Improving out-of-sample forecasting is one of the main issues in financial research. Previous studies have achieved this objective by increasing the number of input variables or changing the kind of input variables. Changing the forecasting model is another possible approach to improve out-of-sample forecasting. Most researches have focused on linear models, while few have studied nonlinear mod...

This article introduces a Bayesian methodology for the prediction for computer experiments having quantitative and qualitative inputs. The proposed model is a hierarchical Bayesian model with conditional Gaussian stochastic process components. For each of the qualitative inputs, our model assumes that the outputs corresponding to different levels of the qualitative input have “similar” function...

The Data Acquisition Backbone Core (DABC) is a software framework to run DAQ with distributed event building on high performance Linux clusters [1]. There are several use cases, especially in test beam times where the setup during data taking is intentionally changed very often, and the interpretation of the acquired detector data depends on the experimental set-up. Usually, the set-up is manag...

Auto-encoders are often used as building blocks of deep network classifier to learn feature extractors, but task-irrelevant information in the input data may lead to bad extractors and result in poor generalization performance of the network. In this paper,via dropping the task-irrelevant input variables the performance of auto-encoders can be obviously improved .Specifically, an importance-bas...

We introduce the separable sets of variables for trees and tree au-tomata. If a set Y of input variables is inseparable for a tree and an automaton then there a non empty family of distributive sets of Y. It is shown that if a tree t has " many " inseparable sets with respect to a tree automaton A then there is an effective way to reduce the complexity of A when running on t.

The exciting study by Steele et al. is the first to use non-negative matrix factorization to quantify electromyography (EMG) muscle synergies during gait in children with cerebral palsy (CP). The identification of fewer synergies during gait in adults with stroke and the relationship of lower synergy number to greater functional limitations are of great interest in neurorehabilitation. So I com...

Harmful algal blooms, which are considered a serious environmental problem nowadays, occur in coastal waters in many parts of the world. They cause acute ecological damage and ensuing economic losses, due to fish kills and shellfish poisoning as well as public health threats posed by toxic blooms. Recently, data-driven models including machine learning (ML) techniques have been employed to mimi...