October 2024 arXiv papers — page 67

Eduard Feireisl, Maria Lukacova-Medvidova, Bangwei She, Yuhuan Yuan

We study random compressible viscous magnetohydrodynamic flows. Combining the Monte Carlo method with a deterministic finite volume method we solve the random system numerically. Quantitative error estimates including statistical and deterministic errors are analyzed up to a stopping time of the exact solution. On the life-span of an exact strong solution we

Shih-Kai Chiu, Yu-Shen Lin

We construct special Lagrangian submanifolds in collapsing Calabi-Yau 3-folds fibered by K3 surfaces. As these 3-folds collapse, the special Lagrangians shrink to 1-dimensional graphs in the base, mirroring the conjectured tropicalization of holomorphic curves in collapsing SYZ torus-fibered Calabi-Yau manifolds. This confirms predictions of Donaldson and Do

A. Nakamula, K. Obuse, N. Sawado, K. Shimasaki

Physics-Informed Neural Networks (PINNs) have emerged as a powerful tool for analyzing nonlinear partial differential equations and identifying governing equations from observational data. In this study, we apply PINNs to investigate vortex-type solutions of quasi-integrable equations in two spatial dimensions, specifically the Zakharov-Kuznetsov (ZK) and th

Maximilian Augustin, Syed Shakib Sarwar, Mostafa Elhoushi, Sai Qian Zhang

Following their success in natural language processing (NLP), there has been a shift towards transformer models in computer vision. While transformers perform well and offer promising multi-tasking performance, due to their high compute requirements, many resource-constrained applications still rely on convolutional or hybrid models that combine the benefits

Xiuji Chen, Zipeng Liu, Si Chen, Duan Gu

In the past decade, the fourth-generation light source based on the combination of Energy Recovery Linac (ERL) and Free-Electron Laser (FEL) using superconducting linear accelerators has garnered significant attention. It holds immense potential, particularly in generating high-power Extreme Ultraviolet (EUV) light sources. This article primarily focuses on

Daniel Charbulák, Zdeněk Stuchlík

The construction of local escape cones and their complementary cones, related to the locally non-rotating frames (LNRFs) orbiting superspinars with the external field described by the Kerr-de Sitter (KdS) naked singularity (NS) spacetimes, is described for all possible classes of the KdS NS spacetimes and all possible positions of the photon emitter. The not

Santosh Kumar Chaudhary, Nitin Gupta

In this paper, we investigate inaccuracy measures based on record values, focusing on the relationship between the distribution of the n-th upper and lower k-record values and the parent distribution. We extend the classical Kerridge inaccuracy measure, originally developed for comparing two distributions, to record values and derive expressions for both upp

Yusheng Liao, Shuyang Jiang, Yanfeng Wang, Yu Wang

Large Language Models (LLMs) have shown promising potential in the medical domain, assisting with tasks like clinical note generation and patient communication. However, current LLMs are limited to text-based communication, hindering their ability to interact with diverse forms of information in clinical environments. Despite clinical agents succeeding in di

He Yu, Jing Liu

Achieving robust networks is a challenging problem due to its NP-hard nature and complex solution space. Current methods, from handcrafted feature extraction to deep learning, have made progress but remain rigid, requiring manual design and large labeled datasets. To address these issues, we propose AutoRNet, a framework that integrates large language models

Dairazalia Sánchez-Cortés, Sergio Burdisso, Esaú Villatoro-Tello, Petr Motlicek

Bias assessment of news sources is paramount for professionals, organizations, and researchers who rely on truthful evidence for information gathering and reporting. While certain bias indicators are discernible from content analysis, descriptors like political bias and fake news pose greater challenges. In this paper, we propose an extension to a recently p

Yi Yu, Junyu Ge, Manlin Luo, In Cheol Seo

Two-dimensional (2D) materials have emerged as promising candidates for next-generation integrated single-photon emitters (SPEs). However, significant variability in the emission energies of 2D SPEs presents a major challenge in producing identical single photons from different SPEs, which may become crucial for various quantum applications including quantum

Ricardo Gallego Torromé

In this work, the notion of spacetime of maximal proper acceleration is motivated as a weak form to implement general covariance and a generalized form of Einstein's equivalence principle from a physical point of view and the fundamental geometric and kinematic properties of such spaces are discussed. Thereafter the Unruh temperature formula is generalized t

Manlin Luo, Junyu Ge, Pengru Huang, Yi Yu

Forming single-photon emitters (SPEs) in insulating hexagonal boron nitride (hBN) has sparked wide interests in the quantum photonics. Despite significant progress, it remains challenging to deterministically create SPEs at precise locations with a specific type of element for creating defects. In this study, we present a straightforward approach to generate

Wendi Bao, Jie Zhang, Wei Rao, Jing Liu

Since the discovery of superconductor one hundred years ago, tremendous theoretical and technological progresses have been achieved. The zero resistance and complete diamagnetism of superconducting materials promise many possibilities in diverse fields. However, the complexity and expensive manufacturing costs associated with the time-consuming superconducto

Jesús Bobadilla, Abraham Gutiérrez

The published method Generative Adversarial Networks for Recommender Systems (GANRS) allows generating data sets for collaborative filtering recommendation systems. The GANRS source code is available along with a representative set of generated datasets. We have tested the GANRS method by creating multiple synthetic datasets from three different real dataset

D. Vanbeveren, N. Mennekens

Mennekens and Vanbeveren (2014) studied the effect of double compact star mergers on the Galactic chemical enrichment of r-process elements. LIGO merger detections since 2015 and new r-process element yields as function of neutron star + neutron star and neutron star + black hole mass requires an update of the 2014 computations. The results of the update are

Harsh Prasad, Vivek Tewary

We prove existence, uniqueness and initial time regularity for variational solutions to nonlocal total variation flows associated with image denoising and deblurring. In particular, we prove existence of parabolic minimisers $u$, that is, $$\int_0^T\int_\Omega u\partial_t\phi\,dx + \textbf{F}(u(t))\,dt\leq \int_0^T \textbf{F}(u+\phi)(t)\,dt,$$ for $\phi\in C

Jon Irureta, Jon Imaz, Aizea Lojo, Javier Fernandez-Marques

Existing Vertical FL (VFL) methods often struggle with realistic and unaligned data partitions, and incur into high communication costs and significant operational complexity. This work introduces a novel approach to VFL, Active Participant Centric VFL (APC-VFL), that excels in scenarios when data samples among participants are partially aligned at training.

Isaac Symes Thompson, Alberto Caron, Chris Hicks, Vasilios Mavroudis

A significant challenge for autonomous cyber defence is ensuring a defensive agent's ability to generalise across diverse network topologies and configurations. This capability is necessary for agents to remain effective when deployed in dynamically changing environments, such as an enterprise network where devices may frequently join and leave. Standard app

S. A. N. Nouwens, M. M. Paulides, W. P. M. H. Heemels

Optimization-based controllers, such as Model Predictive Control (MPC), have attracted significant research interest due to their intuitive concept, constraint handling capabilities, and natural application to multi-input multi-output systems. However, the computational complexity of solving a receding horizon problem at each time step remains a challenge fo

Sunao Ouchi

A system of nonlinear Cauchy problem $\partial_t u_i=f_i(t,x, U, \nabla_xU )$ $u_i(0,x)= u_{i,0}(x)$ is studied in function spaces with asymptotic expansion with respect to $t$. To be specific, it is discussed in Borel summable or multisummable function space.It is recognized that these functions are important classes in asymptotic analysis. We study equatio

Jesús Bobadilla, Jorge Dueñas-Lerín, Fernando Ortega, Abraham Gutierrez

Matrix factorization models are the core of current commercial collaborative filtering Recommender Systems. This paper tested six representative matrix factorization models, using four collaborative filtering datasets. Experiments have tested a variety of accuracy and beyond accuracy quality measures, including prediction, recommendation of ordered and unord

S. A. N. Nouwens, M. M. Paulides, W. P. M. H. Heemels

Accurate state estimates are required for increasingly complex systems, to enable, for example, feedback control. However, available state estimation schemes are not necessarily real-time feasible for certain large-scale systems. Therefore, we develop in this paper, a real-time feasible state-estimation scheme for a class of large-scale systems that approxim

Cheng Yuan, Yutong Ban

Surgical scene segmentation is a fundamental task for robotic-assisted laparoscopic surgery understanding. It often contains various anatomical structures and surgical instruments, where similar local textures and fine-grained structures make the segmentation a difficult task. Vision-specific transformer method is a promising way for surgical scene understan

Fedor V. Fomin, Petr A. Golovach, Tuukka Korhonen, Daniel Lokshtanov

In the Hedge Cut problem, the edges of a graph are partitioned into groups called hedges, and the question is what is the minimum number of hedges to delete to disconnect the graph. Ghaffari, Karger, and Panigrahi [SODA 2017] showed that Hedge Cut can be solved in quasipolynomial-time, raising the hope for a polynomial time algorithm. Jaffke, Lima, Masar\'ik

Giulio Volpato, Stefano Mocatti, Giovanni Marini, Matteo Calandra

Maximally localized Wannier functions use the gauge freedom of Bloch wavefunctions to define the optimally smooth subspace with matrix elements that depend smoothly on crystal momentum. The associated Wannier functions are real-space localized, a feature often used to Fourier interpolate periodic observables in reciprocal space on ultradense momentum grids.

S. A. N. Nouwens, B. de Jager, M. M. Paulides, W. P. M. H. Heemels

Model predictive control (MPC) is an optimization-based control strategy with broad industrial adoption. Unfortunately, the required computation time to solve the receding-horizon MPC optimization problem can become prohibitively large for many applications with a large number of state constraints. This large number of state constraints can, for instance, or

Julien Mordret, Jean-Christophe Le Breton, Gabriel Delhaye, Bruno Lépine

We report on spin atomistic calculations used to model static and dynamic magnetic properties of inhomogeneous ultrathin iron films. Active magnetic layers in next-generation spintronic devices are becoming so thin that they exhibit some variable degree of roughness at the low-scale making them magnetically inhomogeneous. We propose a multiscale approach to

Ziyu Liu, Yuhang Zang, Xiaoyi Dong, Pan Zhang

Visual preference alignment involves training Large Vision-Language Models (LVLMs) to predict human preferences between visual inputs. This is typically achieved by using labeled datasets of chosen/rejected pairs and employing optimization algorithms like direct preference optimization (DPO). Existing visual alignment methods, primarily designed for single-i

Ahmad Berjaoui, Louis Roussel, Eduardo Hugo Sanchez, Elizabeth Cohen-Jonathan Moyal

Glioblastoma is a highly aggressive form of brain cancer characterized by rapid progression and poor prognosis. Despite advances in treatment, the underlying genetic mechanisms driving this aggressiveness remain poorly understood. In this study, we employed multimodal deep learning approaches to investigate glioblastoma heterogeneity using joint image/RNA-se

Faustine Cantalloube, Valentin Christiaens, Carles Cantero Mitjans, Anthony Cioppa

In this communication, we report on the results of the second phase of the Exoplanet Imaging Data Challenge started in 2019. This second phase focuses on the characterization of point sources (exoplanet signals) within multispectral high-contrast images from ground-based telescopes. We collected eight data sets from two high-contrast integral field spectrogr

Wen Yang, Minpeng Liao, Kai Fan

Chain of Thought (CoT) of multi-step benefits from the logical structure of the reasoning steps and task-specific actions, significantly enhancing the mathematical reasoning capabilities of large language models. As the prevalence of long CoT, the number of reasoning steps exceeds manageable token limits and leads to higher computational demands. Inspired by

Wolfgang Bertram

We investigate the problem of defining group or loop structures on spheres, where by ''sphere'' we mean the level set q(x) = c of a general K-valued quadratic form q, for an invertible scalar c. When K is a field and q non-degenerate, then this corresponds to the classical theory of composition algebras; in particular, for K = R and positive definite forms,

François Berteloot

We present a renormalization lemma for certain maps defined on the unit disc of C and taking values in some metric space. We show that the classical renormalization lemmas of Zalcman and Miniowitz can be deduced from our lemma. We also use it to establish a general normality statement for the Pinchuk's scaling method in C^2 and, incidentally, reprove the Cat

Jingyao Zheng, Xian Wang, Simo Hosio, Xiaoxian Xu

Large Language Models (LLMs) are increasingly used in everyday life and research. One of the most common use cases is conversational interactions, enabled by the language generation capabilities of LLMs. Just as between two humans, a conversation between an LLM-powered entity and a human depends on the personality of the conversants. However, measuring the p

Zheng Luo, Ming Feng, Zijian Gao, Jinyang Yu

The emergence of deep learning (DL) has provided great opportunities for the high-throughput analysis of atomic-resolution micrographs. However, the DL models trained by image patches in fixed size generally lack efficiency and flexibility when processing micrographs containing diversified atomic configurations. Herein, inspired by the similarity between the

Hongyu Wang, Xinmin Hou

The Faber-Krahn inequality states that the first Dirichlet eigenvalue among all bounded domains is no less than a Euclidean ball with the same volume in $\mathbb{R}^n$ \cite{Chavel FB}. B{\i}y{\i}ko\u{g}lu and Leydold (J. Comb. Theory, Ser. B., 2007) demonstrated that the Faber-Krahn inequality also holds for the class of trees with boundary with the same de

Yi Yan, Changran Peng, Ercan Engin Kuruoglu

This paper proposes Graph Signal Adaptive Message Passing (GSAMP), a novel message passing method that simultaneously conducts online prediction, missing data imputation, and noise removal on time-varying graph signals. Unlike conventional Graph Signal Processing methods that apply the same filter to the entire graph, the spatiotemporal updates of GSAMP empl

Baiyuan Chen

Robustness is a critical aspect of machine learning models. Existing robustness evaluation approaches often lack theoretical generality or rely heavily on empirical assessments, limiting insights into the structural factors contributing to robustness. Moreover, theoretical robustness analysis is not applicable for direct comparisons between models. To addres

Shiheng Zhao, Zhan Tian, Jiaji Chen, Heng Jiang

Precise control of the polarization and propagation direction of elastic waves is a fundamental challenge in elastodynamics. Achieving efficient mode conversion along arbitrary paths with conventional techniques has proven difficult. In this letter, we propose an innovative harmonimode mechanical metamaterial by integrating classical lattice architecture wit

Yi Yan, Fengfan Zhao, Ercan Engin Kuruoglu

The assumption of using a static graph to represent multivariate time-varying signals oversimplifies the complexity of modeling their interactions over time. We propose a Dynamic Multi-hop model that captures dynamic interactions among time-varying node signals, while also accounting for time-varying edge signals, by extracting latent edges through topologic

George Potter, Gertjan Burghouts, Joris Sijs

Affordances enable robots to have a semantic understanding of their surroundings. This allows them to have more acting flexibility when completing a given task. Capturing object affordances in a machine learning model is a difficult task, because of their dependence on contextual information. Markov Logic Networks (MLN) combine probabilistic reasoning with l

Sheik Mohammad Mostakim Fattah, Athman Bouguettaya

We propose a novel change detection framework to identify changes in the long-term performance behavior of an IaaS service. An IaaS service's long-term performance behavior is represented by an IaaS performance signature. The proposed framework leverages time series similarity measures and a sliding window technique to detect changes in IaaS performance sign

Jie Song, Mengqiao He, Jinhua Feng, Bairong Shen

In recent years, Facial Expression Recognition (FER) has gained increasing attention. Most current work focuses on supervised learning, which requires a large amount of labeled and diverse images, while FER suffers from the scarcity of large, diverse datasets and annotation difficulty. To address these problems, we focus on utilizing large unlabeled Face Rec

Ning Dai, Zheng Wu, Renjie Zheng, Ziyun Wei

Reinforcement learning (RL) with unit test feedback has enhanced large language models' (LLMs) code generation, but relies on sparse rewards provided only after complete code evaluation, limiting learning efficiency and incremental improvements. When generated code fails all unit tests, no learning signal is received, hindering progress on complex tasks. To

Azad I. Ahmadov

In the present paper, we investigate the production of a baryon pair in $e^+e^-$ annihilation to study the structure of baryons. To study the basic structure of the Standard Model, it is also necessary to consider the baryon-antibaryon pair production at the electron-positron linear collider. The production of a baryon pair in electron-positron annihilation

Yuxuan Wu, Jinling Gao, Xiaona Fang, Jin Wang

Brain perceptual rivalry, exemplified by auditory stream segregation of competing tones (A_, B__, ABA_), serves as a core mechanism of brain perception formation. While increasingly recognized as determining by neural connections rather than specific neural groups, the mechanism of brain perception remains uncertain. We demonstrate that auditory stream segre

Juhani Merilehto

This study investigates the effectiveness of Large Language Models (LLMs) in processing semi-structured data from PDF documents into structured formats, specifically examining their application in updating the Finnish Sports Clubs Database. Through action research methodology, we developed and evaluated an AI-assisted approach utilizing OpenAI's GPT-4 and An

Pawel Kryszkiewicz, Adrian Kliks, Pawel Sroka, Michal Sybis

In this letter, we investigate single-slope path loss models complemented with shadowing effects in the context of vehicular communications. We present several models obtained based on extensive measurement campaigns with inter-vehicle transmission conducted at 26.555 GHz in real-traffic experiments, mainly along high-speed roads. Particular attention has be

Mehdi Monemi, Sirous Bahrami, Mehdi Rasti, Matti Latva-aho

We characterize three near-field sub-regions for phased array antennas by elaborating on the boundaries {\it Fraunhofer}, {\it radial-focal}, and {\it non-radiating} distances. The {\it Fraunhofer distance} which is the boundary between near and far field has been well studied in the literature on the principal axis (PA) of single-element center-fed antennas

Jianjun Wei, Yue Liu, Xin Huang, Xin Zhang

This paper explores the applications and challenges of graph neural networks (GNNs) in processing complex graph data brought about by the rapid development of the Internet. Given the heterogeneity and redundancy problems that graph data often have, traditional GNN methods may be overly dependent on the initial structure and attribute information of the graph

Nicola Losacco

We study the rare decays of the $B_c$ meson induced by the flavour changing neutral current $c \to u \gamma$ transition. In the Standard Model they are strongly suppressed by the Glashow-Iliopoulos-Maiani mechanism, therefore they are sensitive to new physics. The difficulty is to get rid of long-distance contributions. We study such effects in radiative $B_

Taichi Uemura

We propose a definition of higher inductive types in $(\infty,1)$-categories with finite limits. We show that the $(\infty,1)$-category of $(\infty,1)$-categories with higher inductive types is finitarily presentable. In particular, the initial $(\infty,1)$-category with higher inductive types exists. We prove a form of canonicity: the global section functor

Sophie Kernchen, Michael Meinel, Stephan Druskat, Michael Fritzsche

Research software is an important output of research and must be published according to the FAIR Principles for Research Software. This can be achieved by publishing software with metadata under a persistent identifier. HERMES is a tool that leverages continuous integration to automate the publication of software with rich metadata. In this work, we describe

Parsa Farzin, Kasra Rouhi, Seyed Ehsan Hosseininejad

Programmable metasurfaces have recently attracted considerable interest for their versatile applications in areas such as beam steering, holography, and wireless communications, utilizing either phase or amplitude modulation. Despite this, programmable amplitude coding modulation has seen limited exploration, primarily due to the difficulties involved in ach

Tao Jiang, Zekun Zhao, Qingqian Kang, Teng Zhao

In the field of quantum precision measurement, enhancing phase sensitivity is crucial for various applications, including quantum metrology and quantum sensing technologies. We theoretically investigate the improvement in phase sensitivity and quantum Fisher information achieved through multiphoton subtraction operations at the output port of an SU(1,1) inte

Dhanya Roy, Sandi Klavžar, Aparna Lakshmanan S

All four invariants of the mutual-visibility problem and, all four invariants of the general position problem are determined for glued binary trees. The number of the corresponding extremal sets is obtained in each of the eight situations. The results are further extended to glued $t$-ary trees, and some of them also to generalized glued binary trees.

Xinpeng Liu, Junxuan Liang, Zili Lin, Haowen Hou

Inverse dynamics (ID), which aims at reproducing the driven torques from human kinematic observations, has been a critical tool for gait analysis. However, it is hindered from wider application to general motion due to its limited scalability. Conventional optimization-based ID requires expensive laboratory setups, restricting its availability. To alleviate

Jianchao Zheng, Tuo Wu, Junteng Yao, Chau Yuen

In this paper, we conduct a theoretical analysis of how to integrate reconfigurable intelligent surfaces (RIS) with cooperative non-orthogonal multiple access (NOMA), considering URLLC. We consider a downlink two-user cooperative NOMA system employing short-packet communications, where the two users are denoted by the central user (CU) and the cell-edge user

Andrii Neronov, Foteini Oikonomou, Dmitri Semikoz

The recently detected extended, very-high-energy gamma-ray emission from the microquasar V4641 Sgr reveals a puzzling 200-parsec-long jet-like structure significantly misaligned with its radio jet. We propose that this gamma-ray structure is produced by high-energy cosmic-ray particles escaping from the microquasar along ordered field lines of the Galactic M

Ángel Paredes, Yihong Zhou, Chaimaa Essayeh, José A. Aguado

The evolving energy landscape has propelled energy communities to the forefront of modern energy management. However, existing research has yet to explore the potential synergies between data centres and energy communities, necessitating an assessment on their collective capabilities for cost efficiency, waste heat optimisation, and market participation. Thi

Muquan Li, Dongyang Zhang, Tao He, Xiurui Xie

Data-free knowledge distillation (DFKD) has emerged as a pivotal technique in the domain of model compression, substantially reducing the dependency on the original training data. Nonetheless, conventional DFKD methods that employ synthesized training data are prone to the limitations of inadequate diversity and discrepancies in distribution between the synt

Shao-Feng Ge, Zhuoni Qian, Michael J. Ramsey-Musolf, Jia Zhou

We explore the prospects for probing new physics (NP) beyond the Standard Model (SM) at future lepton colliders through precision measurements of $e^+e^-\to f{\bar f}$ observables off the $Z$ resonance. We consider interference between SM contributions and those arising from dimension-6, four-fermion effective operators that encode the effects of NP, yieldin

Miguel R. Pebes-Trujillo, Itamar Shenhar, Aravind Harikumar, Ittai Herrmann

We present a probabilistic ranking model to identify the optimal treatment in multiple-response experiments. In contemporary practice, treatments are applied over individuals with the goal of achieving multiple ideal properties on them simultaneously. However, often there are competing properties, and the optimality of one cannot be achieved without compromi

Jan Soeren Schwarz, Minh Cong Pham, Quoc Tuan Tran, Kai Heussen

This paper presents a scaling study on the planning phase of a multi-energy system (MES), which is becoming increasingly prominent in the energy sector. The research aims to investigate the interactions and challenges associated with integrating heat and electrical systems and scaling their components. In this context, interaction between these two domains a

Abhishek Phadke, Alihan Hadimlioglu, Tianxing Chu, Chandra N Sekharan

The intersection of LLMs (Large Language Models) and UAV (Unoccupied Aerial Vehicles) technology represents a promising field of research with the potential to enhance UAV capabilities significantly. This study explores the application of LLMs in UAV control, focusing on the opportunities for integrating advanced natural language processing into autonomous a

Jon Olav Skøien, Nicolas Lampach, Helena Ramos, Rudolf Seljak

We develop a flexible approach by combining the Quadtree-based method with suppression to maximize the utility of the grid data and simultaneously to reduce the risk of disclosing private information from individual units. To protect data confidentiality, we produce a high resolution grid from geo-reference data with a minimum size of 1 km nested in grids wi

Rui Yang, Boming Yang, Aosong Feng, Sixun Ouyang

Knowledge Graphs (KGs) are crucial in the field of artificial intelligence and are widely used in downstream tasks, such as question-answering (QA). The construction of KGs typically requires significant effort from domain experts. Large Language Models (LLMs) have recently been used for Knowledge Graph Construction (KGC). However, most existing approaches f

Jiayi Wu, Hao Sun, Hengyi Cai, Lixin Su

The number of large language models (LLMs) with varying parameter scales and vocabularies is increasing. While they deliver powerful performance, they also face a set of common optimization needs to meet specific requirements or standards, such as instruction following or avoiding the output of sensitive information from the real world. However, how to reuse

Jinyu Yang, Qingwei Wang, Feng Zheng, Peng Chen

Camouflaged Object Detection (COD) aims to detect objects with camouflaged properties. Although previous studies have focused on natural (animals and insects) and unnatural (artistic and synthetic) camouflage detection, plant camouflage has been neglected. However, plant camouflage plays a vital role in natural camouflage. Therefore, this paper introduces a

Habib Ammari, Silvio Barandun, Alexander Uhlmann

The truncated Floquet-Bloch transform can be used to characterise the spectral properties of finite periodic and aperiodic large systems of resonators. This paper aims to provide for the first time the mathematical foundations of this transform.

Kenta Kojin

In this short note, we will give a generalization of the indefinite Schwarz-Pick inequality due to Seto [8]. Our approach is based on a connection between complex geometry and the geometry of reproducing kernel Hilbert spaces, which was crucially used in our previous work [6].

Florian J. Hindenlang, Gabriel G. Plunk, Omar Maj

For the representation of axi-symmetric plasma configurations, it is natural to use cyl. coordinates (R,Z,$\phi$), where $\phi$ is an independent coordinate. The same cyl. coordinates have also been widely used for representing 3D MHD equilibria of non-axisymmetric configurations (stellarators), with cross-sections, defined in RZ-planes, that vary over $\phi

Jiahua Dong, Wenqi Liang, Hongliu Li, Duzhen Zhang

Custom diffusion models (CDMs) have attracted widespread attention due to their astonishing generative ability for personalized concepts. However, most existing CDMs unreasonably assume that personalized concepts are fixed and cannot change over time. Moreover, they heavily suffer from catastrophic forgetting and concept neglect on old personalized concepts

Jun Mitani

This paper focuses on packaging design using origami techniques, specifically designs incorporating curves, known as pillow boxes. While conventional paper packaging boxes are typically cuboid, pillow box designs include curved surfaces, offering both aesthetic and practical advantages. This study analyzes the specific curved folds of pillow boxes, clarifyin

Sejun Park, Kihun Hong, Ganguk Hwang

Over the past decade, there is a growing interest in collaborative learning that can enhance AI models of multiple parties. However, it is still challenging to enhance performance them without sharing private data and models from individual parties. One recent promising approach is to develop distillation-based algorithms that exploit unlabeled public data b

Kai Ma, Tong Li

The laser of an intense electromagnetic field provides an important tool to study the strong-field particle physics. The nonlinear Compton scattering was observed in the collision of an ultra-relativistic electron beam with a laser pulse in 1990s. The precision measurement of the nonlinear Compton scattering shines the light on the studies of strong-field QE

Takuro Fujino, Satoru Takakura, Shahed Shayan Arani, Darcy Barron

At millimeter wavelengths, the atmospheric emission is circularly polarized owing to the Zeeman splitting of molecular oxygen by the Earth's magnetic field. We report a measurement of the signal in the 150 GHz band using 3 years of observational data with the \textsc{Polarbear} project. Non-idealities of a continuously rotating half-wave plate (HWP) partiall

Jhon Yana Galarza, Diego Lorenzo-Oliveira, Thiago Ferreira, Henrique Reggiani

We present HIP 8522, a young solar twin with the lowest detected lithium, potentially a field blue straggler or the result of episodic early accretion. Its stellar parameters ($T_{\rm eff} = 5729 \pm 7$ K, $\log g = 4.532 \pm 0.016$ dex, $\rm{[Fe/H]} = 0.005 \pm 0.010$ dex, $v_{t} = 1.08 \pm 0.02$ km s$^{-1}$) and chemical composition were determined via spe

Junwon Lee, Modan Tailleur, Laurie M. Heller, Keunwoo Choi

Despite significant advancements in neural text-to-audio generation, challenges persist in controllability and evaluation. This paper addresses these issues through the Sound Scene Synthesis challenge held as part of the Detection and Classification of Acoustic Scenes and Events 2024. We present an evaluation protocol combining objective metric, namely Fr\'e

Zhangyuan Guo, Min Ge, You-Qi Zhou, Jiachang Bi

Superconductors, an essential class of functional materials, hold a vital position in both fundamental science and practical applications. However, most superconductors, including MgB$_2$, Bi$_2$Sr$_2$CaCu$_2$O$_{8+\delta}$, and FeSe, are highly sensitive to environmental attacks (such as water and moist air), hindering their wide applications. More importan

Ruyi Tao, Veronica R. Cappelli, Kaiwei Liu, Marcus J. Hamilton

Predicting company growth is a critical yet challenging task because observed dynamics blend an underlying structural growth trend with volatile fluctuations. Here, we propose a Scaling-Theory-Informed Machine Learning (STIML) framework that integrates a scaling-based growth model to capture the mechanism-driven average trend, together with a data-driven for

Shiyu Duan, Runsheng Zhang, Mengmeng Chen, Ziyi Wang

This paper presents a novel method for user interface (UI) generation based on the Transformer architecture, addressing the increasing demand for efficient and aesthetically pleasing UI designs in software development. Traditional UI design relies heavily on designers' expertise, which can be time-consuming and costly. Leveraging the capabilities of Transfor

Nicolas Michel, Ayush Patnaik, Zhaodan Kong, Xinfan Lin

Multirotor unmanned aerial vehicle is a prevailing type of aerial robots with wide real-world applications. The energy efficiency of the robot is a critical aspect of its performance, determining the range and duration of the missions that can be performed. This paper studies the energy-optimal planning of the multirotor, which aims at finding the optimal or

Yashan Wang, Shangda Wu, Xingjian Du, Maosong Sun

This study explores the tokenization of multitrack sheet music in ABC notation, introducing two methods--bar-stream and line-stream patching. We compare these methods against existing techniques, including bar patching, byte patching, and Byte Pair Encoding (BPE). In terms of both computational efficiency and the musicality of the generated compositions, exp

Ankit Bhojak, Surjeet Singh Choudhary, Saurabh Shrivastava

We obtain $L^p-$estimates for the full and lacunary maximal functions associated to the bilinear spherical averages given by \[\mathfrak{A}_t(f_1,f_2)(x,y)=\int_{\mathbb S^{2d-1}}f_1(x+tz_1,y)f_2(x,y+tz_2)\;dσ(z_1,z_2),\;t>0,\] for all dimensions $d\geq1$. We show that the estimates for such operators in dimensions $d\geq2$ essentially rely on the method of

Taiki Miyagawa, Takeru Yokota

We propose the first learning scheme for functional differential equations (FDEs). FDEs play a fundamental role in physics, mathematics, and optimal control. However, the numerical analysis of FDEs has faced challenges due to its unrealistic computational costs and has been a long standing problem over decades. Thus, numerical approximations of FDEs have bee

Bijun Xie, Hangman Chen, Pengfei Wang, Cheng Zhang

Metallic materials under high stress often exhibit deformation localization, manifesting as slip banding. Over seven decades ago, Frank and Read introduced the well-known model of dislocation multiplication at a source, explaining slip band formation. Here, we reveal two distinct types of slip bands (confined and extended) in alloys through multi-scale testi

Yang Hu, Shahriar Talebi, Na Li

To address deviations from expected performance in stochastic systems, we propose a risk-sensitive control synthesis method to minimize certain risk measures over the limiting stationary distribution. Specifically, we extend Worst-case Conditional Value-at-Risk (W-CVaR) optimization for Linear Time-invariant (LTI) systems to handle nonzero-mean noise and aff

Nikolai N. Chugai

I explore observational effects of the cirsumstellar gas around superluminous supernova SN~2018ibb. High velocity Fe II narrow absorptions are reproduced in the model of fragmented cold dense shell. Unusual selective absorption in the emission doublet of [O I] is explained as an effect of the radiation scattering in Si II doublet lines in the supernova envel

Mridul Gupta, Samyak Jain, Vansh Ramani, Hariprasad Kodamana

Graph condensation has emerged as a promising avenue to enable scalable training of GNNs by compressing the training dataset while preserving essential graph characteristics. Our study uncovers significant shortcomings in current graph condensation techniques. First, the majority of the algorithms paradoxically require training on the full dataset to perform

Guijin Son, Dongkeun Yoon, Juyoung Suk, Javier Aula-Blasco

As Large Language Models (LLMs) are now capable of producing fluent and coherent content in languages other than English, it is not imperative to precisely evaluate these non-English outputs. However, when assessing the outputs from mutlilingual LLMs, prior works often employed LLM based evaluators that excel at assessing English outputs, without a thorough

Jiechen Zhao, Ran Shu, Katie Lim, Zewen Fan

Cloud servers use accelerators for common tasks (e.g., encryption, compression, hashing) to improve CPU/GPU efficiency and overall performance. However, users' Service-level Objectives (SLOs) can be violated due to accelerator-related contention. The root cause is that existing solutions for accelerators only focus on isolation or fair allocation of compute

Xinwen Zhu, Zihao Li, Yuxuan Jiang, Jiazhen Xu

The autonomous driving industry is rapidly advancing, with Vehicle-to-Vehicle (V2V) communication systems highlighting as a key component of enhanced road safety and traffic efficiency. This paper introduces a novel Real-time Vehicle-to-Vehicle Communication Based Network Cooperative Control System (VVCCS), designed to revolutionize macro-scope traffic plann

Kenta Endo

In 1979, Gonek presented the hybrid joint universality theorem for Dirichlet $L$-functions and proved the universality theorem for Hurwitz zeta-functions with rational parameter as an application. Following the introduction of the hybrid universality theorem, several generalizations, refinements, and applications have been developed. Despite these advancemen

Mir Imtiaz Mostafiz, Eunseob Kim, Adrian Shuai Li, Elisa Bertino

Cutting state monitoring in the milling process is crucial for improving manufacturing efficiency and tool life. Cutting sound detection using machine learning (ML) models, inspired by experienced machinists, can be employed as a cost-effective and non-intrusive monitoring method in a complex manufacturing environment. However, labeling industry data for tra

Xiaohuan Bi, Xi Li

Federated Learning (FL) enables decentralized model training while preserving privacy. Recently, the integration of Foundation Models (FMs) into FL has enhanced performance but introduced a novel backdoor attack mechanism. Attackers can exploit FM vulnerabilities to embed backdoors into synthetic data generated by FMs. During global model fusion, these backd

Hyunsoo Kim

Recently, urban air mobility (UAM) has attracted attention as an emerging technology that will bring innovation to urban transportation and aviation systems. Since the UAM systems pursue fully autonomous flight without a pilot, wireless communication is a key function not only for flight control signals, but also for navigation and safety information. The es

Mingtai Xie, Wenzhen Zhuo, Yanzhen Cai, Zheng Zhang

The rare-earth chalcogenide $ARECh_{2}$ family ($A =$ alkali metal or monovalent ions, $RE =$ rare earth, $Ch =$ chalcogen) has emerged as a paradigmatic platform for studying frustrated magnetism on a triangular lattice. The family members exhibit a variety of ground states, from quantum spin liquid to exotic ordered phases, providing fascinating insight in

A. F. Krutov, V. E. Troitsky

We extend our nonperturbative essentially relativistic approach, elaborated previously, to perform an approximate estimation of the size of pi0 meson. We present detailed argumentation for choosing the mass mean square radius (MSR) for this purpose. Its value calculated in our approach, using three model quark-antiquark wave functions in pion, is (0.5-0.53)