Concept-Based Visual Analysis of Dynamic Textual Data

Analyzing how interrelated ideas flow within and between multiple social groups helps understand the propagation of information, ideas, and thoughts on social media. The existing dynamic text analysis work on idea flow analysis is mostly based on the topic model. Therefore, when analyzing the reasons behind the flow of ideas, people have to check the textual data of the ideas, which is annoying because of the huge amount and complex structures of these texts. To solve this problem, we propose a concept-based dynamic visual text analytics method, which illustrates how the content of the ideas change and helps users analyze the root cause of the idea flow. We use concepts to summarize the content of the ideas and show the flow of concepts with the flow lines. To ensure the stability of the flow lines, a constrained t-SNE projection algorithm is used to display the change of concepts over time and the correlation between them. In order to better convey the anomalous change of the concepts, we propose a method to detect the time periods with anomalous change of concepts based on anomaly detection and highlight them. A qualitative evaluation and a case study on real-world Twitter datasets demonstrate the correctness and effectiveness of our visual analytics method.Comment: in Chinese languag

Analyzing the noise robustness of deep neural networks

Adversarial examples, generated by adding small but intentionally imperceptible perturbations to normal examples, can mislead deep neural networks (DNNs) to make incorrect predictions. Although much work has been done on both adversarial attack and defense, a fine-grained understanding of adversarial examples is still lacking. To address this issue, we present a visual analysis method to explain why adversarial examples are misclassified. The key is to compare and analyze the datapaths of both the adversarial and normal examples. A datapath is a group of critical neurons along with their connections. We formulate the datapath extraction as a subset selection problem and solve it by constructing and training a neural network. A multi-level visualization consisting of a network-level visualization of data flows, a layer-level visualization of feature maps, and a neuron-level visualization of learned features, has been designed to help investigate how datapaths of adversarial and normal examples diverge and merge in the prediction process. A quantitative evaluation and a case study were conducted to demonstrate the promise of our method to explain the misclassification of adversarial examples

The dynamics of immunoglobulin V-gene usage and clonotype expansion in mice after prime and boost immunizations as analyzed by NGS

In the current study, an improved NGS approach was developed to study the B-cell repertoire evolution in a simple mouse immunization model including only two DNA immunizations. The combination of 5\u27RACE and Ion Torrent long reads enabled unbiased immunoglobulin repertoire analysis even from small amounts of peripheral mouse blood. The B-cell population expanded by the vaccine displayed a relatively strong clonality. Upon priming with the first vaccine dose, we observed a consistent pattern of V-segment gene and CDR3 usage (public specificities). Interestingly, this pattern diversified with the second dose of immunization -it was relatively different in individual mice in spite of having received the same vaccine regimen (private specificities). Nevertheless, there were several instances in which the same public V-segment genes and CDR3s that were expanded after the first dose were further amplified after the second immunization. Taken together, it appears that the major clonotypes expanded by vaccination were originally a homogeneous subset that later diversified after a second dose leading to diverse private clonal compositions in different mice. These results established a new platform valuable to perform longitudinal analyses of the Ig germline gene usage and clonotype evolution throughout an immunization regimen in a commonly used animal model