February 2024 arXiv papers — page 134

Fágner D. Araruna, Enrique Fernández-Cara, Diego A. Souza

This paper deals with the distributed and boundary controllability of the so called Leray-$\alpha$ model. This is a regularized variant of the Navier-Stokes system ($\alpha$ is a small positive parameter) that can also be viewed as a model for turbulent flows. We prove that the Leray-$\alpha$ equations are locally null controllable, with controls bounded ind

Majid Nasiri Khormuji, Alberto Giuseppe Perotti, Qin Yi, Branislav Popovic

This paper introduces a novel physical-layer method labelled as Multi-Modal Concurrent Transmission (MMCT) for efficient transmission of multiple data streams with different reliability-latency performance requirements. The MMCT arranges data from multiple streams within a same physical-layer transport block wherein stream-specific modulation and coding sche

Petar Y. Yordanov, Kalin V. Staykov, Stoytcho S. Yazadjiev, Daniela D. Doneva

Binary pulsars are a powerful tool for probing strong gravity that still outperforms direct gravitational wave observations in a number of directions due to the remarkable accuracy of the pulsar timing. They can constrain very precisely the presence of additional charges of the orbiting neutron stars leading to new channels of energy and angular momentum los

Nicolas M. Müller, Piotr Kawa, Shen Hu, Matthias Neu

Voice faking, driven primarily by recent advances in text-to-speech (TTS) synthesis technology, poses significant societal challenges. Currently, the prevailing assumption is that unaltered human speech can be considered genuine, while fake speech comes from TTS synthesis. We argue that this binary distinction is oversimplified. For instance, altered playbac

Anurag Kumar Patel, Harish Chandra

In this paper we prove that an element $f\in \mathcal{A}(\mathbb{D})$ is a topological divisor of zero(TDZ) if and only if there exists $z_0 \in \mathbb{T}$ such that $f(z_0)=0.$ We also give a characterization of TDZ in the Banach algebra $L^\infty(\mu).$ Further, we prove that the multiplication operator $M_h$ is a TDZ in $\mathcal{B}(L^p(\mu))~(1\leq p\le

Ayush Kumar Tewari

We focus on checking the validity of the half-plane property on two prominent classes of transversal matroids, namely lattice path matroids and bicircular matroids. We show that lattice path matroids satisfy the half-plane property. Subsequently, we show an explicit example of a bicircular matroid that is not a positroid and discuss the negative correlation

Fágner D. Araruna, Enrique Fernández-Cara, Diego A. Souza

This work is devoted to prove the local null controllability of the Burgers-$\alpha$ model. The state is the solution to a regularized Burgers equation, where the transport term is of the form $zy_x$, $z=(Id-\alpha^2\frac{\partial^2}{\partial x^2})^{-1}y$ and $\alpha>0$ is a small parameter. We also prove some results concerning the behavior of the null cont

Miloš Košprdić, Adela Ljajić, Bojana Bašaragin, Darija Medvecki

In this paper, we present the current progress of the project Verif.ai, an open-source scientific generative question-answering system with referenced and verified answers. The components of the system are (1) an information retrieval system combining semantic and lexical search techniques over scientific papers (PubMed), (2) a fine-tuned generative model (M

Elham Nazari

In the energy-momentum squared gravity (EMSG), the matter energy-momentum tensor is not conserved due to nonminimal interaction between the usual and modified matter fields. For this reason, the $N$-body acceleration may host the EMSG effects that can be probed at the solar scale by the perihelion shift of the planets and experimental tests of the Strong Equ

Kirill Antonov, Roman Kalkreuth, Kaifeng Yang, Thomas Bäck

Symbolic regression (SR) poses a significant challenge for randomized search heuristics due to its reliance on the synthesis of expressions for input-output mappings. Although traditional genetic programming (GP) algorithms have achieved success in various domains, they exhibit limited performance when tree-based representations are used for SR. To address t

Livino M. Armijos-Toro, José M. Alonso-Meijide, Manuel A. Mosquera

In this paper, we introduce a notion of mergeable weighted majority games with the aim of providing the first characterization of the Colomer-Mart\'inez power index (Colomer and Mart\'inez in J Theor Polit 7(1):41-63, 1995). Furthermore, we define and characterize a new power index for the family of weighted majority games that combines ideas of the Public G

Lidia Gianne Souza da Rocha, Kenny Anderson Queiroz Caldas, Marco Henrique Terra, Fabio Ramos

Unmanned Aerial Vehicles need an online path planning capability to move in high-risk missions in unknown and complex environments to complete them safely. However, many algorithms reported in the literature may not return reliable trajectories to solve online problems in these scenarios. The Q-Learning algorithm, a Reinforcement Learning Technique, can gene

Sergi G. Leyva, Ignacio Pagonabarraga, Aurora Hernández-Machado, Rodrigo Ledesma-Aguilar

Capillary imbibition underpins many processes of fundamental and applied relevance in fluid mechanics. A limitation to the flow is the coupling to the confining solid, which induces friction forces. Our work proposes a general theoretical framework for the modeling of the transport of liquids in lubricant impregnated surfaces. We show that for sufficiently s

Sergio Martínez-Agüero, Antonio G. Marques, Inmaculada Mora-Jiménez, Joaquín Alvárez-Rodríguez

Electronic health records (EHR) is an inherently multimodal register of the patient's health status characterized by static data and multivariate time series (MTS). While MTS are a valuable tool for clinical prediction, their fusion with other data modalities can possibly result in more thorough insights and more accurate results. Deep neural networks (DNNs)

Kévin Perrot, Sylvain Sené, Léah Tapin

Boolean automata networks (aka Boolean networks) are space-time discrete dynamical systems, studied as a model of computation and as a representative model of natural phenomena. A collection of simple entities (the automata) update their 0-1 states according to local rules. The dynamics of the network is highly sensitive to update modes, i.e., to the schedul

Vijaya Krishna Yalavarthi, Randolf Scholz, Stefan Born, Lars Schmidt-Thieme

Probabilistic forecasting of irregularly sampled multivariate time series with missing values is an important problem in many fields, including health care, astronomy, and climate. State-of-the-art methods for the task estimate only marginal distributions of observations in single channels and at single timepoints, assuming a fixed-shape parametric distribut

Aleksandra K. Adamczyk, Teun A. P. M. Huijben, Karol Kolataj, Fangjia Zhu

Controlling the flow of excitons between organic molecules holds immense promise for various applications, including energy conversion, spectroscopy, photocatalysis, sensing, and microscopy. DNA nanotechnology has shown promise in achieving this control by using synthetic DNA as a platform for positioning and, very recently, for also orienting organic dyes.

Antonio Beltrán, María José Felipe, Carmen Melchor

We summarize several results about non-simplicity, solvability and normal structure of finite groups related to the number of conjugacy classes appearing in the product or the power of conjugacy classes. We also collect some problems that have only been partially solved.

Bhawana Singh, Karim Ahmadi Dastgerdi, Nikolaos Athanasopoulos, Wasif Naeem

We consider a new control strategy for marine navigation, equipped with finite-time convergence characteristics. We provide mathematical guarantees for waypoint reaching and obstacle avoidance for different encounter scenarios, by deriving conditions under which (i) convergence to waypoint and (ii) safe obstacle avoidance is achieved while (iii) satisfying i

Carmen Delgado, Jos é María Sanz, Jeroen Famaey

From the outset, batteries have been the main power source for the Internet of Things (IoT). However, replacing and disposing of billions of dead batteries per year is costly in terms of maintenance and ecologically irresponsible. Since batteries are one of the greatest threats to a sustainable IoT, battery-less devices are the solution to this problem. Thes

Antonio Beltrán, María José Felipe, Carmen Melchor

Let $G$ be a finite group and $N$ a normal subgroup of $G$. We determine the structure of $N$ when the diameter of the graph associated to the $G$-conjugacy classes contained in $N$ is as large as possible, that is, is equal to three.

Gongxi Zhu, Donghao Li, Hanlin Gu, Yuan Yao

Federated Learning (FL) is a promising approach for training machine learning models on decentralized data while preserving privacy. However, privacy risks, particularly Membership Inference Attacks (MIAs), which aim to determine whether a specific data point belongs to a target client's training set, remain a significant concern. Existing methods for implem

João F. Santos, Miguel Correia, Tiago R. Dias

Document management in the rental market is a critical process to ensure the accuracy of financial transactions and regulatory compliance in the sector. In Portugal, the challenges include the complexity of legislation, particularly GDPR non-compliance, lack of transparency, and bureaucratic process inefficiency. With this in mind, a solution based on Hyperl

Olaf Wysocki, Ludwig Hoegner, Uwe Stilla

Although highly-detailed LoD3 building models reveal great potential in various applications, they have yet to be available. The primary challenges in creating such models concern not only automatic detection and reconstruction but also standard-consistent modeling. In this paper, we introduce a novel refinement strategy enabling LoD3 reconstruction by lever

Clara Punzi, Roberto Pellungrini, Mattia Setzu, Fosca Giannotti

Everyday we increasingly rely on machine learning models to automate and support high-stake tasks and decisions. This growing presence means that humans are now constantly interacting with machine learning-based systems, training and using models everyday. Several different techniques in computer science literature account for the human interaction with mach

Caroline Collischon, Michael Klatt, Anthony Banday, Manami Sasaki

Minkowski tensors are comprehensive shape descriptors that robustly capture n-point information in complex random geometries and that have already been extensively applied in the Euclidean plane. Here, we devise a novel framework for Minkowski tensors on the sphere. We first advance the theory by introducing irreducible Minkowski tensors, which avoid the red

Donal Patrick Lynch, Vincent Fusco, Manos M. Tentzeris, Stylianos D. Asimonis

This study presents a superdirective antenna array specifically designed for the sub-6 5G generation frequency range, incorporating pioneering Huygens antenna elements. The optimized structure achieves a realized gain that surpasses Harrington's well-known maximum theoretical limit for antenna directivity, effectively addressing practical concerns related to

Ali Safa, Vikrant Jaltare, Samira Sebt, Kameron Gano

This paper studies the use of Metropolis-Hastings sampling for training Spiking Neural Network (SNN) hardware subject to strong unknown non-idealities, and compares the proposed approach to the common use of the backpropagation of error (backprop) algorithm and surrogate gradients, widely used to train SNNs in literature. Simulations are conducted within a c

Felix Wechsler, Carlo Gigli, Jorge Madrid-Wolff, Christophe Moser

Tomographic Volumetric Additive Manufacturing (TVAM) allows printing of mesoscopic objects within seconds or minutes. Tomographic patterns are illuminated onto a rotating glass vial which contains a photosensitive resin. Current pattern optimization is based on a ray optical assumption which ultimately leads to limited resolution around $20\mu\textrm{m}$ and

Riccardo Cappuzzo, Aimee Coelho, Felix Lefebvre, Paolo Papotti

Machine-learning from a disparate set of tables, a data lake, requires assembling features by merging and aggregating tables. Data discovery can extend autoML to data tables by automating these steps. We present an in-depth analysis of such automated table augmentation for machine learning tasks, analyzing different methods for the three main steps: retrievi

Carmen Delgado, José Ramón Gállego, María Canales, Jorge Ortín

Sensor network virtualization is a promising paradigm to move away from highlycustomized, application-specific wireless sensor networks deployment by opening up to the possibility of dynamically assigning general purpose physical resources to multiple stakeholder applications. In this field, this paper introduces an optimization framework to perform the allo

Emad Awad, Weizhong Dai, Sergey Sobolev

Thermal resonance, in which the temperature amplitude attains a maximum value (peak) in response to an external exciting frequency source, is a phenomenon pertinent to the presence of underdamped thermal oscillations and explicit finite-speed for the thermal wave propagation. The present work investigates the occurrence condition for thermal resonance phenom

Z. Z. Alisultanov, A. Kudlis

We examine the impact of non-magnetic disorder on the electronic states of a multilayer structure comprising layers of both topological and conventional band insulators. Employing the Burkov-Balents model with renormalized tunneling parameters, we generate phase diagrams correlating with disorder, demonstrating that non-magnetic disorder can induce transitio

Antonio Beltrán, Rachel Deborah Camina, María José Felipe, Carmen Melchor

Many results have been established that show how the number of conjugacy classes appearing in the product of classes affect the structure of a finite group. The aim of this paper is to show several results about solvability concerning the case in which the power of a conjugacy class is a union of one or two conjugacy classes. Moreover, we show that the above

Pierre de la Harpe

It is shown that there are groups $\Gamma$ with finite generating sets $S$ such that the adjacency operator of the Cayley graph ${\rm Cay}(\Gamma,S)$ is a disjoint union of $N$ intervals, for arbitrarily large integers $N$.

Wenyu Li, Yinuo Zhu, Xin Lin, Ming Li

Traditional discriminative approaches in mental health analysis are known for their strong capacity but lack interpretability and demand large-scale annotated data. The generative approaches, such as those based on large language models (LLMs), have the potential to get rid of heavy annotations and provide explanations but their capabilities still fall short

In-Jee Jeong, Sung-Jin Oh

We prove the local wellposedness of the Cauchy problems for the electron magnetohydrodynamics equations (E-MHD) without resistivity for possibly large perturbations of nonzero uniform magnetic fields. While the local wellposedness problem for (E-MHD) has been extensively studied in the presence of resistivity (which provides dissipative effects), this seems

Fu Wang, Xinquan Huang, Tariq Alkhalifah

Accurate seismic velocity estimations are vital to understanding Earth's subsurface structures, assessing natural resources, and evaluating seismic hazards. Machine learning-based inversion algorithms have shown promising performance in regional (i.e., for exploration) and global velocity estimation, while their effectiveness hinges on access to large and di

Christoph Zimmer, Mona Meister, Duy Nguyen-Tuong

Learning time-series models is useful for many applications, such as simulation and forecasting. In this study, we consider the problem of actively learning time-series models while taking given safety constraints into account. For time-series modeling we employ a Gaussian process with a nonlinear exogenous input structure. The proposed approach generates da

Artur P. Toshev, Jonas A. Erbesdobler, Nikolaus A. Adams, Johannes Brandstetter

Smoothed particle hydrodynamics (SPH) is omnipresent in modern engineering and scientific disciplines. SPH is a class of Lagrangian schemes that discretize fluid dynamics via finite material points that are tracked through the evolving velocity field. Due to the particle-like nature of the simulation, graph neural networks (GNNs) have emerged as appealing an

Antonio Beltrán, María José Felipe, Carmen Melchor

Suppose that $G$ is a finite group and $K$ a non-trivial conjugacy class of $G$ such that $KK^{-1}=1\cup D\cup D^{-1}$ with $D$ a conjugacy class of $G$. We prove that $G$ is not a non-abelian simple group. We also give arithmetical conditions on the class sizes determining the structure of $\langle K\rangle$ and $\langle D\rangle$. Furthermore, if $D=K$ is

Asghar Daneshvar, Kamran Divaani-Aazar

Noetherian rings have played a fundamental role in commutative algebra, algebraic number theory, and algebraic geometry. Along with their dual, Artinian rings, they have many generalizations, including the notions of isonoetherian and isoartinian rings. In this paper, we prove that the Krull dimension of every isoartinian ring is at most one. We then use thi

Basdouri Imed, Sadraoui Mohamed Amin, Shuangjian Guo

The purpose of the present paper is to investigate cohomologies and deformations of weighted Rota-Baxter Lie algebras as well as weighted Rota-Baxter associative algebras with derivations. First we introduce a notion of weighted Rota-Baxter LieDer and weighted Rota-Baxter AssDer pairs. Then we construct cohomologies of weighted Rota-Baxter LieDer pairs, weig

Konstantinos A. Oikonomidis, Emanuel Laude, Puya Latafat, Andreas Themelis

We show that adaptive proximal gradient methods for convex problems are not restricted to traditional Lipschitzian assumptions. Our analysis reveals that a class of linesearch-free methods is still convergent under mere local H\"older gradient continuity, covering in particular continuously differentiable semi-algebraic functions. To mitigate the lack of loc

Soubhik Chatterjee

The work presented forms a part of the IASc-INSA-NASI Summer Research Fellowship (SRFP) project on the physical & chemical properties of planetary nebulae. Spectral observations of NGC3242 recorded using the VBT Observatory (IIA), Kavalur (TN) for orthogonal slit positions are used for the study. The spectral data were reduced and analyzed using the IRAF dat

Enrique Fernández-Cara, Diego A. Souza

This paper is devoted to prove the local exact controllability to the trajectories for a coupled system, of the Boussinesq kind, with a reduced number of controls. In the state system, the unknowns are the velocity field and pressure of the fluid $(y, p)$, the temperature $\theta$ and an additional variable $c$ that can be viewed as the concentration of a co

Martin Ferianc, Miguel Rodrigues

YAMLE: Yet Another Machine Learning Environment is an open-source framework that facilitates rapid prototyping and experimentation with machine learning (ML) models and methods. The key motivation is to reduce repetitive work when implementing new approaches and improve reproducibility in ML research. YAMLE includes a command-line interface and integrations

Tenghui Wang, Feng Wu, Fei Wang, Xizheng Ma

Fast and high-fidelity qubit initialization is crucial for low-frequency qubits such as fluxonium, and in applications of many quantum algorithms and quantum error correction codes. In a circuit quantum electrodynamics system, the initialization is typically achieved by transferring the state between the qubit and a short-lived cavity through microwave drivi

Peter Vamplew, Ethan, Watkins, Cameron Foale

Multi-objective reinforcement learning (MORL) algorithms extend conventional reinforcement learning (RL) to the more general case of problems with multiple, conflicting objectives, represented by vector-valued rewards. Widely-used scalar RL methods such as Q-learning can be modified to handle multiple objectives by (1) learning vector-valued value functions,

Yao Wei, Alejandro Santana-Bonilla, Lev Kantorovich

The development of novel sub-nanometer clusters (SNCs) catalysts with superior catalytic performance depends on the precise control of clusters' atomistic sizes, shapes, and accurate deposition onto surfaces. The intrinsic complexity of the adsorption process complicates the ability to achieve an atomistic understanding of the most relevant structure-reactiv

Unggi Lee, Minji Jeon, Yunseo Lee, Gyuri Byun

Despite the development of various AI systems to support learning in various domains, AI assistance for art appreciation education has not been extensively explored. Art appreciation, often perceived as an unfamiliar and challenging endeavor for most students, can be more accessible with a generative AI enabled conversation partner that provides tailored que

Dries Dirckx, Mathias Bos, Bastiaan Vandewal, Lander Vanroye

This paper presents a Nonlinear Model Predictive Control (NMPC) scheme targeted at motion planning for mechatronic motion systems, such as drones and mobile platforms. NMPC-based motion planning typically requires low computation times to be able to provide control inputs at the required rate for system stability, disturbance rejection, and overall performan

Siyu Ren, Kenny Q. Zhu

Despite the recent success associated with Large Language Models (LLMs), they are notably cost-prohibitive to deploy in resource-constrained environments due to their excessive memory and computational demands. In addition to model parameters, the key-value cache is also stored in GPU memory, growing linearly with batch size and sequence length. As a remedy,

Marco Baldan, Paolo Di Barba

Physics-informed neural networks (PINNs) are neural networks (NNs) that directly encode model equations, like Partial Differential Equations (PDEs), in the network itself. While most of the PINN algorithms in the literature minimize the local residual of the governing equations, there are energy-based approaches that take a different path by minimizing the v

Maxime Cautrès, Nathan Claudet, Mehdi Mhalla, Simon Perdrix

We study the notion of $k$-stabilizer universal quantum state, that is, an $n$-qubit quantum state, such that it is possible to induce any stabilizer state on any $k$ qubits, by using only local operations and classical communications. These states generalize the notion of $k$-pairable states introduced by Bravyi et al., and can be studied from a combinatori

Panna Gehér, Max Kölbl, Lydia Mirabel Mendoza-Cadena, Daniel P. Szabo

The diameter of a directed graph is the maximum distance between any pair of vertices. We study a problem that generalizes \textsc{Oriented Diameter}: For a given directed graph and a positive integer $d$, what is the minimum number of arc reversals required to obtain a graph with diameter at most $d$? We investigate variants of this problem, considering the

Guillaume Bourcin, Alan Gardin, Jeremy Bourhill, Vincent Vlaminck

We provide a comprehensive analytical description of the effective coupling associated with an antiresonance within a hybrid system comprised of a quasi-closed photonic cavity and a ferrimagnetic material. Whilst so-called level attraction between a resonant system inside an open cavity is well understood, the physical underpinnings of this phenomena within

Parviz Goodarzi

In this work, we consider intermediate inflation in context of the Generalized Non-Minimal Derivative Coupling (GNMDC) model. In this model, inflation is driven by a canonical scalar field that is coupled not only to gravity but also to the derivative of the scalar field. The GNMDC model introduces new dynamics and features during the inflationary epoch. We

Soohaeng Yoo Willow, Dong Geon Kim, R. Sundheep, Amir Hajibabaej

Recent advancements in machine learning potentials (MLPs) have significantly impacted the fields of chemistry, physics, and biology by enabling large-scale first-principles simulations. Among different machine learning approaches, kernel-based MLPs distinguish themselves through their ability to handle small datasets, quantify uncertainties, and minimize ove

Yichuan Mo, Yuji Wang, Zeming Wei, Yisen Wang

While Large Language Models (LLMs) have achieved tremendous success in various applications, they are also susceptible to jailbreaking attacks. Several primary defense strategies have been proposed to protect LLMs from producing harmful information, mostly focusing on model fine-tuning or heuristical defense designs. However, how to achieve intrinsic robustn

Ricardo Gallego Torromé

In this work we consider the emergent origin of gravity in the framework of Hamilton-Randers theory, a theoretical framework for emergent quantum mechanics. After presenting the essential ingredients of the theory, a derivation of the weak equivalence principle from the first principles of the theory follows. Then it is shown that the Newtonian model of grav

Dingding Ren, Tron A. Nilsen, Julie S. Nilsen, Lyubomir Ahtapodov

III-V semiconductor nanowires are considered promising building blocks for advanced photonic devices. One of the key advantages is that the lattice mismatch can easily be accommodated in 1D structures, resulting in superior heteroepitaxial quality compared to thin films. However, few reports break the limitation of using bulk crystalline materials as substra

Mario Cotilla, Diego Cordoba, Diana Nunez

The first morphotectonic model of the Greater Antilles is presented. The model is adjusted to the current dynamics between the Caribbean and North American plates. It is mainly elaborated by Rantsmans methodology. We determined 2 megablocks, 7 macroblocks, 42 mesoblocks, 653 microblocks and 1264 nanoblocks. They constitute a set of active blocks under rotati

Boxue Wang, Liuquan Wang

Mizuno provided 19 examples of generalized rank three Nahm sums with symmetrizer $\mathrm{diag}(1,1,2)$ which are conjecturally modular. We confirm their modularity by establishing Rogers--Ramanujan type identities of index $(1,1,2)$ for these examples. We first reduce these Nahm sums to some double sums or single sums, and then we use known results or apply

Mathias Schäffner

We study local regularity properties of local minimizer of scalar integral functionals with controlled $(p,q)$-growth in the two-dimensional plane. We establish Lipschitz continuity for local minimizer under the condition $1<p\leq q<\infty$ with $q<3p$ which improve upon the classical results valid in the regime $q<2p$. Along the way, we establish an $L^\inf

Chan-Yun Yang, Nilantha Premakumara, Hooman Samani, Chinthaka Premachandra

This paper proposes a new approach to identifying patients with insomnia using a single EEG channel, without the need for sleep stage annotation. Data preprocessing, feature extraction, feature selection, and classification techniques are used to automatically detect insomnia based on features extracted from spectral and temporal domains, including relative

Artis Rušiņš, Krišjānis Nesenbergs, Deniss Tiščenko, Pēteris Paikens

This paper presents an experimental study on radio frequency (RF) fingerprinting of Bluetooth Classic devices. Our research aims to provide a practical evaluation of the possibilities for RF fingerprinting of everyday Bluetooth connected devices that may cause privacy risks. We have built an experimental setup for recording Bluetooth connection in a radio fr

Nandish Chattopadhyay, Amira Guesmi, Muhammad Shafique

Adversarial patch attacks pose a significant threat to the practical deployment of deep learning systems. However, existing research primarily focuses on image pre-processing defenses, which often result in reduced classification accuracy for clean images and fail to effectively counter physically feasible attacks. In this paper, we investigate the behavior

Ge Ye, Mengwei Xie, Chufan Chen, Yanan Zhang

It is intriguing to explore the coexistence and (or) competition between charge-density-wave (CDW) and superconductivity (SC) in many correlated electron systems, such as cuprates, organic superconductors and dichacolgenides. Among them, the recently discovered $\mathbb{Z} _2$ topological kagome metals AV$_3$Sb$_5$ (A=K, Rb, Cs) serve as an ideal platform to

Riku Huttunen, Matias Rusanen, Sami Nikkonen, Henri Korkalainen

Polysomnography (PSG) data is recorded and stored in various formats depending on the recording software. Although the PSG data can usually be exported to open formats, such as the European Data Format (EDF), they are limited in data types, validation, and readability. Moreover, the exported data is not harmonized, which means different datasets need customi

Shuo Liu, Jacky Keung, Zhen Yang, Fang Liu

Compared to Full-Model Fine-Tuning (FMFT), Parameter Efficient Fine-Tuning (PEFT) has demonstrated superior performance and lower computational overhead in several code understanding tasks, such as code summarization and code search. This advantage can be attributed to PEFT's ability to alleviate the catastrophic forgetting issue of Pre-trained Language Mode

H. Nazim Bicer, Cagdas Tuna, Andreas Walther, Emanuël A. P. Habets

Room geometry inference algorithms rely on the localization of acoustic reflectors to identify boundary surfaces of an enclosure. Rooms with highly absorptive walls or walls at large distances from the measurement setup pose challenges for such algorithms. As it is not always possible to localize all walls, we present a data-driven method to jointly detect a

Trishita Das, Manas Ranjan Pandit, Venugopal Raskatla, Purnesh Singh Badavath

Orbital angular momentum (OAM)-carrying beams have gained significant attention in recent years due to their unique properties and potential to improve spectral efficiency and data transmission rates in optical communication systems. However, fully exploiting the capabilities of the entire OAM mode spectrum remains challenging. The emergence of AI-driven OAM

Zequn Yang, Yake Wei, Ce Liang, Di Hu

Multi-modal models have shown a promising capability to effectively integrate information from various sources, yet meanwhile, they are found vulnerable to pervasive perturbations, such as uni-modal attacks and missing conditions. To counter these perturbations, robust multi-modal representations are highly expected, which are positioned well away from the d

Antti Koskela, Rachel Redberg, Yu-Xiang Wang

Private selection mechanisms (e.g., Report Noisy Max, Sparse Vector) are fundamental primitives of differentially private (DP) data analysis with wide applications to private query release, voting, and hyperparameter tuning. Recent work (Liu and Talwar, 2019; Papernot and Steinke, 2022) has made significant progress in both generalizing private selection mec

Antonio Beltrán, María José Felipe, Carmen Melchor

We prove that if a finite group $G$ contains a conjugacy class $K$ whose square is of the form $1 \cup D$, where $D$ is a conjugacy class of $G$, then $\langle K \rangle$ is a solvable proper normal subgroup of $G$ and we completely determine its structure. We also obtain the structure of those groups in which the assumption above is true for all non-central

Meng Zhang, Maosheng Xiang, Yuan-Sen Ting, Jiahui Wang

Stellar abundances for a large number of stars are key information for the study of Galactic formation history. Large spectroscopic surveys such as DESI and LAMOST take median-to-low resolution ($R\lesssim5000$) spectra in the full optical wavelength range for millions of stars. However, line blending effect in these spectra causes great challenges for the e

Ujjal Karmakar, Arnab Mandal

In this article, we explore the quantum symmetry of the direct sum of a finite family of Cuntz algebras $\{\mathcal{O}_{n_i} \}_{i=1}^{m}$, viewing them as graph $C^*$-algebras associated to the graphs $\{L_{n_i}\}_{i=1}^{m}$ (where $L_n$ denotes the graph containing $n$ loops based at a single vertex), in the category introduced by Joardar and Mandal. It ha

Antonio Beltrán, María José Felipe, Carmen Melchor

Let $G$ be a finite group and $N$ a normal subgroup of $G$. We determine the structure of $N$ when the graph $\Gamma_G(N)$, which is the graph associated to the conjugacy classes of $G$ contained in $N$, has no triangles and when the graph consists in exactly one triangle.

Joshua Bon, Christian P Robert

This is a discussion of the paper "Safe testing" by Gr\"unwald, de Heide, and Koolen, Read before The Royal Statistical Society at a meeting organized by the Research Section on Wednesday, 24 January, 2024

Nanxiang Wen, Xuanmin Cao, Jingyi Chao, Hui Liu

We investigate chiral phase transitions and the screening masses, pole masses, and thermal widths of neutral pion meson with finite temperature $T$ and magnetic field $B$ in a lattice-improved AdS/QCD model, which is constructed by fitting the lattice results of the pseudo-critical temperatures ( T_{\text{pc}}(B) ). Specifically, we have that the chiral cond

Antonio Beltrán, María José Felipe, Carmen Melchor

Let $G$ be a finite group and let $N$ be a normal subgroup of $G$. We attach to $N$ two graphs ${\Gamma}_G(N)$ and ${\Gamma}^{\ast}_G(N)$ related to the conjugacy classes of $G$ contained in $N$ and to the set of primes dividing the sizes of these classes, respectively. These graphs are subgraphs of the ordinary ones associated to the conjugacy classes ofG,

Minori Shintaku, Haruki Oga, Hiroki Kusudo, Edward R. Smith

The contact line (CL) is where solid, liquid and vapor phases meet, and Young's equation describes the macroscopic force balance of the interfacial tensions between these three phases. These interfacial tensions are related to the nanoscale stress inhomogeneity appearing around the interface, and for curved CLs, eg a three-dimensional droplet, another force

Swarnendu Sil

For analyzing stationary Yang-Mills connections in higher dimensions, one has to work with Morrey-Sobolev bundles and connections. The transition maps for a Morrey-Sobolev principal $G$-bundles are not continuous and thus the usual notion of topology does not make sense. In this work, we develop the notion of a topological isomorphism class for a bundle-conn

Hao Jiao, Robert Brandenberger, Alexandre Refregier

By means of N-body simulations, we study early structure formation in the presence of a scaling distribution of cosmic string loops. Cosmic string loops dominate the high redshift halo mass function while the fluctuations seeded by the standard structure formation scenario dominate structure at low redshifts. In our study, the effects of the cosmic string lo

Gabriele Cobucci, Armin Tavakoli

Complex forms of quantum entanglement can arise in two qualitatively different ways; either between many qubits or between two particles with higher-than-qubit dimension. While the many-qubit frontier and the high-dimension frontier both are well-established, state-of-the-art quantum technology is becoming increasingly able to create and manipulate entangled

Nanna Bach Munkholm, Robert Alphinas, Torben Tambo

An architectural framework, based on collaborative filtering using K-nearest neighbor and cosine similarity, was developed and implemented to fit the requirements for the company DecorRaid. The aim of the paper is to test different evaluation techniques within the environment to research the recommender systems performance. Three perspectives were found rele

Ronno Das, Dan Petersen

We give a self-contained and streamlined rendition of Andrea Bianchi's recent proof of the Mumford conjecture using moduli spaces of branched covers.

Muning Wen, Junwei Liao, Cheng Deng, Jun Wang

Large Language Models (LLMs) have shown promise as intelligent agents in interactive decision-making tasks. Traditional approaches often depend on meticulously designed prompts, high-quality examples, or additional reward models for in-context learning, supervised fine-tuning, or RLHF. Reinforcement learning (RL) presents a dynamic alternative for LLMs to ov

Navaneet Villodi, Prabhu Ramachandran

The unavailability of accurate boundary treatment methods for compressible Smoothed Particle Hydrodynamics (SPH) severely limits its ability to simulate flows in and around bodies. To this end, challenges specific to compressible flows with SPH are carefully considered. Based on these, robust and widely applicable boundary treatment methods for compressible

Shousuke Ohmori, Yoshihiro Yamazaki

Dynamical properties of a generalized max-plus model for ultradiscrete limit cycles are investigated.This model includes both the negative feedback model and the Sel'kov model. It exhibits the Neimark-Sacker bifurcation, and possesses stable and unstable ultradiscrete limit cycles. The number of discrete states in the limit cycles can be analytically determi

Bhavya Chopra, Yasharth Bajpai, Param Biyani, Gustavo Soares

The widespread availability of Large Language Models (LLMs) within Integrated Development Environments (IDEs) has led to their speedy adoption. Conversational interactions with LLMs enable programmers to obtain natural language explanations for various software development tasks. However, LLMs often leap to action without sufficient context, giving rise to i

Vittorio Nespeca, Rick Quax, Marcel G. M. Olde Rikkert, Hubert P. L. M. Korzilius

Policymaking for complex challenges such as pandemics necessitates the consideration of intricate implications across multiple domains and scales. Computational models can support policymaking, but a single model is often insufficient for such multidomain and scale challenges. Multi-models comprising several interacting computational models at different scal

Semen Goreninskii, Ulyana Chernova, Elisaveta Prosetskaya, Alina Laushkina

Fabrication of tissue engineering scaffolds with tailored physicochemical and biological characteristics is a relevant task in biomedical engineering. The present work was focused at the evaluation of the effect of fabrication approach (single-channel or multi-channel electrospinning) on the properties of the fabricated poly(lactic acid)(PLA)/poly(epsilon-ca

Enrico Russo, Francesco Giulio Blanco, Maurizio Palesi, Giuseppe Ascia

This paper addresses the critical challenge of managing Quality of Service (QoS) in cloud services, focusing on the nuances of individual tenant expectations and varying Service Level Indicators (SLIs). It introduces a novel approach utilizing Deep Reinforcement Learning for tenant-specific QoS management in multi-tenant, multi-accelerator cloud environments

Xiaoyue Liu, Jingze Li, Benoit Montreuil

Modern logistics systems worldwide are facing unprecedented challenges due to the explosive growth of e-commerce, driving the need for resilient systems to tackle problems such as vulnerable supplies, volatile demands, and fragile transportation networks. Motivated by the innovative concept of the Physical Internet, this paper focuses on resilient capacity d

Thuan Pham, Xingpeng Li

Optimal power flow (OPF) is used to perform generation redispatch in power system real-time operations. N-1 OPF can ensure safe grid operations under diverse contingency scenarios. For large and intricate power networks with numerous variables and constraints, achieving an optimal solution for real-time N-1 OPF necessitates substantial computational resource

Vicente Alvarez, Amin Esfahani

In this work, we study the existence of various classes of standing waves for a nonlinear Schr\"odinger system with quadratic interaction, along with a harmonic or partially harmonic potential. We establish the existence of ground-state normalized solutions for this system, which serve as local minimizers of the associated functionals. To address the difficu

Nguyen Anh Minh, Le Dung Muu, Tran Ngoc Thang

We present an adaptive step-size method, which does not include line-search techniques, for solving a wide class of nonconvex multiobjective programming problems on an unbounded constraint set. We also prove convergence of a general approach under modest assumptions. More specifically, the convexity criterion might not be satisfied by the objective function.

Yuhang Liu, Zhen Zhang, Dong Gong, Erdun Gao

Directed Acyclic Graphs (DAGs) are a standard tool in causal modeling, but their suitability for capturing the complexity of large-scale multimodal data is questionable. In practice, real-world multimodal datasets are often collected from heterogeneous generative processes that do not conform to a single DAG. Instead, they may involve multiple, and even oppo