March 2025 arXiv papers — page 84

Jiyong Rao, Brian Nlong Zhao, Yu Wang

Multi-species animal pose estimation has emerged as a challenging yet critical task, hindered by substantial visual diversity and uncertainty. This paper challenges the problem by efficient prompt learning for Vision-Language Pretrained (VLP) models, \textit{e.g.} CLIP, aiming to resolve the cross-species generalization problem. At the core of the solution l

Daniel Rein, Marc Schlegel, Patrick Tollkühn, Werner Vogelsang

We investigate the single-spin asymmetry for the single-inclusive production of hadrons and jets in collisions of transversely polarized nucleons and unpolarized leptons, $\ell N^\uparrow \to (h\,\mathrm{or\,jet})X$. We compute the spin-dependent cross section within collinear twist-3 factorization in perturbative QCD at next-to-leading order (NLO) accuracy.

Richard A. Berk, Amy Braverman

In this paper, we step back from a variety of competing heat wave definitions and forecast directly unusually high temperatures. Our testbed is the Russian Far East in the summers of 2022 and 2023. Remotely sensed data from NASA's Aqua spacecraft are organized into a within-subject design that can reduce nuisance variation in forecasted temperatures. Spatial

Alexandre Verine, Ahmed Mehdi Inane, Florian Le Bronnec, Benjamin Negrevergne

Discriminator Guidance has become a popular method for efficiently refining pre-trained Score-Matching Diffusion models. However, in this paper, we demonstrate that the standard implementation of this technique does not necessarily lead to a distribution closer to the real data distribution. Specifically, we show that training the discriminator using Cross-E

Abdallah Daddi-Moussa-Ider, Lukas Fischer, Marc Pradas, Andreas M. Menzel

For homogeneous and isotropic linearly elastic solids and for incompressible fluids under low-Reynolds-number conditions the fundamental solutions of the associated continuum equations were derived a long time ago for bulk systems. That is, the corresponding Green's functions are available in infinitely extended systems, where boundaries do not play any role

Helmut Harbrecht, Michael Multerer

This chapter is dedicated to recent developments in the field of wavelet analysis for scattered data. We introduce the concept of samplets, which are signed measures of wavelet type and may be defined on sets of arbitrarily distributed data sites in possibly high dimension. By employing samplets, we transfer well-known concepts known from wavelet analysis, n

Devansh Sharma, Amartya Bose

Efficiency of quantum transport through aggregates with multiple end-points or traps proves to be an emergent and a highly non-equilibrium phenomenon. We present a numerically exact approach for computing the emergent time scale and amount of extraction specific to particular traps leveraging a non-Hermitian generalization of the recently introduced state-to

Chelse Swoopes, Tyler Holloway, Elena L. Glassman

Interfaces for interacting with large language models (LLMs) are often designed to mimic human conversations, typically presenting a single response to user queries. This design choice can obscure the probabilistic and predictive nature of these models, potentially fostering undue trust and over-anthropomorphization of the underlying model. In this paper, we

Liang Zhang, Zhiwei Fan, Yiming Pan

We report the formation of a novel soliton, termed event soliton, in nonlinear photonic spacetime crystals (STCs). In these media, simultaneous spatiotemporal periodic modulation of the dielectric constant generates mixed frequency (${\omega}$) and wavevector (k) gaps. Under Kerr nonlinearity, the event solitons emerge as fully localized entities in both spa

Liming Liu, Jiangkai Wu, Haoyang Wang, Peiheng Wang

Traditional video compression algorithms exhibit significant quality degradation at extremely low bitrates. Promptus emerges as a new paradigm for video streaming, substantially cutting down the bandwidth essential for video streaming. However, Promptus is computationally intensive and can not run in real-time on mobile devices. This paper presents PromptMob

Md Khalid Hossain, Farook Rahaman

We propose the back reaction to the charged galactic wormhole spacetime based on Yoshiaki Sofue's exponential dark matter density profile to find exact solutions. The charges act as an additional component to the static wormhole, which is primarily formed by the galactic dark matter density. Unlike traditional mass-based models, this solution incorporates ch

Dagmar Zakova, Peter Frolkovic

This study investigates numerical methods to solve nonlinear transport problems characterized by various sorption isotherms with a focus on the Freundlich type of isotherms. We describe and compare second order accurate numerical schemes, focusing on implicit methods, to effectively model transport phenomena without stability restriction on the choice of tim

Moucheng Yang, Kaixiang Zhu, Lingli Wang, Xuegong Zhou

The conventional LUT is redundant since practical functions in real-world benchmarks only occupy a small proportion of all the functions. For example, there are only 3881 out of more than $10^{14}$ NPN classes of 6-input functions occurring in the mapped netlists of the VTR8 and Koios benchmarks. Therefore, we propose a novel LUT-like architecture, named DSL

Tapio Pitkäranta, Eero Hyvönen

The history of Artificial Intelligence (AI) is a narrative of waves -- rising optimism followed by crashing disappointments. AI winters, such as the early 2000s, are often remembered as barren periods of innovation. This paper argues that such a perspective overlooks a crucial wave of AI that seems to be forgotten: the rise of the Semantic Web, which is base

Patricia Gimeno-Estivill, Tuomas Lappi, Heikki Mäntysaari

We compute the differential cross section for inclusive $\mathrm{D}^0$ production in ultraperipheral collisions within the Color Glass Condensate framework. Our predictions are found to be in relatively good agreement with the CMS data at small transverse momentum, which is the region of validity of our approach. Furthermore, we quantify saturation effects b

Shobhit Singhal, Marta Fochesato, Liviu Aolaritei, Florian Dörfler

Wind power producers (WPPs) participating in short-term power markets face significant imbalance costs due to their non-dispatchable and variable production. While some WPPs have a large enough market share to influence prices with their bidding decisions, existing optimal bidding methods rarely account for this aspect. Price-maker approaches typically model

Mohamad Hassan N C, Divyam Gupta, Mainak Singha, Sai Bhargav Rongali

We introduce Low-Shot Open-Set Domain Generalization (LSOSDG), a novel paradigm unifying low-shot learning with open-set domain generalization (ODG). While prompt-based methods using models like CLIP have advanced DG, they falter in low-data regimes (e.g., 1-shot) and lack precision in detecting open-set samples with fine-grained semantics related to trainin

Alberto Boscaggin, Francesca Colasuonno, Benedetta Noris, Federica Sani

We consider semilinear elliptic problems of the form \[ -\Delta u + \lambda u = f(x,u), \quad u\in H^1_0(A), \] where $A\subset\mathbb{R}^N$, $N\geq3$, is either a bounded or unbounded annulus, and $\lambda \geq0$. We study a broad class of nonlinearities $f$ with superlinear growth at infinity, including exponential- and power-type ones. Under suitable assu

Alexander Ek, Michelle Blom, Philip B. Stark, Peter J. Stuckey

One approach to risk-limiting audits (RLAs) compares randomly selected cast vote records (CVRs) to votes read by human auditors from the corresponding ballot cards. Historically, such methods reduce audit sample sizes by considering how each sampled CVR differs from the corresponding true vote, not merely whether they differ. Here we investigate the latter a

Shilpa Prakash, Mustansir Barma, Kabir Ramola

We study the exact fluctuating hydrodynamics of the scaled Light-Heavy model (sLH), in which two species of particles (light and heavy) interact with a fluctuating surface. This model is similar in definition to the unscaled Light-Heavy model (uLH), except it uses rates scaled with the system size. The consequence, it turns out, is a phase diagram that diffe

Jean Douçot

We give an explicit algorithm to reduce the ramification order of any exponential factor of an irregular connection on $\mathbb P^1$, using the same types of basic operations as in the Katz-Deligne-Arinkin algorithm for rigid irregular connections. The exponential factor reached when the algorithm terminates is, up to admissible deformations, the unique fact

Angelo B. Mingarelli

We correct and update a result of R.G.D. Richardson [13] dealing with the separation of zeros of the real and imaginary parts of non-real eigenfunctions of non-definite Sturm-Liouville eigenvalue problems. We then extend it to the case where the weight function is allowed to be identically zero on a subinterval that excludes the end-points and study the beha

Ángel González-Prieto, Eva Miranda, Daniel Peralta-Salas

In this article, we establish the foundations of a computational field theory, which we term Topological Kleene Field Theory (TKFT), inspired by Stephen Kleene's seminal work on partial recursive functions and drawing parallels with Topological Field Theory. Our central result shows that any computable function can be simulated by the flow on a smooth bordis

Pablo Marchant

Binary stars are pairs of stars that are gravitationally bound, providing in some cases accurate measurements of their masses and radii. As such, they serve as excellent testbeds for the theory of stellar structure and evolution. Moreover, binary stars that orbit each other at a sufficiently small distance will interact during their lifetimes, leading to a m

Yanqin Fan, Hyeonseok Park, Brendan Pass, Xuetao Shi

In this paper, we develop a unified approach to study partial identification of a finite-dimensional parameter defined by a general moment model with incomplete data. We establish a novel characterization of the identified set for the true parameter in terms of a continuum of inequalities defined by conditional optimal transport. For the special case of an a

Daniel Rein, Marc Schlegel, Patrick Tollkühn, Werner Vogelsang

We present next-to-leading order QCD corrections for the cross sections for $\ell p^\uparrow\to hX$, $\ell p^\uparrow\to {\mathrm{jet}}X$ with transversely polarized initial protons. These cross sections are known to be power-suppressed in QCD and probe twist-3 parton correlation functions in the proton. Our calculation exhibits the full complexity of a pert

Lucas Morin, Valéry Weber, Ahmed Nassar, Gerhard Ingmar Meijer

The automated analysis of chemical literature holds promise to accelerate discovery in fields such as material science and drug development. In particular, search capabilities for chemical structures and Markush structures (chemical structure templates) within patent documents are valuable, e.g., for prior-art search. Advancements have been made in the autom

Yahong Guo, Congming Li, Chilin Zhang

We study the singular Lane-Emden-Fowler equation \begin{equation} -\Delta u=f(X)\cdot u^{-\gamma} \end{equation} in a bounded Lipschitz domain $\Omega$, with the Dirichlet boundary condition and a positive, bounded function $f(X)$. A distinguishing feature is that the vanishing boundary condition introduces a singularity in the equation. We focus on the well

Kevine Meugang Toukam

We introduce a modified Benamou-Brenier type approach leading to a Wasserstein type distance that allows global invariance, specifically, isometries, and we show that the problem can be summarized to orthogonal transformations. This distance is defined by penalizing the action with a costless movement of the particle that does not change the direction and sp

Reem I. Masoud, Martin Ferianc, Philip Treleaven, Miguel Rodrigues

Large Language Model (LLM) alignment conventionally relies on supervised fine-tuning or reinforcement learning based alignment frameworks. These methods typically require labeled or preference datasets and involve updating model weights to align the LLM with the training objective or reward model. Meanwhile, in social sciences such as cross-cultural studies,

Marie Bormann

We prove lower bounds for the first non-trivial eigenvalue of the drift Laplacian on manifolds with Wentzell-type boundary condition in terms of some Cheeger-type constants for bulk-boundary interactions. Our results are in the spirit of Cheeger's classical inequality.

Anthony Hastir, Lassi Paunonen

We study the well-posedness and stability of an impedance passive infinite-dimensional linear system under nonlinear feedback of the form $u(t)=\phi(v(t)-y(t))$, where $\phi$ is a monotone function. Our first main result introduces conditions guaranteeing the existence of classical and generalised solutions in a situation where the original linear system is

Abdullah Mamun, Diane J. Cook, Hassan Ghasemzadeh

Adherence to prescribed treatments is crucial for individuals with chronic conditions to avoid costly or adverse health outcomes. For certain patient groups, intensive lifestyle interventions are vital for enhancing medication adherence. Accurate forecasting of treatment adherence can open pathways to developing an on-demand intervention tool, enabling timel

Costantino Di Bello, Édgar Roldán, Ralf Metzler

We consider a general one-dimensional overdamped diffusion model described by the It\^{o} stochastic differential equation (SDE) ${dX_t=\mu(X_t,t)dt+\sigma(X_t,t)dW_t}$, where $W_t$ is the standard Wiener process. We obtain a specific condition that $\mu$ and $\sigma$ must fulfil in order to be able to solve the SDE via mapping the generic process, using a s

Sebastian Haslebacher, Jonas Lill, Patrick Schnider, Simon Weber

We prove that an $\epsilon$-approximate fixpoint of a map $f:[0,1]^d\rightarrow [0,1]^d$ can be found with $\mathcal{O}(d^2(\log\frac{1}{\epsilon} + \log\frac{1}{1-\lambda}))$ queries to $f$ if $f$ is $\lambda$-contracting with respect to an $\ell_p$-metric for some $p\in [1,\infty)\cup\{\infty\}$. This generalizes a recent result of Chen, Li, and Yannakakis

Ian D. Morris

We prove a general measurable Liv\v{s}ic regularity theorem for real-valued cocycles over non-invertible dynamical systems using only abstract hypotheses on an associated transfer operator. As illustrative applications we derive measurable Liv\v{s}ic regularity results in the analytic regularity class for cocycles over real-analytic expanding maps, in the bo

B. C. Lütfüoğlu

We calculate quasinormal modes and gray-body factors of a massive scalar field in the background of three compact objects in the Weyl gravity: Schwarzschild-like black holes, known as Mannheim-Kazanas solution, non-Schwrazschild-like black holes and traversable wormholes found recently in [P. Jizba, K. Mudru\v{n}ka, Phys.Rev.D 110 (2024) 12, 124006]. We show

Gabriela Ghimpeteanu, Hayat Rajani, Josep Quintana, Rafael Garcia

Ensuring food safety and quality is critical in the food processing industry, where the detection of contaminants remains a persistent challenge. This study presents an automated solution for detecting foreign objects on pork belly meat using hyperspectral imaging (HSI). A hyperspectral camera was used to capture data across various bands in the near-infrare

Aung Htet, Alejandro Rodriguez Jimenez, Sarah Hamburg, Alessandro Di Nuovo

We introduce a novel model for updating perceptual beliefs about the environment by extending the concept of Allostasis to the control of internal representations. Allostasis is a fundamental regulatory mechanism observed in animal physiology that orchestrates responses to maintain a dynamic equilibrium in bodily needs and internal states. In this paper, we

Gabriel Martins de Jesus, Felippe Moraes Pereira, João Luiz Rebelatto, Richard Demo Souza

We propose and evaluate age of information (AoI)-aware multiple access mechanisms for the Internet of Things (IoT) in multi-relay two-hop networks. The network considered comprises end devices (EDs) communicating with a set of relays in ALOHA fashion, with new information packets to be potentially transmitted every time slot. The relays, in turn, forward the

Kim William Torre, Joost de Graaf

Stokesian Dynamics (SD) is a numerical framework used for simulating hydrodynamic interactions in particle suspensions at low Reynolds number. It combines far-field approximations with near-field lubrication corrections, offering a balance between accuracy and efficiency. This work reviews SD and provides a perspective on future directions for this approach.

Shuaihao Zhang, Sérgio D. N. Lourenço, Xiangyu Hu

This study introduces a Riemann-based Smoothed Particle Hydrodynamics (SPH) framework for the stable and accurate simulation of surface tension in multiphase flows, with density and viscosity ratios as high as 1000 and 100, respectively. The methodology begins with the computation of surface stress, from which surface tension is derived, ensuring the conserv

Jonas Krumme, Christoph Zetzsche

When interacting with the world robots face a number of difficult questions, having to make decisions when given under-specified tasks where they need to make choices, often without clearly defined right and wrong answers. Humans, on the other hand, can often rely on their knowledge and experience to fill in the gaps. For example, the simple task of organizi

Zhiyuan Liu, Yuting Zhang, Feng Liu, Changwang Zhang

Multimodal Large Language Models (MLLMs) have gained significant traction for their ability to process diverse input data types and generate coherent, contextually relevant outputs across various applications. While supervised fine-tuning (SFT) has been the predominant approach to enhance MLLM capabilities in task-specific optimization, it often falls short

Youngjin Bae, Jung Hee Cheon, Guillaume Hanrot, Jai Hyun Park

Homomorphic encryption is a cryptographic paradigm allowing to compute on encrypted data, opening a wide range of applications in privacy-preserving data manipulation, notably in AI. Many of those applications require significant linear algebra computations (matrix-vector products, and matrix-matrix products). This central role of linear algebra computations

Chiara Rucco, Antonella Longo, Motaz Saad

In today's fast-paced digital world, data has become a critical asset for enterprises across various industries. However, the exponential growth of data presents significant challenges in managing and utilizing the vast amounts of information collected. Data engineering has emerged as a vital discipline addressing these challenges by providing robust platfor

Haobing Zhang, Xintao Fan, Weiwei Wang

Magnetic vortices and skyrmions represent two fundamental classes of topological spin textures in ferromagnetic systems, distinguished by their unique stabilization mechanisms and degrees of freedom. Vortices, characterized by circular in-plane magnetization (chirality) and out-of-plane core polarization, naturally arise in confined geometries due to the int

Nakada Hitoshi, Natsui Rie, Toyosumi Mako

We define a continued fraction map associated with the $\mathfrak o(\sqrt{-3})$-module $\mathcal J = \eta \cdot\mathfrak o(\sqrt{-3})$, $\eta = \frac{3 + \sqrt{-3}}{2}$, which is an Eisenstein field version of the continued fraction map associated with $\mathfrak o(\sqrt{-1}) \cdot (1 + i)$ defined by J.~Hurwitz in the case of the Gaussian field. Together wi

Boris Houska, Matthias A. Müller, Mario E. Villanueva

This paper proposes novel approaches for designing control Lyapunov functions (CLFs) for constrained linear systems. We leverage recent configuration-constrained polyhedral computing techniques to devise piecewise affine convex CLFs. Additionally, we generalize these methods to uncertain systems with both additive and multiplicative disturbances. The propose

Marek Wodzinski, Henning Müller

Lightsheet microscopy is a powerful 3-D imaging technique that addresses limitations of traditional optical and confocal microscopy but suffers from a low penetration depth and reduced image quality at greater depths. Multiview lightsheet microscopy improves 3-D resolution by combining multiple views but simultaneously increasing the complexity and the photo

Vladimir Dotsenko

Kashuba and Mathieu proposed a conjecture on vanishing of some components of the homology of certain Lie algebras, implying a description of the $GL_d$-module structure of the free $d$-generated Jordan algebra. Their conjecture relies on a functorial version of the Tits-Kantor-Koecher construction that builds Lie algebras out of Jordan algebras. Recently, Sh

Jorge J. Martínez de Lejarza, Hsin-Yu Wu, Oleksandr Kyriienko, Germán Rodrigo

Quantum generative modeling is emerging as a powerful tool for advancing data analysis in high-energy physics, where complex multivariate distributions are common. However, efficiently learning and sampling these distributions remains challenging. We propose a quantum protocol for a bivariate probabilistic model based on shifted Chebyshev polynomials, traine

Sergei Berezin, Reza Farahbakhsh, Noel Crespi

Toxicity detection has become core safety infrastructure for online moderation, dataset filtering, and deployed language-model systems. Yet most detectors still treat toxicity as an intrinsic property of isolated text. This position paper argues that toxicity detection should be evaluated as the contextual measurement of situated communicative harm, rather t

Jiale Wei, Shuchi Wu, Ruochen Liu, Xiang Ying

Memory, additional information beyond the training of large language models (LLMs), is crucial to various real-world applications, such as personal assistant. The two mainstream solutions to incorporate memory into the generation process are long-context LLMs and retrieval-augmented generation (RAG). In this paper, we first systematically compare these two t

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Using 20.3$~\rm fb^{-1}$ of $e^+e^-$ annihilation data collected at a center-of-mass energy of 3.773$~\rm GeV$ with the BESIII detector, we report an improved search for the radiative leptonic decay $D^+\to\gamma e^+\nu_e$. An upper limit on its partial branching fraction for photon energies $E_\gamma>10~\rm MeV$ was determined to be $1.2\times10^{-5}$ at 90

Aniek Eijpe, Soufyan Lakbir, Melis Erdal Cesur, Sara P. Oliveira

To improve the prediction of cancer survival using whole-slide images and transcriptomics data, it is crucial to capture both modality-shared and modality-specific information. However, multimodal frameworks often entangle these representations, limiting interpretability and potentially suppressing discriminative features. To address this, we propose Disenta

Longbin Ji, Lei Zhong, Pengfei Wei, Changjian Li

Recent advancements in trajectory-guided video generation have achieved notable progress. However, existing models still face challenges in generating object motions with potentially changing 6D poses under wide-range rotations, due to limited 3D understanding. To address this problem, we introduce PoseTraj, a pose-aware video dragging model for generating 3

Tim Seizinger, Florin-Alexandru Vasluianu, Marcos V. Conde, Zongwei Wu

Bokeh rendering methods play a key role in creating the visually appealing, softly blurred backgrounds seen in professional photography. While recent learning-based approaches show promising results, generating realistic Bokeh with variable strength remains challenging. Existing methods require additional inputs and suffer from unrealistic Bokeh reproduction

Jiayi Su, Shaofeng Zou, Jingyu Qian, Yan Wei

Rejecting outliers before applying classical robust methods is a common approach to increase the success rate of estimation, particularly when the outlier ratio is extremely high (e.g. 90%). However, this method often relies on sensor- or task-specific characteristics, which may not be easily transferable across different scenarios. In this paper, we focus o

Yingmao Miao, Zhanpeng Huang, Rui Han, Zibin Wang

While virtual try-on for clothes and shoes with diffusion models has gained attraction, virtual try-on for ornaments, such as bracelets, rings, earrings, and necklaces, remains largely unexplored. Due to the intricate tiny patterns and repeated geometric sub-structures in most ornaments, it is much more difficult to guarantee identity and appearance consiste

Qiang Zou, Shuli Cheng, Jiayi Chen

Cross-modal hashing is a promising approach for efficient data retrieval and storage optimization. However, contemporary methods exhibit significant limitations in semantic preservation, contextual integrity, and information redundancy, which constrains retrieval efficacy. We present PromptHash, an innovative framework leveraging affinity prompt-aware collab

Zhiyu Cao, Peifeng Li, Qiaoming Zhu, Yaxin Fan

Previous work on Incomplete Utterance Rewriting (IUR) has primarily focused on generating rewritten utterances based solely on dialogue context, ignoring the widespread phenomenon of coreference and ellipsis in dialogues. To address this issue, we propose a novel framework called TEO (\emph{Two-stage approach on Editing Operation}) for IUR, in which the firs

Youhao Shang, Xiangsong Cheng, Jian Liu

We have recently developed the \textit{constraint} coordinate-momentum \textit{phase space} (CPS) formulation for finite-state quantum systems. It has been implemented for the electronic subsystem in nonadiabatic transition dynamics to develop practical trajectory-based approaches. In the generalized CPS formulation for the mapping Hamiltonian of the classic

Asim Ihsan, Muhammad Asif, Hossein Safi, Iman Tavakkolnia

In this paper, we propose an innovative predict-and-optimize algorithm designed for hybrid WiFi/LiFi networks, aiming to achieve service differentiation while maximizing energy efficiency (EE). The proposed framework utilizes multi-access technology real-time intelligent controller (mATRIC) to dynamically predict the appropriate network slice for each user b

R. Negrello, L. Bandiera, N. Canale, P. Fedeli

We present a novel Python tool for the analysis of Geant4 simulations that enhances our understanding of coherent phenomena occurring during the interaction of charged particles with crystal planes. This tool compares the total energy of particles with the potential energy inside crystal channels, enabling a complete examination of coherent effects. By track

Anita Pahi, Kirty Ranjan Sahoo, Biswajit Das, Shuvojit Paul

Active and diffusive motion in Brownian particles are regularly observed in fluidic environments, albeit at different time scales. Here, we experimentally study the dynamics of highly asymmetric microclusters trapped in air employing photophoretic forces generated from a loosely focused laser beam, where the trapped particles display active and diffusive dyn

Xu He, Zhen Huang, Qingsong Yao, Xiaoqian Zhou

Landmark detection plays a crucial role in medical imaging applications such as disease diagnosis, bone age estimation, and therapy planning. However, training models for detecting multiple landmarks simultaneously often encounters the "seesaw phenomenon", where improvements in detecting certain landmarks lead to declines in detecting others. Yet, training a

Yike Yuan, Ziyu Wang, Zihao Huang, Defa Zhu

Diffusion models have emerged as mainstream framework in visual generation. Building upon this success, the integration of Mixture of Experts (MoE) methods has shown promise in enhancing model scalability and performance. In this paper, we introduce Race-DiT, a novel MoE model for diffusion transformers with a flexible routing strategy, Expert Race. By allow

Wanshu Fan, Yue Wang, Cong Wang, Yunzhe Zhang

Single-Image Super-Resolution (SISR) plays a pivotal role in enhancing the accuracy and reliability of measurement systems, which are integral to various vision-based instrumentation and measurement applications. These systems often require clear and detailed images for precise object detection and recognition. However, images captured by visual measurement

Abdelrahman Elsayed, Sarim Hashmi, Mohammed Elseiagy, Hu Wang

The complex nature of medical image segmentation calls for models that are specifically designed to capture detailed, domain-specific features. Large foundation models offer considerable flexibility, yet the cost of fine-tuning these models remains a significant barrier. Parameter-Efficient Fine-Tuning (PEFT) methods, such as Low-Rank Adaptation (LoRA), effi

Andrei M. Beloborodov

When a fast radio burst (FRB) expands from its source through a surrounding tenuous plasma, it strongly heats and compresses the plasma at radii up to $\sim 10^{14}$cm. The likely central engines of FRBs are magnetars, and their ambient plasma at radii $r\gg 10^{10}$cm is a magnetized $e^\pm$ wind. We formulate basic equations of the FRB-plasma interaction,

Maryam Soleymaninia, Hamzeh Khanpour, Majid Azizi, Hadi Hashamipour

We present a quantitative assessment of the anticipated impact of future Electron-Ion Collider (EIC) measurements on the extraction of parton-to-pion fragmentation functions (FFs). Our analysis combines simulated semi-inclusive deep-inelastic scattering (SIDIS) pseudo-data at EIC energies of 45 GeV and 140 GeV with existing single-inclusive electron-positron

Naoyuki Kamiyama

The stable roommates problem is a non-bipartite version of the well-known stable matching problem. Teo and Sethuraman proved that, for each instance of the stable roommates problem in a complete graph, there exists a linear inequality system such that there exists a feasible solution to this system if and only if there exists a stable matching in the given i

Andrei Jelea, Ahmed Nabil Belbachir, Marius Leordeanu

Solving fish segmentation in underwater videos, a real-world problem of great practical value in marine and aquaculture industry, is a challenging task due to the difficulty of the filming environment, poor visibility and limited existing annotated underwater fish data. In order to overcome these obstacles, we introduce a novel two stage unsupervised segment

Takara Kondo, Yuto Nogata

This paper investigates simple $3$-$(2^n+1,13,\lambda)$ designs admitting $\mathrm{PSL}$$(2,2^n)$ as an automorphism group. We determine all possible values of $\lambda$ by systematically analyzing the orbits of $13$-element subsets under the action of $\mathrm{PSL}$$(2, 2^n)$ on the projective line. While previous research has explored this topic by analyzi

Chen-Yu Liu, Samuel Yen-Chi Chen, Kuan-Cheng Chen, Wei-Jia Huang

We present Federated QT-LSTM, a novel framework that combines the Quantum-Train (QT) methodology with Long Short-Term Memory (LSTM) networks in a federated learning setup. By leveraging quantum neural networks (QNNs) to generate classical LSTM model parameters during training, the framework effectively addresses challenges in model compression, scalability,

Michael Goodale, Salvador Mascarenhas, Yair Lakretz

Children acquire language despite being exposed to several orders of magnitude less data than large language models require. Meta-learning has been proposed as a way to integrate human-like learning biases into neural-network architectures, combining both the structured generalizations of symbolic models with the scalability of neural-network models. But wha

Yang Chen, Hui Wang, Shiyao Wang, Junyang Chen

While voice technologies increasingly serve aging populations, current systems exhibit significant performance gaps due to inadequate training data capturing elderly-specific vocal characteristics like presbyphonia and dialectal variations. The limited data available on super-aged individuals in existing elderly speech datasets, coupled with overly simple re

Bisola Faith Kayode, Akinyemi Sadeeq Akintola, Oluwole Fagbohun, Egonna Anaesiuba-Bristol

Denial-of-Service (DoS) attacks remain a critical threat to network security, disrupting services and causing significant economic losses. Traditional detection methods, including statistical and rule-based models, struggle to adapt to evolving attack patterns. To address this challenge, we propose a novel Temporal-Spatial Attention Network (TSAN) architectu

N. Guyot, V. Van Grootel, S. Charpinet, M. Farnir

Context. The Kepler and TESS space missions have revealed the rich gravity (g-)mode pulsation spectra of many hot subdwarf B (sdB) stars in detail. These spectra exhibit complex behaviors, with some stars exhibiting trapped modes interposing in the asymptotic period sequences of regular period spacing, while others do not. Methods. We used our STELlar modeli

J. Garrido, S. Sánchez-Expósito, A. Ruiz-Falcó, J. Ruedas

The SKA Observatory (SKAO), a landmark project in radio astronomy, seeks to address fundamental questions in astronomy. To process its immense data output, approximately 700 PB/year, a global network of SKA Regional Centres (SR-CNet) will provide the infrastructure, tools, computational power needed for scientific analysis and scientific support. The Spanish

Julia Gallini, Zach Baucom, Yorghos Tripodis

Alzheimer's dementia (AD) is of increasing concern as populations achieve longer lifespans. Many of the recent failed AD clinical trials recruiting cognitively intact individuals had a low number of AD events and were thus underpowered. Previous trials have attempted to address this issue by requiring signs of cognitive decline in brain imaging for trial enr

Zhiyu Cao, Peifeng Li, Yaxin Fan, Qiaoming Zhu

Although existing fashionable generation methods on Incomplete Utterance Rewriting (IUR) can generate coherent utterances, they often result in the inclusion of irrelevant and redundant tokens in rewritten utterances due to their inability to focus on critical tokens in dialogue context. Furthermore, the limited size of the training datasets also contributes

Augusto Ciuffoletti, Letizia Chiti

Field-based research projects require a robust suite of ICT services to support data acquisition, documentation, storage, and dissemination. A key challenge lies in ensuring the sustainability of data management - not only during the project's funded period but also beyond its conclusion, when maintenance and support often depend on voluntary efforts. In the

Oluwole Fagbohun, Sai Yashwanth, Akinyemi Sadeeq Akintola, Ifeoluwa Wurola

This study introduces GreenIQ, an AI-powered deep search platform designed to revolutionise carbon market intelligence through autonomous analysis and automated report generation. Carbon markets operate across diverse regulatory landscapes, generating vast amounts of heterogeneous data from policy documents, industry reports, academic literature, and real-ti

Yinghao Hu, Yaoyao Yu, Leilei Gan, Bin Wei

Recent advances in test-time scaling of large language models (LLMs), exemplified by DeepSeek-R1 and OpenAI's o1, show that extending the chain of thought during inference can significantly improve general reasoning performance. However, the impact of this paradigm on legal reasoning remains insufficiently explored. To address this gap, we present the first

Adarsh Saxena, Sudhakar Singh, Shiv Prakash, Tiansheng Yang

DevOps pipeline is a set of automated tasks or processes or jobs that has tasks assigned to execute automatically that allow the Development team and Operations team to collaborate for building and deployment of the software or services. DevOps as a culture includes better collaboration between different teams within an organization and the removal of silos

Antti Mikkonen, Anssi Koskinen, Johanna Tamminen, Hannakaisa Lindqvist

A novel method for monochromatic scalar 3D radiative transfer, designed primarily for modeling remote sensing imaging, is presented. For simulating an observation of an imaging satellite instrument, the method uses a heuristic scattering coupling function to model the inter-pixel scattering of radiation, which is represented with a graph. The GPU-capable cod

Zhihang Liu, Chen-Wei Xie, Pandeng Li, Liming Zhao

Recent Multi-modal Large Language Models (MLLMs) have been challenged by the computational overhead resulting from massive video frames, often alleviated through compression strategies. However, the visual content is not equally contributed to user instructions, existing strategies (\eg, average pool) inevitably lead to the loss of potentially useful informa

Hina Binte Haq, Syed Taha Ali, Asad Salman, Patrick McCorry

The increasing adoption of cryptocurrencies has significantly amplified the resource requirements for operating full nodes, creating substantial barriers to entry. Unlike miners, who are financially incentivized through block rewards and transaction fees, full nodes lack direct economic compensation for their critical role in maintaining the network. A key r

Skander Charfi

We consider the Lax-Oleinik operator $\mathcal{T}$ associated with the non-stationary Hamilton-Jacobi equation $\partial_tu + H(t,x,\partial_xu) = \alpha_0$ for a Tonelli Hamiltonian $H$ and its \Mane critical value $\alpha_0$. It is known from the work of A. Fathi and J.N. Mather \cite{MR1792479} that the convergence of this semigroup fails in the non-auton

Radu Calinescu, Sinem Getir Yaman, Simos Gerasimou, Gricel Vázquez

Given its ability to analyse stochastic models ranging from discrete and continuous-time Markov chains to Markov decision processes and stochastic games, probabilistic model checking (PMC) is widely used to verify system dependability and performance properties. However, modelling the behaviour of, and verifying these properties for many software-intensive s

Timothy J. McSorley, Kaustubh Simha, James E. Corcoran, Izzie J. Catanzaro

Dynamically manipulating carriers in van der Waals heterostructures could enable solid-state quantum simulators with tunable lattice parameters. A key requirement is forming deep potential wells to reliably trap excitations. Here, we report the observation of nonlinear acoustoelectric transport and dynamic carrier modulation in boron nitride-encapsulated gra

Sunqi Fan, Meng-Hao Guo, Shuojin Yang

Video question answering (VideoQA) enables machines to extract and comprehend key information from videos through natural language interaction, which is a critical step towards achieving intelligence. However, the demand for a thorough understanding of videos and high computational costs still limit the widespread applications of VideoQA. To address it, we p

Sai Kartheek Reddy Kasu, Shankar Biradar, Sunil Saumya

In the evolving landscape of online discourse, misinformation increasingly adopts humorous tones to evade detection and gain traction. This work introduces Deceptive Humor as a novel research direction, emphasizing how false narratives, when coated in humor, can become more difficult to detect and more likely to spread. To support research in this space, we

M. Awais, M. Azam

The goal of this work is to develop physical models for spherically symmetric systems in the realm of $f(Q)$ gravity. The field equations are set up for anisotropic fluid and formulate these equations using physical ansatz of Vaidya-Tikekar solution. The intrinsic constants of the model be ascertained by matching the interior solution to the external Schwarz

Jiachang Li, Chao Ma

Let $T:[0,1]^d \rightarrow[0,1]^d$ be a piecewise expanding map with an absolutely continuous (with respect to the $d$-dimensional Lebesgue measure $m_d$) $T$-invariant probability measure $\mu$. Let $\left\{\mathbf{r}_n\right\}$ be a sequence of vectors satisfying the conditons that $\mathbf{r}_n=\left(r_{n, 1}, \ldots, r_{n, d}\right) \in\left(\mathbb{R}_{

Haoyu Lu, Junxiong Jia, Deyu Meng

By formulating the inverse problem of partial differential equations (PDEs) as a statistical inference problem, the Bayesian approach provides a general framework for quantifying uncertainties. In the inverse problem of PDEs, parameters are defined on an infinite-dimensional function space, and the PDEs induce a computationally intensive likelihood function.

Yiming Yang, Deyu Ming, Serge Guillas

Deep Gaussian Processes (DGPs) compose GP layers to warp inputs, enabling improved emulation of computer models with nonstationary input-output behavior compared with ordinary GPs. In contrast to GPs, the predictive uncertainty for DGP gradients remains relatively underexplored. Quantifying DGP gradient uncertainty can support gradient-based tasks in complex

Jamerson Bezerra, Graccyela Salcedo

We study proximal random dynamical systems of homeomorphisms of the circle without a common fixed point. We prove the existence of two random points that govern the behavior of the forward and backward orbits of the system. Assuming the differentiability of the maps, we characterize these random points in terms of the extremal Lyapunov exponents of the rando