Focal Spot, Spring 2005

https://digitalcommons.wustl.edu/focal_spot_archives/1099/thumbnail.jp

"Prescription for Health Care Policy, The Case for Retargeting Tax Subsidies to Health Care"

With health care delivery increasingly shaped by market and budgetary discipline, the provision of health care for all seems an ever-more-distant goal.The high cost of American health care is the inevitable by-product of its method of financing. Cadette proposes shifting the tax subsidies to health care from the tax exclusion of employment-based health insurance to an income-scaled tax credit for the individual purchase of basic health insurance. This plan holds out promise of improving the operation of the health insurance market, making the labor market more efficient, reducing overall health care costs, and providing protection for the unemployed.

Efficient Optimization of Loops and Limits with Randomized Telescoping Sums

We consider optimization problems in which the objective requires an inner loop with many steps or is the limit of a sequence of increasingly costly approximations. Meta-learning, training recurrent neural networks, and optimization of the solutions to differential equations are all examples of optimization problems with this character. In such problems, it can be expensive to compute the objective function value and its gradient, but truncating the loop or using less accurate approximations can induce biases that damage the overall solution. We propose randomized telescope (RT) gradient estimators, which represent the objective as the sum of a telescoping series and sample linear combinations of terms to provide cheap unbiased gradient estimates. We identify conditions under which RT estimators achieve optimization convergence rates independent of the length of the loop or the required accuracy of the approximation. We also derive a method for tuning RT estimators online to maximize a lower bound on the expected decrease in loss per unit of computation. We evaluate our adaptive RT estimators on a range of applications including meta-optimization of learning rates, variational inference of ODE parameters, and training an LSTM to model long sequences

Evidence-Based Health Care for Children: What Are We Missing?

Proposes a new framework for evaluating evidence in health care that takes into account interventions in child health promotion, which aim to change children's physical, social, or emotional environment and may take longer for the effects to show

Problems Affecting Labor

Much experimental work has been devoted in comparing the folding behavior of proteins sharing the same fold but different sequence. The recent design of proteins displaying very high sequence identities but different 3D structure allows the unique opportunity to address the protein-folding problem from a complementary perspective. Here we explored by ℙ-value analysis the pathways of folding of three different heteromorphic pairs, displaying increasingly high-sequence identity (namely, 30%, 77%, and 88%), but different structures called G A (a 3-α helix fold) and G B (an α/β fold). The analysis, based on 132 site-directed mutants, is fully consistent with the idea that protein topology is committed very early along the pathway of folding. Furthermore, data reveals that when folding approaches a perfect two-state scenario, as in the case of the G A domains, the structural features of the transition state appear very robust to changes in sequence composition. On the other hand, when folding is more complex and multistate, as for the G Bs, there are alternative nuclei or accessible pathways that can be alternatively stabilized by altering the primary structure. The implications of our results in the light of previous work on the folding of different members belonging to the same protein family are discussed

Household demand persistence for child micronutrient supplementation.

Addressing early-life micronutrient deficiencies can improve short- and long-term outcomes. In most contexts, private supply chains will be key to effective and efficient preventative supplementation. With established vendors, we conducted a 60-week market trial for a food-based micronutrient supplement in rural Burkina Faso with randomized price and non-price treatments. Repeat purchases - critical for effective supplementation - are extremely price sensitive. Loyalty cards boost demand more than price discounts, particularly in non-poor households where the father is the cardholder. A small minority of households achieved sufficient supplementation for their children through purely retail distribution, suggesting the need for more creative public-private delivery platforms informed by insights into household demand persistence and heterogeneity

The Armenian labor market in transition : issues and options

Reform of the labor market in the former Soviet Union (FSU) is essential to increase productivity. The transition of the FSU economies to a market economy must involve a massive displacement of workers, and will entail labor shortages for certain skills. A key challenge will be to reallocate labor at the lowest social costs. The authors identify key labor market issues in Armenia, reflecting on the dilemmas and options policymakers face both in Armenia and elsewhere in the FSU. Armenians are ardent advocates of radical reform and have already made progress in several areas (including successful privatization of land in 1990). But the Armenian transition is taking place in particularly unfavorable circumstances - including a severe energy crisis because of an economic blockade imposed by neighboring Azerbaijan. In Armenia, current labor policies represent a step in the right direction because they leave primary responsibility for finding a job to the individual. The state's role is simply to provide a social safety net and to create an environment that generates jobs. Tangible progress has been made but the adjustment process has just begun and is hindered by inconsistent labor policies - in some areas too radical and in others smacking of the old interventionism. The authors offer several general policy guidelines. Undertake several initiatives, not just one - possibly worker training as well as job search assistance, self employment grants, and temporary public employment. Use some resources to monitor and evaluate interventions to find out what works. Coordinate active policy interventions and the interface between active and passive instruments. Be prepared to change as the macroeconomic environment changes, and take advantage of the current climate. Under high inflation, for example, consider widening the wedge between wages and various cash and in-kind transfers. When inflation abates, consider paying cash benefits on the basis of prior earnings. Above all, be flexible and sensitive to signals and changes in signals. Among policy options, one size does not fit all.Environmental Economics&Policies,Banks&Banking Reform,Municipal Financial Management,Labor Standards,Health Monitoring&Evaluation

A general guide to applying machine learning to computer architecture

The resurgence of machine learning since the late 1990s has been enabled by significant advances in computing performance and the growth of big data. The ability of these algorithms to detect complex patterns in data which are extremely difficult to achieve manually, helps to produce effective predictive models. Whilst computer architects have been accelerating the performance of machine learning algorithms with GPUs and custom hardware, there have been few implementations leveraging these algorithms to improve the computer system performance. The work that has been conducted, however, has produced considerably promising results. The purpose of this paper is to serve as a foundational base and guide to future computer architecture research seeking to make use of machine learning models for improving system efficiency. We describe a method that highlights when, why, and how to utilize machine learning models for improving system performance and provide a relevant example showcasing the effectiveness of applying machine learning in computer architecture. We describe a process of data generation every execution quantum and parameter engineering. This is followed by a survey of a set of popular machine learning models. We discuss their strengths and weaknesses and provide an evaluation of implementations for the purpose of creating a workload performance predictor for different core types in an x86 processor. The predictions can then be exploited by a scheduler for heterogeneous processors to improve the system throughput. The algorithms of focus are stochastic gradient descent based linear regression, decision trees, random forests, artificial neural networks, and k-nearest neighbors.This work has been supported by the European Research Council (ERC) Advanced Grant RoMoL (Grant Agreemnt 321253) and by the Spanish Ministry of Science and Innovation (contract TIN 2015-65316P).Peer ReviewedPostprint (published version