November 2024 arXiv papers — page 49

Jiaqi Xu, Haoyu Wang, Rang Liu, Josef A. Nossek

In contrast to conventional RIS, the scattering matrix of a non-reciprocal RIS (NR-RIS) is non-symmetric, leading to differences in the uplink and the downlink components of NR-RIS cascaded channels. In this paper, a physically-consistent device model is proposed in which an NR-RIS is composed of multiple groups of two-port elements inter-connected by non-re

Vennela Yarabolu, Govind Waghmare, Sonia Gupta, Siddhartha Asthana

In many real-world applications, continuous machine learning (ML) systems are crucial but prone to data drift, a phenomenon where discrepancies between historical training data and future test data lead to significant performance degradation and operational inefficiencies. Traditional drift adaptation methods typically update models using ensemble techniques

J. G. de Lima Júnior, P. H. R. S. Moraes, E. Brito, J. A. S. Fortunato

We present the first formulation of the recently proposed $f(R,\mathcal{L}_m,T)$ theory of gravity within the Palatini formalism, a well-known alternative variational approach where the metric and connection are treated as independent variables. By applying this formalism, we derive a new set of field equations that exhibit, as expected, distinct properties

Pawan Bharadwaj

We discuss the variational formulation of the Symmetric Autoencoder (SymAE) and its role in achieving disentanglement within the latent space to extract coherent information from a collection of seismic waveforms. Disentanglement involves separating the latent space into components for coherent information shared by all waveforms and components for waveform-

D. K. Weiss, Shifan Xu, Shruti Puri, Yongshan Ding

Proposals for quantum random access memory (QRAM) generally have a binary-tree structure, and thus require hardware that is exponential in the depth of the QRAM. For solid-state based devices, a fabrication yield that is less than $100\%$ implies that certain addresses at the bottom of the tree become inaccessible if a router in the unique path to that addre

Carlo Alberto Barbano, Luca Molinaro, Massimiliano Ciranni, Emanuele Aiello

Humans can visualize new and unknown concepts from their natural language description, based on their experience and previous knowledge. Insipired by this, we present a way to extend this ability to Vision-Language Models (VLMs), teaching them novel concepts by only using a textual description. We refer to this approach as Knowledge Transfer (KT). Our hypoth

George Savvidy

I discuss new non-perturbative solutions of the sourceless Yang-Mills equation representing the superposition of oppositely oriented chromomagnetic flux tubes (vortices) similar in their form to a lattice of superposed Abrikosov-Nielsen-Olesen chromomagnetic vortices. These solutions represent highly degenerate classical vacua of the Yang Mills theory that a

Diogo Sampaio da Silva, Roberto Antonio Cordeiro Prata

We present a brief introduction to a class of interactive fuzzy numbers, called $f$-correlated fuzzy numbers, which consist of pairs of fuzzy numbers where one is dependent on the other by a continuous monotone injective function. We have deduced some equations that can directly calculate the results of the sums and products of $f$-correlated fuzzy numbers,

Ji-Zhe Zhang, Yu Zeng, Qing Qin, Yuan-Hao Yang

A chip-integrated acousto-optic phase modulator tailored for visible optical wavelengths has been developed. Utilizing the lithium niobate on sapphire platform, the modulator employs a 7 GHz surface acoustic wave, excited by an interdigital transducer and aligned perpendicular to the waveguide. This design achieves efficient phase modulation of visible light

Éloi Zablocki, Valentin Gerard, Amaia Cardiel, Eric Gaussier

Understanding the decision processes of deep vision models is essential for their safe and trustworthy deployment in real-world settings. Existing explainability approaches, such as saliency maps or concept-based analyses, often suffer from limited faithfulness, local scope, or ambiguous semantics. We introduce GIFT, a post-hoc framework that aims to derive

Jiawei Zhang, Zijian Wu, Zhiyang Liang, Yicheng Gong

Reconstructing high-fidelity, animatable 3D head avatars from effortlessly captured monocular videos is a pivotal yet formidable challenge. Although significant progress has been made in rendering performance and manipulation capabilities, notable challenges remain, including incomplete reconstruction and inefficient Gaussian representation. To address these

Igor D. Karachentsev, Maxim I. Chazov, Serafim S. Kaisin

The 6-meter BTA telescope has been used to determine radial velocities for 40 galaxies, recently identified in the DESI Legacy Imaging Surveys as nearby objects. Half of them have kinematic distances within 11 Mpc being new probable companions to the bright Local Volume galaxies: NGC628, Maffei2, NGC2787, M81, NGC4605 and NGC4631. Six relatively isolated obj

Sergei Voronin, Abubakar Siddique, Muhammad Iqbal

The rapid progress in machine learning models has significantly boosted the potential for real-world applications such as autonomous vehicles, disease diagnoses, and recognition of emergencies. The performance of many machine learning models depends on the nature and size of the training data sets. These models often face challenges due to the scarcity, nois

Ryan Thalman, Nitish Bhardwaj, Callum E. Flowerday, Jaron C. Hansen

Sulfur dioxide ($\mathrm{SO}_2$) is an important precursor for the formation of atmospheric sulfate aerosol and acid rain. We present an instrument using Broadband Cavity-Enhanced Absorption Spectroscopy (BBCEAS) for the measurement of $\mathrm{SO}_2$ with a minimum limit of detection of 0.75 ppbv (3-$\sigma$) using the spectral range 305.5--312 nm and an av

Xuchen Li, Shiyu Hu, Xiaokun Feng, Dailing Zhang

Vision-language tracking (VLT) extends traditional single object tracking by incorporating textual information, providing semantic guidance to enhance tracking performance under challenging conditions like fast motion and deformations. However, current VLT trackers often underperform compared to single-modality methods on multiple benchmarks, with semantic i

Diogo Sampaio da Silva, Roberto Antonio Cordeiro Prata

This paper presents some concepts of the theory of interactive fuzzy numbers, and mainly, a class of interactive fuzzy numbers, called $f$-correlated fuzzy numbers. We start from the foundations of general fuzzy mathematics and go through operations and the notion of interactivity for fuzzy numbers. The main result is that $f$-correlation preserve the shape

Yu-Yue Li, Deng-Shan Wang

Darboux transformation plays a key role in constructing explicit closed-form solutions of completely integrable systems. This paper provides an algebraic construction of generalized Darboux matrices with the same poles for the $2\times2$ Lax pair, in which the coefficient matrices are polynomials of spectral parameter. The first-order monic Darboux matrix is

Qi Sun, Tong Zhang, Shang Gao, Liuqingqing Yang

This study introduces an advanced gesture recognition and user interface (UI) interaction system powered by deep learning, highlighting its transformative impact on UI design and functionality. By utilizing optimized convolutional neural networks (CNNs), the system achieves high-precision gesture recognition, significantly improving user interactions with di

Jilong Guo, Haobo Yang, Mo Zhou, Xinyu Zhang

Removing adverse weather conditions such as rain, raindrop, and snow from images is critical for various real-world applications, including autonomous driving, surveillance, and remote sensing. However, existing multi-task approaches typically rely on augmenting the model with additional parameters to handle multiple scenarios. While this enables the model t

Yueqiao Jin, Kaixun Yang, Lixiang Yan, Vanessa Echeverria

Learning analytics dashboards (LADs) simplify complex learner data into accessible visualisations, providing actionable insights for educators and students. However, their educational effectiveness has not always matched the sophistication of the technology behind them. Explanatory and interactive LADs, enhanced by generative AI (GenAI) chatbots, hold promis

Md Ashik Khan, Rafath Bin Zafar Auvee

Accurate brain tumor classification in MRI images is critical for timely diagnosis and treatment planning. While deep learning models like ResNet-18, VGG-16 have shown high accuracy, they often come with increased complexity and computational demands. This study presents a comparative analysis of effective yet simple Convolutional Neural Network (CNN) archit

Shaojiang Liu, Jiajun Zou, Zhendan Liu, Meixia Dong

With the widespread application of human body 3D reconstruction technology across various fields, the demands for data transmission and processing efficiency continue to rise, particularly in scenarios where network bandwidth is limited and low latency is required. This paper introduces an Adversarial Feature Learning-based Semantic Communication method (AFL

Jiawei Gu, Xuhui Jiang, Zhichao Shi, Hexiang Tan

Accurate and consistent evaluation is crucial for decision-making across numerous fields, yet it remains a challenging task due to inherent subjectivity, variability, and scale. Large Language Models (LLMs) have achieved remarkable success across diverse domains, leading to the emergence of "LLM-as-a-Judge," where LLMs are employed as evaluators for complex

Yuansong Xu, Jiahe Dong, Yijie Fan, Yuheng Shao

Continuous Medical Education (CME) plays a vital role in physicians' ongoing professional development. Beyond immediate diagnoses, physicians utilize multimodal diagnostic data for retrospective learning, engaging in self-directed analysis and collaborative discussions with peers. However, learning from such data effectively poses challenges for novice physi

Buddhadev Goswami, Adithya B. Somaraj, Prantar Chakrabarti, Ravindra Gudi

Hematological disorders, which involve a variety of malignant conditions and genetic diseases affecting blood formation, present significant diagnostic challenges. One such major challenge in clinical settings is differentiating Erythroblast from WBCs. Our approach evaluates the efficacy of various machine learning (ML) classifiers$\unicode{x2014}$SVM, XG-Bo

Neda Hejazi, Jerry W. Xuan, David R. Coria, Erica Sawczynec

The chemical abundance measurements of host stars and their substellar companions provide a powerful tool to trace the formation mechanism of the planetary systems. We present a detailed high-resolution spectroscopic analysis of a young M-type star, DH Tau A, which is located in the Taurus molecular cloud belonging to the Taurus-Auriga star-forming region. T

Lixiang Yan, Dragan Gašević, Linxuan Zhao, Vanessa Echeverria

Multimodal Learning Analytics (MMLA) leverages advanced sensing technologies and artificial intelligence to capture complex learning processes, but integrating diverse data sources into cohesive insights remains challenging. This study introduces a novel methodology for integrating latent class analysis (LCA) within MMLA to map monomodal behavioural indicato

Sagnik Bhattacharya, Abhishek K. Gupta

An efficient channel estimation is of vital importance to help THz communication systems achieve their full potential. Conventional uplink channel estimation methods, such as least square estimation, are practically inefficient for THz systems because of their large computation overhead. In this paper, we propose an efficient convolutional neural network (CN

Karlo Lelas, Robert Pezer

We show how to adapt the approach introduced for viscous damping in [1] to derive the approximate amplitude decay in the case of damping by a force of constant magnitude (sliding friction) and in the case of damping by a force proportional to the square of velocity (air resistance). We obtain two first-order differential equations from which we obtain the ap

Anubhav Jain, Yuya Kobayashi, Takashi Shibuya, Yuhta Takida

Diffusion models are prone to exactly reproduce images from the training data. This exact reproduction of the training data is concerning as it can lead to copyright infringement and/or leakage of privacy-sensitive information. In this paper, we present a novel perspective on the memorization phenomenon and propose a simple yet effective approach to mitigate

Jinhao Dong, Jun Sun, Wenjie Zhang, Jin Song Dong

Code generation techniques generate code snippets automatically based on the problem requirements in natural language. Recently, large language models (LLMs) achieve the SOTA performance on code generation. However, LLMs still struggle at times to generate accurate code, which diminishes their promised efficiency as developers must spend significant effort e

D. Wiechmann, E. Kempa, E. Kerz, Y. Qiao

Attention-deficit/hyperactivity disorder (ADHD) is a prevalent mental health condition affecting both children and adults, yet it remains severely underdiagnosed. Recent advances in artificial intelligence, particularly in Natural Language Processing (NLP) and Machine Learning (ML), offer promising solutions for scalable and non-invasive ADHD screening metho

Yadong Qu, Yuxin Wang, Bangbang Zhou, Zixiao Wang

Existing scene text recognition (STR) methods struggle to recognize challenging texts, especially for artistic and severely distorted characters. The limitation lies in the insufficient exploration of character morphologies, including the monotonousness of widely used synthetic training data and the sensitivity of the model to character morphologies. To addr

Pranav Jeevan, Neeraj Nixon, Amit Sethi

We introduce a new metric to assess the quality of generated images that is more reliable, data-efficient, compute-efficient, and adaptable to new domains than the previous metrics, such as Fr\'echet Inception Distance (FID). The proposed metric is based on normalizing flows, which allows for the computation of density (exact log-likelihood) of images from a

Yawen Zhang, Han Zhou, Zhoumingju Jiang, Zilu Tang

Cinematic Virtual Reality (CVR) is a narrative-driven VR experience that uses head-mounted displays with a 360-degree field of view. Previous research has explored different viewing modalities to enhance viewers' CVR experience. This study conducted a systematic review and meta-analysis focusing on how different viewing modalities, including intervened rotat

Xiaobao Wei, Qingpo Wuwu, Zhongyu Zhao, Zhuangzhe Wu

Photorealistic reconstruction of street scenes is essential for developing real-world simulators in autonomous driving. While recent methods based on 3D/4D Gaussian Splatting (GS) have demonstrated promising results, they still encounter challenges in complex street scenes due to the unpredictable motion of dynamic objects. Current methods typically decompos

Xinyu Si, Filippo De Lillo, Guido Boffetta, Lei Fang

In turbulent flows, energy flux refers to the transfer of kinetic energy across different scales of motion, a concept that is a cornerstone of turbulence theory. The direction of net energy flux is prescribed by the dimensionality of the fluid system.

Ryugo Morita, Stanislav Frolov, Brian Bernhard Moser, Takahiro Shirakawa

Diffusion models have enabled the generation of high-quality images with a strong focus on realism and textual fidelity. Yet, large-scale text-to-image models, such as Stable Diffusion, struggle to generate images where foreground objects are placed over a chroma key background, limiting their ability to separate foreground and background elements without fi

Jun Gao, Xizhi Liu, Jie Ma, Oleg Pikhurko

For a positive real number $p$, the $p$-norm $\left\lVert G \right\rVert_p$ of a graph $G$ is the sum of the $p$-th powers of all vertex degrees. We study the maximum $p$-norm $\mathrm{ex}_{p}(n,F)$ of $F$-free graphs on $n$ vertices. F\"{u}redi and K\"{u}ndgen \cite{FK06} show that for every bipartite graph $F$, there exists a threshold $p_F$ such that for

Adolf Mirotin

We describe holomorphic functions and fractional powers of Cesáro operators in $L^2(\mathbb{R})$, $L^2(\mathbb{R}_+)$, and $L^2[0,1]$. Logarithms of Cesáro operators are introduced as well and their spectral properties are studied. Several examples are considered.

Albert Kornilov, Tatiana Shavrina

Recent advances in language modeling have demonstrated significant improvements in zero-shot capabilities, including in-context learning, instruction following, and machine translation for extremely under-resourced languages (Tanzer et al., 2024). However, many languages with limited written resources rely primarily on formal descriptions of grammar and voca

Chengyin Li, Hui Zhu, Rafi Ibn Sultan, Hassan Bagher Ebadian

In the diverse field of medical imaging, automatic segmentation has numerous applications and must handle a wide variety of input domains, such as different types of Computed Tomography (CT) scans and Magnetic Resonance (MR) images. This heterogeneity challenges automatic segmentation algorithms to maintain consistent performance across different modalities

Qian Huang, Christian Rohde, Wen-An Yong, Ruixi Zhang

We introduce a new hyperbolic approximation to the incompressible Navier-Stokes equations by incorporating a first-order relaxation and using the artificial compressibility method. With two relaxation parameters in the model, we rigorously prove the asymptotic limit of the system towards the incompressible Navier-Stokes equations as both parameters tend to z

Gabriele Montanaro, Andrea Galimberti, Davide Zoni

Frameworks for the agile development of modern system-on-chips are crucial to dealing with the complexity of designing such architectures. The open-source Vespa framework for designing large, FPGA-based, multi-core heterogeneous system-on-chips enables a faster and more flexible design space exploration of such architectures and their run-time optimization.

A. D. Alhaidari

We obtain simple formulas for the matrix elements of the resolvent operator (the Green's function) in any finite set of square integrable basis. These formulas are suitable for numerical computations whether the basis elements are orthogonal or not. A byproduct of our findings is an expression for the normalized eigenvectors of a matrix in terms of its eigen

Dhairya Patel, Shaifali P. Malukani

Fog computing has gained significant attention for its potential to enhance resource management and service delivery by bringing computation closer to the network edge.While numerous surveys have explored various aspects of fog computing, there is a distinct gap in the literature when it comes to fog federation, a crucial extension that enables collaboration

Shipra Gupta, Amiya Kumar Pani, Sangita Yadav

In this article, a hybridizable discontinuous Galerkin (HDG) method is proposed and analyzed for the Klein-Gordon equation with local Lipschitz-type non-linearity. {\it A priori} error estimates are derived, and it is proved that approximations of the flux and the displacement converge with order $O(h^{k+1}),$ where $h$ is the discretizing parameter and $k$

Yongqi Liang, Changrong Xie, Zechen Guo, Peisheng Huang

Random fluctuations caused by environmental noise can lead to decoherence in quantum systems. Exploring and controlling such dissipative processes is both fundamentally intriguing and essential for harnessing quantum systems to gain practical advantages and deeper insights. In this work, we first demonstrate the diffusive dynamics assisted by controlled deph

Somayeh Aghashahi, Zolfa Zeinalpour-Yazdi, Aliakbar Tadaion, Mahdi Boloursaz Mashhadi

Reconfigurable Intelligent Surfaces (RISs) are envisioned to be employed in next generation wireless networks to enhance the communication and radio localization services. In this paper, we propose novel localization and tracking algorithms exploiting reflections through RISs at multiple receivers. We utilize a single antenna transmitter (Tx) and multiple si

Tekin Karadag, Daniel K. Nakano

In this paper the authors investigate the structure of the Hochschild cohomology for Frobenius kernels. The authors first establish some fundamental constructions to compute Hochschild cohomology by using spectral sequences. This enables us to provide a complete description of the $G$-algebra structure of the Hochschild cohomology for the first Frobenius ker

Shikhamoni Nath, Dhiren Kumar Basnet

Let $q$ be a positive integral power of some prime $p$ and $\mathbb{F}_{q^m}$ be a finite field with $q^m$ elements for some $m \in \mathbb{N}$. Here we establish a sufficient condition for the existence of a non-zero element $\epsilon \in \mathbb{F}_{q^m}$, such that $(f(\epsilon), g(\epsilon))$ is a primitive pair in $\mathbb{F}_{q^m}$ with two prescribed

Kunhai Qing, Xinru Ren, Shuping Jiang, Ping Yang

Multi-regional clinical trial (MRCT) has been common practice for drug development and global registration. The FDA guidance `Demonstrating Substantial Evidence of Effectiveness for Human Drug and Biological Products Guidance for Industry' (FDA, 2019) requires that substantial evidence of effectiveness of a drug/biologic product to be demonstrated for market

Junkai Zhao, Jun Luo, Wei Xie, Zixuan Bai

Biomanufacturing plays an important role in supporting public health and the growth of the bioeconomy. Modeling and studying the interaction effects among various input variables is very critical for obtaining a scientific understanding and process specification in biomanufacturing. In this paper, we use the ShapleyOwen indices to measure the interaction eff

Marcin Łoś, Tomasz Służalec, Maciej Paszyński, Eirik Valseth

We compare several stabilization methods in the context of isogeometric analysis and B-spline basis functions, using an advection-dominated advection\revision{-}diffusion as a model problem. We derive (1) the least-squares finite element method formulation using the framework of Petrov-Galerkin method with optimal test functions in the $L_2$ norm, which guar

Sanjeev Kumar Gupta, Nico Spronk

For rank 1 flat symmetric spaces, continuous orbital measures admit absolutely continuous convolution squares, except for Cartan type AI. Hence $L^1$-$L^2$ dichotomy for these spaces holds true in parallel to the compact and non-compact rank 1 symmetric spaces. We also study $L^1$-$L^2$ dichotomy for flat symmetric spaces of ranks $p=2,3$ of type AIII, i.e.\

T. Beck, A. Gade, B. A. Brown, D. Weisshaar

In-beam $\gamma$-ray spectroscopy was used to study excited states of the neutron-deficient nucleus $^{37}$K populated in fast-beam inelastic-scattering and proton-removal reactions at high-momentum loss. New $\gamma$-ray transitions and $\gamma\gamma$ coincidence relationships were established using the $\gamma$-ray tracking array GRETINA. The extension of

L. Heitz, J. -P. Ebran, E. Khan, D. Verney

A simple pattern of organisation, the nuclear shell structure, emerges from the complex interactions between nucleons in nuclei and determines, to some significant degree, nuclear structure properties. Recent experimental investigations of exotic nuclei revealed a shortfall in our current understanding of nuclear shell evolution and nuclear magicity. We intr

Ram Gopal Jaiswal, Ankik Kumar Giri

A mathematical model for the continuous nonlinear fragmentation equation is considered in the presence of mass transfer. In this paper, we demonstrate the existence of mass-conserving weak solutions to the nonlinear fragmentation equation with mass transfer for collision kernels of the form $\Phi(x,y) = \kappa(x^{{\sigma_1}} y^{{\sigma_2}} + y^{{\sigma_1}} x

Abdullah Al Rabeyah, Fabrício Góes, Marco Volpe, Talles Medeiros

This paper investigates whether large language models (LLMs) show agreement in assessing creativity in responses to the Alternative Uses Test (AUT). While LLMs are increasingly used to evaluate creative content, previous studies have primarily focused on a single model assessing responses generated by the same model or humans. This paper explores whether LLM

Ashutosh K. Mishra, Emma Tolley, Shreyam Parth Krishna, Jean-Paul Kneib

Detecting diffuse radio emission, such as from halos, in galaxy clusters is crucial for understanding large-scale structure formation in the universe. Traditional methods, which rely on X-ray and Sunyaev-Zeldovich (SZ) cluster pre-selection, introduce biases that limit our understanding of the full population of diffuse radio sources. In this work, we provid

Yao Lu, Hao Cheng, Yujie Fang, Zeyu Wang

Although large language models (LLMs) have achieved remarkable success across various domains, their considerable scale necessitates substantial computational resources, posing significant challenges for deployment in resource-constrained environments. Layer pruning, as a simple yet effective compression method, removes layers of a model directly, reducing c

Anxhelo Diko, Antonino Furnari, Luigi Cinque, Giovanni Maria Farinella

Unsupervised domain adaptation remains a critical challenge in enabling the knowledge transfer of models across unseen domains. Existing methods struggle to balance the need for domain-invariant representations with preserving domain-specific features, which is often due to alignment approaches that impose the projection of samples with similar semantics clo

Anxhelo Diko, Tinghuai Wang, Wassim Swaileh, Shiyan Sun

Vision-Language Models (VLMs) are crucial for applications requiring integrated understanding textual and visual information. However, existing VLMs struggle with long videos due to computational inefficiency, memory limitations, and difficulties in maintaining coherent understanding across extended sequences. To address these challenges, we introduce ReWind

Fengfan Zhou, Bangjie Yin, Hefei Ling, Qianyu Zhou

Face Recognition (FR) models are vulnerable to adversarial examples that subtly manipulate benign face images, underscoring the urgent need to improve the transferability of adversarial attacks in order to expose the blind spots of these systems. Existing adversarial attack methods often overlook the potential benefits of augmenting the surrogate model with

Sergey V. Gusev

An algebra that generates a variety with uncountably many subvarieties is said to be of type $2^{\aleph_0}$. We show that the Rees quotient monoid $M(aabb)$ of order ten is of type $2^{\aleph_0}$, thereby affirmatively answering a recent question of Glasson. As a corollary, we exhibit a new example of type $2^{\aleph_0}$ monoid of order six, which turns out

Kaisheng Liang, Xuelong Dai, Yanjie Li, Dong Wang

Deep neural networks (DNNs) exhibit vulnerability to adversarial examples that can transfer across different DNN models. A particularly challenging problem is developing transferable targeted attacks that can mislead DNN models into predicting specific target classes. While various methods have been proposed to enhance attack transferability, they often incu

Pietro Luigi Muzzeddu, Erik Kalz, Andrea Gambassi, Abhinav Sharma

Odd-diffusive systems, characterised by broken time-reversal and/or parity symmetry, have recently been shown to display counterintuitive features such as interaction-enhanced dynamics in the dilute limit. Here we we extend the investigation to the high-density limit of an odd tracer embedded in a soft-Gaussian core medium (GCM) using a field-theoretic appro

Menglin Zhang, Xin Luo, Yunwei Lan, Chang Liu

Recent advances in NeRF inpainting have leveraged pretrained diffusion models to enhance performance. However, these methods often yield suboptimal results due to their ineffective utilization of 2D diffusion priors. The limitations manifest in two critical aspects: the inadequate capture of geometric information by pretrained diffusion models and the subopt

Siddhant Dutta, Iago Leal de Freitas, Pedro Maciel Xavier, Claudio Miceli de Farias

Federated Learning (FL) is a decentralized machine learning approach that has gained attention for its potential to enable collaborative model training across clients while protecting data privacy, making it an attractive solution for the chemical industry. This work aims to provide the chemical engineering community with an accessible introduction to the di

Peter F. Patel-Schneider, Ege Atacan Doğan

Wikidata has a large ontology with classes at several orders. The Wikidata ontology has long been known to have violations of class order and information related to class order that appears suspect. SPARQL queries were evaluated against Wikidata to determine the prevalence of several kinds of violations and suspect information and the results analyzed. Some

Till Hauser

Mean equicontinity is a well studied notion for actions. We propose a definition of mean equicontinuous factor maps that generalizes mean equicontinuity to the relative context. For this we work in the context of countable amenable groups. We show that a factor map is equicontinuous, if and only if it is mean equicontinuous and distal. Furthermore, we show t

N. Pirnay, S. Jerbi, J. -P. Seifert, J. Eisert

One of the core challenges of research in quantum computing is concerned with the question whether quantum advantages can be found for near-term quantum circuits that have implications for practical applications. Motivated by this mindset, in this work, we prove an unconditional quantum advantage in the probably approximately correct (PAC) distribution learn

Krishnendu Gongopadhyay, Lokenath Kundu, Shashank Vikram Singh

The palindromic automorphism group is a subgroup of the automorphism group $Aut(F_3).$ We establish a necessary and sufficient condition for a matrix in $GL_n(\mathbb{Z})$ representing a palindromic automorphism of $F_n.$ We prove that the number of the $z$-classes in $\Pi A(F_n)$ is infinite. We further classify the conjugacy classes of the reducible palind

Rui-Nan Li, Zhen-Yin Zhao, Qin Wu, Shuang-Xi Yi

The structure function (SF) analysis is a powerful tool for studying plasma turbulence. Theoretically, the SF of Faraday rotation measure (RM) is expected to include a geometric component due to the relative orientation of sightlines through an ordered magnetic field. However, observational evidence for this component remains elusive. Here, we report that th

Sowrabh Sudevan, Ramij Rahaman, Sourin Das

In [Phys. Rev. A 77, 060304(R),(2008)], Facchi et al. introduced absolutely maximally entangled (AME) states and also suggested ``majority-agreed key distribution"(MAKD) as a possible application for such states. In MAKD, the qubits of an AME state are distributed one each to many spatially separated parties. AME property makes it necessary that quantum key

Haochen Zhao, Xiangru Tang, Ziran Yang, Xiao Han

The advancement and extensive application of large language models (LLMs) have been remarkable, including their use in scientific research assistance. However, these models often generate scientifically incorrect or unsafe responses, and in some cases, they may encourage users to engage in dangerous behavior. To address this issue in the field of chemistry,

Paul C Bressloff

The run-and-tumble particle (RTP) is one of the simplest examples of an active particle in which the direction of constant motion randomly switches. In the one-dimensional (1D) case this means switching between rightward and leftward velocities. Most theoretical studies of RTPs are based on the analysis of the Chapman-Kolmogorov (CK) differential equation de

P. Marchegiani, V. Vacca, F. Govoni, M. Murgia

We present the results of observations performed with the Sardinia Radio Telescope (SRT) at 1.3-1.8 GHz of the galaxy cluster CL 0217+70 and a $3^\circ \times 3^\circ$ region around it. We combine the SRT data with archival Very Large Array (VLA) data to obtain images having the VLA angular resolution, but sensitive up to largest scales. The SRT+VLA combinat

Hao Tang, Bin Ren, Pingping Wu, Nicu Sebe

In this paper, we present an innovative solution for the challenges of the virtual try-on task: our novel Hierarchical Cross-Attention Network (HCANet). HCANet is crafted with two primary stages: geometric matching and try-on, each playing a crucial role in delivering realistic virtual try-on outcomes. A key feature of HCANet is the incorporation of a novel

Hyelin Nam, Jaemin Kim, Dohun Lee, Jong Chul Ye

While text-to-video diffusion models have made significant strides, many still face challenges in generating videos with temporal consistency. Within diffusion frameworks, guidance techniques have proven effective in enhancing output quality during inference; however, applying these methods to video diffusion models introduces additional complexity of handli

Zhixuan Chen, Yequan Bie, Haibo Jin, Hao Chen

Computed tomography (CT) report generation is crucial to assist radiologists in interpreting CT volumes, which can be time-consuming and labor-intensive. Existing methods primarily only consider the global features of the entire volume, making it struggle to focus on specific regions and potentially missing abnormalities. To address this issue, we propose Re

Jhervey Edric Cheng, Stacy Selena Kalaw, James Patrick Kok, Alyssa Ysabelle Meneses

Gyroscope integration in Xbox controllers offers new possibilities for enhancing gaming experiences, particularly in first-person shooter (FPS) games. To investigate its potential, we conducted an empirical study with 11 participants, comparing aim precision and reaction times across three input methods: a computer mouse, a standard Xbox controller, and a gy

Jing Wu, Mehrtash Harandi

Machine Unlearning (MU) aims to selectively erase harmful behaviors from models while retaining the overall utility of the model. As a multi-task learning problem, MU involves balancing objectives related to forgetting specific concepts/data and preserving general performance. A naive integration of these forgetting and preserving objectives can lead to grad

Joaquim Jusseau, Hamza Jaffali, Frédéric Holweck

In this paper, the CHSH quantum game is extended to four players. This is achieved by exploring all possible 4-variable Boolean functions to identify those that yield a game scenario with a quantum advantage using a specific entangled state. Notably, two new four-player quantum games are presented. In one game, the optimal quantum strategy is achieved when p

Pavel Jolakoski, Jordan Aiko Deja, Klen Čopič Pucihar, Matjaž Kljun

Robots have the potential to enhance teaching of advanced computer science topics, making abstract concepts more tangible and interactive. In this paper, we present Timmy-a GoPiGo robot augmented with projections to demonstrate shortest path algorithms in an interactive learning environment. We integrated a JavaScript-based application that is projected arou

Paul C Bressloff

Most studies of collective phenomena in oscillator networks focus on directly coupled systems as exemplified by the classical Kuramoto model. However, there are growing number of examples in which oscillators interact indirectly via a common external medium, including bacterial quorum sensing (QS) networks, pedestrians walking on a bridge, and centrally coup

Mosab Diab, Ashraf Mohammed, Yinlai Jiang

Electromyography (EMG) is a measure of muscular electrical activity and is used in many clinical/biomedical disciplines and modern human computer interaction. Myo-electric prosthetics analyze and classify the electrical signals recorded from the residual limb. The classified output is then used to control the position of motors in a robotic hand and a moveme

Ben S. Ashby, Veronika Chronholm, Daniel K. Hajnal, Alex Lukyanov

In this work, we present a fundamental mathematical model for proton transport, tailored to capture the key physical processes underpinning Proton Beam Therapy (PBT). The model provides a robust and computationally efficient framework for exploring various aspects of PBT, including dose delivery, linear energy transfer, treatment planning and the evaluation

Lazare Osmanov, Levan Ghaghanidze, Saba Sigua, Temur Begishvili

The official data provided by "Central Election Commission" was analyzed, revealing irregularities that raised reasonable suspicion of election manipulation by the winning ``Georgian Dream Party." However, these suspicions alone were insufficient to provide concrete evidence. A computational approach was developed based on the official data to address this.

Yuxiao Wu, Huizhi Wang, Yong Zeng

In this letter, a fast Fourier transform (FFT)-enhanced low-complexity super-resolution sensing algorithm for near-field source localization with both angle and range estimation is proposed. Most traditional near-field source localization algorithms suffer from excessive computational complexity or incompatibility with existing array architectures. To addres

Germain Tobar, Oscar Berg

The photo-electric effect was a historic milestone in the development of quantum theory, revealing the first evidence of discrete energy of the electromagnetic field through hallmark signatures such as the threshold frequency, intensity-independent energy transfer, and the near instantaneous ejection of photo-electrons. Here, we discuss the photo-electric ef

Yasin Ghafourian, Sajad Movahedi, Azadeh Shakery

CQA services are valuable sources of knowledge that can be used to find answers to users' information needs. In these services, question retrieval aims to help users with their information needs by finding similar questions to theirs. However, finding similar questions is obstructed by the lexical gap that exists between relevant questions. In this work, we

Min Qiu, Yu-Chih Huang, Jinhong Yuan

We consider the uplink multiple access of heterogeneous users, e.g., ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB) users. Each user has its own reliability requirement and blocklength constraint, and users transmitting longer blocks suffer from heterogeneous interference. On top of that, the decoding of URLLC messages

Mohammad Kafini

In this work, we are concerned with a nonlinear wave equation with variable exponents. A distributive delay is imposed into the damping term with variable exponents nonlinearity. Firstly, we show that the global nonexistence time can be dominated. Secondly, global existence of solutions is shown under some suitable conditions on the initial data. Finally, th

Theodor-Adrian Badea, Bogdan Dumitrescu

This paper introduces a novel Laplacian matrix aiming to enable the construction of spectral convolutional networks and to extend the signal processing applications for directed graphs. Our proposal is inspired by a Haar-like transformation and produces a Hermitian matrix which is not only in one-to-one relation with the adjacency matrix, preserving both dir

Rui Hao, Dayu Tan, Qiankun Li, Chunhou Zheng

Accurate segmentation of organs-at-risk (OARs) is vital for safe and precise radiotherapy and surgery. Most existing studies segment only a limited set of organs or regions, lacking a systematic treatment of OARs segmentation. We present a Multi-scale Cascaded Fusion Network (MCFNet) that aggregates features across multiple scales and resolutions. MCFNet con

Tianhao Chen, Pengbo Xu, Pengbo Xu

In recent years, the introduction of AI technologies has brought transformative changes to scientific computing. However, AI models typically focus on single-task and single-modal data processing, limiting their application. To address this, multimodal scientific computing frameworks have become a trend. The Botfip framework aligns function images with symbo

Ganyong Mo, Krishna Kumar Narayanan, David Castells-Rufas, Jordi Carrabina

Maxwell's equations are the fundamental equations for understanding electric and magnetic field interactions and play a crucial role in designing and optimizing sensor systems like capacitive touch sensors, which are widely prevalent in automotive switches and smartphones. Ensuring robust functionality and stability of the sensors in dynamic environments nec

C. Y. Kuo, C. Y. Tai, A. Constantin, J. A. Braatz

We report the detection of H$_2$O maser emission in 4 out of 77 (5.2%) mid-IR red galaxies that meet the color criteria of $W1-W2 > 0.5$ and $W1-W4 > 7$ and are classified as Type-2 AGNs based on optical, near-IR, and mid-IR spectral energy distribution (SED) fitting. Here, $W1$, $W2$, and $W4$ represent the IR magnitudes at 3.4, 4.6, and 22 micron, respecti

Rahul Nihalani, Kushal Shah

This paper presents an improved LLM based model for Grammatical Error Detection (GED), which is a very challenging and equally important problem for many applications. The traditional approach to GED involved hand-designed features, but recently, Neural Networks (NN) have automated the discovery of these features, improving performance in GED. Traditional ru