Many states have recently enacted laws or rules designed to ensure that teachers are well versed in evidence-based reading instruction. Here are some highlights.

Nebraska's education department released an interactive instructional materials map last week, showing what curricula districts have adopted for English-language arts, math, and K-8 science.

Many states have recently enacted laws or rules designed to ensure that teachers are well versed in evidence-based reading instruction. Here are some highlights.

A statewide survey of educators in Tennessee provides critical insights into connections that exist between standards, curriculum, professional development, and ultimately student success.

Many states have recently enacted laws or rules designed to ensure that teachers are well versed in evidence-based reading instruction. Here are some highlights.

Many states have recently enacted laws or rules designed to ensure that teachers are well versed in evidence-based reading instruction. Here are some highlights.

All of Hawaii's public schools were closed down last week in anticipation of a powerful hurricane.

Seek Academy in Newark, N.J., has added a social justice class for students in kindergarten through 4th grades to help them make sense of some of the most controversial issues of the day, including the "take the knee" debate.

Many states have recently enacted laws or rules designed to ensure that teachers are well versed in evidence-based reading instruction. Here are some highlights.

Many states have recently enacted laws or rules designed to ensure that teachers are well versed in evidence-based reading instruction. Here are some highlights.

English language -- Glossaries, vocabularies, etc.

Hutton, George Stewart, 1906-1984.

Edinburgh : And Bell and Bradfute, and E. Balfour, 1801.

London : Rebman, 1910.

London : Rebman, 1903.

[London] : Chapman and Hall, [1877]

London : Philadelphia, 1889.

[London] : printed by C. and J. Adlard, [1843]

Paris (rue de la Vannerie a l'image St. Michel) : Dupuis graveur du Roy, [1721]

London : Published for the proprietors, [between 1800 and 1899]

Paris (28 et 29 rue St. Sulpice) : Maison Bouasse-Lebel, Imp. Edit. et ancienne Maison Basset reunies, [1851?]

London (6 Seymour Place, Euston Square) : Charles Heath ; [London] (Poultry) : Robert Jennings, May 15 1827 ([London] : Printed by McQueen)

London (Charlotte Street, Rathbone Place) : Published as the Act directs ... by Wm. Woollett, engraver to His Majesty, 4 June, 1778.

London : Printed and sold by Thos. and John Bowles, printsellers, [1728]

Many states have recently enacted laws or rules designed to ensure that teachers are well versed in evidence-based reading instruction. Here are some highlights.

Recurrent Neural Networks have been widely used to process sequence data, but have long been criticized for their biological implausibility and training difficulties related to vanishing and exploding gradients. This paper presents a novel algorithm for training recurrent networks, target propagation through time (TPTT), that outperforms standard backpropagation through time (BPTT) on four out of the five problems used for testing. The proposed algorithm is initially tested and compared to BPTT on four synthetic time lag tasks, and its performance is also measured using the sequential MNIST data set. In addition, as TPTT uses target propagation, it allows for discrete nonlinearities and could potentially mitigate the credit assignment problem in more complex recurrent architectures.

Raj Kamal Maurya, Yogesh Mani Tripathi.

Source: Brazilian Journal of Probability and Statistics, Volume 34, Number 2, 345--369.

Abstract:
We consider estimation of the multicomponent stress-strength reliability under progressive Type II censoring under the assumption that stress and strength variables follow Burr XII distributions with a common shape parameter. Maximum likelihood estimates of the reliability are obtained along with asymptotic intervals when common shape parameter may be known or unknown. Bayes estimates are also derived under the squared error loss function using different approximation methods. Further, we obtain exact Bayes and uniformly minimum variance unbiased estimates of the reliability for the case common shape parameter is known. The highest posterior density intervals are also obtained. We perform Monte Carlo simulations to compare the performance of proposed estimates and present a discussion based on this study. Finally, two real data sets are analyzed for illustration purposes.

Data-space inversion (DSI) and related procedures represent a family of methods applicable for data assimilation in subsurface flow settings. These methods differ from model-based techniques in that they provide only posterior predictions for quantities (time series) of interest, not posterior models with calibrated parameters. DSI methods require a large number of flow simulations to first be performed on prior geological realizations. Given observed data, posterior predictions can then be generated directly. DSI operates in a Bayesian setting and provides posterior samples of the data vector. In this work we develop and evaluate a new approach for data parameterization in DSI. Parameterization reduces the number of variables to determine in the inversion, and it maintains the physical character of the data variables. The new parameterization uses a recurrent autoencoder (RAE) for dimension reduction, and a long-short-term memory (LSTM) network to represent flow-rate time series. The RAE-based parameterization is combined with an ensemble smoother with multiple data assimilation (ESMDA) for posterior generation. Results are presented for two- and three-phase flow in a 2D channelized system and a 3D multi-Gaussian model. The RAE procedure, along with existing DSI treatments, are assessed through comparison to reference rejection sampling (RS) results. The new DSI methodology is shown to consistently outperform existing approaches, in terms of statistical agreement with RS results. The method is also shown to accurately capture derived quantities, which are computed from variables considered directly in DSI. This requires correlation and covariance between variables to be properly captured, and accuracy in these relationships is demonstrated. The RAE-based parameterization developed here is clearly useful in DSI, and it may also find application in other subsurface flow problems.