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Matrix concentration inequalities, intimately connected to the Non-Commutative Khintchine inequality, have been an important tool in both applied and pure mathematics. We study tensor versions of these inequalities, and establish non-asymptotic inequalities for the $\ell_p$ injective norm of random tensors with correlated entries. In certain regimes of $p$ and the tensor order, our tensor concentration inequalities are nearly optimal in their dimension dependencies. We illustrate our result with applications to problems including structured models of random tensors and matrices, tensor PCA, and connections to lower bounds in coding theory. Our techniques are based on covering number estimates as opposed to operator theoretic tools, which also provide a geometric proof of a weaker version of the Non-Commutative Khintchine inequality, motivated by a question of Talagrand.

We present a novel variational derivation of the Maxwell-GLM system, which augments the original vacuum Maxwell equations via a generalized Lagrangian multiplier approach (GLM) by adding two supplementary acoustic subsystems and which was originally introduced by Munz et al. for purely numerical purposes in order to treat the divergence constraints of the magnetic and the electric field in the vacuum Maxwell equations within general-purpose and non-structure-preserving numerical schemes for hyperbolic PDE. Among the many mathematically interesting features of the model are: i) its symmetric hyperbolicity, ii) the extra conservation law for the total energy density and, most importantly, iii) the very peculiar combination of the basic differential operators, since both, curl-curl and div-grad combinations are mixed within this kind of system. A similar mixture of Maxwell-type and acoustic-type subsystems has recently been also forwarded by Buchman et al. in the context of a reformulation of the Einstein field equations of general relativity in terms of tetrads. This motivates our interest in this class of PDE, since the system is by itself very interesting from a mathematical point of view and can therefore serve as useful prototype system for the development of new structure-preserving numerical methods. Up to now, to the best of our knowledge, there exists neither a rigorous variational derivation of this class of hyperbolic PDE systems, nor do exactly energy-conserving and asymptotic-preserving schemes exist for them. The objectives of this paper are to derive the Maxwell-GLM system from an underlying variational principle, show its consistency with Hamiltonian mechanics and special relativity, extend it to the general nonlinear case and to develop new exactly energy-conserving and asymptotic-preserving finite volume schemes for its discretization.

We investigate the constraints on the Inert Doublet Model (IDM), a minimal extension of the Standard Model of Particle Physics featuring a scalar dark matter candidate, using data from recent and future gamma-ray observatories. The relevance of the model for indirect searches of dark matter stems from two key features: first, in the high-mass regime, IDM can achieve the correct dark matter relic abundance for masses between approximately 500 GeV and 25 TeV, aligning perfectly with the energy sensitivity of Imaging Atmospheric Cherenkov Telescopes. Second, this regime is dominated by co-annihilation processes, which elevate the thermal-relic velocity-weighted annihilation cross-section to the range of 0.5 $-$ 1.0$\times 10^{-25}$ cm$^3$ s$^{-1}$, thereby enhancing the potential gamma-ray signal from dark matter annihilation. Analyzing recent H.E.S.S. observations of the Galactic Center region, we find that dark matter particle masses within the 1 to 8 TeV range are excluded by current data. Furthermore, we project that the Cherenkov Telescope Array Observatory (CTAO) will comprehensively probe the remaining viable parameter space of the IDM. Our findings are further examined in the context of the most recent theoretical constraints, collider searches, and direct detection results from the LUX-ZEPLIN experiment., Comment: 10 pages, 5 figures

A one-third monolayer of the heavy metals Sn and Pb deposited on semiconductor substrates can lead to a $\sqrt{3}\times\sqrt{3}$ surface reconstruction, constituting an exciting triangular lattice material platform. A long history of experiments identified charge-ordered and magnetic ground states. These discoveries were accompanied by a decades-long debate of whether electron correlations or other effects involving phonons are the driving force of the symmetry-broken states. The most recent discovery of superconductivity in boron-doped Sn/Si(111) with a $T_c$ between 5K and 9K led to a renewed excitement. Here we revisit the electronic and phononic properties of Sn and Pb adatom triangular lattices on Si(111) and SiC(0001). For all materials we compute relativistic bandstructures using DFT+$U$ where $U$ is only applied to the substrate atoms in order to adjust the band gap to match the experimental value; as a consequence, some of the resulting tight-binding parameters of the metallic surface band differ substantially compared to previous studies. Remarkably, for Pb/SiC(0001) we predict Rashba spin-orbit coupling as large as 45% of the nearest-neighbor hopping energy. In addition, we compute the phonon spectra and electron-phonon coupling constants for all materials, and for Pb/Si(111) even relativistically although the inclusion of spin-orbit coupling has surprisingly little effect on the electron-phonon coupling constant. We conclude that the resulting couplings are too weak to account for electron-phonon mediated superconductivity in any of these materials., Comment: 12 pages, 7 figures, 7 tables

With the aim of enabling robots to cooperate with humans, carry out human-like tasks, or navigate among humans, we need to ensure that they are equipped with the ability to comprehend human behaviors and use the extracted knowledge for intelligent decision-making. This ability is particularly important in the safety-critical and human-centred environment of health-care institutions. In the field of robotic navigation, the most cutting-edge approaches to enhancing robot reliability in the application domain of healthcare facilities and in general pertain to augmenting navigation systems with human-aware properties. To implement this in our work, the Co-operative Human-Aware Navigation planner has been integrated into the ROS-based differential-drive robot MARRtina and exhaustively challenged within various simulated contexts and scenarios (mainly modelling the situations relevant in the medical domain) to draw attention to the integrated system's benefits and identify its drawbacks or instances of poor performance while exploring the scope of system capabilities and creating a full characterization of its applicability. The simulation results are then presented to medical experts, and the enhanced robot acceptability within the domain is validated with them as the robot is further planned for deployment.

In the last years, model-related publications have been exploring the application of modeling techniques in different domains. Initially focused on UML and the Model-Driven Architecture approach, the literature has been evolving towards the usage of more general concepts such as Model-Driven Development or Model-Driven Engineering. With the emergence of Low-Code software development platforms, the modeling community has been studying how these two fields may combine and benefit from each other, thus leading to the publication of a number of works in recent years. In this paper, we present a metascience study of Low-Code. Our study has a two-fold approach: (1) to examine the composition (size and diversity) of the emerging Low-Code community; and (2) to investigate how this community differs from the "classical" model-driven community in terms of people, venues, and types of publications. Through this study, we aim to benefit the low-code community by helping them better understand its relationship with the broader modeling community. Ultimately, we hope to trigger a discussion about the current and possible future evolution of the low-code community as part of its consolidation as a new research field.

Photonics has been revolutionized by breakthroughs in optical metasurfaces and layered two-dimensional materials. Yet, integrating these two fields in a singular system has remained challenging. Here, we introduce the concept of van der Waals (vdW) heterostructure metasurfaces, where ultrathin multilayer vdW material stacks are shaped into precisely engineered resonant nanostructures for boosting light-matter interactions. By leveraging quasi-bound states in the continuum to create intrinsic cavities from WS$_2$ monolayers encapsulated in hexagonal boron nitride, we observe room-temperature strong coupling and polaritonic luminescence, which further unveils a saturation of the strong-coupling regime at ultralow fluences <1 nJ/cm2, more than three orders of magnitude smaller than in previous 2D-cavity systems. Our approach, seamlessly merging metasurfaces and vdW materials, unlocks new avenues for ultrathin optical devices with atomic-scale precision and control., Comment: main + supplementary (28 pages, 4 main figures, 9 supplementary figures)

This paper presents the development and comparative evaluation of three voice command pipelines for controlling a Tello drone, using speech recognition and deep learning techniques. The aim is to enhance human-machine interaction by enabling intuitive voice control of drone actions. The pipelines developed include: (1) a traditional Speech-to-Text (STT) followed by a Large Language Model (LLM) approach, (2) a direct voice-to-function mapping model, and (3) a Siamese neural network-based system. Each pipeline was evaluated based on inference time, accuracy, efficiency, and flexibility. Detailed methodologies, dataset preparation, and evaluation metrics are provided, offering a comprehensive analysis of each pipeline's strengths and applicability across different scenarios.

The physical mechanisms regulating the power sharing at the outer targets of L-mode double-null (DN) configurations are investigated using nonlinear, flux-driven, three-dimensional two-fluid simulations. Scans of parameters that regulate the turbulent level, such as the plasma resistivity and the magnetic imbalance, reveal that the power asymmetry in DN configurations is determined by the combined effects of diamagnetic drift, turbulence, and geometrical factor. Leveraging these observations, an analytical theory-based scaling law for the power-sharing asymmetry is derived and compared with nonlinear simulations. These comparisons indicate that the scaling law effectively captures the trends observed in simulations. Validation with experimental data from TCV DN discharges demonstrates agreement of the scaling law with the experimental results., Comment: 30 pages, 9 figures

A new approach for constructing polar-like boundary-conforming coordinates inside a toroid with strongly shaped cross-sections is presented. A coordinate mapping is obtained through a variational approach, which involves identifying extremal points of a proposed action in the mapping space from [0, 2{\pi}] x [0, 2{\pi}] x [0, 1] to a toroidal domain in R3. This approach employs an action built on the squared Jacobian and radial length. Extensive testing is conducted on general toroidal boundaries using a global Fourier-Zernike basis via action minimization. The results demonstrate successful coordinate construction capable of accurately describing strongly shaped toroidal domains. The coordinate construction is successfully applied to the computation of 3D MHD equilibria in the GVEC code where the use of traditional coordinate construction by interpolation from the boundary failed., Comment: 18 pages and 10 figures submitted to the Journal of Plasma Physics

The Local Volume Complete Cluster Survey (LoVoCCS) is an on-going program to observe nearly a hundred low-redshift X-ray-luminous galaxy clusters (redshifts $0.0310^{44}$ erg/s) with the Dark Energy Camera (DECam), capturing data in $u,g,r,i,z$ bands with a $5\sigma$ point source depth of approximately 25-26th AB magnitudes. Here, we map the aperture masses in 58 galaxy cluster fields using weak gravitational lensing. These clusters span a variety of dynamical states, from nearly relaxed to merging systems, and approximately half of them have not been subject to detailed weak lensing analysis before. In each cluster field, we analyze the alignment between the 2D mass distribution described by the aperture mass map, the 2D red-sequence (RS) galaxy distribution, and the brightest cluster galaxy (BCG). We find that the orientations of the BCG and the RS distribution are strongly aligned throughout the interiors of the clusters: the median misalignment angle is 19 deg within 2 Mpc. We also observe the alignment between the orientations of the RS distribution and the overall cluster mass distribution (by a median difference of 32 deg within 1 Mpc), although this is constrained by galaxy shape noise and the limitations of our cluster sample size. These types of alignment suggest long-term dynamical evolution within the clusters over cosmic timescales., Comment: 40 pages, 16 figures, 5 tables; revised and accepted for publication in ApJ

This paper presents a detailed case study examining the application of Large Language Models (LLMs) in the construction of test cases within the context of software engineering. LLMs, characterized by their advanced natural language processing capabilities, are increasingly garnering attention as tools to automate and enhance various aspects of the software development life cycle. Leveraging a case study methodology, we systematically explore the integration of LLMs in the test case construction process, aiming to shed light on their practical efficacy, challenges encountered, and implications for software quality assurance. The study encompasses the selection of a representative software application, the formulation of test case construction methodologies employing LLMs, and the subsequent evaluation of outcomes. Through a blend of qualitative and quantitative analyses, this study assesses the impact of LLMs on test case comprehensiveness, accuracy, and efficiency. Additionally, delves into challenges such as model interpretability and adaptation to diverse software contexts. The findings from this case study contributes with nuanced insights into the practical utility of LLMs in the domain of test case construction, elucidating their potential benefits and limitations. By addressing real-world scenarios and complexities, this research aims to inform software practitioners and researchers alike about the tangible implications of incorporating LLMs into the software testing landscape, fostering a more comprehensive understanding of their role in optimizing the software development process.

High dimension, low sample size (HDLSS) problems are numerous among real-world applications of machine learning. From medical images to text processing, traditional machine learning algorithms are usually unsuccessful in learning the best possible concept from such data. In a previous work, we proposed a dissimilarity-based approach for multi-view classification, the Random Forest Dissimilarity (RFD), that perfoms state-of-the-art results for such problems. In this work, we transpose the core principle of this approach to solving HDLSS classification problems, by using the RF similarity measure as a learned precomputed SVM kernel (RFSVM). We show that such a learned similarity measure is particularly suited and accurate for this classification context. Experiments conducted on 40 public HDLSS classification datasets, supported by rigorous statistical analyses, show that the RFSVM method outperforms existing methods for the majority of HDLSS problems and remains at the same time very competitive for low or non-HDLSS problems., Comment: 23 pages. To be published in statistics and computing (accepted September 26, 2023)

With the popularization of the internet, smartphones and social media, information is being spread quickly and easily way, which implies bigger traffic of information in the world, but there is a problem that is harming society with the dissemination of fake news. With a bigger flow of information, some people are trying to disseminate deceptive information and fake news. The automatic detection of fake news is a challenging task because to obtain a good result is necessary to deal with linguistics problems, especially when we are dealing with languages that not have been comprehensively studied yet, besides that, some techniques can help to reach a good result when we are dealing with text data, although, the motivation of detecting this deceptive information it is in the fact that the people need to know which information is true and trustful and which one is not. In this work, we present the effect the pre-processing methods such as lemmatization and stemming have on fake news classification, for that we designed some classifier models applying different pre-processing techniques. The results show that the pre-processing step is important to obtain betters results, the stemming and lemmatization techniques are interesting methods and need to be more studied to develop techniques focused on the Portuguese language so we can reach better results.