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

A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data

A great improvement to the insight on brain function that we can get from fMRI data can come from effective connectivity analysis, in which the flow of information between even remote brain regions is inferred by the parameters of a predictive dynamical model. As opposed to biologically inspired models, some techniques as Granger causality (GC) are purely data-driven and rely on statistical prediction and temporal precedence. While powerful and widely applicable, this approach could suffer from two main limitations when applied to BOLD fMRI data: confounding effect of hemodynamic response function (HRF) and conditioning to a large number of variables in presence of short time series. For task-related fMRI, neural population dynamics can be captured by modeling signal dynamics with explicit exogenous inputs; for resting-state fMRI on the other hand, the absence of explicit inputs makes this task more difficult, unless relying on some specific prior physiological hypothesis. In order to overcome these issues and to allow a more general approach, here we present a simple and novel blind-deconvolution technique for BOLD-fMRI signal. Coming to the second limitation, a fully multivariate conditioning with short and noisy data leads to computational problems due to overfitting. Furthermore, conceptual issues arise in presence of redundancy. We thus apply partial conditioning to a limited subset of variables in the framework of information theory, as recently proposed. Mixing these two improvements we compare the differences between BOLD and deconvolved BOLD level effective networks and draw some conclusions

Do You Imitate Immediately? The Location Choices for Foreign Direct Investment

This study applies recency effect on interfirm imitation behavior to investigate whether recent location choices of peer firms regarding foreign direct investment (FDI) exert imitation pressure on another firm’s FDI location choices. This study examines the FDI data of listed companies in Taiwan. The results confirm the existence of recency effect. This study further indicates that the remote experience peer firms and a firm’s own experience have negative moderating effects on recency effect

Inkjet-printed vertically emitting solid-state organic lasers

In this paper, we show that Inkjet Printing can be successfully applied to external-cavity vertically-emitting thin-film organic lasers, and can be used to generate a diffraction-limited output beam with an output energy as high as 33.6 uJ with a slope efficiency S of 34%. Laser emission shows to be continuously tunable from 570 to 670 nm using an intracavity polymer-based Fabry-Perot etalon. High-optical quality films with several um thicknesses are realized thanks to ink-jet printing. We introduce a new optical material where EMD6415 commercial ink constitutes the optical host matrix and exhibits a refractive index of 1.5 and an absorption coefficient of 0.66 cm-1 at 550-680 nm. Standard laser dyes like Pyromethene 597 and Rhodamine 640 are incorporated in solution to the EMD6415 ink. Such large size " printed pixels " of 50 mm 2 present uniform and flat surfaces, with roughness measured as low as 1.5 nm in different locations of a 50um x 50um AFM scan. Finally, as the gain capsules fabricated by Inkjet printing are simple and do not incorporate any tuning or cavity element, they are simple to make, have a negligible fabrication cost and can be used as fully disposable items. This works opens the way towards the fabrication of really low-cost tunable visible lasers with an affordable technology that has the potential to be widely disseminated

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

Simulating Ability: Representing Skills in Games

Throughout the history of games, representing the abilities of the various agents acting on behalf of the players has been a central concern. With increasingly sophisticated games emerging, these simulations have become more realistic, but the underlying mechanisms are still, to a large extent, of an ad hoc nature. This paper proposes using a logistic model from psychometrics as a unified mechanism for task resolution in simulation-oriented games

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

An Equalized Margin Loss for Face Recognition

In this paper, we propose a new loss function, termed the equalized margin (EqM) loss, which is designed to make both intra-class scopes and inter-class margins similar over all classes, such that all the classes can be evenly distributed on the hypersphere of the feature space. The EqM loss controls both the lower limit of intra-class similarity by exploiting hard sample mining and the upper limit of inter-class similarity by assuring equalized margins. Therefore, using the EqM loss, we can not only obtain more discriminative features, but also overcome the negative impacts from the data imbalance on the inter-class margins. We also observe that the EqM loss is stable with the variation of the scale in normalized Softmax. Furthermore, by conducting extensive experiments on LFW, YTF, CFP, MegaFace and IJB-B, we are able to verify the effectiveness and superiority of the EqM loss, compared with other state-of-the- art loss functions for face recogniti