December 2024 arXiv papers — page 78

Jose Beltrán Jiménez, Tomi S. Koivisto

The Euclidean path integral approach to quantum gravity is conventionally formulated in terms of the Einstein-Hilbert-York-Gibbons-Hawking action, which requires suitable subtractions to produce the correct black hole partition function. However, there is a unique, canonical teleparallel reformulation which reproduces the same results without subtractions or

Benny Sudakov

A properly edge-colored graph is a graph with a coloring of its edges such that no vertex is incident to two or more edges of the same color. A subgraph is called rainbow if all its edges have different colors. The problem of finding rainbow subgraphs or other restricted structures in edge-colored graphs has a long history, dating back to Euler's work on Lat

Roni Kroll, Yoav Tsori

We present a model for ion-induced nucleation, focusing on the effect of dissociated ions embedded in the fluid surrounding a charged core or colloid. The model includes the ions' direct electrostatic energy and preferential solvation. The integrated ions' free energy has two terms: The first can be short- or long-range, depending on their density. The secon

Gereziher Adhane, Mohammad Mahdi Dehshibi, Dennis Vetter, David Masip

Knowledge distillation (KD) remains challenging due to the opaque nature of the knowledge transfer process from a Teacher to a Student, making it difficult to address certain issues related to KD. To address this, we proposed UniCAM, a novel gradient-based visual explanation method, which effectively interprets the knowledge learned during KD. Our experiment

Beiduo Chen, Siyao Peng, Anna Korhonen, Barbara Plank

Disagreement in human labeling is ubiquitous, and can be captured in human judgment distributions (HJDs). Recent research has shown that explanations provide valuable information for understanding human label variation (HLV) and large language models (LLMs) can approximate HJD from a few human-provided label-explanation pairs. However, collecting explanation

Ewan Cassidy

We extend Friedman's theorem to show that, for any fixed $r>1$, a random $2r$--regular Schreier graph associated with the action of $r$ uniformly random permutations of $[n]$ on $k_{n}$--tuples of distinct elements in $[n]$ has a near--optimal spectral gap with high probability, provided $k_{n}\leq n^{\frac{1}{20}-\epsilon}.$ Previously this was known only f

Bogdan-Vasile Matioc, Lina Sophie Schmitz, Christoph Walker

Quasilinear (and semilinear) parabolic problems of the form $v'=A(v)v+f(v)$ with strict inclusion $\mathrm{dom}(f)\subsetneq \mathrm{dom}(A)$ of the domains of the function $v\mapsto f(v)$ and the quasilinear part $v\mapsto A(v)$ are considered in the framework of time-weighted function spaces. This allows one to establish the principle of linearized stabili

Alexander D. Zemskov, Yao Fu, Runchao Li, Xufei Wang

In Industry 4.0, the digital twin is one of the emerging technologies, offering simulation abilities to predict, refine, and interpret conditions and operations, where it is crucial to emphasize a heightened concentration on the associated security and privacy risks. To be more specific, the adoption of digital twins in the manufacturing industry relies on i

Magnus Christian Ring Merrild, Casper Moldrup Rysgaard, Jens Kristian Refsgaard Schou, Rolf Svenning

In this paper, we study the Contiguous Art Gallery Problem, introduced by Thomas C. Shermer at the 2024 Canadian Conference on Computational Geometry, a variant of the classical art gallery problem from 1973 by Victor Klee. In the contiguous variant, the input is a simple polygon $P$, and the goal is to partition the boundary into a minimum number of polygon

Alan A. Kaptanoglu, Alexander Wiedman, Jacob Halpern, Siena Hurwitz

In this work, we utilize new coil objectives for stellarator optimization with autodifferentiation, including pointwise and net coil-coil forces and torques. We use these methods to perform the first large-scale optimization of planar dipole coil arrays, since arrays of small and geometrically simple coils have been proposed to partially produce the 3D magne

Riccardo Giannini

In the present note, we complete the correspondence between stratum components of translation surfaces in low genus and finite-type Artin groups with defining Dynkin diagram containing $E_6$. In an earlier work, we showed that in genus $3$ the monodromy of the non-hyperelliptic connected components $\mathcal{H}^{\operatorname{odd}}(4)$ and $\mathcal{H}(3,1)$

Malay Pandey, Vaishali Jain, Nimit Godhani, Sachchida Nand Tripathi

In many problem settings that require spatio-temporal forecasting, the values in the time-series not only exhibit spatio-temporal correlations but are also influenced by spatial diffusion across locations. One such example is forecasting the concentration of fine particulate matter (PM2.5) in the atmosphere which is influenced by many complex factors, the mo

O. Adriani, E. Berti, P. Betti, J. Casaus

We present an innovative charge detector with high resolution and wide dynamic range designed to fulfill the requirements of a monitoring system for a high energy ion beam. The detector prototype, constructed using Si photodiodes and a custom readout electronics, underwent extensive testing during HERD and AMS beam tests at CERN SPS facilities. Initial testi

Joanna Reszka, Parvaneh Janbakhshi, Tilak Purohit, Sadegh Mohammadi

In this study, we aim to explore the effect of pre-trained conditional generative speech models for the first time on dysarthric speech due to Parkinson's disease recorded in an ideal/non-noisy condition. Considering one category of generative models, i.e., diffusion-based speech enhancement, these models are previously trained to learn the distribution of c

El Hassan Saidi, Rajae Sammani

We revisit Narain conformal field theories from an algebraic perspective based on finite dimensional Lie algebras $\mathbf{g}$ and representations $\mathcal{R}_{\mathbf{g}}$, and show how the root and weight lattices can encode the momenta and subsequently the partition functions of Narain theories. In this framework, we construct a realisation of the Zamolo

Sebastian Chenery, Stephen Theriault

Gyrations are operations on manifolds that arise in geometric topology, where a manifold $M$ may exhibit distinct gyrations depending on the chosen twisting. For a given $M$, we ask a natural question: do all gyrations of $M$ share the same homotopy type regardless of the twisting? A manifold with this property is said to have gyration stability. Inspired by

José Álamos, Thomas C. Schmidt, Matthias Wählisch

Long range communication with LoRa has become popular as it avoids the complexity of multi-hop communication at low cost and low energy consumption. LoRa is openly accessible, but its packets are particularly vulnerable to collisions due to long time on air in a shared band. This degrades communication performance. Existing techniques for demodulating LoRa s

Ismaïla Balogoun, Jean Auriol, Islam Boussaada, Guilherme Mazanti

In this paper, we establish a necessary and sufficient stability condition for a class of two coupled first-order linear hyperbolic partial differential equations. Through a backstepping transform, the problem is reformulated as a stability problem for an integral difference equation, that is, a difference equation with distributed delay. Building upon a St\

Tyler Maunu, Jiayi Yao

Sampling from high-dimensional distributions has wide applications in data science and machine learning but poses significant computational challenges. We introduce Subspace Langevin Monte Carlo (SLMC), a novel and efficient sampling method that generalizes random-coordinate Langevin Monte Carlo and preconditioned Langevin Monte Carlo by projecting the Lange

Hao-Jie Lin, Song-Bo Zhang, Hai-Zhou Lu, X. C. Xie

An altermagnet is a newly discovered antiferromagnet, characterized by unique anisotropic spin-split energy bands. It has attracted tremendous interest, because of its promising potential in information storage and processing. However, measuring the distinctive spin-split energy bands arising from altermagnetism remains a challenge. Here, we propose to emplo

Ashkan Zarezadeh, Behrooz Khosravi, Zeinab Akhlaghi

Given a finite group G with an irreducible character \chi \in Irr(G), the codegree of \chi is defined by cod(\chi) = |G :\ker \chi|/\chi(1). The set of non-linear irreducible character codegrees of G is denoted by cod(G|G'). In this note, we classify all finite groups G with |cod(G|G')|> 1 and for each pair of distinct elements m, n \in cod(G|G'), m and n ar

Ibrahim Merad, Amos Wolf, Ziad Mazzawi, Yannick Léo

In the quest to overcome language barriers, encoder-decoder models like NLLB have expanded machine translation to rare languages, with some models (e.g., NLLB 1.3B) even trainable on a single GPU. While general-purpose LLMs perform well in translation, open LLMs prove highly competitive when fine-tuned for specific tasks involving unknown corpora. We introdu

Ingrid Beltita, Daniel Beltita

We prove that if a connected and simply connected Lie group $G$ admits connected closed normal subgroups $G_1\subseteq G_2\subseteq \cdots \subseteq G_m=G$ with $\dim G_j=j$ for $j=1,\dots,m$, then its group $C^*$-algebra has closed two-sided ideals $\{0\}=\mathcal{J}_0\subseteq \mathcal{J}_1\subseteq\cdots\subseteq\mathcal{J}_n=C^*(G)$ with $\mathcal{J}_j/\

Ander Corral, Ixak Sarasua, Xabier Saralegi

Large language models (LLMs) are typically optimized for resource-rich languages like English, exacerbating the gap between high-resource and underrepresented languages. This work presents a detailed analysis of strategies for developing a model capable of following instructions in a low-resource language, specifically Basque, by focusing on three key stages

Oliver Goldstein, Ohad Kammar

Probabilistic programming languages, which exist in abundance, are languages that allow users to calculate probability distributions defined by probabilistic programs, by using inference algorithms. However, the underlying inference algorithms are not implemented in a modular fashion, though, the algorithms are presented as a composition of other inference c

Giovanni Battista Carollo, Giuseppe Gonnella, Daniela Moretti, Antonio Suma

We study the behaviour of a Brownian particle in the overdamped regime in the presence of a harmonic potential, assuming its diffusion coefficient to randomly jump between two distinct values. In particular, we characterize the probability distribution of the particle position and provide detailed expressions for the mean square displacement and the kurtosis

Maojun Sun, Ruijian Han, Binyan Jiang, Houduo Qi

In recent years, data science agents powered by Large Language Models (LLMs), known as "data agents," have shown significant potential to transform the traditional data analysis paradigm. This survey provides an overview of the evolution, capabilities, and applications of LLM-based data agents, highlighting their role in simplifying complex data tasks and lo

Alfonso F. Bobadilla, Jose A. R. Cembranos

We investigate the application of deformation quantization to the system of a free particle evolving within a universe described by a Friedmann-Lemaitre-Robertson-Walker (FLRW) geometry. This approach allows us to analyze the dynamics of classical and quantum phase-space distributions in curved spacetime. We demonstrate that when the curvature of the spatial

Jean-Pierre Gazeau, Tomoi Koide, Romain Murenzi, Aidan Zlotak

We present an application of the affine covariant integral quantization (ACIQ) (Adv. Oper. Theory, 5, 2020; Adv. Oper. Theory, 7, 2022) to quantum mechanics on the punctured plane. The associated four-dimensional phase space is identified with the similitude group SIM(2), which comprises translations, rotations, and dilations of the plane. Due to the topolog

Matthias Barkowsky, Holger Giese

The growing size of graph-based modeling artifacts in model-driven engineering calls for techniques that enable efficient execution of graph queries. Incremental approaches based on the RETE algorithm provide an adequate solution in many scenarios, but are generally designed to search for query results over the entire graph. However, in certain situations, a

Shengpeng Ji, Ziyue Jiang, Jialong Zuo, Minghui Fang

Speech watermarking techniques can proactively mitigate the potential harmful consequences of instant voice cloning techniques. These techniques involve the insertion of signals into speech that are imperceptible to humans but can be detected by algorithms. Previous approaches typically embed watermark messages into continuous space. However, intuitively, em

Haolin Wang, Ming Liu, Zifei Yan, Chao Zhou

When embedding objects (foreground) into images (background), considering the influence of photography conditions like illumination, it is usually necessary to perform image harmonization to make the foreground object coordinate with the background image in terms of brightness, color, and etc. Although existing image harmonization methods have made continuou

Lai Kin Man, Xin Wang

This paper introduces an algorithm designed to approximate quantum transformation matrix with a restricted number of gates by using the block decomposition technique. Addressing challenges posed by numerous gates in handling large qubit transformations, the algorithm provides a solution by optimizing gate usage while maintaining computational accuracy. Inspi

David Lenze

We describe the isometry group of $L^2(\Omega, M)$ for Riemannian manifolds $M$ of dimension at least two with irreducible universal cover. We establish a rigidity result for the isometries of these spaces: any isometry arises from an automorphism of $\Omega$ and a family of isometries of $M$, distinguishing these spaces from the classical $L^2(\Omega)$. Add

Fu Wang, Yanghao Zhang, Xiangyu Yin, Guangliang Cheng

Camera-based Bird's Eye View (BEV) perception models receive increasing attention for their crucial role in autonomous driving, a domain where concerns about the robustness and reliability of deep learning have been raised. While only a few works have investigated the effects of randomly generated semantic perturbations, aka natural corruptions, on the multi

Zidong Han, Ruibo Jin, Xiaoyang Li, Bingpeng Zhou

To support future spatial machine intelligence applications, lifelong simultaneous localization and mapping (SLAM) has drawn significant attentions. SLAM is usually realized based on various types of mobile robots performing simultaneous and continuous sensing and communication. This paper focuses on analyzing the energy efficiency of robot operation for lif

Jhonatan Tavori, Hanoch Levy

Deployment of anti-virus software is a common strategy for preventing and controlling the propagation of computer viruses and worms over a computer network. As the deployment of such programs is often limited due to monetary or operational costs, devising optimal strategies for their allocation and deployment can be of high value to the operation, performanc

Felix B. Müller, Georgios Ctistis

This work introduces and characterizes a theoretical model of a reflective polarimetric measurement technique determining the surface stress of a dielectric material, e.g. glass. We have developed a procedure to reconstruct the actual stress state, which is the orientation and value of the principal axes of stress, from the calculated Stokes vector component

Jonathan Clivio

We construct a PROP which encodes 2D-TQFTs with a grading. This defines a graded Frobenius algebra as algebras over this PROP. We also give a description of graded Frobenius algebras in terms of maps and relations. This structure naturally arises as the cohomology of manifolds, loop homology and Hochschild homology of Frobenius algebras. In addition, we give

Xinyuan Shao, Yiqing Shen, Mathias Unberath

Segment Anything Models (SAMs) have gained increasing attention in medical image analysis due to their zero-shot generalization capability in segmenting objects of unseen classes and domains when provided with appropriate user prompts. Addressing this performance gap is important to fully leverage the pre-trained weights of SAMs, particularly in the domain o

Kai Buckenmaier, Richard Neumann, Friedemann Bullinger, Nicolas Kempf

This study pioneers the two-field correlation spectroscopy (COSY) in zero to ultralow field (ZULF) liquid state NMR, employing hyperpolarized [1-$^{13}$C]pyruvate as a model system. We demonstrate the successful integration of signal amplification by reversible exchange (SABRE) for hyperpolarization, enabling the detection of ZULF COSY spectra with increased

Giuseppe Cotardo, Alberto Ravagnani, Ferdinando Zullo

We investigate the Whitney numbers of the first kind of rank-metric lattices, which are closely linked to the open problem of enumerating rank-metric codes having prescribed parameters. We apply methods from the theory of hyperovals and linear sets to compute these Whitney numbers for infinite families of rank-metric lattices. As an application of our result

Jiamin Shen, Yao Chen, Weng-Fai Wong, Ee-Chien Chang

Heterogeneous computing, which incorporates GPUs, NPUs, and FPGAs, is increasingly utilized to improve the efficiency of computer systems. However, this shift has given rise to significant security and privacy concerns, especially when the execution platform is remote. One way to tackle these challenges is to establish a trusted and isolated environment for

Xenia de la Ossa, Mateo Galdeano, Enrico Marchetto

We explore the connection between super $\mathcal{W}$-algebras ($\mathcal{SW}$-algebras) and $\mathrm{G}$-structures with torsion. The former are realised as symmetry algebras of strings with $\mathcal{N}=(1,0)$ supersymmetry on the worldsheet, while the latter are associated with generic string backgrounds with non-trivial Neveu-Schwarz flux $H$. In particu

Dalimil Peša

This paper explores the interactions of absolute continuity of the (quasi)norm with the concepts that are fundamental in the theory of rearrangement-invariant (quasi-)Banach function spaces, such as the Luxemburg representation or the Hardy--Littlewood--P{\' o}lya relation. In order to prove our main results, we give an explicit construction of a particularl

Zihao Zheng, Yuanchun Li, Jiayu Chen, Peng Zhou

Enhancing the computational efficiency of on-device Deep Neural Networks (DNNs) remains a significant challengein mobile and edge computing. As we aim to execute increasingly complex tasks with constrained computational resources, much of the research has focused on compressing neural network structures and optimizing systems. Although many studies have focu

Frej Dahlin

Given a reproducing kernel $k$ on a nonempty set $X$, we define the reproductive boundary of $X$ with respect to $k$. Furthermore, we generalize the well known nontangential and horocyclic approach regions of the unit circle to this new kind of boundary. We also introduce the concept of a composition factor of $k$, an abstract analogue of analytic selfmaps o

Mathis Cohen, Laurent Labonté, Romain Dalidet, Sébastien Tanzilli

The interference between two independent photons stands as a crucial aspect of numerous quantum information protocols and technologies. In this work, we leverage fiber-coupled devices, which encompass fibered photon pair-sources and off-the-shelf optics, to demonstrate Hong-Ou-Mandel interference. We employ two distinct single photon sources, namely an heral

Nicéphore Bonnet, Nicola Marzari

A first-principles approach for calculating ion separation in solution through two-dimensional (2D) membranes is proposed and applied. Ionic energy profiles across the membrane are obtained first, where solvation effects are simulated explicitly with machine-learning molecular dynamics, electrostatic corrections are applied to remove finite-size capacitive e

Alejandro Cholaquidis, Antonio Cuevas, Beatriz Pateiro-López

The problem of estimating, from a random sample of points, the dimension of a compact subset $S$ of the Euclidean space is considered. The emphasis is put on consistency results in the statistical sense. That is, statements of convergence to the true dimension value when the sample size grows to infinity. Among the many available definitions of dimension, we

Imanol Pinto, Álvaro Olazarán, David Jurío, Borja de la Osa

Background: The worst outcomes of diabetic retinopathy (DR) can be prevented by implementing DR screening programs assisted by AI. At the University Hospital of Navarre (HUN), Spain, general practitioners (GPs) grade fundus images in an ongoing DR screening program, referring to a second screening level (ophthalmologist) target patients. Methods: After colle

Yuqiu Liu, Jingxuan Xu, Mauricio Soroco, Yunchao Wei

Recent developments in 3D vision have enabled significant progress in inferring neural fluid fields and realistic rendering of fluid dynamics. However, these methods require dense captures of real-world flows, which demand specialized laboratory setups, making the process costly and challenging. Scientific machine learning (SciML) foundation models, pretrain

Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Mehrdad Karrabi, Harshit J Motwani

The problem of checking satisfiability of linear real arithmetic (LRA) and non-linear real arithmetic (NRA) formulas has broad applications, in particular, they are at the heart of logic-related applications such as logic for artificial intelligence, program analysis, etc. While there has been much work on checking satisfiability of unquantified LRA and NRA

Guilherme Catumba, Atsuki Hiraguchi, George W. -S Hou, Karl Jansen

The custodial Two-Higgs-Doublet-Model with SU(2) gauge fields is studied on the lattice. This model has the same global symmetry structure as the Standard Model but the additional Higgs field enlarges the scalar spectrum and opens the possibility for the occurrence of spontaneous symmetry breaking of the global symmetries. Both the spectrum and the running o

T. Laitinen, S. Dalla

The gradient and curvature of the Parker spiral interplanetary magnetic field give rise to curvature and gradient guiding centre drifts on cosmic rays. The plasma turbulence present in the interplanetary space is thought to suppress the drifts, however the extent to which they are reduced is not clear. We investigate the reduction of the drifts using a new a

Antony Lewis, Ewan Chamberlain

The cosmic microwave background (CMB) and baryon acoustic oscillations (BAO) provide precise measurements of the cosmic expansion history through the comoving acoustic scale. The CMB angular scale measurement $\theta_*$ is particularly robust, constraining the ratio of the sound horizon to the angular diameter distance to last scattering independently of the

Vida Dujmović, Gwenaël Joret, Piotr Micek, Pat Morin

We prove that there exist functions $f,g:\mathbb{N}\to\mathbb{N}$ such that for all nonnegative integers $k$ and $d$, for every graph $G$, either $G$ contains $k$ cycles such that vertices of different cycles have distance greater than $d$ in $G$, or there exists a subset $X$ of vertices of $G$ with $|X|\leq f(k)$ such that $G-B_G(X,g(d))$ is a forest, where

Sotiris A. Tegos, Panagiotis D. Diamantoulakis, Zhiguo Ding, George K. Karagiannidis

This paper addresses, for the first time, the uplink performance optimization of multi-user pinching-antenna (PA) systems, recently developed for next-generation wireless networks. By leveraging the unique capabilities of PAs to dynamically configure wireless channels, we focus on maximizing the minimum achievable data rate between devices to achieve a balan

Wenchang Duan, Zhenguo Gao, Jiwan He, Jinguo Xian

Adaptive Traffic Signal Control (ATSC) system is a critical component of intelligent transportation, with the capability to significantly alleviate urban traffic congestion. Although reinforcement learning (RL)-based methods have demonstrated promising performance in achieving ATSC, existing methods are still prone to making unreasonable policies. Therefore,

Ding Wang, Lei Wang, Huilin Yin, Guoqing Gu

Accurately identifying gas mixtures and estimating their concentrations are crucial across various industrial applications using gas sensor arrays. However, existing models face challenges in generalizing across heterogeneous datasets, which limits their scalability and practical applicability. To address this problem, this study develops two novel deep-lear

Andrei Gaidash, Alexei D. Kiselev, Anton Kozubov, George Miroshnichenko

We develop the algebraic method based on the Lie algebra of quadratic combinations of left and right superoperators associated with matrices to study the Lindblad dynamics of multimode bosonic systems coupled a thermal bath and described by the Liouvillian superoperator that takes into account both dynamical (coherent) and environment mediated (incoherent) i

Hua Yue, Jianli Zhang, Yuhai Ge, Lin Nie

``PeVatrons" refer to astrophysical sources capable of accelerating particles to energies $\sim$PeV and higher, potentially contributing to the cosmic ray spectrum in the knee region. Recently, HAWC and LHAASO have discovered a new type PeVatrons -- X-ray binaries, allowing us to investigate in greater depth of the contributions of these sources to cosmic ra

Saman Ahmadi, Andrea Raith, Guido Tack, Mahdi Jalili

The classic Resource Constrained Shortest Path (RCSP) problem aims to find a cost optimal path between a pair of nodes in a network such that the resources used in the path are within a given limit. Having been studied for over a decade, RCSP has seen recent solutions that utilize heuristic-guided search to solve the constrained problem faster. Building upon

Hadi Hosseini, Shivika Narang, Sanjukta Roy

We initiate the study of matching roommates and rooms wherein the preferences of agents over other agents and rooms are complementary and represented by Leontief utilities. In this setting, 2n agents must be paired up and assigned to n rooms. Each agent has cardinal valuations over the rooms as well as compatibility values over all other agents. Under Leonti

Petr S. Bednyakov, Petr V. Yudin, Alexander K. Tagantsev, Jiří Hlinka

It is known that ferroelectric single crystals can be turned from a polydomain to a monodomain state by the application of an electric field. Here we report an unexpected opposite effect: the formation of through-the-crystal polydomain pattern in a monodomain BaTiO3 crystal in response to the applied electric field favoring the initial orientation of the pol

Aleksandr Azatov, Quoc Trung Ho, Mohamed Mahdi Khalil

We analyze in detail the interactions between non-topological soliton (Q-ball) and its perturbations. We extend the previous literature by carefully identifying the domain of applicability of linear analysis as well discussion of the FLS Q-balls. Applications to the early universe physics are briefly commented.

Ammar Ahmed, Ali Shariq Imran, Mohib Ullah, Zenun Kastrati

The exploration of automated wrist fracture recognition has gained considerable research attention in recent years. In practical medical scenarios, physicians and surgeons may lack the specialized expertise required for accurate X-ray interpretation, highlighting the need for machine vision to enhance diagnostic accuracy. However, conventional recognition te

Martin W. Sommer, Tim Schrabback, Sebastian Grandis

Galaxy cluster masses estimated from parametric modeling of weak lensing shear observations are known to be biased by inaccuracies in observationally determined centers. It has recently been shown that such systematic effects can be non-isotropic when centers are derived from X-ray or Compton-Y (Sunyaev-Zeldovich effect) observations, which is often the case

Mihai C. Suster, Aleksandra Szymańska, Tomasz J. Antosiewicz, Agata Królikowska

Development of fabrication protocols for large-area plasmonic nanostructures with sub-10 nm gaps with a spatially controlled distribution is critical for their real-world applications. In this work, we develop a simple, cleanroom-free protocol for the fabrication of macroscopic-sized plasmonic substrates (>6 cm^2), featuring a tunable multi-resonance optical

Vageesh Saxena, Sharid Loáiciga, Nils Rethmeier

Neural networks have demonstrated significant advancements in Neural Machine Translation (NMT) compared to conventional phrase-based approaches. However, Multilingual Neural Machine Translation (MNMT) in extremely low-resource settings remains underexplored. This research investigates how knowledge transfer across languages can enhance MNMT in such scenarios

Shalini Saini, Anitha Chennamaneni, Babatunde Sawyerr

Deep learning solutions are instrumental in cybersecurity, harnessing their ability to analyze vast datasets, identify complex patterns, and detect anomalies. However, malevolent actors can exploit these capabilities to orchestrate sophisticated attacks, posing significant challenges to defenders and traditional security measures. Adversarial attacks, partic

Yuanhe Zhang, Zhenhong Zhou, Wei Zhang, Xinyue Wang

Large Language Models (LLMs) have demonstrated remarkable performance across diverse tasks yet still are vulnerable to external threats, particularly LLM Denial-of-Service (LLM-DoS) attacks. Specifically, LLM-DoS attacks aim to exhaust computational resources and block services. However, existing studies predominantly focus on white-box attacks, leaving blac

Caitlin Jones, Nico Kraus, Pallavi Bhardwaj, Maximilian Adler

Time series forecasting is a valuable tool for many applications, such as stock price predictions, demand forecasting or logistical optimization. There are many well-established statistical and machine learning models that are used for this purpose. Recently in the field of quantum machine learning many candidate models for forecasting have been proposed, ho

Kun Wu, Chengkai Hou, Jiaming Liu, Zhengping Che

In this paper, we introduce RoboMIND (Multi-embodiment Intelligence Normative Data for Robot Manipulation), a dataset containing 107k demonstration trajectories across 479 diverse tasks involving 96 object classes. RoboMIND is collected through human teleoperation and encompasses comprehensive robotic-related information, including multi-view observations, p

Simon Badger, Matteo Becchetti, Colomba Brancaccio, Heribertus Bayu Hartanto

We present the first benchmark evaluation of the two-loop finite remainders for the production of a top-quark pair in association with a jet at hadron colliders in the gluon channel. We work in the leading colour approximation, and perform the numerical evaluation in the physical phase space. To achieve this result, we develop a new method for expressing the

Mohammed Saqr, Daryn Dever, Sonsoles López-Pernas, Christophe Gernigon

Traditional methods in educational research often fail to capture the complex and evolving nature of learning processes. This chapter examines the use of complex systems theory in education to address these limitations. The chapter covers the main characteristics of complex systems such as non-linear relationships, emergent properties, and feedback mechanism

Jaeyoon Kim, Yoonki Cho, Taeyoung Kim, Sung-Eui Yoon

Nearest neighbor (NN) graph based visual re-ranking has emerged as a powerful approach for improving retrieval accuracy, offering the advantages of effectively exploring high-dimensional manifolds without requiring additional fine-tuning. However, the effectiveness of NN graph-based re-ranking is fundamentally constrained by the quality of its edge connectiv

Baptiste Cerclé, Nathan Huguenin

This article is the first of a two-part series dedicated to studying the symmetries enjoyed by the probabilistic construction of the $\mathfrak{sl}_3$ boundary Toda Conformal Field Theory. Namely in the present document we show that this model enjoys higher-spin symmetry in the form of Ward identities, both local and global. To do so we consider the $\mathfr

Le-Anh Tran, Dong-Chul Park

This paper proposes a lightweight neural network designed for realistic image dehazing, utilizing a Distilled Pooling Transformer Encoder, named DPTE-Net. Recently, while vision transformers (ViTs) have achieved great success in various vision tasks, their self-attention (SA) module's complexity scales quadratically with image resolution, hindering their app

Jianping Li, Xinhang Xu, Jinxin Liu, Kun Cao

Accurate and comprehensive 3D sensing using LiDAR systems is crucial for various applications in photogrammetry and robotics, including facility inspection, Building Information Modeling (BIM), and robot navigation. Motorized LiDAR systems can expand the Field of View (FoV) without adding multiple scanners, but existing motorized LiDAR systems often rely on

Khan Reaz, Gerhard Wunder

Formal verification is a critical process in ensuring the security and correctness of cryptographic protocols, particularly in high-assurance domains. This paper presents a comprehensive formal analysis of the Permission Voucher Protocol, a system designed for secure and authenticated access control in distributed environments. The analysis employs the Tamar

Jiacheng Liu, Peng Tang, Wenfeng Wang, Yuhang Ren

The emergence of large-scale Mixture of Experts (MoE) models represents a significant advancement in artificial intelligence, offering enhanced model capacity and computational efficiency through conditional computation. However, deploying and running inference on these models presents significant challenges in computational resources, latency, and energy ef

CMS Collaboration

In the standard model of particle physics, the masses of the W and Z bosons, the carriers of the weak interaction, are uniquely related. A precise determination of their masses is important because quantum loops of heavy, undiscovered particles could modify this relationship. Although the Z mass is known to the remarkable precision of 22 parts per million (2

Yipeng Zhang, Yifan Liu, Zonghao Guo, Yidan Zhang

Vision transformers (ViTs) are widely employed in multimodal large language models (MLLMs) for visual encoding. However, they exhibit inferior performance on tasks regarding fine-grained visual perception. We attribute this to the limitations of ViTs in capturing diverse multi-modal visual levels, such as low-level details. To address this issue, we present

Thomas W. J. Metzger, Peter Fischer, Takashi Kikkawa, Eiji Saitoh

Femtosecond laser excitation of nanometer thin heterostructures comprising a heavy metal and a magnetically ordered material is known to result in the emission of terahertz radiation. However, the nature of the emitted radiation from heavy metal~/~antiferromagnet heterostructures has sparked debates and controversies in the literature. Here, we unambiguously

A. Lapchuk, O. Prygun, D. Manko, I. Gorbov

To effectively apply passive speckle reduction methods, it is essential to use an illumination system that maximally exploits the non-ideal temporal coherence and angular diversity (spatial coherence reduction) of laser light. This study examines the necessary conditions for these factors to act independently to achieve maximum speckle reduction. A novel des

Marius Lemm, Simone Rademacher, Jingxuan Zhang

We study the quantum many-body dynamics of a Bose-Einstein condensate (BEC) on the lattice in the mean-field regime. We derive a local enhancement of the mean-field approximation: At positive distance $\rho>0$ from the initial BEC, the mean-field approximation error at time $t\leq \rho/v$ is bounded as $\rho^{-n}$, for arbitrarily large $n\geq 1$. This is a

Tulio Ottoni, Jaziel G. Coelho, Rafael C. R. de Lima, Jonas P. Pereira

The strong gravitational potential of neutron stars (NSs) makes them ideal astrophysical objects for testing extreme gravity phenomena. We explore the potential of NS X-ray pulsed lightcurve observations to probe deviations from general relativity (GR) within the scalar-tensor theory (STT) of gravity framework. We compute the flux from a single, circular, fi

Olivier Letoffe, Xuanxiang Huang, Joao Marques-Silva

The ubiquitous use of Shapley values in eXplainable AI (XAI) has been triggered by the tool SHAP, and as a result are commonly referred to as SHAP scores. Recent work devised examples of machine learning (ML) classifiers for which the computed SHAP scores are thoroughly unsatisfactory, by allowing human decision-makers to be misled. Nevertheless, such exampl

Andreea Elena Dragnoiu, Ruxandra F. Olimid

Traditional identity management systems, often centralized, face challenges around privacy, data security, and user control, leaving users vulnerable to data breaches and misuse. This paper explores the potential of using the Arweave network to develop an identity management solution. By harnessing Arweave's permanent storage, our solution offers the users a

Jai Bardhan, Cyrin Neeraj, Mihir Rawat, Subhadip Mitra

Machine learning methods have seen a meteoric rise in their applications in the scientific community. However, little effort has been put into understanding these "black box" models. We show how one can apply integrated gradients (IGs) to understand these models by designing different baselines, by taking an example case study in particle physics. We find th

Kevin T. Grosvenor, Niels A. Obers, Subodh P. Patil

We derive the hydrodynamic equations of perfect fluids without boost invariance [1] from kinetic theory. Our approach is to follow the standard derivation of the Vlasov hierarchy based on an a-priori unknown collision functional satisfying certain axiomatic properties consistent with the absence of boost invariance. The kinetic theory treatment allows us to

Maria Ramos, Tanweer Ahmed, Bao Q. Tu, Eleni Chatzikyriakou

The bulk photovoltaic effect (BPVE) offers a promising avenue to surpass the efficiency limitations of current solar cell technology. However, disentangling intrinsic and extrinsic contributions to photocurrent remains a significant challenge. Here, we fabricate high-quality, lateral devices based on atomically thin ReS2 with minimal contact resistance, prov

Yuzhong Hong, Hanshan Zhang, Junwei Bao, Hongfei Jiang

Since the debut of DPO, it has been shown that aligning a target LLM with human preferences via the KL-constrained RLHF loss is mathematically equivalent to a special kind of reward modeling task. Concretely, the task requires: 1) using the target LLM to parameterize the reward model, and 2) tuning the reward model so that it has a 1:1 linear relationship wi

C. S. Chisholm, S. Hirthe, V. B. Makhalov, R. Ramos

Spin-orbit-coupled Bose-Einstein condensates are a flexible experimental platform to engineer synthetic quantum many-body systems. In particular, they host the so-called stripe phase, an instance of a supersolid state of matter. The peculiar excitation spectrum of the stripe phase, a definite footprint of its supersolidity, has been difficult to measure expe

Sharad Duwal, Suraj Prasai, Suresh Manandhar

Continual learning has emerged as an important research direction due to the infeasibility of retraining large language models (LLMs) from scratch in the event of new data availability. Of great interest is the domain-adaptive pre-training (DAPT) paradigm, which focuses on continually training a pre-trained language model to adapt it to a domain it was not o

Ronen Brilleslijper, Oliver Fabert

In symplectic topology one uses elliptic methods to prove rigidity results about symplectic manifolds and solutions of Hamiltonian equations on them, where the most basic example is given by geodesics on Riemannian manifolds. Harmonic maps from surfaces are the natural 2-dimensional generalizations of geodesics. In this paper, we give the corresponding gener

Martina Fraia, Nadia Loy, Andrea Tosin

In this paper, we introduce a kinetic model which describes a learning process leading individuals to build personal awareness about fake news. Next, we embed the results of this model into another kinetic model, which describes the popularity gained by news on social media conditioned to the reliability of the disseminated information. Both models are formu

Anna Scius-Bertrand, Michael Jungo, Lars Vögtlin, Jean-Marc Spat

Classifying scanned documents is a challenging problem that involves image, layout, and text analysis for document understanding. Nevertheless, for certain benchmark datasets, notably RVL-CDIP, the state of the art is closing in to near-perfect performance when considering hundreds of thousands of training samples. With the advent of large language models (L

Pau Cano, Eva Musulen, Debora Gil

Purpose: This work addresses the detection of Helicobacter pylori (H. pylori) in histological images with immunohistochemical staining. This analysis is a time demanding task, currently done by an expert pathologist that visually inspects the samples. Given the effort required to localise the pathogen in images, a limited number of annotations might be avail