Together alone: a group-based polarization measurement

The growing polarization of our societies and economies has been extensively studied in various disciplines and is subject to public controversy. Yet, measuring polarization is hampered by the discrepancy between how polarization is conceptualized and measured. For instance, the notion of group, especially groups that are identified based on similarities between individuals, is key to conceptualizing polarization but is usually neglected when measuring polarization. To address the issue, this paper presents a new polarization measurement based on a grouping method called “Equal Size Binary Grouping” (ESBG) for both uni- and multi-dimensional discrete data, which satisfies a range of desired properties. Inspired by techniques of clustering, ESBG divides the population into two groups of equal sizes based on similarities between individuals, while overcoming certain theoretical and practical problems afflicting other grouping methods, such as discontinuity and contradiction of reasoning. Our new polarization measurement and the grouping method are illustrated by applying them to a two-dimensional synthetic data set. By means of a so-called “squeezing-and-moving” framework, we show that our measurement is closely related to bipolarization and could help stimulate further empirical research.

SimulaD: A Novel Feature Selection Heuristics for Discrete Data

By applying a running average (with a window-size= d), we could transform Discrete data to broad-range, Continuous values. When we have more than 2 columns and one of them is containing data about the tags of classification (Class Column), we could compare and sort the features (Non-class Columns) based on the R2 coefficient of the regression for running averages. The parameters tuning could help us to select the best features (the non-class columns which have the best correlation with the Class Column). “Window size” and “Ordering” could be tuned to achieve the goal. this optimization problem is hard and we need an Algorithm (or Heuristics) for simplifying this tuning. We demonstrate a novel heuristics, Called Simulated Distillation (SimulaD), which could help us to gain a somehow good results with this optimization problem.

Gender Differences in Teaching and Research Performance of University Teachers Based on Discrete Data Analysis

Numerous studies have found that there were obvious differences between men and women in research performance, but there is little analysis on teaching. At the same time, the existing studies did not classify the teacher sample but only analyzed the overall sample. This study takes both teaching and research into account, and the sample teachers from a certain university in China are divided into three groups such as outstanding teachers in teaching, outstanding teachers in research, and weak teachers in both teaching and research through cluster analysis based on the discrete data analysis of teaching and research workload from 2018 to 2020. The gender differences in teaching and research performance of male and female teachers in every group are analyzed. It is found that there are obvious differences between male and female teachers in the three groups. By analyzing the correlation between male and female teaching and research performance and gender, age, education, and degree of three groups, it is also found that there are differences between men and women in the correlation of individual factors such as degree and type of graduation university. Based on the results, this paper provides several policy recommendations.

Covariance-Invariant Mapping of Data Points to Nonlinear Models

A centroid- and covariance-invariant deterministic mapping of sets of discrete data points to nonlinear models is introduced. Conditions for bijectivity of this mapping are developed. Since the mapping can be accomplished by look-up tables for the special case of equally-spaced data, the resulting mapping algorithm is considered computationally fast. This could be attractive for real-time operations.

PROBLEM OF A DISCRETE DATA ARRAY APPROXIMATION BY A SET OF ELEMENTARY GEOMETRIC ALGORITHMS

Context. In this paper, a problem of a discrete data array approximation by a set of elementary geometric algorithms and a recognition model representation in a form of algorithmic classification tree has been solved. The object of the present study is a concept of a classification tree in a form of an algorithm trees. The subject of this study are the relevant models, methods, algorithms and schemes of different classification tree construction. Objective. The goal of this work is to create a simple and efficient method and algorithmic scheme of building the tree-like recognition and classification models on the basis of the algorithm trees for training selections of large-volume discrete information characterized by a modular structure of independent recognition algorithms assessed in accordance with the initial training selection data for a wide class of applied tasks. Method. A scheme of classification tree (algorithm tree) synthesis has been suggested being based on the data array approximation by a set of elementary geometric algorithms that constructs a tree-like structure (the ACT model) for a preset initial training selection of arbitrary size. The latter consists of a set of autonomous classification/recognition algorithms assessed at each step of the ACT construction according to the initial selection. A method of the algorithmic classification tree construction has been developed with the basic idea of step-by-step arbitrary-volume and structure initial selection approximation by a set of elementary geometric classification algorithms. When forming a current algorithm tree vertex, node and generalized attribute, this method provides alignment of the most effective and high-quality elementary classification algorithms from the initial set and complete construction of only those paths in the ACT structure, where the most of classification errors occur. The scheme of synthesizing the resulting classification tree and the ACT model developed allows one to reduce considerably the tree size and complexity. The ACT construction structural complexity is being assessed on the basis of a number of transitions, vertices and tiers of the ACT structure that allows the quality of its further analysis to be increased, the efficient decomposition mechanism to be provided and the ACT structure to be built in conditions of fixed limitation sets. The algorithm tree synthesis method allows one to construct different-type tree-like recognition models with various sets of elementary classifiers at the preset accuracy for a wide class of artificial intelligence theory problems. Results. The method of discrete training selection approximation by a set of elementary geometric algorithms developed and presented in this work has received program realization and was studied and compared with those of logical tree classification on the basis of elementary attribute selection for solving the real geological data recognition problem. Conclusions. Both general analysis and experiments carried out in this work confirmed capability of developed mechanism of constructing the algorithm tree structures and demonstrate possibility of its promising use for solving a wide spectrum of applied recognition and classification problems. The outlooks of the further studies and approbations might be related to creating the othertype algorithmic classification tree methods with other initial sets of elementary classifiers, optimizing its program realizations, as well experimental studying this method for a wider circle of applied problems.

Implementation of “Epic Healthy Planet” registry for survivorship care coordination.

337 Background: The evolving Commission on Cancer (CoC) reporting mandate and institution’s growing survivorship program led to identifying the need for systematic tracking of survivorship patients, surveillance tests, return appointments and referrals placed. Our aim was to develop an electronic medical record (EMR) integrated registry utilizing discrete data fields to assist our team in tracking key elements of high-quality survivorship care. Methods: Stakeholders from our survivorship team (APP/RN), medical oncology, psychology, research, operations and IT analytics reached consensus on essential discrete EMR fields to be included in the registry. For implementation we utilized the EPIC module, “Healthy Planet”, where patients enter the registry by initiating an “Episode of Care” at their initial survivorship visit. SmartForm fields create unique discrete patient data points identified by the stakeholders. Results: The following domains were identified as important elements of care that require tracking in a dedicated survivorship program. The registry domains populate from two sources: 1) currently existing EMR data fields, 2) domains with no currently discrete data (e.g. lymphedema, peripheral neuropathy) were captured in the developed SmartForm (see Table). From January 1, 2019 to June 1, 2021, 778 patients were entered into the registry. Since September 4, 2020, 112 patient follow-up appointment reminders were sent via EMR which has led to a noticeable increase in return appointments. SmartForm data fields are being amended as additional malignancy types are added to our survivorship program. Conclusions: The utilization of Healthy Planet is an effective and user-friendly way to track survivorship return appointments, remind providers of diagnostic tests that are due, and track referrals for CoC reporting. As the numbers of cancer survivors continues to increase, systematic population management tools are essential to ensure adherence to survivorship guideline recommendations, follow-up care and mandatory reporting.[Table: see text]