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Flamenco, recognized by UNESCO as part of the Intangible Cultural Heritage of Humanity, is a profound expression of cultural identity rooted in Andalusia, Spain. However, there is a lack of quantitative studies that help identify characteristic patterns in this long-lived music tradition. In this work, we present a computational analysis of Flamenco lyrics, employing natural language processing and machine learning to categorize over 2000 lyrics into their respective Flamenco genres, termed as $\textit{palos}$. Using a Multinomial Naive Bayes classifier, we find that lexical variation across styles enables to accurately identify distinct $\textit{palos}$. More importantly, from an automatic method of word usage, we obtain the semantic fields that characterize each style. Further, applying a metric that quantifies the inter-genre distance we perform a network analysis that sheds light on the relationship between Flamenco styles. Remarkably, our results suggest historical connections and $\textit{palo}$ evolutions. Overall, our work illuminates the intricate relationships and cultural significance embedded within Flamenco lyrics, complementing previous qualitative discussions with quantitative analyses and sparking new discussions on the origin and development of traditional music genres., Comment: 21 pages, 29 figures

Galaxy cluster mergers are representative of a wide range of physics, making them an excellent probe of the properties of dark matter and the ionized plasma of the intracluster medium. To date, most studies have focused on mergers occurring in the plane of the sky, where morphological features can be readily identified. To allow study of mergers with arbitrary orientation, we have assembled multi-probe data for the eight-cluster ICM-SHOX sample sensitive to both morphology and line of sight velocity. The first ICM-SHOX paper (Silich+2023) provided an overview of our methodology applied to one member of the sample, MACS J0018.5+1626, in order to constrain its merger geometry. That work resulted in an exciting new discovery of a velocity space decoupling of its gas and dark matter distributions. In this work, we describe the availability and quality of multi-probe data for the full ICM-SHOX galaxy cluster sample. These datasets will form the observational basis of an upcoming full ICM-SHOX galaxy cluster sample analysis., Comment: 6 pages, 6 figures; published in Proc. of the mm Universe 2023 conference, EPJ Web of conferences, EDP Sciences

The objective of digital forgetting is, given a model with undesirable knowledge or behavior, obtain a new model where the detected issues are no longer present. The motivations for forgetting include privacy protection, copyright protection, elimination of biases and discrimination, and prevention of harmful content generation. Effective digital forgetting has to be effective (meaning how well the new model has forgotten the undesired knowledge/behavior), retain the performance of the original model on the desirable tasks, and be scalable (in particular forgetting has to be more efficient than retraining from scratch on just the tasks/data to be retained). This survey focuses on forgetting in large language models (LLMs). We first provide background on LLMs, including their components, the types of LLMs, and their usual training pipeline. Second, we describe the motivations, types, and desired properties of digital forgetting. Third, we introduce the approaches to digital forgetting in LLMs, among which unlearning methodologies stand out as the state of the art. Fourth, we provide a detailed taxonomy of machine unlearning methods for LLMs, and we survey and compare current approaches. Fifth, we detail datasets, models and metrics used for the evaluation of forgetting, retaining and runtime. Sixth, we discuss challenges in the area. Finally, we provide some concluding remarks., Comment: 70 pages

Languages are grouped into families that share common linguistic traits. While this approach has been successful in understanding genetic relations between diverse languages, more analyses are needed to accurately quantify their relatedness, especially in less studied linguistic levels such as syntax. Here, we explore linguistic distances using series of parts of speech (POS) extracted from the Universal Dependencies dataset. Within an information-theoretic framework, we show that employing POS trigrams maximizes the possibility of capturing syntactic variations while being at the same time compatible with the amount of available data. Linguistic connections are then established by assessing pairwise distances based on the POS distributions. Intriguingly, our analysis reveals definite clusters that correspond to well known language families and groups, with exceptions explained by distinct morphological typologies. Furthermore, we obtain a significant correlation between language similarity and geographic distance, which underscores the influence of spatial proximity on language kinships., Comment: 36 pages

Monolayers of molybdenum disulfide (MoS2) are the most studied two-dimensional (2D) transition-metal dichalcogenides (TMDs), due to its exceptional optical, electronic, and opto-electronic properties. Recent studies have shown the possibility of incorporating a small amount of magnetic transition metals (e.g., Fe, Co, Mn, V) into MoS2 to form a 2D dilute magnetic semiconductor (2D-DMS). However, the origin of the observed ferromagnetism has remained elusive, due to the presence of randomly generated sulfur vacancies during synthesis that can pair with magnetic dopants to form complex dopant-vacancy configurations altering the magnetic order induced by the dopants. By combining high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) imaging with first-principles density functional theory (DFT) calculations and magnetometry data, we demonstrate the critical effects of sulfur vacancies and their pairings with vanadium atoms on the magnetic ordering in V-doped MoS2 (V-MoS2) monolayers. Additionally, we fabricated a series of field effect transistors on these V-MoS2 monolayers and observed the emergence of p-type behavior as the vanadium concentration increased. Our study sheds light on the origin of ferromagnetism in V-MoS2 monolayers and provides a foundation for future research on defect engineering to tune the electronic and magnetic properties of atomically thin TMD-based DMSs.

$\epsilon$-Differential privacy (DP) is a well-known privacy model that offers strong privacy guarantees. However, when applied to data releases, DP significantly deteriorates the analytical utility of the protected outcomes. To keep data utility at reasonable levels, practical applications of DP to data releases have used weak privacy parameters (large $\epsilon$), which dilute the privacy guarantees of DP. In this work, we tackle this issue by using an alternative formulation of the DP privacy guarantees, named $\epsilon$-individual differential privacy (iDP), which causes less data distortion while providing the same protection as DP to subjects. We enforce iDP in data releases by relying on attribute masking plus a pre-processing step based on data microaggregation. The goal of this step is to reduce the sensitivity to record changes, which determines the amount of noise required to enforce iDP (and DP). Specifically, we propose data microaggregation strategies designed for iDP whose sensitivities are significantly lower than those used in DP. As a result, we obtain iDP-protected data with significantly better utility than with DP. We report on experiments that show how our approach can provide strong privacy (small $\epsilon$) while yielding protected data that do not significantly degrade the accuracy of secondary data analysis., Comment: 17 pages, 6 figures

Human facial data hold tremendous potential to address a variety of classification problems, including face recognition, age estimation, gender identification, emotion analysis, and race classification. However, recent privacy regulations, such as the EU General Data Protection Regulation and others, have restricted the ways in which human images may be collected and used for research. As a result, several previously published data sets containing human faces have been removed from the internet due to inadequate data collection methods that failed to meet privacy regulations. Data sets consisting of synthetic data have been proposed as an alternative, but they fall short of accurately representing the real data distribution. On the other hand, most available data sets are labeled for just a single task, which limits their applicability. To address these issues, we present the Multi-Task Faces (MTF) image data set, a meticulously curated collection of face images designed for various classification tasks, including face recognition, as well as race, gender, and age classification. The MTF data set has been ethically gathered by leveraging publicly available images of celebrities and strictly adhering to copyright regulations. In this paper, we present this data set and provide detailed descriptions of the followed data collection and processing procedures. Furthermore, we evaluate the performance of five deep learning (DL) models on the MTF data set across the aforementioned classification tasks. Additionally, we compare the performance of DL models over the processed MTF data and over raw data crawled from the internet. The reported results constitute a baseline for further research employing these data. The MTF data set can be accessed through the following link (please cite the present paper if you use the data set): https://github.com/RamiHaf/MTF_data_set, Comment: 21 pages, 2 figures, 9 Tables

The alleged threat of reconstruction attacks has led the U.S. Census Bureau (USCB) to replace in the Decennial Census 2020 the traditional statistical disclosure limitation based on rank swapping with one based on differential privacy (DP). This has resulted in substantial accuracy loss of the released statistics. Worse yet, it has been shown that the reconstruction attacks used as an argument to move to DP are very far from allowing unequivocal reidentification of the respondents, because in general there are a lot of reconstructions compatible with the released statistics. In a very recent paper, a new reconstruction attack has been proposed, whose goal is to indicate the confidence that a reconstructed record was in the original respondent data. The alleged risk of serious disclosure entailed by such confidence-ranked reconstruction has renewed the interest of the USCB to use DP-based solutions. To forestall the potential accuracy loss in future data releases resulting from adoption of these solutions, we show in this paper that the proposed confidence-ranked reconstruction does not threaten privacy. Specifically, we report empirical results showing that the proposed ranking cannot guide reidentification or attribute disclosure attacks, and hence it fails to warrant the USCB's move towards DP. Further, we also demonstrate that, due to the way the Census data are compiled, processed and released, it is not possible to reconstruct original and complete records through any methodology, and the confidence-ranked reconstruction not only is completely ineffective at accurately reconstructing Census records but is trivially outperformed by an adequate interpretation of the released aggregate statistics., Comment: 20 pages, 5 figures, 4 tables

Entropy estimation is a fundamental problem in information theory that has applications in various fields, including physics, biology, and computer science. Estimating the entropy of discrete sequences can be challenging due to limited data and the lack of unbiased estimators. Most existing entropy estimators are designed for sequences of independent events and their performance vary depending on the system being studied and the available data size. In this work we compare different entropy estimators and their performance when applied to Markovian sequences. Specifically, we analyze both binary Markovian sequences and Markovian systems in the undersampled regime. We calculate the bias, standard deviation and mean squared error for some of the most widely employed estimators. We discuss the limitations of entropy estimation as a function of the transition probabilities of the Markov processes and the sample size. Overall, this paper provides a comprehensive comparison of entropy estimators and their performance in estimating entropy for systems with memory, which can be useful for researchers and practitioners in various fields., Comment: 19 pages, 9 figures

The detection of gravitational waves from a binary neutron star merger by Advanced LIGO and Advanced Virgo (GW170817), along with the discovery of the electromagnetic counterparts of this gravitational wave event, ushered in a new era of multimessenger astronomy, providing the first direct evidence that BNS mergers are progenitors of short gamma-ray bursts (GRBs). Such events may also produce very-high-energy (VHE, > 100GeV) photons which have yet to be detected in coincidence with a gravitational wave signal. The Cherenkov Telescope Array (CTA) is a next-generation VHE observatory which aims to be indispensable in this search, with an unparalleled sensitivity and ability to slew anywhere on the sky within a few tens of seconds. New observing modes and follow-up strategies are being developed for CTA to rapidly cover localization areas of gravitational wave events that are typically larger than the CTA field of view. This work will evaluate and provide estimations on the expected number of of gravitational wave events that will be observable with CTA, considering both on- and off-axis emission. In addition, we will present and discuss the prospects of potential follow-up strategies with CTA., Comment: Presented at the 38th International Cosmic Ray Conference (ICRC 2023), 2023 (arXiv:2309.08219)

Identifying and controlling decoherence in single electron sources (SES) is important for their applications in quantum information processing. The recent experiments with ultrashort electron pulses [J. D. Fletcher et al., Nat. Commun. 10, 5298 (2019)] demonstrate strong decoherence that cannot be caused by traditional mechanisms such as electron-electron or electron-phonon interactions. Here we propose timing noise as a universal model, consistent with existing experimental data, to explain strong decoherence of ultrafast SES pulses, without resorting to any specific microscopic mechanism for such decoherence. We also propose a protocol to filter out timing noise which works even in the presence of other decoherence effects, such as those present in, e.g., low-energy SESs., Comment: 12 pages (including references), results are generalized

Motivated by the dramatic disappearance of endangered languages observed in recent years, a great deal of attention has been given to the modeling of language competition in order to understand the factors that promote the disappearance of a language and its unfolding dynamics. With this in mind, we build on existing network models of language competition in bilingual societies. These models deal with the interplay between the usage of a language (link state) and the preference or attitude of the speakers towards the language (node state). In this work, we allow for the case where agents have the freedom to adapt their local interactions in accordance with their language preference. This is modeled by introducing a local rewiring mechanism triggered by the dissatisfaction of an agent with its usage of a given language. Our numerical simulations show that permitting this freedom to agents likely results in linguistically segregated communities for small network sizes. However, for networks of sufficiently large size, the extinction of one of the languages is the most probable scenario. Furthermore, we analyze how the fraction of minority speakers changes with the system size and we find that this fraction grows as the total population increases, which is consistent with existing data. Overall, the results of this work help us understand the impact of speakers' preferences and choices in the complex language landscape of bilingual societies., Comment: 12 pages, 9 figures

Galaxy cluster mergers are rich sources of information to test cluster astrophysics and cosmology. However, cluster mergers produce complex projected signals that are difficult to interpret physically from individual observational probes. Multi-probe constraints on the gas and dark matter cluster components are necessary to infer merger parameters that are otherwise degenerate. We present ICM-SHOX (Improved Constraints on Mergers with SZ, Hydrodynamical simulations, Optical, and X-ray), a systematic framework to jointly infer multiple merger parameters quantitatively via a pipeline that directly compares a novel combination of multi-probe observables to mock observables derived from hydrodynamical simulations. We report a first application of the ICM-SHOX pipeline to MACS J0018.5+1626, wherein we systematically examine simulated snapshots characterized by a wide range of initial parameters to constrain the MACS J0018.5+1626 merger geometry. We constrain the epoch of MACS J0018.5+1626 to the range $0$--$60$ Myr post-pericenter passage, and the viewing angle is inclined $\approx 27$--$40$ degrees from the merger axis. We obtain constraints for the impact parameter ($\lesssim 250$ kpc), mass ratio ($\approx 1.5$--$3.0$), and initial relative velocity when the clusters are separated by 3 Mpc ($\approx 1700$--3000 km s$^{-1}$). The primary and secondary clusters initially (at 3 Mpc) have gas distributions that are moderately and strongly disturbed, respectively. We discover a velocity space decoupling of the dark matter and gas distributions in MACS J0018.5+1626, traced by cluster-member galaxy velocities and the kinematic Sunyaev-Zel'dovich effect, respectively. Our simulations indicate this decoupling is dependent on the different collisional properties of the two distributions for particular merger epochs, geometries, and viewing angles., Comment: 40 pages, 18 figures; accepted for publication in ApJ