November 2024 arXiv papers — page 165

Hamidreza Yaghoubi, Navtaj Randhawa, Igor Ivkić

Traditional access control systems, such as key cards, PIN pads, and physical keys, face challenges in scalability, security, and user experience in today's digital world. We present a cloud-based entry system using Raspberry Pi hardware and Amazon Web Services (AWS) technologies like Lambda, Simple Storage Service (S3), and Rekognition. This solution (AWSec

Solon P. Pissis

We revisit the classic border tree data structure [Gu, Farach, Beigel, SODA 1994] that answers the following prefix-suffix queries on a string $T$ of length $n$ over an integer alphabet $\Sigma=[0,\sigma)$: for any $i,j \in [0,n)$ return all occurrences of $T$ in $T[0\mathinner{.\,.} i]T[j\mathinner{.\,.} n-1]$. The border tree of $T$ can be constructed in $

Bappaditya Roy, Natsuhiko Yoshinaga

We present a data-driven framework for learning hydrodynamic equations from particle-based simulations of active matter. Our method leverages coarse-graining in both space and time to bridge microscopic particle dynamics with macroscopic continuum models. By employing spectral representations and sparse regression, we efficiently estimate partial differentia

Daan Schouten, Giulia Nicoletti, Bas Dille, Catherine Chia

Recent technological advances in healthcare have led to unprecedented growth in patient data quantity and diversity. While artificial intelligence (AI) models have shown promising results in analyzing individual data modalities, there is increasing recognition that models integrating multiple complementary data sources, so-called multimodal AI, could enhance

Kenji Fukushima, Shuhei Minato

In a unified perturbative treatment from the high-density side, we compute the speed of sound and the trace anomaly as functions of the chemical potential $\mu$ for the two-color diquark superfluid, the pion-condensed high-isospin matter, and the 2SC quark matter. We find that the corrections induced by the gap energy $\Delta$ involve nontrivial interplay be

Xinyu Wu, Dan Wu, Eytan Modiano

We consider the problem of network stability in finite-buffer systems. We observe that finite buffer may affect stability even in simplest network structure, and we propose an ordinary differential equation (ODE) model to capture the queuing dynamics and analyze the stability in buffered communication networks with general topology. For single-commodity syst

Maciej Beręsewicz, Marek Wydmuch, Herman Cherniaiev, Robert Pater

The goal of this paper is to develop a multilingual classifier and conditional probability estimator of occupation codes for online job advertisements in accordance with the International Standard Classification of Occupations (ISCO) extended with the Polish Classification of Occupations and Specializations (KZiS), which is analogous to the European Classifi

Héctor Barge, J. J. Sánchez-Gabites, J. M. R. Sanjurjo

The aim of this paper is to study dynamical and topological properties of a flow in the region of influence of an isolated non-saddle set or a $W$-set in a manifold. These are certain classes of compact invariant sets in whose vicinity the asymptotic behaviour of the flow is somewhat controlled. We are mainly concerned with global properties of the dynamics

Ziyan Chen, Yuxin Wang, Kun Jiang, Jiangping Hu

Motivated by recent photoemission measurements on the La$_{0.8}$Sr$_{0.2}$NiO$_2$, we carry out a systematic study of the infinite-layer nickelate using both dynamical mean-field theory and density matrix embedding theory. The renormalized electronic structure and Fermi surface of correlated La$_{0.8}$Sr$_{0.2}$NiO$_2$ are studied in an effective two-band mo

Baptiste Chatelier, José Miguel Mateos-Ramos, Vincent Corlay, Christian Häger

Direction of arrival (DoA) estimation is a common sensing problem in radar, sonar, audio, and wireless communication systems. It has gained renewed importance with the advent of the integrated sensing and communication paradigm. To fully exploit the potential of such sensing systems, it is crucial to take into account potential hardware impairments that can

Yaoyu Yang, Zitan Chen

Motivated by studies of data retrieval in polymer-based storage systems, we consider the problem of reconstructing a multiset of binary strings that have the same length and the same weight from the compositions of their prefixes and suffixes of every possible length. We provide necessary and sufficient conditions for which unique reconstruction up to revers

Chenchuhui Hu

Combinatorial optimization (CO) problems are challenging as the computation time grows exponentially with the input. Graph Neural Networks (GNNs) show promise for researchers in solving CO problems. This study investigates the effectiveness of GNNs in solving the maximum independent set (MIS) problem, inspired by the intriguing findings of Schuetz et al., an

Evgeny Sevost'yanov, Denys Romash, Nataliya Ilkevych

We consider mappings satisfying an upper bound for the distortion of families of curves. We establish lower bounds for the distortion of distances under such mappings. As applications, we obtain theorems on the discreteness of the limit mapping of a sequence of mappings converging locally uniformly. We separately consider cases when mappings are defined in E

Shozen Dan, Joshua Tegegne, Yu Chen, Zhi Ling

Social contact surveys are an important tool to assess infection risks within populations, and the effect of non-pharmaceutical interventions on social behaviour during disease outbreaks, epidemics, and pandemics. Numerous longitudinal social contact surveys were conducted during the COVID-19 era, however data analysis is plagued by reporting fatigue, a phen

Purba Mukherjee, Maria Giovanna Dainotti, Konstantinos F. Dialektopoulos, Jackson Levi Said

The $\Lambda$ Cold Dark Matter ($\Lambda$CDM) cosmological model has been highly successful in predicting cosmic structure and evolution, yet recent precision measurements have highlighted discrepancies, especially in the Hubble constant inferred from local and early-Universe data. Gamma-ray bursts (GRBs) present a promising alternative for cosmological meas

M. Mierzejewski, E. Dagotto, J. Herbrych

Valence bond (VB) states as the formation mechanism of Cooper pairs, eventually leading to high-temperature superconductivity, remain a controversial topic. Although various VB-like states find variational relevance in the description of specific spin models and quantum spin liquids, in the realm of many-body fermionic Hamiltonians, the evidence for such sta

Suhail Khan, Shafqat Ul Islam, Sushant G. Ghosh, Sunil D. Maharaj

Interpreting the cosmological constant as the energy of the vacuum and using a gravitational decoupling approach leads to a new Kerr--anti-de Sitter (AdS) black hole. The metric of the new Kerr--AdS is simpler than the standard Kerr--AdS and exhibits richer geometry, where the effects of rotation appear as warped curvature. We investigate the relationship be

Youssef Mohamed, Runjia Li, Ibrahim Said Ahmad, Kilichbek Haydarov

Research in vision and language has made considerable progress thanks to benchmarks such as COCO. COCO captions focused on unambiguous facts in English; ArtEmis introduced subjective emotions and ArtELingo introduced some multilinguality (Chinese and Arabic). However we believe there should be more multilinguality. Hence, we present ArtELingo-28, a vision-la

Xinnuo Xu, Minyoung Kim, Royson Lee, Brais Martinez

Data point selection (DPS) is becoming a critical topic in deep learning due to the ease of acquiring uncurated training data compared to the difficulty of obtaining curated or processed data. Existing approaches to DPS are predominantly based on a bi-level optimisation (BLO) formulation, which is demanding in terms of memory and computation, and exhibits so

Teppei Kurita, Yuhi Kondo, Legong Sun, Takayuki Sasaki

In this study, we propose a high-performance disparity (depth) estimation method using dual-pixel (DP) images with few parameters. Conventional end-to-end deep-learning methods have many parameters but do not fully exploit disparity constraints, which limits their performance. Therefore, we propose a lightweight disparity estimation method based on a complet

Gabriel Claret, Anna Rozanova-Pierrat, Alexander Teplyaev

We prove the convergence of layer potential operators for the harmonic transmission problem over a sequence of converging two-sided extension domains. Consequently, the Neumann-Poincar{\'e} operators, Calder{\'o}n projectors, and associated Neumann series converge in this setting. As a result, we generalize the notion of Cauchy integrals and, in a sense, of

Xinle Wu, Xingjian Wu, Dalin Zhang, Miao Zhang

Societal and industrial infrastructures and systems increasingly leverage sensors that emit correlated time series. Forecasting of future values of such time series based on recorded historical values has important benefits. Automatically designed models achieve higher accuracy than manually designed models. Given a forecasting task, which includes a dataset

Haotong Yang, Yi Hu, Shijia Kang, Zhouchen Lin

Large language models (LLMs) can solve an increasing number of complex reasoning tasks while making surprising mistakes in basic numerical understanding and processing (such as 9.11 > 9.9). The latter ability is essential for tackling complex arithmetic and mathematical problems and serves as a foundation for most reasoning tasks, but previous work paid litt

Rodolphe Garbit, Julien-Bilal Zinoune

This paper presents a family of Fourier eigenfunctions indexed by the space dimension d. These eigenfunctions are radial and built upon some generalized exponential integral function. For d=1,2,3, they are integrable or square integrable and give new explicit examples of Fourier eigenfunctions in the usual or Fourier-Plancherel sense. For d>3, the functions

Nataliia E. Kopteva, Dmitri R. Yakovlev, Eyüp Yalcin, Ina V. Kalitukha

The great variety of lead halide perovskite semiconductors represents an outstanding platform for studying crystal symmetry effects on the spin-dependent properties. Access to them is granted through optical orientation of exciton and carrier spins by circularly polarized photons. Here, the exciton spin polarization is investigated at $1.6$\,K cryogenic temp

Marco Fenucci, Laura Faggioli, Francesco Gianotto, Davide Bracali Cioci

The NEO Coordination Centre (NEOCC) of the European Space Agency is an operational centre that, among other activities, computes the orbits of near-Earth objects and their probabilities of impact with the Earth. The NEOCC started providing information about near-Earth objects in 2012 on a dedicated web portal, accessible at https://neo.ssa.esa.int/. Since th

Jia-Cheng Tang, Jin Zhao, Haitao Yang, Junlong Tian

We propose a scheme for realizing a deterministic two-photon C-Z gate based on variants of the two-photon quantum Rabi model (QRM), which is feasible within the framework of circuit QED. We begin by utilizing the two-photon interaction to implement the nonlinear sign (NS) gate, and subsequently, we construct the C-Z gate following the KLM scheme. We consider

Marco Moraschini, George Raptis

We show that a surjective homomorphism $\varphi \colon \Gamma \to K$ of (discrete) groups induces an isomorphism $H^\bullet_b(K; V) \to H^\bullet_b(\Gamma; \varphi^{-1} V)$ in bounded cohomology for all dual normed $K$-modules $V$ if and only if the kernel of $\varphi$ is boundedly acyclic. This complements a previous result by the authors that characterized

Martina Matusko, Marion Delehaye

Current state-of-the-art frequency standards are passive optical atomic clocks where the frequency of an optical resonator is stabilized to a narrow atomic transition. Passive clocks have achieved unprecedented stabilities of 6.6 x 10--19 over one hour of averaging time [1]. However, they face intrinsic limitations, particularly due to thermal and mechanical

Bartosz Bieganowski, Robert Ślepaczuk

This paper investigates the enhancement of financial time series forecasting with the use of neural networks through supervised autoencoders (SAE), to improve investment strategy performance. Using the Sharpe and Information Ratios, it specifically examines the impact of noise augmentation and triple barrier labeling on risk-adjusted returns. The study focus

Eliot Beyler, Francis Bach

In this paper, we derive variational inference upper-bounds on the log-partition function of pairwise Markov random fields on the Boolean hypercube, based on quantum relaxations of the Kullback-Leibler divergence. We then propose an efficient algorithm to compute these bounds based on primal-dual optimization. An improvement of these bounds through the use o

Yu Guan, Qinrong Cai, Wei Li, Qiuyun Fan

Diffusion model-based approaches recently achieved re-markable success in MRI reconstruction, but integration into clinical routine remains challenging due to its time-consuming convergence. This phenomenon is partic-ularly notable when directly apply conventional diffusion process to k-space data without considering the inherent properties of k-space sampli

Pablo Romero-Llorente, Jose Manuel Romero-Enrique

In this paper we report a molecular dynamics study of the effect of the application of magnetic fields on the morphology of nematic nanobridges of 32000 oblate Gay-Berne particles in slit pores favouring homeotropic anchoring. In absence of magnetic fields, previous studies show that there are different conformations of the nanobridge, depending on the slit

Ningxin Zhang

We obtain a novel formula for characteristic polynomials of deformations of the Braid arrangement using the notion of levels of regions. As an application, we recover and strengthen results of Chen et al. on the characteristic polynomial of several specific types of hyperplane arrangements via much simpler arguments. Our theorem also generalizes to type B.

Sagar Shrestha, Xiao Fu

Understanding identifiability of latent content and style variables from unaligned multi-domain data is essential for tasks such as domain translation and data generation. Existing works on content-style identification were often developed under somewhat stringent conditions, e.g., that all latent components are mutually independent and that the dimensions o

Mengqian Che, Jinxuan Liang, Yunpeng Cui, Hao Li

Chiral phonons are vibrational modes in a crystal that possess a well-defined handedness or chirality, typically found in materials that lack inversion symmetry. Here we report the discovery of chiral phonon modes in the kagome ferromagnetic Weyl semimetal Co3Sn2S2, a material that preserves inversion symmetry but breaks time-reversal symmetry. Using helicit

Zihan Yu, Jingtao Ding, Yong Li, Depeng Jin

Symbolic regression, a task discovering the formula best fitting the given data, is typically based on the heuristical search. These methods usually update candidate formulas to obtain new ones with lower prediction errors iteratively. However, since formulas with similar function shapes may have completely different symbolic forms, the prediction error does

Yuhao He, Jinyu Tian, Xianwei Zheng, Li Dong

Recent studies have shown that deep learning models are very vulnerable to poisoning attacks. Many defense methods have been proposed to address this issue. However, traditional poisoning attacks are not as threatening as commonly believed. This is because they often cause differences in how the model performs on the training set compared to the validation s

Tatsuya Miura

We solve a variant of Huisken's problem for open curves: we construct migrating elastic flows under the natural boundary conditions, extending previous work from the nonlocal flow to the purely local flow.

A. M. Shirokov, A. I. Mazur, I. A. Mazur, V. A. Kulikov

We discuss isospin analogues of the tetraneutron resonance and their possible manifestations in nuclear structure and reactions.

Xinyu Wu, Eytan Modiano

We quantify the threat of network adversaries to inducing \emph{network overload} through \emph{routing attacks}, where a subset of network nodes are hijacked by an adversary. We develop routing attacks on the hijacked nodes for two objectives related to overload: \emph{no-loss throughput minimization} and \emph{loss maximization}. The first objective attemp

Gleb Fedorovich, Clemens Kuhlenkamp, Atac Imamoglu, Ivan Amelio

In this work we study a system of interacting fermions on a triangular lattice in the presence of an external magnetic field. We neglect spin and fix a density of one third, with one unit of magnetic flux per particle. The infinite density matrix renormalization group algorithm is used to compute the ground state of this generalized Fermi-Hubbard model. Incr

Yuxiao Chen, Gamze Gürsoy, Qi Lei

Federated Learning (FL) is designed to prevent data leakage through collaborative model training without centralized data storage. However, it remains vulnerable to gradient reconstruction attacks that recover original training data from shared gradients. To optimize the trade-off between data leakage and utility loss, we first derive a theoretical lower bou

Xinyue Zhang, Zijia Dai, Wanting Xu, Laurent Kneip

While automatically generated polynomial elimination templates have sparked great progress in the field of 3D computer vision, there remain many problems for which the degree of the constraints or the number of unknowns leads to intractability. In recent years, homotopy continuation has been introduced as a plausible alternative. However, the method currentl

Shuangjie Li, Baoming Zhang, Jianqing Song, Gaoli Ruan

Graph Neural Networks (GNNs) have gained considerable prominence in semi-supervised learning tasks in processing graph-structured data, primarily owing to their message-passing mechanism, which largely relies on the availability of clean labels. However, in real-world scenarios, labels on nodes of graphs are inevitably noisy and sparsely labeled, significant

Ning Ding, Shang Qu, Linhai Xie, Yifei Li

With the development of artificial intelligence, its contribution to science is evolving from simulating a complex problem to automating entire research processes and producing novel discoveries. Achieving this advancement requires both specialized general models grounded in real-world scientific data and iterative, exploratory frameworks that mirror human s

Yoni Choukroun, Shlomi Azoulay, Pavel Kisilev

Distributed machine learning has recently become a critical paradigm for training large models on vast datasets. We examine the stochastic optimization problem for deep learning within synchronous parallel computing environments under communication constraints. While averaging distributed gradients is the most widely used method for gradient estimation, whet

I. V. Anikin, Xurong Chen

We investigate a new class of transverse momentum dependent functions (TMDs), as known as align-spin (AS) functions. In the paper, we propose the most suitable proof of the AS-function existence together with the demonstration of the preponderances if the framework of twisted quark states has been employed. The twisted state corresponds to the elementary par

Md Abrar Jahin, M. F. Mridha, Nilanjan Dey, Md. Jakir Hossen

Feature selection is critical for improving the performance and interpretability of machine learning models, particularly in high-dimensional spaces where complex feature interactions can reduce accuracy and increase computational demands. Existing approaches often rely on static feature subsets or manual intervention, limiting adaptability and scalability.

Go Yatomi, Motoki Nakata

Convergence of a matrix decomposition technique, the multi-field singular value decomposition (MFSVD) which efficiently analyzes nonlinear correlations by simultaneously decomposing multiple fields, is investigated. Toward applications in turbulence studies, we demonstrate that SVD for an artificial matrix with multi-scale structures reproduces the power-law

Bei Yan, Yingfeng Qi, Ziyao Wang, Yan Meng

Recently, topological Dirac-vortex modes in Kekul\'e-distorted photonic lattices have attracted broad interest and exhibited promising applications in robust photonic devices such as topological cavities, lasers, and fibers. However, due to the vectorial nature of electromagnetic waves that results in complicated band dispersions and fails the tight-binding

Kehua Qu, Rui Ding, Jin Tang

Multi-person motion prediction is a complex and emerging field with significant real-world applications. Current state-of-the-art methods typically adopt dual-path networks to separately modeling spatial features and temporal features. However, the uncertain compatibility of the two networks brings a challenge for spatio-temporal features fusion and violate

Josu Aguirrebeitia, Inigo Martin, Iker Heras, Mikel Abasolo

Since Erich Franke produced the first wire race bearings in 1934, they have not been used profusely until these last years in applications such as computerized tomography, X-ray machines, wheels with direct drive... where low weight and inertia constraints are important. Accounting for the structural behaviour of the bearing, there exist a key phenomenon not

Inigo Martin, Iker Heras, Josu Aguirrebeitia, Mikel Abasolo

In wire bearings the rolling process occurs on raceways machined on steel wires, and the rings are made of light materials such as aluminium. This particular architecture provides both weight and inertia savings, but also significantly different behaviour with respect to conventional bearings. For this reason, specific design and analysis tools must be devel

Yunqing Wu, Kai Zheng

In this paper, we obtain gradient estimates and Laplacian estimates for the solution to the singular complex Monge-Amp\`ere equation by applying the integral method.

J. W. Dong, H. F. Mu, M. Qin, H. T. Cui

The quantum Mpemba effect in open quantum systems has been extensively studied, but a comprehensive understanding of this phenomenon remains elusive. In this paper, we conduct an analytical investigation of the dissipative dynamics of single excitations in the The mosaic model. Surprisingly, we discover that the presence of an asymptotic mobility edge, denot

Mikel Armentia, Mikel Abasolo, Ibai Coria, Joseba Albizuri

Statement of problem. Cold rolling is widely used for screw thread manufacturing in industry but is less common in implant dentistry, where cutting is the preferred manufacturing method. Purpose. The purpose of this in vitro study was to compare the surface finish and mechanical performance of a specific model of prosthetic screw used for direct restorations

Nadezhda A. Andreeva, Vitaly V. Chaban

In the contemporary era of rapid advancements in materials science, the development of new compounds and materials is proceeding at an accelerated pace. The concept of the potential energy landscape (PEL) plays a pivotal role in supporting the meticulous engineering of novel structures. This review article examines the historical evolution of the PEL concept

Hermann Kroll, Pascal Sackhoff, Bill Matthias Thang, Maha Ksouri

Digital libraries that maintain extensive textual collections may want to further enrich their content for certain downstream applications, e.g., building knowledge graphs, semantic enrichment of documents, or implementing novel access paths. All of these applications require some text processing, either to identify relevant entities, extract semantic relati

Abdelmajid Essofi, Ridwan Salahuddeen, Munachiso Nwadike, Elnura Zhalieva

The training or fine-tuning of machine learning, vision, and language models is often implemented as a pipeline: a sequence of stages encompassing data preparation, model training and evaluation. In this paper, we exploit pipeline structures to reduce the cost of hyperparameter tuning for model training/fine-tuning, which is particularly valuable for languag

Marlon Tobaben, Mohamed Ali Souibgui, Rubèn Tito, Khanh Nguyen

The Privacy Preserving Federated Learning Document VQA (PFL-DocVQA) competition challenged the community to develop provably private and communication-efficient solutions in a federated setting for a real-life use case: invoice processing. The competition introduced a dataset of real invoice documents, along with associated questions and answers requiring in

Kehua Qu, Rui Ding, Jin Tang

Multi-person motion prediction is an emerging and intricate task with broad real-world applications. Unlike single person motion prediction, it considers not just the skeleton structures or human trajectories but also the interactions between others. Previous methods use various networks to achieve impressive predictions but often overlook that the joints re

Pengfei Lyu, Pak-Hei Yeung, Xiaosheng Yu, Xiufei Cheng

Autonomous aerial vehicle (AAV)-based bi-modal salient object detection (BSOD) aims to segment salient objects in a scene utilizing complementary cues in unaligned RGB and thermal image pairs. However, the high computational expense of existing AAV-based BSOD models limits their applicability to real-world AAV devices. To address this problem, we propose an

Prabal S. Negi

Center-manifold approximations for infinite-dimensional systems are treated in the context of the Navier--Stokes equations extended to include an equation for the parameter evolution. The consequences of system extension are non-trivial and are examined in detail. The extended system is reformulated via an isomorphic transformation, and the application of th

Michael Merry, Patricia Riddle, Jim Warren

We introduce PropNEAT, a fast backpropagation implementation of NEAT that uses a bidirectional mapping of the genome graph to a layer-based architecture that preserves the NEAT genomes whilst enabling efficient GPU backpropagation. We test PropNEAT on 58 binary classification datasets from the Penn Machine Learning Benchmarks database, comparing the performa

Wen Ma, Huikai Wu, Zikai Xiao, Yang Feng

Reconstructing the 3D anatomical structures of the oral cavity, which originally reside in the cone-beam CT (CBCT), from a single 2D Panoramic X-ray(PX) remains a critical yet challenging task, as it can effectively reduce radiation risks and treatment costs during the diagnostic in digital dentistry. However, current methods are either error-prone or only t

Claus D. Hansen, Thuy Hai Le, David Campos

This paper examines the use of computer vision algorithms to estimate aspects of the psychosocial work environment using CCTV footage. We present a proof of concept for a methodology that detects and tracks people in video footage and estimates interactions between customers and employees by estimating their poses and calculating the duration of their encoun

Yu Guan, Kunlong Zhang, Qi Qi, Dong Wang

Diffusion models have recently demonstrated considerable advancement in the generation and reconstruction of magnetic resonance imaging (MRI) data. These models exhibit great potential in handling unsampled data and reducing noise, highlighting their promise as generative models. However, their application in dynamic MRI remains relatively underexplored. Thi

R. Bouet, J. Busto, A. Cadiou, P. Charpentier

To search for $\beta\beta0\nu$ decay with unprecedented sensitivity, the R2D2 collaboration is developing a radial time projection chamber with a fiducial mass of half a tonne of $^{136}$Xe at high pressure. The various approaches implemented to eliminate the radioactive background are presented in terms of detector design, topological recognition of interac

Nadezhda A. Andreeva, Vitaly V. Chaban

Metal-film dielectric capacitors provide lump portions of energy on demand. While the capacities of various capacitor designs are comparable in magnitude, their stabilities make a difference. Dielectric breakdowns - micro-discharges - routinely occur in capacitors due to the inevitable presence of localized structure defects. The application of polymeric die

Botond Tyukodi, Fernando Caballero, Daichi Hayakawa, Douglas M. Hall

Recent advances in synthetic methods enable designing subunits that self-assemble into structures with precise, finite sizes and well-defined architectures, but yields are frequently suppressed by the formation of off-target metastable structures. Increasing the complexity (the number of distinct subunit types) can inhibit off-target structures, but leads to

Ke-Xiong Yan, Yuan Qiu, Yang Xiao, Jie Song

The dynamical Casimir effect (DCE) describes the energy conversion from a mechanical motion to the electromagnetic fields. When the mechanical oscillator is in a mechanically excited state, the free evolution due to the DCE produces radiation in the vacuum, in analogy with the spontaneous emission from an excited atom. In this manuscript, we investigate such

Mahsa Haddadi Moghaddam, Sobhagyam Sharma, Daehwan Park, Dai Sik Kim

The hotspots, which are typically found in nanogaps between metal structures, are critical for the enhancement of the electromagnetic field. Surface-enhanced Raman scattering (SERS), a technique known for its exceptional sensitivity and molecular detection capability, relies on the creation of these hotspots within nanostructures, where localized surface pla

Chuang-Wei Liu, Yikang Zhang, Qijun Chen, Ioannis Pitas

Stereo matching has emerged as a cost-effective solution for road surface 3D reconstruction, garnering significant attention towards improving both computational efficiency and accuracy. This article introduces decisive disparity diffusion (D3Stereo), marking the first exploration of dense deep feature matching that adapts pre-trained deep convolutional neur

Nai-Hui Chia, Kai-Min Chung, Tzu-Hsiang Huang, Chuhan Lu

We begin by establishing structural results for several fundamental quantum complexity classes: p/mBQP, p/mQ(C)MA, $\text{p/mQSZK}_{\text{hv}}$, p/mQIP, p/mBQP/qpoly, p/mBQP/poly, and p/mPSPACE. This includes identifying complete problems, as well as proving containment and separation results among these classes. Here, p/mC denotes the corresponding quantum

Wen-Chin Huang, Erica Cooper, Tomoki Toda

In this paper, we study the task of subjective speech quality assessment (SSQA), which refers to predicting the perceptual quality of speech. Owing to the development of deep neural network models, SSQA has greatly advanced and has been widely applied in scientific papers to evaluate speech generation systems. Nonetheless, the insufficient out-of-domain (OOD

Felix Tempel, Espen Alexander F. Ihlen, Lars Adde, Inga Strümke

In Human Activity Recognition (HAR), understanding the intricacy of body movements within high-risk applications is essential. This study uses SHapley Additive exPlanations (SHAP) to explain the decision-making process of Graph Convolution Networks (GCNs) when classifying activities with skeleton data. We employ SHAP to explain two real-world datasets: one f

Long Shi, Chuanqing Tang, Huangyi Deng, Cai Xu

Recently, multi-view learning has witnessed a considerable interest on the research of trusted decision-making. Previous methods are mainly inspired from an important paper published by Han et al. in 2021, which formulates a Trusted Multi-view Classification (TMC) framework that aggregates evidence from different views based on Dempster's combination rule. A

Feng-Yu Wang, Li-Juan Cheng

By using stochastic analysis, two probability versions of Li-Yau type inequalities are established for diffusion semigroups on a manifold possibly with (non-convex) boundary. The inequalities are explicitly given by the Bakry-Emery curvature-dimension, as well as the lower bound of the second fundamental form if the boundary exists. As applications, a number

Lu Bai, Ziwei Huang, Mingran Sun, Xiang Cheng

In the future sixth-generation (6G) era, to support accurate localization sensing and efficient communication link establishment for intelligent agents, a comprehensive understanding of the surrounding environment and proper channel modeling are indispensable. The existing method, which solely exploits radio frequency (RF) communication information, is diffi

Ye-Hong Chen, Zhi-Cheng Shi, Yu-Ran Zhang, Franco Nori

We derive a modified master equation for the quantum Rabi model in the parameter regime where quantum criticality can occur. The modified master equation can avoid some unphysical predictions, such as excitations in the system at zero temperature and emission of ground-state photons. Due to spectrum collapse, we find that there is mostly no energy relaxation

Zhongliang Tang, Qingrong Cheng, Mengchen Tan, Yongxiang Zhang

Game UI development is essential to the game industry. However, the traditional workflow requires substantial manual effort to integrate pairwise UI and UX designs into a cohesive game user interface (GameUI). The inconsistency between the aesthetic UI design and the functional UX design typically results in mismatches and inefficiencies. To address the issu

Mehedi Hasan Raju, Samantha Aziz, Michael J. Proulx, Oleg V. Komogortsev

We present a real-time gaze-based interaction simulation methodology using an offline dataset to evaluate the eye-tracking signal quality. This study employs three fundamental eye-movement classification algorithms to identify physiological fixations from the eye-tracking data. We introduce the Rank-1 fixation selection approach to identify the most stable f

Muhammad Tayyab Khan, Lequn Chen, Ye Han Ng, Wenhe Feng

Geometric Dimensioning and Tolerancing (GD&T) plays a critical role in manufacturing by defining acceptable variations in part features to ensure component quality and functionality. However, extracting GD&T information from 2D engineering drawings is a time-consuming and labor-intensive task, often relying on manual efforts or semi-automated tools. To addre

Ziqi Lu, Jianbo Ye, John Leonard

We present 3DGS-CD, the first 3D Gaussian Splatting (3DGS)-based method for detecting physical object rearrangements in 3D scenes. Our approach estimates 3D object-level changes by comparing two sets of unaligned images taken at different times. Leveraging 3DGS's novel view rendering and EfficientSAM's zero-shot segmentation capabilities, we detect 2D object

Feyisola Nana, Jordi Salinas San Martín, Jacquelyn Noronha-Hostler

The statistical hadronization model has been successful in extracting information at chemical freeze-out in heavy-ion collisions. At RHIC, with a collision energy of $\sqrt{s_{NN}}=200$ GeV, many different ion species have been used for $A$+$A$ collisions. This allows for a scan across the charge fraction $Y_Q=Z/A$, where $Z$ is the proton number and $A$ is

Ramesh Sreekantan

We construct motivic cohomology cycles in the group $H^3_{\mathcal M}(Z,{\mathbb Q}(2))$ where $Z$ is a K3 surface obtained as a double cover of a del Pezzo surface $X$ branched at a curve in $|-2K_X|$. The construction uses (-1) curves on the del Pezzo and is a generalization of a recent pre-print of Ken Sato arXiv: 2408.09102 where he considers the case of

Biagio Ricceri

In this paper, we apply our minimax theory ([4], [5], [6]) with the one developed by A. Moameni in [2] to formalize a general scheme giving the multiplicity of critical points. Here is a sample of application of the scheme to a critical elliptic problem: Let $\Omega\subset {\bf R}^n$ ($n\geq 3$) be a smooth bounded domain and let $1<q<2\leq p<{{2n}\over {n-2

Abrar Morshed, Abdulla Al Shihab, Md Abrar Jahin, Md Jaber Al Nahian

The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings for detecting COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as a low-

Depanshu Sani, Saket Anand

The growing demand for robust scene understanding in mobile robotics and autonomous driving has highlighted the importance of integrating multiple sensing modalities. By combining data from diverse sensors like cameras and LIDARs, fusion techniques can overcome the limitations of individual sensors, enabling a more complete and accurate perception of the env

Rajender Nath, Satinder Bal

The exponential growth of information source on the web and in turn continuing technological progress of searching the information by using tools like Search Engines gives rise to many problems for the user to know which tool is best for their query and which tool is not. At this time Metasearch Engine comes into play by reducing the user burden by dispatchi

Anaelia Ovalle, Krunoslav Lehman Pavasovic, Louis Martin, Luke Zettlemoyer

Natural-language assistants are designed to provide users with helpful responses while avoiding harmful outputs, largely achieved through alignment to human preferences. Yet there is limited understanding of whether alignment techniques may inadvertently perpetuate or even amplify harmful biases inherited from their pre-aligned base models. This issue is com

Jihyun Park, Andrey Sarantsev

We study a multivariate autoregressive stochastic volatility model for the first 3 principal components (level, slope, curvature) of 10 series of zero-coupon Treasury bond rates with maturities from 1 to 10 years. We fit this model using monthly data from 1990. Unlike classic models with hidden stochastic volatility, here it is observed as VIX: the volatilit

Jinni Yang, Runtong Pan, Jikai Sun, Jianzhong Wu

Classical density functional theory (cDFT) provides a systematic approach to predict the structure and thermodynamic properties of chemical systems through the single-molecule density profiles. Whereas the statistical-mechanical framework is theoretically rigorous, its practical applications are often constrained by challenges in formulating a reliable free-

Jiajun Wu, Mo Song, Jingmin Zhao, Yizhao Gao

Modern transformer-based deep neural networks present unique technical challenges for effective acceleration in real-world applications. Apart from the vast amount of linear operations needed due to their sizes, modern transformer models are increasingly reliance on precise non-linear computations that make traditional low-bitwidth quantization methods and f

Ji Zhang, Yiran Ding, Zixin Liu

3D semantic occupancy prediction is crucial for finely representing the surrounding environment, which is essential for ensuring the safety in autonomous driving. Existing fusion-based occupancy methods typically involve performing a 2D-to-3D view transformation on image features, followed by computationally intensive 3D operations to fuse these with LiDAR f

Mingyu Sheng, Jianan Fan, Dongnan Liu, Ron Kikinis

Surgical instrument segmentation (SIS) is pivotal for robotic-assisted minimally invasive surgery, assisting surgeons by identifying surgical instruments in endoscopic video frames. Recent unsupervised surgical instrument segmentation (USIS) methods primarily rely on pseudo-labels derived from low-level features such as color and optical flow, but these meth

Tiantian Guo

This article proposes a method to improve the switching rate of RF switches based on thermally induced phase change materials.Based on the principle that during the heating process, the increase in heat provided by the heating element plays a major role, while the heat dissipation effect of the bottom heat dissipation layer during the cooling process plays a

Gopinath Guin, Soham Sen, Sunandan Gangopadhyay

In \href{https://link.aps.org/doi/10.1103/PhysRevD.92.103004}{Phys. Rev. D 92 (2015) 103004}, simple analytical solutions of the Friedman equations were obtained for a universe having stiff matter component in the early universe together with a dark matter, and a dark energy component. In this analysis, the universe is considered to be made of a dark fluid w

Peng Gao, Liangyi Zhao

We establish sharp lower bounds for shifted (with two shifts) moments of Dirichlet $L$-function of fixed modulus under the generalized Riemann hypothesis.