March 2025 arXiv papers — page 21

Adam Chalabi, Charlotte Kristjansen, Chenliang Su

We investigate integrability properties of Gukov-Witten 1/2-BPS surface defects in $SU(N)$ $\mathcal{N}=4$ super-Yang-Mills (SYM) theory in the large-$N$ limit. We demonstrate that ordinary Gukov-Witten defects, which depend on a set of continuous parameters, are not integrable except for special sub-sectors. In contrast to these, we show that rigid Gukov-Wi

Ingrid Vazquez-Holm, Andres Luna

We set up a procedure to systematically obtain Compton-like amplitudes in an arbitrary-spin theory, exploiting their factorization properties, and colour-kinematics duality. We furthermore investigate the constraining of Wilson coefficients for arbitrary spinning bodies and its relation to colour-kinematic duality.

Hayato Wakai, Atsuto Seko, Isao Tanaka

Polynomial machine learning potentials (MLPs) based on polynomial rotational invariants have been systematically developed for various systems and applied to efficiently predict crystal structures. In this study, we propose a robust methodology founded on polynomial MLPs to comprehensively enumerate crystal structures under high-pressure conditions and to ev

S. Aaron McClendon, Vishaal Venkatesh, Juan Morinelli

In modern ML Ops environments, model deployment is a critical process that traditionally relies on static heuristics such as validation error comparisons and A/B testing. However, these methods require human intervention to adapt to real-world deployment challenges, such as model drift or unexpected performance degradation. We investigate whether reinforceme

Dirk Tunger, Philipp Schaer

In the context of academic publishing and peer review, this study investigates the relationship between post-publication expert evaluations, their agreement levels, and the subsequent scientific and public recognition of the reviewed research. Using expert statements from the Science Media Center Germany as a dataset, we analyze Research in Context reviews t

Srabani Karmakar, Milind Deo, Imteaz Rahaman, Swomitra Kumar Mohanty

Silica nanoparticles have emerged as key building blocks for advanced applications in electronics, catalysis, energy storage, biomedicine, and environmental science. In this review, we focus on recent developments in both the synthesis and deposition of these nanoparticles, emphasizing the widely used St\"ober method and the versatile technique of electropho

Thomas Boucher, Nicholas Tetlow, Annie Fung, Amy Dewar

Purpose: The distribution of visceral adipose tissue (VAT) in cystectomy patients is indicative of the incidence of post-operative complications. Existing VAT segmentation methods for computed tomography (CT) employing intensity thresholding have limitations relating to inter-observer variability. Moreover, the difficulty in creating ground-truth masks limit

Hernán Barrio-Zhang, Glen McHale, Gary G. Wells, Rodrigo Ledesma-Aguilar

Siliconization is widely used as a coating technique to engineer surface properties, such as in the pharmaceutical and medical device industries to lubricate motion, ensure complete dispensation of product, and to inhibit protein adsorption and biofilm growth. In the hitherto unconnected literature, there has recently been significant progress in understandi

Tobias Rohe, Maximilian Balthasar Mansky, Michael Kölle, Jonas Stein

Training the Variational Quantum Eigensolver (VQE) is a task that requires substantial compute. We propose the use of concepts from transfer learning to considerably reduce the training time when solving similar problem instances. We demonstrate that its utilisation leads to accelerated convergence and provides a similar quality of results compared to circui

Adam Breuer, Bryce J. Dietrich, Michael H. Crespin, Matthew Butler

This paper introduces the largest and most comprehensive dataset of US presidential campaign television advertisements, available in digital format. The dataset also includes machine-searchable transcripts and high-quality summaries designed to facilitate a variety of academic research. To date, there has been great interest in collecting and analyzing US pr

Heiko Renz, Maximilian Krämer, Frank Hoffmann, Torsten Bertram

Visual observation of objects is essential for many robotic applications, such as object reconstruction and manipulation, navigation, and scene understanding. Machine learning algorithms constitute the state-of-the-art in many fields but require vast data sets, which are costly and time-intensive to collect. Automated strategies for observation and explorati

Fengjunjie Pan, Nenad Petrovic, Vahid Zolfaghari, Long Wen

In the domain of model-based engineering, models are essential components that enable system design and analysis. Traditionally, the creation of these models has been a manual process requiring not only deep modeling expertise but also substantial domain knowledge of target systems. With the rapid advancement of generative artificial intelligence, large lang

Madeline Overton, Rebecca G. Martin, Stephen H. Lubow, Stephen Lepp

Motivated by misaligned discs observed in eccentric orbit Be/X-ray binaries, we examine the evolution of a retrograde disc around one component of an eccentric binary with hydrodynamic simulations, $n$-body simulations and linear theory. Forced eccentricity growth from the eccentric orbit binary causes the initially circular disk to undergo eccentricity osci

Kevin Cohen, Laura Manrique-Gómez, Rubén Manrique

This study explores the use of large language models (LLMs) to enhance datasets and improve irony detection in 19th-century Latin American newspapers. Two strategies were employed to evaluate the efficacy of BERT and GPT-4o models in capturing the subtle nuances nature of irony, through both multi-class and binary classification tasks. First, we implemented

Yuta Tomokiyo, Keita Nishimoto, Kimitaka Asatani, Ichiro Sakata

Researchers are no longer limited to producing knowledge; in today's complex world, they also address societal challenges by engaging in policymaking. Although involvement in policymaking has expanded, direct empirical evidence of its career benefits remains underexplored. Prior survey-based studies suggest potential advantages-such as broader professional n

Joseph Kelly, Frank Hu, Arianna Damiani, Michael S. Chen

Two-dimensional electronic spectroscopy (2DES) provides rich information about how the electronic states of molecules, proteins, and solid-state materials interact with each other and their surrounding environment. Atomistic molecular dynamics simulations offer an appealing route to uncover how nuclear motions mediate electronic energy relaxation and their m

Sarubi Thillainathan, Songchen Yuan, En-Shiun Annie Lee, Sanath Jayasena

Fine-tuning multilingual sequence-to-sequence large language models (msLLMs) has shown promise in developing neural machine translation (NMT) systems for low-resource languages (LRLs). However, conventional single-stage fine-tuning methods struggle in extremely low-resource NMT settings, where training data is very limited. This paper contributes to artifici

ATLAS Collaboration

A search for pair-production of vector-like leptons is presented, considering their decays into a third-generation Standard Model (SM) quark and a vector leptoquark ($U_1$) as predicted by an ultraviolet-complete extension of the SM, referred to as the '4321' model. Given the assumed decay of $U_1$ into third-generation SM fermions, the final state can conta

François Petit, Gérard Biau, Raphaël Porcher

We develop a mathematical framework to define an optimal individualized treatment rule (ITR) within the context of prioritized outcomes in a randomized controlled trial. Our optimality criterion is based on the framework of generalized pairwise comparisons. We propose two approaches for estimating optimal ITRs on a pairwise basis. The first approach is a var

B. Kunyangyuen, G. Malinowski, D. Lacour, J. -X. Lin

Single pulse All Optical Helicity-Independent Toggle Switching is observed in CoHo and CoDy alloys single layers. An original reversal mechanism is reported which contrasts with those observed to date. It is shown that the reversal process is on the {\mu}s timescale involving the reorganization / coalescence of domains and domain walls. The toggle switching

Chase Ruff, Tarun Naren, Oliver Wieben, Prashant Nagpal

Objective: Cardiotoxicity is a devastating complication of thoracic radiotherapy. Current radiotherapy imaging protocols are insufficient to decouple and quantify cardiac motion, limiting substructure-specific motion considerations in treatment planning. We propose a 5D-MRI workflow for substructure-specific motion analysis, with future extension to margin c

Iñigo Pikabea, Iñaki Lacunza, Oriol Pareras, Carlos Escolano

Rapid advancements in Visual Language Models (VLMs) have transformed multimodal understanding but are often constrained by generating English responses regardless of the input language. This phenomenon has been termed as Image-induced Fidelity Loss (IFL) and stems from limited multimodal multilingual training data. To address this, we propose a continuous mu

Lyuye Zhang, Chengwei Liu, Jiahui Wu, Shiyang Zhang

The prevalent use of third-party libraries (TPLs) in modern software development introduces significant security and compliance risks, necessitating the implementation of Software Composition Analysis (SCA) to manage these threats. However, the accuracy of SCA tools heavily relies on the quality of the integrated feature database to cross-reference with user

Rajdeep Singh Hundal, Yan Xiao, Xiaochun Cao, Jin Song Dong

Deep Reinforcement Learning (DRL) is a paradigm of artificial intelligence where an agent uses a neural network to learn which actions to take in a given environment. DRL has recently gained traction from being able to solve complex environments like driving simulators, 3D robotic control, and multiplayer-online-battle-arena video games. Numerous implementat

Apurva Patil, Riku Funada, Takashi Tanaka, Luis Sentis

This paper addresses the problem of hierarchical task control, where a robotic system must perform multiple subtasks with varying levels of priority. A commonly used approach for hierarchical control is the null-space projection technique, which ensures that higher-priority tasks are executed without interference from lower-priority ones. While effective, th

Runpeng Dai, Run Yang, Fan Zhou, Hongtu Zhu

Large Language Models (LLMs) and Vision-Language Models (VLMs) have achieved impressive performance across a wide range of tasks, yet they remain vulnerable to carefully crafted perturbations. In this study, we seek to pinpoint the sources of this fragility by identifying parameters and input dimensions (pixels or token embeddings) that are susceptible to su

Junkai Hu, Li Xia

We study a long-run mean-variance team stochastic game (MV-TSG), where each agent shares a common mean-variance objective for the system and takes actions independently to maximize it. MV-TSG has two main challenges. First, the variance metric is neither additive nor Markovian in a dynamic setting. Second, simultaneous policy updates of all agents lead to a

Kar Balan, Robert Learney, Tim Wood

The increasing integration of Artificial Intelligence across multiple industry sectors necessitates robust mechanisms for ensuring transparency, trust, and auditability of its development and deployment. This topic is particularly important in light of recent calls in various jurisdictions to introduce regulation and legislation on AI safety. In this paper,

Federico Manzoni, Matteo Romoli

We investigate higher-order asymptotic symmetries for a $p$-form gauge field in $(p + 2)$-dimensional Minkowski spacetime, where Hodge duality with a scalar holds. Employing symplectic renormalization, we identify $N + 1$ independent asymptotic charges, with each charge being parametrised by an arbitrary function of the angular variables. By means of the Hod

Nóra Frankl, Attila Jung

We present a unified approach to prove Helly-type theorems for monotone properties of boxes, such as having large volume or containing points from a given set. As a corollary, we obtain new proofs for several earlier results regarding specific monotone properties. Our results generalise to $H$-convex sets as well.

Hiroki Kuji, Tetsuro Nikuni, Yuta Shingu

Numerous methodologies have been proposed to implement imaginary time evolution (ITE) on quantum computers. Among these, variational ITE (VITE) methods for noisy intermediate-scale quantum (NISQ) computers have attracted much attention, which uses parametrized quantum circuits to mimic non-unitary dynamics. Although widely studied, conventional variational q

Barbara Hoffmann, Ruben Mayer

This paper examines the critical role of Graph Neural Networks (GNNs) in data preparation for generative artificial intelligence (GenAI) systems, with a particular focus on addressing and mitigating biases. We present a comparative analysis of three distinct methods for bias mitigation: data sparsification, feature modification, and synthetic data augmentati

Hugo Schreckenberg, Zayneb El Omari El Alaoui, Guilhem Gallot

A slightly tilted permanent magnet rotating at high speed can induce a magnetic field capable of trapping another permanent magnet in a gravity independent levitated bound state, bypassing Earnshaw's theorem. During levitation, the floater magnet is locked in a conical orbit at the same frequency as the rotor. This rotation allows the sides of the same polar

Francisc Bozgan

In the current paper, we investigate the fifth order modified KP-I eqaution, namely \begin{equation*} \partial_t u-\partial_{x}^{5}u-\partial_{x}^{-1}\partial_{y}u+\partial_{x}(u^3)=0. \end{equation*} This equation is $L^2$ critical and we prove on $\mathbb{R}\times\mathbb{R}$ that it is globally well posed in the natural energy space if the $L^2$ norm of th

Josh Millar, Yushan Huang, Sarab Sethi, Hamed Haddadi

Efficient on-device neural network (NN) inference offers predictable latency, improved privacy and reliability, and lower operating costs for vendors than cloud-based inference. This has sparked recent development of microcontroller-scale NN accelerators, also known as neural processing units ($\mu$NPUs), designed specifically for ultra-low-power application

Jakob De Moor, Hans Weytjens, Johannes De Smedt, Jochen De Weerdt

Prescriptive Process Monitoring (PresPM) is an emerging area within Process Mining, focused on optimizing processes through real-time interventions for effective decision-making. PresPM holds significant promise for organizations seeking enhanced operational performance. However, the current literature faces two key limitations: a lack of extensive compariso

S. Caliskan, A. M. Amarsi, M. Racca, I. Koutsouridou

The Galactic evolution of copper remains poorly understood, partly due to the strong departures from local thermodynamic equilibrium (LTE) affecting Cu I lines. A key source of uncertainty in non-LTE modelling is the treatment of inelastic Cu+H collisions. We present new rate coefficients based on a combined asymptotic LCAO and free electron model approach,

Ripal Ranpara

In this research paper, we propose a new type of energy-efficient Green AI architecture to support circular economies and address the contemporary challenge of sustainable resource consumption in modern systems. We introduce a multi-layered framework and meta-architecture that integrates state-of-the-art machine learning algorithms, energy-conscious computat

Chiara Lombardo, Andrea Sottini, Sarina Seiter, Gerard Colas des Francs

Interferometric-based microscopies stand as powerful label-free approaches for monitoring and characterising chemical reactions and heterogeneous nanoparticle systems in real time with single particle sensitivity. Nevertheless, coherent artifacts, such as speckle and parasitic interferences, together with limited photon fluxes from spatially incoherent sourc

Moritz Schmidt, Abhoy Kole, Leon Wichette, Rolf Drechsler

Shor's algorithm is one of the most prominent quantum algorithms, yet finding efficient implementations remains an active research challenge. While many approaches focus on low-level modular arithmetic optimizations, a broader perspective can provide additional opportunities for improvement. By adopting a mid-level abstraction, we analyze the algorithm as a

Pasquale Cascarano, Lorenzo Stacchio, Andrea Sebastiani, Alessandro Benfenati

In recent years, Diffusion Models have become the new state-of-the-art in deep generative modeling, ending the long-time dominance of Generative Adversarial Networks. Inspired by the Regularization by Denoising principle, we introduce an approach that integrates a Latent Diffusion Model, trained for the denoising task, into a variational framework using Half

Kanishk Goel, Jayashree Mohan, Nipun Kwatra, Ravi Shreyas Anupindi

The widespread adoption of Large Language Models (LLMs) has enabled diverse applications with very different latency requirements. Existing LLM serving frameworks rely on siloed infrastructure with coarse-grained workload segregation -- interactive and batch -- leading to inefficient resource utilization and limited support for fine-grained Quality-of-Servic

Roy Y. He, Martin Huska, Hao Liu

In this paper, we propose a novel variational model for decomposing images into their respective cartoon and texture parts. Our model characterizes certain non-local features of any Bounded Variation (BV) image by its Total Symmetric Variation (TSV). We demonstrate that TSV is effective in identifying regional boundaries. Based on this property, we introduce

A. Mohandasan, R. L. Smart, C. Reylé, V. Le Brun

Ultracool dwarfs (UCDs) encompass the lowest mass stars and brown dwarfs, defining the stellar substellar boundary. They have significant potential for advancing the understanding of substellar physics; however, these objects are challenging to detect due to their low luminosity. The wide coverage and deep sensitivity of the Euclid survey will increase the n

Lorenzo Clemente

The goal of this paper is to provide exact and terminating algorithms for the formal analysis of deterministic continuous-time control systems with affine input and polynomial state dynamics (in short, polynomial systems). We consider the following semantic properties: zeroness and equivalence, input independence, linearity, and analyticity. Our approach is

Zhendi Gong, Susan Francis, Eleanor Cox, Stamatios N. Sotiropoulos

Multi-organ segmentation holds paramount significance in many clinical tasks. In practice, compared to large fully annotated datasets, multiple small datasets are often more accessible and organs are not labelled consistently. Normally, an individual model is trained for each of these datasets, which is not an effective way of using data for model learning.

Leo de Waal, Matthaios Chouzouris, Marcelo A. Dias

In this work we propose a novel relationship between topology and damage propagation in Maxwell lattices that redefines fracture as a functional design feature rather than mere degradation. We demonstrate that topologically protected modes, inherently robust against perturbations, localise along lattice discontinuities and govern the mechanical response. By

Peng Zhang, Branson Blaylock

Road vehicles contribute to significant levels of greenhouse gas (GHG) emissions. A potential strategy for improving their aerodynamic efficiency and reducing emissions is through active adaptation of their exterior shapes to the aerodynamic environment. In this study, we present a reduced-scale morphing vehicle prototype capable of actively interacting with

Zeno Pavanello, Luigi De Maria, Andrea De Vittori, Michele Maestrini

Ensuring safety for spacecraft operations has become a paramount concern due to the proliferation of space debris and the saturation of valuable orbital regimes. In this regard, the Collision Avoidance Manoeuvre (CAM) has emerged as a critical requirement for spacecraft operators, aiming to efficiently navigate through potentially hazardous encounters. Curre

L. Uhthoff-Rodríguez, A. Hernández-López, E. G. Alonso-Torres, E. Esquivel-Ramírez

Cold atoms experiments employ magnetic fields, commonly generated by coils, as an essential tool to control and manipulate atomic samples. In these experiments, it is often necessary to rapidly switch the magnetic field between two values. However, typical power supplies have a limited switching time for the current flowing through the coil. We present a con

Daniel L. Clarkson, Eduard P. Kontar, Nicolina Chrysaphi, A. Gordon Emslie

Astrophysical radio sources are embedded in turbulent magnetised environments. In the 1 MHz sky, solar radio bursts are the brightest sources, produced by electrons travelling along magnetic field lines from the Sun through the heliosphere. We demonstrate that the magnetic field not only guides the emitting electrons, but also directs radio waves via anisotr

Sapna, Sushant K. Singh, David Wagner

We present a framework for spin dynamics in the quark-gluon plasma created in relativistic heavy-ion collisions. Under the approximation of small polarization, macroscopic spin degrees of freedom decouple from the background, and their evolution equations and transport coefficients have been computed using quantum kinetic theory of massive particles with non

Isabella Käming, Patrick Mehlitz

In this paper, we are concerned with stationarity conditions and qualification conditions for optimization problems with disjunctive constraints. This class covers, among others, optimization problems with complementarity, vanishing, or switching constraints, which are notoriously challenging due to their highly combinatorial structure. The focus of our stud

Riccardo Basilone, Matteo Bruno, Hygor Piaget Monteiro Melo, Michele Avalle

Active mobility is becoming an essential component of the green transition in modern cities. However, the challenge of designing an efficient network of protected bike lanes without disrupting existing road networks for motorised vehicles remains unsolved. This paper focuses on the specific case of Milan, using a network approach that considers street widths

Anja Beck, Michele Atzeni, Eluned Smith

We introduce a novel approach to extract the decay-amplitudes in $B\to V(\to M_1M_2)\ell^+\ell^-$ processes, where $V$ represents a meson with either $J = 0$ (S-wave) or $J = 1$ (P-wave). This approach enables the decay-amplitudes across the dihadron and dilepton invariant-masses to be extracted from data in a model-independent and continuous way. To achieve

Tongyoung Kim, Jeongeun Lee, Soojin Yoon, Sunghwan Kim

Conversational Recommender Systems (CRSs)aim to engage users in dialogue to provide tailored recommendations. While traditional CRSs focus on eliciting preferences and retrieving items, real-world e-commerce interactions involve more complex decision-making, where users consider multiple factors beyond simple attributes. To capture this complexity, we introd

Changshuai Wei, Ming Li, Yalu Wen, Chengyin Ye

With the advance of high-throughput genotyping and sequencing technologies, it becomes feasible to comprehensive evaluate the role of massive genetic predictors in disease prediction. There exists, therefore, a critical need for developing appropriate statistical measurements to access the combined effects of these genetic variants in disease prediction. Pre

Yao Chen, Sophie Sun, Konstantinos Sechidis, Cong Zhang

This paper reviews and compares methods to assess treatment effect heterogeneity in the context of parametric regression models. These methods include the standard likelihood ratio tests, bootstrap likelihood ratio tests, and Goeman's global test motivated by testing whether the random effect variance is zero. We place particular emphasis on tests based on t

Zhuo-Yang Song, Zeyu Li, Qing-Hong Cao, Ming-xing Luo

The geometric evolution of token representations in large language models (LLMs) presents a fundamental paradox: while human language inherently organizes semantic information in low-dimensional spaces ($\sim 10^1$ dimensions), modern LLMs employ high-dimensional embeddings ($\sim 10^3$ dimensions) processed through Transformer architectures. To resolve this

Jorge Almeida

The most developed aspect of the theory of finite semigroups is their classification in pseudovarieties. The main motivation for investigating such entities comes from their connection with the classification of regular languages via Eilenberg's correspondence. This connection prompted the study of various natural operators on pseudovarieties and led to seve

Kaiyuan Yang, Huang Ouyang, Xinyi Wang, Bingjie Lu

This paper introduces Natural-Level Synthesis, an innovative approach for generating hardware using generative artificial intelligence on both the system level and component-level. NLS bridges a gap in current hardware development processes, where algorithm and application engineers' involvement typically ends at the requirements stage. With NLS, engineers c

Michael Mommert, Philipp Bahavar, Robin Barta, Christian Bauer

We use the local curvature derived from velocity vector fields or particle tracks as a surrogate for structure size to compute curvature-based energy spectra. An application to homogeneous isotropic turbulence shows that these spectra replicate certain features of classical energy spectra such as the slope of the inertial range extending towards the equivale

Liang Zhang, Yanan Guo, Junxi Wang, Jinmin Li

Extending two-dimensional photonic integrated circuits (PICs) to three-dimensional (3D) configurations promises great potential for scaling up integration, enhancing functionality, and improving performance of PICs. Silicon-based 3D PICs have made substantial progress due to CMOS compatibility. However, the narrow bandgap of silicon (1.1 eV) limits their use

Michele Bosi, Andrea Lapi, Lumen Boco, Carlos Alonso-Alvarez

We build a semi-empirical framework of galaxy evolution (dubbed StAGE) firmly grounded on stellar archaeology. The latter provides data-driven prescriptions that, on a population statistical ground, allow to define the age and the star formation history for the progenitors of quiescent galaxies (QGs). We exploit StAGE to compute the cosmic star formation rat

Indrajit Jana, Sunita Rani

We consider two $n\times n$ non-Hermitian random matrices such that the $ij$th entry of one matrix is correlated with the $ij$th entry of the other matrix. However, the entries of any particular matrix are i.i.d. random variables. We study the asymptotic behavior of the combined spectrum, and the limit of the linear eigenvalue statistic defined on the combin

Haicheng Liao, Hanlin Kong, Bin Rao, Bonan Wang

Accurate motion forecasting is essential for the safety and reliability of autonomous driving (AD) systems. While existing methods have made significant progress, they often overlook explicit safety constraints and struggle to capture the complex interactions among traffic agents, environmental factors, and motion dynamics. To address these challenges, we pr

Nghiep Khoan Duong, Christian D. Multunas, Thomas Whoriskey, Mehrdad T. Kiani

Intermetallic compounds containing transition metals and group III-V metals tend to possess strong correlations and high catalytic activities, both of which can be enhanced via reduced dimensionality. Nanostructuring is an effective approach to explore this possibility, yet the synthesis of nanostructured intermetallics is challenging due to vast differences

Yijun Quan, Zushu Li, Giovanni Montana

Growing data privacy demands, driven by regulations like GDPR and CCPA, require machine unlearning methods capable of swiftly removing the influence of specific training points. Although verified approaches like SISA, using data slicing and checkpointing, achieve efficient unlearning for single models by reverting to intermediate states, these methods strugg

Gérard Ben Arous, Manuel Cabezas, Alexander Fribergh

We prove a scaling limit theorem for the simple random walk on critical lattice trees in $\mathbb{Z}^d$, for $d\geq 8$. The scaling limit is the Brownian motion on the Integrated Super-Brownian Excursion (BISE) which is the same one that we have identified earlier for other simpler models of anomalous diffusion on critical graphs in large enough dimension. T

Remy Sabathier, Niloy J. Mitra, David Novotny

Reconstructing dynamic assets from video data is central to many in computer vision and graphics tasks. Existing 4D reconstruction approaches are limited by category-specific models or slow optimization-based methods. Inspired by the recent Large Reconstruction Model (LRM), we present the Large Interpolation Model (LIM), a transformer-based feed-forward solu

Matilde N. Lalín, Siva Sankar Nair, Berend Ringeling, Subham Roy

We study the areal Mahler measure of the two-variable, $k$-parameter family $x+y+k$ and prove explicit formulas that demonstrate its relation to the standard Mahler measure of these polynomials. The proofs involve interpreting the areal Mahler measure as a random walk in the complex plane and utilizing the areal analogue of the Zeta Mahler function to arrive

Yue Hu, Alexander Tsymbaliuk

We construct a family of PBWD bases for the positive subalgebras of quantum loop algebras of type $C_n$ and $D_n$, as well as their Lusztig and RTT integral forms, in the new Drinfeld realization. We also establish a shuffle algebra realization of these $\mathbb{Q}(v)$-algebras (proved earlier in arXiv:2102.11269 by completely different tools) and generalize

Chirantan Mitra, Chetan Sriram Madasu, Lucas Gabardos, Chang Chi Kwong

The ability of structured light to mimic exotic topological skyrmion textures, encountered in high-energy physics, cosmology, magnetic materials, and superfluids has recently received considerable attention. Despite their promise as mechanisms for data encoding and storage, there has been a lack of studies addressing the transfer and storage of the topology

Theresa Schollenberger, Christian Rohde, Rainer Helmig

Evaporation-driven salt precipitation occurs in different contexts and leads to challenges in case of e.g. soil salinization or stress-introducing precipitation in building material. During evaporation, brine in porous media gets concentrated due to the loss of water until the solubility limit is reached and salt precipitates. Different models on the REV-sca

Yan-Chuan Cai, Mark Neyrinck

Cosmic voids are low-mass-density regions on intergalactic scales. They are where cosmic expansion and acceleration are most dominant, important places to understand and analyze for cosmology. This entry summarises theoretical underpinnings of cosmic voids, and explores several observational aspects, statistics and applications of voids. The density profiles

Qisheng He, Nicholas Summerfield, Peiyong Wang, Carri Glide-Hurst

Recent studies have shown that diffusion models produce superior synthetic images when compared to Generative Adversarial Networks (GANs). However, their outputs are often non-deterministic and lack high fidelity to the ground truth due to the inherent randomness. In this paper, we propose a novel High-fidelity Brownian bridge model (HiFi-BBrg) for determini

Victor Pettersson, Musa Furkan Keskin, Carina Marcus, Henk Wymeersch

Distributed multi-antenna systems are an important enabling technology for future intelligent transportation systems (ITS), showing promising performance in vehicular communications and near-field (NF) localization applications. This work investigates optimal deployments of phase-coherent sub-arrays on a vehicle for NF localization in terms of a Cram\'er-Rao

Tiago de Souza Farias, Gubio Gomes de Lima, Jonas Maziero, Celso Jorge Villas-Boas

We introduce MixFunn, a novel neural network architecture designed to solve differential equations with enhanced precision, interpretability, and generalization capability. The architecture comprises two key components: the mixed-function neuron, which integrates multiple parameterized nonlinear functions to improve representational flexibility, and the seco

Jamie Mclauchlan, Jim S. Walker, Vatsal Sanjay, Maziyar Jalaal

Intuitively, slow droplets stick to a surface and faster droplets splash or bounce. However, recent work suggests that on non-wetting surfaces, whether microdroplets stick or bounce depends only on their size and fluid properties, but not on the incoming velocity. Here, we show using theory and experiments that even poorly wetting surfaces have a velocity-de

Martin Kišš, Michal Hradiš, Martina Dvořáková, Václav Jiroušek

We introduce the AnnoPage Dataset, a novel collection of 7,550 pages from historical documents, primarily in Czech and German, spanning from 1485 to the present, focusing on the late 19th and early 20th centuries. The dataset is designed to support research in document layout analysis and object detection. Each page is annotated with axis-aligned bounding bo

P. Mas-Buitrago, J. -Y. Zhang, E. Solano, E. L. Martín

Understanding and characterising the magnetic activity of M dwarfs is of paramount importance in the search for Earth-like exoplanets orbiting them. Energetic stellar activity phenomena, such as flares or coronal mass ejections, which are common in these stars, are deeply connected with the habitability and atmospheric evolution of the surrounding exoplanets

Shuze Wang, Yunpeng Mei, Hongjie Cao, Yetian Yuan

Imitation learning (IL) has proven effective for enabling robots to acquire visuomotor skills through expert demonstrations. However, traditional IL methods are limited by their reliance on high-quality, often scarce, expert data, and suffer from covariate shift. To address these challenges, recent advances in offline IL have incorporated suboptimal, unlabel

Patrizia Boccacci, Christine De Mol, Ignace Loris

In the framework of sparsity-enforcing regularisation for linear inverse problems, we consider the minimisation of a square-root Lasso cost function. To solve this problem we devise a simple modification (called SQRT-ISTA) of the Iterative Soft-Thresholding Algorithm (ISTA) for the Lasso problem and we prove convergence for this algorithm. Under some additio

Shuqing Liu, Rong Su, Karl H. Johansson

The pattern formation task is commonly seen in a multi-robot system. In this paper, we study the problem of forming complex shapes with functionally limited mobile robots, which have to rely on other robots to precisely locate themselves. The goal is to decide whether a given shape can be filled by a given set of robots; in case the answer is yes, to complet

Cyril Gavoille, Nicolas Hanusse, Gabriel Le Bouder, Taïssir Marcé

The Freeze Tag Problem consists in waking up a swarm of robots starting with one initially awake robot. Whereas there is a wide literature of the centralized setting, where the location of the robots is known in advance, we focus in the distributed version where the location of the robots $\P$ are unknown, and where awake robots only detect other robots up t

Collin R. Johnson, Stijn de Vries, Kerstin Wohlgemuth, Sergio Lucia

This paper presents a novel dynamic model for slug flow crystallizers that addresses the challenges of spatial distribution without backmixing or diffusion, potentially enabling advanced model-based control. The developed model can accurately describe the main characteristics of slug flow crystallizers, including slug-to-slug variability but leads to a high

Markus Scherer, Markus Uhlmann, Genta Kawahara

We analyse the dynamics within the stability boundary between laminar and turbulent square duct flow with the aid of an edge-tracking algorithm. As for the circular pipe, the edge state turns out to be a chaotic attractor within the edge if the flow is not constrained to a symmetric subspace. The chaotic edge state dynamics is characterised by a sequence of

Jochem Hoogendijk, Ivan Kryven, Rik Versendaal

A multi-type branching process is defined as a random tree with labeled vertices, where each vertex produces offspring independently according to the same multivariate probability distribution. We demonstrate that in realizations of the multi-type branching process, the relative frequencies of the different types in the whole tree converge to a fixed ratio,

Raman Dutt, Harleen Hanspal, Guoxuan Xia, Petru-Daniel Tudosiu

In this work, we undertake the challenge of augmenting the existing generative capabilities of pre-trained text-only large language models (LLMs) with multi-modal generation capability while satisfying two core constraints: C1 preserving the preservation of original language generative capabilities with negligible performance degradation, and C2 adhering to

Samira Alkaee Taleghan, Morteza Karimzadeh, Andrew P. Barrett, Walter N. Meier

Accurate segmentation and mapping of sea ice types is crucial for safe polar navigation, offshore operations, and climate monitoring. While deep learning has demonstrated strong potential for automating sea ice type segmentation, its success often relies on access to extensive expert labeled datasets, which is both resource intensive and time consuming to cr

Gen Ye, Yong Cai

Inflation and dark energy (DE), both featuring accelerated expansion, are crucial components of modern cosmology. As indicated by singularity theorems, null energy condition (NEC) violation is essential for resolving the initial singularity of inflation. The latest DESI DR2 results show that the DE equation of state evolves from $w_{\rm DE} < -1$ at redshift

Masato Konoike, Koji Matsushita

For a lattice polytope $P$, the rank of $P$ is defined by $F-(\dim P+1)$, where $F$ is the number of facets of $P$. In this paper, we study matroid polytopes with small rank. More precisely, we characterize matroid independence polytopes and graphic matroid base polytopes with rank at most three. Furthermore, using this characterization, we investigate their

Martin Kišš, Michal Hradiš

Self-supervised learning has emerged as a powerful approach for leveraging large-scale unlabeled data to improve model performance in various domains. In this paper, we explore masked self-supervised pre-training for text recognition transformers. Specifically, we propose two modifications to the pre-training phase: progressively increasing the masking proba

Wenqiang Luo, Jacky Wai Keung, Boyang Yang, Jacques Klein

Recent advances in leveraging LLMs for APR have demonstrated impressive capabilities in fixing software defects. However, current LLM-based approaches predominantly focus on mainstream programming languages like Java and Python, neglecting less prevalent but emerging languages such as Rust due to expensive training resources, limited datasets, and insufficie

Thorben Kastenholz

In this note we prove that the fouth bounded cohomology of non-abelian free groups with trivial real coefficients is non-zero. In order to prove this, we establish a splitting argument whose simplest form is as follows: Let $M$ denote an $n$-manifold of non-zero simplicial volume and $S$ a codimension two submanifold of $M$, then one can conclude that the $n

Simran Kaur Ghatoray, Yongmin Li

Emotion recognition technology has been studied from the past decade. With its growing importance and applications such as customer service, medical, education, etc., this research study aims to explore its potential and importance in the field of User experience evaluation. Recognizing and keeping track of user emotions in user research video is important t

Vishal Upendran, Durgesh Tripathi, Bhargav Vaidya, Mark Cheung

This paper presents a comparison of plasma dynamics in Coronal Holes (CHs) and Quiet Sun (QS) through 2.5D MHD flux emergence simulations. The magnetic reconnection between the emerging and the pre-existing flux leads to the formation of cool, dense plasmoids with hot boundaries, and hot & cool jets with velocities $\approx50$ km s$^{-1}$. We perform spectra

Andreas Chari, Sean MacAvaney, Iadh Ounis

Globalisation and colonisation have led the vast majority of the world to use only a fraction of languages, such as English and French, to communicate, excluding many others. This has severely affected the survivability of many now-deemed vulnerable or endangered languages, such as Occitan and Sicilian. These languages often share some characteristics, such

Ajaharul Islam, Nora Brambilla, Miguel Ángel Escobedo, Michael Strickland

By employing the potential non-relativistic quantum chromodynamics (pNRQCD) effective field theory within an open quantum system framework, we derive a Lindblad equation governing the evolution of the heavy-quarkonium reduced density matrix, accurate to next-to-leading order (NLO) in the ratio of the state's binding energy to the medium's temperature [1]. Th

Tohid Kargar Tasooji, Sakineh Khodadadi

The design of robust controllers for triple inverted pendulum systems presents significant challenges due to their inherent instability and nonlinear dynamics. Furthermore, uncertainties in system parameters further complicate the control design. This paper investigates a robust control strategy for triple inverted pendulums under parameter uncertainty. Two