3D Cephalometric Normality Range: Auto Contractive Maps (ACM) Analysis in Selected Caucasian Skeletal Class I Age Groups

The objective of this paper is to define normal values of a novel 3D cephalometric analysis and to define the links through an artificial neural network (ANN)

Automated analysis of Physarum network structure and dynamics

We evaluate different ridge-enhancement and segmentation methods to automatically extract the network architecture from time-series of Physarum plasmodia withdrawing from an arena via a single exit. Whilst all methods gave reasonable results, judged by precision-recall analysis against a ground-truth skeleton, the mean phase angle (Feature Type) from intensity-independent, phase-congruency edge enhancement and watershed segmentation was the most robust to variation in threshold parameters. The resultant single pixel-wide segmented skeleton was converted to a graph representation as a set of weighted adjacency matrices containing the physical dimensions of each vein, and the inter-vein regions. We encapsulate the complete image processing and network analysis pipeline in a downloadable software package, and provide an extensive set of metrics that characterise the network structure, including hierarchical loop decomposition to analyse the nested structure of the developing network. In addition, the change in volume for each vein and intervening plasmodial sheet was used to predict the net flow across the network. The scaling relationships between predicted current, speed and shear force with vein radius were consistent with predictions from Murray's law. This work was presented at PhysNet 2015

Optimal utility and probability functions for agents with finite computational precision

When making economic choices, such as those between goods or gambles, humans act as if their internal representation of the value and probability of a prospect is distorted away from its true value. These distortions give rise to decisions which apparently fail to maximize reward, and preferences that reverse without reason. Why would humans have evolved to encode value and probability in a distorted fashion, in the face of selective pressure for reward-maximizing choices? Here, we show that under the simple assumption that humans make decisions with finite computational precision––in other words, that decisions are irreducibly corrupted by noise––the distortions of value and probability displayed by humans are approximately optimal in that they maximize reward and minimize uncertainty. In two empirical studies, we manipulate factors that change the reward-maximizing form of distortion, and find that in each case, humans adapt optimally to the manipulation. This work suggests an answer to the longstanding question of why humans make “irrational” economic choices

A Characterization of Uniquely Representable Graphs

The betweenness structure of a finite metric space M =(X, d) is a pair ℬ (M)=(X, βM) where βM is the so-called betweenness relation of M that consists of point triplets (x, y, z) such that d(x, z)= d(x, y)+ d(y, z). The underlying graph of a betweenness structure ℬ =(X, β)isthe simple graph G(ℬ)=(X, E) where the edges are pairs of distinct points with no third point between them. A connected graph G is uniquely representable if there exists a unique metric betweenness structure with underlying graph G. It was implied by previous works that trees are uniquely representable. In this paper, we give a characterization of uniquely representable graphs by showing that they are exactly the block graphs. Further, we prove that two related classes of graphs coincide with the class of block graphs and the class of distance-hereditary graphs, respectively. We show that our results hold not only for metric but also for almost-metric betweenness structures. © 2021 Péter G.N. Szabó

Social (network) psychology: How networks shape performance, persistence, and access to information

Social psychologists have long been interested in understanding behavior as a function of both individuals and the social structures in which they are embedded. However, since the cognitive revolution of the 1960s, processes internal to individuals have received greater attention than structural influences. This dissertation examines how networks may shape important real-world outcomes beyond intrapsychic phenomena across three studies in varied contexts. In doing so, this work suggests that the networks to which people belong—whether networks of social ties or networks of information—provide both affordances and constraints that affect behavior and outcomes. Chapter I provides a brief introduction to social network analysis as a set of theoretical, methodological, and analytical tools. Chapter II examines the gender gap in negotiation performance. Findings suggest that disparities between male and female MBA students in class social network positions predict this gap more strongly than intrapsychic mechanisms more commonly studied, such as apprehension about negotiating and stereotype threat. Chapter III examines how students’ social networks promote persistence over time in a high-stress science, technology, engineering, and math (STEM) setting. This chapter pulls social network analysis into an experimental context by examining the effects of a randomly assigned social psychological intervention on students’ social networks and subsequent persistence in the biosciences. Chapter IV approaches networks from a different angle, examining how online news media are organized into network structures that may contribute to selective exposure to homogenous information. Finally, Chapter V discusses implications of these three studies. Specifically, I discuss implications for education research, intervention science, and the growing area of social network psychology

Climate Dynamics: A Network-Based Approach for the Analysis of Global Precipitation

Precipitation is one of the most important meteorological variables for defining the climate dynamics, but the spatial patterns of precipitation have not been fully investigated yet. The complex network theory, which provides a robust tool to investigate the statistical interdependence of many interacting elements, is used here to analyze the spatial dynamics of annual precipitation over seventy years (1941-2010). The precipitation network is built associating a node to a geographical region, which has a temporal distribution of precipitation, and identifying possible links among nodes through the correlation function. The precipitation network reveals significant spatial variability with barely connected regions, as Eastern China and Japan, and highly connected regions, such as the African Sahel, Eastern Australia and, to a lesser extent, Northern Europe. Sahel and Eastern Australia are remarkably dry regions, where low amounts of rainfall are uniformly distributed on continental scales and small-scale extreme events are rare. As a consequence, the precipitation gradient is low, making these regions well connected on a large spatial scale. On the contrary, the Asiatic South-East is often reached by extreme events such as monsoons, tropical cyclones and heat waves, which can all contribute to reduce the correlation to the short-range scale only. Some patterns emerging between mid-latitude and tropical regions suggest a possible impact of the propagation of planetary waves on precipitation at a global scale. Other links can be qualitatively associated to the atmospheric and oceanic circulation. To analyze the sensitivity of the network to the physical closeness of the nodes, short-term connections are broken. The African Sahel, Eastern Australia and Northern Europe regions again appear as the supernodes of the network, confirming furthermore their long-range connection structure. Almost all North-American and Asian nodes vanish, revealing that extreme events can enhance high precipitation gradients, leading to a systematic absence of long-range patterns