The regularity of solutions to Navier-Stokes equations at t=0 for bounded space domain

This paper studies the regularity in space-time of local solutions to 3D Navier-Stokes equations at $t=0$. The solutions will belong to $C^\infty(\bar{\Omega}\times[0, T])$ instead of $C^\infty(\bar{\Omega}\times[\epsilon, T])$ , where $\Omega$ is a bounded set of $\mathbb{R}^3$

An Investigation of Three Subjective Rating Scales of Mental Workload in Third Level Education

Mental Workload assessment in educational settings is still recognized as an open research problem. Although its application is useful for instructional design, it is still unclear how it can be formally shaped and which factors compose it. This paper is aimed at investigating a set of features believed to shape the construct of mental workload and aggregating them together in models trained with supervised machine learning techniques. In detail, multiple linear regression and decision trees have been chosen for training models with features extracted respectively from the NASA Task Load Index and the Workload Profile, well-known self-reporting instruments for assessing mental workload. Additionally, a third feature set was formed as a combination of the two aforementioned feature sets and a number of other features believed to contribute to mental workload modeling in education. Models were trained with cross-validation due to the limited sample size. On the one hand, results show how the features of the NASA Task Load index are more expressive for a regression problem than the other two feature sets. On the other hand, results show how the newly formed feature set can lead to the development of models of the mental workload with a lower error when compared to models built with the other two feature sets and when employed for a classification task

Improving Texture Categorization with Biologically Inspired Filtering

Within the domain of texture classification, a lot of effort has been spent on local descriptors, leading to many powerful algorithms. However, preprocessing techniques have received much less attention despite their important potential for improving the overall classification performance. We address this question by proposing a novel, simple, yet very powerful biologically-inspired filtering (BF) which simulates the performance of human retina. In the proposed approach, given a texture image, after applying a DoG filter to detect the "edges", we first split the filtered image into two "maps" alongside the sides of its edges. The feature extraction step is then carried out on the two "maps" instead of the input image. Our algorithm has several advantages such as simplicity, robustness to illumination and noise, and discriminative power. Experimental results on three large texture databases show that with an extremely low computational cost, the proposed method improves significantly the performance of many texture classification systems, notably in noisy environments. The source codes of the proposed algorithm can be downloaded from https://sites.google.com/site/nsonvu/code.Comment: 11 page

The Current Status of Historical Preservation Law in Regularory Takings Jurisprudence: Has the Lucas Missile Dismantled Preservation Programs?

This paper describes our NIHRIO system for SemEval-2018 Task 3 "Irony detection in English tweets". We propose to use a simple neural network architecture of Multilayer Perceptron with various types of input features including: lexical, syntactic, semantic and polarity features.  Our system achieves very high performance in both subtasks of binary and multi-class irony detection in tweets. In particular, we rank at fifth in terms of the accuracy metric and the F1 metric. Our code is available at: https://github.com/NIHRIO/IronyDetectionInTwitte