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A considerable number of asymptotic giant branch (AGB) stars exhibit UV excess and/or X-ray emission indicative of the presence of a binary companion. AGB stars are so bright that they easily outshine their companions, making almost impossible their identification. Y Gem has been known to be a far-UV- and X-ray-bright AGB star for some decades now, but the nature of its companion, whether it is a main-sequence star or a white dwarf (WD) in a symbiotic system (SySt), is disputed. Our goal is to uncover the true nature of Y Gem to help us peer into the possible misidentified population of SySts. Multi-wavelength IR, optical, UV and X-ray observations have been analyzed to investigate the properties of the stellar components and accretion process in Y Gem. In particular, an optical spectrum of Y Gem is presented here for the first time, while X-ray data are interpreted by means of reflection models produced by an accretion disk and material in its vicinity. The optical spectrum exhibits the typical saw-shaped features of molecular absorptions in addition to narrow recombination and forbidden emission lines. The presence of the emission lines and the analysis of the extinction-corrected UV spectrum suggest a hot component with $T_\mathrm{eff}\approx$60,000 K, $L$=140 L$_{\odot}$, and $R$=0.11 R$_{\odot}$, very likely an accreting WD. The late component is found to be an 1.1 M$_\odot$ AGB star with $T_\mathrm{eff}$=3350 K and $R$=240 R$_\odot$. Using IR, optical, UV, and X-ray data, we found that Y Gem is a S-type SySt whose compact component is accreting at an estimated mass accretion rate of $\dot{M}_\mathrm{acc}=2.3\times10^{-7}$ M$_\odot$ yr$^{-1}$. At such accretion rate, the accreting WD has reached the stable and steady burning phase where no recurrent events are expected., Comment: 13 pages, 9 Figures, 4 Tables; Accepted to A&A

The vision of IASIS project is to turn the wave of big biomedical data heading our way into actionable knowledge for decision makers. This is achieved by integrating data from disparate sources, including genomics, electronic health records and bibliography, and applying advanced analytics methods to discover useful patterns. The goal is to turn large amounts of available data into actionable information to authorities for planning public health activities and policies. The integration and analysis of these heterogeneous sources of information will enable the best decisions to be made, allowing for diagnosis and treatment to be personalised to each individual. The project offers a common representation schema for the heterogeneous data sources. The iASiS infrastructure is able to convert clinical notes into usable data, combine them with genomic data, related bibliography, image data and more, and create a global knowledge base. This facilitates the use of intelligent methods in order to discover useful patterns across different resources. Using semantic integration of data gives the opportunity to generate information that is rich, auditable and reliable. This information can be used to provide better care, reduce errors and create more confidence in sharing data, thus providing more insights and opportunities. Data resources for two different disease categories are explored within the iASiS use cases, dementia and lung cancer., Comment: 6 pages, 2 figures, accepted at 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)

Social Network Analysis (SNA) is a set of techniques developed in the field of social and behavioral sciences research, in order to characterize and study the social relationships that are established among a set of individuals. When building a social network for performing an SNA analysis, an initial process of data gathering is achieved in order to extract the characteristics of the individuals and their relationships. This is usually done by completing a questionnaire containing different types of questions that will be later used to obtain the SNA measures needed to perform the study. There are, then, a great number of different possible network generating questions and also many possibilities for mapping the responses to the corresponding characteristics and relationships. Many variations may be introduced into these questions (the way they are posed, the weights given to each of the responses, etc.) that may have an effect on the resulting networks. All these different variations are difficult to achieve manually, because the process is time-consuming and error prone. The tool described in this paper uses semantic knowledge representation techniques in order to facilitate this kind of sensitivity studies. The base of the tool is a conceptual structure, called "ontology" that is able to represent the different concepts and their definitions. The tool is compared to other similar ones, and the advantages of the approach are highlighted, giving some particular examples from an ongoing SNA study about alcohol consumption habits in adolescents.

Alcohol Use Disorder (AUD) is a major concern for public health organizations worldwide, especially as regards the adolescent population. The consumption of alcohol in adolescents is known to be influenced by seeing friends and even parents drinking alcohol. Building on this fact, a number of studies into alcohol consumption among adolescents have made use of Social Network Analysis (SNA) techniques to study the different social networks (peers, friends, family, etc.) with whom the adolescent is involved. These kinds of studies need an initial phase of data gathering by means of questionnaires and a subsequent analysis phase using the SNA techniques. The process involves a number of manual data handling stages that are time consuming and error-prone. The use of knowledge engineering techniques (including the construction of a domain ontology) to represent the information, allows the automation of all the activities, from the initial data collection to the results of the SNA study. This paper shows how a knowledge model is constructed, and compares the results obtained using the traditional method with this, fully automated model, detailing the main advantages of the latter. In the case of the SNA analysis, the validity of the results obtained with the knowledge engineering approach are compared to those obtained manually using the UCINET, Cytoscape, Pajek and Gephi to test the accuracy of the knowledge model.

Context. The galactic winds of starburst galaxies (SBGs) give rise to remarkable structures on kiloparsec scales. However, the evolution and shape of these giant wind bubbles, as well as the properties of the shocks they develop, are not yet fully understood. Aims. We aim to understand what shapes the galactic winds of SBGs, with a particular focus on the role of large-scale magnetic fields in the dynamical evolution of galactic wind-inflated bubbles. In addition, we aim to explore where the conditions for efficient particle acceleration are met in these systems. Methods. We performed magnetohydrodynamic simulations with the AMRVAC code (Adaptive Mesh Refinement Versatile Advection Code) with various configurations of the galactic medium density profile and magnetization. Results. We observe that the large-scale magnetic field, in which galactic winds expand, can impact the structure and evolution of inflated bubbles. However, the typical structures observed in starburst galaxies, such as M82, cannot be solely explained by the magnetic field structures that have been considered. This highlights the importance of other factors, such as the galactic disk, in shaping the galactic bubble. Furthermore, in all the magnetized cases we investigated, the forward wave resulting from the expanding bubbles only results in compression waves, whereas the wind termination shock features high Mach numbers, making it a promising site for diffusive shock acceleration up to $\sim 10^{2}$ PeV. The synthetic X-ray images generated from our models reveal an envelope surrounding the bubbles that extends up to 2 kpc, which could correspond to the polarized emission observed from planar geometry in M82, as well as a large structure inside the bubble corresponding to the shocked galactic wind., Comment: 17 pages

We have studied the evolution of HuBi 1-like planetary nebulae, considering several stages of mass injection. We have carried out numerical ionization+1D hydrodynamics+atomic/ionic rate models with our code Coral 1D to reproduce planetary nebulae that present multiple shells produced by different ejection events around the ionizing source. Furthermore, we are interested in comparing numerical simulations with H$\alpha$ and [NII]$\lambda$6584 emission structures and the position-velocity diagrams observed in HuBi 1. This object also has a phase where it has drastically decreased the injection of ionized photons ejected from the source. The result of these different stages of ejection is a nebula with intense [NII] line emission in the inner part of the planetary nebula and an extended HII recombination line emission around the central zone. The model for HuBi 1 shows the capability of our code to explain the hydrodynamical and photoionization evolution in ionization nebulae. This is our first step with a 1D code to study these two physical phenomena at the same time., Comment: accepted

Pattern discovery is a machine learning technique that aims to find sets of items, subsequences, or substructures that are present in a dataset with a higher frequency value than a manually set threshold. This process helps to identify recurring patterns or relationships within the data, allowing for valuable insights and knowledge extraction. In this work, we propose Information Gained Subgroup Discovery (IGSD), a new SD algorithm for pattern discovery that combines Information Gain (IG) and Odds Ratio (OR) as a multi-criteria for pattern selection. The algorithm tries to tackle some limitations of state-of-the-art SD algorithms like the need for fine-tuning of key parameters for each dataset, usage of a single pattern search criteria set by hand, usage of non-overlapping data structures for subgroup space exploration, and the impossibility to search for patterns by fixing some relevant dataset variables. Thus, we compare the performance of IGSD with two state-of-the-art SD algorithms: FSSD and SSD++. Eleven datasets are assessed using these algorithms. For the performance evaluation, we also propose to complement standard SD measures with IG, OR, and p-value. Obtained results show that FSSD and SSD++ algorithms provide less reliable patterns and reduced sets of patterns than IGSD algorithm for all datasets considered. Additionally, IGSD provides better OR values than FSSD and SSD++, stating a higher dependence between patterns and targets. Moreover, patterns obtained for one of the datasets used, have been validated by a group of domain experts. Thus, patterns provided by IGSD show better agreement with experts than patterns obtained by FSSD and SSD++ algorithms. These results demonstrate the suitability of the IGSD as a method for pattern discovery and suggest that the inclusion of non-standard SD metrics allows to better evaluate discovered patterns.

This work describes the way that topological mixing and chaos in continua, as induced by discrete dynamical systems, can or can't be understood through topological conjugacy with symbolic dynamical systems. For example, there is no symbolic dynamical system that is topologically conjugate to any discrete dynamical system on an entire continuum, and there is no finer topology that can be given to a continuum which simultaneously makes the continuum homeomorphic to a symbolic dynamical system and contains its original topology. However, this paper demonstrates an analytical mechanism by which the existence of topological mixing and/or chaos can be shown through conjugacy with qualitative dynamical systems outside the usual purview of symbolic dynamics. Two examples of this mechanism are demonstrated on classic textbook models of chaotic dynamics; the first proving the existence of topological mixing everywhere in the dyadic map on the interval by showing that there exists a qualitative system that is topologically conjugate to the dyadic map on the interval with a finer topology than the usual Euclidean topology, and the other following a similar approach to demonstrate the existence of Devaney chaos everywhere in the $2$-tent map on the interval. The content is presented in a somewhat self-contained fashion, reiterating some standard results in the field, to aid new learners of topological mixing/chaos., Comment: v.2: Corrected ambiguity issue with definition of semi-conjugacy and related arguments. 13 pages, 5 figures

The intersection of machine learning and dynamical systems has generated considerable interest recently. Neural Ordinary Differential Equations (NODEs) represent a rich overlap between these fields. In this paper, we develop a continuous time neural network approach based on Delay Differential Equations (DDEs). Our model uses the adjoint sensitivity method to learn the model parameters and delay directly from data. Our approach is inspired by that of NODEs and extends earlier neural DDE models, which have assumed that the value of the delay is known a priori. We perform a sensitivity analysis on our proposed approach and demonstrate its ability to learn DDE parameters from benchmark systems. We conclude our discussion with potential future directions and applications., Comment: Comments welcome!

We present reactive gasdynamic, axisymmetric simulations of dense, high velocity clumps for modelling the CO streamers observed in Orion BN/KL. We have considered 15 chemical species, a cooling function for atomic and molecular gas, and heating through cosmic rays. Our numerical simulations explore different ejection velocities, interstellar medium density configurations, and CO content. Using the CO density and temperature, we have calculated the CO ($J=2\to1$) emissivity, and have built CO maps and spatially resolved line profiles, allowing us to see the CO emitting regions of the streamers and to obtain position velocity diagrams to compare with observations. We find that in order to reproduce the images and line profiles of the BN/KL CO streamers and H$_2$ fingers, we need to have clumps that first travel within a dense cloud core, and then emerge into a lower-density environment., Comment: MNRAS, accepted, 10 figures

There is a large number of online documents data sources available nowadays. The lack of structure and the differences between formats are the main difficulties to automatically extract information from them, which also has a negative impact on its use and reuse. In the biomedical domain, the DISNET platform emerged to provide researchers with a resource to obtain information in the scope of human disease networks by means of large-scale heterogeneous sources. Specifically in this domain, it is critical to offer not only the information extracted from different sources, but also the evidence that supports it. This paper proposes EBOCA, an ontology that describes (i) biomedical domain concepts and associations between them, and (ii) evidences supporting these associations; with the objective of providing an schema to improve the publication and description of evidences and biomedical associations in this domain. The ontology has been successfully evaluated to ensure there are no errors, modelling pitfalls and that it meets the previously defined functional requirements. Test data coming from a subset of DISNET and automatic association extractions from texts has been transformed according to the proposed ontology to create a Knowledge Graph that can be used in real scenarios, and which has also been used for the evaluation of the presented ontology.

A new method to generate gummy fingers is presented. A medium-size fake fingerprint database is described and two different fingerprint verification systems are evaluated on it. Three different scenarios are considered in the experiments, namely: enrollment and test with real fingerprints, enrollment and test with fake fingerprints, and enrollment with real fingerprints and test with fake fingerprints. Results for an optical and a thermal sweeping sensors are given. Both systems are shown to be vulnerable to direct attacks., Comment: Published at IEEE International Carnahan Conference on Security Technology (ICCST)

We present a comprehensive study of the ionization structure and kinematics of the planetary nebula (PN) NGC40 (a.k.a. the Bow-tie Nebula). A set of narrow-band images obtained with the ALhambra Faint Object Spectrograph and Camera (ALFOSC) at the Nordic Optical Telescope (NOT) are used to study the turbulent distribution of gas in the main cavity, the ionization stratification and the density of this PN. High-resolution Manchester Echelle Spectrograph (MES) observations obtained at 2.1m telescope of the San Pedro M\'{a}rtir (SPM) Observatory are used to unveil in great detail the kinematic signatures of all morphological features in NGC40. The images and spectra suggest that NGC40 had multiple mass ejections in its recent formation history. We found 4 jet-like ejections not aligned with the main axis of NGC40 (PA=20$^{\circ}$), some of them having pierced the main cavity along the SW-NE direction as well as the southern lobe. Using a tailor-made morpho-kinematic model of NGC40 produced with SHAPE we found that the main cavity has a kinematic age of 6,500 yr and the two pairs of lobes expanding towards the N and S directions have an averaged age of 4,100$\pm$550 yr. NGC40 thus adds to the group of PNe with multiple ejections along different axes that challenge the models of PN formation., Comment: 11 pages, 9 figures, 3 tables; Accepted to MNRAS

Born-again planetary nebulae (PNe) are extremely rare cases in the evolution of solar-like stars. It is commonly accepted that their central stars (CSPN) experienced a very late thermal pulse (VLTP), ejecting H-deficient material inside the evolved H-rich PN. Given the short duration of this event and the fast subsequent evolution of the CSPN, details of the mass ejection are unknown. We present the first morpho-kinematic model of the H-deficient material surrounding a born-again PN, namely A30. New San Pedro M\'{a}rtir observations with the Manchester Echelle Spectrograph were recently obtained to map the inner region of A30 which are interpreted by means of the software SHAPE in conjunction with HST WFC3 images. The SHAPE morpho-kinematic model that best reproduces the observations is composed by a disrupted disk tilted $37^\circ$ with respect to the line of sight and a pair of orthogonal opposite bipolar ejections. We confirm previous suggestions that the structures closer to the CSPN present the highest expansion velocities, that is, the disrupted disk expands faster than the farther bipolar features. We propose that the current physical structure and abundance discrepancy of the H-deficient clumps around the CSPN of A30 can be explained by a common envelope phase following the VLTP event. Our proposed scenario is also compared with other known born-again PNe (A58, A78, HuBi1 and the Sakurai's Object)., Comment: 9 pages, 6 figures, 1 table; Accepted to MNRAS