December 2024 arXiv papers — page 13

Michael fogel, Snigdha Sushil Mishra, Laurent Burlion

Sloshing Platform for In-Orbit Controller Experimentation is an ambitious, student run mission to design and fly a cubesat to study fluid sloshing in spacecraft. The project will examine zero-g propellant sloshing from an experimental standpoint. Despite the small size and limited payload capacity, we intend to use the cubesat platform to mimic larger spacec

Yang Xu, Jun Yan, Kai Zhao

In this paper, we study the asymptotic behavior of globally minimizing orbits of contact Hamiltonian systems. Under some assumptions, we prove that the $\omega$-limit set of globally minimizing orbits is contained in the set of semi-static orbits.

Yonghao Zhang, Qiang He, Yanguang Wan, Yinda Zhang

Generating high-quality whole-body human object interaction motion sequences is becoming increasingly important in various fields such as animation, VR/AR, and robotics. The main challenge of this task lies in determining the level of involvement of each hand given the complex shapes of objects in different sizes and their different motion trajectories, whil

Abdullah Alchihabi, Hao Yan, Yuhong Guo

Class imbalance is pervasive in real-world graph datasets, where the majority of annotated nodes belong to a small set of classes (majority classes), leaving many other classes (minority classes) with only a handful of labeled nodes. Graph Neural Networks (GNNs) suffer from significant performance degradation in the presence of class imbalance, exhibiting bi

Patrick Gould, Guanqun Song, Ting Zhu

This paper analyzes the proportion of Input/output devices made obsolete by changes in technology generations. This obsolescence may be by new software/hardware generations rendering otherwise functional devices unusable. Concluding with brief analysis on the economic and environmental impacts of the e-waste produced.

Hao Guo, Bangan Wu, Qi Li, Zhen Ding

Human trust plays a crucial role in the effectiveness of human-robot collaboration. Despite its significance, the development and maintenance of an optimal trust level are obstructed by the complex nature of influencing factors and their mechanisms. This study investigates the effects of cognitive load on human trust within the context of a hybrid human-robo

Hefei Li, Chao Peng, Chenyang Xu, Zhengfeng Yang

Neural algorithmic reasoning is an emerging area of machine learning that focuses on building neural networks capable of solving complex algorithmic tasks. Recent advancements predominantly follow the standard supervised learning paradigm -- feeding an individual problem instance into the network each time and training it to approximate the execution steps o

Jun Peng, Rakshith Venugopal, Robert Blick, Robert Zierold

In the relentless pursuit of advancing semiconductor technologies, the demand for atomic layer processes has given rise to innovative processes, which have already played a significant role in the continued miniaturization features. Among these, atomic layer etching (ALE) is gaining increasing attention, offering precise control over material removal at the

Keisuke Himeno, Masakazu Teragaito

In knot Floer homology, there are two types of torsion order. One is the minimal power of the action of the variable $U$ to annihilate the $\mathbb{F}_2[U]$-torsion submodule of the minus version of knot Floer homology $\mathrm{HFK}^-(K)$. This is introduced by Juh\'{a}sz, Miller and Zemke, and denoted by $\mathrm{Ord}(K)$. The other, $\mathrm{Ord}'(K)$, int

Mohammad Osaid, Debabrata Dasgupta

Fluid flow and mass transfer inside a bioreactor play a pivotal role in growing bone grafts, as cell proliferation is limited by the transport of nutrients and oxygen, as well as the removal of by-products from cells within the scaffold. Traditionally, perfusion bioreactors are used for tissue-engineered bone grafts. In this study, we modeled electrokinetic

Yousef Yeganeh, Azade Farshad, Ioannis Charisiadis, Marta Hasny

Scaling by training on large datasets has been shown to enhance the quality and fidelity of image generation and manipulation with diffusion models; however, such large datasets are not always accessible in medical imaging due to cost and privacy issues, which contradicts one of the main applications of such models to produce synthetic samples where real dat

Wencan Wang, Huanjun Zhang

This paper is concerned with a general linear quadratic (LQ) control problem of mean-field backward stochastic differential equation (BSDE). Here, the weighting matrices in the cost functional are allowed to be indefinite. Necessary and sufficient conditions for optimality are obtained via a mean-field forward-backward stochastic differential equation (FBSDE

Rosario D. Laureano, Diana A. Mendes, Manuel Alberto M. Ferreira

This article provides a self-contained comprehensive review of the phenomenon of synchronization in dynamical systems, with a particular focus on chaotic systems in both continuous-time and discrete-time contexts. Synchronization, initially observed by Christiaan Huygens in 1665, has evolved from the study of periodic signals to encompass chaotic systems, wh

Wen Liang, Zhenhua Yu

How environments affect dynamics of quantum systems remains a central question in understanding transitions between quantum and classical phenomena and optimizing quantum technologies. A paradigm model to address the above question is the generalized Jaynes-Cummings model, in which a two-level particle is coupled to its environment modeled by a continuum bos

Aaditya Panigrahi, Alexei Tsvelik, Piers Coleman

We demonstrate that the discommensuration between the Fermi surfaces of a conduction sea and an underlying spin liquid provides a natural mechanism for the spontaneous formation of pair density waves. Using a recent formulation of the Kondo lattice model which incorporates a Yao Lee spin liquid proposed by the authors, we demonstrate that doping away from ha

Wei Shen, Ming Fang, Yuxia Wang, Jiafeng Xiao

Text-based person search aims to retrieve the matched pedestrians from a large-scale image database according to the text description. The core difficulty of this task is how to extract effective details from pedestrian images and texts, and achieve cross-modal alignment in a common latent space. Prior works adopt image and text encoders pre-trained on unimo

Lihao Liu, Juexiao Feng, Hui Chen, Ao Wang

Traditional object detection models are constrained by the limitations of closed-set datasets, detecting only categories encountered during training. While multimodal models have extended category recognition by aligning text and image modalities, they introduce significant inference overhead due to cross-modality fusion and still remain restricted by predef

Wonho Bae, Gabriel L. Oliveira, Danica J. Sutherland

Most active learning research has focused on methods which perform well when many labels are available, but can be dramatically worse than random selection when label budgets are small. Other methods have focused on the low-budget regime, but do poorly as label budgets increase. As the line between "low" and "high" budgets varies by problem, this is a seriou

Xiao-Chuan Xu, Yu-Ting Huang

In this paper, we study the direct and inverse spectral problems for the Schrodinger operator with two generalized Regge boundary conditions. For the direct problem, we give the properties of the spectrum, including the asymptotic distribution of the eigenvalues. For the inverse problems, we prove several uniqueness theorems, including the cases: even potent

Md Mahadi Hasan Nahid, Sadid Bin Hasan

Machine learning (ML) models frequently rely on training data that may include sensitive or personal information, raising substantial privacy concerns. Legislative frameworks such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) have necessitated the development of strategies that preserve privacy while maintain

Shuntaro Suzuki, Takaaki Wakamatsu, Yasutaka Shimizu

In this paper, we consider parameter estimation for stochastic differential equations driven by Wiener processes and compound Poisson processes. We assume unknown parameters corresponding to coefficients of the drift term, diffusion term, and jump term, as well as the Poisson intensity and the probability density function of the underlying jump. We propose e

Nima Arkani-Hamed, Carolina Figueiredo, Grant N. Remmen

Open string amplitudes at tree level have been studied for over fifty years, but there is no known analytic form for general $n$-point amplitudes, and their conventional representation in terms of worldsheet integrals does not make many of their most basic physical properties manifest. Recently, a formulation of these amplitudes exposing the underlying "bina

Hyunji Nam, Allen Nie, Ge Gao, Vasilis Syrgkanis

Off-policy policy evaluation (OPE) estimates the outcome of a new policy using historical data collected from a different policy. However, existing OPE methods cannot handle cases when the new policy introduces novel actions. This issue commonly occurs in real-world domains, like healthcare, as new drugs and treatments are continuously developed. Novel actio

Yongchang Li, Yujin Zhu, Tao Yan, Shijian Fan

As real-world knowledge is constantly evolving, ensuring the timeliness and accuracy of a model's knowledge is crucial. This has made knowledge editing in large language models increasingly important. However, existing knowledge editing methods face several challenges, including parameter localization coupling, imprecise localization, and a lack of dynamic i

Xiaoke Gao, Xiaoyu Zhao, Jiawei Wang, Xikui Ma

We propose a medium transformation approach to formulate the adjoint system of space-time modulated photonic crystals (STMPCs), essential for the bi-orthogonal Berry connection when calculating the topological invariant. We show that the non-Abelian Zak phase of STMPCs comprising stacked photonic time crystals and dielectrics is quantized to 0 or 1 for both

Jiawei Zhou, Woojeong Kim, Zhiying Xu, Alexander M. Rush

Understanding the traffic dynamics in networks is a core capability for automated systems to monitor and analyze networking behaviors, reducing expensive human efforts and economic risks through tasks such as traffic classification, congestion prediction, and attack detection. However, it is still challenging to accurately model network traffic with machine

Nguyen Xuan Tung, Le Tung Giang, Bui Duc Son, Seon Geun Jeong

Graph Neural Networks (GNNs) have emerged as a powerful framework for modeling complex interconnected systems, hence making them particularly well-suited to address the growing challenges of next-generation Internet of Things (NG-IoT) networks. Existing studies remain fragmented, and there is a lack of comprehensive guidance on how GNNs can be systematically

Ben Elias, Matthew Hogancamp

The Drinfeld centralizer of a monoidal category $\mathcal{A}$ in a bimodule category $\mathcal{M}$ is the category $\mathcal{Z}(\mathcal{A},\mathcal{M})$ of objects in $\mathcal{M}$ for which the left and right actions by objects of $\mathcal{A}$ coincide, naturally. In this paper we study the interplay between Drinfeld centralizers of $\mathcal{A}$ and its

Jordan Sinclair, Christopher Reardon

Human acceptance of social robots is greatly effected by empathy and perceived understanding. This necessitates accurate and flexible responses to various input data from the user. While systems such as this can become increasingly complex as more states or response types are included, new research in the application of large language models towards human-ro

Haoran Wei, Youyang Yin, Yumeng Li, Jia Wang

Recently, "visual o1" began to enter people's vision, with expectations that this slow-thinking design can solve visual reasoning tasks, especially geometric math problems. However, the reality is that current LVLMs (Large Vision Language Models) can hardly even accurately copy a geometric figure, let alone truly understand the complex inherent logic and spa

Bertrand Teguia Tabuguia

A sequence is difference algebraic (or D-algebraic) if finitely many shifts of its general term satisfy a polynomial relationship; that is, they are the coordinates of a generic point on an affine hypersurface. The corresponding equations are denoted algebraic difference equations (ADEs). We propose a formal definition of D-algebraicity for sequences and inv

Yao Ouyang, Yonghui Sun, Hua-Peng Zhang

Making use of the total variation of particular functions, we give an explicit formula for the pointwise supremum of the set of all copulas with a given curvilinear section. When the pointwise supremum is a copula is characterized. We also characterize the coincidence of the pointwise supremum and the greatest quasi-copula with the same curvilinear section.

Enrica Duchi, Gilles Schaeffer

A celebrated result of Bousquet-M\'elou and Jehanne states that the bivariate power series solutions of so-called combinatorial polynomial equations with one catalytic variable, also known as catalytic equations, are algebraic series. We give a purely combinatorial derivation of this result in the case of order one catalytic equations (those involving only o

Lien-Yung Kao, Giuseppe Martone

We introduce a correlation number for two strictly positive, locally H\"older continuous, independent potentials with strong entropy gaps at infinity on a topologically mixing countable state Markov shift with BIP. We define in this way a correlation number for pairs of cusped Hitchin representations. Furthermore, we explore the connection between the correl

Naoki Kitazawa

We discuss a necessary and sufficient condition for reconstruction of Morse functions with prescribed (regular) level sets on $3$-dimensional manifolds. The present work strengthens a previous result of the author where only sufficient conditions are studied. Our new work is also regarded as a kind of addenda.

Pavel M. Tikhonov, Alexander D. Fedotov, Georgiy A. Solomakha, Anna A. Hurshkainen

Wireless radio frequency coils provide a promising solution for clinical MR applications due to several benefits, such as cable-free connection and compatibility with MR platforms of different vendors. Namely, for the purpose of clinical high-field human breast imaging several wireless transceiver coils are known to the date, those operational principle is b

Aleksandar Pavlović

The main goal of this paper is to investigate relations between topologies obtained by: $\theta$-open sets, $\omega$-open sets, $\theta_\omega$-open sets, local function, and local closure function with ideal of the countable sets. As the result we will obtain some new the relations which hold in general, and, as the ultimate goal, equality of the topology o

Tejas Deshpande, Andra Ionescu, Nicholas Miller, Zhiyuan Wang

Measurements of differential length oscillations of Fabry-Perot cavities provide a sensitive and promising approach to searching for scalar ultralight dark matter (ULDM). The initial demonstration sets direct lower bounds that are one to two orders of magnitude lower for two model ULDM distributions -- a standard galactic halo and a relaxion star bound to Ea

Maria Barbosa, Marcelo Silva, Ednelson Cavalcanti, Kelvin Dias

An essential component for the Fifth Generation of Mobile Networks deployments is the 5G Core (5GC), which bridges the 5G Radio Access Network (RAN) to the rest of the Internet. Some open-source platforms for the 5GC have emerged and been deployed in Common Off-the-Shelf (COTS)-based setups. Despite these open-source 5GC initiatives following the 3GPP specif

Maria Barbosa, Kelvin Dias

Connected Vehicles (CVs) can leverage the unique features of 5G and future 6G/NextG networks to enhance Intelligent Transportation System (ITS) services. However, even with advancements in cellular network generations, CV applications may experience communication interruptions in high-mobility scenarios due to frequent changes of serving base station, also k

Mikkel Have Eriksen, Juan R. Deop-Ruano, Joel D. Cox, Alejandro Manjavacas

We investigate the emergence of self-hybridized thermal magnetoplasmons in doped graphene nanodisks at finite temperatures when subjected to an external magnetic field. Using a semianalytical approach, which fully describes the eigenmodes and polarizability of the graphene nanodisks, we show that the hybridization originates from the coupling of transitions

Ashish Seth, Dinesh Manocha, Chirag Agarwal

Large Vision-Language Models (LVLMs) have demonstrated remarkable performance in complex multimodal tasks. However, these models still suffer from hallucinations, particularly when required to implicitly recognize or infer diverse visual entities from images for complex vision-language tasks. To address this challenge, we propose HALLUCINOGEN, a novel visual

Wenhan Wu, Pengfei Wang, Chen Chen, Aidong Lu

Transformer-based human skeleton action recognition has been developed for years. However, the complexity and high parameter count demands of these models hinder their practical applications, especially in resource-constrained environments. In this work, we propose FreqMixForemrV2, which was built upon the Frequency-aware Mixed Transformer (FreqMixFormer) fo

Jose Guajardo, Ali Niknejad

Digital beamforming forms the foundation for massive MIMO in 6G wireless communications. At their core, digital beamforming architectures provide key benefits such as faster beam search, interference nulling via zero-force beamforming, higher spectral capacity, and more increased flexibility. However, they generally tradeoff power consumption due to the larg

Sawyer Jack Robertson

We consider graphs where edges and their signs are added independently at random from among all pairs of nodes. We establish strong concentration inequalities for adjacency and Laplacian matrices obtained from this family of random graph models. Then, we apply our results to study graphs sampled from the signed stochastic block model. Namely, we take a two-c

Abhirama Subramanyam Penamakuri, Kiran Chhatre, Akshat Jain

In this paper, we introduce Audiopedia, a novel task called Audio Question Answering with Knowledge, which requires both audio comprehension and external knowledge reasoning. Unlike traditional Audio Question Answering (AQA) benchmarks that focus on simple queries answerable from audio alone, Audiopedia targets knowledge-intensive questions. We define three

Abel Thayil, Lasse Ermoneit, Markus Kantner

Electron spin-qubits in silicon-germanium (SiGe) heterostructures are a major candidate for the realization of scalable quantum computers. A critical challenge in strained Si/SiGe quantum wells (QWs) is the existence of two nearly degenerate valley states at the conduction band minimum that can lead to leakage of quantum information. To address this issue, v

Sari Ghanem

We study the Einstein-Yang-Mills system in both the Lorenz and harmonic gauges, where the Yang-Mills fields are valued in any arbitrary Lie algebra $\cal G$, associated to any compact Lie group $G$. This gives a system of hyperbolic partial partial differential that does not satisfy the null condition and that has new complications that are not present for t

Sarwan Ali, Tamkanat E Ali, Imdad Ullah Khan, Murray Patterson

In the realm of data analysis and bioinformatics, representing time series data in a manner akin to biological sequences offers a novel approach to leverage sequence analysis techniques. Transforming time series signals into molecular sequence-type representations allows us to enhance pattern recognition by applying sophisticated sequence analysis techniques

Sarwan Ali, Tamkanat E Ali, Imdad Ullah Khan, Murray Patterson

Accurate molecular sequence analysis is a key task in the field of bioinformatics. To apply molecular sequence classification algorithms, we first need to generate the appropriate representations of the sequences. Traditional numeric sequence representation techniques are mostly based on sequence alignment that faces limitations in the form of lack of accura

Adam Gregory, Zachary Hamaker, Tianyi Yu

Lam, Lee and Shimozono recently introduced backstable double Grothendieck polynomials to represent $K$-theory classes of the infinite flag variety. They used them to define double $\beta$-Stanley symmetric functions, which expand into double stable Grothendieck functions with polynomial coefficients called double $\beta$-Edelman--Greene coefficients. Anderso

Devlin Gualtieri

I present a variant of the Buffon Needle method for determination of the value of the mathematical constant, pi. The original method is based on the random casting of a needle of length l onto a planked floor of plank width L. The described variant involves the random casting of an equilateral triangle with side length l onto a tiled floor consisting of squa

Yulin Fei, Yuhui Gao, Xingyuan Xian, Xiaojin Zhang

With the rise of multimodal large language models, accurately extracting and understanding textual information from video content, referred to as video based optical character recognition (Video OCR), has become a crucial capability. This paper introduces a novel benchmark designed to evaluate the video OCR performance of multi-modal models in videos. Compri

Ziyuan Qin, Jongseok Lee, Rudolph Triebel

Iterative Closest Point (ICP) is a commonly used algorithm to estimate transformation between two point clouds. The key idea of this work is to leverage recent advances in explainable AI for probabilistic ICP methods that provide uncertainty estimates. Concretely, we propose a method that can explain why a probabilistic ICP method produced a particular outpu

Haoxuan Fu, Jiaoyang Huang, Zirui Fan, Bingxin Zhao

In many predictive tasks, there are a large number of true predictors with weak signals, leading to substantial uncertainties in prediction outcomes. The polygenic risk score (PRS) is an example of such a scenario, where many genetic variants are used as predictors for complex traits, each contributing only a small amount of information. Although PRS has bee

Hong-Bin Chen

Recently, [arXiv:2311.08980] demonstrated that, if it exists, the limit free energy of possibly non-convex spin glass models must be determined by a characteristic of the associated infinite-dimensional non-convex Hamilton-Jacobi equation. In this work, we investigate a similar theme purely from the perspective of PDEs. Specifically, we study the unique visc

Nicholas J. Werner

Let $R$ be either the ring of Lipschitz quaternions, or the ring of Hurwitz quaternions. Then, $R$ is a subring of the division ring $\mathbb{D}$ of rational quaternions. For $S \subseteq R$, we study the collection $\rm{Int}(S,R) = \{f \in \mathbb{D}[x] \mid f(S) \subseteq R\}$ of polynomials that are integer-valued on $S$. The set $\rm{Int}(S,R)$ is always

Yousef Yeganeh, Rui Xiao, Goktug Guvercin, Nassir Navab

While conventional computer vision emphasizes pixel-level and feature-based objectives, medical image analysis of intricate biological structures necessitates explicit representation of their complex topological properties. Despite their successes, deep learning models often struggle to accurately capture the connectivity and continuity of fine, sometimes pi

Hong-Bin Chen

We consider the Potts spin glass with additional ferromagnetic interaction parametrized by $t$. It has long been observed that the Potts color symmetry breaking for the spin glass order parameter is closely related to the ferromagnetic phase transition. To clarify this, we identify a single critical value $t_\mathrm{c}$, which marks the onset of both color s

Daniel R. Johnston

The theory of newforms, due to Atkin and Lehner, provides a powerful method for decomposing spaces of modular forms. However, many problems occur when trying to generalise this theory to characteristic $p$. Recently, Deo and Medvedovsky have suggested a way around these problems by using purely algebraic notions to define newforms. In this thesis, we describ

Sean McGrath, Cenhao Zhu, Ryan O'Dea, Min Guo

Low-rank matrix estimation is a fundamental problem in statistics and machine learning with applications across biomedical sciences, including genetics, medical imaging, drug discovery, and electronic health record data analysis. In the context of heterogeneous data generated from diverse sources, a key challenge lies in leveraging data from a source populat

Sarah Chehade, Andrea Delgado, Shuzhou Wang, Zhenhua Wang

In quantum computing, Trotter estimates are critical for enabling efficient simulation of quantum systems and quantum dynamics, help implement complex quantum algorithms, and provide a systematic way to control approximate errors. In this paper, we extend the analysis of Trotter-Suzuki approximations, including third and higher orders, to Jordan-Banach algeb

Alfredo Cuzzocrea, Islam Belmerabet

This paper focuses the attention on privacy-preserving identity and access management in multiple Cloud environments, which is an annoying problem in the modern big data era. Within this conceptual context, the paper describes contemporaneous models and issues, and put the basis for future solid solutions. Finally, we provide a summary table where we embed a

Bimal Kumar, Dmitri Roussinov

Large Language Models (LLMs) such as GPT-4.0 have shown significant promise in addressing the semantic complexities of regulatory documents, particularly in detecting inconsistencies and contradictions. This study evaluates GPT-4.0's ability to identify conflicts within regulatory requirements by analyzing a curated corpus with artificially injected ambiguit

Pei Zhang, M. Paul Laiu, Matthew Norman, Doug Stefanski

Accurate representation of the multiscale features in spatiotemporal physical systems using vision transformer (ViT) architectures requires extremely long, computationally prohibitive token sequences. To address this issue, we propose two adaptive tokenization schemes that dynamically adjust patch sizes based on local features: one ensures convergent behavio

I. Ermeidis, M. Jotz

This paper develops the deformation theory of Lie ideals. It shows that the smooth deformations of an ideal $\mathfrak i$ in a Lie algebra $\mathfrak g$ differentiate to cohomology classes in the cohomology of $\mathfrak g$ with values in its adjoint representation on $\operatorname{Hom}(\mathfrak i, \mathfrak g/\mathfrak i)$. The cohomology associated with

Bouzid Mosbahi, Ahmed Zahari

In this paper, we introduce the concept of inner derivations of low-dimensional Zinbiel algebras and investigate their properties. The primary objective of this study is to develop an algorithm to characterize the inner derivations of any n-dimensional Zinbiel algebra in matrix form. Additionally, we apply this algorithm to two, three and four-dimensional co

Sebastian Toivonen, Kimmo Luoma

We investigate a quantum dot (QD) system coupled to a vibrational environment with a super-Ohmic spectral density and weakly to a leaky cavity mode, a model relevant for semiconductor-based single-photon sources. The phonon coupling induces dephasing and broadens the absorption and emission line shapes, while the weakly coupled cavity mode leads to effective

Aleksei Dorkin, Kairit Sirts

We present GliLem -- a novel hybrid lemmatization system for Estonian that enhances the highly accurate rule-based morphological analyzer Vabamorf with an external disambiguation module based on GliNER -- an open vocabulary NER model that is able to match text spans with text labels in natural language. We leverage the flexibility of a pre-trained GliNER mod

Tomer Garber, Tom Tirer

In recent years, it has become popular to tackle image restoration tasks with a single pretrained diffusion model (DM) and data-fidelity guidance, instead of training a dedicated deep neural network per task. However, such "zero-shot" restoration schemes currently require many Neural Function Evaluations (NFEs) for performing well, which may be attributed to

Dmitri Roussinov, Serge Sharoff, Nadezhda Puchnina

This study demonstrates that the modern generation of Large Language Models (LLMs, such as GPT-4) suffers from the same out-of-domain (OOD) performance gap observed in prior research on pre-trained Language Models (PLMs, such as BERT). We demonstrate this across two non-topical classification tasks: 1) genre classification and 2) generated text detection. Ou

Richárd Balka, Vilma Orgoványi, Alex Rutar

We resolve a few questions regarding the uniformity and size of microsets of subsets of Euclidean space. First, we construct a compact set $K\subset\mathbb{R}^d$ with Assouad dimension arbitrarily close to $d$ such that every microset of $K$ has no Ahlfors--David regular subset with dimension strictly larger than $0$. This answers a question of Orponen. Then

Mykola Khrypchenko

We describe $\sigma$-matching, interchangeable and, as a consequence, totally compatible structures on the strictly upper triangular matrix algebra $UT_n(K)$ for all $n\ge 3$.

Francesco Bajardi, Daniel Blixt, Salvatore Capozziello

We derive the Hamiltonian function for extended teleparallel theories of gravity in their covariant formulation. In particular, we present the Hamiltonian for $f(T)$ gravity and New General Relativity. From this, we obtain the related Hamilton equations, which are presented both in covariant formulation and Weitzenb\"ock gauge. In this framework, teleparalle

Patrick Erik Bradley

The error estimation for eigenvalues and eigenvectors of a small positive symmetric perturbation on the spectrum of a graph Laplacian is related to Gau{\ss} hypergeometric functions. Based on this, a heuristic polynomial-time algorithm for finding an optimal locally ultrametric approximation of a graph-distance power Laplacian matrix via the Vietoris-Rips gr

Jürgen Vollmer, Claudio Giberti, Jordan Orchard, Hannes Reinhard

Strong anomalous diffusion is {often} characterized by a piecewise-linear spectrum of the moments of displacement. The spectrum is characterized by slopes $\xi$ and $\zeta$ for small and large moments, respectively, and by the critical moment $\alpha$ of the crossover. The exponents $\xi$ and $\zeta$ characterize the asymptotic scaling of the bulk and the ta

Slavko Moconja, Predrag Tanović

We introduce the notions of triviality and order-triviality for global invariant types in an arbitrary first-order theory and show that they are well behaved in the NIP context. We show that these two notions agree for invariant global extensions of a weakly o-minimal type, in which case we say that the type is trivial. In the o-minimal case, we prove that e

Albus Li, Nathan Bailey, Will Sumerfield, Kira Kim

Quinn et al propose challenge datasets in their work called ``Kryptonite-N". These datasets aim to counter the universal function approximation argument of machine learning, breaking the notation that machine learning can ``approximate any continuous function" \cite{original_paper}. Our work refutes this claim and shows that universal function approximations

Catharina Stroppel, Paul Wedrich

We consider categories of Soergel bimodules for the symmetric groups S_n in their gl(n)-realizations for all n and assemble them into a locally linear monoidal bicategory. Chain complexes of Soergel bimodules likewise form a locally dg-monoidal bicategory which can be equipped with the structure of a braiding, whose data includes the Rouquier complexes of sh

Yufei Wu, Stefan T. Radev, Francis Tuerlinckx

Contaminant observations and outliers often cause problems when estimating the parameters of cognitive models, which are statistical models representing cognitive processes. In this study, we test and improve the robustness of parameter estimation using amortized Bayesian inference (ABI) with neural networks. To this end, we conduct systematic analyses on a

Fedor Bezrukov, Dmitry Gorbunov, Ekaterina Koreshkova

Dwarf spheroidal galaxies (dSphs) are recognized as being highly dominated by Dark Matter (DM), making them excellent targets for testing DM models through astrophysical observations. One effective method involves estimating the coarse-grained phase-space density (PSD) of the galactic DM component. By comparing this PSD with that of DM particles produced in

Madhavendra Thakur

No-resource languages - those with minimal or no digital representation - pose unique challenges for machine translation (MT). Unlike low-resource languages, which rely on limited but existent corpora, no-resource languages often have fewer than 100 sentences available for training. This work explores the problem of no-resource translation through three dist

Beomjoon Goh, Junwon Kim, Hongchul Choi, Ji Hoon Shim

In this work, we develop a non-equilibrium steady-state non-crossing approximation (NESS-NCA) impurity solver applicable to general impurity problems. The choice of the NCA as the impurity solver enables both a more accurate description of correlation effects with larger Coulomb interaction and scalability to multi-orbital systems. Based on this development,

John Paparrizos, Fan Yang, Haojun Li

Time series, as one of the most fundamental representations of sequential data, has been extensively studied across diverse disciplines, including computer science, biology, geology, astronomy, and environmental sciences. The advent of advanced sensing, storage, and networking technologies has resulted in high-dimensional time-series data, however, posing si

Mahmoud Fawzi, Björn Ross, Walid Magdy

Hadith, the recorded words and actions of the prophet Muhammad, is a key source of the instructions and foundations of Islam, alongside the Quran. Interpreting individual hadiths and verifying their authenticity can be difficult, even controversial, and the subject has attracted the attention of many scholars who have established an entire science of Hadith

Luca Donati, Surahit Chewle, Dominik St. Pierre, Vijay Natarajan

Molecular Dynamics simulations are essential tools for understanding the dynamic behavior of biomolecules, yet extracting meaningful molecular pathways from these simulations remains challenging due to the vast amount of generated data. In this work, we present Molecular Kinetics via Topology (MoKiTo), a novel approach that combines the ISOKANN algorithm to

Erol Vatansever, Mikel Quintana, Andreas Berger

We investigate the crossover of critical behavior for the dynamic phase transition (DPT) in ferromagnetic thin films using Monte Carlo simulations of the kinetic Ising model, focusing on the scaling behavior of the dynamic order parameter under a time-dependent external magnetic field. Specifically, we study the transition of the critical behavior of such mu

Samuel Abreu, Giuseppe De Laurentis, Giulio Falcioni, Einan Gardi

High-energy factorization of 2 -> 2 amplitudes in QCD has been recently pushed to the next-to-next-to-leading logarithmic order by determining the three-loop gluon Regge trajectory. This was based on computing multi-Reggeon exchanges using rapidity evolution in the shock-wave formalism, and disentangling between the Regge pole and Regge cut contributions. In

Samuel Abreu, Giulio Falcioni, Einan Gardi, Calum Milloy

Scattering amplitudes in the high-energy limit can be described in terms of their singularity structure in the complex angular momentum plane, consisting of Regge poles and cuts. In QCD, gluon Reggeization has long been understood as a manifestation of a Regge pole, but until recently Reggeization violation remained largely obscure. New methods, based on ite

Simone Di Giorgio, Sergio Pirozzoli, Alessandro Iafrati

We investigate gas transfer processes occurring at the air-water interface of progressive water waves using high-fidelity numerical simulations. Waves with varying initial steepness, including regular wave patterns, mild spilling and intense plunging breakers are examined. A two-phase solver is employed to model exchange processes enabling precise estimation

Georgios Akrivis, Charalambos G. Makridakis, Costas Smaragdakis

In this paper we consider time-dependent PDEs discretized by a special class of Physics Informed Neural Networks whose design is based on the framework of Runge--Kutta and related time-Galerkin discretizations. The primary motivation for using such methods is that alternative time-discrete schemes not only enable higher-order approximations but also have a c

John Paparrizos, Haojun Li, Fan Yang, Kaize Wu

Distance measures have been recognized as one of the fundamental building blocks in time-series analysis tasks, e.g., querying, indexing, classification, clustering, anomaly detection, and similarity search. The vast proliferation of time-series data across a wide range of fields has increased the relevance of evaluating the effectiveness and efficiency of t

Sao Mai Nguyen

This work in the field of developmental cognitive robotics aims to devise a new domain bridging between reinforcement learning and imitation learning, with a model of the intrinsic motivation for learning agents to learn with guidance from tutors multiple tasks, including sequential tasks. The main contribution has been to propose a common formulation of int

Nacira Agram, Bernt Øksendal, Frank Proske, Olena Tymoshenko

We investigate conditional McKean-Vlasov equations driven by time-space white noise, motivated by the propagation of chaos in an N-particle system with space-time Ornstein-Uhlenbeck dynamics. The framework builds on the stochastic calculus of time-space white noise, utilizing tools such as the two-parameter Ito formula, Malliavin calculus, and orthogonal dec

Youssef Megahed, Anthony Fuller, Saleh Abou-Alwan, Dina El Demellawy

Hirschsprung's disease (HD) is a congenital birth defect diagnosed by identifying the lack of ganglion cells within the colon's muscularis propria, specifically within the myenteric plexus regions. There may be advantages for quantitative assessments of histopathology images of the colon, such as counting the ganglion and assessing their spatial distribution

Anna Ghazaryan, Vahagn Manukian, Jonathan Waldmann, Priscilla Yinzime

We show the existence of traveling front solutions in a diffusive classical SIS epidemic model and the SIS model with a saturating incidence in the size of the susceptible population. We investigate the situation where both susceptible and infected populations move around at a comparable rates, but small compared to the spatial scale. In this case, we show t

Tianxu Wang, Kyunghan Choi, Hao Wang

Highly evolved animals continuously update their knowledge of social factors, refining movement decisions based on both historical and real-time observations. Despite its significance, research on the underlying mechanisms remains limited. In this study, we explore how the use of explicit memory shapes different mathematical models across various ecological

L. D. Abreu, F. Luef, M. Ziyat

We develop an alternative approach to the study of Fourier series, based on the Short-Time-Fourier Transform (STFT) acting on $L_{\nu }^{2}(0,1)$, the space of measurable functions $f$ in ${R}$, square-integrable in $ (0,1)$, and time-periodic up to a phase factor: for fixed $\nu \in \mathbb{R}$, \begin{equation*} f(t+k)=e^{2\pi ik\nu }f(t)\text{, }k\in \mat

David Eelbode, Martin Roelfs, Steven De Keninck

In this paper we use the power of the outer exponential $\Lambda^B$ of a bivector $B$ to see the so-called invariant decomposition from a different perspective. This is deeply connected with the eigenvalues for the adjoint action of $B$, a fact that allows a version of the Cayley-Hamilton theorem which factorises the classical theorem (both the matrix versio

Mark A. Seferian, Jidong J. Yang

Autonomous vehicles face significant challenges in navigating adverse weather, particularly rain, due to the visual impairment of camera-based systems. In this study, we leveraged contemporary deep learning techniques to mitigate these challenges, aiming to develop a vision model that processes live vehicle camera feeds to eliminate rain-induced visual hindr