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A Novel Approach to Jominy Profile Prediction Based on 1D Convolutional Neural Networks and Autoencoders that Supports Transfer Learning
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Quando abbreviare puٍò migliorare: tecniche psicometriche avanzate per la costruzione della forma breve di un test
No full textIndagine comparata sullo strato di attuazione delle quattro Direttive socio-strutturali della CEE in Lombardia, Piemonte, Emilia Romagna
No full textAutomatic steel grades design for Jominy profile achievement through neural networks and genetic algorithms
No full textThe paper proposes an approach to the design of the chemical composition of steel, which is based on neural networks and genetic algorithms and aims at achieving a desired hardenability behavior possibly matching other constraints related to the steel production. Hardenability is a mechanical feature of steel, which is extremely relevant for a wide range of steel applications and refers to the steel capability to improve its hardness following a heat treatment. In the proposed approach, a neural-network-based predictor of the so-called Jominy hardenability profile is exploited, and an optimization problem is formulated, where the optimization function allows taking into account both the desired accuracy in meeting the target Jominy profile and other constraint. The optimization is performed through genetic algorithms. Numerical results are presented and discussed, showing the efficiency of the proposed approach together with its flexibility and easy customization with respect to the user demands and production objectives
Mindfulness
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Mindful creativity: The influence of mindfulness meditation on creative thinking
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January 2014
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Abstract
Mindfulness as a multifaceted construct refers to the ability to regulate with awareness the focus of the attention in the present moment with an open attitude to accept the experience. Originally stemming from Buddhist meditation traditions, this construct has received a great deal of attention in medicine, psychology and neuroscience. Globally, mindfulness-based interventions have been associated with a wealth of psychological benefits, ranging from a decrease of stress and distress and improvement of well-being. A convincing evidence of a close relationship between creativity and mindfulness emerges from the literature as well. Specifically, the two main approaches in the study of the mindfulness-creativity link are here taken into consideration: the study of mindfulness as a unitary construct, which leads to a uniform analysis of mindfulness in association with creativity; the study of the multifold nature of mindfulness, which led to the exploration, through a differential analysis, of the strength and direction of the associations between the mindfulness sub-dimensions and creativity
Thought dynamics: Which role for mind wandering in creativity
No full textFor a long time, mainstream psychological research on cognitive pro- cesses has been focused on the investigation of externally-oriented cognition, namely deliberate processes generated in response to cues provided by the experi- menter and associated with specific experimental paradigms. During the last two decades, there has been a surge of interest in both psychology and neuroscience toward the investigation of internally-oriented cognition, and, among the different kinds, a growing interest has been devoted to mind wandering (MW), which repre- sents a shift in the contents of thought away from an ongoing task and/or from events in the external environment, toward internal mental contents. By definition, MW is characterized by a flow of thought, and it occurs without a fixed course or a drive to reach a specific goal. Creative thinking also involves dynamic shifts between different information and mental states. Does mind wandering contribute to creativ- ity? Here we briefly review mixed findings on the association between MW and creativity and we outline a new multidimensional dynamic approach, in which the associations between different kinds of MW (i.e. spontaneous and deliberate) and different forms of creativity are considered. Practical implications of this approach are discussed
Improving the Stability of the Variable Selection with Small Datasets in Classification and Regression Tasks
No full textWithin the design of a machine learning-based solution for classification or regression problems, variable selection techniques are often applied to identify the input variables, which mainly affect the considered target. The selection of such variables provides very interesting advantages, such as lower complexity of themodel and of the learning algorithm, reduction of computational time and improvement of performances. Moreover, variable selection is useful to gain a profound knowledge of the considered problem. High correlation in variables often produces multiple subsets of equally optimal variables, which makes the traditional method of variable selection unstable, leading to instability and reducing the confidence of selected variables. Stability identifies the reproducibility power of the variable selection method. Therefore, having a high stability is as important as the high precision of the developed model. The paper presents an automatic procedure for variable selection in classification (binary and multi-class) and regression tasks, which provides an optimal stability index without requiring any a priori information on data. The proposed approach has been tested on different small datasets, which are unstable by nature, and has achieved satisfactory results
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