Report on the Expenditure of Lottery Funds Fiscal Year 1998

Abstract pending

Council for School Performance: Report on the Expenditure of Lottery Funds Fiscal Year 1997

Abstract pending

Council for School Performance: Report on the Expenditure of Lottery Funds Fiscal Year 1996

Abstract pending

Quality of Georgia's Pre-Kindergarten Program, 1997-98 School Year

During the 1996-97 and 1997-98 school years, the Council for School Performance and the Applied Research Center of Georgia State University conducted an evaluation of the lottery-funded Georgia Prekindergarten Program. Using data collected through classroom site visits and surveys of teachers in those classrooms, this evaluation compares the quality of classrooms from one year to the next, looks at the relationship between teachers' beliefs and classroom quality, and provides information about the Georgia Prekindergarten Program's teachers and their classrooms

Predictive-State Decoders: Encoding the Future into Recurrent Networks

Recurrent neural networks (RNNs) are a vital modeling technique that rely on internal states learned indirectly by optimization of a supervised, unsupervised, or reinforcement training loss. RNNs are used to model dynamic processes that are characterized by underlying latent states whose form is often unknown, precluding its analytic representation inside an RNN. In the Predictive-State Representation (PSR) literature, latent state processes are modeled by an internal state representation that directly models the distribution of future observations, and most recent work in this area has relied on explicitly representing and targeting sufficient statistics of this probability distribution. We seek to combine the advantages of RNNs and PSRs by augmenting existing state-of-the-art recurrent neural networks with Predictive-State Decoders (PSDs), which add supervision to the network's internal state representation to target predicting future observations. Predictive-State Decoders are simple to implement and easily incorporated into existing training pipelines via additional loss regularization. We demonstrate the effectiveness of PSDs with experimental results in three different domains: probabilistic filtering, Imitation Learning, and Reinforcement Learning. In each, our method improves statistical performance of state-of-the-art recurrent baselines and does so with fewer iterations and less data.Comment: NIPS 201

Prediction of RECRUITment In randomized clinical Trials (RECRUIT-IT)— : —rationale and design for an international collaborative study

Funding: BK has received a project specific grant from the University of Basel to realize this project. In addition, this study is supported by the Swiss National Science Foundation (grant 320030_149496/1) and the Gottfried and Julia Bangerter-Rhyner Foundation. The provided work by BG, JHL, CW, and JY has been supported by the National Cancer Institute Cancer Centre Support Grant P30 CA168524 and used BISR core. The Health Services Research Unit, University of Aberdeen, receives core funding from the Chief Scientist Office of the Scottish Government Health Directorates. DC is supported by a Research Chair from the Canadian Institute for Health Research. The mentioned funding sources have no role in the design and conduct of the study; the collection, management, analysis, and interpretation of the data; the preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication.Peer reviewedPublisher PD

The Long-Baseline Neutrino Experiment: Exploring Fundamental Symmetries of the Universe

The preponderance of matter over antimatter in the early Universe, the dynamics of the supernova bursts that produced the heavy elements necessary for life and whether protons eventually decay --- these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our Universe, its current state and its eventual fate. The Long-Baseline Neutrino Experiment (LBNE) represents an extensively developed plan for a world-class experiment dedicated to addressing these questions. LBNE is conceived around three central components: (1) a new, high-intensity neutrino source generated from a megawatt-class proton accelerator at Fermi National Accelerator Laboratory, (2) a near neutrino detector just downstream of the source, and (3) a massive liquid argon time-projection chamber deployed as a far detector deep underground at the Sanford Underground Research Facility. This facility, located at the site of the former Homestake Mine in Lead, South Dakota, is approximately 1,300 km from the neutrino source at Fermilab -- a distance (baseline) that delivers optimal sensitivity to neutrino charge-parity symmetry violation and mass ordering effects. This ambitious yet cost-effective design incorporates scalability and flexibility and can accommodate a variety of upgrades and contributions. With its exceptional combination of experimental configuration, technical capabilities, and potential for transformative discoveries, LBNE promises to be a vital facility for the field of particle physics worldwide, providing physicists from around the globe with opportunities to collaborate in a twenty to thirty year program of exciting science. In this document we provide a comprehensive overview of LBNE's scientific objectives, its place in the landscape of neutrino physics worldwide, the technologies it will incorporate and the capabilities it will possess.Comment: Major update of previous version. This is the reference document for LBNE science program and current status. Chapters 1, 3, and 9 provide a comprehensive overview of LBNE's scientific objectives, its place in the landscape of neutrino physics worldwide, the technologies it will incorporate and the capabilities it will possess. 288 pages, 116 figure

Building Blocks of Idea Generation and Implementation in Teams: A Meta-Analysis of Team Design and Team Creativity and Innovation

Although organizations increasingly rely on teams to innovate, little systematic knowledge exists about how to design teams to do so. Building on the model of collaborative creativity and innovation and synthesizing findings from published and unpublished studies, this meta-analysis examines the role of team design on team creativity and innovation. We used random-effects meta-analysis to cumulate the correlations between different features of team design and team creativity or team innovation from 134 field studies representing 11,353 teams and 35 studies representing 2,485 student teams. We found that team tenure is curvilinearly related, autonomy-supportive leadership, task interdependence, and goal interdependence are positively related, and demographic diversity and team size are unrelated to team creativity and innovation. Examining meta-analytic path models, we found that task interdependence and supportive leadership positively relate to team creativity and innovation via team collaboration and team potency. In accounting for the literature, we found a dearth of studies examining team processes, some types of diversity such as racial diversity, and the role of team member turnover. We conclude by providing directions for future research and practical guidance about increasing team creativity and innovation through team design