March 2025 arXiv papers — page 130

Gorka Muñoz-Gil, Hans J. Briegel, Michele Caraglio

Target search problems are central to a wide range of fields, from biological foraging to the optimization algorithms. Recently, the ability to reset the search has been shown to significantly improve the searcher's efficiency. However, the optimal resetting strategy depends on the specific properties of the search problem and can often be challenging to det

Marvin Kahra, Michael Breuß

Mathematical morphology, a field within image processing, includes various filters that either highlight, modify, or eliminate certain information in images based on an application's needs. Key operations in these filters are dilation and erosion, which determine the supremum or infimum for each pixel with respect to an order of the tonal values over a subse

Kar Balan, Andrew Gilbert, John Collomosse

The rise of Generative AI (GenAI) has sparked significant debate over balancing the interests of creative rightsholders and AI developers. As GenAI models are trained on vast datasets that often include copyrighted material, questions around fair compensation and proper attribution have become increasingly urgent. To address these challenges, this paper prop

Ruiqian Li, Siyuan Shen, Suan Xia, Ziheng Wang

High quality and high speed videography using Non-Line-of-Sight (NLOS) imaging benefit autonomous navigation, collision prevention, and post-disaster search and rescue tasks. Current solutions have to balance between the frame rate and image quality. High frame rates, for example, can be achieved by reducing either per-point scanning time or scanning density

Andrew Hillier, Ramon Oliver, David Martínez-Gómez

Observations and simulations of coronal rain show that as cold and dense plasma falls through the corona it initially undergoes acceleration by gravity before the downward velocity saturates. Simulations have shown the emergence of an unexpected relation between terminal velocity of the rain and density ratio that has not been explained. Our aim is to explai

Zhendong Chu, Shen Wang, Jian Xie, Tinghui Zhu

Large Language Model (LLM) agents are transforming education by automating complex pedagogical tasks and enhancing both teaching and learning processes. In this survey, we present a systematic review of recent advances in applying LLM agents to address key challenges in educational settings, such as feedback comment generation, curriculum design, etc. We ana

Bijan Saha

In this study, we examine the role of a nonlinear spinor field in the evolution of the Universe within the framework of a Bianchi type-I cosmological model with Lyras geometry. Previous research has explored the nonlinear spinor field in various anisotropic and isotropic cosmological models, revealing that the presence of nontrivial, non-diagonal components

Peter Danchev, Brendan Goldsmith, Fatemeh Karimi

Continuing recent studies of both the hereditary and super properties of certain classes of Abelian groups, we explore in-depth what is the situation in the quite large class consisting of directly finite Abelian groups. Trying to connect some of these classes, we specifically succeeded to prove the surprising criteria that a relatively Hopfian group is here

Hong Qing Yu, Frank McQuade

This paper presents RAG-KG-IL, a novel multi-agent hybrid framework designed to enhance the reasoning capabilities of Large Language Models (LLMs) by integrating Retrieval-Augmented Generation (RAG) and Knowledge Graphs (KGs) with an Incremental Learning (IL) approach. Despite recent advancements, LLMs still face significant challenges in reasoning with stru

Anja Petković Komel, Michael Rawson, Martin Suda

The Vampire automated theorem prover is extended to output machine-checkable proofs in the Dedukti concrete syntax for the LambdaPi-calculus modulo. This significantly reduces the trusted computing base, and in principle eases proof reconstruction in other proof-checking systems. Existing theory is adapted to deal with Vampire's internal logic and inference

Ziyi Wang, Songbai Tan, Gang Xu, Xuerui Qiu

With the success of autoregressive learning in large language models, it has become a dominant approach for text-to-image generation, offering high efficiency and visual quality. However, invisible watermarking for visual autoregressive (VAR) models remains underexplored, despite its importance in misuse prevention. Existing watermarking methods, designed fo

Daniel Andre, Venky P. Krishnan, Clifford Nolan

In recent years, radar technology has seen much improvement, making multistatic Synthetic Aperture Radar (SAR) sensing a realistic possibility, for example in satellite constellations or unmanned aircraft systems. With such systems, there then comes the requirement to investigate useful multistatic SAR geometries. We provide a microlocal analysis of a multis

Lingyu Zhu, Xiangrui Zeng, Bolin Chen, Peilin Chen

By optimizing the rate-distortion-realism trade-off, generative image compression approaches produce detailed, realistic images instead of the only sharp-looking reconstructions produced by rate-distortion-optimized models. In this paper, we propose a novel deep learning-based generative image compression method injected with diffusion knowledge, obtaining t

Yunfan Qing, Wenli Zheng

Dynamic scaling is critical to stream processing engines, as their long-running nature demands adaptive resource management. Existing scaling approaches easily cause performance degradation due to coarse-grained synchronization and inefficient state migration, resulting in system halt or high processing latency. In this paper, we propose DRRS, an on-the-fly

Diana Navas-Nicolás, Cloé Girard-Carillo, Stefan Schoppmann

For several decades now, scintillator detectors have found a wide range of applications in particle physics, including neutrino detection, the search for dark matter and even medical imaging. These detectors so far have strongly relied on the transparency of the scintillating medium, through which light is typically propagated to surrounding photosensors. In

Joona Kareinen, Annaliina Skyttä, Tuomas Eerola, Kaisa Kraft

This paper considers open-set recognition (OSR) of plankton images. Plankton include a diverse range of microscopic aquatic organisms that have an important role in marine ecosystems as primary producers and as a base of food webs. Given their sensitivity to environmental changes, fluctuations in plankton populations offer valuable information about oceans'

The ATLAS Collaboration

This Letter reports the first evidence of production of same-sign $W$ boson pairs where at least one of the $W$ bosons is longitudinally polarized and the most stringent constraint to date for the production of two longitudinally polarized same-sign $W$ bosons. The data set used corresponds to an integrated luminosity of 140 fb$^{-1}$ of proton-proton collis

Qin-Tao Song, O. V. Teryaev, Shinsuke Yoshida

Generalized distribution amplitudes (GDAs) have attracted significant attention in recent years due to their connection with the energy-momentum tensor (EMT) form factors (FFs). The GDAs can be experimentally accessed through the study of amplitudes in $\gamma^{\ast} \gamma \to M_1 M_2$ and $\gamma^{\ast} \to M_1 M_2 \gamma$, where $M_1M_2$ is a pseudoscalar

Jeong Hun Yeo, Hyeongseop Rha, Se Jin Park, Yong Man Ro

Audio-Visual Speech Recognition (AVSR) achieves robust speech recognition in noisy environments by combining auditory and visual information. However, recent Large Language Model (LLM) based AVSR systems incur high computational costs due to the high temporal resolution of audio-visual speech processed by LLMs. In this work, we introduce an efficient multimo

Xinyu Tang, Xiaolei Wang, Zhihao Lv, Yingqian Min

Recent advancements in long chain-of-thoughts(long CoTs) have significantly improved the reasoning capabilities of large language models(LLMs). Existing work finds that the capability of long CoT reasoning can be efficiently elicited by tuning on only a few examples and can easily transfer to other tasks. This motivates us to investigate whether long CoT rea

Beatrice Matilde Ferrari, Cameron James Richard Duncan, Michael Yannai, Raphael Dahan

Spatial and temporal light modulation is a well-established technology that enables dynamic shaping of the phase and amplitude of optical fields, significantly enhancing the resolution and sensitivity of imaging methods. Translating this capability to electron beams is highly desirable within the framework of a transmission electron microscope (TEM) to benef

Juan Antonio De Rus, Mario Montagud, Jesus Lopez-Ballester, Francesc J. Ferri

Precise elevation perception in binaural audio remains a challenge, despite extensive research on head-related transfer functions (HRTFs) and spectral cues. While prior studies have advanced our understanding of sound localization cues, the interplay between spectral features and elevation perception is still not fully understood. This paper presents a compr

A. Hunter, C. Putzke, F. B. Kugler, S. Beck

Interacting electrons can form metallic states beyond the Fermi liquid paradigm, a conceptual frontier of many-body physics mainly explored via bulk thermodynamics and transport. In contrast, the microscopics of anomalous single-particle excitations underlying non-Fermi liquid properties have largely remained in the dark. Here we spectroscopically map such q

Igor Travěnec, Ladislav Šamaj

A limited number of 2D and 3D materials under a constant pressure contract in volume upon heating isobarically; this anomalous phenomenon is known as the negative thermal expansion (NTE). In this paper, the NTE anomaly is observed in 1D fluids of classical particles interacting pairwisely with two competing length scales: the hard-core diameter $a$ and the f

Ali Elkeshawy, Ibrahim Al Ghosh, Haifa Fares, Amor Nafkha

Grant-free random access in massive machine-type communications enables low-latency connectivity with minimal signaling. However, sporadic device activation requires efficient device activity detection. We propose a federated learning-based device activity detection approach, leveraging distributed training to enhance security and privacy while maintaining l

S. Comerón, A. Prieto, P. Dabhade

Coronal lines are forbidden emission lines with a ionisation potential $\chi\gtrsim100\,{\rm eV}$. They are linked to energetic phenomena triggered by AGNs in the circumnuclear medium. We present the first high-angular-resolution integral-field analysis of the $[{\rm Fe\,VII}]\,\lambda6087$ coronal line in a sample of four nearby low-inclination Seyfert gala

Ruben Harris, Claudia Schillings

The Ensemble Kalman Inversion (EKI) method is widely used for solving inverse problems, leveraging ensemble-based techniques to iteratively refine parameter estimates. Despite its versatility, the accuracy of EKI is constrained by the subspace spanned by the initial ensemble, which may poorly represent the solution in cases of limited prior knowledge. This w

Andrei Agrachev, Bettina Kazandjian, Eugenio Pozzoli

We study the small-time controllability problem on the Lie groups $SL_2(\mathbb{R})$ and $SL_2(\mathbb{R})\ltimes H_{d}(\mathbb{R})$ with Lie bracket methods (here $H_{d}(\mathbb{R})$ denotes the $(2d+1)$-dimensional real Heisenberg group). Then, using unitary representations of $SL_2(\mathbb{R})\ltimes H_{d}(\mathbb{R})$ on $L^2(\mathbb{R}^d,\mathbb{C})$ an

Chao Yang, Hongguang Wang, Roberto A. Ortiz, Kelvin Anggara

Infinite layer nickelates have garnered significant attention due to their potential for high-temperature superconductivity. Despite extensive research, the interplay between oxygen stoichiometry and electronic properties in infinite layer nickelates remains inadequately understood. In this study, we employ advanced electron microscopy techniques and theoret

Ali Elkeshawy, Haifa Fares, Amor Nafkha

Grant-free random access (GF-RA) is a promising access technique for massive machine-type communications (mMTC) in future wireless networks, particularly in the context of 5G and beyond (6G) systems. Within the context of GF-RA, this study investigates the efficiency of employing supervised machine learning techniques to tackle the challenges on the device a

Leonardo Farolfi, Filippo Fecit

We compute the first four heat-kernel coefficients required for the renormalization of Linearized Massive Gravity. We focus on the Fierz-Pauli theory in a curved spacetime, describing the propagation of a massive spin $2$ field in a non-flat background. The background must be on-shell, i.e. must be an Einstein space, in order to ensure a consistent extension

Yi Yang

Modified Toda hierarchy is a two-component generalization of the 1st modified KP hierarchy, which has been widely applied to analyze constraints of the Toda hierarchy, including the B--Toda and C--Toda hierarchies. In this paper, we construct additional symmetries for the modified Toda hierarchy and derive the corresponding Adler-Shiota-van Moerbeke formula.

Michael Hanna, Yonatan Belinkov, Sandro Pezzelle

Although large language models (LLMs) are increasingly capable, these capabilities are unevenly distributed: they excel at formal linguistic tasks, such as producing fluent, grammatical text, but struggle more with functional linguistic tasks like reasoning and consistent fact retrieval. Inspired by neuroscience, recent work suggests that to succeed on both

Yuanshuo Zhang, Yuchen Hou, Bohan Tang, Shuo Chen

Agentic workflows invoked by Large Language Models (LLMs) have achieved remarkable success in handling complex tasks. However, optimizing such workflows is costly and inefficient in real-world applications due to extensive invocations of LLMs. To fill this gap, this position paper formulates agentic workflows as computational graphs and advocates Graph Neura

Jiangwei Zhao, Zhengjia Xu, Dongsu Wu, Yingrui Cao

Due to excellent mechanism characteristics of high rigidity, maneuverability and strength-to-weight ratio, 6 Degree-of-Freedom (DoF) Stewart structure is widely adopted to construct flight simulator platforms for replicating motion feelings during training pilots. Unlike conventional serial link manipulator based mechanisms, Upset Prevention and Recovery Tra

Hai Zhao, Hongqiu Wu, Dongjie Yang, Anni Zou

We introduce BriLLM, a brain-inspired large language model that fundamentally redefines the foundations of machine learning through its implementation of Signal Fully-connected flowing (SiFu) learning. This work addresses the critical bottleneck hindering AI's progression toward Artificial General Intelligence (AGI)--the disconnect between language models an

Hristina Hristova, Hristo Iliev, Ivayla Bozhinova, Andon Rangelov

We theoretically propose and experimentally demonstrate a novel composite polarization controller. With our design, which comprises two half-wave plates and two quarter-wave plates the retardance and rotation can be changed continuously by simply rotating the half-wave plates. The idea is universal since any commercial half and quarter-wave plates may be use

Yuhao Du, Hui Liu, Haoxiang Peng, Xinyuan Cheng

Recent years, weather forecasting has gained significant attention. However, accurately predicting weather remains a challenge due to the rapid variability of meteorological data and potential teleconnections. Current spatiotemporal forecasting models primarily rely on convolution operations or sliding windows for feature extraction. These methods are limite

Junhao Yan, Ran Bi, Weijun Lu

This paper delves into the study of mixed super quasi-Einstein manifolds of dimension $n$ (for short, ${\rm M^{n}_{SQE}}$), focusing on their geometric and physical attributes. Initially, we explore several properties of ${\rm M^{n}_{SQE}}$, including conformal Ricci pseudosymmetry, Einstein's field equation, and the space-matter tensor. Subsequently, we cha

Kees Wapenaar

Recently, there has been an increasing interest in employing rotational motion measurements for seismic source inversion, structural imaging and ambient noise analysis. We derive reciprocity and representation theorems for rotational motion. The representations express the rotational motion inside an inhomogeneous anisotropic earth in terms of translational

Martin Výboh, Zuzana Chladná, Gabriela Grmanová, Mária Lucká

Efficiently representing supply and demand curves is vital for energy market analysis and downstream modelling; however, dimensionality reduction often produces reconstructions that violate fundamental economic principles such as monotonicity. This paper evaluates the performance of PCA, Kernel PCA, UMAP, and AutoEncoder across 2d and 3d latent spaces. Durin

Luca Brunelli, Michele Cicoli, Francisco G. Pedro

A novel mechanism for the production of a cosmic network of fundamental superstrings based on a time-varying string tension has been recently proposed in the context of a kinating background driven by the volume modulus of string compactifications. In this paper, we generalise the analysis of this growth mechanism by using dynamical system techniques. We fir

Andrew Starkey, Uduak Idio Akpan, Omaimah AL Hosni, Yaseen Pullissery

There have been several attempts to develop Feature Selection (FS) algorithms capable of identifying features that are relevant in a dataset. Although in certain applications the FS algorithms can be seen to be successful, they have similar basic limitations. In all cases, the global feature selection algorithms seek to select features that are relevant and

Bo Zhang, Jianfeng Zhu, Xiangyu Hu

As a mesh-free method, smoothed particle hydrodynamics (SPH) has been widely used for modeling and simulating fluid-structure interaction (FSI) problems. While the kernel gradient correction (KGC) method is commonly applied in structural domains to enhance numerical consistency, high-order consistency corrections that preserve conservation remain underutiliz

Paris Avgeriou, Nauman bin Ali, Marcos Kalinowski, Daniel Mendez

Increasingly, courses on Empirical Software Engineering research methods are being offered in higher education institutes across the world, mostly at the M.Sc. and Ph.D. levels. While the need for such courses is evident and in line with modern software engineering curricula, educators designing and implementing such courses have so far been reinventing the

Qi Mao, Haobo Hu, Yujie He, Difei Gao

Affective Image Manipulation (AIM) aims to alter visual elements within an image to evoke specific emotional responses from viewers. However, existing AIM approaches rely on rigid \emph{one-to-one} mappings between emotions and visual cues, making them ill-suited for the inherently subjective and diverse ways in which humans perceive and express emotion.To a

Shifna P R, N. Unnikrishnan Nair, S. M. Sunoj

In this paper we present a flexible bivariate distribution specified by a quantile function. The distribution contains as special cases new bivariate exponential, Pareto I, Pareto II, beta, power, log logistic and uniform distributions and also can approximate many other continuous models. Various $L$-moment based properties of the distribution such as covar

Lyes Attouche, Mohamed-Amine Baazizi, Dario Colazzo, Giorgio Ghelli

JSON Schema is a logical language used to define the structure of JSON values. JSON Schema syntax is based on nested schema objects. In all versions of JSON Schema until Draft-07, collectively known as Classical JSON Schema, the semantics of a schema was entirely described by the set of JSON values that it validates. This semantics was the basis for a thorou

Luca Gnoli, Elisabetta Venuti, Matteo Masino, Patrizio Graziosi

We have performed a comprehensive computational study of the vibrational properties and electron-phonon couplings in the three known polymorphs of pentacene. Vibrational patterns and electron-phonon interactions were calculated at several q-points of the Brillouin zone, allowing for a detailed mapping of the phonon landscape and the associated coupling mecha

R. J. Tattersall, A. W. Baggaley, T. P. Billam

An out of equilibrium two-dimensional superfluid relaxes towards equilibrium via a process of coarsening, driven by the annihilation of vortices with antivortices. Here we present a comparison of two different numerical models of this process, a dissipative point vortex model and Fokker-Planck evolution, across a wide range of initial configurations and leve

Philipp Niedermayer, Rahul Singh, Eike Feldmeier, Christian Schömers

Radio frequency knock out resonant slow extraction is a standard method for extracting stored particle beams from synchrotrons by transverse excitation. Excitation signals comprising many betatron sidebands have shown to reduce intensity fluctuations of the extracted beam spill and are used at several facilities. In this contribution, the effect of individua

Akakpo A. Wilfried, Houédanou K. Wilfrid

This paper uses the HCT finite element method and mesh adaptation technology to solve the nonlinear plate bending problem and conducts error analysis on the iterative method, including a priori and a posteriori error estimates. Our investigation exploits Hermite finite elements such as BELL and HSIEH-CLOUGH-TOCHER (HCT) triangles for conforming finite elemen

Wen Xiong, Jinduo Liu, Junzhong Ji, Fenglong Ma

Estimating brain effective connectivity (EC) from functional magnetic resonance imaging (fMRI) data can aid in comprehending the neural mechanisms underlying human behavior and cognition, providing a foundation for disease diagnosis. However, current spatiotemporal attention modules handle temporal and spatial attention separately, extracting temporal and sp

Christelle Schneuwly Diaz, Duy-Thanh Vu, Julien Bodelet, Duy-Cat Can

Alzheimer's disease, a neurodegenerative disorder, is associated with neural, genetic, and proteomic factors while affecting multiple cognitive and behavioral faculties. Traditional AD prediction largely focuses on univariate disease outcomes, such as disease stages and severity. Multimodal data encode broader disease information than a single modality and m

Praveen Shastry, Bhawana Sonawane, Kavya Mohan, Naveen Kumarasami

Background: Accurate spinal structure measurement is crucial for assessing spine health and diagnosing conditions like spondylosis, disc herniation, and stenosis. Manual methods for measuring intervertebral disc height and spinal canal diameter are subjective and time-consuming. Automated solutions are needed to improve accuracy, efficiency, and reproducibil

Bryan Wilie, Samuel Cahyawijaya, Junxian He, Pascale Fung

Large language models (LLMs) trained on massive multilingual datasets hint at the formation of interlingual constructs--a shared subspace in the representation space. However, evidence regarding this phenomenon is mixed, leaving it unclear whether these models truly develop unified interlingual representations, or present a partially aligned constructs. We e

Wei-Jun Deng, Fang-Min Cai, Xin-Qiang Li, Yan Shi

Motivated by the deviations observed between the data and the SM predictions of $\mathcal{B}(\bar{B}_s^0\to D_s^+ \pi^-)$ and $\mathcal{B}(\bar{B}_d^0\to D^+ K^-)$, we revisit the $B_{c}^{-}\to J/\psi(\eta_{c}) L^{-}$ decays, with $L=\pi, K^{(*)}, \rho$, both within the SM and beyond. Since these processes are also mediated by $b\to c \bar{u} d(s)$ transitio

Ana Silva, Efi Efrati

From the integer quantum Hall effect, to swimming at low Reynolds number, geometric phases arise in the description of many different physical systems. In many of these systems the temporal evolution prescribed by the geometric phase can be directly measured by an external observer. By definition, geometric phases rely on the history of the system's internal

Gregory Matousek, Anselm Vossen

Several nuclear physics studies using the CLAS12 detector rely on the accurate reconstruction of neutrons and photons from its forward angle calorimeter system. These studies often place restrictive cuts when measuring neutral particles due to an overabundance of false clusters created by the existing calorimeter reconstruction software. In this work, we pre

Edward Pearce-Crump

Incorporating permutation equivariance into neural networks has proven to be useful in ensuring that models respect symmetries that exist in data. Symmetric tensors, which naturally appear in statistics, machine learning, and graph theory, are essential for many applications in physics, chemistry, and materials science, amongst others. However, existing rese

Yu. M. Andreev, A. Antonov, M. A. Ayala Torres, D. Banerjee

NA64 is a fixed-target experiment at the CERN SPS designed to search for Light particle Dark Matter (LDM) candidates with masses in the sub-GeV range. During the 2016-2022 runs, the experiment obtained the world-leading constraints, leaving however part of the well-motivated region of parameter space suggested by benchmark LDM models still unexplored. To fur

Matteo Baggioli, Kyoung-Bum Huh, Hyun-Sik Jeong, Xuhao Jiang

The study of chaos and complexity in non-Hermitian quantum systems poses significant challenges due to the emergence of complex eigenvalues in their spectra. Recently, the singular value decomposition (SVD) method was proposed to address these challenges. In this work, we identify two critical shortcomings of the SVD approach when analyzing Krylov complexity

Babak Emami, Wesley Dyk, David Haycraft, Carrie Spear

We introduce CVQBoost, a novel classification algorithm that leverages early hardware implementing Quantum Computing Inc's Entropy Quantum Computing (EQC) paradigm, Dirac-3 [Nguyen et. al. arXiv:2407.04512]. We apply CVQBoost to a fraud detection test case and benchmark its performance against XGBoost, a widely utilized ML method. Running on Dirac-3, CVQBoos

Alireza Mousavi-Hosseini, Clayton Sanford, Denny Wu, Murat A. Erdogdu

Theoretical efforts to prove advantages of Transformers in comparison with classical architectures such as feedforward and recurrent neural networks have mostly focused on representational power. In this work, we take an alternative perspective and prove that even with infinite compute, feedforward and recurrent networks may suffer from larger sample complex

Jiaxin Pan, Longwen Zhou

Non-Abelian topological insulators are characterized by matrix-valued, non-commuting topological charges with regard to more than one energy gap. Their descriptions go beyond the conventional topological band theory, in which an additive integer is endowed separately with each (degenerate group of) energy band(s). In this work, we reveal that Floquet (time-p

Dimitrios G. Konstantinides, Remigijus Leipus, Charalampos D. Passalidis, Jonas Siaulys

We reconsider a classical, well-studied problem from applied probability. This is the max-sum equivalence of randomly weighted sums, and the originality is because we manage to include interdependence among the primary random variables, as well as among primary random variables and random weights, as a generalization of previously published results. As a con

Shidi Deng, Maximilian Schiffer, Martin Bichler

Nowadays, a significant share of the business-to-consumer sector is based on online platforms like Amazon and Alibaba and uses AI for pricing strategies. This has sparked debate on whether pricing algorithms may tacitly collude to set supra-competitive prices without being explicitly designed to do so. Our study addresses these concerns by examining the risk

Josef Schicho, Ayush Kumar Tewari, Audie Warren

In this paper we investigate which subsets of the real plane are realisable as the set of points on which a one-layer ReLU neural network takes a positive value. In the case of cones we give a full characterisation of such sets. Furthermore, we give a necessary condition for any subset of $\mathbb R^d$. We give various examples of such one-layer neural netwo

Quanyuan Ruan, Jiabao Lei, Wenhao Yuan, Yanglin Zhang

Differentiable rendering has gained significant attention in the field of robotics, with differentiable robot rendering emerging as an effective paradigm for learning robotic actions from image-space supervision. However, the lack of physical world perception in this approach may lead to potential collisions during action optimization. In this work, we intro

Taehwa Choi, Sangbum Choi, Dipankar Bandyopadhyay

This paper presents a unified rank-based inferential procedure for fitting the accelerated failure time model to partially interval-censored data. A Gehan-type monotone estimating function is constructed based on the idea of the familiar weighted log-rank test, and an extension to a general class of rank-based estimating functions is suggested. The proposed

Huai-Zhen Li, Di-Fu Guo, Long-Hua Qin, Fen Liu

We present photometric observations of the BL Lacertae object S5 0716+714 with a temporal resolution of 120 s in the Sloan i' and r' bands. These observations were conducted using the Comet Search Program telescope at Xingming Observatory from 2018 December 22 to 2020 February 15, and more than 5600 effective images were obtained on each filter across 79 nig

Jonas Utz, Stefan Vocht, Anne Tjorven Buessen, Dennis Possart

In recent years, numerous neural network architectures specifically designed for the instance segmentation of nuclei in microscopic images have been released. These models embed nuclei-specific priors to outperform generic architectures like U-Nets; however, they require large annotated datasets, which are often not available. Generative models (GANs, diffus

Xirui Zhou, Lianlei Shan, Xiaolin Gui

Visual Question Answering (VQA) models, which fall under the category of vision-language models, conventionally execute multiple downsampling processes on image inputs to strike a balance between computational efficiency and model performance. Although this approach aids in concentrating on salient features and diminishing computational burden, it incurs the

Laura Gardini, Davide Radi, Noemi Schmitt, Iryna Sushko

We recently described a specific type of attractors of two-dimensional discontinuous piecewise linear maps, characterized by two discontinuity lines dividing the phase plane into three partitions, related to economic applications. To our knowledge, this type of attractor, which we call a weird quasiperiodic attractor, has not yet been studied in detail. They

Fangli Yang, Liang Chang, Daowen Qiu, Minghua Pan

Free-space quantum cryptography has the potential to enable global quantum communication. However, most existing continuous-variable quantum secret sharing (CV-QSS) schemes rely on fiber channels. In this paper, we present a CV-QSS protocol designed for free-space transmission and construct models of crucial parameters, including channel transmittance, exces

Liying Lu, Raphaël Achddou, Sabine Süsstrunk

Low-light photography produces images with low signal-to-noise ratios due to limited photons. In such conditions, common approximations like the Gaussian noise model fall short, and many denoising techniques fail to remove noise effectively. Although deep-learning methods perform well, they require large datasets of paired images that are impractical to acqu

Gaétan Facchinetti

The optical depth to reionization, a key parameter of the $\Lambda$CDM model, can be computed within astrophysical frameworks for star formation by modeling the evolution of the intergalactic medium. Accurate evaluation of this parameter is thus crucial for joint statistical analyses of CMB data and late-time probes such as the 21 cm power spectrum, requirin

Wayne S Singh

This paper investigates the relationship between smart city initiatives and evolving urbanization trends in the United States. The research addresses the critical issue of rapid urban growth in the U.S. and explores how innovations within the smart city paradigm influence urban development. Utilizing principles from Urban Complexity Theory, this study identi

Xianming Zeng, Sicong Du, Qifeng Chen, Lizhe Liu

Sensor simulation is pivotal for scalable validation of autonomous driving systems, yet existing Neural Radiance Fields (NeRF) based methods face applicability and efficiency challenges in industrial workflows. This paper introduces a Gaussian Splatting (GS) based system to address these challenges: We first break down sensor simulator components and analyze

Mariusz Mirek, Tomasz Z. Szarek, Błażej Wróbel

In this paper, we investigate dimension-free estimates for maximal operators of convolutions with discrete normalized Gaussians (related to the Theta function) in the context of maximal, jump and $r$-variational inequalities on $\ell^p(\mathbb{Z}^d)$ spaces. This is the first instance of a discrete operator in the literature where $\ell^p(\mathbb{Z}^d)$ boun

Ezequiel Albentosa-Ruiz, Iván Martí-Vidal, Ciriaco Goddi, Alejandro Mus

This document presents a novel method for the intra-field calibration and imaging of the radio source SgrA*, observed with the Atacama Large Millimeter/submillimeter Array (ALMA). SgrA* is a complex source comprising two components: the compact core (which exhibits high variability) and the extended minispiral (which is relatively stable over short timescale

Sergio Dorado-Alfaro, Elisa Hernández, Jesús Algaba, Pablo Navarro

Climate emergency has led to the investigation of CO$_{2}$ valorization routes. A competitive process included in this framework is the catalytic CO$_{2}$ cycloaddition to epoxides, to produce cyclic carbonates. Halide-based Ionic liquids (ILs) have been postulated to be a competitive choice. Nevertheless, the structure-performance relation for different ILs

Sahil Kale, Vijaykant Nadadur

As LLMs grow more powerful, their most profound achievement may be recognising when to say "I don't know". Existing studies on LLM self-knowledge have been largely constrained by human-defined notions of feasibility, often neglecting the reasons behind unanswerability by LLMs and failing to study deficient types of self-knowledge. This study aims to obtain i

Long Tan Le, Tung-Anh Nguyen, Han Shu, Suranga Seneviratne

The proliferation of edge devices has dramatically increased the generation of multivariate time-series (MVTS) data, essential for applications from healthcare to smart cities. Such data streams, however, are vulnerable to anomalies that signal crucial problems like system failures or security incidents. Traditional MVTS anomaly detection methods, encompassi

Yonggang Pei, Yubing Lin, Shuai Shao, Mauricio Silva Louzeiro

The scalable adaptive cubic regularization method ($\mathrm{ARC_{q}K}$: Dussault et al. in Math. Program. Ser. A 207(1-2): 191-225, 2024) has been recently proposed for unconstrained optimization. It has excellent convergence properties, well-defined complexity bounds, and promising numerical performance. In this paper, we extend $\mathrm{ARC_{q}K}$ to nonli

Ziyad AlSharawi, Jose S. Cánovas, Sadok Kallel

It is common in stability analysis to linearize a system and investigate the spectrum of the Jacobian matrix. This approach faces the challenge of determining the matrix spectrum when the coefficients depend on parameters or when the characteristic polynomial is more than quartic. In this paper, we reverse the classical process and use the authors' work on g

Haoyang Huang, Guoqing Ma, Nan Duan, Xing Chen

We present Step-Video-TI2V, a state-of-the-art text-driven image-to-video generation model with 30B parameters, capable of generating videos up to 102 frames based on both text and image inputs. We build Step-Video-TI2V-Eval as a new benchmark for the text-driven image-to-video task and compare Step-Video-TI2V with open-source and commercial TI2V engines usi

Lea Friedli, Athénaïs Gautier, Anna Broccard, David Ginsbourger

Sequential design of real and computer experiments via Gaussian Process (GP) models has proven useful for parsimonious, goal-oriented data acquisition purposes. In this work, we focus on acquisition strategies for a GP model that needs to be accurate within a predefined range of the response of interest. Such an approach is useful in various fields including

Viet-Hoang Tran, Thanh T. Chu, Khoi N. M. Nguyen, Trang Pham

Sliced Optimal Transport (OT) simplifies the OT problem in high-dimensional spaces by projecting supports of input measures onto one-dimensional lines and then exploiting the closed-form expression of the univariate OT to reduce the computational burden of OT. Recently, the Tree-Sliced method has been introduced to replace these lines with more intricate str

Vojtech Cahlik, Rodrigo Alves, Pavel Kordik

We propose a large language model explainability technique for obtaining faithful natural language explanations by grounding the explanations in a reasoning process. When converted to a sequence of tokens, the outputs of the reasoning process can become part of the model context and later be decoded to natural language as the model produces either the final

Cédric Goeury, Fabien Souillé

Numerical modeling of morphodynamics presents significant challenges in engineering due to uncertainties arising from inaccurate inputs, model errors, and limited computing resources. Accurate results are essential for optimizing strategies and reducing costs. This paper presents a step-by-step Bayesian methodology to conduct an uncertainty analysis of 2D nu

Andong Lu, Yuanzhi Guo, Wanyu Wang, Chenglong Li

Current RGBT tracking methods often overlook the impact of fusion location on mitigating modality gap, which is key factor to effective tracking. Our analysis reveals that shallower fusion yields smaller distribution gap. However, the limited discriminative power of shallow networks hard to distinguish task-relevant information from noise, limiting the poten

Aatiz Ghimire, Shahnawaz Alam, Siman Giri, Madhav Prasad Ghimire

The growing demand for computational power is driven by advancements in deep learning, the increasing need for big data processing, and the requirements of scientific simulations for academic and research purposes. Developing countries like Nepal often struggle with the resources needed to invest in new and better hardware for these purposes. However, optimi

Ziwei Shi, Xiaoran Zhang, Wenjing Xu, Yan Xia

We tackle the challenge of LiDAR-based place recognition, which traditionally depends on costly and time-consuming prior 3D maps. To overcome this, we first construct LiRSI-XA dataset, which encompasses approximately $110,000$ remote sensing submaps and $13,000$ LiDAR point cloud submaps captured in urban scenes, and propose a novel method, L2RSI, for cross-

Khoi N. M. Nguyen, Hoang Duy Nguyen Do, Huyen Thao Le, Thanh Tuan Dao

Performance modeling, a pivotal domain in program cost analysis, currently relies on manually crafted models constrained by various program and hardware limitations, especially in the intricate landscape of GPGPU. Meanwhile, Large Language Models (LLMs) have demonstrated their effectiveness in addressing diverse programming challenges. Our work establishes a

Nayana Venkatareddy, Partha Sarathi Mondal, Jaydeep Mandal, Shradha Mishra

Two temperature induced phase separation(2-TIPS) is a phenomenon observed in mixtures of active and passive particles modeled by scalar activity where the temperature of the particle is proportional to its activity. The binary mixture of 'hot' and 'cold' particles phase separate when the relative temperature difference between hot and cold particles defined

Sahar Diskin, Mihyun Kang, Lyuben Lichev

Fix a sequence of $d$-regular graphs $(G_d)_{d\in \mathbb{N}}$ and denote by $G_{d,p}$ the graph obtained from $G_d$ after edge-percolation with probability $p=c/d$, for a constant $c>0$. We prove a quantitative local convergence of $(G_{d,p})_{d\in \mathbb{N}}$. In combination with results of Bordenave, Lelarge and Salez, it implies that the rescaled matchi

Jun Yu, Xilong Lu

Compound Expression Recognition (CER) is crucial for understanding human emotions and improving human-computer interaction. However, CER faces challenges due to the complexity of facial expressions and the difficulty of capturing subtle emotional cues. To address these issues, we propose a novel approach leveraging Large Vision-Language Models (LVLMs). Our m

Zijing Hu, Fengda Zhang, Long Chen, Kun Kuang

Diffusion models have achieved remarkable success in text-to-image generation. However, their practical applications are hindered by the misalignment between generated images and corresponding text prompts. To tackle this issue, reinforcement learning (RL) has been considered for diffusion model fine-tuning. Yet, RL's effectiveness is limited by the challeng

Maxim Fadeev, Anastasiya Ponosova, Qingquan Peng, Anqi Huang

We report a new type of vulnerability in practical implementations of quantum key distribution systems. We show that it is possible to increase the pulse energy of a source laser diode not only by injection-locking it by external light near its emission wavelength of 1550 nm, but also by optically pumping it at a much shorter wavelength. We demonstrate 10% i