The Emergence of Organizing Structure in Conceptual Representation

Nostradamus: A novel event propagation prediction approach with spatio-temporal characteristics in non-Euclidean space

The prediction of event propagation has received extensive attention from the knowledge discovery community for applications such as virus spread analytics, social network analysis, earthquake location prediction, and typhoon tracking. The data describing these phenomena are multidimensional asynchronous event data that affect each other and show complex dynamic patterns in the continuous-time domain. Unlike the discrete characteristics formed by sampling at equal intervals of asynchronous time series, the timestamps of asynchronous events are in the continuous-time field. The study of these dynamic processes and the mining of their potential correlations provide a foundation for applying event propagation prediction, traceability, and causal inference at both the micro and macro levels. Most of the existing methods represent data as being embedded in the Euclidean space. However, when embedding a real-world graph with a tree-likeliness graph, Euclidean space cannot visually represent a graph. Inspired by the characteristics of hyperbolic space, we propose a model called Nostradamus to capture the propagation of the events of interest from historical events in a graph via the hyperbolic graph neural Hawkes process with Spatio-temporal characteristics. The Nostradamus' core concept is to integrate the Hawkes process's conditional intensity function with a hyperbolic graph convolutional recurrent neural network.

EGF rs4444903 polymorphism is associated with risk of HCV-related cirrhosis and HBV/HCV-related hepatocellular carcinoma

OBJECTIVE

The epidermal growth factor (EGF) rs4444903 polymorphism is associated with aberrant expression of EGF, which was a characteristic of cirrhotic liver diseases, induces highly malignant hepatocellular carcinoma (HCC). Numerous studies have uncovered the association of this polymorphism with the risk of liver disease, but with inconsistent findings.

MATERIALS AND METHODS

Therefore, this meta-analysis was performed to evaluate whether EGF rs4444903 polymorphism conferred susceptibility to liver disease. Totally 18 eligible articles were identified by searching PubMed, Google, CNKI and EMBASE up to December 1, 2020.

RESULTS

Our results indicated that there was no significant difference in the minor G allele frequency of rs4444903 polymorphism between HBV/HCV carriers and healthy controls. In other words, EGF rs4444903 polymorphism was not associated with the risk of HBV/HCV. Interestingly, this polymorphism increased the risk of liver cirrhosis in the controls with HCV infection. Additionally, EGF rs4444903 polymorphism is associated with the increased risk of HCC under the five models. Subgroup analysis by ethnicity shows that rs4444903 polymorphism intensifies the risk of HCC among Asians and Caucasians. Strong correlation is also reported in controls with cirrhosis or HCV infection and studies using PCR-RFLP genotyping.

CONCLUSIONS

The study supports that EGF rs4444903 polymorphism is a genetic contributor to liver cirrhosis and HCC in the overall population. Nevertheless, this conclusion must be confirmed by larger studies with more diverse ethnic populations.

Network architectures supporting learnability

Human learners acquire complex interconnected networks of relational knowledge. The capacity for such learning naturally depends on two factors: the architecture (or informational structure) of the knowledge network itself and the architecture of the computational unit-the brain-that encodes and processes the information. That is, learning is reliant on integrated network architectures at two levels: the epistemic and the computational, or the conceptual and the neural. Motivated by a wish to understand conventional human knowledge, here, we discuss emerging work assessing network constraints on the learnability of relational knowledge, and theories from statistical physics that instantiate the principles of thermodynamics and information theory to offer an explanatory model for such constraints. We then highlight similarities between those constraints on the learnability of relational networks, at one level, and the physical constraints on the development of interconnected patterns in neural systems, at another level, both leading to hierarchically modular networks. To support our discussion of these similarities, we employ an operational distinction between the modeller (e.g. the human brain), the model (e.g. a single human's knowledge) and the modelled (e.g. the information present in our experiences). We then turn to a philosophical discussion of whether and how we can extend our observations to a claim regarding explanation and mechanism for knowledge acquisition. What relation between hierarchical networks, at the conceptual and neural levels, best facilitate learning? Are the architectures of optimally learnable networks a topological reflection of the architectures of comparably developed neural networks? Finally, we contribute to a unified approach to hierarchies and levels in biological networks by proposing several epistemological norms for analysing the computational brain and social epistemes, and for developing pedagogical principles conducive to curious thought. This article is part of the theme issue 'Unifying the essential concepts of biological networks: biological insights and philosophical foundations'.

What does the mind learn? A comparison of human and machine learning representations

We present a brief review of modern machine learning techniques and their use in models of human mental representations, detailing three notable branches: spatial methods, logical methods and artificial neural networks. Each of these branches contains an extensive set of systems, and demonstrate accurate emulations of human learning of categories, concepts and language, despite substantial differences in operation. We suggest that continued applications will allow cognitive researchers the ability to model the complex real-world problems where machine learning has recently been successful, providing more complete behavioural descriptions. This will, however, also require careful consideration of appropriate algorithmic constraints alongside these methods in order to find a combination which captures both the strengths and weaknesses of human cognition.