Uncertainty Analysis for the Classification of Multispectral Satellite Images Using SVMs and SOMs

Abstract: Classification of multispectral remotely sensed data with textural features is investigated with a special focus on uncertainty analysis in the produced land-cover maps. Much effort has already been directed into the research of satisfactory accuracy-assessment techniques in image classification, but a common approach is not yet universally adopted. We look at the relationship between hard accuracy and the uncertainty on the produced answers, introducing two measures based on maximum probability and a quadratic entropy. Their impact differs depending on the type of classifier. In this paper, we deal with two different classification strategies, based on support vector machines (SVMs) and Kohonen's self-organizingmaps (SOMs), both suitably modified to give soft answers. Once the multiclass probability answer vector is available for each pixel in the image, we studied the behavior of the overall classification accuracy as a function of the uncertainty associated with each vector, given a hard-labeled test set. The experimental results show that the SVM with one-versus-one architecture and linear kernel clearly outperforms the other supervised approaches in terms of overall accuracy. On the other hand, our analysis reveals that the proposed SOM-based classifier, despite its unsupervised learning procedure, is able to provide soft answers which are the best candidates for a fusion with supervised results

Effects of Noise in a Cortical Neural Model

Recently Segev et al. (Phys. Rev. E 64,2001, Phys.Rev.Let. 88, 2002) made long-term observations of spontaneous activity of in-vitro cortical networks, which differ from predictions of current models in many features. In this paper we generalize the EI cortical model introduced in a previous paper (S.Scarpetta et al. Neural Comput. 14, 2002), including intrinsic white noise and analyzing effects of noise on the spontaneous activity of the nonlinear system, in order to account for the experimental results of Segev et al.. Analytically we can distinguish different regimes of activity, depending from the model parameters. Using analytical results as a guide line, we perform simulations of the nonlinear stochastic model in two different regimes, B and C. The Power Spectrum Density (PSD) of the activity and the Inter-Event-Interval (IEI) distributions are computed, and compared with experimental results. In regime B the network shows stochastic resonance phenomena and noise induces aperiodic collective synchronous oscillations that mimic experimental observations at 0.5 mM Ca concentration. In regime C the model shows spontaneous synchronous periodic activity that mimic activity observed at 1 mM Ca concentration and the PSD shows two peaks at the 1st and 2nd harmonics in agreement with experiments at 1 mM Ca. Moreover (due to intrinsic noise and nonlinear activation function effects) the PSD shows a broad band peak at low frequency. This feature, observed experimentally, does not find explanation in the previous models. Besides we identify parametric changes (namely increase of noise or decreasing of excitatory connections) that reproduces the fading of periodicity found experimentally at long times, and we identify a way to discriminate between those two possible effects measuring experimentally the low frequency PSD.Comment: 25 pages, 10 figures, to appear in Phys. Rev.

Postpartum depression screening through artificial intelligence: preliminary data through the Talking About algorithm

Postpartum depression (PD) is the most widespread perinatal psy­chiatric disorder, also representing the most frequent non-obstetric birth-related complication. From an epidemiological point of view, it has an average prevalence of 17-18% worldwide. This psychiatric disorder may have long-standing effects on the health of both the mother and the child, but also on the relationship with the partner (including paternal PD). Therefore, an early diagnosis is fundamental to treat this disorder immediately and avoid such complications. Talking About, by the company GPI (Trento, Italy), is a project focused on voice analysis as a medium to access human emotions. It consists of a series of Speech Emotion Recognition (SER) algorithms. The aim of the study is to evaluate the application of the artificial intelligence (AI) algorithm Talking About on the mothers’ emotions analysis. Talking About investigates the unconscious aspects of voice that usually cannot be controlled or voluntarily modified, aiming at identifying the subjects’ emotions. Thus, all bias, characterising all classic screening questionnaires, should be neutralised, achieving a sharper overview of the mothers’ emotional state. The mother’s emotional results are displayed in 5 main categories: 2 positive, 3 negative. This study has engaged a total of 154 mothers who gave birth at the “Policlinico Universitario D. Casula” and/or carried out a pediatric examination at the “ambulatorio SOS MAMI” (which is a PostNatal Care Service). They underwent both the Edinburgh Postnatal Depression Scale (EPDS) test and the Talking About voice test. Despite this study’s sample limitations, our preliminary data related to PD symptoms identification are promising and encouraging, leading the way to further investigations related to the application of AI as a PD screening support. Indeed, further studies are needed to improve our knowledge on this topic and possibly apply this tool in clinical practice in the future, even considering PD of the father

Elaboración de papel ecológico a partir de fibras vegetales para uso artístico

El propósito de este proyecto de investigación es la elaboración de papel ecológico a partir de la experimentación con diversas fibras vegetales de la provincia de Misiones para ser utilizado en el campo de las artes plásticas. Pretende optimizar los recursos humanos existentes, a través de la conformación de un equipo interdisciplinario que aúne conocimientos científico-tecnológicos y artísticos, sistematizar el proceso de producción del papel, ampliar su gama de posibilidades cromáticas utilizando vegetales de la zona y fomentar la elaboración de papel como soporte y medio de expresión artística. La metodología aplicada en el desarrollo del proyecto consta de dos etapas, una heurística y otra hermenéutica. (Párrafo extraído del texto a modo de resumen) Facultad de Bellas Arte

Elaboración de papel ecológico a partir de fibras vegetales para uso artístico

El propósito de este proyecto de investigación es la elaboración de papel ecológico a partir de la experimentación con diversas fibras vegetales de la provincia de Misiones para ser utilizado en el campo de las artes plásticas. Pretende optimizar los recursos humanos existentes, a través de la conformación de un equipo interdisciplinario que aúne conocimientos científico-tecnológicos y artísticos, sistematizar el proceso de producción del papel, ampliar su gama de posibilidades cromáticas utilizando vegetales de la zona y fomentar la elaboración de papel como soporte y medio de expresión artística. La metodología aplicada en el desarrollo del proyecto consta de dos etapas, una heurística y otra hermenéutica. (Párrafo extraído del texto a modo de resumen) Facultad de Bellas Arte

Elaboración de papel ecológico a partir de fibras vegetales para uso artístico

El propósito de este proyecto de investigación es la elaboración de papel ecológico a partir de la experimentación con diversas fibras vegetales de la provincia de Misiones para ser utilizado en el campo de las artes plásticas. Pretende optimizar los recursos humanos existentes, a través de la conformación de un equipo interdisciplinario que aúne conocimientos científico-tecnológicos y artísticos, sistematizar el proceso de producción del papel, ampliar su gama de posibilidades cromáticas utilizando vegetales de la zona y fomentar la elaboración de papel como soporte y medio de expresión artística. La metodología aplicada en el desarrollo del proyecto consta de dos etapas, una heurística y otra hermenéutica. (Párrafo extraído del texto a modo de resumen) Facultad de Bellas Arte

Reducing Blindness from Retinopathy of Prematurity (ROP) in Argentina Through Collaboration, Advocacy and Policy Implementation.

Retinopathy of prematurity (ROP) is a largely avoidable cause of blindness in children worldwide, requiring high-quality neonatal care, early detection and treatment. In middle-income countries throughout Latin America, Eastern Europe and South Asia, there has been a rise in ROP blindness due to a combination of increased survival of preterm infants, resource-scarce medical environments and lack of policies, training and human resources. However, Argentina is an example of country where rates of ROP blindness have declined and ROP programmes have been successfully and effectively embedded within the health and legal system. The purpose of this study is to describe the activities and stakeholders, including Ministry of Health (MoH) and UNICEF, involved in the process, from recognition of an epidemic of ROP blindness to the development of national guidelines, policies and legislation for control. Using a retrospective mixed methods case study design, data on rates of severe ROP was collected from 13 neonatal intensive care units from 1999 to 2012, and on the proportion of children blind from ROP in nine blind schools in seven provinces. Legislative document review, focus group discussions and key informant interviews were conducted with neonatologists, ophthalmologists, neonatal nurses, parents, MoH officials, clinical societies, legislators and UNICEF officials in seven provinces. Results are presented combining the stages heuristic policy framework and Shiffman including: agenda setting, policy formulation, implementation and evaluation. By 2012, ROP had declined as a cause of blindness in children in schools for the blind as had rates of severe ROP needing treatment in the NICUs visited. Multiple factors played a role in reducing blindness from ROP in Argentina and successfully coordinating its control including national advocacy, leadership, legislation and international collaboration. Lessons learned in Argentina can potentially be scaled to other LMICs in Latin America and beyond with further context-specific research