April 2024 arXiv papers — page 115

Lasse Grimmelt, Jori Merikoski

We prove a theorem that allows one to count solutions to determinant equations twisted by a periodic weight with high uniformity in the modulus. It is obtained by using spectral methods of $\operatorname{SL}_2(\mathbb{R})$ automorphic forms to study Poincar\'e series over congruence subgroups. By keeping track of interactions between multiple orbits we get a

Ting Dong, Haoxin Wang, Hengxi Zhang, Wenbo Ding

When addressing the challenge of complex multi-objective optimization problems, particularly those with non-convex and non-uniform Pareto fronts, Decomposition-based Multi-Objective Evolutionary Algorithms (MOEADs) often converge to local optima, thereby limiting solution diversity. Despite its significance, this issue has received limited theoretical explor

Wolf-Jürgen Beyn, Christian Döding

We consider the stability of front-type modulated waves in the complex Ginzburg-Landau equation (CGL). The waves occur in the bistable regime (e.g. of the quintic CGL) and connect the zero state to a spatially homogenous state oscillating in time. For initial perturbations that decay at a certain algebraic rate, we prove convergence to the wave with asymptot

Guido Mazzuca

In recent years, a lot of effort has been put in describing the hydrodynamic behavior of integrable systems. In this paper, we describe such picture for the Volterra lattice. Specifically, we are able to explicitly compute the susceptibility matrix and the current-field correlation matrix in terms of the density of states of the Volterra lattice endowed with

Tianning Xiao, Dingyun Yao, Chao Zhang, Zhijie Fan

The crossover between short-range and long-range (LR) universal behaviors remains a central theme in the physics of long-range interacting systems. The competition between LR coupling and the Berezinskii-Kosterlitz-Thouless mechanism makes the problem more subtle and less understood in the two-dimensional (2D) XY model, a cornerstone for investigating low-di

Mina Montazeri, Chetan Kulkarni, Olga Fink

Urban Air Mobility (UAM) aims to expand existing transportation networks in metropolitan areas by offering short flights either to transport passengers or cargo. Electric vertical takeoff and landing aircraft powered by lithium-ion battery packs are considered promising for such applications. Efficient mission planning is cru-cial, maximizing the number of f

Enric Florit

Let $A$ be a simple abelian variety over a number field $k$ such that $\operatorname{End}(A)$ is noncommutative. We show that $A$ splits modulo all but finitely many primes of $k$. We prove this by considering the subalgebras of $\operatorname{End}(A_{\mathfrak p})\otimes\mathbb{Q}$ which have prime Schur index. Our main tools are Tate's characterization of

Jonathan D. Chang, Wenhao Zhan, Owen Oertell, Kianté Brantley

Reinforcement Learning (RL) from Human Preference-based feedback is a popular paradigm for fine-tuning generative models, which has produced impressive models such as GPT-4 and Claude3 Opus. This framework often consists of two steps: learning a reward model from an offline preference dataset followed by running online RL to optimize the learned reward model

Simon Oddershede Gregersen, Alejandro Aguirre, Philipp G. Haselwarter, Joseph Tassarotti

Almost-sure termination is an important correctness property for probabilistic programs, and a number of program logics have been developed for establishing it. However, these logics have mostly been developed for first-order programs written in languages with specific syntactic patterns for looping. In this paper, we consider almost-sure termination for hig

Garry Goldstein

The success behind many pseudopotential methods, such as the Projected Augmented Waves (PAW) and the Phillips-Kleinman pseudopotential methods, is that these methods are nearly all electron methods in disguise. For the Phillips-Kleinman and PAW pseudopotential methods we show that there is an explicit all electron reformulation (which is nearly equivalent).

Siting Estee Lu

The growing adoption of large language models (LLMs) presents potential for deeper understanding of human behaviours within game theory frameworks. Addressing research gap on multi-player competitive games, this paper examines the strategic interactions among multiple types of LLM-based agents in a classical beauty contest game. LLM-based agents demonstrate

Haozhe Zhao, Zefan Cai, Shuzheng Si, Liang Chen

Large-scale multilingual Pretrained Language Models (mPLMs) yield impressive performance on cross-language tasks, yet significant performance disparities exist across different languages within the same mPLM. Previous studies endeavored to narrow these disparities by supervise fine-tuning the mPLMs with multilingual data. However, obtaining labeled multiling

Jiangjing Hu, Fengyu Wang, Wenjun Xu, Hui Gao

Intelligent task-oriented semantic communications~(SemComs) have witnessed great progress with the development of deep learning~(DL), where multi-task SemComs that perform multiple tasks simultaneously attach great importance due to its high efficiency. However, the study of robust multi-task-oriented semantics transmission is still in early stages. In this

Jing Yao, Danfeng Hong, Chenyu Li, Jocelyn Chanussot

Recurrent neural networks and Transformers have recently dominated most applications in hyperspectral (HS) imaging, owing to their capability to capture long-range dependencies from spectrum sequences. However, despite the success of these sequential architectures, the non-ignorable inefficiency caused by either difficulty in parallelization or computational

Stefano De Paoli

This paper proposes a test to perform Thematic Analysis (TA) with Large Language Model (LLM) on data which is in a different language than English. While there has been initial promising work on using pre-trained LLMs for TA on data in English, we lack any tests on whether these models can reasonably perform the same analysis with good quality in other langu

W. Jussiau, C. Leclercq, F. Demourant, P. Apkarian

This paper presents advances towards the data-based control of periodic oscillator flows, from their fully-developed regime to their equilibrium stabilized in closed-loop, with linear time-invariant (LTI) controllers. The proposed approach directly builds upon Leclercq et al. (2019) and provides several improvements for an efficient online implementation, ai

Jesse Pajwani, Herman Rohrbach, Anna M. Viergever

The compactly supported $\mathbb{A}^1$-Euler characteristic, introduced by Hoyois and later refined by Levine and others, is an anologue in motivic homotopy theory of the classical Euler characteristic of complex topological manifolds. It is an invariant on the Grothendieck ring of varieties $\mathrm{K}_0(\mathrm{Var}_k)$ taking values in the Grothendieck-Wi

Giuliano Guarino, Pablo Antolin, Alberto Milazzo, Annalisa Buffa

This work focuses on the coupling of trimmed shell patches using Isogeometric Analysis, based on higher continuity splines that seamlessly meet the $C^1$ requirement of Kirchhoff-Love-based discretizations. Weak enforcement of coupling conditions is achieved through the symmetric interior penalty method, where the fluxes are computed using their correct vari

Russell Avdek

We describe symplectic mapping class relations between products of positive Dehn twists along Lagrangian spheres in Weinstein $4$-manifolds, all of which are affine $\mathbb{C}$ varieties. The relations are obtained by applying classification results for Fano $3$-folds and polarized $K3$ surfaces of small genus to a general methodology -- finding pencil pair

Ahmad Halimi Razlighi, Carsten Bockelmann, Armin Dekorsy

In this paper, we investigated semantic communication for multi-task processing using an information-theoretic approach. We introduced the concept of a "semantic source", allowing multiple semantic interpretations from a single observation. We formulated an end-to-end optimization problem taking into account the communication channel, maximizing mutual infor

Nick Klemmer, Janek Fleper, Valentin Jonas, Ameneh Sheikhan

We investigate the experimental control of pair tunneling in a double-well potential using Floquet engineering. We demonstrate a crossover from a regime with density-assisted tunneling to dominant pair tunneling by tuning the effective interactions. Furthermore, we show that the pair tunneling rate can be enhanced not only compared to the Floquet-reduced sin

Snehashish Sarkar, Sutapa Mandal, Pinaki Pal

We investigate the instabilities and associated bifurcation structure near the onset of rotating magnetoconvection of low Prandtl number fluids by performing three dimensional direct numerical simulations. Previous studies considered zero magnetic Prandtl number ($\mathrm{Pm}$) limit for the investigation of bifurcation structure near the onset of convection

Ozan Evkaya, Miguel de Carvalho

As a result of recent advancements in generative AI, the field of data science is prone to various changes. The way practitioners construct their data science workflows is now irreversibly shaped by recent advancements, particularly by tools like OpenAI's Data Analysis plugin. While it offers powerful support as a quantitative co-pilot, its limitations deman

Hugo Plombat, Denis Puy

Context. To understand the formation of the first stars, a detailed description of the thermal and chemical processes in collapsing gas clouds is essential. Molecular cooling, particularly via H2, plays a significant role in triggering thermal instabilities that lead to star formation. The 21 cm hydrogen line serves as a potential probe of the first collapsi

Arne Sachtler, Davide Calzolari, Maximilian Raff, Annika Schmidt

We identify the nonlinear normal modes spawning from the stable equilibrium of a double pendulum under gravity, and we establish their connection to homoclinic orbits through the unstable upright position as energy increases. This result is exploited to devise an efficient swing-up strategy for a double pendulum with weak, saturating actuators. Our approach

Youssef Chahir, Messaoud Mostefai, Hamza Saida

Basic patterns are the source of a wide range of more or less complex geometric structures. We will exploit such patterns to develop new efficient visual markers. Besides being projective invariants, the proposed markers allow producing rich panel of unique identifiers, highly required for resource-intensive navigation and augmented reality applications. The

Hai-Vy Nguyen, Fabrice Gamboa, Reda Chhaibi, Sixin Zhang

We measure the Out-of-domain uncertainty in the prediction of Neural Networks using a statistical notion called ``Lens Depth'' (LD) combined with Fermat Distance, which is able to capture precisely the ``depth'' of a point with respect to a distribution in feature space, without any assumption about the form of distribution. Our method has no trainable param

Hirbod Assa, Peng Liu

This paper introduces and studies factor risk measures. While risk measures only rely on the distribution of a loss random variable, in many cases risk needs to be measured relative to some major factors. In this paper, we introduce a double-argument mapping as a risk measure to assess the risk relative to a vector of factors, called factor risk measure. The

Patrick Lederer, Dominik Peters, Tomasz Wąs

For the problem of aggregating several rankings into one ranking, Kemeny (1959) proposed two methods: the median rule which selects the ranking with the smallest total swap distance to the input rankings, and the mean rule which minimizes the squared swap distances to the input rankings. The median rule has been extensively studied since and is now known sim

Zenon Jan Jabłoński, Il Bong Jung, Carlos Kubrusly, Jan Stochel

This paper is concerned with the convergence of power sequences and stability of Hilbert space operators, where "convergence" and "stability" refer to weak, strong and norm topologies. It is proved that an operator has a convergent power sequence if and only if it is a (not necessarily orthogonal) direct sum of an identity operator and a stable operator. Thi

Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu

Time series data, characterized by its intrinsic long and short-range dependencies, poses a unique challenge across analytical applications. While Transformer-based models excel at capturing long-range dependencies, they face limitations in noise sensitivity, computational efficiency, and overfitting with smaller datasets. In response, we introduce a novel T

Chao Xu, Jiang Zeng

In 1977 Carlitz and Scoville introduced the cycle $(\alpha,t)$-Eulerian polynomials $A^{\mathrm{cyc}}_n(x,y, t\,|\,\alpha)$ by enumerating permutations with respect to the number of excedances, drops, fixed points and cycles. In this paper, we introduce a nine-variable generalization of the Eulerian polynomials $A_n(u_1,u_2,u_3,u_4, f, g, t\,|\,\alpha, \beta

Michel Reniers, Kai Cai

In the Ramadge-Wonham supervisory control theory the only interaction mechanism between supervisor and plant is that the supervisor may enable/disable events from the plant and the plant makes a final decision about which of the enabled events is actually taking place. In this paper, the interaction between supervisor and plant is enriched by allowing the su

Ivor van der Hoog, Daniel Rutschmann

Sorting has a natural generalization where the input consists of: (1) a ground set $X$ of size $n$, (2) a partial oracle $O_P$ specifying some fixed partial order $P$ on $X$ and (3) a linear oracle $O_L$ specifying a linear order $L$ that extends $P$. The goal is to recover the linear order $L$ on $X$ using the fewest number of linear oracle queries. In this

Mijanur Islam, Kuntal Bhattacharyya, Saurabh Basu

We present impelling evidence of topological phase transitions induced by electron-phonon (e-ph) coupling in an $\alpha$-$T_3$ Haldane-Holstein model that presents smooth tunability between graphene ($\alpha=0$) and a dice lattice $(\alpha=1)$. The e-ph coupling has been incorporated via the Lang-Firsov transformation which adequately captures the polaron ph

Eric P. Glasbrenner, Yannik Gerdes, Sándor Varró, Wolfgang P. Schleich

We present two different approaches towards the Landau-Zener problem: (i) The Markov approximation in the integro-differential equation for one of the two probability amplitudes, and (ii) an amplitude-and-phase analysis of the linear second order differential equation for same probability amplitude. Our treatment shows that the Markov approximation neglects

Kazuki Morita, Andrew M. Rappe

Double perovskites Cs$_2$Au$_2$X$_6$ (X=Cl, Br, I) are prototypical materials that exhibit charge disproportionation of gold into 1+ and 3+ states. It is known that the disproportionation is resolved under high pressures, and this has stimulated many studies into the pressurization of these materials. At present, the phase changes in these materials are stil

W. M. Stuckey, Michael Silberstein, Timothy McDevitt

We explain how the disparate kinematics of quantum mechanics (finite-dimensional Hilbert space of QM) and special relativity (Minkowski spacetime from the Lorentz transformations of SR) can both be based on one principle (relativity principle). This is made possible by the axiomatic reconstruction of QM via information-theoretic principles, which has success

A. M. Smolovich, A. V. Frolov, L. D. Klebanov, I. D. Laktaev

An optical security element containing an area of random rough relief is proposed. It combines the low cost of mass replication inherent in traditional security holograms with the impossibility of holographic copying, when the wave restored by the hologram is rewritten as a copy of this hologram. The proposed optical element is also protected from contact an

Sophia Julia Feriani, Matthias Cosnefroy, Allan Peter Engsig-Karup, Tim Warburton

In outdoor acoustics, the calculations of sound propagating in air can be computationally heavy if the domain is chosen large enough to fulfil the Sommerfeld radiation condition. By strategically truncating the computational domain with a efficient boundary treatment, the computational cost is lowered. One commonly used boundary treatment is the perfectly ma

Jonas Niederle, Simon Koop, Marc Pagès-Gallego, Vlado Menkovski

Nanopore sequencing offers the ability for real-time analysis of long DNA sequences at a low cost, enabling new applications such as early detection of cancer. Due to the complex nature of nanopore measurements and the high cost of obtaining ground truth datasets, there is a need for nanopore simulators. Existing simulators rely on handcrafted rules and para

Rasmus Jensen, Ralf Zimmermann

Riemannian optimization is concerned with problems, where the independent variable lies on a smooth manifold. There is a number of problems from numerical linear algebra that fall into this category, where the manifold is usually specified by special matrix structures, such as orthogonality or definiteness. Following this line of research, we investigate too

CMS Collaboration

A search for Higgs boson pair (HH) production in association with a vector boson V (W or Z boson) is presented. The search is based on proton-proton collision data at a center-of-mass energy of 13 TeV, collected with the CMS detector at the LHC, corresponding to an integrated luminosity of 138 fb$^{-1}$. Both hadronic and leptonic decays of V bosons are used

Changho Shin, Jitian Zhao, Sonia Cromp, Harit Vishwakarma

Popular zero-shot models suffer due to artifacts inherited from pretraining. One particularly detrimental issue, caused by unbalanced web-scale pretraining data, is mismatched label distribution. Existing approaches that seek to repair the label distribution are not suitable in zero-shot settings, as they have mismatching requirements, such as needing access

Orfeas Menis Mastromichalakis, Jason Liartis, Giorgos Stamou

Explainable Artificial Intelligence (XAI) has emerged as a critical area of research aimed at enhancing the transparency and interpretability of AI systems. Counterfactual Explanations (CFEs) offer valuable insights into the decision-making processes of machine learning algorithms by exploring alternative scenarios where certain factors differ. Despite the g

Steven Kim, Fabian Hassler

A chaotic light source is characterized by the fact that many independent emitters radiate photons with a random optical phase. This is similar compared to a tunnel junction where many independent channels are able to emit photons due to a coupling to an electromagnetic environment. However, in a recent experiment it has been observed that a tunnel junction

A. Mazzolari, H. Backe, L. Bandiera, N. Canale

We observed highly efficient manipulation of the 530 MeV positron beam at Mainz Microtron via bent crystals. The low beam divergence revealed a fine structure in the angular distribution of channeled particles. A compact analytical model, supported by Monte Carlo including multiple scattering, accounts for the measurements. We established a criterion for des

Emile van Krieken, Pasquale Minervini, Edoardo M. Ponti, Antonio Vergari

State-of-the-art neurosymbolic learning systems use probabilistic reasoning to guide neural networks towards predictions that conform to logical constraints over symbols. Many such systems assume that the probabilities of the considered symbols are conditionally independent given the input to simplify learning and reasoning. We study and criticise this assum

Jiaxin Shi, Yuan Gao, Rui Pan, Hansheng Wang

In this study, we develop a latent factor model for analysing high-dimensional binary data. Specifically, a standard probit model is used to describe the regression relationship between the observed binary data and the continuous latent variables. Our method assumes that the dependency structure of the observed binary data can be fully captured by the contin

Lorenc Kapllani, Long Teng

In this work, we propose a novel backward differential deep learning-based algorithm for solving high-dimensional nonlinear backward stochastic differential equations (BSDEs), where the deep neural network (DNN) models are trained not only on the inputs and labels but also the differentials of the corresponding labels. This is motivated by the fact that diff

Rodrigo Hernangómez, Jochen Fink, Renato L. G. Cavalcante, Zoran Utkovski

In this paper, we formalize an optimization framework for analog beamforming in the context of monostatic integrated sensing and communication (ISAC), where we also address the problem of self-interference in the analog domain. As a result, we derive semidefinite programs to approach detection-optimal transmit and receive beamformers, and we devise a superio

Yunlong Liu, Xiangdong Zhang

In this paper, we calculate the frequencies of geodesic orbits in self-dual spacetime on the equatorial plane and obtain the leading-order effects of loop quantum parameters $P$ on the energy flux and angular momentum flux in eccentric extreme mass ratio inspirals. The gravitational waveform under different eccentricity is carried out by improved "analytic-k

Mulugeta Weldezgina Asres, Christian Walter Omlin, Jay Dittmann, Pavel Parygin

Identifying outlier behavior among sensors and subsystems is essential for discovering faults and facilitating diagnostics in large systems. At the same time, exploring large systems with numerous multivariate data sets is challenging. This study presents a lightweight interconnection and divergence discovery mechanism (LIDD) to identify abnormal behavior in

Chenqi Kong, Anwei Luo, Peijun Bao, Yi Yu

Deepfakes have recently raised significant trust issues and security concerns among the public. Compared to CNN face forgery detectors, ViT-based methods take advantage of the expressivity of transformers, achieving superior detection performance. However, these approaches still exhibit the following limitations: (1) Fully fine-tuning ViT-based models from I

Xinyuan Zhai, Thomas Corre, Ataollah Mesgarnejad, Alain Karma

The propagation path of quasistatic cracks under monotonic loading is known to be strongly influenced by the anisotropy of the fracture energy in crystalline solids or engineered materials with a regular microstructure. Such cracks generally follow directions close to minima of the fracture energy. Here we demonstrate both experimentally and computationally

Xianhua He, Dashuang Liang, Song Yang, Zhanlong Hao

Face recognition systems are frequently subjected to a variety of physical and digital attacks of different types. Previous methods have achieved satisfactory performance in scenarios that address physical attacks and digital attacks, respectively. However, few methods are considered to integrate a model that simultaneously addresses both physical and digita

Jingrui Ye, Zongkai Zhang, Yujiao Jiang, Qingmin Liao

Rendering dynamic 3D human from monocular videos is crucial for various applications such as virtual reality and digital entertainment. Most methods assume the people is in an unobstructed scene, while various objects may cause the occlusion of body parts in real-life scenarios. Previous method utilizing NeRF for surface rendering to recover the occluded are

Guilong Gui, Yancan Li

We construct in the paper the low-regularity strong solutions to the viscous surface wave equations in anisotropic Sobolev spaces. Here we use the Lagrangian structure of the system to homogenize the free boundary conditions, and establish a new iteration scheme on a known equilibrium domain to get the low-regularity strong solutions, in which no nonlinear c

Xiaowen Jiang, Anton Rodomanov, Sebastian U. Stich

Federated learning is a distributed optimization paradigm that allows training machine learning models across decentralized devices while keeping the data localized. The standard method, FedAvg, suffers from client drift which can hamper performance and increase communication costs over centralized methods. Previous works proposed various strategies to mitig

Pengfei Qu, Peng Jin, Guangdi Zhou, Zhen Wang

In this paper, 2-inch free-standing diamonds were prepared by using heteroepitaxy on composite Ir/YSZ/Si (001) substrates. To release stress, patterned templates were fabricated using laser etching after the initial growth of 50-nm-diamond. Then, the subsequent growth was completed on a patterned template. The full width at half maximum of the diamond (400)

Hanchen Li, Chaofeng Zhu

In this paper, we prove the stability theorems for the isotropic perturbations of maximal isotropic subspaces in symplectic Banach spaces. Then we prove a stability theorem for the mod $2$ dimensions of kernel of skew-adjoint linear Fredholm relations between real Banach spaces with index $0$. Finally we gives the two path components of the set of skew-adjoi

Cui Zhang, Xiao Xu, Qiong Wu, Pingyi Fan

In vehicle edge computing (VEC), asynchronous federated learning (AFL) is used, where the edge receives a local model and updates the global model, effectively reducing the global aggregation latency.Due to different amounts of local data,computing capabilities and locations of the vehicles, renewing the global model with same weight is inappropriate.The abo

Raia Abu Ahmad, Jennifer D'Souza, Matthäus Zloch, Wolfgang Otto

Search engines these days can serve datasets as search results. Datasets get picked up by search technologies based on structured descriptions on their official web pages, informed by metadata ontologies such as the Dataset content type of schema.org. Despite this promotion of the content type dataset as a first-class citizen of search results, a vast propor

Guillaume Bonnet, Andrea Cangiani, Ricardo H. Nochetto

We propose and analyze an $H^2$-conforming Virtual Element Method (VEM) for the simplest linear elliptic PDEs in nondivergence form with Cordes coefficients. The VEM hinges on a hierarchical construction valid for any dimension $d \ge 2$. The analysis relies on the continuous Miranda-Talenti estimate for convex domains $\Omega$ and is rather elementary. We p

G. V. Lipunova, A. S. Tavleev, K. L. Malanchev

We studied the thermal stability of non-self-gravitating turbulent $\alpha$-discs around supermassive black holes (SMBHs) to test a new type of high-amplitude galactic nucleus flares. By calculating the disc structures, we computed the critical points of equilibrium curves for discs around SMBHs, which cover a wide range of accretion rates and resemble the s

Zhanhao Zhang, Steen Hørsholt, John Bagterp Jørgensen

This paper presents the numerical discretization methods of the continuous-time linear-quadratic optimal control problems (LQ-OCPs) with time delays. We describe the weight matrices of the LQ-OCPs as differential equations systems, allowing us to derive the discrete equivalent of the continuous-time LQ-OCPs. Three numerical methods are introduced for solving

A. F. Lanza, S. N. Breton

WASP-18 is an F6V star that hosts a planet with a mass of about 10 Jupiter masses and an orbital period of 0.94 days. In spite of its relatively fast rotation and young age, the star remains undetected in X-rays, thus implying a very low level of magnetic activity. To account for such unexpected properties, we propose a mechanism that modifies the internal s

Robert Klar, Niklas Arvidsson, Vangelis Angelakis

Digital twinning of vehicles is an iconic application of digital twins, as the concept of twinning dates back to the twinning of NASA space vehicles. Although digital twins (DTs) in the automotive industry have been recognized for their ability to improve efficiency in design and manufacturing, their potential to enhance land vehicle operation has yet to be

Soumyadeep Kundu, Muktish Acharyya

Following our recent study (S. Kundu and M. Acharyya, International Journal of Modern Physics C, 35 (2024) 2450094), we have introduced stochasticity (random noise) in the Fairen-Velarde deterministic differential equations. The role of such noise on time scales is studied. The probability of reaching the domain of fixed points in a given interval is also ca

Jia-Xin Peng, Baiqiang Zhu, Weiping Zhang, Keye Zhang

The detrimental impact of noise on sensing performance in quantum metrology has been widely recognized by researchers in the field. However, there are no explicit fundamental laws of physics stating that noise invariably weakens quantum metrology. We reveal that phase-covariant (PC) noise either degrades or remains neutral to sensing precision, whereas non-p

Satoshi Iso, Jinyang Li, Nobuyuki Matsumoto, Katsuta Sakai

We propose an experiment for constructing a spatial cat state of a suspended mirror with an order of $\mathcal{O}$(mg). The mirror is set at the center of two mirrors, creating two optical cavities and optical springs. The induced potential exhibits a double-well shape, and its deformation resembles a second-order phase transition as a function of laser powe

Patricia A. Apellániz, Juan Parras, Santiago Zazo

The rising use of machine learning in various fields requires robust methods to create synthetic tabular data. Data should preserve key characteristics while addressing data scarcity challenges. Current approaches based on Generative Adversarial Networks, such as the state-of-the-art CTGAN model, struggle with the complex structures inherent in tabular data.

Linhuang Wang, Xin Kang, Fei Ding, Satoshi Nakagawa

Unlike typical video action recognition, Dynamic Facial Expression Recognition (DFER) does not involve distinct moving targets but relies on localized changes in facial muscles. Addressing this distinctive attribute, we propose a Multi-Scale Spatio-temporal CNN-Transformer network (MSSTNet). Our approach takes spatial features of different scales extracted b

Mary Knapp, Lenny Paritsky, Ekaterina Kononov, Melodie M. Kao

The low-frequency sky below $\sim$15 MHz (20 m) is obscured by the Earth's ionosphere, the layer of charged particles above the neutral atmosphere. Single spacecraft have made measurements in this band, but cannot achieve high or even moderate angular resolution because a telescope's resolution ($\theta$) is set by $\theta = \lambda/D$, where $\lambda$ is th

Ankit Kumar Panda, Partha Bagchi, Hiranmaya Mishra, Victor Roy

We investigate the impact of baryon stopping on the temporal evolution of electromagnetic fields in vacuum at low-energy Au+Au collisions with $\sqrt{s_{NN}} = 4$-$20$ GeV. Baryon stopping is incorporated into the Monte-Carlo Glauber model by employing a parameterized velocity profile of participant nucleons with non-zero deceleration. The presence of these

Peter Kristel, Benedikt Peterseim

Probability monads on categories of topological spaces are classical objects of study in the categorical approach to probability theory, with important applications in the semantics of probabilistic programming languages. We construct a probability monad on the category of compactly generated weakly Hausdorff (CGWH) spaces, a (if not the) standard choice of

Yibin Huang, Muchun Yang, D. L. Zhou

Quantum autoencoder (QAE) compresses a bipartite quantum state into its subsystem by a self-checking mechanism. How to characterize the lost information in this process is essential to understand the compression mechanism of QAE\@. Here we investigate how to decrease the lost information in QAE for any input mixed state. We theoretically show that the lost i

Ian Stewart

Hopf bifurcation in networks of coupled ODEs creates periodic states in which the relative phases of nodes are well defined near bifurcation. When the network is a fully inhomogeneous nearest-neighbour coupled unidirectional ring, and node spaces are 1-dimensional, we derive constraints on these phase shifts that apply to any ODE that respects the ring topol

Gertjan Lippertz, Oliver Breunig, Rafael Fister, Anjana Uday

The selective-area epitaxy (SAE) is a useful technique to grow epitaxial films with a desired shape on a pre-patterned substrate. Although SAE of patterned topological-insulator (TI) thin films has been performed in the past, there has been no report of SAE-grown TI structures that are bulk-insulating. Here we report the successful growth of Hall-bars and na

Fabio Mason, Manuel Koller, Eva Cantoni, Paolo Ghisletta

Statistical inference is a major scientific endeavor for many researchers. In terms of inferential methods implemented to mixed-effects models, significant progress has been made in the R software. However, these advances primarily concern classical estimators (ML, REML) and mainly focus on fixed effects. In the confintROB package, we have implemented variou

Mattia Alberto Ubertini, Emanuele Locatelli, Angelo Rosa

We present an in-depth multi-scale analysis of the conformations and dynamics of polar active polymers, comparing very dilute and very dense conditions. We unveil characteristic length and time scales, common to both dilute and dense systems, that recapitulate the conformational and dynamical properties of these active polymers upon varying both the polymer

Hassan Ali, Philipp Allgeuer, Stefan Wermter

Human intention-based systems enable robots to perceive and interpret user actions to interact with humans and adapt to their behavior proactively. Therefore, intention prediction is pivotal in creating a natural interaction with social robots in human-designed environments. In this paper, we examine using Large Language Models (LLMs) to infer human intentio

Maeghal Jain, Ziya Uddin, Wubshet Ibrahim

The outbreak of COVID-19 has highlighted the intricate interplay between public health and economic stability on a global scale. This study proposes a novel reinforcement learning framework designed to optimize health and economic outcomes during pandemics. The framework leverages the SIR model, integrating both lockdown measures (via a stringency index) and

Nicola Bellumat

Following the theory of tensor triangular support introduced by Sanders, which generalizes the Balmer-Favi support, we prove the local version of the result of Zou that the Balmer spectrum being Hochster weakly scattered implies the local-to-global principle. That is, given an object $t$ of a tensor triangulated category $\mathcal{T}$ we show that if the ten

Robin Schön, Julian Lorenz, Katja Ludwig, Rainer Lienhart

The interactive segmentation task consists in the creation of object segmentation masks based on user interactions. The most common way to guide a model towards producing a correct segmentation consists in clicks on the object and background. The recently published Segment Anything Model (SAM) supports a generalized version of the interactive segmentation pr

Yiran Xu, Haina Li

In this paper, we consider the model of 3D incompressible Navier-Stokes equations and 2D supercritical Surface Quasi-Geostrophic equations with time oscillation in the nonlinear term. We obtain that there exists global smooth solution of these two equations for any initial data $u_0\in H^2$.

Yuelong Li, Tengfei Xiao, Lei Geng, Jianming Wang

Pose diversity is an inherent representative characteristic of 2D images. Due to the 3D to 2D projection mechanism, there is evident content discrepancy among distinct pose images. This is the main obstacle bothering pose transformation related researches. To deal with this challenge, we propose a fine-grained incremental evolution centered pose generation f

P. François, G. Cescutti, P. Bonifacio, E. Caffau

The MINCE (Measuring at Intermediate metallicity Neutron-Capture Elements) project aims to gather the abundances of neutron-capture elements but also of light elements and iron peak elements in a large sample of giant stars in this metallicity range. T The aim of this work is to study the chemical evolution of galactic sub-components recently identified (i.e

William Fleshman, Aleem Khan, Marc Marone, Benjamin Van Durme

Large language models (LLMs) are increasingly capable of completing knowledge intensive tasks by recalling information from a static pretraining corpus. Here we are concerned with LLMs in the context of evolving data requirements. For instance: batches of new data that are introduced periodically; subsets of data with user-based access controls; or requireme

Martti Raidal, Ville Vaskonen, Hardi Veermäe

We review the theory behind the formation of primordial black hole binaries and their merger rates. We consider the binary formation in the early and late Universe, emphasising the former as it gives the dominant contribution of the present primordial black hole merger rate. The binaries formed in the early Universe are highly eccentric so their interactions

Manosij Ghosh Dastidar, Michael Wallner

A relaxed $k$-ary tree is an ordered directed acyclic graph with a unique source and sink in which every node has out-degree $k$. These objects arise in the compression of trees in which some repeated subtrees are factored and repeated appearances are replaced by pointers. We prove an asymptotic theta-result for the number of relaxed $k$-ary tree with $n$ no

Rongguang Ye, Longcan Chen, Jinyuan Zhang, Hisao Ishibuchi

Recently, Pareto Set Learning (PSL) has been proposed for learning the entire Pareto set using a neural network. PSL employs preference vectors to scalarize multiple objectives, facilitating the learning of mappings from preference vectors to specific Pareto optimal solutions. Previous PSL methods have shown their effectiveness in solving artificial multi-ob

Tomoya Shiota, Kenji Ishihara, Wataru Mizukami

Computational screening is indispensable for the efficient design of high-entropy alloys (HEAs), which hold considerable potential for catalytic applications. However, the chemical space of HEAs is exponentially vast with respect to the number of constituent elements, making even machine learning-based screening calculations time-intensive. To address this c

Siming Shan, Pengkai Wang, Song Chen, Jiaxu Liu

The use of machine learning in fluid dynamics is becoming more common to expedite the computation when solving forward and inverse problems of partial differential equations. Yet, a notable challenge with existing convolutional neural network (CNN)-based methods for data fidelity enhancement is their reliance on specific low-fidelity data patterns and distri

Soichiro Shimamori

We explore higher-dimensional conformal field theories (CFTs) in the presence of a conformal defect that itself hosts another sub-dimensional defect. We refer to this new kind of conformal defect as the composite defect. We elaborate on the various conformal properties of the composite defect CFTs, including correlation functions, operator expansions, and co

H. H. Wang, H. D. Yan, L. C. -C. Lin, J. Takata

We report the analysis results of X-ray and gamma-ray data of the nova FM Cir taken by Swift and Fermi-LAT. The gamma-ray emission from FM Cir can be identified with a significance level of 3sigma within 40 days after the nova eruption (2018 January 19) while we bin the light curve per day. The significance can further exceed 4 sigma confidence level if we a

Hongtao Wang, Li Long, Jiangshe Zhang, Xiaoli Wei

Contemporary automatic first break (FB) picking methods typically analyze 1D signals, 2D source gathers, or 3D source-receiver gathers. Utilizing higher-dimensional data, such as 2D or 3D, incorporates global features, improving the stability of local picking. Despite the benefits, high-dimensional data requires structured input and increases computational d

Patrick Brkic, Emil Wiedemann

We consider the Cauchy problem for the 3D incompressible axisymmetric swirl-free Euler equations. The convex integration method developed by De Lellis and Sz\'ekelyhidi rules out the possibility that the Euler equations admit unique admissible weak solutions. It had remained conceivable, though, that axisymmetry of the solution might serve as a selection cri

Zhe Li, Haiwei Pan, Kejia Zhang, Yuhua Wang

Multi-modality image fusion (MMIF) aims to integrate complementary information from different modalities into a single fused image to represent the imaging scene and facilitate downstream visual tasks comprehensively. In recent years, significant progress has been made in MMIF tasks due to advances in deep neural networks. However, existing methods cannot ef

Chong Li, Yang-Yang Lyu, Wen-Cheng Yue, Peiyuan Huang

Symmetry-breaking plays a pivotal role in unlocking intriguing properties and functionalities in material systems. For example, the breaking of spatial and temporal symmetries leads to a fascinating phenomenon of superconducting diode effect. However, generating and precisely controlling the superconducting diode effect poses significant challenges. Here, we