In this paper, we present an application of evolved neural networks using a real coded genetic algorithm for simulations of monthly groundwater levels in a coastal aquifer located in the Shabestar Plain, Iran. After initializing the model with groundwater elevations observed at a given time, the developed hybrid genetic algorithm-back propagation (GA-BP) should be able to reproduce groundwater ...

We consider secure multi-party computation in the asynchronous model and present an efficient protocol with optimal resilience. For n parties, up to t < n/3 of them being corrupted, and security parameter κ, a circuit with c gates can be securely computed with communication complexity O(cnκ) bits. In contrast to all previous asynchronous protocols with optimal resilience, our protocol requires ...

To address intensifying social and environmental challenges, development policy must learn from inclusions exclusions of past discourses. We analyse Kenya's post-colonial agricultural discourse. Our analysis reveals a near-exclusive focus on the promotion modernisation based industrial farm inputs, bureaucratic state and/or ‘the liberalised market’. It was with this thrust to modernise that sma...

The firing reliability and precision of an isopotential membrane patch consisting of a realistically large number of ion channels is investigated using a stochastic Hodgkin-Huxley (HH) model. In sharp contrast to the deterministic HH model, the biophysically inspired stochastic model reproduces qualitatively the different reliability and precision characteristics of spike firing in response to ...

Abstract This paper demonstrates the acute sensitivity of education program effectiveness to choices inputs and outcome measures, using a randomized evaluation mother-tongue literacy program. The raises reading scores by 0.64 SD writing 0.45 SD. A reduced-cost version instead yields statistically insignificant gains some large negative effects (?0.33 SDs) on advanced writing. We combine concept...

Many data sets have missing values in practical application contexts, but the majority of commonly studied machine learning methods cannot be applied directly when there are incomplete samples. However, most such methods only depend on the relative differences between samples instead of their particular values, and thus one useful approach is to directly estimate the pairwise distances between ...

Advancements in data collection techniques and the heterogeneity of resources can yield high percentages missing observations on variables, such as block-wise data. Under missing-data scenarios, traditional methods simple average, k-nearest neighbor, multiple, regression imputations may lead to results that are unstable or unable be computed. Motivated by concept semi-supervised learning, we pr...