December 2024 arXiv papers — page 30

Oscar Ocampo, José Gregorio Rodríguez-Nieto, Olga Patricia Salazar-Díaz

In this paper, we present a brief overview of the concept of doodles from the perspective of J.S. Carter's work on classifying immersed curves and the work of J.S. Carter, S. Kamada, and M. Saito on stable equivalence of knots on surfaces and virtual knot cobordisms. We use the homology intersection number and the work of G. Cairns and D. Elton on the Gauss

Johann Cigler, Hans-Christian Herbig

We present a proof of a conjecture of Goh and Wildberger on the factorization of the spread polynomials. We indicate how the factors can be effectively calculated and exhibit a connection to the factorization of Fibonacci numbers into primitive parts.

Muhammad Umer, Muhammad Ahmed Mohsin, Aryan Kaushik, Qurrat-ul-Ain Nadeem

Reconfigurable intelligent surface (RIS)-assisted aerial non-terrestrial networks (NTNs) offer a promising paradigm for enhancing wireless communications in the era of 6G and beyond. By integrating RIS with aerial platforms such as unmanned aerial vehicles (UAVs) and high-altitude platforms (HAPs), these networks can intelligently control signal propagation,

Jimmy Lin, Pankaj Gupta, Will Horn, Gilad Mishne

When you have a question, the most effective way to have the question answered is to directly connect with experts on the topic and have a conversation with them. Prior to the invention of writing, this was the only way. Although effective, this solution exhibits scalability challenges. Writing allowed knowledge to be materialized, preserved, and replicated,

Julien Guinot, Elio Quinton, György Fazekas

Contrastive learning has proven effective in self-supervised musical representation learning, particularly for Music Information Retrieval (MIR) tasks. However, reliance on augmentation chains for contrastive view generation and the resulting learnt invariances pose challenges when different downstream tasks require sensitivity to certain musical attributes.

Nadav Z. Cohen, Oron Nir, Ariel Shamir

Balancing content fidelity and artistic style is a pivotal challenge in image generation. While traditional style transfer methods and modern Denoising Diffusion Probabilistic Models (DDPMs) strive to achieve this balance, they often struggle to do so without sacrificing either style, content, or sometimes both. This work addresses this challenge by analyzin

Zhirayr Avetisyan, Alexey Karapetyants, Irina Smirnova

We introduce and study weighted spaces of functions with mixed norm on the upper half-plane, defined in terms of Fourier transform. We give a characterization of analytic functions within these spaces, and in particular, we provide an analog of the Paley-Wiener theorem in this setting. As an application, we consider Toeplitz operators with vertical symbols i

P. A. Alekseev, I. A. Milekhin, K. A. Gasnikova, I. A. Eliseyev

Transition metal dichalcogenides (TMDCs) have emerged as highly promising materials for nanophotonics and optoelectronics due to their exceptionally high refractive indices, strong excitonic photoluminescence (PL) in monolayer configurations, and the versatility to engineer van der Waals (vdW) heterostructures. In this work, we exploit the intense excitonic

Navid Nayyem, Abdullah Rakin, Longwei Wang

This paper explores the intricate relationship between interpretability and robustness in deep learning models. Despite their remarkable performance across various tasks, deep learning models often exhibit critical vulnerabilities, including susceptibility to adversarial attacks, over-reliance on spurious correlations, and a lack of transparency in their dec

Muhammet Esat Kalfaoglu, Halil Ibrahim Ozturk, Ozsel Kilinc, Alptekin Temizel

Understanding road topology is crucial for autonomous driving. This paper introduces TopoBDA (Topology with Bezier Deformable Attention), a novel approach that enhances road topology comprehension by leveraging Bezier Deformable Attention (BDA). TopoBDA processes multi-camera 360-degree imagery to generate Bird's Eye View (BEV) features, which are refined th

Tobias Breiten, Shubhaditya Burela, Philipp Schulze

Solving optimal control problems for transport-dominated partial differential equations (PDEs) can become computationally expensive, especially when dealing with high-dimensional systems. To overcome this challenge, we focus on developing and deriving reduced-order models that can replace the full PDE system in solving the optimal control problem. Specifical

A. O. Ivanov, E. S. Lychagina, A. A. Tuzhilin

In the present paper a distinguishability of bounded metric spaces by the set of the Gromov--Hausdorff distances to so-called simplexes (metric spaces with unique non-zero distance) is investigated. It is easy to construct an example of non-isometric metric spaces such that the Gromov--Hausdorff distance between them vanishes. Such spaces are non-distinguish

Ruiqi Chen, Yangxintong Lyu, Han Bao, Bruno da Silva

Multiplication is a core operation in modern neural network (NN) computations, contributing significantly to energy consumption. The linear-complexity multiplication (L-Mul) algorithm is specifically proposed as an approximate multiplication method for emerging NN models, such as large language model (LLM), to reduce the energy consumption and computational

Paweł Pietrzycki, Marcel Scherer, Jan Stochel

In this paper, we study hyperrigidity for $C^*$-algebras. We will show that hyperrigidity can be expressed solely in terms of representations, without the need to involve general unital completely positive maps.

Kaiwen Zuo, Yirui Jiang

Medical Large Language Models (MLLMs) have demonstrated potential in healthcare applications, yet their propensity for hallucinations -- generating medically implausible or inaccurate information -- presents substantial risks to patient care. This paper introduces MedHallBench, a comprehensive benchmark framework for evaluating and mitigating hallucinations

Yassine Chemingui, Aryan Deshwal, Honghao Wei, Alan Fern

Offline safe reinforcement learning (OSRL) involves learning a decision-making policy to maximize rewards from a fixed batch of training data to satisfy pre-defined safety constraints. However, adapting to varying safety constraints during deployment without retraining remains an under-explored challenge. To address this challenge, we introduce constraint-ad

Sijie Xu, Runqi Wang, Wei Zhu, Dejia Song

Diffusion-based stylization methods typically denoise from a specific partial noise state for image-to-image and video-to-video tasks. This multi-step diffusion process is computationally expensive and hinders real-world application. A promising solution to speed up the process is to obtain few-step consistency models through trajectory distillation. However

S. R. Bhoyar, Yash B. Ingole

This study explores a distinctive logarithmic parameterization of the deceleration parameter within the $f(Q, C)$ gravity framework, incorporating a nonlinear functional form $f(Q, C) = \gamma_1 Q^n + \gamma_2 C$, where $Q$ and $C$ denote the nonmetricity scalar and boundary term, respectively, and $n \geq 1$. This approach provides a unique perspective on t

Bohdan Feshchenko

This article is devoted to the study of a certain class of smooth circle-valued functions on a cylinder $S^1\times [0,1]$, a torus $T^2$, a disk $D^2$ and a sphere $S^2$ which is a generalization of Morse-Bott functions without saddles. We established a "normal form" for functions from this class, namely, we proved that any such function $f$ can be presented

Milan Damnjanovic, Ivanka Milosevic

Possible forms of obstructed atomic limits in quasi-one-dimensional systems are studied using line group symmetry. This is accomplished by revisiting the standard theory with an emphasis on its group-theoretical background, synthesizing the insights into a theorem that effectively identifies potential cases. The framework is then applied across the classes o

Pengwei Zhao, Jiahao Yang, Gang v. Chen

Ferroelectricity has been one major focus in modern fundamental research and technological application. We consider the physical origin of improper ferroelectricity in Mott insulating materials. Beyond the well-known Katsura-Nagaosa-Balatsky's inverse Dzyaloshinskii-Moriya mechanism for the noncollinearly ordered magnets, we point out the induction of the el

Hiroyuki Sagawa, Tomoya Naito, Xavier Roca-Maza, Tetsuo Hatsuda

A new framework is introduced to connect between a charge symmetry breaking (CSB) energy density functional (EDF) and the low-energy constants derived from quantum chromodynamics (QCD). By constructing a QCD-based CSB EDF, this method provides new insights into the Okamoto-Nolen-Schiffer anomaly, a long-standing puzzle in the energy differences of mirror nuc

Xiao-Yu Sun, Chuan Zhang, Huai-Ning Wu, Xian-Fu Zhang

This paper focuses on switching event-triggered output feedback control for a class of parabolic partial differential equation (PDE) systems subject to unknown nonlinearities and external bounded disturbance. Initially, the PDE systems is properly separated into a finite-dimensional ordinary differential equation (ODE) slow system and an infinite-dimensional

Yewon Kim, Sung-Ju Lee, Chris Donahue

Songwriting is often driven by multimodal inspirations, such as imagery, narratives, or existing music, yet songwriters remain unsupported by current music AI systems in incorporating these multimodal inputs into their creative processes. We introduce Amuse, a songwriting assistant that transforms multimodal (image, text, or audio) inputs into chord progress

Rupshali Roy, Archisman Ghosh, Swaroop Ghosh

Many third-party cloud providers set up quantum hardware as a service that includes a wide range of qubit technologies and architectures to maximize performance at minimal cost. However, there is little visibility to where the execution of the circuit is taking place. This situation is similar to the classical cloud. The difference in the quantum scenario is

Abdulaziz M. Alanazi, Augustine O. Munagi, Manjil P. Saikia

We consider properties of overpartitions that are simultaneously {\ell}-regular and {\mu}-regular, where {\ell} and {\mu} are positive relatively prime integers. We prove a seven-way combinatorial identity related to these overpartitions. We also prove several congruence properties satisfied by this class of partitions (and a further related class) using bot

A. Shchagin, G. Kube, A. Potylitsyn, S. Strokov

This paper discusses the Doppler effect for radiation emitted by a relativistic oscillator in vacuum described geometrically as an ellipsoid in momentum space. Spectral and angular properties of the Doppler X-ray radiation emitted by a relativistic oscillator moving through a homogeneous medium are considered. Additionally, we show that radiation from an ele

Gregory M. Dickinson

Current consumer-protection debates focus on the powerful new data-analysis techniques that have disrupted the balance of power between companies and their customers. Online tracking enables sellers to amass troves of historical data, apply machine-learning tools to construct detailed customer profiles, and target those customers with tailored offers that be

Chang Pan, Shuai Hu, Fanli Yang, Dongchao Yang

Spin current plays a central role in spintronics for driving exotic spin-dependent phenomena and high-performance device applications. Recently, a magnetic spin Hall effect has been discovered in spin-split antiferromagnets including noncollinear antiferromagnets and altermagnets, allowing the efficient generation of unconventional spin currents even in the

Bauyrzhan K. Primkulov, Davis J. Evans, Valeri Frumkin, Pedro J. Sáenz

The Kapitza-Dirac effect is the diffraction of quantum particles by a standing wave of light. We here report an analogous phenomenon in pilot-wave hydrodynamics, wherein droplets walking across the surface of a vibrating liquid bath are deflected by a standing Faraday wave. We show that, in certain parameter regimes, the statistical distribution of the dropl

Jan Warnken, Dmytro Velychko, Simon Damm, Asja Fischer

The evidence lower bound (ELBO) is one of the most central objectives for probabilistic unsupervised learning. For the ELBOs of several generative models and model classes, we here prove convergence to entropy sums. As one result, we provide a list of generative models for which entropy convergence has been shown, so far, along with the corresponding express

Yingqi Zhao, Kuo Zhan, Pei-Lin Xin, Zuyan Chen

Discriminating the low-abundance hydroxylated proline from hydroxylated proline is crucial for monitoring diseases and eval-uating therapeutic outcomes that require single-molecule sensors. While the plasmonic nanopore sensor can detect the hydrox-ylation with single-molecule sensitivity by surface enhanced Raman spectroscopy (SERS), it suffers from intrinsi

Libo Zhang, Zhaoning Zhang, Baizhou Xu, Rui Li

With the continuous advancement in the performance of large language models (LLMs), their demand for computational resources and memory has significantly increased, which poses major challenges for efficient inference on consumer-grade devices and legacy servers. These devices typically feature relatively weaker GPUs and stronger CPUs. Although techniques su

Yixiao Li, Xiaoyuan Yang, Weide Liu, Xin Jin

As super-resolution (SR) techniques introduce unique distortions that fundamentally differ from those caused by traditional degradation processes (e.g., compression), there is an increasing demand for specialized video quality assessment (VQA) methods tailored to SR-generated content. One critical factor affecting perceived quality is temporal inconsistency,

Ritik Mehta, Olha Jureckova, Mark Stamp

The proliferation of malware variants poses a significant challenges to traditional malware detection approaches, such as signature-based methods, necessitating the development of advanced machine learning techniques. In this research, we present a novel approach based on a hybrid architecture combining features extracted using a Hidden Markov Model (HMM), w

Hengameh R. Dehkordi

The increasing frequency and intensity of wildfires underscore the need for accurate predictive models to enhance wildfire management. Traditional models, such as Rothermel and FARSITE, provide foundational insights but often oversimplify the complex dynamics of wildfire spread. Advanced methods, employing sophisticated mathematical techniques, offer more pr

W. He, Z. Huang, X. Meng, X. Qi

In the era of pre-trained models, image clustering task is usually addressed by two relevant stages: a) to produce features from pre-trained vision models; and b) to find clusters from the pre-trained features. However, these two stages are often considered separately or learned by different paradigms, leading to suboptimal clustering performance. In this pa

Xiaoning Bai, Shangzhi Zeng, Jin Zhang, Lezhi Zhang

In this paper, we study a class of bilevel optimization problems where the lower-level problem is a convex composite optimization model, which arises in various applications, including bilevel hyperparameter selection for regularized regression models. To solve these problems, we propose an Alternating Gradient-type algorithm with Inexact Lower-level Solutio

Lunhao Duan, Shanshan Zhao, Wenjun Yan, Yinglun Li

Recently, text-to-image generation models have achieved remarkable advancements, particularly with diffusion models facilitating high-quality image synthesis from textual descriptions. However, these models often struggle with achieving precise control over pixel-level layouts, object appearances, and global styles when using text prompts alone. To mitigate

Sang-Ho Kim, Yongseok Oh, Sangyeong Son, S. Sakinah

Recent experiments of photon-nucleon and meson-nucleon scatterings have accumulated a lot of data for various meson production processes. One of the purposes of those experiments is to search for the missing resonances which are not discovered until now but whose existence was predicted by hadron models. The analyses of the data requires the development of d

Dapeng Zhao, Yue Qi

3D face reconstruction technology aims to generate a face stereo model naturally and realistically. Previous deep face reconstruction approaches are typically designed to generate convincing textures and cannot generalize well to multiple occluded scenarios simultaneously. By introducing bump mapping, we successfully added mid-level details to coarse 3D face

Rui Sun, Yumin Zhang, Varun Ojha, Tejal Shah

We propose Exemplar-Condensed federated class-incremental learning (ECoral) to distil the training characteristics of real images from streaming data into informative rehearsal exemplars. The proposed method eliminates the limitations of exemplar selection in replay-based approaches for mitigating catastrophic forgetting in federated continual learning (FCL)

Junying Chen, Zhenyang Cai, Ke Ji, Xidong Wang

The breakthrough of OpenAI o1 highlights the potential of enhancing reasoning to improve LLM. Yet, most research in reasoning has focused on mathematical tasks, leaving domains like medicine underexplored. The medical domain, though distinct from mathematics, also demands robust reasoning to provide reliable answers, given the high standards of healthcare. H

Jacob Bradd, Nigel Higson, Robert Yuncken

David Vogan proved that if $G$ is a real reductive group, and if $K$ is a maximal compact subgroup of $G$, then every irreducible representation of $K$ is included as a minimal $K$-type in precisely one tempered, irreducible unitary representation of $G$ with real infinitesimal character, and that moreover it is included there with multiplicity one and is th

Dohyun Kim, Woojoo Shim

The aim of this article is to investigate the convergence properties of a heterogeneous consensus model on Stiefel manifolds. We consider each agent, without interaction, moving according to the flow determined by the fundamental vector field of the right multiplication action of the orthogonal group on the Stiefel manifold. We analyze the asymptotic behavio

Jian Zhang, Ping Zhu, Chris C. Hegna

A hybrid spectral/finite-element code is developed to numerically solve the resistive finite-pressure magnetohydrodynamic equilibria without the necessity of postulating nested magnetic flux surfaces in the non-axisymmetric toroidal systems. The adopted approach integrates a hyperbolic parallel damping equation for pressure updating, along with a dynamic res

Dapeng Zhao, Yue Qi

Single-view 3D face reconstruction is a fundamental Computer Vision problem of extraordinary difficulty. Current systems often assume the input is unobstructed faces which makes their method not suitable for in-the-wild conditions. We present a method for performing a 3D face that removes eyeglasses from a single image. Existing facial reconstruction methods

Brett T. Lopez, Jean-Jacques Slotine

We present two quaternion-based sliding variables for controlling the orientation of a manipulator's end-effector. Both sliding variables are free of singularities and represent global exponentially convergent error dynamics that do not exhibit unwinding when used in feedback. The choice of sliding variable is dictated by whether the end-effector's angular v

Dapeng Zhao, Yue Qi

Over the past few years, single-view 3D face reconstruction methods can produce beautiful 3D models. Nevertheless,the input of these works is unobstructed faces.We describe a system designed to reconstruct convincing face texture in the case of occlusion.Motivated by parsing facial features,we propose a complete face parsing map generation method guided by l

Eldar Knar

This study proposes an approach to describing personality dynamics through mathematical modelling of introversion, extroversion, and ambiversion processes. Introversion is interpreted as a recursive process characterized by deep self-awareness and inner reflection; extroversion is presented as an iterative process of accumulating and processing external stim

Yingchen Wei, Xihe Qiu, Xiaoyu Tan, Jingjing Huang

Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a common sleep disorder caused by upper airway blockage, leading to oxygen deprivation and disrupted sleep. Traditional diagnosis using polysomnography (PSG) is expensive, time-consuming, and uncomfortable. Existing deep learning methods using facial image analysis lack accuracy due to poor facial feature

Krisvarish V, Priyadarshini T, K P Abhishek Sri Saai, Vaidehi Vijayakumar

This paper describes a memory-efficient transformer model designed to drive a reduction in memory usage and execution time by substantial orders of magnitude without impairing the model's performance near that of the original model. Recently, new architectures of transformers were presented, focused on parameter efficiency and computational optimization; how

Sanjoeng Wong

Automatic prohibited item detection in X-ray images is crucial for public safety. However, most existing detection methods either rely on expensive box annotations to achieve high performance or use weak annotations but suffer from limited accuracy. To balance annotation cost and detection performance, we study Weakly Semi-Supervised X-ray Prohibited Item De

Yi-Chia Chen, Wei-Hua Li, Chu-Song Chen

Open-vocabulary panoptic segmentation remains a challenging problem. One of the biggest difficulties lies in training models to generalize to an unlimited number of classes using limited categorized training data. Recent popular methods involve large-scale vision-language pre-trained foundation models, such as CLIP. In this paper, we propose OMTSeg for open-

Tommaso Taddei, Xuejun Xu, Lei Zhang

We introduce optimization-based full-order and reduced-order formulations of fluid structure interaction problems. We study the flow of an incompressible Newtonian fluid which interacts with an elastic body: we consider an arbitrary Lagrangian Eulerian formulation of the fluid problem and a fully Lagrangian formulation of the solid problem; we rely on a fini

A. A. Khartov

We consider a new class $\boldsymbol{Q}$ of distribution functions $F$ that have the property of rational-infinite divisibility: there exist some infinitely divisible distribution functions $F_1$ and $F_2$ such that $F_1=F*F_2$. A distribution function of the class $\boldsymbol{Q}$ is quasi-infinitely divisible in the sense that its characteristic function a

Dulhan Jayalath, James Bradley Wendt, Nicholas Monath, Sandeep Tata

Long-range tasks demand reasoning over long inputs. However, existing solutions are limited, e.g., long-context models require large compute budgets, parameter-efficient fine-tuning (PEFT) needs training data, and retrieval-augmented generation (RAG) entails complex task-specific designs. Though in-context approaches overcome many of these issues, methods wi

Zixuan Li, Shulin He, Xueliang Zhang

In multi-speaker environments the direction of arrival (DOA) of a target speaker is key for improving speech clarity and extracting target speaker's voice. However, traditional DOA estimation methods often struggle in the presence of noise, reverberation, and particularly when competing speakers are present. To address these challenges, we propose RTS-DOA, a

Guozheng Dai, Zhonggen Su, Hanchao Wang

Let $M$ be an $n\times n$ random i.i.d. matrix. This paper studies the deviation inequality of $s_{n-k+1}(M)$, the $k$-th smallest singular value of $M$. In particular, when the entries of $M$ are subgaussian, we show that for any $\gamma\in (0, 1/2), \varepsilon>0$ and $\log n\le k\le c\sqrt{n}$ \begin{align} \textsf{P}\{s_{n-k+1}(M)\le \frac{\varepsilon}{\

Chang Zou, Shikang Zheng, Evelyn Zhang, Runlin Guo

Diffusion Transformers (DiT) have become the dominant methods in image and video generation yet still suffer substantial computational costs. As an effective approach for DiT acceleration, feature caching methods are designed to cache the features of DiT in previous timesteps and reuse them in the next timesteps, allowing us to skip the computation in the ne

Situo Zhang, Hankun Wang, Da Ma, Zichen Zhu

Speculative Decoding (SD) is a popular lossless technique for accelerating the inference of Large Language Models (LLMs). We show that the decoding speed of SD frameworks with static draft structures can be significantly improved by incorporating context-aware adaptive draft structures. However, current studies on adaptive draft structures are limited by the

Xiaobing Li, Ranran Guo, Mingmei Xu, Jinghua Fu

Using the Metropolis algorithm, we simulate the relaxation process of the three-dimensional kinetic Ising model. Starting from a random initial configuration, we first present the average equilibration time across the entire phase boundary. It is observed that the average equilibration time increases significantly as the temperature decreases far from the cr

JiaCheng Li

With the explosive growth of Chinese text data and advancements in natural language processing technologies, Chinese text classification has become one of the key techniques in fields such as information retrieval and sentiment analysis, attracting increasing attention. This paper conducts a comparative study on three deep learning models:TextCNN, TextRNN, a

Carl Qi, Dan Haramati, Tal Daniel, Aviv Tamar

Object manipulation is a common component of everyday tasks, but learning to manipulate objects from high-dimensional observations presents significant challenges. These challenges are heightened in multi-object environments due to the combinatorial complexity of the state space as well as of the desired behaviors. While recent approaches have utilized large

Guozheng Dai, Zeyan Song, Hanchao Wang

Let A be an n*n random matrix with mean zero and independent inhomogeneous non-constant subgaussian entries. We get that for any k<c\sqrt{n}, the probability of the matrix has a lower rank than n-k that is sub-exponential. Furthermore, we get a deviation inequality for the singular values of A. This extends earlier results of Rudelson's paper in 2024 by remo

Akshay Nagesh Kamthe, Vishnudatta Thota, Aashi Shrinate, Twinkle Tripathy

In this work, we consider a group of n agents which interact with each other in a cooperative framework. A Laplacian-based model is proposed to govern the evolution of opinions in the group when the agents are subjected to external biases like agents' traits, news, etc. The objective of the paper is to design a control input which leads to any desired opinio

Zhonghua Jiang, Jimin Xu, Shengyu Zhang, Tao Shen

Federated learning (FL) is a promising technology for data privacy and distributed optimization, but it suffers from data imbalance and heterogeneity among clients. Existing FL methods try to solve the problems by aligning client with server model or by correcting client model with control variables. These methods excel on IID and general Non-IID data but pe

Yuuya Chiba

We prove that the Ising models with transverse and longitudinal fields on the hypercubic lattices with dimensions higher than one have no local conserved quantities other than the Hamiltonian. This holds for any value of the longitudinal field, including zero, as far as the transverse field and the Ising interactions are nonzero. The conserved quantity consi

Shigeyuki Kondo, Shigeru Mukai

We calculate the automorphism group of the Kummer surface associated with a curve of genus 2 or the product of two elliptic curves in characteristic two under the assumption that the Kummer surface is a $K3$ surface. Moreover we discuss the complex $K3$ surfaces with the same Picard lattice as these Kummer surfaces. The paper has two appendices.

Lasha Ephremidze

Granger causality has become an indispensable tool for analyzing causal relationships between time series. In this paper, we provide a detailed overview of its mathematical foundations, trace its historical development, and explore how recent computational advancements can enhance its application in various fields. We will not hesitate to present the proofs

Varun Varma Jaganath, Ben Steinfurth

Energy harvesting from vortex-induced vibrations is a promising technology that relies on the vibrations of bluff bodies due to vortex shedding. Increasing the vibration amplitude at a given free stream kinetic energy is therefore equivalent to enhancing the efficiency of the harvesting device. In this study, we assess the potential of alternate slot blowing

Masahiro Sato

This study examines whether collective reasoning among generative agents can facilitate novel and coherent thinking that leads to innovation. To achieve this, it proposes GAI, a new LLM-empowered framework designed for reflection and interaction among multiple generative agents to replicate the process of innovation. The core of the GAI framework lies in an

Enxun Huang, Tengyou Zhu

Let $1<c<d$ be two relatively prime integers, $g_{c,d}=cd-c-d$ and $\mathbb{P}$ is the set of primes. For any given integer $k \geq 1$, we prove that $$\#\left\{p^k\le g_{c,d}:p\in \mathbb{P}, ~p^k=cx+dy,~x,y\in \mathbb{Z}_{\geqslant0} \right\}\sim \frac{k}{k+1}\frac{g^{1/k}}{\log g} \quad (\text{as}~c\rightarrow\infty),$$ which gives an extension of a recen

Sangita Dutta, Prosenjit Kundu, Pitambar Khanra, Ludovico Minati

This study aims to develop a generalised concept that will enable double explosive transitions in the forward and backward directions or a combination thereof. We found two essential factors for generating such phase transitions: the use of higher-order (triadic) interactions and the partial adaptation of a global order parameter acting on the triadic coupli

Senlei Li, Shane P. Kelly, Jingcheng Zhou, Hanyi Lu

Superconducting circuits are arguably taking a leading role in driving the ongoing quantum technological revolution. A detailed knowledge of the microscopic fluctuating electromagnetic properties plays an important role in advancing the circuitry design, testing, and material integration of cutting-edge superconducting quantum electronics. Here we report sca

Wen-Hao Zhou, Che Ming Ko, Kai-Jia Sun

Because of their weak interactions with the strongly interacting matter produced in relativistic heavy-ion collisions, dileptons provide an ideal probe of the early dynamics of these collisions. Here, we study dilepton production using a partonic transport model that is based on an extended Nambu-Jona-Lasinio (NJL) model. In this model, the in-medium quark m

Serkan Salturk, Irem Sayin, Ibrahim Cem Balci, Taha Emre Pamukcu

Lumbar disk segmentation is essential for diagnosing and curing spinal disorders by enabling precise detection of disk boundaries in medical imaging. The advent of deep learning has resulted in the development of many segmentation methods, offering differing levels of accuracy and effectiveness. This study assesses the effectiveness of several sophisticated

Gang Ma, Jianfeng Wang, Guang Li, Sandi Klavžar

A connected graph $\Gamma$ of diameter ${\rm diam}(\Gamma) \ge \ell$ is $\ell$-distance-balanced if $|W_{xy}(\Gamma)|=|W_{yx}(\Gamma)|$ for every $x,y\in V(\Gamma)$ with $d_{\Gamma}(x,y)=\ell$, where $W_{xy}(\Gamma)$ is the set of vertices of $\Gamma$ that are closer to $x$ than to $y$. $\Gamma$ is said to be highly distance-balanced if it is $\ell$-distance

Alejandra Garrido, Colin D. Reid

In this article we give sufficient conditions for a group to have simple derived subgroup; the argument is based on generalising properties observed for extremely proximal micro-supported actions on the Cantor space, and generalises previous results of Matui, Le Boudec and others in this direction. We give a sufficient condition for a non-trivial normal subg

Ping Guo, Qingfu Zhang, Xi Lin

The discovery of symbolic solutions -- mathematical expressions, logical rules, and algorithmic structures -- is fundamental to advancing scientific and engineering progress. However, traditional methods often struggle with search efficiency and fail to integrate knowledge effectively. While recent large language model-based (LLM-based) approaches have demon

Marius Sparn, Elinor Kath, Nikolas Liebster, Jelte Duchene

We experimentally study cosmological particle production in a two-dimensional Bose-Einstein condensate, whose density excitations map to an analog cosmology. The expansion of spacetime is realized with tunable interactions. The particle spectrum can be understood through an analogy to quantum mechanical scattering, in which the dynamics of the spacetime metr

A. O. Ivanov, I. N. Mikhailov, A. A. Tuzhilin

In this paper, we study metric trees, without any finiteness restrictions. For subsets of such trees, a condition that guarantees that the Hausdorff and Gromov--Hausdorff distances from the subset to the entire metric tree are the same is obtained. This result allows to construct a new class of shortest geodesics (in the proper class of all metric spaces) co

Junwei Ji, Dongyuan Shi, Boxiang Wang, Xiaoyi Shen

The Kalman filter (KF)-based active noise control (ANC) system demonstrates superior tracking and faster convergence compared to the least mean square (LMS) method, particularly in dynamic noise cancellation scenarios. However, in environments with extremely high noise levels, the power of the control signal can exceed the system's rated output power due to

Ganlin Liu, Ziling Liang, Xiaowei Huang, Xinping Yi

Despite impressive capability in learning over graph-structured data, graph neural networks (GNN) suffer from adversarial topology perturbation in both training and inference phases. While adversarial training has demonstrated remarkable effectiveness in image classification tasks, its suitability for GNN models has been doubted until a recent advance that s

Wuyang Zhang, Yansong Li, Zeyu Dong, Yu Wu

Large Language Models (LLMs) have helped programmers increase efficiency through code generation, comprehension, and repair. However, their application to large-scale projects remains challenging due to complex interdependencies and the extensive size of modern codebases. Although Knuth's concept of Literate Programming (LP) combines code and natural languag

Osamu Ishimura, Yoshihide Yoshimoto

This paper proposes integrating Aspect-oriented Programming (AOP) into Julia, a language widely used in scientific and High-Performance Computing (HPC). AOP enhances software modularity by encapsulating cross-cutting concerns, such as logging, caching, and parallelizing, into separate, reusable aspects. Leveraging Julia's powerful metaprogramming and abstrac

B. Ayed. Sabria, T. Saanouni

This work investigates the long time asymptotic behavior of some inhomogeneous non-linear Schr\"odinger type equations. We give sharp a threshold of scattering versus non-scattering of mass solutions, depending on the source term. This work complements the literature to the inhomogeneous regime.

Di Wu, Feng Yang, Benlian Xu, Pan Liao

The application of vision-based multi-view environmental perception system has been increasingly recognized in autonomous driving technology, especially the BEV-based models. Current state-of-the-art solutions primarily encode image features from each camera view into the BEV space through explicit or implicit depth prediction. However, these methods often o

Reyhaneh Hosseininejad, Megh Shukla, Saeed Saadatnejad, Mathieu Salzmann

Human pose forecasting is inherently multimodal since multiple futures exist for an observed pose sequence. However, evaluating multimodality is challenging since the task is ill-posed. Therefore, we first propose an alternative paradigm to make the task well-posed. Next, while state-of-the-art methods predict multimodality, this requires oversampling a larg

Yong-Peng Guo, Geng-Yan Zou, Xing Ding, Qi-Hang Zhang

Fusing small resource states into a larger, fully connected graph-state is essential for scalable photonic quantum computing. Theoretical analysis reveals that this can only be achieved when the success probability of the fusion gate surpasses a specific percolation threshold of 58.98% by using three-photon GHZ states as resource states. However, such an imp

Yifeng Liu

We introduce the notion of ordinary distributions for unitary groups and their Rankin--Selberg products, based on which we (re)define the ordinary eigenvarieties. In both definite and indefinite cases, we construct the Bessel period on the Rankin--Selberg eigenvariety as an ordinary distribution and as an element in the Selmer group of ordinary distributions

Naira R. Arakelyan, Sergey V. Pilipenko, Stefan Gottlöber, Noam I. Libeskind

Investigations of trajectories of various objects orbiting the Milky Way (MW) halo with modern precision, achievable in observations by Gaia, requires sophisticated, non-stationary models of the Galactic potential. In this paper we analyze the evolution of the spherical harmonics expansion of MW analogues potential in constrained simulations of the Local Gro

Qingxiang Zhao, Runfeng Zhu, Xin Zhong, Baitao Lin

As soft continuum manipulators characterize terrific compliance and maneuverability in narrow unstructured space, low stiffness and limited dexterity are two obvious shortcomings in practical applications. To address the issues, a novel asymmetric coaxial antagonistic tubular robot (CATR) arm with high stiffness has been proposed, where two asymmetrically pa

Hongqi Liu, Junjun Jia, Menghui Jia, Chengcan Han

Traditional metal and n-type doped semiconductor materials serve as emerging epsilon-near-zero (ENZ) materials, showcasing great potential for nonlinear photonic applications. However, a significant limitation for such materials is the lack of versatile ENZ wavelength tuning, and thus dynamic tuning of the ENZ wavelength remains a technical challenge, thereb

Yichen Xu, Faliang Chang, Chunsheng Liu, Dexin Wang

In robotic manipulation tasks, achieving a designated target state for the manipulated object is often essential to facilitate motion planning for robotic arms. Specifically, in tasks such as hanging a mug, the mug must be positioned within a feasible region around the hook. Previous approaches have enabled the generation of multiple feasible target states f

Guangyi Zhang, Kequan Zhou, Yunlong Cai, Qiyu Hu

Semantic communication (SC) is emerging as a pivotal innovation within the 6G framework, aimed at enabling more intelligent transmission. This development has led to numerous studies focused on designing advanced systems through powerful deep learning techniques. Nevertheless, many of these approaches envision an analog transmission manner by formulating the

Niccolò Urbinat, Marco LiCalzi

We study competitive equilibria in exchange economies when a continuum of goods is conflated into a finite set of commodities. The design of conflation choices affects the allocation of scarce resources among agents, by constraining trading opportunities and shifting competitive pressures. We demonstrate the consequences on relative prices, trading positions

Alireza Sedighi Moghaddam, Fatemeh Anvari, Mohammadjavad Mirshekari Haghighi, Mohammadali Fakhari

Person Re-Identification (ReID) is a fundamental task in computer vision with critical applications in surveillance and security. Despite progress in recent years, most existing ReID models often struggle to generalize across diverse cultural contexts, particularly in Islamic regions like Iran, where modest clothing styles are prevalent. Existing datasets pr

Guiyu Zhao, Zhentao Guo, Zewen Du, Hongbin Ma

Due to the density inconsistency and distribution difference between cross-source point clouds, previous methods fail in cross-source point cloud registration. We propose a density-robust feature extraction and matching scheme to achieve robust and accurate cross-source registration. To address the density inconsistency between cross-source data, we introduc

Hidetoshi Wada, Tiantian Zhang, Shuichi Murakami

Recent studies showed that topologically trivial insulators may have fractionally quantized corner charges due to the topological invariant called a filling anomaly. Such crystal shapes in three dimensions are restricted to vertex-transitive polyhedra, which are classified into spherical and cylindrical families. The previous works derived formulas of the fr