March 2025 arXiv papers — page 30

Haozhen Cheng, Veit Hagenmeyer, Hüseyin K. Çakmak

This paper presents a novel modeling approach for building performance simulation, characterized as a white-box model with a high degree of modularity and flexibility, enabling direct integration into complex large-scale energy system co-simulations. The introduced model is described in detail, with a focus on its modular structure, and proposes various conf

Anders Björn, Jana Björn, Lukáš Malý

We construct various examples of Sobolev-type functions, defined via upper gradients in metric spaces, that fail to be quasicontinuous or weakly quasicontinuous. This is done with quasi-Banach function lattices $X$ as the function spaces defining the smoothness of the Sobolev-type functions. These results are in contrast to the case $X=L^p$ with $1\le p<\inf

Enrico Pasqualetto, Giacomo Enrico Sodini

Given a unital algebra $\mathscr A$ of locally Lipschitz functions defined over a metric measure space $({\mathrm X},{\mathsf d},\mathfrak m)$, we study two associated notions of function of bounded variation and their relations: the space ${\mathrm BV}_{\mathrm H}({\mathrm X};\mathscr A)$, obtained by approximating in energy with elements of $\mathscr A$, a

Bethan Humphreys, Alex J. Matthies, Hannah J. Williams

We present the python package DiPolMol-Py, which can be used to calculate the rotational and hyperfine structure of $^2\Sigma$ molecules. The calculations can be performed in the presence of dc magnetic fields, dc electric fields and far off-resonant optical fields. We additionally include functions to calculate the polarisability of the molecule and the tra

J. Dai, A. Antezak, W. Broad, M. Thees

YbNi$_4$P$_2$ is the first known ferromagnetic metal showing a second-order quantum phase transition. Current theoretical understanding rules out second order ferromagnetic quantum criticality in centrosymmetric 2D and 3D metals. Thus, studying the electronic structure of YbNi$_4$P$_2$ is of prime fundamental importance. Using angle-resolved photoemission sp

M. M. Ahmadi-Jahmani, A. Parvizi

Fractons, characterized by restricted mobility and governed by higher-moment conservation laws, represent a novel phase of matter with deep connections to tensor gauge theories and emergent gravity. This work systematically explores the duality between fractons and non-Lorentzian particles-Carroll and Galilean-within electromagnetic (EM) fields. By construct

Kuang Wu, Chuan Yang, Zhanbin Li

Vectorized high-definition (HD) maps are essential for an autonomous driving system. Recently, state-of-the-art map vectorization methods are mainly based on DETR-like framework to generate HD maps in an end-to-end manner. In this paper, we propose InteractionMap, which improves previous map vectorization methods by fully leveraging local-to-global informati

Thijs Hazenberg, Daniel Braig, Johannes Mich, Arne Scholtissek

This article presents numerical simulations of the response of an iron dust Bunsen flame to particle seeding changes. A validated numerical model is used to study the impact of particle seeding fluctuations on flame stability. Simulations are conducted for the Bunsen setup in the right-side up and up-side down configuration. No significant differences in fla

Sudhagar Suyamprakasam, Sreekanth Harikumar, Paweł Ciecieląg, Przemysław Figura

Detection of quasi-monochromatic, long-duration (continuous) gravitational wave radiation emitted by, e.g., asymmetric rotating neutron stars in our Galaxy requires a long observation time to distinguish it from the detector's noise. If this signal is additionally microlensed by a lensing object located in the Galaxy, its magnitude would be temporarily magni

Yi-Kai Zhang, Jin Wang, Xu-Xiang Zhong, De-Chuan Zhan

Model merging acquires general capabilities without extra data or training by combining multiple models' parameters. Previous approaches achieve linear mode connectivity by aligning parameters into the same loss basin using permutation invariance. In this paper, we introduce Model Assembly Learning (MAL), a novel paradigm for model merging that iteratively i

Faizan Bhat, Aninda Sinha

In 1914, Ramanujan unveiled 17 extraordinary infinite series for $1/\pi$. In this work, we uncover their physics origin by relating them to 2D logarithmic conformal field theories (LCFTs), which emerge in diverse settings such as the fractional quantum Hall effect, percolation, polymers, and even holography. Through this LCFT connection, we reinterpret such

Keren Censor-Hillel, Tomer Even, Virginia Vassilevska Williams

Dell, Lapinskas and Meeks [DLM SICOMP 2022] presented a general reduction from approximate counting to decision for a class of fine-grained problems that can be viewed as hyperedge counting or detection problems in an implicit hypergraph, thus obtaining tight equivalences between approximate counting and decision for many key problems such as $k$-clique, $k$

Sharon Heung, Lucy Jiang, Shiri Azenkot, Aditya Vashistha

Disabled people on social media often experience ableist hate and microaggressions. Prior work has shown that platform moderation often fails to remove ableist hate leaving disabled users exposed to harmful content. This paper examines how personalized moderation can safeguard users from viewing ableist comments. During interviews and focus groups with 23 di

Desmond Coles, Martin Ulirsch

Let $G$ be a connected reductive algebraic group over an algebraically closed field of characteristic zero carrying the trivial valuation. In this article we discuss two candidates for what could be the tropicalization of $G$. Our first suggestion is the extended affine building associated to $G$. This perspective makes makes use of Berkovich's embedding of

Jingwei Chen, Jun Ye, Jinwen Chen, Zhidong Wang

We propose the first $\alpha$-parameterized framework for solving time-changed stochastic differential equations (TCSDEs), explicitly linking convergence rates to the driving parameter of the underlying stochastic processes. Theoretically, we derive exact moment estimates and exponential moment estimates of inverse $\alpha$-stable subordinator $E$ using Mitt

Bryan Sanctuary

We present an analysis of the Dirac equation when the spin symmetry is changed from SU(2) to the quaternion group, $Q_8$, achieved by multiplying one of the gamma matrices by the imaginary number, $i$. The reason for doing this is to introduce a bivector into the spin algebra, which complexifies the Dirac field. It then separates into two distinct and comple

Andreas Leipner, Alexey Kupriyanov, Arthur Reis, Annike Knabe

Future satellite gravimetry missions seek to surpass the performance of CHAMP, GOCE, GRACE, and GRACE-FO to meet increasing scientific and operational demands. These missions will integrate advanced technologies, including optical and quantum accelerometers, high-precision inter-satellite laser ranging, and micro-Newton electric thrusters. However, increased

Fernando Almaguer-Angeles, Pedro R. Dieguez, Akshata Shenoy H., Marcin Pawłowski

We demonstrate a hardware vulnerability in quantum computing systems by exploiting cross-talk effects on an available commercial quantum computer (IBM). Specifically, based on the cross-talk produced by certain quantum gates, we implement a row hammer attack that ultimately allows us to flip a qubit. Both single-qubit and two-qubit operations are performed a

Hugo França, Maziyar Jalaal

Flows with deformable interfaces are commonly controlled by applying an external field or modifying the boundaries that interact with the fluid, but realizing such solutions can be demanding or impractical in various scenarios. Here, we demonstrate that fluids with broken symmetries can self-control their mechanics. We present a continuum model of a viscous

Jayce R. Getz, Miao Pam Gu, Chun-Hsien Hsu, Spencer Leslie

We introduce multi-variable zeta integrals which unfold to Euler products representing the triple product $L$-function times a product of $L$-functions with known analytic properties. We then formulate a generalization of the Poisson summation conjecture and show how it implies the analytic properties of triple product $L$-functions. Finally, we propose a st

Nikita Medvedev

This proceeding discusses nonequilibrium effects in matter exposed to XUV/X-ray irradiation. When ultrashort, intense XUV/X-ray pulses interact with materials, they trigger a complex sequence of processes, including electronic excitation, nonequilibrium electron kinetics, energy exchange with the atomic system, electronic thermalization, and subsequent atomi

Thomas Monks, Alison Harper, Amy Heather

Discrete-event simulation (DES) is widely used in healthcare Operations Research, but the models themselves are rarely shared. This limits their potential for reuse and long-term impact in the modelling and healthcare communities. This study explores the feasibility of using generative artificial intelligence (AI) to recreate published models using Free and

Rafiqul Rabin, Sean McGregor, Nick Judd

This paper explores the risk that a large language model (LLM) trained for code generation on data mined from software repositories will generate content that discloses sensitive information included in its training data. We decompose this risk, known in the literature as ``unintended memorization,'' into two components: unintentional disclosure (where an LL

Sinisa Grgic

This study provides a comparative evaluation of global diplomatic mission directories. DiplomaticMonitor.org, EmbassyPages.com, and WikiData.org are strategically selected among the top ten global services. After analyzing nearly all available online global diplomatic directory services, these three platforms are selected as they represent fundamentally diff

Bin Guo, Shaun D. Hampton

Symmetric orbifold CFTs contain twist operators that can join and split copies of the CFT. In this paper, we study the effects of four twist-2 operators on two copies of a single free boson. A recent study analyzed their effects on the vacuum, finding a nontrivial left-right mixing that arises from the fact that the covering surface is a torus, while the eff

Robert Auffarth, Jorge Duque Franco

The rank $\rho$ of the N\'eron-Severi group of a complex torus $X$ of dimension $g$ satisfies $0\leq\rho\leq g^2=h^{1,1}.$ The degree $\mathfrak{d}$ of the extension field generated over $\mathbb{Q}$ by the entries of a period matrix of $X$ imposes constraints on its Picard number $\rho$ and, consequently, on the structure of $X$. In this paper, we show that

Jiwon Shin, C. Y. Hui, Sangin Kim, Kwangmin Oh

Using public data collected by the Fermi Large Area Telescope (LAT) over 16 years, and the 1523 days of survey data (3HWC) from the High Altitude Water Cherenkov (HAWC) observatory, we searched for possible GeV-TeV connections in globular clusters (GCs). In addition to the confirmed $\gamma-$ray GCs in the 4FGL catalog, we report a GeV detection at the posit

Yongshuai Liu, Taeyeong Choi, Xin Liu

Machine learning has been successful in building control policies to drive a complex system to desired states in various applications (e.g. games, robotics, etc.). To be specific, a number of parameters of policy can be automatically optimized from the observations of environment to be able to generate a sequence of decisions leading to the best performance.

Ilmun Kim, Aaditya Ramdas

This paper tackles a fundamental inference problem: given $n$ observations from a distribution $P$ over $\mathbb{R}^d$ with unknown mean $\boldsymbol{\mu}$, we must form a confidence set for the index (or indices) corresponding to the smallest component of $\boldsymbol{\mu}$. By duality, we reduce this to testing, for each $r$ in $1,\ldots,d$, whether $\mu_r

Samuel J. Edwards, Michael D. Levine

A method to rapidly estimate extreme ship response events is developed in this paper. The method involves training by a Long Short-Term Memory (LSTM) neural network to correct a lower-fidelity hydrodynamic model to the level of a higher-fidelity simulation. More focus is placed on larger responses by isolating the time-series near peak events identified in t

Roman Malyshev, Bjørnulf Brekke, Ingeborg-Helene Svenum, Sverre M. Selbach

CuFeS$_2$ is an altermagnetic semiconductor that is lattice-matched with silicon and has a high N\'eel temperature. It is nonpolar and magnetically compensated in its structural ground state. However, the crystal belongs to a magnetic symmetry class allowing simultaneous piezoelectricity and -magnetism, indicating that distortion by shear strain may enable f

Leon Bein, Niels Martin, Luise Pufahl

Resource allocation in business process management involves assigning resources to open tasks while considering factors such as individual roles, aptitudes, case-specific characteristics, and regulatory constraints. Current information systems for resource allocation often require extensive manual effort to specify and maintain allocation rules, making them

Giorgio Almirante, Michael Urban

The question of whether there are enough superfluid neutrons in the inner crust of neutron stars to explain pulsar glitches remains a topic of debate. Previous band structure calculations suggest that the entrainment effect significantly reduces the superfluid density. In this letter, a new derivation of the BCS expression for the superfluid density is given

Yassir Lairgi

The accurate determination of the beginning of each Hijri month is essential for religious, cultural, and administrative purposes. Manazel (The code and datasets are available at https://github.com/lairgiyassir/manazel) addresses this challenge in Morocco by leveraging 13 years of crescent visibility data to refine the ODEH criterion, a widely used standard

Y. Sinnwell, A. Maksakov, S. Palis, S. Antonyuk

This study investigates the influence of particle morphology on spray characteristics in low-pressure cold gas spraying (LPCGS) by analyzing three copper powders with distinct shapes and microstructures. A comprehensive morphology analysis was conducted using both 2D and 3D imaging techniques. Light microscopy combined with image processing quantified partic

David Emanuele Corrado Raphael Catania, Alessandro Buratto, Giovanni Perin

Wireless sensing and the internet of things (IoT) are nowadays pervasive in 5G and beyond networks, and they are expected to play a crucial role in 6G. However, a centralized optimization of a distributed system is not always possible and cost-efficient. In this paper, we analyze a setting in which two sensors collaboratively update a common server seeking t

Wenxiang Yan, Zheng Yuan, Yuan Gao, Xian Long

Optical orbital angular momentum (OAM) has traditionally relied on vortex beams with helical phase fronts imparting quantized intrinsic OAM. Here, we introduce a fundamentally vortex_free framework where intrinsic OAM arises from the natural curvature of lights energy flow, specifically, the caustic geometry of self_accelerating beams whose curved trajectori

Francesco Cecere, Matteo Lapucci, Davide Pucci, Marco Sciandrone

In this paper, we consider the problem of minimizing a smooth function, given as finite sum of black-box functions, over a convex set. In order to advantageously exploit the structure of the problem, for instance when the terms of the objective functions are partially separable, noisy, costly or with first-order information partially accessible, we propose a

Marta Galbiati, Kevin Ambrogioni, Leonardo Francesco Claudio Monaco, Maria Sole Galli De Magistris

A source of high-energy photons, ions, and positrons can be attained with the interaction of ultra-intense femtosecond laser pulses with advanced nanostructured targets. We present and characterise a numerical model that mimics the foam deposition process on solid substrates, as it occurs in Double-Layer Target (DLT) manufacturing. The model is integrated in

Saeyoung Rho, Andrew Tang, Noah Bergam, Rachel Cummings

In causal inference with observational studies, synthetic control (SC) has emerged as a prominent tool. SC has traditionally been applied to aggregate-level datasets, but more recent work has extended its use to individual-level data. As they contain a greater number of observed units, this shift introduces the curse of dimensionality to SC. To address this,

Karlo Palenzuela, Ali Dadras, Alp Yurtsever, Tommy Löfstedt

Multiple local steps are key to communication-efficient federated learning. However, theoretical guarantees for such algorithms, without data heterogeneity-bounding assumptions, have been lacking in general non-smooth convex problems. Leveraging projection-efficient optimization methods, we propose FedMLS, a federated learning algorithm with provable improve

Guangyao Zhu, Yan Jiang, Zhuang Zhao, Mengping Zhang

This paper presents a novel inverse Lax-Wendroff (ILW) boundary treatment for finite difference Hermite weighted essentially non-oscillatory (HWENO) schemes to solve hyperbolic conservation laws on arbitrary geometries. The complex geometric domain is divided by a uniform Cartesian grid, resulting in challenge in boundary treatment. The proposed ILW boundary

Friedemann Brock, Francesco Chiacchio

In this paper we investigate the reverse isoperimetric inequality with respect to the Gaussian measure for convex sets in $\mathbb{R}^{2}$. While the isoperimetric problem for the Gaussian measure is well understood, many relevant aspects of the reverse problem have not yet been investigated. In particular, to the best of our knowledge, there seem to be no r

Léo Legrand, Louis-Martin Poitras, Nicolas Sator, Matthieu Micoulaut

The electric properties of a model fast-ion electrolyte ((100-x)SiS2-xNa2S) glass are investigated by means of classical molecular dynamics simulations. These systems appear promising for battery applications and the conductivity is thought to be essentially driven by the concentration of charge carriers (Na) so that the usual amount is found to be of about

Paweł Hatka, Marcel Garczyk, Paweł Płaczkiewicz, Dawid Brząkała

Reconfigurable Intelligent Surfaces (RIS) have gained significant attention for some time. Thanks to the possibility of individual steering of each reflecting element of the boards, they are envisaged to impact the propagation environment significantly. In this work, we concentrate on the practical verification of this concept. We present the results of deta

Lars Heckler-Kram, Jan-Hendrik Neudeck, Ulla Scheler, Rebecca König

In recent years, performance on existing anomaly detection benchmarks like MVTec AD and VisA has started to saturate in terms of segmentation AU-PRO, with state-of-the-art models often competing in the range of less than one percentage point. This lack of discriminatory power prevents a meaningful comparison of models and thus hinders progress of the field,

Valentin Slepukhin, Víctor Peris Yagüe, Christian Westendorf, Birgit Koch

Bacteria frequently colonize natural microcavities such as gut crypts, plant apoplasts, and soil pores. Recent studies have shown that the physical structure of these spaces plays a crucial role in shaping the stability and resilience of microbial populations (Karita et al., PNAS 2022, Postek et al. PNAS 2024). Here, we demonstrate that protected microhabita

Zhengxi Lu, Yuxiang Chai, Yaxuan Guo, Xi Yin

The recent DeepSeek-R1 has showcased the emergence of reasoning capabilities in LLMs through reinforcement learning (RL) with rule-based rewards. Despite its success in language models, its application in multi-modal domains, particularly in graphic user interface (GUI) agent tasks, remains under-explored. To address this issue, we propose UI-R1, the first f

Michael Magee

We survey a research program on the strong convergence of unitary and permutation representations of discrete groups. We also take the opportunity to flesh out details that have not appeared elsewhere.

Biyi Wang, Karl Meerbergen, Raf Vandebril, Hengbin An

We present a rational filter for computing all eigenvalues of a symmetric definite eigenvalue problem lying in an interval on the real axis. The linear systems arising from the filter embedded in the subspace iteration framework, are solved via a preconditioned Krylov method. The choice of the poles of the filter is based on two criteria. On the one hand, th

Ahatsham Hayat, Bilal Khan, Mohammad Rashedul Hasan

We propose a novel approach to leveraging pre-trained language models (LMs) for early forecasting of academic trajectories in STEM students using high-dimensional longitudinal experiential data. This data, which captures students' study-related activities, behaviors, and psychological states, offers valuable insights for forecasting-based interventions. Key

Jiahui Chen, Yang Huan, Runhua Shi, Chanfan Ding

Gestures are essential for enhancing co-speech communication, offering visual emphasis and complementing verbal interactions. While prior work has concentrated on point-level motion or fully supervised data-driven methods, we focus on co-speech gestures, advocating for weakly supervised learning and pixel-level motion deviations. We introduce a weakly superv

Vikas Kushwaha, Sruti Srinivasa Ragavan, Subhajit Roy

Successful agent-human partnerships require that any agent generated information is understandable to the human, and that the human can easily steer the agent towards a goal. Such effective communication requires the agent to develop a finer-level notion of what is understandable to the human. State-of-the-art agents, including LLMs, lack this detailed notio

Xiaoye Qu, Yafu Li, Zhao-Chen Su, Weigao Sun

Recent Large Reasoning Models (LRMs), such as DeepSeek-R1 and OpenAI o1, have demonstrated strong performance gains by scaling up the length of Chain-of-Thought (CoT) reasoning during inference. However, a growing concern lies in their tendency to produce excessively long reasoning traces, which are often filled with redundant content (e.g., repeated definit

Javier Coronado-Blázquez

We study the ability of large language models (LLMs) to generate comprehensive and accurate book summaries solely from their internal knowledge, without recourse to the original text. Employing a diverse set of books and multiple LLM architectures, we examine whether these models can synthesize meaningful narratives that align with established human interpre

Daniel Wachsmuth

We investigate a globalized inexact semismooth Newton method applied to strongly convex optimization problems in Hilbert spaces. Here, the semismooth Newton method is appplied to the dual problem, which has a continuously differentiable objective. We prove global strong convergence of iterates as well as transition to local superlinear convergence. The latte

Tony Lindeberg

This paper presents an analysis of the orientation selectivity properties of idealized models of complex cells in terms of affine quasi quadrature measures, which combine the responses of idealized models of simple cells in terms of affine Gaussian derivatives by (i) pointwise squaring, (ii) summation of responses for different orders of spatial derivation a

Konstantinos Dimopoulos, Christian Dioguardi, Gert Hütsi, Antonio Racioppi

Palatini $F(R,X)$ gravity, with $X$ the inflaton kinetic term, proved to be a powerful framework for generating asymptotically flat inflaton potentials. Here we show that a quadratic Palatini $F(R,X)$ restores compatibility with the observational data of the Peebles-Vilenkin quintessential inflation model. Moreover, the same can be achieved with an exponenti

Haopeng Wang, Haiwei Dong, Abdulmotaleb El Saddik

Extended reality (XR) is rapidly advancing, and poised to revolutionize content creation and consumption. In XR, users integrate various sensory inputs to form a cohesive perception of the virtual environment. This survey reviews the state-of-the-art in XR streaming, focusing on multiple paradigms. To begin, we define XR and introduce various XR headsets alo

Damianos Iosifidis, Manthos Karydas, Anastasios Petkou, Konstantinos Siampos

In a flat background, the canonical energy momentum tensor of Lorentz and conformally invariant matter field theories can be improved to a symmetric and traceless tensor that gives the same conserved charges. We argue that the geometric origin of this improvement process is unveiled when the matter theory is coupled to Metric-Affine Gravity. In particular, w

Ye Tian, Sanyou Wu, Long Feng

Identifying low-dimensional latent structures within high-dimensional data has long been a central topic in the machine learning community, driven by the need for data compression, storage, transmission, and deeper data understanding. Traditional methods, such as principal component analysis (PCA) and autoencoders (AE), operate in an unsupervised manner, ign

Mengyuan Wang, Yang Liu, Haopeng Wang, Haiwei Dong

This paper presents an empirical evaluation of the Matterport Pro3, a consumer-grade 3D scanning device, for large-scale environment reconstruction. We conduct detailed scanning (1,099 scanning points) of a six-floor building (17,567 square meters) and assess the device's effectiveness, limitations, and performance enhancements in diverse scenarios. Challeng

Suvadip Masanta, Chumki Nayak, Premananda Chatterjee, Atindra Nath Pal

Heterobilayers formed by stacking two-dimensional atomic crystals are particularly promising for low-dimensional semiconductor optics, as they host interlayer excitons, bound states of electrons and holes residing in different layers. They inherit the valley-contrasting physics of the individual monolayers, leading to a range of unique properties that distin

Matheus Rakes, Maíra Chagas Morais, Maria Eduarda Sperotto, Odimar Zanuzo Zanardi

The present study investigates the compatibility of mycoinsecticides based on isolates IBCB66 and Simbi BB15 of Beauveria bassiana and Esalq-1296 of Cordyceps javanica, which are registered for the management of Dalbulus maidis in Brazil, with synthetic fungicides. Irrespective of the fungicide, a total inhibition in the number of colony-forming units (CFUs)

Jie Zang, Pascal Helson, Shenquan Liu, Arvind Kumar

Neurons in the brain show great diversity in their individual properties and their connections to other neurons. To develop an understanding of how neuronal diversity contributes to brain dynamics and function at large scales we start with a linearized version of the Wilson-Kowan model and introduce a random anisotropy to inter-neuron connection. The resulta

Yufei He, Xucong Zhang, Arno H. A. Stienen

Human intention detection with hand motion prediction is critical to drive the upper-extremity assistive robots in neurorehabilitation applications. However, the traditional methods relying on physiological signal measurement are restrictive and often lack environmental context. We propose a novel approach that predicts future sequences of both hand poses an

Martin Donati, Lars Eric Hientzsch, Christophe Lacave, Evelyne Miot

The evolution of highly concentrated vorticity around rings in the three-dimensional axisymmetric Euler equations is studied in a regime for which the leapfrogging dynamics predicted by Helmholtz is expected to occur. We provide in this paper the first result deriving this phenomenon for a general class of initial data in the suitable regime. The singular in

Mladen Banović, Petar Atanasijević, Antonios Prapas, Christos Pappas

The threads of photonics are eagerly awaited to redefine the future of neuromorphic data processing, especially as the computing-intensive artificial intelligence models become an unavoidable part of our everyday lives. Still, there is much to be improved within the domain of photonic nonlinear activation functions, as the programmable, all-optical, energy-e

Karime Maamari, Connor Landy, Amine Mhedhbi

Recent advancements in Text-to-SQL, driven by large language models, are democratizing data access. Despite these advancements, enterprise deployments remain challenging due to the need to capture business-specific knowledge, handle complex queries, and meet expectations of continuous improvements. To address these issues, we designed and implemented GenEdit

Ying Yu, Siyao Li, Yixuan Jiang, Hang Xiao

Human Activity Recognition (HAR) is a fundamental technology for numerous human - centered intelligent applications. Although deep learning methods have been utilized to accelerate feature extraction, issues such as multimodal data mixing, activity heterogeneity, and complex model deployment remain largely unresolved. The aim of this paper is to address issu

Johannes Voigt, Peter Jiacheng Gu, Peter Rost

The use of higher frequencies in mobile communication systems leads to smaller cell sizes, resulting in the deployment of more base stations and an increase in handovers to support user mobility. This can lead to frequent radio link failures and reduced data rates. In this work, we propose a handover optimization method using proximal policy optimization (PP

Ye-Huang Pang, Xue Zhang, Qing-Guo Huang

Recent findings from the Dark Energy Spectroscopic Instrument (DESI) Data Release 2 (DR2) favor a dynamical dark energy characterized by a phantom crossing feature. This result also implies a lower value of the Hubble constant, thereby intensifying the so-called Hubble tension. To alleviate the Hubble tension, we consider the early dark energy and explore it

Georgios Mylonopoulos, Giovanni Interdonato, Stefano Buzzi, Pei Liu

Cell-free (CF) massive multiple-input multiple-output (MIMO) is a promising approach for next-generation wireless networks, enabling scalable deployments of multiple small access points (APs) to enhance coverage and service for multiple user equipments (UEs). While most existing research focuses on low-frequency bands with Rayleigh fading models, emerging 5G

Johan Wahréus, Ahmed Hussain, Panos Papadimitratos

Large Language Models (LLMs) have transformed task automation and content generation across various domains while incorporating safety filters to prevent misuse. We introduce a novel jailbreaking framework that employs distributed prompt processing combined with iterative refinements to bypass these safety measures, particularly in generating malicious code.

Pinlong Zhao, Weiyao Zhu, Pengfei Jiao, Di Gao

Deep learning has become a cornerstone of modern artificial intelligence, enabling transformative applications across a wide range of domains. As the core element of deep learning, the quality and security of training data critically influence model performance and reliability. However, during the training process, deep learning models face the significant t

Siyu Han, Lihan Jia, Lanzhe Guo

This work focuses on the limitations about the insufficient fitting capability of current quantum machine learning methods, which results from the over-reliance on a single data embedding strategy. We propose a novel quantum machine learning framework that integrates multiple quantum data embedding strategies, allowing the model to fully exploit the diversit

Chen Tang, Yi Ling, Qing-Quan Jiang

We investigate the quasi-normal modes (QNMs) under the gravitational field perturbations and the grey-body factors for a class of regular black holes with sub-Planckian curvature and Minkowski core. Specifically, we compute the QNMs with the pseudospectral method and the WKB method. It is found that as the deviation parameter of the regular black hole change

István Márton, Erika Bene, Péter Diviánszky, Gábor Drótos

The presented program aims at speeding up the brute force computation of the so-called $L_d$ norm of a matrix $M$ using graphics processing units (GPUs). Alternatives for CPUs have also been implemented, and the algorithm is applicable to any parallel environment. The $n\times m$ matrix $M$ has real elements which may represent coefficients of a bipartite Be

Jincheng Yan, Yun Wang, Xiaoyan Luo, Yu-Wing Tai

Person re-identification (ReID) plays a critical role in applications such as security surveillance and criminal investigations. Most traditional image-based ReID methods face challenges including occlusions and lighting changes, while text provides complementary information to mitigate these issues. However, the integration of both image and text modalities

Abhishek Dhyani, Amirreza Haqshenas Mojaveri, Chengqian Zhang, Dhanika Mahipala

This paper introduces AUTOBargeSim, a simulation toolbox for autonomous inland vessel guidance and control system design. AUTOBargeSim is developed using MATLAB and provides an easy-to-use introduction to various aspects of autonomous inland navigation, including mapping, modelling, control design, and collision avoidance, through examples and extensively do

Ludovic Gardré, Swen Helstroffer, Pierre Muller, Fabrice Thalmann

Most organic and inorganic surfaces (e.g., glass, nucleic acids or lipid membranes) become charged in aqueous solutions. The resulting ionic distribution induces effective interactions between the charged surfaces. Stacks of like-charged lipid bilayers immersed in multivalent ion solutions exhibit strong coupling (SC) effects, where ion correlations cause co

Yoann Boget

Discrete Diffusion and Flow Matching models have significantly advanced generative modeling for discrete structures, including graphs. However, the dependencies between intermediate noisy states lead to error accumulation and propagation during the reverse denoising process - a phenomenon known as compounding denoising errors. To address this problem, we pro

Yarden Sharon, Alex Geftler, Hanna Kossowsky Lev, Ilana Nisky

Objective: We aim to investigate long-term robotic surgical skill acquisition among surgical residents and the effects of training intervals and fatigue on performance. Methods: For six months, surgical residents participated in three training sessions once a month, surrounding a single 26-hour hospital shift. In each shift, they participated in training ses

Zorar Ahmadi, Bashir Mojaveri

This study presents a comparative analysis of three quantum thermal engines utilizing a two-qubit Heisenberg XXZ chain as the working substance. A novel generalized quantum Otto cycle (GQOC) is introduced, featuring two distinct coupling configurations to thermal reservoirs. The GQOC exhibits the potential for 100\% efficiency, surpassing the efficiency of t

F. Acerbi, C. Andreopoulos, I. Angelis, A. Baratto Roldan

A new generation of neutrino cross-section experiments at the GeV scale is crucial in the precision era of oscillation physics and lepton flavor studies. In this document, we present a novel neutrino beam design that leverages the experience and R&D achievements of the NP06/ENUBET and NuTag Collaborations and explore its potential implementation at CERN. Thi

Guang Zhao, Xihaier Luo, Seungjun Lee, Yihui Ren

Mesoscale ocean dynamics play a critical role in climate systems, governing heat transport, hurricane genesis, and drought patterns. However, simulating these processes at high resolution remains computationally prohibitive due to their nonlinear, multiscale nature and vast spatiotemporal domains. Implicit neural representations (INRs) reduce the computation

Iván León, Riccardo Muolo, Shigefumi Hata, Hiroya Nakao

Phase reduction is a powerful technique in the study of nonlinear oscillatory systems. Under certain assumptions, it allows us to describe each multidimensional oscillator by a single phase variable, giving rise to simple phase models such as the Kuramoto model. Classically, the method has been applied in the case where the interactions are only pairwise (tw

Sudip Mandal, Sandip Halder, Kalpataru Pradhan

Correlated electron systems with competing interactions provide a valuable platform for examining exotic magnetic phases. Theoretical models often focus on nearest-neighbor interactions, although long-range interactions can have a considerable impact on the behavior of the system, creating distinct and complicated magnetic phases. We investigate the conseque

Haixu Wang, Jiguo Cao

High-dimensional functional time series (HDFTS) are often characterized by nonlinear trends and high spatial dimensions. Such data poses unique challenges for modeling and forecasting due to the nonlinearity, nonstationarity, and high dimensionality. We propose a novel probabilistic functional neural network (ProFnet) to address these challenges. ProFnet int

Marco Canducci, Petra Awad, Abolfazl Taghribi, Mohammad Mohammadi

Filaments are ubiquitous in astronomical data sets. Be it in particle simulations or observations, filaments are always tracers of a perturbation in the equilibrium of the studied system and hold essential information on its history and future evolution. However, the recovery of such structures is often complicated by the presence of a large amount of backgr

Edouard B. Sonin

The Letter presents the theory of planar ballistic SNS junctions at $T=0$ for any normal layer thickness $L$ taking into account phase gradients in superconducting leads. The current-phase relation was derived in the model of the steplike pairing potential analytically and is exact in the limit of large ratio of the Fermi energy to the superconducting gap. A

A. C. Cem Say

We present new results on the landscape of problems that can be solved by quantum Turing machines (QTM's) employing severely limited amounts of memory. In this context, we demonstrate two infinite time hierarchies of complexity classes within the ``small space'' regime: For all $i\geq 0$, there is a language that can be recognized by a constant-space machine

Liuyue Xie, Jiancong Guo, Ozan Cakmakci, Andre Araujo

Accurate camera calibration is a fundamental task for 3D perception, especially when dealing with real-world, in-the-wild environments where complex optical distortions are common. Existing methods often rely on pre-rectified images or calibration patterns, which limits their applicability and flexibility. In this work, we introduce a novel framework that ad

Fabian Bäuerlein, Samuele Riccò, Leah Schätzler

We prove global higher integrability for functionals of double-phase type under a uniform local capacity density condition on the complement of the considered domain $\Omega \subset \mathbb{R}^n$. In this context, we investigate a new natural notion of variational capacity associated to the double-phase integrand. Under the related fatness condition for the

Weronika Ormaniec, Michael Vollenweider, Elisa Hoskovec

In this paper, we explore the idea of combining GCNs into one model. To that end, we align the weights of different models layer-wise using optimal transport (OT). We present and evaluate three types of transportation costs and show that the studied fusion method consistently outperforms the performance of vanilla averaging. Finally, we present results sugge

Sourav Bhattacharya, Kinsuk Roy

We consider a scalar field theory with quartic self interaction, Yukawa coupled to fermions in the inflationary de Sitter spacetime background. The scalar has a classical background plus quantum fluctuations, whereas the fermions are taken to be quantum. We derive for this system the effective action and the effective potential via the two particle irreducib

Chris D. A. Blair

Eleven-dimensional supergravity has a non-relativistic variant obtained by taking a limit associated with the M2 brane. Consistency of this non-relativistic supergravity requires constraints. There is one choice of constraints which keeps the maximal amount of supersymmetry transformations, and another which only keeps half. I discuss supersymmetric solution

Tobias Fritz, Tomáš Gonda, Antonio Lorenzin, Paolo Perrone

The Glivenko--Cantelli theorem is a uniform version of the strong law of large numbers. It states that for every IID sequence of random variables, the empirical measure converges to the underlying distribution (in the sense of uniform convergence of the CDF). In this work, we provide tools to study such limits of empirical measures in categorical probability

Biswanath Dutta, Debanjali Bain

The increasing use of Electronic Health Records (EHR) has emphasized the need for standardization and interoperability in healthcare data management. The Ministry of Health and Family Welfare, Government of India, has introduced the Electronic Health Record Minimum Data Set (EHRMDS) to facilitate uniformity in clinical documentation. However, the compatibili

José María Domínguez-Vázquez, Olga Caballero-Calero, Ketan Lohani, José J. Plata

This study demonstrates the direct correlation between the presence of the L2$_1$ ordered phase and the large enhancement in the thermoelectric performance of Fe$_2$VAl thin films deposited on MgO and Al$_2$O$_3$ substrates at temperatures varying between room temperature and 950$^{\circ}$C. We employ both experimental techniques and computational modeling t