December 2024 arXiv papers — page 132

Christopher Dunne

Quantum key distribution algorithms are considered secure because they leverage quantum phenomena to provide security. As such, eavesdroppers can be detected by analyzing the error rate in the shared key obtained by the parties performing the key exchange. Nevertheless, this paper developed and investigated a novel attack strategy capable of being undetected

Zijian Zhou, Shikun Liu, Xiao Han, Haozhe Liu

Controllable person image generation aims to generate a person image conditioned on reference images, allowing precise control over the person's appearance or pose. However, prior methods often distort fine-grained textural details from the reference image, despite achieving high overall image quality. We attribute these distortions to inadequate attention t

Tolga Altinoluk, Guillaume Beuf, Etienne Blanco, Swaleha Mulani

We study dijet production in pA collisions at forward rapidities at next-to-eikonal accuracy. We restrict ourselves to the next-to-eikonal corrections that are induced by the quark background field of the target. We consider all possible channels, compute scattering amplitudes both in general kinematics and in the back-to-back limit. By using these results,

Alexander Valverde

Modern mesh generation pipelines whether learning-based or classical often produce outputs requiring post-processing to achieve production-quality geometry. This work introduces MeshCone, a convex optimization framework for guided mesh refinement that leverages reference geometry to correct deformed or degraded meshes. We formulate the problem as a second-or

Qi Lü, Zhonghua Liao

In this paper, we study two kinds of inverse problems for Mean Field Games (MFGs) with common noise. Our focus is on MFGs described by a coupled system of stochastic Hamilton-Jacobi-Bellman and Fokker-Planck equations. Firstly, we establish the Lipschitz and H\"older stability for determining the solutions of a coupled system of stochastic Hamilton-Jacobi-Be

Tapas Kumar Dutta, Snehashis Majhi, Deepak Ranjan Nayak, Debesh Jha

Polyp segmentation in colonoscopy is crucial for detecting colorectal cancer. However, it is challenging due to variations in the structure, color, and size of polyps, as well as the lack of clear boundaries with surrounding tissues. Traditional segmentation models based on Convolutional Neural Networks (CNNs) struggle to capture detailed patterns and global

Aditya Shukla, Mikhail Erementchouk, Pinaki Mazumder

Dynamical Ising machines achieve accelerated solving of complex combinatorial optimization problems by remapping the convergence to the ground state of the classical spin networks to the evolution of specially constructed continuous dynamical systems. The main adapted principle of constructing such systems is based on requiring that, on the one hand, the sys

Min Hou, Yueying Wu, Chang Xu, Yu-Hao Huang

As one of the most successful generative models, diffusion models have demonstrated remarkable efficacy in synthesizing high-quality images. These models learn the underlying high-dimensional data distribution in an unsupervised manner. Despite their success, diffusion models are highly data-driven and prone to inheriting the imbalances and biases present in

Sumaiya Zoha, Jeong-Gun Lee, Young-Woong Ko

Domain Generalization (DG) seeks to transfer knowledge from multiple source domains to unseen target domains, even in the presence of domain shifts. Achieving effective generalization typically requires a large and diverse set of labeled source data to learn robust representations that can generalize to new, unseen domains. However, obtaining such high-quali

Patrick Sattler, Johannes Zirngibl, Fahad Hilal, Oliver Gasser

DNS is one of the cornerstones of the Internet. Nowadays, a substantial fraction of DNS queries are handled by public resolvers (e.g., Google Public DNS and Cisco's OpenDNS) rather than ISP nameservers. This behavior makes it difficult for authoritative nameservers to provide answers based on the requesting resolver. The impact is especially important for en

Ahmed Rafi Hasan, Niloy Kumar Kundu, Saad Hasan, Mohammad Rashedul Hoque

The Alternate Wetting and Drying (AWD) method is a rice-growing water management technique promoted as a sustainable alternative to Continuous Flooding (CF). Climate change has placed the agricultural sector in a challenging position, particularly as global water resources become increasingly scarce, affecting rice production on irrigated lowlands. Rice, a s

Mangesh Daspute, Amri Wandel, Ravi Kumar Kopparapu, Volker Perdelwitz

We analyze four super-Earth exoplanets, LHS 1140 b, K2-18 b, TOI-1452 b, and TOI-1468 c, which orbit M-dwarf stars in the habitable zone. Their relative proximity, within 40 parsecs, makes them prime candidates for follow-up observations and atmospheric and habitability studies. This paper aims to assess their internal structure and habitability, considering

Junwen Wang, Yuanyuan Zhang, Yanjun Chu

In this paper, we first define extending datums and unified products of Rota-Baxter family Hom-associative algebras, and theoretically solve the extending structure problem. Moreover, we consider flag datums as an application, and give an example of the extending structure problem. Second, we introduce matched pairs of Rota-Baxter family Hom-associative alge

Huiyuan Lai, Esther Ploeger, Rik van Noord, Antonio Toral

Neural machine translation (NMT) systems amplify lexical biases present in their training data, leading to artificially impoverished language in output translations. These language-level characteristics render automatic translations different from text originally written in a language and human translations, which hinders their usefulness in for example crea

Martina Vanelli, Julien M. Hendrickx

We study the identifiability of nonlinear network systems with partial excitation and partial measurement when the network dynamics is linear on the edges and nonlinear on the nodes. We assume that the graph topology and the nonlinear functions at the node level are known, and we aim to identify the weight matrix of the graph. Our main result is that, for al

Gaoneng Dong, Ali Nawaz Babar, Rasmus Ellebæk Christiansen, Søren Engelberth Hansen

The emergence of dielectric bowtie cavities enable optical confinement with ultrahigh quality factor and ultra-small optical mode volumes with perspectives for enhanced light-matter interaction. Experimental work has so far emphasized the realization of these nanocavities. Here, we experimentally investigate the ultrafast dynamics of a topology-optimized die

Claude Marion, Pavel Zalesskii

A group is said to have the Magnus Property (MP) if whenever two elements have the same normal closure then they are conjugate or inverse-conjugate. We show that a profinite MP group $G$ is prosolvable and any quotient of it is again MP. As corollaries we obtain that the only prime divisors of $|G|$ are $2$, $3$, $5$ and $7$, and the second derived subgroup

Jyh-Haur Teh

Semi-topological Galois theory associates a canonical finite splitting covering to a monic Weierstrass polynomial. The inverse limit of the corresponding deck groups defines the absolute semi-topological Galois group, $\PiST(X,x)$. This paper develops a cohomology theory for $\PiST(X,x)$ with discrete torsion coefficients, establishing its fundamental proper

Haosheng Li, Weixin Mao, Weipeng Deng, Chenyu Meng

Multi-hand semantic grasp generation aims to generate feasible and semantically appropriate grasp poses for different robotic hands based on natural language instructions. Although the task is highly valuable, due to the lack of multihand grasp datasets with fine-grained contact description between robotic hands and objects, it is still a long-standing diffi

Zun Wang, Jialu Li, Yicong Hong, Songze Li

Creating high-quality data for training robust language-instructed agents is a long-lasting challenge in embodied AI. In this paper, we introduce a Self-Refining Data Flywheel (SRDF) that generates high-quality and large-scale navigational instruction-trajectory pairs by iteratively refining the data pool through the collaboration between two models, the ins

Giuseppe Esposito, Juan David Guerrero-Balaguera, Josie Esteban Rodriguez Condia, Matteo Sonza Reorda

The reliability of Neural Networks has gained significant attention, prompting efforts to develop SW-based hardening techniques for safety-critical scenarios. However, evaluating hardening techniques using application-level fault injection (FI) strategies, which are commonly hardware-agnostic, may yield misleading results. This study for the first time compa

Rachel A. Matson, Rebecca Gore, Steve B. Howell, David R. Ciardi

M dwarfs have become increasingly important in the detection of exoplanets and the study of Earth-sized planets and their habitability. However, 20-30% of M dwarfs have companions that can impact the formation and evolution of planetary systems. We use high-resolution imaging and Gaia astrometry to detect stellar companions around M dwarf exoplanet hosts dis

Bao-Bing Li, Yi Gu, Shao-Feng Wu

Machine learning can uncover physical concepts or physical equations when prior knowledge from the other is available. However, these two aspects are often intertwined and cannot be discovered independently. We extend SciNet, which is a neural network architecture that simulates the human physical reasoning process for physics discovery, by proposing a model

Mu Zhang, Yunfan Liu, Yue Liu, Yuzhong Zhao

Existing image synthesis methods for natural scenes focus primarily on foreground control, often reducing the background to simplistic textures. Consequently, these approaches tend to overlook the intrinsic correlation between foreground and background, which may lead to incoherent and unrealistic synthesis results in remote sensing (RS) scenarios. In this p

Rosario Patanè, Nadjib Achir, Andrea Araldo, Lila Boukhatem

Edge computing (EC) consists of deploying computation resources close to the users, thus enabling low-latency applications, such as augmented reality and online gaming. However, large-scale deployment of edge nodes can be highly impractical and expensive. Besides EC, there is a rising concept known as Vehicular Cloud Computing (VCC). VCC is a computing parad

Gauri Jain, Pradeep Varakantham, Haifeng Xu, Aparna Taneja

Public health practitioners often have the goal of monitoring patients and maximizing patients' time spent in "favorable" or healthy states while being constrained to using limited resources. Restless multi-armed bandits (RMAB) are an effective model to solve this problem as they are helpful to allocate limited resources among many agents under resource cons

H. Denes, V. A. Capa

The properties of galaxies follow scaling relations related to the physics that govern galaxy evolution. Based on these, we can identify galaxies undergoing specific evolutionary processes such as HI-excess galaxies, which have relatively high HI mass compared to their stellar mass. The possible reasons for this could be either recent gas accretion or an ine

Zhi-Ming Geng, Jin-Shan Yao, Ying-Bin Cheng, Xue-Jun Yan

Phonons, the quanta of lattice vibrations, are primary heat carriers for semiconductors and dielectrics. The demand of effective phonon manipulation urgently emerges, because the thermal management is crucial for the ongoing development of micro/nano semiconductor devices towards higher integration and power densities1, 2. Phonons also show wave-particle dua

Fermin Orozco, Pedro Porto Buarque de Gusmão, Hongkai Wen, Johan Wahlström

Deep-learning based traffic prediction models require vast amounts of data to learn embedded spatial and temporal dependencies. The inherent privacy and commercial sensitivity of such data has encouraged a shift towards decentralised data-driven methods, such as Federated Learning (FL). Under a traditional Machine Learning paradigm, traffic flow prediction m

Stjepan Salatovic, Sebastian Krumscheid, Florian Wittemann, Luise Kärger

Fiber orientation is decisive for the mechanical performance of composite materials. During manufacturing, variations in material and process parameters can influence fiber orientation. We employ multilevel polynomial surrogates to model the propagation of uncertain material properties in the injection molding process. To ensure reliable uncertainty quantifi

Jonathan B. Hill, Saraswata Chaudhuri

We study the probability tail properties of Inverse Probability Weighting (IPW) estimators of the Average Treatment Effect (ATE) when there is limited overlap between the covariate distributions of the treatment and control groups. Under unconfoundedness of treatment assignment conditional on covariates, such limited overlap is manifested in the propensity s

Wen-Chao Hu, Wang-Zhou Dai, Yuan Jiang, Zhi-Hua Zhou

Neuro-Symbolic (NeSy) AI could be regarded as an analogy to human dual-process cognition, modeling the intuitive System 1 with neural networks and the algorithmic System 2 with symbolic reasoning. However, for complex learning targets, NeSy systems often generate outputs inconsistent with domain knowledge and it is challenging to rectify them. Inspired by th

Félix Belzunce, Alba M. Franco-Pereira, Julio Mulero

In this paper we provide a new criterion for the comparison of claims, when we have conditional claims arising in stop loss contracts or contracts with franchise deductible. These stochastic comparisons are made on the basis of the Tail Value at Risk (also known as conditional tail expectation), just for a fixed level and beyond. In particular, we explain th

Himangshu Hazarika, Giorgos Kapetanakis, Dhiren Kumar Basnet

Let $\Fm$ be finite fields of order $q^m$, where $m\geq 2$ and $q$, a prime power. Given $\F$-affine hyperplanes $A_1,\ldots, A_m$ of $\Fm$ in general position, we study the existence of primitive element $\alpha$ of $\Fm$, such that $f(\alpha)$ is also primitive, where $ax^2+bx+c\in \Fm[x]$ ($a\neq 0$ and $b^2\neq 4ac$) in $\Fm$ and the primitive pair $(\al

Nguyen Thi Thu Huong

This paper studies approximate solutions of a linear fractional vector optimization problem without requiring boundedness of the constraint set. We establish necessary and sufficient conditions for approximating weakly efficient points of such a problem via some properties of the objective function and a technical lemma related to the intersection of the top

Paul Geuchen, Palina Salanevich, Olov Schavemaker, Felix Voigtlaender

In this paper, we prove sharp upper and lower bounds for the approximation of Sobolev functions by sums of multivariate ridge functions, i.e., for approximation by functions of the form $\mathbb{R}^d \ni x \mapsto \sum_{k=1}^n \varrho_k(A_k x) \in \mathbb{R}$ with $\varrho_k : \mathbb{R}^\ell \to \mathbb{R}$ and $A_k \in \mathbb{R}^{\ell \times d}$. We show

Rainer Schicker

The ALICE detector at the LHC has undergone a major upgrade in the long shutdown 2019-2021 to be able to take data at much-increased rates in Runs 3 and 4. The upgrades of the detector systems used for analysing double gap events are described, and the improvement in data taking capability for such double gap events is presented.

Zhen-Cheng Liao, Cong Xiao, Zhi Wang, Qian Niu

We propose two thermo-spintronic responses in proximity induced superconductors with spin-orbit coupled band - spin Nernst effect and thermal Edelstein effect stemming respectively from momentum-space and mixed superconducting Berry curvatures. We unveil that the Bloch band spin-orbit coupling and pairing are entangled in shaping the superconducting Berry cu

Hanru Wang, Yihan Jiao, Fanyu Meng, Xu Zhang

We present the ultralow-temperature thermal conductivity measurements on single crystals of transition-metal dichalcogenide material 4Hb-TaS$_{2}$, which has recently been proposed as a topological superconductor candidate. In zero field, a small residual linear term $\kappa_{0}/T$ is observed, indicating the existence of a residual density of states in the

Daniel Scolnic, Paula Boubel, Jakob Byrne, Adam G. Riess

Boubel et al. 2024 (B24) recently used the Tully-Fisher (TF) relation to measure calibrated distances in the Hubble flow and found $H_0= 73.3 \pm 2.1 (stat) \pm 3.5 (sys)$ km/s/Mpc. The large systematic uncertainty was the result of propagating the conflict between two sources of empirical distance calibration: a difference in zeropoint when calibrating the

Ganesh Ji Omar, Pierluigi Gargiani, Manuel Valvidares, Zhi Shiuh Lim

The balance between the orbital and spin magnetic moments in a magnetic system is the heart of many intriguing phenomena. Here, we show experimental evidence of a large orbital moment, which competes with its spin counterpart in a ferrimagnetic insulator thulium iron garnet, Tm3Fe5O12. Leveraging element-specific X-ray magnetic circular dichroism (XMCD), we

Zhen Li

Rotating regular black hole, as a promising extension beyond general relativity, offer a phenomenological model that resolves spacetime singularities. In this study, we investigate the observational features of thin accretion disk around a well-known rotating regular black hole, which introduce an exponential converge factor $e^{-k/r}$ to the black hole mass

Gaetano Lambiase, Tanmay Kumar Poddar

The high speeds seen in rapidly rotating pulsars after supernova explosions present a longstanding puzzle in astrophysics. Numerous theories have been suggested over the years to explain this sudden "kick" imparted to the neutron star, yet each comes with its own set of challenges and limitations. Key explanations for pulsar kicks include hydrodynamic instab

Dzmitry Bahdanau, Nicolas Gontier, Gabriel Huang, Ehsan Kamalloo

We present TapeAgents, an agent framework built around a granular, structured log tape of the agent session that also plays the role of the session's resumable state. In TapeAgents we leverage tapes to facilitate all stages of the LLM Agent development lifecycle. The agent reasons by processing the tape and the LLM output to produce new thought and action st

Irene Valladares Duque, Philipp Strasberg

We analyze a toy model that obeys environmentally induced decoherence and quantum Darwinism and satisfies the decoherent histories criterion and Leggett-Garg inequalities with respect to the pointer basis. Yet, the resulting "classical" dynamics are extremely fragile and recohere after a seemingly innocent control operation. This challenges the idea that cla

Yuan Liu, Le Tian, Xiao Zhou, Xinyu Gao

Vision-language models have made significant strides recently, demonstrating superior performance across a range of tasks, e.g. optical character recognition and complex diagram analysis. Building on this trend, we introduce a new vision-language model, POINTS1.5, designed to excel in various real-world applications. POINTS1.5 is an enhancement of POINTS1.0

Andrew Szot, Bogdan Mazoure, Omar Attia, Aleksei Timofeev

We examine the capability of Multimodal Large Language Models (MLLMs) to tackle diverse domains that extend beyond the traditional language and vision tasks these models are typically trained on. Specifically, our focus lies in areas such as Embodied AI, Games, UI Control, and Planning. To this end, we introduce a process of adapting an MLLM to a Generalist

Oleg Mushkarov, Nikolai Nikolov

An analog of Picard's little theorem for entire functions of matrices is proved.

Chenglong Li, Tao Wang, Zhaodong Ding, Yun Xiao

RGBT tracking usually suffers from various challenging factors of low resolution, similar appearance, extreme illumination, thermal crossover and occlusion, to name a few. Existing works often study complex fusion models to handle challenging scenarios, but can not well adapt to various challenges, which might limit tracking performance. To handle this probl

Alfonso J. Bello, Julio Mulero, Miguel A. Sordo, Alfonso Suárez-Llorens

In risk theory, financial asset returns often follow heavy-tailed distributions. Investors and risk managers used to compare risk measures as the value at risk or tail value at risk in order over the whole confidence levels to avoid the exposure to to large risks. In this paper we analyze the comparison between tail values at risk from a confidence level and

Cong Chen, Mo Huang, Xuekui Zhang

FDA's Project Optimus initiative for oncology drug development emphasizes selecting a dose that optimizes both efficacy and safety. When an inferentially adaptive Phase 2/3 design with dose selection is implemented to comply with the initiative, the conventional inverse normal combination test is commonly used for Type I error control. However, indiscriminat

Daniele Peri

An extremely schematic model of the forces acting an a sailing yacht equipped with a system of foils is here presented and discussed. The role of the foils is to raise the hull from the water in order to reduce the total resistance and then increase the speed. Some CFD simulations are providing the total resistance of the bare hull at some values of speed an

Bruno Kahn

We associate an $L$-function $L^{\mathrm{near}}(M,s)$ to any geometric motive over a global field $K$ in the sense of Voevodsky. This is a Dirichlet series which converges in some half-plane and has an Euler product factorisation. When $M$ is the dual of $M(X)$ for $X$ a smooth projective variety, $L^{\mathrm{near}}(M,s)$ differs from the alternating product

Piotr Pokora

We define a combinatorial object that can be associated with any conic-line arrangement with ordinary singularities, which we call the combinatorial Poincar\'e polynomial. We prove a Terao-type factorization statement on the splitting of such a polynomial over the rationals under the assumption that our conic-line arrangements are free and admit ordinary qua

Lifan Zhao, Yanyan Shen

Time series forecasting always faces the challenge of concept drift, where data distributions evolve over time, leading to a decline in forecast model performance. Existing solutions are based on online learning, which continually organize recent time series observations as new training samples and update model parameters according to the forecasting feedbac

Guochao Jiang, Ziqin Luo, Chengwei Hu, Zepeng Ding

Many previous models of named entity recognition (NER) suffer from the problem of Out-of-Entity (OOE), i.e., the tokens in the entity mentions of the test samples have not appeared in the training samples, which hinders the achievement of satisfactory performance. To improve OOE-NER performance, in this paper, we propose a new framework, namely S+NER, which

Alex Bishop, Daniele D'Angeli, Francesco Matucci, Tatiana Nagnibeda

We are interested in the subgroup membership problem in groups acting on rooted $d$-regular trees and a natural class of subgroups, the stabilisers of infinite rays emanating from the root. These rays, which can also be viewed as infinite words in the alphabet with d letters, form the boundary of the tree. Stabilisers of infinite rays are not finitely genera

Artemy Kolchinsky, Andreas Dechant, Kohei Yoshimura, Sosuke Ito

In genuine nonequilibrium systems that undergo continuous driving, the thermodynamic forces are nonconservative, meaning they cannot be described by any free energy potential. Nonetheless, we show that the dynamics of such systems are governed by a "generalized free energy" that is derived from a large-deviations variational principle. This variational princ

A. Zdeb, M. Warda, L. M. Robledo, S. A. Giuliani

Pairing plays a crucial role in the microscopic description of nuclear fission. Microscopic methods provide access to three quantities related to pairing, namely, the pairing gap ($\Delta$), the particle number fluctuations ($ \Delta \hat{N}^2 $), and the quenching factor (QF). The aim of this work is to analyse the impact of each of these quantities on the

Xiao Luo, Sean O'Connell, Shamima Mithun

This paper provides an in-depth evaluation of three state-of-the-art Large Language Models (LLMs) for personalized career mentoring in the computing field, using three distinct student profiles that consider gender, race, and professional levels. We evaluated the performance of GPT-4, LLaMA 3, and Palm 2 using a zero-shot learning approach without human inte

Laura Duarte, Victor P. Goncalves, Daniel E. Martins

The impact of Lorentz violating (LV) terms on the exclusive $e^+e^-$ production in ultraperipheral heavy-ion collisions at the Large Hadron Collider (LHC) is investigated, considering vectorial and axial couplings. Results for the differential and total cross-sections are presented, and the sensitivity to a time-like coupling is estimated. Our results indica

Martin Schuck, Dinushka Orrin Dahanaggamaarachchi, Ben Sprenger, Vedant Vyas

Drone swarm performances -- synchronized, expressive aerial displays set to music -- have emerged as a captivating application of modern robotics. Yet designing smooth, safe choreographies remains a complex task requiring expert knowledge. We present SwarmGPT, a language-based choreographer that leverages the reasoning power of large language models (LLMs) t

Lisbeth Carrero, Alexander Quaas, Andres Zuniga

In this paper, we introduce a new class of quasilinear operators, which represents a nonlocal version of the operator studied by Stuart and Zhou [1], inspired by models in nonlinear optics. We will study the existence of at least one or two solutions in the cone $X=\{u\in H^s_0(\Omega): u\geq 0\}$ using variational methods. For this purpose, we analyze two s

Rixin Yu, Marco Herbert, Markus Klein, Erdzan Hodzic

Predicting the evolution of complex systems governed by partial differential equations (PDEs) remains challenging, especially for nonlinear, chaotic behaviors. This study introduces Koopman-inspired Fourier Neural Operators (kFNO) and Convolutional Neural Networks (kCNN) to learn solution advancement operators for flame front instabilities. By transforming d

Lucas Martin, Martin Parlanti, Martin Schvellinger

We study high-energy scattering at fixed angle in the planar limit of non-Abelian gauge field theories from type IIB superstring theory scattering amplitudes. Firstly, we consider four-glueball scattering described in terms of the four-dilaton string theory scattering amplitude. We explicitly calculate some angular integrals of four scalar spherical harmonic

Alexander Tyurin

We focus on the classification problem with a separable dataset, one of the most important and classical problems from machine learning. The standard approach to this task is logistic regression with gradient descent (LR+GD). Recent studies have observed that LR+GD can find a solution with arbitrarily large step sizes, defying conventional optimization theor

Manuel Blickle, Daniel Fink, Alexandria Wheeler, Wenliang Zhang

Let $R$ be a regular $F$-finite ring of prime characteristic $p$. We prove that the injective dimension of every unit Frobenius module $M$ in the category of unit Frobenius modules is at most $\operatorname{dim}(\operatorname{Supp}_R(M))+1$. We further show that for unit Cartier modules the same bound holds over any noetherian $F$-finite ring $A$ of prime ch

S. V. Amitonov, A. Aprà, M. Asker, B. Barry

Confining electrons or holes in quantum dots formed in the channel of industry-standard fully depleted silicon-on-insulator CMOS structures is a promising approach to scalable qubit architectures. In this communication, we present measurement results of a commercial nanostructure fabricated using the GlobalFoundries 22FDX(TM) industrial process. We demonstra

Yi Zhong, Weize Quan, Dong-ming Yan, Jie Jiang

Point cloud completion aims to reconstruct the complete 3D shape from incomplete point clouds, and it is crucial for tasks such as 3D object detection and segmentation. Despite the continuous advances in point cloud analysis techniques, feature extraction methods are still confronted with apparent limitations. The sparse sampling of point clouds, used as inp

Netzer Moriya

Ill-posed configurations, such as collinear or coplanar point arrangements, are a persistent challenge in computational geometry, complicating tasks as in triangulation and convex hull construction. This paper discusses the probability of such configurations arising in two scenarios: (1) data sampled randomly from a uniform distribution, and (2) data collect

Farooq Ahmad Wani, Maria Sofia Bucarelli, Andrea Giuseppe Di Francesco, Oleksandr Pryymak

Graph Neural Networks (GNNs) are powerful at solving graph classification tasks, yet applied problems often contain noisy labels. In this work, we study GNN robustness to label noise, demonstrate GNN failure modes when models struggle to generalise on low-order graphs, low label coverage, or when a model is over-parameterized. We establish both empirical and

Jonas Benjamin Ohland, Nathalie Lebas, Vincent Deo, Olivier Guyon

Traditional wavefront control in high-energy, high-intensity laser systems usually lacks real-time capability, failing to address dynamic aberrations. This limits experimental accuracy due to shot-to-shot fluctuations and necessitates long cool-down phases to mitigate thermal effects, particularly as higher repetition rates become essential, e.g. in Inertial

Yuxiang Liu, Ligong Wang

Let $Q(G)=D(G)+A(G)$ be the signless Laplacian matrix of a simple graph $G$, where $D(G)$ and $A(G)$ are the degree diagonal matrix and the adjacency matrix of $G$, respectively. The largest eigenvalue of $Q(G)$, denoted by $q(G)$, is called the signless Laplacian spectral radius of $G$. Let $\theta(l_{1},l_{2},l_{3})$ denote the theta graph which consists o

Luis A. Vargas-Mieles, Paul D. W. Kirk, Chris Wallace

Biclustering has gained interest in gene expression data analysis due to its ability to identify groups of samples that exhibit similar behaviour in specific subsets of genes (or vice versa), in contrast to traditional clustering methods that classify samples based on all genes. Despite advances, biclustering remains a challenging problem, even with cutting-

Jiayuan Ma, Hongbin Na, Zimu Wang, Yining Hua

Mental manipulation severely undermines mental wellness by covertly and negatively distorting decision-making. While there is an increasing interest in mental health care within the natural language processing community, progress in tackling manipulation remains limited due to the complexity of detecting subtle, covert tactics in conversations. In this paper

Seung-Il Choi, Sun-Young Nam, Young-Tak Oh

Let $\mathrm{Int}(n)$ denote the set of nonempty left weak Bruhat intervals in the symmetric group $\mathfrak{S}_n$. We investigate the equivalence relation $\overset{D}{\simeq}$ on $\mathrm{Int}(n)$, where $I \overset{D}{\simeq} J$ if and only if there exists a descent-preserving poset isomorphism between $I$ and $J$. For each equivalence class $C$ of $(\ma

Songchun Zhang, Chunhui Zhao

Inferring 3D structures from sparse, unposed observations is challenging due to its unconstrained nature. Recent methods propose to predict implicit representations directly from unposed inputs in a data-driven manner, achieving promising results. However, these methods do not utilize geometric priors and cannot hallucinate the appearance of unseen regions,

Riccardo Borsato, Sibylle Driezen

We consider a string on a Jordanian deformation of the $AdS_5\times S^5$ spacetime. This model belongs to the larger class of Homogeneous Yang-Baxter deformations, which preserve classical integrability in the sense that one can construct an explicit Lax connection. To study the scattering of bosonic worldsheet excitations, we fix light-cone gauge and expand

Zhuoran Yang, Xi Guo, Chenjing Ding, Chiyu Wang

Autonomous driving requires robust perception models trained on high-quality, large-scale multi-view driving videos for tasks like 3D object detection, segmentation and trajectory prediction. While world models provide a cost-effective solution for generating realistic driving videos, challenges remain in ensuring these videos adhere to fundamental physical

Anis Maddi, Gaelle Poignand, Vassos Achilleos, Vincent Pagneux

A passive loudspeaker mounted in a duct acts as a reciprocal scatterer for plane waves impinging on either of its sides. However, the reciprocity can be broken by means of an asymmetric electroacoustic feedback which supplies to the loudspeaker a signal picked-up from a microphone facing only one of its sides. This simple modification offers new opportunitie

Zoltán M. Balogh, Alexandru Kristály, Ágnes Mester

The paper is devoted to proving Allard-Michael-Simon-type $L^p$-Sobolev inequalities $(p>1)$ with explicit constants in the setting of Euclidean minimal submanifolds of arbitrary codimension. Our results require separate discussions for the cases $p\geq 2$ and $1<p<2$, respectively. In particular, for $p\geq 2$, we obtain an asymptotically sharp and codimens

Huan Tran, Chiho Kim, Rishi Gurnani, Oliver Hvidsten

Polymer composite performance depends significantly on the polymer matrix, additives, processing conditions, and measurement setups. Traditional physics-based optimization methods for these parameters can be slow, labor-intensive, and costly, as they require physical manufacturing and testing. Here, we introduce a first step in extending Polymer Informatics,

Neng Dong, Shuanglin Yan, Liyan Zhang, Jinhui Tang

Visible-infrared person re-identification (VIReID) retrieves pedestrian images with the same identity across different modalities. Existing methods learn visual content solely from images, lacking the capability to sense high-level semantics. In this paper, we propose an Embedding and Enriching Explicit Semantics (EEES) framework to learn semantically rich c

Zhi Sun, Tianyue Li, Shiqi Kuang, Xue Yun

Metasurfaces are reshaping traditional optical paradigms and are increasingly required in complex applications that demand substantial computational resources to numerically solve Maxwell's equations-particularly for large-scale systems, inhomogeneous media, and densely packed metadevices. Conventional forward design using electromagnetic solvers is based on

D. Trotta, F. Valentini, D. Burgess, S. Servidio

Shocks and turbulence are ubiquitous phenomena, responsible for particle acceleration to very high energies in a large collection of astrophysical systems. Using self-consistent, hybrid-kinetic simulations with and without pre-existing turbulence, we study the transition of a shock from ``laminar'' to turbulent. We show that the changes in upstream proton tr

Ali Forootani, Danial Esmaeili Aliabadi, Daniela Thraen

Wind power forecasting plays a critical role in modern energy systems, facilitating the integration of renewable energy sources into the power grid. Accurate prediction of wind energy output is essential for managing the inherent intermittency of wind power, optimizing energy dispatch, and ensuring grid stability. This paper proposes the use of Deep Neural N

Wilken Aldair Misael, Andre Severo Pereira Gomes

We investigate the core-level ionization energies of the bare uranyl ion (UO$_2^{2+}$) and its interaction with X-rays when it is hosted in the Cs$_2$UO$_2$Cl$_4$ crystalline environment using a recent implementation of the core-valence-separated relativistic equation-of-motion coupled-cluster method (CVS-EOM-CC). Our study evaluates different relativistic H

Benito A. Juárez-Aubry

We consider semiclassical gravity with a Klein-Gordon field with mass $m^2 \geq 0$ and curvature coupling $\xi = 1/2$. We identify a special class of Hadamard two-point functions for which the semiclassical system is quasi-linear hyperbolic and, within this class, provide the first well-posedness result for semiclassical gravity without spacetime symmetries.

You Qi, Louis-Hadrien Robert, Joshua Sussan, Emmanuel Wagner

We give a new proof of a theorem due to Shumakovitch and Wang on base point independence of Khovanov--Rozansky homology in characteristic $p$. Some further symmetries of $\mathfrak{gl}(p)$-homology in characteristic $p$ are also discussed.

Shun Watanabe, Geoffrey Wolfer

It is known that the set of lumpable Markov chains over a finite state space, with respect to a fixed lumping function, generally does not form an exponential family of stochastic matrices. In this work, we explore efficiently verifiable necessary and sufficient conditions for families of lumpable transition matrices to form exponential families. To this end

Shigeki Akiyama

We introduce a new generalization of Wythoff Nim using three piles of stones. We show that its P-positions have finite difference properties and produce a partition of positive integers. Further, we give a conjecture that the P-positions approximate a half-line whose slope is described by algebraic numbers of degree 5.

Joao Carvalho, An T. Le, Philipp Jahr, Qiao Sun

Grasping objects successfully from a single-view camera is crucial in many robot manipulation tasks. An approach to solve this problem is to leverage simulation to create large datasets of pairs of objects and grasp poses, and then learn a conditional generative model that can be prompted quickly during deployment. However, the grasp pose data is highly mult

Xiang Ji, Zhen-Ya Zheng, Ru-Qiu Lin, Hai-Cheng Feng

Dual and lensed quasars are valuable astrophysical targets in many aspects. Dual quasars, considered as the precursors of supermassive black hole binaries, can provide crucial insights into how black hole mergers drive the growth of supermassive black holes and influence the evolution of galaxies. Lensed quasars, formed by the gravitational deflection of a b

Ruqiu Lin, Zhen-Ya Zheng, Chunyan Jiang, Fang-Ting Yuan

Recently, the James Webb Space Telescope (JWST) has revealed a new class of high redshift (high-$z$, $z>4$) compact galaxies which are red in the rest-frame optical and blue in the rest-frame UV as V-shaped spectral energy distributions (SEDs), referred to as "Little Red Dots" (LRDs). It is very likely that LRDs host obscured broad-line active galactic nucle

Shuairu Zhu, Zhen-ya Zheng, Fang-Ting Yuan, Chunyan Jiang

Understanding the ionizing photon escape from galaxies is essential for studying Cosmic Reionization. With a sample of 23 Lyman Continuum (LyC) leakers at $3<z<4.5$ in the GOODS-S field, we investigate their morphologies using high-resolution data from the Hubble Space Telescope (HST) and the James Webb Space Telescope (JWST). We find that 20 of the 23 LyC l

Kaiyuan Chen, Jin Wang, Xuejie Zhang

Small language models (SLMs) are more efficient, cost-effective, and customizable than large language models (LLMs), though they often underperform in specific areas like reasoning. Past methods for enhancing SLMs' reasoning, such as supervised fine-tuning and distillation, often depend on costly external signals, resulting in SLMs being overly confident wit

Wessel Poelman, Miryam de Lhoneux

Multilingual natural language processing is getting increased attention, with numerous models, benchmarks, and methods being released for many languages. English is often used in multilingual evaluation to prompt language models (LMs), mainly to overcome the lack of instruction tuning data in other languages. In this position paper, we lay out two roles of E

Shengwei Liu, Hongwei Liu, Frederique Oggier

Generalized Reed-Solomon codes form the most prominent class of maximum distance separable (MDS) codes, codes that are optimal in the sense that their minimum distance cannot be improved for a given length and code size. The study of codes that are MDS yet not generalized Reed-Solomon codes, called non-generalized Reed-Solomon MDS codes, started with the wor

R. Luo, J. Z. Wang, X. Zhang, D. H. Quan

Aims. We present observational results of H$_{2}$S 1$_{10}$-1$_{01}$, H$_{2}$$^{34}$S 1$_{10}$-1$_{01}$, H$_{2}$CS 5$_{14}$-4$_{14}$, HCS$^{+}$ 4-3, SiO 4-3, HC$_{3}$N 19-18 and C$^{18}$O 1-0 toward a sample of 51 late-stage massive star-forming regions, to study relationships among H$_{2}$S, H$_{2}$CS, HCS$^{+}$ and SiO in hot cores. Chemical connections of

Jing Ye, Lu Xiang, Yaping Zhang, Chengqing Zong

Large Language Models (LLMs) have demonstrated promising potential in providing empathetic support during interactions. However, their responses often become verbose or overly formulaic, failing to adequately address the diverse emotional support needs of real-world scenarios. To tackle this challenge, we propose an innovative strategy-enhanced role-playing