The Interaction Between PDE and Graphs in Multiscale Modeling

In this article an upscaled model is presented, for complex networks with highly clustered regions exchanging some abstract quantities in both, microscale and macroscale level. Such an intricate system is approximated by a partitioned open map in $\mathbb{R}^{2}$ or $\mathbb{R}^{3}$. The behavior of the quantities is modeled as flowing in the map constructed and thus it is subject to be described by partial differential equations. We follow this approach using the Darcy Porous Media, saturated fluid flow model in mixed variational formulation.Comment: 14 pages, 4 figure

Does Eco-Certification Have Environmental Benefits? Organic Coffee in Costa Rica

Eco-certification of coffee, timber and other high-value agricultural commodities is increasingly widespread. In principle, it can improve commodity producers’ environmental performance, even in countries where state regulation is weak. However, evidence needed to evaluate this hypothesis is virtually nonexistent. To help fill this gap, we use detailed farm-level data to analyze the environmental impacts of organic coffee certification in central Costa Rica. We use propensity score matching to control for self-selection bias. We find that organic certification improves coffee growers’ environmental performance. It significantly reduces chemical input use and increases adoption of some environmentally friendly management practices.certification, coffee, Costa Rica, propensity score matching

Does Eco-Certification Have Environmental Benefits? Organic Coffee in Costa Rica

Eco-certification of coffee, timber and other high-value agricultural commodities is increasingly widespread. In principle, it can improve commodity producers’ environmental performance, even in countries where state regulation is weak. However, evidence needed to evaluate this hypothesis is virtually nonexistent. To help fill this gap, we use detailed farm-level data to analyze the environmental impacts of organic coffee certification in central Costa Rica. We use propensity score matching to control for self-selection bias. We find that organic certification improves coffee growers’ environmental performance. It significantly reduces chemical input use and increases adoption of some environmentally friendly management practices.certification, coffee, Costa Rica, propensity score matching

Farmers’ Adaptation to Climate Change: A Framed Field Experiment

The risk of losing income and productive means due to adverse weather can differ significantly among farmers sharing a productive landscape and is, of course, hard to estimate or even “guesstimate” empirically. Moreover, the costs associated with investments in adaptation to climate are likely to exhibit economies of scope. We explore the implications of these characteristics on Costa Rican coffee farmers’ decisions to adapt to climate change, using a framed field experiment. Despite having a baseline of high levels of risk aversion, we still found that farmers more frequently chose the safe options when the setting is characterized by unknown risk (that is, poor or unreliable risk information). Second, we found that farmers, to a large extent, coordinated their decisions to secure a lower adaptation cost and that communication among farmers strongly facilitated coordination.risk, ambiguity, technology adoption, climate change, field experiment

Logic Negation with Spiking Neural P Systems

Nowadays, the success of neural networks as reasoning systems is doubtless. Nonetheless, one of the drawbacks of such reasoning systems is that they work as black-boxes and the acquired knowledge is not human readable. In this paper, we present a new step in order to close the gap between connectionist and logic based reasoning systems. We show that two of the most used inference rules for obtaining negative information in rule based reasoning systems, the so-called Closed World Assumption and Negation as Finite Failure can be characterized by means of spiking neural P systems, a formal model of the third generation of neural networks born in the framework of membrane computing.Comment: 25 pages, 1 figur

Topology-based Representative Datasets to Reduce Neural Network Training Resources

One of the main drawbacks of the practical use of neural networks is the long time required in the training process. Such a training process consists of an iterative change of parameters trying to minimize a loss function. These changes are driven by a dataset, which can be seen as a set of labelled points in an n-dimensional space. In this paper, we explore the concept of are representative dataset which is a dataset smaller than the original one, satisfying a nearness condition independent of isometric transformations. Representativeness is measured using persistence diagrams (a computational topology tool) due to its computational efficiency. We prove that the accuracy of the learning process of a neural network on a representative dataset is "similar" to the accuracy on the original dataset when the neural network architecture is a perceptron and the loss function is the mean squared error. These theoretical results accompanied by experimentation open a door to reducing the size of the dataset to gain time in the training process of any neural network

Non-equilibrium Effects in the Thermal Switching of Underdamped Josephson Junctions

We study the thermal escape problem in the low damping limit. We find that finiteness of the barrier is crucial for explaining the thermal activation results. In this regime low barrier non-equilibrium corrections to the usual theories become necessary. We propose a simple theoretical extension accounting for these non-equilibrium processes which agrees numerical results. We apply our theory to the understanding of switching current curves in underdamped Josephson junctions.Comment: 4 pages + 4 figure