February 2024 arXiv papers — page 167

José Joaquín Bernal, Ángel del Río, Juan Jacobo Simón

A (left) group code of length n is a linear code which is the image of a (left) ideal of a group algebra via an isomorphism from FG to Fn which maps G to the standard basis of Fn. Many classical linear codes have been shown to be group codes. In this paper we obtain a criterion to decide when a linear code is a group code in terms of its intrinsical properti

Zita Abreu, Joachim Rosenthal, Michael Schaller

The free distance of a convolutional code is a reliable indicator of its performance. However its computation is not an easy task. In this paper, we present some algorithms to compute the free distance with good efficiency that work for convolutional codes of all rates and over any field. Furthermore we discuss why an algorithm which is claimed to be very ef

Zhu-Qin Li, Qun-Li Lei, Yu-qiang Ma

Cells not only can be motile by crawling but are also capable of non-motility active motions like periodic contraction or pulsation. In this work, based on a Voronoi cell model, we show how this non-motility activity affects the structure, dynamic and density fluctuations of cellular monolayers. Our model shows that random cell pulsation fluidizes solid epit

Md Muzakkir Quamar, Ali Nasir

This comprehensive review article delves into the intricate realm of fault-tolerant control (FTC) schemes tailored for robotic manipulators. Our exploration spans the historical evolution of FTC, tracing its development over time, and meticulously examines the recent breakthroughs fueled by the synergistic integration of cutting-edge technologies such as art

Rüdiger Ehlers, Ayrat Khalimov

Generalized Reactivity(1) (GR(1)) synthesis is a reactive synthesis approach in which the specification is split into two parts: a symbolic game graph, describing the safe transitions of a system, a liveness specification in a subset of Linear Temporal Logic (LTL) on top of it. Many specifications can naturally be written in this restricted form, and the res

Sumit Kumar, Matthew Kenworthy, Henry Ginn, Xavier Rojas

Leveraging advancements in cavity optomechanics, we explore Optomechanically Induced Transparency/Absorption (OMIT/OMIA) in the microwave domain at ambient temperature. Contrary to previous works employing cryogenic temperatures, this work exploits a 3D microwave cavity architecture to observe these effects at ambient temperature, broadening the scope of pos

Haya Majid Qureshi, Wolfgang Faber

Metamodeling is a general approach to expressing knowledge about classes and properties in an ontology. It is a desirable modeling feature in multiple applications that simplifies the extension and reuse of ontologies. Nevertheless, allowing metamodeling without restrictions is problematic for several reasons, mainly due to undecidability issues. Practical l

Hyomin Kim, Yunhui Jang, Jaeho Lee, Sungsoo Ahn

In this paper, we study hybrid neural representations for spherical data, a domain of increasing relevance in scientific research. In particular, our work focuses on weather and climate data as well as comic microwave background (CMB) data. Although previous studies have delved into coordinate-based neural representations for spherical signals, they often fa

Kien Do, Duc Kieu, Toan Nguyen, Dang Nguyen

We propose a systematic training-free method to transform the probability flow of a "linear" stochastic process characterized by the equation X_{t}=a_{t}X_{0}+\sigma_{t}X_{1} into a straight constant-speed (SC) flow, reminiscent of Rectified Flow. This transformation facilitates fast sampling along the original probability flow via the Euler method without t

Arthur da Cunha, Kasper Green Larsen, Martin Ritzert

In boosting, we aim to leverage multiple weak learners to produce a strong learner. At the center of this paradigm lies the concept of building the strong learner as a voting classifier, which outputs a weighted majority vote of the weak learners. While many successful boosting algorithms, such as the iconic AdaBoost, produce voting classifiers, their theore

Nicolò Penzo, Antonio Longa, Bruno Lepri, Sara Tonelli

Current text classification approaches usually focus on the content to be classified. Contextual aspects (both linguistic and extra-linguistic) are usually neglected, even in tasks based on online discussions. Still in many cases the multi-party and multi-turn nature of the context from which these elements are selected can be fruitfully exploited. In this w

A. P Serebrov, O. M. Zherebtsov, R. M. Samoilov, N. S. Budanov

In this work, we study the appearance of neutrino asymmetries during the expansion of the Universe. A mathematical model based on differential equations was used to describe the processes of neutrino oscillations considering CP violation and neutrino collisions. An analysis of the emergence of neutrino asymmetry due to collisions of neutrinos with each other

Shuohan Wu, Zihao Li, Luyi Yan, Weimin Chen

Given the growing importance of smart contracts in various applications, ensuring their security and reliability is critical. Fuzzing, an effective vulnerability detection technique, has recently been widely applied to smart contracts. Despite numerous studies, a systematic investigation of smart contract fuzzing techniques remains lacking. In this paper, we

Junyoung Seo, Susung Hong, Wooseok Jang, Inès Hyeonsu Kim

Text-to-3D generation has achieved significant success by incorporating powerful 2D diffusion models, but insufficient 3D prior knowledge also leads to the inconsistency of 3D geometry. Recently, since large-scale multi-view datasets have been released, fine-tuning the diffusion model on the multi-view datasets becomes a mainstream to solve the 3D inconsiste

Naeem Akhtar, Xiaosen Yang, Muhammad Asjad, Jia-Xin Peng

Superposed photon-added and photon-subtracted squeezed-vacuum states exhibit sub-Planck phase-space structures and metrological potential similar to the original compass states (superposition of four coherent states), but are more closely tied to modern experiments. Here, we observe that these compasslike states are highly susceptible to loss of quantum cohe

Arash Ghorbanalizadeh, Monire Mikaeili Nia

In this study, $(1,1)-$weak type boundedness of square function $S_{\alpha,\psi}$ is obtained by using Nazarov-Treil and Volberg technique. Also using this result, the $(1,1)-$ weak type boundedness of $g^{*}_{\lambda,\psi}$ operator is investigated.

Simone Bombari, Marco Mondelli

Understanding the reasons behind the exceptional success of transformers requires a better analysis of why attention layers are suitable for NLP tasks. In particular, such tasks require predictive models to capture contextual meaning which often depends on one or few words, even if the sentence is long. Our work studies this key property, dubbed word sensiti

Sheng Luo, Wei Chen, Wanxin Tian, Rui Liu

Foundation models have indeed made a profound impact on various fields, emerging as pivotal components that significantly shape the capabilities of intelligent systems. In the context of intelligent vehicles, leveraging the power of foundation models has proven to be transformative, offering notable advancements in visual understanding. Equipped with multi-m

Agnieszka Anna Corley-Wiciak, Diana Ryzhak, Marvin Hartwig Zoellner, Costanza Lucia Manganelli

Temperature dependence of vibrational modes in semiconductors depends on lattice thermal expansion and anharmonic phonon-phonon scattering. Evaluating the two contributions from experimental data is not straightforward, especially for epitaxial layers that present mechanical deformation and anisotropic lattice expansion. In this work, a temperature-dependent

Manuel Friedrich, Joscha Seutter

We study the atomistic-to-continuum limit for a model of a quasi-static crack evolution driven by time-dependent boundary conditions. We consider a two-dimensional atomic mass spring system whose interactions are modeled by classical interaction potentials, supplemented by a suitable irreversibility condition accounting for the breaking of atmoic bonding. In

Ramón González Rodríguez

In this paper we introduce the notion of $a$-monoidal distributive law between two Hopf quasigroups $A$ and $H$. We prove that every $a$-monoidal distributive law induce a product on $A\otimes H$, called the wreath product, thanks to which $A\otimes H$ becomes in a Hopf quasigroup. Finally, using this construction, we show that double cross products of Hopf

Paul Hagemann, Johannes Hertrich, Maren Casfor, Sebastian Heidenreich

Motivated by indirect measurements and applications from nanometrology with a mixed noise model, we develop a novel algorithm for jointly estimating the posterior and the noise parameters in Bayesian inverse problems. We propose to solve the problem by an expectation maximization (EM) algorithm. Based on the current noise parameters, we learn in the E-step a

Polina Kurtser, Kailun Feng, Thomas Olofsson, Aitor De Andres

We present a label-free method for detecting anomalies during thermographic inspection of building envelopes. It is based on the AI-driven prediction of thermal distributions from color images. Effectively the method performs as a one-class classifier of the thermal image regions with high mismatch between the predicted and actual thermal distributions. The

Artem Shalev, Konstantin Ladutenko, Igor Lobanov, Vassilios Yannopapas

We present here Multem 3, an updated and revised version of Multem 2, which syntax has been upgraded to Fortran 2018, with the source code being divided into modules. Multem 3 is equipped with LAPACK, the state-of-the art Faddeeva complex error function routine, and the Bessel function package AMOS. The amendments significantly improve both the speed, conver

André Silva, Nuno Saavedra, Martin Monperrus

Bug-fix benchmarks are essential for evaluating methodologies in automatic program repair (APR) and fault localization (FL). However, existing benchmarks, exemplified by Defects4J, need to evolve to incorporate recent bug-fixes aligned with contemporary development practices. Moreover, reproducibility, a key scientific principle, has been lacking in bug-fix

Giuseppe Negro, Louise C. Head, Livio N. Carenza, Tyler N. Shendruk

Active emulsions and liquid crystalline shells are intriguing and experimentally realisable types of topological matter. Here we numerically study the morphology and spatiotemporal dynamics of a double emulsion, where one or two passive small droplets are embedded in a larger active droplet. We find activity introduces a variety of rich and nontrivial nonequ

Jay Jorgenson, Min Lee, Lejla Smajlovic

Let $M$ be a finite volume hyperbolic Riemann surface with arbitrary signature, and let $\chi$ be an arbitrary $m$-dimensional multiplier system of weight $k$. Let $R(s,\chi)$ be the associated Ruelle zeta function, and $\varphi(s,\chi)$ the determinant of the scattering matrix. We prove the functional equation that $R(s,\chi)\varphi(s,\chi) = R(-s,\chi)\var

N. I. Izhovkina, S. N. Artekha, N. S. Erokhin, L. A. Mikhailovskaya

Numerous sources produce the ionization impact on the planetary atmosphere. The tropospheric cloudiness therewith is originated at such altitudes, which coincide with places of the maximum of the atmospheric ionization that caused by space radiation. Components of cosmic radiation, penetrating down to the stratospheric and tropospheric altitudes, produce in

Abhishek Mondal, Deepak Mishra, Ganesh Prasad, George C. Alexandropoulos

Effective solutions for intelligent data collection in terrestrial cellular networks are crucial, especially in the context of Internet of Things applications. The limited spectrum and coverage area of terrestrial base stations pose challenges in meeting the escalating data rate demands of network users. Unmanned aerial vehicles, known for their high agility

Hamed Amini Amirkolaee, Miaojing Shi, Lianghua He, Mark Mulligan

The process of estimating and counting tree density using only a single aerial or satellite image is a difficult task in the fields of photogrammetry and remote sensing. However, it plays a crucial role in the management of forests. The huge variety of trees in varied topography severely hinders tree counting models to perform well. The purpose of this paper

Woojung Han, Seil Kang, Kyobin Choo, Seong Jae Hwang

Leveraging semantically precise pseudo masks derived from image-level class knowledge for segmentation, namely image-level Weakly Supervised Semantic Segmentation (WSSS), still remains challenging. While Class Activation Maps (CAMs) using CNNs have steadily been contributing to the success of WSSS, the resulting activation maps often narrowly focus on class-

Aitor Balmaseda, Davide Lonigro, Juan Manuel Pérez-Pardo

We provide sufficient conditions for the approximate controllability of infinite-dimensional quantum control systems corresponding to form perturbations of the drift Hamiltonian modulated by a control function. We rely on previous results on controllability of quantum bilinear control systems and obtain a priori $L^1$-bounds of the controls for generic initi

Johan Peralez, Aurélien Delage, Olivier Buffet, Jilles S. Dibangoye

A recent theory shows that a multi-player decentralized partially observable Markov decision process can be transformed into an equivalent single-player game, enabling the application of \citeauthor{bellman}'s principle of optimality to solve the single-player game by breaking it down into single-stage subgames. However, this approach entangles the decision

Jiahao Liu, Jun Zeng, Fabio Pierazzi, Ziqi Yang

With the rapid advancement of machine learning (ML), ML-based Android malware detection has gained significant popularity due to its ability to automatically learn malicious patterns from Android apps. However, the lack of an in-depth and systematic analysis of existing research makes it difficult to obtain a holistic understanding of the state of the art in

Huy Nguyen, Nhat Ho, Alessandro Rinaldo

Mixture of experts (MoE) model is a statistical machine learning design that aggregates multiple expert networks using a softmax gating function in order to form a more intricate and expressive model. Despite being commonly used in several applications owing to their scalability, the mathematical and statistical properties of MoE models are complex and diffi

Ron Dorfman, Naseem Yehya, Kfir Y. Levy

Byzantine-robust learning has emerged as a prominent fault-tolerant distributed machine learning framework. However, most techniques focus on the static setting, wherein the identity of Byzantine workers remains unchanged throughout the learning process. This assumption fails to capture real-world dynamic Byzantine behaviors, which may include intermittent m

Yankai Rong, Guoshun Nan, Minwei Zhang, Sihan Chen

Recently proliferated deep learning-based semantic communications (DLSC) focus on how transmitted symbols efficiently convey a desired meaning to the destination. However, the sensitivity of neural models and the openness of wireless channels cause the DLSC system to be extremely fragile to various malicious attacks. This inspires us to ask a question: "Can

Kun Fang, Qinghua Tao, Kexin Lv, Mingzhen He

Out-of-Distribution (OoD) detection is vital for the reliability of Deep Neural Networks (DNNs). Existing works have shown the insufficiency of Principal Component Analysis (PCA) straightforwardly applied on the features of DNNs in detecting OoD data from In-Distribution (InD) data. The failure of PCA suggests that the network features residing in OoD and In

Kerwan Morin, Delphine Lagarde, Angélique Gillet, Xavier Marie

Rydberg states of excitons can reach microns in size and require extremely pure crystals. We introduce an experimental method for the rapid and spatially-resolved characterization of Rydberg excitons in copper oxide (Cu2O) with sub-micron resolution over large zones. Our approach involves illuminating and imaging the entire sample on a camera to realize a sp

Felipe Albino dos Santos, Mikhail Neklyudov, Vyacheslav Futorny

We construct two families of orthogonal polynomials associated with the universal central extensions of the superelliptic Lie algebras. These polynomials satisfy certain fourth order linear differential equations, and one of the families is a particular collection of associated ultraspherical polynomials. We show that the generating functions of the polynomi

Alexandra Zhabitskaya, Alexander Sheshkus, Vladimir L. Arlazarov

In this paper, we introduce HoughToRadon Transform layer, a novel layer designed to improve the speed of neural networks incorporated with Hough Transform to solve semantic image segmentation problems. By placing it after a Hough Transform layer, "inner" convolutions receive modified feature maps with new beneficial properties, such as a smaller area of proc

Sarikul Islam, Nitin Gupta

An extension of Archimax copula class in more than two random variables ( Multivariate ) was introduced in (J\'agr 2011) for describing dependency structures among random variables in higher dimension, and some properties of Archimax copula were explored in (Charpentier et al. 2014). In this article, some results for stochastic ordering of extreme order stat

Zhibin Li

In this study, we investigate the behavior of entanglement properties in the QCD phase diagram using holographic Einstein-Maxwell-Dilaton (EMD) models. We consider two representative holographic QCD models and examine various entanglement measures, including entanglement entropy, conditional mutual information, and entanglement of purification. We find that

Yehui Tang, Yunhe Wang, Jianyuan Guo, Zhijun Tu

Transformer plays a vital role in the realms of natural language processing (NLP) and computer vision (CV), specially for constructing large language models (LLM) and large vision models (LVM). Model compression methods reduce the memory and computational cost of Transformer, which is a necessary step to implement large language/vision models on practical de

Olga Maria Luque Alcaraz, Pilar Aparicio-Martínez, Antonio Gomera, Manuel Vaquero-Abellán

Objective: To analyze the current scientific knowledge and research lines focused on environmentally sustainable health systems, including the role of nurses. Background: There seem to be differences between creating interventions focused on environmentally sustainable health systems, including nurses, and the scarcity of research on this topic, framed on th

Samy Blusseau

Morphological neural networks, or layers, can be a powerful tool to boost the progress in mathematical morphology, either on theoretical aspects such as the representation of complete lattice operators, or in the development of image processing pipelines. However, these architectures turn out to be difficult to train when they count more than a few morpholog

Emma Fernández-Alvar, Georges Kordopatis, Vanessa Hill, Giuseppina Battaglia

The emergence of the disc in our Galaxy and the relation of the thick and thin disc formation and evolution is still a matter of debate. The chemo-dynamical characterization of disc stars is key to resolve this question, in particular at parameter regimes where both disc components overlap, such as the region around [Fe/H] $\sim$ $-0.7$ corresponding to the

Adrienne L. Traxler, Camila Mani Dias do Amaral, Charles Henderson, Evan LaForge

Network analysis has become a well-recognized methodology in physics education research (PER), with study topics including student performance and persistence, faculty change, and the structure of conceptual networks. The social network analysis side of this work has focused on quantitative analysis of whole-network cases, such as the structure of networks i

Haruna Yunusa, Shiyin Qin, Abdulrahman Hamman Adama Chukkol, Abdulganiyu Abdu Yusuf

The hybrid of Convolutional Neural Network (CNN) and Vision Transformers (ViT) architectures has emerged as a groundbreaking approach, pushing the boundaries of computer vision (CV). This comprehensive review provides a thorough examination of the literature on state-of-the-art hybrid CNN-ViT architectures, exploring the synergies between these two approache

Mattia Coccolo, Jesús M. Seoane, Miguel A. F. Sanjuán

The interaction between the fractional order parameter and the damping parameter can play a relevant role for introducing different dynamical behaviors in a physical system. Here, we study the Duffing oscillator with a fractional damping term. Our findings show that for certain values of the fractional order parameter, the damping parameter, and the forcing

Konstantin Holzhausen, Mia Merlid, Håkon Olav Torvik, Anders Malthe-Sørenssen

Backpropagation-optimized artificial neural networks, while precise, lack robustness, leading to unforeseen behaviors that affect their safety. Biological neural systems do solve some of these issues already. Unlike artificial models, biological neurons adjust connectivity based on neighboring cell activity. Understanding the biological mechanisms of robustn

Gaspard Junot, Andrés Javier Manzano González, Pietro Tierno

Driven or self-propelling particles moving in viscoelastic fluids recently emerge as novel class of active systems showing a complex yet rich set of phenomena due to the non-Newtonian nature of the dispersing medium. Here we investigate the one-dimensional growth of clusters made of active colloidal shakers, which are realized by oscillating magnetic rotors

Ji-Beom Kim, Je-Bum Choi, Eun-Sung Jung

With the increasing importance of data in the modern business environment, effective data man-agement and protection strategies are gaining increasing research attention. Data protection in a cloud environment is crucial for safeguarding information assets and maintaining sustainable services. This study introduces a system structure that integrates Kubernet

G. Dilillo, E. J. Marchesini, G. Della Casa, G. Baroni

The HERMES Technologic and Scientific Pathfinder project is a constellation of six CubeSats aiming to observe transient high-energy events such as the Gamma Ray Bursts (GRBs). HERMES will be the first space telescope to include a siswich detector, able to perform spectroscopy in the 2 keV to 2 MeV energy band. The particular siswich architecture, which combi

Hessa Abdulrahman Alawwad, Areej Alhothali, Usman Naseem, Ali Alkhathlan

Textbook question answering (TQA) is a challenging task in artificial intelligence due to the complex nature of context needed to answer complex questions. Although previous research has improved the task, there are still some limitations in textual TQA, including weak reasoning and inability to capture contextual information in the lengthy context. We propo

Li Yu, Yanjun Gao, Farhad Pakdaman, Moncef Gabbouj

Deep learning-based methods have demonstrated encouraging results in tackling the task of panoramic image inpainting. However, it is challenging for existing methods to distinguish valid pixels from invalid pixels and find suitable references for corrupted areas, thus leading to artifacts in the inpainted results. In response to these challenges, we propose

DRHBc Mass Table Collaboration, Peng Guo, Xiaojie Cao, Kangmin Chen

The mass table in the deformed relativistic Hartree-Bogoliubov theory in continuum (DRHBc) with the PC-PK1 density functional has been established for even-$Z$ nuclei with $8\le Z\le120$, extended from the previous work for even-even nuclei [Zhang $\it{et.~al.}$ (DRHBc Mass Table Collaboration), At. Data Nucl. Data Tables 144, 101488 (2022)]. The calculated

Sultan Saburov, Sergei V. Ketov

A modified gravity model of Starobinsky inflation and primordial black hole production was proposed in good (within $1\sigma$) agreement with current measurements of the cosmic microwave background radiation. The model is an extension of the singularity-free Appleby-Battye-Starobinsky model by the $R^4$-term with different values of the parameters whose fine

Vinitra Swamy, Syrielle Montariol, Julian Blackwell, Jibril Frej

In human-centric settings like education or healthcare, model accuracy and model explainability are key factors for user adoption. Towards these two goals, intrinsically interpretable deep learning models have gained popularity, focusing on accurate predictions alongside faithful explanations. However, there exists a gap in the human-centeredness of these ap

Mrinmoy Sarkar, Santosh Joshi, Peter de Cat

In this paper, we report the detection of amplitude modulation in a delta Scuti star HD118660. We found that the p-mode frequency at 24.3837 c/d varies periodically in amplitude with frequency 0.0558 c/d. However, all other modes are stable in both amplitude and phase which is clear evidence of non-conservation of visible pulsation mode energy. We constructe

Anna Bringmann, Anastasia Zhukova

This literature review gives an overview of current approaches to perform domain adaptation in a low-resource and approaches to perform multilingual semantic search in a low-resource setting. We developed a new typology to cluster domain adaptation approaches based on the part of dense textual information retrieval systems, which they adapt, focusing on how

Till Domröse, Claus Ropers

Ultrafast optical switching of materials properties is of great relevance both for future technological applications as well as gaining fundamental physical insights to microscopic couplings and nonequilibrium phenomena. Transition-metal dichalcogenides (TMDCs) combine photo-sensitivity with strong correlations, furthering rich phase diagrams and enhanced tu

Florentia Afentaki, Michael Hefenbrock, Georgios Zervakis, Mehdi B. Tahoori

Printed Electronics (PE) stands out as a promisingtechnology for widespread computing due to its distinct attributes, such as low costs and flexible manufacturing. Unlike traditional silicon-based technologies, PE enables stretchable, conformal,and non-toxic hardware. However, PE are constrained by larger feature sizes, making it challenging to implement com

Shuangshuang Meng, Yu Han, Wei Zhao, Yueqiang Zhu

The zeta ($\zeta$) potential is a pivotal metric for characterizing the electric field topology within an electric double layer - an important phenomenon on phase interface. It underpins critical processes in diverse realms such as chemistry, biomedical engineering, and micro/nanofluidics. Yet, local measurement of $\zeta$ potential at the interface has hist

Kenichi Shimizu

We introduce the notion of a quasi-Frobenius algebra in a finite tensor category $\mathcal{C}$ and give equivalent conditions for an algebra in $\mathcal{C}$ to be quasi-Frobenius. A quasi-Frobenius algebra in $\mathcal{C}$ is not necessarily Frobenius, however, we show that an algebra $A$ in $\mathcal{C}$ is quasi-Frobenius if and only if $A$ is Morita equi

Roland Gruber, Johann Christopher Engster, Markus Michen, Nele Blum

Instance segmentation of compound objects in XXL-CT imagery poses a unique challenge in non-destructive testing. This complexity arises from the lack of known reference segmentation labels, limited applicable segmentation tools, as well as partially degraded image quality. To asses recent advancements in the field of machine learning-based image segmentation

Diego García-Maroto, Luis Durán, Miguel Ángel de Pablo Hernández

Snow poles are inexpensive systems composed of a wooden mast with temperature sensors affixed at varying heights with the purpose of estimating the snow depth. They are frequently utilised in cold, remote regions where the maintenance of complex monitoring instruments becomes impractical. In this study, snow cover thickness is determined using different meth

V. S. D. S. Mahesh Akavarapu, Arnab Bhattacharya

Identification of cognates across related languages is one of the primary problems in historical linguistics. Automated cognate identification is helpful for several downstream tasks including identifying sound correspondences, proto-language reconstruction, phylogenetic classification, etc. Previous state-of-the-art methods for cognate identification are mo

Alina Torbunova, Per Erik Strandberg, Ivan Porres

Regression testing in software development checks if new software features affect existing ones. Regression testing is a key task in continuous development and integration, where software is built in small increments and new features are integrated as soon as possible. It is therefore important that developers are notified about possible faults quickly. In t

Nancy Elias-Rosa, Seán J. Brennan, Stefano Benetti, Enrico Cappellaro

We present photometric and spectroscopic data sets for SN 2020pvb, a Type IIn-P supernova (SN) similar to SNe 1994W, 2005cl, 2009kn and 2011ht, with a precursor outburst detected (PS1 w-band ~ -13.8 mag) around four months before the B-band maximum light. SN 2020pvb presents a relatively bright light curve peaking at M_B = -17.95 +- 0.30 mag and a plateau la

Niloy Ghosh, Sarang Pendharker

This paper develops a theoretical framework for enabling seamless transfer of digital information from classical microwave domain to the quantum optical domain in wireless-to-optical converters. A quantum mechanical network model is introduced to characterize microwave-to-optical digital information mapping in antenna-coupled electro-optic modulator-based co

Umut Cem Entok, Firas Laakom, Farhad Pakdaman, Moncef Gabbouj

Most scenes are illuminated by several light sources, where the traditional assumption of uniform illumination is invalid. This issue is ignored in most color constancy methods, primarily due to the complex spatial impact of multiple light sources on the image. Moreover, most existing multi-illuminant methods fail to preserve the smooth change of illuminatio

Mehdi Acheli, Daniela Grigori, Matthias Weidlich

Process mining provides methods to analyse event logs generated by information systems during the execution of processes. It thereby supports the design, validation, and execution of processes in domains ranging from healthcare, through manufacturing, to e-commerce. To explore the regularities of flexible processes that show a large behavioral variability, i

G. Ferrandi, M. E. Hochstenbach, M. R. Oliveira

A subspace method is introduced to solve large-scale trace ratio problems. This approach is matrix-free, requiring only the action of the two matrices involved in the trace ratio. At each iteration, a smaller trace ratio problem is addressed in the search subspace. Additionally, the algorithm is endowed with a restarting strategy, that ensures the monotonici

Andronikos Paliathanasis

We investigate the asymptotic behavior of the cosmological field equations in Symmetric Teleparallel General Relativity, where a nonlinear function of the boundary term is introduced instead of the cosmological constant to describe the acceleration phase of the universe. Our analysis reveals constraints on the free parameters necessary for the existence of a

Fabio Caceffo, Sara Murciano, Vincenzo Alba

Recently, the entanglement asymmetry emerged as an informative tool to understand dynamical symmetry restoration in out-of-equilibrium quantum many-body systems after a quantum quench. For integrable systems the asymmetry can be understood in the space-time scaling limit via the quasiparticle picture, as it was pointed out in Ref. [1]. However, a quasipartic

Yuya Suzuki, Toni Karvonen

This paper studies function approximation in Gaussian Sobolev spaces over the real line and measures the error in a Gaussian-weighted $L^p$-norm. We construct two linear approximation algorithms using $n$ function evaluations that achieve the optimal or almost optimal rate of worst-case convergence in a Gaussian Sobolev space of order $\alpha$. The first alg

Nick Collins, Mick Grierson

As future musical AIs adhere closely to human music, they may form their own attachments to particular human artists in their databases, and these biases may in the worst case lead to potential existential threats to all musical history. AI super fans may act to corrupt the historical record and extant recordings in favour of their own preferences, and prese

Yangkendi Deng, Chenjie Fan, Kailong Yang, Zehua Zhao

We study local-in-time and global-in-time bilinear Strichartz estimates for the Schr\"odinger equation on waveguides. As applications, we apply those estimates to study global well-posedness of nonlinear Schr\"odinger equations on these waveguides.

Jessica Nieder, Johann-Mattis List

Closely related languages show linguistic similarities that allow speakers of one language to understand speakers of another language without having actively learned it. Mutual intelligibility varies in degree and is typically tested in psycholinguistic experiments. To study mutual intelligibility computationally, we propose a computer-assisted method using

Upalaparna Banerjee, Sabyasachi Chakraborty, Suraj Prakash, Shakeel Ur Rahaman

A first order electroweak phase transition probes physics beyond the Standard Model on multiple frontiers and therefore is of immense interest for theoretical exploration. We conduct a model-independent study of the effects of relevant dimension 6 and dimension 8 operators, of the Standard Model effective field theory, on electroweak phase transition. We use

Milosz Zdybal, Marcin Kucharczyk, Marcin Wolter

A proof-of-concept solution based on the machine learning techniques has been implemented and tested within the MUonE experiment designed to search for New Physics in the sector of anomalous magnetic moment of a muon. The results of the DNN based algorithm are comparable to the classical reconstruction, reducing enormously the execution time for the pattern

M. A. V. Costa, Y. B. C. Carranza, C. Morales-Rodrigo, A. Suarez

In this paper, we discuss the existence and uniqueness of coexistence states for a class of non-local elliptic system. This problem models the behaviour of a bacteria and a living nutrient, whose diffusion depends on the population of the bacteria in a non-local and nonlinear way. Mainly, we employ bifurcation methods and the Implicit Function Theorem to obt

D. Pugliese, Z. Stuchlik

We study the accretion flows towards a central Kerr super-spinning attractor, discussing the formation of the flow inversion points, defined by condition $u^{\phi}=0$ on the particles flow axial velocity. We locate two closed surfaces, defining \emph{inversion coronas} (spherical shells), surrounding the central attractor. The coronas analysis highlights obs

Meng Shang, Lenore Dedeyne, Jolan Dupont, Laura Vercauteren

The Otago Exercise Program (OEP) represents a crucial rehabilitation initiative tailored for older adults, aimed at enhancing balance and strength. Despite previous efforts utilizing wearable sensors for OEP recognition, existing studies have exhibited limitations in terms of accuracy and robustness. This study addresses these limitations by employing a sing

Lina Morkunaite, Justas Kardoka, Darius Pupeikis, Paris Fokaides

Buildings energy efficiency is a widely researched topic, which is rapidly gaining popularity due to rising environmental concerns and the need for energy independence. In Northern Europe heating energy alone accounts for up to 70 percent of the total building energy consumption. Industry 4.0 technologies such as IoT, big data, cloud computing and machine le

Dalton N. Oliveira, Roman Chertovskih, Erico L. Rempel, Francis F. Franco

Hydrodynamic and magnetohydrodynamic convective attractors in three-dimensional rotating Rayleigh-B\'enard convection are studied numerically by varying the Taylor and Rayleigh numbers as control parameters. First, an analysis of hydrodynamic attractors and their bifurcations is conducted, where routes to chaos via quasiperiodicity are identified. Second, th

Alexander Dunlap, Chiranjib Mukherjee

We study long-term behavior and stationary distributions for stochastic heat equations forced simultaneously by a multiplicative noise and an independent additive noise with the same distribution. We prove that nontrivial space-time translation-invariant measures exist for all values of the parameters. We also show that if the multiplicative noise is suffici

Bernard Spiegl, Andrea Perin, Stéphane Deny, Alexander Ilin

Deep learning is providing a wealth of new approaches to the problem of novel view synthesis, from Neural Radiance Field (NeRF) based approaches to end-to-end style architectures. Each approach offers specific strengths but also comes with limitations in their applicability. This work introduces ViewFusion, an end-to-end generative approach to novel view syn

Valentin Carlier, Martin Campos-Pinto

We propose a new class of finite element approximations to ideal compressible magnetohydrodynamic equations in smooth regime. Following variational approximations developed for fluid models in the last decade, our discretizations are built via a discrete variational principle mimicking the continuous Euler-Poincar\'e principle, and to further exploit the geo

Hao Yu, Zebin Huang, Qingbo Liu, Ignacio Carlucho

This study presents a pioneering effort to replicate human neuromechanical experiments within a virtual environment utilising a digital human model. By employing MyoSuite, a state-of-the-art human motion simulation platform enhanced by Reinforcement Learning (RL), multiple types of impedance identification experiments of human elbow were replicated on a musc

Xiang-Kun Dong, Teng Ji, Feng-Kun Guo, Ulf-G. Meißner

The primary objective of this study is to investigate hadronic molecules of $K^*\bar K_1(1270)$ using a one-boson-exchange model, which incorporates exchanges of vector and pseudoscalar mesons in the $t$-channel, as well as the pion exchange in the $u$-channel. Additionally, careful consideration is given to the three-body effects resulting from the on-shell

Jerome Droniou, Gianmarco Manzini, Liam Yemm

This paper introduces a novel eXtended virtual element method, an extension of the conforming virtual element method. The XVEM is formulated by incorporating appropriate enrichment functions in the local spaces. The method is designed to handle highly generic enrichment functions, including singularities arising from fractured domains. By achieving consisten

Silvio Sergio Cerri

Alfv\'en waves (AWs) excited by the cosmic-ray (CR) streaming instability (CRSI) are a fundamental ingredient for CR confinement. The effectiveness of self-confinement relies on a balance between CRSI growth rate and damping mechanisms acting on quasi-parallel AWs excited by CRs. One relevant mechanism is the so-called turbulent damping, in which an AW packe

Hauyu Baobab Liu, Takayuki Muto, Mihoko Konishi, Chia-Ying Chung

The previous, high angular resolution 225 GHz ($\sim$1.3 mm) continuum observations on the transitional disk DM Tau have resolved an outer ring at 20-120 au radii that is weakly azimuthally asymmetric. We aimed to examine dust growth and filtration in the outer ring. We performed the $\sim$0$''$.06 ($\sim$8.7 au) resolution Karl G. Jansky Very Large Array (J

Shanshan Wang, Soumya Tripathy, Toni Heittola, Annamaria Mesaros

In Self-Supervised Learning (SSL), Audio-Visual Correspondence (AVC) is a popular task to learn deep audio and video features from large unlabeled datasets. The key step in AVC is to randomly sample audio and video clips from the dataset and learn to minimize the feature distance between the positive pairs (corresponding audio-video pair) while maximizing th

Marianne A Jonker, Hassan Pazira, Anthony CC Coolen

To estimate accurately the parameters of a regression model, the sample size must be large enough relative to the number of possible predictors for the model. In practice, sufficient data is often lacking, which can lead to overfitting of the model and, as a consequence, unreliable predictions of the outcome of new patients. Pooling data from different data

Carlos Lozano, Jorge Ponsin

Numerical solutions to the adjoint Euler equations have been found to diverge with mesh refinement near walls for a variety of flow conditions and geometry configurations. The issue is reviewed and an explanation is provided by comparing a numerical incompressible adjoint solution with an analytic adjoint solution, showing that the anomaly observed in numeri

Ivar Frisch, Mario Giulianelli

While both agent interaction and personalisation are vibrant topics in research on large language models (LLMs), there has been limited focus on the effect of language interaction on the behaviour of persona-conditioned LLM agents. Such an endeavour is important to ensure that agents remain consistent to their assigned traits yet are able to engage in open,

Amin Coja-Oghlan, Max Hahn-Klimroth, Lukas Hintze, Dominik Kaaser

We study the problem of identifying a small set $k\sim n^\theta$, $0<\theta<1$, of infected individuals within a large population of size $n$ by testing groups of individuals simultaneously. All tests are conducted concurrently. The goal is to minimise the total number of tests required. In this paper we make the (realistic) assumption that tests are noisy,