Polygenic Adaptation to an Environmental Shift: Temporal Dynamics of Variation Under Gaussian Stabilizing Selection and Additive Effects on a Single Trait.

Predictions about the effect of natural selection on patterns of linked neutral variation are largely based on models involving the rapid fixation of unconditionally beneficial mutations. However, when phenotypes adapt to a new optimum trait value, the strength of selection on individual mutations decreases as the population adapts. Here, I use explicit forward simulations of a single trait with additive-effect mutations adapting to an "optimum shift." Detectable "hitchhiking" patterns are only apparent if (i) the optimum shifts are large with respect to equilibrium variation for the trait, (ii) mutation rates to large-effect mutations are low, and (iii) large-effect mutations rapidly increase in frequency and eventually reach fixation, which typically occurs after the population reaches the new optimum. For the parameters simulated here, partial sweeps do not appreciably affect patterns of linked variation, even when the mutations are strongly selected. The contribution of new mutations vs. standing variation to fixation depends on the mutation rate affecting trait values. Given the fixation of a strongly selected variant, patterns of hitchhiking are similar on average for the two classes of sweeps because sweeps from standing variation involving large-effect mutations are rare when the optimum shifts. The distribution of effect sizes of new mutations has little effect on the time to reach the new optimum, but reducing the mutational variance increases the magnitude of hitchhiking patterns. In general, populations reach the new optimum prior to the completion of any sweeps, and the times to fixation are longer for this model than for standard models of directional selection. The long fixation times are due to a combination of declining selection pressures during adaptation and the possibility of interference among weakly selected sites for traits with high mutation rates

The federal debt: too little revenue or too much spending

The rise in the national debt... is entirely a consequence of the federal government’s increase of expenditures without an offsetting increase in revenues.Budget deficits ; Debts, Public ; Economic conditions - United States

How good are the government’s deficit and debt projections and should we care?

Each year, the Congressional Budget Office (CBO) publishes its Budget and Economic Outlook. The CBO’s deficit projections for the current fiscal year (FY) and the next 10 FYs are widely followed because they provide an assessment of the medium-term budget outlook based on current law and a presumed path for the economy over the next decade. Admittedly, this task is more difficult because of the required assumption that the laws governing future outlays and revenues do not change. Nevertheless, given its nonpartisan nature and the CBO’s well-respected staff of professional economists and budget analysts, its projections are closely followed. In this article, the authors update their 2001 assessment of the accuracy of the CBO’s short- and medium-term budget projections by adding an additional 10 years of data. Such analysis is useful in light of the dramatic change in actual and expected fiscal policy, especially over the past few years. In addition, they investigate the extent to which the CBO’s projection errors are affected by errors in forecasting key economic variables and the extent to which the errors relate more to inaccurate projections of revenues or expenditures.Budget deficits ; Debts, Public ; Fiscal policy

The expected federal budget surplus: how much confidence should the public and policymakers place in the projections?

When the government runs a deficit, it can borrow from the public—that is, it can create debt. Conversely, when the government runs a surplus, it can retire that debt. For the past three years, the federal government has recorded budget surpluses, and both the White House Office of Management and Budget and the Congressional Budget Office project that these surpluses will increase for at least the next decade. If these projections prove to be accurate, the $3.5 trillion of publicly held federal debt could be eliminated by around 2010. This article, which was written prior to the updated estimates published in January 2001, assesses the likelihood that these projected surpluses will materialize, and consequently eliminate the public debt, by comparing previous budget projections with actual outcomes. The authors show that the long-term budget projections have not provided a useful indicator of actual experience. Principally, these errors occur because of changes in macroeconomic conditions or unforeseen legislative actions, which both result in unanticipated increases or decreases in revenues or outlays. Not surprisingly, the projections have proven to be less reliable the longer the projection horizon. Moreover, over the period of available data, the projections have been biased upward, i.e., the actual deficits have been larger than projected. Accordingly, the authors suggest that prospects for eliminating the public debt may be overstated.Budget ; Fiscal policy ; Expenditures, Public

The federal debt: what’s the source of the increase in spending?

The government increased payments to individuals without reducing spending elsewhere in the budget.Budget deficits ; Debts, Public ; Economic conditions - United States

Does a mild recession imply a weak recovery?

Recessions

Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms

Many different machine learning algorithms exist; taking into account each algorithm's hyperparameters, there is a staggeringly large number of possible alternatives overall. We consider the problem of simultaneously selecting a learning algorithm and setting its hyperparameters, going beyond previous work that addresses these issues in isolation. We show that this problem can be addressed by a fully automated approach, leveraging recent innovations in Bayesian optimization. Specifically, we consider a wide range of feature selection techniques (combining 3 search and 8 evaluator methods) and all classification approaches implemented in WEKA, spanning 2 ensemble methods, 10 meta-methods, 27 base classifiers, and hyperparameter settings for each classifier. On each of 21 popular datasets from the UCI repository, the KDD Cup 09, variants of the MNIST dataset and CIFAR-10, we show classification performance often much better than using standard selection/hyperparameter optimization methods. We hope that our approach will help non-expert users to more effectively identify machine learning algorithms and hyperparameter settings appropriate to their applications, and hence to achieve improved performance.Comment: 9 pages, 3 figure