STROOPWAFEL: Simulating rare outcomes from astrophysical populations, with application to gravitational-wave sources

Gravitational-wave observations of double compact object (DCO) mergers are providing new insights into the physics of massive stars and the evolution of binary systems. Making the most of expected near-future observations for understanding stellar physics will rely on comparisons with binary population synthesis models. However, the vast majority of simulated binaries never produce DCOs, which makes calculating such populations computationally inefficient. We present an importance sampling algorithm, STROOPWAFEL, that improves the computational efficiency of population studies of rare events, by focusing the simulation around regions of the initial parameter space found to produce outputs of interest. We implement the algorithm in the binary population synthesis code COMPAS, and compare the efficiency of our implementation to the standard method of Monte Carlo sampling from the birth probability distributions. STROOPWAFEL finds $\sim$25-200 times more DCO mergers than the standard sampling method with the same simulation size, and so speeds up simulations by up to two orders of magnitude. Finding more DCO mergers automatically maps the parameter space with far higher resolution than when using the traditional sampling. This increase in efficiency also leads to a decrease of a factor $\sim$3-10 in statistical sampling uncertainty for the predictions from the simulations. This is particularly notable for the distribution functions of observable quantities such as the black hole and neutron star chirp mass distribution, including in the tails of the distribution functions where predictions using standard sampling can be dominated by sampling noise.Comment: Accepted. Data and scripts to reproduce main results is publicly available. The code for the STROOPWAFEL algorithm will be made publicly available. Early inquiries can be addressed to the lead autho

Noisy Business Cycles

This paper investigates a real-business-cycle economy that features dispersed information about the underlying aggregate productivity shocks, taste shocks, and—potentially—shocks to monopoly power. We show how the dispersion of information can (i) contribute to significant inertia in the response of macroeconomic outcomes to such shocks; (ii) induce a negative shortrun response of employment to productivity shocks; (iii) imply that productivity shocks explain only a small fraction of high-frequency fluctuations; (iv) contribute to significant noise in the business cycle; (v) formalize a certain type of demand shocks within an RBC economy; and (vi) generate cyclical variation in observed Solow residuals and labor wedges. Importantly, none of these properties requires significant uncertainty about the underlying fundamentals: they rest on the heterogeneity of information and the strength of trade linkages in the economy, not the level of uncertainty. Finally, none of these properties are symptoms of inefficiency: apart from undoing monopoly distortions or providing the agents with more information, no policy intervention can improve upon the equilibrium allocations

Consequences of long-term infrastructure decisions—the case of self-healing roads and their CO2 emissions

What could be the reduction in greenhouse gas emissions if the conventional way of maintaining roads is changed? Emissions of greenhouse gases must be reduced if global warming is to be avoided, and urgent political and technological decisions should be taken. However, there is a lock-in in built infrastructures that is limiting the rate at which emissions can be reduced. Self-healing asphalt is a new type of technology that will reduce the need for fossil fuels over the lifetime of a road pavement, at the same time as prolonging the road lifespan. In this study we have assessed the benefits of using self-healing asphalt as an alternative material for road pavements employing a hybrid input–output-assisted Life-Cycle Assessment, as only by determining the plausible scenarios of future emissions will policy makers identify pathways that might achieve climate change mitigation goals. We have concluded that self-healing roads could prevent a considerable amount of emissions and costs over the global road network: 16% lower emissions and 32% lower costs compared to a conventional road over the lifecycle

Appropriate Economic Space for Transnational Infrastructural Projects: Gateways, Multimodal Corridors, and Special Economic Zones

This study addresses three questions that arise in Asia when formulating, financing, implementing, and maintaining transnational linkages versus purely domestic connections. Firstly, how is optimal economic space to be defined as a useful starting point? Secondly, how can relevant criteria be developed to define the emerging spatial economy and identify efficient transnational transport networks? Thirdly, what are the main investment opportunities in physical infrastructure that would result in more efficient and effective regional cooperation and integration (making special reference to the potential role of cross-border special economic zones (SEZs) or their equivalents)?asia transnational infrastructure; asia regional cooperation

Graphics Processing Unit–Enhanced Genetic Algorithms for Solving the Temporal Dynamics of Gene Regulatory Networks

Understanding the regulation of gene expression is one of the key problems in current biology. A promising method for that purpose is the determination of the temporal dynamics between known initial and ending network states, by using simple acting rules. The huge amount of rule combinations and the nonlinear inherent nature of the problem make genetic algorithms an excellent candidate for finding optimal solutions. As this is a computationally intensive problem that needs long runtimes in conventional architectures for realistic network sizes, it is fundamental to accelerate this task. In this article, we study how to develop efficient parallel implementations of this method for the fine-grained parallel architecture of graphics processing units (GPUs) using the compute unified device architecture (CUDA) platform. An exhaustive and methodical study of various parallel genetic algorithm schemes—master-slave, island, cellular, and hybrid models, and various individual selection methods (roulette, elitist)—is carried out for this problem. Several procedures that optimize the use of the GPU’s resources are presented. We conclude that the implementation that produces better results (both from the performance and the genetic algorithm fitness perspectives) is simulating a few thousands of individuals grouped in a few islands using elitist selection. This model comprises 2 mighty factors for discovering the best solutions: finding good individuals in a short number of generations, and introducing genetic diversity via a relatively frequent and numerous migration. As a result, we have even found the optimal solution for the analyzed gene regulatory network (GRN). In addition, a comparative study of the performance obtained by the different parallel implementations on GPU versus a sequential application on CPU is carried out. In our tests, a multifold speedup was obtained for our optimized parallel implementation of the method on medium class GPU over an equivalent sequential single-core implementation running on a recent Intel i7 CPU. This work can provide useful guidance to researchers in biology, medicine, or bioinformatics in how to take advantage of the parallelization on massively parallel devices and GPUs to apply novel metaheuristic algorithms powered by nature for real-world applications (like the method to solve the temporal dynamics of GRNs)

Recreational, Cultural and Aesthetic Services from Estuarine and Coastal Ecosystems

The role of economic analysis in guiding the sustainable development of estuarine and coastal ecosystems is investigated based on a comprehensive review of the literature on the valuation of the recreation, cultural and aesthetic services. The implications of the findings for the sustainable management of coral reefs, Marine Protected Areas, and Small Island Developing States are discussed. Finally, the potential of meta-analytical benefit transfer and scaling up of values at various aggregation levels is demonstrated in the context of coastal tourism and recreation in Europe. The results of the study support the conclusion that the non-material values provided by coastal and estuarine ecosystems in terms of recreational, cultural and aesthetic services represent a substantial component of human well-being.Aesthetic Values, Coastal Recreation, Coral Reefs, Cultural Values, Ecosystem Services Valuation, Ecosystem Services, Estuarine Ecosystems, Marine Protected Areas, Non-market Valuation, Non-use Values, Passive Values, Recreational Fishing, Small Island Developing States, Spiritual and Religious Values.

Analysis of the optimal deployment location for tidal energy converters in the mesotidal Ria de Vigo (NW Spain)

The potential power output expected from the installation of a tidal farm near the mesotidal Ria de Vigo (NW Spain) is assessed using two different tidal stream energy converters (TEC). For this, the results of a previous resource assessment based on a 28-day long hydrodynamic simulation are used. From this data we identify the areas susceptible of hosting the farms, select the optimal location for them, and assess the total available and extractable energy for each turbine type. Finally, using a simple farm design based on standard inter-turbine separation, we estimate the expected power supplied by the farm. Irrespective of the site, the total available tidal power in the areas susceptible of hosting the farms is around 150 MW; at the optimal location, the hourly extractable power is about 22.5 MW, of which only between 10% and 15% can be harnessed by the designed farms, powering between 4411 and 6638 homes. A local analysis of the most energetic subregions within these sites increases this ratio up to 30%. Nevertheless, the power output is sufficient to fulfil the needs of between 1660 and 2213 households, depending on the chosen site and the selected TEC.Peer ReviewedPostprint (author's final draft