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The Statistical Classification of Economic Activities in the European Community (NACE) is the standard classification system for the categorization of economic and industrial activities within the European Union. This paper proposes a novel approach to transform the NACE classification into low-dimensional embeddings, using state-of-the-art models and dimensionality reduction techniques. The primary challenge is the preservation of the hierarchical structure inherent within the original NACE classification while reducing the number of dimensions. To address this issue, we introduce custom metrics designed to quantify the retention of hierarchical relationships throughout the embedding and reduction processes. The evaluation of these metrics demonstrates the effectiveness of the proposed methodology in retaining the structural information essential for insightful analysis. This approach not only facilitates the visual exploration of economic activity relationships, but also increases the efficacy of downstream tasks, including clustering, classification, integration with other classifications, and others. Through experimental validation, the utility of our proposed framework in preserving hierarchical structures within the NACE classification is showcased, thereby providing a valuable tool for researchers and policymakers to understand and leverage any hierarchical data.

The equation of state of dilute Bose gases, in which the energy only depends on the $s$-wave scattering length, is rather unknown beyond the universal limit. We have carried out a bunch of diffusion Monte Carlo calculations up to gas parameters of $10^{-2}$ to explore how the departure from the universality emerges. Using different model potentials, we calculate the energies of the gas in an exact way, within some statistical noise, and report the results as a function of the three relevant scattering parameters: the $s$-wave scattering length $a_0$, the $s$-wave effective range $r_0$, and the $p$-wave scattering length $a_1$. If the effective range is not large we observe universality in terms of $a_0$ and $r_0$ up to gas parameters of $10^{-2}$. If $r_0$ grows the regime of universality in these two parameters is reduced and effects of $a_1$ start to be observed. In the $(a_0,r_0)$ universal regime we propose an analytical law that reproduces fairly well the exact energies., Comment: 16 pages

Itinerant ferromagnetism is one of the most studied quantum phase transitions, the transition point and the nature of this phase transition being widely discussed. In dilute Fermi liquids, this analysis has been carried out up to second-order in the gas parameter, where the results for any spin degeneracy are universal in terms of only the s-wave scattering length $a_0$. We extend this analysis to third-order where energies depend, not only on $a_0$, but also on the s-wave effective range $r_0$ and the p-wave scattering length $a_1$. The introduction in the theory of these new parameters changes the transition point, with respect to the second-order estimation, and also can modify the nature of the phase transition itself. We analyze these interaction effects on the phase transition for different spin values. The emerging phase diagram shows that the type of ferromagnetic transition changes dramatically as a function of $r_0$ and $a_1$ and, importantly, that this classification is not solely determined by the spin value, as happens at second order., Comment: 9 pages

The global proliferation of social media platforms has transformed political communication, making the study of online interactions between politicians and voters crucial for understanding contemporary political discourse. In this work, we examine the dynamics of political messaging and voter behavior on Twitter during the 2022 Italian general elections. Specifically, we focus on voters who changed their political preferences over time (swing voters), identifying significant patterns of migration and susceptibility to propaganda messages. Our analysis reveals that during election periods, the popularity of politicians increases, and there is a notable variation in the use of persuasive language techniques, including doubt, loaded language, appeals to values, and slogans. Swing voters are more vulnerable to these propaganda techniques compared to non-swing voters, with differences in vulnerability patterns across various types of political shifts. These findings highlight the nuanced impact of social media on political opinion in Italy.

Children come across various media items online, many of which are selected by recommender systems (RS) primarily designed for adults. The specific nature of the content selected by RS to display on online platforms used by children - although not necessarily targeting them as a user base - remains largely unknown. This raises questions about whether such content is appropriate given children's vulnerable stages of development and the potential risks to their well-being. In this position paper, we reflect on the relationship between RS and children, emphasizing the possible adverse effects of the content this user group might be exposed to online. As a step towards fostering safer interactions for children in online environments, we advocate for researchers, practitioners, and policymakers to undertake a more comprehensive examination of the impact of RS on children - one focused on harms. This would result in a more holistic understanding that could inform the design and deployment of strategies that would better suit children's needs and preferences while actively mitigating the potential harm posed by RS; acknowledging that identifying and addressing these harms is complex and multifaceted., Comment: 7 pages, 2 figures, DCDW 2024

Building on the recent release of a new \emph{Gaia}-based blue straggler star catalog in Galactic open star clusters (OCs), we explored the properties of these stars in a cluster sample spanning a wide range in fundamental parameters. We employed \emph{Gaia} EDR3 to assess the membership of any individual blue or yellow straggler to their parent cluster. We then made use of the \texttt{ASteCA} code to estimate the fundamental parameters of the selected clusters, in particular, the binary fraction. With all this at hand, we critically revisited the relation of the blue straggler population and the latter. For the first time, we found a correlation between the number of blue stragglers and the host cluster binary fraction and binaries. This supports the hypothesis that binary evolution is the most viable scenario of straggler formation in Galactic star clusters. The distribution of blue stragglers in the Gaia color-magnitude diagram was then compared with a suite of composite evolutionary sequences derived from binary evolutionary models that were run by exploring a range of binary parameters: age, mass ratio, period, and so forth. The excellent comparison between the bulk distribution of blue stragglers and the composite evolutionary sequences loci further supports the binary origin of most stragglers in OCs and paves the way for a detailed study of individual blue stragglers, Comment: 15 pages, in press in Astronomy and Astrophysics

Narratives can be powerful tools for inspiring action on pressing societal issues such as climate change. While social science theories offer frameworks for understanding the narratives that arise within collective movements, these are rarely applied to the vast data available from social media platforms, which play a significant role in shaping public opinion and mobilizing collective action. This gap in the empirical evaluation of online narratives limits our understanding of their relationship with public response. In this study, we focus on plant-based diets as a form of pro-environmental action and employ natural language processing to operationalize a theoretical framework of moral narratives specific to the vegan movement. We apply this framework to narratives found in YouTube videos promoting environmental initiatives such as Veganuary, Meatless March, and No Meat May. Our analysis reveals that several narrative types, as defined by the theory, are empirically present in the data. To identify narratives with the potential to elicit positive public engagement, we used text processing to estimate the proportion of comments supporting collective action across narrative types. Video narratives advocating social fight, whether through protest or through efforts to convert others to the cause, are associated with a stronger sense of collective action in the respective comments. These narrative types also demonstrate increased semantic coherence and alignment between the message and public response, markers typically associated with successful collective action. Our work offers new insights into the complex factors that influence the emergence of collective action, thereby informing the development of effective communication strategies within social movements., Comment: 15 pages, 7 figures, 7 tables. Accepted at ICWSM 2024

Public discourse on critical issues such as climate change is progressively shifting to social media platforms that prioritize short-form video content. Content creators acting on those platforms play a pivotal role in shaping the discourse, yet the dynamics of communication and audience reactions across platforms remain underexplored. To improve our understanding of this transition, we studied the video content produced by 21 prominent YouTube creators who have expanded their influence to TikTok as information disseminators. Using dictionary-based tools and BERT-based embeddings, we analyzed the transcripts of nearly 7k climate-related videos across both platforms and the 574k comments they received. We found that, when publishing on TikTok, creators use a more emotionally resonant, self-referential, and action-oriented language compared to YouTube. We also observed a strong semantic alignment between videos and comments, with creators who excel at diversifying their TikTok content from YouTube typically receiving responses that more closely align with their produced content. This suggests that tailored communication strategies hold greater promise in directing public discussion toward desired topics, which bears implications for the design of effective climate communication campaigns., Comment: Accepted at WebSci 2024. 6 pages, 5 figures, 4 tables

Exploring behavioral change in the digital age is imperative for societal progress in the context of 21st-century challenges. We analyzed 148 articles (2000-2023) and built a map that categorizes behaviors and change detection methodologies, platforms of reference, and theoretical frameworks that characterize online behavior change. Our findings uncover a focus on sentiment shifts, an emphasis on API-restricted platforms, and limited theory integration. We call for methodologies able to capture a wider range of behavioral types, diverse data sources, and stronger theory-practice alignment in the study of online behavioral change.

We introduce a novel re-ranking model that aims to augment the functionality of standard search engines to support classroom search activities for children (ages 6 to 11). This model extends the known listwise learning-to-rank framework by balancing risk and reward. Doing so enables the model to prioritize Web resources of high educational alignment, appropriateness, and adequate readability by analyzing the URLs, snippets, and page titles of Web resources retrieved by a given mainstream search engine. Experimental results, including an ablation study and comparisons with existing baselines, showcase the correctness of the proposed model. The outcomes of this work demonstrate the value of considering multiple perspectives inherent to the classroom setting, e.g., educational alignment, readability, and objectionability, when applied to the design of algorithms that can better support children's information discovery., Comment: Extended version of the manuscript to appear in proceedings of the 22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology

We introduce the Unified Cluster Catalogue, the largest catalogue of stellar clusters currently listing nearly 14000 objects. In this initial release it exclusively contains Milky Way open clusters, with plans to include other objects in future updates. Each cluster is processed using a novel probability membership algorithm, which incorporates the coordinates, parallax, proper motions, and their associated uncertainties for each star into the probability assignment process. We employ Gaia DR3 data up to a G magnitude of 20, resulting in the identification of over a million probable members. The catalogue is accompanied by a publicly accessible website designed to simplify the search and data exploration of stellar clusters. The website can be accessed at https://ucc.ar.

In the realm of Computational Social Science (CSS), practitioners often navigate complex, low-resource domains and face the costly and time-intensive challenges of acquiring and annotating data. We aim to establish a set of guidelines to address such challenges, comparing the use of human-labeled data with synthetically generated data from GPT-4 and Llama-2 in ten distinct CSS classification tasks of varying complexity. Additionally, we examine the impact of training data sizes on performance. Our findings reveal that models trained on human-labeled data consistently exhibit superior or comparable performance compared to their synthetically augmented counterparts. Nevertheless, synthetic augmentation proves beneficial, particularly in improving performance on rare classes within multi-class tasks. Furthermore, we leverage GPT-4 and Llama-2 for zero-shot classification and find that, while they generally display strong performance, they often fall short when compared to specialized classifiers trained on moderately sized training sets., Comment: Accepted at EACL 2024. 14 pages, 4 figures, 2 tables

This paper aims to provide a careful and self-contained introduction to the theory of topological degree in Euclidean spaces. It is intended for people mostly interested in analysis and, in general, a heavy background in algebraic or differential topology is not required., Comment: 25 pages

Using online information discovery as a case study, in this position paper we discuss the need to design, develop, and deploy (conversational) agents that can -- non-intrusively -- guide children in their quest for online resources rather than simply finding resources for them. We argue that agents should "let children learn" and should be built to take on a teacher-facilitator function, allowing children to develop their technical and critical thinking abilities as they interact with varied technology in a broad range of use cases., Comment: 6 pages

The most important parameters in the study of low-energy scattering are the s-wave and p-wave scattering lengths and the s-wave effective range. We solve the scattering problem and find two useful formulas for the scattering length and the effective range for any angular momentum, as long as the Wigner threshold law holds. Using that formalism, we obtain a set of useful formulas for the angular-momentum scattering parameters of four different model potentials: hard-sphere, soft-sphere, spherical well, and well-barrier potentials. The behavior of the scattering parameters close to Feshbach resonances is also analyzed. Our derivations can be useful as hands-on activities for learning scattering theory., Comment: Main document with two supplementary materials

Studying systems where many individual bodies in motion interact with one another is a complex and interesting area. Simple mechanisms that may be determined for biological, chemical, or physical reasons can lead to astonishingly complex results that require a further understanding of the moving bodies. With the increasing interaction between computation and various scientific areas, it has become more useful, feasible, and important to create models for these systems. Here, we present two families of models, deterministic and nondeterministic, along with three distinct and realistic interaction mechanisms. These are combined in a unique way to provide the groundwork for particle system models across multiple disciplines. This work has applications that range from biology to chemistry and physics. In addition to describing the motivations and math behind all the models, software is provided that allows researchers to quickly adjust and implement what is described here., Comment: 11 pages, 1 figure

Fuel cell electric vehicles have earned substantial attentions in recent decades due to their high-efficiency and zero-emission features, while the high operating costs remain the major barrier towards their large-scale commercialization. In such context, this paper aims to devise an energy management strategy for an urban postal-delivery fuel cell electric vehicle for operating cost mitigation. First, a data-driven dual-loop spatial-domain battery state-of-charge reference estimator is designed to guide battery energy depletion, which is trained by real-world driving data collected in postal delivery missions. Then, a fuzzy C-means clustering enhanced Markov speed predictor is constructed to project the upcoming velocity. Lastly, combining the state-of-charge reference and the forecasted speed, a model predictive control-based cost-optimization energy management strategy is established to mitigate vehicle operating costs imposed by energy consumption and power-source degradations. Validation results have shown that 1) the proposed strategy could mitigate the operating cost by 4.43% and 7.30% in average versus benchmark strategies, denoting its superiority in term of cost-reduction and 2) the computation burden per step of the proposed strategy is averaged at 0.123ms, less than the sampling time interval 1s, proving its potential of real-time applications., Comment: Submission to journal