Search results for "Descent"
showing 10 items of 99 documents
Light transmission and ultraviolet protection of contact lenses under artificial illumination
2016
Purpose: To determine the spectral transmission of contact lenses (CLs), with and without an ultraviolet (UV) filter to evaluate their capacity for protection under UV radiation from artificial illumination (incandescent, fluorescent, xenon (Xe) lamps, or white LEDs (light-emitting diode)). Methods: The transmission curves of nine soft CLs were obtained by using a PerkinElmer Lambda 35 UV-vis spectrophotometer. A CIE standard was used for the emission spectra of incandescent and fluorescent lamps, and Xe lamps and white LEDs were measured by using an International Light Technologies ILT-950 spectroradiometer. Results: Five of the nine soft CLs analysed state that they incorporate UV filters…
Accelerated Proximal Gradient Descent in Metric Learning for Kernel Regression
2018
The purpose of this paper is to learn a specific distance function for the Nadayara Watson estimator to be applied as a non-linear classifier. The idea of transforming the predictor variables and learning a kernel function based on Mahalanobis pseudo distance througth an low rank structure in the distance function will help us to lead the development of this problem. In context of metric learning for kernel regression, we introduce an Accelerated Proximal Gradient to solve the non-convex optimization problem with better convergence rate than gradient descent. An extensive experiment and the corresponding discussion tries to show that our strategie its a competitive solution in relation to p…
PAH gene mutations in the Sicilian population: association with minihaplotypes and expression analysis.
2001
Abstract The molecular basis of PAH deficiency in the Sicilian population is characterized by a marked heterogeneity, with 44 mutations at a single locus identified by a "gene-scanning" approach and accounting for a detection rate of 91%. The remaining 9% of PAH alleles does not bear mutations in any of the 13 exons and 24 exon/intron junctions. Three mutations IVS10nt-11 G > A, R261Q, and A300S accounted for 30.5%, whereas the remaining mutations were found at relative frequencies of less than 5% and 20 mutations were observed once only. Five mutations have been detected only in Sicilians so far. By studying the association of mutations with intragenic STR-VNTR haplotypes ("minihaplotypes"…
Management of undescended testes: European Association of Urology/European Society for Paediatric Urology Guidelines.
2016
Summary Context Undescended testis is the most common endocrinological disease in the male newborn period. Incidence varies between 1.0% and 4.6% in full-term neonates, with rates as high as 45% in preterm neonates. Failure or delay of treatment can result in reduced fertility and/or increased testicular cancer risk in adulthood. Objective To provide recommendations for the diagnosis and treatment of boys with undescended testes which reduce the risk of impaired fertility and testicular cancer in adulthood. Evidence acquisition Embase and Pubmed were searched for all relevant publications, from 1990 to 2015 limited to English language. Data were narratively synthesized in light of methodolo…
Identification of Risk Factors Associated with Obesity and Overweight-A Machine Learning Overview.
2020
Social determining factors such as the adverse influence of globalization, supermarket growth, fast unplanned urbanization, sedentary lifestyle, economy, and social position slowly develop behavioral risk factors in humans. Behavioral risk factors such as unhealthy habits, improper diet, and physical inactivity lead to physiological risks, and &ldquo
Time scales of adaptive behavior and motor learning in the presence of stochastic perturbations.
2009
In this paper, the major assumptions of influential approaches to the structure of variability in practice conditions are discussed from the perspective of a generalized evolving attractor landscape model of motor learning. The efficacy of the practice condition effects is considered in relation to the theoretical influence of stochastic perturbations in models of gradient descent learning of multiple dimension landscapes. A model for motor learning is presented combining simulated annealing and stochastic resonance phenomena against the background of different time scales for adaptation and learning processes. The practical consequences of the model's assumptions for the structure of pract…
Fast Convergence of Neural Networks by Application of a New Min-Max Algorithm
1992
Abstract The paper presents a new application of the min-max method, an original algorithm previously successfully applied in other areas and based on a combination of the quasi-Newton and steepest descent methods in order to find the weights minimising the error function of a feed forward neural networks. Preliminary results, obtained by applying the proposed method to a simple 2-2-1 architecture on small Boolean learning problems, are very promising.
A New Min-Max Optimisation Approach for Fast Learning Convergence of Feed-Forward Neural Networks
1993
One of the most critical aspect for a wide use of neural networks to real world problems is related to the learning process which is known to be computational expensive and time consuming.
The distributed assembly permutation flowshop scheduling problem
2013
Nowadays, improving the management of complex supply chains is a key to become competitive in the twenty-first century global market. Supply chains are composed of multi-plant facilities that must be coordinated and synchronised to cut waste and lead times. This paper proposes a Distributed Assembly Permutation Flowshop Scheduling Problem (DAPFSP) with two stages to model and study complex supply chains. This problem is a generalisation of the Distributed Permutation Flowshop Scheduling Problem (DPFSP). The first stage of the DAPFSP is composed of f identical production factories. Each one is a flowshop that produces jobs to be assembled into final products in a second assembly stage. The o…
Indirect Methods for Optimal Control Problems
2003
This chapter is dedicated to the numerical approximation of Optimal Control Problems. The algorithms are based on the necessary conditions for optimality which allow us to use a descent method for the minimization of the cost functional.