Artificial Neural Networks: The Missing Link Between Curiosity and Accuracy

Artificial Neural Networks, as the name itself suggests, are biologically inspired algorithms designed to simulate the way in which the human brain processes information. Like neurons, which consist of a cell nucleus that receives input from other neurons through a web of input terminals, an Artificial Neural Network includes hundreds of single units, artificial neurons or processing elements, connected with coefficients (weights), and are organized in layers. The power of neural computations comes from connecting neurons in a network: in fact, in an Artificial Neural Network it is possible to manage a different number of information at the same time. What is not fully understood is which is the most efficient way to train an Artificial Neural Network, and in particular what is the best mini-batch size for maximize accuracy while minimizing training time. The idea that will be developed in this study has its roots in the biological world, that inspired the creation of Artificial Neural Network in the first place. Humans have altered the face of the world through extraordinary adaptive and technological advances: those changes were made possible by our cognitive structure, particularly the ability to reasoning and build causal models of external events. This dynamism is made possible by a high degree of curiosity. In the biological world, and especially in human beings, curiosity arises from the constant search of knowledge and information: behaviours that support the information sampling mechanism range from the very small (initial mini-batch size) to the very elaborate sustained (increasing mini-batch size). The goal of this project is to train an Artificial Neural Network by increasing dynamically, in an adaptive manner (with validation set), the mini-batch size; our hypothesis is that this training method will be more efficient (in terms of time and costs) compared to the ones implemented so far

Ritz-like values in steplength selections for stochastic gradient methods

The steplength selection is a crucial issue for the effectiveness of the stochastic gradient methods for large-scale optimization problems arising in machine learning. In a recent paper, Bollapragada et al. (SIAM J Optim 28(4):3312–3343, 2018) propose to include an adaptive subsampling strategy into a stochastic gradient scheme, with the aim to assure the descent feature in expectation of the stochastic gradient directions. In this approach, theoretical convergence properties are preserved under the assumption that the positive steplength satisfies at any iteration a suitable bound depending on the inverse of the Lipschitz constant of the objective function gradient. In this paper, we propose to tailor for the stochastic gradient scheme the steplength selection adopted in the full-gradient method knows as limited memory steepest descent method. This strategy, based on the Ritz-like values of a suitable matrix, enables to give a local estimate of the inverse of the local Lipschitz parameter, without introducing line search techniques, while the possible increase in the size of the subsample used to compute the stochastic gradient enables to control the variance of this direction. An extensive numerical experimentation highlights that the new rule makes the tuning of the parameters less expensive than the trial procedure for the efficient selection of a constant step in standard and mini-batch stochastic gradient methods

Scaled Gradient Projection Methods for Astronomical Imaging

This book is a collection of 19 articles which reflect the courses given at the Collège de France/Summer school “Reconstruction d'images − Applications astrophysiques“ held in Nice and Fréjus, France, from June 18 to 22, 2012. The articles presented in this volume address emerging concepts and methods that are useful in the complex process of improving our knowledge of the celestial objects, including Earth

Exploring the attitude towards the adoption of a sustainable diet: a cross-country comparison

Purpose: Understanding the determinants that influence consumers' attitude to adopt sustainable diets represents an important area of research to promote sustainable food consumption. The aim of this study is to investigate how (1) the individual openness to new foods (ONFs), (2) the involvement in food trends (IFTs) and (3) the social media use (SMU) can potentially impact the attitude towards the adoption of a sustainable diet (ATSD). Design/methodology/approach: The authors conducted a structured survey in eight countries: Italy, Germany, Poland, USA, Brazil, Japan, Korea and China. The final sample of 5,501 individuals was analysed applying a structural equation model. Findings: The main results show that attitude towards the ATSD is influenced differently by the antecedents investigated in each country. In particular, the ONF positively influences the ATSD only in Italy, USA and Germany. IFT positively influences the ATSD only in Italy, Poland and USA, while negatively in Germany. SMU has a positive influence on the ATSD only in Japan, USA and Germany, while a negative one in Brazil and Korea. Originality/value: This study presents a cross-country comparison about the antecedents of attitude towards the ATSD, thus providing evidence for the need of ad hoc marketing strategies by companies and policies by institutions at single country level

Global public health policies: gathering public health associations' perspectives

Background Advocacy is one of the core functions of public health and is a key tool for achieving Sustainable Development Goals. Public health associations play a key role in advocating for the development and implementation of strategies to prevent diseases and promote health and well-being. Objective This study aims to map out the focus of public health advocacy carried out by selected national public health associations over 4 years, between 2018 and 2021, in order to identify gaps and strengths and support associations and professionals in their advocacy efforts. Methods Twelve national public health associations participated in the study. Official policy documents produced between 2018 and 2021 were collected and analysed. The title and summary of the policy documents were examined line by line and coded into the main subject categories and themes. A qualitative thematic analysis was conducted. Policies were assessed from global and regional perspectives. Results A total of 220 policy documents were analysed. Overall, the largest number of policy documents came from high-income countries and dealt with environmental health and communicable diseases, including COVID-19, with, however, important differences among regions. In the African region, public health advocacy focused mainly on strengthening health systems; Europe and South America were mostly concerned with communicable diseases and pandemic management; and North America and the Western Pacific regions focused primarily on climate change. Limited attention was paid to international health and health as a human right in all regions. Conclusion Our study showed that, especially in high-income countries, public health associations actively engage in advocacy; however, more effort needs to be devoted to implementing a more international and intersectoral approach at the global level, anchored in health as a human right and aligned with the Sustainable Development Goals

The virtuous cycle of stakeholder engagement in developing a sustainability culture: Salcheto winery

Stakeholder engagement in sustainability represents a powerful driver for value creation. Drawing from stakeholder theory, this paper explores how a firm with a proactive sustainable behaviour engages stakeholders in developing innovation and creating value. A longitudinal, single case study of the Salcheto winery was carried out. Since the late 1990s, Salcheto has been at the forefront of wine eco-innovation and it has played a key role in the development of Montepulciano (Tuscany, Italy) as one of the first sustainable wine clusters worldwide. The development of a sustainable wine culture is one of the firm's various innovations. In doing so, the firm has had to face three challenges - identity creation, legitimization and enhancement - and has engaged its stakeholders through three specific mechanisms (adoption and development; co-creation and diffusion; exploitation and contamination). This virtuous cycle of stakeholder engagement has resulted in value creation at a firm, stakeholder and local level

Effect of heat treatment and defects on the tensile behavior of a hot work tool steel manufactured by laser powder bed fusion

Microstructure and tensile properties of a hot work tool steel manufactured via laser powder bed fusion (LPBF) were investigated. Specimens were built under two different orientations and subjected to two quenching and tempering heat treatments, featuring different austenitizing and tempering temperatures and the eventual presence of a sub-zero step. Microstructural analyses revealed a homogeneous tempered martensite structure after both heat treatments, with the only distinction of a higher alloying segregation at a sub micrometric scale length in samples subjected to the highest tempering temperatures. Hardness and tensile tests indicated a negligible effect of building orientation on mechanical properties, but a significant influence of heat treatment parameters. The treatment featuring the lower tempering temperatures and the sub-zero step resulted in higher hardness, tensile strength, and elongation, attributed to a lower martensite tempering and alloying segregation. Tensile fracture occurred via crack initiation and unstable propagation from large LPBF defects in all the investigated conditions

A stochastic gradient method with variance control and variable learning rate for Deep Learning

In this paper we study a stochastic gradient algorithm which rules the increase of the minibatch size in a predefined fashion and automatically adjusts the learning rate by means of a monotone or non -monotone line search procedure. The mini -batch size is incremented at a suitable a priori rate throughout the iterative process in order that the variance of the stochastic gradients is progressively reduced. The a priori rate is not subject to restrictive assumptions, allowing for the possibility of a slow increase in the mini -batch size. On the other hand, the learning rate can vary non -monotonically throughout the iterations, as long as it is appropriately bounded. Convergence results for the proposed method are provided for both convex and non -convex objective functions. Moreover it can be proved that the algorithm enjoys a global linear rate of convergence on strongly convex functions. The low per -iteration cost, the limited memory requirements and the robustness against the hyperparameters setting make the suggested approach well -suited for implementation within the deep learning framework, also for GPGPU-equipped architectures. Numerical results on training deep neural networks for multiclass image classification show a promising behaviour of the proposed scheme with respect to similar state of the art competitors

GPU acceleration of a model-based iterative method for Digital Breast Tomosynthesis

Digital Breast Tomosynthesis (DBT) is a modern 3D Computed Tomography X-ray technique for the early detection of breast tumors, which is receiving growing interest in the medical and scientific community. Since DBT performs incomplete sampling of data, the image reconstruction approaches based on iterative methods are preferable to the classical analytic techniques, such as the Filtered Back Projection algorithm, providing fewer artifacts. In this work, we consider a Model-Based Iterative Reconstruction (MBIR) method well suited to describe the DBT data acquisition process and to include prior information on the reconstructed image. We propose a gradient-based solver named Scaled Gradient Projection (SGP) for the solution of the constrained optimization problem arising in the considered MBIR method. Even if the SGP algorithm exhibits fast convergence, the time required on a serial computer for the reconstruction of a real DBT data set is too long for the clinical needs. In this paper we propose a parallel SGP version designed to perform the most expensive computations of each iteration on Graphics Processing Unit (GPU). We apply the proposed parallel approach on three different GPU boards, with computational performance comparable with that of the boards usually installed in commercial DBT systems. The numerical results show that the proposed GPU-based MBIR method provides accurate reconstructions in a time suitable for clinical trials