Self-Organizing Type-2 Fuzzy Double Loop Recurrent Neural Network for Uncertain Nonlinear System Control.

Nonlinear systems, such as robotic systems, play an increasingly important role in our modern daily life and have become more dominant in many industries; however, robotic control still faces various challenges due to diverse and unstructured work environments. This article proposes a double-loop recurrent neural network (DLRNN) with the support of a Type-2 fuzzy system and a self-organizing mechanism for improved performance in nonlinear dynamic robot control. The proposed network has a double-loop recurrent structure, which enables better dynamic mapping. In addition, the network combines a Type-2 fuzzy system with a double-loop recurrent structure to improve the ability to deal with uncertain environments. To achieve an efficient system response, a self-organizing mechanism is proposed to adaptively adjust the number of layers in a DLRNN. This work integrates the proposed network into a conventional sliding mode control (SMC) system to theoretically and empirically prove its stability. The proposed system is applied to a three-joint robot manipulator, leading to a comparative study that considers several existing control approaches. The experimental results confirm the superiority of the proposed system and its effectiveness and robustness in response to various external system disturbances.

Uncovering the Over-Smoothing Challenge in Image Super-Resolution: Entropy-Based Quantification and Contrastive Optimization.

PSNR-oriented models are a critical class of super-resolution models with applications across various fields. However, these models tend to generate over-smoothed images, a problem that has been analyzed previously from the perspectives of models or loss functions, but without taking into account the impact of data properties. In this paper, we present a novel phenomenon that we term the center-oriented optimization (COO) problem, where a model's output converges towards the center point of similar high-resolution images, rather than towards the ground truth. We demonstrate that the strength of this problem is related to the uncertainty of data, which we quantify using entropy. We prove that as the entropy of high-resolution images increases, their center point will move further away from the clean image distribution, and the model will generate over-smoothed images. Implicitly optimizing the COO problem, perceptual-driven approaches such as perceptual loss, model structure optimization, or GAN-based methods can be viewed. We propose an explicit solution to the COO problem, called Detail Enhanced Contrastive Loss (DECLoss). DECLoss utilizes the clustering property of contrastive learning to directly reduce the variance of the potential high-resolution distribution and thereby decrease the entropy. We evaluate DECLoss on multiple super-resolution benchmarks and demonstrate that it improves the perceptual quality of PSNR-oriented models. Moreover, when applied to GAN-based methods, such as RaGAN, DECLoss helps to achieve state-of-the-art performance, such as 0.093 LPIPS with 24.51 PSNR on 4× downsampled Urban100, validating the effectiveness and generalization of our approach.

Variation p.R1045H in MYH7 correlated with hypertrophic cardiomyopathy in a Chinese pedigree.

BACKGROUND: Inherited hypertrophic cardiomyopathy (HCM) is a common heart muscle disease that damages heart function and may cause the heart to suddenly stop beating. Genetic factors play an important role in HCM. Pedigree analysis is a good way to identify the genetic defects that cause disease. METHODS: An HCM pedigree was determined in Yunnan, China. Whole-exome sequencing was performed to identify the genetic variants of HCM. Another 30 HCM patients and 200 healthy controls were also used to investigate the frequency of the variants by customized TaqMan genotyping assay. RESULTS: The variant NM_000257.4:c.3134G > A (NP_000248.2:p.Arg1045His, rs397516178, c.3134G > A in short) was found to cosegregate with the clinical phenotype of HCM. Moreover, the variant was not found in the 200 control subjects. After genotyping the variant in 30 HCM patients, there was one patient who carried the variant and had a family history. CONCLUSIONS: Our findings suggest that this variant may be closely related to the occurrence of the disease. According the ACMG guidelines, the c.3134G > A variant should be classified as "Likely pathogenic".