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Industrial development has greatly promoted China's economic growth. Under the dual pressure of resources and the environment, high-quality economic development has become an inevitable requirement of China's new era. Under the new development pattern of the Yangtze River Economic Belt, which is 'to step up conservation of the Yangtze River and stop its over-development,' it is of great significance to explore the transmission mechanism of the impact of environmental regulations on the economic belt's high-quality economic development. Based on the panel data of 108 cities in the Yangtze River Economic Belt from 2005 to 2019, this paper constructed an index system of high-quality economic development and verified the mediation effect of environmental regulations on its high-quality economic development by using the mediation effect model. Intermediate variables, including technological input and industrial structure, were used. In addition, the dynamic threshold model was used to verify the impact of environmental regulations on high-quality economic development under different threshold variable levels. The results found that: (1) The results of the mediation effect test showed that both technological input and industrial structure had a partial mediation effect in the impact of environmental regulations on high quality economic development. Environmental regulations helped to improve the level of technological input and optimize the adjustment of industrial structure, which in turn had a positive impact on the quality of economic development. (2) The results of the threshold model showed that when technological input and industrial structure were the threshold variables of environmental regulations, environmental regulations had a threshold effect on high quality economic development. In regions with high technology input and a backward industrial structure, environmental regulations could better promote high quality economic development. (3) The results of the heterogeneity of the threshold effects showed that a temporal heterogeneity existed in the threshold effects of technology input and industrial structure variables and that factors such as the policy orientation of technological inputs and the rigidity of industrial structure affected the implementation effect of environmental regulation policies. Based on the above conclusions, the following policy recommendations are put forward: (1) Formulate differentiated environmental regulation policies and promote the effective implementation of environmental regulation policies. (2) Improve the level of technological input and provide new impetus for high-quality economic development. (3) Optimize industrial structure, and accelerate the green development and transformation of industries. (4) Clarify the mechanism of environmental compensation and special governance, and promote the synergistic development of regional economy and environmental regulations. [ABSTRACT FROM AUTHOR]

Human chromosomes carry genetic information about our life. Chromosome classification is crucial for karyotype analysis. Existing chromosome classification methods do not take into account reasoning, such as: analyzing the relationship between variables, modeling uncertainty, and performing causal reasoning. In this paper, we introduce a knowledge engine for reasoning-based human chromosome classification that stores knowledge of chromosomes via a novel representation structure, the Chromosome Part Description (CPD), and reasons over CPDs by utilizing the probability tree model (PTM) for classification. Each CPD keeps information on a particular feature of chromosomes, while the PTM provides causal reasoning capability taking CPDs as nodes and dependencies between CPDs and types as edges. Experimental results show that the proposed knowledge engine's performance increases when providing more CPDs and achieves 100% classification accuracy with more than three CPDs. [ABSTRACT FROM AUTHOR]

This article proposes a mask refinement method for chromosome instance segmentation. The proposed method exploits the knowledge representation capability of Neural Knowledge DNA (NK-DNA) to capture the semantics of the chromosome's shape, texture, and key points, and then it uses the captured knowledge to improve the accuracy and smoothness of the masks. We validate the method's effectiveness on our latest high-resolution chromosome image dataset. The experimental results show that our proposed method's mask average precision (MaskAP) is 3.66% higher than Mask R-CNN and outperforms advanced Cascade Mask R-CNN by 1.35%. [ABSTRACT FROM AUTHOR]

Karyotyping requires chromosome instances to be segmented and classified from the metaphase images. One of the difficulties in chromosome segmentation is that the chromosomes are randomly positioned in the image, and there is a great chance for chromosomes to be touched or overlap with others. It is always much easier for operators and automatic programs to tackle images without overlapping chromosomes than ones with largely overlapped chromosomes. In order to reduce the processing difficulty, adding a smart image selection procedure ahead of segmentation is practical and necessary. In this paper, we introduce the Smart Karyotyping Image Selection (SKIS) based on Commonsense Knowledge Reasoning. The initial experiment demonstrates that the proposed approach can select the expected images based on reasoning and benefit following karyotyping processes. [ABSTRACT FROM AUTHOR]

In this paper, we propose a knowledge sharing oriented approach to enable a robot to reuse other robots' knowledge by adapting itself to the inverse dynamics model of the knowledge-sharing robot. The purpose of this work is to remove the heavy fine-tuning procedure required before using a new robot for a task via reusing other robots' knowledge. We use the Neural Knowledge DNA (NK-DNA) to help robots gain empirical knowledge and introduce a Knowledge Adaption Module (KAM) utilizing the deep neural networks (DNN) for knowledge reuse. The initial experiment shows that the target robot can adapt to the inverse dynamic model of the source robot via our KAM and reuse the knowledge shared by the source robot. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach that adds the interpretability to Neural Knowledge DNA (NK-DNA) via generating a decision tree. The NK-DNA is a promising knowledge representation approach for acquiring, storing, sharing, and reusing knowledge among machines and computing systems. We introduce the decision tree-based generative method for knowledge extraction and representation to make the NK-DNA more explainable. We examine our approach through an initial case study. The experiment results show that the proposed method can transform the implicit knowledge stored in the NK-DNA into explicitly represented decision trees bringing fair interpretability to neural network-based intelligent systems. [ABSTRACT FROM AUTHOR]