Emotion is considered to be an important factor in human decision making and consciousness. Due to its perceived importance, recently a number of works try to explore emotion in IR tasks. However, the complexity of emotion extractors and lack of understanding on their effectiveness hinder the progress of such works. In this paper, we conduct a comparative study on the effectiveness of three emo...

This paper presents and evaluates two linear feature extractors based on mutual information. These feature extractors consider general dependencies between features and class labels, as opposed to well known linear methods such as PCA which does not consider class labels and LDA, which uses only simple low order dependencies. As evidenced by several simulations on high dimensional data sets, th...

We show two sets of results applying the theory of extractors to resource-bounded Kolmogorov complexity: Most strings in easy sets have nearly optimal polynomial-time CD complexity. This extends work of Sipser Sip83] and Buhrman and Fortnow BF97]. We use extractors to extract the randomness of strings. In particular we show how to get from an arbitrary string, an incompressible string which enc...

In this paper, we present a novel approach to microaneurysm candidate extraction. To strengthen the accuracy of individual algorithms, we propose an ensemble of state-of-the-art candidate extractors. We apply a simulated annealing based method to select an optimal combination of such algorithms for a particular dataset. We also present a novel classification technique, which is based on a paral...

In previous research, Shelton et al. presented a geneticbased method for evolving feature extractors for facial recognition. The technique presented evolved feature extractors that consisted of non-uniform, overlapping patches and did not cover the entire image. In this paper, we compare the use of non-uniform, overlapping patches with uniform, overlapping patches. Our results show a statistica...

We give the first explicit construction of deterministic extractors for affine sources over F2, with entropy k ≥ log n for some large enough constant C, where n is the length of the source. Previously the best known results are by Bourgain [Bou07], Yehudayoff [Yeh11] and Li [Li11b], which require the affine source to have entropy at least Ω(n/ √ log log n). Our extractor outputs one bit with er...