December 2024 arXiv papers — page 37

Mathan Kumar Mounagurusamy, Thiyagarajan V S, Abdur Rahman, Shravan Chandak

Early management and better clinical outcomes for epileptic patients depend on seizure prediction. The accuracy and false alarm rates of existing systems are often compromised by their dependence on static thresholds and basic Electroencephalogram (EEG) properties. A novel Recurrent Neural Network (RNN)-based method for seizure start prediction is proposed i

A. S. Viz, M. M. Botana, J. C. Verde, M. V. Ramallo

By using a Ginzburg-Landau functional in the Gaussian approximation, we calculate the energy of superconducting fluctuations above the transition, at zero external magnetic field, of a system composed by a small number $N$ of parallel two-dimensional superconducting planes, each of them Josephson coupled to its first neighbour, with special focus in the $N=2

Teresa Toscano, Sol N. Molina, José L. Gómez, Ai-Ling Zeng

We present a study on rotation measure (RM) of the quasar 3C 273. This analysis aims to discern the magnetic field structure and its temporal evolution. The quasar 3C 273 is one of the most studied active galactic nuclei due to its high brightness, strong polarization, and proximity, which enables resolving the transverse structure of its jet in detail. We u

Usman Ali, Waqas Ali, Umer Ramzan

Early detection of faults in induction motors is crucial for ensuring uninterrupted operations in industrial settings. Among the various fault types encountered in induction motors, bearing, rotor, and stator faults are the most prevalent. This paper introduces a Weighted Probability Ensemble Deep Learning (WPEDL) methodology, tailored for effectively diagno

Majid Ali, Hina Shakir, Asia Samreen, Sohaib Ahmed

Accurate diagnosis of Parkinson disease, especially in its early stages, can be a challenging task. The application of machine learning techniques helps improve the diagnostic accuracy of Parkinson disease detection but only few studies have presented work towards the prediction of disease progression. In this research work, Long Short Term Memory LSTM was t

Dou El Kefel Mansouri, Seif-Eddine Benkabou, Khalid Benabdeslem

Fr\'echet regression extends linear regression to model complex responses in metric spaces, making it particularly relevant for multi-label regression, where eachinstance can have multiple associated labels. However, addressing noise and dependencies among predictors within this framework remains un derexplored. In this paper, we present an extension of the

Petr Akhmet'ev

To solve MHD problems within the framework of the theory of two-scale mean fields, it is important to study the invariants of magnetic lines. Such invariants are constructed on the basis of invariants of classical links, which must satisfy the asymptotic property. We choose the simplest asymptotic invariant $M_3$ of three-component links, which is not expres

Cao Thi Vi Ba, Nguyen Quang Bau, Anh-Tuan Tran, Tang Thi Dien

General analytic expressions for the total absorption coefficient of strong electromagnetic waves caused by confined electrons in Infinite semi-parabolic plus Semi-inverse Squared Quantum Wells (ISPSISQW) are obtained by using the quantum kinetic equation for electrons in the case of electron-optical phonon scattering. A second-order multi-photon process is

Kai Yang, Qidong Fu, Henrique C. Prates, Peng Wang

Topological transport is determined by global properties of physical media where it occurs and is characterized by quantized amounts of adiabatically transported quantities. Discovered for periodic potentials it was also explored in disordered and discrete quasi-periodic systems. Here we report on experimental observation of pumping of a light beam in a genu

Bingsen Wang, Xiaohui Zhang, Shuai Wang, Li Chen

Low Earth Orbit (LEO) satellite networks have attracted considerable attention for their ability to deliver global, low-latency broadband Internet services. In this paper, we present a large-scale measurement study of the Starlink network, the largest LEO satellite constellation to date. We first propose an efficient method for discovering active Starlink us

Mohammad Noori, Joseph D. Berry, Dalton J. E. Harvie

The present study simulates shear-induced migration (SIM) in semi-dilute pressure-driven Stokes suspension flows using a multi-fluid (MF) model. Building on analysis from a companion paper (Harvie, 2024), the specific formulation uses volume-averaged phase stresses that are linked to the binary hydrodynamic interaction of spheres and suspension microstructur

Rong Shan, Jianghao Lin, Chenxu Zhu, Bo Chen

Graph neural networks (GNNs) have emerged as state-of-the-art methods to learn from graph-structured data for recommendation. However, most existing GNN-based recommendation methods focus on the optimization of model structures and learning strategies based on pre-defined graphs, neglecting the importance of the graph construction stage. Earlier works for gr

J. C. Verde, A. S. Viz, M. M. Botana, C. Montero-Orille

We calculate the effects of doping nanostructuration and the patterning of thin films of high-temperature superconductors (HTS) with the aim of optimizing their functionality as sensing materials for resistive transition-edge bolometer devices (TES). We focus, in particular, on spatial variations of the carrier doping into the CuO$_2$ layers due to oxygen of

Pengcheng Zhao, Jiang Bian, Zekun Ni, Weixin Jin

In recent years, Artificial Intelligence Weather Prediction (AIWP) models have achieved performance comparable to, or even surpassing, traditional Numerical Weather Prediction (NWP) models by leveraging reanalysis data. However, a less-explored approach involves training AIWP models directly on observational data, enhancing computational efficiency and impro

Junrui Liang, Min Jiang, Zhongming Huang, Junhong He

The speckle field yielded by disordered media is extensively employed for spectral measurements. Existing speckle reconstructive spectrometers (RSs) implemented by neural networks primarily rely on supervised learning, which necessitates large-scale spectra-speckle pairs. However, beyond system stability requirements for prolonged data collection, generating

Kentaro Kaba, Reo Shimizu, Masayuki Ohzeki, Yuki Sughiyama

Generative diffusion models use time-forward and backward stochastic differential equations to connect the data and prior distributions. While conventional diffusion models (e.g., score-based models) only learn the backward process, more flexible frameworks have been proposed to also learn the forward process by employing the Schr\"odinger bridge (SB). Howev

Artem Khodzitskii

Starting with the work S.H. Zheng, L. Guo and M. Rosenkranz (2015), Rota-Baxter operators are studied on the polynomial algebra. Injective Rota-Baxter operators of weight zero on $F[x]$ were described in 2021. We classify the following classes of monomial Rota-Baxter operators of weight zero on the polynomial algebra $F[x,y]$ and its augmentation ideal $F_0[

Haiyan Lan, Shujun Li, Mingjie Xie, Xuanjia Zhao

Local climate zone (LCZ) classification is of great value for understanding the complex interactions between urban development and local climate. Recent studies have increasingly focused on the fusion of synthetic aperture radar (SAR) and multi-spectral data to improve LCZ classification performance. However, it remains challenging due to the distinct physic

Afek Steinberg, Ran Eisenberg, Ofir Lindenbaum

Temporal alignment of sequences is a fundamental challenge in many applications, such as computer vision and bioinformatics, where local time shifting needs to be accounted for. Misalignment can lead to poor model generalization, especially in high-dimensional sequences. Existing methods often struggle with optimization when dealing with high-dimensional spa

Andrey Zhukov, Andrey Lebedev, Walter Pogosov

We propose a methodology for implementing Grover's algorithm in the digital quantum simulation of disordered Ising models. The core concept revolves around using the evolution operator for the Ising model as the quantum oracle within Grover's search. This operator induces phase shifts for the eigenstates of the Ising Hamiltonian, with the most pronounced shi

Minju Seo, Jinheon Baek, Seongyun Lee, Sung Ju Hwang

Long Context Language Models (LCLMs) have emerged as a new paradigm to perform Information Retrieval (IR), which enables the direct ingestion and retrieval of information by processing an entire corpus in their single context, showcasing the potential to surpass traditional sparse and dense retrieval methods. However, processing a large number of passages wi

Yan Zhang, Guoqiang Wu, Bingzheng Wang, Teng Pang

In Continual Learning (CL), while existing work primarily focuses on the multi-class classification task, there has been limited research on Multi-Label Learning (MLL). In practice, MLL datasets are often class-imbalanced, making it inherently challenging, a problem that is even more acute in CL. Due to its sensitivity to imbalance, Macro-AUC is an appropria

Jiaqi Wu, Shihao Zhang, Simin Chen, Lixu Wang

Edge computing has emerged as a key paradigm for deploying deep learning-based object detection in time-sensitive scenarios. However, existing edge detection methods face challenges: 1) difficulty balancing detection precision with lightweight models, 2) limited adaptability of generalized deployment designs, and 3) insufficient real-world validation. To add

Murat Babaarslan, Ahmet Sungur

Special curves and surfaces have an important place in mathematics, engineering and other fields of science. Loxodromes are special curves which cut all meridians on the Earth's surface at a constant angle and they are very popular in engineering. Ships sailing and airplanes flying along a fixed magnetic compass course move along this curve. The Mercator pro

Usman Ali

A method for determining the current signature faults using Fractional Fourier Transform (FrFT) has been developed. The method has been applied to the real-time steady-state current of the inverter-fed high power induction motor for fault determination. The method incorporates calculating the relative norm error to find the threshold value between healthy an

Logan J. Prust, Gabriel Kumar, Lars Bildsten

Type Ia supernovae are triggered by accretion onto a white dwarf from a companion which is most likely Roche lobe-filling at the time of the explosion. The collision between the ejecta and a surviving companion carves out a conical wake, which could manifest as an asymmetry when the ejecta reaches the remnant phase. We simulate the companion interaction usin

Jango Zhang

With the rapid growth of blockchain technology, smart contracts are now crucial to Decentralized Finance (DeFi) applications. Effective vulnerability detection is vital for securing these contracts against hackers and enhancing the accuracy and efficiency of security audits. In this paper, we present SimilarGPT, a unique vulnerability identification tool for

Koichi Ito, Yihan Zhu, Mahmoud Abdelrahman, Xiucheng Liang

Street view imagery (SVI) has been instrumental in many studies in the past decade to understand and characterize street features and the built environment. Researchers across a variety of domains, such as transportation, health, architecture, human perception, and infrastructure have employed different methods to analyze SVI. However, these applications and

Peijin Xie, Lin Sun, Bingquan Liu, Dexin Wang

Distinguishing spatial relations is a basic part of human cognition which requires fine-grained perception on cross-instance. Although benchmarks like MME, MMBench and SEED comprehensively have evaluated various capabilities which already include visual spatial reasoning(VSR). There is still a lack of sufficient quantity and quality evaluation and optimizati

Yang Jinlong, Yang JingXu

Background: African swine fever is among the most devastating viral diseases of pigs. Despite nearly a century of research, there is still no safe and effective vaccine available. The current situation is that either vaccines are safe but not effective, or they are effective but not safe.Findings: The ASF vaccine prepared using the inactivation method with p

Yuhan Wang, Zhen Xu, Yue Yao, Jinsong Liu

With the development of the financial industry, credit default prediction, as an important task in financial risk management, has received increasing attention. Traditional credit default prediction methods mostly rely on machine learning models, such as decision trees and random forests, but these methods have certain limitations in processing complex data

Xianfeng Song, Yi Zou, Zheng Shi, Zheng Liu

Feature-based image matching has extensive applications in computer vision. Keypoints detected in images can be naturally represented as graph structures, and Graph Neural Networks (GNNs) have been shown to outperform traditional deep learning techniques. Consequently, the paradigm of image matching via GNNs has gained significant prominence in recent academ

Hongyu Chen, Zian Wang, Peixin Qin, Ziang Meng

The recently discovered altermagnets, featured by the exotic correlation of magnetic exchange interaction and alternating crystal environments, have offered exciting cutting-edge opportunities for spintronics. Nevertheless, the altermagnetism of RuO2, one of the earliest-discovered altermagnets, is currently under intense debate. Here we try to resolve this

S. E. Kuratov, I. S. Galtsov, S. A. Dyachkov, S. Yu. Igashov

The existence of macroscopic shell structure of submicron metal clusters is known for several decades. Since the most studies provide theoretical analysis for clusters of spherical shape, the electron density inhomogeneities caused by shell effects are spherically symmetric and do not provide long range electrostatic fields. However, similar shell structure

Takuma Fukuda, Hiroshi Kera, Kazuhiko Kawamoto

We propose Adapter Merging with Centroid Prototype Mapping (ACMap), an exemplar-free framework for class-incremental learning (CIL) that addresses both catastrophic forgetting and scalability. While existing methods involve a trade-off between inference time and accuracy, ACMap consolidates task-specific adapters into a single adapter, thus achieving constan

Hamid Bostani, Jacopo Cortellazzi, Daniel Arp, Fabio Pierazzi

Adversarial Training (AT) is a key defense against Machine Learning evasion attacks, but its effectiveness for real-world malware detection remains poorly understood. This uncertainty stems from a critical disconnect in prior research: studies often overlook the inherent nature of malware and are fragmented, examining diverse variables like realism or confid

Shaoxiang Dang, Tetsuya Matsumoto, Yoshinori Takeuchi, Hiroaki Kudo

The topic of speech separation involves separating mixed speech with multiple overlapping speakers into several streams, with each stream containing speech from only one speaker. Many highly effective models have emerged and proliferated rapidly over time. However, the size and computational load of these models have also increased accordingly. This is a dis

Mengyang Wu, Yuzhi Zhao, Jialun Cao, Mingjie Xu

Controversial contents largely inundate the Internet, infringing various cultural norms and child protection standards. Traditional Image Content Moderation (ICM) models fall short in producing precise moderation decisions for diverse standards, while recent multimodal large language models (MLLMs), when adopted to general rule-based ICM, often produce class

Nikolai Lipscomb, Xin Liu, Vidyadhar G. Kulkarni

We consider the optimal appointment scheduling problem that incorporates patients' unpunctual behavior, where the unpunctuality is assumed to be time dependent, but additive. Our goal is to develop an optimal scheduling method for a large patient system to maximize expected net revenue. Methods for deriving optimal appointment schedules for large-scale syste

Zhen Zhang, Tao Peng, Liang Liao, Jing Xiao

Vehicle detection and tracking in satellite video is essential in remote sensing (RS) applications. However, upon the statistical analysis of existing datasets, we find that the dim vehicles with low radiation intensity and limited contrast against the background are rarely annotated, which leads to the poor effect of existing approaches in detecting moving

Daniel R. Terno

We treat the semiclassical Einstein equation as a quantum-classical hybrid and demonstrate the formal equivalence of its two derivation methods. This approach identifies the left-hand side of the equation as the expectation value of the Einstein tensor given the state of matter, and not its actual value in each realization of the set-up. As a result, standar

Changfu Xu, Jianxiong Guo, Wanyu Lin, Haodong Zou

Artificial Intelligence Generated Content (AIGC) has gained significant popularity for creating diverse content. Current AIGC models primarily focus on content quality within a centralized framework, resulting in a high service delay and negative user experiences. However, not only does the workload of an AIGC task depend on the AIGC model's complexity rathe

Yuya Onishi, Ryosuke Ota

Coincidence time resolution (CTR) in time-of-flight positron emission tomography (TOF-PET) has significantly improved with advancements in scintillators, photodetectors, and readout electronics. Achieving a CTR of 100 ps remains challenging due to the need for sufficiently thick scintillators-typically 20 mm-to ensure adequate sensitivity because the photon

Shangbin Yang, Joerg Buechner, Jean Carlo Santos, Jan Skala

An instability criterion in the MHD with the open boundary of magnetic field is proposed in this paper. We use a series of linear force-free extrapolation field, in which the normal part of magnetic field is fixed, to obtain the linear fitting coefficient called relative alpha by using the co-joined value of magnetic free energy and magnetic flux at the open

Elena Litvinova

These notes summarise the lectures given at the International School of Physics "Enrico Fermi" in Summer 2024 in Varenna (Italy) about the strongly coupled quantum many-body theory and its applications to nuclear structure. The lectures present a rather short overview of the subject with an emphasis on the analytical aspects of the nuclear many-body problem,

H. A. Kebede, M. M. Assen, M. A. Sharew

Over the past few decades, crime and delinquency rates have increased drastically in many countries; nevertheless, it is important to note that crime trends can differ significantly by geographic region. This study's primary goal was to use geographic technology to map and analyze Dessie City's crime patterns. To investigate the geographic clustering of crim

Thet Htar Su, Shaswot Shresthamali, Masaaki Kondo

This paper introduces a quantum framework for addressing reinforcement learning (RL) tasks, grounded in the quantum principles and leveraging a fully quantum model of the classical Markov decision process (MDP). By employing quantum concepts and a quantum search algorithm, this work presents the implementation and optimization of the agent-environment intera

Liang Du, Henghui Jiang, Xiaodong Li, Yiqing Guo

Multi-view clustering (MVC) aims to integrate complementary information from multiple views to enhance clustering performance. Late Fusion Multi-View Clustering (LFMVC) has shown promise by synthesizing diverse clustering results into a unified consensus. However, current LFMVC methods struggle with noisy and redundant partitions and often fail to capture hi

David Favero, Pouya Layeghi

We give a topological description of Ext groups between simple representations of categories via a nerve type construction. We use it to show that the Koszulity of indiscretely based category algebras is equivalent to the locally bouquet property of this nerve. We also provide a class of functors which preserve the Koszulity of category algebras called almos

Bidisha Sengupta, Mousa Alrubayan, Yibin Wang, Esther Mallet

Biofilms are resistant microbial cell aggregates that pose risks to health and food industries and produce environmental contamination. Accurate and efficient detection and prevention of biofilms are challenging and demand interdisciplinary approaches. This multidisciplinary research reports the application of a deep learning-based artificial intelligence (A

Shashikanta Sahoo

In competitive combat sports like boxing, analyzing a boxers's performance statics is crucial for evaluating the quantity and variety of punches delivered during bouts. These statistics provide valuable data and feedback, which are routinely used for coaching and performance enhancement. We introduce BoxMAC, a real-world boxing dataset featuring 15 professio

Shangbin Yang, Suo Liu, Jiangtao Su, Yuanyong Deng

Magnetic helicity is a key geometrical parameter to describe the structure and evolution of solar coronal magnetic fields. The accumulation of magnetic helicity is correlated with the non-potential magnetic field energy, which is released in the solar eruptions. Moreover, the relative magnetic helicity fluxes can be estimated only relying on the line-of-sigh

Zhuohuan Hu, Richard Yu, Zizhou Zhang, Haoran Zheng

This paper leverages machine learning algorithms to forecast and analyze financial time series. The process begins with a denoising autoencoder to filter out random noise fluctuations from the main contract price data. Then, one-dimensional convolution reduces the dimensionality of the filtered data and extracts key information. The filtered and dimensionali

Geoffrey Hutinet, J. E. Pascoe

We give an abstract perspective on quadratic programming with an eye toward long portfolio theory geared toward explaining sparsity via maximum principles. Specifically, in optimal allocation problems, we see that support of an optimal distribution lies in a variety intersect a kind of distinguished boundary of a compact subspace to be allocated over. We dem

Shyam Kumar Shrestha, Shiva Raj Pokhrel, Jonathan Kua

The new transmission control protocol (TCP) relies on Deep Learning (DL) for prediction and optimization, but requires significant manual effort to design deep neural networks (DNNs) and struggles with generalization in dynamic environments. Inspired by the success of large language models (LLMs), this study proposes TCP-LLM, a novel framework leveraging LLM

Usman Ali, Sahil Ranmbail, Muhammad Nadeem, Hamid Ishfaq

Extracting medication names from handwritten doctor prescriptions is challenging due to the wide variability in handwriting styles and prescription formats. This paper presents a robust method for extracting medicine names using a combination of Mask R-CNN and Transformer-based Optical Character Recognition (TrOCR) with Multi-Head Attention and Positional Em

Tomoyuki Tsuyama, Takeshi Kaneshita, Akira Matsui, Kohei Ochiai

We report the mechanisms of atomic ordering in Fe$_{1-x}$Pt$_{x}$ alloys using density functional theory (DFT) and machine-learning interatomic potential Monte Carlo (MLIP-MC) simulations. We clarified that the formation enthalpy of the ordered phase was significantly enhanced by spin polarization compared to that of the disordered phase. Analysis of the den

Hiroyuki Chihara

We directly compute the symbol of the normal operator for the d-plane transform on the Euclidean space. We show that this symbol is the product of the symbol of the power of the Laplacian of order -d/2 and a constant given by an invariant integral over excess-dimensional spaces. This leads to an alternative derivation of the filtered backprojection formula f

Zeru Shi, Zhenting Wang, Yongye Su, Weidi Luo

While automatic prompt generation methods have recently received significant attention, their robustness remains poorly understood. In this paper, we introduce PertBench, a comprehensive benchmark dataset that includes a wide range of input perturbations, designed to systematically evaluate the robustness of current auto-prompting techniques. Our analysis re

Rustem N. Garifullin, Ismagil T. Habibullin

In this paper, a class of particular solutions of the Ishimori equation is found. This equation is known as the spatially two-dimensional version of the Heisenberg equation, which has important applications in the theory of ferromagnets. It is shown that the two-dimensional Toda-type lattice found earlier by Ferapontov, Shabat and Yamilov is a dressing chain

Shiduo Zhang, Zhe Xu, Peiju Liu, Xiaopeng Yu

General-purposed embodied agents are designed to understand the users' natural instructions or intentions and act precisely to complete universal tasks. Recently, methods based on foundation models especially Vision-Language-Action models (VLAs) have shown a substantial potential to solve language-conditioned manipulation (LCM) tasks well. However, existing

Paige Bright, Manik Dhar

We obtain new bounds for (a variant of) the Furstenberg set problem for high dimensional flats over $\mathbb{R}^n$. In particular, let $F\subset \mathbb{R}^n$, $1\leq k \leq n-1$, $s\in (0,k]$, and $t\in (0,k(n-k)]$. We say that $F$ is a $(s,t;k)$-spread Furstenberg set if there exists a $t$-dimensional set of subspaces $\mathcal P \subset \mathcal G(n,k)$ s

Pabel Shahrear, Md. Shahedul Islam, Md. Abu Bakkar, Anika Bushra

The ever-changing world of disease study heavily relies on mathematical models. They are key in finding and controlling infectious diseases. We aim to explore these mathematical tools used for studying disease spread in biology. The SEIR model holds our focus. It is a super important tool known for being flexible and useful. We look at the modified SEIR mode

Shu Kawaguchi, Kazuhiko Yamaki

Let $A$ be an abelian variety over an algebraically closed field $k$ that is complete with respect to a nontrivial nonarchimedean absolute value. Let $A^{\mathrm{an}}$ denote the analytification of $A$ in the sense of Berkovich, and let $\Sigma$ be the canonical skeleton of $A^{\mathrm{an}}$. In this paper, we obtain a faithful tropicalization of $\Sigma$ by

Xuechen Liu, Junichi Yamagishi, Md Sahidullah, Tomi kinnunen

This study investigates the explainability of embedding representations, specifically those used in modern audio spoofing detection systems based on deep neural networks, known as spoof embeddings. Building on established work in speaker embedding explainability, we examine how well these spoof embeddings capture speaker-related information. We train simple

Andre Rusli, Makoto Shishido

This paper analyses how traditional baseline metrics, such as BLEU and TER, and neural-based methods, such as BERTScore and COMET, score several NMT models performance on chat translation and how these metrics perform when compared to human-annotated scores. The results show that for ranking NMT models in chat translations, all metrics seem consistent in dec

Xiang Yu, Linlin Zhang, Yaw, Adu-Gyamfi

Even though Truck Mounted Attenuators (TMA)/Autonomous Truck Mounted Attenuators (ATMA) and traffic control devices are increasingly used in mobile work zones to enhance safety, work zone collisions remain a significant safety concern in the United States. In Missouri, there were 63 TMA-related crashes in 2023, a 27% increase compared to 2022. Currently, all

Andre Rusli, Makoto Shishido

This research explores the applicability of cross-lingual transfer learning from English to Japanese and Indonesian using the XLM-R pre-trained model. The results are compared with several previous works, either by models using a similar zero-shot approach or a fully-supervised approach, to provide an overview of the zero-shot transfer learning approach's ca

Nikita Louison, Wayne Goodridge, Koffka Khan

Existing Sign Language Learning applications focus on the demonstration of the sign in the hope that the student will copy a sign correctly. In these cases, only a teacher can confirm that the sign was completed correctly, by reviewing a video captured manually. Sign Language Translation is a widely explored field in visual recognition. This paper seeks to e

Shuanglong Han, Zhiqiang Fan, Ousi Pan, Xiaohang Chen

In this paper, we simulate the function of hot-carrier photocatalysts (HCPCs) with quantum well and quantum dot energy-selective contacts (ESCs) in the water-splitting reaction. The transport equations for these ESCs are derived by using ballistic transport theory. The results indicate that thermalization loss from non-ideal ESCs is a primary factor diminish

Yucong Luo, Mingyue Cheng, Jie Ouyang, Xiaoyu Tao

Text-to-image generative models excel in creating images from text but struggle with ensuring alignment and consistency between outputs and prompts. This paper introduces TextMatch, a novel framework that leverages multimodal optimization to address image-text discrepancies in text-to-image (T2I) generation and editing. TextMatch employs a scoring strategy p

Haoyu Zhang, Rayan Saab

Quantization and pruning are two essential techniques for compressing neural networks, yet they are often treated independently, with limited theoretical analysis connecting them. This paper introduces a unified framework for post-training quantization and pruning using stochastic path-following algorithms. Our approach builds on the Stochastic Path Followin

Roxanne M. Tutchton, Jean-Pierre Julien, Qimiao Si, Jian-Xin Zhu

Since the discovery of heavy-fermion superconductivity in CeCu$_{2}$Si$_{2}$, the material has attracted great interest particularly with regard to the nature of the superconducting pairing and its mechanism. Consequently, it is essential to better understand the electronic Fermi surface topology and its role in strong antiferromagnetic fluctuations. The sta

Stylianos Kampakis

This paper introduces JANUS, a Stablecoin 3.0 protocol designed to address the stablecoin trilemma--simultaneously improving decentralization (D), capital efficiency (E), and safety-stability (S). Building upon insights from previous stablecoin generations, JANUS leverages a dual-token system (Alpha and Omega), integrates crypto-assets and real-world assets

Tadahiro Katsuoka

Ihara and Birch obtained a formula expressing the sum of powers of the traces of elliptic curves over a fixed finite field of characteristic $p$ in terms of the traces of Hecke operators for $\mathrm{SL}_2(\mathbb{Z})$. Generalizing the theorems of Ihara and Birch, for a finite abelian group $A$ whose order is coprime to $p$, Kaplan and Petrow gave a formula

Hisayoshi Nanmo, Manabu Kuroki

For a data-generating process for random variables that can be described with a linear structural equation model, we consider a situation in which (i) a set of covariates satisfying the back-door criterion cannot be observed or (ii) such a set can be observed, but standard statistical estimation methods cannot be applied to estimate causal effects because of

Le Tieu Nien, Pham Van Cuong, Nguyen Phuc Anh, Vu Ngoc Son

DC motors have been widely used in many industrial applications, from small jointed robots with multiple degrees of freedom to household appliances and transportation vehicles such as electric cars and trains. The main function of these motors is to ensure stable positioning performance and speed for mechanical systems based on pre-designed control methods.

Jincheng An, Ganpathy Murthy

Charge-neutral graphene in the quantum Hall regime is an example of a quantum Hall ferromagnet in a complex spin-valley space. This system exhibits a plethora of phases, with the particular spin-valley order parameters chosen by the system depending sensitively on the short-range anisotropic couplings, the Zeeman field, and the sublattice symmetry breaking f

Shicheng Yin, Kaixuan Yin, Weixing Chen, Enbo Huang

Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) are two dominant models for image analysis. While CNNs excel at extracting multi-scale features and ViTs effectively capture global dependencies, both suffer from high computational costs, particularly when processing high-resolution images. Recently, state-space models (SSMs) and recurrent

Sihao Liu, Yibo Yang, Xiaojie Li, David A. Clifton

Online continual learning (OCL) seeks to learn new tasks from data streams that appear only once, while retaining knowledge of previously learned tasks. Most existing methods rely on replay, focusing on enhancing memory retention through regularization or distillation. However, they often overlook the adaptability of the model, limiting the ability to learn

Davoud Yousefi, Hassan Yari, Farzad Osouli, Mohammad Ebrahimi

Nowadays, the use of soft computational techniques in power systems under the umbrella of machine learning is increasing with good reception. In this paper, we first present a deep learning approach to find the optimal configuration for HetNet systems. We used a very large number of radial configurations of a test system for training purposes. We also studie

Yucong Luo, Qitao Qin, Hao Zhang, Mingyue Cheng

Sequential recommendation (SR) systems have evolved significantly over the past decade, transitioning from traditional collaborative filtering to deep learning approaches and, more recently, to large language models (LLMs). While the adoption of LLMs has driven substantial advancements, these models inherently lack collaborative filtering information, relyin

Ruihong Ma, Engui Fan

We consider soliton gas solutions of the modified Korteweg-de Vries (mKdV) equation, where the point spectrum of the condensate is located within a bounded domain in the upper half-plane. We first demonstrate that when the domain is a quadrature and the soliton density is an analytic function, the corresponding deterministic soliton gas coincides with a fini

Haohang Li, Yupeng Cao, Yangyang Yu, Shashidhar Reddy Javaji

Recent advancements have underscored the potential of large language model (LLM)-based agents in financial decision-making. Despite this progress, the field currently encounters two main challenges: (1) the lack of a comprehensive LLM agent framework adaptable to a variety of financial tasks, and (2) the absence of standardized benchmarks and consistent data

Qiming Wang, Wanfang Shen, Wenbin Liu

In this paper, the optimal strong error estimates for stochastic parabolic optimal control problem with additive noise and integral state constraint are derived based on time-implicit and finite element discretization. The continuous and discrete first-order optimality conditions are deduced by constructing the Lagrange functional, which contains forward-bac

Chang-Chun He, Shao-Gang Xu, Yu-Jun Zhao, Hu Xu

The unique electron deficiency of boron makes it challenging to determine the stable structures, leading to a wide variety of forms. In this work, we introduce a statistical model based on grand canonical ensemble theory that incorporates the octet rule to determine electron density in boron systems. This parameter-free model, referred to as the bonding free

Xiaomeng Hu, Pin-Yu Chen, Tsung-Yi Ho

Large Language Models (LLMs) are increasingly being integrated into services such as ChatGPT to provide responses to user queries. To mitigate potential harm and prevent misuse, there have been concerted efforts to align the LLMs with human values and legal compliance by incorporating various techniques, such as Reinforcement Learning from Human Feedback (RL

Yuhan Zhao, Rui Chen, Qilong Han, Hongtao Song

Collaborative filtering (CF) stands as a cornerstone in recommender systems, yet effectively leveraging the massive unlabeled data presents a significant challenge. Current research focuses on addressing the challenge of unlabeled data by extracting a subset that closely approximates negative samples. Regrettably, the remaining data are overlooked, failing t

Rongxin Cheng, Yuxin Lai, Xingda Wei, Rong Chen

Serving LLMs with a cluster of GPUs is common nowadays, where the serving system must meet strict latency SLOs required by applications. However, the stateful nature of LLM serving requires maintaining huge states (i.e., KVCache) in limited GPU memory. Under spikes in real-world workloads, GPU memory can be easily throttled, leading to orders of magnitude hi

Yuhan Zhao, Rui Chen, Li Chen, Shuang Zhang

Intuitively, an ideal collaborative filtering (CF) model should learn from users' full rankings over all items to make optimal top-K recommendations. Due to the absence of such full rankings in practice, most CF models rely on pairwise loss functions to approximate full rankings, resulting in an immense performance gap. In this paper, we provide a novel anal

Kohei Sato, Hiromasa Watanabe, Takeshi Yamazaki for PACS Collaboration

We report our preliminary results for the charge radii of $\pi^{+}$ and $K^{+}$ mesons with the PACS10 configuration generated at the physical point using the Iwasaki gauge action and $N_{f}=2+1$ stout-smeared nonperturbatively $\mathcal{O}(a)$ improved Wilson quark action, especially at $0.085$ fm corresponding lattice size $128^4$. The charge radii are obt

Suwesh Prasad Sah

Autonomous driving in high-speed racing, as opposed to urban environments, presents significant challenges in scene understanding due to rapid changes in the track environment. Traditional sequential network approaches may struggle to meet the real-time knowledge and decision-making demands of an autonomous agent covering large displacements in a short time.

Shruti Nath, Julie Carreau, Kai Kornhuber, Peter Pfleiderer

High-impact climate damages are often driven by compounding climate conditions. For example, elevated heat stress conditions can arise from a combination of high humidity and temperature. To explore future changes in compounding hazards under a range of climate scenarios and with large ensembles, climate emulators can provide light-weight, data-driven comple

Yinbin Han, Meisam Razaviyayn, Renyuan Xu

Diffusion models have emerged as powerful tools for generative modeling, demonstrating exceptional capability in capturing target data distributions from large datasets. However, fine-tuning these massive models for specific downstream tasks, constraints, and human preferences remains a critical challenge. While recent advances have leveraged reinforcement l

Xingyu Xiao, Peng Chen, Ben Qi, Jingang Liang

As climate change and other global challenges increase the likelihood of unforeseen emergencies, the limitations of human-driven strategies in critical situations become more pronounced. Inadequate pre-established emergency plans can lead operators to become overwhelmed during complex systems malfunctions. This study addresses the urgent need for agile decis

Rasika Ransing, Mohammed Amaan Dhamaskar, Ayush Rajpurohit, Amey Dhoke

India's vast linguistic diversity presents unique challenges and opportunities for technological advancement, especially in the realm of Natural Language Processing (NLP). While there has been significant progress in NLP applications for widely spoken languages, the regional languages of India, such as Marathi and Hindi, remain underserved. Research in the f

Mohamed Elrashidy, Mudassir Masood, Ali Arshad Nasir

Cell-free massive multiple input multiple output (MIMO) systems can provide reliable connectivity and increase user throughput and spectral efficiency of integrated sensing and communication (ISAC) systems. This can only be achieved through intelligent beamforming design. While many works have proposed optimization methods to design beamformers for cell-free

Shray Mathur, Noah van der Vleuten, Kevin Yager, Esther Tsai

Scientific user facilities, such as synchrotron beamlines, are equipped with a wide array of hardware and software tools that require a codebase for human-computer-interaction. This often necessitates developers to be involved to establish connection between users/researchers and the complex instrumentation. The advent of generative AI presents an opportunit

Xuting Lan, Mingliang Zhou, Jielu Yan, Xuekai Wei

Given that the factors influencing image quality vary significantly with scene, content, and distortion type, particularly in the context of regional heterogeneity, we propose an adaptive multi-quality factor (AMqF) framework to represent image quality in a dictionary space, enabling the precise capture of quality features in non-uniformly distorted regions.

Shubham Sharma, Deepak Kumar

Vibrated granular matter constitutes a useful system for studying the physics of active matter. Usually, self-propulsion is induced in grains through suitable asymmetry in the particle design. In this paper, we show that a symmetrical mini wheel placed on a vibrating plate self-propels along circular trajectories, showing chiral active dynamics. The chiral a