Deep Learning for Single Image Super-Resolution: A Brief Review

Single image super-resolution (SISR) is a notoriously challenging ill-posed problem, which aims to obtain a high-resolution (HR) output from one of its low-resolution (LR) versions. To solve the SISR problem, recently powerful deep learning algorithms have been employed and achieved the state-of-the-art performance. In this survey, we review representative deep learning-based SISR methods, and group them into two categories according to their major contributions to two essential aspects of SISR: the exploration of efficient neural network architectures for SISR, and the development of effective optimization objectives for deep SISR learning. For each category, a baseline is firstly established and several critical limitations of the baseline are summarized. Then representative works on overcoming these limitations are presented based on their original contents as well as our critical understandings and analyses, and relevant comparisons are conducted from a variety of perspectives. Finally we conclude this review with some vital current challenges and future trends in SISR leveraging deep learning algorithms.Comment: Accepted by IEEE Transactions on Multimedia (TMM

Increase in soil organic carbon by agricultural intensification in northern China

Acknowledgements. This research was supported by National Natural Science Foundation of China (no. 31370527 and 31261140367) and the National Science and Technology Support Program of China (no. 2012BAD14B01-2). The authors gratefully thank the Huantai Agricultural Station for providing of the Soil Fertility Survey data. We also thank Zheng Liang from China Agricultural University for the soil sampling and analysis in 2011. Thanks are extended to Jessica Bellarby for helpful discussion and suggestions.Peer reviewedPublisher PD

Provision of secondary frequency regulation by coordinated dispatch of industrial loads and thermal power plants

Demand responsive industrial loads with high thermal inertia have potential to provide ancillary service for frequency regulation in the power market. To capture the benefit, this study proposes a new hierarchical framework to coordinate the demand responsive industrial loads with thermal power plants in an industrial park for secondary frequency control. In the proposed framework, demand responsive loads and generating resources are coordinated for optimal dispatch in two-time scales: (1) the regulation reserve of the industrial park is optimally scheduled in a day-ahead manner. The stochastic regulation signal is replaced by the specific extremely trajectories. Furthermore, the extremely trajectories are achieved by the day-ahead predicted regulation mileage. The resulting benefit is to transform the stochastic reserve scheduling problem into a deterministic optimization; (2) a model predictive control strategy is proposed to dispatch the industry park in real time with an objective to maximize the revenue. The proposed technology is tested using a real-world industrial electrolysis power system based upon Pennsylvania, Jersey, and Maryland (PJM) power market. Various scenarios are simulated to study the performance of the proposed approach to enable industry parks to provide ancillary service into the power market. The simulation results indicate that an industrial park with a capacity of 500 MW can provide up to 40 MW ancillary service for participation in the secondary frequency regulation. The proposed strategy is demonstrated to be capable of maintaining the economic and secure operation of the industrial park while satisfying performance requirements from the real world regulation market

Composition design and physical properties prediction of mold flux for continuous casting of high Mn–HIGH Al steel

The deterioration of CaO-SiO2 based mold flux caused by the reaction of steel -slag interface is a bottleneck restricting the high Mn-Al steel continuous casting production efficiently. Therefore, the development of low-reactivity mold flux has become a research hotspot. In this paper, the scheme of high Al2O3 and low SiO2 was adopted to suppress or reduce the occurrence of steel-slag reaction. Drawing binary phase diagram of mold flux based on the CaO–Al2O3 composition, the influence of different solvents on the melting characteristics of the mold flux were investigated and the reasonable mass ratio of CaO/Al2O3and the content of SiO2, SrO, MgO, Na2O and B2O3 were determined. According to the viscosity and the melting temperature model calculation, the physical property is beneficial for the composition design of low-reactivity mold flux

Composition design and physical properties prediction of mold flux for continuous casting of high Mn–HIGH Al steel

The deterioration of CaO-SiO2 based mold flux caused by the reaction of steel -slag interface is a bottleneck restricting the high Mn-Al steel continuous casting production efficiently. Therefore, the development of low-reactivity mold flux has become a research hotspot. In this paper, the scheme of high Al2O3 and low SiO2 was adopted to suppress or reduce the occurrence of steel-slag reaction. Drawing binary phase diagram of mold flux based on the CaO–Al2O3 composition, the influence of different solvents on the melting characteristics of the mold flux were investigated and the reasonable mass ratio of CaO/Al2O3and the content of SiO2, SrO, MgO, Na2O and B2O3 were determined. According to the viscosity and the melting temperature model calculation, the physical property is beneficial for the composition design of low-reactivity mold flux

Characterization of sensory neuron membrane proteins (SNMPs) in cotton bollworm Helicoverpa armigera (Lepidoptera: Noctuidae)

Sensory neuron membrane proteins (SNMPs) play a critical role in insect chemosensory system. Previously, three SNMPs were identified, characterized and functionally investigated in a lepidopteran model insect, Bombyx mori . However, whether these results are consistent across other lepidopteran species are unknown. Here genome and transcriptome data analysis, expression profiling, quantitative real‐time PCR (qRT‐PCR) and the yeast hybridization system were utilized to examine snmp genes of Helicoverpa armigera , one of the most destructive lepidopteran pests in cropping areas. In silico expression and qRT‐PCR analyses showed that, just as the B. mori snmp genes, H. armigera snmp1 (Harmsnmp1 ) is specifically expressed in adult antennae. Harmsnmp2 is broadly expressed in multiple tissues including adult antennae, tarsi, larval antennae and mouthparts. Harmsnmp3 is specifically expressed in larval midguts. Further RNAseq analysis suggested that the expression levels of Harmsnmp2 and Harmsnmp3 differed significantly depending on the plant species on which the larvae fed, indicating they may be involved in plant‐feeding behaviours. Yeast hybridization results revealed a protein–protein interaction between HarmSNMP1 and the sex pheromone receptor, HarmOR13. This study demonstrated that SNMPs may share same functions and mechanisms in different lepidopteran species, which improved our understanding of insect snmp genes and their functions in lepidopterans

Genome-Wide Localization of Protein-DNA Binding and Histone Modification by a Bayesian Change-Point Method with ChIP-seq Data

Next-generation sequencing (NGS) technologies have matured considerably since their introduction and a focus has been placed on developing sophisticated analytical tools to deal with the amassing volumes of data. Chromatin immunoprecipitation sequencing (ChIP-seq), a major application of NGS, is a widely adopted technique for examining protein-DNA interactions and is commonly used to investigate epigenetic signatures of diffuse histone marks. These datasets have notoriously high variance and subtle levels of enrichment across large expanses, making them exceedingly difficult to define. Windows-based, heuristic models and finite-state hidden Markov models (HMMs) have been used with some success in analyzing ChIP-seq data but with lingering limitations. To improve the ability to detect broad regions of enrichment, we developed a stochastic Bayesian Change-Point (BCP) method, which addresses some of these unresolved issues. BCP makes use of recent advances in infinite-state HMMs by obtaining explicit formulas for posterior means of read densities. These posterior means can be used to categorize the genome into enriched and unenriched segments, as is customarily done, or examined for more detailed relationships since the underlying subpeaks are preserved rather than simplified into a binary classification. BCP performs a near exhaustive search of all possible change points between different posterior means at high-resolution to minimize the subjectivity of window sizes and is computationally efficient, due to a speed-up algorithm and the explicit formulas it employs. In the absence of a well-established "gold standard" for diffuse histone mark enrichment, we corroborated BCP's island detection accuracy and reproducibility using various forms of empirical evidence. We show that BCP is especially suited for analysis of diffuse histone ChIP-seq data but also effective in analyzing punctate transcription factor ChIP datasets, making it widely applicable for numerous experiment types

RGB Guided Depth Map Super-Resolution with Coupled U-Net

The depth maps captured by RGB-D cameras usually are of low resolution, entailing recent efforts to develop depth super-resolution (DSR) methods. However, several problems remain in existing DSR methods. First, conventional DSR methods often suffer from unexpected artifacts. Secondly, high-resolution (HR) RGB features and low-resolution (LR) depth features are often fused in shallow layers only. Thirdly, only the last layer of features is used for reconstruction. To address the above problems, we propose Coupled U-Net (CU-Net), a new color image guided DSR method built on two U-Net branches for HR color images and LR depth maps, respectively. The CU-Net embeds a dual skip connection structure to leverage the feature interaction of the two branches, and a multi-scale fusion to fuse the deeper and multi-scale features of two branch decoders for more effective feature reconstruction. Moreover, a channel attention module is proposed to eliminate artifacts. Extensive experiments show that the proposed CU-Net outperforms state-of-the-art methods