May 2024 arXiv papers — page 21

Stylianos Loukas Vasileiou, William Yeoh

Traditional belief revision frameworks often rely on the principle of minimalism, which advocates minimal changes to existing beliefs. However, research in human cognition suggests that people are inherently driven to seek explanations for inconsistencies, thereby striving for explanatory understanding rather than minimal changes when revising beliefs. Tradi

Ruchika Chavhan, Da Li, Timothy Hospedales

While large-scale text-to-image diffusion models have demonstrated impressive image-generation capabilities, there are significant concerns about their potential misuse for generating unsafe content, violating copyright, and perpetuating societal biases. Recently, the text-to-image generation community has begun addressing these concerns by editing or unlear

Tilmann Matthaei

Solving discretized versions of the Dirac equation represents a large share of execution time in lattice Quantum Chromodynamics (QCD) simulations. Many high-performance computing (HPC) clusters use graphics processing units (GPUs) to offer more computational resources. Our solver program, DDalphaAMG, previously was unable to fully take advantage of GPUs to a

Amir Hossein Karbasi, Hao Yang, Saiedeh Razavi

In transportation networks, intersections pose significant risks of collisions due to conflicting movements of vehicles approaching from different directions. To address this issue, various tools can exert influence on traffic safety both directly and indirectly. This study focuses on investigating the impact of adaptive signal control and connected and auto

Eduardo Rodriguez, Alessandro Zocco

We construct a description of Ion Temperature Gradient (ITG) driven localised linear modes which retains both wave-particle and magnetic drift resonant effects while capturing the field-line dependence of the electrostatic potential. We exploit the smallness of the magnetic drift and the strong localisation of the mode to resolve the problem with a polynomia

Mohammadreza Sadeghi, Zihan Wang, Narges Armanfard

Unsupervised Continual Learning (UCL) is a burgeoning field in machine learning, focusing on enabling neural networks to sequentially learn tasks without explicit label information. Catastrophic Forgetting (CF), where models forget previously learned tasks upon learning new ones, poses a significant challenge in continual learning, especially in UCL, where l

Jacob Bedrossian, Siming He, Sameer Iyer, Fei Wang

In this article, we study the regularity theory for two linear equations that are important in fluid dynamics: the passive scalar equation for (time-varying) shear flows close to Couette in $\mathbb T \times [-1,1]$ with vanishing diffusivity $\nu \to 0$ and the Poisson equation with right-hand side behaving in similar function spaces to such a passive scala

Maria Stamatopoulou, Jianwei Liu, Dimitrios Kanoulas

We present DiPPeST, a novel image and goal conditioned diffusion-based trajectory generator for quadrupedal robot path planning. DiPPeST is a zero-shot adaptation of our previously introduced diffusion-based 2D global trajectory generator (DiPPeR). The introduced system incorporates a novel strategy for local real-time path refinements, that is reactive to c

Bowen Xu, Yiwen Huang, Chuan Hong, Shuangning Li

Conditional independence tests are crucial across various disciplines in determining the independence of an outcome variable $Y$ from a treatment variable $X$, conditioning on a set of confounders $Z$. The Conditional Randomization Test (CRT) offers a powerful framework for such testing by assuming known distributions of $X \mid Z$; it controls the Type-I er

Ed Davis, Ian Gallagher, Daniel John Lawson, Patrick Rubin-Delanchy

Dynamic graphs provide a flexible data abstraction for modelling many sorts of real-world systems, such as transport, trade, and social networks. Graph neural networks (GNNs) are powerful tools allowing for different kinds of prediction and inference on these systems, but getting a handle on uncertainty, especially in dynamic settings, is a challenging probl

Stylianos Loukas Vasileiou, William Yeoh, Alessandro Previti, Tran Cao Son

Explanation generation frameworks aim to make AI systems' decisions transparent and understandable to human users. However, generating explanations in uncertain environments characterized by incomplete information and probabilistic models remains a significant challenge. In this paper, we propose a novel framework for generating probabilistic monolithic expl

Fatemeh Delavari, Seyyed Mohammad Reza Hashemi Golpayegani, Mohammad Ali Ahmadi-Pajouh

Motor Imagery (MI) is gaining traction in both rehabilitation and sports settings, but its immediate influence on human postural control is not yet clearly understood. The focus of this study is to examine the effects of MI on the dynamics of the Center of Pressure (COP), a crucial metric for evaluating postural stability. In the experiment, thirty healthy y

Chang-Goo Kim, Eve C. Ostriker, Jeong-Gyu Kim, Munan Gong

We present a new simulation suite for the star-forming interstellar medium (ISM) in galactic disks using the TIGRESS-NCR framework. Distinctive aspects of our simulation suite are: (1) sophisticated and comprehensive numerical treatments of essential physical processes including magnetohydrodynamics, self-gravity, and galactic differential rotation, as well

Honglin Lin, Siyu Li, Guoshun Nan, Chaoyue Tang

Image retrieval from contextual descriptions (IRCD) aims to identify an image within a set of minimally contrastive candidates based on linguistically complex text. Despite the success of VLMs, they still significantly lag behind human performance in IRCD. The main challenges lie in aligning key contextual cues in two modalities, where these subtle cues are

Bijan Mazaheri, Chandler Squires, Caroline Uhler

Heterogeneous data from multiple populations, sub-groups, or sources is often represented as a ``mixture model'' with a single latent class influencing all of the observed covariates. Heterogeneity can be resolved at multiple levels by grouping populations according to different notions of similarity. This paper proposes grouping with respect to the causal r

Anna Breger, Clemens Karner, Ian Selby, Janek Gröhl

Image quality assessment (IQA) is standard practice in the development stage of novel machine learning algorithms that operate on images. The most commonly used IQA measures have been developed and tested for natural images, but not in the medical setting. Reported inconsistencies arising in medical images are not surprising, as they have different propertie

Manfred Buchacher, Manuel Kauers

For a given ideal I in K[x_1,...,x_n,y_1,...,y_m] in a polynomial ring with n+m variables, we want to find all elements that can be written as f-g for some f in K[x_1,...,x_n] and some g in K[y_1,...,y_m], i.e., all elements of I that contain no term involving at the same time one of the x_1,...,x_n and one of the y_1,...,y_m. For principal ideals and for id

Anej Svete, Franz Nowak, Anisha Mohamed Sahabdeen, Ryan Cotterell

The recent successes and spread of large neural language models (LMs) call for a thorough understanding of their computational ability. Describing their computational abilities through LMs' \emph{representational capacity} is a lively area of research. However, investigation into the representational capacity of neural LMs has predominantly focused on their

Seyedmehdi Orouji, Martin C. Liu, Tal Korem, Megan A. K. Peters

Machine learning techniques are steadily becoming more important in modern biology, and are used to build predictive models, discover patterns, and investigate biological problems. However, models trained on one dataset are often not generalizable to other datasets from different cohorts or laboratories, due to differences in the statistical properties of th

Ravil Mussabayev

Evaluating natural language generation models, particularly for method name prediction, poses significant challenges. A robust metric must account for the versatility of method naming, considering both semantic and syntactic variations. Traditional overlap-based metrics, such as ROUGE, fail to capture these nuances. Existing embedding-based metrics often suf

Mareike Dressler, Simon Foucart, Mioara Joldes, Etienne de Klerk

We consider a new multivariate generalization of the classical monic (univariate) Chebyshev polynomial that minimizes the uniform norm on the interval $[-1,1]$. Let $\Pi^*_n$ be the subset of polynomials of degree at most $n$ in $d$ variables, whose homogeneous part of degree $n$ has coefficients summing up to $1$. The problem is determining a polynomial in

Azeem Hasan, Shani Meynet, Daniele Migliorati

Recent proposals for the Symmetry Topological Field Theory (SymTFT) of Maxwell theory admit a 0-form symmetry compatible with the classical $SL_2(\mathbb{R})$ duality of electromagnetism. We describe how to realize these automorphisms of the SymTFT in terms of its operators and we detail their effects on the dynamical theory and its global variants. In the p

Yue Xia, Maximilian Egger, Christoph Hofmeister, Rawad Bitar

Privacy of the clients' data and security against Byzantine clients are key challenges in Federated Learning (FL). Existing solutions to joint privacy and security incur sacrifices on the privacy guarantee. We introduce LoByITFL, the first communication-efficient information-theoretically private and secure FL scheme that makes no sacrifices on the privacy g

Paul Skoufranis

In this paper, a connection between bi-free probability and the asymptotics of random quantum channels and tensor products of random matrices is established. Using bi-free matrix models, it is demonstrated that the spectral distribution of certain self-adjoint quantum channels and tensor products of random matrices tend to a distribution that can be obtained

Björn Gustafsson

The paper reviews some parts of classical potential theory with applications to two dimensional fluid dynamics, in particular vortex motion. Energy and forces within a system of point vortices are similar to those for point charges when the vortices are kept fixed, but the dynamics is different in the case of free vortices. Starting from Bernoulli's equation

Miriam Buck, Stefano Modena

In a previous work (arXiv:2306.05948), we constructed by convex integration examples of energy dissipating solutions to the 2D Euler equations on $\mathbb{R}^2$ with vorticity in the real Hardy space $H^p(\mathbb{R}^2)$. In the present paper, we develop tools that significantly improve that result in two ways: Firstly, we achieve vorticities in $H^p(\mathbb{

ChonLam Lao, Jiaqi Gao, Ganesh Ananthanarayanan, Aditya Akella

Traditional ML inference is evolving toward modeless inference, which abstracts the complexity of model selection from users, allowing the system to automatically choose the most appropriate model for each request based on accuracy and resource requirements. While prior studies have focused on modeless inference within data centers, this paper tackles the pr

Charles Westphal, Stephen Hailes, Mirco Musolesi

In this paper, we introduce Partial Information Decomposition of Features (PIDF), a new paradigm for simultaneous data interpretability and feature selection. Contrary to traditional methods that assign a single importance value, our approach is based on three metrics per feature: the mutual information shared with the target variable, the feature's contribu

Keltin Grimes, Collin Abidi, Cole Frank, Shannon Gallagher

Machine learning models are vulnerable to adversarial attacks, including attacks that leak information about the model's training data. There has recently been an increase in interest about how to best address privacy concerns, especially in the presence of data-removal requests. Machine unlearning algorithms aim to efficiently update trained models to compl

Paulo Neves, Joerg K. Wegner, Philippe Schwaller

Ensuring high-quality data is paramount for maximizing the performance of machine learning models and business intelligence systems. However, challenges in data quality, including noise in data capture, missing records, limited data production, and confounding variables, significantly constrain the potential performance of these systems. In this study, we pr

Eduard Hogea, Darian Onchis

In this paper, a deep neural network approach and a neuro-symbolic one are proposed for classification and regression. The neuro-symbolic predictive models based on Logic Tensor Networks are capable of discriminating and in the same time of explaining the characterization of bad connections, called alerts or attacks, and of normal connections. The proposed h

Ziyang Wang, Shoubin Yu, Elias Stengel-Eskin, Jaehong Yoon

Long-form video understanding is complicated by the high redundancy of video data and the abundance of query-irrelevant information. To tackle these challenges, we propose VideoTree, a training-free framework which builds a query-adaptive and hierarchical video representation for LLM reasoning over long-form videos. First, VideoTree extracts query-relevant i

Guillermo Gamboa Quintero, Martín Matamala, Juan Pablo Peña

Chen and Chv\'atal conjectured in 2008 that in any finite metric space either there is a line containing all the points - a universal line -, or the number of lines is at least the number of points. This is a generalization of a classical result due to Erd\H{o}s that says that a set of $n$ non-collinear points in the Euclidean plane defines at least $n$ diff

Ridong Wu, Shuhong Chen, Xiangbiao Su, Yuankai Zhu

With the rapid development of large-scale language models, Retrieval-Augmented Generation (RAG) has been widely adopted. However, existing RAG paradigms are inevitably influenced by erroneous retrieval information, thereby reducing the reliability and correctness of generated results. Therefore, to improve the relevance of retrieval information, this study p

Xuan Son Nguyen, Shuo Yang, Aymeric Histace

Deep neural networks (DNNs) on Riemannian manifolds have garnered increasing interest in various applied areas. For instance, DNNs on spherical and hyperbolic manifolds have been designed to solve a wide range of computer vision and nature language processing tasks. One of the key factors that contribute to the success of these networks is that spherical and

M'bark Amghar, Mohamed Amazioug

The connections between thermodynamics and quantum information processing are of paramount importance. Here, we address a bipartite entanglement via extracted work in a cavity magnomechanical system contained inside an yttrium iron garnet (YIG) sphere. The photons and magnons interact through an interaction between magnetic dipoles. A magnetostrictive intera

Michail Mamalakis, Héloïse de Vareilles, Shun-Chin Jim Wu, Ingrid Agartz

In the last decade, computer vision has witnessed the establishment of various training and learning approaches. Techniques like adversarial learning, contrastive learning, diffusion denoising learning, and ordinary reconstruction learning have become standard, representing state-of-the-art methods extensively employed for fully training or pre-training netw

Weitian Zhang, Yichao Yan, Yunhui Liu, Xingdong Sheng

This paper aims to introduce 3D Gaussian for efficient, expressive, and editable digital avatar generation. This task faces two major challenges: (1) The unstructured nature of 3D Gaussian makes it incompatible with current generation pipelines; (2) the expressive animation of 3D Gaussian in a generative setting that involves training with multiple subjects

Renato M. Silva, Gregorio F. Azevedo, Matheus V. V. Berto, Jean R. Rocha

Traffic incidents involving vulnerable road users (VRUs) constitute a significant proportion of global road accidents. Advances in traffic communication ecosystems, coupled with sophisticated signal processing and machine learning techniques, have facilitated the utilization of data from diverse sensors. Despite these advancements and the availability of ext

Justin Deschenaux, Igor Krawczuk, Grigorios Chrysos, Volkan Cevher

Denoising Diffusion Probabilistic Models (DDPMs) exhibit remarkable capabilities in image generation, with studies suggesting that they can generalize by composing latent factors learned from the training data. In this work, we go further and study DDPMs trained on strictly separate subsets of the data distribution with large gaps on the support of the laten

Xuanqin Mou, Jiayu Duan

In this study, we proposed a universal n-th order partial differential equation (PDE) of 2-D Radon transform to disclose the relationship of Radon transform over a neighborhood of the integral line, named as local correlation equation (LCE). It is independent to the imaging object while in present CT theory, the relationship of Radon transform over neighbori

Lin Ma, Lam Tran, David White

This paper examines the association between police drug seizures and drug overdose deaths in Ohio from 2014 to 2018. We use linear regression, ARIMA models, and categorical data analysis to quantify the effect of drug seizure composition and weight on drug overdose deaths, to quantify the lag between drug seizures and overdose deaths, and to compare the weig

David Lee, Joel D. R. Vernet, Roland Bacon, Alexandre Jeanneau

WST - Widefield Spectroscopic Telescope: We summarise the design challenges of instrumentation for a proposed 12m class Telescope that aims to provide a large (>2.5 square degree) field of view and enable simultaneous Multi-object (> 20,000 objects) and Integral Field spectroscopy (inner 3x3 arcminutes field of view), initially at visible wavelengths. For th

Jubin Thomas

When managing an organization, planners often encounter numerous challenging scenarios. In such instances, relying solely on intuition or managerial experience may not suffice, necessitating a quantitative approach. This demand is further accentuated in the era of big data, where the sheer scale and complexity of constraints pose significant challenges. Ther

John A. Baldwin, Steven Sivek

The A-polynomial of a knot is defined in terms of SL(2,C) representations of the knot group, and encodes information about essential surfaces in the knot complement. In 2005, Dunfield-Garoufalidis and Boyer-Zhang proved that it detects the unknot using Kronheimer-Mrowka's work on the Property P conjecture. Here we use more recent results from instanton Floer

Igor Bogush, Dmitri Gal'tsov, Gérard Clément

We extend the derivation of mass formulas for stationary axisymmetric asymptotically locally flat solutions with string singularities on the polar axis to general supergravity actions containing vector and scalar fields. It is based on the rod structure of the solutions in Weyl coordinates and is applicable to black holes with Dirac and Misner strings. The o

Henry W. Longo, Richard C. Remsing

The surface of Titan, Saturn's largest moon, is rich in organics and is often suggested to model early Earth environments. Titan's surface is cold, at a temperature of approximately 90 K, which prohibits most thermally activated chemical reactions. However, quantum effects become more important at low temperatures and reactions that are classically prohibite

Hongen Liu, Di Sun, Jiahao Wang, Yi Liu

Video text spotting (VTS) aims to simultaneously localize, recognize and track text instances in videos. To address the limited recognition capability of end-to-end methods, recent methods track the zero-shot results of state-of-the-art image text spotters directly, and achieve impressive performance. However, owing to the domain gap between different datase

Jiaxing Yuan, Tine Curk

Polyelectrolyte (PE) hydrogels can dynamically respond to external stimuli, such as changes in pH and temperature, which benefits their use for smart materials and nanodevices with tunable properties. We investigate equilibrium conformations and phase transition dynamics of pH-responsive nanogels using hybrid molecular dynamics/Monte Carlo simulations with f

S. Annabi, E. Arrighi, A. Peugeot, H. Riechert

We present a technique for integrating ultraclean carbon nanotubes into superconducting circuits, aiming to realize Josephson junctions based on one-dimensional elementary quantum conductors. This technique primarily involves depositing the nanotube in the final step, thus preserving it from the inherent contaminations of nanofabrication and maintaining cont

Inbar Ben Yaacov, Yotam Dikstein, Gal Maor

High dimensional expanders (HDXs) are a hypergraph generalization of expander graphs. They are extensively studied in the math and TCS communities due to their many applications. Like expander graphs, HDXs are especially interesting for applications when they are bounded degree, namely, if the number of edges adjacent to every vertex is bounded. However, onl

Wei-Shu Hou, Mohamed Krab

We study the discovery prospects for a charged Higgs boson via the $b g\to c H^- \to c \bar t b$ process at the Large Hadron Collider (LHC). Focusing on the general Two Higgs Doublet Model (G2HDM) that possesses extra Yukawa couplings, the process is controlled by extra top couplings $\rho_{tc}$ and $\rho_{tt}$, which can drive electroweak baryogenesis (EWBG

Kaiwen Zhou, Tianyu Wang

In this paper, we introduce an innovative application of artificial intelligence in the realm of interior design through the integration of Stable Diffusion and Dreambooth models. This paper explores the potential of these advanced generative models to streamline and democratize the process of room interior generation, offering a significant departure from c

Amina A. Abdu, Lauren M. Chambers, Deirdre K. Mulligan, Abigail Z. Jacobs

Emerging discussions on the responsible government use of algorithmic technologies propose transparency and public participation as key mechanisms for preserving accountability and trust. But in practice, the adoption and use of any technology shifts the social, organizational, and political context in which it is embedded. Therefore translating transparency

Laura Fieback, Jakob Spiegelberg, Hanno Gottschalk

Large Vision Language Models (LVLMs) have shown remarkable capabilities in multimodal tasks like visual question answering or image captioning. However, inconsistencies between the visual information and the generated text, a phenomenon referred to as hallucinations, remain an unsolved problem with regard to the trustworthiness of LVLMs. To address this prob

Jean-Christophe Pain, Yuri Ralchenko

The intriguing law of anomalous numbers, also named Benford's law, states that the significant digits of data follow a logarithmic distribution favoring the smallest values. In this work, we test the compliance with this law of the atomic databases developed at the National Institute of Standards and Technology (NIST) focusing on line energies, oscillator st

Yufan Kang, Rongsheng Zhang, Wei Shao, Flora D. Salim

Dynamic Vehicle Routing Problem (DVRP), is an extension of the classic Vehicle Routing Problem (VRP), which is a fundamental problem in logistics and transportation. Typically, DVRPs involve two stakeholders: service providers that deliver services to customers and customers who raise requests from different locations. Many real-world applications can be for

Khang Truong Giang, Yongjae Kim, Andrea Finazzi

This paper addresses imitation learning for motion prediction problem in autonomous driving, especially in multi-agent setting. Different from previous methods based on GAN, we present the conditional latent ordinary differential equation (cLODE) to leverage both the generative strength of conditional VAE and the continuous representation of neural ODE. Our

Michel Kulhandjian, Hovannes Kulhandjian, Gunes Karabulut Kurt, Halim Yanikomeroglu

We address the challenge of developing an orthogonal time-frequency space (OTFS)-based non-orthogonal multiple access (NOMA) system where each user is modulated using orthogonal pulses in the delay Doppler domain. Building upon the concept of the sufficient (bi)orthogonality train-pulse [1], we extend this idea by introducing Hermite functions, known for the

Jubin Thomas

In response to global warming and the dwindling reservoirs of fossil fuels, Thailand has increasingly embraced alternative energy sources. Central to its energy development strategy is the Alternative Energy Development Plan (AEDP), which aims to reduce energy intensity, capitalize on residual resources, and mitigate greenhouse gas emissions. While significa

Alessandra Buonanno, Gustav Mogull, Raj Patil, Lorenzo Pompili

Driven by advances in scattering amplitudes and worldline-based methods, recent years have seen significant progress in our ability to calculate gravitational two-body scattering observables. These observables effectively encapsulate the gravitational two-body problem in the weak-field and high-velocity regime (post-Minkowskian, PM), with applications to the

Iva Vilović, Dirk Schulze-Makuch, René Heller

Stars with about 45 to 80% the mass of the Sun, so-called K dwarf stars, have previously been proposed as optimal host stars in the search for habitable extrasolar worlds. These stars are abundant, have stable luminosities over billions of years longer than Sun-like stars, and offer favorable space environmental conditions. So far, the theoretical and experi

Saurabh Pathak, Samridha Shrestha, Abdelrahman AlMahmoud

Object detection forms a key component in Unmanned Aerial Vehicles (UAVs) for completing high-level tasks that depend on the awareness of objects on the ground from an aerial perspective. In that scenario, adversarial patch attacks on an onboard object detector can severely impair the performance of upstream tasks. This paper proposes a novel model-agnostic

Purba Mukherjee, Anjan Ananda Sen

In this work, we implement Gaussian process regression to reconstruct the expansion history of the universe in a model-agnostic manner, using the Pantheon-Plus SN-Ia compilation in combination with two different BAO measurements (SDSS-IV and DESI DR1). In both the reconstructions, the $\Lambda$CDM model is always included in the 95\% confidence intervals. We

W. Garrett Levine, Shreyas Vissapragada, Adina D. Feinstein, George W. King

Aeronomy, the study of Earth's upper atmosphere and its interaction with the local space environment, has long traced changes in the thermospheres of Earth and other solar system planets to solar variability in the X-ray and extreme ultraviolet (collectively, "XUV") bands. Extending comparative aeronomy to the short-period extrasolar planets may illuminate w

Eduard Hogea, Josem Rocafortf

A hot topic of Artificial Intelligence right now is image generation from prompts. DALL-E 2 is one of the biggest names in this domain, as it allows people to create images from simple text inputs, to even more complicated ones. The company that made this possible, OpenAI, has assured everyone that visited their website that their mission is to ensure that a

Andrew Jacobsen, Ashok Cutkosky

We develop algorithms for online linear regression which achieve optimal static and dynamic regret guarantees \emph{even in the complete absence of prior knowledge}. We present a novel analysis showing that a discounted variant of the Vovk-Azoury-Warmuth forecaster achieves dynamic regret of the form $R_{T}(\vec{u})\le O\left(d\log(T)\vee \sqrt{dP_{T}^{\gamm

Maroua Ltifi

This study delves into a comprehensive examination of the three-dimensional $(3D)$ incompressible magneto-hydrodynamic $(MHD)$ equations in $H^{1}(\R^{3})$. The modification involves incorporating a power term in the nonlinear convection component, a particularly relevant adjustment in porous media scenarios, especially when the fluid adheres to the Darcy-Fo

Sabrina Cynthia Triess, Timo Leitritz, Christian Jauch

With rising technologies, the protection of privacy-sensitive information is becoming increasingly important. In industry and production facilities, image or video recordings are beneficial for documentation, tracing production errors or coordinating workflows. Individuals in images or videos need to be anonymized. However, the anonymized data should be reus

Fatemeh Attarzadeh, Ahmad Abbasi, Ali Behtoei

WE study the clique number and the chromatic number of generalized Sierpinski graphs in which the base graph is an arbitrary simple graph.

G. Bezhanishvili, F. Dashiell, M. A. Moshier, J. Walters-Wayland

Completions play an important r\^ole for studying structure by supplying elements that in some sense ``ought to be." Among these, the Dedekind-MacNeille completion is of particular importance. In 1968 Janowitz provided necessary and sufficient conditions for it to be subfit or Boolean. Another natural separation axiom connected to these is regularity. We exp

Robin Herkert, Patrick Buchfink, Tizian Wenzel, Bernard Haasdonk

We address the challenging application of 3D pore scale reactive flow under varying geometry parameters. The task is to predict time-dependent integral quantities, i.e., breakthrough curves, from the given geometries. As the 3D reactive flow simulation is highly complex and computationally expensive, we are interested in data-based surrogates that can give a

Niseem Magdy

This study explores the transverse momentum ($p_T$) dependencies of Symmetric and Asymmetric Correlations (SC and ASC) with one and two particles of interest in Au+Au collisions at 200 GeV. Leveraging the AMPT model, the investigation delves into the sensitivity of these correlations to the final state effects, providing valuable insights into their potentia

Shouhei Honda, Andrea Mondino

In this short note, we provide a quantitative global Poincar\'e inequality for one forms on a closed Riemannian four manifold, in terms of an upper bound on the diameter, a positive lower bound on the volume, and a two-sided bound on Ricci curvature. This seems to be the first non-trivial result giving such an inequality without any higher curvature assumpti

Gabriel Ducrocq, Lukas Grunewald, Sebastian Westenhoff, Fredrik Lindsten

The three-dimensional structure of proteins plays a crucial role in determining their function. Protein structure prediction methods, like AlphaFold, offer rapid access to a protein structure. However, large protein complexes cannot be reliably predicted, and proteins are dynamic, making it important to resolve their full conformational distribution. Single-

Seong Youl Choi, Jaehoon Jeong, Dong Woo Kang

We develop an effective and methodical algorithm for the construction of general covariant four-point $H\ell\ell Z$ vertices, accommodating leptons $\ell=e, \mu$, and designed to handle a boson $H$ of any integer spin, not merely confined to spins up to 2. While our numerical analysis assumes the $H$-boson mass to be $m_H=125\,{\rm GeV}$, the analytical fram

Benjamin Shih, Ahmad Peyvan, Zhongqiang Zhang, George Em Karniadakis

Neural operator learning models have emerged as very effective surrogates in data-driven methods for partial differential equations (PDEs) across different applications from computational science and engineering. Such operator learning models not only predict particular instances of a physical or biological system in real-time but also forecast classes of so

Minsung Kho, Rak-Kyeong Seong

We study the algebraic structure of the mesonic moduli spaces of bipartite field theories by computing the Hilbert series. Bipartite field theories form a large family of 4d N=1 supersymmetric gauge theories that are defined by bipartite graphs on Riemann surfaces with boundaries. By calculating the Hilbert series, we are able to identify the generators and

Sounak Lahiri, Sumit Pai, Tim Weninger, Sanmitra Bhattacharya

Electronic Discovery (eDiscovery) requires identifying relevant documents from vast collections for legal production requests. While artificial intelligence (AI) and natural language processing (NLP) have improved document review efficiency, current methods still struggle with legal entities, citations, and complex legal artifacts. To address these challenge

N. Banerjee, A. Romanov, M. Wallbank

We are commissioning a 2.5-MeV proton beam for the Integrable Optics Test Accelerator at Fermilab, allowing experiments in the strong space-charge regime with incoherent betatron tune shifts nearing 0.5. Accurate modelling of space-charge dynamics is vital for understanding planned experiments. We compare anticipated emittance growth and beam loss in the bar

Sarthak Mittal, Eric Elmoznino, Leo Gagnon, Sangnie Bhardwaj

Large autoregressive models like Transformers can solve tasks through in-context learning (ICL) without learning new weights, suggesting avenues for efficiently solving new tasks. For many tasks, e.g., linear regression, the data factorizes: examples are independent given a task latent that generates the data, e.g., linear coefficients. While an optimal pred

Shuxiang Xu, Cui-Qun Chen, Mengwu Huo, Deyuan Hu

In the intricate phase diagram of unconventional superconductors characterized by intertwined electronic orders and superconductivity, a key step in understanding the superconducting mechanism is to investigate the parent compounds from which superconductivity emerges through doping or pressure. In this study, we employed optical spectroscopy and ultrafast r

Leon Shaposhnikov, Eduardo Barredo-Alamilla, Frank Wilczek, Maxim A. Gorlach

Spatial structuring of materials at subwavelength scales underlies the concept of metamaterials possessing exotic properties beyond those of the constituent media. Temporal modulation of material parameters enables further functionalities. Here, we show that high-frequency oscillations of spatially uniform magnetization generate an effective dynamic axion fi

Hakuba Murota, Hideaki Yamamoto, Nobuaki Monma, Shigeo Sato

In vitro neuronal culture is an important research platform in cellular and network neuroscience. However, neurons cultured on a homogeneous scaffold form dense, randomly connected networks and display excessively synchronized activity; this phenomenon has limited their applications in network-level studies, such as studies of neuronal ensembles, or coordina

Mikhail A. Komarov

Let $\Pi_n$ be the class of algebraic polynomials $P$ of degree $n$, all of whose zeros lie on the segment $[-1,1]$. In 1995, S.P. Zhou has proved the following Tur\'{a}n type reverse Markov-Nikol'skii inequality: $\|P'\|_{L_p[-1,1]}>c\, {(\sqrt{n})}^{1-1/p+1/q}\, \|P\|_{L_q[-1,1]}$, $P\in \Pi_n$, where $0<p\le q\le \infty$, $1-1/p+1/q\ge 0$ ($c>0$ is a cons

Michael J. Maher

The class of defeasible logics is only vaguely defined -- it is defined by a few exemplars and the general idea of efficient reasoning with defeasible rules. The recent definition of the defeasible logic $DL(\partial_{||})$ introduced new features to such logics, which have repercussions that we explore. In particular, we define a class of logics that accomm

Robi Bhattacharjee, Nick Rittler, Kamalika Chaudhuri

Many machine learning models appear to deploy effortlessly under distribution shift, and perform well on a target distribution that is considerably different from the training distribution. Yet, learning theory of distribution shift bounds performance on the target distribution as a function of the discrepancy between the source and target, rarely guaranteei

Han-Ze Li, Minhui Wan, Jian-Xin Zhong

The Wannier-Stark localization dynamically alters the entanglement behavior of non-Hermitian free fermions. Utilizing the single-particle correlation matrix technique, we analyze the effective Hamiltonian of these fermions with a Wannier-Stark ladder. Under open boundary conditions, we observe the steady state half-chain entanglement entropy and identify two

Shishir Sankhyayan, Pratik Dabhade

We investigated the prevalence of giant radio galaxies (GRGs), some of the largest structures powered by supermassive black holes, within supercluster environments, and the influence of such environments on their properties. Utilising two large catalogues of superclusters (401) and GRGs (1446), we established the existence of 77 GRGs (5.3%) residing in 64 su

Arthur Juliani, Jordan T. Ash

Continual learning with deep neural networks presents challenges distinct from both the fixed-dataset and convex continual learning regimes. One such challenge is plasticity loss, wherein a neural network trained in an online fashion displays a degraded ability to fit new tasks. This problem has been extensively studied in both supervised learning and off-po

M. Krasilnikov, Z. Aboulbanine, G. Adhikari, N. Aftab

Advanced experiments using THz pump and X-ray probe pulses at modern free-electron lasers (FELs) like the European X-ray FEL require a frequency-tunable, high-power, narrow-band THz source maintaining the repetition rate and pulse structure of the X-ray pulses. This paper reports the first results from a THz source, that is based on a single-pass high-gain T

Ofir Gorodetsky, Mo Dick Wong

Let $\alpha \colon \mathbb{N} \to S^1$ be the Steinhaus multiplicative function: a completely multiplicative function such that $(\alpha(p))_{p\text{ prime}}$ are i.i.d.~random variables uniformly distributed on the complex unit circle $S^1$. Helson conjectured that $\mathbb{E}|\sum_{n\le x}\alpha(n)|=o(\sqrt{x})$ as $x \to \infty$, and this was solved in a

Manash Jyoti Sarmah, Himangshu Prabal Goswami

We analyze the efficiency fluctuations of a coherent quantum heat engine coupled to a unimodal cavity using a standard full-counting statistics procedure. The engine's most likely efficiency obtained by computing the large-deviation function corresponds to the quantum efficiency obtained by defining a useful work obtainable from a steady-state fluctuation th

Xintong Jiang, Yaxiong Wang, Mengjian Li, Yujiao Wu

Composed Image Retrieval (CIR) involves searching for target images based on an image-text pair query. While current methods treat this as a query-target matching problem, we argue that CIR triplets contain additional associations beyond this primary relation. In our paper, we identify two new relations within triplets, treating each triplet as a graph node.

Hsiao-yu Chen, Egor Larionov, Ladislav Kavan, Gene Lin

Well-fitted clothing is essential for both real and virtual garments to enable self-expression and accurate representation for a large variety of body types. Common practice in the industry is to provide a pre-made selection of distinct garment sizes such as small, medium and large. While these may cater to certain groups of individuals that fall within this

Anuj Dawar

We construct a pair of non-isomorphic, bipartite graphs which are not distinguished by counting the number of homomorphisms to any tree. This answers a question motivated by Atserias et al. (LICS 2021). In order to establish the construction, we analyse the equivalence relations induced by counting homomorphisms to trees of diameter two and three and obtain

Jacopo Teneggi, Jeremias Sulam

Recent works have extended notions of feature importance to semantic concepts that are inherently interpretable to the users interacting with a black-box predictive model. Yet, precise statistical guarantees, such as false positive rate and false discovery rate control, are needed to communicate findings transparently and to avoid unintended consequences in

Swati Shukla, Subhra Sankar Dhar, Shalabh

This article introduces an L-estimator for the semiparametric Tobit model with endogenous regressors. The estimation procedure follows a two-stage approach: the first stage employs least squares, while the second stage utilizes the L-estimation technique. We establish the large-sample properties of the proposed estimators under weakly dependent data. The uti

Xiaofang Gao, Igor Lima, Rulin Shen

Let $G$ be a finite group and $n_p(G)$ the number of Sylow $p$-subgroups of $G$. In this paper, we prove if $n_p(G)<p^2$ then almost all numbers $n_p(G)$ are a power of a prime.

Diab W. Abueidda, Panos Pantidis, Mostafa E. Mobasher

The modern digital engineering design often requires costly repeated simulations for different scenarios. The prediction capability of neural networks (NNs) makes them suitable surrogates for providing design insights. However, only a few NNs can efficiently handle complex engineering scenario predictions. We introduce a new version of the neural operators c