Enhancing Privacy-Preserving Cancer Classification with Convolutional Neural Networks.

Precision medicine significantly enhances patients prognosis, offering personalized treatments. Particularly for metastatic cancer, incorporating primary tumor location into the diagnostic process greatly improves survival rates. However, traditional methods rely on human expertise, requiring substantial time and financial resources.

The Desiccation and Catastrophic Refilling of the Mediterranean: 50 Years of Facts, Hypotheses, and Myths Around the Messinian Salinity Crisis.

According to some authors, the Messinian salinity crisis was ended by a giant waterfall or megaflood 5.33 million years ago, when the Atlantic Ocean reconnected in a catastrophic way with the desiccated Mediterranean, creating the Strait of Gibraltar. An erosional surface deeply cutting upper Miocene or older rocks and sealed by lower Pliocene sediments is the geological feature that inspired this fascinating hypothesis.

Echo-Endoscopy Combined with Virtual Reality: A Whole Perspective of Laparoscopic Common Bile Duct Exploration in Children.

Endoscopic procedures are performed more frequently in children due to technological advances that can be safely performed in an adequate setting with a support of a multidisciplinary team. Pediatric indications for ERCP (endoscopic retrograde cholangiopancreatography) and EUS (endoscopic ultrasound) occur mainly due to congenital malformations. In a pediatric case series, we report the application of EUS combined with duodenoscopy, eventually associated with ERCP and minimally invasive surgery, highlighting the importance of defining a tailored dedicated management pathway for each patient.

Tiny Machine Learning for Concept Drift.

Tiny machine learning (TML) is a new research area whose goal is to design machine and deep learning (DL) techniques able to operate in embedded systems and the Internet-of-Things (IoT) units, hence satisfying the severe technological constraints on memory, computation, and energy characterizing these pervasive devices. Interestingly, the related literature mainly focused on reducing the computational and memory demand of the inference phase of machine and deep learning models. At the same time, the training is typically assumed to be carried out in cloud or edge computing systems (due to the larger memory and computational requirements).

A Markovian model for the spread of the SARS-CoV-2 virus.

We propose a Markovian stochastic approach to model the spread of a SARS-CoV-2-like infection within a closed group of humans. The model takes the form of a Partially Observable Markov Decision Process (POMDP), whose states are given by the number of subjects in different health conditions. The model also exposes the different parameters that have an impact on the spread of the disease and the various decision variables that can be used to control it (e.