1,720,984 research outputs found
Fine-tuning the fuzziness of strong fuzzy partitions through PSO
Full text linkWe study the influence of fuzziness of trapezoidal fuzzy sets in the strong fuzzy partitions (SFPs) that constitute the database of a fuzzy rule-based classifier. To this end, we develop a particular representation of the trapezoidal fuzzy sets that is based on the concept of cuts, which are the cross-points of fuzzy sets in a SFP and fix the position of the fuzzy sets in the Universe of Discourse. In this way, it is possible to isolate the parameters that characterize the fuzziness of the fuzzy sets, which are subject to fine-tuning through particle swarm optimization (PSO). In this paper, we propose a formulation of the parameter space that enables the exploration of all possible levels of fuzziness in a SFP. The experimental results show that the impact of fuzziness is strongly dependent on the defuzzification procedure used in fuzzy rule-based classifiers. Fuzziness has little influence in the case of winner-takes-all defuzzification, while it is more influential in weighted sum defuzzification, which however may pose some interpretation problems
La valutazione della sperimentazione del Reddito Minimo di Inserimento nel comune di Napoli
No full textA Python Library for PRUF
No full textIn 1978, Lotfi Zadeh proposed the Possibilistic Relational Universal Fuzzy (PRUF) language to represent and reason about imprecise knowledge. This paper introduces pyPRUF, a Python library implementing PRUF constructs for approximate reasoning. Through examples involving fuzzy relational databases and natural-language propositions, we illustrate how pyPRUF can facilitate the modelling and inference of fuzzy concepts
A Bayesian Interpretation of Fuzzy C-Means
No full textIn Explainable Artificial Intelligence, the interpretation of the decisions provided by a model is of primary importance. In this context, we consider Fuzzy C-Means (FCM), which is a clustering algorithm that induces a model from data by assigning, to each data-point, a degree of membership to each cluster such that the sum of memberships is one. A fuzzification parameter is also used to tune the degree of fuzziness of clusters. The distribution of membership degrees suggests an interpretation of membership degrees within the Probability Theory. This paper shows that the membership degrees resulting from FCM can be interpreted as posterior probabilities derived from a Bayesian model, which assumes that data are generated through a specific probability density function. The results give a clear interpretation of the membership degrees of FCM, as well as its fuzzification parameter, within a sound theoretical framework, and shed light on possible extensions of the algorithm
Descriptive Stability of Fuzzy Rule-Based Systems
Full text linkFuzzy Rule-Based Systems (FRBSs) are endowed with a knowledge base that can be used to provide model and outcome explanations. Usually, FRBSs are acquired from data by applying some learning methods: it is expected that, when modeling the same phenomenon, the FRBSs resulting from the application of a learning method should provide almost the same explanations. This requires a stability in the description of the knowledge bases that can be evaluated through the proposed measure of Descriptive Stability. The measure has been applied on three methods for generating FRBSs based on three benchmark datasets. The results show that, under same settings, different methods may produce FRBSs with varying stability, which impacts on their ability to provide trustful explanations
Therapeutic potential and activity modulation of the protein lysine deacylase sirtuin 5
No full textSirtiun 5 (SIRT5) is a NAD+-dependent protein lysine deacylase primarily located in mitochondria. SIRT5 displays an affinity for negatively charged acyl groups and mainly catalyzes lysine deglutarylation, desuccinylation, and demalonylation while possessing weak deacetylase activity. SIRT5 substrates play crucial roles in metabolism and reactive oxygen species (ROS) detoxification, and SIRT5 activity is protective in neuronal and cardiac physiology. Moreover, SIRT5 exhibits a dichotomous role in cancer, acting as context-dependent tumor promoter or suppressor. Given its multifaceted activity, SIRT5 is a promising target in the design of activators or inhibitors that might act as therapeutics in many pathologies, including cancer, cardiovascular disorders, and neurodegeneration. To date, few cellular-active peptide-based SIRT5 inhibitors (SIRT5i) have been described, and potent and selective small-molecule SIRT5i have yet to be discovered. In this perspective, we provide an outline of SIRT5's roles in different biological settings and describe SIRT5 modulators in terms of their mode of action, pharmacological activity, and structure-activity relationships
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
Effect of fuzziness in fuzzy rule-based classifiers defined by strong fuzzy partitions and winner-takes-all inference
Full text linkWe study the impact of fuzziness on the behavior of Fuzzy Rule-Based Classifiers (FRBCs) defined by trapezoidal fuzzy sets forming Strong Fuzzy Partitions. In particular, if an FRBC selects the class related to the rule with the highest activation (so-called Winner-Takes-All approach), then fuzziness, as quantified by the slope of the membership functions, has no impact in classifying data in regions of the input space where rules dominate. On the other hand, fuzziness affects the behaviour of the FRBC in regions where the confidence in classification is low. As a consequence, in the context of Explainable Artificial Intelligence, fuzziness is profitable in FRBCs only if classification is accompanied by an explanation of the confidence of the provided outputs
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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