The brain processes distinct attributes such as colour and motion in anatomically largely segregated systems. Moreover, these two attributes are perceived with different latencies. Here, we show that the time required to bind these two attributes differs too. In psychophysical experiments, we determined minimal presentation times required to perceptually pair spatially separate pairs of stimuli...

In supervised learning, discretization of the continuous explanatory attributes enhances the accuracy of decision tree induction algorithms and naive Bayes classifier. Many discretization methods have been developped, leading to precise and comprehensible evaluations of the amount of information contained in one single attribute with respect to the target one. In this paper, we discuss the mult...

BACKGROUND Efforts to stem the diabetes epidemic in the United States and other countries must take into account a complex array of individual, social, economic, and built environmental factors. Increasingly, scientists use information visualization tools to "make sense" of large multivariate data sets. Recently, ring map visualization has been explored as a means of depicting spatially referen...

The interval neutrosophic set (INS) can be easier to express the incomplete, indeterminate and inconsistent information, and TOPSIS is one of the most commonly used and effective method for multiple attribute decision making, however, in general, it can only process the attribute values with crisp numbers. In this paper, we have extended TOPSIS to INS, and with respect to the multiple attribute...

We present a theoretical and computational study of the impact of inserting a new attribute and removing an old attribute in a DEA model. Our objective is to obviate a portion of the computational effort needed to process such model changes by studying how the efficient/inefficient status of DMUs is affected. Reducing computational efforts is important since DEA is known to be computationally i...

The development, optimization, and analysis of downstream processes are challenged by a high number of potentially critical process parameters that need to be investigated using lab-scale experiments. These process parameters are spread across multiple unit operations and potentially show interactions across unit operations. In this contribution, we present a novel strategy for bioprocess devel...

A new multivariate approach is introduced to identify the middle class of a society and to measure its possible decline. The middle class is a properly defined central region in attribute space that contains a fixed portion of the population. The decline of the middle class is measured by comparing volumes of the middle class. The use of different notions of central regions is discussed in one ...

Product quality heterogeneities, such as a trisulfide bond (TSB) formation, can be influenced by multiple interacting process parameters. Identifying their root cause is a major challenge in biopharmaceutical production. To address this issue, this paper describes the novel application of advanced multivariate data analysis (MVDA) techniques to identify the process parameters influencing TSB fo...

Attribute reduction is the process of selecting a minimal attribute subset from a problem domain while retaining a suitably high accuracy in representing the original attributes. In this work, we propose a new attribute reduction algorithm called record-to-record travel (RRT) algorithm and employ a rough set theory as a mathematical tool to evaluate the quality of the obtained solutions. RRT is...