Associations among Family Environment, Attention, and School Readiness for At-Risk Children

This study examined the developmental pathways from children’s family environment to school readiness within an at-risk sample (N = 1,701). Measures of the family environment (maternal parenting behaviors and maternal mental health) across early childhood were related to children’s observed sustained attention as well as to academic and behavioral outcomes at age 5 years. Results suggest specificity in the associations among attention and its correlates. Maternal parenting behaviors but not mental health explained individual differences in sustained attention, which in turn were associated with variability in children’s academic school readiness. Mediation tests confirmed that sustained attention partially accounted for the link between parenting behaviors and academic school readiness. While maternal mental health was associated with children’s behavioral school readiness, sustained attention did not play a mediating role. Findings indicate sustained attention as a potential target for efforts aimed at enhancing academic school readiness among predominantly poor and minority children.child development, educational success, parenting behaviors, school readiness, mental health

Low Complexity Regularization of Linear Inverse Problems

Inverse problems and regularization theory is a central theme in contemporary signal processing, where the goal is to reconstruct an unknown signal from partial indirect, and possibly noisy, measurements of it. A now standard method for recovering the unknown signal is to solve a convex optimization problem that enforces some prior knowledge about its structure. This has proved efficient in many problems routinely encountered in imaging sciences, statistics and machine learning. This chapter delivers a review of recent advances in the field where the regularization prior promotes solutions conforming to some notion of simplicity/low-complexity. These priors encompass as popular examples sparsity and group sparsity (to capture the compressibility of natural signals and images), total variation and analysis sparsity (to promote piecewise regularity), and low-rank (as natural extension of sparsity to matrix-valued data). Our aim is to provide a unified treatment of all these regularizations under a single umbrella, namely the theory of partial smoothness. This framework is very general and accommodates all low-complexity regularizers just mentioned, as well as many others. Partial smoothness turns out to be the canonical way to encode low-dimensional models that can be linear spaces or more general smooth manifolds. This review is intended to serve as a one stop shop toward the understanding of the theoretical properties of the so-regularized solutions. It covers a large spectrum including: (i) recovery guarantees and stability to noise, both in terms of $\ell^2$-stability and model (manifold) identification; (ii) sensitivity analysis to perturbations of the parameters involved (in particular the observations), with applications to unbiased risk estimation ; (iii) convergence properties of the forward-backward proximal splitting scheme, that is particularly well suited to solve the corresponding large-scale regularized optimization problem

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Measuring the impact of the investment climate on total factor productivity : the cases of China and Brazil

This study measures the impact of investment climate factors on total factor productivity (TFP) of firms in Brazil and China. The analysis is conducted in two steps: first an econometric production function is estimated to produce a measure of TFP at the firm level. In the second step, variation in TFP across firms is statistically related to a indicators of the investment climate as well as firm characteristics. The results yield a number of insights about the factors underlying productivity. In both countries, and in a variety of industry groups, indicators of poor investment climate, especially delays in customs clearance and interruptions in utility services, have significant negative effects on TFP. Reducing customs clearance time by one day in China could increase TFP by 2-6 percent. Indicators such as email usage have positive effects on TFP. In the case of China, state-owned firms and firms located in the interior are shown to be much less productive than privately owned firms and firms located in the east. In Brazil, the results present an interesting contrast between the apparel industry and the electronics industry. In the apparel industry, older firms in competitive markets are more productive, while in the case of electronics, newer firms with higher market shares are more productive.Economic Theory&Research,Technology Industry,Water and Industry,ICT Policy and Strategies,Economic Growth