LATTES: a novel detector concept for a gamma-ray experiment in the Southern hemisphere

The Large Array Telescope for Tracking Energetic Sources (LATTES), is a novel concept for an array of hybrid EAS array detectors, composed of a Resistive Plate Counter array coupled to a Water Cherenkov Detector, planned to cover gamma rays from less than 100 GeV up to 100 TeVs. This experiment, to be installed at high altitude in South America, could cover the existing gap in sensitivity between satellite and ground arrays. The low energy threshold, large duty cycle and wide field of view of LATTES makes it a powerful tool to detect transient phenomena and perform long term observations of variable sources. Moreover, given its characteristics, it would be fully complementary to the planned Cherenkov Telescope Array (CTA) as it would be able to issue alerts. In this talk, a description of its main features and capabilities, as well as results on its expected performance, and sensitivity, will be presented.Comment: Proceedings of the 35th International Cosmic Ray Conference (ICRC2017), Busan, South Korea. Presented by R. Concei\c{c}\~{a}o. 8 pages; v2: correct affiliation + journal referenc

STARTUPV: Different approaches in mentoring and tutorship for entrepreneurs in the three stages of a university entrepreneurial ecosystem

Year after year, a crowd of students from the Universitat Politècnica de València (UPV), a polytechnic university in Valencia (Spain) with over 30,000 students, are encouraged to start their own business projects. Since 1992, IDEAS UPV, the Entrepreneurial service at UPV, has been mentoring entrepreneurs. Up till now, IDEAS UPV has helped in the generation of close to 1000 new businesses with a survival rate of over 60% in five years. In 2012, IDEAS UPV introduced new mentoring and tutorship activities for students by the creation of a business incubator within the university campus. StartUPV is currently a 5-year startup incubation programme and an entrepreneurial ecosystem with more than 300 startups and more than 50 million euros of overall private investment StartUPV programme is divided into three different stages: (i) STAND UP, in which startups define a business model and complete a validation process; (ii) START UP, in which startups achieve a targeted market share and build their company management team; and (iii) SCALE UP, in which startups achieve maturity and scale to other international markets. As university students and their startups face different needs in every step of the programme, different approaches for mentoring and tutorship are applied in every stage. For instance, a startup in the first stage is mentored in business modelling or market segmentation, while a scale up requires a more specific mentorship in dealing with corporates and venture capital. These different approaches are analysed in this work including the main findings of the 10 years of this programme

LATTES: A new gamma-ray detector concept for South America

Currently the detection of Very High Energy gamma-rays for astrophysics rely on the measurement of the Extensive Air Showers (EAS) either using Cherenkov detectors or EAS arrays with larger field of views but also larger energy thresholds. In this talk we present a novel hybrid detector concept for a EAS array with an improved sensitivity in the lower energies (~ 100 GeV). We discuss its main features, capabilities and present preliminary results on its expected perfomances and sensitivities.This wide field of view experiment is planned to be installed at high altitude in South America making it a complementary project to the planned Cherenkov telescope experiments and a powerful tool to trigger further observations of variable sources and to detect transients phenomena

Manipulating host resistance structure reveals impact of pathogen dispersal and environmental heterogeneity on epidemics

Understanding how variation in hosts, parasites, and the environment shapes patterns of disease is key to predicting ecological and evolutionary outcomes of epidemics. Yet in spatially structured populations, variation in host resistance may be spatially confounded with variation in parasite dispersal and environmental factors that affect disease processes. To tease apart these disease drivers, we paired surveys of natural epidemics with experiments manipulating spatial variation in host susceptibility to infection. We mapped epidemics of the wind-dispersed powdery mildew pathogen Podosphaera plantaginis in five populations of its plant host, Plantago lanceolata. At 15 replicate sites within each population, we deployed groups of healthy potted 'sentinel' plants from five allopatric host lines. By tracking which sentinels became infected in the field and measuring pathogen connectivity and microclimate at those sites, we could test how variation in these factors affected disease when spatial variation in host resistance and soil conditions was minimized. We found that the prevalence and severity of sentinel infection varied over small spatial scales in the field populations, largely due to heterogeneity in pathogen prevalence on wild plants and unmeasured environmental factors. Microclimate was critical for disease spread only at the onset of epidemics, where humidity increased infection risk. Sentinels were more likely to become infected than initially healthy wild plants at a given field site. However, in a follow-up laboratory inoculation study we detected no significant differences between wild and sentinel plant lines in their qualitative susceptibility to pathogen isolates from the field populations, suggesting that primarily non-genetic differences between sentinel and wild hosts drove their differential infection rates in the field. Our study leverages a multi-faceted experimental approach to disentangle important biotic and abiotic drivers of disease patterns within wild populations.Peer reviewe

The Optical System for the Large Size Telescope of the Cherenkov Telescope Array

The Large Size Telescope (LST) of the Cherenkov Telescope Array (CTA) is designed to achieve a threshold energy of 20 GeV. The LST optics is composed of one parabolic primary mirror 23 m in diameter and 28 m focal length. The reflector dish is segmented in 198 hexagonal, 1.51 m flat to flat mirrors. The total effective reflective area, taking into account the shadow of the mechanical structure, is about 368 m$^2$. The mirrors have a sandwich structure consisting of a glass sheet of 2.7 mm thickness, aluminum honeycomb of 60 mm thickness, and another glass sheet on the rear, and have a total weight about 47 kg. The mirror surface is produced using a sputtering deposition technique to apply a 5-layer coating, and the mirrors reach a reflectivity of $\sim$94% at peak. The mirror facets are actively aligned during operations by an active mirror control system, using actuators, CMOS cameras and a reference laser. Each mirror facet carries a CMOS camera, which measures the position of the light spot of the optical axis reference laser on the target of the telescope camera. The two actuators and the universal joint of each mirror facet are respectively fixed to three neighboring joints of the dish space frame, via specially designed interface plate.Comment: In Proceedings of the 34th International Cosmic Ray Conference (ICRC2015), The Hague, The Netherlands. All CTA contributions at arXiv:1508.0589

Multimodal Emotion Recognition on RAVDESS Dataset Using Transfer Learning

Emotion Recognition is attracting the attention of the research community due to the multiple areas where it can be applied, such as in healthcare or in road safety systems. In this paper, we propose a multimodal emotion recognition system that relies on speech and facial information. For the speech-based modality, we evaluated several transfer-learning techniques, more specifically, embedding extraction and Fine-Tuning. The best accuracy results were achieved when we fine-tuned the CNN-14 of the PANNs framework, confirming that the training was more robust when it did not start from scratch and the tasks were similar. Regarding the facial emotion recognizers, we propose a framework that consists of a pre-trained Spatial Transformer Network on saliency maps and facial images followed by a bi-LSTM with an attention mechanism. The error analysis reported that the frame-based systems could present some problems when they were used directly to solve a videobased task despite the domain adaptation, which opens a new line of research to discover new ways to correct this mismatch and take advantage of the embedded knowledge of these pre-trained models. Finally, from the combination of these two modalities with a late fusion strategy, we achieved 80.08% accuracy on the RAVDESS dataset on a subject-wise 5-CV evaluation, classifying eight emotions. The results revealed that these modalities carry relevant information to detect users’ emotional state and their combination enables improvement of system performance