Yael Ben-Haim

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A new algorithm for building decision tree classifiers is proposed. The algorithm is executed in a distributed environment and is especially designed for classifying large datasets and streaming data. It is empirically shown to be as accurate as standard decision tree classifiers, while being scalable to infinite streaming data and multiple processors.
Exact Minimum Density of Codes Identifying Vertices in the Square Grid Yael Ben-Haim and Simon Litsyn School of Electrical Engineering Tel-Aviv University Tel-Aviv 69978 Israel An identifying code C is a subset of the vertices of the square grid Z with the property that for each element v of Z, the collection of elements from C at distance at most one from(More)
One of the major challenges in regenerative medicine is the ability to recreate the stem cell niche, which is defined by its signaling molecules, the creation of cytokine gradients, and the modulation of matrix stiffness. A wide range of scaffolds has been developed in order to recapitulate the stem cell niche, among them hydrogels. This paper reports the(More)
We study the problem of encoding cardinality constraints (threshold functions) on Boolean variables into CNF. Specifically, we propose new encodings based on (perfect) hashing that are efficient in terms of the number of clauses, auxiliary variables, and propagation strength. We compare the properties of our encodings to known ones, and provide experimental(More)
Recent advances in single-cell RNA-sequencing (scRNA-seq) technology increase the understanding of immune differentiation and activation processes, as well as the heterogeneity of immune cell types. Although the number of available immune-related scRNA-seq datasets increases rapidly, their large size and various formats render them hard for the wider(More)