Roundtable's Rx for the economy: first, do no harm

An overview of the January 24, 1992 meeting of the Fourth District Economists' Roundtable, at which participants discussed how longer-term structural adjustments taking place in many of the nation's industries may be restraining the recovery.Business forecasting

The Lives of Stars: Insights From the TGAS-RAVE-LAMOST Dataset

In this paper we investigate how the chemical and kinematic properties of stars vary as a function of age. Using data from a variety of photometric, astrometric and spectroscopic surveys, we calculate the ages, phase space information and orbits for $\sim$125,000 stars covering a wide range of stellar parameters. We find indications that the inner regions of the disk reached high levels of enrichment early, while the outer regions were more substantially enriched in intermediate and recent epochs. We consider these enrichment histories through comparison of the ages of stars, their metallicities, and kinematic properties, such as their angular momentum in the solar neighborhood (which is a proxy for orbital radius). We calculate rates at which the velocity dispersions evolve, investigate the Oort constants for different aged populations (finding a slightly negative $\partial V_{C} / \partial R$ and $\partial V_{R} / \partial R$ for all ages, being most negative for the oldest stars), as well as examine the behavior of the velocity vertex deviation angle as a function of age (which we find to fall from $\sim$15 degrees for the 2 Gyr aged population to $\sim$6 degrees at around 6.5 Gyr of age, after which it remains unchanged). We find evidence for stellar churning, and find that the churned stars have a slightly younger age distribution than the rest of the data.Comment: 18 Pages, 14 Figures, Accepted Ap

An Assessment of the Economic, Environmental and Social Impacts of NSW Agriculture's Wheat Breeding Program

The Wagga wheat breeding program has been operating for over 100 years. In that time, it has released a flow of new wheat varieties for wheat growers in south-eastern Australia. Those varieties have led to increases in both yields and grain quality. The average annual rate of yield improvement in NSW has been 3.2% compared to the average for Australia of 2.4% with a significant proportion of these productivity gains arising from new varieties. In this analysis, the investment in that program from 1980 to 2003 has been evaluated. Given the lags inherent in wheat breeding investments, the benefits from those investments are being measured from 1993 to 2020. The broad structure of the program has remained relatively stable for most of the period since 1980. The program consists of 2-3 wheat breeders, one breeder-pathologist, and a cereal chemist, with appropriate technical and field support, totaling approximately 15 full-time equivalents per year. The costs of the program have averaged approximately $1.2 million per year over the period. In assessing the Wagga wheat breeding program it is important to consider how the industry would have developed without the program. The benefits of the program were measured as the difference in returns from improved wheat varieties in NSW over that period and the returns that would have been achieved in the absence of the Wagga breeding program. The assumption used to determine the impact without the Wagga program was that the rate of yield improvement in NSW would have been the same as for the rest of Australia. For quality, without the Wagga program the assumption was that in southern NSW the increase in quality would have been 20$321 million. The economic benefits of the breeding program are shared by producers, processors and consumers in the wheat industry, some of whom live overseas. Because Australia is largely a price taker on world wheat markets and because the wheat processing and distribution sector in Australia is generally considered to be competitive, most of the benefits of the wheat breeding program are likely to remain with producers. However these gains are offset by declines in the world price in response to advancing technology throughout the world. These economic benefits have positive social consequences, largely through their contribution to the incomes of farmers and those who handle and process wheat in regional NSW. Some of these gains are in the form of new marketing and processing industries around the increasingly specialised industry segments resulting directly from the changes that have occurred in wheat varieties. Perhaps these new skills add to the social capital of towns in the wheat belt of NSW. In environmental terms, the wheat breeding program itself is not likely to have major impacts, since the wheat industry would have been very similar whether or not there was a Wagga breeding program. However, to the extent that improved productivity from the Wagga program's varieties has allowed an expansion of the wheat industry, there could be some negative environmental consequences of the breeding program, such as those arising from the clearing of land, increased cultivation and increased use of herbicides. On the other hand, the high levels of disease resistance developed and maintained has meant that wheat production is not associated with large-scale fungicide use, and hence the danger of chemical contamination of the environment is less than it would have been without the resistance developed in this program. Some of these environmental impacts affect the costs and incomes of wheat farmers and hence are reflected in economic benefits and some spill over to the broader community and have not been valued here. It is not clear that these social and environmental impacts would be much different without the Wagga breeding program, except through the extent to which the Wagga program has allowed the wheat industry in NSW to develop more than it otherwise would have. Without the Wagga program the slower gains in yield and quality would also be associated with some social and environmental impacts, and it is the difference that is critical in evaluating the Wagga program. The costs of this program have been met partly by the NSW taxpayers through NSW Agriculture and partly by the grains industry through levies from the Grains Research and Development Corporation (GRDC). The recent introduction of variety royalty payments ("end-point royalties") has not yet led to significant funding, but may be expected to do so in the future. The nature of the outputs of plant breeding programs is that there are large economic benefits that flow directly to producers, processors and consumers in the industry. However the social and environmental impacts on the broader community, while not explicitly valued here, are considered to be small relative to economic benefits and relative to some other programs of NSW Agriculture that have been evaluated. Hence it is appropriate that the industry, though GRDC levies and royalties on production, has increasingly funded the operations of the wheat breeding program. Recent institutional changes for the wheat breeding program have made it even more commercially-based for the future and less reliant on government funding. The new institutional arrangements for wheat breeding programs and the strengthening role of the private sector in supplying varieties traditionally supplied by the public sector mean that the place of public wheat breeding programs is being re-assessed. A key question is whether publicly-operated programs, can offer some additional benefits either to the industry or to the community, which would not result from the complete privatisation of the wheat breeding sector. While those issues have not been addressed directly in this analysis, the results indicate that past investments in public wheat breeding program at Wagga have certainly been a productive use of public funds over the past 20 years or so.Research and Development/Tech Change/Emerging Technologies,

Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates

We establish optimal convergence rates for a decomposition-based scalable approach to kernel ridge regression. The method is simple to describe: it randomly partitions a dataset of size N into m subsets of equal size, computes an independent kernel ridge regression estimator for each subset, then averages the local solutions into a global predictor. This partitioning leads to a substantial reduction in computation time versus the standard approach of performing kernel ridge regression on all N samples. Our two main theorems establish that despite the computational speed-up, statistical optimality is retained: as long as m is not too large, the partition-based estimator achieves the statistical minimax rate over all estimators using the set of N samples. As concrete examples, our theory guarantees that the number of processors m may grow nearly linearly for finite-rank kernels and Gaussian kernels and polynomially in N for Sobolev spaces, which in turn allows for substantial reductions in computational cost. We conclude with experiments on both simulated data and a music-prediction task that complement our theoretical results, exhibiting the computational and statistical benefits of our approach

High-dimensional Ising model selection using ℓ1{\ell_1}-regularized logistic regression

We consider the problem of estimating the graph associated with a binary Ising Markov random field. We describe a method based on $\ell_1$-regularized logistic regression, in which the neighborhood of any given node is estimated by performing logistic regression subject to an $\ell_1$-constraint. The method is analyzed under high-dimensional scaling in which both the number of nodes $p$ and maximum neighborhood size $d$ are allowed to grow as a function of the number of observations $n$. Our main results provide sufficient conditions on the triple $(n,p,d)$ and the model parameters for the method to succeed in consistently estimating the neighborhood of every node in the graph simultaneously. With coherence conditions imposed on the population Fisher information matrix, we prove that consistent neighborhood selection can be obtained for sample sizes $n=\Omega(d^3\log p)$ with exponentially decaying error. When these same conditions are imposed directly on the sample matrices, we show that a reduced sample size of $n=\Omega(d^2\log p)$ suffices for the method to estimate neighborhoods consistently. Although this paper focuses on the binary graphical models, we indicate how a generalization of the method of the paper would apply to general discrete Markov random fields.Comment: Published in at http://dx.doi.org/10.1214/09-AOS691 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Tolerability, safety, and efficacy of adjunctive brivaracetam for focal seizures in older patients: A pooled analysis from three phase III studies

Introduction: This analysis was conducted to assess the tolerability, safety, and efficacy of brivaracetam (BRV) for adjunctive treatment of focal (partial-onset) seizures in patients aged ≥65 years. Methods: Safety/tolerability and efficacy data for patients aged ≥65 years were pooled from three randomized, double-blind, placebo-controlled, fixed-dose Phase III studies (NCT00490035, NCT00464269, and NCT01261325). Data were pooled by treatment group: placebo or the proposed therapeutic dose range of 50–200 mg/day: BRV 50, 100, 200 mg/day. Results: Thirty-two patients aged ≥65 years were randomized to placebo or BRV 50–200 mg/day. Of these, 30 patients (93.8%) completed their respective study. In the safety population (n = 32), 87.5% placebo- vs 73.3% BRV-treated patients reported treatment-emergent adverse events (TEAEs) during the treatment period; most commonly, headache (25.0% vs 12.5%), paresthesia (0% vs 12.5%), and somnolence (50.0% vs 12.5%) for placebo- vs BRV-treated patients, respectively. During the treatment period, drug-related TEAEs were reported by 62.5% of placebo- vs 53.3% of BRV-treated patients, and serious TEAEs (SAEs) were reported by 0% of placebo- and 4.2% of BRV-treated patients; there were no drug-related SAEs and no deaths. Three SAEs (placebo 1/8; BRV 2/24) and two deaths (placebo 1/8; BRV 1/24) occurred in the post-treatment period. In the efficacy population (n = 31), median percent reduction from baseline in focal seizure frequency/28 days was 14.0% for placebo vs 25.5%, 49.6%, and 74.9% for BRV 50, 100, and 200 mg/day, respectively. The ≥50% responder rate was 14.3% for placebo vs 25.0%, 50.0%, and 66.7% for BRV 50, 100, and 200 mg/day, respectively. Conclusions: Safety/tolerability and efficacy findings in this small subgroup of older patients treated with adjunctive BRV are consistent with those observed in the much larger overall pooled population. BRV may be a suitable adjunctive treatment for older patients with uncontrolled focal seizures. Further larger studies in this population are warranted

Robust Iterative Solution of a Class of Time-Dependent Optimal Control Problems

The fast iterative solution of optimal control problems, and in particular PDE-constrained optimization problems, has become an active area of research in applied mathematics and numerical analysis. In this paper, we consider the solution of a class of time-dependent PDE-constrained optimization problems, specifically the distributed control of the heat equation. We develop a strategy to approximate the (1,1)-block and Schur complement of the saddle point system that results from solving this problem, and therefore derive a block diagonal preconditioner to be used within the MINRES algorithm. We present numerical results to demonstrate that this approach yields a robust solver with respect to step-size and regularization parameter

Randomized Smoothing for Stochastic Optimization

We analyze convergence rates of stochastic optimization procedures for non-smooth convex optimization problems. By combining randomized smoothing techniques with accelerated gradient methods, we obtain convergence rates of stochastic optimization procedures, both in expectation and with high probability, that have optimal dependence on the variance of the gradient estimates. To the best of our knowledge, these are the first variance-based rates for non-smooth optimization. We give several applications of our results to statistical estimation problems, and provide experimental results that demonstrate the effectiveness of the proposed algorithms. We also describe how a combination of our algorithm with recent work on decentralized optimization yields a distributed stochastic optimization algorithm that is order-optimal.Comment: 39 pages, 3 figure