Recently, deep reinforcement learning (RL) has achieved remarkable empirical success by integrating neural networks into RL frameworks. However, these algorithms often require a large number of training samples and admit little theoretical understanding. To mitigate issues, we propose theoretically principled nearest neighbor (NN) function approximator that can replace the value in methods. Ins...

The contact process (CP) is an irreversible lattice model involving nearest neighbor interactions only [1,2]. This model incorporates spontaneous desorption and nearest-neighbor induced adsorption. This stochastic process can be used to mimic epidemic spread as well as catalytic reactions. This model belongs to a general class of nonequilibrium models exhibiting a continuous phase transition. N...

We investigate the tight-binding approximation for the dispersion of the p and p* electronic bands in graphene and carbon nanotubes. The nearest-neighbor tight-binding approximation with a fixed g0 applies only to a very limited range of wave vectors. We derive an analytic expression for the tight-binding dispersion including up to third-nearest neighbors. Interaction with more distant neighbor...

Self-nested trees present a systematic form of redundancy in their subtrees and thus achieve optimal compression rates by DAG compression. A method for quantifying the degree of self-similarity of plants through self-nested trees has been introduced by Godin and Ferraro in 2010. The procedure consists in computing a self-nested approximation, called the nearest embedding self-nested tree, that ...

Nonlinear dimensionality reduction methods often rely on the nearest-neighbors graph to extract low-dimensional embeddings that reliably capture the underlying structure of high-dimensional data. Research however has shown that computing nearest neighbors of a point from a highdimensional data set generally requires time proportional to the size of the data set itself, rendering the computation...