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Active learning is a subfield of machine learning that focuses on improving the data collection efficiency in expensive-to-evaluate systems. Active learning-applied surrogate modeling facilitates cost-efficient analysis of demanding engineering systems, while the existence of heterogeneity in underlying systems may adversely affect the performance. In this article, we propose the partitioned active learning that quantifies informativeness of new design points by circumventing heterogeneity in systems. The proposed method partitions the design space based on heterogeneous features and searches for the next design point with two systematic steps. The global searching scheme accelerates exploration by identifying the most uncertain subregion, and the local searching utilizes circumscribed information induced by the local Gaussian process (GP). We also propose Cholesky update-driven numerical remedies for our active learning to address the computational complexity challenge. The proposed method consistently outperforms existing active learning methods in three real-world cases with better prediction and computation time.

 

Subsidies are widely criticized in fisheries management for promoting global fishing capacity growth and overharvesting. Scientists worldwide have thus called for a ban on “harmful” subsidies that artificially increase fishing profits, resulting in the recent agreement among members of the World Trade Organization to eliminate such subsidies. The argument for banning harmful subsidies relies on the assumption that fishing will be unprofitable after eliminating subsidies, incentivizing some fishermen to exit and others to refrain from entering. These arguments follow from open-access governance regimes where entry has driven profits to zero. Yet many modern-day fisheries are conducted under limited-access regimes that limit capacity and maintain economic profits, even without subsidies. In these settings, subsidy removal will reduce profits but perhaps without any discernable effect on capacity. Importantly, until now, there have been no empirical studies of subsidy reductions to inform us about their likely quantitative impacts. In this paper, we evaluate a policy reform that reduced fisheries subsidies in China. We find that China’s subsidy reductions accelerated the rate at which fishermen retired their vessels, resulting in reduced fleet capacity, particularly among older and smaller vessels. Notably, the reduction of harmful subsidies was only partly responsible for reducing fleet capacity; an increase in vessel retirement subsidies was also a necessary driver of capacity reduction. Our study demonstrates that the efficacy of removing harmful subsidies depends on the policy environment in which removals occur. 

Understanding the cytoarchitecture and wiring of the brain requires improved methods to record and stimulate large groups of neurons with cellular specificity. This requires miniaturized neural interfaces that integrate into brain tissue without altering its properties. Existing neural interface technologies have been shown to provide high-resolution electrophysiological recording with high signal-to-noise ratio. However, with single implantation, the physical properties of these devices limit their access to one, small brain region. To overcome this limitation, we developed a platform that provides three-dimensional coverage of brain tissue through multisite multifunctional fiber-based neural probes guided in a helical scaffold. Chronic recordings from the spatially expandable fiber probes demonstrate the ability of these fiber probes capturing brain activities with a single-unit resolution for long observation times. Furthermore, usingThy1-ChR2-YFPmice we demonstrate the application of our probes in simultaneous recording and optical/chemical modulation of brain activities across distant regions. Similarly, varying electrographic brain activities from different brain regions were detected by our customizable probes in a mouse model of epilepsy, suggesting the potential of using these probes for the investigation of brain disorders such as epilepsy. Ultimately, this technique enables three-dimensional manipulation and mapping of brain activities across distant regions in the deep brain with minimal tissue damage, which can bring new insights for deciphering complex brain functions and dynamics in the near future.