Attending Category Disentangled Global Context for Image Classification

In this paper, we propose a general framework for image classification using the attention mechanism and global context, which could incorporate with various network architectures to improve their performance. To investigate the capability of the global context, we compare four mathematical models and observe the global context encoded in the category disentangled conditional generative model could give more guidance as "know what is task irrelevant will also know what is relevant". Based on this observation, we define a novel Category Disentangled Global Context (CDGC) and devise a deep network to obtain it. By attending CDGC, the baseline networks could identify the objects of interest more accurately, thus improving the performance. We apply the framework to many different network architectures and compare with the state-of-the-art on four publicly available datasets. Extensive results validate the effectiveness and superiority of our approach. Code will be made public upon paper acceptance.Comment: Under revie

Joint Learning-based Causal Relation Extraction from Biomedical Literature

Causal relation extraction of biomedical entities is one of the most complex tasks in biomedical text mining, which involves two kinds of information: entity relations and entity functions. One feasible approach is to take relation extraction and function detection as two independent sub-tasks. However, this separate learning method ignores the intrinsic correlation between them and leads to unsatisfactory performance. In this paper, we propose a joint learning model, which combines entity relation extraction and entity function detection to exploit their commonality and capture their inter-relationship, so as to improve the performance of biomedical causal relation extraction. Meanwhile, during the model training stage, different function types in the loss function are assigned different weights. Specifically, the penalty coefficient for negative function instances increases to effectively improve the precision of function detection. Experimental results on the BioCreative-V Track 4 corpus show that our joint learning model outperforms the separate models in BEL statement extraction, achieving the F1 scores of 58.4% and 37.3% on the test set in Stage 2 and Stage 1 evaluations, respectively. This demonstrates that our joint learning system reaches the state-of-the-art performance in Stage 2 compared with other systems.Comment: 15 pages, 3 figure

Global Exponential Stability of Almost Periodic Solution for Neutral-Type Cohen-Grossberg Shunting Inhibitory Cellular Neural Networks with Distributed Delays and Impulses

A kind of neutral-type Cohen-Grossberg shunting inhibitory cellular neural networks with distributed delays and impulses is considered. Firstly, by using the theory of impulsive differential equations and the contracting mapping principle, the existence and uniqueness of the almost periodic solution for the above system are obtained. Secondly, by constructing a suitable Lyapunov functional, the global exponential stability of the unique almost periodic solution is also investigated. The work in this paper improves and extends some results in recent years. As an application, an example and numerical simulations are presented to demonstrate the feasibility and effectiveness of the main results

Bayesian Optimized 1-Bit CNNs

Deep convolutional neural networks (DCNNs) have dominated the recent developments in computer vision through making various record-breaking models. However, it is still a great challenge to achieve powerful DCNNs in resource-limited environments, such as on embedded devices and smart phones. Researchers have realized that 1-bit CNNs can be one feasible solution to resolve the issue; however, they are baffled by the inferior performance compared to the full-precision DCNNs. In this paper, we propose a novel approach, called Bayesian optimized 1-bit CNNs (denoted as BONNs), taking the advantage of Bayesian learning, a well-established strategy for hard problems, to significantly improve the performance of extreme 1-bit CNNs. We incorporate the prior distributions of full-precision kernels and features into the Bayesian framework to construct 1-bit CNNs in an end-to-end manner, which have not been considered in any previous related methods. The Bayesian losses are achieved with a theoretical support to optimize the network simultaneously in both continuous and discrete spaces, aggregating different losses jointly to improve the model capacity. Extensive experiments on the ImageNet and CIFAR datasets show that BONNs achieve the best classification performance compared to state-of-the-art 1-bit CNNs

Finger Vein Image Deblurring Using Neighbors-Based Binary-GAN (NB-GAN)

Vein contraction and venous compression typically caused by low temperature and excessive placement pressure can blur the captured finger vein images and severely impair the quality of extracted features. To improve the quality of captured finger vein image, this paper proposes a 26-layer generator network constrained by Neighbors-based Binary Patterns (NBP) texture loss to recover the clear image (guessing the original clear image). Firstly, by analyzing various types and degrees of blurred finger vein images captured in real application scenarios, a method to mathematically model the local and global blurriness using a pair of defocused and mean blur kernels is proposed. By iteratively and alternatively convoluting clear images with both kernels in a multi-scale window, a polymorphic blur training set is constructed for network training. Then, NBP texture loss is used for training the generator to enhance the deblurring ability of the network on images. Lastly, a novel network structure is proposed to retain more vein texture feature information, and two residual connections are added on both sides of the residual module of the 26-layer generator network to prevent degradation and overfitting. Theoretical analysis and simulation results show that the proposed neighbors-based binary-GAN (NB-GAN) can achieve better deblurring performance than the the-state-of-the-art approaches

The association between FGF21 and diabetic erectile dysfunction: Evidence from clinical and animal studies

Erectile dysfunction (ED), a complication of diabetes mellitus (DM), affects 50–75% of men with diabetes. Fibroblast growth factor 21 (FGF21) is a liver-derived metabolic regulator which plays a role in insulin-independent glucose uptake in adipocytes. We designed a clinical study and an animal experiment to investigate the relationship between FGF21 and DM-induced ED. The clinical study enrolled 93 participants aged \u3e 18 years (61 patients with type 2 DM and 32 healthy controls) from Taian City Central Hospital (TCCH) in Shandong Province, China, amongst whom the association between serum FGF21 and diabetic ED was analyzed. To further validate this association, we developed animal model of diabetic ED using Sprague-Dawley (SD) rats. Serum FGF21 concentration and FGF21 mRNA expression in penile samples of the rats were determined with Western blotting and quantitative real-time PCR. Among the 93 participants, the level of serum FGF21 was negatively correlated with the IIEF-5 score (r = -0.74, P \u3c 0.001). The analysis on the performance of FGF21 for ED diagnosis showed that the area under the receiver operating characteristic (ROC) curve was 0.875 (95% confidence interval [CI]: 0.803 to 0.946). In the animal experiment, the levels of serum FGF21, 2-Δ Δ Ct values of FGF21 mRNA expression, and relative levels of FGF21 in penile samples were higher in the ED group compared to the DM and control groups. Our findings demonstrated an association between the FGF21 level and diabetic ED, indicating the potential of this cytokine in predicting diabetic ED

Increased expression of pigment epithelium-derived factor in aged mesenchymal stem cells impairs their therapeutic efficacy for attenuating myocardial infarction injury.

AIMS: Mesenchymal stem cells (MSCs) can ameliorate myocardial infarction (MI) injury. However, older-donor MSCs seem less efficacious than those from younger donors, and the contributing underlying mechanisms remain unknown. Here, we determine how age-related expression of pigment epithelium-derived factor (PEDF) affects MSC therapeutic efficacy for MI. METHODS AND RESULTS: Reverse transcriptase-polymerized chain reaction and enzyme-linked immunosorbent assay analyses revealed dramatically increased PEDF expression in MSCs from old mice compared to young mice. Morphological and functional experiments demonstrated significantly impaired old MSC therapeutic efficacy compared with young MSCs in treatment of mice subjected to MI. Immunofluorescent staining demonstrated that administration of old MSCs compared with young MSCs resulted in an infarct region containing fewer endothelial cells, vascular smooth muscle cells, and macrophages, but more fibroblasts. Pigment epithelium-derived factor overexpression in young MSCs impaired the beneficial effects against MI injury, and induced cellular profile changes in the infarct region similar to administration of old MSCs. Knocking down PEDF expression in old MSCs improved MSC therapeutic efficacy, and induced a cellular profile similar to young MSCs administration. Studies in vitro showed that PEDF secreted by MSCs regulated the proliferation and migration of cardiac fibroblasts. CONCLUSIONS: This is the first evidence that paracrine factor PEDF plays critical role in the regulatory effects of MSCs against MI injury. Furthermore, the impaired therapeutic ability of aged MSCs is predominantly caused by increased PEDF secretion. These findings indicate PEDF as a promising novel genetic modification target for improving aged MSC therapeutic efficacy