November 2024 arXiv papers — page 45

Michele Battisti, Valentino Dardanoni, Stefano Demichelis

This paper studies inter-firm heterogeneity in production. Unlike much of the existing research, which primarily addresses heterogeneous production through unobserved fixed effects, our approach also focuses on differences in factors' output elasticities. Using manufacturing data from Chile, Colombia, and Japan, we apply an innovative Empirical Bayes methodo

Arthur Azevedo de Amorim, Cheng Zhang, Marco Gaboardi

We prove that the equational theory of Kleene algebra with commutativity conditions on primitives (or atomic terms) is undecidable, thereby settling a longstanding open question in the theory of Kleene algebra. While this question has also been recently solved independently by Kuznetsov, our results hold even for weaker theories that do not support the induc

Ryan Dancy, Nancy A. Day, Owen Zila, Khadija Tariq

Alloy is a well-known, formal, declarative language for modelling systems early in the software development process. Currently, it uses the Kodkod library as a back-end for finite model finding. Kodkod translates the model to a SAT problem; however, this method can often handle only problems of fairly low-size sets and is inherently finite. We present Portus

Piotr Stachura

This article presents a differential groupoid with ``coaction'' of the groupoid underlying the Quantum Euclidean Group (i.e. its $C^*$-algebra is the $C^*$-algebra of this quantum group). The dual of the Lie algebroid is a Poisson manifold that can be identified with the space of oriented lines in Euclidean space equipped with a Poisson action of the Poisson

Ruiqiang Xiao, Songning Lai, Yijun Yang, Jiemin Wu

Adapting machine learning models to new domains without labeled data, especially when source data is inaccessible, is a critical challenge in applications like medical imaging, autonomous driving, and remote sensing. This task, known as Source-Free Unsupervised Domain Adaptation (SFUDA), involves adapting a pre-trained model to a target domain using only unl

David Peter Wallis Freeborn

Manifold learning and effective model building are generally viewed as fundamentally different types of procedure. After all, in one we build a simplified model of the data, in the other, we construct a simplified model of the another model. Nonetheless, I argue that certain kinds of high-dimensional effective model building, and effective field theory const

Shivalee Shah, Mayank Vatsa

This paper introduces a novel quantum diffusion model designed for Noisy Intermediate-Scale Quantum (NISQ) devices. Unlike previous methods, this model efficiently processes higher-dimensional images with complex pixel structures, even on qubit-limited platforms. This is accomplished through a pairwise Bell-state entangling technique, which reduces space com

Hesameddin Mohammadi, Mohammad Tinati, Stephen Tu, Mahdi Soltanolkotabi

The symmetric low-rank matrix factorization serves as a building block in many learning tasks, including matrix recovery and training of neural networks. However, despite a flurry of recent research, the dynamics of its training via non-convex factorized gradient-descent-type methods is not fully understood especially in the over-parameterized regime where t

Chiranjeevi Bura, Praveen Kumar Myakala

Generative AI is transforming education by enabling personalized learning, enhancing administrative efficiency, and fostering creative engagement. This paper explores the opportunities and challenges these tools bring to pedagogy, proposing actionable frameworks to address existing equity gaps. Ethical considerations such as algorithmic bias, data privacy, a

Basile Radisson, Hadrien Bense, Emmanuel Siéfert, Lucie Domino

Parallel assemblies of slender structures forming brushes are common in our daily life from sweepers to pastry brushes and paintbrushes. This type of porous objects can easily trap liquid in their interstices when removed from a liquid bath. This property is exploited to transport liquids in many applications ranging from painting, dip-coating, brush-coating

Hiroki Nakai, Chisa Hotta

We explore the quantum pseudospin-1 pyrochlore magnet featuring Fe$^{2+}$-based spinel oxides that addresses the formation of amplitude-modulated spin-density waves. We propose that the relatively small spin-orbit coupling and the small extra crystal field splitting in these materials create anisotropic exchange interactions and strong single-ion anisotropy,

J. Miquel Martínez

Let $p$ and $q$ be different primes and let $G$ be a finite $q$-solvable group. We prove that $\mathrm{Irr}_{p'}(G)\subseteq \mathrm{Irr}_{q'}(G)$ if and only if $\mathbf{N}_G(P)\subseteq \mathbf{N}_G(Q)$ and $\mathbf{C}_{Q'}(P)=1$ for some $P\in\mathrm{Syl}_p(G)$ and $Q\in\mathrm{Syl}_q(G)$. Further, if $B$ is a $q$-block of $G$ and $p$ does not divide the

Pierre Mackenzie, Mika Senghaas, Raphael Achddou

The use of deep learning in stylistic effect generation has seen increasing use over recent years. In this work, we use simple convolutional neural networks to model Cinestill800T film given a digital input. We test the effect of different loss functions, the addition of an input noise channel and the use of random scales of patches during training. We find

Soumava Paul, Prakhar Kaushik, Alan Yuille

In this work, we introduce a generative approach for pose-free (without camera parameters) reconstruction of 360 scenes from a sparse set of 2D images. Pose-free scene reconstruction from incomplete, pose-free observations is usually regularized with depth estimation or 3D foundational priors. While recent advances have enabled sparse-view reconstruction of

Amy S. Inwood, Peter J. Smith, Philippa A. Martin, Graeme K. Woodward

Phase selection design for reconfigurable intelligent surfaces (RISs) is a significant research challenge, as a closed-form optimal solution for a multi-user (MU) system is believed to be intractable. While existing methods achieve strong near-optimal performance, they typically entail high computational complexity. In this work, we take a different approach

Pierre-Etienne Druet

For single components fluids, vanishing isothermal compressibility implies that the mass density is constant, but the same conclusion is unknown for multicomponent fluids. Here the volume remains affected by changes of the composition. In the present paper we discuss an apparently natural way to conceptualise, based on derivatives of the Gibbs function g, th

Antonio Trovato, Manuel De Stefano, Fabiano Pecorelli, Dario Di Nucci

Maintaining software quality is crucial in the dynamic landscape of software development. Regression testing ensures that software works as expected after changes are implemented. However, re-executing all test cases for every modification is often impractical and costly, particularly for large systems. Although very effective, traditional test suite optimiz

Ayesha Baig, Li Zhouxin

We investigate the existence, non-existence, and multiplicity of positive solutions to a class of quasilinear Schrodinger equations with a prescribed mass condition in higher dimensions. Using the dual approach, the equation is transformed into a corresponding semilinear form. A global branch approach is employed to address nonlinearities that may be mass su

J. L. Han, D. J. Zhou, C. Wang, W. Q. Su

The Five-hundred-meter Aperture Spherical radio Telescope (FAST) is the most sensitive telescope at the $L$-band (1.0-1.5 GHz) and has been used to carry out the FAST Galactic Plane Pulsar Snapshot (GPPS) survey in the last 5 yr. Up to now, the survey has covered one-fourth of the planned areas within $\pm10^{\circ}$ from the Galactic plane visible by FAST,

Lang Xie, J. L. Han, Z. L. Yang, W. C. Jing

Magnetars are slowly rotating, highly magnetized young neutron stars that can show transient radio phenomena for radio pulses and fast radio bursts. We conducted radio observations of from two magnetars SGR$~$J1935+2154 and 3XMM$~$J185246.6+003317 and a high-magnetic field pulsar PSR$~$J1846$-$0258 using the Five-hundred-meter Aperture Spherical radio Telesc

D. I. Novikov, K. O. Parfenov

We demonstrate an approach that allows separating two-point correlations created by a Gaussian random field from correlations created by cosmic foregrounds such as polarized dust emission, gravitational lensing and other non-Gaussian signals. The result of traditional approaches should typically be a 'foreground-cleaned' two-dimensional CMB map of the anisot

Enea Monzio Compagnoni, Tianlin Liu, Rustem Islamov, Frank Norbert Proske

Despite the vast empirical evidence supporting the efficacy of adaptive optimization methods in deep learning, their theoretical understanding is far from complete. This work introduces novel SDEs for commonly used adaptive optimizers: SignSGD, RMSprop(W), and Adam(W). These SDEs offer a quantitatively accurate description of these optimizers and help illumi

Leonardo Bossi, Luca Griguolo, Jacopo Papalini, Lorenzo Russo

We provide non-trivial checks of the recently proposed duality between double-scaled SYK and a 2d dilaton gravity model with sine potential, studying the path integral at one-loop level. Specifically, we compute the logarithmic correction to the free energy of sine-dilaton gravity and, up to potential ordering ambiguities, we find a match with the correspond

Rafail Psyroukis

We investigate the analytic properties of a Dirichlet series involving the Fourier-Jacobi coefficients of two cusp forms for orthogonal groups of signature $(2,n+2)$. Using an orthogonal Eisenstein series of Klingen type, we obtain an integral representation for this Dirichlet series. In the case when the corresponding lattice has only one $1$-dimensional cu

Melih Şahin, Ozgur B. Akan

Inter-symbol interference (ISI) limits reliability in diffusion-based molecular communication (MC) channels. We propose RLIM, a family of run-length-limited (RLL) codes that form fixed-size codebooks by minimizing the total number of 1-bits, increasing the per-symbol molecule budget under standard power normalizations and thus improving reliability. We devel

G. S. Nahum

We introduce a symmetric, gradient exclusion process within the class of non-cooperative kinetically constrained lattice gases, modelling a non-linear diffusivity in which the exchange of occupation values between two neighbouring sites depends on the local density in specific boxes surrounding the pair. The existence of such a model satisfying the gradient

Ankit Shaw

This paper presents a comprehensive overview of exploration strategies utilized in both 2D and 3D environments, focusing on autonomous multi-robot systems designed for building exploration and fire detection. We explore the limitations of traditional algorithms that rely on prior knowledge and predefined maps, emphasizing the challenges faced when environmen

T. M. Aliev, Y. Sarac

The semileptonic $B_{(s)} \rightarrow T(J^P=2^-)l^+l^-$ decays induced by flavor changing neural currents are investigated within the light cone QCD sum rule method. We apply the $B$ meson distribution amplitudes up to twist-4 and calculate the relevant form factors of the $B_{(s)} \rightarrow T$ transitions, where $T=K_2,~a_2,~f_2,~\phi_2$ with $J^P=2^-$. T

Joar Skalse, Alessandro Abate

The aim of Inverse Reinforcement Learning (IRL) is to infer a reward function $R$ from a policy $\pi$. This problem is difficult, for several reasons. First of all, there are typically multiple reward functions which are compatible with a given policy; this means that the reward function is only *partially identifiable*, and that IRL contains a certain funda

Alex Buchel

We study thermal states of the strongly coupled Klebanov-Witten ${\cal N}=1$ superconformal gauge theory with R-symmetry chemical potential. The theory has two distinct dimension $\Delta=3$ chiral primary operators that develop a condensate at the same critical value of the chemical potential. One instability is associated with the spontaneous fluxification

Maximilian Dreisbach, Elham Kiyani, Jochen Kriegseis, George Karniadakis

Two-phase flow phenomena underpin critical technologies such as hydrogen fuel cells, spray cooling, and combustion, where droplet dynamics govern performance and efficiency. Conventional optical diagnostics, including shadowgraphy and particle image velocimetry, provide valuable insights but are limited to two-dimensional projections of inherently three-dime

Amir Hossein Hadavi, Mohammad M. Mojahedian, Mohammad Reza Aref

Adaptive data analysis (ADA) involves a dynamic interaction between an analyst and a dataset owner, where the analyst submits queries sequentially, adapting them based on previous answers. This process can become adversarial, as the analyst may attempt to overfit by targeting non-generalizable patterns in the data. To counteract this, the dataset owner intro

Ayesha Baig, Li Zhouxin

We investigate the existence of standing wave solutions for quasilinear Schrodinger systems. To address the challenges posed by non differentiability, we adopt the dual approach introduced by Colin and Jeanjean. The existence of solutions is established using Del Pino and Felmer's penalization technique, with refinements inspired by Alves' arguments.

Stéphane Launois, Isaac Oppong

By a theorem of Dixmier, primitive quotients of enveloping algebras of finite-dimensional complex nilpotent Lie algebras are isomorphic to Weyl algebras. In view of this result, it is natural to consider simple quotients of positive parts of quantized enveloping algebras (and more generally of uniparameter Quantum Nilpotent Algebras) as quantum analogues of

Star, Liu

This tutorial investigates the convergence of statistical mechanics and learning theory, elucidating the potential enhancements in machine learning methodologies through the integration of foundational principles from physics. The tutorial delves into advanced techniques like entropy, free energy, and variational inference which are utilized in machine learn

Xinzhe Cao, Yadong Xu, Xiaofeng Yang

Accurately predicting customer Lifetime Value (LTV) is crucial for companies to optimize their revenue strategies. Traditional deep learning models for LTV prediction are effective but typically provide only point estimates and fail to capture model uncertainty in modeling user behaviors. To address this limitation, we propose a novel approach that enhances

Naoki Kitazawa

Morse functions are important objects and tools in understanding topologies of manifolds since the 20th century. Their classification has been natural and difficult problems, and surprisingly, this is recently developing. Since the 2010's, results for cases of surfaces have been presented by Gelbukh, Marzantowicz and Michalak for example. We have also longed

Yifei Wu, Juming Xiong, Tianyuan Yao, Ruining Deng

Chronic rhinosinusitis (CRS) is characterized by persistent inflammation in the paranasal sinuses, leading to typical symptoms of nasal congestion, facial pressure, olfactory dysfunction, and discolored nasal drainage, which can significantly impact quality-of-life. Eosinophils (Eos), a crucial component in the mucosal immune response, have been linked to di

Haoyang He, Jiangning Zhang, Yuxuan Cai, Hongxu Chen

Previous research on lightweight models has primarily focused on CNNs and Transformer-based designs. CNNs, with their local receptive fields, struggle to capture long-range dependencies, while Transformers, despite their global modeling capabilities, are limited by quadratic computational complexity in high-resolution scenarios. Recently, state-space models

Søren Brinck Knudstorp

To formalize patterns of information increase and decrease, Van Benthem (1996) proposed modal information logic (MIL), a modal logic over partial orders. In MIL, points are interpreted as information states and least upper bounds, when existent, as informational sums. A natural counterpart to this logic is the modal logic of minimal upper bounds (MIN), inter

David Wallauch

We establish Strichartz estimates, including estimates involving spatial derivatives, for radial wave equations with potentials in similarity variables. This is accomplished for all spatial dimensions $d\geq 3$ and almost all regularities above energy and below the threshold $\frac d2$. These estimates provide a unified framework that allows one to derive op

David Strack, Joachim Saur

We investigate how the spatiotemporal structure of induced magnetic fields outside of Callisto is affected by their propagation with the magnetohydrodynamic (MHD) modes. At moons that are surrounded by dense magnetized plasmas like the Galilean moons, low-frequency induced magnetic fields cannot propagate with the ordinary electromagnetic mode as is implicit

Ferhat Ozgur Catak, Murat Kuzlu, Taylor Patrick

In recent years, vision-language models (VLMs) have been applied to various fields, including healthcare, education, finance, and manufacturing, with remarkable performance. However, concerns remain regarding VLMs' consistency and uncertainty, particularly in critical applications such as healthcare, which demand a high level of trust and reliability. This p

Chris Allton, Gert Aarts, M. Naeem Anwar, Ryan Bignell

The FASTSUM Collaboration has developed a comprehensive research programme in thermal lattice QCD using 2+1 flavour ensembles. We review our recent hadron spectrum analyses of open charm mesons and charm baryons at non-zero temperature. We also detail our determination of the interquark potential in the bottomonium system using NRQCD quarks. All of our work

Geoff Twardokus, William Joslin, Hanif Rahbari, William Layton

Within 1-2 decades, quantum computers may become powerful enough to break current public-key cryptography, prompting authorities such as the IETF and NIST to push for adopting quantum-resistant cryptography (QRC) in ecosystems like Internet Protocol Security (IPsec). Yet, IPsec struggles to adopt QRC, primarily because Internet Key Exchange Protocol Version

Alvaro Ribot, Emil Horobet, Anna Seigal, Ettore Teixeira Turatti

Matrices can be decomposed via rank-one approximations: the best rank-one approximation is a singular vector pair, and the singular value decomposition writes a matrix as a sum of singular vector pairs. The singular vector tuples of a tensor are the critical points of its best rank-one approximation problem. In this paper, we study tensors that can be decomp

Evan Carroll, Nicholas Recchione, Jean Paul Rojas Henao, Vincent G. Capone

Inhalers with corticosteroids and muscle relaxants are prescribed by pulmonologists to mitigate bronchospasms that happen due to tightening of the chest. An adhesive patch attached to a patient with chronic obstructive pulmonary disease can relieve episodes of bronchospasms in the event the prescribed inhaler is not nearby. This paper shows the design and pr

Klara Janouskova, Cristian Gavrus, Jiri Matas

In object recognition, both the subject of interest (referred to as foreground, FG, for simplicity) and its surrounding context (background, BG) may play an important role. However, standard supervised learning often leads to unintended over-reliance on the BG, limiting model robustness in real-world deployment settings. The problem is mainly addressed by su

Vladimir Gol'dshtein, Alexander Ukhlov

In this article, we consider $(p,q)$-extension operators, $1 < q \le p < \infty$, on Sobolev spaces. Based on composition operators on Sobolev spaces, we construct the extension operators in outward cuspidal domains with estimates of their norms. Using these $(p,q)$-extension operators, we prove estimates for the non-linear Neumann eigenvalues of the $p$-Lap

Deep Chakraborty, Yann LeCun, Tim G. J. Rudner, Erik Learned-Miller

A number of different architectures and loss functions have been applied to the problem of self-supervised learning (SSL), with the goal of developing embeddings that provide the best possible pre-training for as-yet-unknown, lightly supervised downstream tasks. One of these SSL criteria is to maximize the entropy of a set of embeddings in some compact space

Michael B. Giles

It is well known that the Euler-Maruyama discretisation of an autonomous SDE using a uniform timestep $h$ has a strong convergence error which is $O(h^{1/2})$ when the drift and diffusion are both globally Lipschitz. This note proves that the same is true for the approximation of the path sensitivity to changes in a parameter affecting the drift and diffusio

Demetrius Gulewicz, Uduak Inyang-Udoh, Trevor Bird, Neera Jain

Model predictive control has gained popularity for its ability to satisfy constraints and guarantee robustness for certain classes of systems. However, for systems whose dynamics are characterized by a high state dimension, substantial nonlinearities, and stiffness, suitable methods for online nonlinear MPC are lacking. One example of such a system is a vehi

Zheng Yang, Wanyue Xiao, Hengzhi Li, Hao Pan

Optical systems breaking Lorentz reciprocity have attracted broad attention due to their intriguing physics and applications. Nonreciprocal metasurfaces can enable one-way light transmission and reflection with essential applications in optical communication. Conventional nonreciprocal metasurfaces rely on using magneto-optic or nonlinear materials to induce

Haebin Shin, Lei Ji, Yeyun Gong, Sungdong Kim

Prompts used in recent large language model based applications are often fixed and lengthy, leading to significant computational overhead. To address this challenge, we propose Generative Prompt Internalization (GenPI), a lightweight method that employs a joint training approach. GenPI not only replicates the behavior of models with prompt inputs but also ge

Ali Abrishami, Jafar Habibi, AmirAli Jarrahi, Dariush Amiri

Financial markets are integral to a country's economic success, yet their complex nature raises challenging issues for predicting their behaviors. There is a growing demand for an integrated system that explores the vast and diverse data in financial reports with powerful machine-learning models to analyze financial markets and suggest appropriate investment

Shumeet Baluja, David Marwood, Ashwin Baluja

Simply by rearranging the regions of an image, we can create a new image of any subject matter. The definition of regions is user definable, ranging from regularly and irregularly-shaped blocks, concentric rings, or even individual pixels. Our method extends and improves recent work in the generation of optical illusions by simultaneously learning not only t

Jonathan H. Brown, Lisa Orloff Clark, Adam H. Fuller

Let $D \subseteq A$ be a quasi-Cartan pair of algebras. Then there exists a unique discrete groupoid twist $\Sigma \to G$ whose twisted Steinberg algebra is isomorphic to $A$ in a way that preserves $D$. In this paper, we show there is a lattice isomorphism between wide open subgroupoids of $G$ and subalgebras $C$ such that $D\subseteq C\subseteq A$ and $D \

Saba Zahid, Sajid Ghuffar, Obaid-ur-Rehman, Syed Roshaan Ali Shah

This study explores the effectiveness of multi-temporal satellite imagery for better functional field boundary delineation using deep learning semantic segmentation architecture on two distinct geographical and multi-scale farming systems of Netherlands and Pakistan. Multidate images of April, August and October 2022 were acquired for PlanetScope and Sentine

Chia-Ming Lee, Ching-Heng Cheng, Yu-Fan Lin, Yi-Ching Cheng

Recent advances in All-in-One (AiO) RGB image restoration have demonstrated the effectiveness of prompt learning in handling multiple degradations within a single model. However, extending these approaches to hyperspectral image (HSI) restoration is challenging due to the domain gap between RGB and HSI features, information loss in visual prompts under sever

Yi Ran, Zhichang Guo, Jia Li, Yao Li

The removal of multiplicative Gamma noise is a critical research area in the application of synthetic aperture radar (SAR) imaging, where neural networks serve as a potent tool. However, real-world data often diverges from theoretical models, exhibiting various disturbances, which makes the neural network less effective. Adversarial attacks can be used as a

Nana Kankam Gyimah, Judith Mwakalonge, Gurcan Comert, Saidi Siuhi

In this paper, we present an automated machine learning (AutoML) approach for network intrusion detection, leveraging a stacked ensemble model developed using the MLJAR AutoML framework. Our methodology combines multiple machine learning algorithms, including LightGBM, CatBoost, and XGBoost, to enhance detection accuracy and robustness. By automating model s

Marcin Maździarz

The paper gathers and unifies mechanical stability conditions for all symmetry classes of 3D and 2D materials under arbitrary load. The methodology is based on the spectral decomposition of the fourth-order stiffness tensors mapped to second-order tensors using orthonormal (Mandel) notation, and the verification of the positivity of the so-called Kelvin modu

Bao-Quan Huang, Tong Liu, Guo-Yu Li

Polarization observations provide a unique way to probe the nature of jet magnetic fields in gamma-ray bursts (GRBs). Currently, some GRBs have been detected to be polarized in their early optical afterglows. However, the measured polarization degrees (PDs) of these GRBs are much lower than those predicted by theoretical models. In this work, we investigate

Carl A. Kukkonen

Superconductivity and the normal state electrical resistivity which varies as $T^2$ are strongly enhanced near the compressibility and charge density wave instabilities in the electron-positive fermion gas. The additional screening from the positive fermions introduces an attractive term in the effective electron-electron interaction that is the basis for su

Sourav Banerjee, Ayushi Agarwal, Promila Ghosh

Automatic Speech Recognition (ASR) systems in the clinical domain face significant challenges, notably the need to recognise specialised medical vocabulary accurately and meet stringent precision requirements. We introduce United-MedASR, a novel architecture that addresses these challenges by integrating synthetic data generation, precision ASR fine-tuning,

Chen-Lung Lu, Honglu He, Jinhan Ren, Joni Dhar

Robotic Wire Arc Additive Manufacturing (WAAM) is a metal additive manufacturing technology, offering flexible 3D printing while ensuring high quality near-net-shape final parts. However, WAAM also suffers from geometric imprecision, especially for low-melting-point metal such as aluminum alloys. In this paper, we present a multi-robot framework for WAAM pro

Kaihan Lin, Xing Gao

The Non-Markovian Stochastic Schrodinger Equation (NMSSE) offers a promising approach for open quantum simulations, especially in large systems, owing to its low scaling complexity and suitability for parallel computing. However, its application at low temperatures faces significant convergence challenges. While short-time evolution converges quickly, long-t

Shuyue Fu, Shengping Gong, Peng Shi

This paper develops an analytical strategy for solving the linear quadratic pursuit-evasion game in arbitrary Keplerian reference orbits. The motion of the pursuer and evader is described using the controlled Tschauner-Hempel equations, and the optimal game strategies of the pursuer and evader are presented by the solution of the differential Riccati equatio

Jian Chen, Rixing Huang, Xueqian Zhao, Qingxi Fan

The coupling between dual-band or multi-band quasi-bound states in the continuum (q-BICs) is of great interest for their rich physics and promising applications. Here, we report tunable collective electromagnetic induced transparency-like (EIT-like) phenomenon due to coupling between dual-band collective electric dipolar and magnetic quadrupolar q-BICs, whic

L. Rogić, N. Somun, S. Griffitt, A. Najev

We present the design of a continuous-wave, highly sensitive optical spectrometer for millimeter-wave frequencies between 50 and 1000 GHz. The spectrometer uses photomixing of near-infrared light to generate radiation in a wide frequency range, and the absorbed optical power is determined directly through measurements of the sample temperature. This enables

Antoine Georges, Gabriel Kotliar

Atomic physics has a profound impact on the physical properties of correlated electron materials. This article describes a prime example of this phenomenon. We provide a non-technical introduction to the physics of Hund metals, a broad class of materials which include in particular iron pnictides and chalcogenides, as well as oxides of the 4d transition-meta

Soham Bakshi, Walter Dempsey, Snigdha Panigrahi

Causal effect moderation investigates how the effect of interventions (or treatments) on outcome variables changes based on observed characteristics of individuals, known as potential effect moderators. With advances in data collection, datasets containing many observed features as potential moderators have become increasingly common. High-dimensional analys

Yongzhen Wang

The geopolitical tensions between China and the US have dramatically reshaped the American scientific workforce's landscape. To gain a deeper understanding of this circumstance, this study selects the discipline of computer science as a representative case for empirical investigations, aiming to explore the current situation of US-based Chinese-descent compu

Bryn Davies, Clemens Thalhammer

We study the convergence of two of the most widely used and intuitive approaches for computing the spectra of differential operators with quasiperiodic coefficients: the supercell method and the superspace method. In both cases, Floquet-Bloch theory for periodic operators can be used to compute approximations to the spectrum. We illustrate our results with e

Matthias Stiefenhofer

We give conditions for local diagonalization of an analytic operator family to a diagonal operator polynomial. The families are acting between real or complex Banach spaces. The basic assumption is given by stabilization of the Jordan chains at length k in the sense that no root elements with finite rank above k are allowed to exist. Jordan chains with infin

Elio Quiroga Rodriguez

This work compares the performance of different antenna configurations in radio astronomy interferometry, including the golden spiral, a grid, a random arrangement, and the "Y" configuration similar to the Very Large Array. One hundred antennas are simulated in each configuration, and the resulting UV coverage and image quality are analyzed. The results show

Yanming Shao, Chenxi Xiao

Humans naturally perform bimanual skills to handle large and heavy objects. To enhance robots' object manipulation capabilities, generating effective bimanual grasp poses is essential. Nevertheless, bimanual grasp synthesis for dexterous hand manipulators remains underexplored. To bridge this gap, we propose the BimanGrasp algorithm for synthesizing bimanual

Akanksha Gupta, Jaya Kumar Alageshan, Kolluru Venkata Kiran, Rahul Pandit

We show that flocking of microswimmers in a turbulent flow can enhance the efficacy of reinforcement-learning-based path-planning of microswimmers in turbulent flows. In particular, we develop a machine-learning strategy that incorporates Vicsek-model-type flocking in microswimmer assemblies in a statistically homogeneous and isotropic turbulent flow in two

R. Herrmann, S. Bose, I. Filip, D. Medina

Advancing towards high automation and autonomous operations is crucial for the future of inland waterway transport (IWT) systems. These systems necessitate robust and precise onboard sensory technologies that can perceive the environment under all weather conditions, including static features for local positioning techniques such as Simultaneous Localization

Adittya Pal, Rolf Fagerberg, Jakob Lykke Andersen, Christoph Flamm

The search for pathways that optimize the formation of a particular target molecule in a reaction network is a key problem in many settings, including reactor systems. Chemical reaction networks are mathematically well represented as hypergraphs, modeling that facilitates the search for pathways by computational means. We propose to enrich an existing search

Dongsun Yoon, Sungkyu Jung

We propose a novel estimator for the principal component (PC) subspace tailored to the high-dimension, low-sample size (HDLSS) context. The method, termed Adaptive Reference-Guided (ARG) estimator, is designed for data exhibiting spiked covariance structures and seeks to improve upon the conventional sample PC subspace by leveraging auxiliary information fro

Nikoleta Iliakopoulou, Jovan Stojkovic, Chloe Alverti, Tianyin Xu

The widespread adoption of LLMs has driven an exponential rise in their deployment, imposing substantial demands on inference clusters. These clusters must handle numerous concurrent queries for different LLM downstream tasks. To handle multi-task settings with vast LLM parameter counts, methods like Low-Rank Adaptation (LoRA) enable task-specific fine-tunin

Mounira Nihad Zitouni, Amal Ahmed Anda, Sahil Rajpal, Daniel Amyot

Over the past decade, different domain-specific languages (DSLs) were proposed to formally specify requirements stated in legal contracts, mainly for analysis but also for code generation. Symboleo is a promising language in that area. However, writing formal specifications from natural-language contracts is a complex task, especial for legal experts who do

Rachel Yovel, Eran Treister

We present a novel block-preconditioner for the elastic Helmholtz equation, based on a reduction to acoustic Helmholtz equations. Both versions of the Helmholtz equations are challenging numerically. The elastic Helmholtz equation is larger, as a system of PDEs, and harder to solve due to its more complicated physics. It was recently suggested that the elast

Cinzia Bisi, Joerg Winkelmann

Let $A$ be one of the following Clifford algebras : $\mathbb{R}_2 \cong \mathbb{H}$ or $\mathbb{R}_3$. For the algebra $A$, the automorphism group $Aut(A)$ and its invariants are well known. In this paper we will describe the invariants of the automorphism group of the algebra of slice regular functions over $A$.

C. Xiao, W. An, Y. Zhang, Z. Su

Moving object detection in satellite videos (SVMOD) is a challenging task due to the extremely dim and small target characteristics. Current learning-based methods extract spatio-temporal information from multi-frame dense representation with labor-intensive manual labels to tackle SVMOD, which needs high annotation costs and contains tremendous computationa

Haiyang Huang, Yingfan Wang, Cynthia Rudin

Parametric dimensionality reduction methods have gained prominence for their ability to generalize to unseen datasets, an advantage that traditional approaches typically lack. Despite their growing popularity, there remains a prevalent misconception among practitioners about the equivalence in performance between parametric and non-parametric methods. Here,

Joonwoo Kwon, Heehwan Wang, Jinwoo Lee, Sooyoung Kim

In this paper, we introduce RevisitAffectiveMemory, a novel task designed to reconstruct autobiographical memories through audio-visual generation guided by affect extracted from electroencephalogram (EEG) signals. To support this pioneering task, we present the EEG-AffectiveMemory dataset, which encompasses textual descriptions, visuals, music, and EEG reco

Chengxin Wang, Gary Tan, Swagato Barman Roy, Beng Chin Ooi

Urban spatio-temporal (ST) forecasting is crucial for various urban applications such as intelligent scheduling and trip planning. Previous studies focus on modeling ST correlations among urban locations in offline settings, which often neglect the non-stationary nature of urban ST data, particularly, distribution shifts over time. This oversight can lead to

Shiriny Akthar, Md. Wali Hossain

We present a general parametrization for energy density of a quintessence field, a minimally coupled canonical scalar field which rolls down slowly during the late time. This parametrization can mimic all classes of quintessence dynamics, namely scaling-freezing, tracker and thawing dynamics for any redshift. For thawing dynamics the parametrization needs tw

Ziyu Chen, Zhiqing Xiao, Xinbei Jiang, Junbo Zhao

Large Language Models (LLMs) and Reinforcement Learning (RL) are two powerful approaches for building autonomous agents. However, due to limited understanding of the game environment, agents often resort to inefficient exploration and trial-and-error, struggling to develop long-term strategies or make decisions. We propose a method that extracts experience f

Donald L. Kreher, William J. Martin, Douglas R. Stinson

We show that a "mate'' $B$ of a set $A$ in a near-factorization $(A,B)$ of a finite group $G$ is unique. Further, we describe how to compute the mate $B$ very efficiently using an explicit formula for $B$. We use this approach to give an alternate proof of a theorem of Wu, Yang and Feng, which states that a strong circular external difference family cannot h

Getachew K. Befekadu

In this paper, further extensions of the result of the paper "A successive approximation method in functional spaces for hierarchical optimal control problems and its application to learning, arXiv:2410.20617 [math.OC], 2024" concerning a class of learning problem of point estimations for modeling of high-dimensional nonlinear functions are given. In particu

Houssam Razouk, Leonie Benischke, Georg Niess, Roman Kern

In this paper, we consider the process of transforming causal domain knowledge into a representation that aligns more closely with guidelines from causal data science. To this end, we introduce two novel tasks related to distilling causal domain knowledge into causal variables and detecting interaction entities using LLMs. We have determined that contemporar

Carlo Mercuri, Kanishka Perera

We develop novel variational methods for solving scaled equations that do not have the mountain pass geometry, classical linking geometry based on linear subspaces, or $\mathbb Z_2$ symmetry, and therefore cannot be solved using classical variational arguments. Our contributions here include new critical group estimates for scaled functionals, nonlinear sadd

Xinliang An, Haoyang Chen, Sifan Yu

We study the elastic wave system in three spatial dimensions. For admissible harmonic elastic materials, we prove a desired low-regularity local well-posedness result for the corresponding elastic wave equations. For such materials, we can split the dynamics into the divergence-part and the curl-part, and each part satisfies a distinct coupled quasilinear wa

J Morava

The Euclidean renormalization bundle considered in QFT by Connes, Kreimer, and Marcolli has been extended, in a remarkable series of papers by S Agarwala, to Riemannian manifolds $(X,g)$: in particular by the construction of a flat connection on that bundle, regarded as defined over a thickening of $X$ by an infinitesimal disk. The theory of Fourier integral

Donald L. Kreher, Maura B. Paterson, Douglas R. Stinson

We investigate near-factorizations of nonabelian groups, concentrating on dihedral groups. We show that some known constructions of near-factorizations in dihedral groups yield equivalent near-factorizations. In fact, there are very few known examples of nonequivalent near-factorizations in dihedral or other nonabelian groups; we provide some new examples wi

Biao Zhang, Shu-Qing Zhong, Long Li, Zi-Gao Dai

The X-ray afterglows of some gamma-ray bursts (GRBs) exhibit plateaus, which can be explained by the internal dissipation of a newborn millisecond magnetar wind. In the early phase of these newborn magnetars, the magnetic inclination angle undergoes periodic changes due to precession, leading to periodic modulation of the injection luminosity due to magnetic

Hong Xu, Shireen Y. Elhabian

Particle-based shape modeling (PSM) is a popular approach to automatically quantify shape variability in populations of anatomies. The PSM family of methods employs optimization to automatically populate a dense set of corresponding particles (as pseudo landmarks) on 3D surfaces to allow subsequent shape analysis. A recent deep learning approach leverages im