We report on an introductory study used to gauge the significance of random weakedge disorder on the coherent transport properties of ultra-thin zig-zag nanoribbons (ZGNRs) beyond the simple (i.e., first nearest-neighbour) tight-binding approximation. Such extensions include up to third nearest-neighbour hopping in an extended tight-binding model, as well as a mean-field Hubbard-U . The effect ...

We investigate the paramagnetic-metal-to-antiferromagnetic-metal and antiferromagnetic-metal-toantiferromagnetic-insulator transitions using a slave-boson mean-field theory. To this effect, we discuss the ground state of the half-filled Hubbard model as a function of t8/t and correlation strength U, where t and t8 are the hopping amplitudes between nearest and next-nearest neighbors, respective...

Nearest neighbour (NN) searches and k nearest neighbour (k-NN) searches are widely used in pattern recognition and image retrieval. An NN (k-NN) search finds the closest object (closest k objects) to a query object. Although the definition of the distance between objects depends on applications, its computation is generally complicated and time-consuming. It is therefore important to reduce the...

ProbabilisticK-nearest neighbour (PKNN) classification has been introduced to improve the performance of the original K-nearest neighbour (KNN) classification algorithm by explicitly modelling uncertainty in the classification of each feature vector. However, an issue common to both KNN and PKNN is to select the optimal number of neighbours, K. The contribution of this paper is to incorporate t...

We consider the problem of model-free reinforcement learning for infinite-horizon discounted Markov Decision Processes (MDPs) with a continuous state space and unknown transition kernels, when only a single sample path of the system is available. We focus on the classical approach of Q-learning where the goal is to learn the optimal Q-function. We propose the Nearest Neighbor Q-Learning approac...

Graphs are a prevalent tool in data science, as they model the inherent structure of the data. They have been used successfully in unsupervised and semi-supervised learning. Typically they are constructed either by connecting nearest samples, or by learning them from data, solving an optimization problem. While graph learning does achieve a better quality, it also comes with a higher computatio...

Predicting the folding of an RNA sequence, while allowing general pseudoknots (PK), consists in finding a minimal free-energy matching of its n positions. Assuming independently contributing basepairs, the problem can be solved in Θ(n)-time using a variant of the maximal weighted matching. By contrast, the problem was previously proven NP-Hard in the more realistic nearest-neighbor energy model...

Abstract Diversity can play a very vital role in improving the performance of a communication system. The achievable performance with signal space diversity (SSD) is analyzed and a closed form expression for an upper bound of average probability of bit error (Pb) for M -ary phase shift keying (MPSK) in Rayleigh fading channels is presented. Previously in the literature, the problem of calculati...

Interactions with environment have large impact on the properties of light–harvesting (LH) pigment– protein complexes. Some of these interactions could be modeled by different types of static disorder. Fluctuations of bacteriochlorophyll’s positions in B850 ring from LH2 complex of purple bacteria are investigated in present paper. The nearest neighbour approximation model of the ring is consid...

We investigate envelope solitary waves on square with two degrees of freedom and nonlinear nearest and next-nearest neighbor interactions. We consider solitary waves which are localized in the direction of their motion and periodically modulated along the perpendicular direction. In the quasi– monochromatic approximation and low–amplitude limit a system of two coupled nonlinear Schrödinger equa...