Related papers: Cluster-based Superconducting Tunneling Networks
Clustering is a technique used in network routing to enhance the performance and conserve the network resources. This paper presents a cluster-based routing protocol for VANET utilizing a new addressing scheme in which each node gets an…
We present a novel deep neural network architecture for unsupervised subspace clustering. This architecture is built upon deep auto-encoders, which non-linearly map the input data into a latent space. Our key idea is to introduce a novel…
We study the percolation of strongly connected clusters (SCCs), in which sites are mutually reachable through directed paths, in systems with randomly oriented bonds by extensive simulations on hypercubic lattices from dimension $d=2$ to…
There is a wide recognition that Josephson-junction-like structures intrinsic to the layered cuprate high temperature superconductors offer an attractive stage for exploiting possible applications to new quantum technologies. On the other…
The CuO-planes of high-T_c superconductors were found to consist of geometric stripes with alternating superconducting and antiferromagnetic areas. Here we will investigate the repulsive Hubbard model of striped clusters as a possible…
We perform a numeric study of the flux transitions in a superconducting ring at fixed temperature, while the applied field is swept at an ideally slow rate. The current around the ring and its free energy are evaluated. We partially explain…
The problem of dimension reduction is of increasing importance in modern data analysis. In this paper, we consider modeling the collection of points in a high dimensional space as a union of low dimensional subspaces. In particular we…
Despite the success of convolution- and attention-based models in vision tasks, their rigid receptive fields and complex architectures limit their ability to model irregular spatial patterns and hinder interpretability, therefore posing…
The proper design of DNA sequences allows for the formation of well defined supramolecular units with controlled interactions via a consecution of self-assembling processes. Here, we benefit from the controlled DNA self-assembly to…
A unique property of metal nanoclusters is the "superatom" shell structure of their delocalized electrons. The electronic shell levels are highly degenerate and therefore represent sharp peaks in the density of states. This can enable…
Theoretical results for the ab-plane tunneling conductance in the d-wave model for high Tc superconductors are presented. The d-wave model predicts surface bound states below the maximum gap. A sub-dominant order parameter, stabilized by…
We studied by means of computer simulations the low temperature properties of two-dimensional parahydrogen clusters comprising between 7 and 30 molecules. Computed energetics is in quantitative agreement with that reported in the only…
The new concept of multilevel network is introduced in order to embody some topological properties of complex systems with structures in the mesoscale which are not completely captured by the classical models. This new model, which…
We demonstrate a technique for creating high quality, large area tunnel junction barriers for normal-insulating- superconducting or superconducting-insulating-superconducting tunnel junctions. We use atomic layer depo- sition and an…
We demonstrate that graphs embedded on surfaces are a powerful and practical tool to generate, characterize and simulate networks with a broad range of properties. Remarkably, the study of topologically embedded graphs is non-restrictive…
Clustering high-dimensional datasets is hard because interpoint distances become less informative in high-dimensional spaces. We present a clustering algorithm that performs nonlinear dimensionality reduction and clustering jointly. The…
In a hybrid Superconductor - Insulator - Normal metal tunnel junction biased just below the gap, the extraction of hot electrons out of the normal metal results in electronic cooling effect. The quasiparticles injected in the superconductor…
Clustering is widely used in unsupervised learning method that deals with unlabeled data. Deep clustering has become a popular study area that relates clustering with Deep Neural Network (DNN) architecture. Deep clustering method…
We consider the densities of clusters, at the percolation point of a two-dimensional system, which are anchored in various ways to an edge. These quantities are calculated by use of conformal field theory and computer simulations. We find…
Since network data commonly consists of observations from a single large network, researchers often partition the network into clusters in order to apply cluster-robust inference methods. Existing such methods require clusters to be…