since long tried to reproduce this process automatically, but most of the methods were typically restricted to specific domains or based on brute-force enumeration methods. In this work we propose and implement methods for generating conjectures by using statistical analogies extracted from large formal

and comparison of the adequacy of parsing models. In this paper, we review our experience with constructing one such large annotated corpus--the Penn Treebank, a corpus 1 consisting of over 4.5 million words of American English. During the first three-year phase of the Penn Treebank Project (1989

. Pascal conjectured that human choice behavior could be approximated by this procedure. 106 SCHULTZ Despite its advantages, expected value theory has limits when comparing very small with very large rewards or when comparing values at different start positions. Rather than following physical sizes

investments, using a comprehensive sample of U.S. based VCs over the period 1980 to 2003. VC funds whose parent firms enjoy more influential network positions have significantly better performance, as measured by the proportion of portfolio company investments that are successfully exited through an initial

task, and in particular (iii) to combine such methods with learning-assisted automated reasoning that will serve as a strong seman-tic component. We describe these ideas, the initial set of corpora, and some initial experiments done over them. 1

easily mis-led by irrelevant knowledge in this setting, and finding deeper proofs is typi-cally more difficult. Both large-theory AI/ATP methods, and translation and data-mining techniques of large formal corpora, have significantly developed recently, providing enough data for an initial comparison

their boundaries. We test our theory in an analysis of the rate of dyadic communication in an email data set comprising thousands of employees in a large information technology firm. These findings have implications for research on homophily, gender relations in organizations, and formal and informal

;meaningful" if its number of occurrences would be very small in a random situation: Geometric structures are then characterized as large deviations from randomness. In two previous works, we have applied this principle to the detection of orientation alignments and boundaries in a digital image. In this paper, we

'i State School Readiness Assessment. SPARK-HI children with some ECE experiences outperformed SPARK-HI children without any ECE experience; SPARK-HI girls with ECE experiences exhibited a large effectsize advantage in mathematics over SPARK-HI girls without ECE experiences. Despite our not having random

Abstract—In this paper we present a novel strategy to discover the community structure of (possibly, large) networks. This approach is based on the well-know concept of network modularity optimization. To do so, our algorithm exploits a novel measure of edge centrality, based on the κ