Pictograph Portrays What It Is

As one origin of Chinese characters, pictograph has a graphical structure based on its referent. Are the aesthetic qualities of the reference objects reflected in the neural processing of pictographs? In the study reviewed in this chapter, participants were scanned while making aesthetic judgments of pictographs and their referents. The conjunction analysis revealed the common involvement of the bilateral inferior occipital gyri and inferior frontal gyri, the right superior occipital gyrus, the left middle occipital gyrus, and the inferior orbitofrontal cortex for the aesthetic judgments of pictographs and object images referring to beautiful objects. Moreover, only the beautiful judgments for pictographs but not object images activated the motor areas, implying that an approach motivation was elicited during the aesthetic perception of novel pictographs. Results indicate that not only the object images, but also the corresponding pictographs arouse a sense of beauty that relies on common neural mechanisms during aesthetic judgments.

Golden Ratio Butterflies

This piece is a rumination on flow, pattern, and edges/transitions, focusing on polynomials of odd degree and overlaying/underlaying the flow of the graphical structure with a rainbow to suggest the central importance of queer visibility in mathematics.

Transistor Sizing using Hybrid Reinforcement Learning and Graph Convolution Neural Network Algorithm

Transistor sizing is one the developing field in VLSI. Many researches have been conducted to achieve automatic transistor sizing which is a complex task due to its large design area and communication gap between different node and topology. In this paper, automatic transistor sizing is implemented using a combinational methods of Graph Convolutional Neural Network (GCN) and Reinforcement Learning (RL). In the graphical structure the transistor are represented as apexes and the wires are represented as boundaries. Reinforcement learning techniques acts a communication bridge between every node and topology of all circuit. This brings proper communication and understanding among the circuit design. Thus the Figure of Merit (FOM) is increased and the experimental results are compared with different topologies. It is proved that the circuit with prior knowledge about the system, performs well.

Weaving the Rainbow (Odd I)

This piece is a rumination on flow, pattern, and edges/transitions, focusing on polynomials of odd degree and overlaying/underlaying the flow of the graphical structure with a rainbow to suggest the central importance of queer visibility in mathematics.

A Ladder of Causal Distances

Causal discovery, the task of automatically constructing a causal model from data, is of major significance across the sciences. Evaluating the performance of causal discovery algorithms should ideally involve comparing the inferred models to ground-truth models available for benchmark datasets, which in turn requires a notion of distance between causal models. While such distances have been proposed previously, they are limited by focusing on graphical properties of the causal models being compared. Here, we overcome this limitation by defining distances derived from the causal distributions induced by the models, rather than exclusively from their graphical structure. Pearl and Mackenzie [2018] have arranged the properties of causal models in a hierarchy called the ``ladder of causation'' spanning three rungs: observational, interventional, and counterfactual. Following this organization, we introduce a hierarchy of three distances, one for each rung of the ladder. Our definitions are intuitively appealing as well as efficient to compute approximately. We put our causal distances to use by benchmarking standard causal discovery systems on both synthetic and real-world datasets for which ground-truth causal models are available.

Graphical structure of extended b-metric spaces: an application to the transverse oscillations of a homogeneous bar

The purpose of this article is to present the notion of graphical extended b-metric spaces, blending the concepts of graph theory and metric fixed point theory. We discuss the structure of an open ball of the new proposed space and elaborate on the newly introduced ideas in a novel way by portraying suitably directed graphs. We also provide some examples in graph structure to show that our results are sharp as compared to the results in the existing state-of-art. Furthermore, an application to the transverse oscillations of a homogeneous bar is entrusted to affirm the applicability of the established results. Additionally, we evoke some open problems for enthusiastic readers for the future aspects of the study.

CRAN package bayesvl on CSIRO

bayesvl: Visually Learning the Graphical Structure of Bayesian Networks and Performing MCMC with 'Stan'

CRAN package bayesvl on Microsoft

bayesvl: Visually Learning the Graphical Structure of Bayesian Networks and Performing MCMC with 'Stan'