Deep learning for supervised classification

One of the most recent area in the Machine Learning research is Deep Learning. Deep Learning algorithms have been applied successfully to computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics. The key idea of Deep Learning is to combine the best techniques from Machine Learning to build powerful general‑purpose learning algorithms. It is a mistake to identify Deep Neural Networks with Deep Learning Algorithms. Other approaches are possible, and in this paper we illustrate a generalization of Stacking which has very competitive performances. In particular, we show an application of this approach to a real classification problem, where a three-stages Stacking has proved to be very effective

Statistics in the Big Data era

It is estimated that about 90% of the currently available data have been produced over the last two years. Of these, only 0.5% is effectively analysed and used. However, this data can be a great wealth, the oil of 21st century, when analysed with the right approach. In this article, we illustrate some specificities of these data and the great interest that they can represent in many fields. Then we consider some challenges to statistical analysis that emerge from their analysis, suggesting some strategies

A Review of Atrial Fibrillation Detection Methods as a Service

Atrial Fibrillation (AF) is a common heart arrhythmia that often goes undetected, and even if it is detected, managing the condition may be challenging. In this paper, we review how the RR interval and Electrocardiogram (ECG) signals, incorporated into a monitoring system, can be useful to track AF events. Were such an automated system to be implemented, it could be used to help manage AF and thereby reduce patient morbidity and mortality. The main impetus behind the idea of developing a service is that a greater data volume analyzed can lead to better patient outcomes. Based on the literature review, which we present herein, we introduce the methods that can be used to detect AF efficiently and automatically via the RR interval and ECG signals. A cardiovascular disease monitoring service that incorporates one or multiple of these detection methods could extend event observation to all times, and could therefore become useful to establish any AF occurrence. The development of an automated and efficient method that monitors AF in real time would likely become a key component for meeting public health goals regarding the reduction of fatalities caused by the disease. Yet, at present, significant technological and regulatory obstacles remain, which prevent the development of any proposed system. Establishment of the scientific foundation for monitoring is important to provide effective service to patients and healthcare professionals

Charakter wyspiarski turystyki: przykład Sycylii i Sardynii

Treść artykułu podzielona jest na 4 części, w których autorka omawia kolejno: — specyfikę turyzmu w autonomicznym regionie wyspiarskich Włoch, — sycylijski model turystyki, — sardyński model turystyki, — doświadczenia regionów wyspiarskich w turystyce. Sycylia i Sardynia — mimo położenia na trasie wielkich, śródziemnomorskich potoków turystycznych — pozostają ciągle na uboczu. Głównymi powodami są: dość trudna dostępność, wygórowane ceny, polityka faworyzująca turystykę luksusową etc

Le modifiche agli schemi di bilancio contenute nella proposta OIC di attuazione della direttiva 2003/51/CE.

L'elaborato analizza le modifiche agli schemi di bilancio contenute nella proposta OIC di attuazione della Direttiva 2003/51/CEE. Tale direttiva, emanata il 18 giugno 2003, è volta a ridurre i problemi di incompatibilità esistenti tra le direttive contabili europee e i principi contabili internazionali. Le modifiche che essa apporta alla quarta e settima direttiva introducono novità rilevanti sia nella forma che nel contenuto del bilancio, e consentono di fatto, ai singoli Stati membri di estendere a tutte le società la facoltà di redigere bilanci secondo gli IAS-IFRS

Cerebrospinal Fluid Analysis in Multiple Sclerosis Diagnosis: An Update

Multiple sclerosis (MS) is an immune-mediated demyelinating disease of the central nervous system (CNS) with brain neurodegeneration. MS patients present heterogeneous clinical manifestations in which both genetic and environmental factors are involved. The diagnosis is very complex due to the high heterogeneity of the pathophysiology of the disease. The diagnostic criteria have been modified several times over the years. Basically, they include clinical symptoms, presence of typical lesions detected by magnetic resonance imaging (MRI), and laboratory findings. The analysis of cerebrospinal fluid (CSF) allows an evaluation of inflammatory processes circumscribed to the CNS and reflects changes in the immunological pattern due to the progression of the pathology, being fundamental in the diagnosis and monitoring of MS. The detection of the oligoclonal bands (OCBs) in both CSF and serum is recognized as the “gold standard” for laboratory diagnosis of MS, though presents analytical limitations. Indeed, current protocols for OCBs assay are time-consuming and require an operator-dependent interpretation. In recent years, the quantification of free light chain (FLC) in CSF has emerged to assist clinicians in the diagnosis of MS. This article reviews the current knowledge on CSF biomarkers used in the diagnosis of MS, in particular on the validated assays and on the alternative biomarkers of intrathecal synthesis

Integration of a physically-based hydrological model with spatial soil data and GIS: an application to the Hafren Catchment, Wales

The present research aims to illustrate and evaluate the effect of spatially variable soil data on the modelling of catchment rainfall-runoff transformations, using the hydrological model Topmodel. The soil-topographic wetness index used in Topmodel has always allowed for a spatially variable To - lateral saturated transmissivity - yet very little published research has focussed on the use of spatial soil datasets to derive To. In recent years the availability of soil hydrologic parameters, either from soil classifications and/or from new measurement techniques has increased significantly and, especially with regards to remote sensing, there is still great potential for further advances. It is therefore important that models like Topmodel should be able to incorporate such distributed soil data and assess if its' inclusion may allow a better representation of rainfall-runoff transformation processes. In particular, one of the key issues is the need to use distributed data to predict internal catchment conditions — such as runoff source areas — and not only global volumetric outflows. This aspect is of importance both at the catchment scale, for improved integrated catchment management (i.e. in the presence of land-use changes), and at the GCM modelling scale for the simulation of regional land-atmosphere interactions.With regard to the soil data, particular importance is associated to soil hydraulic parameters such as porosity and saturated conductivities. Traditionally, such data have only been available from measurements on single soil samples. But in recent years, various analytical methods and hydromorphic classification schemes have been developed which allow us to estimate the above parameters or, alternatively, provide qualitative indeces of the soils behaviour in terms of runoff generation. The present research has therefore evaluated the effect of different soil classification schemes with respect to their ability to improve the prediction of soil moisture deficit using TOPMODEL.Given the strengths of GIS in storing and analysing spatial data, the research has also evaluated if and how GIS can be used to better understand the effect of spatial classification schemes applied to the soil input data. Though GIS cannot substitute the theoretical knowledge of the processes occurring, it can certainly provide the spatial functionalities often lacking in hydrological models. It is this spatial perspective that can allow us to visualise synoptically the phenomena being studied, while at the same time exploring, highlighting, and verifying the prominent spatial variables that control the rainfallrunoff transformation processes.The integration of the three different modelling perspectives was pursued to allow the user to carry out a more thorough validation of both data and modelling methods used. Ultimately, it is hoped that this multidisciplinary approach will help to better assess the validity of the adopted methodology within the context of integrated catchment management

The community as lab for service learning

Teachers can teach students about community service by encouraging their actual participation in community development. This type of service learning promotes the self-esteem of the students and makes them aware of their values in the community. Service learning also improves social skills

Face recognition with the RGB-D sensor

Face recognition in unconstrained environments is still a challenge, because of the many variations of the facial appearance due to changes in head pose, lighting conditions, facial expression, age, etc. This work addresses the problem of face recognition in the presence of 2D facial appearance variations caused by 3D head rotations. It explores the advantages of the recently developed consumer-level RGB-D cameras (e.g. Kinect). These cameras provide color and depth images at the same rate. They are affordable and easy to use, but the depth images are noisy and in low resolution, unlike laser scanned depth images. The proposed approach to face recognition is able to deal with large head pose variations using RGB-D face images. The method uses the depth information to correct the pose of the face. It does not need to learn a generic face model or make complex 3D-2D registrations. It is simple and fast, yet able to deal with large pose variations and perform pose-invariant face recognition. Experiments on a public database show that the presented approach is effective and efficient under significant pose changes. Also, the idea is used to develop a face recognition software that is able to achieve real-time face recognition in the presence of large yaw rotations using the Kinect sensor. It is shown in real-time how this method improves recognition accuracy and confidence level. This study demonstrates that RGB-D sensors are a promising tool that can lead to the development of robust pose-invariant face recognition systems under large pose variations