November 2024 arXiv papers — page 55

Ben Wang, Kaimin Zheng, Qian Xie, Aonan Zhang

The estimation of multiple parameters is a ubiquitous requirement in many quantum metrology applications. However, achieving the ultimate precision limit, i.e. the quantum Cram\'er-Rao bound, becomes challenging in these scenarios compared to single parameter estimation. To address this issue, optimizing the parameters encoding strategies with the aid of ant

Zhijie Fan, Ji Li, Fedor Sukochev, Dmitriy Zanin

Let $R_{\lambda,j}$ be the $j$-th Bessel--Riesz transform, where $n\geq 1$, $\lambda>0$, and $j=1,\ldots,n+1$. In this article, we establish a Weyl type asymptotic for $[M_f,R_{\lambda,j}]$, the commutator of $R_{\lambda,j}$ with multiplication operator $M_f$, based on building a preliminary result that the endpoint weak Schatten norm of $[M_f,R_{\lambda,j}]

Zhenwei Yang, Jilei Mao, Wenxian Yang, Yibo Ai

Temporal perception, defined as the capability to detect and track objects across temporal sequences, serves as a fundamental component in autonomous driving systems. While single-vehicle perception systems encounter limitations, stemming from incomplete perception due to object occlusion and inherent blind spots, cooperative perception systems present their

Idir Arab, Tommaso Lando, Paulo Eduardo Oliveira

Stochastic dominance of a random variable by a convex combination of its independent copies has recently been shown to hold within the relatively narrow class of distributions with concave odds function, and later extended to broader families of distributions. A simple consequence of this surprising result is that the sample mean can be stochastically larger

Linqi Lu, Yifan Deng, Chuan Tian, Sijia Yang

This study introduces Purrfessor, an innovative AI chatbot designed to provide personalized dietary guidance through interactive, multimodal engagement. Leveraging the Large Language-and-Vision Assistant (LLaVA) model fine-tuned with food and nutrition data and a human-in-the-loop approach, Purrfessor integrates visual meal analysis with contextual advice to

Ingrid Zukerman

In this report, I provide a brief summary of the literature in philosophy, psychology and cognitive science about Explanatory Virtues, and link these concepts to eXplainable AI.

Reymond Akpanya, Vanishree Krishna Kirekod, Alice C. Niemeyer, Daniel Robertz

In 1957, Steinhaus proved that a chain of regular tetrahedra, meeting face-to-face and forming a closed loop does not exist. Over the years, various modifications of this statement have been considered and analysed. Weakening the statement by only requiring the tetrahedra of a chain to be wild, i.e. having all faces congruent, results in various examples of

Mohammed Akkaoui

Muscular Dystrophy is a group of genetic disorders that progressively affect the strength and functioning of muscles, thereby affecting millions of people worldwide. The lifetime nature of MD requires continuous follow-up care due to its progressive nature. This conceptual paper proposes an Internet of Things-based system to support the management of MD thro

Zewen Long, Liang Wang, Shu Wu, Qiang Liu

With their vast open-world knowledge and reasoning abilities, large language models (LLMs) have become a promising tool for sequential recommendation. Researchers have explored various methods to harness these capabilities, but most existing approaches rely on simple input-output prompting, failing to effectively bridge the gap between LLMs' general knowledg

Yifan Gao, Xinyi Li, Yifan Li, Runsheng Liu

We show that a domain in $\mathbb{R}^3$ with the trace of a 3D Brownian motion removed almost surely satisfies the boundary Harnack principle (BHP). Then, we use it to prove that the intersection exponents for 3D Brownian motion are analytic.

M. E. Kutepov, V. E. Kaydashev, D. V. Stryukov, A. S. Konstantinov

Vanadium dioxide with metal-to-insulator transition (MIT) that is triggered by heat, current or light is a promising material for modern active THz/mid-IR metasurfaces and all-optical big data processing systems. Multilayer VO2-based active metasurfaces are urgently needed however several important issues related to VO2 properties in VO2/TiO2/Al2O3 films sho

Zhaolin Wang, Chongjun Ouyang, Yuanwei Liu

The optimal beamforming design for multi-user continuous aperture array (CAPA) systems is proposed. In contrast to conventional spatially discrete array (SPDA), the beamformer for CAPA is a continuous function rather than a discrete vector or matrix, rendering beamforming optimization a non-convex integral-based functional programming. To address this challe

Syamlal Sankaran Kunnath, Mateusz Zelent, Mathieu Moalic, Maciej Krawczyk

Artificial spin-ice systems, consisting of arrays of interacting ferromagnetic nanoelements, offer a versatile platform for reconfigurable magnonics with potential in GHz logic and neuromorphic computing. However, weak dipolar coupling between nanoelements severely limits their functionality. We numerically demonstrate a rich spin-wave spectrum in a square s

Aurel X. Appius, Emiland Garrabe, Francois Helenon, Mahdi Khoramshahi

Task-aware robotic grasping is a challenging problem that requires the integration of semantic understanding and geometric reasoning. This paper proposes a novel framework that leverages Large Language Models (LLMs) and Quality Diversity (QD) algorithms to enable zero-shot task-conditioned grasp synthesis. The framework segments objects into meaningful subpa

Vladimir A. Dogiel, Chung-Ming Ko

Two enigmatic gamma-ray features in the Galactic central region, known as Fermi Bubbles (FBs), were found from Fermi-LAT data. An energy release (e.g., by tidal disruption events in the Galactic center, GC), generates a cavity with a shock that expands into the local ambient medium of the Galactic halo. A decade or so ago, a phenomenological model of the FBs

Amirafshar Moshtaghpour, Abner Velazco-Torrejon, Alex W. Robinson, Nigel D. Browning

The extended Ptychographical Iterative Engine (ePIE) is a widely used phase retrieval algorithm for Electron Ptychography from 4-dimensional (4-D) Scanning Transmission Electron Microscopy (4-D STEM) measurements acquired with a focused or defocused electron probe. However, ePIE relies on redundancy in the data and hence requires adjacent illuminated areas t

Moritz Staudinger, Wojciech Kusa, Florina Piroi, Aldo Lipani

Systematic literature reviews (SLRs) are a cornerstone of academic research, yet they are often labour-intensive and time-consuming due to the detailed literature curation process. The advent of generative AI and large language models (LLMs) promises to revolutionize this process by assisting researchers in several tedious tasks, one of them being the genera

Huy Le, Tai Hoang, Miroslav Gabriel, Gerhard Neumann

Learning diverse policies for non-prehensile manipulation is essential for improving skill transfer and generalization to out-of-distribution scenarios. In this work, we enhance exploration through a two-fold approach within a hybrid framework that tackles both discrete and continuous action spaces. First, we model the continuous motion parameter policy as a

Pablo Avilés, David González Méndez, Vicente Garzó

A kinetic model for granular mixtures is considered to study three different non-equilibrium situations. The model is based on the equivalence between a gas of elastic hard spheres subjected to a drag force proportional to the particle velocity and a gas of inelastic hard spheres. As a first problem, the relaxation of the velocity moments to their forms in t

A. Bonfanti, I. Amateis, D. Gandolfi, L. Borsato

TOI-396 is an F6V star ($V\approx6.4$) orbited by three transiting planets. The orbital periods of the two innermost planets are close to the 5:3 commensurability ($P_b \sim3.6$ d and $P_c \sim6.0$ d). To measure the masses of the three planets, refine their radii, and investigate whether planets b and c are in MMR, we carried out HARPS RV observations and r

Songshaptak De, Atri Dey, Tousik Samui

The search for heavy $W'$ bosons in their decay modes to a lepton and a heavy neutrino offers a promising avenue for probing new physics beyond the Standard Model. This work focuses on such a signature with an energetic lepton plus a fat jet, originating from the heavy neutrino and containing a lepton. We have employed the jet substructure techniques to isol

Jean-Pierre Gazeau, Hamed Pejhan

We investigate the matter-antimatter properties of elementary systems, modeled as free quantum fields, within the global structure of de Sitter spacetime. By leveraging the distinctive causal and analytic properties of de Sitter spacetime, we propose that matter-antimatter asymmetry could emerge as an observer-dependent effect shaped by time orientation with

Gennaro Notomista, Gary P. T. Choi, Matteo Saveriano

This paper presents a robot control algorithm suitable for safe reactive navigation tasks in cluttered environments. The proposed approach consists of transforming the robot workspace into the \emph{ball world}, an artificial representation where all obstacle regions are closed balls. Starting from a polyhedral representation of obstacles in the environment,

Prashanth Thattai Ravikumar

Quantifying and aligning music AI model representations with human behavior is an important challenge in the field of MIR. This paper presents a platform for exploring the Direct alignment between AI music model Representations and Human Musical judgments (DAIRHuM). It is designed to enable musicians and experimentalists to label similarities in a dataset of

Linrui Gong, Jiuming Liu, Junyi Ma, Lihao Liu

Diffusion models have shown the great potential in the point cloud registration (PCR) task, especially for enhancing the robustness to challenging cases. However, existing diffusion-based PCR methods primarily focus on instance-level scenarios and struggle with outdoor LiDAR points, where the sparsity, irregularity, and huge point scale inherent in LiDAR poi

Hiroki Matui, Takehiko Mori

This paper explores the cup and cap products within the cohomology and homology groups of ample groupoids, focusing on their applications and fundamental properties. Ample groupoids, which are \'etale groupoids with a totally disconnected unit space, play a crucial role in the study of topological dynamical systems and operator algebras. We introduce the cup

Muhammad Rafsan Kabir, Md. Mohibur Rahman Nabil, Mohammad Ashrafuzzaman Khan

Sentence-level embedding is essential for various tasks that require understanding natural language. Many studies have explored such embeddings for high-resource languages like English. However, low-resource languages like Bengali (a language spoken by almost two hundred and thirty million people) are still under-explored. This work introduces two lightweigh

Shota Fujii, Rei Yamagishi

Large language models (LLMs) are becoming more advanced and widespread and have shown their applicability to various domains, including cybersecurity. Static malware analysis is one of the most important tasks in cybersecurity; however, it is time-consuming and requires a high level of expertise. Therefore, we conducted a demonstration experiment focusing on

Irina Barašin, Blaž Bertalanič, Mihael Mohorčič, Carolina Fortuna

Time series classification is a relevant step supporting decision-making processes in various domains, and deep neural models have shown promising performance in this respect. Despite significant advancements in deep learning, the theoretical understanding of how and why complex architectures function remains limited, prompting the need for more interpretabl

Anjana Uday, Gertjan Lippertz, Bibek Bhujel, Alexey A. Taskin

Chiral one-dimensional transport can be realized in thin films of a surface-insulating ferromagnetic topological insulator called quantum anomalous Hall insulator (QAHI). When superconducting (SC) pairing correlations are induced in the surface of such a material by putting an $s$-wave superconductor on the top, the resulting topological superconductivity gi

Wei Kou, Xurong Chen

Understanding the interactions between quark-antiquark pairs is essential for elucidating quark confinement within the framework of quantum chromodynamics (QCD). This study investigates the field distribution patterns that arise between these pairs by employing advanced machine learning techniques, namely multilayer perceptrons (MLP) and Kolmogorov-Arnold ne

Tanveer Hannan, Md Mohaiminul Islam, Jindong Gu, Thomas Seidl

Large language models (LLMs) excel at retrieving information from lengthy text, but their vision-language counterparts (VLMs) face difficulties with hour-long videos, especially for temporal grounding. Specifically, these VLMs are constrained by frame limitations, often losing essential temporal details needed for accurate event localization in extended vide

Hang Guo, Yong Guo, Yaohua Zha, Yulun Zhang

The Mamba-based image restoration backbones have recently demonstrated significant potential in balancing global reception and computational efficiency. However, the inherent causal modeling limitation of Mamba, where each token depends solely on its predecessors in the scanned sequence, restricts the full utilization of pixels across the image and thus pres

Leander Claes, Johannes Lankeit, Michael Winkler

A model for the generation of heat due to mechanical losses during acoustic wave propagation in a solid is considered in a Kelvin-Voigt type framework. In contrast to previous studies on related thermoviscoelastic models, in line with recent experimental findings the present manuscript focuses on situations in which elastic parameters depend on temperature.

José E. Chacón, Andrea Meilán-Vila

This paper presents an alternative formulation of the geodesic normal distribution on the sphere, building on the work of Hauberg (2018). While the isotropic version of this distribution is naturally defined on the sphere, the anisotropic version requires projecting points from the hypersphere onto the tangent space. In contrast, our approach removes the dep

Pedro Sancho

Vacuum fluctuations play a central role in spontaneous emission. Recently, it has been suggested that these fluctuations could also be fundamental in the absorption dynamics, breaking the superposition inherent to the linear quantum evolution. We analyze the consistency of that proposal with previous results in double spontaneous emission. Moreover, for the

Yanhui Wang, Pei Gao, Xueming Ren

We introduce the class of network right $*$-abundant semigroups. These are based on networks that extend the notion of a directed graph. This class properly contains the class of graph inverse semigroups. We investigate the structure of network right $*$-abundant semigroups. We show that two network right $*$-abundant semigroups are isomorphic if and only if

Anna Glazkova, Olga Zakharova

Large language models (LLMs) play a crucial role in natural language processing (NLP) tasks, improving the understanding, generation, and manipulation of human language across domains such as translating, summarizing, and classifying text. Previous studies have demonstrated that instruction-based LLMs can be effectively utilized for data augmentation to gene

M. Burgay, L. Nieder, C. J. Clark, P. C. C. Freire

This paper presents the results of a joint radio and gamma-ray timing campaign on the nine millisecond pulsars (MSPs) discovered as part of the L-band targeted survey of Fermi-LAT sources performed in the context of the Transients and Pulsars with MeerKAT (TRAPUM) Large Survey Project. Out of these pulsars, eight are members of binary systems; of these eight

Dai Imaike

A Calabi-Yau 4-fold of Camere-Garbagnati-Mongardi is a crepant resolution of the quotient of a hyperk\"ahler 4-fold by an antisymplectic involution. In this paper, we compare two different types of holomorphic torsion invariants; one is the BCOV invariant of the Calabi-Yau 4-fold of Camere-Garbagnati-Mongardi, and the other is the invariant of the correspond

Gábor Mészáros, Johannes Wachs

Software libraries are the elementary building blocks of open source software ecosystems, extending the capabilities of programming languages beyond their standard libraries. Although ecosystem health is often quantified using data on libraries and their interdependencies, we know little about the rate at which new libraries are developed and used. Here we s

Ruijun Shi, Yue Zhou, Tianyu Zhao, Zhixiang Ren

Accurate waveform templates of binary black holes (BBHs) with eccentric orbits are essential for the detection and precise parameter estimation of gravitational waves (GWs). While SEOBNRE produces accurate time-domain waveforms for eccentric BBH systems, its generation speed remains a critical bottleneck in analyzing such systems. Accelerating template gener

Sayantan Mondal, Biman Bagchi

The dielectric properties of a bulk dipolar liquid have been subjects of intense interest during the past decades. A surprising result was the discovery of a strong wavenumber dependence in the bulk homogeneous state. Such behaviour seems to suggest the possibility of a strong system size dependence of the dielectric constant (DC) of a nanoconfined liquid, a

Nitay Derei, Shmuel Balberg, Shay I. Heizler, Elad Steinberg

We derive a family of similarity solutions to the nonlinear non-equilibrium Marshak wave problem for an inhomogeneous planar medium which is coupled to a time dependent radiation driving source. We employ the non-equilibrium gray diffusion approximation in the supersonic regime. The solutions constitute a generalization of the non-equilibrium nonlinear solut

Yifeng Du, Yufeng Liu, Chengdong Yang, Xiaodong Zheng

Quantum cryptographic conferencing (QCC) allows sharing secret keys among multiple distant users and plays a crucial role in quantum networks. Because of the fragility and low generation rate of genuine multipartite entangled states required in QCC, realizing and extending QCC with the entanglement-based protocol is challenging. Measurement-device-independen

Junzhe Chen, Tianshu Zhang, Shiyu Huang, Yuwei Niu

Despite the recent breakthroughs achieved by Large Vision Language Models (LVLMs) in understanding and responding to complex visual-textual contexts, their inherent hallucination tendencies limit their practical application in real-world scenarios that demand high levels of precision. Existing methods typically either fine-tune the LVLMs using additional dat

Boštjan Brešar, Jaka Hedžet, Michael A. Henning

Let $p \in \mathbb{N}$ and $q \in \mathbb{N} \cup \lbrace \infty \rbrace$. We study a dynamic coloring of the vertices of a graph $G$ that starts with an initial subset $S$ of blue vertices, with all remaining vertices colored white. If a white vertex~$v$ has at least~$p$ blue neighbors and at least one of these blue neighbors of~$v$ has at most~$q$ white ne

Pedro A. Parra, Eliezer F. Oliveira

Using molecular dynamics simulations and the ReaxFF force field, we studied a composite based on cubic (c-BN) and hexagonal (h-BN) boron nitride subjected to different temperatures to verify the possibility of a c-BN to h-BN phase transition. Our results demonstrate that the surface termination of c-BN (whether B- or N-terminated) is a crucial factor in the

César Piñeiro, Juan C. Pichel

Python demonstrates lower performance in comparison to traditional high performance computing (HPC) languages such as C, C++, and Fortran. This performance gap is largely due to Python's interpreted nature and the Global Interpreter Lock (GIL), which hampers multithreading efficiency. However, the latest version of Python includes the necessary changes to ma

Juan Miguel Lopez Alcaraz, Nils Strodthoff

This study investigates the feasibility of using electrocardiogram (ECG) data combined with basic patient metadata to estimate and monitor prompt laboratory abnormalities. We use the MIMIC-IV dataset to train multimodal deep learning models on ECG waveforms, demographics, biometrics, and vital signs. Our model is a structured state space classifier with late

Yu Rong, Peng Wang

We investigate the alignment of non-red early-type galaxies (ETGs) with blue or green colors within large-scale filaments and compare this alignment pattern with that of red ETGs. Our analysis reveals a significant alignment of the major axes of red ETGs with the orientations of their host cosmic filaments, consistent with prior research. In contrast, non-re

Theofilos Spyrou, Said Hamdioui, Haralampos-G. Stratigopoulos

Neuromorphic computing and spiking neural networks (SNNs) are gaining traction across various artificial intelligence (AI) tasks thanks to their potential for efficient energy usage and faster computation speed. This comparative advantage comes from mimicking the structure, function, and efficiency of the biological brain, which arguably is the most brillian

Immanuel M. Bomze, Daniel de Vicente

The standard quadratic optimization problem (StQP) consists of minimizing a quadratic form over the standard simplex. Without convexity or concavity of the quadratic form, the StQP is NP-hard. This problem has many relevant real-life applications ranging portfolio optimization to pairwise clustering and replicator dynamics. Sometimes, the data matrix is unce

Yuheng Xu, Taiping Zhang

Domain-invariant representation learning is a powerful method for domain generalization. Previous approaches face challenges such as high computational demands, training instability, and limited effectiveness with high-dimensional data, potentially leading to the loss of valuable features. To address these issues, we hypothesize that an ideal generalized rep

Shengbin Fu, Wenting Huang, Fei Jiang

Recently, Jiang--Jiang (J. Differential Equations 282, 2021) showed the existence of unique strong solutions in spatial periodic domain (denoted by $\mathbb{T}^3$), whenever the elasticity coefficient is larger than the initial velocity perturbation of the rest state. Motivated by Jiang--Jiang's result, we revisit the Cauchy problem of the compressible visco

Kate Mowbray, Thomas Neukirch, James Threlfall

In this paper we provide a detailed investigation of the energisation processes in two-dimensional, two and a half-dimensional and three-dimensional collapsing magnetic trap models. Using kinematic magnetohydrodynamic models of collapsing magnetic traps, we examine the importance of Fermi acceleration in comparison with betatron acceleration in these models.

Ibrahem Shatnawi, Jeyhun I. Mikayilov

The adoption of more stringent fuel economy standards represents a pivotal pathway toward achieving net zero emissions in the transportation sector. By steadily increasing the fuel efficiency of vehicles, this approach drives a gradual but consistent decline in emissions. When coupled with the simultaneous integration of electric and alternative fuel vehicle

Wanqiu Long, Bonnie Webber

Implicit discourse relation recognition involves determining relationships that hold between spans of text that are not linked by an explicit discourse connective. In recent years, the pre-train, prompt, and predict paradigm has emerged as a promising approach for tackling this task. However, previous work solely relied on manual verbalizers for implicit dis

Aaron Joy, Ben Soh, Zhi Zhang, Sri Parameswaran

Trusted Execution Environments (TEEs) are critical components of modern secure computing, providing isolated zones in processors to safeguard sensitive data and execute secure operations. Despite their importance, TEEs are increasingly vulnerable to fault injection (FI) attacks, including both physical methods, such as Electromagnetic Fault Injection (EMFI),

Arno Simons

I present Astro-HEP-BERT, a transformer-based language model specifically designed for generating contextualized word embeddings (CWEs) to study the meanings of concepts in astrophysics and high-energy physics. Built on a general pretrained BERT model, Astro-HEP-BERT underwent further training over three epochs using the Astro-HEP Corpus, a dataset I curated

Anita Behme, Sebastian Mentemeier

We study the long-time behaviour of matrix-valued stochastic exponentials of L\'evy processes, i.e. of multiplicative L\'evy processes in the general linear group. In particular, we prove laws of large numbers as well as central limit theorems for the logarithmised norm, logarithmised entries and the logarithmised determinant of the stochastic exponential. W

Yanyun Ding, Zhenghua Yao, Peili Li, Yunhai Xiao

The elastic net penalty is frequently employed in high-dimensional statistics for parameter regression and variable selection. It is particularly beneficial compared to lasso when the number of predictors greatly surpasses the number of observations. However, empirical evidence has shown that the $\ell_q$-norm penalty (where $0 < q < 1$) often provides bette

Arpan Roy, Arnab Laha, Abhijit Biswas, Adam Miranowicz

The unique properties of exceptional point (EP) singularities, arising from non-Hermitian physics, have unlocked new possibilities for manipulating light-matter interactions. A tailored gain-loss variation, while encircling higher-order EPs dynamically, can significantly enhance the control of the topological flow of light in multi-level photonic systems. In

Midriem Mirdanies, Roni Permana Saputra, Edwar Yazid, Rozeha A. Rashid

This paper describes the implementation of a learning-based lane detection algorithm on an Autonomous Mobile Robot. It aims to implement the Ultra Fast Lane Detection algorithm for real-time application on the SEATER P2MC-BRIN prototype using a camera and optimize its performance on the Jetson Nano platform. Preliminary experiments were conducted to evaluate

Duc V. Dinh, Zhuohui Chen, Oliver Brandt

We investigate the synthesis of 340-nm-thick Sc$_x$Al$_{1-x}$N layers with $0 \leq x \leq 0.35$ on AlN-buffered Si(111) by plasma-assisted molecular beam epitaxy. We employ an AlN nucleation layer under conditions giving rise to single-domain N-polar [(000$\bar{1}$)-oriented] layers, as demonstrated by the ($3 \times 3$) pattern observed in reflection high-e

Dingyuan Shi, Yong Wang, Hangyu Li, Xiangxiang Chu

Diffusion models have shown remarkable success in text-to-image generation, making preference alignment for these models increasingly important. The preference labels are typically available only at the terminal of denoising trajectories, which poses challenges in optimizing the intermediate denoising steps. In this paper, we propose to conduct Denoised Dist

Kunle Adegoke

We give a short proof of the well-known Knuth's old sum and provide some generalizations. Our approach utilizes the binomial theorem and integration formulas derived using the Beta function. Several new polynomial identities and combinatorial identities are derived.

Sebastian Stock, Jannik Dunkelau, Atif Mashkoor

Context: With artificial intelligence (AI) being well established within the daily lives of research communities, we turn our gaze toward formal methods (FM). FM aim to provide sound and verifiable reasoning about problems in computer science. Objective: We conduct a systematic mapping study to overview the current landscape of research publications that app

Herve Zwirn

In this article, we propose to use the formalism of quantum mechanics to describe and explain the so-called "abnormal" behaviour of agents in certain decision or choice contexts. The basic idea is to postulate that the preferences of these agents are indeterminate (in the quantum sense of the term) before the choice is made or the decision is taken. An agent

Xuewu Lin, Tianwei Lin, Lichao Huang, Hongyu Xie

In embodied intelligence systems, a key component is 3D perception algorithm, which enables agents to understand their surrounding environments. Previous algorithms primarily rely on point cloud, which, despite offering precise geometric information, still constrain perception performance due to inherent sparsity, noise, and data scarcity. In this work, we i

Anjali Nambiyar Rajkumar Kannan

Edge detection has been one of the most difficult challenges in computer vision because of the difficulty in identifying the borders and edges from the real-world images including objects of varying kinds and sizes. Methods based on ensemble learning, which use a combination of backbones and attention modules, outperformed more conventional approaches, such

Katarzyna Kowalczyk-Gajewska, Stephane Berbenni, Sebastien Mercier

Mean-field modeling based on the Eshelby inclusion problem poses some difficulties when the non-linear Maxwell-type constitutive law is used for elasto-viscoplasticity. One difficulty is that this behavior involves different orders of time differentiation, which leads a long-term memory effect. One of the possible solutions to this problem is the additive in

Carlos Batlle, José Figueroa-O'Farrill, Joaquim Gomis, Girish Vishwa

We generalise BMS algebras in three dimensions by the introduction of an arbitrary real parameter $\lambda$, recovering the standard algebras (BMS, extended BMS and Weyl-BMS) for $\lambda=-1$. We exhibit a realisation of the (centreless) Weyl $\lambda$-BMS algebra in terms of the symplectic structure on the space of solutions of the massless Klein-Gordon equ

Zhonghua Yi, Hao Shi, Qi Jiang, Yao Gao

Optical flow estimation is extensively used in autonomous driving and video editing. While existing models demonstrate state-of-the-art performance across various benchmarks, the robustness of these methods has been infrequently investigated. Despite some research focusing on the robustness of optical flow models against adversarial attacks, there has been a

Federico Bassetti, Lucia Ladelli, Pietro Rotondo

We study the distributional properties of linear neural networks with random parameters in the context of large networks, where the number of layers diverges in proportion to the number of neurons per layer. Prior works have shown that in the infinite-width regime, where the number of neurons per layer grows to infinity while the depth remains fixed, neural

Dominik Dold, Amy Thomas, Nicole Rosi, Jai Grover

Recently, a class of mechanical lattices with reconfigurable, zero-stiffness structures has been proposed, called Totimorphic lattices. In this work, we introduce a computational framework that enables continuous reprogramming of a Totimorphic lattice's effective properties, such as mechanical and optical behaviour, through geometric changes alone, demonstra

Jaehoon Kim, Kyoungjae Lee, Lizhen Lin

We propose the first Bayesian methods for detecting change points in high-dimensional mean and covariance structures. These methods are constructed using pairwise Bayes factors, leveraging modularization to identify significant changes in individual components efficiently. We establish that the proposed methods consistently detect and estimate change points

Jeongsol Kim, Beomsu Kim, Jong Chul Ye

Diffusion models (DMs), which enable both image generation from noise and inversion from data, have inspired powerful unpaired image-to-image (I2I) translation algorithms. However, they often require a larger number of neural function evaluations (NFEs), limiting their practical applicability. In this paper, we tackle this problem with Schrodinger Bridges (S

Bo-Wang Zhang, Cheng Shang, J. Y. Sun, Zhuo-Cheng Gu

In recent years, extensive research has been dedicated to the study of parity-time ($\mathcal{PT}$) symmetry, which involves the engineered balance of gain and loss in non-Hermitian optics. Complementary to $\mathcal{PT}$ symmetry, the concept of anti-$\mathcal{PT}$ symmetry has emerged as a natural framework for describing the dynamics of open systems with

Abdur-Rahman Ibrahim Sayyid-Ali, Abdul Rafay, Muhammad Abdullah Soomro, Muhammad Hamad Alizai

Batteryless IoT systems face energy constraints exacerbated by checkpointing overhead. Approximate computing offers solutions but demands manual expertise, limiting scalability. This paper presents CheckMate, an automated framework leveraging LLMs for context-aware code approximations. CheckMate integrates validation of LLM-generated approximations to ensure

Giung Nam, Juho Lee

While ensembling deep neural networks has shown promise in improving generalization performance, scaling current ensemble methods for large models remains challenging. Given that recent progress in deep learning is largely driven by the scale, exemplified by the widespread adoption of large-scale neural network architectures, scalability emerges an increasin

Nataliya Vasylyeva

In the paper, we discuss the two-dimensional contact Muskat problem with zero surface tension of a free boundary. The initial shape of the unknown interface is a smooth simple curve which forms acute corners $\delta_{0}$ and $\delta_{1}$ with fixed boundaries. Under suitable assumptions on the given data, the one-to-one local classical solvability of this pr

Alberto Bernardi, Luca Costabello

Knowledge Graph Embedding models, representing entities and edges in a low-dimensional space, have been extremely successful at solving tasks related to completing and exploring Knowledge Graphs (KGs). One of the key aspects of training most of these models is teaching to discriminate between true statements positives and false ones (negatives). However, the

Won Jun Kim, Hyungjin Chung, Jaemin Kim, Sangmin Lee

Gradient-based methods are a prototypical family of explainability techniques, especially for image-based models. Nonetheless, they have several shortcomings in that they (1) require white-box access to models, (2) are vulnerable to adversarial attacks, and (3) produce attributions that lie off the image manifold, leading to explanations that are not actuall

Claudia Faggian, Gaetan Lopez, Benoît Valiron

Quantum lambda calculus has been studied mainly as an idealized programming language -- the evaluation essentially corresponds to a deterministic abstract machine. Very little work has been done to develop a rewriting theory for quantum lambda calculus. Recent advances in the theory of probabilistic rewriting give us a way to tackle this task with tools unav

Teodor Alexandru Szente, James Harrison, Mihai Zanfir, Cristian Sminchisescu

Fractional gradient descent has been studied extensively, with a focus on its ability to extend traditional gradient descent methods by incorporating fractional-order derivatives. This approach allows for more flexibility in navigating complex optimization landscapes and offers advantages in certain types of problems, particularly those involving non-lineari

Jacek Dobrzyniecki, Paula Heim, Michał Tomza

We propose a scheme for constructing versatile quantum simulators using ultracold Rydberg atoms in long-lived circular and elliptical states. By exciting different subspaces of internal atomic states, the atoms can be used to simulate two effective spin species with different spin-spin interactions. The strengths of transverse and longitudinal spin-spin inte

Yong Zhou, Ze-yan Peng, Yan Xiao, Wen-mei Guo

The Wheatstone bridge experiment is fundamental for precise measurement of electrical resistance, holding significant value in both undergraduate physics education and real-life scientific research. This study reimagines the experiment by integrating computational simulation with traditional methods, enhancing its educational and practical value. By analyzin

Clara Escañuela Nieves, Felix Werner, Jim Hinton

High energy cosmic-rays generate air showers when they enter Earth's atmosphere. Ground-based gamma-ray astronomy is possible using either direct detection of shower particles at mountain altitudes, or with arrays of imaging air-Cherenkov telescopes (IACTs). Advances in the technique and larger collection areas have increased the rate at which air-shower eve

Gourab Kumar Sar, Kevin O'Keeffe, Dibakar Ghosh

We study a variant of the one-dimensional swarmalator model where the units' interactions have a controllable length scale or range. We tune the model from the long-range regime, which is well studied, into the short-range regime, which is understudied, and find diverse collective states: sync dots, where the swarmalators arrange themselves into k>1 delta po

Jonas Pleyer

This thesis calculates the special-relativistic internal energy of a non-interacting gas. We derive an equation of state (EoS) which we apply to the Tollmann-Oppenheimer Volkoff (TOV) equation. Furthermore we present numerical results of the TOV equation for various configurations of a polytropic EoS. These results show that the zero values of the TOV equati

Kamil Khadiev, Danil Serov

Let us consider the Multiple String Matching Problem. In this problem, we consider a long string, denoted by $t$, of length $n$. This string is referred to as a text. We also consider a sequence of $m$ strings, denoted by $S$, which we refer to as a dictionary. The total length of all strings from the dictionary is represented by the variable L. The objectiv

A. Urdangarin, T. Goicoa, P. Congdon, MD. Ugarte

Ischaemic heart disease (IHD) remains the primary cause of mortality in the US. This study focuses on using spatio-temporal disease mapping models to explore the temporal trends of IHD at the county level from 1999 to 2021. To manage the computational burden arising from the high-dimensional data, we employ scalable Bayesian models using a "divide and conque

Kunal Shukre, Dipankar Chakrabarti, Subhasish Basak

Lattice chiral perturbation theory is developed for Karsten-Wilczek fermions, a variant of minimally doubled fermions. As a first step, we consider the n\"aive fermionic field on lattice without its doubler. Once the symmetries of the action, the Symanzik effective theory and the spurion structure are established for the single fermion, we extend our study t

Zhening Liu, Yingdong Hu, Xinjie Zhang, Rui Song

The recent development of 3D Gaussian Splatting (3DGS) has led to great interest in 4D dynamic spatial reconstruction. Existing approaches mainly rely on full-length multi-view videos, while there has been limited exploration of online reconstruction methods that enable on-the-fly training and per-timestep streaming. Current 3DGS-based streaming methods trea

Gabriella Agazie, Akash Anumarlapudi, Anne M. Archibald, Zaven Arzoumanian

Evidence has emerged for a stochastic signal correlated among 67 pulsars within the 15-year pulsar-timing data set compiled by the NANOGrav collaboration. Similar signals have been found in data from the European, Indian, Parkes, and Chinese PTAs. This signal has been interpreted as indicative of the presence of a nanohertz stochastic gravitational wave back

Ik Soo Lim, Naoki Masuda

Trust and reciprocation of it form the foundation of economic, social and other interactions. While the Trust Game is widely used to study these concepts for interactions between two players, often alternating different roles (i.e., investor and trustee), its extensions to multi-player scenarios have been restricted to instances where players assume only one

Xinyu Bai, Zeng Lian, Xiao Ma, Hang Zhao

In this paper, we consider a class of affined Anosov mappings with quasi-periodic forces, and show that there is a unique positive integer $m$, which only depends on the system, such that the exponential growth rate of the cardinality of invariant tori of degree $m$ is equal to the topological entropy.

Saurabh Tiwari, Supriyo Ghosh

Spatiotemporal variation of the thermal gradient in the melt pool inherited from different heat input patterns or other non-equilibrium transient effects during additive manufacturing can significantly affect the resulting subgrain microstructure evolution. To examine the impact of this variation, we approximate the thermal gradient by various isotherm patte

Carl Chalmers, Paul Fergus, Serge Wich, Steven N Longmore

Effective monitoring of wildlife is critical for assessing biodiversity and ecosystem health, as declines in key species often signal significant environmental changes. Birds, particularly ground-nesting species, serve as important ecological indicators due to their sensitivity to environmental pressures. Camera traps have become indispensable tools for moni