November 2024 arXiv papers — page 101

Adaku Uchendu, Thai Le

The surge of data available on the Internet has driven the adoption of a wide range of computational methods for analyzing and extracting insights from large-scale data. Among these, Machine Learning (ML) has become a central paradigm, offering powerful tools for pattern discovery, prediction, and representation learning across many domains. At the same time

Philipp Karg, Balint Varga, Sören Hohmann

In this work, we propose novel offline and online Inverse Differential Game (IDG) methods for nonlinear Differential Games (DG), which identify the cost functions of all players from control and state trajectories constituting a feedback Nash equilibrium. The offline approach computes the sets of all equivalent cost function parameters that yield the observe

Semu Serunjogi

In this paper, we investigate the parking process on a uniform random rooted binary tree with $n$ vertices. Viewing each vertex as a single parking space, a random number of cars independently arrive at and attempt to park on each vertex one at a time. If a car attempts to park on an occupied vertex, it traverses the unique path on the tree towards the root,

Minjun Kim, Sina Falaki

The once mythological 51% attack has moved beyond the hypothetical and now poses a legitimate widespread threat to blockchain technology. Current blockchains provide inferior throughput capacity when compared to that of centralized systems, creating an obvious vulnerability which allows the 51% attack to occur within decentralized systems. Despite recent adv

Hui Huang, Hicham Kouhkouh

A self-interacting dynamics that mimics the standard Consensus-Based Optimization (CBO) model is introduced. This single-particle dynamics is shown to converge to a unique invariant measure that approximates the global minimum of a given function. As an application, its connection to CBO with Personal Best introduced by C. Totzeck and M.-T. Wolfram (Math. Bi

Jin Han, Balaraju Battu, Ivan Romić, Talal Rahwan

Large language models (LLMs) are increasingly used to model human social behavior, with recent research exploring their ability to simulate social dynamics. Here, we test whether LLMs mirror human behavior in social dilemmas, where individual and collective interests conflict. Humans generally cooperate more than expected in laboratory settings, showing less

Ryoma Yataka, Adriano Cardace, Pu Perry Wang, Petros Boufounos

Indoor radar perception has seen rising interest due to affordable costs driven by emerging automotive imaging radar developments and the benefits of reduced privacy concerns and reliability under hazardous conditions (e.g., fire and smoke). However, existing radar perception pipelines fail to account for distinctive characteristics of the multi-view radar s

Florian Seitz, Matteo Rosati, Ángeles Vázquez-Castro, Janis Nötzel

It is a common belief that quantum key distribution systems are the one and only information-theoretically secure physical layer security protocol that enables secure data transmission without a need for the legitimate parties to have any channel knowledge. It is also known that this high security profile results in severe rate restrictions for the parties u

Xu Wang, Mohammad Ali Maleki, Muhammad Waqar Azhar, Pedro Trancoso

With their potential to significantly reduce traffic accidents, enhance road safety, optimize traffic flow, and decrease congestion, autonomous driving systems are a major focus of research and development in recent years. Beyond these immediate benefits, they offer long-term advantages in promoting sustainable transportation by reducing emissions and fuel c

Shangdi Yu, Jessica Shi, Jamison Meindl, David Eisenstat

We introduce the ParClusterers Benchmark Suite (PCBS) -- a collection of highly scalable parallel graph clustering algorithms and benchmarking tools that streamline comparing different graph clustering algorithms and implementations. The benchmark includes clustering algorithms that target a wide range of modern clustering use cases, including community dete

Bernadette Charron-Bost, Louis Penet de Monterno

We study the problem of clock synchronization in a networked system with arbitrary starts for all nodes. We consider a synchronous network of $n$ nodes, where each node has a local clock that is an integer counter. Eventually, clocks must be all equal and increase by one in each round modulo some period $P$. The purpose of this paper is to study whether cloc

Yacin Ameur, Joakim Cronvall

We consider a class of external potentials on the complex plane $\mathbb{C}$ for which the coincidence set to the obstacle problem contains a Jordan curve in the exterior of the droplet. We refer to this curve as a spectral outpost. We study the corresponding Coulomb gas at $\beta=2$. Generalizing recent work in the radially symmetric case, we prove that the

Joshua H. Tyler, Mohamed K. M. Fadul, Donald R. Reising

Modern cryptography, such as Rivest Shamir Adleman (RSA) and Secure Hash Algorithm (SHA), has been designed by humans based on our understanding of cryptographic methods. Neural Network (NN) based cryptography is being investigated due to its ability to learn and implement random cryptographic schemes that may be harder to decipher than human-designed algori

Brandon Shane, Blakesley Burkhart, Laura Fissel, Susan E. Clark

The characterization of magnetic fields within molecular clouds is fundamental to understanding star formation processes. Accurately gauging the three-dimensional structure of these fields presents a challenge, as observational techniques such as dust polarization and the Zeeman effect each provide only partial information on the orientation and line-of-sigh

Pedro Palacios, Rafael Medina, Jean-Luc Rouas, Giovanni Ansaloni

Efficient deployment of resource-intensive transformers on edge devices necessitates cross-stack optimization. We thus study the interrelation between structured pruning and systolic acceleration, matching the size of pruned blocks with the systolic array dimensions. In this setting, computations of pruned weight blocks can be skipped, reducing run-time and

Keshav Ranjan, Meghana Nasre, Prajakta Nimbhorkar

We consider the Hospitals/Residents (HR) problem in the presence of ties in hospital preferences. Among the three notions of stability, namely weak stability, strong stability, and super-stability, we focus on the notion of strong stability. Strong stability has many desirable properties, both theoretically and in practice; however, its existence is not guar

Gunnar Birke, Christian Engwer, Jan Giesselmann, Sandra May

In this work we present an a priori error analysis for solving the unsteady advection equation on cut cell meshes along a straight ramp in two dimensions. The space discretization uses a lowest order upwind-type discontinuous Galerkin scheme involving a \textit{Domain of Dependence} (DoD) stabilization to correct the update in the neighborhood of small cut c

Claudio Bravo, Auguste Hébert, Diego Izquierdo, Benoit Loisel

Let $\A$ be a finite dimensional vector space and $\Phi$ be a finite root system in $\A$. To this data is associated an affine poly-simplicial complex. Motivated by a forthcoming construction of connectified higher buildings, we study "affine weight polytopes" associated to these data. We prove that these polytopes tesselate $\A$. We also prove a kind of "mi

Quang P. M. Pham, Khoi T. N. Nguyen, Lan C. Ngo, Truong Do

Scene graphs have proven to be highly effective for various scene understanding tasks due to their compact and explicit representation of relational information. However, current methods often overlook the critical importance of preserving symmetry when generating scene graphs from 3D point clouds, which can lead to reduced accuracy and robustness, particula

Tim Elsner, Paula Usinger, Julius Nehring-Wirxel, Gregor Kobsik

In language processing, transformers benefit greatly from text being condensed. This is achieved through a larger vocabulary that captures word fragments instead of plain characters. This is often done with Byte Pair Encoding. In the context of images, tokenisation of visual data is usually limited to regular grids obtained from quantisation methods, without

Si Chen, Jingyi Xie, Ge Wang, Haizhou Wang

This study investigates how 18-year-old students, parents, and experts in China utilize artificial intelligence (AI) tools to support decision-making in college applications during college entrance exam -- a highly competitive, score-driven, annual national exam. Through 32 interviews, we examine the use of Quark GaoKao, an AI tool that generates college app

Jiajun Zhou, Jiacheng Yao, Xuanze Chen, Shanqing Yu

Lateral movement is a crucial component of advanced persistent threat (APT) attacks in networks. Attackers exploit security vulnerabilities in internal networks or IoT devices, expanding their control after initial infiltration to steal sensitive data or carry out other malicious activities, posing a serious threat to system security. Existing research sugge

Francesco Giffoni, Emanuela Sirtori, Louis Colnot

This paper addresses how to assign a monetary value to scientific publications, particularly in the case of multi-author papers arising from large-scale research collaborations. Contemporary science increasingly relies on extensive and varied collaborations to tackle global challenges in fields such as life sciences, climate science, energy, high-energy phys

Xingbo Wei, Kewei Feng, Tian-Cheng Yi, Tong Liu

We investigate the phase transitions in a one-dimensional system with colored noise. Previous studies indicated that the phase diagram of this system included extended and disorder-induced localized phases. However, by studying the properties of wave functions, we find that this phase diagram can be further refined, revealing the existence of a bound phase f

Peter Spacek, Charles Wang

We construct a family of polytopes, which we call Chevalley polytopes, associated to homogeneous spaces $X=G/P$ in their projective embeddings $X\hookrightarrow \mathbb{P}(V_{\varpi})$ together with a choice of reduced expression for the minimal coset representative $w^P$ of $w_0$ in $W/W_P$. When $X$ is minuscule in its minimal embedding, we describe our co

Sergio M. de Paco, Antonio Agudo

We present a coarse-to-fine neural deformation model to simultaneously recover the camera pose and the 4D reconstruction of an unknown object from multiple RGB sequences in the wild. To that end, our approach does not consider any pre-built 3D template nor 3D training data as well as controlled illumination conditions, and can sort out the problem in a self-

Tyler Lane

We produce twisted derived equivalences between torsors under abelian varieties and their moduli spaces of simple semi-homogeneous sheaves. We also establish the natural converse to this result and show that a large class of twisted derived equivalences, including all derived equivalences, between torsors arise in this way. As corollaries, we obtain partial

Pathirage N. Deelaka, Tharindu Wickremasinghe, Devin Y. De Silva, Lisara N. Gajaweera

Model interpretability is a key challenge that has yet to align with the advancements observed in contemporary state-of-the-art deep learning models. In particular, deep learning aided vision tasks require interpretability, in order for their adoption in more specialized domains such as medical imaging. Although the field of explainable AI (XAI) developed me

Xiaodong Chen, Yuxuan Hu, Xiaokang Zhang, Yanling Wang

Pruning has become a widely adopted technique for reducing the hardware requirements of large language models (LLMs). To recover model performance after pruning, post-training is commonly employed to mitigate the resulting performance degradation. While post-training benefits from larger datasets, once the dataset size is already substantial, increasing the

Christof Kuelske, Niklas Schubert

We consider $\mathbb{Z}_q$-valued clock models on a regular tree, for general classes of ferromagnetic nearest neighbor interactions which have a discrete rotational symmetry. It has been proved recently that, at strong enough coupling, families of homogeneous Markov chain Gibbs states $\mu_A$ coexist whose single-site marginals concentrate on $A\subset \mat

Xinmin Qiu, Bonan Li, Zicheng Zhang, Congying Han

Blind face restoration (BFR) is fundamentally challenged by the extensive range of degradation types and degrees that impact model generalization. Recent advancements in diffusion models have made considerable progress in this field. Nevertheless, a critical limitation is their lack of awareness of specific degradation, leading to potential issues such as un

S. Bagnulo, J. D. Landstreet

Many magnetic white dwarfs exhibit a polarised spectrum that periodically varies as the star rotates because the magnetic field is not symmetric about the rotation axis. In this work, we report the discovery that while weakly magnetic white dwarfs of all ages with M < 1Mo show polarimetric variability with a period between hours and several days, the large m

Yohann Bouilly, Gianluca Faraco, Arnaud Maret

We prove that infinite mapping class group orbits are dense in the character variety of Deroin-Tholozan representations. In other words, the action is minimal except for finite orbits. Our arguments rely on the symplectic structure of the character variety, emphasizing this geometric perspective over its algebraic properties.

Hossein Hassani, Ehsan Hallaji, Roozbeh Razavi-Far, Mehrdad Saif

Reinforcement learning (RL) is a sub-domain of machine learning, mainly concerned with solving sequential decision-making problems by a learning agent that interacts with the decision environment to improve its behavior through the reward it receives from the environment. This learning paradigm is, however, well-known for being time-consuming due to the nece

Ciprian Gal, Chandan Ghosh, Sanghwa Park, Devi Adhikari

We propose to measure the beam normal single spin asymmetry in elastic scattering of transversely polarized electron from target nuclei with 12 $\leq Z \leq$ 90 at Q$^2$ = 0.0092 GeV$^2$ to study its nuclear dependence. While the theoretical calculations based on two-photon exchange suggest no nuclear dependence at this kinematics, the results of 208Pb from

Leon J. Sieke, Mattis Harhoff, Sören Schlichting, Lorenz von Smekal

We study the critical dynamics of a scalar field theory with $Z_2$ symmetry in the dynamic universality class of Model A in two and three spatial dimensions with classical-statistical lattice simulations. In particular, we measure the non-equilibrium behavior of the system under a quench protocol in which the symmetry-breaking external field is changed at a

Tommaso Morone, Roberto Tateo

We show how solutions to the Ricci flow on Lorentzian manifolds, along with its generalizations, can be linked to Einstein's field equations. The approach involves deformations of the matter sector that are generated by quadratic functionals of the stress-energy tensor. We provide illustrative examples by explicitly constructing analytical solutions within m

Zixiong Yuan, Wen-Cheng Yue, Peiyuan Huang, Yang-Yang Lyu

Artificial spin ices provide a controlled platform for investigating diverse physical phenomena, such as geometric frustration, magnetic monopoles, and phase transitions, via deliberate design. Here, we introduce a novel approach by developing artificial spin ice on the surfaces of a three-dimensional cube, which leads to emergent geometric frustration media

K. James Sangston

We use the theory of Bernstein functions, completely monotonic functions, and Levy processes to define a positive random process $\tau(t)$. For radar clutter one may think of $\tau(t)$ as the instantaneous power of the scattered radar signal that is described by a compound-Gaussian model. Thus the results herein give a mechanism for defining and simulating a

Tao Wang, Yapeng Li, Zihao Mo, Wesley Cooke

The safety monitoring for nonlinear dynamical systems with embedded neural network components is addressed in this paper. The interval-observer-based safety monitor is developed consisting of two auxiliary neural networks derived from the neural network components of the dynamical system. Due to the presence of nonlinear activation functions in neural networ

Hao Wang, Minghui Liao, Zhouyi Xie, Wenyu Liu

The task of partial scene text retrieval involves localizing and searching for text instances that are the same or similar to a given query text from an image gallery. However, existing methods can only handle text-line instances, leaving the problem of searching for partial patches within these text-line instances unsolved due to a lack of patch annotations

Grigorios Giotopoulos, Hisham Sati, Urs Schreiber

Super $L_\infty$-algebras unify extended super-symmetry with rational classifying spaces for higher flux densities: The super-invariant super-fluxes which control super $p$-branes and their supergravity target super-spaces are, together with their (non-linear) Bianchi identities, neatly encoded in (non-abelian) super-$L_\infty$ cocycles. These are the ration

Zuhra Amiri, Florian Seitz, Janis Nötzel

Low latency and high data rate performance are essential in wireless communication systems. This paper explores trade-offs between latency and data rates for optical wireless communication. We introduce a latency-optimized model utilizing compound codes as one corner case and a data rate-optimized model employing channel estimation via pilot signals and feed

Qi Liu, Yanchen Liu, Ruifeng Li, Chenhong Cao

The integration of intelligent and connected technologies in modern vehicles, while offering enhanced functionalities through Electronic Control Unit (ECU) and interfaces like OBD-II and telematics, also exposes the vehicle's in-vehicle network (IVN) to potential cyberattacks. Unlike prior work, we identify a new time-exciting threat model against IVN. These

Nikolas Adaloglou, Tim Kaiser, Damir Iagudin, Markus Kollmann

Guidance is a widely used technique for diffusion models to enhance sample quality. Technically, guidance is realised by using an auxiliary model that generalises more broadly than the primary model. Using a 2D toy example, we first show that it is highly beneficial when the auxiliary model exhibits similar but stronger generalisation errors than the primary

Yago Herrera, Daniel Muñoz Vela, Glòria Sala, Jordi José

X-Ray bursts (XRBs) are powerful thermonuclear events on the surface of accreting neutron stars (NSs), which can synthesize intermediate-mass elements. Although the high surface gravity prevents an explosive ejection, a small fraction of the envelope may be ejected by radiation-driven winds. In our previous works, we have developed a non-relativistic radiati

Mohammed Yaseen Jabarulla, Theodor Uden, Thomas Jack, Philipp Beerbaum

Pediatric heart diseases present a broad spectrum of congenital and acquired diseases. More complex congenital malformations require a differentiated and multimodal decision-making process, usually including echocardiography as a central imaging method. Artificial intelligence (AI) offers considerable promise for clinicians by facilitating automated interpre

Abdullah Abdullah, Fannya Ratana Sandjaja, Ayesha Abdul Majeed, Gyan Wickremasinghe

This study investigates uncertainty quantification (UQ) using quantum-classical hybrid machine learning (ML) models for applications in complex and dynamic fields, such as attaining resiliency in supply chain digital twins and financial risk assessment. Although quantum feature transformations have been integrated into ML models for complex data tasks, a gap

Hassan Firouzjahi

We calculate the two-loop corrections in primordial power spectrum in models of single field inflation incorporating an intermediate USR phase employed for PBHs formation. Among the total eleven one-particle irreducible Feynman diagrams, we calculate the corrections from the "double scoop" two-loop diagram involving two vertices of quartic Hamiltonians. We d

Jingru Yang, Huan Yu, Yang Jingxin, Chentianye Xu

Multimodal Large Language Models (MLLMs) excel at descriptive tasks within images but often struggle with precise object localization, a critical element for reliable visual interpretation. In contrast, traditional object detection models provide high localization accuracy but frequently generate detections lacking contextual coherence due to limited modelin

Jingru Yang, Chengzhi Cao, Chentianye Xu, Zhongwei Xie

Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs) face inherent challenges in image matting, particularly in preserving fine structural details. ViTs, with their global receptive field enabled by the self-attention mechanism, often lose local details such as hair strands. Conversely, CNNs, constrained by their local receptive field, rely on

Thiebout Delabie, Juhani Koivisto, François Le Maître, Romain Tessera

A well-known result of Shalom says that lattices in SO$(n,1)$ are $\mathrm{L}^p$ measure equivalent for all $p<n-1$. His proof actually yields the following stronger statement: the natural coupling resulting from a suitable choice of fundamental domains from a uniform lattice to a non-uniform one is $(\mathrm{L}^p,\mathrm{L}^{\infty})$. Moreover, it is easy

Paolo Barucca, Carlo Campajola, Jiahua Xu

Forks in the Bitcoin network result from the natural competition in the blockchain's Proof-of-Work consensus protocol. Their frequency is a critical indicator for the efficiency of a distributed ledger as they can contribute to resource waste and network insecurity. We introduce a model for the estimation of natural fork rates in a network of heterogeneous m

Zhiwei Yun

These are lectures notes of my talks at the IHES summer school on the Langlands program in 2022. We give an introduction to the notion of Shtukas, their relation with more familiar geometric objects, their moduli spaces and applications to automorphic forms.

Sourav Bhattacharjee, Piotr Sierant, Marek Dudyński, Jan Wehr

We examine the onset of Anderson localization in three-dimensional systems with structural disorder in the form of lattice irregularities and in the absence of any on-site disordered potential. Analyzing two models with distinct types of lattice regularities, we show that the Anderson localization transition occurs when the strength of the structural disorde

Jiangang Hao, Wenju Cui, Patrick Kyllonen, Emily Kerzabi

Collaborative problem solving (CPS) is widely recognized as a critical 21st-century skill. Assessing CPS depends heavily on coding the communication data using a construct-relevant framework, and this process has long been a major bottleneck to scaling up such assessments. Based on five datasets and two coding frameworks, we demonstrate that ChatGPT can code

A. Feijoo, V. Mantovani Sarti, J. Nieves, A . Ramos

We study the compatibility between the $K^-\Lambda$ correlation function, recently measured by the ALICE collaboration, and the LHCb $K^-\Lambda$ invariant mass distribution obtained in the $\Xi^-_b \to J/\psi \Lambda K^-$ decay. The $K^-\Lambda$ invariant mass distribution associated with the $\Xi^-_b$ decay has been calculated within the framework of Unita

Michele Fossati, Colin Rylands, Pasquale Calabrese

We examine the behavior of the entanglement asymmetry in the ground state of a (1+1)-dimensional conformal field theory with a boundary condition that explicitly breaks a bulk symmetry. Our focus is on the asymmetry of a subsystem $A$ originating from the symmetry-breaking boundary and extending into a semi-infinite bulk. By employing the twist field formali

Jiaping Liao, Shuaizheng Lu, Tao Wang, Weiming Xiang

In this paper, a data-driven approach is developed for controller design for a class of discrete-time large-scale systems, where a large-scale system can be expressed in an equivalent data-driven form and the decentralized controllers can be parameterized by the data collected from its subsystems, i.e., system state, control input, and interconnection input.

Michael Aerni, Javier Rando, Edoardo Debenedetti, Nicholas Carlini

Large language models memorize parts of their training data. Memorizing short snippets and facts is required to answer questions about the world and to be fluent in any language. But models have also been shown to reproduce long verbatim sequences of memorized text when prompted by a motivated adversary. In this work, we investigate an intermediate regime of

C. D. Fosco, G. Hansen

We formulate fermionic versions, for any number of spatial dimensions, of the van der Waals and Casimir-Polder interactions, and study their properties. In both cases, the systems we introduce contain localized `atoms': two-level systems, coupled to a vacuum Dirac field. This Dirac field plays here a role akin to the electromagnetic field in the van der Waal

Yejiang Yang, Zihao Mo, Weiming Xiang

This paper proposes a neural network hybrid modeling framework for dynamics learning to promote an interpretable, computationally efficient way of dynamics learning and system identification. First, a low-level model will be trained to learn the system dynamics, which utilizes multiple simple neural networks to approximate the local dynamics generated from d

Somayeh Ahmadkhani, Katharina Boguslawsk, Paweł Tecmer

In this work, we derive working equations for the Linear Response pair Coupled Cluster Doubles (LR-pCCD) ansatz and its extension to singles (S), LR-pCCD+S. These methods allow us to compute electronic excitation energies and transition dipole moments based on a pCCD reference function. We benchmark the LR-pCCD+S model against the {linear response} coupled-c

Michael Widom

The B-Pt alloy system contains several Pt-rich phases exhibiting complex structures, many with partial site occupation. It also exhibits a deep (nearly 1000 degrees C) eutectic. We evaluate the ab-initio total energies of the crystalline solids to clarify the identity and character of the phases. Our work identifies inconsistencies in materials databases suc

Tao Wang, Xinlin Zhang, Zhenxuan Zhang, Yuanbo Zhou

In clinical medicine, precise image segmentation can provide substantial support to clinicians. However, obtaining high-quality segmentation typically demands extensive pixel-level annotations, which are labor-intensive and expensive. Scribble annotations offer a more cost-effective alternative by improving labeling efficiency. Nonetheless, using such sparse

B. Miao, J. E. Shrock, E. Rockafellow, A. Sloss

Pushing the high energy frontier of laser wakefield electron acceleration (LWFA) to 10 GeV and beyond requires extending the propagation of relativistic intensity pulses to ~1 m in a low density ($N_e\sim 10^{17} cm^{-3}$) plasma waveguide. We present the development and characterization of two types of supersonic gas jet for meter-scale multi-GeV laser wake

Arthur Stéphanovitch

We extend the classical regularity theory of optimal transport to non-optimal transport maps generated by heat flow for perturbations of Gaussian measures. Considering probability measures of the form $d\mu(x) = \exp\left(-\frac{|x|^2}{2} + a(x)\right)dx$ on $\mathbb{R}^d$ where $a$ has H\"older regularity $C^\beta$ with $\beta\geq 0$; we show that the Lange

J. Bieniek, M. Rahouti, D. C. Verma

As the boundaries of human computer interaction expand, Generative AI emerges as a key driver in reshaping user interfaces, introducing new possibilities for personalized, multimodal and cross-platform interactions. This integration reflects a growing demand for more adaptive and intuitive user interfaces that can accommodate diverse input types such as text

David Buchberger, Bernd Schmidt

We derive the time-dependent von K\'arm\'an plate equations from three dimensional, purely atomistic particle models. In particular, we prove that a thin structure of interacting particles whose dynamics is governed by Newton's laws of motion is effectively described by the von K\'arm\'an equations in the limit of vanishing interatomic distance $\eps$ and va

Xingxi Yin, Zhi Li, Jingfeng Zhang, Chenglin Li

Text-to-image (T2I) diffusion models, with their impressive generative capabilities, have been adopted for image editing tasks, demonstrating remarkable efficacy. However, due to attention leakage and collision between the cross-attention map of the object and the new color attribute from the text prompt, text-guided image editing methods may fail to change

Sanath Budakegowdanadoddi Nagaraju, Brian Bernhard Moser, Tobias Christian Nauen, Stanislav Frolov

Transformer-based architectures have recently advanced the image reconstruction quality of super-resolution (SR) models. Yet, their scalability remains limited by quadratic attention costs and coarse patch embeddings that weaken pixel-level fidelity. We propose TaylorIR, a plug-and-play framework that enforces 1x1 patch embeddings for true pixel-wise reasoni

Jana Motowilowa, Maurizio Vergari, Tanja Kojić, Maximilian Warsinke

This study explored a concept for using Augmented Reality (AR) glasses to customize augmented table setup and lighting in a restaurant. The aim was to provide insights into AR usage in restaurants and contribute to existing research by introducing an extendable and versatile concept for scholars and restaurateurs. A controlled laboratory study, using a withi

Hubie Chen, Stefan Mengel

A central computational task in database theory, finite model theory, and computer science at large is the evaluation of a first-order sentence on a finite structure. In the context of this task, the \emph{width} of a sentence, defined as the maximum number of free variables over all subformulas, has been established as a crucial measure, where minimizing wi

Siddhartha Kumar, Mohammad Hossein Moghaddam, Andreas Wolfgang, Tommy Svensson

We consider a mesh network at the edge of a wireless network that connects users to the core network via multiple base stations. For this scenario, we present a novel tree-search-based algorithm that strives to identify effective communication path to the core network for each user by maximizing the signal-to-noise-plus-interference ratio (SNIR) along the ch

Pablo Rosillo-Rodes, Maxi San Miguel, David Sanchez

There are different ways of measuring diversity in complex systems. In particular, in language, lexical diversity is characterized in terms of the type-token ratio and the word entropy. We here investigate both diversity metrics in six massive linguistic datasets in English, Spanish, and Turkish, consisting of books, news articles, and tweets. These gigaword

Alejandro Miranda

We have revisited the isospin-breaking corrections relating $\sigma(e^+e^-\to\pi^+\pi^-)$ and $\Gamma(\tau^-\to\pi^-\pi^0\nu_\tau)$. We confirm that the associated uncertainty is under control, so that tau data can also be used to predict accurately the leading hadronic contribution to the muon anomalous magnetic moment and precision conserved vector current

Matthew Nyaaba, Bismark Nyaaba Akanzire, Macharious Nabang

This study explores the perceptions, challenges, and opportunities associated with using Virtual Reality (VR) as a tool in teacher education among pre-service teachers in a resource-limited setting. Utilizing a qualitative case study design, the study draws on the experiences and reflections of 36 Ghanaian pre-service teachers who engaged with VR in a facili

Jianping Zhang, Qiang Liu, Zhenzhong Hu, Jiarui Lin

Interoperability remains the key problem in multi-discipline collaboration based on building information modeling (BIM). Although various methods have been proposed to solve the technical issues of interoperability, such as data sharing and data consistency; organizational issues, including data ownership and data privacy, remain unresolved to date. These or

Blaž Bortolato, Jernej F. Kamenik, Michele Tammaro

We evaluate the consistency of hadronic interaction models in the CORSIKA simulation package with publicly available fluorescence telescope data from the Pierre Auger Observatory. By comparing the first few central moments of the extended air shower depth maximum distributions, as extracted from measured events, to those predicted by the best-fit inferred co

Yao Lu, Peixin Zhang, Jingyi Wang, Lei Ma

Deep learning has revolutionized computing in many real-world applications, arguably due to its remarkable performance and extreme convenience as an end-to-end solution. However, deep learning models can be costly to train and to use, especially for those large-scale models, making it necessary to optimize the original overly complicated models into smaller

Chang-Gi Lee, Byeong-Gyu Chae, I-Jun Ro, Kyuseon Jang

Atom probe tomography (APT) enables near atomic scale three dimensional elemental mapping through the controlled field evaporation of surface atoms triggered by the combined application of a DC voltage and either voltage or laser pulses. As the selected laser wavelength for the atom probes transitioned from the near-infrared (1050 nm) to shorter wavelengths

Iason Efraimidis, Pavel Gumenyuk

We prove that the sublevel set $\big\{z\in\mathbb D\colon k_{\mathbb D}\big(z,z_0\big)-k_{\mathbb D}\big(f(z),w_0\big)<\mu\big\}$, ${\mu\in\mathbb R}$, is geodesically convex with respect to the Poincar\'e distance $k_{\mathbb D}$ in the unit disk $\mathbb D$ for every ${z_0,w_0\in\mathbb D}$ and every holomorphic ${f:\mathbb D\to\mathbb D}$ if and only if $

Miranda Nichols, D. Hanstorp, R. Cabrera-Trujillo

The charge exchange and energy loss processes provide insights into fundamental processes across physical, chemical, and engineered systems. While this field has been thoroughly investigated, a clear study on trajectory effects is lacking, particularly in the context of inelastic processes at low energies. In this work, we address this gap by solving the tim

Yilin Ye, Adrien Chaigneau, Denis S. Grebenkov

We propose an efficient numerical approach to simulate the boundary local time of reflected Brownian motion, as well as the time and position of the associated reaction event on a smooth boundary of a Euclidean domain. This approach combines the standard walk-on-spheres algorithm in the bulk with the approximate solution of the escape problem in a boundary l

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

An angular analysis of the $B_s^0 \rightarrow \phi e^+e^-$ decay is performed using the proton-proton collision dataset collected between 2011 and 2018 by the LHCb experiment, corresponding to an integrated luminosity of $9\,{\rm fb}^{-1}$ at centre-of-mass energies of 7, 8 and $13\,{\rm TeV}$. The analysis is performed in the very low dielectron invariant m

Federica Stolf, Antonio Canale

Tucker tensor decomposition offers a more effective representation for multiway data compared to the widely used PARAFAC model. However, its flexibility brings the challenge of selecting the appropriate latent multi-rank. To overcome the issue of pre-selecting the latent multi-rank, we introduce a Bayesian adaptive Tucker decomposition model that infers the

Rajiv V. Gavai, Mischa E. Jaensch, Olaf Kaczmarek, Frithjof Karsch

The non-singlet part of the chiral symmetry in QCD with two light flavors is known to be restored through a crossover transition at a pseudo-critical temperature. However, the temperature dependence of the singlet part of the chiral symmetry and whether it is effectively restored at the same temperature is not well understood. Using (2+1)-flavor QCD configur

Riccardo Catena, Taylor R. Gray, Andreas Lund

Fixed target experiments where beam electrons are focused upon a thin target have shown great potential for probing new physics, including the sub-GeV dark matter (DM) paradigm. However, a signal in future experiments such as the light dark matter experiment (LDMX) would require an independent validation to assert its DM origin. To this end, we propose to co

Roger B. Scott, Jeffrey W. Reep, Mark G. Linton, Stephen J. Bradshaw

Thermal nonequilibrium (TNE) is a condition of the plasma in the solar corona in which the local rate of energy loss due to radiation increases to the point that it cannot be sustained by the various heating terms acting on the plasma, precluding the existence of a steady state. The limit cycles of precipitation and evaporation that result from TNE have been

Wannes Goethals, Tom Bultreys, Steffen Berg, Matthieu N. Boone

Time-resolved high-resolution X-ray Computed Tomography (4D $\mu$CT) is an imaging technique that offers insight into the evolution of dynamic processes inside materials that are opaque to visible light. Conventional tomographic reconstruction techniques are based on recording a sequence of 3D images that represent the sample state at different moments in ti

Vu-Anh Le, Haruko Murakami Wainwright, Hansell Gonzalez-Raymat, Carol Eddy-Dilek

Monitored Natural Attenuation (MNA) is gaining prominence as an effective method for managing soil and groundwater contamination due to its cost-efficiency and minimal environmental disruption. Despite its benefits, MNA necessitates extensive groundwater monitoring to ensure that contaminant levels decrease to meet safety standards. This study expands the ca

Rupchand Sutradhar, Gopinath Sadhu, D C Dalal

Hepatitis B virus (HBV) is considered as etiological agent of the lethal liver disease hepatitis B. Globally, hepatitis B is recognized as one of the prevailing infectious diseases with a significant impact on human health. In spite of being non-infectious in nature, sub-viral particles (SVPs) , composed with mainly viral surface proteins, play critical role

Xiangxin Meng, Zexiong Ma, Pengfei Gao, Chao Peng

Large language models (LLMs) and LLM-based Agents have been applied to fix bugs automatically, demonstrating the capability in addressing software defects by engaging in development environment interaction, iterative validation and code modification. However, systematic analysis of these agent systems remain limited, particularly regarding performance variat

Youngjoon Lee, Jinu Gong, Joonhyuk Kang

Given sufficient data from multiple edge devices, federated learning (FL) enables training a shared model without transmitting private data to the central server. However, FL is generally vulnerable to Byzantine attacks from compromised edge devices, which can significantly degrade the model performance. In this work, we propose an intuitive plugin that seam

Xinhua Xie

We theoretically demonstrate x-ray second harmonic generation (SHG) with strong-field-driven high harmonic generation (HHG) from core electrons in gas-phase atoms, highlighting its potential as a powerful spectroscopic tool for studying atomic and molecular systems. Our simulations confirm the generation of HHG with x-ray SHG (HHG-XSHG) from atomic core elec

E. M. de Gouveia Dal Pino, J. C. Rodríguez-Ramírez, M. V. del Valle

Measurements from astroparticle experiments, such as the 2017 flare associated with the source TXS 0506+056, indicate that blazars act as multi-messenger (MM; radiation and neutrinos) factories. Theoretically, the particle acceleration mechanisms responsible for blazar emissions and the precise location within the jet where this occurs remain undetermined. T

Kang Chen, Jiyuan Zhang, Zecheng Hao, Yajing Zheng

Spike cameras, as an innovative neuromorphic camera that captures scenes with the 0-1 bit stream at 40 kHz, are increasingly employed for the 3D reconstruction task via Neural Radiance Fields (NeRF) or 3D Gaussian Splatting (3DGS). Previous spike-based 3D reconstruction approaches often employ a casecased pipeline: starting with high-quality image reconstruc

Filip Jonsson Kling, Samuel Lundqvist, Fatemeh Mohammadi, Matthias Orth

For the almost complete intersection ideals $(x_1^2, \dots, x_n^2, (x_1 + \cdots + x_n)^k)$, we compute their reduced Gr\"obner basis for any term ordering, revealing a combinatorial structure linked to lattice paths, elementary symmetric polynomials, and Catalan numbers. Using this structure, we classify the weak Lefschetz property for these ideals. Additio

Kosuke Tahara, Shin-ichi Tamura, Haruko Toyama, Jotaro J. Nakane

The silicon vacancy center in Silicon Carbide (SiC) provides an optically addressable qubit at room temperature in its spin-$\frac{3}{2}$ electronic state. However, optical spin initialization and readout are less efficient compared to those of spin-1 systems, such as nitrogen-vacancy centers in diamond, under non-resonant optical excitation. Spin-dependent

Alexis Langlois-Rémillard, Élise Raphael, Erika Roldan

Can outreach inspire and lead to research and vice versa? In this work, we introduce our approach to the gamification of research in mathematics and computer science through three illustrative examples. We discuss our primary motivations and provide insights into what makes our proposed gamification effective for three research topics in discrete and computa