4303 research outputs found
Sort by
Mechanisms for Codon Usage Bias and its Influence on Translational Efficiency and Gene Expression
No full textA Social Intuitionist Perspective On Morality And Persuasion
No full textThe study of how and why people are persuaded has been very interdisciplinary with studies conducted in fields ranging from philosophy to economics. A key focus in the literature is the use of moral appeals in persuasive messaging with findings suggesting that calling attention to individual and societal moral principles often have strong persuasive effects on individuals and/or groups. This paper aims to clearly define the underlying mechanisms behind moral appeals and understand what makes them such an effective means of persuasion by reviewing literature from different disciplines. Using Haidt’s (2001) social intuitionist model (SIM) as a framework for morality, I explore the connection between morality and affect and the influence our morals have on our social connections and networks. As both affect and social influence are key persuasive tools, I propose that these characteristics of morality are what drive its influence on beliefs and perspectives. The implications and limitations of this review will also be discussed
Classification Models for Statistical Graphics
No full textThis paper presents an interpretable approach to detecting patterns in scatter plots, which can help automate the process of checking statistical assumptions (e.g., linearity in residual plots for linear regression). Scatter plot diagnostics (scagnostics) are used to quantify aspects of a scatter plot’s characteristics, such as shape and trend, on a scale from 0 to 1. Scatter plots with varying appearances were generated and their scagnostics were used as explanatory variables to train statistical learning models—logistic regression, generalized additive models (GAMs), and random forest models—to classify plots with distinct patterns. The accuracy levels for these models were promising, ranging from 97.4% to 99.9%, with performance similar to that of a convolutional neural network (CNN). While CNNs offered a comparable level of accuracy and robustness, the interpretability of the statistical models allows for a better understanding of their limitations. Further testing on the models’ sensitivity to training data reveals that the models perform exceptionally well on distributions they have been trained on, but tend to struggle significantly on additional testing distributions
Social Bot Detection
No full textSocial bots on Twitter are accounts controlled by software that can manipulate opinions in important events such as elections. In order to detect these bots, we want to achieve both scalability and generalizability, to enable faster real-time detection algorithms and to make the classification more robust to new bots that are different from previous ones. We replicate the paper Scalable and Generalizable Social Bot Detection through Data Selection (Yang, Varol, Hui, Menczer 2020) and test the robustness of its results. The paper proposed an approach for classification through training random forest models on just user profile information and only on a subset of the available training data. Our results align with Yang, et al. (2020) in that training on subsets of datasets with user metadata improves classifier performance. We differ from their models as we achieved better model performance than their top models using a fewer number of datasets for training. These differences might be due to datasets differences, feature calculation discrepancies, API call rate limit, and our relatively newer sampling time for account features of certain datasets. Relationships across all training and testing datasets are investigated to explain top models’ selections of training subsets, and feature importance in the training datasets are analyzed
Evolution of the early nitrogen cycle in the face of the Great Oxidation Event (GOE): How transition metal abundances influenced the evolution of the modern nitrogen cycle
No full textStupid Little Place
No full textMy name is Molly Furlong. Four years ago, I decided to attend a small liberal arts college in Northfield, Minnesota. It\u27s called Carleton. Ever since then, I\u27ve been thinking about my place at Carleton and Carleton\u27s place in Northfield. Some nice people did interviews to help me figure some stuff out
