The Role of Dust in Models of Population Synthesis

We have employed state-of-the-art evolutionary models of low and intermediate-mass AGB stars, and included the effect of circumstellar dust shells on the spectral energy distribution (SED) of AGB stars, to revise the Padua library of isochrones (Bertelli et al. 1994). The major revision involves the thermally pulsing AGB phase, that is now taken from fully evolutionary calculations by Weiss & Ferguson (2009). Two libraries of about 600 AGB dust-enshrouded SEDs each have also been calculated, one for oxygen-rich M-stars and one for carbon-rich C-stars. Each library accounts for different values of input parameters like the optical depth {\tau}, dust composition, and temperature of the inner boundary of the dust shell. These libraries of dusty AGB spectra have been implemented into a large composite library of theoretical stellar spectra, to cover all regions of the Hertzsprung-Russell Diagram (HRD) crossed by the isochrones. With the aid of the above isochrones and libraries of stellar SEDs, we have calculated the spectro-photometric properties (SEDs, magnitudes, and colours) of single-generation stellar populations (SSPs) for six metallicities, more than fifty ages (from 3 Myr to 15 Gyr), and nine choices of the Initial Mass Function. The new isochrones and SSPs have been compared to the colour-magnitude diagrams (CMDs) of field populations in the LMC and SMC, with particular emphasis on AGB stars, and the integrated colours of star clusters in the same galaxies, using data from the SAGE (Surveying the Agents of Galaxy Evolution) catalogues. We have also examined the integrated colours of a small sample of star clusters located in the outskirts of M31. The agreement between theory and observations is generally good. In particular, the new SSPs reproduce the red tails of the AGB star distribution in the CMDs of field stars in the Magellanic Clouds.Comment: Accepted for publication in MNRA

Clefts in context : A QUD-perspective on c'est / il y a utterances in spoken French

In this paper we present the results of a pragmatic analysis of French full clefts and monoclausal c'est/il y a utterances (e.g. c'est la femme qui l'a tué 'it's the wife who killed him' vs. c'est la femme 'it's the wife' respectively in answer to the question 'who killed him?'), when these structures are used as pragmatic strategies to focalize the subject in spoken French. Unlike full cleft sentences, monoclausal c'est and il y a utterances have received less attention in the literature, especially with regard to focus and its realization in spontaneous speech. Investigating the opposition between full clefts and monoclausal forms as well as the questions that these clefts answer allows us to arrive at a more precise understanding of the discourse functions of these structures and the pragmatic contexts in which they are felicitous. The corpus that is used (sgs, spontaneous spoken French) contains many question-answer pairs due to its interactive setup, thus enabling a clear analysis of the types of Question Under Discussion that the clefts answer. The data show that monoclausal utterances are more likely to answer highly active QUDs, whereas full clefts are more likely to answer less active QUDs. The level of activation is determined in terms of proximity and implicitness of the QUD (immediately-preceding the cleft, further away or implicit), and - when the question is uttered explicitly - modality (wh or yes/no) also plays a role

Effect of the star formation histories on the SFR-M_* relation at z ≥ 2

We investigate the effect of different star formation histories (SFHs) on the relation between stellar mass (M_∗) and star formation rate (SFR) using a sample of galaxies with reliable spectroscopic redshift z_(spec)> 2 drawn from the VIMOS Ultra-Deep Survey (VUDS). We produce an extensive database of dusty model galaxies, calculated starting from a new library of single stellar population (SSPs) models, weighted by a set of 28 different star formation histories based on the Schmidt function, and characterized by different ratios of the gas infall timescale τ_(infall) to the star formation efficiency ν. Dust extinction and re-emission were treated by means of the radiative transfer calculation. The spectral energy distribution (SED) fitting technique was performed by using GOSSIP+, a tool able to combine both photometric and spectroscopic information to extract the best value of the physical quantities of interest, and to consider the intergalactic medium (IGM) attenuation as a free parameter. We find that the main contribution to the scatter observed in the SFR-M_∗ plane is the possibility of choosing between different families of SFHs in the SED fitting procedure, while the redshift range plays a minor role. The majority of the galaxies, at all cosmic times, are best fit by models with SFHs characterized by a high τ_(infall)/ν ratio. We discuss the reliability of a low percentage of dusty and highly star-forming galaxies in the context of their detection in the far infrared (FIR)

Sheaf Neural Networks for Graph-based Recommender Systems

Recent progress in Graph Neural Networks has resulted in wide adoption by many applications, including recommendation systems. The reason for Graph Neural Networks' superiority over other approaches is that many problems in recommendation systems can be naturally modeled as graphs, where nodes can be either users or items and edges represent preference relationships. In current Graph Neural Network approaches, nodes are represented with a static vector learned at training time. This static vector might only be suitable to capture some of the nuances of users or items they define. To overcome this limitation, we propose using a recently proposed model inspired by category theory: Sheaf Neural Networks. Sheaf Neural Networks, and its connected Laplacian, can address the previous problem by associating every node (and edge) with a vector space instead than a single vector. The vector space representation is richer and allows picking the proper representation at inference time. This approach can be generalized for different related tasks on graphs and achieves state-of-the-art performance in terms of F1-Score@N in collaborative filtering and Hits@20 in link prediction. For collaborative filtering, the approach is evaluated on the MovieLens 100K with a 5.1% improvement, on MovieLens 1M with a 5.4% improvement and on Book-Crossing with a 2.8% improvement, while for link prediction on the ogbl-ddi dataset with a 1.6% refinement with respect to the respective baselines.Comment: 9 pages, 7 figure

Modelling galaxy spectra in presence of interstellar dust-III. From nearby galaxies to the distant Universe

Improving upon the standard evolutionary population synthesis (EPS) technique, we present spectrophotometric models of galaxies whose morphology goes from spherical structures to discs, properly accounting for the effect of dust in the interstellar medium (ISM). These models enclose three main physical components: the diffuse ISM composed by gas and dust, the complexes of molecular clouds (MCs) where active star formation occurs and the stars of any age and chemical composition. These models are based on robust evolutionary chemical models that provide the total amount of gas and stars present at any age and that are adjusted in order to match the gross properties of galaxies of different morphological type. We have employed the results for the properties of the ISM presented in Piovan, Tantalo & Chiosi (2006a) and the single stellar populations calculated by Cassar\`a et al. (2013) to derive the spectral energy distributions (SEDs) of galaxies going from pure bulge to discs passing through a number of composite systems with different combinations of the two components. The first part of the paper is devoted to recall the technical details of the method and the basic relations driving the interaction between the physical components of the galaxy. Then, the main parameters are examined and their effects on the spectral energy distribution of three prototype galaxies are highlighted. We conclude analyzing the capability of our galaxy models in reproducing the SEDs of real galaxies in the Local Universe and as a function of redshift.Comment: 22 pages, 10 figures, submitted to MNRA

A Federated Channel Modeling System using Generative Neural Networks

The paper proposes a data-driven approach to air-to-ground channel estimation in a millimeter-wave wireless network on an unmanned aerial vehicle. Unlike traditional centralized learning methods that are specific to certain geographical areas and inappropriate for others, we propose a generalized model that uses Federated Learning (FL) for channel estimation and can predict the air-to-ground path loss between a low-altitude platform and a terrestrial terminal. To this end, our proposed FL-based Generative Adversarial Network (FL-GAN) is designed to function as a generative data model that can learn different types of data distributions and generate realistic patterns from the same distributions without requiring prior data analysis before the training phase. To evaluate the effectiveness of the proposed model, we evaluate its performance using Kullback-Leibler divergence (KL), and Wasserstein distance between the synthetic data distribution generated by the model and the actual data distribution. We also compare the proposed technique with other generative models, such as FL-Variational Autoencoder (FL-VAE) and stand-alone VAE and GAN models. The results of the study show that the synthetic data generated by FL-GAN has the highest similarity in distribution with the real data. This shows the effectiveness of the proposed approach in generating data-driven channel models that can be used in different region

How Generative Models Improve LOS Estimation in 6G Non-Terrestrial Networks

With the advent of 5G and the anticipated arrival of 6G, there has been a growing research interest in combining mobile networks with Non-Terrestrial Network platforms such as low earth orbit satellites and Geosynchronous Equatorial Orbit satellites to provide broader coverage for a wide range of applications. However, integrating these platforms is challenging because Line-Of-Sight (LOS) estimation is required for both inter satellite and satellite-to-terrestrial segment links. Machine Learning (ML) techniques have shown promise in channel modeling and LOS estimation, but they require large datasets for model training, which can be difficult to obtain. In addition, network operators may be reluctant to disclose their network data due to privacy concerns. Therefore, alternative data collection techniques are needed. In this paper, a framework is proposed that uses generative models to generate synthetic data for LOS estimation in non-terrestrial 6G networks. Specifically, the authors show that generative models can be trained with a small available dataset to generate large datasets that can be used to train ML models for LOS estimation. Furthermore, since the generated synthetic data does not contain identifying information of the original dataset, it can be made publicly available without violating privac