November 2024 arXiv papers — page 111

Louise Friot-Giroux, Françoise Peyrin, Voichita Maxim

Abstract Objective. Cone-beam computed tomography is becoming more and more popular in applications such as 3D dental imaging. Iterative methods compared to the standard Feldkamp algorithm have shown improvements in image quality of reconstruction of low-dose acquired data despite their long computing time. An interesting aspect of iterative methods is their

Pierre Lissy

The Douglas' majorization and factorization theorem characterizes the inclusion of operator ranges in Hilbert spaces. Notably, it reinforces the well-established connections between the inclusion of kernels of operators in Hilbert spaces and the (inverse) inclusion of the closures of the ranges of their adjoints. This note aims to present a ''mixed'' version

Pierfrancesco Leonardi, Vincenza Torrisi, Andrea Araldo, Matteo Ignaccolo

By adapting bus routes to users' requests, Demand-Responsive Transit (DRT) can serve low-demand areas more efficiently than conventional fixed-line buses. However, a main barrier to its adoption of DRT is its unpredictability, i.e., it is not possible to know a-priori how much time a certain trip will take, especially when no large prebooking is imposed. To

Yunteng Jiang, Zhentao Xiao, Zonghao Zhang, Juncheng Zhang

In this paper, we propose a simulation-based novel Split-Gate Trench MOSFET structure with an optimized fabrication process to enhance power efficiency, switching speed, and thermal stability for high-performance semiconductor applications. Integrating high-k pillars with superjunction structures beneath the split gate enhancing breakdown performance by redu

Chris Hinds

Ethnomethodological fieldwork has long been acknowledged as a potentially valuable way of informing the design of technology. However, there is relatively little methodological support for this activity, particularly in relation to the systematic approaches to development advocated in mainstream software and requirements engineering. This thesis focuses on t

Soowon Kim, Ha-Na Jo, Eunyeong Ko

In this study, we propose an ensemble learning framework for electroencephalogram-based overt speech classification, leveraging denoising diffusion probabilistic models with varying convolutional kernel sizes. The ensemble comprises three models with kernel sizes of 51, 101, and 201, effectively capturing multi-scale temporal features inherent in signals. Th

Zhenshi Li, Dilxat Muhtar, Feng Gu, Xueliang Zhang

Automatically and rapidly understanding Earth's surface is fundamental to our grasp of the living environment and informed decision-making. This underscores the need for a unified system with comprehensive capabilities in analyzing Earth's surface to address a wide range of human needs. The emergence of multimodal large language models (MLLMs) has great pote

Arnab Bhattacharya, Afsar Ahmed, Apurba Dutta, Ajay Kumar

While recent theoretical studies have positioned noncollinear polar magnets with $C_{nv}$ symmetry as compelling candidates for realizing topological magnetic phases and substantial intrinsic anomalous Hall conductivity, experimental realizations of the same in strongly correlated systems remain rare. Here, we present a large intrinsic anomalous Hall effect

Simone Arreghini, Antonio Paolillo, Gabriele Abbate, Alessandro Giusti

Social robots are required not only to understand human intentions but also to effectively communicate their intentions or own internal states to users. This study explores the use of sonification to provide explicit auditory feedback, enhancing mutual understanding in HRI. We introduce a novel sonification approach that conveys the robot's internal state, l

Siqi Zhang, Furen Deng, Youjun Lu, Shenghua Yu

The Gravitation Wave (GW) signals from a large number of double white dwarfs (DWDs) in the Galaxy are expected to be detected by space GW detectors, e.g., the Laser Interferometer Space Antenna (LISA), Taiji, and Tianqin in the millihertz band. In this paper, we present an alternative method by directly using the time-domain GW signal detected by space GW de

Chenlong Zhang, Tong Zhou, Pengfei Cao, Zhuoran Jin

The rapid proliferation of online news has posed significant challenges in tracking the continuous development of news topics. Traditional timeline summarization constructs a chronological summary of the events but often lacks the flexibility to meet the diverse granularity needs. To overcome this limitation, we introduce a new paradigm, Dynamic-granularity

Franck Rothen, Samuel Klein, Matthew Leigh, Tobias Golling

Machine learning is becoming increasingly popular in the context of particle physics. Supervised learning, which uses labeled Monte Carlo (MC) simulations, remains one of the most widely used methods for discriminating signals beyond the Standard Model. However, this paper suggests that supervised models may depend excessively on artifacts and approximations

Vera V. Lendyashova, Igor V. Ilkiv, Vadim G. Talalaev, Talgat Shugabaev

The influence of growth regimes of the silicon capping layer on the optical properties of heterostructures with submonolayer InAs quantum dots embedded in a silicon matrix has been studied. The photoluminescence signal at 1650 nm from submonolayer quantum dots at low temperatures up to 120 K was obtained. It was established that the use of a two-stage method

Gabriele Abbate, Alessandro Giusti, Luca Randazzo, Antonio Paolillo

We propose a machine learning-based estimator of the hand state for rehabilitation purposes, using light exoskeletons. These devices are easy to use and useful for delivering domestic and frequent therapies. We build a supervised approach using information from the muscular activity of the forearm and the motion of the exoskeleton to reconstruct the hand's o

Chenyang Wang, Wenjie An, Kui Jiang, Xianming Liu

Existing face super-resolution (FSR) methods have made significant advancements, but they primarily super-resolve face with limited visual information, original pixel-wise space in particular, commonly overlooking the pluralistic clues, like the higher-order depth and semantics, as well as non-visual inputs (text caption and description). Consequently, these

Jiyu Wang, Xiaodian Chen, Licai Deng, Jianxing Zhang

Based on the LAMOST spectroscopy and TESS time-series photometry, we have obtained a main-sequence star sample of $\delta$ Scuti and $\gamma$ Doradus stars. The sample includes 1534 $\delta$ Sct stars, 367 $\gamma$ Dor stars, 1703 $\delta$ Sct$| \gamma$ Dor stars, 270 $\gamma$ Dor$| \delta$ Sct stars, along with 105 '$\delta$ Sct candidates' and 32 '$\gamma$

Alan Riquier, Emmanuel Dormy

Theoretical results on water waves almost always start by assuming irrotationality of the flow in order to simplify the formulation. In this work, we investigate the well-foundedness of this hypothesis via numerical simulations of the free-surface Navier-Stokes equations. We show that, in the presence of a non-flat bathymetry, either angular or smooth, a gra

Chengzhang Sun

For a parabolically convex domain $M\subseteq \mathbb{H}^n$, $n\ge 3$, we prove that if $f:(N,\bar g)\to (M,g)$ has nonzero degree, where $N$ is spin with scalar curvature $R_N\ge -n(n-1)$, and if $f|_{\partial N}$ does not increase the distance and the mean curvature, then $N$ is hyperbolic, and $\partial N$ is isometric to $\partial M$. This is a partial g

Dilxat Muhtar, Yelong Shen, Yaming Yang, Xiaodong Liu

In-context learning (ICL) allows large language models (LLMs) to adapt to new tasks directly from the given demonstrations without requiring gradient updates. While recent advances have expanded context windows to accommodate more demonstrations, this approach increases inference costs without necessarily improving performance. To mitigate these issues, We p

Frederik Munko, Catherine Cruz Luukkonen, Ismael S. S. Carrasco, Fábio D. A. Aarão Reis

Island formation in strain-free heteroepitaxial deposition of thin films is analyzed using kinetic Monte Carlo simulations of two minimal lattice models and scaling approaches. The transition from layer-by-layer (LBL) to island (ISL) growth is driven by a weaker binding strength of the substrate which, in the kinetic model, is equivalent to an increased diff

Tianpei Lu, Bingsheng Zhang, Lichun Li, Kui Ren

With the increasing emphasis on privacy regulations, such as GDPR, protecting individual privacy and ensuring compliance have become critical concerns for both individuals and organizations. Privacy-preserving machine learning (PPML) is an innovative approach that allows for secure data analysis while safeguarding sensitive information. It enables organizati

Ke Xu, Ziliang Wang, Wei Zheng, Yuhao Ma

Cross-domain recommendation (CDR) methods are proposed to tackle the sparsity problem in click through rate (CTR) estimation. Existing CDR methods directly transfer knowledge from the source domains to the target domain and ignore the heterogeneities among domains, including feature dimensional heterogeneity and latent space heterogeneity, which may lead to

Thomas Crozon

The present document corresponds to the 4 th chapter of my thesis, the problem setting is not definitive, what matters most here are the mathematical results and the methodology of the existence proofs. In this work, we propose a framework and some tools for establishing the existence of solutions to numerical schemes in the case of the two-phase flow model.

Charlie Leprince, Victor Gondret, Clothilde Lamirault, Rui Dias

We demonstrate the effect of pulse shaping in momentum selective atomic Bragg diffraction. We compare temporal square pulses, which produce sidelobes in momentum space, with other shapes which can produce more nearly square momentum distributions. We produce pulses that simultaneously address two sets of velocity classes and demonstrate that we can control t

Harini G., Aiman Farooq, Deepak Mishra

Thoracic trauma often results in rib fractures, which demand swift and accurate diagnosis for effective treatment. However, detecting these fractures on rib CT scans poses considerable challenges, involving the analysis of many image slices in sequence. Despite notable advancements in algorithms for automated fracture segmentation, the persisting challenges

Nan Bai, Mao-Zhong Shao

In this paper we investigate the integrable boundary state in ABJM theory. We find an integrability condition for the two-site integrable matrix product state (MPS) similar to the KT-relation. We also construct a class of non-trivial MPSs from the projected K-matrices.

Ponaki Das, Sainkupar Marwein Mawiong

In this paper, we develop the concept of multiple cylinder of relations which is a generalization of the relation cylinder, extending the multiple non-Hausdorff mapping cylinder to sequences of finite T0-spaces linked by a series of relations. This construction is important in capturing complex homotopical structures across chains of finite spaces and, when

Terol Sébastien, Costes Antony, Malmert Alexandre, Brunet Emmanuel

Extensive research has been dedicated to optimizing the cyclist's position on the bike to enhance aerodynamic performance. This study aims to further investigate the aerobars position effect on cycling speed. Drawing from previous work (Fintelman et al., 2015), a relationship is established between position variations and hip angle, a critical determinant of

Sebastián Rojas-Innocenti, Enrique Baeyens, Alejandro Martín-Crespo, Sergio Saludes-Rodil

The increasing integration of renewable energy sources into power systems is intensifying the demand for greater flexibility among industrial electricity consumers. However, operational constraints, production requirements, and market dynamics pose significant challenges to achieving optimal flexibility. This paper presents an enhanced mixed integer linear p

Chunyang Cao, Fukun Liu, Shuo Li, Xian Chen

When a binary of early-type stars from the young stellar populations in the Galactic center (GC) region is scattered to the vicinity of the supermassive black hole (SMBH) Sgr~$\rm{A}^{*}$, one of the components would be tidally ejected as an early-type hypervelocity star (HVS) and the counterpart would be captured on a tight orbit around Sgr~$\rm{A}^{*}$. Do

Krishna Rani Sahoo, Manoj Talluri, Dipak Maity, Suman Mundlia

Two-dimensional (2D) transition metal di-chalcogenide layers with high electrical conductivity and spin-orbit coupling (SOC) can find huge potential in spintronic devices. With limited success of 2D spin Hall material development, we demonstrate vanadium (V) substitutionally doped monolayer MoS2 (VMS) as a potential spin Hall material having tunable electric

Bohuslav Matouš

This paper connects two methods for finding the functional of entropy in F(R)-Gravity: Padmanabhan's and Hammad's. The resulting approach is simple to follow and yields entropy functional, which can be separated into two parts. The part unknown in General Relativity is often called in the literature as an internal entropy and this paper points on incompatibi

Ziyang Men, Zheqi Shen, Yan Gu, Yihan Sun

The $k$d-tree is one of the most widely used data structures to manage multi-dimensional data. Due to the ever-growing data volume, it is imperative to consider parallelism in $k$d-trees. However, we observed challenges in existing parallel kd-tree implementations, for both constructions and updates. The goal of this paper is to develop efficient in-memory $

Michel Chipot, Daniel Hauer

The goal of this note is to consider Liouville type theorem for p-Laplacian type operators. In particular guided by the Laplacian case one establishes analogous results for the p-Laplacian and operators of this type.

Xiang Zhang, Senyu Li, Ning Shi, Bradley Hauer

Recent developments in multimodal methodologies have marked the beginning of an exciting era for models adept at processing diverse data types, encompassing text, audio, and visual content. Models like GPT-4V, which merge computer vision with advanced language processing, exhibit extraordinary proficiency in handling intricate tasks that require a simultaneo

S. D. Korolkov, V. V. Izmodenov

A subsonic flow of an ideal gas through a flat channel in the presence of a mass force field is considered. The forces acting on the gas are reduced to the attraction to the channel axis in a certain region of the channel. It has been shown that the occurrence of a zone of increased pressure along the axis of a channel leads to the formation of a pressure gr

Alessandro Morbidelli, Thorsten Kleine, Francis Nimmo

The dominant accretion process leading to the formation of the terrestrial planets of the Solar System is a subject of intense scientific debate. Two radically different scenarios have been proposed. The classic scenario starts from a disk of planetesimals which, by mutual collisions, produce a set of Moon to Mars-mass planetary embryos. After the removal of

R. Fischer, A. Bock, S. S. Denk, A. Medvedeva. M. Salewski

A major challenge in nuclear fusion research is the coherent combination of data from heterogeneous diagnostics and modelling codes for machine control and safety as well as physics studies. Measured data from different diagnostics often provide information about the same subset of physical parameters. Additionally, information provided by some diagnostics m

Vamsi Krishna Kommineni, Birgitta König-Ries, Sheeba Samuel

Deep Learning (DL) techniques are increasingly applied in scientific studies across various domains to address complex research questions. However, the methodological details of these DL models are often hidden in the unstructured text. As a result, critical information about how these models are designed, trained, and evaluated is challenging to access and

Guanwen Feng, Zhihao Qian, Yunan Li, Siyu Jin

While existing one-shot talking head generation models have achieved progress in coarse-grained emotion editing, there is still a lack of fine-grained emotion editing models with high interpretability. We argue that for an approach to be considered fine-grained, it needs to provide clear definitions and sufficiently detailed differentiation. We present LES-T

Pablo Fernández-Piñeiro, Manuel Ferández-Veiga, Rebeca P. Díaz-Redondo, Ana Fernández-Vilas

In prototype-based federated learning, the exchange of model parameters between clients and the master server is replaced by transmission of prototypes or quantized versions of the data samples to the aggregation server. A fully decentralized deployment of prototype-based learning, without a central agregartor of prototypes, is more robust upon network failu

Sahibzada Adil Shahzad, Ammarah Hashmi, Yan-Tsung Peng, Yu Tsao

Multimodal deepfakes involving audiovisual manipulations are a growing threat because they are difficult to detect with the naked eye or using unimodal deep learningbased forgery detection methods. Audiovisual forensic models, while more capable than unimodal models, require large training datasets and are computationally expensive for training and inference

Zheng Zhou, Wenquan Feng, Shuchang Lyu, Guangliang Cheng

Dataset Distillation (DD) is an emerging technique that compresses large-scale datasets into significantly smaller synthesized datasets while preserving high test performance and enabling the efficient training of large models. However, current research primarily focuses on enhancing evaluation accuracy under limited compression ratios, often overlooking cri

Tim Browning, Stephanie Chan

The large sieve is used to estimate the density of integral quadratic polynomials $Q$, such that there exists an odd degree integral polynomial which has resultant $\pm 1$ with $Q$. Given a monic integral polynomial $R$ of odd degree, this is used to show that for almost all integral quadratic polynomials $Q$, there exists a prime $p$ such that $Q$ and $R$ s

Hu Wang, Congbo Ma, Ibrahim Almakky, Ian Reid

Model merging, particularly through weight averaging, has shown surprising effectiveness in saving computations and improving model performance without any additional training. However, the interpretability of this technique works remains unclear. In this work, we reinterpret weight-averaged model merging through the lens of interpretability and provide empi

Matthew J. Hayes, Alberto Saldana-Lopez, Annalisa Citro, Bethan L. James

We determine the spectroscopic properties of ~1000 ostensibly star-forming galaxies at redshifts (z=4-10) using prism spectroscopy from JWST/NIRSpec. With rest-wavelength coverage between Lya and [S II] in the optical, we stack spectra as a function of nebular conditions, and compare UV spectral properties with stellar age. This reveals UV lines of N III], N

Umar Alkafaween, Ibrahim Albluwi, Paul Denny

Automatically graded programming assignments provide instant feedback to students and significantly reduce manual grading time for instructors. However, creating comprehensive suites of test cases for programming problems within automatic graders can be time-consuming and complex. The effort needed to define test suites may deter some instructors from creati

Xuannan Liu, Xing Cui, Peipei Li, Zekun Li

The rapid evolution of multimodal foundation models has led to significant advancements in cross-modal understanding and generation across diverse modalities, including text, images, audio, and video. However, these models remain susceptible to jailbreak attacks, which can bypass built-in safety mechanisms and induce the production of potentially harmful con

Wenchao Xu, Xinyu Zhang

Asymptotic optimality is a key theoretical property in model averaging. Due to technical difficulties, existing studies rely on restricted weight sets or the assumption that there is no true model with fixed dimensions in the candidate set. The focus of this paper is to overcome these difficulties. Surprisingly, we discover that when the penalty factor in th

M. Dhillon, K. K. Kataria

In this paper, we study the composition of two independent GCPs which we call the iterated generalized counting process (IGCP). Its distributional properties such as the transition probabilities, probability generating function, state probabilities and its corresponding L\'evy measure are obtained. We study some integrals of the IGCP. Also, we study some of

Anna Jenčová

We study higher order quantum maps in the context of a *-autonomous category of affine subspaces. We show that types of higher order maps can be identified with certain Boolean functions that we call type functions. By an extension of this identification, the algebraic structure of Boolean functions is inherited by some sets of quantum objects including high

Jean Seo, Jongwon Lim, Dongjun Jang, Hyopil Shin

We introduce DAHL, a benchmark dataset and automated evaluation system designed to assess hallucination in long-form text generation, specifically within the biomedical domain. Our benchmark dataset, meticulously curated from biomedical research papers, consists of 8,573 questions across 29 categories. DAHL evaluates fact-conflicting hallucinations in Large

Aditi Saxena, Twinkle Tripathy, Rajasekhar Anguluri

Laplacian flows model the rate of change of each node's state as being proportional to the difference between its value and that of its neighbors. Typically, these flows capture diffusion or synchronization dynamics and are well-studied. Expanding on these classical flows, we introduce a pseudoinverse Laplacian flow system, substituting the Laplacian with it

Takahiro Anan, Takahiro Morimoto

The emergent inductor, which is a concept of an inductor employing quantum mechanics on helical magnets, has been studied actively from both theoretical and experimental aspects. Interestingly, finite inductance has been observed not only in spiral magnetic phases but also across various other magnetic phases although the underlying mechanism behind emergent

Andrea Vaiuso

Multimedia streaming protocols are becoming increasingly popular in Crowdsensing due to their ability to deliver high-quality video content over the internet in real-time. Streaming multimedia content, as in the context of live video streaming, requires high bandwidth and large storage capacity to ensure a sufficient throughput. Crowdsensing can distribute i

Weilin Ruan, Wenzhuo Wang, Siru Zhong, Wei Chen

Predicting spatio-temporal traffic flow presents significant challenges due to complex interactions between spatial and temporal factors. Existing approaches often address these dimensions in isolation, neglecting their critical interdependencies. In this paper, we introduce the Spatio-Temporal Unitized Model (STUM), a unified framework designed to capture b

Dunwei Tu, Huiyu Yi, Tieyi Zhang, Ruotong Li

Few-shot class-incremental learning (FSCIL) aims to continually learn new classes from only a few samples without forgetting previous ones, requiring intelligent agents to adapt to dynamic environments. FSCIL combines the characteristics and challenges of class-incremental learning and few-shot learning: (i) Current classes occupy the entire feature space, w

Kota Tanabe, Masanori Hirano, Kazuki Matoya, Kentaro Imajo

The domain adaptation of language models, including large language models (LLMs), has become increasingly important as the use of such models continues to expand. This study demonstrates the effectiveness of Composition to Augment Language Models (CALM) in adapting to the financial domain. CALM is a model to extend the capabilities of existing models by intr

Shantanu Desai, Shalini Ganguly

We reanalyze the spectral lag data for GRB 160625B using frequentist inference in order to constrain the energy scale ($E_{QG}$) of Lorentz Invariance Violation (LIV). For this purpose, we use profile likelihood to deal with the astrophysical nuisance parameters. This is in contrast to Bayesian inference implemented in previous works, where marginalization w

Y. Kawamura, T. Tanaka

Spontaneous rotation of an ultra-small satellite was observed and its driving torque was explained by the thermal interaction between the air molecules and the surfaces of the satellite heated by the radiation from the earth. This mechanism has the similarity with a usual radiometer, except the point that the velocity of the satellite is sufficiently faster

Pavan Adroja, Sanjay Amrutiya

In this article, we study the various fundamental groupoid schemes corresponding to Tannakian categories of certain types of vector bundles. We compute fundamental groupoid scheme of anisotropic conic, Klein bottle and abelian varieties. Additionally, we study the relation among various fundamental groupoid schemes by considering their representations.

Qifei Ma, Huaizhou Jin, Xiaoxiao Shang, Tamas Pardy

Emulsions are ubiquitous in everyday life and find applications in various industries. Optical tweezers (OTs) have emerged as the preferred method for studying emulsion dynamics. In this review, we first introduce the theory of optical trapping and emulsion stability. We then survey applications in the manipulation of emulsions, stability mechanism, the proc

Yating Wang, Zhengya Yang, Wing Tat Leung

Solving multiscale diffusion problems is often computationally expensive due to the spatial and temporal discretization challenges arising from high-contrast coefficients. To address this issue, a partially explicit temporal splitting scheme is proposed. By appropriately constructing multiscale spaces, the spatial multiscale property is effectively captured,

Jung-Sun Lee, Ha-Na Jo, Seo-Hyun Lee

Brain signals accompany various information relevant to human actions and mental imagery, making them crucial to interpreting and understanding human intentions. Brain-computer interface technology leverages this brain activity to generate external commands for controlling the environment, offering critical advantages to individuals with paralysis or locked-

Samir Adly, Bui Trong Kien

In this paper, we derive first and second-order optimality conditions of KKT type for locally optimal solutions to a class of multiobjective optimal control problems with endpoint constraint and mixed pointwise constraints. We give some sufficient conditions for normality of multipliers. Namely, we show that if the linearized system is controllable or some c

Jinjie Liu, Hang Qiu

Machine learning models deployed on edge devices have enabled numerous exciting new applications, such as humanoid robots, AR glasses, and autonomous vehicles. However, the computing resources available on these edge devices are not catching up with the ever-growing number of parameters in these models. As the models become bigger and more complicated, the n

Jan Vykopal, Valdemar Švábenský, Michael Tuscano Lopez, Pavel Čeleda

Improving cybersecurity education has become a priority for many countries and organizations worldwide. Computing societies and professional associations have recognized cybersecurity as a distinctive computing discipline and created specialized cybersecurity curricular guidelines. Higher education institutions are introducing new cybersecurity programs, att

Lewis Powell, Wenjun Kuang, Gabriel Hawkins-Pottier, Rashid Jalil

Unconventional superconductivity, where electron pairing does not involve electron-phonon interactions, is often attributed to magnetic correlations in a material. Well known examples include high-T_c cuprates and uranium-based heavy fermion superconductors. Less explored are unconventional superconductors with strong spin-orbit coupling, where interactions

Xuanhao Pan, Chenguang Wang, Chaolong Ying, Ye Xue

The ``Heatmap + Monte Carlo Tree Search (MCTS)'' paradigm has recently emerged as a prominent framework for solving the Travelling Salesman Problem (TSP). While considerable effort has been devoted to enhancing heatmap sophistication through advanced learning models, this paper rigorously examines whether this emphasis is justified, critically assessing the

Yasmine Marani, Israel Filho, Tareq Al-Naffouri, Taous-Meriem Laleg-Kirati

Contraction analysis offers, through elegant mathematical developments, a unified way of designing observers for a general class of nonlinear systems, where the observer correction term is obtained by solving an infinite dimensional inequality that guarantees global exponential convergence. However, solving the matrix partial differential inequality involved

Soumya Chakrabarti, Chiranjeeb Singha

We argue that a spherically symmetric traversable wormhole solution of the Einstein field equations can be supported by minimally coupled self-interacting scalar field which allows a spontaneous symmetry breaking of the field around the wormhole throat. We study two cases : (i) the phantom wormhole solution of Bronnikov and (ii) a generalized Kiselev wormhol

Junteng Yao, Liangxiao Xin, Tuo Wu, Ming Jin

This letter considers a fluid antenna system (FAS)-aided secure and covert communication system, where the transmitter adjusts multiple fluid antennas' positions to achieve secure and covert transmission under the threat of an eavesdropper and the detection of a warden. This letter aims to maximize the secrecy rate while satisfying the covertness constraint.

Elizabeth Thomas, S. Vineeth, Noble P. Abraham

Global attention has been focused on extreme climatic changes. This paper investigates the relationship between different phases of solar activity and extreme precipitation events in Kerala, India. Sunspot number and rainfall data were analysed over 122 years (1901-2022) on an annual scale. A negative correlation was observed in the winter and post-monsoon s

Christian Ecker, Florian Ecker, Daniel Grumiller

We identify a new critical parameter in Choptuik's gravitational collapse: the angle at which null energy condition (NEC) saturation lines intersect at the center of the critical spacetime. These NEC lines coincide with regions of vanishing curvature, dividing spacetime into stripes of positive and negative curvature. By numerically solving Choptuik's origin

B. J. Frei, P. Ulbl, J. Trilaksono, F. Jenko

This paper presents the first gyrokinetic (GK) simulations of edge and scrape-off layer (SOL) turbulence accelerated by a velocity-space spectral approach in the full-f GK code GENE-X. Building upon the original grid velocity-space discretization, we derive and implement a new spectral formulation and verify the numerical implementation using the method of m

Kexian Liu, Jianfeng Guan, Xiaolong Hu, Jing Zhang

To meet the diverse needs of users, the rapid advancement of cloud-edge-device collaboration has become a standard practice. However, this complex environment, particularly in untrusted (non-collaborative) scenarios, presents numerous security challenges. Authentication acts as the first line of defense and is fundamental to addressing these issues. Although

Xiao-Song Yang

One fundamental problem in studying dynamical process is whether it is possible and how to construct prediction model for an unknown system via sampled time series, especially in the modern big data era. The research in this area is beneficial to experimentalists in physics, chemistry, especially, in biological science, where it is hard to construct a predic

Kexian Liu, Jianfeng Guan, Xiaolong Hu, Jianli Liu

The growing complexity of Internet of Things (IoT) environments, particularly in cross-domain data sharing, presents significant security challenges. Existing data-sharing schemes often rely on computationally expensive cryptographic operations and centralized key management, limiting their effectiveness for resource-constrained devices. To address these iss

Andrés Fábrega, Carolina Ortega Pérez, Armin Namavari, Ben Nassi

We explore an emerging threat model for end-to-end (E2E) encrypted applications: an adversary sends chosen messages to a target client, thereby "injecting" adversarial content into the application state. Such state is subsequently encrypted and synchronized to an adversarially-visible storage. By observing the lengths of the resulting cloud-stored ciphertext

Shigeki Matsutani

Euler derived the differential equations of elastica by the variational method in 1744, but his original derivation has never been properly interpreted or explained in terms of modern mathematics. We elaborate Euler's original derivation of elastica and show that Euler used Noether's theorem concerning the translational symmetry of elastica, although Noether

Zhehao Yi, Yanying Liang, Haozhen Situ

Combining classical optimization with parameterized quantum circuit evaluation, variational quantum algorithms (VQAs) are among the most promising algorithms in near-term quantum computing. Similar to neural networks (NNs), VQAs iteratively update circuit parameters to optimize a cost function. However, the training of variational quantum circuits (VQCs) is

Damianos Michaelides, Antony Overstall, Dave Woods

This paper describes the R package fdesigns that implements a methodology for identifying Bayesian optimal experimental designs for models whose factor settings are functions, known as profile factors. This type of experiments which involve factors that vary dynamically over time, presenting unique challenges in both estimation and design due to the infinite

Md Kamrul Siam, Huanying Gu, Jerry Q. Cheng

Our everyday lives now heavily rely on artificial intelligence (AI) powered large language models (LLMs). Like regular users, programmers are also benefiting from the newest large language models. In response to the critical role that AI models play in modern software development, this study presents a thorough evaluation of leading programming assistants, i

Amanda Vallentin

The diagnostic imaging departments are under great pressure due to a growing workload. The number of required scans is growing and there is a shortage of qualified labor. AI solutions for medical imaging applications have shown great potential. However, very few diagnostic imaging models have been approved for hospital use and even fewer are being implemente

Shota Saito, Yuji Tachikawa

We study if and when mod-2 anomalies can be canceled by the Green-Schwarz mechanism with the introduction of an antisymmetric tensor field $B_{\mu\nu}$. As explicit examples, we examine $SU(2)$ and more general $Sp(n)$ gauge theories in four and eight dimensions. We find that the mod-2 anomalies of 8d $\mathcal{N}=1$ $Sp(n)$ gauge theory can be canceled, as

Aviv Ovadya, Kyle Redman, Luke Thorburn, Quan Ze Chen

This position paper argues that effectively "democratizing AI" requires democratic governance and alignment of AI, and that this is particularly valuable for decisions with systemic societal impacts. Initial steps -- such as Meta's Community Forums and Anthropic's Collective Constitutional AI -- have illustrated a promising direction, where democratic proces

Gayani Rathnayake, Akanksha Negi, Otavio Bartalotti, Xueyan Zhao

We consider the identification of average treatment effects on the treated (ATT) in difference-in-differences (DiD) settings in the presence of endogenous sample selection. We first establish that the conventional DiD estimand generally fails to recover causally meaningful treatment effects, even if selection and treatment assignment are independent. We then

Hitesh Khungla, Mohit Kumar

Pandora temporal fault tree, as one notable extension of the fault tree, introduces temporal gates and temporal laws. Pandora Temporal Fault Tree(TFT) enhances the capability of fault trees and enables the modeling of system failure behavior that depends on sequences. The calculation of system failure probability in Pandora TFT relies on precise probabilisti

Xiaoxue Gao, Zexin Li, Yiming Chen, Cong Liu

Given the extensive research and real-world applications of automatic speech recognition (ASR), ensuring the robustness of ASR models against minor input perturbations becomes a crucial consideration for maintaining their effectiveness in real-time scenarios. Previous explorations into ASR model robustness have predominantly revolved around evaluating accura

Yuheng Shi, Minjing Dong, Chang Xu

While Contrastive Language-Image Pre-training (CLIP) has advanced open-vocabulary predictions, its performance on semantic segmentation remains suboptimal. This shortfall primarily stems from its spatial-invariant semantic features and constrained resolution. While previous adaptations addressed spatial invariance semantic by modifying the self-attention in

Augusto Cerqua, Marco Letta, Gabriele Pinto

We provide the first systematic assessment of data leakage issues in the use of machine learning on panel data. Our organizing framework clarifies why neglecting the cross-sectional and longitudinal structure of these data leads to hard-to-detect data leakage, inflated out-of-sample performance, and an inadvertent overestimation of the real-world usefulness

Sally Junsong Wang, Kexin Pei, Junfeng Yang

Smart contracts are software programs that enable diverse business activities on the blockchain. Recent research has identified new classes of "machine un-auditable" bugs that arise from both transactional contexts and source code. Existing detection methods require human understanding of underlying transaction logic and manual reasoning across different sou

Ryan K. Krueger, Megan C. Engel, Ryan Hausen, Michael P. Brenner

Developing physics-based models for molecular simulation requires fitting many unknown parameters to diverse experimental datasets. Traditionally, this process is piecemeal and difficult to reproduce, leading to a fragmented landscape of models. Here, we establish a systematic, extensible framework for fitting coarse-grained molecular models to macroscopic e

Renzo Kenyi Takagui Perez

Building upon the concept of utilizing quasi-parabolic approximations to determine plasma frequency profiles from ionograms, we present a refined multi-quasi-parabolic method for modeling the E and F layers. While a recent study AIP Advances 14 065034 introduced an approach in this direction, we identified several inaccuracies in its mathematical treatment a

Aniket Deroy, Subhankar Maity

The widespread use of social media platforms like Twitter and Facebook has enabled people of all ages to share their thoughts and experiences, leading to an immense accumulation of user-generated content. However, alongside the benefits, these platforms also face the challenge of managing hate speech and offensive content, which can undermine rational discou

Nghia Trung Ngo, Chien Van Nguyen, Franck Dernoncourt, Thien Huu Nguyen

Retrieval-augmented generation (RAG) has emerged as a promising approach to enhance the performance of large language models (LLMs) in knowledge-intensive tasks such as those from medical domain. However, the sensitive nature of the medical domain necessitates a completely accurate and trustworthy system. While existing RAG benchmarks primarily focus on the

Hue T. B. Do, Gregory K. Ngirmang, Wu Lin, Michel Bosman

We introduce a new mechanism for second-harmonic generation through geometrically rectifying-funneling-ballistic electrons in THz optical resonators. Our resonant rectifiers inherently act as second-order harmonic generators, rectifying currents without the presence of a potential barrier. Particle-in-cell simulations reveal that femtosecond electron-surface

Ji-Ha Park, Seo-Hyun Lee, Soowon Kim, Seong-Whan Lee

Interpreting human neural signals to decode static speech intentions such as text or images and dynamic speech intentions such as audio or video is showing great potential as an innovative communication tool. Human communication accompanies various features, such as articulatory movements, facial expressions, and internal speech, all of which are reflected i

Yinghao Ma, Jiaxi Su, Dong-Ling Deng

Classical verification of quantum learning allows classical clients to reliably leverage quantum computing advantages by interacting with untrusted quantum servers. Yet, current quantum devices available in practice suffers from a variety of noises and whether existed classical verification protocols carry over to noisy scenarios remains unclear. Here, we pr

Xuyang Cao, Guoxin Wang, Sheng Shi, Jun Zhao

Audio-driven portrait animation has made significant advances with diffusion-based models, improving video quality and lipsync accuracy. However, the increasing complexity of these models has led to inefficiencies in training and inference, as well as constraints on video length and inter-frame continuity. In this paper, we propose JoyVASA, a diffusion-based