Hausdorff clustering of financial time series

A clustering procedure, based on the Hausdorff distance, is introduced and tested on the financial time series of the Dow Jones Industrial Average (DJIA) index.Comment: 9 pages, 3 figure

Deep Learning for Short-Term Prediction of Available Bikes on Bike-Sharing Stations

Bike-sharing is adopted as a valid option replacing traditional public transports since they are eco-friendly, prevent traffic congestions, reduce any possible risk of social contacts which happen mostly on public means. However, some problems may occur such as the irregular distribution of bikes on related stations/racks/areas, and the difficulty of knowing in advance what the rack status will be like, or predicting if there will be bikes available in a specific bike-station at a certain time of the day, or if there will be a free slot to leave the rented bike. Thus, providing predictions can be useful to improve the service quality, especially in those cases where bike racks are used for e-bikes, which need to be recharged. This paper compares the state-of-the-art techniques to predict the number of available bikes and free bike-slots in bike-sharing stations (i.e., bike racks). To this end, a set of features and predictive models were compared to identify the best models and predictors for short-term predictions, namely of 15, 30, 45, and 60 minutes. The study has demonstrated that deep learning and in particular Bidirectional Long Short-Term Memory networks (Bi-LSTM) offers a robust approach for the implementation of reliable and fast predictions of available bikes, even with a limited amount of historical data. This paper has also reported an analysis of feature relevance based on SHAP that demonstrated the validity of the model for different cluster behaviours. Both solution and its validation were derived by using data collected in bike-stations in the cities of Siena and Pisa (Italy), in the context of Sii-Mobility National Research Project on Mobility and Transport and Snap4City Smart City IoT infrastructure

Magnetic Monopoles in Ferromagnetic Spin-Triplet Superconductors

Using the $\phi$-mapping method, we argue that ferromagnetic spin-triplet superconductors allow formation of unstable magnetic monopoles. In particular, we show that the limit points and the bifurcation points of the $\phi$-mapping will serve as the interaction points of these magnetic monopoles.Comment: 4 pages, no figure

Essential Role of the cVRG in the Generation of Both the Expiratory and Inspiratory Components of the Cough Reflex.

As stated by Korpáš and Tomori (1979), cough is the most important airway protective reflex which provides airway defensive responses to nociceptive stimuli. They recognized that active expiratory efforts, due to the activation of caudal ventral respiratory group (cVRG) expiratory premotoneurons, are the prominent component of coughs. Here, we discuss data suggesting that neurons located in the cVRG have an essential role in the generation of both the inspiratory and expiratory components of the cough reflex. Some lines of evidence indicate that cVRG expiratory neurons, when strongly activated, may subserve the alternation of inspiratory and expiratory cough bursts, possibly owing to the presence of axon collaterals. Of note, experimental findings such as blockade or impairment of glutamatergic transmission to the cVRG neurons lead to the view that neurons located in the cVRG are crucial for the production of the complete cough motor pattern. The involvement of bulbospinal expiratory neurons seems unlikely since their activation affects differentially expiratory and inspiratory muscles, while their blockade does not affect baseline inspiratory activity. Thus, other types of cVRG neurons with their medullary projections should have a role and possibly contribute to the fine tuning of the intensity of inspiratory and expiratory efforts

HIV-1 superinfection with a triple-class drug-resistant strain in a patient successfully controlled with antiretroviral treatment.

We report a case of HIV-1 superinfection (HSI) with a clade B, triple-class resistant virus in a patient successfully controlling viremia with continuous combination antiretroviral therapy started 8 years earlier during primary HIV infection. The course of HIV infection prior to HSI was monitored in both the source partner and recipient (8 and 11 years, respectively) and 4 years following HSI. This case report demonstrates re-infection with HIV-1 despite effective combination antiretroviral therapy

HIV-1 superinfection with a triple-class drug-resistant strain in a patient successfully controlled with antiretroviral treatment.

We report a case of HIV-1 superinfection (HSI) with a clade B, triple-class resistant virus in a patient successfully controlling viremia with continuous combination antiretroviral therapy started 8 years earlier during primary HIV infection. The course of HIV infection prior to HSI was monitored in both the source partner and recipient (8 and 11 years, respectively) and 4 years following HSI. This case report demonstrates re-infection with HIV-1 despite effective combination antiretroviral therapy

Country-level factors dynamics and ABO/Rh blood groups contribution to COVID-19 mortality

The identification of factors associated to COVID-19 mortality is important to design effective containment measures and safeguard at-risk categories. In the last year, several investigations have tried to ascertain key features to predict the COVID-19 mortality tolls in relation to country-specific dynamics and population structure. Most studies focused on the first wave of the COVID-19 pandemic observed in the first half of 2020. Numerous studies have reported significant associations between COVID-19 mortality and relevant variables, for instance obesity, healthcare system indicators such as hospital beds density, and bacillus Calmette-Guerin immunization. In this work, we investigated the role of ABO/Rh blood groups at three different stages of the pandemic while accounting for demographic, economic, and health system related confounding factors. Using a machine learning approach, we found that the “B+” blood group frequency is an important factor at all stages of the pandemic, confirming previous findings that blood groups are linked to COVID-19 severity and fatal outcome