Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network

Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled to the high resolution (HR) space using a single filter, commonly bicubic interpolation, before reconstruction. This means that the super-resolution (SR) operation is performed in HR space. We demonstrate that this is sub-optimal and adds computational complexity. In this paper, we present the first convolutional neural network (CNN) capable of real-time SR of 1080p videos on a single K2 GPU. To achieve this, we propose a novel CNN architecture where the feature maps are extracted in the LR space. In addition, we introduce an efficient sub-pixel convolution layer which learns an array of upscaling filters to upscale the final LR feature maps into the HR output. By doing so, we effectively replace the handcrafted bicubic filter in the SR pipeline with more complex upscaling filters specifically trained for each feature map, whilst also reducing the computational complexity of the overall SR operation. We evaluate the proposed approach using images and videos from publicly available datasets and show that it performs significantly better (+0.15dB on Images and +0.39dB on Videos) and is an order of magnitude faster than previous CNN-based methods

Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network

Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled to the high resolution (HR) space using a single filter, commonly bicubic interpolation, before reconstruction. This means that the super-resolution (SR) operation is performed in HR space. We demonstrate that this is sub-optimal and adds computational complexity. In this paper, we present the first convolutional neural network (CNN) capable of real-time SR of 1080p videos on a single K2 GPU. To achieve this, we propose a novel CNN architecture where the feature maps are extracted in the LR space. In addition, we introduce an efficient sub-pixel convolution layer which learns an array of upscaling filters to upscale the final LR feature maps into the HR output. By doing so, we effectively replace the handcrafted bicubic filter in the SR pipeline with more complex upscaling filters specifically trained for each feature map, whilst also reducing the computational complexity of the overall SR operation. We evaluate the proposed approach using images and videos from publicly available datasets and show that it performs significantly better (+0.15dB on Images and +0.39dB on Videos) and is an order of magnitude faster than previous CNN-based methods

A Proof Strategy Language and Proof Script Generation for Isabelle/HOL

We introduce a language, PSL, designed to capture high level proof strategies in Isabelle/HOL. Given a strategy and a proof obligation, PSL's runtime system generates and combines various tactics to explore a large search space with low memory usage. Upon success, PSL generates an efficient proof script, which bypasses a large part of the proof search. We also present PSL's monadic interpreter to show that the underlying idea of PSL is transferable to other ITPs.Comment: This paper has been submitted to CADE2

Synthesis of cubic diamond in the graphite-magnesium carbonate and graphite-K2Mg(CO3)(2) systems at high pressure of 9-10 GPa region

Cubic diamond was synthesized with two systems, (1) graphite with pure magnesium carbonate (magnesite) and (2) graphite with mixed potassium and magnesium carbonate at pressures and temperatures above 9.5 GPa, 1600 degrees C and 9 GPa, 1650 degrees C, respectively. At these conditions (1) the pure magnesite is solid, whereas (2) the mixed carbonate exists as a melt. In this pressure range, graphite seems to be partially transformed into hexagonal diamond. Measured carbon isotope delta(13)C values for all the materials suggest that the origin of the carbon source to form cubic diamond was the initial graphite powder, and not the carbonates

Class II Phosphoinositide 3-Kinases Contribute to Endothelial Cells Morphogenesis

PMCID: PMC3539993This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Demonstration of the Presence of the "Deleted" MIR122 Gene in HepG2 Cells

MicroRNA 122 (miR-122) is highly expressed in the liver where it influences diverse biological processes and pathways, including hepatitis C virus replication and metabolism of iron and cholesterol. It is processed from a long non-coding primary transcript (~7.5 kb) and the gene has two evolutionarily-conserved regions containing the pri-mir-122 promoter and pre-mir-122 hairpin region. Several groups reported that the widely-used hepatocytic cell line HepG2 had deficient expression of miR-122, previously ascribed to deletion of the pre-mir-122 stem-loop region. We aimed to characterise this deletion by direct sequencing of 6078 bp containing the pri-mir-122 promoter and pre-mir-122 stem-loop region in HepG2 and Huh-7, a control hepatocytic cell line reported to express miR-122, supported by sequence analysis of cloned genomic DNA. In contrast to previous findings, the entire sequence was present in both cell lines. Ten SNPs were heterozygous in HepG2 indicating that DNA was present in two copies. Three validation isolates of HepG2 were sequenced, showing identical genotype to the original in two, whereas the third was different. Investigation of promoter chromatin status by FAIRE showed that Huh-7 cells had 6.2 ± 0.19- and 2.7 ± 0.01- fold more accessible chromatin at the proximal (HNF4α-binding) and distal DR1 transcription factor sites, compared to HepG2 cells (p=0.03 and 0.001, respectively). This was substantiated by ENCODE genome annotations, which showed a DNAse I hypersensitive site in the pri-mir-122 promoter in Huh-7 that was absent in HepG2 cells. While the origin of the reported deletion is unclear, cell lines should be obtained from a reputable source and used at low passage number to avoid discrepant results. Deficiency of miR-122 expression in HepG2 cells may be related to a relative deficiency of accessible promoter chromatin in HepG2 versus Huh-7 cells

Local Moment Instability of Os in Honeycomb Li2.15Os0.85O3.

Compounds with honeycomb structures occupied by strong spin orbit coupled (SOC) moments are considered to be candidate Kitaev quantum spin liquids. Here we present the first example of Os on a honeycomb structure, Li2.15(3)Os0.85(3)O3 (C2/c, a = 5.09 Å, b = 8.81 Å, c = 9.83 Å, β = 99.3°). Neutron diffraction shows large site disorder in the honeycomb layer and X-ray absorption spectroscopy indicates a valence state of Os (4.7 ± 0.2), consistent with the nominal concentration. We observe a transport band gap of Δ = 243 ± 23 meV, a large van Vleck susceptibility, and an effective moment of 0.85 μB, much lower than expected from 70% Os(+5). No evidence of long range order is found above 0.10 K but a spin glass-like peak in ac-susceptibility is observed at 0.5 K. The specific heat displays an impurity spin contribution in addition to a power law ∝T(0.63±0.06). Applied density functional theory (DFT) leads to a reduced moment, suggesting incipient itineracy of the valence electrons, and finding evidence that Li over stoichiometry leads to Os(4+)-Os(5+) mixed valence. This local picture is discussed in light of the site disorder and a possible underlying quantum spin liquid state

Random planar graphs and the London street network

In this paper we analyse the street network of London both in its primary and dual representation. To understand its properties, we consider three idealised models based on a grid, a static random planar graph and a growing random planar graph. Comparing the models and the street network, we find that the streets of London form a self-organising system whose growth is characterised by a strict interaction between the metrical and informational space. In particular, a principle of least effort appears to create a balance between the physical and the mental effort required to navigate the city

Monsoonal influences on evapotranspiration of savanna vegetation of northern Australia

Data from savannas of northern Australia are presented for net radiation, latent and sensible heat, ecosystem surface conductance (Gs) and stand water use for sites covering a latitudinal range of 5° or 700 km. Measurements were made at three locations of increasing distance from the northern coastline and represent high- (1,750 mm), medium- (890 mm) and low- (520 mm) rainfall sites. This rainfall gradient arises from the weakened monsoonal influence with distance inland. Data were coupled to seasonal estimates of leaf area index (LAI) for the tree and understorey strata. All parameters were measured at the seasonal extremes of late wet and dry seasons. During the wet season, daily rates of evapotranspiration were 3.1-3.6 mm day-1 and were similar for all sites along the rainfall gradient and did not reflect site differences in annual rainfall. During the dry season, site differences were very apparent with evapotranspiration 2-18 times lower than wet season rates, the seasonal differences increasing with distance from coast and reduced annual rainfall. Due to low overstorey LAI, more than 80% of water vapour flux was attributed to the understorey. Seasonal differences in evapotranspiration were mostly due to reductions in understorey leaf area during the dry season. Water use of individual trees did not differ between the wet and dry seasons at any of the sites and stand water use was a simple function of tree density. Gs declined markedly during the dry season at all sites, and we conclude that the savanna water (and carbon) balance is largely determined by Gs and its response to atmospheric and soil water content and by seasonal adjustments to canopy leaf area

IEEE 802.11n MAC frame aggregation mechanisms for next-generation high-throughput WLANs [Medium access control protocols for wireless LANs]

IEEE 802.11n is an ongoing next-generation wireless LAN standard that supports a very highspeed connection with more than 100 Mb/s data throughput measured at the medium access control layer. This article investigates the key MAC enhancements that help 802.11n achieve high throughput and high efficiency. A detailed description is given for various frame aggregation mechanisms proposed in the latest 802.11n draft standard. Our simulation results confirm that A-MSDU, A-MPDU, and a combination of these methods improve extensively the channel efficiency and data throughput. We analyze the performance of each frame aggregation scheme in distinct scenarios, and we conclude that overall, the two-level aggregation is the most efficacious