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The nucleation of graphene on a transition metal (TM) surface, either on a terrace or near a step edge, is systematically explored using density functional theory (DFT) calculations and applying the two-dimensional (2D) crystal nucleation…
We present O-CNN, an Octree-based Convolutional Neural Network (CNN) for 3D shape analysis. Built upon the octree representation of 3D shapes, our method takes the average normal vectors of a 3D model sampled in the finest leaf octants as…
The nucleation field for surface superconductivity, $H_{c3}$, depends on the geometrical shape of the mesoscopic superconducting sample and is substantially enhanced with decreasing sample size. As an example we studied circular, square,…
A central question in the field of graphene-related research is how graphene behaves when it is patterned at the nanometer scale with different edge geometries. Perhaps the most fundamental shape relevant to this question is the graphene…
Single-walled carbon nanotubes exist in a variety of different geometries, so-called chiralities, that define their electronic properties. Chiral selectivity has been reported in catalytic chemical vapor deposition synthesis experiments,…
We present a convolutional network that is equivariant to rigid body motions. The model uses scalar-, vector-, and tensor fields over 3D Euclidean space to represent data, and equivariant convolutions to map between such representations.…
An elongation method based on ab initio quantum chemistry approaches is presented. It allows to study electronic structures and coherent electron transportation properties of single-walled carbon nanotubes (SWCNTs) up to 22nm in length…
Spiking Neural Networks (SNN) are more closely related to brain-like computation and inspire hardware implementation. This is enabled by small networks that give high performance on standard classification problems. In literature, typical…
The kinematical properties of the Galactic Thick Disk are studied using absolute proper motions from the SPM3 Catalog and 2MASS near-infrared photometry for a sample of ~1200 red giants in the direction of the South Galactic Pole. The…
Semantic understanding and completion of real world scenes is a foundational primitive of 3D Visual perception widely used in high-level applications such as robotics, medical imaging, autonomous driving and navigation. Due to the curse of…
Spiking neural networks (SNNs) promise energy-efficient computation by mimicking biological neural dynamics, yet existing plasticity rules focus on isolated spike pairs and fail to leverage the synchronous activity patterns that drive…
Single-walled carbon nanotubes (SWNTs) are potential materials for future nanoelectronics. Since the electronic and optical properties of SWNTs strongly depend on tube diameter and chirality, obtaining SWNTs with narrow (n,m) chirality…
Hyperspectral imaging (HSI) has been extensively utilized for a number of real-world applications. HSI classification (HSIC) is a challenging task due to high inter-class similarity, high intra-class variability, overlapping, and nested…
Advanced Monte Carlo simulations are used to study the effect of nano-slit confinement on metric and topological properties of model DNA chains. We consider both linear and circularised chains with contour lengths in the 1.2--4.8 $\mu$m…
While state-of-the-art 3D Convolutional Neural Networks (CNN) achieve very good results on action recognition datasets, they are computationally very expensive and require many GFLOPs. While the GFLOPs of a 3D CNN can be decreased by…
The growth of an actin network against an obstacle that stimulates branching locally is studied using several variants of a kinetic rate model based on the orientation-dependent number density of filaments. The model emphasizes the effects…
Using Molecular Dynamics (MD) and Monte Carlo (MC) simulations interfacial properties of crystal-fluid interfaces are investigated for the hard sphere system and the one-component metallic system Ni (the latter modeled by a potential of the…
Chiral magnets have attracted a large amount of research interest in recent years because they support a variety of topological defects, such as skyrmions and bimerons, and allow for their observation and manipulation through several…
Spiking Neural Networks (SNNs) have sparse, event driven processing that can leverage neuromorphic applications. In this work, we introduce a multi-threading kernel that enables neuromorphic applications running at the edge, meaning they…
It is now possible to create perfect crystal nanowires of many metals. The deformation of such objects requires a good understanding of the processes involved in plasticity at the nanoscale. Isotropic compression of such nanometre scale…