Diversity and the Undertreatment of Pain

The undertreatment of pain is a major public health problem and has enormous costs -- to the individual, the health care system and society. Undertreatment is complex and many studies have clarified the factors that may contribute. Clinicians who become aware of these factors are better able to assess accurately, treat appropriately, and educate about pain. Among other factors, undertreatmentof pain has been associated with race, sex, ethnicity, and culture

Prediction in Photovoltaic Power by Neural Networks

The ability to forecast the power produced by renewable energy plants in the short and middle term is a key issue to allow a high-level penetration of the distributed generation into the grid infrastructure. Forecasting energy production is mandatory for dispatching and distribution issues, at the transmission system operator level, as well as the electrical distributor and power system operator levels. In this paper, we present three techniques based on neural and fuzzy neural networks, namely the radial basis function, the adaptive neuro-fuzzy inference system and the higher-order neuro-fuzzy inference system, which are well suited to predict data sequences stemming from real-world applications. The preliminary results concerning the prediction of the power generated by a large-scale photovoltaic plant in Italy confirm the reliability and accuracy of the proposed approaches

Distributed Learning for Multiple Source Data

Distributed learning is the problem of inferring a function when data to be analyzed is distributed across a network of agents. Separate domains of application may largely impose different constraints on the solution, including low computational power at every location, limited underlying connectivity (e.g. no broadcasting capability) or transferability constraints related to the enormous bandwidth requirement. Thus, it is no longer possible to send data in a central node where traditionally learning algorithms are used, while new techniques able to model and exploit locally the information on big data are necessary. Motivated by these observations, this thesis proposes new techniques able to efficiently overcome a fully centralized implementation, without requiring the presence of a coordinating node, while using only in-network communication. The focus is given on both supervised and unsupervised distributed learning procedures that, so far, have been addressed only in very specific settings only. For instance, some of them are not actually distributed because they just split the calculation between different subsystems, others call for the presence of a fusion center collecting at each iteration data from all the agents; some others are implementable only on specific network topologies such as fully connected graphs. In the first part of this thesis, these limits have been overcome by using spectral clustering, ensemble clustering or density-based approaches for realizing a pure distributed architecture where there is no hierarchy and all agents are peer. Each agent learns only from its own dataset, while the information about the others is unknown and obtained in a decentralized way through a process of communication and collaboration among the agents. Experimental results, and theoretical properties of convergence, prove the effectiveness of these proposals. In the successive part of the thesis, the proposed contributions have been tested in several real-word distributed applications. Telemedicine and e-health applications are found to be one of the most prolific area to this end. Moreover, also the mapping of learning algorithms onto low-power hardware resources is found as an interesting area of applications in the distributed wireless networks context. Finally, a study on the generation and control of renewable energy sources is also analyzed. Overall, the algorithms presented throughout the thesis cover a wide range of possible practical applications, and trace the path to many future extensions, either as scientific research or technological transfer results

Intimate Relationships: Reality and Normativity

Close interpersonal relationships are a part of everyone\u27s life at some point. For most people these relationships are actually prominent parts of their everyday lives. As such, it is important to figure out whether and how they fit into different normative theories of ethics. Relationships like those that exist as romantic couples, close friendships, and parent-child relationships share certain features with other close interpersonal relationships that I define as \u27intimate relationships\u27 in this dissertation. Intimate relationships are those that exist between people when they wish one another well, act for one another, do so mutually, treat one another as ends in themselves, and trust one another. If a normative theory is to account for moral value in intimate relationships then it must meet a set of necessary and sufficient conditions. My purpose in this dissertation is to synthesize these conditions after analyzing four distinct normative theories\u27 accounts of moral value in intimate relationships: Kantianism, G.E. Moore\u27s consequentialism, Aristotle\u27s virtue ethics, and Virginia Held\u27s ethics of care. These four theorists\u27 approaches to normative ethics were selected because each theorist claims to value intimate relationships yet each provides a different account of that value. I first show that each theorist considers intimate relationships to be morally valuable and then analyze their theories\u27 abilities to account for that value using four value terms: intrinsic good, extrinsic good, instrumental good, and final good. I then identify the components of each normative theory that either allow it to capture or prevent it from capturing the moral value of intimate relationships. This leads to the conclusion that a normative theory must allow for value in each of the components of an intimate relationship and appraise the relationships themselves to be more than instrumentally valuable in order to account for any moral value in the relationships. Those people who treat the intimate relationships in their lives as having moral value will be able to gauge the applicability of a particular normative theory to their lives by that theory\u27s ability to meet the aforementioned criteria

A comparison of machine learning classifiers for smartphone-based gait analysis

This paper proposes a reliable monitoring scheme that can assist medical specialists in watching over the patient's condition. Although several technologies are traditionally used to acquire motion data of patients, the high costs as well as the large spaces they require make them difficult to be applied in a home context for rehabilitation. A reliable patient monitoring technique, which can automatically record and classify patient movements, is mandatory for a telemedicine protocol. In this paper, a comparison of several state-of-the-art machine learning classifiers is proposed, where stride data are collected by using a smartphone. The main goal is to identify a robust methodology able to assure a suited classification of gait movements, in order to allow the monitoring of patients in time as well as to discriminate among a pathological and physiological gait. Additionally, the advantages of smartphones of being compact, cost-effective and relatively easy to operate make these devices particularly suited for home-based rehabilitation programs. Graphical Abstract. This paper proposes a reliable monitoring scheme that can assist medical specialists in watching over the patient's condition. Although several technologies are traditionally used to acquire motion data of patients, the high costs as well as the large spaces they require make them difficult to be applied in a home context for rehabilitation. A reliable patient monitoring technique, which can automatically record and classify patient movements, is mandatory for a telemedicine protocol. In this paper, a comparison of several state-of-the-art machine learning classifiers is proposed, where stride data are collected and processed by using a smartphone(see figure). The main goal is to identify a robust methodology able to assure a suited classification of gait movements, in order to allow the monitoring of patients in time as well as to discriminate among a pathological and physiological gait. Additionally, the advantages of smartphones of being compact, cost-effective and relatively easy to operate make these devices particularly suited for home-based rehabilitation programs

Análisis comparativo entre OCDE y políticas tributarias domésticas : aplicación del "Método sexto" en materia de commodities en la argentina

Fil: Díaz Altilio, Victoria. Universidad de San Andrés. Escuela de Administración y Negocios; Argentina.Diez, Gustavo Eduard

Epitalamio

Di Gabriele Altilio Poete famoso a' tempi del Sanazzaro, Sopra Le Nozze Di Giovan-Galeazzo Sforza, allora Duca di Milano, con Isabella d'Aragona ...; Tradotto elegantamente di Latino in Ottava Rima per suo ecersizio Dall'Abate Giovam-Batista Carminati Patrizio VenetoHandschriftlicher Schenkungsvermerk Johann Jakob Bodmers an die Stadtbibliothek Zürich (1776) auf dem vorderen Vorsatzblatt des Sammelbande

A smartphone-based application using machine learning for gesture recognition. Using feature extraction and template matching via Hu image moments to recognize gestures

The rapid development of smart devices, such as smartphones and tablets, leads to new challenges and ushers in a new stage of human-computer interaction. In this context, it becomes essential to develop methods and techniques for a better and more natural interaction with these devices. In this article, we address the problem of gesture segmentation and recognition, taking into account the limited computational resources of smartphone devices. We introduce a methodology for designing efficient and useful applications that, by using low-cost and widely diffused technologies, can be used in telemedicine, home-based rehabilitation, and other biomedical applications for patients with specific disabilities. To this end, we have designed a new machine-learning algorithm that is able to identify hand gestures through the use of Hu image moments, due to their invariance to rotation, translation, scaling, and their low computational cost. The experimental results collected from a case study show an excellent gesture recognition performance and an affordable real-time execution speed on smartphones and other mobile devices