Stony Brook University

About: Stony Brook University is a education organization based out in Stony Brook, New York, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 32534 authors who have published 68218 publications receiving 3035131 citations. The organization is also known as: State University of New York at Stony Brook & SUNY Stony Brook.

Oral language and code-related precursors to reading: Evidence from a longitudinal structural model.

Abstract: This study examined code-related and oral language precursors to reading in a longitudinal study of 626 children from preschool through 4th grade. Code-related precursors, including print concepts and phonological awareness, and oral language were assessed in preschool and kindergarten. Reading accuracy and reading comprehension skills were examined in 1st through 4th grades. Results demonstrated that (a) the relationship between code-related precursors and oral language is strong during preschool; (b) there is a high degree of continuity over time of both code-related and oral language abilities; (c) during early elementary school, reading ability is predominantly determined by the level of print knowledge and phonological awareness a child brings from kindergarten; and (d) in later elementary school, reading accuracy and reading comprehension appear to be 2 separate abilities that are influenced by different sets of skills.

Historical biogeography, ecology and species richness

Abstract: Ecology and historical (phylogeny-based) biogeography have much to offer one another, but exchanges between these fields have been limited. Historical biogeography has become narrowly focused on using phylogenies to discover the history of geological connections among regions. Conversely, ecologists often ignore historical biogeography, even when its input can be crucial. Both historical biogeographers and ecologists have more-or-less abandoned attempts to understand the processes that determine the large-scale distribution of clades. Here, we describe the chasm that has developed between ecology and historical biogeography, some of the important questions that have fallen into it and how it might be bridged. To illustrate the benefits of an integrated approach, we expand on a model that can help explain the latitudinal gradient of species richness.

Combined Measurement of the Higgs Boson Mass in pp Collisions at √s=7 and 8 TeV with the ATLAS and CMS Experiments

Abstract: A measurement of the Higgs boson mass is presented based on the combined data samples of the ATLAS and CMS experiments at the CERN LHC in the H→γγ and H→ZZ→4l decay channels. The results are obtained from a simultaneous fit to the reconstructed invariant mass peaks in the two channels and for the two experiments. The measured masses from the individual channels and the two experiments are found to be consistent among themselves. The combined measured mass of the Higgs boson is mH=125.09±0.21 (stat)±0.11 (syst) GeV.

Describing Textures in the Wild

Abstract: Patterns and textures are key characteristics of many natural objects: a shirt can be striped, the wings of a butterfly can be veined, and the skin of an animal can be scaly. Aiming at supporting this dimension in image understanding, we address the problem of describing textures with semantic attributes. We identify a vocabulary of forty-seven texture terms and use them to describe a large dataset of patterns collected "in the wild". The resulting Describable Textures Dataset (DTD) is a basis to seek the best representation for recognizing describable texture attributes in images. We port from object recognition to texture recognition the Improved Fisher Vector (IFV) and Deep Convolutional-network Activation Features (DeCAF), and show that surprisingly, they both outperform specialized texture descriptors not only on our problem, but also in established material recognition datasets. We also show that our describable attributes are excellent texture descriptors, transferring between datasets and tasks, in particular, combined with IFV and DeCAF, they significantly outperform the state-of-the-art by more than 10% on both FMD and KTH-TIPS-2b benchmarks. We also demonstrate that they produce intuitive descriptions of materials and Internet images.