Three-dimensional Radial Visualization of High-dimensional Datasets with Mixed Features

We develop methodology for 3D radial visualization (RadViz) of high-dimensional datasets. Our display engine is called RadViz3D and extends the classical 2D RadViz that visualizes multivariate data in the 2D plane by mapping every record to a point inside the unit circle. We show that distributing anchor points at least approximately uniformly on the 3D unit sphere provides a better visualization with minimal artificial visual correlation for data with uncorrelated variables. Our RadViz3D methodology therefore places equi-spaced anchor points, one for every feature, exactly for the five Platonic solids, and approximately via a Fibonacci grid for the other cases. Our Max-Ratio Projection (MRP) method then utilizes the group information in high dimensions to provide distinctive lower-dimensional projections that are then displayed using Radviz3D. Our methodology is extended to datasets with discrete and continuous features where a Gaussianized distributional transform is used in conjunction with copula models before applying MRP and visualizing the result using RadViz3D. A R package radviz3d implementing our complete methodology is available.Comment: 12 pages, 10 figures, 1 tabl

A Signaling Model of Quality and Export: with application to dumping

Extending the literature on quality and trade and supported by the empirical evidence obtained from China, this paper demonstrates that in a developing country, a firm’s export to developed countries has a potential signaling effect on domestic consumers’ perception of its product quality. The model analyzes the signaling and imitating strategies of different types of firms in their decisions to export, and characterizes the conditions for the separating, pooling, and hybrid equilibria. Next, the analysis shows that the strategic exporting of low-quality producers under informational asymmetry can result in dumping. Moreover, the model shows that the implementation of antidumping measures of foreign countries can lead to a Pareto improvement for the firms and consumers of the home country under some circumstances.

A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels

The recent success of deep neural networks is powered in part by large-scale well-labeled training data. However, it is a daunting task to laboriously annotate an ImageNet-like dateset. On the contrary, it is fairly convenient, fast, and cheap to collect training images from the Web along with their noisy labels. This signifies the need of alternative approaches to training deep neural networks using such noisy labels. Existing methods tackling this problem either try to identify and correct the wrong labels or reweigh the data terms in the loss function according to the inferred noisy rates. Both strategies inevitably incur errors for some of the data points. In this paper, we contend that it is actually better to ignore the labels of some of the data points than to keep them if the labels are incorrect, especially when the noisy rate is high. After all, the wrong labels could mislead a neural network to a bad local optimum. We suggest a two-stage framework for the learning from noisy labels. In the first stage, we identify a small portion of images from the noisy training set of which the labels are correct with a high probability. The noisy labels of the other images are ignored. In the second stage, we train a deep neural network in a semi-supervised manner. This framework effectively takes advantage of the whole training set and yet only a portion of its labels that are most likely correct. Experiments on three datasets verify the effectiveness of our approach especially when the noisy rate is high

Tokenized Ownership in Decentralized Autonomous Organizations: Evidence from Steemit

Decentralized autonomous organizations (DAOs) operate on an incentive network powered by crypto tokens, which are attached with payment rights (i.e., transactional tokens) and ownership rights (i.e., governance tokens). Tokenized ownership is a special incentive way that supports automated operations of DAOs. Our study focuses on this new incentive scheme and construct a quasi-experiment setting to empirically test the incentive effects of tokenized ownership. We find that the intended choice of governance tokens leads to higher post length and readability and higher curation quality compared with transactional tokens. This study contributes to the literature of blockchain and cryptocurrency from an operational perspective and provides practical suggestions for the design of incentive mechanisms in DAOs

Three-dimensional Radial Visualization of High-dimensional Continuous or Discrete Data

This paper develops methodology for 3D radial visualization of high-dimensional datasets. Our display engine is called RadViz3D and extends the classic RadViz that visualizes multivariate data in the 2D plane by mapping every record to a point inside the unit circle. The classic RadViz display has equally-spaced anchor points on the unit circle, with each of them associated with an attribute or feature of the dataset. RadViz3D obtains equi-spaced anchor points exactly for the five Platonic solids and approximately for the other cases via a Fibonacci grid. We show that distributing anchor points at least approximately uniformly on the 3D unit sphere provides a better visualization than in 2D. We also propose a Max-Ratio Projection (MRP) method that utilizes the group information in high dimensions to provide distinctive lower-dimensional projections that are then displayed using Radviz3D. Our methodology is extended to datasets with discrete and mixed features where a generalized distributional transform is used in conjuction with copula models before applying MRP and RadViz3D visualization