March 2025 arXiv papers — page 102

Ziyao Ling, Giovanni Delnevo, Paola Salomoni, Silvia Mirri

Chinese porcelain holds immense historical and cultural value, making its accurate classification essential for archaeological research and cultural heritage preservation. Traditional classification methods rely heavily on expert analysis, which is time-consuming, subjective, and difficult to scale. This paper explores the application of DL and transfer lear

Shimi Chettiparambil Mohanan, Christina Surulescu, Neslihan Nesliye Pelen

We propose two approaches to a model deduction for Buruli ulcer spread in a tissue, prove global existence of solutions to the obtained macroscopic cross-diffusion PDE-ODE systems, and perform numerical simulations to illustrate the behavior of solutions under various scenarios and compare the outcome of the two approaches.

Nobuhiko Wakai, Satoshi Sato, Yasunori Ishii, Takayoshi Yamashita

Person detection in overhead fisheye images is challenging due to person rotation and small persons. Prior work has mainly addressed person rotation, leaving the small-person problem underexplored. We remap fisheye images to equirectangular panoramas to handle rotation and exploit panoramic geometry to handle small persons more effectively. Conventional dete

Eduardo Treviño, Hugo Contant, James Ngai, Graham Neubig

The integration of tools has extended the capabilities of language models (LMs) beyond vanilla text generation to versatile scenarios. However, tool-augmented language models (TaLMs) often assume 'perfect' information access and tool availability, which may not hold in the real world. To systematically study TaLMs' imperfections, we introduce the FAIL-TALMS

Huaifeng Zhang, Ahmed Ali-Eldin

Software bloat refers to code and features that is not used by a software during runtime. For Machine Learning (ML) systems, bloat is a major contributor to their technical debt leading to decreased performance and resource wastage. In this work, we present, Negativa-ML, a novel tool to identify and remove bloat in ML frameworks by analyzing their shared lib

Josephine Evans, Daniel Morris, Havva Yoldaş

We consider a class of nonlinear, spatially inhomogeneous kinetic equations of BGK-type with density dependent collision rates. These equations share the same superlinearity as the Boltzmann equation, and fall into the class of run and tumble equations appearing in mathematical biology. We prove that the Cauchy problem is well-posed, and the solutions propag

Andrés E. Piatti

In this work we present results of the first in-depth analysis of extra-tidal mock stars of Milky Way globular clusters recently generated by Grondin et al. (2024). Particularly, we selected a sample of globular clusters with a general consensus of being formed in the bulge or in the disk of the Milky Way. From the catalog we estimated the width and the disp

Fabio Bagarello, Francesco Gargano, Polina Khrennikova

In this work, we introduce a quantum-inspired epidemic model to study the dynamics of an infectious disease in a population divided into compartments. By treating the healthy population as a large reservoir, we construct a framework based on open quantum systems and a Hilbert space formalism to model the spread of the infection. This approach allows for a ma

Katayoun Eshkofti, Matthieu Barreau

In a more connected world, modeling multi-agent systems with hyperbolic partial differential equations (PDEs) offers a compact, physics-consistent description of collective dynamics. However, classical control tools need adaptation for these complex systems. Physics-informed neural networks (PINNs) provide a powerful framework to fix this issue by inferring

Yuxuan Guo, Sheng Yang, Xue-Jia Yu

Symmetry breaking has been a central theme in classifying quantum phases and phase transitions. Recently, this concept has been extended to the mixed states of open systems, attracting considerable attention due to the emergence of novel physics beyond closed systems. In this work, we reveal a new type of phase transition in mixed states, termed \emph{quantu

Adam J. Stone, Emmanuel Ogundimu, John Paul Gosling

The Additive Voronoi Tessellations (AddiVortes) model is a multivariate regression model that uses multiple Voronoi tessellations to partition the covariate space for an additive ensemble model. In this paper, the AddiVortes framework is extended to binary classification by incorporating a probit model with a latent variable formulation. Specifically, we uti

ChungHa Lee, Jin-Hyuk Hong

Music visualization is an important medium that enables synesthetic experiences and creative inspiration. However, previous research focused mainly on the technical and theoretical aspects, overlooking users' everyday interaction with music visualizations. This gap highlights the pressing need for research on how music visualization influences users in synes

Yizhou Li, Yusuke Monno, Masatoshi Okutomi, Yuuichi Tanaka

Recent advances in Neural Radiance Fields (NeRF) have shown great potential in 3D reconstruction and novel view synthesis, particularly for indoor and small-scale scenes. However, extending NeRF to large-scale outdoor environments presents challenges such as transient objects, sparse cameras and textures, and varying lighting conditions. In this paper, we pr

George Panagopoulos, Johannes F. Lutzeyer, Sofiane Ennadir, Michalis Vazirgiannis

Genomic studies face a vast hypothesis space, while interventions such as gene perturbations remain costly and time-consuming. To accelerate such experiments, gene perturbation models predict the transcriptional outcome of interventions. Since constructing the training set is challenging, active learning is often employed in a "lab-in-the-loop" process. Whil

Tennison Liu, Nicolas Huynh, Mihaela van der Schaar

Decision trees are a crucial class of models offering robust predictive performance and inherent interpretability across various domains, including healthcare, finance, and logistics. However, current tree induction methods often face limitations such as suboptimal solutions from greedy methods or prohibitive computational costs and limited applicability of

Henry Dakin

We study the canonical mixed Hodge module structure associated to the $\mathscr{D}_X$-module $\mathscr{M}(f^{-\alpha}):=\mathscr{O}_X(*f)f^{-\alpha}$. We particularly focus on the weight filtration and extend many known results to the weighted setting. We obtain new relations between Hodge theory and birational geometry. We derive a general formula for the H

Yuanyuan Lian, Pieralberto Sicbaldi

In this paper we obtain rigidity results for bounded positive solutions of the general capillary overdetermined problem \begin{equation} \left\{ \begin{array} {ll} \mathrm{div} \left(\frac{\nabla u}{\sqrt{1+|\nabla u|^2}}\right) + f(u) = 0 & \mbox{in }\; \Omega,\\[1mm] u= 0 & \mbox{on }\; \partial \Omega,\\[1mm] \partial_{\nu} u=\kappa &\mbox{on }\; \partial

Mariano Cadoni, Leonardo Modesto, Mirko Pitzalis, Andrea Pierfrancesco Sanna

We present a class of Lorentzian traversable wormholes in conformal gravity, constructed via Weyl rescaling of Minkowski spacetime. As a result, these wormholes are solutions of every theory of gravity that is both conformally invariant and admits Minkowski spacetime as a solution. We specifically examine the case of a wormhole possessing a Morris-Thorne sha

Ashraf Ghiye, Baptiste Barreau, Laurent Carlier, Michalis Vazirgiannis

Graph Neural Networks have significantly advanced research in recommender systems over the past few years. These methods typically capture global interests using aggregated past interactions and rely on static embeddings of users and items over extended periods of time. While effective in some domains, these methods fall short in many real-world scenarios, e

Bharath Srivathsan, Rafal Gartman, Robert J. A. Francis-Jones, Peter Mosley

The coherent storage, buffering and retrieval of photons in a quantum memory enables the scalable creation of photonic entangled states via linear optics and repeat-until-success, unlocking applications in quantum communications and photonic quantum computing. Quantum memories based on off-resonant cascaded absorption (ORCA) in atomic vapors allow this stora

Gillian M Raab, Sophie McCall, Liam Cavin

Confidential administrative data is usually only available to researchers within a trusted research environment (TRE). Recently, some UK groups have proposed that low-fidelity synthetic data (LFSD) is available to researchers outside the TRE to allow code-testing and data discovery. There is a need for transparency so that those who access LFSD know how it h

Bingyan Xie, Yongpeng Wu, Feng Shu, Jiangzhou Wang

This paper focuses on a typical uplink transmission scenario over multiple-input multiple-output multiple access channel (MIMO-MAC) and thus propose a multi-user learnable CSI fusion semantic communication (MU-LCFSC) framework. It incorporates CSI as the side information into both the semantic encoders and decoders to generate a proper feature mask map in or

Maicon Hespanha, Ademir Pastor

This paper is concerned with a cubic nonlinear Schr\"odinger system modeling the interaction between an optical beam and its third harmonic in a material with Kerr-type nonlinear response. We are mainly interested in the so-called energy-critical case, that is, in dimension four. Our main result states that radially symmetric solutions with initial energy be

Saif Ur Rehman Khan, Muhammad Nabeel Asim, Sebastian Vollmer, Andreas Dengel

Diabetic retinopathy is a leading cause of blindness in diabetic patients and early detection plays a crucial role in preventing vision loss. Traditional diagnostic methods are often time-consuming and prone to errors. The emergence of deep learning techniques has provided innovative solutions to improve diagnostic efficiency. However, single deep learning m

Norman Khan, Erwan Quintin, Natalie A. Webb, Robbie Webbe

The XMM-Newton observatory has accumulated a vast archive of over 17,000 X-ray observations over the last 25 years. However, the standard data processing pipelines may fail to detect certain types of transient X-ray sources due to their short-lived or dim nature. Identifying these transient sources is important for understanding the full range of temporal X-

Rumeshika Pallewela, Yuyang Liu, Filip Elvander

In this work, we consider the problem of jointly estimating a set of room impulse responses (RIRs) corresponding to closely spaced microphones. The accurate estimation of RIRs is crucial in acoustic applications such as speech enhancement, noise cancellation, and auralization. However, real-world constraints such as short excitation signals, low signal-to-no

Yurui Lu, Xueke Pu

In this paper, we study the linear stability of Couette flow for 2D compressible Navier-Stokes-Poisson system at high Reynolds number in the domain $\mathbb{T}\times\mathbb{R}$ with initial perturbation in Sobolev spaces. We establish the upper bounds for the solutions of linearized system near Couette flow. In particular, we show that the irrotational compo

Sunwoo Lee

Sharpness-aware minimization (SAM) is known to improve the generalization performance of neural networks. However, it is not widely used in real-world applications yet due to its expensive model perturbation cost. A few variants of SAM have been proposed to tackle such an issue, but they commonly do not alleviate the cost noticeably. In this paper, we propos

Van Tuan Vo, Andreas Dechant, Keiji Saito

Nonequilibrium current fluctuations represent one of the central topics in nonequilibrium physics. The thermodynamic uncertainty relation (TUR) is widely acclaimed for rigorously establishing a lower bound on current fluctuations, expressed in terms of the entropy production rate and the average current. In this study, we focus on an upper bound for the fluc

Hao Ma, Zhiqiang Pu, Shijie Wang, Boyin Liu

Trajectory prediction facilitates effective planning and decision-making, while constrained trajectory prediction integrates regulation into prediction. Recent advances in constrained trajectory prediction focus on structured constraints by constructing optimization objectives. However, handling unstructured constraints is challenging due to the lack of diff

John Severn, Thomas Vacus, Eric Lauga

Muscle contraction, both in skeletal and cardiac tissue, is driven by sarcomeres, the microscopic units inside muscle cells where thick myosin and thin actin filaments slide past each other. During contraction and relaxation, the sarcomere's volume changes, causing sarcoplasm (intra-sarcomeric fluid) to flow out during contraction and back in as the sarcomer

Alessandro Giagnorio, Alberto Martin-Lopez, Gabriele Bavota

Deep learning (DL)-based code completion tools have transformed software development by enabling advanced code generation. These tools leverage models trained on vast amounts of code from numerous repositories, capturing general coding patterns. However, the impact of fine-tuning these models for specific organizations or developers to boost their performanc

Aissa Bouhali, Issam Louhichi, Abdel Rahman Yousef

A major open problem in the Theory of Toeplitz operators on the analytic Bergman space over the unit disk is the characterization of the commutant of a given Toeplitz operator--that is, the set of all bounded Toeplitz operators that commute with it. In this paper, we provide a complete description of bounded Toeplitz operators $T_f$, where the symbol $f$ has

Bartosz Wasilewski, Wojciech Marciniak, Mirosław Werwiński

The cubic Laves phases including YFe$_2$, YCo$_2$, ZrFe$_2$, and ZrCo$_2$ are considered as promising candidates for application in hydrogen storage and magnetic refrigeration. While YFe$_2$ and ZrFe$_2$ are ferromagnets, alloying with Co decreases magnetic moments and Curie temperatures ($T_\mathrm{C}$) of pseudobinary Zr(Fe$_{1-x}$Co$_x$)$_2$ and Y(Fe$_{1-

Junjin Xiao, Qing Zhang, Yonewei Nie, Lei Zhu

This paper presents RoGSplat, a novel approach for synthesizing high-fidelity novel views of unseen human from sparse multi-view images, while requiring no cumbersome per-subject optimization. Unlike previous methods that typically struggle with sparse views with few overlappings and are less effective in reconstructing complex human geometry, the proposed m

Giulia Sormani, Alex Rodriguez, Ali Hassanali

Machine learning has emerged as a powerful tool in atomistic simulations, enabling the identification of complex patterns in molecular systems limiting human intervention and bias. However, the practical implementation of these methods presents significant technical challenges, particularly in the selection of hyperparameters and in the physical interpretabi

Krystian Roslon

We present an efficient and cost-effective way to train operators of complex systems with limited accessibility, such as sub-detectors of big experiments at the CERN Large Hadron Collider (LHC). Our coaching station was developed to train on-call shifters for the ALICE Fast Interaction Trigger (FIT). This coaching station significantly reduces the training p

Rui Cao, Wei Tu, Dongsheng Chen, Wenyu Zhang

The shift toward high-quality urbanization has brought increased attention to the issue of "urban villages", which has become a prominent social problem in China. However, there is a lack of available geospatial data on urban villages, making it crucial to prioritize urban village mapping. In order to assess the current progress in urban village mapping and

Vincent C. Müller

This paper investigates the prospects of AI without representation in general, and the proposals of Rodney Brooks in particular. What turns out to be characteristic of Brooks' proposal is the rejection of central control in intelligent agents; his systems has as much or as little representation as traditional AI. The traditional view that representation is n

Yilin Wang

Autonomous driving systems require a deep understanding of human driving behaviors to achieve higher intelligence and safety.Despite advancements in deep learning, challenges such as long-tail distribution due to scarce samples and confusion from similar behaviors hinder effective driving behavior detection.Existing methods often fail to address sample confu

M. Icaza-Lizaola, E. L. Sirks, Yong-Seon Song, Peder Norberg

The analysis of state-of-the-art cosmological surveys like the Dark Energy Spectroscopic Instrument (DESI) survey requires high-resolution, large-volume simulations. However, the computational cost of hydrodynamical simulations at these scales is prohibitive. Instead, dark matter (DM)-only simulations are used, with galaxies populated a posteriori, typically

Sascha Caron, Andreas Ipp, Gert Aarts, Gábor Bíró

Artificial intelligence (AI) is transforming scientific research, with deep learning methods playing a central role in data analysis, simulations, and signal detection across particle, nuclear, and astroparticle physics. Within the JENA communities-ECFA, NuPECC, and APPEC-and as part of the EuCAIF initiative, AI integration is advancing steadily. However, br

Adrian Constantin, Pierre Germain, Zhiwu Lin, Hao Zhu

We investigate some qualitative aspects of the dynamics of the Euler equation on a rotating sphere that are relevant or stratospheric flows. Zonal flow dominates the dynamics of the stratosphere and for most known planetary stratospheres the observed flow pattern is a small perturbation of an n-jet, which corresponds to choosing the Legendre polynomial of de

Yongqi Li, Lu Yang, Jian Wang, Runyang You

Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in multimodal understanding, reasoning, and interaction. Given the extensive applications of MLLMs, the associated safety issues have become increasingly critical. Due to the effectiveness of preference optimization in aligning MLLMs with human preferences, there is an urgent

Niels Tripier-Mondancin, Ilya Karuseichyk, Mattia Walschaers, Valentina Parigi

Fast and precise characterization of Gaussian states is crucial for their effective use in quantum technologies. In this work, we apply a multi-parameter moment-based estimation method that enables rapid and accurate determination of squeezing, antisqueezing, and the squeezing angle of the squeezed vacuum state. Compared to conventional approaches, our metho

Yanghai Yu, Jinlu Li

In this paper, we consider the inviscid limit problem to the higher dimensional incompressible Navier-Stokes equations in the whole space. It was proved in \cite[J. Funct. Anal., 276 (2019)]{GZ} that given initial data $u_0\in B^{s}_{p,r}$ with $1\leq r<\infty$, the solution of the Navier-Stokes equations converges strongly in $B^{s}_{p,r}$ to the solution o

Mehdi Testouri, Gamal Elghazaly, Faisal Hawlader, Raphael Frank

Teleoperated driving enables remote human intervention in autonomous vehicles, addressing challenges in complex driving environments. However, its effectiveness depends on ultra-low latency, high-reliability communication. This paper evaluates teleoperated driving over 5G networks, analyzing key performance metrics such as glass-to-glass (G2G) latency, RTT a

Wuwei Huang, Dexin Wang, Deyi Xiong

In end-to-end speech translation, acoustic representations learned by the encoder are usually fixed and static, from the perspective of the decoder, which is not desirable for dealing with the cross-modal and cross-lingual challenge in speech translation. In this paper, we show the benefits of varying acoustic states according to decoder hidden states and pr

Atharva Ghotavadekar, František Nekovář, Martin Saska, Jan Faigl

Agile trajectory planning can improve the efficiency of multi-rotor Uncrewed Aerial Vehicles (UAVs) in scenarios with combined task-oriented and kinematic trajectory planning, such as monitoring spatio-temporal phenomena or intercepting dynamic targets. Agile planning using existing non-linear model predictive control methods is limited by the number of plan

Aleksandr Shefer, Igor Engel, Stanislav Alekseev, Daniil Berezun

Although formal methods are capable of producing reliable software, they have seen minimal adoption in everyday programming. Automatic code generation using large language models is becoming increasingly widespread, but it rarely considers producing strong correctness guarantees. In this study, we explore the ability of LLMs to produce verified code in three

Bozhou Zhang, Nan Song, Xin Jin, Li Zhang

End-to-end autonomous driving unifies tasks in a differentiable framework, enabling planning-oriented optimization and attracting growing attention. Current methods aggregate historical information either through dense historical bird's-eye-view (BEV) features or by querying a sparse memory bank, following paradigms inherited from detection. However, we argu

Hayden Robertson, Joanne Zimmer, Anuar Sifuentes Name, Cassia Lux

For studying the structure of microgel particles at the air/water interface, specular and off-specular X-ray reflectivity (OSR/XRR) allows in situ measurements without any labelling techniques. Herein we investigate the vertical and lateral structure of poly(N-isopropylacrylamide) (PNIPAM) microgels (MGs) at the air/water interface and the effect of Langmuir

Vesa Kaarnioja, André-Alexander Zepernick

Let $K_n$ denote the set of all nonsingular $n\times n$ lower triangular $(0,1)$-matrices. Hong and Loewy (2004) introduced the number sequence $$ c_n=\min\{\lambda\mid\lambda~\text{is an eigenvalue of}~XX^{\rm T},~X\in K_n\},\quad n\in\mathbb Z_+. $$ There have been a number of attempts in the literature to obtain bounds on the numbers $c_n$ by Mattila (201

Rika Shimomura, Yuuichi Tabe, Hisaaki Shinkai

Independent component analysis (ICA) is a method to extract a set of time-series data using ``statistical independency" of each component. We applied ICA to extract gravitational wave (GW) signals directly from the detector data. Our idea is to extract a coherent signal that is included in multiple detectors and find it by shifting the data set around its ar

Liyi Zhang, Yujie Peng, Yi Lian, Mengru Xue

With the development of technology, digital games have permeated into family and parent-child relationships, leading to cognitive deficiencies and inter-generational conflicts that have yet to be effectively addressed. Building on previous research on digital games and parent-child relationships, we have developed Figame, a Joint Media Engagement (JME) based

Mohamad Al Ahdab, Zheng-Hua Tan, John Leth

We present a direct parametrization for continuous-time stochastic state-space models that ensures external stability via the stochastic bounded-real lemma. Our formulation facilitates the construction of probabilistic priors that enforce almost-sure stability, which are suitable for sampling-based Bayesian inference methods. We validate our work with a simu

Lirui Jia, Wenguang Zhai

Suppose $a,~b$ are fixed algebraic numbers with $1\leq a<b$. Let $\Delta_{a,b}(x)$ be the error term for the number of lattice points in a two-dimensional area $h^ar^b\leq x $ with $h, r$ positive integers. In this paper, we establish an asymptotic formula for the mean square of $\Delta_{a,b}(x)$ when $a, b$ are fixed algebraic numbers such that $\dfrac{a}{b

Nadir Fasola, Michele Graffeo, Danilo Lewański, Andrea T. Ricolfi

Let $(S,p)$ be a smooth pointed surface. In the first part of this paper we study motivic invariants of punctual nested Hilbert schemes attached to $(S,p)$ using the Hilbert-Samuel stratification. We compute two infinite families of motivic classes of punctual nested Hilbert schemes, corresponding to nestings of the form $(2,n)$ and $(3,n)$. As a consequence

Natalie Butterfield, Larry Morgan, Ashley Barnes, Adam Ginsburg

We present the detection of a previously unknown giant molecular cloud (GMC) located at the midpoint of the Galactic Bar Dust Lanes (M4.7--0.8), using spectral line observations taken with the Green Bank Telescope (GBT). This $\sim$60 pc long GMC is associated with accreting material that is transitioning from the quieter Galactic disk environment to the mor

Guillem Cadevall Ferreres, Marc Serrano Sanz, Marc Bardeli Gámez, Pol Gerdt Basullas

Named Entity Recognition (NER) is a critical component of Natural Language Processing (NLP) for extracting structured information from unstructured text. However, for low-resource languages like Catalan, the performance of NER systems often suffers due to the lack of high-quality annotated datasets. This paper introduces NERCat, a fine-tuned version of the G

Kenneth L. Baker, Marc Kegel, Kimihiko Motegi

The first and last named authors have demonstrated the existence of knots for which every integral slope is non-characterizing. In this short note, we extend this result in two ways. There exists a knot that shares for every integer n the same n-trace with infinitely many mutually distinct knots. Moreover, every knot is concordant to a knot that is not detec

Simon Niedermayr, Christoph Neuhauser Rüdiger Westermann

We introduce an image upscaling technique tailored for 3D Gaussian Splatting (3DGS) on lightweight GPUs. Compared to 3DGS, it achieves significantly higher rendering speeds and reduces artifacts commonly observed in 3DGS reconstructions. Our technique upscales low-resolution 3DGS renderings with a marginal increase in cost by directly leveraging the analytic

Xin Zhao, Mingzhe Liu, Yu Chen, Qi Zhang

The PL6 color center in 4H-SiC, known for its excellent ambient-temperature spin and optical properties, has an unresolved microscopic origin. In this first-principles study, we systematically investigate potential structures to clarify its nature. We first rigorously examine the DV-antisite hypothesis (a divacancy paired with a carbon antisite, $\mathrm{C_{

Julian Brockmeier, Timon Schapeler, Nina Amelie Lange, Jan Philipp Höpker

Cointegration of heralded single-photon generation and on-chip detection requires the ability to differentiate between pump light and single photons. We explored the dispersion-induced temporal separation of optical pulses to reach this goal. Our method exploits the distinct group velocities of pump light and single photons, as well as single-photon detector

Lin Ma, Qiyuan An, Jing Chen, Xinggang Hou

Extensive research has confirmed the positive relationship between exposure to natural environments and human cognitive, behavioral, physical, and mental health. However, only some have easy access to nature. With electronic information and simulation technology advancements, digital nature experiences are widely used across various devices and scenarios. It

Mirosław Werwiński, Andrzej Szajek

The five crystal structures of selected UNi$_{x}$Sb$_2$ compositions are investigated by density functional theory supercell calculations. The considered phases are USb$_2$, UNi$_{0.33}$Sb$_2$, UNi$_{0.5}$Sb$_2$, UNi$_{0.66}$Sb$_2$, and UNiSb$_2$ ($x$ = 0, 1/3, 1/2, 2/3, 1). The occupation deficiency of Ni is modeled by removing the Ni layers from constructe

Jiefeng Liu, Tongtong Yue, Qi Wang

We introduce a notion of a para-K\"{a}hler strict Lie 2-algebra, which can be viewed as a categorification of a para-K\"{a}hler Lie algebra. In order to study para-K\"{a}hler strict Lie 2-algebra in terms of strict pre-Lie 2-algebras, we introduce the Manin triples, matched pairs and bialgebra theory for strict pre-Lie 2-algebras and the equivalent relations

Hiroki Takahasi

We investigate periodic points of the Dyck shift from the viewpoint of large deviations. We establish the level-2 Large Deviation Principle with the rate function given in terms of Kolmogorov-Sinai entropies of shift-invariant Borel probability measures. Unlike topologically mixing Markov shifts, the level-2 rate function is non-convex and level-1 rate funct

Andrew Mummery, Muryel Guolo, James Matthews, Megan Newsome

Stars in galactic centers are occasionally scattered so close to the central supermassive black hole that they are completely disrupted by tidal forces, initiating a transient accretion event. The aftermath of such a tidal disruption event (TDE) produces a bright-and-blue accretion flow which is known to persist for at least a decade (observationally) and ca

Zongyun Zhang, Jiacheng Ruan, Xian Gao, Ting Liu

Industrial Anomaly Detection (IAD) is critical to ensure product quality during manufacturing. Although existing zero-shot defect segmentation and detection methods have shown effectiveness, they cannot provide detailed descriptions of the defects. Furthermore, the application of large multi-modal models in IAD remains in its infancy, facing challenges in ba

Yiqi Zhu, Ziyue Wang, Can Zhang, Peng Li

Vision-Language Models (VLMs) have recently witnessed significant progress in visual comprehension. As the permitting length of image context grows, VLMs can now comprehend a broader range of views and spaces. Current benchmarks provide insightful analysis of VLMs in tasks involving complex visual instructions following, multi-image understanding and spatial

Minh Nhat Vu, Gerald Ebmer, Alexander Watcher, Marc-Philip Ecker

Autonomous large-scale machine operations require fast, efficient, and collision-free motion planning while addressing unique challenges such as hydraulic actuation limits and underactuated joint dynamics. This paper presents a novel two-step motion planning framework designed for an underactuated forestry crane. The first step employs GPU-accelerated stocha

Yu-Peng Zhang, Shao-Wen Wei, Yu-Xiao Liu

This work devotes to investigate the dynamical emergence of black hole shadow from gravitational lensing in dynamical spacetime by using the collapsing boson star. Two characterized scenarios are adopted with or without considering the time delay of light propagation. As the boson star evolves, new Einstein rings emerge from the lensing center, with their ra

Eduardo Abi Jaber, Shaun, Li

We introduce the two-factor Quintic Ornstein-Uhlenbeck (OU) model, where volatility is modelled as a degree-five polynomial of the sum of two Ornstein-Uhlenbeck processes driven by the same Brownian motion, each mean-reverting at a different speed. We demonstrate that the model effectively captures the volatility surfaces of SPX and VIX while aligning with t

Víctor J. Maciá

The theory of Khinchin families connects Probability Theory and Complex Analysis. Along this PhD thesis, we exploit this connection to obtain, using a variety of local central limit theorems, asymptotic formulas for the coefficients of analytic functions with non-negative coefficients. We give criteria for a power series to have certain specific, and in most

Claudio Galelli

Characterizing the temporal variability of astrophysical sources is key to understanding the underlying physical processes driving their emissions. This work introduces a gammapy_SyLC, a Python package that offers tools to simulate and fit time-domain data, with a focus on Active Galactic Nuclei (AGN) variability. The package was developed taking into accoun

Muxuan Yang, Dongyang Yan, Lei Gao, Wei Liu

Dirac-like cones, featuring conical linear dispersions intersecting with flat bands, typically arise from accidental degeneracy of multiple modes that requires precise tuning of material and structural parameters, inherently limiting their robustness and applications. In this work, by introducing electromagnetic duality symmetry into photonic crystals, we de

Liya Guo, Zun Wang, Chang Liu, Junzhe Li

The ensemble average of physical properties of molecules is closely related to the distribution of molecular conformations, and sampling such distributions is a fundamental challenge in physics and chemistry. Traditional methods like molecular dynamics (MD) simulations and Markov chain Monte Carlo (MCMC) sampling are commonly used but can be time-consuming a

Zhang Chen, Shuai Wan, Siyu Ren, Fuzheng Yang

One of the main challenges in point cloud compression (PCC) is how to evaluate the perceived distortion so that the codec can be optimized for perceptual quality. Current standard practices in PCC highlight a primary issue: while single-feature metrics are widely used to assess compression distortion, the classic method of searching point-to-point nearest ne

Changran Xu, Yi Liu, Yunhao Zhou, Shan Huang

The rapid advancement of large language models (LLMs) has revolutionized code generation tasks across various programming languages. However, the unique characteristics of programming languages, particularly those like Verilog with specific syntax and lower representation in training datasets, pose significant challenges for conventional tokenization and dec

Iman Peivaste, Ahmed Makradi, Salim Belouettar

Microstructural evolution, particularly grain growth, plays a critical role in shaping the physical, optical, and electronic properties of materials. Traditional phase-field modeling accurately simulates these phenomena but is computationally intensive, especially for large systems and fine spatial resolutions. While machine learning approaches have been emp

B. Blok, R. Segev

We consider the double parton scattering (DPS) initiated by direct photons in $\gamma p$ and $\gamma A$ collisions. We extend the previously known results for photoproduction to the case of electroproduction with nonzero photon virtuality $Q^2$, and then consider the DPS for both photo and electroproduction for HERA and IEC ( electron ion collider) kinematic

Yong Zhong, Zhuoyi Yang, Jiayan Teng, Xiaotao Gu

We present Concat-ID, a unified framework for identity-preserving video generation. Concat-ID employs variational autoencoders to extract image features, which are then concatenated with video latents along the sequence dimension. It relies exclusively on inherent 3D self-attention mechanisms to incorporate them, eliminating the need for additional parameter

Yihang Zhou, Ruige Kong, Zhengsen Xu, Linlin Xu

Facing the escalating threat of global wildfires, numerous computer vision techniques using remote sensing data have been applied in this area. However, the selection of deep learning methods for wildfire prediction remains uncertain due to the lack of comparative analysis in a quantitative and explainable manner, crucial for improving prevention measures an

Niklas Zorbach, Jan Philipp Klinger, Owe Philipsen, Jens Braun

Lattice Monte Carlo (MC) simulations and the functional Renormalization Group (RG) are powerful approaches that allow for quantitative studies of non-perturbative phenomena such as bound-state formation, spontaneous symmetry breaking and phase transitions. While results from both methods have recently shown remarkable agreement for many observables, e.g., in

Simone Marchi, David Vokrouhlický, David Nesvorný, William F. Bottke

The NASA Lucy mission is scheduled to fly-by the main belt asteroid (52246) Donaldjohanson on April 20, 2025. Donaldjohanson (DJ hereafter) is a member of the primitive (C-type class) Erigone collisional asteroid family located in the inner main belt in proximity of the source regions of asteroid (101955)~Bennu and (162173)~Ryugu, visited respectively by OSI

Fereshteh Felegary, Thammarong Eadkhong, Farruh Atamurotov, Phongpichit Channuie

This work investigates a single-field inflationary model, a specific class of the K-essence models where a coupling term exists between canonical Lagrangian and the potential. This coupling term has many effects on key inflationary parameters consisting of the power spectral, the spectral index, the tensor-to-scalar ratio, the Hubble parameter, the equation

Nadav Auerbach, Surajit Dutta, Matan Uzan, Yaar Vituri

The tunable band structure and nontrivial topology of multilayer rhombohedral graphene lead to a variety of correlated electronic states with isospin orders-meaning ordered states in the combined spin and valley degrees of freedom-dictated by the interplay of spin-orbit coupling and Hunds exchange interactions. However, methods for mapping local isospin text

Vincenzo Calabrese, Silvia Nardone, Amy Q. Shen, Simon J. Haward

Capillary thinning of polymeric fluids is central to biological and industrial processes, yet the mechanisms governing thinning dynamics remain unresolved, especially for semi-flexible polymers. Using ideal solutions of semi-flexible DNA, we validate a predictive model for exponential capillary thinning that accounts for each polymer in the molecular weight

Maximilian Binzer, František Šanda, Lars Mewes, Erling Thyrhaug

Transient absorption (TA) is the most widespread method to follow ultrafast dynamics in molecules and materials. The related method of TA anisotropy (TAA) reports on the ultrafast reorientation dynamics of transition dipole moments, reporting on phenomena ranging from electronic dephasing to orientational diffusion. While these are fundamental aspects comple

Tidiane Camaret Ndir, Robin Tibor Schirrmeister, Tonio Ball

Deep networks for electroencephalogram (EEG) decoding are often only trained to solve one specific task, such as pathology or age decoding. A more general task-agnostic approach is to train deep networks to match a (clinical) EEG recording to its corresponding textual medical report and vice versa. This approach was pioneered in the computer vision domain ma

Jing Li, Pinhao Wang, Emilia Barakova, Jun Hu

We found that children in elementary school often experience stress during task performance. Limited coping skills and lack of stress awareness restrict children's ability to manage their stress. Many designs and studies have proposed different stress detection and intervention solutions. Still, they often overlook the potential of enhancing everyday objects

Michele Caselli, Mattia Freguglia, Nicola Picenni

We consider sequences of maps from an $(n+m)$-dimensional domain into the $(n-1)$-sphere, which satisfy a natural $p$-energy growth, as $p$ approaches $n$ from below. We prove that, up to subsequences, the Jacobians of such maps converge in the flat topology to an integral $m$-current, and that the $p$-energy Gamma-converges to the mass of the limit current.

Yuming Fu, Xue-Bing Wu, R. J. Bouwens, Karina I. Caputi

The Gaia DR3 has provided a large sample of more than 6.6 million quasar candidates with high completeness but low purity. Previous work on the CatNorth quasar candidate catalog has shown that including external multiband data and applying machine-learning methods can efficiently purify the original Gaia DR3 quasar candidate catalog and improve the redshift

Zining Wang, Tongkun Guan, Pei Fu, Chen Duan

Multi-modal Large Language Models (MLLMs) have introduced a novel dimension to document understanding, i.e., they endow large language models with visual comprehension capabilities; however, how to design a suitable image-text pre-training task for bridging the visual and language modality in document-level MLLMs remains underexplored. In this study, we intr

Omogolo Omaatla Morake, Mengru Xue

Psychological stress encompasses emotional tension and pressure experienced by people, which usually arises from situations people find challenging. However, more is needed to know about the pressures faced by international college students studying in China. The goal of this study is to investigate the various stressors that international college students i

Siddharth D Jaiswal, Sagnik Basu, Sandipan Sikdar, Animesh Mukherjee

Automated Face Recognition Systems (FRSs), developed using deep learning models, are deployed worldwide for identity verification and facial attribute analysis. The performance of these models is determined by a complex interdependence among the model architecture, optimization/loss function and datasets. Although FRSs have surpassed human-level accuracy, th

Roberto Mauri, Massimiliano Giona

Applying the least action principle to the motion of an ideal gas, we find Bernoulli's equation where the local velocity is expressed as the gradient of a velocity potential, while the internal energy depends on the interaction among the particles of the gas. Then, assuming that the internal energy is proportional non-locally to the logarithm of the mass den

Ankit Dutta, Nabarup Ghosh, Ankush Chatterjee

Large Language models have demonstrated excellent domain-specific question-answering capabilities when finetuned with a particular dataset of that specific domain. However, fine-tuning the models requires a significant amount of training time and a considerable amount of hardware. In this work, we propose CARE (Customer Assistance and Response Engine), a lig

Karl Palmskog, Andreas Lindner, Scott Constable, Roberto Guanciale

Many types of formal verification establish properties about abstract high-level program representations, leaving a large gap to programs at runtime. Although gaps can sometimes be narrowed by techniques such as refinement, a verified program's trusted computing base may still include compilers and inlined assembly. In contrast, verification of binaries foll