Bar-Ilan University

About: Bar-Ilan University is a education organization based out in Ramat Gan, Israel. It is known for research contribution in the topics: Population & Poison control. The organization has 12835 authors who have published 34964 publications receiving 995648 citations. The organization is also known as: Bar Ilan University & BIU.

High‐quality relationships, psychological safety, and learning from failures in work organizations

Abstract: How can organizations support employees to engage in learning from failures? In this paper, we draw on the concept of high-quality relationships to explore the relational underpinnings of learning from failures in organizations. We focus on relational coordination as a specific manifestation of high-quality relationships and examine how the relational dimensions of relational coordination—shared goals, shared knowledge, and mutual respect—foster psychological safety and thus enable organizational members to engage in learning from failures. The results of two separate studies support our mediation model where psychological safety mediates the link between high-quality relationships and learning from failures in organizations. Copyright © 2008 John Wiley & Sons, Ltd.

Structure, physical properties, and applications of SrRuO3 thin films

Abstract: SrRuO3 is endowed with three remarkable features. First, it is a moderately correlated material that exhibits several novel physical properties; second, it permits the epitaxial growth of essentially single-crystal films; and third, because it is a good conductor, it has attracted interest as a conducting layer in epitaxial heterostructures with a variety of functional oxides. In this review, the present state of knowledge of SrRuO3 thin films is summarized. Their role as a model system for studying magnetism and electron transport characterized by intermediate electron correlation and large magnetocrystalline anisotropy is demonstrated. The materials science of SrRuO3 thin film growth is reviewed, and its relationship to electronic, magnetic, and other physical properties is discussed. Finally, it is argued that, despite all that has been learned, a comprehensive understanding of SrRuO3 is still lacking and challenges remain.

Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data

Abstract: Advances in high-throughput sequencing technologies now allow for large-scale characterization of B cell immunoglobulin (Ig) repertoires. The high germline and somatic diversity of the Ig repertoire presents challenges for biologically meaningful analysis, which requires specialized computational methods. We have developed a suite of utilities, Change-O, which provides tools for advanced analyses of large-scale Ig repertoire sequencing data. Change-O includes tools for determining the complete set of Ig variable region gene segment alleles carried by an individual (including novel alleles), partitioning of Ig sequences into clonal populations, creating lineage trees, inferring somatic hypermutation targeting models, measuring repertoire diversity, quantifying selection pressure, and calculating sequence chemical properties. All Change-O tools utilize a common data format, which enables the seamless integration of multiple analyses into a single workflow. Availability and implementation Change-O is freely available for non-commercial use and may be downloaded from http://clip.med.yale.edu/changeo. Contact steven.kleinstein@yale.edu.

Committee-based sampling for training probabilistic classifiers

Abstract: In many real-world learning tasks, it is expensive to acquire a sufficient number of labeled examples for training. This paper proposes a general method for efficiently training probabilistic classifiers, by selecting for training only the more informative examples in a stream of unlabeled examples. The method, committee-based sampling , evaluates the informativeness of an example by measuring the degree of disagreement between several model variants. These variants (the committee) are drawn randomly from a probability distribution conditioned by the training set selected so far (Monte-Carlo sampling). The method is particularly attractive because it evaluates the expected information gain from a training example implicitly, making the model both easy to implement and generally applicable. We further show how to apply committee-based sampling for training Hidden Markov Model classifiers, which are commonly used for complex classification tasks. The method was implemented and tested for the task of tagging words in natural language sentences with parts-of-speech. Experimental evaluation of committee-based sampling versus standard sequential training showed a substantial improvement in training efficiency.