December 2023 arXiv papers — page 85

Christian Millichap, Yeeka Yau

In this article, we create an artificial neural network (ANN) that combines both classical and modern techniques for determining the key length of a Vigen\`{e}re cipher. We provide experimental evidence supporting the accuracy of our model for a wide range of parameters. We also discuss the creation and features of this ANN along with a comparative analysis

Abdul Wasi, O. Jeba Shiney

Images acquired in hazy conditions have degradations induced in them. Dehazing such images is a vexed and ill-posed problem. Scores of prior-based and learning-based approaches have been proposed to mitigate the effect of haze and generate haze-free images. Many conventional methods are constrained by their lack of awareness regarding scene depth and their i

Dominik Francoeur

A group $G$ is said to be intersection-saturated if for every strictly positive integer $n$ and every map $c\colon \mathcal{P}(\{1,\dots, n\})\setminus \emptyset \rightarrow \{0,1\}$, one can find subgroups $H_1,\dots, H_n\leq G$ such that for every non-empty subset $I\subseteq \{1,\dots, n\}$, the intersection $\bigcap_{i\in I}H_i$ is finitely generated if

Yousef Emami

Unmanned aerial vehicle (UAV)-assisted sensor networks (UASNets), which play a crucial role in creating new opportunities, are experiencing significant growth in civil applications worldwide. UASNets improve disaster management through timely surveillance and advance precision agriculture with detailed crop monitoring, thereby significantly transforming the

Pascal Zimmer, Sébastien Andreina, Giorgia Azzurra Marson, Ghassan Karame

Although promising, existing defenses against query-based attacks share a common limitation: they offer increased robustness against attacks at the price of a considerable accuracy drop on clean samples. In this work, we show how to efficiently establish, at test-time, a solid tradeoff between robustness and accuracy when mitigating query-based attacks. Give

Yuanbo Hou, Qiaoqiao Ren, Siyang Song, Yuxin Song

WHO's report on environmental noise estimates that 22 M people suffer from chronic annoyance related to noise caused by audio events (AEs) from various sources. Annoyance may lead to health issues and adverse effects on metabolic and cognitive systems. In cities, monitoring noise levels does not provide insights into noticeable AEs, let alone their relations

S. Prajnanaswaroopa

This work discusses the Alon-Tarsi number of line graphs and total graphs. In addition, we also discuss the Alon-Tarsi number of some Erdos-Faber-Lovasz (EFL) graphs.

Cedric Derstroff, Mattia Cerrato, Jannis Brugger, Jan Peters

Peer learning is a novel high-level reinforcement learning framework for agents learning in groups. While standard reinforcement learning trains an individual agent in trial-and-error fashion, all on its own, peer learning addresses a related setting in which a group of agents, i.e., peers, learns to master a task simultaneously together from scratch. Peers

Kristof Albrecht, Juliane Entzian, Armin Iske

This work concerns the construction and characterization of product kernels for multivariate approximation from a finite set of discrete samples. To this end, we consider composing different component kernels, each acting on a low-dimensional Euclidean space. Due to Aronszajn (1950), the product of positive semi-definite kernel functions is again positive se

Kaushik Roy, Vedant Khandelwal, Harshul Surana, Valerie Vera

Systematic reviews (SRs) - the librarian-assisted literature survey of scholarly articles takes time and requires significant human resources. Given the ever-increasing volume of published studies, applying existing computing and informatics technology can decrease this time and resource burden. Due to the revolutionary advances in (1) Generative AI such as

Johanna Walker, Elisavet Koutsiana, Joe Massey, Gefion Thuermer

Can large language models assist in data discovery? Data discovery predominantly happens via search on a data portal or the web, followed by assessment of the dataset to ensure it is fit for the intended purpose. The ability of conversational generative AI (CGAI) to support recommendations with reasoning implies it can suggest datasets to users, explain why

Olivier Barrois, Thomas Gastine, Christopher C. Finlay

We present dynamos computed using a hybrid QG-3D numerical scheme in a thick spherical shell geometry. Our model is based on a quasi-geostrophic convection code extended with a 3D treatment of heat transport and magnetic induction. We find a collection of self-sustained, multipolar, weak field dynamos with magnetic energy one or two orders of magnitude lower

Ankit Rai

Given a constructible sheaf $F$ on a complex manifold, Kashiwara-Schapira defined the notion of singular support and characteristic cycle of $F$. On the other hand for a Zariski constructible \'{e}tale sheaf $F$ on an algebraic variety $X$, Beilinson defined the notion of singular support of $F$ and Saito defined the notion of characteristic cycle of $F$. In

Mattia Merluzzi, Francesca Costanzo, Konstantinos D. Katsanos, George C. Alexandropoulos

This work lies at the intersection of two cutting edge technologies envisioned to proliferate in future 6G wireless systems: Multi-access Edge Computing (MEC) and Reconfigurable Intelligent Surfaces (RISs). While the former will bring a powerful information technology environment at the wireless edge, the latter will enhance communication performance, thanks

Belén Costanza, Claudia G. Scóccola, Matías Zaldarriaga

The accurate reconstruction of Cosmic Microwave Background (CMB) maps and the measurement of its power spectrum are crucial for studying the early universe. In this paper, we implement a convolutional neural network to apply the Wiener Filter to CMB temperature maps, and use it intensively to compute an optimal quadratic estimation of the power spectrum. Our

Olivier Barrois, Nicolas Gillet, Julien Aubert

We invert for motions at the surface of Earth's core under spatial and temporal constraints that depart from the mathematical smoothings usually employed to ensure spectral convergence of the flow solutions. Our spatial constraints are derived from geodynamo simulations. The model is advected in time using stochastic differential equations coherent with the

J. Douglas Wright

We provide a rigorous validation that the infinite Calogero-Moser lattice can be well-approximated by solutions of the Benjamin-Ono equation in a long-wave limit.

Ayoub Belhadji, Rémi Gribonval

Compressive learning is an emerging approach to drastically reduce the memory footprint of large-scale learning, by first summarizing a large dataset into a low-dimensional sketch vector, and then decoding from this sketch the latent information needed for learning. In light of recent progress on information preservation guarantees for sketches based on rand

Sahil Nokhwal, Suman Nokhwal, Saurabh Pahune, Ankit Chaudhary

In this pioneering research paper, we present a groundbreaking exploration into the synergistic fusion of classical and quantum computing paradigms within the realm of Generative Adversarial Networks (GANs). Our objective is to seamlessly integrate quantum computational elements into the conventional GAN architecture, thereby unlocking novel pathways for enh

Milad Kazemi, Mateo Perez, Fabio Somenzi, Sadegh Soudjani

We present a modular approach to \emph{reinforcement learning} (RL) in environments consisting of simpler components evolving in parallel. A monolithic view of such modular environments may be prohibitively large to learn, or may require unrealizable communication between the components in the form of a centralized controller. Our proposed approach is based

Rui Song, Bili Wang, Kai Feng, Jia Yao

With the rise of two-dimensional (2D) materials, unique properties that are completely distinct from bulk counterparts continue to emerge at low-dimensional scales, presenting numerous opportunities and challenges. It also provides a new perspective for the study of transition metal system. Here, based on density functional theory (DFT), the physical propert

Kimin Park, Petr Marek, Radim Filip

Quantum simulations provide means to probe challenging problems within controllable quantum systems. However, implementing or simulating deep-strong nonlinear couplings between bosonic oscillators on physical platforms remains a challenge. We present a deterministic simulation technique that efficiently and accurately models nonlinear bosonic dynamics. This

Yuxin Cao, Ziyu Zhao, Xi Xiao, Derui Wang

Video recognition systems are vulnerable to adversarial examples. Recent studies show that style transfer-based and patch-based unrestricted perturbations can effectively improve attack efficiency. These attacks, however, face two main challenges: 1) Adding large stylized perturbations to all pixels reduces the naturalness of the video and such perturbations

Krishnat Masalkar, Anil Khairnar, Anita Lande, Lata Kadam

For a ring $R$, the zero-divisor graph is a simple graph $\Gamma(R)$ whose vertex set is the set of all non-zero zero-divisors in a ring $R$, and two distinct vertices $x$ and $y$ are adjacent if and only if $xy=0$ or $yx=0$ in $R$. By using Weyl's inequality we give bounds on eigenvalues of adjacency matrix of $\Gamma(M_2(F))$, where $M_2(F)$ is a $2 \times

Mamoru Ueda

We construct a homomorphism from the affine Yangian $Y_{\hbar,\varepsilon}(\widehat{\mathfrak{sl}}(n))$ to the affine Yangian $Y_{\hbar,\varepsilon}(\widehat{\mathfrak{sl}}(n+1))$. We also give the relationship between this homomorphism and the one from the affine Yangian $Y_{\hbar,\varepsilon}(\widehat{\mathfrak{sl}}(n))$ to the universal enveloping algebra

Yuxin Zi, Hariram Veeramani, Kaushik Roy, Amit Sheth

Natural language understanding (NLU) using neural network pipelines often requires additional context that is not solely present in the input data. Through Prior research, it has been evident that NLU benchmarks are susceptible to manipulation by neural models, wherein these models exploit statistical artifacts within the encoded external knowledge to artifi

AS Jooste, K. Jordaan

In this paper we consider connection formulae for orthogonal polynomials in the context of Christoffel transformations for the case where a weight function, not necessarily even, is multiplied by an even function $c_{2k}(x),k\in N_0$, to determine new lower bounds for the largest zero and upper bounds for the smallest zero of a Meixner-Pollaczek polynomial.

Ha Thi Khanh Linh, Andreas H Hamel

Recently defined expectile regions capture the idea of centrality with respect to a multivariate distribution, but fail to describe the tail behavior while it is not at all clear what should be understood by a tail of a multivariate distribution. Therefore, cone expectile sets are introduced which take into account a vector preorder for the multi-dimensional

Federica Gattere, Massimo Zanolini, Davide Gatti, Matteo Bernardini

The ability of streamwise-travelling waves of spanwise velocity to reduce the turbulent skin friction drag is assessed in the compressible regime. Direct numerical simulations are carried out to compare drag reduction in subsonic, transonic and supersonic channel flows. Compressibility improves the benefits of the travelling waves, in a way that depends on t

Amit Sheth, Kaushik Roy

The rapid progression of Artificial Intelligence (AI) systems, facilitated by the advent of Large Language Models (LLMs), has resulted in their widespread application to provide human assistance across diverse industries. This trend has sparked significant discourse centered around the ever-increasing need for LLM-based AI systems to function among humans as

Paulo E Ibañez-Almaguer, Germán Rojas-Lorenzo, Maykel Márquez-Mijares, Jesús Rubayo-Soneira

A comprehensive numerical investigation has been conducted on the angular distribution and spectrum of radiation emitted by 855 MeV electron and positron beams while traversing a 'quasi-mosaic' bent silicon (111) crystal. This interaction of charged particles with a bent crystal gives rise to various phenomena such as channeling, dechanneling, volume reflect

Lei Chen, Xiaohui Zhong, Hao Li, Jie Wu

Skillful subseasonal forecasts are crucial for various sectors of society but pose a grand scientific challenge. Recently, machine learning based weather forecasting models outperform the most successful numerical weather predictions generated by the European Centre for Medium-Range Weather Forecasts (ECMWF), but have not yet surpassed conventional models at

Mohsen Yavartanoo, Sangmin Hong, Reyhaneh Neshatavar, Kyoung Mu Lee

CNC manufacturing is a process that employs computer numerical control (CNC) machines to govern the movements of various industrial tools and machinery, encompassing equipment ranging from grinders and lathes to mills and CNC routers. However, the reliance on manual CNC programming has become a bottleneck, and the requirement for expert knowledge can result

Loïc Marsot

In this article, we consider some Carrollian dynamical systems as effective models on null hypersurfaces in a Lorentzian spacetime. We show that we can realize Carroll models from more usual ``relativistic'' theories. In particular, we show how ambient null geodesics imply the classical ``no Carroll motion'' and, more interestingly, we find that the ambient

E. Djuissi, J. Boffelli, R. Hassaine, N. Pop

Our previous studies of dissociative recombination, and vibrational excitation/de-excitation of the BeH$^+$ ion, based on the multichannel quantum defect theory, are extended to collision energies above the dissociation threshold, taking into account the vibrational continua of the BeH$^+$ ion and, consequently, its dissociative excitation. We have also sign

Jason Chun Lok Li, Rui Lin, Jiajun Zhou, Edmund Yin Mun Lam

Despite the decomposition of convolutional kernels for lightweight CNNs being well studied, existing works that rely on tensor network diagrams or hyperdimensional abstraction lack geometry intuition. This work devises a new perspective by linking a 3D-reshaped kernel tensor to its various slice-wise and rank-1 decompositions, permitting a straightforward co

Fátima Castro-Jul, Rebeca Díaz Redondo, Ana Fernández-Vilas, Sophie Chabridon

Since smart cities aim at becoming self-monitoring and self-response systems, their deployment relies on close resource monitoring through large-scale urban sensing. The subsequent gathering of massive amounts of data makes essential the development of event-filtering mechanisms that enable the selection of what is relevant and trustworthy. Due to the rise o

Eduardo Arancibia-Rojas, Monica Zorotovic, Maja Vučković, Alexey Bobrick

Hot subdwarf B (sdB) stars are helium core burning stars that have lost almost their entire hydrogen envelope due to binary interaction. Their assumed canonical mass of $\rm M_{\mathrm{sdB}}\sim0.47 M_{\odot}$ has recently been debated given a broad range found both from observations as well as from the simulations. Here, we revise and refine the mass range

Chiara Perinati

We describe and analyze a quasi-Trefftz DG method for solving boundary value problems for the homogeneous diffusion-advection-reaction equation with piecewise-smooth coefficients. Trefftz schemes are high-order Galerkin methods whose discrete functions are elementwise exact solutions of the underlying PDE. Trefftz basis functions can be computed for many PDE

L. F. Canto, V. A. B. Zagatto, J. Lubian, R. Donangelo

We present an improved version of the Wong formula for heavy-ion fusion, where the parameters of the parabolic approximation of the Coulomb barrier are replaced by parameters of the l-dependent potential at an effective partial-wave. A pocket formula for this l-dependence is given. This version reproduces the fusion cross sections of quantum mechanical calcu

Di Zhou, Yinxian Zhang

The rising popularity of ChatGPT and other AI-powered large language models (LLMs) has led to increasing studies highlighting their susceptibility to mistakes and biases. However, most of these studies focus on models trained on English texts. Taking an innovative approach, this study investigates political biases in GPT's multilingual models. We posed the s

Adrian Dumitrescu, János Pach, Géza Tóth

We consider partitions of a point set into two parts, and the lengths of the minimum spanning trees of the original set and of the two parts. If $w(P)$ denotes the length of a minimum spanning tree of $P$, we show that every set $P$ of $n \geq 12$ points admits a bipartition $P= R \cup B$ for which the ratio $\frac{w(R)+w(B)}{w(P)}$ is strictly larger than $

Craig Goodwin, Sandra Woolley, Ed de Quincey, Tim Collins

Obsolete devices add to the rising levels of electronic waste, a major environmental concern, and a contributing factor to climate change. In recent years, device manufacturers have established environmental commitments and launched initiatives such as supporting the recycling of obsolete devices by making more ways available for consumers to safely dispose

Rico Hager, Andreas H Hamel, Frank Heyde

Motivated by situations in which the removal of a zero (a.k.a., an absorbing element) from a semigroup yields a subsemigroup with another zero, sets of quasi-zeros (a.k.a., quasi-absorbing elements) are introduced as well as primitive elements, minimal ideals and simple semigroups all with respect to a given ideal. An application to set optimization is discu

Lukas Radl, Michael Steiner, Andreas Kurz, Markus Steinberger

Due to the omnipresence of Neural Radiance Fields (NeRFs), the interest towards editable implicit 3D representations has surged over the last years. However, editing implicit or hybrid representations as used for NeRFs is difficult due to the entanglement of appearance and geometry encoded in the model parameters. Despite these challenges, recent research ha

Harris Papadopoulos

Venn Prediction (VP) is a new machine learning framework for producing well-calibrated probabilistic predictions. In particular it provides well-calibrated lower and upper bounds for the conditional probability of an example belonging to each possible class of the problem at hand. This paper proposes five VP methods based on Neural Networks (NNs), which is o

Xueyao Zhang, Liumeng Xue, Yicheng Gu, Yuancheng Wang

Amphion is an open-source toolkit for Audio, Music, and Speech Generation, targeting to ease the way for junior researchers and engineers into these fields. It presents a unified framework that includes diverse generation tasks and models, with the added bonus of being easily extendable for new incorporation. The toolkit is designed with beginner-friendly wo

Ghafar Ahmadi, Shahpoor Saeidian, Ghasem Naeimi

The time evolution of quantum Fisher information, quantum coherence, and non-Markovianity of a V-type three-level atom embedded in free space or a photonic band gap crystal have been investigated. It has been demonstrated that the photonic band gap crystal, as a structured environment, significantly influences the preservation and enhancement of these quantu

Beatriz Barbero-Lucas, Iván Blanco-Chacón, Raúl Durán-Díaz, Rodrigo Martín Sánchez-Ledesma

We extend two of the attacks on the PLWE problem presented in (Y. Elias, K. E. Lauter, E. Ozman, and K. E. Stange, Ring-LWE Cryptography for the Number Theorist, in Directions in Number Theory, E. E. Eischen, L. Long, R. Pries, and K. E. Stange, eds., vol. 3 of Association for Women in Mathematics Series, Cham, 2016, Springer International Publishing, pp. 27

Wei Xie, Feng Yang, Liangcai Xu, Xiaokang Li

We present a study of electrical and thermal transport in Weyl semimetal WTe$_2$ down to 0.3 K. The Wiedemann-Franz law holds below 2 K and a downward deviation starts above. The deviation is more pronounced in cleaner samples, as expected in the hydrodynamic picture of electronic transport, where a fraction of electron-electron collisions conserve momentum.

Zhengqiang Zhang, Ruihuang Li, Shi Guo, Yang Cao

Online video super-resolution (online-VSR) highly relies on an effective alignment module to aggregate temporal information, while the strict latency requirement makes accurate and efficient alignment very challenging. Though much progress has been achieved, most of the existing online-VSR methods estimate the motion fields of each frame separately to perfor

Sreekanth Harikumar, Laur Järv, Margus Saal, Aneta Wojnar

Accelerated expansion of the Universe prompted searches of modified gravity theory beyond general relativity, instead of adding a mysterious dark energy component with exotic physical properties. One such alternative gravity approach is metric-affine Palatini $f(\hat{R})$ theory. By now routine gravitational wave detections have opened a promising avenue of

Eric Bonvin, Louisiane Devaud, Massimiliano Rossi, Andrei Militaru

We present a hybrid trapping platform that allows us to levitate a charged nanoparticle in high vacuum using either optical fields, radio-frequency fields, or a combination thereof. Our hybrid approach combines an optical dipole trap with a linear Paul trap while maintaining a large numerical aperture (0.77 NA). We detail a controlled transfer procedure that

Thorben Schomacker, Tillmann Dönicke, Marina Tropmann-Frick

In this paper, we apply transformer-based Natural Language Generation (NLG) techniques to the problem of text simplification. Currently, there are only a few German datasets available for text simplification, even fewer with larger and aligned documents, and not a single one with narrative texts. In this paper, we explore to which degree modern NLG technique

Thomas Bi, Raffaello D'Andrea

Motivated by the challenge of achieving rapid learning in physical environments, this paper presents the development and training of a robotic system designed to navigate and solve a labyrinth game using model-based reinforcement learning techniques. The method involves extracting low-dimensional observations from camera images, along with a cropped and rect

Batoul Tarhini

A path P(k,l,r) is an oriented path consisting of k forward arcs, followed by l backward arcs, and then by r forward arcs. We prove the existence of any oriented path of length n-1 with three blocks having the middle block of length one in any (2n-3)- chromatic digraph, which is an improvement of the latest bound reached in this case. Concerning the general

Nathaniel Gallup, Stephen Sawin

We prove a version of Gabriel's theorem for (possibly infinite dimensional) representations of infinite quivers. More precisely, we show that the representation theory of quiver $\Omega$ is of unique type (each dimension vector has at most one associated indecomposable) and infinite Krull-Schmidt (every, possibly infinite dimensional, representation is a dir

Verena Brehm, Pawel Sobieszczyk, Jostein Kløgetvedt, Richard F. L. Evans

The microscopic origin of the topological magnon band gap in CrI$_3$ ferromagnets has been a subject of controversy for years since two main models with distinct characteristics, i.e., Dzyaloshinskii-Moriya (DM) and Kitaev, provided possible explanations with different outcome implications. Here we investigate the angular magnetic field dependence of the mag

Danilo Radovic, Markus Hofer, Faruk Pasic, Enrico M. Vitucci

The role of wireless communications in various domains of intelligent transportation systems is significant; it is evident that dependable message exchange between nodes (cars, bikes, pedestrians, infrastructure, etc.) has to be guaranteed to fulfill the stringent requirements for future transportation systems. A precise site-specific digital twin is seen as

Xinyu Lin, Wenjie Wang, Jujia Zhao, Yongqi Li

Collaborative Filtering (CF) recommender models highly depend on user-item interactions to learn CF representations, thus falling short of recommending cold-start items. To address this issue, prior studies mainly introduce item features (e.g., thumbnails) for cold-start item recommendation. They learn a feature extractor on warm-start items to align feature

Debajyoti Sengupta, Matthew Leigh, John Andrew Raine, Samuel Klein

We introduce a new technique called Drapes to enhance the sensitivity in searches for new physics at the LHC. By training diffusion models on side-band data, we show how background templates for the signal region can be generated either directly from noise, or by partially applying the diffusion process to existing data. In the partial diffusion case, data c

J. H. Ramirez-Gonzalez, Ying Sun

Modeling the trajectories of animals is challenging due to the complexity of their behaviors, the influence of unpredictable environmental factors, individual variability, and the lack of detailed data on their movements. Additionally, factors such as migration, hunting, reproduction, and social interactions add additional layers of complexity when attemptin

Youssef Azouzi, Marwa Masmoudi

In the recent surge of papers on ergodic theory within Riesz spaces, this article contributes by introducing enhanced characterizations of ergodicity. Our work extends and strengthens prior results from both the authors and Homann, Kuo, and Watson. Specifically, we show that in a conditional expectation preserving system (E,T,S,e), S can be extended to the n

Yizhe Zhang, Shuo Wang, Tao Zhou, Qi Dou

Segmentation quality assessment (SQA) plays a critical role in the deployment of a medical image based AI system. Users need to be informed/alerted whenever an AI system generates unreliable/incorrect predictions. With the introduction of the Segment Anything Model (SAM), a general foundation segmentation model, new research opportunities emerged in how one

Christopher M. Graney

This paper argues that Tycho Brahe's "principal argument against Copernicus" (as the astronomer Christiaan Huygens called it) likely derived from a much older argument regarding the sizes of the "two great lights" described in the first chapter of the book of Genesis. Brahe's argument, that in the Copernican system stars would have to be absurdly large, play

Nelson Perez-Rojas, Saul Calderon-Ramirez, Martin Solis-Salazar, Mario Romero-Sandoval

Text simplification, crucial in natural language processing, aims to make texts more comprehensible, particularly for specific groups like visually impaired Spanish speakers, a less-represented language in this field. In Spanish, there are few datasets that can be used to create text simplification systems. Our research has the primary objective to develop a

Michał Czakon, Felix Eschment, Marco Niggetiedt, Rene Poncelet

We evaluate the top-bottom interference contribution to the fully-inclusive Higgs production cross section at next-to-next-to-leading order in QCD. Although bottom-quark-mass effects are power-suppressed, the accuracy of state-of-the-art theory predictions makes an exact determination of this effect indispensable. The total effect of the interference at 13 T

Suwon Shon, Kwangyoun Kim, Prashant Sridhar, Yi-Te Hsu

When performing tasks like automatic speech recognition or spoken language understanding for a given utterance, access to preceding text or audio provides contextual information can improve performance. Considering the recent advances in generative large language models (LLM), we hypothesize that an LLM could generate useful context information using the pre

Shengyi Hua, Fang Yan, Tianle Shen, Lei Ma

Large amounts of digitized histopathological data display a promising future for developing pathological foundation models via self-supervised learning methods. Foundation models pretrained with these methods serve as a good basis for downstream tasks. However, the gap between natural and histopathological images hinders the direct application of existing me

Sadeq S. Kadijani, Nicolás Del Grosso, Thomas L. Schmidt, M. Belén Farias

A transmission line coupled to an externally driven superconducting quantum interference device (SQUID) can exhibit the Dynamical Casimir Effect (DCE). Employing this setup, we quantize the SQUID degrees of freedom and show that it gives rise to a three-body interaction Hamiltonian with the cavity modes. By considering only two interacting modes from the cav

Claudio Giorgi, Federico Zullo

The aim of this paper is to develop a general constitutive scheme within continuum thermodynamics to describe the behavior of heat flow in deformable media. Starting from a classical thermodynamic approach, the rate-type constitutive equations are defined in the material (Lagrangian) description where the standard time derivative satisfies the principle of o

Oleg Karpenkov, Fatemeh Mohammadi, Christian Müller, Bernd Schulze

In 1864, J. C. Maxwell introduced a link between self-stressed frameworks in the plane and piecewise linear liftings to 3-space. This connection has found numerous applications in areas such as discrete geometry, control theory and structural engineering. While there are some generalisations of this theory to liftings of $d$-complexes in $d$-space, extension

Vivi Nastase, Paola Merlo

Sentence embeddings induced with various transformer architectures encode much semantic and syntactic information in a distributed manner in a one-dimensional array. We investigate whether specific grammatical information can be accessed in these distributed representations. Using data from a task developed to test rule-like generalizations, our experiments

Fabian Hebestreit, Jan Steinebrunner

We give a simple proof that complete Segal animae are equivalent to categories.

Victor A. Mateevitsi, Mathis Bode, Nicola Ferrier, Paul Fischer

In the realm of Computational Fluid Dynamics (CFD), the demand for memory and computation resources is extreme, necessitating the use of leadership-scale computing platforms for practical domain sizes. This intensive requirement renders traditional checkpointing methods ineffective due to the significant slowdown in simulations while saving state data to dis

Felipe Álvarez-Barrientos, Mariana Salinas-Camus, Simone Pezzuto, Francisco Sahli Costabal

The identification of the Purkinje conduction system in the heart is a challenging task, yet essential for a correct definition of cardiac digital twins for precision cardiology. Here, we propose a probabilistic approach for identifying the Purkinje network from non-invasive clinical data such as the standard electrocardiogram (ECG). We use cardiac imaging t

Matthew Luzum, João Paulo Picchetti, Mauricio Hippert, Jean-Yves Ollitrault

Recently, a method was developed for implementing arbitrary short-range nucleon-nucleon correlations in Monte Carlo sampled nuclei (as well as deformations of the 1-body nuclear density). We use this method to implement realistic 2-body correlations in a sample of nuclei for use in simulations of relativistic heavy-ion collisions and we quantify the statisti

Max Lahn

We study through the lens of Anosov representations the dynamical properties of reducible suspensions of linear representations of non-elementary hyperbolic groups, which are linear representations preserving and acting weakly unipotently on a proper non-zero subspace. We characterize when reducible suspensions are discrete and (almost) faithful, quasi-isome

Jayesh Malaviya, Anirban Dasgupta, Rachit Chhaya

While coresets have been growing in terms of their application, barring few exceptions, they have mostly been limited to unsupervised settings. We consider supervised classification problems, and non-decomposable evaluation measures in such settings. We show that stratified uniform sampling based coresets have excellent empirical performance that are backed

Christian Röver, Tim Friede

Meta-analyses are commonly performed based on random-effects models, while in certain cases one might also argue in favour of a common-effect model. One such case may be given by the example of two "study twins" that are performed according to a common (or at least very similar) protocol. Here we investigate the particular case of meta-analysis of a pair of

Alexander Volker, Janis Viktor Schmidt, Dominik Dannheim, Peter Svihra

A reliable and cost-effective interconnect technology is required for the development of hybrid pixel detectors. The interconnect technology needs to be adapted for the pitch and die sizes of the respective applications. For small-scale applications and during the ASIC and sensor development phase, interconnect technologies must also be suitable for the asse

Anna M. Viergever

Levine has constructed motivic analogues of virtual fundamental classes, living in cohomology of Witt sheaves. We use this to define motivic Donaldson-Thomas invariants $\tilde{I}_n$ for $\mathbb{P}^3$ over $\mathbb{R}$. We show that for $n$ odd, $\tilde{I}_n = 0$ and we compute $\tilde{I}_2 = 10, \tilde{I}_4 = 25$ and $\tilde{I}_6 = -50$. We then make a con

Shunxin Guo, Hongsong Wang, Xin Geng

Federated learning shows promise as a privacy-preserving collaborative learning technique. Existing heterogeneous federated learning mainly focuses on skewing the label distribution across clients. However, most approaches suffer from catastrophic forgetting and concept drift, mainly when the global distribution of all classes is extremely unbalanced and the

Aditya Parikh

Information extraction (IE) from unstructured documents remains a critical challenge in data processing pipelines. Traditional optical character recognition (OCR) methods and conventional parsing engines demonstrate limited effectiveness when processing large-scale document datasets. This paper presents a comprehensive framework for information extraction th

Anuvind Asokan

Recent lattice analyses of the $D\pi$ scattering by Hadron Spectrum Collaboration(HadSpec) report only one pole in the $D_0^*$ channel. This is in odds with the unitarised chiral perturbation theory analyses, which predict the $D_0^*(2300)$ as the interplay of two poles. We provide an explanation for this contradiction $-$ the exsistence of a hidden pole. We

Olivier Barrois, Magnus D. Hammer, Christopher C. Finlay, Yannick Martin

We jointly invert for magnetic and velocity fields at the core surface over the period 1997-2017, directly using ground-based observatory time series and measurements from the CHAMP and Swarm satellites. Satellite data are reduced to the form of virtual observatory time series distributed on a regular grid in space. Such a sequential storage helps incorporat

Faïcel Chamroukhi, Nhat Thien Pham

Mixture-of-experts (MoE) models provide a flexible statistical framework for modeling heterogeneity and nonlinear relationships. In many modern applications, however, datasets are naturally distributed across multiple machines due to storage, computational, or governance constraints. We consider a distributed model aggregation setting in which local MoE mode

Aditya Parikh

In this project we have designed and described a model which colourize a gray-scale image, with no human intervention. We propose a fully automatic process of colouring and re-colouring faded or gray-scale image with vibrant and pragmatic colours. We have used Convolutional Neural Network to hallucinate input images and feed-forwarded by training thousands o

Roy Maartens, Jessica Santiago, Chris Clarkson, Basheer Kalbouneh

The disagreement between low- and high-redshift measurements of the Hubble parameter is emerging as a serious challenge to the standard model of cosmology. We develop a covariant cosmographic analysis of the Hubble parameter in a general spacetime, which is fully model-independent and can thus be used as part of a robust assessment of the tension. Here our f

Artur Gesla, Yohann Duguet, Patrick Le Quéré, Laurent Martin Witkowski

Rotor-stator cavity flows are known to exhibit unsteady flow structures in the form of circular and spiral rolls. While the origin of the spirals is well understood, that of the circular rolls is not. In the present study the axisymmetric flow in an aspect ratio $R/H=10$ cavity is revisited {numerically using recent concepts and tools from bifurcation theory

Vincent Pfenninger

In this note, we prove that every even regular multigraph on $n$ vertices with multiplicity at most $r$ and minimum degree at least $rn/2 + o(n)$ has a Hamilton decomposition. This generalises a result of Vaughan who proved an asymptotic version of the multigraph $1$-factorisation conjecture. We derive our result by proving a more general result which states

Matthew Kiser, Anna Schroeder, Gian-Luca R. Anselmetti, Chandan Kumar

Quantum-enhanced auxiliary field quantum Monte Carlo (QC-AFQMC) uses output from a quantum computer to increase the accuracy of its classical counterpart. The algorithm requires the estimation of overlaps between walker states and a trial wavefunction prepared on the quantum computer. We study the applicability of this algorithm in terms of the number of mea

Alexander M. Moore, Randy C. Paffenroth, Kenneth T. Ngo, Joshua R. Uzarski

Multivariate time series data are ubiquitous in the application of machine learning to problems in the physical sciences. Chemiresistive sensor arrays are highly promising in chemical detection tasks relevant to industrial, safety, and military applications. Sensor arrays are an inherently multivariate time series data collection tool which demand rapid and

G. E. Volovik

We discuss the two-step transitions in superconductors, where the intermediate state between the Cooper pair state and the normal metal is the 4-fermion condensate, which is called the intertwined vestigial order. We discuss different types of the vestigial order, which are possible in the spin-triplet superfluid $^3$He, and the topological objects in the ve

Mikaëla Ngamboé, Xiao Niu, Benoit Joly, Steven P Biegler

The Automatic Dependent Surveillance-Broadcast (ADS-B) is a surveillance technology that mandated in many airspaces. It improves safety, increases efficiency and reduces air traffic congestion by broadcasting aircraft navigation data. Yet, ADS-B is vulnerable to spoofing attacks as it lacks mechanisms to ensure the integrity and authenticity of the data bein

Minbiao Han, Michael Albert, Haifeng Xu

We study a ubiquitous learning challenge in online principal-agent problems during which the principal learns the agent's private information from the agent's revealed preferences in historical interactions. This paradigm includes important special cases such as pricing and contract design, which have been widely studied in recent literature. However, existi

Nicolas Fröhlich, Arne Meier

Expressing system specifications using Computation Tree Logic (CTL) formulas, formalising programs using Kripke structures, and then model checking the system is an established workflow in program verification and has wide applications in AI. In this paper, we consider the task of model enumeration, which asks for a uniform stream of output systems that sati

Adam Martin

In this paper we explore $pp \to W^\pm (\ell^\pm \nu) \gamma$ to $\mathcal O(1/\Lambda^4)$ in the SMEFT expansion. Calculations to this order are necessary to properly capture SMEFT contributions that grow with energy, as the interference between energy-enhanced SMEFT effects at $\mathcal O(1/\Lambda^2)$ and the Standard Model is suppressed. We find that the

Tianchen Deng, Guole Shen, Tong Qin, Jianyu Wang

Neural implicit scene representations have recently shown encouraging results in dense visual SLAM. However, existing methods produce low-quality scene reconstruction and low-accuracy localization performance when scaling up to large indoor scenes and long sequences. These limitations are mainly due to their single, global radiance field with finite capacity

Marius Urbonas, Temitope Ajileye, Paul Gainer, Douglas Pires

Designing reward functions that guide generative molecular design (GMD) algorithms to desirable areas of chemical space is of critical importance in AI-driven drug discovery. Traditionally, this has been a manual and error-prone task; the selection of appropriate computational methods to approximate biological assays is challenging and the aggregation of com