Urban traffic emissions represent a significant concern due to their detrimental impacts on both public health and the environment. Consequently, decision-makers have flagged their reduction as a crucial goal. In this study, we first analyze the correlation between traffic flux and pollution in the city of Valencia, Spain. Our results demonstrate that traffic has a significant impact on the levels of certain pollutants (especially $\text{NO}_\text{x}$). Secondly, we develop an alarm system to predict if a street is likely to experience unusually high traffic in the next 30 minutes, using an independent three-tier level for each street. To make the predictions, we use traffic data updated every 10 minutes and Long Short-Term Memory (LSTM) neural networks. We trained the LSTM using traffic data from 2018, and tested it using traffic data from 2019.

We use Google's MusicVAE, a Variational Auto-Encoder with a 512-dimensional latent space to represent a few bars of music, and organize the latent dimensions according to their relevance in describing music. We find that, on average, most latent neurons remain silent when fed real music tracks: we call these "noise" neurons. The remaining few dozens of latent neurons that do fire are called "music neurons". We ask which neurons carry the musical information and what kind of musical information they encode, namely something that can be identified as pitch, rhythm or melody. We find that most of the information about pitch and rhythm is encoded in the first few music neurons: the neural network has thus constructed a couple of variables that non-linearly encode many human-defined variables used to describe pitch and rhythm. The concept of melody only seems to show up in independent neurons for longer sequences of music.

We will exemplify the use of this method in a nontrivial Algorithms that detect anomalies have to learn normal task in our field-domain, Particle Physics. In the context behaviour to be able to identify anomalous behaviour. of the Large Hadron Collider (LHC) searches for new Sometimes we do know what types of anomalies we need phenomena, we show how Anomaly Awareness can help to search for, and then use supervised Machine Learning making these searches more robust, less dependent on the (ML) methods to find them. As anomalies are, by definition, specific scenarios one has in mind. This model-independence rarer than normal events, these supervised techniques need to of LHC searches is particularly important now that be adapted to unbalanced datasets and made robust against the traditional ways of thinking in Particle Physics are fluctuations in the dominant normal or in-distribution dataset.