December 2024 arXiv papers — page 82

Sithu Aung, Min-Cheol Sagong, Junghyun Cho

We address an advanced challenge of predicting pedestrian occupancy as an extension of multi-view pedestrian detection in urban traffic. To support this, we have created a new synthetic dataset called MVP-Occ, designed for dense pedestrian scenarios in large-scale scenes. Our dataset provides detailed representations of pedestrians using voxel structures, ac

Juan M. Z. Pretel, Mariana Dutra, Sergio B. Duarte

We analyze the effect of a Chaplygin dark fluid (CDF) core on neutron stars (NSs). To address this study, we focus on the relativistic structure of stellar configurations composed by a dark-energy core, described by a Chaplygin-like equation of state (EoS), and an ordinary-matter crust which is described by a polytropic EoS. We examine the impact of the rate

Dieter Bothe, Kohei Soga

A passively advected sharp interface can be represented as the zero level set of a level set function $f$. The linear transport equation $\partial_tf+v\cdot \nabla f =0$ is the simplest governing equation for such a level set function. While the signed distance of the interface is a geometrically convenient function, e.g., the norm of the gradient is everywh

Elias Pescoller, Marie Eder, Iva Březinová

In the search for accurate approximate solutions of the many-body Schr\"odinger equation, reduced density matrices play an important role, as they allow to formulate approximate methods with polynomial scaling in the number of particles. However, these methods frequently encounter the issue of $N$-representability, whereby in self-consistent applications of

Xiaole Xian, Xilin He, Zenghao Niu, Junliang Zhang

For efficient and high-fidelity local facial attribute editing, most existing editing methods either require additional fine-tuning for different editing effects or tend to affect beyond the editing regions. Alternatively, inpainting methods can edit the target image region while preserving external areas. However, current inpainting methods still suffer fro

Minh Hoang Trinh, Nhat-Minh Le-Phan, Hyo-Sung Ahn

In this chapter, an input-output economic model with multiple interactive economic systems is considered. The model captures the multi-dimensional nature of the economic sectors or industries in each economic system, the interdependencies among industries within an economic system and across different economic systems, and the influence of demand. To determi

Sevilay Kirci Serenbay, Rabia Aktaş Karaman

In the present paper, we deal with Bernstein-Chlodowsky type operators for approximating functions on the domain. We first present Bernstein-Chlodowsky type operators in two variables and then we discuss some examples of these operators under a domain transformation. Finally, we give bivarite shifted mth Bernstein-Chlodowsky-Stancu operators and we present s

E. Marchi, G. Tiana

Starting from the reported experimental evidence that the residence time of contacts between the ends of biopolymers is length dependent, we investigate the kinetics of contact breaking in simple polymer models from a theoretical point of view. We solved Kramers equation first for an ideal chain and then for a polymer with attracting ends, and compared the p

Enhao Jia, Kui Wu, Yong Du, Yuyang Wu

The Carnian Pluvial Episode (CPE) was a major global climate change event in the early Late Triassic that significantly affected marine ecosystems and carbon cycles. One of the most prominent features of the CPE is the coupled multiple negative carbonate-organic carbon isotope excursions. However, at Erguan and Xiashulao from eastern Tethys, a decoupling bet

Allan John Gerrard, Kohei Motegi, Kazumitsu Sakai

We introduce and investigate a class of $\mathfrak{gl}_{M+1}$ partition functions which is an extension of the one introduced by Foda-Manabe. We characterize the partition functions by a nested version of Izergin-Korepin analysis, and determine the explicit forms, for each of the rational, trigonometric and elliptic versions. The resulting multisymmetric fun

Balázs Dóra, Cătălin Paşcu Moca

Stabilizer entropies and quantum magic have been extensively explored in real-space formulations of quantum systems within the framework of resource theory. However, interesting and transparent physics often emerges in momentum space, such as Cooper pairing. Motivated by this, we investigate the momentum-space structure of Pauli strings and stabilizer entrop

Achintha Wijesinghe, Suchinthaka Wanninayaka, Weiwei Wang, Yu-Chieh Chao

The recent rise of semantic-style communications includes the development of goal-oriented communications (GOCOMs) remarkably efficient multimedia information transmissions. The concept of GO-COMS leverages advanced artificial intelligence (AI) tools to address the rising demand for bandwidth efficiency in applications, such as edge computing and Internet-of

Mengyan Zhang, Shahine Bouabid, Cheng Soon Ong, Seth Flaxman

We develop the framework of Indirect Query Bayesian Optimization (IQBO), a new class of Bayesian optimization problems where the integrated feedback is given via a conditional expectation of the unknown function $f$ to be optimized. The underlying conditional distribution can be unknown and learned from data. The goal is to find the global optimum of $f$ by

Changsun Lee, Sangjoon Park, Cheong-Il Shin, Woo Hee Choi

Recent medical vision-language models (VLMs) have shown promise in 2D medical image interpretation. However extending them to 3D medical imaging has been challenging due to computational complexities and data scarcity. Although a few recent VLMs specified for 3D medical imaging have emerged, all are limited to learning volumetric representation of a 3D medic

Na Fu, Jianping Sun

This article introduces the $L_p$-Gauss dual curvature measure and proposes its related $L_p$-Gauss dual Minkowski problem as: for $p,q\in\mathbb{R}$, under what necessary and/or sufficient condition on a non-zero finite Borel measure $\mu$ on unit sphere does there exist a convex body $K$ such that $\mu$ is the $L_p$ Gauss dual curvature measure? If $K$ exi

Taichi Abe, Yasunobu Morita

This study examines the relationship between the concept of serious leisure and user innovation. We adopted the characteristics of innovative consumers identified by Luthje (2004)-product use experience, information exchange, and new product adoption speed-to analyze their correlation with serious leisure engagement. The analysis utilized consumer behavior s

Shivadharshan S, Akilesh P, Rajrupa Chattaraj, Sridhar Chimalakonda

With the increasing complexity of modern software and the demand for high performance, energy consumption has become a critical factor for developers and researchers. While much of the research community is focused on evaluating the energy consumption of machine learning and artificial intelligence systems -- often implemented in Python -- there is a gap whe

Nicolas Pope, Juho Kahila, Henriikka Vartiainen, Mohammed Saqr

This paper, submitted to the special track on resources for teaching AI in K-12, presents an explainable AI (XAI) education tool designed for K-12 classrooms, particularly for students in grades 4-9. The tool was designed for interventions on the fundamental processes behind social media platforms, focusing on four AI- and data-driven core concepts: data col

Hangming Zhang, Alexander Sboev, Roman Rybka, Qiang Yu

Spiking Neural Networks have attracted significant attention in recent years due to their distinctive low-power characteristics. Meanwhile, Transformer models, known for their powerful self-attention mechanisms and parallel processing capabilities, have demonstrated exceptional performance across various domains, including natural language processing and com

Chenghao Gu, Zhenzhe Li, Zhengqi Zhang, Yunpeng Bai

3D editing has shown remarkable capability in editing scenes based on various instructions. However, existing methods struggle with achieving intuitive, localized editing, such as selectively making flowers blossom. Drag-style editing has shown exceptional capability to edit images with direct manipulation instead of ambiguous text commands. Nevertheless, ex

Xuhan Zuo, Minghao Wang, Tianqing Zhu, Shui Yu

Large language models (LLMs) have transformed the way computers understand and process human language, but using them effectively across different organizations remains still difficult. When organizations work together to improve LLMs, they face several main challenges. First, organizations hesitate to share their valuable data with others. Second, competiti

Peng Su, Shudong Huang, Weihong Ma, Deng Xiong

Previous multi-view contrastive learning methods typically operate at two scales: instance-level and cluster-level. Instance-level approaches construct positive and negative pairs based on sample correspondences, aiming to bring positive pairs closer and push negative pairs further apart in the latent space. Cluster-level methods focus on calculating cluster

Cheng Qian, Peixuan Han, Qinyu Luo, Bingxiang He

Language model agents excel in long-session planning and reasoning, but existing benchmarks primarily focus on goal-oriented tasks with explicit objectives, neglecting creative adaptation in unfamiliar environments. To address this, we introduce EscapeBench, a benchmark suite of room escape game environments designed to challenge agents with creative reasoni

Jingxiang Guo, Jiayu Luo, Zhenyu Wei, Yiwen Hou

Teleoperation provides an effective way to collect robot data, which is crucial for learning from demonstrations. In this field, teleoperation faces several key challenges: user-friendliness for new users, safety assurance, and transferability across different platforms. While collecting real robot dexterous manipulation data by teleoperation to train robots

Ankit Dhiman, Tao Lu, R Srinath, Emre Arslan

Novel-view synthesis plays a crucial role in computer vision with applications in 3D reconstruction, mixed reality, and robotics. Recent approaches, such as 3D Gaussian Splatting (3DGS), have emerged as state-of-the-art solutions, offering high-quality novel view synthesis in real time. However, training 3DGS models remains slow, particularly for high-resolu

Conghan Dong, Yu Li

We establish a uniform entropy bound for simply connected Ricci shrinkers with a finite second homotopy group and a uniform curvature bound. Additionally, we extend the non-collapsing result to a broader class of smooth metric measure spaces satisfying Bakry-\'Emery conditions.

Habib Ammari, Jinghao Cao, Erik Orvehed Hiltunen, Liora Rueff

This paper provides a general framework for deriving effective material properties of one-dimensional, time-modulated systems of subwavelength resonators. It applies to subwavelength resonator systems with a general form of time-dependent parameters. We show that the resonators can be accurately described by a point-scattering formulation when the width of t

Zheng Hu, Zhe Li, Ziyun Jiao, Satoshi Nakagawa

In recent years, knowledge graphs have been integrated into recommender systems as item-side auxiliary information, enhancing recommendation accuracy. However, constructing and integrating structural user-side knowledge remains a significant challenge due to the improper granularity and inherent scarcity of user-side features. Recent advancements in Large La

Yunbin Tu, Liang Li, Li Su, Qingming Huang

Video has emerged as a favored multimedia format on the internet. To better gain video contents, a new topic HIREST is presented, including video retrieval, moment retrieval, moment segmentation, and step-captioning. The pioneering work chooses the pre-trained CLIP-based model for video retrieval, and leverages it as a feature extractor for other three chall

Yanhua Li, Xiaocao Ouyang, Chaofan Pan, Jie Zhang

Open intent classification is critical for the development of dialogue systems, aiming to accurately classify known intents into their corresponding classes while identifying unknown intents. Prior boundary-based methods assumed known intents fit within compact spherical regions, focusing on coarse-grained representation and precise spherical decision bounda

Jingyao Wang, Wenwen Qiang, Changwen Zheng, Fuchun Sun

Fine-grained emotion recognition (FER) plays a vital role in various fields, such as disease diagnosis, personalized recommendations, and multimedia mining. However, existing FER methods face three key challenges in real-world applications: (i) they rely on large amounts of continuously annotated data to ensure accuracy since emotions are complex and ambiguo

Yingjie Zhu, Xuefeng Bai, Kehai Chen, Yang Xiang

Large Vision-Language Models (LVLMs) have demonstrated remarkable performance across diverse tasks. Despite great success, recent studies show that LVLMs encounter substantial limitations when engaging with visual graphs. To study the reason behind these limitations, we propose VGCure, a comprehensive benchmark covering 22 tasks for examining the fundamental

Lu Chen, Wai Lee Chan, Yu Lv

Conjugate heat transfer is a challenging fluid-structure coupling problem that can significantly influence flame stabilization and thermoacoustic instabilities. To properly capture combustion phenomena that involve conjugate heat transfer, careful modeling of chemical reactions in the fluid domain and heat transfer in the solid body is necessary and remains

Lars Grüne, Mario Zanon

It is known that receding horizon control with a strictly pre-dissipative optimal control problem yields a practically asymptotically stable closed loop when suitable state constraints are imposed. In this note we show that alternatively suitably bounded terminal costs can be used for stabilizing the closed loop.

Yoshihito Tanaka

In this paper, we discuss models of the common knowledge logic. The common knowledge logic is a multi-modal logic that includes the modal operators $\mathsf{K}_{i}$ ($i\in\mathcal{I}$, where $\mathcal{I}$ is a finite set of agents) and $\mathsf{C}$ in the language. The intended meanings of $\mathsf{K}_{i}\phi$ ($i\in\mathcal{I}$) and $\mathsf{C}\phi$ are ''t

Kejie Chen, Lin Wang, Qinghai Zhang, Renjun Xu

Recent studies have highlighted the limitations of large language models in mathematical reasoning, particularly their inability to capture the underlying logic. Inspired by meta-learning, we propose that models should acquire not only task-specific knowledge but also transferable problem-solving skills. We introduce MetaRuleGPT, a novel Transformer-based ar

Zheng Liu, Feifan Shi, Jing Yao, Yang Yang

In this paper, we investigate the cumulative distribution functions (CDFs) of the maximum and minimum of multivariate Poisson distributions with three dependence structures, namely, the common shock, comonotonic shock and thinning-dependence models. In particular, we formulate the definition of a thinning-dependent multivariate Poisson distribution based on

Xin Du, Kumiko Tanaka-Ishii

We present {\em generative clustering} (GC) for clustering a set of documents, $\mathrm{X}$, by using texts $\mathrm{Y}$ generated by large language models (LLMs) instead of by clustering the original documents $\mathrm{X}$. Because LLMs provide probability distributions, the similarity between two documents can be rigorously defined in an information-theore

Zonghui Yang, Shijian Gao, Xiang Cheng

Integrated sensing and communication (ISAC) is anticipated to be widely used in future sub-terahertz (sub-THz) systems. With the line-of-sight (LoS) propagation characteristics of sub-THz channels, ISAC transmitter design largely parallels analog precoder optimization. However, balancing both sensing and communication functionalities is challenging due to th

Chenxuan Xiang, Jumin Qiu, Qiegen Liu, Shuyuan Xiao

The multiplexing capability of metasurfaces has been successfully demonstrated in applications such as holography and diffractive neural networks. However, identifying a suitable structure that simultaneously satisfies the phase requirements across multiple channels remains a significant challenge in many multiplexing design scenarios. In this study, we prop

Cheng Chen, Zhixin Zhang, Fan Lei, Haifeng Weng

Superlubricity, a state where friction between two contact surfaces is nearly zero, has a great potential to revolutionize various mechanical systems by significantly reducing energy dissipation and enhancing efficiency. It can be realized either by structural incommensurate contact between crystalline surfaces or by creating highly passive interfaces to can

Jiaxing Qi, Chang Zeng, Zhongzhi Luan, Shaohan Huang

Log-based anomaly detection (LogAD) is the main component of Artificial Intelligence for IT Operations (AIOps), which can detect anomalous that occur during the system on-the-fly. Existing methods commonly extract log sequence features using classical machine learning techniques to identify whether a new sequence is an anomaly or not. However, these classica

Xue Wen Tan, Kenneth See, Stanley Kok

The increase in global cellphone usage has led to a spike in instant messaging scams, causing extensive socio-economic damage with yearly losses exceeding half a trillion US dollars. These scams pose a challenge to the integrity of justice systems worldwide due to their international nature, which complicates legal action. Scams often exploit emotional vulne

Mingwei Fu, Bin Shi

Nesterov's accelerated gradient method (NAG) achieves faster convergence than gradient descent for convex optimization but lacks monotonicity in function values. To address this, Beck and Teboulle [2009b] proposed a monotonic variant, M-NAG, and extended it to the proximal setting as M-FISTA for composite problems such as Lasso. However, establishing the lin

Fanshuang Kong, Richong Zhang, Zhijie Nie, Hang Zhou

Model merging offers a scalable alternative to multi-task learning but often yields suboptimal performance on classification tasks. We attribute this degradation to a geometric misalignment between the merged encoder and static task-specific classifier heads. Existing methods typically rely on auxiliary parameters to enforce strict representation alignment.

Jialiang Tang, Shuo Chen, Chen Gong

Data-free knowledge distillation aims to learn a compact student network from a pre-trained large teacher network without using the original training data of the teacher network. Existing collection-based and generation-based methods train student networks by collecting massive real examples and generating synthetic examples, respectively. However, they inev

I Gusti Ngurah Yudi Handayana, Yi-Lin Tsao, H. H. Jen

Quantum correlations are essential to the emergent behaviors of quantum systems, supporting key phenomena such as localization or delocalization of particles, quantum avalanches in many-body localized systems, and quantum information transfer. In open atom-nanophotonic systems characterized by long-range spin-exchange interactions, we examine the influence o

Yike Wang, Yusha Chen, Jingzhen Liu, Zhenyu Cui

The monotone mean-variance (MMV) preference proposed by Maccheroni, et al. (Math. Finance 19(3): 487-521, 2009) fails to differentiate strictly dominant payoffs, which may cause inconsistency in portfolio decision-making. This paper introduces a broader class of strictly monotone mean-variance (SMMV) preferences and demonstrates its applications to portfolio

Bui Duc Manh, Chi-Hieu Nguyen, Dinh Thai Hoang, Diep N. Nguyen

This work proposes a novel privacy-preserving cyberattack detection framework for blockchain-based Internet-of-Things (IoT) systems. In our approach, artificial intelligence (AI)-driven detection modules are strategically deployed at blockchain nodes to identify real-time attacks, ensuring high accuracy and minimal delay. To achieve this efficiency, the mode

Yike Wang, Jingzhen Liu, Alain Bensoussan, Ka-Fai Cedric Yiu

In this paper, we focus on a class of time-inconsistent stochastic control problems, where the objective function includes the mean and several higher-order central moments of the terminal value of state. To tackle the time-inconsistency, we seek both the closed-loop and the open-loop Nash equilibrium controls as time-consistent solutions. We establish a par

Yi Huang, Fangyin Cheng, Fan Zhou, Jiahui Li

In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities in data analytics when integrated with Multi-Agent Systems (MAS). However, these systems often struggle with complex tasks that involve diverse functional requirements and intricate data processing challenges, necessitating customized solutions that lack broad applicabili

Shivasankaran Vanaja Pandi, Bharath Ramsundar

Protein language models (PLMs) have shown promise in improving the understanding of protein sequences, contributing to advances in areas such as function prediction and protein engineering. However, training these models from scratch requires significant computational resources, limiting their accessibility. To address this, we integrate a PLM into DeepChem,

Pei-Jie Chang, Qi-Bo Zeng, Jinghui Pi, Dong Ruan

Reentrant localization has recently been observed in systems with quasi-periodic nearest-neighbor hopping, where the interplay between dimerized hopping and staggered disorder is identified as the driving mechanism. However, the robustness of reentrant localization in the presence of long-range hopping remains an open question. In this work, we investigate t

Jakkapat Seeyangnok, Udomsilp Pinsook, Graeme John Ackland

Since the discovery of MgB2 with Tc=39K, various metal diborides of MB2 have been intensively studied to find possible conventional high-temperature superconductors. A possible 2D structure of the metal diboride has been shown to be in the form of M2B2. Using density functional theory, we investigated phase stability and possible conventional superconductors

Jiahui Li, Tai-Wei Chang, Kun Kuang, Ximing Li

Noisy labels are both inevitable and problematic in machine learning methods, as they negatively impact models' generalization ability by causing overfitting. In the context of learning with noise, the transition matrix plays a crucial role in the design of statistically consistent algorithms. However, the transition matrix is often considered unidentifiable

Seonwoo Kim, Claudio Landim

Consider a sequence of continuous-time Markov chains $(X^{(n)}_t:t\ge 0)$ evolving on a fixed finite state space $V$. Let $I_n$ be the measure-current large deviations rate functional for $X^{(n)}_t$, as $t\to\infty$. Under a hypothesis on the jump rates, we prove that $I_n$ can be written as $I_n = \mathbf I^{(0)} \,+\, \sum_{1\le p\le \mathfrak q} (1/\thet

Mayank Sanganeria, Rohan Gala

Recent AI-driven step-function advances in several longstanding problems in music technology are opening up new avenues to create the next generation of music education tools. Creating personalized, engaging, and effective learning experiences are continuously evolving challenges in music education. Here we present two case studies using such advances in mus

WenKui Zhao, ShengYi Wang, HanZhuo Kuang, Hao Luo

Maximum structural chirality refers to the highest selectivity for circularly polarized light (CPL) in nanostructures, often manifested as maximum circular dichroism (CD), optical rotation (OR), and spin-orbit coupling (SOC). However, the underlying physical mechanisms that lead to maximum chirality remain unclear. In this work, we demonstrate that maximum c

Mauricio Diaz, Ivan Gonzalez, Igor Kondrashuk, Eduardo A. Notte-Cuello

Mellin-Barnes integral representation of one-loop off-shell box massless diagram is five-fold by construction. On the other hand, it is known from the year 1992 that it may be reduced to certain two-fold Mellin-Barnes integral. We propose a way to reduce the number of the Mellin-Barnes integration contours from five to two by using the Mellin-Barnes integral

Rui Bai, Di Lu, Shihao Ran, Elizabeth Olson

Natural Language Processing (NLP) of news articles can play an important role in understanding the dynamics and causes of violent conflict. Despite the availability of datasets categorizing various conflict events, the existing labels often do not cover all of the fine-grained violent conflict event types relevant to areas like the Horn of Africa. In this pa

Rui Cai, Zhiyu Dong, Jianfeng Dong, Xun Wang

Existing cross-modal retrieval methods typically rely on large-scale vision-language pair data. This makes it challenging to efficiently develop a cross-modal retrieval model for under-resourced languages of interest. Therefore, Cross-lingual Cross-modal Retrieval (CCR), which aims to align vision and the low-resource language (the target language) without u

Yunqi Guo, Kaiyuan Hou, Heming Fu, Hongkai Chen

Understanding sensor data can be difficult for non-experts because of the complexity and different semantic meanings of sensor modalities. This leads to a need for intuitive and effective methods to present sensor information. However, creating intuitive sensor data visualizations presents three key challenges: the variability of sensor readings, gaps in dom

Jingwei Bao, Jinhua Hao, Pengcheng Xu, Ming Sun

High-resolution (HR) images are commonly downscaled to low-resolution (LR) to reduce bandwidth, followed by upscaling to restore their original details. Recent advancements in image rescaling algorithms have employed invertible neural networks (INNs) to create a unified framework for downscaling and upscaling, ensuring a one-to-one mapping between LR and HR

David Noever, Forrest McKee

This study demonstrates a novel approach to facial camouflage that combines targeted cosmetic perturbations and alpha transparency layer manipulation to evade modern facial recognition systems. Unlike previous methods -- such as CV dazzle, adversarial patches, and theatrical disguises -- this work achieves effective obfuscation through subtle modifications t

Chang-Yeon Chough

We develop some foundations for the theory of formal derived algebraic geometry, which parallel the theory of formal spectral algebraic geometry by Jacob Lurie. For this, we establish a close connection between algebro-geometric objects in the derived and spectral settings. We apply this construction to prove a version of the formal GAGA theorem in the deriv

Matthew B. Weiss

Can the state-space of $d$-dimensional quantum theory be derived from studying the behavior of a single "reference" measuring device? The answer is yes, if the measuring device corresponds to a complex-projective 3-design. In this privileged case, not only does each quantum state correspond to a probability-distribution over the outcomes of a single measurem

Siyang Dai, Jun Liu, Ngai-Man Cheung

Urbanization as a global trend has led to many environmental challenges, including the urban heat island (UHI) effect. The increase in temperature has a significant impact on the well-being of urban residents. Air temperature ($T_a$) at 2m above the surface is a key indicator of the UHI effect. How land use land cover (LULC) affects $T_a$ is a critical resea

Khai Phan Tran, Wen Hua, Xue Li

Document-level Relation Extraction (DocRE) aims to identify relationships between entity pairs within a document. However, most existing methods assume a uniform label distribution, resulting in suboptimal performance on real-world, imbalanced datasets. To tackle this challenge, we propose a novel data augmentation approach using generative models to enhance

Huan Lei, Hongdong Li, Andreas Geiger, Anthony Dick

3D shape analysis has been largely focused on traditional 3D representations of point clouds and meshes, but the discrete nature of these data makes the analysis susceptible to variations in input resolutions. Recent development of neural fields brings in level-set parameters from signed distance functions as a novel, continuous, and numerical representation

Dang Nguyen, Jian Chen, Yu Wang, Gang Wu

Graphical User Interface (GUI) agents, powered by Large Foundation Models, have emerged as a transformative approach to automating human-computer interaction. These agents autonomously interact with digital systems or software applications via GUIs, emulating human actions such as clicking, typing, and navigating visual elements across diverse platforms. Mot

Yinan Zhao, Xavier Dumusque, Michael Cretignier, Khaled Al Moulla

One of the main obstacles in exoplanet detection when using the radial velocity (RV) technique is the presence of stellar activity signal induced by magnetic regions. In this context, a realistic simulated dataset that can provide photometry and spectroscopic outputs is needed for method development. The goal of this paper is to describe two realistic simula

David Fernández-Bretón, Jareb Navarro-Castillo, Jesús A. Soria-Rojas

We study ultrafilters from the perspective of the algebra in the \v{C}ech-Stone compactification of the natural numbers, and idempotent elements therein. The first two results that we prove establish that, if $p$ is a Q-point (resp. a selective ultrafilter) and $\mathscr F^p$ (resp. $\mathscr G^p$) is the smallest family containing $p$ and closed under itera

Feifei Long, Yian Zhao, Yunjiao Zhang, Chenguang Wan

The tearing mode, a large-scale MHD instability in tokamak, typically disrupts the equilibrium magnetic surfaces, leads to the formation of magnetic islands, and reduces core electron temperature and density, thus resulting in significant energy losses and may even cause discharge termination. This process is unacceptable for ITER. Therefore, the accurate id

Manzi Nan, Pengcheng Li, Wei Zuo, Qingfeng Li

The production, dynamic evolution, and decay of $\Delta$ particles play a crucial role in understanding the properties of high baryon density nuclear matter in intermediate-energy heavy-ion collisions. In this work, the energy-, density-, and isospin-dependent nucleon-$\Delta$ elastic cross section ($\sigma^{*}_{N \Delta}$) is studied within the relativistic

Pengbo Guo, Chengxu Liu, Xingsong Hou, Xueming Qian

Fisheye image rectification aims to correct distortions in images taken with fisheye cameras. Although current models show promising results on images with a similar degree of distortion as the training data, they will produce sub-optimal results when the degree of distortion changes and without retraining. The lack of generalization ability for dealing with

Dong Qiao, Xinxian Ma, Jicong Fan

High-dimensional data visualization is crucial in the big data era and these techniques such as t-SNE and UMAP have been widely used in science and engineering. Big data, however, is often distributed across multiple data centers and subject to security and privacy concerns, which leads to difficulties for the standard algorithms of t-SNE and UMAP. To tackle

E. Rofors, N. Abgrall, M. S. Bandstra, R. J. Cooper

Sparse static detector networks in urban environments can be used in efforts to detect illicit radioactive sources, such as stolen nuclear material or radioactive "dirty bombs". We use detailed simulations to evaluate multiple configurations of detector networks and their ability to detect sources moving through a $6\times6$ km$^2$ area of downtown Chicago.

Vijay Kumar, Purnesh Singh Badavath

Optical angular momentum (OAM) in light beams is manifested as the two-dimensional spatial distribution of its complex amplitude, necessitating a 2D detector for its measurement. Here we present a novel speckle-based machine learning approach for OAM recognition, which enables recognition using a 1-D array detector or even a 0-D single-pixel detector.

Changxin Huang, Yanbin Chang, Junfan Lin, Junyang Liang

The ability to autonomously explore and resolve tasks with minimal human guidance is crucial for the self-development of embodied intelligence. Although reinforcement learning methods can largely ease human effort, it's challenging to design reward functions for real-world tasks, especially for high-dimensional robotic control, due to complex relationships a

Manuel Ayala, Dennice Gayme, Charles Meneveau

A new model to evaluate the equivalent hydrodynamic length or surface roughness, z0, of ocean waves is developed and tested. The proposed Surface Wave-Aerodynamic Roughness Length (SWARL) model requires maps of the wave surface height at consecutive times and the air flow characteristic Reynolds number as inputs. Pressure drag is accounted for by approximati

Ahmet Oğuz Saltık, Alicia Allmendinger, Anthony Stein

This paper presents a comprehensive evaluation of state-of-the-art object detection models, including YOLOv9, YOLOv10, and RT-DETR, for the task of weed detection in smart-spraying applications focusing on three classes: Sugarbeet, Monocot, and Dicot. The performance of these models is compared based on mean Average Precision (mAP) scores and inference times

Yunuo Cen, Zhiwei Zhang, Zixuan Wang, Yimin Wang

It is challenging to scale Ising machines for industrial-level problems due to algorithm or hardware limitations. Although higher-order Ising models provide a more compact encoding, they are, however, hard to physically implement. This work proposes a theoretical framework of a higher-order Ising simulator, IsingSim. The Ising spins and gradients in IsingSim

Xinxin Liu, Aaron Thomas, Cheng Zhang, Jianyi Cheng

Parameter-Efficient Fine-Tuning (PEFT) has gained prominence through low-rank adaptation methods like LoRA. In this paper, we focus on sparsity-based PEFT (SPEFT), which introduces trainable sparse adaptations to the weight matrices in the model, offering greater flexibility in selecting fine-tuned parameters compared to low-rank methods. We conduct the firs

Baolong Bi, Shaohan Huang, Yiwei Wang, Tianchi Yang

Reliable responses from large language models (LLMs) require adherence to user instructions and retrieved information. While alignment techniques help LLMs align with human intentions and values, improving context-faithfulness through alignment remains underexplored. To address this, we propose $\textbf{Context-DPO}$, the first alignment method specifically

Mahir Akgun, Sacip Toker

This study explores the role of pretesting when integrated with conversational AI tools, specifically ChatGPT, in enhancing learning outcomes. Drawing on existing research, which demonstrates the benefits of pretesting in memory activation and retention, this experiment extends these insights into the context of digital learning environments. A randomized tr

Zhenhong Sun, Yifu Wang, Yonhon Ng, Yongzhi Xu

2D concept art generation for 3D scenes is a crucial yet challenging task in computer graphics, as creating natural intuitive environments still demands extensive manual effort in concept design. While generative AI has simplified 2D concept design via text-to-image synthesis, it struggles with complex multi-instance scenes and offers limited support for str

Yue Xu, Xiao-Dong Zhang

A balanced 2-partition of a graph is a bipartition $A,A^c$ of $V(G)$ such that $|A|=|A^c|$. Balogh, Clemen, and Lidick\'y conjectured that for every $K_4$-free graph on $n$ (even) vertices, there exists a balanced 2-partition $A,A^c$ such that $\max\{e(A),e(A^c)\}\leq n^2/16$ edges. In this paper, we present a family of counterexamples to the conjecture and

Kancharla Aditya Hari, Manish Gupta, Vasudeva Varma

Curriculum learning has been used to improve the quality of text generation systems by ordering the training samples according to a particular schedule in various tasks. In the context of data-to-text generation (DTG), previous studies used various difficulty criteria to order the training samples for monolingual DTG. These criteria, however, do not generali

Jenny Gonzalez-Jara, Patricia B. Tissera, Antonela Monachesi, Emanuel Sillero

Stellar halos around galaxies contain key information about their formation and assembly history. Using simulations, we can trace the origins of different stellar populations in these halos, contributing to our understanding of galaxy evolution. We aim to investigate the assembly of stellar halos and their chemical abundances in 28 galaxies from CIELO projec

Sergei Kladov, Sergei Nagaitsev, Alex H. Lumpkin, Jinhao Ruan

This article investigates electron bunch density fluctuations in the 1 - 10 $\mu m$ wavelength range, focusing on their impact on coherent electron cooling (CEC) in hadron storage rings. In this study, we thoroughly compare the shot-noise model with experimental observations of optical transition radiation (OTR) generated by a relativistic electron bunch ($\

Yu-Peng Zhang, Si-Jiang Yang, Shao-Wen Wei, Wen-Di Guo

In this paper, we present a nonlinear numerical investigation on the dynamical scalarization process of a Reissner-Nordstr\"om black hole, incorporating an axionic scalar potential within the framework of the Einstein-Maxwell-dilaton theory. By scrutinizing the evolution of the irreducible mass of the black hole and the value of scalar field on the apparent

Tananun Songdechakraiwut, Yutong Wu

The human brain is a complex system, and understanding its mechanisms has been a long-standing challenge in neuroscience. The study of the functional connectome, which maps the functional connections between different brain regions, has provided valuable insights through various advanced analysis techniques developed over the years. Similarly, neural network

Yvonne Alama Bronsard, Xi Chen, Matthieu Dolbeault

We construct fully-discrete schemes for the Benjamin-Ono, Calogero-Sutherland DNLS, and cubic Szeg\H{o} equations on the torus, which are $\textit{exact in time}$ with $\textit{spectral accuracy}$ in space. We prove spectral convergence for the first two equations, of order $K^{-s+1}$ in $L^2$ norm for initial data in $H^s(\mathbb T)$, $s>1$, with an error c

Hanzhong Guo, Hongwei Yi, Daquan Zhou, Alexander William Bergman

Latent diffusion models have made great strides in generating expressive portrait videos with accurate lip-sync and natural motion from a single reference image and audio input. However, these models are far from real-time, often requiring many sampling steps that take minutes to generate even one second of video-significantly limiting practical use. We intr

Sepideh Maleki, Jan-Christian Huetter, Kangway V. Chuang, David Richmond

Predicting transcriptional responses to novel drugs provides a unique opportunity to accelerate biomedical research and advance drug discovery efforts. However, the inherent complexity and high dimensionality of cellular responses, combined with the extremely limited available experimental data, makes the task challenging. In this study, we leverage single-c

Qingyu Zheng, Guijun Han, Wei Li, Lige Cao

Advances in data assimilation (DA) methods have greatly improved the accuracy of Earth system predictions. To fuse multi-source data and reconstruct the nonlinear evolution missing from observations, geoscientists are developing future-oriented DA methods. In this paper, we redesign a purely data-driven latent space DA framework (DeepDA) that employs a gener

Song-Tao Yu, Ming-Gen He, Sheng Fang, Youjin Deng

Optical simulators for the Ising model have demonstrated great promise for solving challenging problems in physics and beyond. Here, we develop a spatial optical simulator for a variety of classical statistical systems, including the clock, $XY$, Potts, and Heisenberg models, utilizing a digital micromirror device composed of a large number of tiny mirrors.

Bowen Chen, Namgi Han, Yusuke Miyao

The lack of data transparency in Large Language Models (LLMs) has highlighted the importance of Membership Inference Attack (MIA), which differentiates trained (member) and untrained (non-member) data. Though it shows success in previous studies, recent research reported a near-random performance in different settings, highlighting a significant performance

Kevin Haninger, Luka Peternel

For successful goal-directed human-robot interaction, the robot should adapt to the intentions and actions of the collaborating human. This can be supported by musculoskeletal or data-driven human models, where the former are limited to lower-level functioning such as ergonomics, and the latter have limited generalizability or data efficiency. What is missin

Xianqi Jiao, Jia Liu, Zhiping Chen

Gradient Descent (GD) and Conjugate Gradient (CG) methods are among the most effective iterative algorithms for solving unconstrained optimization problems, particularly in machine learning and statistical modeling, where they are employed to minimize cost functions. In these algorithms, tunable parameters, such as step sizes or conjugate parameters, play a