March 2025 arXiv papers — page 23

Jan Lewen, Max Pargmann, Jenia Jitsev, Mehdi Cherti

Concentrating Solar Power (CSP) plants are a key technology in the transition toward sustainable energy. A critical factor for their safe and efficient operation is the distribution of concentrated solar flux on the receiver. However, flux distributions from individual heliostats are sensitive to surface imperfections. Measuring these surfaces across many he

Bruno Oziel Fernandez, Rodrigo Bloot, Marcelo Moret

Binary optimization problems are emerging as potential candidates for useful applications of quantum computing. Among quantum algorithms, the quantum approximate optimization algorithm (QAOA) is currently considered the most promising method to obtain a quantum advantage for such problems. The QAOA method uses a classical counterpart to perform optimization

C. Granados, B. Kumar Das, M. F. Ciappina, W. Gao

The interaction of light with matter serves as a fundamental tool for probing material properties across a wide range of energy regimes. Recent breakthroughs in tailoring the topology of coherent electromagnetic fields have opened new avenues for exploring how matter uniquely responds to the topological characteristics of light. In this work, we conduct a co

Yizhang Zhu, Runzhi Jiang, Boyan Li, Nan Tang

Text-to-SQL automatically translates natural language queries to SQL, allowing non-technical users to retrieve data from databases without specialized SQL knowledge. Despite the success of advanced LLM-based Text-to-SQL approaches on leaderboards, their unsustainable computational costs--often overlooked--stand as the "elephant in the room" in current leader

Zhonglin Jiang, Qian Tang, Zequn Wang

Large Language Models (LLMs) have demonstrated remarkable in-context learning capabilities, enabling flexible utilization of limited historical information to play pivotal roles in reasoning, problem-solving, and complex pattern recognition tasks. Inspired by the successful applications of LLMs in multiple domains, this paper proposes a generative design met

Ada Gorgun, Bernt Schiele, Jonas Fischer

Neural networks are widely adopted to solve complex and challenging tasks. Especially in high-stakes decision-making, understanding their reasoning process is crucial, yet proves challenging for modern deep networks. Feature visualization (FV) is a powerful tool to decode what information neurons are responding to and hence to better understand the reasoning

David Fischinger, Martin Boyer

The orchestrated manipulation of public opinion, particularly through manipulated images, often spread via online social networks (OSN), has become a serious threat to society. In this paper we introduce the Digital Forensics Net (DF-Net), a deep neural network for pixel-wise image forgery detection. The released model outperforms several state-of-the-art me

Vida Adeli, Soroush Mehraban, Majid Mirmehdi, Alan Whone

Gait analysis is crucial for the diagnosis and monitoring of movement disorders like Parkinson's Disease. While computer vision models have shown potential for objectively evaluating parkinsonian gait, their effectiveness is limited by scarce clinical datasets and the challenge of collecting large and well-labelled data, impacting model accuracy and risk of

Josu Yeregui, Iker Lopetegi, Sergio Fernandez, Erik Garayalde

This paper presents a novel physical parameter estimation framework for on-site model characterization, using a two-phase modelling strategy with Physics-Informed Neural Networks (PINNs) and transfer learning (TL). In the first phase, a PINN is trained using only the physical principles of the single particle model (SPM) equations. In the second phase, the m

Tereza Vrabcová, Marek Kadlčík, Petr Sojka, Michal Štefánik

Negations are key to determining sentence meaning, making them essential for logical reasoning. Despite their importance, negations pose a substantial challenge for large language models (LLMs) and remain underexplored. We constructed and published two new textual entailment datasets NoFEVER-ML and NoSNLI-ML in four languages (English, Czech, German, and Ukr

Rulin Zhou, Wenlong He, An Wang, Qiqi Yao

Accurate tissue point tracking in endoscopic videos is critical for robotic-assisted surgical navigation and scene understanding, but remains challenging due to complex deformations, instrument occlusion, and the scarcity of dense trajectory annotations. Existing methods struggle with long-term tracking under these conditions due to limited feature utilizati

Mylène Sauty, Jean-Philippe Banon, Nicolas M. S. Lopes, Tanay Tak

The electron density of states in the open quantum well formed by the downward band bending region at the surface of cesiated p-type GaN is investigated. We theoretically predict the existence of metastable resonant states in this non confining potential with an intrinsic lifetime around 20 fs. Their experimental observation requires access to the empty cond

Xiao Jiang, Grace J. Gang, J. Webster Stayman

We have previously introduced Spectral Diffusion Posterior Sampling (Spectral DPS) as a framework for accurate one-step material decomposition by integrating analytic spectral system models with priors learned from large datasets. This work extends the 2D Spectral DPS algorithm to 3D by addressing potentially limiting large-memory requirements with a pre-tra

Md Fazle Rabbi, Rajshakhar Paul, Arifa Islam Champa, Minhaz F. Zibran

Vulnerabilities in software libraries and reusable components cause major security challenges, particularly in dependency-heavy ecosystems such as Maven. This paper presents a large-scale analysis of vulnerabilities in the Maven ecosystem using the Goblin framework. Our analysis focuses on the aspects and implications of vulnerability types, documentation de

Alheydis Geiger

For certain tropical quartic curves the existing techniques could not predict the lifting behavior of their bitangents over the real numbers. We close this gap by using patchworking techniques. Further, this paper provides an analysis of the combinatorial types of real tropical quartic curves according to their real topology and number of real bitangents. Th

Dawid Płudowski, Francesco Spinnato, Piotr Wilczyński, Krzysztof Kotowski

Counterfactual explanations provide an intuitive way to understand model decisions by identifying minimal changes required to alter an outcome. However, applying counterfactual methods to time series models remains challenging due to temporal dependencies, high dimensionality, and the lack of an intuitive human-interpretable representation. We introduce MASC

Zhiyu Yang, Shuo Wang, Yukun Yan, Yang Deng

LLMs are transforming software development, yet current code generation and code repair benchmarks mainly assess syntactic and functional correctness in simple, single-error cases. LLMs' capabilities to autonomously find and fix runtime logical errors in complex data science code remain largely unexplored. To address this gap, we introduce DSDBench: the Data

Alexei V. Finkelstein

The name "Mpemba effect" was given to the finding that "If two systems are cooled, the water that starts hotter may freeze first", confirmed by numerous of observations. Now this paradoxical state-ment obtained a more general form "the state that is initially more distant from its equilibrium state comes to this state earlier". Though seemingly violating the

S M Sivalingam, V Govindaraj, A. S. Hendy

The study of parametric differential equations plays a crucial role in weather forecasting and epidemiological modeling. These phenomena are better represented using fractional derivatives due to their inherent memory or hereditary effects. This paper introduces a novel scientific machine learning approach for solving parametric time-fractional differential

Bohang Fang, Yue Chen, Changhong Zhao

The concept of dispatchable region plays a pivotal role in quantifying the capacity of power systems to accommodate renewable generation. In this paper, we extend the previous approximations of the dispatchable regions on direct current (DC), linearized, and nonlinear single-phase alternating current (AC) models to unbalanced three-phase radial (tree) networ

Christophe Piveteau, Lukas Schmitt, David Sutter

Circuit cutting is a technique for simulating large quantum circuits by partitioning them into smaller subcircuits, which can be executed on smaller quantum devices. The results from these subcircuits are then combined in classical post-processing to accurately reconstruct the expectation value of the original circuit. Circuit cutting introduces a sampling o

Jazer Jose H. Togonon, Jean-Noel Chotard, Alessandro Longo, Lorenzo Stievano

The interest in Ni-rich layered oxide positive electrode materials has been increasing due to its wide applicability particularly in electric vehicles as high capacity and high energy density electrode materials. However, the Ni-O bond array which builds the overall framework and plays a critical role in the charge compensation mechanism of the material requ

CMS Collaboration

A search for resonances in top quark pair ($\text{t}\bar{\text{t}}$) production in final states with two charged leptons and multiple jets is presented, based on proton-proton collision data collected by the CMS experiment at the CERN LHC at $\sqrt{s}$ = 13 TeV, corresponding to 138 fb$^{-1}$. The analysis explores the invariant mass of the $\text{t}\bar{\te

Zeljko Cuckovic, Jari Taskinen

Pointwise lower bounds on the open unit disc $\bbD$ for the sum of the moduli of two analytic functions $f$ and $g$ (or their derivatives) are known in several cases, like $f,g$ belonging to the Bloch space $\cB$, $BMOA$ or the weighted Hardy space $H_\omega^\infty$. We find complementary results of Ramey-Ullrich and Abakumov-Doubtsov for functions with litt

Jakob Murauer, Rajiv Krishnakumar, Sabine Tornow, Michaela Geierhos

Existing approaches to quantum reservoir computing can be broadly categorized into restart-based and continuous protocols. Restart-based methods require reinitializing the quantum circuit for each time step, while continuous protocols use mid-circuit measurements to enable uninterrupted information processing. A gap exists between these two paradigms: while

Stephen Meisenbacher, Chaeeun Joy Lee, Florian Matthes

The task of $\textit{Differentially Private Text Rewriting}$ is a class of text privatization techniques in which (sensitive) input textual documents are $\textit{rewritten}$ under Differential Privacy (DP) guarantees. The motivation behind such methods is to hide both explicit and implicit identifiers that could be contained in text, while still retaining t

Tianshu Chen, Aiqun Hu, Shiqi Zhang

Eliminating the influence of temporally varying channel components on the radio frequency fingerprint (RFF) extraction has been an enduring and challenging issue. To overcome this problem, we propose a channel-independent RFF extraction method inspired by the idea of 'fighting fire with fire'. Specifically, we derive the linear differential spectrum and the

Filip Filipi

We give a comprehensive description of conjugation quandles and their connectedness. In this context, we find a characterization of Hayashi's conjecture (2013) in terms of a centrality condition of groups. This condition is thus a conjecture itself and it states that powers of elements of a finite and generating conjugacy classes should be central whenever t

Abhinava Danish, Shubham Mishra, Sourav Pal, Aditya Srivastav

Correlators of Wilson-line, which capture eikonal contributions, are known to exponentiate in non-abelian gauge theories, and their logarithms can be organised in terms of collections of Feynman diagrams called webs. In~\cite{Agarwal:2020nyc} the concept of correlator web (Cweb), which is a set of skeleton diagrams built with connected gluon correlators and

Christian Steinhauser, Philipp Reis, Hubert Padusinski, Jacob Langner

Precise perception of the environment is essential in highly automated driving systems, which rely on machine learning tasks such as object detection and segmentation. Compression of sensor data is commonly used for data handling, while virtualization is used for hardware-in-the-loop validation. Both methods can alter sensor data and degrade model performanc

Giulio Federico, Giuseppe Amato, Fabio Carrara, Claudio Gennaro

Understanding the nature of human sketches is challenging because of the wide variation in how they are created. Recognizing complex structural patterns improves both the accuracy in recognizing sketches and the fidelity of the generated sketches. In this work, we introduce ViSketch-GPT, a novel algorithm designed to address these challenges through a multi-

V. Tomas Mari Surkau, Urko Reinosa

We investigate the heavy-quark corner of the Columbia plot using the gluon potential derived from the Curci-Ferrari extension of the Faddeev-Popov gauge fixing in the center-symmetric Landau gauge, as a proxy for the Polyakov loop potential. In line with the observation that Landau gauge couplings are not that large in the case of heavy-quark QCD, we conside

Tobias Hainer, Erik Fransson, Sangita Dutta, Julia Wiktor

Understanding the phase behavior of mixed-cation halide perovskites is critical for optimizing their structural stability and optoelectronic performance. Here, we map the phase diagram of MA$_{1-x}$FA$_x$PbI$_3$ using a machine-learned interatomic potential in molecular dynamics simulations. We identify a morphotropic phase boundary (MPB) at approximately 27

Csaba Farkas, Alessio Fiscella, Ky Ho, Patrick Winkert

In this paper we study problems with critical and sandwich-type growth represented by \begin{align*} -\operatorname{div}\Big(|\nabla u|^{p-2}\nabla u + a(x)|\nabla u|^{q-2}\nabla u\Big)= \lambda w(x)|u|^{s-2}u+\theta B\left(x,u\right) \quad \text{in } \Omega,\quad u= 0 \quad\text{on } \partial \Omega, \end{align*} where $\Omega\subset\mathbb{R}^N$ is a bound

Haofei Lu, Yifei Dong, Zehang Weng, Florian T. Pokorny

We introduce the sequential multi-object robotic grasp sampling algorithm SeqGrasp that can robustly synthesize stable grasps on diverse objects using the robotic hand's partial Degrees of Freedom (DoF). We use SeqGrasp to construct the large-scale Allegro Hand sequential grasping dataset SeqDataset and use it for training the diffusion-based sequential gras

Andreas Dzemski, Ryo Okui, Wenjie Wang

Significant treatment effects are often emphasized when interpreting and summarizing empirical findings in studies that estimate multiple, possibly many, treatment effects. Under this kind of selective reporting, conventional treatment effect estimates may be biased and their corresponding confidence intervals may undercover the true effect sizes. We propose

Johannes B. S. Petersen, Akbar Davoodi, Thomas Gärtner, Marc Hellmuth

We present an exact algorithm for computing all common subgraphs with the maximum number of vertices across multiple graphs. Our approach is further extended to handle the connected Maximum Common Subgraph (MCS), identifying the largest common subgraph in terms of either vertices or edges across multiple graphs, where edges or vertices may additionally be la

Shakul Awasthi, Hyunggyu Park, Jae Sung Lee

One of the key objectives in investigating small stochastic systems is the development of micrometer-sized engines and the understanding of their thermodynamics. However, the primary mathematical tool used for this purpose, the overdamped approximation, has a critical limitation: it fails to fully capture the thermodynamics when the temperature varies over t

Martin Bladt, Laurits Glargaard, Theodor Henningsen

We study the consistency and weak convergence of the conditional tail function and conditional Hill estimators under broad dependence assumptions for a heavy-tailed response sequence and a covariate sequence. Consistency is established under $\alpha$-mixing, while asymptotic normality follows from $\beta$-mixing and second-order conditions. A key aspect of o

Svetlana Kulagina, Anne Benoit, Henning Meyerhenke

The analysis of massive scientific data often happens in the form of workflows with interdependent tasks. When such a scientific workflow needs to be scheduled on a parallel or distributed system, one usually represents the workflow as a directed acyclic graph (DAG). The vertices of the DAG represent the tasks, while its edges model the dependencies between

Nandakishor M, Vrinda Govind, Anuradha Puthalath, Anzy L

Force estimation in human-object interactions is crucial for various fields like ergonomics, physical therapy, and sports science. Traditional methods depend on specialized equipment such as force plates and sensors, which makes accurate assessments both expensive and restricted to laboratory settings. In this paper, we introduce ForcePose, a novel deep lear

Yuan He, Bailan He, Zifeng Ding, Alisia Lupidi

Understanding and mitigating hallucinations in Large Language Models (LLMs) is crucial for ensuring reliable content generation. While previous research has primarily focused on "when" LLMs hallucinate, our work explains "why" and directly links model behaviour to the pre-training data that forms their prior knowledge. Specifically, we demonstrate that an as

Niklas Rottmayer, Claudia Redenbach

The assessment of segmentation quality plays a fundamental role in the development, optimization, and comparison of segmentation methods which are used in a wide range of applications. With few exceptions, quality assessment is performed using traditional metrics, which are based on counting the number of erroneous pixels but do not capture the spatial distr

Jelena Ćelić, Rodrigo Guedes Lang, Simon Steinmassl, Jim Hinton

This study introduces a time-based cleaning method for H.E.S.S. using CT5 in monoscopic mode and presents an optimization workflow for image-cleaning algorithms to enhance telescope sensitivity while minimizing systematic biases. We evaluate three methods - tail-cut cleaning and two flavours of time-based cleaning TIME3D and TIME4D - and find best-cut config

Tomoaki Okayama, Yuito Kuwashita, Ao Kondo

F. Stenger proposed efficient approximation formulas for derivatives over infinite intervals. These formulas were derived by combining the Sinc approximation with appropriate conformal maps. It has been demonstrated that these formulas can attain root-exponential convergence. In this study, we enhance the convergence rate by improving the conformal maps empl

Jiahao Xia, Min Xu, Wenjian Huang, Jianguo Zhang

Despite the similar structures of human faces, existing face alignment methods cannot learn unified knowledge from multiple datasets with different landmark annotations. The limited training samples in a single dataset commonly result in fragile robustness in this field. To mitigate knowledge discrepancies among different datasets and train a task-agnostic u

Matheus Rodrigues Felizardo, Nuno Miguel Feixa Rodrigues, António Coelho, Sónia Silva Sousa

Background: Executive functions (EFs) are cognitive processes essential for controlling impulses, staying focused, thinking before acting, and managing information. Childhood is a critical period for EF development, but there is a lack of standardized tools that combine EF tasks with physical activity in a gamified approach. Objectives: This scoping review m

Meghyn Bienvenu, Diego Figueira, Pierre Lafourcade

The Shapley value, originating from cooperative game theory, has been employed to define responsibility measures that quantify the contributions of database facts to obtaining a given query answer. For non-numeric queries, this is done by considering a cooperative game whose players are the facts and whose wealth function assigns 1 or 0 to each subset of the

Hadrien Reynaud, Alberto Gomez, Paul Leeson, Qingjie Meng

Advances in deep learning have significantly enhanced medical image analysis, yet the availability of large-scale medical datasets remains constrained by patient privacy concerns. We present EchoFlow, a novel framework designed to generate high-quality, privacy-preserving synthetic echocardiogram images and videos. EchoFlow comprises four key components: an

Zhongquan Chen, Pim van der Hoorn, Bjoern Baumeier

This study investigates the impact of exchange-correlation functional choices on the predictive accuracy of multiscale models for charge transport in organic semiconductors (OSCs). A hybrid functional approach is applied to analyze uncertainties in key parameters influencing charge mobility, focusing on the Hartree--Fock exchange fraction. Using 2-methyl-9,1

Jie Ma, Chu-Han Wang, Xiao-Yun Xu, Chang-Kun Shi

In the information explosion era, the demand for high-density stable storage technologies is soaring. Multi-dimensional optical storage with femtosecond laser writing offers a potential solution for massive data storage. However, equipment instability and reduced voxel resolution inevitably lead to data errors. Here, we propose and demonstrate a paradigm exe

Félix Hoffet, Alexey Vylegzhanin, Emanuele Distante, Lukas Heller

A promising platform for quantum information research relies on cavity coupled atomic spin-waves, enabling efficient operations such as quantum memories, quantum light generation and entanglement distribution. In this work, we study the strong coupling between non-classical collective spin excitations generated by Raman scattering in a cold $^{87}\mathrm{Rb}

Yubo Li, Yidi Miao, Xueying Ding, Ramayya Krishnan

Large Language Models (LLMs) have shown remarkable capabilities across various tasks, but their deployment in high-stake domains requires consistent and coherent behavior across multiple rounds of user interaction. This paper introduces a comprehensive framework for evaluating and improving LLM response consistency, making three key contributions. Code and d

Barış Batuhan Topal, Umut Özyurt, Zafer Doğan Budak, Ramazan Gokberk Cinbis

Recent advancements in text-to-image generative models, particularly latent diffusion models (LDMs), have demonstrated remarkable capabilities in synthesizing high-quality images from textual prompts. However, achieving identity personalization-ensuring that a model consistently generates subject-specific outputs from limited reference images-remains a funda

Byeongjun Kwon, Munchurl Kim

Zero-shot depth estimation (DE) models exhibit strong generalization performance as they are trained on large-scale datasets. However, existing models struggle with high-resolution images due to the discrepancy in image resolutions of training (with smaller resolutions) and inference (for high resolutions). Processing them at full resolution leads to decreas

Filippo Sarti, Alessio Savini

We define bounded cohomology of $t$-discrete measured groupoids with coefficients into measurable bundles of Banach spaces. Our approach via homological algebra extends the classic theory developed by Ivanov and by Monod. As a consequence, we show that the bounded cohomology of a $t$-discrete groupoid $\mathcal{G}$ can be computed using any amenable $\mathca

Li-Heng Chen, Zi-Xin Zou, Chang Liu, Tianjiao Jing

Accurate surface reconstruction from unposed images is crucial for efficient 3D object or scene creation. However, it remains challenging, particularly for the joint camera pose estimation. Previous approaches have achieved impressive pose-free surface reconstruction results in dense-view settings, but could easily fail for sparse-view scenarios without suff

Christos Charmousis, Simon Iteanu, David Langlois, Karim Noui

We study axial perturbations of static black holes with primary hair in a family of degenerate higher-order scalar-tensor (DHOST) theories. These solutions possess a scalar charge, fully independent of the mass, leading to a continuous one-parameter deformation of the standard Schwarzschild black hole. Starting from these solutions, we also construct new bla

T. M. Aliev, A. Elpe, I. Turan, L. Selbuz

The Belle II Collaboration reported the first measurement on ${\rm Br}(B^+\rightarrow K^+\nu\bar{\nu})$, which lies 2.7$\sigma$ away from the Standard Model expectation. This result may be manifestation of new physics beyond the Standard Model. In present work, motivated by the Belle II measurement, we investigate the effect of a dark photon/dark $Z$ on the

Ruifeng Luo, Zhengjie Liu, Tianxiao Cheng, Jie Wang

Recognizing symbols in architectural CAD drawings is critical for various advanced engineering applications. In this paper, we propose a novel CAD data annotation engine that leverages intrinsic attributes from systematically archived CAD drawings to automatically generate high-quality annotations, thus significantly reducing manual labeling efforts. Utilizi

Maria Padilla Engstrøm, Anders Sundnes Løvlie

We present a work in progress that explores using a Large Language Model (LLM) as a design material for an interactive museum installation. LLMs offer the possibility of creating chatbots that can facilitate dynamic and human-like conversation, engaging in a form of role play to bring historical persons to life for visitors. However, LLMs are prone to produc

Huiang He, Minghui Hu, Chuanxia Zheng, Chaoyue Wang

Recent advances in style and appearance transfer are impressive, but most methods isolate global style and local appearance transfer, neglecting semantic correspondence. Additionally, image and video tasks are typically handled in isolation, with little focus on integrating them for video transfer. To address these limitations, we introduce a novel task, Sem

Qing-Hua Zhu

Due to the short gravitational timescale of Sgr A*, variable emissions near the galactic center are expected in the Very-long-baseline interferometry observations. Phenomenologically, the high-flux variable emissions could be interpreted as occasional events from hotspots within accretion disks. It provides a probe of black hole (BH) geometry and accretion m

Zhihang Lin, Mingbao Lin, Yuan Xie, Rongrong Ji

This paper introduces Completion Pruning Policy Optimization (CPPO) to accelerate the training of reasoning models based on Group Relative Policy Optimization (GRPO). GRPO, while effective, incurs high training costs due to the need to sample multiple completions for each question. Our experiment and theoretical analysis reveal that the number of completions

Mingqi Sun, Kai Liao

Gravitationally lensed quasars have served as a powerful tool for studying the composition of dark matter (DM) in lensing galaxies. In this work, we propose a novel method to investigate stellar-mass primordial black holes (PBHs) by using the microlensing effect of strongly lensed Type Ia supernovae (SNe Ia). Using the parameters of the lensed quasar system

Elisabetta Matricardi, Lorenzo Pucci, Elia Favarelli, Enrico Paolini

In this work, we investigate a multistatic MIMO-OFDM joint sensing and communication (JSC) system that leverages cooperation among spatially distributed base stations (BSs) to detect and localize multiple targets through soft fusion of range-angle maps. We propose an innovative selective data fusion strategy that combines only the most reliable regions of ra

J. Terasaki, O. Civitarese

We calculate the nuclear matrix element (NME), effective axial-vector current coupling $g_A^\mathrm{eff}$, and half-life of the double-$\beta$ ($\beta\beta$) decay using the transition operator perturbed by the nuclear interaction. The correction terms for the NME are obtained by extending the hadron sector to a higher order in terms of the Rayleigh-Schr\"{o

Shrikant Malviya, Pablo Arnau-González, Miguel Arevalillo-Herráez, Stamos Katsigiannis

The rapid advancement of large language models (LLMs) has introduced new challenges in distinguishing human-written text from AI-generated content. In this work, we explored a pipelined approach for AI-generated text detection that includes a feature extraction step (i.e. prompt-based rewriting features inspired by RAIDAR and content-based features derived f

F. Stefani, G. M. Horstmann, G. Mamatsashvili, T. Weier

This work builds on a recently developed self-consistent synchronization model of the solar dynamo which attempts to explain Rieger-type periods, the Schwabe/Hale cycle and the Suess-de Vries and Gleissberg cycles in terms of resonances of various wave phenomena with gravitational forces exerted by the orbiting planets. We start again from the basic concept

Anna Cisek, Antoni Szczurek

We discuss prompt production of $J/\psi$ mesons in proton-proton collisions at the $\sqrt{s}$ $=$ $68.5$ $GeV$ energy within NRQCD $k_t$-factorization approach using different unintegrated gluon distributions functions (UGDFs). We include both direct color-singlet production ($g g \to J/\psi g$) as well as a feed-down from $\chi_c \to J/\psi \gamma$ and $\ps

Cecilia Holmgren, Jasper Ischebeck, Daniel Krenn, Florian Lesny

We consider a multi-pivot QuickSort algorithm using $K\in\mathbb{N}$ pivot elements to partition a nonsorted list into $K+1$ sublists in order to proceed recursively on these sublists. For the partitioning stage, various strategies are in use. We focus on the strategy that minimizes the expected number of key comparisons in the standard random model, where t

Felix Reichel

Bessel's correction adjusts the denominator in the sample variance formula from n to n-1 to ensure an unbiased estimator of the population variance. This paper provides rigorous algebraic derivations geometric interpretations and visualizations to reinforce the necessity of this correction. It further introduces the concept of Bariance an alternative dispers

Mahdi Anbarloei

In this paper, we introduce (weakly) square-difference factor absorbing hyperideals in a multiplicative hyperring

M. Ban, P. Voloshyn, R. Adomaviciene, E. Bachelet

We report the analysis of a planetary microlensing event AT2021uey. The event was observed outside the Galactic bulge and was alerted by both space- (Gaia) and ground-based (ZTF and ASAS-SN) surveys. From the observed data, we find that the lens system is located at a distance of 1 kpc and comprises an M-dwarf host star of about half a solar mass, orbited by

Ziping Dong, Chao Shuai, Zhongjie Ba, Peng Cheng

Invisible Image Watermarking is crucial for ensuring content provenance and accountability in generative AI. While Gen-AI providers are increasingly integrating invisible watermarking systems, the robustness of these schemes against forgery attacks remains poorly characterized. This is critical, as forging traceable watermarks onto illicit content leads to f

Louis Owen, Nilabhra Roy Chowdhury, Abhay Kumar, Fabian Güra

Motivated in part by their relevance for low-precision training and quantization, massive activations in large language models (LLMs) have recently emerged as a topic of interest. However, existing analyses are limited in scope, and generalizability across architectures is unclear. This paper helps address some of these gaps by conducting an analysis of mass

Yancong Lin, Shiming Wang, Liangliang Nan, Julian Kooij

Scene flow estimation aims to recover per-point motion from two adjacent LiDAR scans. However, in real-world applications such as autonomous driving, points rarely move independently of others, especially for nearby points belonging to the same object, which often share the same motion. Incorporating this locally rigid motion constraint has been a key challe

Pascal Börner, Max Klimm, Annette Lutz, Marc E. Pfetsch

The construction of a cost minimal network for flows obeying physical laws is an important problem for the design of electricity, water, hydrogen, and natural gas infrastructures. We formulate this problem as a mixed-integer non-linear program with potential-based flows. The non-convexity of the constraints stemming from the potential-based flow model togeth

Wadim Strielkowski

This paper focuses on assessing the potentials for the efficient low carbon development in green hydrogen and ammonia economy using an example of Ukraine as a case study. It describes the country prerequisites for the transition to renewable energy sources and outlines the ongoing green hydrogen projects. Moreover, it offers a comprehensive SWOT analysis of

Yali Du, Hui Sun, Ming Li

Code translation migrates codebases across programming languages. Recently, large language models (LLMs) have achieved significant advancements in software mining. However, handling the syntactic structure of source code remains a challenge. Classic syntax-aware methods depend on intricate model architectures and loss functions, rendering their integration i

Sören Wilkening, Andreea-Iulia Lefterovici, Lennart Binkowski, Marlene Funck

Solving combinatorial optimization problems is a promising application area for quantum algorithms in real-world scenarios. In this work, we extend the "Quantum Tree Generator" (QTG), previously proposed for the 0-1 Knapsack Problem, to the 0-1 Quadratic Knapsack Problem (QKP) and the Multidimensional Knapsack Problem (MDKP). The QTG constructs a superpositi

Chenyang Xu, XingGuo Deng, Rui Zhong

The 3D Gaussian Splatting (3D-GS) is a novel method for scene representation and view synthesis. Although Scaffold-GS achieves higher quality real-time rendering compared to the original 3D-GS, its fine-grained rendering of the scene is extremely dependent on adequate viewing angles. The spectral bias of neural network learning results in Scaffold-GS's poor

Toshihisa Kubo, Bent Ørsted

The main objective of this paper is twofold. One is to classify and construct $SL(3,\mathbb{R})$-intertwining differential operators between vector bundles over the real projective space $\mathbb{RP}^2$. It turns out that two kinds of operators appear. We call them Cartan operators and PRV operators. The second objective is then to study the representations

E. de la Hoz, P. Diego-Palazuelos, J. Errard, A. Gruppuso

Cosmic birefringence (CB) is the rotation of the photons' linear polarisation plane during propagation. Such an effect is a tracer of parity-violating extensions of standard electromagnetism and would probe the existence of a new cosmological field acting as dark matter or dark energy. It has become customary to employ cosmic microwave background (CMB) polar

Justinas Smertinas, Nicolaj Hans Nielsen, Matthias Y. C. Van Hove, Peder Bacher

This work presents a scalable Bayesian modeling framework for evaluating building energy performance using smart-meter data from 2,788 Danish single-family homes. The framework leverages Bayesian statistical inference integrated with Energy Signature (ES) models to characterize thermal performance in buildings. This approach quantifies key parameters such as

Jannes Kordilla, Marco Dentz, Juan J. Hidalgo

We introduce the open-source Python-based code openKARST for flow in karst conduit networks. Flow and transport in complex karst systems remain a challenging area of hydrogeological research due to the heterogeneous nature of conduit networks. Flow regimes in these systems are highly dynamic, with transitions from free-surface to fully pressurized and lamina

Daichi Hayashi, Graham E. Leigh

In Hayashi and Leigh (2024), the authors formulate classical number realisability for first-order arithmetic and a corresponding axiomatic system based on Krivine's classical realisability interpretation. This paper presents a self-referential generalisation of previous results in the spirit of Friedman and Sheard (1987).

K. Białas, J. Spiechowicz

Active matter is one of the hottest topics in physics nowadays. As a prototype of living systems operating in viscous environments it has usually been modeled in terms of the overdamped dynamics. Recently, active matter in the underdamped regime has gained a place in the spotlight. In this work we unveil another remarkable face of active matter. In doing so

Femke J. Witmans, Mathijs G. C. Mientjes, Maarten J. G. Kamphuis, Vince van de Sande

We report on a variety of quantum transport experiments in SnTe nanowire devices. Research on these particular nanowire devices is relevant because of their topological properties and their potential to distinguish surface states owing to their high surface-to-volume ratio that suppresses the bulk contribution to the conductance. We observe a low-resistance

L. Chappuis, D. Eckert, M. Sereno, A. Gavidia

The nature of the elusive dark matter can be probed by comparing the predictions of the cold dark matter framework with the gravitational field of massive galaxy clusters. However, a robust test of dark matter can only be achieved if the systematic uncertainties in the reconstruction of the gravitational potential are minimized. Techniques based on the prope

Nils B. Weidmann, Mats Faulborn, David García

Current political developments worldwide illustrate that research on democratic backsliding is as important as ever. A recent exchange in Political Science & Politics (2/2024) has highlighted again a fundamental challenge in this literature: the measurement of democracy. With many democracy indicators consisting of subjective assessments rather than factual

Helge Øystein Maakestad

In the paper I introduce a new characteristic class $c(E)$ for a finite rank vector bundle $E$ on an affine scheme $S:=Spec(A)$ - the fundamental class of $E$. The class $c(E)$ is not a characteristic class in the classical sense in the sense that it lives in a pointed cohomology torsor $\operatorname{Ext}^1(L, \operatorname{End}_A(E))$. Most characteristic

Zenghui Chang, Yang Zhang, Hu Tan, Hong Cai Chen

Nonlinear dynamics system identification is crucial for circuit emulation. Traditional continuous-time domain modeling approaches have limitations in fitting capability and computational efficiency when used for modeling circuit IPs and device behaviors.This paper presents a novel continuous-time domain hybrid modeling paradigm. It integrates neural network

Cody A Carr, Aaron Smith, Viraj Pandya, Christopher C. Hayward

$\require{mediawiki-texvc}$Galactic outflows shape galaxy evolution, but their mass, energy, and momentum transfer remain uncertain. High-resolution spectroscopy can help, but systematic discrepancies hinder model interpretation. In this study, we evaluate the performance of semi-analytical line transfer (SALT) and empirical partial covering models (PCMs) to

Ilia Grishmanovskii, Taesoo Song, Carsten Greiner, Elena Bratkovskaya

We extend our investigation of heavy quark transport coefficients in the effective dynamical quasiparticle model (DQPM) -- which reproduces nonperturbative QCD phenomena in the strongly interacting quark-gluon plasma (sQGP) according to lattice QCD data -- by including inelastic $2 \to 3$ processes with massive gluon radiation, in addition to elastic $2 \to

Colin Cros, Laurent Ferro-Famil

This paper presents a method for estimating the characteristics of a diffuse source from interferometric measurements in the context of SAR tomography. The proposed method is based on the use of central moments of the reflectivity density and does not use any a priori model. The method's performance is discussed as a function of antenna array parameters (res

Zakaria Laskar, Tomas Vojir, Matej Grcic, Iaroslav Melekhov

Before deployment in the real-world deep neural networks require thorough evaluation of how they handle both knowns, inputs represented in the training data, and unknowns (anomalies). This is especially important for scene understanding tasks with safety critical applications, such as in autonomous driving. Existing datasets allow evaluation of only knowns o

Aleksa Marusic, Sao Mai Nguyen, Adriana Tapus

Physical rehabilitation exercises suggested by healthcare professionals can help recovery from various musculoskeletal disorders and prevent re-injury. However, patients' engagement tends to decrease over time without direct supervision, which is why there is a need for an automated monitoring system. In recent years, there has been great progress in quality

Donald Woukeng

In this paper, we introduce a novel persistence framework for Morse decompositions in Markov chains using combinatorial multivector fields. Our approach provides a structured method to analyze recurrence and stability in finite-state stochastic processes. In our setting filtrations are governed by transition probabilities rather than spatial distances. We co

Noël Brunetière

During the wear process of surfaces in sliding friction, there is a running-in period during which the topography of surfaces changes with time before reaching the steady wear regime. In the steady wear regime, the statistical parameters used to describe the topography of the surfaces remain almost constant. Some experimental studies have shown that starting