scholarly journals Basic properties and information theory of Audic-Claverie statistic for analyzing cDNA arrays

2009 ◽  
Vol 10 (1) ◽  
Author(s):  
Peter Tiňo
2020 ◽  
Vol 107 (121) ◽  
pp. 45-51
Author(s):  
Dragan Djurcic ◽  
Nebojsa Elez ◽  
Valentina Timotic

We introduce a proper subclass of the class of rapidly varying sequences (logarithmic (translationally) rapidly varying sequences), motivated by a notion in information theory (self-information of the system). We prove some of its basic properties. In the main result, we prove that Rothberger?s and Kocinac?s selection principles hold, when this class is on the second coordinate, and on the first coordinate we have the class of positive and unbounded sequences


2009 ◽  
Vol 07 (05) ◽  
pp. 1009-1019 ◽  
Author(s):  
P. W. LAMBERTI ◽  
M. PORTESI ◽  
J. SPARACINO

We study in detail a very natural metric for quantum states. This new proposal has two basic ingredients: entropy and purification. The metric for two mixed states is defined as the square root of the entropy of the average of representative purifications of those states. Some basic properties are analyzed and its relation to other distances is investigated. As an illustrative application, the proposed metric is evaluated for one-qubit mixed states.


Author(s):  
Charles A. Doan ◽  
Ronaldo Vigo

Abstract. Several empirical investigations have explored whether observers prefer to sort sets of multidimensional stimuli into groups by employing one-dimensional or family-resemblance strategies. Although one-dimensional sorting strategies have been the prevalent finding for these unsupervised classification paradigms, several researchers have provided evidence that the choice of strategy may depend on the particular demands of the task. To account for this disparity, we propose that observers extract relational patterns from stimulus sets that facilitate the development of optimal classification strategies for relegating category membership. We conducted a novel constrained categorization experiment to empirically test this hypothesis by instructing participants to either add or remove objects from presented categorical stimuli. We employed generalized representational information theory (GRIT; Vigo, 2011b , 2013a , 2014 ) and its associated formal models to predict and explain how human beings chose to modify these categorical stimuli. Additionally, we compared model performance to predictions made by a leading prototypicality measure in the literature.


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