Abstract: The present invention relates to a process for the purification of (R)-2-amino-3-phenylpropyl carbamate in perspective of the subsequent conversion in the corresponding HCl salt (SOLRIAMFETOL). The invention further relates to the process for preparing a batch of purified solriamfetol, as well as to the purified (R)-2-amino-3-phenylpropyl carbamate or its hydrochloride salt.

Abstract: A tuning system and related computer implemented tuning method carried on an IT system including a System Under Test (SUT) including a stack of software layers, provided with a number of adjustable parameters are disclosed. The method includes the steps of supplying a characterization and prediction module, a tuner module, and a knowledge base (KB). The KB is composed by N tuples, (si, {right arrow over (w)}i, {right arrow over (x)}i, yi) being gathered over iterative tuning sessions where each iteration is started by applying to the SUT si a configuration {right arrow over (xl)} suggested by the tuner module, exposing the system si to an external working condition wi and gathering performance metrics resulting in a performance indicator score yi.

Abstract: There is disclosed a method for generating movie recommendations, based on automatic extraction of features from a multimedia content, wherein the extracted features are visual features representing mise-en-scène characteristics of the movie defined on the basis of Applied Media Aesthetic theory, said extracted features being then fed to content-based recommendation algorithm in order to generate personalized recommendation.

Abstract: There is disclosed a method for generating movie recommendations, based on automatic extraction of features from a multimedia content, wherein the extracted features are visual features representing mise-en-scène characteristics of the movie defined on the basis of Applied Media Aesthetic theory, said extracted features being then fed to content-based recommendation algorithm in order to generate personalised recommendation.

Abstract: A method and a client-side Recommender system on One-Way Broadcast Network includes at least a one-way channel connecting a broadcasting station to a set-top-box device of a first set of users, the set-top-box including a memory, a computation and driving unit for tracking the user activity, storing a user profile, generating and displaying a ranking of recommended media items, the ranking being determined by the computation unit through a recommender model stored on the memory. The system further includes a collaborative dual module, a first back-end module running on a auxiliary server connected to a two-way network and belonging to an auxiliary domain, this first back-end module leveraging external data sources to collect community based information on at least part of the media items, while the recommender model stored on the memory being a second front-end module, wherein the broadcasting station and the set-top-box device belong to a target domain.

Abstract: Embodiments provide a method for performing an automatic execution of a Box and Jenkins method for forecasting the behavior of said dataset. The method may include pre-processing the dataset including providing one or more missing values to the dataset, removing level discontinuities and outliers, and removing one or more last samples from the dataset, obtaining a trend of the pre-processed dataset including identifying and filtering the trend out of the dataset based on a coefficient of determination methodology, detecting seasonality to obtain a resulting stationary series including computing an auto correlation function of the dataset, repeating the detecting step on an aggregate series of a previous dataset, and removing detected seasonality based on a seasonal differencing process, and modeling the resulting stationary series under an autoregressive-moving-average (ARMA) model.

Abstract: Embodiments provide a method for upgrading resources in a system including normalizing a collected dataset, scattering data from the normalized dataset, obtaining a plurality of clusters based on the scattered data, discarding one or more clusters from the plurality of clusters with less than a percentage of a total number of observations, in each cluster, performing clusterwise regression and obtaining linear sub-clusters in a defined number, reducing one or more sub-clusters including applying a refinement procedure, removing one or more sub-clusters that fit to outliers and merging pairs of clusters that fit an equivalent model, updating one or more clusters with the reduced sub-clusters, removing one or more globular clusters, reducing a number of clusters with the refinement procedure, and de-normalizing one or more results.

Abstract: Embodiments provide a method for performing an automatic execution of a Box and Jenkins method for forecasting the behavior of said dataset. The method may include pre-processing the dataset including providing one or more missing values to the dataset, removing level discontinuities and outliers, and removing one or more last samples from the dataset, obtaining a trend of the pre-processed dataset including identifying and filtering the trend out of the dataset based on a coefficient of determination methodology, detecting seasonality to obtain a resulting stationary series including computing an auto correlation function of the dataset, repeating the detecting step on an aggregate series of a previous dataset, and removing detected seasonality based on a seasonal differencing process, and modeling the resulting stationary series under an autoregressive-moving-average (ARMA) model.

Abstract: Embodiments provide a method for performing an automatic execution of a Box and Jenkins method for forecasting the behavior of said dataset. The method may include pre-processing the dataset including providing one or more missing values to the dataset, removing level discontinuities and outliers, and removing one or more last samples from the dataset, obtaining a trend of the pre-processed dataset including identifying and filtering the trend out of the dataset based on a coefficient of determination methodology, detecting seasonality to obtain a resulting stationary series including computing an auto correlation function of the dataset, repeating the detecting step on an aggregate series of a previous dataset, and removing detected seasonality based on a seasonal differencing process, and modeling the resulting stationary series under an autoregressive-moving-average (ARMA) model.

Abstract: A method and a client-side Recommender system on One-Way Broadcast Network includes at least a one-way channel connecting a broadcasting station to a set-top-box device of a first set of users, the set-top-box including a memory, a computation and driving unit for tracking the user activity, storing a user profile, generating and displaying a ranking of recommended media items, the ranking being determined by the computation unit through a recommender model stored on the memory. The system further includes a collaborative dual module, a first back-end module running on a auxiliary server connected to a two-way network and belonging to an auxiliary domain, this first back-end module leveraging external data sources to collect community based information on at least part of the media items, while the recommender model stored on the memory being a second front-end module, wherein the broadcasting station and the set-top-box device belong to a target domain.

Abstract: Compounds which are 3-arylsulfonyl-2-methyl propanoic acid derivatives of formula (I): wherein X is HO—NH— or HO—, R1 is selected from phenyl, 4-chlorophenyl, 4-florophenyl, 4-cyanophenyl, benzamido (i.e., —NH—CO-Ph) and benzamido substituted on the terminal phenyl ring by C1-C4 alkyl, fluoro, chloro, cyano or C1-4 alkoxy; R2 is selected from (a) —S—Ar or —S—CH2—Ar wherein Ar is an aromatic moiety; (b) —O—Ar wherein Ar is as defined above; (c) —S-Het or —S—CH2-Het wherein Het is a heterocyclic ring; and (d) 2,5-dioxo-1-imidazolidinyl or 2,4-dioxo-1-imidazolinyl; and the pharmaceutically acceptable salts thereof; have potent and selective inhibitory activity against matrix metalloproteinases (MMPs) and can thus be used in the treatment and prevention of diseases mediated by MMPs.

Abstract: The present invention relates to amino-benzothiazole compounds of formula (I) wherein X, Y, Z and R1 are as defined in the specification, their pharmaceutically acceptable salts, processes for their preparation, pharmaceutical composition comprising such compounds and their use as neuroprotective agents.