October 2024 arXiv papers — page 4

Natasha Morrison, Shannon Ogden

The $r$-bond bootstrap percolation process on a graph $G$ begins with a set $S$ of infected edges of $G$ (all other edges are healthy). At each step, a healthy edge becomes infected if at least one of its endpoints is incident with at least $r$ infected edges (and it remains infected). If $S$ eventually infects all of $E(G)$, we say $S$ percolates. In this p

Elaf Musa, Ilya Agapov, Tessa Charles

The development of ultra-low emittance storage rings, such as the e+/e- Future Circular Collider (FCC-ee) with a circumference of about 90 km, aims to achieve unprecedented luminosity and beam size. One significant challenge is correcting the optics, which becomes increasingly difficult as we target lower emittances. In this paper, we investigate optics corr

Jia Lin Hau, Erick Delage, Esther Derman, Mohammad Ghavamzadeh

In Markov decision processes (MDPs), quantile risk measures such as Value-at-Risk are a standard metric for modeling RL agents' preferences for certain outcomes. This paper proposes a new Q-learning algorithm for quantile optimization in MDPs with strong convergence and performance guarantees. The algorithm leverages a new, simple dynamic program (DP) decomp

Ryotaro Suzuki, Hosho Katsura, Yosuke Mitsuhashi, Tomohiro Soejima

Random circuits giving rise to unitary designs are key tools in quantum information science and many-body physics. In this work, we investigate a class of random quantum circuits with a specific gate structure. Within this framework, we prove that one-dimensional structured random circuits with non-Haar random local gates can exhibit substantially more globa

Dominic Sobhani, Amir Feder, David Blei

Probabilistic topic models are a powerful tool for extracting latent themes from large text datasets. In many text datasets, we also observe per-document covariates (e.g., source, style, political affiliation) that act as environments that modulate a "global" (environment-agnostic) topic representation. Accurately learning these representations is important

K. S. Babu, Ajay Kaladharan

Left-right symmetric models which employ a generalized seesaw mechanism to generate quark and charged lepton masses are known to solve the strong CP problem via parity symmetry, without the need for the axion. These models lead to naturally light Dirac neutrinos with their masses arising through radiatve corrections. In this work, we show how baryogenesis vi

Lorenzo Guerra, Paolo Salvatore

We show that a certain conjecture by Atiyah and Sutcliffe implies the existence of an $ E_3 $-algebra (respectively $ E_2 $-algebra) structure on the disjoint union of all complex (respectively real) full flag manifolds modulo symmetric groups. Moreover, we show that these structures are liftings of exotic $ E_3 $ (respectively $ E_2 $) structures on the fre

Hideki Todo, Yuki Koyama, Kunihiro Sakai, Akihiro Komiya

Our animation studio has developed a practical style transfer pipeline for creating stylized 3D animation, which is suitable for complex real-world production. This paper presents the insights from our development process, where we explored various options to balance quality, artist control, and workload, leading to several key decisions. For example, we cho

W. Callum Wareham, David A. Sivak

Biological molecular machines convert free energy between different forms in cells, often at high efficiency. Optimal control theory provides a framework to elucidate design principles governing energetically efficient driving. Here, we use linear-response theory to design efficient protocols exercising dynamic control of trap center and stiffness in a model

Gholamreza Davarpanah, Sajjad Mohammadi, James L. Kirtley

This paper focuses on designing new motors with high torque density, which is crucial for applications ranging from electric vehicles to robotics. We propose a double-teeth C-core switched reluctance motor with hybrid excitation, integrating permanent magnets and a novel drive technique to enhance motor torque density. We explore three magnet placement confi

Alyssa Gadsby, Tian Liu, Robert Samstein, Jiahan Zhang

This study is to evaluate the impact of lung volume choices on predicting radiation pneumonitis (RP) risk in patients with locally advanced NSCLC undergoing radiotherapy. Dosimetric variables V20, V5, and mean lung dose (MLD) were extracted from the treatment plans of 442 patients enrolled in the NRG Oncology RTOG 0617 trial. Three lung volumes were defined:

Zachary Roch, Ramya Akula

The full implementation of the metaverse requires the integration of the physical and digital worlds. Applications built on Distributed Ledger Technology (DLT) hold the power to move society closer towards the ideal metaverse through innovations like Non-Fungible Tokens (NFTs). Due to a combination of the infancy of this technology and the significant implic

Akash Dhruv, Anshu Dubey

The emergence of foundational models and generative artificial intelligence (GenAI) is poised to transform productivity in scientific computing, especially in code development, refactoring, and translating from one programming language to another. However, because the output of GenAI cannot be guaranteed to be correct, manual intervention remains necessary.

R. Rajesh, V. Subashri, Oleg Zaboronski

We study probabilities of rare events in the general coalescence process, $kA\rightarrow \ell A$, where $k>\ell$. For arbitrary $k, \ell$, by rewriting these probabilities in terms of an effective action, we derive the large deviation function describing the probability of finding $N$ particles at time $t$, when starting with $M$ particles initially. Additio

Ali Reza Ibrahimzada, Kaiyao Ke, Mrigank Pawagi, Muhammad Salman Abid

Code translation transforms programs from one programming language (PL) to another. Several rule-based transpilers have been designed to automate code translation between different pairs of PLs. However, the rules can become obsolete as the PLs evolve and cannot generalize to other PLs. Recent studies have explored the automation of code translation using La

Amir Kazemi, Qurat ul ain Fatima, Volodymyr Kindratenko, Christopher W. Tessum

Image labeling is a critical bottleneck in the development of computer vision technologies, often constraining machine learning performance due to the time-intensive nature of manual annotations. This work introduces a novel approach that leverages outpainting to mitigate annotated data scarcity by generating artificial contexts and annotations, significantl

S. Aiello, A. Albert, A. R. Alhebsi, M. Alshamsi

The KM3NeT Collaboration has tackled a common challenge faced by the astroparticle physics community, namely adapting the experiment-specific simulation software to work with the CORSIKA air shower simulation output. The proposed solution is an extension of the open source code gSeaGen, which allows the transport of muons generated by CORSIKA to a detector o

Neil Chowdhury, Franklin Wang, Sumedh Shenoy, Douwe Kiela

Multimodal models leverage large-scale pre-training to achieve strong but still imperfect performance on tasks such as image captioning, visual question answering, and cross-modal retrieval. In this paper, we present a simple and efficient method for correcting errors in trained contrastive image-text retrieval models with no additional training, called Near

Giorgia Bellomonte, Stefan Ivkovic, Camillo Trapani

In this paper, we consider representations induced by general positive and completely positive sesquilinear maps with values in ordered Banach bimodules, such as the space of trace-class operators and the spaces of bounded linear operators from a von Neumann algebra into the dual of another von Neumann algebra. Also, we deduce some new inequalities for these

Md Mohaiminul Haque, Joonas Sae, Juho Pirskanen, Mikko Valkama

Digital Enhanced Cordless Telecommunications 2020 New Radio (DECT-2020 NR) has garnered recognition as an alternative for cellular 5G technology in the internet of things industry. This paper presents a study centered around the analysis of the link distance performance in varying environments for DECT-2020 NR. The study extensively examines and analyzes rec

Rodolfo A. Quintero, Juan C. Vera, Luis F. Zuluaga

In recent years, there has been a surge of interest in studying different ways to reformulate nonconvex optimization problems, especially those that involve binary variables. This interest surge is due to advancements in computing technologies, such as quantum and Ising devices, as well as improvements in quantum and classical optimization solvers that take

Jeremy Mendez, Yae Eun Kim, Nafisah Chowdhury, Alexios Tziranis

Electrochemical biosensors ("E-AB" or "E-DNA" type sensors) that utilize square-wave voltammetry originated in academic labs with a few standard experimental configurations for the electrochemical cell and data analysis. We report here on adaptations of these approaches that are friendly to novice scientists such as those in undergraduate laboratories. These

F. Blaschke, T. Romańczukiewicz, K. Sławińska, A. Wereszczyński

Using a renormalization-inspired perturbation expansion we show that oscillons in a generic field theory in (1+1) dimensions arise as dressed $Q$-balls of a universal (up to the leading nonlinear order) complex field theory. This theory reveals a close similarity to the integrable complex sine-Gordon model which possesses exact multi-$Q$-balls. We show that

Kai Yan, Alexander G. Schwing, Yu-Xiong Wang

Decision Transformers have recently emerged as a new and compelling paradigm for offline Reinforcement Learning (RL), completing a trajectory in an autoregressive way. While improvements have been made to overcome initial shortcomings, online finetuning of decision transformers has been surprisingly under-explored. The widely adopted state-of-the-art Online

Kim Louisa Auth, Jim Brouzoulis, Magnus Ekh

In this study, we address damage initiation and micro-crack formation in ductile failure of polycrystalline metals. We show how our recently published thermodynamic framework for ductile phase-field fracture of single crystals can be extended to polycyrstalline structures. A key feature of this framework is that is accounts for size effects by adopting gradi

Denis Korzhenkov, Christos Louizos

The problem of heterogeneous clients in federated learning has recently drawn a lot of attention. Spectral model sharding, i.e., partitioning the model parameters into low-rank matrices based on the singular value decomposition, has been one of the proposed solutions for more efficient on-device training in such settings. In this work, we present two samplin

Nabeel Seedat, Mihaela van der Schaar

Schema matching -- the task of finding matches between attributes across disparate data sources with different tables and hierarchies -- is critical for creating interoperable machine learning (ML)-ready data. Addressing this fundamental data-centric problem has wide implications, especially in domains like healthcare, finance and e-commerce -- but also has

Martin G. Herold, Evangelos Kipouridis, Joachim Spoerhase

We initiate the study of the following general clustering problem. We seek to partition a given set $P$ of data points into $k$ clusters by finding a set $X$ of $k$ centers and assigning each data point to one of the centers. The cost of a cluster, represented by a center $x\in X$, is a monotone, symmetric norm $f$ (inner norm) of the vector of distances of

Cai-Chang Li, Jun-Nan Lu, Gui-Jun Ding

We perform a comprehensive bottom-up study of all the simplest lepton models based on non-holomorphic $A_{5}$ modular flavor symmetry, in which neutrinos are assumed to be Majorana particles and their masses are generated by the Weinberg operator or the type I seesaw mechanism. In the case that the generalized CP (gCP) symmetry is not considered, we find tha

Joel Schmitz

Counterexamples to Lagrangian Poincar\'e recurrence were recently found in dimensions greater than six by Bro\'ci\'c and Shelukhin. We construct counterexamples in dimension four using almost toric fibrations.

Samuel A. Myers, Ellen S. Howell, Christopher Magri, Ronald J. Vervack

Understanding the properties of near-Earth asteroids (NEAs) is key for many aspects of planetary science, particularly planetary defense. Our current knowledge of NEA sizes and regolith properties is heavily dependent on simple thermal models. These models are often used to analyze data from missions such as NEOWISE because they are well suited to deal with

Moritz Haas, Jin Xu, Volkan Cevher, Leena Chennuru Vankadara

Sharpness Aware Minimization (SAM) enhances performance across various neural architectures and datasets. As models are continually scaled up to improve performance, a rigorous understanding of SAM's scaling behaviour is paramount. To this end, we study the infinite-width limit of neural networks trained with SAM, using the Tensor Programs framework. Our fin

Robin K. S. Hankinn

In this short article I introduce the evitaicossa package which provides functionality for antiassociative algebras in the R programming language; it is available on CRAN at https://CRAN.R-project.org/package=evitaicossa.

Rachel Longjohn, Markelle Kelly, Sameer Singh, Padhraic Smyth

In machine learning research, it is common to evaluate algorithms via their performance on standard benchmark datasets. While a growing body of work establishes guidelines for -- and levies criticisms at -- data and benchmarking practices in machine learning, comparatively less attention has been paid to the data repositories where these datasets are stored,

S. Abe, I. Alekseev, T. Arai, T. Arihara

The magnetised near detector (ND280) of the T2K long-baseline neutrino oscillation experiment has been recently upgraded aiming to satisfy the requirement of reducing the systematic uncertainty from measuring the neutrinonucleus interaction cross section, which is the largest systematic uncertainty in the search for leptonic charge-parity symmetry violation.

Clemens Karner, Janek Gröhl, Ian Selby, Judith Babar

When developing machine learning models, image quality assessment (IQA) measures are a crucial component for the evaluation of obtained output images. However, commonly used full-reference IQA (FR-IQA) measures have been primarily developed and optimized for natural images. In many specialized settings, such as medical images, this poses an often overlooked

Danilo Polo Ojito, Emil Prodan, Tom Stoiber

In space-adiabatic approaches one can approximate Hamiltonians that are modulated slowly in space by phase-space functions that depend on position and momentum. In this paper, we establish a rigorous relation between this approach and the operator-theoretic approach for topological insulators with defects, which employs $C^*$-algebras and operator K-theory.

Nabil Omi, Hosein Hasanbeig, Hiteshi Sharma, Sriram K. Rajamani

In this paper we propose a formal, model-agnostic meta-learning framework for safe reinforcement learning. Our framework is inspired by how parents safeguard their children across a progression of increasingly riskier tasks, imparting a sense of safety that is carried over from task to task. We model this as a meta-learning process where each task is synchro

Chenyue Zhang, Shangyuan Liu, Hoi-To Wai, Anthony Man-Cho So

Learning the graph topology of a complex network is challenging due to limited data availability and imprecise data models. A common remedy in existing works is to incorporate priors such as sparsity or modularity which highlight on the structural property of graph topology. We depart from these approaches to develop priors that are directly inspired by comp

Ping Zhao, Wenwan Yang, Long Feng, Zhaojun Wang

In this paper, we investigate sphericity testing in high-dimensional settings, where existing methods primarily rely on sum-type test procedures that often underperform under sparse alternatives. To address this limitation, we propose two max-type test procedures utilizing the sample covariance matrix and the sample spatial-sign covariance matrix, respective

M. M. Korshunov, Yu. N. Togushova

We consider the effect of the nematic order on the formation of the superconducting state in iron pnictides and chalcogenides. Nematic order with the $B_{2g}$ symmetry is modelled as the $d$-type Pomeranchuk instability and treated within the mean-field approach. Calculated nematic order parameter depends on the nematic interaction coefficient and abruptly c

Lamine Diop, Marc Plantevit, Arnaud Soulet

Efficient learning from streaming data is important for modern data analysis due to the continuous and rapid evolution of data streams. Despite significant advancements in stream pattern mining, challenges persist, particularly in managing complex data streams like sequential and weighted itemsets. While reservoir sampling serves as a fundamental method for

Ricardo N. Ferreira, Marta Guimarães, Cláudia Soares

The amount of debris in orbit has increased significantly over the years. With the recent growth of interest in space exploration, conjunction assessment has become a central issue. One important metric to evaluate conjunction risk is the miss distance. However, this metric does not intrinsically take into account uncertainty distributions. Some work has bee

Binghao Huang, Yixuan Wang, Xinyi Yang, Yiyue Luo

Tactile and visual perception are both crucial for humans to perform fine-grained interactions with their environment. Developing similar multi-modal sensing capabilities for robots can significantly enhance and expand their manipulation skills. This paper introduces \textbf{3D-ViTac}, a multi-modal sensing and learning system designed for dexterous bimanual

Zhenbiao Cao, Yuanlei Zheng, Zhihao Fan, Xiaojin Zhang

Text-to-SQL generation aims to translate natural language questions into SQL statements. In Text-to-SQL based on large language models, schema linking is a widely adopted strategy to streamline the input for LLMs by selecting only relevant schema elements, therefore reducing noise and computational overhead. However, schema linking faces risks that require c

Carolina Higuera, Akash Sharma, Chaithanya Krishna Bodduluri, Taosha Fan

In this work, we introduce general purpose touch representations for the increasingly accessible class of vision-based tactile sensors. Such sensors have led to many recent advances in robot manipulation as they markedly complement vision, yet solutions today often rely on task and sensor specific handcrafted perception models. Collecting real data at scale

Joongkyu Lee, Min-hwan Oh

In this work, we prove that, in linear MDPs, the feature dimension $d$ is lower bounded by $S/U$ in order to aptly represent transition probabilities, where $S$ is the size of the state space and $U$ is the maximum size of directly reachable states. Hence, $d$ can still scale with $S$ depending on the direct reachability of the environment. To address this l

Gerold Schefer

We prove a Galois equidistribution result for torsion points in $\mathbb G_m^n$ in the $p$-adic setting for test functions of the form $\log |F|_p$ where $F$ is a nonzero polynomial with coefficients in the $p$-adic numbers. Our result includes a power saving quantitative estimate of the decay rate rate of the equidistribution. As an application we show that

Abhimanyu Das, Matthew Faw, Rajat Sen, Yichen Zhou

Motivated by the recent success of time-series foundation models for zero-shot forecasting, we present a methodology for $\textit{in-context fine-tuning}$ of a time-series foundation model. In particular, we design a pretrained foundation model that can be prompted (at inference time) with multiple time-series examples, in order to forecast a target time-ser

Luigi Camerano, Dario Mastrippolito, Debora Pierucci, Ji Dai

The wave-like nature of electrons is evident from quantum interference effects observed during the photoemission process. When there are different nuclei in the unit cell of a crystal and/or structural distortions, photo-electron wavefunctions can interfere, giving rise to peculiar intensity modulation of the spectrum, which can also hide energy states in a

Yulia Gorginyan

Let (X,I,J,K) be a compact hypercomplex manifold, i.e. a smooth manifold X with an action of the quaternion algebra (Id,I,J,K) on the tangent bundle TX, inducing integrable almost complex structures. For any $(a, b, c) \in S^2$, the linear combination $L := aI + bJ + cK$ defines another complex structure on X. This results in a $C P^1$-family of complex stru

Florian Paysan, David Melching, Eric Breitbarth

The link between microscopic mechanisms and macroscopic behaviour, represented by the $da/dN-\Delta K$ curve, plays an increasingly important role in relating the fatigue crack growth curve required for component design to the underlying physics. High-resolution digital image correlation (HR-DIC) allows for in-depth analysis of microscopic fatigue crack grow

Meijing Chen, Bin Liu, Ying Liu, Tianrui Li

Glass composition screening is essential for advancing new glass materials, yet the inherent complexity of multicomponent systems presents significant challenges. Current supervised learning methods for this task rely heavily on large amounts of high-quality data and are prone to overfitting on noisy samples, which limits their generalization ability. In thi

Maxwell G. A. Maltz, Peter A. Thomas, Christoper C. Lovell, William J. Roper

Understanding the galaxy-halo relationship is not only key for elucidating the interplay between baryonic and dark matter, it is essential for creating large mock galaxy catalogues from N-body simulations. High-resolution hydrodynamical simulations are limited to small volumes by their large computational demands, hindering their use for comparisons with wid

Dejun Xu, Kai Ye, Zimo Zheng, Tao Zhou

Bilevel optimization problems are characterized by an interactive hierarchical structure, where the upper level seeks to optimize its strategy while simultaneously considering the response of the lower level. Evolutionary algorithms are commonly used to solve complex bilevel problems in practical scenarios, but they face significant resource consumption chal

Dillon Z. Chen, Sylvie Thiébaux

Graph learning is naturally well suited for use in symbolic, object-centric planning due to its ability to exploit relational structures exhibited in planning domains and to take as input planning instances with arbitrary numbers of objects. Numeric planning is an extension of symbolic planning in which states may now also exhibit numeric variables. In this

Jinlin Lai, Justin Domke, Daniel Sheldon

Bayesian reasoning in linear mixed-effects models (LMMs) is challenging and often requires advanced sampling techniques like Markov chain Monte Carlo (MCMC). A common approach is to write the model in a probabilistic programming language and then sample via Hamiltonian Monte Carlo (HMC). However, there are many ways a user can transform a model that make inf

Erik Skibsted

We review Yafaev's approach to asymptotic completeness for systems of particles mutually interacting with short-range potentials. The theory is based on computation of commutators with time-independent (mostly bounded) observables yielding a sufficient supply of Kato smoothness bounds.

Amos Kaminski

A classical result, the Stone embedding, characterizes profinite sets as totally disconnected, compact Hausdorff spaces. Building on "Pyknotic objects, I. Basic notions", which introduced a derived Stone embedding of the pro-category of $\pi$-finite spaces into pyknotic spaces, this paper uses the $\infty$-topoi machinery to partially characterize the essent

Yi-Zen Chu, Afidah Zuroida

We analyze the second order perturbations of the Deser-Woodard II (DWII), Vardanyan-Akrami-Amendola-Silvestri (VAAS) and Amendola-Burzilla-Nersisyan (ABN) nonlocal gravity models in an attempt to extract their associated gravitational wave energy-momentum fluxes. In Minkowski spacetime, the gravitational spatial momentum density is supposed to scale at most

Mohamad Hakam Shams Eddin, Juergen Gall

The spatio-temporal relations of impacts of extreme events and their drivers in climate data are not fully understood and there is a need of machine learning approaches to identify such spatio-temporal relations from data. The task, however, is very challenging since there are time delays between extremes and their drivers, and the spatial response of such d

Xiaokun Zhao, Marija Iloska, Yousef El-Laham, Mónica F. Bugallo

An important and often overlooked aspect of particle filtering methods is the estimation of unknown static parameters. A simple approach for addressing this problem is to augment the unknown static parameters as auxiliary states that are jointly estimated with the time-varying parameters of interest. This can be impractical, especially when the system of int

Lisi Xia, Peter J. M. van der Slot, Chris Toebes, Klaus -J. Boller

We experimentally investigate on-chip control and analysis of spatially multimode nonlinear interactions in silicon nitride waveguide circuits. Using widely different dispersion of transverse supermodes in a strongly-coupled dual-core waveguide section, and using integrated pairs of input and output single-mode waveguides, we enable controlled excitation of

Kevin F. Dunnell, Andrew P. Stoddard

This paper presents "Biotic Browser," an innovative AI assistant leveraging StreamingLLM to transform web navigation and task execution. Characterized by its ability to simulate the experience of a passenger in an autonomous vehicle, the Biotic Browser excels in managing extended interactions and complex, multi-step web-based tasks. It marks a significant ad

Marcin Glowacki, Khee-Gan Lee

Despite the first detection of fast radio bursts (FRBs) being as recent as 2007, they have already been proven to be a fantastic tool as a unique cosmological probe. In this chapter, after a brief introduction to FRBs and how they are currently detected, we describe various cosmological questions and how FRB research has both aided previous studies and can c

Santiago Casas, Christian Fidler

We present TOPO (Time-Ordered Provable Outputs), a tool designed to enhance reproducibility and data integrity in astrophysical research, providing a trustless alternative to data analysis blinding. Astrophysical research frequently involves probabilistic algorithms, high computational demands, and stringent data privacy requirements, making it difficult to

Davide Maran, Alberto Maria Metelli, Matteo Papini, Marcello Restelli

Achieving the no-regret property for Reinforcement Learning (RL) problems in continuous state and action-space environments is one of the major open problems in the field. Existing solutions either work under very specific assumptions or achieve bounds that are vacuous in some regimes. Furthermore, many structural assumptions are known to suffer from a prova

Quentin Guilhot, Michał Wójcik, Jascha Achterberg, Rui Ponte Costa

Methods for analyzing representations in neural systems have become a popular tool in both neuroscience and mechanistic interpretability. Having measures to compare how similar activations of neurons are across conditions, architectures, and species, gives us a scalable way of learning how information is transformed within different neural networks. In contr

Eirini Angeloudi, Marc Huertas-Company, Jesús Falcón-Barroso, Regina Sarmiento

Galaxies grow through star formation (in-situ) and accretion (ex-situ) of other galaxies. Reconstructing the relative contribution of these two growth channels is crucial for constraining the processes of galaxy formation in a cosmological context. In this on-going work, we utilize a conditional variational autoencoder along with a normalizing flow - trained

Sheng Fang, Qing Lin, Jun Meng, Bingsheng Chen

Percolation is a cornerstone concept in physics, providing crucial insights into critical phenomena and phase transitions. In this study, we adopt a kinetic perspective to reveal the scaling behaviors of higher-order gaps in the largest cluster across various percolation models, spanning from latticebased to network systems, encompassing both continuous and

Stefano Albini, Lara Orlandic, Jonathan Dan, Jérôme Thevenot

Continuous cough monitors can greatly aid doctors in home monitoring and treatment of respiratory diseases. Although many algorithms have been proposed, they still face limitations in data privacy and short-term monitoring. Edge-AI offers a promising solution by processing privacy-sensitive data near the source, but challenges arise in deploying resource-int

YuQianqian Ma, Peng Zhang, Leyang Xue

Individual decisions and behaviors are shaped not only by direct interactions with others but also by the collective emotional dynamics within groups. In this work, we introduce the signed simplicial contagion model, integrating both pairwise and emotional group interactions to investigate contagion dynamics in signed networks. Through mean field analysis an

Toyo Taniguchi

We construct a non-commutative analogue of the modular vector field on a Poisson manifold for a given pair of a double bracket and a connection on a space of 1-forms. The key ingredient, the triple divergence map, is directly constructed from a connection on a linear category to deal with multiple base points. As an application, we give an algebraic descript

Matthias Köhne, Jürgen Saal, Laura Westermann

We consider the Stokes equations subject to Navier boundary conditions on a two-dimensional wedge domain with opening angle $\theta_0 \in (0,\,\pi)$. We prove existence and uniqueness of solutions with optimal regularity in an $L^p$-setting. The results are based on optimal regularity results for the Stokes equations subject to perfect slip boundary conditio

R. Maarleveld, H. E. J. Veeger, F. C. T. van der Helm, J. Son

Musculoskeletal (MSK) models offer a non-invasive way to understand biomechanical loads on joints and tendons, which are difficult to measure directly. Variations in muscle strength, especially relative differences between muscles, significantly impact model outcomes. Typically, scaled generic MSK models use maximum isometric forces that are not adjusted for

Anjeza Bekolli, Luis A. Guardiola, Ana Meca

Agricultural industries face increasing pressure to optimize efficiency and reduce costs in a competitive and resource-constrained global market. As firms seek innovative ways to enhance productivity, cooperative strategies have emerged as a promising solution to address these challenges. In this context, game theory provides a powerful framework for analyzi

Anna Trindade Falcao, G. Fabbiano, M. Elvis, A. Paggi

We present a detailed imaging analysis of 260 ks of sub-arcsecond resolution Chandra Advanced CCD Imaging Spectrometer (ACIS-S) observations of the nearby Seyfert 2 galaxy NGC 5728. Our study focuses on the bright and diffuse soft X-ray emission within the galaxy's inner ~1 kpc. By comparing the X-ray emission across different energy bands, we identify local

Xiang Li, Yixiang Dai, Qing Qu

In this work, we study the generalizability of diffusion models by looking into the hidden properties of the learned score functions, which are essentially a series of deep denoisers trained on various noise levels. We observe that as diffusion models transition from memorization to generalization, their corresponding nonlinear diffusion denoisers exhibit in

Tianyu Chen, Kevin Bello, Francesco Locatello, Bryon Aragam

We consider the linear causal representation learning setting where we observe a linear mixing of $d$ unknown latent factors, which follow a linear structural causal model. Recent work has shown that it is possible to recover the latent factors as well as the underlying structural causal model over them, up to permutation and scaling, provided that we have a

Dhrumil Patel, Mark M. Wilde

Thermal states play a fundamental role in various areas of physics, and they are becoming increasingly important in quantum information science, with applications related to semi-definite programming, quantum Boltzmann machine learning, Hamiltonian learning, and the related task of estimating the parameters of a Hamiltonian. Here we establish formulas underl

Oriol Llopis-Almela, J. Tomas Lazaro, Santiago F. Elena, Josep Sardanyes

The dynamics of wild-type (wt) RNA viruses and their defective viral genomes (DVGs) have been extensively studied both experimentally and theoretically. This research has paid special attention to the interference effects of DVGs on wt accumulation, transmission, disease severity, and induction of immunological responses. This subject is currently a highly a

A. Qamesh, R. Ahmad, M. Karagounis, P. Kind

The upcoming ATLAS Phase II upgrade mandates replacing the tracking system with the all-silicon Inner Tracker (ITK), featuring a pixel detector as its core element. The monitoring data of the new system will be aggregated from an on-detector ASIC, Monitoring Of Pixel System (MOPS), and channeled to the Detector Control System (DCS) via a newly developed FPGA

Lokendra Poudel, Sushant Jha, Ryan Meeker, Duy-Nhat Phan

Ultrasonic Additive Manufacturing (UAM) employs ultrasonic welding to bond similar or dissimilar metal foils to a substrate, resulting in solid, consolidated metal components. However, certain processing conditions can lead to inter-layer defects, affecting the final product's quality. This study develops a method to monitor in-process quality using deep lea

Diana Cai, Chirag Modi, Charles C. Margossian, Robert M. Gower

We develop EigenVI, an eigenvalue-based approach for black-box variational inference (BBVI). EigenVI constructs its variational approximations from orthogonal function expansions. For distributions over $\mathbb{R}^D$, the lowest order term in these expansions provides a Gaussian variational approximation, while higher-order terms provide a systematic way to

Iman Kazemian, Murat Yildirim, Paritosh Ramanan

Operations and maintenance (O&M) is a fundamental problem in wind energy systems with far reaching implications for reliability and profitability. Optimizing O&M is a multi-faceted decision optimization problem that requires a careful balancing act across turbine level failure risks, operational revenues, and maintenance crew logistics. The resulting O&M pro

Lili Fan, Ziwei Wang, Chenfei Liao, Jingwei Wang

Plasma surface high-order harmonics generation (SHHG) driven by intense laser pulses on plasma targets enables a high-quality extreme ultraviolet source with high pulse energy and outstanding spatiotemporal coherence. Optimizing the performance of SHHG is important for its applications in single-shot imaging and absorption spectroscopy. In this work, we demo

Muhammed Saeed, Elgizouli Mohamed, Mukhtar Mohamed, Shaina Raza

Large language models (LLMs) are widely used but raise ethical concerns due to embedded social biases. This study examines LLM biases against Arabs versus Westerners across eight domains, including women's rights, terrorism, and anti-Semitism and assesses model resistance to perpetuating these biases. To this end, we create two datasets: one to evaluate LLM

Leonhard M. Lohr, Richard Ciesielski, Vinh-Binh Truong, Victor Soltwisch

Scatterometry is a tested method for measuring periodic semiconductor structures. Since the sizes of modern semiconductor structures have reached the nanoscale regime, the challenge is to determine the shape of periodic nanostructures with sub-nanometer accuracy. To increase the resolution of scatterometry, short-wavelength radiation like soft X-rays can be

Wilson S. Martins, Joseph W. P. Wilkinson, Markus Hennrich, Igor Lesanovsky

Trapped ions are among the most advanced platforms for quantum simulation and computation. Their capabilities can be further augmented by making use of electronically highly excited Rydberg states, which enable the realization of long-ranged electric dipolar interactions. Most experimental and theoretical studies so far focus on the excitation of ionic Rydbe

Junliang Du, Yiru Cang, Tong Zhou, Jiacheng Hu

This study introduces the Hybrid Multi-modal VGG (HM-VGG) model, a cutting-edge deep learning approach for the early diagnosis of glaucoma. The HM-VGG model utilizes an attention mechanism to process Visual Field (VF) data, enabling the extraction of key features that are vital for identifying early signs of glaucoma. Despite the common reliance on large ann

R. Moukhader, D. R. Rodrigues, A. Riveros, A. Koujok

Control of the microwave signal generated by spin-transfer torque oscillators (STOs) is crucial for their applications in spin wave generation and neuromorphic computing. This study investigates injection locking of a DC-driven vortex STO using surface acoustic waves (SAWs) to enhance the STO's signal and allow for its synchronization with external inputs. W

Antony Della Vecchia, Michael Joswig, Fabian Lenzen

We study algebraic shifting of uniform hypergraphs and finite simplicial complexes in the exterior algebra with respect to matrices which are not necessarily generic. Several questions raised by Kalai (2002) are addressed. For instance, it turns out that the combinatorial shifting of Erd\H{o}s$\unicode{x2013}$Ko$\unicode{x2013}$Rado (1961) arises as a specia

Anish Kulkarni, Kohei Kawabata, Shinsei Ryu

Symmetry of non-Hermitian matrices underpins many physical phenomena. In particular, chaotic open quantum systems exhibit universal bulk spectral correlations classified on the basis of time-reversal symmetry$^{\dagger}$ (TRS$^{\dagger}$), coinciding with those of non-Hermitian random matrices in the same symmetry class. Here, we analytically study the spect

Schuyler G. Wolff, András Gáspár, George H. Rieke, Jarron M. Leisenring

We present a provisory scattered light detection of the Vega debris disk using deep Hubble Space Telescope coronagraphy (PID 16666). At only 7.7 parsecs, Vega is immensely important in debris disk studies both for its prominence and also because it allows the highest physical resolution among all debris systems relative to temperature zones around the star.

Brian Colquhoun, Christine T. H. Davies, G. Peter Lepage

We calculate the decay rate for $\eta_b \to \gamma \gamma$ in lattice QCD for the first time, providing a precise prediction for the Belle II experiment. Our calculation includes $u$, $d$, $s$ and $c$ quarks in the sea, using gluon field configurations generated by the MILC collaboration, at three values of the lattice spacing from $0.06\;\mathrm{fm}$ to $0.

Leonardo Roveri, Francesco Triggiano

We consider the 2D Euler equation with bounded initial vorticity and perturbed by rough transport noise. We show that there exists a unique solution, which coincides with the starting condition advected by the Lagrangian flow. Moreover, the stability of the solution map with respect to the initial vorticity and the rough perturbation yields a Wong-Zakai resu

Wenhao Liu, Jiazhi Wu, Quanwei Lin, Handong Luo

The low-orbit mega-constellation network (LMCN) is an important part of the space-air-ground integrated network system. An effective satellite-ground interconnection design can result in a stable constellation topology for LMCNs. A naive solution is accessing the satellite with the longest remaining service time (LRST), which is widely used in previous desig

V. Cianciolo, R. Golub, B. W. Filippone, P. R. Huffman

The nEDM experiment at the SNS (nEDM@SNS) is the first measurement of the neutron EDM to directly measure the precession frequency of the neutron spin due to magnetic and electric fields. Previous measurements have inferred the precession frequency by measuring the residual polarization of neutrons after a long period of free precession. This difference prov

Gennadiy V. Sotnikov, Kostyantyn V. Galaydych, Jay L. Hirshfield, Peter I. Markov

To mitigate the BBU instability and improve characteristics of accelerated bunches in Dielectric Wakefield Accelerator one can be used the isotropic plasma filling of the transport channel. Here we present the results of analytical and numerical studies of the dynamics of accelerated electron/positron and drive electron bunches under wake acceleration in a p

Sunjae Yoon, Gwanhyeong Koo, Younghwan Lee, Chang D. Yoo

Human image animation aims to generate a human motion video from the inputs of a reference human image and a target motion video. Current diffusion-based image animation systems exhibit high precision in transferring human identity into targeted motion, yet they still exhibit irregular quality in their outputs. Their optimal precision is achieved only when t