November 2024 arXiv papers — page 164

Francisco Sena, Romeo Rizzi, Alexandru I. Tomescu

A path-covering problem on a directed acyclic graph (DAG) requires finding a set of source-to-sink paths that cover all the nodes, all the arcs, or subsets thereof, and additionally they are optimal with respect to some function. In this paper we study safe sequences of nodes or arcs, namely sequences that appear in some path of every path cover of a DAG. We

Pankhuri Gupta, Artem Litvinenko, Akash Kumar, Pranaba Kishor Muduli

Ultra-fast spectrum analysis concept based on rapidly tuned spintronic nano-oscillators has been under development for the last few years and has already demonstrated promising results. Here, we demonstrate an ultra-fast microwave spectrum analyzer based on a chain of five mutually synchronized nano-constriction spin Hall nano-oscillators (SHNOs). As mutual

Glenn J. White, L. Barrufet, S. Serjeant, C. P. Pearson

This paper presents a 610 MHz radio survey covering 1.94 square degrees around the North Ecliptic Pole (NEP), which includes parts of the AKARI (ADF-N) and Euclid, Deep Fields North. The median 5-sigma sensitivity is 28 microJy beam per beam, reaching as low as 19 microJy per beam, with a synthesised beam of 3.6 x 4.1 arcsec. The catalogue contains 1675 radi

P. K. Batrakov, V. O. Yurovsky, I. Kudryashov

The paper presents a theoretical model describing the full power spectra of synchrotron radiation generated by relativistic electrons in a turbulent magnetic field. Using the theoretical model, numerical calculations of the complete power spectra of synchrotron radiation were performed for a turbulent field generated by the harmonic method. Additionally, a m

Abhishek Sebastian, Pragna R, Sonaa Rajagopal, Muralikrishnan Mani

Fiber Specklegram Sensors (FSS) are vital for environmental monitoring due to their high temperature sensitivity, but their complex data poses challenges for predictive models. This study introduces Prion-ViT, a prion-inspired Vision Transformer model, inspired by biological prion memory mechanisms, to improve long-term dependency modeling and temperature pr

Ryo Kishikawa, Nanase Harada, Toshiki Saito, Susanne Aalto

It is essential to examine the physical or chemical properties of molecular gas in starburst galaxies to reveal the underlying mechanisms characterizing starbursts. We used non-negative matrix factorization (NMF) to extract individual molecular or physical components involved in the star formation process in NGC\,253. We used images of 148 transitions from 4

Shashi Kumar, Iuliia Thorbecke, Sergio Burdisso, Esaú Villatoro-Tello

Recent research has demonstrated that training a linear connector between speech foundation encoders and large language models (LLMs) enables this architecture to achieve strong ASR capabilities. Despite the impressive results, it remains unclear whether these simple approaches are robust enough across different scenarios and speech conditions, such as domai

Yizhe Huang, Xingbo Wang, Hao Liu, Fanqi Kong

Traditional interactive environments limit agents' intelligence growth with fixed tasks. Recently, single-agent environments address this by generating new tasks based on agent actions, enhancing task diversity. We consider the decision-making problem in multi-agent settings, where tasks are further influenced by social connections, affecting rewards and inf

L. R. Ivey, A. C. Fabian, J. S. Sanders, C. Pinto

The radiative cooling time of hot gas in the cool cores of many galaxy clusters and massive elliptical galaxies drops in the centre to below 100 million years. The mass cooling rates inferred from simple modelling of X-ray observations of these objects are very low, indicating that either AGN feedback is tightly balanced or that soft X-rays from cooling gas

A. A. Kozhberov

We study the electrostatic energy of binary ionic mixtures (BIMs) in the form of Coulomb crystals with the main focus on ordered crystals. We consider 15 different binary bcc-like lattices, accurately calculate their electrostatic energies and approximate them by a unified equation. These results extend those available in the literature. A detailed compariso

Huayang Huang, Yu Wu, Qian Wang

Watermarking generative content serves as a vital tool for authentication, ownership protection, and mitigation of potential misuse. Existing watermarking methods face the challenge of balancing robustness and concealment. They empirically inject a watermark that is both invisible and robust and passively achieve concealment by limiting the strength of the w

Joseph Geo Benjamin, Mothilal Asokan, Mohammad Yaqub, Karthik Nandakumar

One of the most common defense strategies against Byzantine clients in federated learning (FL) is to employ a robust aggregator mechanism that makes the training more resilient. While many existing Byzantine robust aggregators provide theoretical convergence guarantees and are empirically effective against certain categories of attacks, we observe that certa

Cristina Flaut, Dana Piciu

Divisible residuated lattices are algebraic structures corresponding to a more comprehensive logic than Hajek's basic logic with an important significance in the study of fuzzy logic. The purpose of this paper is to investigate commutative rings whose the lattice of ideals can be equipped with a structure of divisible residuated lattice. We show that these r

Yuanshao Zhu, James Jianqiao Yu, Xiangyu Zhao, Xun Zhou

Building a universal trajectory foundation model is a promising solution to address the limitations of existing trajectory modeling approaches, such as task specificity, regional dependency, and data sensitivity. Despite its potential, data preparation, pre-training strategy development, and architectural design present significant challenges in constructing

Saeed Ahmed, Javed Hussain

In this paper, we are interested in proving the existence and uniqueness of the local, local maximal, and global solutions of the equation projected on the Hilbert manifold. Furthermore, we show that, for any given initial data in the Hilbert manifold $\mathcal{M}$, the solution to this equation is also in the Hilbert manifold $\mathcal{M}$. Finally, we demo

Qizhe Wu, Letian Zhao, Yuchen Gui, Huawen Liang Xiaotian Wang

Graph Convolutional Networks (GCNs) are state-of-the-art deep learning models for representation learning on graphs. However, the efficient training of GCNs is hampered by constraints in memory capacity and bandwidth, compounded by the irregular data flow that results in communication bottlenecks. To address these challenges, we propose a message-passing arc

Cristina Flaut, Andreea Baias

In this paper, by using matix representation for quaternions andoctonions, we provide a procedure to obtain some example of k potent matrices of order 4 or 8, over the real field or over the field ZP, with p a prime number.

Masakazu Yoshimura, Teruaki Hayashi, Yota Maeda

An ecosystem of Transformer-based models has been established by building large models with extensive data. Parameter-efficient fine-tuning (PEFT) is a crucial technology for deploying these models to downstream tasks with minimal cost while achieving effective performance. Recently, Mamba, a State Space Model (SSM)-based model, has attracted attention as a

Gergely Kiss, Itay Londner, Máté Matolcsi, Gábor Somlai

Coven and Meyerowitz formulated two conditions which have since been conjectured to characterize all finite sets that tile the integers by translation. By periodicity, this conjecture is reduced to sets which tile a finite cyclic group $\mathbb{Z}_M$. In this paper we consider a natural relaxation of this problem, where we replace sets with nonnegative funct

Peter Reinholdt, Erik Rosendahl Kjellgren, Karl Michael Ziems, Sonia Coriani

Quantum computing presents a promising avenue for solving complex problems, particularly in quantum chemistry, where it could accelerate the computation of molecular properties and excited states. This work focuses on hybrid quantum-classical algorithms for near-term quantum devices, combining the quantum linear response (qLR) method with a polarizable embed

Aditya Gupta, Souvik Das, Debasish Chatterjee

This article presents a zeroth order probabilistic global optimization algorithm -- SwiftNav -- for (not necessarily convex) functions over a compact domain. A discretization procedure is deployed on the compact domain, starting with a small step-size $h > 0$ and subsequently adaptively refining it in the course of a simulated annealing routine utilizing the

Chiranjib Mukhopadhyay, Victor Montenegro, Abolfazl Bayat

Quantum sensors are now universally acknowledged as one of the most promising near-term quantum technologies. The traditional formulation of quantum sensing introduces a concrete bound on ultimate precision through the so-called local sensing framework, in which a significant knowledge of prior information about the unknown parameter value is implicitly assu

Yu-Han Ma, Cong Fu

We present a general framework for determining the power-efficiency trade-off relations across arbitrary thermal machines, addressing the lack of unified optimization results stemming from their diverse functionalities (e.g., heat engines, refrigerators, and heat pumps). For time-dependent cycle irreversibility $A(\tau)$ following a $\tau^{-\alpha}$ power la

Santtu Tikka, Juha Karvanen

Monotone missingness is commonly encountered in practice when a missing measurement compels another measurement to be missing. Because of the simpler missing data pattern, monotone missing data is often viewed as beneficial from the perspective of practical data analysis. However, in graphical missing data models, monotonicity has implications for the identi

Peihao Li

With the widespread adoption of edge computing technologies and the increasing prevalence of deep learning models in these environments, the security risks and privacy threats to models and data have grown more acute. Attackers can exploit various techniques to illegally obtain models or misuse data, leading to serious issues such as intellectual property in

Riccardo Aragona, Norberto Gavioli, Giuseppe Nozzi

Starting with an integral domain $D$ of characteristic $0$, we consider a class of iterated wreath product $W_n$ of $n$ copies of $D$. In order that $W_n$ be transfinite hypercentral, it is necessary to restrict to the case of wreath products defined by way of numerical polynomials. We also associate to each of these groups a Lie ring, providing a correspond

Weishuo Ma, Yanbo Wang, Xiyuan Wang, Muhan Zhang

Recent advancements in graph neural networks (GNNs) for link prediction have introduced sophisticated training techniques and model architectures. However, reliance on outdated baselines may exaggerate the benefits of these new approaches. To tackle this issue, we systematically explore Graph Autoencoders (GAEs) by applying model-agnostic tricks in recent me

Dongliang Cai, Borui Chen, Liang Zhang, Kexin Li

Attribute-Based Encryption (ABE) is a promising solution for access control in cloud services. However, the heavy decryption overhead hinders its widespread adoption. A general approach to address this issue is to outsource decryption to decryption cloud service(DCS). Existing schemes have utilized various methods to enable users to verify outsourced results

Guy Bunin, Olivier Rivoire

We present a simple physical model that recapitulates several features of biological evolution, while being based only on thermally-driven attachment and detachment of elementary building blocks. Through its dynamics, this model samples a large and diverse array of non-equilibrium steady states, both within and between independent trajectories. These dynamic

Gregorio Paci, Omar Zanusso

We use the ambient space construction, in which spacetime is mapped into a special lightcone of a higher dimensional manifold, to derive the integrable terms of the trace anomaly in even dimensions. We argue that the natural topological anomaly is the so-called $Q$-curvature, which, when projected from the ambient space, always comes with a Weyl covariant op

Alena Ulke, Michael Schuster, Simone Göttlich

We prove an existence result for the steady state flow of gas mixtures on networks. The basis of the model are the physical principles of the isothermal Euler equation, coupling conditions for the flow and pressure, and the mixing of incoming flow at nodes. The state equation is based on a convex combination of the ideal gas equations of state for natural ga

Kai Sandbrink, Jan P. Bauer, Alexandra M. Proca, Andrew M. Saxe

Animals survive in dynamic environments changing at arbitrary timescales, but such data distribution shifts are a challenge to neural networks. To adapt to change, neural systems may change a large number of parameters, which is a slow process involving forgetting past information. In contrast, animals leverage distribution changes to segment their stream of

Lukas Hintze, Lena Krieg, Olga Scheftelowitsch, Haodong Zhu

In group testing, the task is to identify defective items by testing groups of them together using as few tests as possible. We consider the setting where each item is defective with a constant probability $\alpha$, independent of all other items. In the (over-)idealized noiseless setting, tests are positive exactly if any of the tested items are defective.

Yoshimoto Ribayashi, Yuta Sahara, Shogo Sawaguchi, Kazuhiro Miyama

For robots to become more versatile and expand their areas of application, their bodies need to be suitable for contact with the environment. When the human body comes into contact with the environment, it is possible for it to continue to move even if the positional relationship between muscles or the shape of the muscles changes. We have already focused on

Alessandra D'Alise, Davide Iacobacci, Francesco Sannino

We show that the epidemiological Renormalization Group (eRG) framework is a useful and minimal tool to effectively describe the temporal evolution of the Dengue multi-wave pandemics. We test the framework on the Dengue history of several countries located in both Latin America and Asia. We also observe a strong correlation between the total number of infecte

M. Ögren, G. M. Kavoulakis

We consider a quantum droplet which is confined in a ring potential. We investigate the so-called "yrast" state, i.e., the lowest-energy state of the droplet assuming that it has some fixed expectation value of the angular momentum. Two are the most interesting aspects of this problem, the nonlinear term -- which is partly attractive and partly repulsive --

Públio Elon Correa da Silva, Jurandy Almeida

Deep learning (DL) technologies can transform agriculture by improving crop health monitoring and management, thus improving food safety. In this paper, we explore the potential of edge computing for real-time classification of leaf diseases using thermal imaging. We present a thermal image dataset for plant disease classification and evaluate deep learning

Haozhe Yi, Junyi Liu, Maolin Yang, Zewei Chen

Controller Area Networks (CANs) are widely adopted in real-time automotive control and are increasingly standard in factory automation. Considering their critical application in safety-critical systems, The error rate of the system must be accurately predicted and guaranteed. Through simulation, it is possible to obtain a low-precision overview of the system

Dieter Teichrib, Moritz Schulze Darup

Neural networks (NN) have been successfully applied to approximate various types of complex control laws, resulting in low-complexity NN-based controllers that are fast to evaluate. However, when approximating control laws using NN, performance and stability guarantees of the original controller may not be preserved. Recently, it has been shown that it is po

K. Migkas, M. W. Sommer, T. Schrabback, E. R. Carrasco

Galaxy cluster mergers are excellent laboratories for studying a wide variety of different physical phenomena. Such a unique system is the distant SPT-CLJ2228-5828 cluster merger located at $z\approx 0.77$. Previous analyses via Sunyaev-Zeldovich and weak lensing data suggested that the system potentially was a dissociative cluster post-merger. In this work,

Rotem Ben-Hur, Orian Leitersdorf, Ronny Ronen, Lidor Goldshmidt

Genome analysis has revolutionized fields such as personalized medicine and forensics. Modern sequencing machines generate vast amounts of fragmented strings of genome data called reads. The alignment of these reads into a complete DNA sequence of an organism (the read mapping process) requires extensive data transfer between processing units and memory, lea

Eric Behar, Julien Romero, Amel Bouzeghoub, Katarzyna Wegrzyn-Wolska

Job recommendation gathers many challenges well-known in recommender systems. First, it suffers from the cold start problem, with the user (the candidate) and the item (the job) having a very limited lifespan. It makes the learning of good user and item representations hard. Second, the temporal aspect is crucial: We cannot recommend an item in the future or

Tianbo Li, Min Lin, Stephen Dale, Zekun Shi

We present a novel approach to address the challenges of variable occupation numbers in direct optimization of density functional theory (DFT). By parameterizing both the eigenfunctions and the occupation matrix, our method minimizes the free energy with respect to these parameters. As the stationary conditions require the occupation matrix and the Kohn-Sham

Clarence A. Antipona, Romeo R. Magsino, Raymund M. Dioses, Khatalyn E. Mata

This study is focused on enhancing the Haar Cascade Algorithm to decrease the false positive and false negative rate in face matching and face detection to increase the accuracy rate even under challenging conditions. The face recognition library was implemented with Haar Cascade Algorithm in which the 128-dimensional vectors representing the unique features

Heng Yang, Jiang Zhou

Let $0<t<\infty$, $0<\alpha<n$, $1<p<r<\infty$ and $1<q<s<\infty$. In this paper, we prove that $b\in B M O\left(\mathbb{R}^{n}\right)$ if and only if the commutator $[b, T_{\Omega,\alpha}]$ generated by the fractional integral operator with the rough kernel $T_{\Omega,\alpha}$ and the locally integrable function $b$ is bounded from the slice space $(E_{p}^{

Zhitong Gao, Bingnan Li, Mathieu Salzmann, Xuming He

In open-world scenarios, where both novel classes and domains may exist, an ideal segmentation model should detect anomaly classes for safety and generalize to new domains. However, existing methods often struggle to distinguish between domain-level and semantic-level distribution shifts, leading to poor out-of-distribution (OOD) detection or domain generali

Idir Arab, Milto Hadjikyriakou, Paulo Eduardo Oliveira

Building on recent developments in models focused on the shape properties of odds ratios, this paper introduces two new models that expand the class of available distributions while preserving specific shape characteristics of an underlying baseline distribution. The first model offers enhanced control over odds and log-odds functions, facilitating adjustmen

Tianhao He, Andrija Stankovic, Evangelos Niforatos, Gerd Kortuem

Ideation is a critical component of video-based design (VBD), where videos serve as the primary medium for design exploration and inspiration. The emergence of generative AI offers considerable potential to enhance this process by streamlining video analysis and facilitating idea generation. In this paper, we present DesignMinds, a prototype that integrates

Jae-Hong Jeong, Jeong-Eun Lee, Seonjae Lee, Giseon Baek

We present an investigation of Complex Organic Molecules (COMs) in the spatially resolved Keplerian disk around V883 Ori, an eruptive young star, based on a spectral survey carried out with ALMA in Band 6 (220.7$-$274.9 GHz). We identified about 3,700 molecular emission lines and discovered 23 COMs in the disk. We estimated the column densities of COMs detec

C. Sánchez Contreras, D. Tafoya, J. P. Fonfria, J. Alcolea

We present interferometric observations at 1 and 3\,mm with the Atacama Large Millimeter Array (ALMA) of the free-free continuum and mm-wavelength recombination line (mRRL) emission of the ionized core (within $\lsim$130\,au) of the young Planetary Nebula (PN) candidate M\,2-9.These inner regions are concealed in the vast majority of similar objects. A spect

Thor Hvid-Olsen, Christina H. Christoffersen, Damon J. Carrad, Nicolas Gauquelin

The $t_{2g}$ band-structure of SrTiO$_3$-based two-dimensional electron gasses (2DEGs), have been found to play a role in features such as the superconducting dome, high-mobility transport, and the magnitude of spin-orbit coupling. This adds to the already very diverse range of phenomena, including magnetism and extreme magnetoresistance, exhibited by this p

Dingjie Song, Sicheng Lai, Mingxuan Wang, Shunian Chen

The rapid advancement of multimodal large language models (MLLMs) has significantly enhanced performance across benchmarks. However, data contamination-unintentional memorization of benchmark data during model training-poses critical challenges for fair evaluation. Existing detection methods for unimodal large language models (LLMs) are inadequate for MLLMs

Đorđe Dangić, Yue-Wen Fang, Tiago F. T. Cerqueira, Antonio Sanna

Recent predictions of metastable high-temperature hydride superconductors give hope that superconductivity at ambient conditions is within reach. In this work, we predict RbPH$_3$ as a new compound with a superconducting critical temperature around 100 K at ambient pressure, dynamically stabilized thanks to ionic quantum anharmonic effects. RbPH$_3$ is therm

James Cranch, Georg Struth

With a view on applications in computing, in particular concurrency theory and higher-dimensional rewriting, we develop notions of $n$-fold monoid and comonoid objects in $n$-fold monoidal categories and bicategories. We present a series of examples for these structures from various domains, including a categorical model for a communication protocol and a la

Tyler Clark, Mark Towers, Christine Evers, Jonathon Hare

Rainbow Deep Q-Network (DQN) demonstrated combining multiple independent enhancements could significantly boost a reinforcement learning (RL) agent's performance. In this paper, we present "Beyond The Rainbow" (BTR), a novel algorithm that integrates six improvements from across the RL literature to Rainbow DQN, establishing a new state-of-the-art for RL usi

Xi Yang, Xu Gu, Xingyilang Yin, Xinbo Gao

The proliferation of 2D foundation models has sparked research into adapting them for open-world 3D instance segmentation. Recent methods introduce a paradigm that leverages superpoints as geometric primitives and incorporates 2D multi-view masks from Segment Anything model (SAM) as merging guidance, achieving outstanding zero-shot instance segmentation resu

J. -K. Krogager, A. De Cia, K. E. Heintz, J. P. U. Fynbo

We study the properties of long gamma-ray burst (GRB) host galaxies using a statistical modelling framework derived to model damped Lyman-$\alpha$ absorbers (DLAs) in quasar spectra at high redshift. The distribution of NHI for GRB-DLAs is $\sim$10 times higher than what is found for quasar-DLAs at similar impact parameters. We interpret this as a temporal s

Zhirui Deng, Zhicheng Dou, Yutao Zhu, Ji-Rong Wen

The outstanding capabilities of large language models (LLMs) render them a crucial component in various autonomous agent systems. While traditional methods depend on the inherent knowledge of LLMs without fine-tuning, more recent approaches have shifted toward the reinforcement learning strategy to further enhance agents' ability to solve complex interactive

Mikhail Glazkov, Timofey Shilkin

In this paper we investigate the existence, uniqueness and stability of weak solutions of the initial boundary value problem with the Dirichlet boundary conditions for a parabolic equation with a drift $b\in L_2$. We prove $L_1$-stability of solutions with respect to perturbations of the drift $b$ in $L_2$ in the case if the drift satisfies the ``non-spectra

Leonhard Alkewitz, Timo Zuccarello, Alexander Raschke, Matthias Tichy

A flight trajectory defines how exactly a quadrocopter moves in the three-dimensional space from one position to another. Automatic flight trajectory planning faces challenges such as high computational effort and a lack of precision. Hence, when low computational effort or precise control is required, programming the flight route trajectory manually might b

Fengxiang Wang, Ranjie Duan, Peng Xiao, Xiaojun Jia

Large Language Models (LLMs) demonstrate outstanding performance in their reservoir of knowledge and understanding capabilities, but they have also been shown to be prone to illegal or unethical reactions when subjected to jailbreak attacks. To ensure their responsible deployment in critical applications, it is crucial to understand the safety capabilities a

Peter Gladbach, Eva Kopfer

The aim of this paper is to examine the large-scale behavior of dynamical optimal transport on stationary random graphs embedded in $\R^n$. Our primary contribution is a stochastic homogenization result that characterizes the effective behavior of the discrete problems in terms of a continuous optimal transport problem, where the homogenized energy density r

Tianhua Tao, Junbo Li, Bowen Tan, Hongyi Wang

Large Language Models (LLMs) specializing in code generation (which are also often referred to as code LLMs), e.g., StarCoder and Code Llama, play increasingly critical roles in various software development scenarios. It is also crucial for code LLMs to possess both code generation and natural language abilities for many specific applications, such as code s

Ioannis Caragiannis, Nick Gravin, Zhile Jiang

The problem of identifying the satisfiability threshold of random $3$-SAT formulas has received a lot of attention during the last decades and has inspired the study of other threshold phenomena in random combinatorial structures. The classical assumption in this line of research is that, for a given set of $n$ Boolean variables, each clause is drawn uniform

Claude Formanek, Callum Rhys Tilbury, Jonathan P. Shock

South Africa stands at a crucial juncture, grappling with interwoven socio-economic challenges such as poverty, inequality, unemployment, and the looming climate crisis. The government's Just Transition framework aims to enhance climate resilience, achieve net-zero greenhouse gas emissions by 2050, and promote social inclusion and poverty eradication. Accord

Zhihai Xiang

Our previous study [1] has demonstrated that the gauge theory is a proper framework for characterizing the local temporal and spatial interactions in inhomogeneous elastic media. However, in that study temporal interactions were interpreted as the compensation for the loss of kinetic energy resulting from homogenization process, distinct from damping effects

Erich Round, Louise Esher, Sacha Beniamine

Autonomous morphology, such as inflection class systems and paradigmatic distribution patterns, is widespread and diachronically resilient in natural language. Why this should be so has remained unclear given that autonomous morphology imposes learning costs, offers no clear benefit relative to its absence and could easily be removed by the analogical forces

Chengrui Qu, Laixi Shi, Kishan Panaganti, Pengcheng You

Online Reinforcement learning (RL) typically requires high-stakes online interaction data to learn a policy for a target task. This prompts interest in leveraging historical data to improve sample efficiency. The historical data may come from outdated or related source environments with different dynamics. It remains unclear how to effectively use such data

Gregory Debruyne

We provide a general quantified Ingham-Karamata Tauberian theorem with a flexible one-sided Tauberian condition under several types of boundary behavior for the Laplace transform. Our results in particular improve a theorem by Stahn, removing a vexing restriction on the growth of the Laplace transform. Improving existing optimality results, we also show that

Salma Hassan, Dawlat Akaila, Maryam Arjemandi, Vijay Papineni

In the complex realm of cognitive disorders, Alzheimer's disease (AD) and vascular dementia (VaD) are the two most prevalent dementia types, presenting entangled symptoms yet requiring distinct treatment approaches. The crux of effective treatment in slowing neurodegeneration lies in early, accurate diagnosis, as this significantly assists doctors in determi

Xuan Wang, Wenbin Lin, Bo Yang, Guansheng He

The gravitational deflection of light signals restricted in the polar-axis plane of a moving Kerr-Newman (KN) black hole with a constant velocity along the polar axis is studied within the second post-Minkowskian (PM) approximation. For this purpose, the Lorentz boosting technique is adopted to obtain the exact metric of a moving KN black hole with an arbitr

Jilan Mei, Junbo Li, Cai Meng

This paper proposes a new method for accurate and robust 6D pose estimation of novel objects, named GS2Pose. By introducing 3D Gaussian splatting, GS2Pose can utilize the reconstruction results without requiring a high-quality CAD model, which means it only requires segmented RGBD images as input. Specifically, GS2Pose employs a two-stage structure consistin

Aleksei Pastushkov

We examine the dynamics of informational efficiency in a market with asymmetrically informed, boundedly rational traders who adaptively learn optimal strategies using simple multiarmed bandit (MAB) algorithms. The strategies available to the traders have two dimensions: on the one hand, the traders must endogenously choose whether to acquire a costly informa

Hiu Ting Lau, Arkaitz Zubiaga

Natural Language Generation has been rapidly developing with the advent of large language models (LLMs). While their usage has sparked significant attention from the general public, it is important for readers to be aware when a piece of text is LLM-generated. This has brought about the need for building models that enable automated LLM-generated text detect

Yiming Li, Fang Li, Kirk Roberts, Licong Cui

Generating discharge summaries is a crucial yet time-consuming task in clinical practice, essential for conveying pertinent patient information and facilitating continuity of care. Recent advancements in large language models (LLMs) have significantly enhanced their capability in understanding and summarizing complex medical texts. This research aims to expl

M Muhsin, F Adersh, Mamata Sahoo

We analytically explore the dynamics of a charged active particle coupled to two thermal baths kept at two different temperatures in two dimensions. The particle is confined to an asymmetric harmonic potential and a magnetic field of constant magnitude is applied perpendicular to the plane of motion of the particle. For such a system, as opposed to Brownian

Marco Pozza, Antonio Siconolfi, Alfonso Sorrentino

We prove a homogenization result for a family of time-dependent Hamilton-Jacobi equations, rescaled by a parameter $\varepsilon$ tending to zero, posed on a periodic network, with a suitable notion of periodicity that will be defined. As $\varepsilon$ becomes infinitesimal, we derive a limiting Hamilton-Jacobi equation in a Euclidean space, whose dimension i

Nanxiang Zhou, Jing Dong, Yutian Li, Baoxiang Wang

To understand the complexity of the dynamic of learning in differential games, we decompose the game into components where the dynamic is well understood. One of the possible tools is Helmholtz's theorem, which can decompose a vector field into a potential and a harmonic component. This has been shown to be effective in finite and normal-form games. However,

Stavros Garoufalidis, Seokbeom Yoon

Motivated by the conjectured asymptotics of the Kashaev invariant, Dimofte and the first author introduced a power series associated to a suitable ideal triangulation of a cusped hyperbolic 3-manifold, proved that its constant (1-loop) term is a topological invariant and conjectured that it equals to the adjoint Reidemeister torsion. We prove this conjecture

Youning Li, Junfeng Huang, Chao Zhang, Jun Li

This work presented a perturbational decomposition method for simulating quantum evolution under the one-dimensional Ising model with both longitudinal and transverse fields. By treating the transverse field terms as perturbations in the expansion, our approach is particularly effective in systems with moderate longitudinal fields and weak to moderate transv

Edvin Listo Zec, Adam Breitholtz, Fredrik D. Johansson

Federated learning enables multiple actors to collaboratively train models without sharing private data. Existing algorithms are successful and well-justified in this task when the intended target domain, where the trained model will be used, shares data distribution with the aggregate of clients, but this is often violated in practice. A common reason is la

Hossam Amer, Joe Osborne, Michael Zaki, Mohamed Afify

Emojis improve communication quality among smart-phone users that use mobile keyboards to exchange text. To predict emojis for users based on input text, we should consider the on-device low memory and time constraints, ensure that the on-device emoji classifier covers a wide range of emoji classes even though the emoji dataset is typically imbalanced, and a

Mohit, Ranjana Jain

We identify the smooth points of $L^1(\mu,X)$, and provide some necessary and sufficient conditions for left and right symmetry of points with respect to Birkhoff-James orthogonality in $L^p(\mu,X), 1\leq p<\infty$, where $\mu$ is any complete positive measure and $X$ is a Banach space with some suitable properties.

Hasna Lammaihri, Marwa Erramla, Norjihan Abdul Ghani

This report presents an approach for optimizing metro station locations and line layouts in the area of Selangor, located in Malaysia. The project utilized the genetic algorithm in identifying the locations and lines layout. With population in Selangor projected to reach 7.3 million by 2024, the existing transport infrastructure is under increasing strain. T

Qianyun Miao, Fa Peng, Yuan Zhou

We establish a nonlinear Calder\'on-Zygmund $L^2$-theory to the Dirichlet problem $$-|Du|^{\gamma}\Delta^N_p u=f\in L^2(\Omega)\quad {\rm in}\quad \Omega; \quad u=0 \ \mbox{on $\partial\Omega$} $$ for $n\ge2$, $ p>1$ and a large range of $\gamma>-1$, in particular, for all $p>1$ and all $ \gamma>-1$ when $n=2$. Here $\Omega\subset \mathbb{R}^n$ is a bounded

Ziheng Jia, Zicheng Zhang, Jiaying Qian, Haoning Wu

The advent and proliferation of large multi-modal models (LMMs) have introduced new paradigms to computer vision, transforming various tasks into a unified visual question answering framework. Video Quality Assessment (VQA), a classic field in low-level visual perception, focused initially on quantitative video quality scoring. However, driven by advances in

Tomáš Karella, Adam Harmanec, Jan Kotera, Jan Blažek

CNNs exhibit inherent equivariance to image translation, leading to efficient parameter and data usage, faster learning, and improved robustness. The concept of translation equivariant networks has been successfully extended to rotation transformation using group convolution for discrete rotation groups and harmonic functions for the continuous rotation grou

Nils Friederich, Angelo Jovin Yamachui Sitcheu, Annika Nassal, Matthias Pesch

Microfluidic Live-Cell Imaging (MLCI) generates high-quality data that allows biotechnologists to study cellular growth dynamics in detail. However, obtaining these continuous data over extended periods is challenging, particularly in achieving accurate and consistent real-time event classification at the intersection of imaging and stochastic biology. To ad

Najib Khachiaa

The aim of this paper is to study $K$-frames for quaternionic Hilbert spaces. First, we present the quaternionic version of Douglas's theorem and then investigate $K$-frames for a quaternionic Hilbert space $\mathcal{H}$, where $K \in \mathbb{B}(\mathcal{H})$. Given two quaternionic Hilbert spaces $\mathcal{H}_1$ and $\mathcal{H}_2$, along with two right $\m

Philipp A. Guth, Vesa Kaarnioja

There has been a surge of interest in uncertainty quantification for parametric partial differential equations (PDEs) with Gevrey regular inputs. The Gevrey class contains functions that are infinitely smooth with a growth condition on the higher-order partial derivatives, but which are nonetheless not analytic in general. Recent studies by Chernov and Le (C

Ya-Rong Wang, Xiao-Hai Liu, Cheng-Qun Pang, Hao Chen

The BESIII Collaboration recently reported the observation of two new resonances, $X(1910)$ and $X(2300)$, which have sparked our interest in studying the light hadron family with $J^{PC}=1^{+-}$ . In this work, we investigate the mass spectra and OZI-allowed two-body strong decays of $b_1$, $h_1$, and $h_1^\prime$ using the MGI model and QPC model with newl

K. E. Harborne, C. del P. Lagos, S. M. Croom, J. van de Sande

This work considers the impact of resolution in the construction of mock observations of simulated galaxies. In particular, when building mock integral field spectroscopic observations from galaxy formation models in cosmological simulations, we investigate the possible systematics that may arise given the assumption that all galaxies above some stellar mass

Najib Khachiaa

The aim of this work is to study frame theory in quaternionic Hilbert spaces. We provide a characterization of frames in these spaces through the associated operators. Additionally, we examine frames of the form $\{Lu_i\}_{i \in I}$, where $L$ is a right $\mathbb{H}$-linear bounded operator and $\{u_i\}_{i \in I}$ is a frame.

Jie Zhao, Ming Li, Yu Li, Patrick Matgen

Understanding the extent of urban flooding is crucial for assessing building damage, casualties and economic losses. Synthetic Aperture Radar (SAR) technology offers significant advantages for mapping flooded urban areas due to its ability to collect data regardless weather and solar illumination conditions. However, the wide range of existing methods makes

Danny Ofek

We introduce a technique for proving lower bounds on the essential dimension of split reductive groups. As an application, we strengthen the best previously known lower bounds for various split simple algebraic groups, most notably for the exceptional group $E_8$. In the case of the projective linear group $\operatorname{PGL}_n$, we recover A. Merkurjev's ce

Antonin Gagnere, Geoffroy Peeters, Slim Essid

In this paper, we propose a novel Self-Supervised-Learning scheme to train rhythm analysis systems and instantiate it for few-shot beat tracking. Taking inspiration from the Contrastive Predictive Coding paradigm, we propose to train a Log-Mel-Spectrogram Transformer encoder to contrast observations at times separated by hypothesized beat intervals from thos

Mathis Caprais, André Bergeron

In this study, the Method of Characteristics (MOC) for Delayed Neutron Precursors (DNPs) is used to solve the precursors balance equation with turbulent diffusion. The diffusivity of DNPs, significantly higher than molecular diffusivity, emerges in turbulent flows from the time-averaging of the DNPs mass balance equation. To integrate this effect within the

Songmei Qin, Jing Zhong, Tong Tang, Yueyue Jiang

As a well-known open cluster, NGC 2323 (also called M50) has been widely investigated for over a hundred years and has always been considered a classical single cluster. In this work, with the help of Gaia DR3, we study the binary structure nature of this cluster. Although indistinguishable in the spatial space, the small but undeniable difference in the pro

Lawrence Stewart, Matthew Trager, Sujan Kumar Gonugondla, Stefano Soatto

Speculative decoding aims to speed up autoregressive generation of a language model by verifying in parallel the tokens generated by a smaller draft model.In this work, we explore the effectiveness of learning-free, negligible-cost draft strategies, namely $N$-grams obtained from the model weights and the context. While the predicted next token of the base m

Xiao Liang, Yu-Sen An, Chen-Hao Wu, Ya-Peng Hu

In this work, we study the evaporation behaviors of asymptotically flat charged black holes in the Einstein-Horndeski gravity theory. Based on the thermodynamics of the Horndeski black hole, we present a physical understanding of the scalar charge of the Horndeski black hole and also clarify its connection to the Einstein vector theory. As the presence of no