December 2024 arXiv papers — page 157

Marco Ghirlanda

We classify the non-toric, $\mathbb{Q}$-factorial, Gorenstein, Fano threefolds of Picard number one with an effective $\mathbb{K}^*$-action and maximal orbit quotient $\mathbb{P}_2$.

Harald Monsuur, Robin Smeets, Rob Stevenson

We construct least squares formulations of PDEs with inhomogeneous essential boundary conditions, where boundary residuals are not measured in unpractical fractional Sobolev norms, but which formulations nevertheless are shown to yield a quasi-best approximations from the employed trial spaces. Dual norms do enter the least-squares functional, so that solvin

Şevval Çakıcı, Dilara Karaduman, Mehmet Akif Çırlan, Ali Hürriyetoğlu

In recent years, sentiment analysis has gained increasing significance, prompting researchers to explore datasets in various languages, including Turkish. However, the limited availability of Turkish datasets has led to their multifaceted usage in different studies, yielding diverse outcomes. To overcome this challenge, a rigorous review was conducted of res

Taveena Lotey, Aman Verma, Partha Pratim Roy

Electroencephalography (EEG) is widely researched for neural decoding in Brain Computer Interfaces (BCIs) as it is non-invasive, portable, and economical. However, EEG signals suffer from inter- and intra-subject variability, leading to poor performance. Recent technological advancements have led to deep learning (DL) models that have achieved high performan

R. M. Khakimov, M. T. Makhammadaliev, U. A. Rozikov

We investigate splitting Gibbs measures (SGMs) of a three-state (wand-graph) hardcore SOS model on Cayley trees of order $ k \geq 2 $. Recently, this model was studied for the hinge-graph with $ k = 2, 3 $, while the case $ k \geq 4 $ remains unresolved. It was shown that as the coupling strength $\theta$ increases, the number of translation-invariant SGMs (

Yong Yuan, Ao-Ran Wang, Zhuo-Tao Li, Gang Yu

Gravitational waves (GWs) from core-collapse supernovae (CCSNe) have been proposed as a means to probe the internal physical properties of supernovae. However, due to their complex time-frequency structure, effectively searching for and extracting GW signals from CCSNe remains an unsolved challenge. In this paper, we apply the improved multisynchrosqueezing

Qiao Feng, Yuanwang Yang, Yebin Liu, Yu-Kun Lai

We introduce FOF-X for real-time reconstruction of detailed human geometry from a single image. Balancing real-time speed against high-quality results is a persistent challenge, mainly due to the high computational demands of existing 3D representations. To address this, we propose Fourier Occupancy Field (FOF), an efficient 3D representation by learning the

Dezhi Song, Fuyang Huang, Gang Yao, Haimin Zhang

We obtain in single van-der-Waals layer of MnBi2Te4 the spin-valve-like ferromagnet/spin glass (SG)/ferromagnet architecture, where the switch of individual spin states in the SG-like layer appears as the resistance switch behavior. The characteristic temperature of SG can be effectively tuned by fine-control of Bi-doping in the SG layer. A doping- and tempe

Dezhi Song, Fuyang Huang, Gang Yao, Haimin Zhang

MnBi2Te4 (MBT) is a typical magnetic topological insulator with an A-type antiferromagnetic (AFM) ground state. Here we prepared ultra-thin MBT films with controlled anti-site defects and observed rich doping-dependent magnetic behaviors. We find in one-septuple-layer MBT films a ferrimagnetic ground state and in two-septuple-layer ones a kind of re-entrant

Adem Ait, Javier Luis Cánovas Izquierdo, Jordi Cabot

Large Language Models (LLMs) have facilitated the definition of autonomous intelligent agents. Such agents have already demonstrated their potential in solving complex tasks in different domains. And they can further increase their performance when collaborating with other agents in a multi-agent system. However, the orchestration and coordination of these a

Yiyan Li, Zhenghao Zhou, Jian Ping, Xiaoyuan Xu

Graph motif, defined as the microstructure that appears repeatedly in a large graph, reveals important topological characteristics of the large graph and has gained increasing attention in power system analysis regarding reliability, vulnerability and resiliency. However, searching motifs within the large-scale power system is extremely computationally chall

Yiyuan Pan, Yiheng Xie, Steven Low

The deployment of distributed energy resource (DER) devices plays a critical role in distribution grids, offering multiple value streams, including decarbonization, provision of ancillary services, non-wire alternatives, and enhanced grid flexibility. However, existing research on capacity expansion suffers from two major limitations that undermine the reali

Takeshi Seki, Hiroto Masuda, Varun K. Kushwaha, Takumi Yamazaki

The antisymmetric-type long-range exchange interaction between two ferromagnetic layers through a nonmagnetic layer, called antisymmetric interlayer exchange coupling (AIEC), has recently been discovered and attracted much attention. This paper reports that the AIEC is naturally built in synthetic antiferromagnets depending on the thin film growth condition.

D. Beretta

This article presents an open-source Python package for simulating micro-thermoelectric generators, based on the work by D. Beretta et al. (Sustainable Energy Fuels, 2017). Featuring a user-friendly graphical user interface and robust computational capabilities, the tool is designed for use by scientists, researchers, and engineers to analyze and optimize de

Juan Guillermo Garrido, Pedro Pérez-Aros, Emilio Vilches

This work investigates a dynamical system functioning as a nonsmooth adaptation of the continuous Newton method, aimed at minimizing the sum of a primal lower-regular and a locally Lipschitz function, both potentially nonsmooth. The classical Newton method's second-order information is extended by incorporating the graphical derivative of a locally Lipschitz

Juan Yeo, Jinkwan Jang, Kyubyung Chae, Seongkyu Mun

Recent studies show that pretrained vision models can boost performance in audio downstream tasks. To enhance the performance further, an additional pretraining stage with large scale audio data is typically required to infuse audio specific knowledge into the vision model. However, such approaches require extensive audio data and a carefully designed object

Chao Yang, Chaochao Lu, Yingchun Wang, Bowen Zhou

Ensuring Artificial General Intelligence (AGI) reliably avoids harmful behaviors is a critical challenge, especially for systems with high autonomy or in safety-critical domains. Despite various safety assurance proposals and extreme risk warnings, comprehensive guidelines balancing AI safety and capability remain lacking. In this position paper, we propose

Josué Knorst, Christian Olivera, Alexandre B. de Souza

We derive quantitative estimates for large stochastic systems of interacting particles perturbed by both idiosyncratic and environmental noises, as well as singular kernels. We prove that the (mollified) empirical process converges to the solution of the nonlinear stochastic Fokker-Planck equation. The proof is based on It\^o's formula for $H_{q}^{1}$-valued

Jiawei Huang, Aimin Wang, Geng Sun, Jiahui Li

Unmanned aerial vehicles (UAVs) can be utilized as relay platforms to assist maritime wireless communications. However, complex channels and multipath effects at sea can adversely affect the quality of UAV transmitted signals. Collaborative beamforming (CB) can enhance the signal strength and range to assist the UAV relay for remote maritime communications.

Yuxuan Zeng, Wei Cao, Yijing Zuo, Tan Peng

Lattice thermal conductivity (LTC) is a critical parameter for thermal transport properties, playing a pivotal role in advancing thermoelectric materials and thermal management technologies. Traditional computational methods, such as Density Functional Theory (DFT) and Molecular Dynamics (MD), are resource-intensive, limiting their applicability for high-thr

Akhil S. Nair, Lucas Foppa, Matthias Scheffler

The efficiency of active learning (AL) approaches to identify materials with desired properties relies on the knowledge of a few parameters describing the property. However, these parameters are unknown if the property is governed by a high intricacy of many atomistic processes. Here, we develop an AL workflow based on the sure-independence screening and spa

Louis M. T. Arts

The recent discovery of little red dots - a population of extremely compact and highly dust-reddened high redshift galaxies - by the James Webb Space Telescope presents a new challenge to the fields of astrophysics and cosmology. Their remarkably high luminosities at redshifts 5 < z < 10, appear to challenge LambdaCDM cosmology and galaxy formation models, a

Liuqing Yang, Zexin Wang

Let $R = \mathbb{K}[x_1, \ldots, x_n]$ be a polynomial ring over a field $\mathbb{K}$, and let $I \subseteq R$ be a monomial ideal of height $h$. We provide a formula for the multiplicity of the powers of $I$ when all the primary ideals of height $h$ in the irredundant reduced primary decomposition of $I$ are irreducible. This is a generalization of \cite[Th

Jianyi Chen, Stuart Calder, Joseph A. M. Paddison, Gina Angelo

Magnetoplumbites are one of the most broadly studied families of hexagonal ferrites, typically with high magnetic ordering temperatures, making them excellent candidates for permanent magnets. However, magnetic frustration was rarely observed in magnetoplumbites. Herein, we report the discovery, synthesis and characterization of the first Mn-based magnetoplu

Jie Ning, Jiebao Sun, Shengzhu Shi, Zhichang Guo

Deep learning-based image denoising models demonstrate remarkable performance, but their lack of robustness analysis remains a significant concern. A major issue is that these models are susceptible to adversarial attacks, where small, carefully crafted perturbations to input data can cause them to fail. Surprisingly, perturbations specifically crafted for o

Yuwen Huang

In this thesis, we leverage finite graph covers to analyze the SPA and the Bethe partition function for both S-FGs and DE-FGs. There are two main contributions in this thesis. The first main contribution concerns a special class of S-FGs where the partition function of each S-FG equals the permanent of a nonnegative square matrix. The Bethe partition functio

Jian Chai, Shan Cheng

We propose to introduce the intrinsic transversal momentum distribution functions (iTMDs), in conjunction with the light-cone distribution amplitudes (LCDAs), to elucidate the probability amplitude of encountering a meson state wherein the partons swiftly traverse along the longitudinal axis while gently oscillating in the transversal plane. The primary moti

Yuan Xu, Kui Huang, Weichao Guo, Leyi Du

This paper explores the digital modeling and robotic reproduction of traditional Chinese medicine (TCM) massage techniques. We adopt an adaptive admittance control algorithm to optimize force and position control, ensuring safety and comfort. The paper analyzes key TCM techniques from kinematic and dynamic perspectives, and designs robotic systems to reprodu

Xiao Xu, Tianhao Niu, Yuxi Xie, Libo Qin

Multimodal Large Language Models (MLLMs) excel in vision--language tasks by pre-training solely on coarse-grained concept annotations (e.g., image captions). We hypothesize that integrating fine-grained concept annotations (e.g., object labels and object regions) will further improve performance, as both data granularities complement each other in terms of b

Naveed Ur Rehman Junejo, Muhammad Wasim Nawaz, Qingsheng Huang, Xiaoqing Dong

The ability to accurately predict and analyze student performance in online education, both at the outset and throughout the semester, is vital. Most of the published studies focus on binary classification (Fail or Pass) but there is still a significant research gap in predicting students' performance across multiple categories. This study introduces a novel

Sakhinana Sagar Srinivas, Akash Das, Shivam Gupta, Venkataramana Runkana

Process Flow Diagrams (PFDs) and Process and Instrumentation Diagrams (PIDs) are critical tools for industrial process design, control, and safety. However, the generation of precise and regulation-compliant diagrams remains a significant challenge, particularly in scaling breakthroughs from material discovery to industrial production in an era of automation

Peter Beelen, Trygve Johnsen, Prasant Singh

The study of linear codes over a finite field of odd cardinality, derived from determinantal varieties obtained from symmetric matrices of bounded rank, was initiated in a recent paper by the authors. There, one found the minimum distance of the code obtained from evaluating homogeneous linear functions at all symmetric matrices with rank, which is, at most,

Francesco Nobili

We review recent results regarding the problem of the stability of Sobolev inequalities on Riemannian manifolds with Ricci curvature lower bounds. We shall describe techniques and methods from smooth and non-smooth geometry, the fruitful combination of which revealed particularly effective. Furthermore, we present a self-contained overview of the proof of th

Ma Teng, Jia Xiaojun, Duan Ranjie, Li Xinfeng

With the rapid advancement of multimodal large language models (MLLMs), concerns regarding their security have increasingly captured the attention of both academia and industry. Although MLLMs are vulnerable to jailbreak attacks, designing effective jailbreak attacks poses unique challenges, especially given the highly constrained adversarial capabilities in

Xi Shao, Philip G. Edwards, Jamie Stevens, Minfeng Gu

We present multi-frequency radio data for a sample of narrow-line Seyfert 1 galaxies. We first focus on the sub-class of gamma-ray emitting narrow-line Seyfert 1 galaxies, studying the long-term radio variability of five sources and comparing it to their gamma-ray state. We then extend the observations of the southern narrow-line Seyfert 1 galaxy sample of C

Vaibhav Katyal, A. Chakraborty, B. K. Sahoo, Ben Ohayon

Precise evaluation of the isotope shift (IS) factors for seven low-lying potassium (K) states is achieved using relativistic coupled-cluster (RCC) theory. The energies of these states are assessed and compared with experimental data to confirm the accuracy of the wave functions calculated at varying RCC theory approximations and highlight the significance of

Xiangkai Sun, Liang He, Xianjun Long

This paper deals with a second-order primal-dual dynamical system with Hessian-driven damping and Tikhonov regularization terms in connection with a convex-concave bilinear saddle point problem. We first obtain a fast convergence rate of the primal-dual gap along the trajectory generated by the dynamical system, and provide some integral estimates. Then, bas

P. A. Almeida, E. Vernek, E. V. Anda, S. E. Ulloa

A composite impurity in a metal can explore different configurations, where its net magnetic moment may be screened by the host electrons. An example is the two-stage Kondo (TSK) system, where screening occurs at successively smaller energy scales. Alternatively, impurities may prefer a local singlet disconnected from the metal. This competition is influence

Haoran Li, Yuli Tian, Yonghui Wang, Yong Liao

Distilling 3D representations from pretrained 2D diffusion models is essential for 3D creative applications across gaming, film, and interior design. Current SDS-based methods are hindered by inefficient information distillation from diffusion models, which prevents the creation of photorealistic 3D contents. In this paper, we first reevaluate the SDS approa

Jiaxin Chen, Zunjie Huang, Haisen Zhang

A Halpern-type relaxed inertial inexact progressive hedging algorithm (PHA) is proposed for solving multi-stage stochastic variational inequalities in general probability spaces. The subproblems in this algorithm are allowed to be calculated inexactly. It is found that the Halpern-type relaxed inertial inexact PHA is closely related to the Halpern-type relax

Yuan Gao

Inspired by the simple fact that a compact n-dimensional manifold-with-boundary which satisfies Poincar\'e-Lefschetz duality of dimension n has a boundary which itself satisfies Poincar\'e duality of dimension n, we show that the categorical formal punctured neighborhood of infinity, a canonical categorical construction associated to every $A_{\infty}$ categ

Xingyu Zheng, Xianglong Liu, Yichen Bian, Xudong Ma

Diffusion models (DMs) have been significantly developed and widely used in various applications due to their excellent generative qualities. However, the expensive computation and massive parameters of DMs hinder their practical use in resource-constrained scenarios. As one of the effective compression approaches, quantization allows DMs to achieve storage

Mariem Chemingui, Ahmed Elzanaty, Rahim Tafazolli

The rollout of the fifth-generation (5G) networks has raised some concerns about potential health effects from increased exposure to electromagnetic fields (EMF). To address these concerns, we design a novel EMF-aware architecture for uplink communications. Specifically, we propose an aerial reconfigurable intelligent surface (ARIS) assisted multi-user multi

Gideon A. Ngwa, Bime M. Ghakanyuy, Miranda I. Teboh-Ewungkem, Jacek Banasiak

A deterministic nonlinear ordinary differential equation model for mosquito dynamics in which the mosquitoes can quest for blood either within a human population or within non-human/vertebrate populations is derived and studied. The model captures both the mosquito's aquatic and terrestrial forms and includes a mechanism to investigate the impact of mating o

Len Brandes, Wolfram Weise

As an update to our previously performed Bayesian inference analyses of the neutron star matter equation-of-state and related quantities, the additional impact of the recently published NICER data of PSR J0437-4751 is examined. Including the mass and radius distributions of this pulsar in our data base results in modest shifts from previously inferred median

Sergey Kharintsev, Elina Battalova

In this Letter, we study the optical heating of spatially dispersive solids due to electronic light scattering (ELS), a phenomenon driven by indirect optical transitions. In this process, a spatial heterogeneity generates an optical near-field photon with expanded momentum, leading to electron-photon momentum matching, followed by thermalization of the elect

Thomas Vecchiato

Developing increasingly efficient and accurate algorithms for approximate nearest neighbor search is a paramount goal in modern information retrieval. A primary approach to addressing this question is clustering, which involves partitioning the dataset into distinct groups, with each group characterized by a representative data point. By this method, retriev

Yangtao Deng, Qiaolin He

Cell collective migration plays a crucial role in a variety of physiological processes. In this work, we propose the Runge-Kutta random feature method to solve the nonlinear and strongly coupled multiphase flow problems of cells, in which the random feature method in space and the explicit Runge-Kutta method in time are utilized. Experiments indicate that th

Ganesh Neelakanta Iyer, Andrew Goh Yisheng, Metilda Chee Heng Er, Weng Xian Choong

DevOps can be best explained as people working together to conceive, build and deliver secure software at top speed. DevOps practices enable software development (dev) and operations (ops) teams to accelerate delivery through automation, collaboration, fast feedback, and iterative improvement. It is now an integral part of the information technology industry

Bora Kim

In estimating spillover effects under network interference, practitioners often use linear regression with either the number or fraction of treated neighbors as regressors. An often overlooked fact is that the latter is undefined for units without neighbors (``isolated nodes"). The common practice is to impute this fraction as zero for isolated nodes. This p

Zhijie Yuan, Ganzhao Yuan, Lei Sun

Coordinate descent algorithms are widely used in machine learning and large-scale data analysis due to their strong optimality guarantees and impressive empirical performance in solving non-convex problems. In this work, we introduce Block Coordinate Descent (BCD) method for structured nonconvex optimization with nonseparable constraints. Unlike traditional

A. Shahriar, M. Abbasiyan-Motlaq, M. Mohsenzadeh, E. Yusofi

This paper investigates the thermodynamic evolution of the universe within the framework of a quadratic equation of state (EoS). Building upon the basis of the quadratic EoS model, as a phenomenological extension to dark energy models, we analyze the implications for cosmic dynamics, including energy density evolution of effective dark matter and dark energy

Ke-Ching Chang, Chung-Chi Chen, An-Zi Yen

Large Language Models (LLMs) have demonstrated significant capabilities in machine translation. However, their translation quality is sometimes questioned, as the generated outputs may deviate from expressions typically used by native speakers. These deviations often arise from differences in sentence structure between language systems. To address this issue

Timothy G. Backert, Fabian Brauneis, Matija Čufar, Joachim Brand

We study a one-dimensional system of two-component fermions in the limit of strong attractive particle-particle interactions. First, we analyze scattering in the corresponding few-body problem, which is analytically solvable via Bethe ansatz. This allows us to engineer effective interactions between the system's effective degrees of freedom: fermions and bos

Daheng Ju, Qihang Jing

In non-well-founded set theory, which anti-foundation axiom is philosophically justified, BAFA, FAFA, SAFA, AFA, or some other? In this paper, we investigate a general approach to answering this question: first, consider which identity condition for sets is justified; second, consider which anti-foundation axiom it justifies. Specifically, we study in detail

Omar Lakkis, Charalambos Makridakis

We derive aposteriori error estimates for fully discrete approximations to solutions of linear parabolic equations on the space-time domain. The space discretization uses finite element spaces, that are allowed to change in time. Our main tool is an appropriate adaptation of the elliptic reconstruction technique, introduced by Makridakis and Nochetto (2003).

Lukas Einkemmer, Katharina Kormann, Jonas Kusch, Ryan G. McClarren

Time-dependent kinetic models are ubiquitous in computational science and engineering. The underlying integro-differential equations in these models are high-dimensional, comprised of a six--dimensional phase space, making simulations of such phenomena extremely expensive. In this article we demonstrate that in many situations, the solution to kinetics probl

Alexandre Andorra, Maximilian Göbel

Evaluating a soccer player's performance can be challenging due to the high costs and small margins involved in recruitment decisions. Raw observational statistics further complicate an accurate individual skill assessment as they do not abstract from the potentially confounding factor of team strength. We introduce the Soccer Factor Model (SFM), which corre

Manu Mannattil, Haim Diamant, David Andelman

Inspired by recent experiments, we present a phase-field model of microphase separation in an elastomer swollen with a solvent. The imbalance between the molecular scale of demixing and the mesoscopic scale beyond which elasticity operates produces effective long-range interactions, forming stable finite-sized domains. Our predictions concerning the dependen

Yiheng Zhao

The Neumann problem in balls $\Omega\subset\mathbb{R}^n$, $n\in\{3,4\}$, for the chemotaxis system \begin{equation*} \left\{ \begin{array}{ll} u_t = \Delta u - \nabla \cdot (u\nabla v), \\[1mm] 0 = \Delta v - \mu^{(w)}(t) + w, \quad \mu^{(w)}(t) = \frac{1}{|\Omega|}\int_\Omega w \\[1mm] w_t = \Delta w - w + f(u), \end{array} \right. \end{equation*} is consid

Jianing Zhang, Yuchao Zheng, Ziwei Li, Qionghai Dai

Gaussian splatting has gained attention for its efficient representation and rendering of 3D scenes using continuous Gaussian primitives. However, it struggles with sparse-view inputs due to limited geometric and photometric information, causing ambiguities in depth, shape, and texture. we propose GBR: Generative Bundle Refinement, a method for high-fidelity

Yan Chang, Yukun Guo, Zhipeng Yang, Yue Zhao

This paper concerns the inverse source scattering problems of recovering random sources for acoustic and elastic waves. The underlying sources are assumed to be random functions driven by an additive white noise. The inversion process aims to find the essential statistical characteristics of the mean and variance from the radiated random wave field at multip

Lucky Li

We study the discrete-time linear-quadratic (LQ) control model using reinforcement learning (RL). Using entropy to measure the cost of exploration, we prove that the optimal feedback policy for the problem must be Gaussian type. Then, we apply the results of the discrete-time LQ model to solve the discrete-time mean-variance asset-liability management proble

Mauro Bernardi, Claudio Busatto, Manuela Cattelan

This paper introduces fast R updating algorithms specifically designed for statistical applications, including regression, filtering, and model selection, where data structures change frequently. Although traditional QR decomposition is essential for matrix operations, it becomes computationally intensive when dynamically updating the design matrix in statis

Asif Alif, Khondokar Fida Hasan, Jesse Laeuchli, Mohammad Jabed Morshed Chowdhury

The Internet of Things (IoT) has transformed healthcare, facilitating remote patient monitoring, enhanced medication adherence, and chronic disease management. However, this interconnected ecosystem faces significant vulnerabilities with the advent of quantum computing, which threatens to break existing encryption standards protecting sensitive patient data

Zhizhong Huang

Let $F$ be a non-degenerate integral ternary quadratic form and let $m_0\in\mathbb{Z}_{\neq 0}$. We study growth of rational points on the affine quadric $(F=m_0)$ and show that they are equidistributed in the adelic space off a finite place. This is closely related to Linnik's problem. Our approach is based on the $\delta$-variant of the Hardy--Littlewood c

Andrea Poiatti, Ulisse Stefanelli

We investigate the initial-value problem for the incompressible tangential Navier-Stokes equation with variable viscosity on a given two-dimensional surface without boundary. Existence of global weak and strong solutions under inhomogeneous forcing is proved by a fixed-point and continuation argument. Continuous dependence on data, backward uniqueness, and i

Sertac Kilickaya, Cansu Celebioglu, Levent Eren, Murat Askar

Condition monitoring of induction machines is crucial to prevent costly interruptions and equipment failure. Mechanical faults such as misalignment and rotor issues are among the most common problems encountered in industrial environments. To effectively monitor and detect these faults, a variety of sensors, including accelerometers, current sensors, tempera

Mathieu Carrière, Seunghyun Kim, Woojin Kim

The generalized persistence diagram (GPD) is a natural extension of the classical persistence barcode to the setting of multi-parameter persistence and beyond. The GPD is defined as an integer-valued function whose domain is the set of intervals in the indexing poset of a persistence module, and is known to be able to capture richer topological information t

Yuanzhi Zhu, Hanshu Yan, Huan Yang, Kai Zhang

Generative models, particularly diffusion models, have made significant success in data synthesis across various modalities, including images, videos, and 3D assets. However, current diffusion models are computationally intensive, often requiring numerous sampling steps that limit their practical application, especially in video generation. This work introdu

Jakob Ziewer, Abyay Ghosh, Michaela Hanušová, Luka Pirker

Gold assisted exfoliation (GAE) is a groundbreaking mechanical exfoliation technique, producing centimeter scale single crystal monolayers of 2D materials. Such large, high quality films offer unparalleled advantages over the micron sized flakes typically produced by conventional exfoliation techniques, significantly accelerating the research and technologic

Jun Nie, Yonggang Zhang, Tongliang Liu, Yiu-ming Cheung

We introduce a novel framework for AI-generated image detection through epistemic uncertainty, aiming to address critical security concerns in the era of generative models. Our key insight stems from the observation that distributional discrepancies between training and testing data manifest distinctively in the epistemic uncertainty space of machine learnin

Weizhuo Li, Zhigang Wang, Yu Gu, Ge Yu

Recently the generative Large Language Model (LLM) has achieved remarkable success in numerous applications. Notably its inference generates output tokens one-by-one, leading to many redundant computations. The widely-used KV-Cache framework makes a compromise between time and space complexities. However, caching data generates the increasingly growing memor

Arlene John, Barry Cardiff, Deepu John

The growing demand for accurate, continuous, and non-invasive health monitoring has propelled multi-sensor data fusion to the forefront of healthcare technology. This review aims to provide an overview of the development of fusion frameworks in the literature and common terminology used in fusion literature. The review introduces the fusion classification st

Parthib Roy, Srinivasa Perisetla, Shashank Shriram, Harsha Krishnaswamy

Human-interactive robotic systems, particularly autonomous vehicles (AVs), must effectively integrate human instructions into their motion planning. This paper introduces doScenes, a novel dataset designed to facilitate research on human-vehicle instruction interactions, focusing on short-term directives that directly influence vehicle motion. By annotating

Ruoxi Cheng, Yizhong Ding, Shuirong Cao, Ranjie Duan

Understanding the vulnerabilities of Large Vision Language Models (LVLMs) to jailbreak attacks is essential for their responsible real-world deployment. Most previous work requires access to model gradients, or is based on human knowledge (prompt engineering) to complete jailbreak, and they hardly consider the interaction of images and text, resulting in ina

Alejandro Mesa Dame, Ian E. Ochs, Nathaniel J. Fisch

In a magnetic mirror fusion reactor, capturing the energy of fusion-produced alpha particles is essential to sustaining the reaction. However, since alpha particles are born at energies much higher than the confining potential, a substantial fraction are lost due to pitch-angle scattering before they can transfer their energy to the plasma via drag. The ener

Peilun He, Gareth W. Peters, Nino Kordzakhia, Pavel V. Shevchenko

In the analysis of commodity futures, it is commonly assumed that futures prices are driven by two latent factors: short-term fluctuations and long-term equilibrium price levels. In this study, we extend this framework by introducing a novel state-space functional regression model that incorporates yield curve dynamics. Our model offers a distinct advantage

Donghang Lyu, Ruochen Gao, Marius Staring

Medical image segmentation involves partitioning medical images into meaningful regions, with a focus on identifying anatomical structures and lesions. It has broad applications in healthcare, and deep learning methods have enabled significant advancements in automating this process. Recently, the introduction of the Segmentation Anything Model (SAM), the fi

Heidi Kivijärvi, Arto Viitanen, Timm Mörstedt, Mikko Möttönen

We use a transmon qubit and its dispersively coupled readout resonator to measure the Fock state populations of another microwave resonator, to which we have attached a quantum-circuit refrigerator (QCR). First, we apply noise generated at room temperature to the resonator and show that such noise drive leads to a thermal distribution of the resonator Fock s

Kristoffer Simula, Evelin Martine Corvid Christlmaier, Maria-Andreea Filip, J. Philip Haupt

The transcorrelated (TC) method performs a similarity transformation on the electronic Schr\"odinger equation via Jastrow factorization of the wave function. This has demonstrated significant advancements in computational electronic structure theory by improving basis set convergence and compactifying the description of the wave function. In this work, we in

Muhammad Imran, Abdul Wahab Ziaullah, Kai Chen, Ferda Ofli

The widespread use of microblogging platforms like X (formerly Twitter) during disasters provides real-time information to governments and response authorities. However, the data from these platforms is often noisy, requiring automated methods to filter relevant information. Traditionally, supervised machine learning models have been used, but they lack gene

Qingyou He, Mingyue Zhang

We consider the Keller-Segel system with a volume-filling effect and study its incompressible limit. Due to the presence of logistic-type sensitivity, $K=1$ is the critical threshold. When $K>1$, as the diffusion exponent tends to infinity, by supposing the weak limit of $u^2_m$, we prove that the limiting system becomes a Hele-Shaw type free boundary proble

Kerui Wu, Ka-Ho Chow, Wenqi Wei, Lei Yu

As Graph Neural Networks (GNNs) become increasingly popular for learning from large-scale graph data across various domains, their susceptibility to adversarial attacks when using graph reduction techniques for scalability remains underexplored. In this paper, we present an extensive empirical study to investigate the impact of graph reduction techniques, sp

Huajian Feng, Guoxiao Zhang, Yadong Zhang, Yi We

Accurate post-click conversion rate (CVR) estimation is crucial for online advertising systems. Despite significant advances in causal approaches designed to address the Sample Selection Bias problem, CVR estimation still faces challenges due to Covariate Shift. Given the intrinsic connection between the distribution of covariates in the click and non-click

Usman Anjum, Chris Trentman, Elrod Caden, Justin Zhan

Understanding the effect of uncertainty and noise in data on machine learning models (MLM) is crucial in developing trust and measuring performance. In this paper, a new model is proposed to quantify uncertainties and noise in data on MLMs. Using the concept of signal-to-noise ratio (SNR), a new metric called deterministic-non-deterministic ratio (DDR) is pr

Leonid Antsfeld, Boris Chidlovskii

We address the problem of 3D inconsistency of image inpainting based on diffusion models. We propose a generative model using image pairs that belong to the same scene. To achieve the 3D-consistent and semantically coherent inpainting, we modify the generative diffusion model by incorporating an alternative point of view of the scene into the denoising proce

Gianluca Genovese, Ivan Heibi, Silvio Peroni, Sofia Pescarin

We investigated the use of virtual technologies to digitise and enhance cultural heritage (CH), aligning with Open Science and FAIR principles. Through case studies in museums, we developed reproducible workflows, 3D models, and tools fostering accessibility, inclusivity, and sustainability of CH. Applications include interdisciplinary research, educational

Detlef Müller

As main result, we show that a pseudodifferential operator in the Weyl calculus, whose symbol has compact Fourier support, lies in the Schatten class $\mathcal S^p$ if and only if its symbol lies in the Lebesgue space $L^p$ on phase space. As an immediate consequence, this gives an alternative and very lucid proof of a recent result by Luef and Samuelsen, wh

Wei Qu, Chi Tin Hon, Yiqiao Zhang, Tao Qian

We introduce a new fundamental algorithm called Matrix-POAFD to solve the matrix least square problem. The method is based on the matching pursuit principle. The method directly extracts, among the given features as column vectors of the measurement matrix, in the order of their importance, the decisive features for the observing vector. With competitive com

Josef Salzmann, Ulrich Schmid

Investigating the temporal behavior of digital circuits is a crucial step in system design, usually done via analog or digital simulation. Analog simulators like SPICE iteratively solve the differential equations characterizing the circuits components numerically. Although unrivaled in accuracy, this is only feasible for small designs, due to the high comput

Xuefeng Ni, Linshan Wu, Jiaxin Zhuang, Qiong Wang

3D medical image analysis is pivotal in numerous clinical applications. However, the scarcity of labeled data and limited generalization capabilities hinder the advancement of AI-empowered models. Radiology reports are easily accessible and can serve as weakly-supervised signals. However, large-scale vision-language pre-training (VLP) remains underexplored i

V. P. Mineev

A theory of the normal and superconducting states of piezomagnetic metals, which are altermagnetic materials, has been developed. This has been done in comparison with the corresponding theoretical description for metals that do not have spatial inversion symmetry. Particular attention has been paid to the problem of the anomalous Hall effect, and its absenc

Chetan Lodha, Ajay Kumar Rai

Motivated by recent developments in tetraquark studies, we investigate the mass spectra and decay properties of light-strange tetraquarks ($sq\bar{s}\bar{q},ss\bar{q}\bar{q}$) in diquark-antidiquark formalism. By considering different internal quark structures and internal color structures, mass spectra are generated in semi-relativistic and non-relativistic

Mohd Saif Shaikh, Mircea-Traian Catuneanu, Ahmad Echresh, Rang Li

Photonic integrated circuits (PICs) are crucial for advancing optical communications, promising substantial gains in data transmission speed, bandwidth, and energy efficiency compared to conventional electronics. Telecom-wavelength photodetectors, operating near 1550 nm, are indispensable in PICs, where they enable the sensitive and low-noise conversion of o

Yugang Chang, Hongyu Chen, Fei Wang, Chengcheng Chen

This paper introduces the Maritime Ship Navigation Behavior Dataset (MID), designed to address challenges in ship detection within complex maritime environments using Oriented Bounding Boxes (OBB). MID contains 5,673 images with 135,884 finely annotated target instances, supporting both supervised and semi-supervised learning. It features diverse maritime sc

Y. -Y. Chen, K. Li, L. Zhang, Y. -K. Wu

The requirement for Hermiticity in quantum mechanics ensures the reality of energies, while the parity-time symmetry offers an alternative route to achieve this goal. Interestingly, in a three-level system, the parity-time symmetry-breaking can lead to a third-order exceptional point with distinctive topological properties and enhanced sensitivity. To experi

Dibyajyoti Sahu, Suhas Gangadharaiah

Majorana Bound States (MBS) have emerged as promising candidates for robust quantum computing due to their non-Abelian statistics and topological protection. In this study, we focus on the dynamical transport of MBS in the semiconductor-superconductor (SM-SC) heterostructure via the piano key-type setup, wherein each of the keys of the wire can be tuned from

Vijayalaxmi Sahadevan, Sushil Mario, Yash Jaiswal, Divyanshu Bajpai

Ontology-based knowledge graphs (KG) are desirable for effective knowledge management and reuse in various decision making scenarios, including design. Creating and populating extensive KG based on specific ontological models can be highly labour and time-intensive unless automated processes are developed for knowledge extraction and graph creation. Most res

Haijun Jia

In this paper, we generalize a work of Rohrlich. Let $K/\mathbb{Q}$ be an imaginary quadratic field and $\phi$ be a Hecke character of $K$ of infinite type (1,0) whose restriction to $\mathbb{Q}$ is the quadratic character corresponding to $K/\mathbb{Q}$. We consider a class of Hecke characters $\chi$, which are anticyclotomic twists of $\phi$ with ramificat