Antithrombin III and activated protein C for the treatment of severe sepsis associated to coagulopathy

Improving Irony and Stereotype Spreaders Detection using Data Augmentation and Convolutional Neural Network

In this paper we describe a deep learning model based on a Data Augmentation (DA) layer followed by a Convolutional Neural Network (CNN). The proposed model was developed by our team for the Profiling Irony and Stereotype Spreaders (ISSs) task proposed by the PAN 2022 organizers. As a first step, to classify an author as ISS or not (nISS), we developed a DA layer that expands each sample in the dataset provided. Using this augmented dataset we trained the CNN. Then, to submit our predictions, we apply our DA layer on the samples within the unlabeled test set too. Finally we fed our trained CNN with the augmented test set to generate our final predictions. To develop and test our model we us…

Hybrid VLC/WiFi Architectures with Priority Feedback Channels

In this paper, we consider integrating Visible Light Communication (VLC) and WiFi technologies in an ultra-dense (massive) Internet of Things scenario, where WiFi links are used for complementing VLC links and vice-versa. Indeed, in case of mobility and/or bidirectional communications, limited coverage areas and self-generated interference can be an issue. We consider hybrid VLC/WiFi nodes where the VLC link is used for the majority of (data) traffic and the WiFi link is employed as a feedback channel (e.g. transmitting TCP ACKs). In presence of intense WiFi traffic, the WiFi network may be congested and thus, to fully exploit the VLC bandwidth, we discuss possible priority mechanisms to fu…