February 2024 arXiv papers — page 133

Nathan Albin, Vincenzo Nesi, Mariapia Palombaro

We study the differential inclusion $Du\in K$, where $K$ is an unbounded and rotationally invariant subset of the real symmetric $3\times 3$ matrices. We exhibit a subset of all possible average fields. The corresponding microgeometries are laminates of infinite rank. The problem originated in the search for the effective conductivity of polycrystalline comp

Remi Carles

We consider the cubic Schrodinger equation on the line, for which the scattering theory requires modifications due to long range effects. We revisit the construction of the modified wave operator, and recall the construction of its inverse, in order to describe the asymptotic behavior of these operators near the origin. At leading order, these operators, who

Swapan Jana, Sourav Pal, Nitin Tomar

We study the operator-valued positive definite functions on a group using positive block matrices. We give an alternative proof to Brehmer positivity for doubly commuting contractions. We classify all commuting unitary representations over a finite group. We show by examples that the power of a positive-definite function may not be positive definite and also

Varqa Abyaneh, Parsa Ghorbani

According to quantum Zeno dynamics (QZD), the evolution of a quantum system can be restricted to a subspace of its Hilbert space by frequent measurements. A crucial question in QZD of a particle's position is: how short the time interval between successive measurements should be, in order to confine the particle in its initial spatial region? To address this

Helena Bergold, Manfred Scheucher, Felix Schröder

In this article, we show that the completion problem, i.e. the decision problem whether a partial structure can be completed to a full structure, is NP-complete for many combinatorial structures. While the gadgets for most reductions in literature are found by hand, we present an algorithm to construct gadgets in a fully automated way. Using our framework wh

Tianfu Qi, Jun Wang

Non-Gaussian impulsive noise (IN) with memory exists in many practical applications. When it is mixed with white Gaussian noise (WGN), the resultant mixed noise will be bursty. The performance of communication systems will degrade significantly under bursty mixed noise if the bursty characteristic is ignored. A proper model for the bursty mixed noise and cor

Frédérique Bassino, Mathilde Bouvel, Valentin Féray, Lucas Gerin

We obtain the scaling limits of random graphs drawn uniformly in three families of intersection graphs: permutation graphs, circle graphs, and unit interval graphs. The two first families typically generate dense graphs, in these cases we prove a.s. convergence to an explicit deterministic graphon. Uniform unit interval graphs are nondense and we prove conve

Daniel Roberts, Mark Gieles, Denis Erkal, Jason L. Sanders

Nearly a hundred progenitor-less, thin stellar streams have been discovered in the Milky Way, thanks to Gaia and related surveys. Most streams are believed to have formed from star clusters and it was recently proposed that extended star clusters -- rich in stellar-mass black holes (BHs) -- are efficient in creating streams. To understand the nature of strea

Juan Jiménez-Sánchez, Carmen Ortega-Sabater, Philip K. Maini, Víctor M. Pérez-García

Evolvability is defined as the ability of a population to generate heritable variation to facilitate its adaptation to new environments or selection pressures. In this article, we consider evolvability as a phenotypic trait subject to evolution and discuss its implications in the adaptation of populations of asexual individuals. We explore the evolutionary d

Giuseppina Barbieri, Francisco Javier García-Pacheco, Soledad Moreno-Pulido

We study measures defined on effect algebras. We characterize real-valued measures on effect algebras and find a class of effect algebras, that include the natural effect algebras of sets, on which sigma-additive measures with values in a finite dimensional Banach space are always bounded. We also prove that in effect algebras the Nikodym and the Grothendiec

Georgii Stanishevskii, Jakub Steczkiewicz, Tomasz Szczepanik, Sławomir Tadeja

Numerous emerging deep-learning techniques have had a substantial impact on computer graphics. Among the most promising breakthroughs are the rise of Neural Radiance Fields (NeRFs) and Gaussian Splatting (GS). NeRFs encode the object's shape and color in neural network weights using a handful of images with known camera positions to generate novel views. In

Aven-Le Zhou, Yu-Ao Wang, Wei Wu, Kang Zhang

With the advancement of neural generative capabilities, the art community has actively embraced GenAI (generative artificial intelligence) for creating painterly content. Large text-to-image models can quickly generate aesthetically pleasing outcomes. However, the process can be non-deterministic and often involves tedious trial-and-error, as users struggle

Stefana Anita, Gabriel Turinici

Although Multi Armed Bandit (MAB) on one hand and the policy gradient approach on the other hand are among the most used frameworks of Reinforcement Learning, the theoretical properties of the policy gradient algorithm used for MAB have not been given enough attention. We investigate in this work the convergence of such a procedure for the situation when a $

Pablo Rodriguez-Perez, Ruben Fraile, Miguel Garcia-Escrig, Nicolas Saenz-Lechon

A transversal study of the pitch variability of parkinsonian voices in read speech is presented. 30 patients suffering from Parkinson's disease (PD) and 32 healthy speakers were recorded while reading a text without voiceless phonemes. The fundamental frequency contours were calculated from the recordings, and the following measures were used for describing

Ryota Maniwa, Naoki Ichijo, Yuta Nakahara, Toshiyasu Matsushima

A decision tree is one of the most popular approaches in machine learning fields. However, it suffers from the problem of overfitting caused by overly deepened trees. Then, a meta-tree is recently proposed. It solves the problem of overfitting caused by overly deepened trees. Moreover, the meta-tree guarantees statistical optimality based on Bayes decision t

Dragos Costea, Alina Marcu, Cristina Lazar, Marius Leordeanu

Modeling face-to-face communication in computer vision, which focuses on recognizing and analyzing nonverbal cues and behaviors during interactions, serves as the foundation for our proposed alternative to text-based Human-AI interaction. By leveraging nonverbal visual communication, through facial expressions, head and body movements, we aim to enhance enga

Ruben Laso, Diego Krupitza, Sascha Hunold

Since the advent of parallel algorithms in the C++17 Standard Template Library (STL), the STL has become a viable framework for creating performance-portable applications. Given multiple existing implementations of the parallel algorithms, a systematic, quantitative performance comparison is essential for choosing the appropriate implementation for a particu

Francisco Javier García-Pacheco, Clemente Cobos-Sánchez, Soledad Moreno-Pulido, Alberto Sánchez-Alzola

The supporting vectors of a matrix A are the solutions of max || x ||_2 =1 {||Ax||_2^2}. The generalized supporting vectors of matrices A_1 , . . . , A_k are the solutions of max || x ||_2 =1 {||A_1x||_2^2 + ||A_2x||_2^2 + ... + ||A_kx||_2^2}. Notice that the previous optimization problem is also a boundary element problem since the maximum is attained on th

Narayanaswamy Balakrishnan, María Jaenada, Leandro Pardo

Many products in engineering are highly reliable with large mean lifetimes to failure. Performing lifetests under normal operations conditions would thus require long experimentation times and high experimentation costs. Alternatively, accelerated lifetests shorten the experimentation time by running the tests at higher than normal stress conditions, thus in

Lasha Ephremidze

In this paper, the Janashia-Lagvilava matrix spectral factorization algorithm, which is designed for power spectral density functions defined on the unit circle, is extended to the real line. The proposed algorithm can be used directly for continuous-time models

Yuhao Wang, Ming Gao, Wai Ming Tai, Bryon Aragam

We develop optimal algorithms for learning undirected Gaussian trees and directed Gaussian polytrees from data. We consider both problems of distribution learning (i.e. in KL distance) and structure learning (i.e. exact recovery). The first approach is based on the Chow-Liu algorithm, and learns an optimal tree-structured distribution efficiently. The second

Ioannis N. Tzortzis, Konstantinos Makantasis, Ioannis Rallis, Nikolaos Bakalos

Limited amount of data and data sharing restrictions, due to GDPR compliance, constitute two common factors leading to reduced availability and accessibility when referring to medical data. To tackle these issues, we introduce the technique of Learning Using Privileged Information. Aiming to substantiate the idea, we attempt to build a robust model that impr

Zhuoran Zheng, Jun Zhang

In endoscopic imaging, the recorded images are prone to exposure abnormalities, so maintaining high-quality images is important to assist healthcare professionals in performing decision-making. To overcome this issue, We design a frequency-domain based network, called FD-Vision Mamba (FDVM-Net), which achieves high-quality image exposure correction by recons

Judith Stute, Sabine Pelka, Matthias Kühnbach, Marian Klobasa

In 2022, energy prices skyrocketed across Europe, with average day-ahead spot market prices in Germany 2.43 times higher than the previous year, hinting at future trends. At the same time, electricity infrastructure is expected to be overutilized in some regions in the future due to the uptake of electric vehicles, heat pumps, PV systems, and other appliance

Ressi Bonti Muhammad, Apoorv Srivastava, Sergey Alyaev, Reidar Brumer Bratvold

Geosteering, a key component of drilling operations, traditionally involves manual interpretation of various data sources such as well-log data. This introduces subjective biases and inconsistent procedures. Academic attempts to solve geosteering decision optimization with greedy optimization and Approximate Dynamic Programming (ADP) showed promise but lacke

Konstantin Sonntag, Bennet Gebken, Georg Müller, Sebastian Peitz

The efficient optimization method for locally Lipschitz continuous multiobjective optimization problems from [1] is extended from finite-dimensional problems to general Hilbert spaces. The method iteratively computes Pareto critical points, where in each iteration, an approximation of the subdifferential is computed in an efficient manner and then used to co

I. Orue, L. Marcano, P. Bender, A. García-Prieto

Magnetospirillum gryphiswaldense is a microorganism with the ability to biomineralize magnetite nanoparticles, called magnetosomes, and arrange them into a chain that behaves like a magnetic compass. Rather than straight lines, magnetosome chains are slightly bent, as evidenced by electron cryotomography. Our experimental and theoretical results suggest that

Claudio Bonati, Andrea Pelissetto, Ettore Vicari

We investigate the nature of the deconfinement transitions in three-dimensional lattice Abelian Higgs models, in which a complex scalar field of integer charge $Q\ge 2$ is minimally coupled with a compact $U(1)$ gauge field. Their phase diagram presents two phases separated by a transition line where static charges $q$, with $q<Q$, deconfine. We argue that t

Daniel Gómez, Javier Castro, Inmaculada Gutiérrez, Rosa Espínola

In this paper we formally define the hierarchical clustering network problem (HCNP) as the problem to find a good hierarchical partition of a network. This new problem focuses on the dynamic process of the clustering rather than on the final picture of the clustering process. To address it, we introduce a new ierarchical clustering algorithm in networks, bas

Denis S. Grebenkov

We study the spectral properties of the Dirichlet-to-Neumann operator and the related Steklov problem in spheroidal domains ranging from a needle to a disk. An explicit matrix representation of this operator for both interior and exterior problems is derived. We show how the anisotropy of spheroids affects the eigenvalues and eigenfunctions of the operator.

Raul K. C. Araújo, Enrique Fernández-Cara, Juan Límaco, Diego A. Souza

This paper concerns the null controllability of the two-phase 1D Stefan problem with distributed controls. This is a free-boundary problem that models solidification or melting processes. In each phase, a parabolic equation, completed with initial and boundary conditions must be satisfied; the phases are separated by a phase-change interface where an additio

A. Sánchez-Lavega, T. del Río-Gaztelurrutia, R. Hueso, S. Pérez-Hoyos

We investigate the long-term motion of Saturn's North-Pole Hexagon and the structure of its associated eastward jet, using Cassini ISS and ground-based images from 2008 to 2014. We show that both are persistent features that have survived the long polar night, the jet profile remaining essentially unchanged. During those years the hexagon vertices showed a s

Zu-Jian Ying

The Jaynes-Cummings Model (JCM) is a fundamental model and building block for light-matter interactions, quantum information and quantum computation. We analytically analyze the topological feature manifested by the JCM in the presence of non-Hermiticity which may be effectively induced by dissipation and decay rates. Indeed, the eigenstates of the JCM are t

Soledad Moreno-Pulido, Giuseppina Barbieri, Fernando León-Saavedra, Francisco Javier Pérez-Fernández

In this manuscript we characterize the completeness of a normed space through the strong lacunary (N-theta) and lacunary statistical convergence (S-theta) of series. A new characterization of weakly unconditionally Cauchy series through N-theta and S-theta is obtained. We also relate the summability spaces associated with these summabilities with the strong

Georgios Mylonopoulos, Behrooz Makki, Stefano Buzzi, Gábor Fodor

This paper considers the problem of downlink localization and user equipments (UEs) tracking with an adaptive procedure for a range of distances. We provide the base station (BS) with two signaling schemes and the UEs with two localization algorithms, assuming far-field (FF) and near-field (NF) conditions, respectively. The proposed schemes employ different

Hojjat Karami, David Atienza, Anisoara Ionescu

Irregular sampling of time series in electronic health records (EHRs) is one of the main challenges for developing machine learning models. Additionally, the pattern of missing data in certain clinical variables is not at random but depends on the decisions of clinicians and the state of the patient. Point process is a mathematical framework for analyzing ev

Adrien Pommellet, Daniel Stan, Simon Scatton

The CTL learning problem consists in finding for a given sample of positive and negative Kripke structures a distinguishing CTL formula that is verified by the former but not by the latter. Further constraints may bound the size and shape of the desired formula or even ask for its minimality in terms of syntactic size. This synthesis problem is motivated by

Tathagata Gupta, Shayeef Murshid, Somshubhro Bandyopadhyay

We consider the problem of determining the state of an unknown quantum sequence without error. The elements of the given sequence are drawn with equal probability from a known set of linearly independent pure quantum states with the property that their mutual inner products are all real and equal. This problem can be posed as an instance of unambiguous state

Riccardo Montalto, Federico Murgante, Stefano Scrobogna

In this paper we consider the generalized surface quasi-geostrophic $\alpha$-SQG equations, in the "sublinear regime" $\alpha \in (0, 1)$ and we study the stability of vortex patches close to vortex discs. We shall prove that for regular, Sobolev initial vortex patches $\varepsilon$-close to a vortex disc, the solutions stay $\varepsilon$-close to a vortex d

Sizhe Chen, Julien Piet, Chawin Sitawarin, David Wagner

Recent advances in Large Language Models (LLMs) enable exciting LLM-integrated applications, which perform text-based tasks by utilizing their advanced language understanding capabilities. However, as LLMs have improved, so have the attacks against them. Prompt injection attacks are an important threat: they trick the model into deviating from the original a

Bharti, Andreas Carlson, Tak Shing Chan

We study the Plateau-Rayleigh instability of a viscoelastic soft solid layer coated on a rigid cylinder i.e., a soft fibre with a rigid core. The onset of instability is examined using a linear stability analysis. We find that increasing the rigid cylinder radius reduce the growth rate of the fastest growing mode. For each rigid cylinder radius, a critical e

Alessandro Georgoudis, Carlo Heissenberg, Rodolfo Russo

We consider the frequency-domain LO and NLO post-Minkowskian (PM) waveforms obtained from the tree-level and one-loop amplitudes describing the scattering of two massive scalar objects and the emission of one graviton. We explicitly calculate their post-Newtonian (PN) limit obtaining an expansion up to the third subleading PN order in all ingredients: the tr

Haocheng Liu, Teysir Baoueb, Mathieu Fontaine, Jonathan Le Roux

Diffusion models are receiving a growing interest for a variety of signal generation tasks such as speech or music synthesis. WaveGrad, for example, is a successful diffusion model that conditionally uses the mel spectrogram to guide a diffusion process for the generation of high-fidelity audio. However, such models face important challenges concerning the n

Peiyuan Gong, Jiamian Li, Jiaxin Mao

Collaborative search supports multiple users working together to accomplish a specific search task. Research has found that designing lightweight collaborative search plugins within instant messaging platforms aligns better with users' collaborative habits. However, due to the complexity of multi-user interaction scenarios, it is challenging to implement a f

Narayanaswamy Balakrishnan, María Jaenada, Leandro Pardo

Accelerated life tests (ALTs) play a crucial role in reliability analyses, providing lifetime estimates of highly reliable products. Among ALTs, step-stress design increases the stress level at predefined times, while maintaining a constant stress level between successive changes. This approach accelerates the occurrence of failures, reducing experimental du

Jona te Lintelo, Stefanos Koffas, Stjepan Picek

Sponge attacks aim to increase the energy consumption and computation time of neural networks. In this work, we present a novel sponge attack called SkipSponge. SkipSponge is the first sponge attack that is performed directly on the parameters of a pretrained model using only a few data samples. Our experiments show that SkipSponge can successfully increase

Giovanni Landi, Chiara Pagani

We study two classes of quantum spheres and hyperboloids which are $*$-quantum spaces for the quantum orthogonal group $\mathcal{O}(SO_q(3))$. We construct line bundles over the quantum homogeneous space of invariant elements for the quantum subgroup $SO(2)$ of $SO_q(3)$. These are associated to the quantum principal bundle via corepresentations of $SO(2)$ a

Jose A. Carrillo, Gissell Estrada-Rodriguez, Laszlo Mikolas, Sui Tang

We address the inverse problem of identifying nonlocal interaction potentials in nonlinear aggregation-diffusion equations from noisy discrete trajectory data. Our approach involves formulating and solving a regularized variational problem, which requires minimizing a quadratic error functional across a set of hypothesis functions, further augmented by a spa

Diego Fernández de la Pradilla, Esteban Moreno, Johannes Feist

Master equations play a pivotal role in investigating open quantum systems. In particular, the Bloch-Redfield equation stands out due to its relation to a concrete physical environment. However, without further approximations it does not lead to a Lindblad master equation that guarantees that the density matrix stays completely positive, which has raised som

Amelia Jiménez-Sánchez, Natalia-Rozalia Avlona, Dovile Juodelyte, Théo Sourget

Medical Imaging (MI) datasets are fundamental to artificial intelligence in healthcare. The accuracy, robustness, and fairness of diagnostic algorithms depend on the data (and its quality) used to train and evaluate the models. MI datasets used to be proprietary, but have become increasingly available to the public, including on community-contributed platfor

Jaron Fontaine, Adnan Shahid, Eli De Poorter

Artificial intelligence (AI) plays an important role in the dynamic landscape of wireless communications, solving challenges unattainable by traditional approaches. This paper discusses the evolution of wireless AI, emphasizing the transition from isolated task-specific models to more generalizable and adaptable AI models inspired by recent successes in larg

Dimitris Karakostas, Aggelos Kiayias, Thomas Zacharias

We analyze bribing attacks in Proof-of-Stake distributed ledgers from a game theoretic perspective. In bribing attacks, an adversary offers participants a reward in exchange for instructing them how to behave, with the goal of attacking the protocol's properties. Specifically, our work focuses on adversaries that target blockchain safety. We consider two typ

Arya Marda, Shubham Kulkarni, Karthik Vaidhyanathan

Addressing runtime uncertainties in Machine Learning-Enabled Systems (MLS) is crucial for maintaining Quality of Service (QoS). The Machine Learning Model Balancer is a concept that addresses these uncertainties by facilitating dynamic ML model switching, showing promise in improving QoS in MLS. Leveraging this concept, this paper introduces SWITCH, an exemp

Enrique Fernández-Cara, Maurício C. Santos, Diego A. Souza

This paper deals with the boundary controllability of inviscid incompressible fluids for which thermal effects are important. They will be modeled through the so called Boussinesq approximation. In the zero heat diffusion case, by adapting and extending some ideas from J.-M.~Coron and O.~Glass, we establish the simultaneous global exact controllability of th

Fabian Mohn, Konrad Scheffler, Justin Ackers, Agnes Weimer

Objective. The availability of magnetic nanoparticles with medical approval for human intervention is fundamental to the clinical translation of magnetic particle imaging (MPI). In this work, we thoroughly evaluate and compare the magnetic properties of an magnetic resonance imaging (MRI) approved tracer to validate its performance for MPI in future human tr

Lee Smith, Peter Brookes Chambers, Daniel R. Naylor, Angela Dyson

The effects of confinement on electron transport in GaN have been studied via an ensemble Monte-Carlo code. Excellent agreement is obtained with experimental data from the literature up to moderate fields. In agreement with experimental results, negative-differential-conductivity is not observed in velocity-field curves. The reasons for this are discussed in

Archit Sood, Shweta Jain, Sujit Gujar

Restless multi-armed bandits (RMABs) generalize the multi-armed bandits where each arm exhibits Markovian behavior and transitions according to their transition dynamics. Solutions to RMAB exist for both offline and online cases. However, they do not consider the distribution of pulls among the arms. Studies have shown that optimal policies lead to unfairnes

Katsuki Muraoka, Naoto Kojiguchi, Junpei Ito, Daisaku Nogami

We present our optical photometric observations of the 2022 eruption of the recurrent nova U Scorpii (U Sco) using 49,152 data points over 70 d following the optical peak. We have also analyzed its soft X-ray (0.3--1 keV) light curve by the Neil Gehrels Swift Observatory. During the 2022 eruption, the optical plateau stage started 13.8--15.0 d and ended 23.8

Carlos Pérez, Javier Verón, Francisca Pérez, M Ángeles Moraga

The total energy cost of computing activities is steadily increasing and projections indicate that it will be one of the dominant global energy consumers in the coming decades. However, perhaps due to its relative youth, the video game sector has not yet developed the same level of environmental awareness as other computing technologies despite the estimated

Soledad Moreno-Pulido, Francisco Javier García-Pacheco, Clemente Cobos-Sánchez, Alberto Sánchez-Alzola

In this manuscript we provide an exact solution to the maxmin problem max ||Ax|| subject to ||Bx||<= 1, where A and B are real matrices. This problem comes from a remodeling of max ||Ax|| subject to min ||Bx||, because the latter problem has no solution. Our mathematical method comes from the Abstract Operator Theory, whose strong machinery allows us to redu

Martin H. Essink, Stefan Karpitschka, Hamza K. Khattak, Kari Dalnoki-Veress

Liquid drops slide more slowly over soft, deformable substrates than over rigid solids. This phenomenon can be attributed to the viscoelastic dissipation induced by the moving wetting ridge, which inhibits a rapid motion, and is called "viscoelastic braking". Experiments on soft dynamical wetting have thus far been modelled using linear theory, assuming smal

E. S. Moreira

At first glance, the title of this work seems to be improper. And the reason is well known. Since blackbody pressure depends only on temperature, one cannot take the derivative of the thermodynamic quantities with respect to one of them, keeping the other constant. That is, the heat capacity at constant pressure, $C_{P}$, as well as, the coefficient of therm

Harritxu Gete, Thierry Etchegoyhen

Standard context-aware neural machine translation (NMT) typically relies on parallel document-level data, exploiting both source and target contexts. Concatenation-based approaches in particular, still a strong baseline for document-level NMT, prepend source and/or target context sentences to the sentences to be translated, with model variants that exploit e

Xuanzhong Chen, Xiaohao Mao, Qihan Guo, Lun Wang

Generalist Large Language Models (LLMs), such as GPT-4, have shown considerable promise in various domains, including medical diagnosis. Rare diseases, affecting approximately 300 million people worldwide, often have unsatisfactory clinical diagnosis rates primarily due to a lack of experienced physicians and the complexity of differentiating among many rare

M. Sahoo, I. J. Onuorah, L. C. Folkers, E. V. Chulkov

Magnetic topological insulators (TIs) herald a wealth of applications in spin-based technologies, relying on the novel quantum phenomena provided by their topological properties. Particularly promising is the (MnBi$_2$Te$_4$)(Bi$_2$Te$_3$)$_n$ layered family of established intrinsic magnetic TIs that can flexibly realize various magnetic orders and topologic

Sam Nerenberg, Oliver D. Neill, Giulia Marcucci, Daniele Faccio

Neuromorphic processors improve the efficiency of machine learning algorithms through the implementation of physical artificial neurons to perform computations. However, whilst efficient classical neuromorphic processors have been demonstrated in various forms, practical quantum neuromorphic platforms are still in the early stages of development. Here we pro

Édouard Bonnet, Carl Feghali, Tung Nguyen, Alex Scott

In 1972, Mader showed that every graph without a 3-connected subgraph is 4-degenerate and thus 5-colorable}. We show that the number 5 of colors can be replaced by 4, which is best possible.

Tao Wu, Bo Liu, Haohao Hao, Fang Yuan

In water electrolysis, bubbles form on the electrode and interact through processes such as collision and coalescence. However, the impact of bubble coalescence a fundamental process governing electrolytic bubble behaviour-on electrolysis efficiency remains unclear. Here, we show that enhancing bubble coalescence improves electrolysis efficiency by more than

Aleksey S. Gvozdarev

The research presents the closed-form outage analysis of the newly presented $\alpha$-modification of the shadowed Beaulieu-Xie fading model for wireless communications. For the considered channel, the closed-form analytical expressions for the outage probability (including its upper and lower bounds), raw moments, amount of fading, and channel quality estim

Brais González-Rodríguez, Joe Naoum-Sawaya

This paper presents a new approach to quadrify a polynomial programming problem, i.e. reduce the polynomial program to a quadratic program, before solving it. The proposed approach, QUAD-RLT, exploits the Reformulation-Linearization Technique (RLT) structure to obtain smaller relaxations that can be solved faster and still provide high quality bounds. QUAD-R

Pardeep Kumar, Manil T. Mohan

In this article, we discuss the local exact controllability to trajectories of the following convective Brinkman-Forchheimer (CBF) equations (or damped Navier-Stokes equations) defined in a bounded domain $\Omega \subset\mathbb{R}^d$ ($d=2,3$) with smooth boundary: \begin{align*} \frac{\partial\boldsymbol{u}}{\partial t}-\mu \Delta\boldsymbol{u}+(\boldsymbol

Fernando Ferraretto, Thiago Laitz, Roberto Lotufo, Rodrigo Nogueira

ExaRanker recently introduced an approach to training information retrieval (IR) models, incorporating natural language explanations as additional labels. The method addresses the challenge of limited labeled examples, leading to improvements in the effectiveness of IR models. However, the initial results were based on proprietary language models such as GPT

José M. Alonso-Meijide, Livino M. Armijos-Toro, Balbina V. Casas-Méndez, Manuel A. Mosquera

Based on Holler (1982) and Armijos-Toro et al. (2021) we propose two power indices to measure the influence of the players in the class of weighted majority games in partition function form. We compare these new power indices with their original versions on the class of games in characteristic function form. Finally, we use both pairs of power indices for ga

Huaiyuan Ying, Shuo Zhang, Linyang Li, Zhejian Zhou

The math abilities of large language models can represent their abstract reasoning ability. In this paper, we introduce and open-source our math reasoning LLMs InternLM-Math which is continue pre-trained from InternLM2. We unify chain-of-thought reasoning, reward modeling, formal reasoning, data augmentation, and code interpreter in a unified seq2seq format

Joanna Komorniczak, Pawel Ksieniewicz

In recent years Deep Neural Network-based systems are not only increasing in popularity but also receive growing user trust. However, due to the closed-world assumption of such systems, they cannot recognize samples from unknown classes and often induce an incorrect label with high confidence. Presented work looks at the evaluation of methods for Open Set Re

Zonggui Tian, Du Zhang, Hong-Ning Dai

Recently, continual graph learning has been increasingly adopted for diverse graph-structured data processing tasks in non-stationary environments. Despite its promising learning capability, current studies on continual graph learning mainly focus on mitigating the catastrophic forgetting problem while ignoring continuous performance improvement. To bridge t

Peimian Du, Qicheng Guo, Yanru Li

This study proposes a Network to recognize displacement of a RC frame structure from a video by a monocular camera. The proposed Network consists of two modules which is FlowNet2 and POFRN-Net. FlowNet2 is used to generate dense optical flow as well as POFRN-Net is to extract pose parameter H. FlowNet2 convert two video frames into dense optical flow. POFRN-

Luís Maia

We prove an It\^o-Wentzell formula for the fractional Brownian motion. As an application we derive an existence and uniqueness result for a class of stochastic differential equations driven by this stochastic process.

Radka Vozabova, Boris Tomasik

We calculate the elliptic flow of deuterons in Pb+Pb collisions at 2.76 TeV per colliding nucleon-nucleon pair and show that it can be used to discriminate between direct statistical production and coalescence. The emission from the fireball is parametrized and tuned to reproduce transverse momentum spectra and the elliptic flow of protons and pions. Coalesc

Xi Chen, Siwei Zhang, Yun Xiong, Xixi Wu

Temporal Interaction Graphs (TIGs) are widely utilized to represent real-world systems. To facilitate representation learning on TIGs, researchers have proposed a series of TIG models. However, these models are still facing two tough gaps between the pre-training and downstream predictions in their ``pre-train, predict'' training paradigm. First, the tempora

Alberto Garzás-Villar, Caspar Boersma, Alexis Derumigny, Arkady Zgonnikov

The provision of robotic assistance during motor training has proven to be effective in enhancing motor learning in some healthy trainee groups as well as patients. Personalizing such robotic assistance can help further improve motor (re)learning outcomes and cater better to the trainee's needs and desires. However, the development of personalized haptic ass

Fernando León-Saavedra, Soledad Moreno-Pulido, Antonio Sala-Pérez

In this paper we will characterize the completeness and barrelledness of a normed space through the strong p-Ces\`aro summability of series. A new characterization of weakly unconditionally Cauchy series and unconditionally convergent series through the strong p-Ces\`aro summability is obtained.

Gon Buzaglo, Itamar Harel, Mor Shpigel Nacson, Alon Brutzkus

Background. A main theoretical puzzle is why over-parameterized Neural Networks (NNs) generalize well when trained to zero loss (i.e., so they interpolate the data). Usually, the NN is trained with Stochastic Gradient Descent (SGD) or one of its variants. However, recent empirical work examined the generalization of a random NN that interpolates the data: th

P. Saura-Bastida, A. Das, G. Sierra, J. Molina-Vilaplana

We propose a symmetry resolution of entanglement for categorical non-invertible symmetries (CaT-SREE) in (1 + 1)-dimensional CFTs. The definition parallels that of group-like invertible symmetries, employing the concept of symmetric boundary states with respect to a categorical symmetry. Our examination extends to rational CFTs, where the behavior of CaT-SRE

Antonio Beltrán, María José Felipe, Carmen Melchor

Landau's theorem on conjugacy classes asserts that there are only finitely many finite groups, up to isomorphism, with exactly $k$ conjugacy classes for any positive integer $k$. We show that, for any positive integers $n$ and $s$, there exists only a finite number of finite groups $G$, up to isomorphism, having a normal subgroup $N$ of index $n$ which conta

Angus Phillips, Hai-Dang Dau, Michael John Hutchinson, Valentin De Bortoli

Denoising diffusion models have become ubiquitous for generative modeling. The core idea is to transport the data distribution to a Gaussian by using a diffusion. Approximate samples from the data distribution are then obtained by estimating the time-reversal of this diffusion using score matching ideas. We follow here a similar strategy to sample from unnor

Luis Costero, Francisco D. Igual, Katzalin Olcoz, Francisco Tirado

We present a family of policies that, integrated within a runtime task scheduler (Nanox), pursue the goal of improving the energy efficiency of task-parallel executions with no intervention from the programmer. The proposed policies tackle the problem by modifying the core operating frequency via DVFS mechanisms, or by enabling/disabling the mapping of tasks

Hojjat Karami, Mary-Anne Hartley, David Atienza, Anisoara Ionescu

Time series in Electronic Health Records (EHRs) present unique challenges for generative models, such as irregular sampling, missing values, and high dimensionality. In this paper, we propose a novel generative adversarial network (GAN) model, TimEHR, to generate time series data from EHRs. In particular, TimEHR treats time series as images and is based on t

A. Kievsky, E. Garrido, M. Viviani, M. Gattobigio

The correlation function is a useful tool to study the interaction between hadrons. The theoretical description of this observable requires the knowledge of the scattering wave function, whose asymptotic part is distorted when two or more particles are charged. For a system of three (or more) particles, with more than two particles asymptotically free and at

Diana Nunez, Diego Cordoba, Eduard Kissling

The contact between the Caribbean and North American plates is a tectonically complicated boundary where the deformation is accommodated in north and south of Hispaniola by the Enriquillo Plantain Garden and Septentrional Oriente Fault Zones. We present a crustal and tectonic study of the Northeastern Caribbean Plate Boundary from wide-angle seismic data acq

Behnaz Alafi, Saeid Moradi

The performance of an artificial neural network (ANN) in forecasting crash risk is shown in this paper. To begin, some traffic and weather data are acquired as raw data. This data is then analyzed, and relevant characteristics are chosen to utilize as input data based on additional tree and Pearson correlation. Furthermore, crash and non-crash time data are

Florian Grötschla, Joël Mathys, Robert Veres, Roger Wattenhofer

Graph Visualization, also known as Graph Drawing, aims to find geometric embeddings of graphs that optimize certain criteria. Stress is a widely used metric; stress is minimized when every pair of nodes is positioned at their shortest path distance. However, stress optimization presents computational challenges due to its inherent complexity and is usually s

Xiaoxuan Zhang, Quan Pan, Salvador García

Deep learning (DL)-based sea\textendash land clutter classification for sky-wave over-the-horizon-radar (OTHR) has become a novel research topic. In engineering applications, real-time predictions of sea\textendash land clutter with existing distribution discrepancies are crucial. To solve this problem, this article proposes a novel Multisource Semisupervise

D. W. Kurtz, H. Saio, D. L. Holdsworth, Santosh Joshi

HD 12098 is an roAp star pulsating in the most distorted dipole mode yet observed in this class of star. Using TESS Sector 58 observations we show that there are photometric spots at both the magnetic poles of this star. It pulsates obliquely primarily in a strongly distorted dipole mode with a period of $P_{\rm puls} = 7.85$ min ($\nu_{\rm puls} = 183.34905

Abhishek Palchoudhary, Simone Peter, Vincent Maurel, Cristian Ovalle

This paper introduces a new local plastic correction algorithm that is aimed at accelerating elasto-plastic finite element (FE) simulations for structural problems exhibiting localised plasticity (around e.g. notches, geometrical defects). The proposed method belongs to the category of generalised multi-axial Neuber-type methods, which process the results of

Anurag Kumar Patel, Harish Chandra

In this paper, we establish necessary and sufficient conditions that must be met for weighted composition operators to act as zero divisors in $\mathcal{B}(\ell^p).$ We also give a necessary condition and a sufficient condition for a composition operators to act as zero divisors in $\mathcal{B}(L^p(\mu)).$ Subsequently, we characterize TDZ in $C(X)$. Afterwa

Dietmar Gallistl, Moritz Hauck, Yizhou Liang, Daniel Peterseim

We establish an a priori error analysis for the lowest-order Raviart-Thomas finite element discretisation of the nonlinear Gross-Pitaevskii eigenvalue problem. Optimal convergence rates are obtained for the primal and dual variables as well as for the eigenvalue and energy approximations. In contrast to conformal approaches, which naturally imply upper energ

Mira Sharma, David P. DiVincenzo

The $\bf{g}$ tensor, which determines the reaction of Kramers-degenerate states to an applied magnetic field, is of increasing importance in the current design of spin qubits. It is affected by details of heterostructure composition, disorder, and electric fields, but it inherits much of its structure from the effect of the spin-orbit interaction working at

Franka Baaske, Hans-Jürgen Schmeißer, Hans Triebel

We present a new proof of the caloric smoothing related to the fractional Gauss-Weierstrass semi-group in Triebel-Lizorkin spaces. This property will be used to prove existence and uniqueness of mild and strong solutions of the Cauchy problem for a fractional nonlinear heat equation.

Elena Zappon, Matteo Salvador, Roberto Piersanti, Francesco Regazzoni

When generating in-silico clinical electrophysiological outputs, such as electrocardiograms (ECGs) and body surface potential maps (BSPMs), mathematical models have relied on single physics, i.e. of the cardiac electrophysiology (EP), neglecting the role of the heart motion. Since the heart is the most powerful source of electrical activity in the human body