Electronic dummy for acoustical testing

Electronic Dummy /ED/ used for acoustical testing represents the average male torso from the Xiphoid process upward and includes an acoustic replica of the human head. This head simulates natural flesh, and has an artificial voice and artificial ears that measure sound pressures at the eardrum or the entrance to the ear canal

Towards a phenotype of the amphibious company:an illustrative case from the chemical industry

The paper explores the phenotype of the amphibious company, which is intended as the fittest economic species in today’s hypercompetitive business arenas and hence the most likely to survive and prosper. Four behavioral traits are proposed and discussed as distinctive of amphibious companies: doing different jobs good, diversifying in multiple market arenas, brokering and bridging across business networks and absorbing knowledge from the outside. The paper illustrates these arguments through a paradigmatic case study of an Italian firm operating in the chemical industry, which has been able to survive a challenging crisis by adopting an amphibious behavior.<br

Age limit in bronchiolitis diagnosis: 6 or 12 months?

Aim: The most frequent cause of lower respiratory tract infection in infants is bronchiolitis. Up to now there is no agreement on the upper limit age of bronchiolitis. Our aim was to identify if there are clinical differences in infants hospitalized for bronchiolitis between 0–6 months and 6–12 months of age. A secondary aim was to establish whether there was differences in terms of recurrent wheezing at 12, 24, and 36 months of follow-up. Methods: We retrospectively analyzed clinical and virological records of 824 infants hospitalized for bronchiolitis during 11 consecutive epidemic seasons. From each infant at admission to the hospital nasopharyngeal washing was collected, clinical severity was assessed and clinical data were extracted from a structured questionnaire. At 12–24–36 months after discharge, parents were interviewed seeking information on recurrent wheezing. Results: A total of 773 infants (Group1) were ≤6 months of age, while 51 were &gt;6 months (Group 2). No differences between family history for atopy and passive smoking exposure were observed between the two groups. Respiratory syncyzial virus was detected more frequently in Group 1 and human bocavirus in Group 2. The clinical severity score (p = 0.011) and the use of intravenous fluids (p = 0.0001) were higher in Group 1 with respect to Group 2 infants. At 36 months follow-up 163/106 (39.4%) Group 1 and 9/9 Group 2 infants experienced recurrent wheezing (p = 0.149). Conclusion: We demonstrated that 0-6 months old infants bronchiolitis differs from &gt; 6 months bronchiolitis

On the Importance of Word Boundaries in Character-level Neural Machine Translation

Neural Machine Translation (NMT) models generally perform translation using a fixed-size lexical vocabulary, which is an important bottleneck on their generalization capability and overall translation quality. The standard approach to overcome this limitation is to segment words into subword units, typically using some external tools with arbitrary heuristics, resulting in vocabulary units not optimized for the translation task. Recent studies have shown that the same approach can be extended to perform NMT directly at the level of characters, which can deliver translation accuracy on-par with subword-based models, on the other hand, this requires relatively deeper networks. In this paper, we propose a more computationally-efficient solution for character-level NMT which implements a hierarchical decoding architecture where translations are subsequently generated at the level of words and characters. We evaluate different methods for open-vocabulary NMT in the machine translation task from English into five languages with distinct morphological typology, and show that the hierarchical decoding model can reach higher translation accuracy than the subword-level NMT model using significantly fewer parameters, while demonstrating better capacity in learning longer-distance contextual and grammatical dependencies than the standard character-level NMT model

Constraining the dark matter contribution of γ\gamma rays in Cluster of galaxies using Fermi-LAT data

Clusters of galaxies are the largest gravitationally-bound systems in the Universe. Their dynamics are dominated by dark matter (DM), which makes them among the best targets for indirect DM searches. We analyze 12 years of data collected by the Fermi Large Area Telescope (Fermi-LAT) in the direction of 49 clusters of galaxies selected for their proximity to the Earth and their high X-ray flux, which makes them the most promising targets. We first create physically motivated models for the DM density around each cluster considering different assumptions for the substructure distribution. Then we perform a combined search for a $\gamma$-ray signal in the {\it Fermi}-LAT data between 500 MeV and 1 TeV. We find a signal of $\gamma$ rays potentially associated with DM that is at a statistical significance of $2.5\sigma-3.0\sigma$ when considering a slope for the subhalo mass distribution $\alpha=1.9$ and minimum mass of $M_{\rm{min}}=10^{-6}$ $M_{\odot}$. The best-fit DM mass and annihilation cross-sections for a $b\bar{b}$ annihilation channel are $m_{\chi}=40-60$ GeV and $\langle \sigma v \rangle = (2-4) \times 10^{-25}$ cm$^3$/s. When we consider $\alpha=2.0$ and $M_{\rm{min}}=10^{-9}$ $M_{\odot}$, the best-fit of the cross section reduces to $\langle \sigma v \rangle = (4-10) \times 10^{-26}$ cm$^3$/s. For both DM substructure models there is a tension between the values of $\langle \sigma v \rangle$ that we find and the upper limits obtained with the non-detection of a $\gamma$-ray flux from Milky Way dwarf spheroidal galaxies. This signal is thus more likely associated with $\gamma$ rays produced in the intracluster region by cosmic rays colliding with gas and photon fields.Comment: 27 Pages, 13 Figures. Accepted for publication in the PRD journa

Deep Neural Machine Translation with Weakly-Recurrent Units

Recurrent neural networks (RNNs) have represented for years the state of the art in neural machine translation. Recently, new architectures have been proposed, which can leverage parallel computation on GPUs better than classical RNNs. Faster training and inference combined with different sequence-to-sequence modeling also lead to performance improvements. While the new models completely depart from the original recurrent architecture, we decided to investigate how to make RNNs more efficient. In this work, we propose a new recurrent NMT architecture, called Simple Recurrent NMT, built on a class of fast and weakly-recurrent units that use layer normalization and multiple attentions. Our experiments on the WMT14 English-to-German and WMT16 English-Romanian benchmarks show that our model represents a valid alternative to LSTMs, as it can achieve better results at a significantly lower computational cost

Data Augmentation for End-to-End Speech Translation: FBK@IWSLT '19

This paper describes FBK’s submission to the end-to-end speech translation (ST) task at IWSLT 2019. The task consists in the “direct” translation (ie without intermediate discrete representation) of English speech data derived from TED Talks or lectures into German texts. Our participation had a twofold goal: i) testing our latest models, and ii) evaluating the contribution to model training of different data augmentation techniques. On the model side, we deployed our recently proposed S-Transformer with logarithmic distance penalty, an ST-oriented adaptation of the Transformer architecture widely used in machine translation (MT). On the training side, we focused on data augmentation techniques recently proposed for ST and automatic speech recognition (ASR). In particular, we exploited augmented data in different ways and at different stages of the process. We first trained an end-to-end ASR system and used the weights of its encoder to initialize the decoder of our ST model (transfer learning). Then, we used an English-German MT system trained on large data to translate the English side of the English-French training set into German, and used this newly-created data as additional training material. Finally, we trained our models using SpecAugment, an augmentation technique that randomly masks portions of the spectrograms in order to make them different at every training epoch. Our synthetic corpus and SpecAugment resulted in an improvement of 5 BLEU points over our baseline model on the test set of MuST-C En-De, reaching the score of 22.3 with a single end-to-end system

Food-borne Lactiplantibacillus plantarum protect normal intestinal cells against inflammation by modulating reactive oxygen species and IL-23/IL-17 axis

Food-associated Lactiplantibacillus plantarum (Lpb. plantarum) strains, previously classified as Lactobacillus plantarum, are a promising strategy to face intestinal inflammatory diseases. Our study was aimed at clarifying the protective role of food-borne Lpb. plantarum against inflammatory damage by testing the scavenging microbial ability both in selected strains and in co-incubation with normal mucosa intestinal cells (NCM460). Here, we show that Lpb. plantarum endure high levels of induced oxidative stress through partially neutralizing reactive oxygen species (ROS), whereas they elicit their production when co-cultured with NCM460. Moreover, pre-treatment with food-borne Lpb. plantarum significantly reduce pro-inflammatory cytokines IL-17F and IL-23 levels in inflamed NCM460 cells. Our results suggest that food-vehicled Lpb. plantarum strains might reduce inflammatory response in intestinal cells by directly modulating local ROS production and by triggering the IL-23/IL-17 axis with future perspectives on health benefits in the gut derived by the consumption of functional foods enriched with selected strains