Learning Single-Image Depth from Videos using Quality Assessment Networks

Depth estimation from a single image in the wild remains a challenging problem. One main obstacle is the lack of high-quality training data for images in the wild. In this paper we propose a method to automatically generate such data through Structure-from-Motion (SfM) on Internet videos. The core of this method is a Quality Assessment Network that identifies high-quality reconstructions obtained from SfM. Using this method, we collect single-view depth training data from a large number of YouTube videos and construct a new dataset called YouTube3D. Experiments show that YouTube3D is useful in training depth estimation networks and advances the state of the art of single-view depth estimation in the wild

Substrate-induced half-metallic property in epitaxial silicene

For most practical applications in electronic devices, two-dimensional materials should be transferred onto semiconducting or insulating substrates, since they are usually generated on metallic substrates. However, the transfer often leads to wrinkles, damages, contaminations and so on which would destroy the intrinsic properties of samples. Thus, generating two-dimensional materials directly on nonmetallic substrates has been a desirable goal for a long time. Here, via a swarm structure search method and density functional theory, we employed an insulating N-terminated cubic boron nitride(111) surface as a substrate for the generation of silicene. The result shows that the silicene behaves as a ferromagnetic half-metal because of the strong interaction between silicon and surface nitrogen atoms. The magnetic moments are mainly located on surface nitrogen sites without bonding silicon atoms and the value is about 0.12 uB. In spin-up channel, it behaves as a direct band gap semiconductor with a gap of around 1.35 eV, while it exhibits metallic characteristic in spin-down channel, and the half-metallic band gap is about 0.11 eV. Besides, both the magnetic and electronic properties are not sensitive to the external compressive strain. This work maybe open a way for the utility of silicene in spintronic field

Graphene-like quaternary compound SiBCN: a new wide direct band gap semiconductor predicted by a first-principles study

Due to the lack of two-dimensional silicon-based semiconductors and the fact that most of the components and devices are generated on single-crystal silicon or silicon-based substrates in modern industry, designing two-dimensional silicon-based semiconductors is highly desired. With the combination of a swarm structure search method and density functional theory in this work, a quaternary compound SiBCN with graphene-like structure is found and displays a wide direct band gap as expected. The band gap is of ~2.63 eV which is just between ~2.20 and ~3.39 eV of the highlighted semiconductors SiC and GaN. Notably, the further calculation reveals that SiBCN possesses high carrier mobility with ~5.14x10^3 and ~13.07x10^3 cm^2V^-1s^-1 for electron and hole, respectively. Furthermore, the ab initio molecular dynamics simulations also show that the graphene-like structure of SiBCN can be well kept even at an extremely high temperature of 2000 K. The present work tells that designing ulticomponent silicides may be a practicable way to search for new silicon-based low-dimensional semiconductors which can match well with the previous Si-based substrates

Single-cluster dynamics for the random-cluster model

We formulate a single-cluster Monte Carlo algorithm for the simulation of the random-cluster model. This algorithm is a generalization of the Wolff single-cluster method for the $q$-state Potts model to non-integer values $q>1$. Its results for static quantities are in a satisfactory agreement with those of the existing Swendsen-Wang-Chayes-Machta (SWCM) algorithm, which involves a full cluster decomposition of random-cluster configurations. We explore the critical dynamics of this algorithm for several two-dimensional Potts and random-cluster models. For integer $q$, the single-cluster algorithm can be reduced to the Wolff algorithm, for which case we find that the autocorrelation functions decay almost purely exponentially, with dynamic exponents $z_{\rm exp} =0.07 (1), 0.521 (7)$, and $1.007 (9)$ for $q=2, 3$, and 4 respectively. For non-integer $q$, the dynamical behavior of the single-cluster algorithm appears to be very dissimilar to that of the SWCM algorithm. For large critical systems, the autocorrelation function displays a range of power-law behavior as a function of time. The dynamic exponents are relatively large. We provide an explanation for this peculiar dynamic behavior.Comment: 7 figures, 4 table