November 2024 arXiv papers — page 35

Hua Qiu, Qi Wang

For a self-affine carpet $K$ of Bara\'{n}ski, we establish a dichotomy: $ \text{either }\quad 0<\mathcal{H}^{\dim_{\text{H}} K}(K)<+\infty \quad\text{ or } \quad\mathcal{H}^{\dim_{\text{H}} K}(K)=+\infty. $ We introduce four types of uniform fibre condition for $K$: Hausdorff ($\textbf{u.f.H}$), Box ($\textbf{u.f.B}$), Assouad ($\textbf{u.f.A}$), and Lower (

Zhenchen Wan, Yanwu Xu, Zhaoqing Wang, Feng Liu

Recent advancements in Virtual Try-On (VTO) have demonstrated exceptional efficacy in generating realistic images and preserving garment details, largely attributed to the robust generative capabilities of text-to-image (T2I) diffusion backbones. However, the T2I models that underpin these methods have become outdated, thereby limiting the potential for furt

Qing Han, Jiongduo Xie

In this paper, we study the regularity of solutions to uniformly degenerate elliptic equations in bounded domains under the condition that the characteristic polynomials have varying characteristic exponents.

Mingyu Cao, Gen Li, Jie Ji, Jiaqi Zhang

Mixture-of-Experts (MoE) has garnered significant attention for its ability to scale up neural networks while utilizing the same or even fewer active parameters. However, MoE does not alleviate the massive memory requirements of networks, which limits their practicality in real-world applications, especially in the era of large language models (LLMs). While

Yuchen Wu, Shengxin Li, Shizhen Zhang, Xingbo Wang

Academic Oral Presentation (AOP) allows English-As-Foreign-Language (EFL) students to express ideas, engage in academic discourse, and present research findings. However, while previous efforts focus on training efficiency or speech assistance, EFL students often face the challenge of seamlessly integrating verbal, nonverbal, and visual elements into their p

Jean-Luc Lupien, Abdullah Alhadlaq, Yuhan Tang, Jiayu Joyce Chen

In urban environments, parking has proven to be a significant source of congestion and inefficiency. In this study, we propose a methodology that offers a systematic solution to minimize the time spent by drivers in finding parking spaces. Drawing inspiration from statistical mechanics, we utilize an entropy model to predict the distribution of available par

Emmy Brown, Sean T. Vittadello

At the heart of many contemporary theories of life is the concept of biological self-organisation: organisms have to continuously produce and maintain the conditions of their own existence in order to stay alive. The way in which these accounts articulate this concept, however, differs quite significantly. As a result, it can be difficult to identify self-or

Shijian Deng, Wentian Zhao, Yu-Jhe Li, Kun Wan

Self-improvement in multimodal large language models (MLLMs) is crucial for enhancing their reliability and robustness. However, current methods often rely heavily on MLLMs themselves as judges, leading to high computational costs and potential pitfalls like reward hacking and model collapse. This paper introduces a novel, model-level judge-free self-improve

Manuel S. Drehwald, Asma Jamali, Rodrigo A. Vargas-Hernández

In this work, we present MOLPIPx, a versatile library designed to seamlessly integrate Permutationally Invariant Polynomials (PIPs) with modern machine learning frameworks, enabling the efficient development of linear models, neural networks, and Gaussian process models. These methodologies are widely employed for parameterizing potential energy surfaces acr

Spencer Chapman, Eli B. Dugan, Shadi Gaskari, Emi Lycan

A factorization of an element $x$ in a monoid $(M, \cdot)$ is an expression of the form $x = u_1^{z_1} \cdots u_k^{z_k}$ for irreducible elements $u_1, \ldots, u_k \in M$, and the length of such a factorization is $z_1 + \cdots + z_k$. We introduce the notion of $p$-length, a generalized notion of factorization length obtained from the $\ell_p$-norm of the s

Baiqiang Wang, Dongfang Zhao

Order-preserving encryption (OPE) is a fundamental cryptographic tool for enabling efficient range queries on encrypted data in outsourced databases. Despite its importance, existing OPE schemes face critical limitations that hinder their practicality. Stateful designs require clients to maintain plaintext-to-ciphertext mappings, imposing significant storage

José Roberto Castilho Piqueira, Felipe Freitas, Luis Antonio Aguirre

Phase-locked loop (PLL), conceived in 1932 by H. Bellescize, has been the basic electronic component in the development of communication technology from the early analog radio receptors to modern digital civil and military facilities. Traditionally, the analysis is conducted by modeling the dynamical behavior of phase and frequency errors, hence following a

Qian Zhai, Jujia Zhang, Weili Lin, Paolo Mazzali

We present photometric and spectroscopic observations of supernova (SN) 2014C, primarily emphasizing the initial month after the explosion at approximately daily intervals. During this time, it was classified as a Type Ib SN exhibiting a notably higher peak luminosity ($L_{\rm peak}\approx4.3\times10^{42}\rm erg\,s^{-1}$), a faster rise to brightness ($t_{\r

Tae Bong Jeong, Myoungwon Jeon, Hyunmi Song, Volker Bromm

Recent findings from photometric and spectroscopic JWST surveys have identified examples of high-redshift galaxies at $z \gtrsim 10$. These high-$z$ galaxies appear to form much earlier and exhibit greater UV luminosity than predicted by theoretical work. In this study, our goal is to reproduce the brightness of these sources by simulating high-redshift gala

Craig Iaboni, Pramod Abichandani

Spiking Neural Networks (SNNs) represent a biologically inspired paradigm offering an energy-efficient alternative to conventional artificial neural networks (ANNs) for Computer Vision (CV) applications. This paper presents a systematic review of datasets, architectures, learning methods, implementation techniques, and evaluation methodologies used in CV-bas

Vikas Arora, Pramoda K Nayak, Victor S Muthu, A K Sood

Two-dimensional van der Waals heterostructures (HS) exhibit twist-angle ($\theta$) dependent interlayer charge transfer, driven by moir\'e potential that tunes the electronic band structure with varying $\theta$. Apart from the magic angles of $\sim$3$^\circ$ and $\sim$57.5$^\circ$ that show flat valence bands (twisted WSe$_2$ bilayer), the commensurate angl

Kamil Erdayandi, Mustafa Asan Mustafa

In this paper, we propose a novel Privacy-Preserving clearance mechanism for Local Energy Markets (PP-LEM), designed for computational efficiency and social welfare. PP-LEM incorporates a novel competitive game-theoretical clearance mechanism, modelled as a Stackelberg Game. Based on this mechanism, a privacy-preserving market model is developed using a part

Mateo Muro

In this paper, we prove the following characterization: an abelian Mal'cev variety is finitely based if and only it has finite type, its ring of idempotent binary terms is finitely presented, and its module of unary terms is finitely presented.

Qiangqiang Mao, Yankai Cao

The prevailing mindset is that a single decision tree underperforms classic random forests in testing accuracy, despite its advantages in interpretability and lightweight structure. This study challenges such a mindset by significantly improving the testing accuracy of an oblique regression tree through our gradient-based entire tree optimization framework,

Shambhavi Mishra, Julio Silva-Rodriguez, Ismail Ben Ayed, Marco Pedersoli

Large pre-trained vision-language models (VLMs), such as CLIP, have shown unprecedented zero-shot performance across a wide range of tasks. Nevertheless, these models may be unreliable under distributional shifts, as their performance is significantly degraded. In this work, we investigate how to efficiently utilize class text information to mitigate distrib

Carl A. Kukkonen

The electron-positive fermion gas in three dimensions and $T=0$ is modeled as two independent fermion gases interacting via the coulomb interaction. The main advantage of the simple model is that all existing results from the electron gas can be directly used for the positive fermion gas, which is the same as the electron gas, but scaled for the mass of the

Caleb S. Spradlin, Jordan A. Caraballo-Vega, Jian Li, Mark L. Carroll

Foundation models have the potential to transform the landscape of remote sensing (RS) data analysis by enabling large computer vision models to be pre-trained on vast amounts of remote sensing data. These models can then be fine-tuned with small amounts of labeled training and applied to a variety of applications. Most existing foundation models are designe

Shipeng Liu, FNU Shrutika, Boshen Zhang, Zhehui Huang

Effective human-robot collaboration requires robot to adopt their roles and levels of support based on human needs, task requirements, and complexity. Traditional human-robot teaming often relies on a pre-determined robot communication scheme, restricting teamwork adaptability in complex tasks. Leveraging strong communication capabilities of Large Language M

Samuel G. Gessow, David Thorne, Brett T. Lopez

This paper develops a new framework for preventing localization failures in mobile systems that must estimate their state using measurements. Safety is guaranteed by imposing the nonlinear least squares optimization solved in modern localization algorithms remains well-conditioned. Specifically, the eigenvalues of the Hessian matrix are made to be always pos

Nanae Domoto, Shinya Wanajo, Masaomi Tanaka, Daiji Kato

Kilonova spectra provide us with the direct information of r-process nucleosynthesis in neutron star mergers. In this paper, we study the signatures of elements beyond the third r-process peak expected to be produced in neutron-rich ejecta in the photospheric spectra of kilonova. Ra II, Ac III, and Th III are our candidates because they have a small number o

Jon Geist

Everett's "Relative State Formulation of Quantum Mechanics" (RSQM), which appeared in Reviews of Modern Physics, is based on his thesis "The Theory of the Universal Wavefunction". The most noteworthy property of these works is the claim by other authors that these works are the seminal contribution to Many Worlds theories of branching realities and the claim

Amar Kulkarni, Shangtong Zhang, Madhur Behl

Ensuring the safety of autonomous vehicles (AVs) requires identifying rare but critical failure cases that on-road testing alone cannot discover. High-fidelity simulations provide a scalable alternative, but automatically generating realistic and diverse traffic scenarios that can effectively stress test AV motion planners remains a key challenge. This paper

Samprit Ghosh

The higher Euler-Kronecker constants of a number field $K$ are the coefficients in the Laurent series expansion of the logarithmic derivative of the Dedekind zeta function about $s=1$. These coefficients are mysterious and seem to contain a lot of arithmetic information. In this article, we study these coefficients. We prove arithmetic formulas satisfied by

David Gray Widder, Sireesh Gururaja, Lucy Suchman

In the context of unprecedented U.S. Department of Defense (DoD) budgets, this paper examines the recent history of DoD funding for academic research in algorithmically based warfighting. We draw from a corpus of DoD grant solicitations from 2007 to 2023, focusing on those addressed to researchers in the field of artificial intelligence (AI). Considering the

Matthias Paulsen, Sönke Rollenske, Konstantin Wehler

We show that a compact complex parallelisable nilmanifold has unobstructed deformations if and only if its associated Lie algebra satisfies a reality condition and is a free Lie algebra in a variety of Lie algebras, that is, defined by a verbal ideal in a free Lie algebra. We provide a partial classification of verbal ideals and show that there are finitely

Xiangjian Qian, Rongyi Lv, Jong Yeon Lee, Mingpu Qin

We propose a generalized Shastry-Sutherland model which bridges the Shastry-Sutherland model and the $J_1$-$J_2$ Heisenberg model. By employing large scale Density Matrix Renormalization Group and Fully Augmented Matrix Product State calculations, combined with careful finite-size scaling, we find the phase transition between the plaquette valence bond state

Jonas Beddrich, Stephan B. Lunowa, Barbara Wohlmuth

We address the numerical challenge of solving the Hookean-type time-fractional Navier--Stokes--Fokker--Planck equation, a history-dependent system of PDEs defined on the Cartesian product of two $d$-dimensional spaces in the turbulent regime. Due to its high dimensionality, the non-locality with respect to time, and the resolution required to resolve turbule

Shubham Bhardwaj, Ashwin Vinod, Soumojit Bhattacharya, Aryan Koganti

Point cloud representation has gained traction due to its efficient memory usage and simplicity in acquisition, manipulation, and storage. However, as point cloud sizes increase, effective down-sampling becomes essential to address the computational requirements of downstream tasks. Classical approaches, such as furthest point sampling (FPS), perform well on

Mohammad R. Salmanpour, Arman Gorji, Amin Mousavi, Ali Fathi Jouzdani

Objective: This study explores a semi-supervised learning (SSL), pseudo-labeled strategy using diverse datasets to enhance lung cancer (LCa) survival predictions, analyzing Handcrafted and Deep Radiomic Features (HRF/DRF) from PET/CT scans with Hybrid Machine Learning Systems (HMLS). Methods: We collected 199 LCa patients with both PET & CT images, obtained

Michael Ginn

Linguists have long held that a key aspect of natural language syntax is the recursive organization of language units into constituent structures, and research has suggested that current state-of-the-art language models lack an inherent bias towards this feature. A number of alternative models have been proposed to provide inductive biases towards constituen

Chenhan Zhang, Benjamin Zi Hao Zhao, Hassan Asghar, Dali Kaafar

Users may inadvertently upload personally identifiable information (PII) to Machine Learning as a Service (MLaaS) providers. When users no longer want their PII on these services, regulations like GDPR and COPPA mandate a right to forget for these users. As such, these services seek efficient methods to remove the influence of specific data points. Thus the

Keyur D. Shah, James A. Shackleford, Nagarajan Kandasamy, Gregory C. Sharp

Purpose: Deformable image registration (DIR) is critical in adaptive radiation therapy (ART) to account for anatomical changes. Conventional intensity-based DIR methods often fail when image intensities differ. This study evaluates a hybrid similarity metric combining intensity and structural information, leveraging CycleGAN-based intensity correction and au

Yao Fu, Yin Yu, Xiaotian Han, Runchao Li

Knowledge distillation (KD) has become a widely adopted approach for compressing large language models (LLMs) to reduce computational costs and memory footprints. However, the availability of complex teacher models is a prerequisite for running most KD pipelines. Thus, the traditional KD procedure can be unachievable or budget-unfriendly, particularly when r

Gregory J. Brault, Christopher James Augeri, Barry E. Mullins, Rusty O. Baldwin

A skip graph is a resilient application-layer routing structure that supports range queries of distributed k-dimensional data. By sorting deterministic keys into groups based on locally computed random membership vectors, nodes in a standard skip graph can optimize range query performance in mobile networks such as unmanned aerial vehicle swarms. We propose

Hamid Kamangir, Brent. S. Sams, Nick Dokoozlian, Luis Sanchez

Crop yield prediction is essential for agricultural planning but remains challenging due to the complex interactions between weather, climate, and management practices. To address these challenges, we introduce a deep learning-based multi-model called Climate-Management Aware Vision Transformer (CMAViT), designed for pixel-level vineyard yield predictions. C

Najib Khachiaa

The aim of this work is to study (Multi-window) Gabor systems in the space \(\ell^2(\mathbb{Z} \times \mathbb{Z}, \mathbb{H})\), denoted by $\mathcal{G}(g,L,M,N)$, and defined by: \[ \left\{ (k_1,k_2)\in \mathbb{Z}^2\mapsto e^{2\pi i \frac{m_1}{M}k_1} g_l(k - nN) e^{2\pi j \frac{m_2}{M}k_2} \right\}_{l \in \mathbb{N}_L, (m_1, m_2) \in \mathbb{N}_M^2, n \in \

Bruno Gomes, Samih Eisa, David R. Matos, Miguel L. Pardal

Users increasingly rely on identity providers for accessing online services and resources. However, centralized identity systems often compromise user privacy due to online activity tracking or data breaches. At the same time, many online services require digital copies of physical documents for validation in claims processes, such as providing proof of resi

Guopeng Xu, Chunli Huang

Recent scanning tunneling microscopy experiments have found Kekul\'e-Distorted (KD) ordering in graphene subjected to strong magnetic fields, a departure from the antiferromagnetic (AF) state identified in earlier transport experiments on double-encapsulated devices with larger dielectric screening constant $\epsilon$. This variation suggests that the magnet

Tim Hartill

Pretrained large Language Models (LLMs) are able to answer questions that are unlikely to have been encountered during training. However a diversity of potential applications exist in the broad domain of reasoning systems and considerations such as latency, cost, available compute resource and internet connectivity are relevant in determining an appropriate

Jean-Claude Cuenin

We prove eigenvalue bounds for Schr\"odinger operator $-\Delta_g+V$ on compact manifolds with complex potentials $V$. The bounds depend only on an $L^q$-norm of the potential, and they are shown to be optimal, in a certain sense, on the round sphere and more general Zoll manifolds. These bounds are natural analogues of Frank's \cite{MR2820160} results in the

Antonio De Felice, François Larrouturou

In a cosmological context, the Einstein-Gauss-Bonnet theory contains, in $d+4$ dimensions, a dynamical compactification scenario in which the additional dimensions settle down to a configuration with a constant radion/scale factor. Sadly however this work demonstrates that such a quite appealing framework is plagued by instabilities, either from the backgrou

N. Nikuradze, Z. N. Osmanov

Our aim is to evaluate the maximum attainable energies of electrons accelerated by means of the magneto-centrifugal mechanism. We examine how the range of maximum possible energies, as well as the primary limiting factors, vary with black hole mass. Additionally, we analyse the dependence of the maximum relativistic factor on the initial distance from the bl

Yang Xu, Yue Wu, Robert L. Jackson

Electrical contact is fundamental to almost every aspect of modern industry, including the fast-growing electric vehicle industry. In metallic contacts in atmospheric conditions, most of the electrical current passes via the micro-junctions formed between two electrodes. The classic electrical contact theory predicts an infinite current density at the circul

Songqinghao Yang, Prakash Murali

Circuit cutting allows quantum circuits larger than the available hardware to be executed. Cutting techniques split circuits into smaller subcircuits, run them on the hardware, and recombine results through classical post-processing. Circuit cutting techniques have been extensively researched over the last five years and it been adopted by major quantum hard

Jacob Oloketuyi, Yu Liu, Linhua Deng, Abouazza Elmhamdi

Understanding coronal structure and dynamics can be facilitated by analyzing green-line emission, which enables the investigation of diverse coronal structures such as coronal loops, streamers, coronal holes, and various eruptions in the solar atmosphere. In this study, we investigated the spatiotemporal behaviors of green-line emissions in both low and high

Naoki Masuda, Saiful Islam, Si Thu Aung, Takamitsu Watanabe

We review a class of energy landscape analysis method that uses the Ising model and takes multivariate time series data as input. The method allows one to capture dynamics of the data as trajectories of a ball from one basin to a different basin to yet another, constrained on the energy landscape specified by the estimated Ising model. While this energy land

Weiguang Liu

We establish normal approximation in the Wasserstein metric for both non-degenerate and degenerate second-order U-statistics under cross-sectional dependence using Stein's method. For the non-degenerate case, our results extend recent studies on the asymptotic properties of sums of cross-sectionally dependent random variables. The degenerate case is more cha

Dieudonné Zirhumanana Balike, Maria Rosaria Mattei, Luigi Frunzo

We present the stability analysis of two free boundary problems arising in biofilm modelling. The first, introduced in the 1980s by Wanner and Gujer, is related to the competition between autotrophic and heterotrophic bacteria in a biofilm bioreactor. It is a free boundary problem consisting of a system of hyperbolic PDEs modelling biofilm growth and the com

Francisco Faria, Samih Eisa, David R. Matos, Miguel L. Pardal

Blockchain technology supports decentralized, consensus-driven data storage and processing, ensuring integrity and auditability. It is increasingly adopted for use cases with multiple stakeholders with shared ownership scenarios like digital identity and supply chain management. However, real-world deployments face challenges with mistakes and intrusions. Th

Zan Chaudhry, Naoko Mizuno

Hyperparameter tuning remains a significant challenge for the training of deep neural networks (DNNs), requiring manual and/or time-intensive grid searches, increasing resource costs and presenting a barrier to the democratization of machine learning. The global initial learning rate for DNN training is particularly important. Several techniques have been pr

Joseph Fowler, Ian Fogarty Florang, Nathan Nakamura, Daniel Swetz

Naturally occurring radiation backgrounds cause correlated decoherence events in superconducting qubits. These backgrounds include both gamma rays produced by terrestrial radioisotopes and cosmic rays. We use the particle-transport code Geant4 and the PARMA summary of the cosmic-ray spectrum to model both sources of natural radiation and to study their effec

Hrishikesh D Vinod

We build a context-free, comprehensive, flexible, and sound footing for measuring the dependence of two variables based on three new axioms, updating Renyi's (1959) seven postulates. We illustrate the superior footing of axioms by Vinod's (2014) asymmetric matrix of generalized correlation coefficients R*. We list five limitations explaining the poorer footi

Rambod Azimi, Yijian Kong, Dusan Gostimirovic, James J. Clark

Integrated silicon photonic devices, which manipulate light to transmit and process information on a silicon-on-insulator chip, are highly sensitive to structural variations. Minor deviations during nanofabrication-the precise process of building structures at the nanometer scale-such as over- or under-etching, corner rounding, and unintended defects, can si

Amirabbas Afzali, Hesam Hosseini, Mohmmadamin Mirzai, Arash Amini

Time series data analysis is prevalent across various domains, including finance, healthcare, and environmental monitoring. Traditional time series clustering methods often struggle to capture the complex temporal dependencies inherent in such data. In this paper, we propose the Variational Mixture Graph Autoencoder (VMGAE), a graph-based approach for time s

Ju-Hyung Lee, Joohan Lee, Andreas F. Molisch

Massive MIMO (mMIMO) systems are essential for 5G/6G networks to meet high throughput and reliability demands, with machine learning (ML)-based techniques, particularly autoencoders (AEs), showing promise for practical deployment. However, standard AEs struggle under noisy channel conditions, limiting their effectiveness. This work introduces a Vector Quanti

Daniel Pranjić, Florian Knäble, Philipp Kunst, Damian Kutzias

Whether in fundamental physics, cybersecurity or finance, the detection of anomalies with machine learning techniques is a highly relevant and active field of research, as it potentially accelerates the discovery of novel physics or criminal activities. We provide a systematic analysis of the generalization properties of the One-Class Support Vector Machine

Srikar Yellapragada, Alexandros Graikos, Kostas Triaridis, Prateek Prasanna

Diffusion models have revolutionized image generation, yet several challenges restrict their application to large-image domains, such as digital pathology and satellite imagery. Given that it is infeasible to directly train a model on 'whole' images from domains with potential gigapixel sizes, diffusion-based generative methods have focused on synthesizing s

Kevin Gomez, Ken Ono, Hasan Saad, Ajit Singh

We revisit Euler's partition function recurrence, which asserts, for integers $n\geq 1,$ that $$ p(n)=p(n-1)+p(n-2)-p(n-5)-p(n-7)+\dots = \sum_{k\in \mathbb{Z}\setminus \{0\}} (-1)^{k+1} p(n-\omega(k)), $$ where $\omega(m):=(3m^2+m)/2$ is the $m$th pentagonal number. We prove that this classical result is the $\nu=0$ case of an infinite family of ``pentagona

Dénes Berta, Balduin Katzer, Katrin Schulz, Péter Dusán Ispánovity

Acoustic emission signals have been shown to accompany avalanche-like events in materials, such as dislocation avalanches in crystalline solids, collapse of voids in porous matter or domain wall movement in ferroics. The data provided by acoustic emission measurements is tremendously rich, but it is rather challenging to precisely connect it to the character

Eli M. Levenson-Falk, Sadman Ahmed Shanto

In this short review we describe the process of designing a superconducting circuit device for quantum information applications. We discuss the factors that must be considered to implement a desired effective Hamiltonian on a device. We describe the translation between a device's physical layout, the circuit graph, and the effective Hamiltonian. We go over t

Masayo Fujimura, Matti Vuorinen

We prove a sharp result for the distortion of a hyperbolic type metric under $K$-quasiregular mappings of the upper half plane. The proof makes use of a new kind of Bernoulli inequality and the Schwarz lemma for quasiregular mappings.

Katherine Piper, Vinamra Agrawal

Despite advances in manufacturing making metal functionally graded materials (FGMs) more common, numerical methods for predicting fracture in ductile functionally graded materials remain limited. In this work we study the crack propagation in ductile FGMs, specifically focusing on crack propagation within the gradient region of an FGM. We investigate the dir

Catalina M Jaramillo, Paul Squires, Julian Togelius

Fairness,the impartial treatment towards individuals or groups regardless of their inherent or acquired characteristics [20], is a critical challenge for the successful implementation of Artificial Intelligence (AI) in multiple fields like finances, human capital, and housing. A major struggle for the development of fair AI models lies in the bias implicit i

Yuming Feng, Zhiyang Dou, Ling-Hao Chen, Yuan Liu

Modeling temporal characteristics and the non-stationary dynamics of body movement plays a significant role in predicting human future motions. However, it is challenging to capture these features due to the subtle transitions involved in the complex human motions. This paper introduces MotionWavelet, a human motion prediction framework that utilizes Wavelet

Nimrod Megiddo

Combinatorial optimization can be described as the problem of finding a feasible subset that maximizes a objective function. The paper discusses combinatorial optimization problems, where for each dimension the set of feasible subsets is fixed. It is demonstrated that in some cases fixing the structure makes the problem easier, whereas in general the problem

Joseph Nyangon

Climate change significantly increases risks to power systems, exacerbating issues such as aging infrastructure, evolving regulations, cybersecurity threats, and fluctuating demand. This paper focuses on the utilization of Grid Enhancing Technologies (GETs) to strengthen power system resilience in the face of extreme weather events. GETs are pivotal in optim

Lining Yu, Mengmeng Yin, Ruining Deng, Quan Liu

Segmenting glomerular intraglomerular tissue and lesions traditionally depends on detailed morphological evaluations by expert nephropathologists, a labor-intensive process susceptible to interobserver variability. Our group previously developed the Glo-In-One toolkit for integrated detection and segmentation of glomeruli. In this study, we leverage the Glo-

Christian Magliano, Giovanni Covone, Enrico Corsaro, Laura Inno

The search for exoplanets has led to the identification of intriguing patterns in their distributions, one of which is the so-called sub-Jovian and Neptune desert. The occurrence rate of Neptunian exoplanets with an orbital period $P\lesssim 4$ days sharply decreases in this region in period-radius and period-mass space. We present a novel approach to deline

Ezra Ameperosa, Jeremy A. Collins, Mrinal Jain, Animesh Garg

Imitation learning in robotics faces significant challenges in generalization due to the complexity of robotic environments and the high cost of data collection. We introduce RoCoDA, a novel method that unifies the concepts of invariance, equivariance, and causality within a single framework to enhance data augmentation for imitation learning. RoCoDA leverag

H. Yu, Z. Garai, M. Cretignier, Gy. M. Szabó

The AU Microscopii planetary system is only 24 Myr old, and its geometry may provide clues about the early dynamical history of planetary systems. Here, we present the first measurement of the Rossiter-McLaughlin effect for the warm sub-Neptune AU Mic c, using two transits observed simultaneously with the European Southern Observatory's (ESO's) Very Large Te

Yu. D. Panov, S. V. Nuzhin, V. S. Ryumshin, A. S. Moskvin

The peculiarities of phase states of the triplet boson model for orthonickelates are investigated analytically and by means of numerical simulations. The conditions of thermodynamic stability of homogeneous phases are found. It is shown that the description of the phase inhomogeneous state in the mean-field approximation qualitatively agrees with the observe

Nawaf Alampara, Mara Schilling-Wilhelmi, Martiño Ríos-García, Indrajeet Mandal

Recent advancements in artificial intelligence have sparked interest in scientific assistants that could support researchers across the full spectrum of scientific workflows, from literature review to experimental design and data analysis. A key capability for such systems is the ability to process and reason about scientific information in both visual and t

Kamal Acharya, Mehul Lad, Liang Sun, Houbing Song

In recent years, the rapid pace of urbanization has posed profound challenges globally, exacerbating environmental concerns and escalating traffic congestion in metropolitan areas. To mitigate these issues, Advanced Air Mobility (AAM) has emerged as a promising transportation alternative. However, the effective implementation of AAM requires robust demand mo

Xiaoteng, Liu, Pavly Halim

Analytical framework for predicting General Matrix Multiplication (GEMM) performance on modern GPUs, focusing on runtime, power consumption, and energy efficiency. Our study employs two approaches: a custom-implemented tiled matrix multiplication kernel for fundamental analysis, and NVIDIA's CUTLASS library for comprehensive performance data collection acros

Massine El Khader, Elias Al Bouzidi, Abdellah Oumida, Mohammed Sbaihi

Recent advances in Diffusion Models have enabled the generation of images from text, with powerful closed-source models like DALL-E and Midjourney leading the way. However, open-source alternatives, such as StabilityAI's Stable Diffusion, offer comparable capabilities. These open-source models, hosted on Hugging Face, come equipped with ethical filter protec

Henry H. Kim, Takuya Yamauchi

By using the degenerate Whittaker functions, we study the Fourier expansion of the Gan-Gurevich lifts which are Hecke eigen quaternionic cusp forms of weight $k$ ($k\geq 2$, even) on the split exceptional group $G_2$ over $\mathbb{Q}$ which come from elliptic newforms of weight $2k$ without supercuspidal local components. In particular, our results give a pa

Nicholas J. Moore, Brendan Foerster

The Gibbs ensemble of the truncated KdV (TKdV) equation has been shown to accurately describe the anomalous wave statistics observed in laboratory experiments, in particular the emergence of extreme events. Here, we introduce a novel proposal distribution that facilitates efficient rejection sampling of the TKdV Gibbs measure. Within parameter regimes access

Valeri V. Makarov, Phil Cigan, David Gordon, Megan C. Johnson

Published analyses of very long baseline interferometry (VLBI) data for the sources included in the third International Celestial Reference Frame (ICRF3) catalog have revealed object-specific, excess astrometric variability and quasi-coherent trajectories as functions of time. A fraction of these sources show markedly elongated distributions of positions on

Damir D. Dzhafarov, Reed Solomon, Andrea Volpi

A relational structure is \emph{strongly indivisible} if for every partition $M = X_0 \sqcup X_1$, the induced substructure on $X_0$ or $X_1$ is isomorphic to $\mathcal{M}$. Cameron (1997) showed that a graph is strongly indivisible if and only if it is the complete graph, the completely disconnected graph, or the random graph. We analyze the strength of Cam

Guoping Xu, Xiaoxue Qian, Hua Chieh Shao, Jax Luo

This study introduces SAMatch, a SAM-guided Match-based framework for semi-supervised medical image segmentation, aimed at improving pseudo label quality in data-scarce scenarios. While Match-based frameworks are effective, they struggle with low-quality pseudo labels due to the absence of ground truth. SAM, pre-trained on a large dataset, generalizes well a

Greg Schreiner, Alex Krolewski, Shahab Joudaki, Will J. Percival

Super sample covariance (SSC) is important when estimating covariance matrices using a set of mock catalogues for galaxy surveys. If the underlying cosmological simulations do not include the variation in background parameters appropriate for the simulation sizes, then the scatter between mocks will be missing the SSC component. The coupling between large an

Susanne Albers, Sebastian Schubert

The $b$-matching problem is an allocation problem where the vertices on the left-hand side of a bipartite graph, referred to as servers, may be matched multiple times. In the setting with stochastic rewards, an assignment between an incoming request and a server turns into a match with a given success probability. Mehta and Panigrahi (FOCS 2012) introduced o

Jakub Maksymilian Fober

Lens Distortion Encoding System (LDES) allows for a distortion-accurate workflow, with a seamless interchange of high quality motion picture images regardless of the lens source. This system is similar in a concept to the Academy Color Encoding System (ACES), but for distortion. Presented solution is fully compatible with existing software/plug-in tools for

Edson D. Leonel, Diego F. M. Oliveira

The probability distribution for multiple collisions observed in the chaotic low energy domain in the bouncing ball model is shown to be scaling invariant concerning the control parameters. The model considers the dynamics of a bouncing ball particle colliding elastically with two rigid walls. One is fixed, and the other one moves periodically in time. The d

Hyun Min Lee

We propose the microscopic origin of the pole inflation from the scalar fields of broken non-compact isometry in Weyl gravity. We show that the $SO(1,N)$ isometry in the field space in combination with the Weyl symmetry relates the form of the non-minimal couplings to the one of the potential in the Jordan frame. In the presence of an explicit breaking of th

B. Ram, L. P. Chitta, S. Mandal, H. Peter

Coronal loops generally trace magnetic lines of force in the upper solar atmosphere. Understanding the loop morphology and its temporal evolution has implications for coronal heating models that rely on plasma heating due to reconnection at current sheets. Simultaneous observations of coronal loops from multiple vantage points are best suited for this purpos

Sumit Chaudhary, Shahram Dehdashti, Igor Litvin, Janis Nötzel

Many quantum communication systems operate based on weak light pulses which by design are assumed to operate in isolation from regular data traffic. With the widespread availability and commercialization of these systems comes a need for seamless integration already at the physical layer. In particular for optical fiber links where wavelength division multip

A. V. Belitsky, L. V. Bork

We study off-shell n-particle form factors of half-BPS operators built from n complex scalar fields at the two-loop order in the planar maximally supersymmetric Yang-Mills theory (sYM). These are known as minimal form factors. We construct their representation as a sum of independent scalar Feynman integrals relying on two complementary techniques. First, by

Georgina Nuthall, Richard Bowden, Oscar Mendez

As robots increasingly coexist with humans, they must navigate complex, dynamic environments rich in visual information and implicit social dynamics, like when to yield or move through crowds. Addressing these challenges requires significant advances in vision-based sensing and a deeper understanding of socio-dynamic factors, particularly in tasks like navig

Christian Camilo Silva Villamil

Let $\varphi$ be a smooth conservative diffeomorphism of a compact surface $S$ and let $\Lambda$ be a mixing horseshoe of $\varphi$. Given a smooth real function $f$ defined on $S$, we define for points $\eta$ in the unstable Cantor set of the pair $(\varphi,\Lambda)$, a generalization, $k_{\varphi,\Lambda,f}(\eta)$, of the best constant of Diophantine appro

Arnab Kumar Maity, Jhanvi Garg, Cynthia Basu

The reliability of clinical trial outcomes is crucial, especially in guiding medical decisions. In this paper, we introduce the Fragility Index (FI) for time-to-event endpoints in single-arm clinical trials - a novel metric designed to quantify the robustness of study conclusions. The FI represents the smallest number of censored observations that, when recl

Yang Zhou, Sixu Li, Wissam Kontar, Fan Pu

This paper presents an analytical approximation framework to understand the dynamics of traffic wave propagation for Automated Vehicles (AVs) during traffic oscillations. The framework systematically unravels the intricate relationships between the longitudinal control model of the AVs and the properties of traffic waves. We apply Laplacian Transformation an

Kamyar Zeinalipour, Achille Fusco, Asya Zanollo, Marco Maggini

In this work, we unveil a novel tool for generating Italian crossword puzzles from text, utilizing advanced language models such as GPT-4o, Mistral-7B-Instruct-v0.3, and Llama3-8b-Instruct. Crafted specifically for educational applications, this cutting-edge generator makes use of the comprehensive Italian-Clue-Instruct dataset, which comprises over 30,000 e

M. Dannenberg, W. Hagerstrom, G. Hart, A. Iosevich

We solve a variant of the classical Buffon Needle problem. More specifically, we inspect the probability that a randomly oriented needle of length $l$ originating in a bounded convex set $X\subset\mathbb{R}^2$ lies entirely within $X$. Using techniques from convex geometry, we prove an isoperimetric type inequality, showing that among sets $X$ with equal per

Callum E. Flowerday, Ryan Thalman, Matthew C. Asplund, Jaron C. Hansen

Glyoxal CHOCHO is a trace gas in the atmosphere, often used as an indicator of biogenic emissions. It is frequently compared to formaldehyde concentrations, which serve as indicators of anthropogenic emissions, to gain insights into the characteristics of the environmental source. This study employed broadband cavity-enhanced absorption spectroscopy to detec