hvEEGNet: exploiting hierarchical VAEs on EEG data for neuroscience applications

With the recent success of artificial intelligence in neuroscience, a number of deep learning (DL) models were proposed for classification, anomaly detection, and pattern recognition tasks in electroencephalography (EEG). EEG is a multi-channel time-series that provides information about the individual brain activity for diagnostics, neuro-rehabilitation, and other applications (including emotions recognition). Two main issues challenge the existing DL-based modeling methods for EEG: the high variability between subjects and the low signal-to-noise ratio making it difficult to ensure a good quality in the EEG data. In this paper, we propose two variational autoencoder models, namely vEEGNet-ver3 and hvEEGNet, to target the problem of high-fidelity EEG reconstruction. We properly designed their architectures using the blocks of the well-known EEGNet as the encoder, and proposed a loss function based on dynamic time warping. We tested the models on the public Dataset 2a - BCI Competition IV, where EEG was collected from 9 subjects and 22 channels. hvEEGNet was found to reconstruct the EEG data with very high-fidelity, outperforming most previous solutions (including our vEEGNet-ver3 ). Furthermore, this was consistent across all subjects. Interestingly, hvEEGNet made it possible to discover that this popular dataset includes a number of corrupted EEG recordings that might have influenced previous literature results. We also investigated the training behaviour of our models and related it with the quality and the size of the input EEG dataset, aiming at opening a new research debate on this relationship. In the future, hvEEGNet could be used as anomaly (e.g., artefact) detector in large EEG datasets to support the domain experts, but also the latent representations it provides could be used in other classification problems and EEG data generation

Impact of Transmission Delays Over Age of Information Under Finite Horizon Scheduling

Sensor communications in real-time systems may be required to report status updates to minimize the so-called age of information metric, which quantifies the freshness of exchanged data. This situation can be heavily impacted by the delays in data transmission, as some updates may reach the destination when their information content is already stale. In this paper, we consider the problem of scheduling sensing updates over a finite time horizon, and discuss the impact of their transmission delay on the resulting data freshness. We tackle the adjustments required for an offline schedule aimed at minimizing the average age of information which, as long as they do not cause the updates to go off the boundaries of the finite horizon, are only dependent on the average transmission delay. We derive a closed form expression for the average age of information, also verified through simulations, and the resulting performance is evaluated under different system conditions. This can be used to further explore the task of delivering timely system updates under general scenarios, e.g., when the statistics of the delay is not known a priori, or under other non idealities

The Leather Industry: A Chemistry Insight Part I: an Overview of the Industrial Process

A panoramic overview of the leather world market is given. The industrial tanning process is schematically explained giving a general outline of how an animal skin is transformed into a durable material having many different characteristics according to its specific future use. All the tanning industrial steps are overviewed starting from soaking, liming and after various steps ending up with finishing. An insight of collagen chemistry is also given

Trajetórias da Educomunicação nas Políticas Públicas e a Formação de seus Profissionais

Esta obra é composta com os trabalhos apresentados no primeiro subtema, TRAJETÓRIA – Educação para a Comunicação como Política pública, nas perspectivas da Educomunicação e da Mídia-Educação, do II Congresso Internacional de Comunicação e Educação. Os artigos pretendem propiciar trocas de informações e produzir reflexões com os leitores sobre os caminhos percorridos, e ainda a percorrer, tendo como meta a expansão e a legitimação das práticas educomunicativas e/ou mídia-educativas como política pública para o atendimento à formação de crianças, adolescentes, jovens e adultos, no Brasil e no mundo

Malattie alimentari: sindrome di Guillain Barrè

Durante i tre anni del Corso di Laurea in Sicurezza Igienico-Sanitaria degli Alimenti abbiamo studiato materie come Chimica, Patologia e Microbiologia, che ci hanno permesso di conoscere le Malattie Alimentari. In questa tesi di laurea abbiamo approfondito la conoscenza di una tra le numerose malattie rare che possono avere origine alimentare, la sindrome di Guillain-Barrè, che a livello mondiale ha un’incidenza di 0,6-4 casi ogni 100.000 persone. Scoperta durante la Prima Guerra Mondiale da tre giovani medici francesi specializzandi in neurologia, George Charles Guillain, Jean Alexandre Barrè e Andrè Strohl, sembrava insorgere senza un’apparente causa. Da allora si sono susseguiti numerosi studi che hanno chiarito, anche se ancora non del tutto, le cause probabili di questa sindrome, tra cui infezioni del soggetto colpito antecedenti lo svilupparsi della malattia da parte Campylobacter jejuni, vari tipi di vaccinazioni (anti-papilloma virus, anti-meningococco, antiinfluenza suina), e traumi chirurgici post-operatori. La sindrome di Guillain-Barrè si manifesta con un’iniziale difficoltà di movimento articolare, sviluppandosi in seguito come una vera e propria paralisi totale, con il paziente impossibilitato a muoversi e a svolgere le più semplici azioni, come parlare o aprire gli occhi: in questa sindrome, infatti, si ha un attacco del sistema immunitario all’organismo stesso (patologia autoimmunitaria), provocando la distruzione della mielina che riveste l’assone dei nervi del sistema nervoso periferico. Nella tesi approfondiremo, oltre alle malattie alimentari, l’impatto economico della sindrome, nozioni sul Campylobacter, la legislazione riguardante le malattie rare e i batteri degli alimenti, le associazioni che si occupano della GBS, nonché saranno dati e cenni sul sistema nervoso ed immunitario, i principali sistemi colpiti da questa malattia

Tackling Age of Information in Access Policies for Sensing Ecosystems

Recent technological advancements such as the Internet of Things (IoT) and machine learning (ML) can lead to a massive data generation in smart environments, where multiple sensors can be used to monitor a large number of processes through a wireless sensor network (WSN). This poses new challenges for the extraction and interpretation of meaningful data. In this spirit, age of information (AoI) represents an important metric to quantify the freshness of the data monitored to check for anomalies and operate adaptive control. However, AoI typically assumes a binary representation of the information, which is actually multi-structured. Thus, deep semantic aspects may be lost. In addition, the ambient correlation of multiple sensors may not be taken into account and exploited. To analyze these issues, we study how correlation affects AoI for multiple sensors under two scenarios of (i) concurrent and (ii) time-division multiple access. We show that correlation among sensors improves AoI if concurrent transmissions are allowed, whereas the benefits are much more limited in a time-division scenario. Furthermore, we discuss how ML can be applied to extract relevant information from data and show how it can further optimize the transmission policy with savings of resources. Specifically, we demonstrate, through simulations, that ML techniques can be used to reduce the number of transmissions and that classification errors have no influence on the AoI of the system

Status Update Scheduling in Remote Sensing Under Variable Activation and Propagation Delays

Sensor data exchanges in IoT applications can experience a variable delay due to changes in the communication environment and sharing of processing capabilities. This variability can impact the performance and effectiveness of the systems being controlled, and is especially reflected on age of information (AoI), a performance metric that quantifies the freshness of updates in remote sensing. In this work, we discuss the quantitative impact of activation and propagation delays, both taken as random variables, on AoI. In our analysis we consider an offline scheduling over a finite horizon, we derive a closed form solution to evaluate the average AoI, and we validate our results through numerical simulation. We also provide further analysis on which type of delay has more influence on the system, as well as the probability that the system fails to deliver all the scheduled updates due to excessive delays of either kind

Avaliação agronômica de composições granulométricas de calcário

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A Practical, Enantioselective Synthesis of the Fragrances Canthoxal and Silvial (R), and Evaluation of Their Olfactory Activity

The fragrances (S)-(+)- and (R)-(-)-canthoxal [(S)-(+)- and (R)-(-)-3-(4-methoxyphenyl)-2-methylpropanal] and (+)- and (-)-Silvial® [(+)- and (-)-3-(4-isobutylphenyl)-2-methylpropanal] have been synthesized in high enantiopurity via a simple four-step strategy starting from the commercially available 4-substituted benzaldehydes. The key synthetic step is the catalytic asymmetric hydrogenation of the appropriate 3-aryl-2-methylacrylic acid which has been carried out employing an in situ prepared ruthenium/axially chiral phosphine catalyst (up to 98% ee). The olfactory activity of the single enantiomers has been evaluated.The fragrances (S)-(+)- and (R)-(-)-canthoxal [(S)-(+)- and (R)-(-)-3-(4-methoxyphenyl)-2-methylpropanal] and (+)- and (-)-Silvial® [(+)- and (-)-3-(4-isobutylphenyl)-2-methylpropanal] have been synthesized in high enantiopurity via a simple four-step strategy starting from the commercially available 4-substituted benzaldehydes. The key synthetic step is the catalytic asymmetric hydrogenation of the appropriate 3-aryl-2-methylacrylic acid which has been carried out employing an in situ prepared ruthenium/axially chiral phosphine catalyst (up to 98% ee). The olfactory activity of the single enantiomers has been evaluated