Search results for "Biase"
showing 10 items of 67 documents
Multispectral image denoising with optimized vector non-local mean filter
2016
Nowadays, many applications rely on images of high quality to ensure good performance in conducting their tasks. However, noise goes against this objective as it is an unavoidable issue in most applications. Therefore, it is essential to develop techniques to attenuate the impact of noise, while maintaining the integrity of relevant information in images. We propose in this work to extend the application of the Non-Local Means filter (NLM) to the vector case and apply it for denoising multispectral images. The objective is to benefit from the additional information brought by multispectral imaging systems. The NLM filter exploits the redundancy of information in an image to remove noise. A …
G protein biased signaling by non-catechol dopamine D1 receptor agonists
2020
Dopamine is a catecholamine neurotransmitter with essential roles in voluntary movement, working memory, attention, and reward. Dopamine acts through five G protein coupled receptors with the D1 and D5 receptors (D1R) stimulating Galphas/olf activation and increasing neuronal excitability. Deficits in D1R signaling are implicated in Parkinson’s disease motor deficits as well as cognitive deficits in schizophrenia and attention deficit hyperactivity disorder. For more than 40 years, academic and industry scientists have been searching for a drug-like D1R agonist, but this has remained elusive. The challenge in developing D1R selective agonists is that all previous agonists possess a common p…
Muestreos utilizados en investigación educativa en España
2002
En este trabajo se expone un estudio bibliográfico de distintas investigaciones que se han publicado recientemente en España, en las actas de congresos sobre educación. De todas las investigaciones que se han revisado, se analiza el tipo de muestreo que han empleado, cómo han sido seleccionadas y la aplicación de los instrumentos para la recogida de información, así como la forma en que han sido recogidos esos datos. Se pretende resaltar la importancia de la selección de muestras representativas, sobre todo para que los resultados tengan mayor relevancia y repercusión en el desarrollo del conocimiento educativo.
Genetic diversity and trait genomic prediction in a pea diversity panel
2014
Background Pea (Pisum sativum L.), a major pulse crop grown for its protein-rich seeds, is an important component of agroecological cropping systems in diverse regions of the world. New breeding challenges imposed by global climate change and new regulations urge pea breeders to undertake more efficient methods of selection and better take advantage of the large genetic diversity present in the Pisum sativum genepool. Diversity studies conducted so far in pea used Simple Sequence Repeat (SSR) and Retrotransposon Based Insertion Polymorphism (RBIP) markers. Recently, SNP marker panels have been developed that will be useful for genetic diversity assessment and marker-assisted selection. Resu…
Consumer biases in the perception of organizational greed
2022
This article extends current models of how consumers judge or perceive organizations as greedy by employing the theoretical framework of motivated moral reasoning. We show that inherent features of an organization (size and “black sheep” status) and its behavior (relative frequency) bias consumer perceptions of organizational greed. We use an experimental methodology, present subjects with vignettes describing different scenarios, validate our questionnaire using confirmatory factor analysis, and test our hypotheses by employing a general linear model with covariates. Our findings suggest that consumer perceptions of organizational greed are subject to three effects: the underdog effect (St…
A novel Stochastic Discretized Weak Estimator operating in non-stationary environments
2012
The task of designing estimators that are able to track time-varying distributions has found promising applications in many real-life problems. A particularly interesting family of distributions are the binomial/multiomial distributions. Existing approaches resort to sliding windows that track changes by discarding old observations. In this paper, we report a novel estimator referred to as the Stochastic Discretized Weak Estimator (SDWE), that is based on the principles of Learning Automata (LA). In brief, the estimator is able to estimate the parameters of a time varying binomial distribution using finite memory. The estimator tracks changes in the distribution by operating on a controlled…
Finite Sample Efficiency and Drawbacks: An Illustration
2011
Historically, finite-sample efficiency was the first notion of optimality introduced and it is still encountered in introductory statistics texts. The definition has several drawbacks however, one being that it is restricted to the class of unbiased estimators. An example is given to illustrate this.
Unbiased Branches: An Open Problem
2007
The majority of currently available dynamic branch predictors base their prediction accuracy on the previous k branch outcomes. Such predictors sustain high prediction accuracy but they do not consider the impact of unbiased branches, which are difficult-to-predict. In this paper, we evaluate the impact of unbiased branches in terms of prediction accuracy on a range of branch difference predictors using prediction by partial matching, multiple Markov prediction and neural-based prediction. Since our focus is on the impact that unbiased branches have on processor performance, timing issues and hardware costs are out of scope of this investigation. Our simulation results, with the SPEC2000 in…
Effective state estimation of stochastic systems
2003
In the present paper, for constructing the minimum risk estimators of state of stochastic systems, a new technique of invariant embedding of sample statistics in a loss function is proposed. This technique represents a simple and computationally attractive statistical method based on the constructive use of the invariance principle in mathematical statistics. Unlike the Bayesian approach, an invariant embedding technique is independent of the choice of priors. It allows one to eliminate unknown parameters from the problem and to find the best invariant estimator, which has smaller risk than any of the well‐known estimators. There exists a class of control systems where observations are not …
Constrained minimum variance control of nonsquare LTI MIMO systems
2010
Constrained minimum variance control is offered for nonsquare LTI MIMO systems. A constrained control design takes advantage of the so-called control zeros. The new control strategy is compared with familiar generalized minimum variance control and possible application areas of the two are discussed.