

Qianhang Ding, Minxi He, Hui-Yu Zhu

Dark matter (DM) can form dense condensates around black holes (BHs), such as superradiant clouds and ultracompact mini halos, which can significantly affect the orbital evolution of their companion objects through dynamical friction (DF). In this work, we define a novel quantity to quantify such effects in the emitted gravitational waves (GWs) in terms of G

J. A. DeVine, J. Terwisscha van Scheltinga, S. Ioppolo, K. -J. Chuang

Ethanol (CH3CH2OH) has been detected in interstellar ices within regions associated with the early stages of star and planet formation. Its solid-phase pathways can lead to diverse conditions that can significantly influence its photostability and -chemistry. Laboratory studies have explored the effects of energetic processing on pure ethanol ices, there is

Aditya Parikh, Sneha Das, Aasa Feragen

Deep learning models aim to improve diagnostic workflows, but fairness evaluation remains underexplored beyond classification, e.g., in image segmentation. Unaddressed segmentation bias can lead to disparities in the quality of care for certain populations, potentially compounded across clinical decision points and amplified through iterative model developme

Angana Borah, Adrija Datta, Ashish S. Kumar, Raviraj Dave

Efforts to green cities for cooling are succeeding unevenly because the same vegetation that cools surfaces can also intensify how hot the air feels. Previous studies have identified humid heat as a growing urban hazard, yet how physiologically active vegetation governs this trade-off between cooling and moisture accumulation remains poorly understood, leavi

Roman Freiberg, Alexander Qualmann, Ngo Anh Vien, Gerhard Neumann

Multi-embodiment grasping focuses on developing approaches that exhibit generalist behavior across diverse gripper designs. Existing methods often learn the kinematic structure of the robot implicitly and face challenges due to the difficulty of sourcing the required large-scale data. In this work, we present a data-efficient, flow-based, equivariant grasp s

Kowshik Balasubramanian, Andre Williams, Ismail Butun

This paper introduces a novel framework for enhancing Random Forest classifiers by integrating probabilistic feature sampling and hyperparameter tuning via Simulated Annealing. The proposed framework exhibits substantial advancements in predictive accuracy and generalization, adeptly tackling the multifaceted challenges of robust classification across divers

Tian Liang, Wenxiang Jiao, Zhiwei He, Jiahao Xu

Large Reasoning Models (LRMs) have demonstrated impressive capabilities but suffer from cognitive inefficiencies like "overthinking" simple problems and "underthinking" complex ones. While existing methods that use supervised fine-tuning (SFT) or reinforcement learning (RL) with token-length rewards can improve efficiency, they often do so at the cost of acc

Junfeng Lu, Yueyan Li

Advances in large language models are making personalized AI agents a new research focus. While current agent systems primarily rely on personalized external memory databases to deliver customized experiences, they face challenges such as memory redundancy, memory staleness, and poor memory-context integration, largely due to the lack of effective memory upd

Jarne Besjes, Robbe Nooyens, Tolgahan Bardakci, Mutlu Beyazit

Representational State Transfer (REST) APIs are a cornerstone of modern cloud native systems. Ensuring their reliability demands automated test suites that exercise diverse and boundary level behaviors. Nevertheless, designing such test cases remains a challenging and resource intensive endeavor. This study extends prior work on Large Language Model (LLM) ba

Taiyo Nakamura, Kensuke Homma

We propose a novel telescope concept based on Earth's gravitational lensing effect, optimized for the detection of distant dark matter sources, particularly axion-like particles (ALPs). When a unidirectional flux of dark matter passes through Earth at sufficiently high velocity, gravitational lensing can concentrate the flux at a distant focal region in spac

Mikel Artola, Ismael Ayuso, Ruth Lazkoz, Gonzalo Olmo

The $f(Q,C)$ framework of gravity enables the depiction of an effective dark energy fluid that emerges from geometry itself, thus leading to modifications in the cosmological phenomenology of General Relativity. We pursue this approach to discover new and observationally supported (effective) evolving dark energy models. We propose a general $f(Q,C)$ formula

Francis Atta Howard, Kinvi Kangni

In this study we investigate the nexus between the $\Spin (\frac12)$ and the $\SU(1,1)$-quasi boson Lie structure and reveal related properties as well as some decomposition of spin particles. We show that the $\SU(1,1)$-quasi boson has a left invariant Haar measure and we ascertain its spherical Fourier transformation. We finally show that this spherical Fo

Tommaso Del Carro, Gerson Portilla, Alexandre Seuret, Rafael Vazquez

This paper presents the design of a state-feedback control law for spacecraft rendezvous, formulated using the Hill-Clohessy-Wiltshire equations. The proposed method introduces an impulsive control strategy to regulate thruster operations. Specifically, a state-dependent switching framework is developed to determine both the control input magnitudes and the

Bruno Puri, Jim Berend, Sebastian Lapuschkin, Wojciech Samek

Interpretability is crucial for building safe, reliable, and controllable language models, yet existing interpretability pipelines remain costly and difficult to scale. Interpreting a new model typically requires training model-specific components (e.g., sparse autoencoders), followed by manual or semi-automated labeling and validation, imposing a growing "t

Mahipal Gurram

In this paper,we develop a novel representation of the zeta function expressed as the limiting difference between two structured double sums. This approach leads to a new and elegant identity involving maximum functions and additive terms, providing theoretical insights. The derivation relies on generalized harmonic series and polygamma functions, linking cl

Jianwen Sun, Yukang Feng, Yifan Chang, Chuanhao Li

A fundamental bottleneck in human-AI collaboration is the ``intention expression gap," the difficulty for humans to effectively convey complex, high-dimensional thoughts to AI. This challenge often traps users in inefficient trial-and-error loops and is exacerbated by the diverse expertise levels of users. We reframe this problem from passive instruction fol

Felipe E. Sanchez, Thomas A. Lake, Jason A. Galvis, Chris Jones

Data on livestock farm locations and demographics are essential for disease monitoring, risk assessment, and developing spatially explicit epidemiological models. Our semantic segmentation model achieved an F2 score of 92 % and a mean Intersection over Union of 76 %. An initial total of 194,474 swine barn candidates were identified in the Southeast (North Ca

Zhixing Zhao, Zhong-Ying Fan, Xiaobao Wang, Minyong Guo

While theoretically established for decades, the Penrose process - energy extraction from rotating black holes - still lacks clear observational evidence. A promising theoretical framework posits magnetic reconnection in the ergosphere as a trigger, causing a plasmoid to separate into an escaping positive-energy fragment and an infalling negative-energy one.

Nelson Mattié, Arturo Sanchez-Azofeifa, Pablo Crespo-Peremarch, Juan-Ygnacio López-Hernández

According to the Paris Climate Change Agreement, all nations are required to submit reports on their greenhouse gas emissions and absorption every two years by 2024. Consequently, forests play a crucial role in reducing carbon emissions, which is essential for meeting these obligations. Recognizing the significance of forest conservation in the global battle

Alp Öktem, Farida Boudichat

This paper presents Awal, a community-powered initiative for developing language technology resources for Tamazight. We provide a comprehensive review of the NLP landscape for Tamazight, examining recent progress in computational resources, and the emergence of community-driven approaches to address persistent data scarcity. Launched in 2024, awaldigital.org

Hao Fang, Jing Chi, Xiao-Qiu Qi, Yong-Hui Zhang

High-precision measurements of the fine-structure splittings in helium high Rydberg states have been reported, yet corresponding ab initio benchmarks for direct comparison remain unavailable. In this work, we extend the correlated B-spline basis function (C-BSBF) method to calculate the fine-structure splittings of high Rydberg states in $^4$He. The calculat

Shuhei Kobayashi

This paper presents a unified formulation of the origin of the arrow of time in classical and quantum mechanics. We begin with a mechanical analysis of a one-dimensional three-particle system, which provides a concrete example in which macroscopic irreversibility emerges despite microscopically reversible dynamics. By abstracting this mechanism, we identify

Ruifeng Leng, Cheng-Yang Lee, Siyi Zhou

Unitarity is a cornerstone of quantum theory, ensuring the conservation of probability and information. Although non-Hermitian Hamiltonians are typically associated with open or dissipative systems, pseudo-Hermitian quantum mechanics shows that real spectra and unitary evolution can still emerge through a suitably defined inner product. Motivated by this ins

Junkang Liu, Fanhua Shang, Junchao Zhou, Hongying Liu

The core bottleneck of Federated Learning (FL) lies in the communication rounds. That is, how to achieve more effective local updates is crucial for reducing communication rounds. Existing FL methods still primarily use element-wise local optimizers (Adam/SGD), neglecting the geometric structure of the weight matrices. This often leads to the amplification o

Imaan Hameed, Huma Umar, Fozia Umber, Maryam Mustafa

In recent years, LLM-based maternal health chatbots have been widely deployed in low-resource settings, but they often ignore real-world contexts where women may not own phones, have limited literacy, and share decision-making within families. Through the deployment of a WhatsApp-based maternal health chatbot with 48 pregnant women in Lahore, Pakistan, we ex

Jiahao Liu, Zijian Wang, Kuo Zhao, Dong Hu

Knowledge editing has emerged as an efficient approach for updating factual knowledge in large language models (LLMs). It typically locates knowledge storage modules and then modifies their parameters. However, most existing methods focus on the weights of multilayer perceptron (MLP) modules, which are often identified as the main repositories of factual inf

Yu Wang, Rongguo Yang, Jing Zhang, Xiaomin Liu

The precise measurement of spatial attitude parameters is critical for applications in inertial navigation, industrial monitoring, instrument calibration, quantum metrology, etc. In this work, we theoretically investigate and experimentally realize the simultaneous measurement of the yaw and pitch angles using a Hermite-Gaussian-postselected double weak valu

Joshua S. Harvey, Guanchao Feng, Sai Anusha Meesala, Tina Zhao

Despite their enormous predictive power, machine learning models are often unsuitable for applications in regulated industries such as finance, due to their limited capacity to provide explanations. While model-agnostic frameworks such as Shapley values have proved to be convenient and popular, they rarely align with the kinds of causal explanations that are

Damilola Fasiku, Wentao Tang

To reduce complexity and achieve scalable performance in high-dimensional black-box settings, we propose a distributed method for nonconvex derivative-free optimization of continuous variables with an additively separable objective, subject to linear equality constraints. The approach is built upon the alternating direction method of multipliers (ADMM) as th

Masanobu Kaneko, Masato Kuwata

We provide an explicit description of two torsion points on the classical Bianchi elliptic quintic curve in terms of Ramanujan's functions. As a byproduct, we describe generators and defining equations of several modular function fields of level 10 using those functions.

Yuhao Zhang, Guangjin Pan, Musa Furkan Keskin, Ossi Kaltiokallio

In this paper, we propose a unified localization framework (called UNILocPro) that integrates model-based localization and channel charting (CC) for mixed line-of-sight (LoS)/non-line-of-sight (NLoS) scenarios. Specifically, based on LoS/NLoS identification, an adaptive activation between the model-based and CC-based methods is conducted. Aiming for unsuperv

Sebastian Anita, Vincenzo Capasso, Simone Scacchi

In this paper investigations by the same authors on environmental issues concerning the control of the pollution produced by human activities have been extended to include costs related to environmental interventions. The proposed model consists of a spatially structured dynamic economic growth model which takes into account the level of pollution induced by

Sales Aribe

The rapid evolution of generative adversarial networks (GANs) and diffusion models has made synthetic media increasingly realistic, raising societal concerns around misinformation, identity fraud, and digital trust. Existing deepfake detection methods either rely on deep learning, which suffers from poor generalization and vulnerability to distortions, or fo

Ian Rabago, Giuseppe Lodato, Stefano Facchini, Zhaohuan Zhu

In binary systems with a strongly misaligned disk, the central binary stars can travel a significant vertical distance above and below the disk's orbital plane. This can cause large changes in illumination of the disk over the course of the binary orbital period. We use both analytic and radiative transfer models to examine the effect of changes in stellar i

John Southworth

UZ Dra is a detached and totally-eclipsing binary containing two late-F stars in a circular orbit of period 3.261 d. It has been observed by the Transiting Exoplanet Survey Satellite in 41 sectors, yielding a total of 664,809 high-quality flux measurements. We model these data and published radial velocities to determine the physical properties of the system

Patrick Laloyaux, Mihai Alexe, Eulalie Boucher, Peter Lean

The Data Assimilation (DA) community has been developing various diagnostics to understand the importance of the observing system in accurately forecasting the weather. They usually rely on the ability to compute the derivatives of the physical model output with respect to its initial condition. For example, the Forecast Sensitivity-based Observation Impact

Qiyuan Chen, Ke Ye

Structures of multilinear maps are characterized by invariants. In this paper we introduce two invariants, named the isotropy index and the completeness index. These invariants capture the tensorial structure of the kernel of a multilinear map. We establish bounds on both indices in terms of the partition rank, geometric rank, analytic rank and height, and p

Nikita Kornilov, Alexander Korotin

Flow Matching (FM) method in generative modeling maps arbitrary probability distributions by constructing an interpolation between them and then learning the vector field that defines ODE for this interpolation. Recently, it was shown that FM can be modified to map distributions optimally in terms of the quadratic cost function for any initial interpolation.

Hansjörg Albrecher, Jinxia Zhu

This paper explores the optimal policy for using an allocated carbon emission budget over time with the objective to maximize profit, by explicitly taking into account present-biased preferences of decision-makers, accounting for time-inconsistent preferences. The setup can be adapted to be applicable for either a (present-biased) individual or also for a co

Yueyang Wang, Mehmet Dogar, Gustav Markkula

Modelling pedestrian-driver interactions is critical for understanding human road user behaviour and developing safe autonomous vehicle systems. Existing approaches often rely on rule-based logic, game-theoretic models, or 'black-box' machine learning methods. However, these models typically lack flexibility or overlook the underlying mechanisms, such as sen

Sagar Dutta, Banani Basu, Fazal Ahmed Talukdar

Early fault diagnosis is imperative for the proper functioning of rotating machines. It can reduce economic losses in the industry due to unexpected failures. Existing fault analysis methods are either expensive or demand expertise for the installation of the sensors. This article proposes a novel method for the detection of bearing faults and imbalance in i

Shuang Li, Zhiyuan Lin, Sen Li, Mohan Zhang

A robust pre-emptive kill switch for cold atom experiments is introduced to significantly reduce costly system reassembly or replacement. The design incorporates upper (alarm) and lower (evaporation) event detection mechanisms based on predefined thresholds. Meanwhile, a duty cycle timing methodology is used to avert unintentional activation of the dispenser

Johannes Schrotshamer, Bernd Kolar, Markus Schöberl

In this contribution we discuss flat discrete-time nonlinear systems in a general setting including two special subclasses, namely, forward- and backward-flat systems. We relate rank conditions for certain submatrices of the Jacobian of the flat parameterization to the mentioned subclasses. Motivated by these rank conditions, for the case of two-input system

Sales G. Aribe

Spiking Neural Networks (SNNs) represent the latest generation of neural computation, offering a brain-inspired alternative to conventional Artificial Neural Networks (ANNs). Unlike ANNs, which depend on continuous-valued signals, SNNs operate using distinct spike events, making them inherently more energy-efficient and temporally dynamic. This study present

Austin Meek, Eitan Sprejer, Iván Arcuschin, Austin J. Brockmeier

Chain-of-thought (CoT) outputs let us read a model's step-by-step reasoning. Since any long, serial reasoning process must pass through this textual trace, the quality of the CoT is a direct window into what the model is thinking. This visibility could help us spot unsafe or misaligned behavior (monitorability), but only if the CoT is transparent about its i

Jan Wójcik

The mean squared displacement has been widely used as the primary metric for comparing quantum and classical random walks, with quantum walks showing quadratic scaling versus linear scaling for classical walks. However, this comparison may not capture the full picture: while the mean squared displacement is well-suited for Gaussian distributions, quantum wal

He-Guang Xu, Shujie Cheng

The Dicke-Hubbard model, describing an ensemble of interacting atoms in a cavity, provides a rich platform for exploring collective quantum phenomena. However, its potential for quantum thermodynamic applications remains largely uncharted. Here, we study a quantum Otto heat engine whose working substance is a system governed by the Dicke-Hubbard Hamiltonian.

Jean-Marc Couveignes, Reynald Lercier

We study natural evaluation and interpolation problems for elliptic functions and prove that they allow a recursive treatment using a variant of classical butterflies first introduced by Gauss. We deduce the existence of straight-line programs with complexity scaling with $d\log(d)$ for these problems and present applications to finite field arithmetic, codi

Josep M. Trigo-Rodríguez, Pau Grèbol-Tomàs, Jordi Ibáñez-Insa, Jacinto Alonso-Azcárate

Undifferentiated asteroids, particularly the parent bodies of carbon-rich chondrite groups, might be promising candidates for future space resource utilization due to their primitive composition and potential to host valuable metals and rare earth elements. However, our understanding of their bulk elemental composition remains limited, as most data are deriv

Sumanjit Chakraborty, Gopi K. Seemala

The present investigation is directed at exploring southern polar ionospheric responses to intense space weather events and their correlations with plasma convection and auroral precipitation. The main phases of six geomagnetic storms occurring in the year 2023 (ascending phase of the present solar cycle) are considered for this study. The ionospheric Total

Sagar Dutta, Banani Basu, Fazal Ahmed Talukdar

This article investigates how the creeping wave propagation around the human thigh could be used to monitor the leg movements. The propagation path around the human thigh gives information regarding leg motions that can be used for the classification of activities. The variation of the transmission coefficient is measured between two on-body polyethylene ter

Jaroslav Vlcek, Michal Kaufman, Elnaz M. Nia, Jiri Houska

We report a high-performance thermochromic VO2-based coating prepared by using a three-step process, consisting of magnetron sputter depositions of SiO2 films and V-W films and their postannealing, on standard glass at a low substrate temperature of 350 {\deg}C without opening the vacuum chamber to atmosphere. It is formed by four layers of W-doped VO2 nanop

Tosin Adewumi, Martin Karlsson, Marcus Liwicki, Mikael Sjödahl

We present a comprehensive systematic survey of the application of natural language processing (NLP) along the entire battery life cycle, instead of one stage or method, and introduce a novel technical language processing (TLP) framework for the EU's proposed digital battery passport (DBP) and other general battery predictions. We follow the Preferred Report

Michał Eckstein, Tomasz Miller, Karol Życzkowski

Quasimetric spaces form a natural framework to study distance problems with an inherent directional asymmetry. We introduce a simple novel class of quasimetrics on probability simplices, inspired by the Chebyshev distance. It is shown that such quasimetrics have expedient geometric properties -- they induce the Euclidean topology and a Finslerian infinitesim

Anson Wen Han Cheong, Vahid Badali, Sean Kiely, Iman Roohani

Achieving an optimal biomechanical environment within bone scaffolds is critical for promoting tissue regeneration, particularly in load-bearing anatomical sites where rigid fixation can induce stress shielding and compromise healing. Functionally graded (FG) scaffolds, which incorporate controlled variations in porosity or material properties, have attracte

Sam Fatehmanesh Vegas, Matt Thomson, James Gornet, David Prober

Neural dynamics underlie behaviors from memory to sleep, yet identifying mechanisms for higher-order phenomena (e.g., social interaction) is experimentally challenging. Existing whole-brain models often fail to scale to single-neuron resolution, omit behavioral readouts, or rely on PCA/conv pipelines that miss long-range, non-linear interactions. We introduc

Touqeer Ahmad, Abid Hussain

Count regression models are necessary for examining discrete dependent variables alongside covariates. Nonetheless, when data display outliers, overdispersion, and an abundance of zeros, traditional methods like the zero-inflated negative binomial (ZINB) model sometimes do not yield a satisfactory fit, especially in the tail regions. This research presents a

Meftun Akarsu, Kerem Catay, Sedat Bin Vedat, Enes Kutay Yarkan

We present a practical pipeline for fine-tuning open-source video diffusion transformers to synthesize cinematic scenes for television and film production from small datasets. The proposed two-stage process decouples visual style learning from motion generation. In the first stage, Low-Rank Adaptation (LoRA) modules are integrated into the cross-attention la

Mengjie Deng, Guanting Dong, Zhicheng Dou

Recently, large language models (LLMs) have demonstrated remarkable problem-solving capabilities by autonomously integrating with external tools for collaborative reasoning. However, due to the inherently complex and diverse nature of multimodal information, enabling multimodal large language models (MLLMs) to flexibly and efficiently utilize external tools

Sławomir Dinew, Dan Popovici

Given an $n$-dimensional compact K\"ahler manifold, we continue our study of $m$-positivity in two ways. We first propose generalisations of the notions of pseudo-effective and big Bott-Chern cohomology classes of bidegree $(1,\,1)$ by relaxing the standard positivity hypotheses to their $m$-counterparts after we have proved a Lamari-type duality lemma in bi