Image Super-Resolution Using Deep Convolutional Networks Chao Dong; Loy, Chen Change; Kaiming He ...
IEEE transactions on pattern analysis and machine intelligence,
2016-Feb.-1, 2016-Feb, 2016-2-1, 20160201, Volume:
38, Issue:
2
Journal Article
Peer reviewed
Open access
We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep ...convolutional neural network (CNN) that takes the low-resolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. But unlike traditional methods that handle each component separately, our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art restoration quality, and achieves fast speed for practical on-line usage. We explore different network structures and parameter settings to achieve trade-offs between performance and speed. Moreover, we extend our network to cope with three color channels simultaneously, and show better overall reconstruction quality.
Transformer-based methods have shown impressive performance in low-level vision tasks, such as image super-resolution. However, we find that these networks can only utilize a limited spatial range of ...input information through attribution analysis. This implies that the potential of Transformer is still not fully exploited in existing networks. In order to activate more input pixels for better reconstruction, we propose a novel Hybrid Attention Transformer (HAT). It combines both channel attention and window-based self-attention schemes, thus making use of their complementary advantages of being able to utilize global statistics and strong local fitting capability. Moreover, to better aggregate the cross-window information, we introduce an overlapping cross-attention module to enhance the interaction between neighboring window features. In the training stage, we additionally adopt a same-task pre-training strategy to exploit the potential of the model for further improvement. Extensive experiments show the effectiveness of the proposed modules, and we further scale up the model to demonstrate that the performance of this task can be greatly improved. Our overall method significantly outperforms the state-of-the-art methods by more than 1dB.
Species are fundamental units in biological research and can be defined on the basis of various operational criteria. There has been growing use of molecular approaches for species delimitation. ...Among the most widely used methods, the generalized mixed Yule-coalescent (GMYC) and Poisson tree processes (PTP) were designed for the analysis of single-locus data but are often applied to concatenations of multilocus data. In contrast, the Bayesian multispecies coalescent approach in the software Bayesian Phylogenetics and Phylogeography (BPP) explicitly models the evolution of multilocus data. In this study, we compare the performance of GMYC, PTP, and BPP using synthetic data generated by simulation under various speciation scenarios. We show that in the absence of gene flow, the main factor influencing the performance of these methods is the ratio of population size to divergence time, while number of loci and sample size per species have smaller effects. Given appropriate priors and correct guide trees, BPP shows lower rates of species overestimation and underestimation, and is generally robust to various potential confounding factors except high levels of gene flow. The single-threshold GMYC and the best strategy that we identified in PTP generally perform well for scenarios involving more than a single putative species when gene flow is absent, but PTP outperforms GMYC when fewer species are involved. Both methods are more sensitive than BPP to the effects of gene flow and potential confounding factors. Case studies of bears and bees further validate some of the findings from our simulation study, and reveal the importance of using an informed starting point for molecular species delimitation. Our results highlight the key factors affecting the performance of molecular species delimitation, with potential benefits for using these methods within an integrative taxonomic framework.
Li–CO2 batteries are an attractive technology for converting CO2 into energy. However, the decomposition of insulating Li2CO3 on the cathode during discharge is a barrier to practical application. ...Here, it is demonstrated that a high loading of single Co atoms (≈5.3%) anchored on graphene oxide (adjacent Co/GO) acts as an efficient and durable electrocatalyst for Li–CO2 batteries. This targeted dispersion of atomic Co provides catalytically adjacent active sites to decompose Li2CO3. The adjacent Co/GO exhibits a highly significant sustained discharge capacity of 17 358 mA h g−1 at 100 mA g−1 for >100 cycles. Density functional theory simulations confirm that the adjacent Co electrocatalyst possesses the best performance toward the decomposition of Li2CO3 and maintains metallic‐like nature after the adsorption of Li2CO3.
Targeted synergy between adjacent Co atoms on graphene oxide is an efficient new electrocatalyst for Li–CO2 batteries. Due to a targeted high mass‐loading, neighboring single Co atoms generate a synergetic interaction and provide continuous catalytic active sites for electrocatalysis of decomposition of Li2CO3 with excellent capacity and cycling stability toward Li–CO2 batteries.
Stipa purpurea, an endemic forage species on the Tibetan Plateau, is highly resistant to cold and drought, but the mechanisms underlying its responses to drought stress remain elusive. An ...understanding of such mechanisms may be useful for developing cultivars that are adaptable to water deficit. In this study, we analyzed the physiological and proteomic responses of S. purpurea under increasing drought stress. Seedlings of S. purpurea were subjected to a drought gradient in a controlled experiment, and proteins showing changes in abundance under these conditions were identified by two-dimensional electrophoresis followed by mass spectrometry analysis. A western blotting analysis was conducted to confirm the increased abundance of a heat-shock protein, NCED2, and a dehydrin in S. purpurea seedlings under drought conditions. We detected carbonylated proteins to identify oxidation-sensitive proteins in S. purpurea seedlings, and found that ribulose-1, 5-bisphosphate carboxylase oxygenase (RuBisCO) was one of the oxidation-sensitive proteins under drought. Together, these results indicated drought stress might inhibit photosynthesis in S. purpurea by oxidizing RuBisCO, but the plants were able to maintain photosynthetic efficiency by a compensatory upregulation of unoxidized RuBisCO and other photosynthesis-related proteins. Further analyses confirmed that increased abundance of antioxidant enzymes could balance the redox status of the plants to mitigate drought-induced oxidative damage.
Unmanned aerial vehicle (UAV)-enabled mobile edge computing has been recognized as a promising technology to flexibly and efficiently handle computation-intensive and latency-sensitive tasks in the ...era of fifth generation (5G) and beyond. In this paper, we study the problem of Service Provisioning for UAV-enabled mobile edge computiNg (SPUN). Specifically, under task latency requirements and various resource constraints, we jointly optimize the service placement, UAV movement trajectory, task scheduling, and computation resource allocation, to minimize the overall energy consumption of all terrestrial user equipments (UEs). Due to the non-convexity of the SPUN problem as well as complex coupling among mixed integer variables, it is a non-convex mixed integer nonlinear programming (MINLP) problem. To solve this challenging problem, we propose two alternating optimization-based suboptimal solutions with different time complexities. In the first solution with relatively high complexity in the worst case, the joint service placement and task scheduling subproblem, and UAV trajectory subproblem are iteratively solved by the Branch and Bound (BnB) method and successive convex approximation (SCA), respectively, while the optimal solution to the computation resource allocation subproblem is efficiently obtained in the closed form. To avoid the high complexity caused by BnB, in the second solution, we propose a novel approximation algorithm based on relaxation and randomized rounding techniques for the joint service placement and task scheduling subproblem, while the other two subproblems are solved in the same way as that of the first solution. Extensive simulations demonstrate that the proposed solutions achieve significantly lower energy consumption of UEs compared to three benchmarks.
Human-induced biodiversity change impairs ecosystem functions crucial to human well-being. However, the consequences of this change for ecosystem multifunctionality are poorly understood beyond ...effects of plant species loss, particularly in regions with high biodiversity across trophic levels. Here we adopt a multitrophic perspective to analyze how biodiversity affects multifunctionality in biodiverse subtropical forests. We consider 22 independent measurements of nine ecosystem functions central to energy and nutrient flow across trophic levels. We find that individual functions and multifunctionality are more strongly affected by the diversity of heterotrophs promoting decomposition and nutrient cycling, and by plant functional-trait diversity and composition, than by tree species richness. Moreover, cascading effects of higher trophic-level diversity on functions originating from lower trophic-level processes highlight that multitrophic biodiversity is key to understanding drivers of multifunctionality. A broader perspective on biodiversity-multifunctionality relationships is crucial for sustainable ecosystem management in light of non-random species loss and intensified biotic disturbances under future environmental change.
Insects are the focus of many recent studies suggesting population declines, but even invaluable pollination service providers such as bees lack a modern distributional synthesis. Here, we combine a ...uniquely comprehensive checklist of bee species distributions and >5,800,000 public bee occurrence records to describe global patterns of bee biodiversity. Publicly accessible records are sparse, especially from developing countries, and are frequently inaccurate throughout much of the world, consequently suggesting different biodiversity patterns from checklist data. Global analyses reveal hotspots of species richness, together generating a rare bimodal latitudinal richness gradient, and further analyses suggest that xeric areas, solar radiation, and non-forest plant productivity are among the most important global drivers of bee biodiversity. Together, our results provide a new baseline and best practices for studies on bees and other understudied invertebrates.
•Bees show a rare bimodal latitudinal gradient with highest richness at mid-latitudes•Xeric and temperate zones host higher richness than tropical areas•Plant productivity and richness are important drivers when forests are excluded•A global bee species richness reconstruction is presented for the first time
A modern, quantitative synthesis on bee distribution and its drivers at a global scale. Orr et al. show that bees exhibit a rare bimodal pattern of higher species richness at mid-latitudes, based on their great success in xeric and some temperate areas, further supported by a driver analysis. Bee species richness is also reprojected worldwide.
This paper gives a criterion for the non-vanishing of the Dirac cohomology of
L
S
(
Z
)
, where
L
S
(
⋅
)
is the cohomological induction functor, while the inducing module
Z
is irreducible, ...unitarizable, and in the good range. As an application, we give a formula counting the number of strings in the Dirac series. Using this formula, we classify all the irreducible unitary representations of
E
6(2)
with non-zero Dirac cohomology. Our calculation continues to support Conjecture 5.7’ of Salamanca-Riba and Vogan (Ann. Math.,
148
(3), 1067–1133
1998
). Moreover, we find more unitary representations for which cancellation happens between the even part and the odd part of their Dirac cohomology.
Neutrophil to lymphocyte ratio (NLR) might be associated with the mortality or major adverse cardiac events (MACEs) in acute coronary syndrome (ACS) patients. We performed a meta-analysis to evaluate ...the correlation between NLR and mortality/MACEs in ACS.
We assessed clinical trials through Pubmed, EMBASE, the Cochrane Library and Web of science in investigating the association between NLR and mortality/MACEs in ACS patients up to August 15, 2017. The primary outcome was mortality or recurrent MACEs.
In total, 8 studies of 9406 patients were included in the systematic and meta-analysis. Our analysis indicated that elevated pretreatment NLR was a poor prognostic marker for patients with recent ACS in predicting medium to long-term mortality/MACEs (OR 1.26, 95%CI 1.13–1.41). And the analysis indicated that higher pretreatment NLR value was associated with higher in-hospital mortality in ACS patients (OR 6.39, 95%CI 1.49–27.38, p<0.001). The NLR value of 5.0 maybe a cut-off value for ACS risk.
In patients with a recent ACS, an elevated pretreatment NLR value is effective in predicting the risk of mortality/MACEs.
•NLR value is effective in predicting the risk of mortality/MACEs.•NLR predicted medium to long-term mortality/MACEs in ACS patients.•NLR in predicting mortality/MACEs is obvious in large sample size, high cut-off vales, and in STEMI patients.•Higher pretreatment NLR value was associated with higher in-hospital mortality in ACS patients.
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