Massachusetts Institute of Technology

About: Massachusetts Institute of Technology is a education organization based out in Cambridge, Massachusetts, United States. It is known for research contribution in the topics: Population & Laser. The organization has 116795 authors who have published 268000 publications receiving 18272025 citations. The organization is also known as: MIT & M.I.T..

Intercalation compounds of graphite

Abstract: A broad review of recent research work on the preparation and the remarkable properties of intercalation compounds of graphite, covering a wide range of topics from the basic chemistry, physics and materials science to engineering applications.

Head-Driven Statistical Models for Natural Language Parsing

Abstract: This article describes three statistical models for natural language parsing. The models extend methods from probabilistic context-free grammars to lexicalized grammars, leading to approaches in which a parse tree is represented as the sequence of decisions corresponding to a head-centered, top-down derivation of the tree. Independence assumptions then lead to parameters that encode the X-bar schema, subcategorization, ordering of complements, placement of adjuncts, bigram lexical dependencies, wh-movement, and preferences for close attachment. All of these preferences are expressed by probabilities conditioned on lexical heads. The models are evaluated on the Penn Wall Street Journal Treebank, showing that their accuracy is competitive with other models in the literature. To gain a better understanding of the models, we also give results on different constituent types, as well as a breakdown of precision/recall results in recovering various types of dependencies. We analyze various characteristics of the models through experiments on parsing accuracy, by collecting frequencies of various structures in the treebank, and through linguistically motivated examples. Finally, we compare the models to others that have been applied to parsing the treebank, aiming to give some explanation of the difference in performance of the various models.

Deep learning with coherent nanophotonic circuits

Abstract: Artificial Neural Networks have dramatically improved performance for many machine learning tasks. We demonstrate a new architecture for a fully optical neural network that enables a computational speed enhancement of at least two orders of magnitude and three orders of magnitude in power efficiency over state-of-the-art electronics.

Positive Accounting Theory: A Ten Year Perspective

Abstract: This paper reviews and critiques the positive accounting literature following the publication of Watts and Zimmerman (1978, 1979), The 1978 paper helped generate the positive accounting literature that offers an explanation of accounting practice, suggests the importance of contracting costs, and has led to the discovery of some previously unknown empirical regularities. The 1979 paper produced a methodological debate that has not been very productive. This paper attempts to remove some common misconceptions about methodology that surfaced in that debate. It also suggests ways to improve positive research in accounting choice. The most important of these improvements is tighter links between the theory and the empirical tests. A second suggested improvement is the development of models that recognize the endogeneity among the variables in the regressions. A third improvement is reduction in measurement errors in both the dependent and independent variables in the regressions.

Systematic discovery of regulatory motifs in human promoters and 3′ UTRs by comparison of several mammals

Abstract: Comprehensive identification of all functional elements encoded in the human genome is a fundamental need in biomedical research. Here, we present a comparative analysis of the human, mouse, rat and dog genomes to create a systematic catalogue of common regulatory motifs in promoters and 3' untranslated regions (3' UTRs). The promoter analysis yields 174 candidate motifs, including most previously known transcription-factor binding sites and 105 new motifs. The 3'-UTR analysis yields 106 motifs likely to be involved in post-transcriptional regulation. Nearly one-half are associated with microRNAs (miRNAs), leading to the discovery of many new miRNA genes and their likely target genes. Our results suggest that previous estimates of the number of human miRNA genes were low, and that miRNAs regulate at least 20% of human genes. The overall results provide a systematic view of gene regulation in the human, which will be refined as additional mammalian genomes become available.