December 2023 arXiv papers — page 38

Max Schölpple, Ingo Steinwart

Given a Banach space $E$ consisting of functions, we ask whether there exists a reproducing kernel Hilbert space $H$ with bounded kernel such that $E\subset H$. More generally, we consider the question, whether for a given Banach space consisting of functions $F$ with $E\subset F$, there exists an intermediate reproducing kernel Hilbert space $E\subset H\sub

Alessandro Georgoudis, Carlo Heissenberg, Ingrid Vazquez-Holm

In this short note, we address the calculation of the contribution to the one-loop gravitational waveform arising from the difference of the unitarity cuts associated to the $s$- and $s'$-channels recently pointed out in arXiv:2308.02125, providing its explicit expression for minimally-coupled massive scalars in momentum space.

Jan Manschot

This article surveys invariants of four-manifolds and their relation to Donaldson-Witten theory, and other topologically twisted Yang-Mills theories. The article is written for the second edition of the Encyclopedia of Mathematical Physics, and focuses on the period since the first edition in 2006.

Sourabrata Mukherjee, Zdeněk Kasner, Ondřej Dušek

Text sentiment transfer aims to flip the sentiment polarity of a sentence (positive to negative or vice versa) while preserving its sentiment-independent content. Although current models show good results at changing the sentiment, content preservation in transferred sentences is insufficient. In this paper, we present a sentiment transfer model based on pol

Eugenia Loiudice

We study the Boothby-Wang fibration of para-Sasakian manifolds and introduce the class of para-Sasakian $\phi$-symmetric spaces, canonically fibering over para-Hermitian symmetric spaces. Using this fibration we give a method to explicitly construct semisimple para-Sasakian $\phi$-symmetric spaces. We provide moreover an example of non-semisimple para-Sasaki

Elias Marks, Jonas Bömer, Federico Magistri, Anurag Sah

Agricultural production is facing severe challenges in the next decades induced by climate change and the need for sustainability, reducing its impact on the environment. Advancements in field management through non-chemical weeding by robots in combination with monitoring of crops by autonomous unmanned aerial vehicles (UAVs) and breeding of novel and more

Yifei Chen, Binfeng Zou, Zhaoxin Guo, Yiyu Huang

Pulmonary embolism (PE) is a prevalent lung disease that can lead to right ventricular hypertrophy and failure in severe cases, ranking second in severity only to myocardial infarction and sudden death. Pulmonary artery CT angiography (CTPA) is a widely used diagnostic method for PE. However, PE detection presents challenges in clinical practice due to limit

Mouhamadou Hassane Saley, Jaouad El-hassouny, Abderrahim El Mouhafid, Ahmed Jellal

We study the transport properties of Dirac fermions in ABC trilayer graphene (ABC-TLG) superlattices. More specifically, we analyze the impact of varying the physical parameters -- the number of cells, barrier/well width, and barrier heights -- on electron tunneling in the ABC-TLG. In the initial stage, we solved the eigenvalue equation to determine the ener

L. D. Valdez

Quarantine measures are one of the first lines of defense against the spread of infectious diseases. However, maintaining these measures over extended periods can be challenging due to a phenomenon known as quarantine fatigue. In this paper, we investigate the impact of quarantine fatigue on the spread of infectious diseases by using an epidemic model on ran

Lucy M. Hogarth, Amélie Saintonge, Tim A. Davis, Sara L. Ellison

We investigate whether barred galaxies are statistically more likely to harbour radial molecular gas flows and what effect those flows have on their global properties. Using 46 galaxies from the ALMA-MaNGA QUEnching and STar formation (ALMaQUEST) survey, we identify galaxies hosting optical bars using a combination of the morphological classifications in Gal

J. R. Canivete Cuissa, O. Steiner

Context. Ubiquitous small-scale vortical motions are seen to occur in the solar atmosphere both in simulations and observations. They are thought to play a significant role in the local heating of the quiet chromosphere and corona. In a previous paper, we proposed a new method for the automated identification of vortices based on the accurate estimation of c

V. N. Mantsevich, D. S. Smirnov

We describe theoretically the spin Nernst effect for electrons tunneling to a quantum dot from a quantum wire with the heat flowing along it. Such a tunneling spin Nernst effect is shown to take place due to the spin-dependent electron tunneling produced by the spin-orbit coupling. The Coulomb interaction of electrons in the quantum dot is taken into account

Dalia Artenstein, Janina C. Letz, Amrei Oswald, Andrea Solotar

We give an explicit description of a diagonal map on the Bardzell resolution for any monomial algebra, and we use this diagonal map to describe the cup product on Hochschild cohomology. Then, we prove that the cup product is zero in positive degrees for triangular monomial algebras. Our proof uses the graded-commutativity of the cup product on Hochschild coh

Naoufal El Bekri, Lucas Drumetz, Franck Vermet

The generative paradigm has become increasingly important in machine learning and deep learning models. Among popular generative models are normalizing flows, which enable exact likelihood estimation by transforming a base distribution through diffeomorphic transformations. Extending the normalizing flow framework to handle time-indexed flows gave dynamic no

Joaquin Rodriguez, Lew-Fock-Chong Lew-Yan-Voon, Renato Martins, Olivier Morel

Polarization information of the light can provide rich cues for computer vision and scene understanding tasks, such as the type of material, pose, and shape of the objects. With the advent of new and cheap polarimetric sensors, this imaging modality is becoming accessible to a wider public for solving problems such as pose estimation, 3D reconstruction, unde

Sagak Ayvazyan

The "typical" asymptotic behavior of the weighted sums of independent, identically distibuted random vectors in k-dimensional space is considered. It is shown that under finitnes of fifth absolute moment of an individual term the rate of convergence by Edgeworth correction in the multivariate central limit theorem is of order O(1/n^3/2 ). This extends the on

Leszek Hadasz, Błażej Ruba

We construct highest weight vectors of ${\widehat{\mathfrak{sl}_2}}_{,k+1} \oplus \mathsf{Vir}$ in tensor products of highest weight modules of ${\widehat{\mathfrak{sl}_2}}_{,k}$ and ${\widehat{\mathfrak{sl}_2}}_{,1}$, and thus for generic weights we find the decomposition of the tensor product into irreducibles of ${\widehat{\mathfrak{sl}_2}}_{,k+1} \oplus

A. Kerbizi, X. Artru

We present a recursive quantum mechanical model for the fragmentation of a string stretched between a quark and an antiquark with entangled spin states. The quarks are assumed to be produced in the $e^+e^-$ annihilation process via the exchange of a virtual photon and the correlations between their spin states are described by a joint spin density matrix. Th

Alfred Gautschy

Helium-star models were dynamically evolved into the region of the HR Diagram where R CrB variables are found. The MESA stellar evolution code was able to pick up pulsational instabilities with cycle lengths that are compatible with the periods observed in pulsating R CrB variables. The properties of the computed pulsations hint at their being strange modes.

Ludovico Napoli, Vedran Sekara, Manuel García-Herranz, Márton Karsai

Socioeconomic segregation patterns in networks usually evolve gradually, yet they can change abruptly in response to external shocks. The recent COVID-19 pandemic and the subsequent government policies induced several interruptions in societies, potentially disadvantaging the socioeconomically most vulnerable groups. Using large-scale digital behavioral obse

Yannis Bekri, Anatoli Juditsky, Arkadi Nemirovski

It was recently shown [7, 9] that "properly built" linear and polyhedral estimates nearly attain minimax accuracy bounds in the problem of recovery of unknown signal from noisy observations of linear images of the signal when the signal set is an ellitope. However, design of nearly optimal estimates relies upon solving semidefinite optimization problems with

Youcheng Niu, Jinming Xu, Ying Sun, Yan Huang

This paper consider solving a class of nonconvex-strongly-convex distributed stochastic bilevel optimization (DSBO) problems with personalized inner-level objectives. Most existing algorithms require computational loops for hypergradient estimation, leading to computational inefficiency. Moreover, the impact of data heterogeneity on convergence in bilevel pr

Raymond Pomponio, Bailey K. Fosdick, Julia Wrobel, Ryan A. Peterson

It is increasingly common to collect pre-post data with pseudonyms or self-constructed identifiers. On survey responses from sensitive populations, identifiers may be made optional to encourage higher response rates. The ability to match responses between pre- and post-intervention phases for every participant may be impossible in such applications, leaving

Nicolas Boullé, Alex Townsend

Operator learning aims to discover properties of an underlying dynamical system or partial differential equation (PDE) from data. Here, we present a step-by-step guide to operator learning. We explain the types of problems and PDEs amenable to operator learning, discuss various neural network architectures, and explain how to employ numerical PDE solvers eff

Ioannis Mavromatis, Theodoros Spyridopoulos, Pietro Carnelli, Woon Hau Chin

The way we travel is changing rapidly, and Cooperative Intelligent Transportation Systems (C-ITSs) are at the forefront of this evolution. However, the adoption of C-ITSs introduces new risks and challenges, making cybersecurity a top priority for ensuring safety and reliability. Building on this premise, this paper presents an envisaged Cybersecurity Centre

Patrick Concha, Octavio Fierro, Evelyn Rodríguez

In this paper we present the Hietarinta Chern-Simons supergravity theory in three space-time dimensions which extends the simplest Poincar\'e supergravity theory. After approaching the construction of the action using the Chern-Simons formalism, the analysis of the corresponding asymptotic symmetry algebra is considered. For this purpose, we first propose a

Hugo J. Ladret, Christian Casanova, Laurent Udo Perrinet

Both biological and artificial neural networks inherently balance their performance with their operational cost, which balances their computational abilities. Typically, an efficient neuromorphic neural network is one that learns representations that reduce the redundancies and dimensionality of its input. This is for instance achieved in sparse coding, and

Minghan Chu

Turbulent flow has been extensively studied using computational fluid dynamics (CFD) simulations since turbulent flow regime is so frequently encountered in both academic and engineering applications. The high-fidelity simulation of the Direct Numerical Simulation (DNS) requires a sufficiently fine mesh to resolve the smallest Kolmogorov length scale of turb

B. Šlaus, V. Smolcic, Ž. Ivezic, S. Fotopoulou

We model the evolution of active galactic nuclei by constructing their radio luminosity functions. We use a set of surveys of varying area and depth, namely the deep COSMOS survey of $1,916$ AGN sources, the wide shallow 3CRR, 7C and 6CE surveys, containing together $356$ AGNs, and the intermediate XXL-North and South fields consisting of $899$ and $1,484$ s

Daan Mulder, Pieter Rein ten Wolde, Thomas E. Ouldridge

We study a finite-time cyclic copy protocol that creates persisting correlations between a memory and a data bit. The average work to copy the two states of the data bit consists of the mutual information created between the memory and data bit after copying, a cost due to the difference between the initial and final states of the memory bit, and a finite-ti

Raffaele Marino, Lorenzo Buffoni, Lorenzo Chicchi, Lorenzo Giambagli

EODECA (Engineered Ordinary Differential Equations as Classification Algorithm) is a novel approach at the intersection of machine learning and dynamical systems theory, presenting a unique framework for classification tasks [1]. This method stands out with its dynamical system structure, utilizing ordinary differential equations (ODEs) to efficiently handle

Xinyang Dong, Emanuel Gull, Lei Wang

The many-body Green's function provides access to electronic properties beyond density functional theory level in ab inito calculations. In this manuscript, we propose a deep learning framework for predicting the finite-temperature Green's function in atomic orbital space, aiming to achieve a balance between accuracy and efficiency. By predicting the self-en

Andreas Ringwald

SM*A*S*H is an extension of the Standard Model of particle physics which has just the minimal number of fields in order to solve six puzzles of particle physics and cosmology in one smash: vacuum stability, inflation, baryon asymmetry, neutrino masses, strong CP, and dark matter. The parameters of SM*A*S*H are constrained by symmetries and requirements to so

Ramón J. Aliaga, Guillaume Grelier, Antonín Procházka

We prove that several classical Banach space properties are equivalent to separability for the class of Lipschitz-free spaces, including Corson's property ($\mathcal{C}$), Talponen's Countable Separation Property, or being a G\^ateaux differentiability space. On the other hand, we single out more general properties where this equivalence fails. In particular

Zeyu Li, Chenghui Shi, Yuwen Pu, Xuhong Zhang

The widespread use of deep learning technology across various industries has made deep neural network models highly valuable and, as a result, attractive targets for potential attackers. Model extraction attacks, particularly query-based model extraction attacks, allow attackers to replicate a substitute model with comparable functionality to the victim mode

Jasper Müller, Sai Kireet Patri, Gabriele Di Rosa, Achim Autenrieth

Optical transport network operators typically follow a pay-as-you-grow strategy for their network deployment. We propose a proactive multi-period planning approach based on heuristic network planning, supporting this deployment strategy while enabling efficient network utilization through next-generation technology. We report 60% less provisioned lightpaths.

Huaimin Li, Qing Wang

In this paper, we investigate the affine ageing algebra $\widehat{\mathfrak{age}}(1)$, which is a central extension of the loop algebra of the 1-spatial ageing algebra $\mathfrak{age}(1)$. Certain Verma-type modules including Verma modules and imaginary Verma modules of $\widehat{\mathfrak{age}}(1)$ are studied.Particularly, the simplicity of these modules a

Jessica Bariffi, Hannes Bartz, Gianluigi Liva, Joachim Rosenthal

Most low-density parity-check (LDPC) code constructions are considered over finite fields. In this work, we focus on regular LDPC codes over integer residue rings and analyze their performance with respect to the Lee metric. Their error-correction performance is studied over two channel models, in the Lee metric. The first channel model is a discrete memoryl

Francesco Salamida

The Pierre Auger Observatory is a unique facility designed to study ultra-high energy cosmic rays, with energies up to 10$^{20}$ eV and beyond. The Observatory is located in Argentina and comprises more than 1600 water Cherenkov detectors spread over an area of 3000 square kilometers overlooked by Fluorescence detectors. The first phase of the Observatory's

Sergei Shumilin, Alexander Ryabov, Serguei Barannikov, Evgeny Burnaev

Voronoi tessellation, also known as Voronoi diagram, is an important computational geometry technique that has applications in various scientific disciplines. It involves dividing a given space into regions based on the proximity to a set of points. Autodifferentiation is a powerful tool for solving optimization tasks. Autodifferentiation assumes constructin

Oliver Knill

A discrete d-manifold is a finite simple graph G=(V,E) where all unit spheres are (d-1)-spheres. A d-sphere is a d-manifold for which one can remove a vertex to make it contractible. A graph is contractible if one can remove a vertex with contractible unit sphere to get a contractible graph. We prove a discrete Morse-Sard theorem: if G=(V,E) is a d-manifold

Alessandro Antonucci, Gregorio Piqué, Marco Zaffalon

We evaluate the ability of large language models (LLMs) to infer causal relations from natural language. Compared to traditional natural language processing and deep learning techniques, LLMs show competitive performance in a benchmark of pairwise relations without needing (explicit) training samples. This motivates us to extend our approach to extrapolating

Romain Rieger, Alexandre Danescu

We explore the macroscopic elastic behavior of the aperiodic but hyperuniform (single tile) tiling.

Liwei Hu, Wenyong Wang, Yu Xiang, Stefan Sommer

We propose to incorporate Riemannian geometric features from the geometry of aircraft wing surfaces in the prediction of coefficient of pressure (CP) on the aircraft wing. Contrary to existing approaches that treat the wing surface as a flat object, we represent the wing as a piecewise smooth manifold and calculate a set of Riemannian geometric features (Rie

Yuya Shishikura, Hiroki Ohta

We propose a statistical physics model of a neutral community, where each agent can represent identical plant species growing in the vertical direction with sunlight in the form of rich-get-richer competition. Disturbance added to this ecosystem, which makes an agent restart from the lowest growth level, is realized as a stochastic resetting. We show that in

Qianrui Zhou, Hua Xu, Hao Li, Hanlei Zhang

Multimodal intent recognition aims to leverage diverse modalities such as expressions, body movements and tone of speech to comprehend user's intent, constituting a critical task for understanding human language and behavior in real-world multimodal scenarios. Nevertheless, the majority of existing methods ignore potential correlations among different modali

B-G Andersson, Janik Karoly, Pierre Bastien, Archana Soam

We present SCUBA-2/POL-2 850 $\mu$m polarimetric observations of the circumstellar envelope (CSE) of the carbon-rich asymptotic giant branch (AGB) star IRC+10216. Both FIR and optical polarization data indicate grains aligned with their long axis in the radial direction relative to the central star. The 850 $\mu$m polarization does not show this simple struc

Fernando Valadares, Ni-Ni Huang, Kyle Chu, Aleksandr Dorogov

The diverse applications of light-matter interactions in science and technology stem from the qualitatively distinct ways these interactions manifest, prompting the development of physical platforms that can interchange between regimes on demand. Bosonic cQED employs the light field of high-Q superconducting cavities coupled to non-linear circuit elements, h

Miriam Jäger, Steven Landgraf, Boris Jutzi

In the fields of computer graphics, computer vision and photogrammetry, Neural Radiance Fields (NeRFs) are a major topic driving current research and development. However, the quality of NeRF-generated 3D scene reconstructions and subsequent surface reconstructions, heavily relies on the network output, particularly the density. Regarding this critical aspec

G. Martinez-Lema, V. Chepel, A. Roy, A. Breskin

We report on the first observation of electroluminescence amplification with a Microstrip Plate immersed in liquid xenon. The electroluminescence of the liquid, induced by alpha-particles, was observed in an intense non-uniform electric field in the vicinity of 8-$\mu$m narrow anode strips interlaced with wider cathode ones, deposited on the same side of a g

Guillermo Badia, Daniel Gaina, Alex Knapp, Tomasz Kowalski

There are known characterisations of several fragments of hybrid logic by means of invariance under bisimulations of some kind. The fragments include $\{\store, \jump\}$ with or without nominals (Areces, Blackburn, Marx), $\jump$ with or without nominals (ten Cate), and $\store$ without nominals (Hodkinson, Tahiri). Some pairs of these characterisations, how

Bastian B. Brandt, Gergely Endrődi, José Javier Hernández Hernández, Gergely Markó

In this proceedings article we present a selected set of our lattice results regarding the effect that background electromagnetic fields have on the topology of QCD. In particular, we report on the lattice spacing-dependence of the axion-photon coupling as well as on the response of the topological susceptibility to strong magnetic fields at nonzero temperat

Cristiana De Filippis, Lukas Koch, Jan Kristensen

We introduce a new quantification of nonuniform ellipticity in variational problems via convex duality, and prove higher differentiability and $2d$-smoothness results for vector valued minimizers of possibly degenerate functionals. Our framework covers convex, anisotropic polynomials as prototypical model examples - in particular, we improve in an essentiall

Matteo Scerbo, Lauri Savioja, Enzo De Sena

Room acoustic synthesis can be used in Virtual Reality (VR), Augmented Reality (AR) and gaming applications to enhance listeners' sense of immersion, realism and externalisation. A common approach is to use Geometrical Acoustics (GA) models to compute impulse responses at interactive speed, and fast convolution methods to apply said responses in real time. A

Syama Sundar Rangapuram, Jan Gasthaus, Lorenzo Stella, Valentin Flunkert

This paper presents non-parametric baseline models for time series forecasting. Unlike classical forecasting models, the proposed approach does not assume any parametric form for the predictive distribution and instead generates predictions by sampling from the empirical distribution according to a tunable strategy. By virtue of this, the model is always abl

Sebastiaan A. Terwijn

We investigate completions of partial combinatory algebras (pcas), in particular of Kleene's second model $\mathcal{K}_2$ and generalizations thereof. We consider weak and strong notions of embeddability and completion that have been studied before in the literature. It is known that every countable pca can be weakly embedded into $\mathcal{K}_2$, and we gen

Paul Stoewer, Achim Schilling, Andreas Maier, Patrick Krauss

Cognitive maps are a proposed concept on how the brain efficiently organizes memories and retrieves context out of them. The entorhinal-hippocampal complex is heavily involved in episodic and relational memory processing, as well as spatial navigation and is thought to built cognitive maps via place and grid cells. To make use of the promising properties of

Christian Henriksen, Carsten Lunde Petersen, Eva Uhre

Let $\Omega \in \mathbb{C}$ be a domain such that $K:= \mathbb{C} \setminus \Omega$ is compact and non-polar. Let $g_\Omega$ be the Green's function with a logarithmic pole at infinity, and let $\omega = \omega_K$ be the equilibrium distribution on $K$. Let $(q_k)_{k>0}$ be a sequence of polynomials with $n_k$, the degree of $q_k$ satisfying $n_k \to \infty$

Bjarne Kosmeijer, Hessel Posthuma

We compute the Hochschild cohomology of universal enveloping algebras of Lie-Rinehart algebras in terms of the Poisson cohomology of the associated graded quotient algebras. Central in our approach are two cochain complexes of "nonlinear Chevalley-Eilenberg" cochains whose origins lie in Lie-Rinehart modules "up to homotopy", one on the Hochschild cochains o

Samuele Sottile

In this paper we prove an inverse resonance theorem for the half-solid with vanishing stresses on the surface via Weyl-Titchmarsh function. Using a semi-classical approach it is possible to simplify this three-dimensional problem of the elastic wave equation for the half-solid as a Schr\"odinger equation with Robin boundary conditions on the half-line. The g

Alnour Altoum, Hasan Arslan, Mariam Zaarour

In this paper, we will introduce the Cauchy numbers of both kinds in type B and produce their corresponding exponential generating functions. Then we will provide some identities involving Cauchy, Lah, and Stirling numbers in type B through combinatorial methods.

Patricia A. Apellániz, Juan Parras, Santiago Zazo

As in many fields of medical research, survival analysis has witnessed a growing interest in the application of deep learning techniques to model complex, high-dimensional, heterogeneous, incomplete, and censored medical data. Current methods often make assumptions about the relations between data that may not be valid in practice. In response, we introduce

Zihua Liu, Yizhou Li, Masatoshi Okutomi

Despite the remarkable progress facilitated by learning-based stereo-matching algorithms, the performance in the ill-conditioned regions, such as the occluded regions, remains a bottleneck. Due to the limited receptive field, existing CNN-based methods struggle to handle these ill-conditioned regions effectively. To address this issue, this paper introduces

Veronika Ertl, Sally Gilles, Wiesława Nizioł

We study the image of the Hodge-Tate logarithm map (in any cohomological degree), defined by Heuer, in the case of smooth Stein varieties. Heuer, motivated by the computations for the affine space of any dimension, raised the question whether this image is always equal to the group of closed differential forms. We show that it indeed always contains such for

Orhan Aygn, Bertan Turhan

India has enacted an intricate affirmative action program through a reservation system since the 1950s. Notably, in 2008, a historic judgment by the Supreme Court of India (SCI) in the case of Ashoka Kumar Thakur vs. Union of India mandated a 27 percent reservation to the Other Backward Classes (OBC). The SCI's ruling suggested implementing the OBC reservati

Alaa Saleh, Roberto Morabito, Sasu Tarkoma, Susanna Pirttikangas

In today's digital world, Generative Artificial Intelligence (GenAI) such as Large Language Models (LLMs) is becoming increasingly prevalent, extending its reach across diverse applications. This surge in adoption has sparked a significant increase in demand for data-centric GenAI models, highlighting the necessity for robust data communication infrastructur

Hongda Sun, Hongzhan Lin, Rui Yan

Electronic health records (EHRs) have become the foundation of machine learning applications in healthcare, while the utility of real patient records is often limited by privacy and security concerns. Synthetic EHR generation provides an additional perspective to compensate for this limitation. Most existing methods synthesize new records based on real EHR d

Sandro Mereghetti, Michela Rigoselli, Ruben Salvaterra, Dominik P. Pacholski

Giant flares, short explosive events releasing up to 10$^{47}$ erg of energy in the gamma-ray band in less than one second, are the most spectacular manifestation of magnetars, young neutron stars powered by a very strong magnetic field, 10$^{14-15}$ G in the magnetosphere and possibly higher in the star interior. The rate of occurrence of these rare flares

Jan C. Louw, Linda M. van Manen, Rishabh Jha

It has been known that the large-$q$ complex SYK model falls under the same universality class as that of van der Waals (mean-field) and saturates the Maldacena-Shenker-Stanford bound, both features shared by various black holes. This makes the SYK model a useful tool in probing the fundamental nature of quantum chaos and holographic duality. This work estab

S. B. Korolev, E. N. Bashmakova, A. K. Tagantsev, T. Yu. Golubeva

The generation of squeezed Fock states by the one or more photon subtraction from a two-mode entangled Gaussian (TMEG) state is theoretically addressed. We showed that an arbitrary order Fock state can be generated this way and we obtained a condition that should be imposed on the parameters of the TMEG state to guaranty such a generation. We called the regi

Rainer Picard, Sascha Trostorff, Marcus Waurick

We provide certain compatibility conditions for m-accretive operators such that the adjoint of the sum is given by the closure of the sum of the respective adjoint. We revisit the proof of well-posedness of the abstract class of partial differential-algebraic equations known as evolutionary equations. We show that the general mechanism provided here can be a

Piotr Bozek

The rapid expansion of the fireball created in a heavy-ion collision causes strong departures from local equilibrium. Such effects are especially important in the very early phase of the collision, bringing a substantial pressure asymmetry. We investigate effects of this early stage pressure asymmetry in a kinetic model without boost-invariance. In the kinet

Omer Angel, Delphin Sénizergues

In this paper we prove a scaling limit result for the component of the root in the Wired Minimal Spanning Forest (WMSF) of the Poisson-Weighted Infinite Tree (PWIT), where the latter tree arises as the local weak limit of the Minimal Spanning Tree (MST) on the complete graph endowed with i.i.d. weights on its edges. The limiting object can be obtained by agg

Pu Liu, Chaoxi Cui, Zhi-Ming Yu

The TiSiCO-family monolayer $X_2Y$CO$_2$($X$=Ti, Zr, Hf; $Y$=Si, Ge) is a two-dimensional second-order topological insulator with unique valley-layer coupling in equilibrium condition. In this work, based on the four-band tight-binding (TB) model of monolayer Ti$_2$SiCO$_2$ (ML-TiSiCO) and the Floquet theory, we study the non-equilibrium properties of the ML

Mohamed Badi, Chaouki Ben Issaid, Anis Elgabli, Mehdi Bennis

The growing number of wireless edge devices has magnified challenges concerning energy, bandwidth, latency, and data heterogeneity. These challenges have become bottlenecks for distributed learning. To address these issues, this paper presents a novel approach that ensures energy efficiency for distributionally robust federated learning (FL) with over air co

John Fry, Tom Brighton, Silvio Fanzon

Two natural ways of modelling Formula 1 race outcomes are a probabilistic approach, based on the exponential distribution, and econometric modelling of the ranks. Both approaches lead to exactly soluble race-winning probabilities. Equating race-winning probabilities leads to a set of equivalent parametrisations. This time-rank duality is attractive theoretic

N. I. Shvetsov-Shilovski, M. Lein

We apply deep learning for retrieval of the time-dependent bond length in the dissociating two-dimensional H$_2^{+}$ molecule using photoelectron momentum distributions. We consider a pump-probe scheme and calculate electron momentum distributions from strong-field ionization by treating the motion of the nuclei classically, semiclassically or quantum mechan

Yitong Deng, Hong-Xing Yu, Diyang Zhang, Jiajun Wu

We introduce Neural Flow Maps, a novel simulation method bridging the emerging paradigm of implicit neural representations with fluid simulation based on the theory of flow maps, to achieve state-of-the-art simulation of inviscid fluid phenomena. We devise a novel hybrid neural field representation, Spatially Sparse Neural Fields (SSNF), which fuses small ne

Suna Bensch, Amanda Eriksson

We investigate interaction patterns for humans interacting with explainable and non-explainable robots. Non-explainable robots are here robots that do not explain their actions or non-actions, neither do they give any other feedback during interaction, in contrast to explainable robots. We video recorded and analyzed human behavior during a board game, where

Borna Kalhor, Sanchari Das

Virtual assistants (VAs) have seen increased use in recent years due to their ease of use for daily tasks. Despite their growing prevalence, their security and privacy implications are still not well understood. To address this gap, we conducted a study to evaluate the security and privacy postures of eight widely used voice assistants: Alexa, Braina, Cortan

William H. Oliver, Pascal J. Elahi, Geraint F. Lewis, Tobias Buck

We present AstroLink, an efficient and versatile clustering algorithm designed to hierarchically classify astrophysically-relevant structures from both synthetic and observational data sets. We build upon CluSTAR-ND, a hierarchical galaxy/(sub)halo finder, so that AstroLink now generates a two-dimensional representation of the implicit clustering structure a

J. M. Flynn, A. Jüttner, J. T. Tsang

We discuss our recently proposed model-independent framework for fitting hadronic form-factor data, which are often only available at discrete kinematical points, using parameterisations based on unitarity and analyticity. The accompanying dispersive bound on the form factors (unitarity constraint) is used to regulate the ill-posed fitting problem and allow

Ali Abdari, Alex Falcon, Giuseppe Serra

Recently, the Metaverse is becoming increasingly attractive, with millions of users accessing the many available virtual worlds. However, how do users find the one Metaverse which best fits their current interests? So far, the search process is mostly done by word of mouth, or by advertisement on technology-oriented websites. However, the lack of search engi

Max J. Ruckriegel, Lisa M. Gächter, David Kealhofer, Mohsen Bahrami Panah

We implement circuit quantum electrodynamics (cQED) with quantum dots in bilayer graphene, a maturing material platform for semiconductor qubits that can host long-lived spin and valley states. The presented device combines a high-impedance ($Z_\mathrm{r} \approx 1 \mathrm{k{\Omega}}$) superconducting microwave resonator with a double quantum dot electrostat

Hongliu Cao

In light of emerging legal requirements and policies focused on privacy protection, there is a growing trend of companies across various industries adopting Federated Learning (FL). This decentralized approach involves multiple clients or silos, collaboratively training a global model under the coordination of a central server while utilizing their private l

Tristan da Câmara Santa Clara Gomes, Nicolas Marchal, Joaquín de la Torre Medina, Flavio Abreu Araujo

Macroscopic-scale nanostructures, situated at the interface of nanostructures and bulk materials, hold significant promise in the realm of thermoelectric materials. Nanostructuring presents a compelling avenue for enhancing material thermoelectric performance as well as unlocking intriguing nanoscale phenomena, including spin-dependent thermoelectric effects

Yuehao Yin, Huiyan Qi, Bin Zhu, Jingjing Chen

Large Multi-modal Models (LMMs) have made impressive progress in many vision-language tasks. Nevertheless, the performance of general LMMs in specific domains is still far from satisfactory. This paper proposes FoodLMM, a versatile food assistant based on LMMs with various capabilities, including food recognition, ingredient recognition, recipe generation, n

Yujie Li, Xin Yang, Hao Wang, Xiangkun Wang

This paper studies the problem of continual learning in an open-world scenario, referred to as Open-world Continual Learning (OwCL). OwCL is increasingly rising while it is highly challenging in two-fold: i) learning a sequence of tasks without forgetting knowns in the past, and ii) identifying unknowns (novel objects/classes) in the future. Existing OwCL me

Iris Dominguez-Catena, Daniel Paternain, Mikel Galar

In the last few years, Artificial Intelligence systems have become increasingly widespread. Unfortunately, these systems can share many biases with human decision-making, including demographic biases. Often, these biases can be traced back to the data used for training, where large uncurated datasets have become the norm. Despite our knowledge of these biase

Bobin Yang, Jie Deng, Zhenghan Chen, Ruoxue Wu

The task of deducing three-dimensional molecular configurations from their two-dimensional graph representations holds paramount importance in the fields of computational chemistry and pharmaceutical development. The rapid advancement of machine learning, particularly within the domain of deep generative networks, has revolutionized the precision of predicti

Taha Eghtesad, Sirui Li, Yevgeniy Vorobeychik, Aron Laszka

The increasing reliance of drivers on navigation applications has made transportation networks more susceptible to data-manipulation attacks by malicious actors. Adversaries may exploit vulnerabilities in the data collection or processing of navigation services to inject false information, and to thus interfere with the drivers' route selection. Such attacks

Ekaterina Mukhanova, Weijun Zeng, Elica Anne Heredia, Chun-Wei Wu

We have looked into cobalt disilicide (CoSi$_2$) as a potential building block for superconducting quantum circuits. In order to achieve this, we annealed a thin layer of Co to create 10-105 nm thick microwave cavities from CoSi$_2$ embedded in the silicon substrate. The cavity properties were measured as a function of temperature and power. In films measuri

Joonas Iivanainen, Tony R. Carter, Jonathan E. Dhombridge, Timothy S. Read

We present a novel four-channel OPM sensor for magnetoencephalography that utilizes a two-color pump/probe scheme on a single optical axis. We characterize its performance across 18 built sensor modules. The new sensor implements several improvements over our previously developed sensor including lower vapor-cell operating temperature, improved probe-light d

CMS HGCAL collaboration

This paper describes the experience with the calibration, reconstruction and evaluation of the timing capabilities of the CMS HGCAL prototype in the beam tests in 2018. The calibration procedure includes multiple steps and corrections ranging from tens of nanoseconds to a few hundred picoseconds. The timing performance is studied using signals from positron

Martina Ahlberg, Sheng Jiang, Roman Khymyn, Sunjae Chung

Magnetic droplets are nanoscale, non-topological, dynamical solitons that can be nucleated in different spintronic devices, such as spin torque nano-oscillators (STNOs) and spin Hall nano-oscillators (SHNOs). This chapter first briefly discusses the theory of spin current driven dissipative magnetic droplets in ferromagnetic thin films with uniaxial anisotro

Max Bannach, Markus Hecher

Algorithmic meta-theorems state that problems definable in a fixed logic can be solved efficiently on structures with certain properties. An example is Courcelle's Theorem, which states that all problems expressible in monadic second-order logic can be solved efficiently on structures of small treewidth. Such theorems are usually proven by algorithms for the

Yifu Liu, Xiaoxia Li, Zhiling Luo, Wei Zhou

Existing data-driven methods for garment animation, usually driven by linear skinning, although effective on tight garments, do not handle loose-fitting garments with complex deformations well. To address these limitations, we develop a garment generative model based on deformation decomposition to efficiently simulate loose garment deformation without direc

Riccardo March, Orfeu Bertolami, Marco Muccino, Simone Dell'Agnello

We analyze the dynamics of the Sun-Earth-Moon system in the context of a particular class of theories of gravity where curvature and matter are nonminimally coupled (NMC). These theories can potentially violate the Equivalence Principle as they give origin to a fifth force and a extra non-Newtonian force that may imply that Earth and Moon fall differently to

Jaš Bensa

We study the exponential relaxation of observables, propagated with a non-Hermitian transfer matrix, an example being out-of-time-ordered correlations (OTOC) in brickwall (BW) random quantum circuits. Until a time that scales as the system size, the exponential decay of observables is not usually determined by the second largest eigenvalue of the transfer ma

D. Tsiklauri

The problem of explaining observed soft X-ray fluxes during solar flares, which invokes acceleration of large fraction of electrons, if the acceleration takes places at the solar coronal loop-top, can potentially be solved by postulating that flare at loop-top creates dispersive Alfven waves (DAWs) which propagate towards the foot-points. As DAWs move in pro