March 2024 arXiv papers — page 63

Li Xie, Liangyan Li, Jun Chen, Zhongshan Zhang

The distortion-rate function of output-constrained lossy source coding with limited common randomness is analyzed for the special case of squared error distortion measure. An explicit expression is obtained when both source and reconstruction distributions are Gaussian. This further leads to a partial characterization of the information-theoretic limit of qu

Philip Charles, Deep Ray

Entropy conditions play a crucial role in the extraction of a physically relevant solution for systems of conservation laws, thus motivating the construction of entropy stable schemes that satisfy a discrete analogue of such conditions. TeCNO schemes (Fjordholm et al. 2012) form a class of arbitrary high-order entropy stable finite difference solvers, which

Emilio J. B. Zanatta, Ruben Sanchéz-Janssen, Rafael S. de Souza, Ana L. Chies-Santos

Nuclear star clusters (NSCs) are dense star clusters located at the centre of galaxies spanning a wide range of masses and morphologies. Analysing NSC occupation statistics in different environments provides an invaluable window into investigating early conditions of high-density star formation and mass assembly in clusters and group galaxies. We use HST/ACS

Géry de Saxcé

In this paper, we revisit the Kaluza-Klein theory from the perspective of the classification of elementary particles based on the coadjoint orbit method. We study the momentum map of the corresponding symmetry group $G_1$ which conserves the hyperbolic metric. We show that the electric charge is not frame-invariant, in contradiction with the experimental obs

Brian R. Dennis, Kenneth J. H. Phillips

We review the terms, spectral radiance and spectral irradiance, and show how their precise definitions are crucial for interpreting observations made with different instruments covering widely different energy or wavelength ranges. We show how the use of column and volume emission measures in different solar physics and astrophysics communities has led to co

Jan Verschelde, Kylash Viswanathan

PHCpack is a software package for polynomial homotopy continuation, which provides a robust path tracker [Telen, Van Barel, Verschelde, SISC 2020]. This tracker computes the radius of convergence of Newton's method, estimates the distance to the nearest path, and then applies Pad\'{e} approximants to predict the next point on the path. A priori step size con

Mohamed Issa, Ahmed Abdelwahed

This Paper discusses the growing popularity of online medical diagnosis as an alternative to traditional doctor visits. It highlights the limitations of existing tools and emphasizes the advantages of using ChatGPT, which provides real-time, personalized medical diagnosis at no cost. The paragraph summarizes a research study that evaluated the performance of

Haoyue Dai, Ignavier Ng, Gongxu Luo, Peter Spirtes

Gene regulatory network inference (GRNI) is a challenging problem, particularly owing to the presence of zeros in single-cell RNA sequencing data: some are biological zeros representing no gene expression, while some others are technical zeros arising from the sequencing procedure (aka dropouts), which may bias GRNI by distorting the joint distribution of th

Haoyue Dai, Ignavier Ng, Yujia Zheng, Zhengqing Gao

Local causal discovery is of great practical significance, as there are often situations where the discovery of the global causal structure is unnecessary, and the interest lies solely on a single target variable. Most existing local methods utilize conditional independence relations, providing only a partially directed graph, and assume acyclicity for the g

Yawer H. Shah, Luigi Palatella, Korosh Mahmoodi, Orazio S. Santonocito

The analysis of glioblastoma (GB) cell locomotion and its modeling inspired by Levy random walks is presented herein. We study such walks occurring on a two-dimensional plane where the walk is similar to the motion of a bird flying with a constant velocity, but with random changes of direction in time. The intelligence of the bird is signaled by the instanta

T. van der Zwaard, L. A. Grzelak, C. W. Oosterlee

Affine Diffusion dynamics are frequently used for Valuation Adjustments (xVA) calculations due to their analytic tractability. However, these models cannot capture the market-implied skew and smile, which are relevant when computing xVA metrics. Hence, additional degrees of freedom are required to capture these market features. In this paper, we address this

Enora Rice, Ali Marashian, Luke Gessler, Alexis Palmer

Canonical morphological segmentation is the process of analyzing words into the standard (aka underlying) forms of their constituent morphemes. This is a core task in language documentation, and NLP systems have the potential to dramatically speed up this process. But in typical language documentation settings, training data for canonical morpheme segmentati

Gerry Chen, Sunil Kumar Narayanan, Thomas Gautier Ottou, Benjamin Missaoui

Hyperspectral Imagery (HSI) has been used in many applications to non-destructively determine the material and/or chemical compositions of samples. There is growing interest in creating 3D hyperspectral reconstructions, which could provide both spatial and spectral information while also mitigating common HSI challenges such as non-Lambertian surfaces and tr

Jesús Guillermo Falcón-Cardona, Mahboubeh Nezhadmoghaddam, Emilio Bernal-Zubieta

The distribution of objective vectors in a Pareto Front Approximation (PFA) is crucial for representing the associated manifold accurately. Distribution Indicators (DIs) assess the distribution of a PFA numerically, utilizing concepts like distance calculation, Biodiversity, Entropy, Potential Energy, or Clustering. Despite the diversity of DIs, their streng

Opher Bar Nathan, Deborah Levy, Tali Treibitz, Dan Rosenbaum

Underwater image restoration is a challenging task because of water effects that increase dramatically with distance. This is worsened by lack of ground truth data of clean scenes without water. Diffusion priors have emerged as strong image restoration priors. However, they are often trained with a dataset of the desired restored output, which is not availab

Zining Cheng, Guanzhou Ji

This paper presents the use of panoramic 3D estimation in lighting simulation. Conventional lighting simulation necessitates detailed modeling as input, resulting in significant labor effort and time cost. The 3D layout estimation method directly takes a single panorama as input and generates a lighting simulation model with room geometry and window aperture

P Moodley, S Roux

We study four wave mixing in a dielectric medium and calculate the detection probability of signal and idler photons landing on a screen. The outgoing photons are theoretically well characterized and can be used as probes for experimental investigations. The intensity plots are presented which compare well with experimental results.

Adrien Bourgoin, Pierre Teyssandier, Paolo Tortora, Marco Zannoni

Solving the null geodesic equations for a ray of light is a difficult task even considering a stationary spacetime. The problem becomes even more difficult if the electromagnetic signal propagates through a flowing optical medium. Indeed, because of the interaction between light and matter, the signal does not follow a null geodesic path of the spacetime met

Marco Forgione, Manas Mejari, Dario Piga

With a specific emphasis on control design objectives, achieving accurate system modeling with limited complexity is crucial in parametric system identification. The recently introduced deep structured state-space models (SSM), which feature linear dynamical blocks as key constituent components, offer high predictive performance. However, the learned represe

Alberto Rodriguez-Ardila, Marcos Antonio Fonseca-Faria, Denimara Dias dos Santos, Swayamtrupta Panda

We use near-infrared (NIR) spectroscopy covering simultaneously the $zJHK$ bands to look for outflowing gas from the nuclear environment of 1H0707-495 taking advantage that this region is dominated by low-ionization broad line region (BLR) lines, most of them isolated. We detect broad components in HI, FeII and OI, at rest to the systemic velocity, displayin

Eli Orvis

Recent work by Arpin, Chen, Lauter, Scheidler, Stange, and Tran counted the number of cycles of length $r$ in supersingular $\ell$-isogeny graphs. In this paper, we extend this work to count the number of cycles that occur along the spine. We provide formulas for both the number of such cycles, and the average number as $p \to \infty$, with $\ell$ and $r$ fi

Zeya Wang, Chenglong Ye

Deep clustering partitions complex high-dimensional data using deep neural networks for clustering. It involves projecting data into lower-dimensional embeddings before partitioning, which embarks unique evaluation challenges. Traditional clustering validation measures, designed for low-dimensional spaces, are problematic for deep clustering for two reasons:

F. M. Castro-Macías, P. Morales-Álvarez, Y. Wu, R. Molina

Multiple Instance Learning (MIL) is a weakly supervised paradigm that has been successfully applied to many different scientific areas and is particularly well suited to medical imaging. Probabilistic MIL methods, and more specifically Gaussian Processes (GPs), have achieved excellent results due to their high expressiveness and uncertainty quantification ca

Alberto Baldrati, Davide Morelli, Marcella Cornia, Marco Bertini

Fashion illustration is a crucial medium for designers to convey their creative vision and transform design concepts into tangible representations that showcase the interplay between clothing and the human body. In the context of fashion design, computer vision techniques have the potential to enhance and streamline the design process. Departing from prior r

Zach Goldthorpe

The purpose of this note is to resolve a conjecture in arXiv:2307.00442(4), regarding the initial algebra for the enrichment endofunctor $(-)\mathbf{Cat}$ over general symmetric monoidal $(\infty, 1)$-categories. We prove that Ad\'amek's construction of an initial algebra for $(-)\mathbf{Cat}$ does not terminate; more precisely, we show that Ad\'amake's cons

Miha Brešar, Aleksandar Mijatović

We provide a criterion for establishing lower bounds on the rate of convergence in $f$-variation of a continuous-time ergodic Markov process to its invariant measure. The criterion consists of novel super- and submartingale conditions for certain functionals of the Markov process. It provides a general approach for proving lower bounds on the tails of the in

A. Kozina, M. Aguilar, O. Pizio, S. Sokołowski

We perform the analysis of predictions of a classical density functional theory for associating fluids with different association strength concerned with wetting of solid surfaces. The four associating sites water-like models with non-associative square-well attraction parametrized by Clark et al. [Mol. Phys., 2006, 104, 3561] are considered. The fluid-solid

Vadim E. Levit, David Tankus

A graph $G$ is well-covered if all maximal independent sets are of the same cardinality. Let $w:V(G) \longrightarrow\mathbb{R}$ be a weight function. Then $G$ is $w$-well-covered if all maximal independent sets are of the same weight. An edge $xy \in E(G)$ is relating if there exists an independent set $S$ such that both $S \cup \{x\}$ and $S \cup \{y\}$ are

T. Staszewski, M. Borówko

We investigate rearrangements of a single hairy particle at a liquid-liquid interface using coarse-grained molecular dynamics simulations. We consider the particles with the same (symmetrical interactions) and different (asymmetrical interactions) affinity to the liquids. We show how ligand mobility affects the behavior of the hairy particle at the liquid-li

Jie Wang, Rui Gao, Yao Xie

We present a new framework to address the non-convex robust hypothesis testing problem, wherein the goal is to seek the optimal detector that minimizes the maximum of worst-case type-I and type-II risk functions. The distributional uncertainty sets are constructed to center around the empirical distribution derived from samples based on Sinkhorn discrepancy.

Peipei Song, Jing Zhang, Piotr Koniusz, Nick Barnes

Existing eye fixation prediction methods perform the mapping from input images to the corresponding dense fixation maps generated from raw fixation points. However, due to the stochastic nature of human fixation, the generated dense fixation maps may be a less-than-ideal representation of human fixation. To provide a robust fixation model, we introduce Gauss

M. Ali Al-Radhawi, Krishna Manoj, Dhruv D. Jatkar, Alon Duvall

In the context of epigenetic transformations in cancer metastasis, a puzzling effect was recently discovered, in which the elimination (knock-out) of an activating regulatory element leads to increased (rather than decreased) activity of the element being regulated. It has been postulated that this paradoxical behavior can be explained by activating and repr

J. M. Gómez-Verdú, B. Martínez-Haya, A. Cuetos

This study uses a coarse-grained Monte Carlo algorithm to model and simulate the coadsorption of a binary mixture of counterionic colloids in Gibbs monolayers. These monolayers form at a idealized air-water interface, with one non-soluble species confined at the interface and the second one partially soluble in the aqueous phase. The investigation focuses on

N. I. Lebovka, M. R. Petryk, N. V. Vygornitskii

Connectedness percolation phenomena in the two-dimensional (2D) packing of binary mixtures of disks with different diameters were studied numerically. The packings were produced using random sequential adsorption (RSA) model with simultaneous deposition of disks. The ratio of the particle diameters was varied within the range $D=1$-$10$, and the selection pr

Daniele Dorigoni, Mehregan Doroudiani, Joshua Drewitt, Martijn Hidding

We study non-holomorphic modular forms built from iterated integrals of holomorphic modular forms for SL$(2,\mathbb Z)$ known as equivariant iterated Eisenstein integrals. A special subclass of them furnishes an equivalent description of the modular graph forms appearing in the low-energy expansion of string amplitudes at genus one. Notably the Fourier expan

Ari Meir Brodsky, Assaf Rinot, Shira Yadai

The parameterized proxy principles were introduced by Brodsky and Rinot in a 2017 paper, as new foundations for the construction of $\kappa$-Souslin trees in a uniform way that does not depend on the nature of the (regular uncountable) cardinal $\kappa$. Since their introduction, these principles have facilitated construction of Souslin trees with complex co

Hannah R. Lawrence, Renee A. Schneider, Susan B. Rubin, Maja J. Mataric

Global rates of mental health concerns are rising, and there is increasing realization that existing models of mental health care will not adequately expand to meet the demand. With the emergence of large language models (LLMs) has come great optimism regarding their promise to create novel, large-scale solutions to support mental health. Despite their nasce

Yongming Liu

A new dimensional reduction (DR) and data visualization method, Curvature-Augmented Manifold Embedding and Learning (CAMEL), is proposed. The key novel contribution is to formulate the DR problem as a mechanistic/physics model, where the force field among nodes (data points) is used to find an n-dimensional manifold representation of the data sets. Compared

Qizheng He, Carlo Sanna

Let $b \geq 3$ be a positive integer. A natural number is said to be a base-$b$ Zuckerman number if it is divisible by the product of its base-$b$ digits. Let $\mathcal{Z}_b(x)$ be the set of base-$b$ Zuckerman numbers that do not exceed $x$, and assume that $x \to +\infty$. First, we prove an upper bound of the form $|\mathcal{Z}_b(x)| < x^{z_b^{+} + o(1)}$

Aleksandr Melkozerov, Ashot Avanesov, Ivan Dyakonov, Stanislav Straupe

Bell state measurements (BSM) play a significant role in quantum information and quantum computing, in particular, in fusion-based quantum computing (FBQC). The FBQC model is a framework for universal quantum computing provided that we are able to perform entangling measurements, called fusions, on qubits within small entangled resource states. Here we analy

Paul Leask

In this letter we study soliton crystals in the $(2+1)$-dimensional analogue model of the $(3+1)$-dimensional Adkins--Nappi model of nuclear physics. The baby $\omega$-Skyrme model studied here is an $O(3)$ nonlinear $\sigma$ model coupled to a massive vector meson, the $\omega$-meson. Using recently developed methods in the $(3+1)$-dimensional $\omega$-Skyr

Fei Xia, Caio Vaz Rimoli, Walther Akemann, Cathie Ventalon

The intricate nature of the brain necessitates the application of advanced probing techniques to comprehensively study and understand its working mechanisms. Neurophotonics offers minimally invasive methods to probe the brain using optics at cellular and even molecular levels. However, multiple challenges persist, especially concerning imaging depth, field o

Nigel G. Ward, Divette Marco

Automatic measures of similarity between utterances are invaluable for training speech synthesizers, evaluating machine translation, and assessing learner productions. While there exist measures for semantic similarity and prosodic similarity, there are as yet none for pragmatic similarity. To enable the training of such measures, we developed the first coll

He-Ran Wang, Xiao-Yang Yang, Zhong Wang

Characterizing nonequilibrium dynamics in quantum many-body systems is a challenging frontier of physics. In this Letter, we systematically construct solvable nonintegrable quantum circuits that exhibit exact hidden Markovian subsystem dynamics. This feature thus enables accurately calculating local observables for arbitrary evolution time. Utilizing the inf

Shupeng Ning, Hanqing Zhu, Chenghao Feng, Jiaqi Gu

In recent decades, the demand for computational power has surged, particularly with the rapid expansion of artificial intelligence (AI). As we navigate the post-Moore's law era, the limitations of traditional electrical digital computing, including process bottlenecks and power consumption issues, are propelling the search for alternative computing paradigms

Shang-Min Tsai, Hamish Innes, Nicholas F. Wogan, Edward W. Schwieterman

Theoretical predictions and observational data indicate a class of sub-Neptune exoplanets may have water-rich interiors covered by hydrogen-dominated atmospheres. Provided suitable climate conditions, such planets could host surface liquid oceans. Motivated by recent JWST observations of K2-18 b, we self-consistently model the photochemistry and potential de

Rowan Kelleher, Anselm Vossen

This study focuses on the novel application of a normalizing flow as a method of domain adaptation. Normalizing flows offer a way to transform data points between two different distributions. The present study investigates a method of transforming latent representations of physics data to a normal distribution and then to a physics distribution again. The fi

Han Shu, Jacob Mays

Disputes over cost allocation can present a significant barrier to investment in shared infrastructure. While it may be desirable to allocate cost in a way that corresponds to expected benefits, investments in long-lived projects are made under conditions of substantial uncertainty. In the context of electricity transmission, uncertainty combined with the in

Mahsa Sinaei Hamed, Pak Kwan, Matthew Klich, Jillian Aurisano

With the growth of data sizes, visualizing them becomes more complex. Desktop displays are insufficient for presenting and collaborating on complex data visualizations. Large displays could provide the necessary space to demo or present complex data visualizations. However, designing and developing visualizations for such displays pose distinct challenges. I

Junyoung Kim, Jingye Yang, Kai Wang, Chunhua Weng

Phenotype-driven gene prioritization is a critical process in the diagnosis of rare genetic disorders for identifying and ranking potential disease-causing genes based on observed physical traits or phenotypes. While traditional approaches rely on curated knowledge graphs with phenotype-gene relations, recent advancements in large language models have opened

Edrina Gashi, Jiankang Deng, Ismail Elezi

We conduct a comprehensive evaluation of state-of-the-art deep active learning methods. Surprisingly, under general settings, no single-model method decisively outperforms entropy-based active learning, and some even fall short of random sampling. We delve into overlooked aspects like starting budget, budget step, and pretraining's impact, revealing their si

Dimitri Marques Abramov, Henrique Santos Lima, Vladimir Lazarev, Paulo Ricardo Galhanone

There are reasons to suggest that a number of mental disorders may be related to alteration in the neural complexity (NC). Thus, quantitative analysis of NC could be helpful in classifying mental and understanding conditions. Here, focusing on a methodological procedure, we have worked with young individuals, typical and with attention-deficit/hyperactivity

Nicholas Waltz

Economic policy and research rely on the correct evaluation of the billions of high-frequency data points that we collect every day. Consistent clustering algorithms, like DBSCAN, allow us to make sense of the data in a useful way. However, while there is a large literature on the consistency of various clustering algorithms for high-dimensional static clust

Gaurav Bhatt, James Ross, Leonid Sigal

Modern pre-trained architectures struggle to retain previous information while undergoing continuous fine-tuning on new tasks. Despite notable progress in continual classification, systems designed for complex vision tasks such as detection or segmentation still struggle to attain satisfactory performance. In this work, we introduce a memory-based detection

Andrew Coles, Erez Karpas, Andrey Lavrinenko, Wheeler Ruml

Standard temporal planning assumes that planning takes place offline and then execution starts at time 0. Recently, situated temporal planning was introduced, where planning starts at time 0 and execution occurs after planning terminates. Situated temporal planning reflects a more realistic scenario where time passes during planning. However, in situated tem

Shashank Kushwaha, Jaewan Park, Seid Koric, Junyan He

Unlike classical artificial neural networks, which require retraining for each new set of parametric inputs, the Deep Operator Network (DeepONet), a lately introduced deep learning framework, approximates linear and nonlinear solution operators by taking parametric functions (infinite-dimensional objects) as inputs and mapping them to complete solution field

Shashi Shekhar Roy, S. V. Raghurama Rao

In this paper, we present a kinetic model with flexible velocities that satisfy positivity preservation conditions for the Euler equations. Our 1D kinetic model consists of two velocities and employs both the asymmetrical and symmetrical models. Switching between the two models is governed by our formulation of kinetic relative entropy along with an addition

Arianna I. Renzini, Thomas A. Callister, Katerina Chatziioannou, Will M. Farr

The vast majority of gravitational-wave signals from stellar-mass compact binary mergers are too weak to be individually detected with present-day instruments and instead contribute to a faint, persistent background. This astrophysical background is targeted by searches that model the gravitational-wave ensemble collectively with a small set of parameters. T

Abel Souza, Shruti Jasoria, Basundhara Chakrabarty, Alexander Bridgwater

There has been a significant societal push towards sustainable practices, including in computing. Modern interactive workloads such as geo-distributed web-services exhibit various spatiotemporal and performance flexibility, enabling the possibility to adapt the location, time, and intensity of processing to align with the availability of renewable and low-ca

Jimin Mun, Liwei Jiang, Jenny Liang, Inyoung Cheong

General purpose AI, such as ChatGPT, seems to have lowered the barriers for the public to use AI and harness its power. However, the governance and development of AI still remain in the hands of a few, and the pace of development is accelerating without a comprehensive assessment of risks. As a first step towards democratic risk assessment and design of gene

Luca Piano, Pietro Basci, Fabrizio Lamberti, Lia Morra

Generative techniques for image anonymization have great potential to generate datasets that protect the privacy of those depicted in the images, while achieving high data fidelity and utility. Existing methods have focused extensively on preserving facial attributes, but failed to embrace a more comprehensive perspective that considers the scene and backgro

Claudio Vittorio Ragaglia, Francesco Guarnera, Sebastiano Battiato

{The study of frequency components derived from Discrete Cosine Transform (DCT) has been widely used in image analysis. In recent years it has been observed that significant information can be extrapolated from them about the lifecycle of the image, but no study has focused on the analysis between them and the source resolution of the image. In this work, we

Junyan He, Seid Koric, Diab Abueidda, Ali Najafi

Modern digital engineering design process commonly involves expensive repeated simulations on varying three-dimensional (3D) geometries. The efficient prediction capability of neural networks (NNs) makes them a suitable surrogate to provide design insights. Nevertheless, few available NNs can handle solution prediction on varying 3D shapes. We present a nove

Steffen Dereich

In this article, we develop a theory for understanding the traces left by a random walk in the vicinity of a randomly chosen reference vertex. The analysis is related to interlacements but goes beyond previous research by showing weak limit theorems for the vicinity around the reference vertex together with the path segments of the random walk in this vicini

Michael Zingale, Zhi Chen, Eric T. Johnson, Max P. Katz

We build on the simplified spectral deferred corrections (SDC) coupling of hydrodynamics and reactions to handle the case of nuclear statistical equilibrium (NSE) and electron/positron captures/decays in the cores of massive stars. Our approach blends a traditional reaction network on the grid with a tabulated NSE state from a very large, O(100) nuclei, netw

Anthony Salib

Savin's small perturbation approach has had far reaching applications in the theory of non-linear elliptic and parabolic PDE. In this short note, we revisit his seminal proof of De-Giorgi's improvement of flatness theorem for minimal surfaces and provide an approach based on the Harnack inequality that avoids the use of compactness arguments.

Michele Masini, Marie Ioannou, Nicolas Brunner, Stefano Pironio

Photon loss represents a major challenge for the implementation of quantum communication protocols with untrusted devices, e.g. in the device-independent (DI) or semi-DI approaches. Determining critical loss thresholds is usually done in case-by-case studies. In the present work, we develop a general framework for characterizing the admissible levels of loss

R. Pascale, C. Nipoti, F. Calura, A. Della Croce

It has been recently claimed that a surprisingly massive black hole (BH) is present in the core of the dwarf spheroidal galaxy (dSph) Leo I. Based on integral field spectroscopy, this finding challenges the typical expectation of dSphs hosting BHs of intermediate-mass, since such a BH would better be classified as supermassive. Indeed, the analysis points to

Bojan Banjac, Branko Malesevic, Milos Micovic, Bojana Mihailovic

In this paper, we generalize Cristinel Mortici's results on Wilker-Cusa-Huygens inequalities using stratified families of functions and SimTheP - a system for automated proving of MTP inequalities.

Bowen Jiang, Zhijun Zhuang, Shreyas S. Shivakumar, Dan Roth

This work explores the zero-shot capabilities of foundation models in Visual Question Answering (VQA) tasks. We propose an adaptive multi-agent system, named Multi-Agent VQA, to overcome the limitations of foundation models in object detection and counting by using specialized agents as tools. Unlike existing approaches, our study focuses on the system's per

Iman Emtiazi Naeini, Zahra Saberi, Khadijeh Hassanzadeh

This study employs the Causal Machine Learning (CausalML) statistical method to analyze the influence of electricity pricing policies on carbon dioxide (CO2) levels in the household sector. Investigating the causality between potential outcomes and treatment effects, where changes in pricing policies are the treatment, our analysis challenges the conventiona

Alancoc dos Santos Alencar, Keti Tenenblat

We consider the inverse mean curvature flow by parallel hypersurfaces in space forms. We show that such a flow exists if and only if the initial hypersurface is isoparametric. The flow is characterized by an algebraic equation satisfied by the distance function of the parallel hypersurfaces. The solutions to the flow are obtained explicitly when the distinct

Adam Karvonen

Language models have shown unprecedented capabilities, sparking debate over the source of their performance. Is it merely the outcome of learning syntactic patterns and surface level statistics, or do they extract semantics and a world model from the text? Prior work by Li et al. investigated this by training a GPT model on synthetic, randomly generated Othe

Shenhao Zhu, Junming Leo Chen, Zuozhuo Dai, Qingkun Su

In this study, we introduce a methodology for human image animation by leveraging a 3D human parametric model within a latent diffusion framework to enhance shape alignment and motion guidance in curernt human generative techniques. The methodology utilizes the SMPL(Skinned Multi-Person Linear) model as the 3D human parametric model to establish a unified re

Evangelos Psomiadis, Dipankar Maity, Panagiotis Tsiotras

This paper investigates the task-driven exploration of unknown environments with mobile sensors communicating compressed measurements. The sensors explore the area and transmit their compressed data to another robot, assisting it to reach its goal location. We propose a novel communication framework and a tractable multi-agent exploration algorithm to select

Kyrylo Muliarchyk

We prove a bi-ordered version of Rivas' result for free products of left-order groups. Namely, we show that a free product of bi-ordered groups does not admit isolated bi-ordering. Our method relies on the dynamical realization of bi-ordered groups. We also show that the natural action of the automorphism group $Aut(F_2)$ on $F_2$ does not have dense orbits.

Qianyu Guo, Jiaming Fu, Yawen Lu, Dongming Gan

In Virtual Reality (VR), adversarial attack remains a significant security threat. Most deep learning-based methods for physical and digital adversarial attacks focus on enhancing attack performance by crafting adversarial examples that contain large printable distortions that are easy for human observers to identify. However, attackers rarely impose limitat

E. O. Asante-Asamani, A. Kleefeld, B. A. Wade

A fourth-order exponential time differencing (ETD) Runge-Kutta scheme with dimensional splitting is developed to solve multidimensional non-linear systems of reaction-diffusion equations (RDE). By approximating the matrix exponential in the scheme with the A-acceptable Pad\'e (2,2) rational function, the resulting scheme (ETDRK4P22-IF) is verified empiricall

S. Heinemeyer, M. Mühlleitner, K. Radchenko, G. Weiglein

The results obtained at the LHC for constraining the trilinear Higgs self-coupling of the detected Higgs boson at about 125 GeV, $\lambda_{hhh}$, via the Higgs pair production process have significantly improved during the last years. We investigate the impact of potentially large higher-order corrections and interference effects on the comparison between th

Laura O'Mahony, David JP O'Sullivan, Nikola S. Nikolov

Out-of-distribution data and anomalous inputs are vulnerabilities of machine learning systems today, often causing systems to make incorrect predictions. The diverse range of data on which these models are used makes detecting atypical inputs a difficult and important task. We assess a tool, Benford's law, as a method used to quantify the difference between

Zhenrong Liu, Zongze Li, Yi Gong, Yik-Chung Wu

In mobile edge computing (MEC) systems, the wireless channel condition is a critical factor affecting both the communication power consumption and computation rate of the offloading tasks. This paper exploits the idea of cooperative transmission and employing reconfigurable intelligent surface (RIS) in MEC to improve the channel condition and maximize comput

Yiwei Zhou, Xiaobo Xia, Zhiwei Lin, Bo Han

The vulnerability of deep neural networks to imperceptible adversarial perturbations has attracted widespread attention. Inspired by the success of vision-language foundation models, previous efforts achieved zero-shot adversarial robustness by aligning adversarial visual features with text supervision. However, in practice, they are still unsatisfactory due

Roberto Henschel, Levon Khachatryan, Hayk Poghosyan, Daniil Hayrapetyan

Text-to-video diffusion models enable the generation of high-quality videos that follow text instructions, making it easy to create diverse and individual content. However, existing approaches mostly focus on high-quality short video generation (typically 16 or 24 frames), ending up with hard-cuts when naively extended to the case of long video synthesis. To

Sayanton V. Dibbo, Adam Breuer, Juston Moore, Michael Teti

Recent model inversion attack algorithms permit adversaries to reconstruct a neural network's private and potentially sensitive training data by repeatedly querying the network. In this work, we develop a novel network architecture that leverages sparse-coding layers to obtain superior robustness to this class of attacks. Three decades of computer science re

Robert Szalai

We show the existence and uniqueness of invariant foliations about invariant tori in analytic discrete-time dynamical systems. The parametrisation method is used prove the result. Our theory is a foundational block of data-driven model order reduction, that can only be carried out using invariant foliations. The theory is illustrated by two mechanical exampl

Katie Lim, Matthew Giordano, Theano Stavrinos, Irene Zhang

Direct-attached accelerators, where application accelerators are directly connected to the datacenter network via a hardware network stack, offer substantial benefits in terms of reduced latency, CPU overhead, and energy use. However, a key challenge is that modern datacenter network stacks are complex, with interleaved protocol layers, network management fu

Quang Nguyen, Ruitong Jiang, Meg Ellingwood, Ronald Yurko

Tackling is a fundamental defensive move in American football, with the main purpose of stopping the forward motion of the ball-carrier. However, current tackling metrics are manually recorded outcomes that are inherently flawed due to their discrete and subjective nature. Using player tracking data, we present a novel framework for assessing tackling contri

E. Langmann, J. Lenells

We study the 3D anisotropic Hubbard model on a cubic lattice with hopping parameter $t$ in the $x$- and $y$-directions and a possibly different hopping parameter $t_z$ in the $z$-direction; this model interpolates between the 2D and 3D Hubbard models corresponding to the limiting cases $t_z=0$ and $t_z=t$, respectively. We first derive all-order asymptotic e

Himangshu Paul, Alexander Nikolaev

Must we trace and block all fake content in a social commerce network so that genuine users may enjoy fake-free information? Such efforts largely fail, because, as we get better at spam detection, spammers use the same advances for anti-detection. As a fundamentally new approach, we show that an online platform can aggregate and route user-generated content

Riccarda Bonsignori, Viktor Eisler

We study the entanglement Hamiltonian for the ground state of one-dimensional free fermions in the presence of an inhomogeneous chemical potential. In particular, we consider a lattice with a linear, as well as a continuum system with a quadratic potential. It is shown that, for both models, conformal field theory predicts a Bisognano-Wichmann form for the e

Ronan Gautier, Élie Genois, Alexandre Blais

We present a framework that combines the adjoint-state method together with reverse-time backpropagation to solve prohibitively large open-system quantum control problems. Our approach enables the optimization of arbitrary cost functions with fully general controls applied on large open quantum systems described by a Lindblad master equation. It is scalable,

Julia Amoros-Binefa, Jan Kolodynski

Sensing a magnetic field with an atomic magnetometer operated in real time presents significant challenges, primarily due to sensor non-linearity, the presence of noise, and the need for one-shot estimation. To address these challenges, we propose a comprehensive approach that integrates measurement, estimation and control strategies. Specifically, this invo

Yago Bea, Raul Jimenez, David Mateos, Shuheng Liu

Holography relates gravitational theories in five dimensions to four-dimensional quantum field theories in flat space. Under this map, the equation of state of the field theory is encoded in the black hole solutions of the gravitational theory. Solving the five-dimensional Einstein's equations to determine the equation of state is an algorithmic, direct prob

Antonio Capanema, Yago Porto, Maria Manuela Saez

Predicting the flavor composition of neutrinos from supernovae is a challenging task, primarily due to the high neutrino densities at their core. In such an environment, neutrino self-interactions give rise to collective effects that have dramatic yet poorly understood consequences for their flavor evolution. In this paper, however, we show that standard mat

Grigory Ivanov

The classical Steinitz theorem asserts that if the origin lies within the interior of the convex hull of a set $S \subset \mathbb{R}^d$, then there are at most $2d$ points in $S$ whose convex hull contains the origin within its interior. B\'ar\'any, Katchalski, and Pach established a quantitative version of Steinitz's theorem, showing that for a convex polyt

Weipeng Deng, Jihan Yang, Runyu Ding, Jiahui Liu

Rapid advancements in 3D vision-language (3D-VL) tasks have opened up new avenues for human interaction with embodied agents or robots using natural language. Despite this progress, we find a notable limitation: existing 3D-VL models exhibit sensitivity to the styles of language input, struggling to understand sentences with the same semantic meaning but wri

Manimala Chakraborti, Sven Heinemeyer, Ipsita Saha

The quest for supersymmetric (SUSY) particles is among the main search channels currently pursued at the LHC. Particularly, electroweak (EW) particles with masses as low as a few hundred GeV are still viable. Recent searches for the ``golden channel'', $pp \to \tilde \chi_2^{\rm 0} \tilde \chi_1^{\rm \pm} \to \tilde \chi_1^{\rm 0} Z^{(*)} \, \tilde \chi_1^{\

P. Rodríguez-Beltrán, M. Cerviño, A. Vazdekis, M. A. Beasley

The goal of this work is to scrutinise the surface brightness fluctuation (SBF) calculation methodology. We analysed the SBF derivation procedure, measured the accuracy of the fitted SBF under controlled conditions, retrieved the uncertainty associated with the variability of a system that is inherently stochastic, and studied the SBF reliability under a wid

Mateusz Duch, Alessandro Strumia, Arsenii Titov

We propose a theory that preserves spin-summed scattering and decay rates at tree level while affecting particle spins. This is achieved by breaking the Lorentz group in a non-local way that tries avoiding stringent constraints, for example leaving unbroken the maximal sub-group SIM(2). As a phenomenological application, this new physics can alter the spins

Khachatur G. Nazaryan, Liang Fu

We uncover a new superconducting state with partial spin polarization induced by a magnetic field. This state, which we call "magnonic superconductor", lacks a conventional pairing order parameter, but is characterized instead by a composite order parameter that represents the binding of electron pairs and magnons. We rigorously demonstrate the existence of