1,721,094 research outputs found
The role of aging on the control of contractile force by Na+-Ca2+ exchange in rat papillary muscle
No full textReduced aerobic metabolic efficiency in postischemic myocardium dysfunction in rats: role of aging.
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An Evolutionary Approach to Time-Optimal Control of Robotic Manipulators
No full textTime-optimal control of robotic manipulators along specified paths is a well-known problem in robotics. It concerns the minimization of the trajectory-tracking time subject to a constrained path and actuator torque limits. Calculus of variations reveals that time-optimal control is of bang-bang type, meaning that at least one actuator is in saturation for every point on the path. Unfortunately, this rule is broken at singular points, where the enforcement of the maximal and/or minimal torque at the bounding actuator would cause the violation of the path constraint. At these particular points, and, sometimes, at critical ones too, the selection of the torques is cumbersome and may introduce jitters in the control references. In this paper, the time-optimal control is addressed in the phase plane with a genetic approach. Results of calculus of variations are ignored and bang-bang control is re-found for the most of the trajectory, while in the neighborhoods of singular points, torques are automatically selected in order to minimize the trajectory-tracking time. Compared to other techniques, the problem is solved directly, without intermediate steps requiring, for example, the explicit computation of the switching points and the management of torques at critical points. The algorithm is validated in simulation on a canonical 2R planar robot in order to ease the comparison with previous works
Cartesian genetic programming for diagnosis of Parkinson disease through handwriting analysis: Performance vs. interpretability issues
No full textIn the last decades, early disease identification through non-invasive and automatic methodologies has gathered increasing interest from the scientific community. Among others, Parkinson's disease (PD) has received special attention in that it is a severe and progressive neuro-degenerative disease. As a consequence, early diagnosis would provide more effective and prompt care strategies, that cloud successfully influence patients’ life expectancy. However, the most performing systems implement the so called black-box approach, which do not provide explicit rules to reach a decision. This lack of interpretability, has hampered the acceptance of those systems by clinicians and their deployment on the field. In this context, we perform a thorough comparison of different machine learning (ML) techniques, whose classification results are characterized by different levels of interpretability. Such techniques were applied for automatically identify PD patients through the analysis of handwriting and drawing samples. Results analysis shows that white-box approaches, such as Cartesian Genetic Programming and Decision Tree, allow to reach a twofold goal: support the diagnosis of PD and obtain explicit classification models, on which only a subset of features (related to specific tasks) were identified and exploited for classification. Obtained classification models provide important insights for the design of non-invasive, inexpensive and easy to administer diagnostic protocols. Comparison of different ML approaches (in terms of both accuracy and interpretability) has been performed on the features extracted from the handwriting and drawing samples included in the publicly available PaHaW and NewHandPD datasets. The experimental findings show that the Cartesian Genetic Programming outperforms the white-box methods in accuracy and the black-box ones in interpretability
Mimicking the immune system to diagnose Parkinson's disease from handwriting
No full textWe introduce a method adopting the Negative Selection Algorithm, which mimics the way the human immune system learns to discriminate body cells from external antigens, for the computer-aided diagnosis of Parkinson's disease from online handwriting. The major advantage of the proposed method with respect to the current state-of-the-art machine learning methods is that it is trained only on data from healthy subjects, thus avoiding the burden of collecting patients' data. Moreover, it has only two parameters to set, and its implementation is by far simpler than those of most of, if not all, the methods proposed in the literature. The performance of the proposed method is evaluated on the PaHaW dataset, which includes handwriting samples drawn by 75 subjects. The results show that it outperforms the state-of-the-art methods and uses fewer features
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Investigating One-Class Classifiers to Diagnose Alzheimer’s Disease from Handwriting
No full textThe analysis of handwriting and drawing has been adopted since the early studies to help diagnose neurodegenerative diseases, such as Alzheimer’s and Parkinson’s. Departing from the current state-of-the-art methods that approach the problem of discriminating between healthy subjects and patients by using two- or multi-class classifiers, we propose to adopt one-class classifier models, as they require only data by healthy subjects to build the classifier, thus avoiding to collect patient data, as requested by competing techniques. In this framework, we evaluated the performance of three models of one-class classifiers, namely the Negative Selection Algorithm, the Isolation Forest and the One-Class Support Vector Machine, on the DARWIN dataset, which includes 174 subjects performing 25 handwriting/drawing tasks. The comparison with the state-of-the-art shows that the methods achieve state-of-the-art performance, and therefore may represent a viable alternative to the dominant approach
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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