November 2024 arXiv papers — page 72

Owais Ahmed, M Huzaifa, M Areeb, Hamza Ali Khan

In response to the growing challenges of manual labor and efficiency in warehouse operations, Amazon has embarked on a significant transformation by incorporating robotics to assist with various tasks. While a substantial number of robots have been successfully deployed for tasks such as item transportation within warehouses, the complex process of object pi

Miles Simon

In this paper we survey some results on Ricci flowing non-smooth initial data. Among other things, we give a non-exhaustive list of various weak initial data which can be evolved with the Ricci flow. We also survey results which show that various curvature lower bounds will, possibly up to a constant, be preserved, if we start with such possibly non-smooth i

Antonino Visalli, Francesco Maria Calistroni, Margherita Calderan, Francesco Donnarumma

Evidence Accumulation Models (EAMs) have been widely used to investigate speeded decision-making processes, but they have largely neglected the role of predictive processes emphasized by theories of the predictive brain. In this paper, we present the Predictive evidence Accumulation Models (PAM), a novel computational framework that integrates predictive pro

Ahmad Abdi, Mahsa Dalirrooyfard, Meike Neuwohner

Given a connected graph $G=(V,E)$ and a crossing family $\mathcal{C}$ over ground set $V$ such that $|\delta_G(U)|\geq 2$ for every $U\in \mathcal{C}$, we prove there exists a strong orientation of $G$ for $\mathcal{C}$, i.e., an orientation of $G$ such that each set in $\mathcal{C}$ has at least one outgoing and at least one incoming arc. This implies the m

Avinash M, Srikrishna Bhashyam

In this paper, we propose a bistatic sensing-assisted beam tracking method for simultaneous communication and tracking of user vehicles navigating arbitrary-shaped road trajectories. Prior work on simultaneous communication and tracking assumes a colocated radar receiver at the transmitter for sensing measurements using the reflected Integrated Sensing and C

Harrie Passier, Lex Bijlsma, Ruurd Kuiper, Kees Huizing

To improve the quality of programs we provide an approach to guidance in the process of program development. At the higher level the various activities and their dependencies to structure the process are identified. At the lower level, detailed, practical rules are given for the decision-making in the development steps during these activities. The approach c

Dali Liu, Haolei Weng

In this paper, we demonstrate how a class of advanced matrix concentration inequalities, introduced in \cite{brailovskaya2024universality}, can be used to eliminate the dimensional factor in the convergence rate of matrix completion. This dimensional factor represents a significant gap between the upper bound and the minimax lower bound, especially in high d

Yuexing Ding, Jun Wang, Hongbing Lyu

In the field of medical image segmentation, challenges such as indistinct lesion features, ambiguous boundaries,and multi-scale characteristics have long revailed. This paper proposes an improved method named Intensity-Spatial Dual Masked AutoEncoder (ISD-MAE). Based on the tissue-contrast semi-masked autoencoder, a Masked AutoEncoder (MAE) branch is introdu

Andrea Giudici, Colin Please, Jon Chapman

Over the long timescale of many charge/discharge cycles, gas formation can result in large bulging deformations of a Lithium-ion pouch cell, which is a key failure mechanism in batteries. Guided by recent experimental X-ray tomography data of a bulging cell, we propose a homogenised mechanical model to predict the shape of the deformation and the stress dist

Luuk J. Blaauw, Detlef Lohse, Sander G. Huisman

Bubbly drag reduction is considered as one of the most promising techniques to reduce the energy consumption of marine vessels. With this technique bubbles are injected under the hull where they then lubricate the hull, thus reducing the drag of the vessel. Understanding the effects of salts on bubbly drag reduction is therefore of crucial importance in the

C. J. Muller

A molecular MCB junction in the partially wet phase has been used to probe effects related to open quantum systems. Although the exact quantum system, environment, and coupling, are not known the nature of the experiments shows a measurement influenced next measurement. The quantum system senses the measurement outcome and prepares itself in a state other th

Verena Ruf, Dominik Thues, Sarah Malone, Stefan Kuechemann

Graphs are essential representations in the professions and education concerning the science, technology, engineering, and mathematics (STEM) disciplines. Beyond their academic relevance, graphs find extensive utility in everyday scenarios, ranging from news media to educational materials. This underscores the importance of people's being able to understand

André Freitas, Kiwon Um, Mathieu Desbrun, Michele Buzzicotti

This work studies an a posteriori data-driven approach (known as solver-in-the-loop) for sub-grid modeling of a shell model for turbulence. This approach takes advantage of the differentiable physics paradigm of deep learning, allowing a neural network model to interact with the differential equation solver over time during the training process. The closure

Eli Bagno, Estrella Eisenberg, Shulamit Reches, Moriha Sigron

In a recent study, Tenner introduced the concept of the interval poset of a permutation to effectively represent all intervals and their inclusions within a permutation. In this paper, we present a new geometric viewpoint on interval posets. We establish a one-to-one correspondence between the set of interval posets for permutations of size $n$ and a specifi

Anup Mishra, Nikolaos Pappas, Čedomir Stefanović, Onur Ayan

Achieving a flexible and efficient sharing of wireless resources among a wide range of novel applications and services is one of the major goals of the sixth-generation of mobile systems (6G). Accordingly, this work investigates the performance of a real-time system that coexists with a broadband service in a frame-based wireless channel. Specifically, we co

Juan E. Galeote-Cazorla, Alejandro Ramírez-Arroyo, José-María Molina-García-Pardo, María-Teresa Martínez-Inglés

The fifth generation (5G) of mobile communications relies on extremely high data transmission rates utilizing a wide range of frequency bands, including FR1 (sub-6 GHz) and FR2 (mmWave). Future mobile communications systems are envisaged to operate at the electromagnetic spectrum beyond FR2, above 100 GHz, known as sub-THz band. These new frequencies open up

Aditya Dubey, Zeki Zeybek, Fabian Köhler, Rick Mukherjee

A fundamental longstanding problem in studying spin models is the efficient and accurate numerical simulation of the long-time behavior of larger systems. The exponential growth of the Hilbert space and the entanglement accumulation at long times pose major challenges for current methods. To address these issues, we employ the multilayer multiconfiguration t

Tom David Müller, Arslan Siraj, Axel Walter, Jihyung Kim

Liquid Chromatography Mass Spectrometry (LC-MS) is an indispensable analytical technique in proteomics, metabolomics, and other life sciences. While OpenMS provides advanced open-source software for MS data analysis, its complexity can be challenging for non-experts. To address this, we have developed OpenMS WebApps, a framework for creating user-friendly MS

Yong Xie, Weijie Zheng, Hanxun Huang, Guangnan Ye

As deep learning models are increasingly deployed in safety-critical applications, evaluating their vulnerabilities to adversarial perturbations is essential for ensuring their reliability and trustworthiness. Over the past decade, a large number of white-box adversarial robustness evaluation methods (i.e., attacks) have been proposed, ranging from single-st

Anup Mishra, Israel Leyva-Mayorga, Petar Popovski

Future wireless networks are envisioned to facilitate the seamless coexistence of communication and sensing functionalities, thereby enabling the much-touted integrated sensing and communication (ISAC) paradigm. A key challenge in ISAC is managing inter-functionality interference while maintaining a balanced performance trade-off. In this work, we propose a

Erica Coppolillo, Federico Cinus, Marco Minici, Francesco Bonchi

Large Language Models (LLMs) demonstrate significant persuasive capabilities in one-on-one interactions, but their influence within social networks, where interconnected users and complex opinion dynamics pose unique challenges, remains underexplored. This paper addresses the research question: \emph{Can LLMs generate meaningful content that maximizes user e

Chengjie Huang, Vahdat Abdelzad, Sean Sedwards, Krzysztof Czarnecki

Input aggregation is a simple technique used by state-of-the-art LiDAR 3D object detectors to improve detection. However, increasing aggregation is known to have diminishing returns and even performance degradation, due to objects responding differently to the number of aggregated frames. To address this limitation, we propose an efficient adaptive method, w

Martin Zlatić, Marko Čanađija

The aim of this work is to develop a neural network for modelling incompressible hyperelastic behaviour with isotropic damage, the so-called Mullins effect. This is obtained through the use of feed-forward neural networks with special attention to the architecture of the network in order to fulfil several physical restrictions such as objectivity, polyconvex

Kevin Riehl, Michail Makridis, Anastasios Kouvelas

Advancements in computer science, artificial intelligence, and control systems of the recent have catalyzed the emergence of cybernetic societies, where algorithms play a significant role in decision-making processes affecting the daily life of humans in almost every aspect. Algorithmic decision-making expands into almost every industry, government processes

Erin Carson, Xinye Chen, Fei He, Cheng Kang

The expansion of time-series data from sensors and monitoring systems has made compact representations increasingly important. Such representations should retain signal structure while cutting storage, transmission and computation costs. Adaptive Brownian Bridge-based Aggregation (ABBA) addresses this need by converting long numerical series into short symbo

Veronica Henao Isaza, David Aguillon, Carlos Andres Tobon Quintero, Francisco Lopera

Background: Dementia, marked by cognitive decline, is a global health challenge. Alzheimer's disease (AD), the leading type, accounts for ~70% of cases. Electroencephalography (EEG) measures show promise in identifying AD risk, but obtaining large samples for reliable comparisons is challenging. Objective: This study integrates signal processing, harmonizati

Kuiran Wang, Xuehui Yu, Wenwen Yu, Guorong Li

Single object tracking(SOT) relies on precise object bounding box initialization. In this paper, we reconsidered the deficiencies in the current approaches to initializing single object trackers and propose a new paradigm for single object tracking algorithms, ClickTrack, a new paradigm using clicking interaction for real-time scenarios. Moreover, click as a

Wencong Sun, Haoshen Ye, Li Liang, Ning Ding

Altermagnetism, as a new branch of magnetism independent of traditional ferromagnetism and antiferromagnetism, has attracted extensive attention recently. At present, researchers have proved several kinds of three-dimensional altermagnets, but research on two-dimensional (2D) altermagnets remains elusive. Here, we propose a method for designing altermagnetis

Luigi Celona, Simone Bianco, Paolo Napoletano

The classification of distracted drivers is pivotal for ensuring safe driving. Previous studies demonstrated the effectiveness of neural networks in automatically predicting driver distraction, fatigue, and potential hazards. However, recent research has uncovered a significant loss of accuracy in these models when applied to samples acquired under condition

Szymon Lis

This study conducted a comprehensive review of 71 papers published between 2000 and 2021 that employed various measures of investor sentiment to model returns. The analysis indicates that higher complexity of sentiment measures and models improves the coefficient of determination. However, there was insufficient evidence to support that models incorporating

Erik Tegler, Magnus Oskarsson, Kalle Åström

Time delay estimation or Time-Difference-Of-Arrival estimates is a critical component for multiple localization applications such as multilateration, direction of arrival, and self-calibration. The task is to estimate the time difference between a signal arriving at two different sensors. For the audio sensor modality, most current systems are based on class

Mikhail Zaitsev

We propose a universal matrix Capelli identity and explain how to derive Capelli identities for all quantum immanants in the Reflection Equation algebra and in the universal enveloping algebra U(gl_(M|N)).

Caixing Gu, In Sung Hwang, Hyoung Joon Kim, Woo Young Lee

In this paper we formulate the almost invariant subspaces theorems of backward shift operators in terms of the ranges or kernels of product of Toeplitz and Hankel operators. This approach simplifies and gives more explicit forms of these almost invariant subspaces which are derived from related nearly backward shift invariant subspaces with finite defect. Fu

Vidar Gudmundsson, Vram Mughnetsyan, Hsi-Sheng Goan, Jeng-Da Chai

We model a spin-phase transition in a two-dimensional square array, or a lateral superlattice, of quantum rings in an external perpendicular homogeneous magnetic field. The electron system is placed in a circular cylindrical far-infrared photon cavity with a single circularly symmetric photon mode. Our numerical results reveal that the spin ordering of the t

Meili Guo, Haiyan Jiang, Tiao Lu, Wenqi Yao

The 1D Schr\"odinger equation closed with the transparent boundary conditions(TBCs) is known as a successful model for describing quantum effects, and is usually considered with a self-consistent Poisson equation in simulating quantum devices. We introduce discrete fourth order transparent boundary conditions(D4TBCs), which have been proven to be essentially

Paweł Swaczyna, Maciej Bzowski, Marzena A. Kubiak

Energetic Neutral Atom (ENA) observations provide valuable insights into the plasma conditions in the heliosphere and the surrounding interstellar medium. Unlike plasma detectors, which measure charged particles tied to the magnetic fields at their location, ENA detectors capture former ions that were neutralized in distant regions and traverse the heliosphe

Hongliu Cao

The rapid advancement of Language Model technologies has opened new opportunities, but also introduced new challenges related to bias and fairness. This paper explores the uncharted territory of potential biases in state-of-the-art universal text embedding models towards specific document and query writing styles within Information Retrieval (IR) systems. Ou

Mario Molina, Lorenzo J. Tardon, Ana M. Barbancho, Irene De-Torres

Electroencephalography (EEG) provides a way to understand, and evaluate neurotransmission. In this context, time-locked EEG activity or event-related potentials (ERPs) are often used to capture neural activity related to specific mental processes. Normally, they are considered on the basis of averages across a number of trials. However, there exist notable v

Fedor V. Fomin, Petr A. Golovach, Tanmay Inamdar, Saket Saurabh

The parameterized analysis of graph modification problems represents the most extensively studied area within Parameterized Complexity. Given a graph $G$ and an integer $k\in\mathbb{N}$ as input, the goal is to determine whether we can perform at most $k$ operations on $G$ to transform it into a graph belonging to a specified graph class $\mathcal{F}$. Typic

Tianping Zhang, Mingxuan Zhong

We prove that the Kloosterman sum $\text{Kl}(1,q)$ changes sign infinitely many times, as $q\rightarrow +\infty$ with at most six prime factors. As a consequence, our result improved the best known result of Xi(IMRN, 2022). The novelty of our method comes from introducing a new truncated divisor function whose selection depends on the number of prime factors

Peng Wang, Li Shen, Zerui Tao, Yan Sun

Averaging iterations of Stochastic Gradient Descent (SGD) have achieved empirical success in training deep learning models, such as Stochastic Weight Averaging (SWA), Exponential Moving Average (EMA), and LAtest Weight Averaging (LAWA). Especially, with a finite weight averaging method, LAWA can attain faster convergence and better generalization. However, i

Georgii Oblapenko, Arseniy Tarnovskiy, Moritz Ertl, Manuel Torrilhon

In the present work, we extend the Discontinuous Galerkin Spectral Element Method (DGSEM) to high-enthalpy reacting gas flows with internal degrees of freedom. An entropy- and kinetic energy-preserving flux function is proposed which allows for use of arbitrary expressions for the internal energies of the constituent gas species. The developed method is appl

Antoine Marie, Pina Romaniello, Xavier Blase, Pierre-François Loos

The Bethe-Salpeter equation has been extensively employed to compute the two-body electron-hole propagator and its poles which correspond to the neutral excitation energies of the system. Through a different time-ordering, the two-body Green's function can also describe the propagation of two electrons or two holes. The corresponding poles are the double ion

Thibaut Lacroix, Dario Cilluffo, Susana F. Huelga, Martin B. Plenio

Quantum collision describe open quantum systems through repeated interactions with a coarse-grained environment. However, a complete certification of these models is lacking, as no complete error bounds on the simulation of system observables have been established. Here, we show that Markovian and non-Markovian collision models can be recovered analytically

Tilman Enss

Transport in strongly correlated fermions cannot be understood by fermionic quasiparticles alone. We present a theoretical framework for quantum transport that incorporates strong local correlations of fermion pairs. These contact correlations add essential contributions to viscous, thermal and sound transport coefficients. The bulk viscosity, in particular,

Qifeng Lin, Nghia Vuong, Kewei Song, Phuoc Thanh Tran-Ngoc

The advancement of insect-computer hybrid robots holds significant promise for navigating complex terrains and enhancing robotics applications. This study introduced an automatic assembly method for insect-computer hybrid robots, which was accomplished by mounting backpack with precise implantation of custom-designed bipolar electrodes. We developed a stimul

Nabeel Seedat, Caterina Tozzi, Andrea Hita Ardiaca, Mihaela van der Schaar

The reuse of historical clinical trial data has significant potential to accelerate medical research and drug development. However, interoperability challenges, particularly with missing medical codes, hinders effective data integration across studies. While Large Language Models (LLMs) offer a promising solution for automated coding without labeled data, cu

Bingzhe Wang, Ruohan Qian, Yuejia Dou, Qi Qi

The autobidding system generates huge revenue for advertising platforms, garnering substantial research attention. Existing studies in autobidding systems focus on designing Autobidding Incentive Compatible (AIC) mechanisms, where the mechanism is Incentive Compatible (IC) under ex ante expectations. However, upon deploying AIC mechanisms in advertising plat

Hikaru Manabe

In an amalgamation Nim, players are allowed to use a move from the traditional form of Nim or to amalgamate two piles when they are not empty. No formula that describes the set of P-positions of Amalgamation Nim is known. The author gives a condition on the amalgamation of two piles. Players can amalgamate two piles only when the number of stones in these tw

Zengzhu Guo, Zhiqi Ma

Peptides are biomolecules comprised of amino acids that play an important role in our body. In recent years, peptides have received extensive attention in drug design and synthesis, and peptide prediction tasks help us better search for functional peptides. Typically, we use the primary sequence and structural information of peptides for model encoding. Howe

Jad C. Halimeh, Masanori Hanada, Shunji Matsuura, Franco Nori

We provide a universal framework for the quantum simulation of SU(N) Yang--Mills theories on fault-tolerant digital quantum computers adopting the orbifold lattice formulation. As warm-up examples, we also consider simple models, including scalar field theory and the Yang--Mills matrix model, to illustrate the universality of our formulation, which shows up

Ya-Nan Lv, Yan-Lei Zhang, Xu-Bo Zou, Guang-Can Guo

Efficient microwave-to-optical frequency conversion (MOC) is crucial for applications such as radiometry, electrometry, quantum microwave illumination and quantum networks. Rydberg atoms provide a unique platform for realizing free-space MOC, promising wide-bandwidth, scalable, and flexible quantum interfaces. Here, we develop a theoretical framework to eval

Jiawei Yu, Yuang Li, Xiaosong Qiao, Huan Zhao

Text-to-speech (TTS) models have been widely adopted to enhance automatic speech recognition (ASR) systems using text-only corpora, thereby reducing the cost of labeling real speech data. Existing research primarily utilizes additional text data and predefined speech styles supported by TTS models. In this paper, we propose Hard-Synth, a novel ASR data augme

Yan-Lei Zhang, Ming Li, Xin-Biao Xu, Chun-Hua Dong

Quantum frequency converters that enable the interface between the itinerant photons and qubits are indispensable for realizing long-distance quantum network. However, the cascaded connection between converters and qubits usually brings additional insertion loss and intermediate noises. Here, we propose a cooperative quantum interface (CQI) that integrates t

Yi Gu, Dongjun Ye, Zhaorui Wang, Jiaxu Wang

Neural surface reconstruction relies heavily on accurate camera poses as input. Despite utilizing advanced pose estimators like COLMAP or ARKit, camera poses can still be noisy. Existing pose-NeRF joint optimization methods handle poses with small noise (inliers) effectively but struggle with large noise (outliers), such as mirrored poses. In this work, we f

Lars Doorenbos, Raphael Sznitman, Pablo Márquez-Neila

The reliability of supervised classifiers is severely hampered by their limitations in dealing with unexpected inputs, leading to great interest in out-of-distribution (OOD) detection. Recently, OOD detectors trained on synthetic outliers, especially those generated by large diffusion models, have shown promising results in defining robust OOD decision bound

Hyun Ryu, Eric Kim

Efficient inference in large language models (LLMs) has become a critical focus as their scale and complexity grow. Traditional autoregressive decoding, while effective, suffers from computational inefficiencies due to its sequential token generation process. Speculative decoding addresses this bottleneck by introducing a two-stage framework: drafting and ve

Alice James, Avishkar Seth, Subhas Mukhopadhyay

We introduce MecQaBot, an open-source, affordable, and modular autonomous mobile robotics framework developed for education and research at Macquarie University, School of Engineering, since 2019. This platform aims to provide students and researchers with an accessible means for exploring autonomous robotics and fostering hands-on learning and innovation. O

Go Kato, Masaki Owari, Koji Maruyama

The necessary time required to control a many-body quantum system is a critically important issue for the future development of quantum technologies. However, it is generally quite difficult to analyze directly, since the time evolution operator acting on a quantum system is in the form of time-ordered exponential. In this work, we examine the Baker-Campbell

Zhicong Li, Jiahao Wang, Zhishu Jiang, Hangyu Mao

Large language models often encounter challenges with static knowledge and hallucinations, which undermine their reliability. Retrieval-augmented generation (RAG) mitigates these issues by incorporating external information. However, user queries frequently contain noise and intent deviations, necessitating query rewriting to improve the relevance of retriev

Ameera Bawazir, Kebin Wu, Wenbin Li

Recent advancements in vision-language pre-training via contrastive learning have significantly improved performance across computer vision tasks. However, in the medical domain, obtaining multimodal data is often costly and challenging due to privacy, sensitivity, and annotation complexity. To mitigate data scarcity while boosting model performance, we intr

Kai Tanaka, Mineichi Kudo, Keigo Kimura, Atsuyoshi Nakamura

The proportion of elderly people is increasing worldwide, particularly those living alone in Japan. As elderly people get older, their risks of physical disabilities and health issues increase. To automatically discover these issues at a low cost in daily life, sensor-based detection in a smart home is promising. As part of the effort towards early detection

Houcheng Su, Mengzhu Wang, Jiao Li, Nan Yin

In semi-supervised domain adaptation (SSDA), the model aims to leverage partially labeled target domain data along with a large amount of labeled source domain data to enhance its generalization capability for the target domain. A key advantage of SSDA is its ability to significantly reduce reliance on labeled data, thereby lowering the costs and time associ

Lucas Sippel, Michael Forbes

The method of fragments was recently proposed, and its effectiveness has been empirically shown for three specialised pickup and delivery problems. We propose an enhanced fragment algorithm that for the first time, effectively solves the Pickup and Delivery Problem with Time Windows. Additionally, we describe the approach in general terms to exemplify its th

Christoph Reinders, Radu Berdan, Beril Besbinar, Junji Otsuka

Current deep learning approaches in computer vision primarily focus on RGB data sacrificing information. In contrast, RAW images offer richer representation, which is crucial for precise recognition, particularly in challenging conditions like low-light environments. The resultant demand for comprehensive RAW image datasets contrasts with the labor-intensive

Thomas Pöllabauer

Localizing target objects in images is an important task in computer vision. Often it is the first step towards solving a variety of applications in autonomous driving, maintenance, quality insurance, robotics, and augmented reality. Best in class solutions for this task rely on deep neural networks, which require a set of representative training data for be

Johannes Pitz, Lennart Röstel, Leon Sievers, Berthold Bäuml

In-hand manipulation with multi-fingered hands is a challenging problem that recently became feasible with the advent of deep reinforcement learning methods. While most contributions to the task brought improvements in robustness and generalization, this paper addresses the critical performance measure of the speed at which an in-hand manipulation can be per

Mengzhu Wang, Jiao Li, Houcheng Su, Nan Yin

Semi-supervised learning (SSL) has made notable advancements in medical image segmentation (MIS), particularly in scenarios with limited labeled data and significantly enhancing data utilization efficiency. Previous methods primarily focus on complex training strategies to utilize unlabeled data but neglect the importance of graph structural information. Dif

Chao Liu, Roland Geyer, Shanying Hu

Robust and credible material flow data are required to support the ongoing efforts to reconcile the economic and social benefits of plastics with their human and environmental health impacts. This study presents a global, but regionalized, life cycle material flow analysis (MFA) of all plastic polymers and applications for the period 1950-2020. It also illus

Guillaume Lambard

The Erd\"{o}s-Moser equation $ \sum_{i=1}^{m - 1} i^k = m^k $ is a longstanding challenge in number theory, with the only known integer solution being $ (k,m) = (1,3) $. Here, we investigate whether other solutions might exist by using the Euler-MacLaurin formula to approximate the discrete sum $ S(m-1,k) $ with a continuous function $ S_{\mathbb{R}}(m-1,k)

Wenjie Peng, Hongxiang Huang, Tianshui Chen, Quhui Ke

Hard negative generation aims to generate informative negative samples that help to determine the decision boundaries and thus facilitate advancing deep metric learning. Current works select pair/triplet samples, learn their correlations, and fuse them to generate hard negatives. However, these works merely consider the local correlations of selected samples

Naen Xu, Changjiang Li, Tianyu Du, Minxi Li

Text-to-image diffusion models have emerged as powerful tools for generating high-quality images from textual descriptions. However, their increasing popularity has raised significant copyright concerns, as these models can be misused to reproduce copyrighted content without authorization. In response, recent studies have proposed various copyright protectio

Yanze Chen

We computed the first Whittaker coefficient of an Eisenstein series on a global metaplectic group induced from the torus and related the result with a Weyl group multiple Dirichlet series attached to the (dual) root system of the group under a mild assumption on the root system and the degree of the metaplectic cover. This confirms a conjecture of Brubaker-B

Yingkai Ouyang, Yumang Jing, Gavin K. Brennen

Transversal gates on quantum error correction codes have been a promising approach for fault-tolerant quantum computing, but are limited by the Eastin-Knill no-go theorem. Existing solutions like gate teleportation and magic state distillation are resource-intensive. We present a measurement-free code-switching protocol for universal quantum computation, swi

Pradeesha Ashok, Gautam K. Das, Arti Pandey, Kaustav Paul

Given a graph $G=(V,E)$, a function $f:V\to \{0,1,2\}$ is said to be a \emph{Roman Dominating function} (RDF) if for every $v\in V$ with $f(v)=0$, there exists a vertex $u\in N(v)$ such that $f(u)=2$. A Roman Dominating function $f$ is said to be an \emph{Independent Roman Dominating function} (IRDF), if $V_1\cup V_2$ forms an independent set, where $V_i=\{v

Zimao Sheng, Hongan Yang, Jiakang Wang, Tong Zhang

The PID controller remains the most widely adopted control architecture, with groundbreaking success across extensive implications. However, optimal parameter tuning for PID controller remains a critical challenge. Existing theories predominantly focus on linear time-invariant systems and Single-Input Single-Output (SISO) scenarios, leaving a research gap in

Bishal Sonar, Satyam Guragain, Ravi Srivastava

The paper focuses on studying strong geodetic sets and numbers in the context of corona-type products of graphs. Our primary focus is on three variations of the corona products: the generalized corona, generalized edge corona, and generalized neighborhood corona products. A strong geodetic set is a minimal subset of vertices that covers all vertices in the g

Yi-Ting Huang, Ying-Ren Guo, Yu-Sheng Yang, Guo-Wei Wong

With the increasing sophistication of Advanced Persistent Threats (APTs), the demand for effective detection and mitigation strategies and methods has escalated. Program execution leaves traces in the system audit log, which can be analyzed to detect malicious activities. However, collecting and analyzing large volumes of audit logs over extended periods is

Zepeng Zhang, Olga Fink

Over the last decade, graph neural networks (GNNs) have made significant progress in numerous graph machine learning tasks. In real-world applications, where domain shifts occur and labels are often unavailable for a new target domain, graph domain adaptation (GDA) approaches have been proposed to facilitate knowledge transfer from the source domain to the t

Torsten Linß, Goran Radojev

A class of linear parabolic equations is considered. We derive a framework for the a posteriori error analysis of time discretisations by Richardson extrapolation of arbitrary order combined with finite element discretisations in space. We use the idea of elliptic reconstructions and certain bounds for the Green's function of the parabolic operator. The cruc

Xin Wang, Kai Chen, Jiaming Zhang, Jingjing Chen

Large pre-trained Vision-Language Models (VLMs) such as CLIP have demonstrated excellent zero-shot generalizability across various downstream tasks. However, recent studies have shown that the inference performance of CLIP can be greatly degraded by small adversarial perturbations, especially its visual modality, posing significant safety threats. To mitigat

S. S. Kuptsov, S. A. Paston, A. A. Sheykin

The group-theoretic method for constructing symmetric isometric embeddings is used to describe all possible four-dimensional surfaces in flat $(1,9)$-dimensional space, whose induced metric is static and spherically symmetric. For such surfaces, we propose a classification related to the dimension of the elementary blocks forming the embedding function. All

Margot Ferrand, Vincent Labatut

In historical studies, the older the sources, the more common it is to have access to data that are only partial, and/or unreliable or imprecise. This can make it difficult, or even impossible, to perform certain tasks of interest, such as the segmentation of some urban space based on the location of its constituting elements. Indeed, traditional approaches

Cillian Doherty, Konstantinos Kavvadias, Jason Miller

Suppose that $h$ is an instance of the Gaussian free field (GFF) on a simply connected domain $D \subseteq {\mathbf C}$ and $x,y \in \partial D$ are distinct. Fix $\kappa \in (0,4)$ and for each $\theta \in {\mathbf R}$ let $\eta_\theta$ be the flow line of $h$ from $x$ to $y$. Recall that for $\theta_1 < \theta_2$ the fan ${\mathbf F}(\theta_1,\theta_2)$ of

Jia Shen, Yanni Wang, Haohao Zheng

This paper presents an investigation into the high-order asymptotic expansion for 2D and 3D cubic nonlinear Klein-Gordon equations in the non-relativistic limit regime. There are extensive numerical and analytic results concerning that the solution of NLKG can be approximated by first-order modulated Schr\"odinger profiles in terms of $e^{i\frac t {\varepsil

Tomoki Matsumoto

This paper proposes a Bayesian method for estimating the parameters of a normal distribution when only limited summary statistics (sample mean, minimum, maximum, and sample size) are available. To estimate the parameters of a normal distribution, we introduce a data augmentation approach using the Gibbs sampler, where intermediate values are treated as missi

Gabriel Turinici

Herd immunity is a critical concept in epidemiology, describing a threshold at which a sufficient proportion of a population is immune, either through infection or vaccination, thereby preventing sustained transmission of a pathogen. In the classic Susceptible-Infectious-Recovered (SIR) model, which has been widely used to study infectious disease dynamics,

Z. M. Balogh, E. Bubani, I. D. Platis

We define linear and radial stretch maps in the affine-additive group, and prove that they are minimizers of the mean quasiconformal distortion functional. For the proofs we use a method based on the notion of modulus of a curve family and the minimal stretching property (MSP) of the afore-mentioned maps. MSP relies on certain given curve families compatible

Jiyu Wang, Ying Chen, Xiancong Lu

Three dimensional (3D) third-order topological insulators (TIs) have zero-dimensional (0D) corner states, which are three dimensions lower than bulk. Here we investigate the third-order TIs on breathing pyrochlore lattices with p-orbital freedom. The tight-binding Hamiltonian is derived for the p-orbital model, in which we find that the two orthogonal ${\pi}

Xuechao Zou, Shun Zhang, Kai Li, Shiying Wang

Cloud segmentation is a critical challenge in remote sensing image interpretation, as its accuracy directly impacts the effectiveness of subsequent data processing and analysis. Recently, vision foundation models (VFM) have demonstrated powerful generalization capabilities across various visual tasks. In this paper, we present a parameter-efficient adaptive

Guy Barles, Olivier Ley, Erwin Topp

In this article, we consider nonlocal Hamilton-Jacobi Equations on networks with Kirchhoff type conditions for the interior vertices and Dirichlet boundary conditions for the boundary ones: our aim is to provide general existence and comparison results in the case when the integro-differential operators are of order strictly less than 1. The main originality

Dilian Gurov, Reiner Hähnle

We present an expressive logic over trace formulas, based on binary state predicates, chop, and least fixed-points, for precise specification of programs with recursive procedures. Both, programs and trace formulas, are equipped with a direct-style, fully compositional, denotational semantics that on programs coincides with the standard SOS of recursive prog

Fabrice Etienne

We introduce a generalisation of norm relations in the group algebra Q[G], where G is a finite group. We give some properties of these relations, and use them to obtain relations between the S-unit groups of different subfields of the same Galois extension of Q, of Galois group G. Then we deduce an algorithm to compute the class groups of some number fields

Fakeha Farooq, Jameel-Un Nabi, Ramoona Shehzadi

The knowledge of beta decay transitional probabilities and GamowTeller (GT) strength functions from highly excited states of nuclides is of particular importance for applications to astrophysical network calculations of nucleosynthesis in explosive stellar events. These quantities are challenging to achieve from measurements or computations using various nuc

Shenglan Sun, Fei Wang, Huawei Zhang, Xiang-Xiang Xue

Motivated by the vast gap between photometric and spectroscopic data volumes, there is great potential in using 5D kinematic information to identify and study substructures of the Milky Way. We identify substructures in the Galactic halo using 46,575 RR Lyrae stars (RRLs) from Gaia DR3 with the photometric metallicities and distances newly estimated by Li et

Yijie Zhang, Luzhe Huang, Nir Pillar, Yuzhu Li

Imaging mass spectrometry (IMS) is a powerful tool for untargeted, highly multiplexed molecular mapping of tissue in biomedical research. IMS offers a means of mapping the spatial distributions of molecular species in biological tissue with unparalleled chemical specificity and sensitivity. However, most IMS platforms are not able to achieve microscopy-level

Yusuf Yüksel

We have performed Monte Carlo simulations for the investigation of dynamic phase transitions on a honeycomb lattice which has garnered a significant amount of interest from the viewpoint of tailoring the intrinsic magnetism in two-dimensional materials. For the system under the influence of time-dependent magnetic field sequences exhibiting the half-wave ant

Stefan Graser, Martin Schrepp, Stephan Böhm

Questionnaires are a widely used tool for measuring the user experience (UX) of products. There exists a huge number of such questionnaires that contain different items (questions) and scales representing distinct aspects of UX, such as efficiency, learnability, fun of use, or aesthetics. These items and scales are not independent; they often have semantic o

Stefan Graser, Stephan Böhm, Martin Schrepp

Measuring User Experience (UX) with standardized questionnaires is a widely used method. A questionnaire is based on different scales that represent UX factors and items. However, the questionnaires have no common ground concerning naming different factors and the items used to measure them. This study aims to identify general UX factors based on the formula

Jameel-Un Nabi, Tuncay Bayram, Muhammad Riaz, Asim Ullah

This study re examines the effect of nuclear deformation on the calculated Gamow Teller (GT) strength distributions of neutron deficient (178 192Hg, 185 194Pb and 196 206Po) nuclei. The nuclear ground state properties and shape parameters were calculated using the Relativistic Mean Field model. Three different density dependent interactions were used in the