Aesthetic-Driven Image Enhancement by Adversarial Learning

We introduce EnhanceGAN, an adversarial learning based model that performs automatic image enhancement. Traditional image enhancement frameworks typically involve training models in a fully-supervised manner, which require expensive annotations in the form of aligned image pairs. In contrast to these approaches, our proposed EnhanceGAN only requires weak supervision (binary labels on image aesthetic quality) and is able to learn enhancement operators for the task of aesthetic-based image enhancement. In particular, we show the effectiveness of a piecewise color enhancement module trained with weak supervision, and extend the proposed EnhanceGAN framework to learning a deep filtering-based aesthetic enhancer. The full differentiability of our image enhancement operators enables the training of EnhanceGAN in an end-to-end manner. We further demonstrate the capability of EnhanceGAN in learning aesthetic-based image cropping without any groundtruth cropping pairs. Our weakly-supervised EnhanceGAN reports competitive quantitative results on aesthetic-based color enhancement as well as automatic image cropping, and a user study confirms that our image enhancement results are on par with or even preferred over professional enhancement

Visual question answering model for fruit tree disease decision-making based on multimodal deep learning

Visual Question Answering (VQA) about diseases is an essential feature of intelligent management in smart agriculture. Currently, research on fruit tree diseases using deep learning mainly uses single-source data information, such as visible images or spectral data, yielding classification and identification results that cannot be directly used in practical agricultural decision-making. In this study, a VQA model for fruit tree diseases based on multimodal feature fusion was designed. Fusing images and Q&A knowledge of disease management, the model obtains the decision-making answer by querying questions about fruit tree disease images to find relevant disease image regions. The main contributions of this study were as follows: (1) a multimodal bilinear factorized pooling model using Tucker decomposition was proposed to fuse the image features with question features: (2) a deep modular co-attention architecture was explored to simultaneously learn the image and question attention to obtain richer graphical features and interactivity. The experiments showed that the proposed unified model combining the bilinear model and co-attentive learning in a new network architecture obtained 86.36% accuracy in decision-making under the condition of limited data (8,450 images and 4,560k Q&A pairs of data), outperforming existing multimodal methods. The data augmentation is adopted on the training set to avoid overfitting. Ten runs of 10-fold cross-validation are used to report the unbiased performance. The proposed multimodal fusion model achieved friendly interaction and fine-grained identification and decision-making performance. Thus, the model can be widely deployed in intelligent agriculture

Topological Single Photon Emission from Quantum Emitter Chains

We develop a scheme of generating highly indistinguishable single photons from an active quantum Su-Schrieffer-Heeger chain made from a collection of noisy quantum emitters. Surprisingly, the single photon emission spectrum of the active quantum chain is extremely narrow compared to that of a single emitter or topologically trivial chain. Moreover, this effect becomes dramatically strong close to the non-trivial-to-trivial phase transition point. Using this effect, we show that the single photon linewidth of a long topological quantum chain can become arbitrarily narrow, constituting an ideal source of indistinguishable single photons. Finally, taking specific examples of actual quantum emitters, we provide a microscopic and quantitative analysis of our model and analyze the most important parameters in view of the experimental realization

Induction of hyporesponsiveness to intact foreign protein via retroviral-mediated gene expression: The IgG scaffold is important for induction and maintenance of immune hyporesponsiveness

IgG molecules can be highly tolerogenic carriers for associated antigens. Previously, we reported that recipients of bone marrow or lipopolysaccharide-stimulated B-cell blasts, both of which were retrovirally gene-transferred with an immunodominant peptide in-frame with the variable region of a murine IgG heavy chain, were rendered profoundly unresponsive to that epitope. To further investigate whether tolerance to larger molecules can be achieved via this approach and whether the IgG scaffold is important for induction and maintenance of immunological tolerance, we engineered two retroviral constructs encoding the cI λ repressor (MBAE-1–102 and MBAE-1–102-IgG) for gene transfer. Our results show that recipients of bone marrow or peripheral B cells, transduced with the MBAE-1–102-IgG recombinant, are hyporesponsive to p1–102. In addition, the self-IgG scaffold enhanced the induction and maintenance of such an immune hyporesponsiveness. Thus, our studies demonstrate that in vivo-expressed IgG heavy chain fusion protein can be processed and presented on the appropriate MHC class II, resulting in hyporesponsiveness to that antigen and offering an additional therapeutic approach to autoimmune diseases

Higher order numerical methods for solving fractional differential equations

The final publication is available at Springer via http://dx.doi.org/10.1007/s10543-013-0443-3In this paper we introduce higher order numerical methods for solving fractional differential equations. We use two approaches to this problem. The first approach is based on a direct discretisation of the fractional differential operator: we obtain a numerical method for solving a linear fractional differential equation with order 0 0. The order of convergence of the numerical method is O(h^3) for α ≥ 1 and O(h^(1+2α)) for 0 < α ≤ 1 for sufficiently smooth solutions. Numerical examples are given to show that the numerical results are consistent with the theoretical results

Spermine Synthase and MYC Cooperate to Maintain Colorectal Cancer Cell Survival by Repressing Bim Expression

Dysregulation of polyamine metabolism has been linked to the development of colorectal cancer (CRC), but the underlying mechanism is incompletely characterized. Here, we report that spermine synthase (SMS), a polyamine biosynthetic enzyme, is overexpressed in CRC. Targeted disruption of SMS in CRC cells results in spermidine accumulation, which inhibits FOXO3a acetylation and allows subsequent translocation to the nucleus to transcriptionally induce expression of the proapoptotic protein Bim. However, this induction is blunted by MYC-driven expression of miR-19a and miR-19b that repress Bim production. Pharmacological or genetic inhibition of MYC activity in SMS-depleted CRC cells dramatically induces Bim expression and apoptosis and causes tumor regression, but these effects are profoundly attenuated by silencing Bim. These findings uncover a key survival signal in CRC through convergent repression of Bim expression by distinct SMS- and MYC-mediated signaling pathways. Thus, combined inhibition of SMS and MYC signaling may be an effective therapy for CRC