This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2020 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2020 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2020 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2020 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

Gov. Kate Brown ordered the public release of annual school performance ratings last week after Oregon's biggest newspaper reported that a Brown appointee had delayed the release of the statistical rankings until after the high-stakes gubernatorial election Nov. 6.

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2020 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2020 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2020 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This report provides an overview of the implementation of the Water plan (Mary Basin) 2006 and summarises the findings of the assessments undertaken for the last five years. The plan supports urban water supply and a variety of industries including significant agricultural production, a growing tourism industry and fisheries.

Lyricists -- Australia -- Biography.

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2020 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2020 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2020 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2020 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2020 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2020 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2020 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2020 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2020 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2020 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

A Paris (rue St Denis No. 214) : chez Bance aîné, [1810?]

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2020 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2020 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2020 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

Washington : Dept. of Transportation, National Highway Traffic Safety Administration, 1976.

Arvind Prasadan, Raj Rao Nadakuditi, Debashis Paul.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 345--385.

Abstract:
Singular value decomposition (SVD) based principal component analysis (PCA) breaks down in the high-dimensional and limited sample size regime below a certain critical eigen-SNR that depends on the dimensionality of the system and the number of samples. Below this critical eigen-SNR, the estimates returned by the SVD are asymptotically uncorrelated with the latent principal components. We consider a setting where the left singular vector of the underlying rank one signal matrix is assumed to be sparse and the right singular vector is assumed to be equisigned, that is, having either only nonnegative or only nonpositive entries. We consider six different algorithms for estimating the sparse principal component based on different statistical criteria and prove that by exploiting sparsity, we recover consistent estimates in the low eigen-SNR regime where the SVD fails. Our analysis reveals conditions under which a coordinate selection scheme based on a sum-type decision statistic outperforms schemes that utilize the $ell _{1}$ and $ell _{2}$ norm-based statistics. We derive lower bounds on the size of detectable coordinates of the principal left singular vector and utilize these lower bounds to derive lower bounds on the worst-case risk. Finally, we verify our findings with numerical simulations and a illustrate the performance with a video data where the interest is in identifying objects.

We consider the standard model of distributed optimization of a sum of functions $F(mathbf z) = sum_{i=1}^n f_i(mathbf z)$, where node $i$ in a network holds the function $f_i(mathbf z)$. We allow for a harsh network model characterized by asynchronous updates, message delays, unpredictable message losses, and directed communication among nodes. In this setting, we analyze a modification of the Gradient-Push method for distributed optimization, assuming that (i) node $i$ is capable of generating gradients of its function $f_i(mathbf z)$ corrupted by zero-mean bounded-support additive noise at each step, (ii) $F(mathbf z)$ is strongly convex, and (iii) each $f_i(mathbf z)$ has Lipschitz gradients. We show that our proposed method asymptotically performs as well as the best bounds on centralized gradient descent that takes steps in the direction of the sum of the noisy gradients of all the functions $f_1(mathbf z), ldots, f_n(mathbf z)$ at each step.

Beginning from a basic neural-network architecture, we test the potential benefits offered by a range of advanced techniques for machine learning, in particular deep learning, in the context of a typical classification problem encountered in the domain of high-energy physics, using a well-studied dataset: the 2014 Higgs ML Kaggle dataset. The advantages are evaluated in terms of both performance metrics and the time required to train and apply the resulting models. Techniques examined include domain-specific data-augmentation, learning rate and momentum scheduling, (advanced) ensembling in both model-space and weight-space, and alternative architectures and connection methods.

Following the investigation, we arrive at a model which achieves equal performance to the winning solution of the original Kaggle challenge, whilst being significantly quicker to train and apply, and being suitable for use with both GPU and CPU hardware setups. These reductions in timing and hardware requirements potentially allow the use of more powerful algorithms in HEP analyses, where models must be retrained frequently, sometimes at short notice, by small groups of researchers with limited hardware resources. Additionally, a new wrapper library for PyTorch called LUMINis presented, which incorporates all of the techniques studied.

Nicole Bohme Carnegie.

Source: Statistical Science, Volume 34, Number 1, 90--93.

Abstract:
With a thorough exposition of the methods and results of the 2016 Atlantic Causal Inference Competition, Dorie et al. have set a new standard for reproducibility and comparability of evaluations of causal inference methods. In particular, the open-source R package aciccomp2016, which permits reproduction of all datasets used in the competition, will be an invaluable resource for evaluation of future methodological developments. Building upon results from Dorie et al., we examine whether a set of potential modifications to Bayesian Additive Regression Trees (BART)—multiple chains in model fitting, using the propensity score as a covariate, targeted maximum likelihood estimation (TMLE), and computing symmetric confidence intervals—have a stronger impact on bias, RMSE, and confidence interval coverage in combination than they do alone. We find that bias in the estimate of SATT is minimal, regardless of the BART formulation. For purposes of CI coverage, however, all proposed modifications are beneficial—alone and in combination—but use of TMLE is least beneficial for coverage and results in considerably wider confidence intervals.