November 2024 arXiv papers — page 3

Lan Feng, Fan Nie, Yuejiang Liu, Alexandre Alahi

We propose TAROT, a targeted data selection framework grounded in optimal transport theory. Previous targeted data selection methods primarily rely on influence-based greedy heuristics to enhance domain-specific performance. While effective on limited, unimodal data (i.e., data following a single pattern), these methods struggle as target data complexity inc

Stefan Meisenbacher, Kaleb Phipps, Oskar Taubert, Marie Weiel

Optimizing smart grid operations relies on critical decision-making informed by uncertainty quantification, making probabilistic forecasting a vital tool. Designing such forecasting models involves three key challenges: accurate and unbiased uncertainty quantification, workload reduction for data scientists during the design process, and limitation of the en

Yu Shi, Yiqi Wang, WeiXuan Lang, Jiaxin Zhang

Nodes in the real-world graphs exhibit diverse patterns in numerous aspects, such as degree and homophily. However, most existent node predictors fail to capture a wide range of node patterns or to make predictions based on distinct node patterns, resulting in unsatisfactory classification performance. In this paper, we reveal that different node predictors

Steffen Dick, Christoph Bockisch, Harrie Passier, Lex Bijlsma

Correctness is one of the more important criteria of qualitative software. However, it is often taught in isolation and most students consider it only as an afterthought. They also do not receive sufficient feedback on code quality and tests unless specified in the assignment. To improve this, we developed a procedural guidance that guides students to an imp

Shaofei Huang, Christopher M. Poskitt, Lwin Khin Shar

Evolving cybersecurity threats in complex cyber-physical systems pose significant risks to system functionality and safety. This experience report introduces ACTISM (Automotive Consequence-Driven and Threat-Informed Security Modelling), an integrated security modelling framework that enhances the resilience of automotive systems by dynamically updating their

Hongxuan Lu, Biao Li

Data augmentation is a widely adopted technique utilized to improve the robustness of automatic speech recognition (ASR). Employing a fixed data augmentation strategy for all training data is a common practice. However, it is important to note that there can be variations in factors such as background noise, speech rate, etc. among different samples within a

Sergey Pavluchenko

In the current review, we provide a summary of the recent progress made in the cosmological aspect of extra-dimensional Lovelock gravity. Our review covers a wide variety of particular model/matter source combinations: Einstein--Gauss--Bonnet as well as cubic Lovelock gravities with vacuum, cosmological constant, perfect fluid, spatial curvature, and some of

Satoshi Masaki

In this article, we investigate the large-time behavior of small solutions to a system of one-dimensional cubic nonlinear Schr\"odinger equations with two components. In previous studies, a structural condition on the nonlinearity has been employed to guarantee the existence of a coercive, mass-type conserved quantity. We identify a new class of systems that

Philip Kennerberg, Ernst C. Wit

The aim of this paper is to extend worst risk minimization, also called worst average loss minimization, to the functional realm. This means finding a functional regression representation that will be robust to future distribution shifts on the basis of data from two environments. In the classical non-functional realm, structural equations are based on a tra

Mohammad Hasan Rahmani, Rafael Berkvens, Maarten Weyn

Emotions have a profound impact on our daily lives, influencing our thoughts, behaviors, and interactions, but also our physiological reactions. Recent advances in wearable technology have facilitated studying emotions through cardio-respiratory signals. Accelerometers offer a non-invasive, convenient, and cost-effective method for capturing heart- and pulmo

Yingchao Wang, Wenqi Niu

Personalized Federated Learning (PFL) focuses on tailoring models to individual IIoT clients in federated learning by addressing data heterogeneity and diverse user needs. Although existing studies have proposed effective PFL solutions from various perspectives, they overlook the issue of forgetting both historical personalized knowledge and global generaliz

Guangqing Wang, Suixin He

Let $T_{a,\varphi}$ be a Fourier integral operator defined with $a\in S^{m}_{0,\delta}(0\leq\delta<1)$ and $\varphi\in \Phi^{2}$ satisfying the strong non-degenerate condition. We demonstrate that when the order satisfies $$m\leq-\frac{n}{2}-\frac{n}{p}\delta+\frac{n}{p},$$ the operator $T_{a,\varphi}$ becomes bounded on $L^{p}(\mathbb{R}^n)$ for $2< p<\inft

Sai Kiran Narayanaswami, Gopalakrishnan Srinivasan, Balaraman Ravindran

As machine learning gets deployed more and more widely, and model sizes continue to grow, improving computational efficiency during model inference has become a key challenge. In many commonly used model architectures, including Transformers, a significant portion of the inference computation is comprised of exponential non-linearities such as Softmax. In th

Peter L. Walters, Mohammad U. Sherazi, Fei Wang

The simulation of non-Markovian quantum dynamics plays an important role in the understanding of charge and exciton dynamics in the condensed phase environment, and yet it remains computationally expensive on classical computers. We have developed a variational quantum algorithm that is capable of simulating non-Markovian quantum dynamics. The algorithm capt

C. McGuigan, R. Y. Teh, P. D. Drummond, M. D Reid

The Einstein-Podolsky-Rosen (EPR) paradox was presented as an argument that quantum mechanics is an incomplete description of physical reality. However, the premises on which the argument is based are falsifiable by Bell experiments. In this paper, we examine the EPR paradox from the perspective of Schrodinger's reply to EPR. Schrodinger pointed out that the

Brahim Boukanjime, Mohamed Maama

In this paper we consider a stochastic SEIQR (susceptible-exposed-infected-quarantined-recovered) epidemic model with a generalized incidence function. Using the Lyapunov method, we establish the existence and uniqueness of a global positive solution to the model, ensuring that it remains well-defined over time. Through the application of Young's inequality

Yuwei Fan, Tao Song, Chenlong Feng, Keyu Song

The remarkable achievements of large models in the fields of natural language processing (NLP) and computer vision (CV) have sparked interest in their application to time series forecasting within industrial contexts. This paper explores the application of a pre-trained large time series model, Timer, which was initially trained on a wide range of time serie

Weikai Xie, Li Zhang, Shihe Wang, Rongjie Yi

The growing capabilities of large language models in natural language understanding significantly strengthen existing agentic systems. To power performant on-device mobile agents for better data privacy, we introduce DroidCall, the first training and testing dataset for accurate Android intent invocation. With a highly flexible and reusable data generation p

Weian Guo, Wuzhao Li, Li Li, Lun Zhang

Vehicular Ad-hoc Networks (VANETs) operate in highly dynamic environments characterized by high mobility, time-varying channel conditions, and frequent network disruptions. Addressing these challenges, this paper presents a novel temporal-aware multi-objective robust optimization framework, which for the first time formally incorporates temporal continuity i

Chen Zhou, Marlen Neubert, Yuri Koide, Yumeng Zhang

Constructing datasets representative of the target domain is essential for training effective machine learning models. Active learning (AL) is a promising method that iteratively extends training data to enhance model performance while minimizing data acquisition costs. However, current AL workflows often require human intervention and lack parallelism, lead

Sebastian V. Pios, Maxim F. Gelin, Wolfgang Domcke, Lipeng Chen

Graphitic carbon nitride ($g$-CN) has attracted vast interest as a promising inexpensive metal-free photocatalyst for water splitting with solar photons. The heptazine (Hz) molecule is the building block of graphitic carbon nitride. The photochemistry of the Hz molecule and derivatives thereof in protic environments has been the subject of several recent exp

Anatolii V. Tushev

In the paper we study irreducible representations of some nilpotent groups of finite abelian total rank. The main result of the paper states that if a torsion-free minimax group $G$ of nilpotency class 2 admits a faithful irreducible representation $\varphi $ over a finitely generated field $k$ such that $chark \notin Sp(G)$ then there exist a subgroup $N$ a

Yatian Pang, Bin Zhu, Bin Lin, Mingzhe Zheng

In this work, we present DreamDance, a novel method for animating human images using only skeleton pose sequences as conditional inputs. Existing approaches struggle with generating coherent, high-quality content in an efficient and user-friendly manner. Concretely, baseline methods relying on only 2D pose guidance lack the cues of 3D information, leading to

Daehwa Kim, Mario Srouji, Chen Chen, Jian Zhang

Humanoid robots have significant gaps in their sensing and perception, making it hard to perform motion planning in dense environments. To address this, we introduce ARMOR, a novel egocentric perception system that integrates both hardware and software, specifically incorporating wearable-like depth sensors for humanoid robots. Our distributed perception app

Martin Ziegler, Andres Felipe Posada-Moreno, Friedrich Solowjow, Sebastian Trimpe

Foundation models have demonstrated remarkable generalization, data efficiency, and robustness properties across various domains. In this paper, we explore the feasibility of foundation models for applications in the control domain. The success of these models is enabled by large-scale pretaining on Internet-scale datasets. These are available in fields like

Fei Huang, Tianbo Liu, Yu-Kun Song, Shu-Yi Wei

The spin correlation of final-state hadrons provides a novel platform to explore the hadronization mechanism of polarized partons in unpolarized high-energy collisions. In this work, we investigate the helicity correlation of two hadrons originating from the same single parton. The production of such a dihadron system is formally described by the interferenc

Min Zhang, Zimo Zhu, Qijia Zhai, Xiaoping Xie

This paper develops an hybridizable discontinuous Galerkin (HDG) finite element method of arbitrary order for the steady thermally coupled incompressible Magnetohydrodynamics (MHD) flow. The HDG scheme uses piecewise polynomials of degrees $k(k\geq 1),k,k-1,k-1$, and $k$ respectively for the approximations of the velocity, the magnetic field, the pressure, t

Zehao Li, Wenwei Han, Yujun Cai, Hao Jiang

While 3D Gaussian Splatting enables high-quality real-time rendering, existing Gaussian-based frameworks for 3D semantic segmentation still face significant challenges in boundary recognition accuracy. To address this, we propose a novel 3DGS-based framework named GradiSeg, incorporating Identity Encoding to construct a deeper semantic understanding of scene

M. V. Fistul, O. Neyenhuys, B. Pernack, I. M. Eremin

We study collective quantum phases and quantum phase transitions occurring in frustrated sawtooth arrays of small quantum Josephson junctions. Frustration is introduced through the periodic arrangement of $0$- and $\pi$- Josephson junctions with the Josephson coupling energies $\alpha E_\mathrm{J}$ of different signs, $-1\leq \alpha \leq 1$. The complexity o

Tiantian Hao, Feng Shao, Dongyi Wei, Ping Zhang

In this paper, we study the global well-posedness of the 3-D inhomogeneous incompressible Navier-Stokes system (INS in short) with initial density $\rho_0$ being discontinuous and initial velocity $u_0$ belonging to some critical space. Firstly, if $\rho_0u_0$ is sufficiently small in the space $\dot{B}^{-1+\frac{3}{p}}_{p,\infty}(\mathbb{R}^3)$ and $\rho_0$

Artyom Radomskii

We obtain an upper bound for the sum $\sum_{n\leq N} (a_{n}/\varphi (a_{n}))^{s}$, where $\varphi$ is Euler's totient function, $s\in \mathbb{N}$, and $a_{1},\ldots, a_{N}$ are positive integers (not necessarily distinct) with some restrictions. As applications, for any $t>0$, we obtain an upper bound for the number of $n\in [1,N]$ such that $a_{n}/ \varphi

Wang Shiwei, Alexander Zorin, Marina Konyaeva, Mikhail Malykh

The issue of inheriting periodicity of an exact solution of a dynamic system by a difference scheme is considered. It is shown that some difference schemes (midpoint scheme, Kahan scheme) in some special cases provide approximate solutions of differential equations, which are periodic sequences. Such solutions are called periodic. A purely algebraic method f

Daniel Camazón Portela, Álvaro Otero Sánchez, Juan Antonio López Ramos

The advent of large-scale quantum computers implies that our existing public-key cryptography infrastructure has become insecure. That means that the privacy of many mobile applications involving dynamic peer groups, such as multicast messaging or pay-per-view, could be compromised. In this work we propose a generalization of the well known group key exchang

Chiya Zhang, Ting Wang, Rubing Han, Yuanxiang Gong

Unmanned Aerial Vehicles (UAVs) are increasingly utilized in wireless communication, yet accurate channel loss prediction remains a significant challenge, limiting resource optimization performance. To address this issue, this paper leverages Artificial Intelligence Generated Content (AIGC) for the efficient construction of Channel Knowledge Maps (CKM) and U

Andrei Moroianu, Uwe Semmelmann, Gregor Weingart

Compact Hermitian symmetric spaces are K\"ahler manifolds with constant scalar curvature and non-negative sectional curvature. A famous result by A. Gray states that, conversely, a compact simply connected K\"ahler manifold with constant scalar curvature and non-negative sectional curvature is a Hermitian symmetric space. The aim of the present article is to

Chenlu Ding, Jiancan Wu, Yancheng Yuan, Jinda Lu

The advent of Large Language Models (LLMs) has revolutionized natural language processing, enabling advanced understanding and reasoning capabilities across a variety of tasks. Fine-tuning these models for specific domains, particularly through Parameter-Efficient Fine-Tuning (PEFT) strategies like LoRA, has become a prevalent practice due to its efficiency.

Chengyu Li, Debo Cheng, Guixian Zhang, Yi Li

Graph Neural Networks (GNNs) have demonstrated strong performance in graph representation learning across various real-world applications. However, they often produce biased predictions caused by sensitive attributes, such as religion or gender, an issue that has been largely overlooked in existing methods. Recently, numerous studies have focused on reducing

Xinyao Zhang

In this article, we prove the remaining open cases of the Fontaine-Mazur conjecture on two-dimensional regular Galois representations over $\Gal(\overline{\Q}/\Q)$ when $p=3$, hence concluding the conjecture in the regular case for all odd primes. Our result is a sequel to Pan's work, based on some recent progress on $p$-adic Langlands correspondence, Galois

Zhaoxing Gan, Guangnan Ye

Layout Generation aims to synthesize plausible arrangements from given elements. Currently, the predominant methods in layout generation are Generative Adversarial Networks (GANs) and diffusion models, each presenting its own set of challenges. GANs typically struggle with handling discrete data due to their requirement for differentiable generated samples a

Shinichi Deguchi, Satoshi Okano

In this paper, we show that the mass-shell constraints in the gauged twistor formulation of a massive particle given in [Deguchi and Okano, Phys. Rev. D 93, 045016 (2016) [Erratum 93, 089906(E) (2016)]] are incorporated in an action automatically by extending the local $U(2)$ transformation to its inhomogeneous extension denoted by $IU(2)$. Therefore, it tur

The LHAASO Collaboration, Zhen Cao, F. Aharonian, Axikegu

Identifying Galactic PeVatrons (PeV particle accelerators) from the ultra-high-energy (UHE, >100 TeV) $\gamma$-ray sources plays a crucial role in revealing the origin of Galactic cosmic rays. The UHE source 1LHAASO J1857+0203u is suggested to be associated with HESS J1858+020, which may be attributed to the possible PeVatron candidate supernova remnant (SNR

Changqing Ji

In the application of brain-computer interface (BCI), being able to accurately decode brain signals is a critical task. For the multi-class classification task of brain signal ECoG, how to improve the classification accuracy is one of the current research hotspots. ECoG acquisition uses a high-density electrode array and a high sampling frequency, which make

Jing-Yi Wang, Kai Li, Xiang Gao, Di-Fu Guo

Eclipsing binaries (EBs) play an important astrophysical role in studying stellar properties and evolution. By analyzing photometric data in the LAMOST Medium-Resolution Survey field, RA: $23^h$$01^m$$51.00^s$, Dec: +34$^\circ$36$^\prime$45$^{\prime \prime}$, 48 EBs are detected and 2 are newly discovered. This specific field has been observed 52 times by th

Jie Xiong, Xu Yang, Xiaowen Zhou

We consider a system of two stochastic differential equations (SDEs) with competing two-way interactions driven by Brownian motions and spectrally positive $\alpha$-stable random measures. Such a SDE system can be identified as a Lotka-Volterra type population model. We find nearly sharp conditions for one of the population to become extinct or extinguished.

Danilo Costarelli, Michele Piconi

In this paper, we provide two algorithms based on the theory of multidimensional neural network (NN) operators activated by hyperbolic tangent sigmoidal functions. Theoretical results are recalled to justify the performance of the here implemented algorithms. Specifically, the first algorithm models multidimensional signals (such as digital images), while th

Chetan Madan, Mayuna Gupta, Soumen Basu, Pankaj Gupta

We focus on the problem of Gallbladder Cancer (GBC) detection from Ultrasound (US) images. The problem presents unique challenges to modern Deep Neural Network (DNN) techniques due to low image quality arising from noise, textures, and viewpoint variations. Tackling such challenges would necessitate precise localization performance by the DNN to identify the

Dongfang Zhao

Multimodal tasks, such as image-text retrieval and generation, require embedding data from diverse modalities into a shared representation space. Aligning embeddings from heterogeneous sources while preserving shared and modality-specific information is a fundamental challenge. This paper provides an initial attempt to integrate algebraic geometry into multi

Jim Solomon, Laleh Jalilian, Alexander Vilesov, Meryl Mathew

Human-machine teaming in medical AI requires us to understand to what degree a trained clinician should weigh AI predictions. While previous work has shown the potential of AI assistance at improving clinical predictions, existing clinical decision support systems either provide no explainability of their predictions or use techniques like saliency and Shapl

Martin Doležal, Wiesław Kubiś

We define and study a natural category of graph limits. The objects are pairs $(\pi,\mu)$, where $\pi$ (the distribution of vertices) is an abstract probability measure on some abstract measurable space $(X,\mathcal{A})$ and $\mu$ (the distribution of edges) is an abstract finite measure on the square $(X,\mathcal{A})^2$. Morphisms are random maps between th

Yang Li, Xing Zhang, Bo Lei, Qianying Zhao

Edge computing (EC), positioned near end devices, holds significant potential for delivering low-latency, energy-efficient, and secure services. This makes it a crucial component of the Internet of Things (IoT). However, the increasing number of IoT devices and emerging services place tremendous pressure on edge servers (ESs). To better handle dynamically ar

Daniel Severo, Ashish Khisti, Alireza Makhzani

We present an optimal method for encoding cluster assignments of arbitrary data sets. Our method, Random Cycle Coding (RCC), encodes data sequentially and sends assignment information as cycles of the permutation defined by the order of encoded elements. RCC does not require any training and its worst-case complexity scales quasi-linearly with the size of th

Kousuke Nakano, Benjamin X. Shi, Dario Alfè, Andrea Zen

Basis set incompleteness error (BSIE) is a common source of error in quantum chemistry (QC) calculations, but it has not been comprehensively studied in fixed-node Diffusion Monte Carlo (FN-DMC) calculations. FN-DMC, being a projection method, is often considered minimally affected by basis set biases. Here, we show that this assumption is not always valid.

Ziqi Chen, Jun Du, Chunxiao Jiang, Zhu Han

As the demand for underwater communication continues to grow, underwater acoustic RIS (UARIS), as an emerging paradigm in underwater acoustic communication (UAC), can significantly improve the communication rate of underwater acoustic systems. However, in open underwater environments, the location of the source node is highly susceptible to being obtained by

Weihang Guo, Zachary Kingston, Kaiyu Hang, Lydia E. Kavraki

Multi-robot motion planning for high degree-of-freedom manipulators in shared, constrained, and narrow spaces is a complex problem and essential for many scenarios such as construction, surgery, and more. Traditional coupled methods plan directly in the composite configuration space, which scales poorly; decoupled methods, on the other hand, plan separately

B. L. Alterman, R. D'Amicis

The two-state solar wind paradigm is based on observations showing that slow and fast solar wind have distinct properties like helium abundances, kinetic signatures, elemental composition, and charge-state ratios. Nominally, the fast wind originates from solar sources that are continuously magnetically open to the heliosphere like coronal holes while the slo

Huadong Tang, Youpeng Zhao, Yan Huang, Min Xu

It is widely agreed that open-vocabulary-based approaches outperform classical closed-set training solutions for recognizing unseen objects in images for semantic segmentation. Existing open-vocabulary approaches leverage vision-language models, such as CLIP, to align visual features with rich semantic features acquired through pre-training on large-scale vi

Kouki Wakita, Youhei Akimoto, Atsuo Maki

In the field of Maritime Autonomous Surface Ships (MASS), the accurate modeling of ship maneuvering motion for harbor maneuvers is a crucial technology. Non-parametric system identification (SI) methods, which do not require prior knowledge of the target ship, have the potential to produce accurate maneuvering models using observed data. However, the modelin

Swapnil Patel, Kenneth R. Brown

We present high-resolution rovibronic spectroscopy of calcium monohydride molecular ions (CaH$^+$) co-trapped in a Coulomb crystal with calcium ions ($^{40}$Ca$^+$), focusing on rotational transitions in the $|X^1\Sigma^+, \nu" = 0> \rightarrow |A^1\Sigma^+, \nu' = 2>$ manifold. By resolving individual P and R branch transitions with record precision and usi

S M Rafee Adnan, Muhammad Jobair Hasan, Ahmad Al-Imtiaz, Sulyman H. Robin

Strong lensing by massive galaxies probes their mass distribution, thus providing a window to study their internal structure, i.e., the distributions of luminous and dark matter. In this paper, we investigate the relation between the internal structure of massive elliptical galaxies and their environment using a sample of 15 strong lensing systems. We perfor

Yongke Wu, Xiaoping Xie

In this paper, we consider mixed finite element semi-/full discretizations of the Rosensweig ferrofluid flow model. We first establish some regularity results for the model under several basic assumptions. Then we show that the energy stability of the weak solutions is preserved exactly for both the semi-discrete and fully discrete finite element solutions.

Md Kowsher, Nusrat Jahan Prottasha, Chun-Nam Yu, Ozlem Ozmen Garibay

The success of self-attention lies in its ability to capture long-range dependencies and enhance context understanding, but it is limited by its computational complexity and challenges in handling sequential data with inherent directionality. This work introduces a shared weight self-attention-based BERT model that only learns one weight matrix for (Key, Val

Arash Ashuri, Vasilis Gkatzelis

The two standard fairness notions in the resource allocation literature are proportionality and envy-freeness. If there are n agents competing for the available resources, then proportionality requires that each agent receives at least a 1/n fraction of their total value for the set of resources. On the other hand, envy-freeness requires that each agent weak

Sanghyun Kim, Moonseok Choi, Jinwoo Shin, Juho Lee

Fine-tuning text-to-image diffusion models is widely used for personalization and adaptation for new domains. In this paper, we identify a critical vulnerability of fine-tuning: safety alignment methods designed to filter harmful content (e.g., nudity) can break down during fine-tuning, allowing previously suppressed content to resurface, even when using ben

Wesley H. Holliday

Challenges to classical logic have emerged from several sources. According to recent work, the behavior of epistemic modals in natural language motivates weakening classical logic to orthologic, a logic originally discovered by Birkhoff and von Neumann in the study of quantum mechanics. In this paper, we consider a different tradition of thinking that the be

Huayang Song, Xia Wan

We introduce a non-linear representation of ultraviolet~(UV) complete model, $U$ representation, under which matching HEFT to general scalar extensions of the standard model is straightforward. The main idea is to express a scalar multiplet in its linear form rotated by $U$ matrices, where $U$ matrix is exponential form of Goldstones based on Pauli matrices

Geethan Karunaratne, Michael Hersche, Abu Sebastian, Abbas Rahimi

To efficiently factorize high-dimensional distributed representations to the constituent atomic vectors, one can exploit the compute-in-superposition capabilities of vector-symbolic architectures (VSA). Such factorizers however suffer from the phenomenon of limit cycles. Applying noise during the iterative decoding is one mechanism to address this issue. In

Shanu Kumar, Saish Mendke, Karody Lubna Abdul Rahman, Santosh Kurasa

Chain-of-thought (CoT) prompting has significantly enhanced the capability of large language models (LLMs) by structuring their reasoning processes. However, existing methods face critical limitations: handcrafted demonstrations require extensive human expertise, while trigger phrases are prone to inaccuracies. In this paper, we propose the Zero-shot Uncerta

Marcelo A. Feitosa, Wesley B. Cardoso, Dionisio Bazeia, Ardiley T. Avelar

In this paper we employ a configurational information measure, specifically the differential configurational complexity (DCC), to quantify the information content of lump-type solutions in various scalar field models, including two modified inverted $\phi^{4}$ models, the modified $\phi^{3}$ model, as well as two additional families of lump models. Our objec

Phuoc-Nguyen Bui, Duc-Tai Le, Junghyun Bum, Hyunseung Choo

Traditional deep learning methods in medical imaging often focus solely on segmentation or classification, limiting their ability to leverage shared information. Multi-task learning (MTL) addresses this by combining both tasks through shared representations but often struggles to balance local spatial features for segmentation and global semantic features fo

Alim Ablat, Sayipjamal Dulat, Tie-Jiun Hou, Joey Huston

In this study, we investigate the impact of new LHC inclusive jet and dijet measurements on parton distribution functions (PDFs) that describe the proton structure, with a particular focus on the gluon distribution at large momentum fraction, $x$, and the corresponding partonic luminosities. We assess constraints from these datasets using next-to-next-to-lea

Honghui Liu, Askar B. Abdikamalov, Temurbek Mirzaev, Cosimo Bambi

X-ray reflection spectroscopy is a powerful tool to study the strong gravity region of black holes. The next generation of astrophysical X-ray missions promises to provide unprecedented high-quality data, which could permit us to get very precise measurements of the properties of the accretion flow and of the spacetime geometry in the strong gravity region a

Wei Zhou, Li Yang, Lei Zhao, Runyu Zhang

Traffic Surveillance Systems (TSS) have become increasingly crucial in modern intelligent transportation systems, with vision technologies playing a central role for scene perception and understanding. While existing surveys typically focus on isolated aspects of TSS, a comprehensive analytical framework bridging low-level and high-level perception tasks, pa

Pham Truong Xuan

In this paper, we investigate the existence, uniqueness, and exponential decay of asymptotically almost periodic (AAP-) mild solutions for the parabolic-parabolic Keller-Segel systems on a bounded domain $\Omega \subset \mathbb{R}^n$ with a smooth boundary. First, we establish the well-posedness of mild solutions for the corresponding linear systems by utili

Han Li, Fei Liu, Zhi Zheng, Yu Zhang

Vehicle Routing Problems (VRPs) are significant Combinatorial Optimization (CO) problems holding substantial practical importance. Recently, Neural Combinatorial Optimization (NCO), which involves training deep learning models on extensive data to learn vehicle routing heuristics, has emerged as a promising approach due to its efficiency and the reduced need

Takayuki Osogami, Hirota Kinoshita, Segev Wasserkrug

We analytically derive a class of optimal solutions to a linear program (LP) for automated mechanism design that satisfies efficiency, incentive compatibility, strong budget balance (SBB), and individual rationality (IR), where SBB and IR are enforced in expectation. These solutions can be expressed using a set of essential variables whose cardinality is exp

Chandni Menapara, Ajay Kumar Rai

The light, strange baryons have been studied through various approaches and attempted to be looked for rigorously in experiments. The screened potential has been applied to heavy baryon sector as well as meson systems in earlier works. Here, this article attempts to compare the results for linear and screened potential for light strange baryons. Also, the Re

Jackson Kulik, Keith A. LeGrand

Many problems in navigation and tracking require increasingly accurate characterizations of the evolution of uncertainty in nonlinear systems. Nonlinear uncertainty propagation approaches based on Gaussian mixture density approximations offer distinct advantages over sampling based methods in their computational cost and continuous representation. State-of-t

Nadeen Fathallah, Monika Bhole, Steffen Staab

In today's digital age, video content is prevalent, serving as a primary source of information, education, and entertainment. However, the Deaf and Hard of Hearing (DHH) community often faces significant challenges in accessing video content due to the inadequacy of automatic speech recognition (ASR) systems in providing accurate and reliable captions. This

Hana Satou, Alan Mitkiy

The convergence of cross-modal adversarial learning and physics-driven methods represents a cutting-edge direction for tackling challenges in complex multi-modal tasks and scientific computing. This review focuses on systematically analyzing how these two approaches can be synergistically integrated to enhance performance and robustness across diverse applic

Zhaoming Wang, Guanghui Shi, Guanghui Wang, Man Wang

Sodium-ion batteries (SIBs) have garnered significant attention in recent years as a promising alternative to lithium-ion batteries (LIBs) due to their low cost, abundant sodium resources, and excellent cycling performance. Hard carbon materials, characterized by their high specific capacity, outstanding cycling stability, and low cost, have emerged as poten

Conrad Zimmerman, Jenna DiVincenzo

Previously, gradual verification has been developed using overapproximating logics such as Hoare logic. We show that the static verification component of gradual verification is also connected to underapproximating logics like incorrectness logic. To do this, we use a novel definition of gradual verification and a novel gradualization of exact logic [Maksimo

Zewen Xiao, Chen Qiu, Su-Huai Wei, Hideo Hosono

Wide-bandgap two-dimensional (2D) beta-TeO2 has been reported as a high-mobility p-type transparent semiconductor (Nat. Electron. 2021, 4, 277-283), attracting significant attention. This "breakthrough" not only challenges the conventional characterization of TeO2 as an insulator but also conflicts with the anticipated difficulty in hole doping of TeO2 by es

Johannes Rauch, Dieter Rautenbach

Confirming a conjecture posed by Caro, it was shown by Chen and Yu that every graph $G$ with $n$ vertices and at most $2n-4$ edges has a stable cutset, which is a stable set of vertices whose removal disconnects the graph. Le and Pfender showed that all graphs with $n$ vertices and $2n-3$ edges without stable cutset arise recursively glueing together triangl

Sergi Elizalde, Amya Luo

Nonnesting permutations are permutations of the multiset $\{1,1,2,2,\dots,n,n\}$ that avoid subsequences of the form $abba$ for any $a\neq b$. These permutations have recently been studied in connection to noncrossing (also called quasi-Stirling) permutations, which are those that avoid subsequences of the form $abab$, and in turn generalize the well-known S

Gongwei Liu, Yi Peng, Peng Li

This paper deals with a class of semilinear wave equation with nonlinear damping term $|u_{t}|^{m-2}u_t $ and nonlinear source term $g(x)|u|^{p-2}u$ on the manifolds with conical singularities. Firstly, we prove the local existence and uniqueness of the solution by the semigroup method. Secondly, we establish the global existence, the energy decay estimate a

Meihan Wu, Tao Chang, Cui Miao, Jie Zhou

Federated learning research has recently shifted from Convolutional Neural Networks (CNNs) to Vision Transformers (ViTs) due to their superior capacity. ViTs training demands higher computational resources due to the lack of 2D inductive biases inherent in CNNs. However, efficient federated training of ViTs on resource-constrained edge devices remains unexpl

Junli Deng, Yihao Luo

Dynamic scene rendering has taken a leap forward with the rise of 4D Gaussian Splatting, but there's still one elusive challenge: how to make 3D Gaussians move through time as naturally as they would in the real world, all while keeping the motion smooth and consistent. In this paper, we unveil a fresh approach that blends state-space modeling with Wasserste

Kota Tani, Shunji Tsuchiya, Seiichiro Tani, Yuki Takeuchi

Grover's quantum algorithm can find a marked item from an unstructured database faster than any classical algorithm, and hence it has been used for several applications such as cryptanalysis and optimization. When there exist multiple marked items, Grover's algorithm has the property of finding one of them uniformly at random. To further broaden the applicat

Xin Xu, Kai Li, Fei Liu, Qian-Xue Yan

We performed photometric and spectroscopic studies of three large amplitude contact binaries, NSVS 2418361, ATLAS J057.1170+31.2384 and NSVS 7377875. The amplitudes of three systems' light curves are more than 0.7 magnitude. We analyzed the light curves using Wilson-Devinney code to yield physical parameters. The photometric solutions suggested that NSVS 737

Martim Veiga, Carlos J. Costa

In recent years, we have witnessed a marked development and growth in Artificial Intelligence. The growth of the data volume generated by sensors and machines, combined with the information flow resulting from the user actions on the Internet, with high investments of the governments and the companies in this area, provided the practice and developed the alg

Negar Alizadeh, Boris Belchev, Nishant Saurabh, Patricia Kelbert

The use of generative AI-based coding assistants like ChatGPT and Github Copilot is a reality in contemporary software development. Many of these tools are provided as remote APIs. Using third-party APIs raises data privacy and security concerns for client companies, which motivates the use of locally-deployed language models. In this study, we explore the t

Vincent Corlay, Tatsuya Nakazato, Kanako Yamaguchi, Akinori Nakajima

The advent of deep learning and recurrent neural networks revolutionized the field of time-series processing. Therefore, recent research on spectrum prediction has focused on the use of these tools. However, spectrum prediction, which involves forecasting wireless spectrum availability, is an older field where many "classical" tools were considered around th

N. Bukharskii, Ph. Korneev

The effects leading to generation of quasi-stationary and propagating electromagnetic fields during the propagation of a laser-driven current discharge pulse in extended targets are considered. The results of numerical modeling describe the interaction of a relativistically intense ultrashort laser pulse with an extended dense target and its transformation i

Yirong Cai, Hanyuan Deng

Let $P(G)=(P_{0}(G),P_{1}(G),\cdots, P_{\rho}(G))$ be the path sequence of a graph $G$, where $P_{i}(G)$ is the number of paths with length $i$ and $\rho$ is the length of a longest path in $G$. In this paper, we first give the path sequences of some graphs and show that the number of paths with length $h$ in a starlike tree is completely determined by its b

Jongmin Jung, Andreas Jansson, Dasaem Jeong

MusicGen is a music generation language model (LM) that can be conditioned on textual descriptions and melodic features. We introduce MusicGen-Chord, which extends this capability by incorporating chord progression features. This model modifies one-hot encoded melody chroma vectors into multi-hot encoded chord chroma vectors, enabling the generation of music

Daomin Ji, Hui Luo, Zhifeng Bao, Shane Culpepper

Table integration aims to create a comprehensive table by consolidating tuples containing relevant information. In this work, we investigate the challenge of integrating multiple tables from a data lake, focusing on three core tasks: 1) pairwise integrability judgment, which determines whether a tuple pair is integrable, accounting for any occurrences of sem

Yasuaki Sumita, Koh Takeuchi, Hisashi Kashima

Large Language Models (LLMs) are trained on large corpora written by humans and demonstrate high performance on various tasks. However, as humans are susceptible to cognitive biases, which can result in irrational judgments, LLMs can also be influenced by these biases, leading to irrational decision-making. For example, changing the order of options in multi

Yen Chin Ong

It has been almost 40 years since the proposal of the idea that Hawking radiation of black holes does not lead to a complete evaporation but rather a "remnant" state. Though traditionally viewed with great criticisms especially from the high energy physics community, in recent years, various approaches have demonstrated that black hole remnants remain a viab

Shu-Qing Zhong, Wen-Jin Xie, Jia-Hong Gu

In this paper, through analysis, modelings, and simulations, we show that if the spectra of fast radio bursts (FRBs) are intrinsically quasi-periodic spectra, likely produced by coherent curvature radiation from quasi-periodic structured bunches, then the observed steep and shallow spectra, narrow and broadband spectra, multi-frequency simultaneous spectra,

Dana Jones, Nils Siemonsen, Ling Sun, William E. East

Ultralight bosons are a hypothetical class of particles predicted under various extensions of Standard Model physics. As a result of the superradiance mechanism, we expect ultralight bosons, should they exist in certain mass ranges, to form macroscopic clouds around rotating black holes, so that we can probe their existence by looking for the long-transient