DUAL REPRESENTATIONS OF STRONGLY MONOTONIC UTILITY FUNCTIONS

We present theorems that establish dualities, i.e., bijections, be- tween speci¯ed sets of direct utility functions, indirect utility functions and expenditure functions. The substantive properties characterizing the speci¯ed set of direct utility functions are strong monotonicity, upper semicontinuity and quasi-concavity. Our results are strictly in- termediate between two classes of analogous results in the literature. We also provide applications that use all the three classes of duality results.Direct utility function, indirect utility function, ex-penditure function, duality, strong monotonicity

In Praise of a Cumulative Prevention Science

Durlak and Wells (1997) provide a pivotal appraisal of prevention research on children and adolescents. Their meta-analytic approach has the advantages of reducing scientific misjudgments based on single studies, and providing a more balanced evaluation of impact of various interventions; it provides an opportunity for hypothesis finding, helps set methodological standards, allows assessment of working classifications in the field, and an evaluation of the maturity of the prevention field itself. New developmental tasks for the field include incorporating and pursuing the leads produced by these findings, conducting similar research syntheses with other populations and outcomes, and using the results as an impetus to increased operational precision and parsimony.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44045/1/10464_2004_Article_423269.pd

A deep analysis on high resolution dermoscopic image classification

[EN] Convolutional neural networks (CNNs) have been broadly employed in dermoscopic image analysis, mainly as a result of the large amount of data gathered by the International Skin Imaging Collaboration (ISIC). As in many other medical imaging domains, state-of-the-art methods take advantage of architectures developed for other tasks, frequently assuming full transferability between enormous sets of natural images (e.g. ImageNet) and dermoscopic images, which is not always the case. A comprehensive analysis on the effectiveness of state-of-the-art deep learning techniques when applied to dermoscopic image analysis is provided. To achieve this goal, the authors consider several CNNs architectures and analyse how their performance is affected by the size of the network, image resolution, data augmentation process, amount of available data, and model calibration. Moreover, taking advantage of the analysis performed, a novel ensemble method to further increase the classification accuracy is designed. The proposed solution achieved the third best result in the 2019 official ISIC challenge, with an accuracy of 0.593.Juan Maroñas is supported by grant FPI-UPV, grant agreement No 825,111 DeepHealth Project, and by the Spanish National Ministry of Education through grant RTI2018-098091-B-I00. The research leading to these results has received funding from the European Union through Programa Operativo del Fondo Europeo de Desarrollo Regional (FEDER) from Comunitat Valencia (2014-2020) under project Sistemas de frabricación inteligentes para la indústria 4.0 (grant agreement IDIFEDER/2018/025).Pollastri, F.; Parreño Lara, M.; Maroñas-Molano, J.; Bolelli, F.; Paredes Palacios, R.; Ramos, D.; Grana, C. (2021). A deep analysis on high resolution dermoscopic image classification. IET Computer Vision. 15(7):514-526. https://doi.org/10.1049/cvi2.1204851452615

Bayesian Approaches For Image Restoration

Ph.DDOCTOR OF PHILOSOPH