Causally Disentangled Generative Variational AutoEncoder

We present a new supervised learning technique for the Variational AutoEncoder (VAE) that allows it to learn a causally disentangled representation and generate causally disentangled outcomes simultaneously. We call this approach Causally Disentangled Generation (CDG). CDG is a generative model that accurately decodes an output based on a causally disentangled representation. Our research demonstrates that adding supervised regularization to the encoder alone is insufficient for achieving a generative model with CDG, even for a simple task. Therefore, we explore the necessary and sufficient conditions for achieving CDG within a specific model. Additionally, we introduce a universal metric for evaluating the causal disentanglement of a generative model. Empirical results from both image and tabular datasets support our findings

OH + Isoprene: A Direct Dynamics Study

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137441/1/bkcs11145.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137441/2/bkcs11145_am.pd

Influential factors in the out-of-class activities of Korean college students

This study aimed to explore who participates in what kinds of out-of-class activities in Korea\u27s universities. Therefore, the researchers examine whether differences exist in the pattern of out-of-class experiences according to the individual characteristics of the students, including gender, grade, household income level, high school performance and major. The researchers also aimed to examine the empirical evidence to determine the relationships between the patterns in out-of-class activities and the institutional characteristics of the university that the student attends. In terms of the institutional characteristics, this study is concerned with the location and size of the university. To explore these questions, the researchers analyzed K-NSSE data with hierarchical linear modeling. In sum, the findings of the statistical analysis of this study support the results of the preceding research in which different personal and institutional characteristics are related to five types of out-of-class activities. (DIPF/Orig.

Fast Knowledge Graph Completion using Graphics Processing Units

Knowledge graphs can be used in many areas related to data semantics such as question-answering systems, knowledge based systems. However, the currently constructed knowledge graphs need to be complemented for better knowledge in terms of relations. It is called knowledge graph completion. To add new relations to the existing knowledge graph by using knowledge graph embedding models, we have to evaluate $N\times N \times R$ vector operations, where $N$ is the number of entities and $R$ is the number of relation types. It is very costly. In this paper, we provide an efficient knowledge graph completion framework on GPUs to get new relations using knowledge graph embedding vectors. In the proposed framework, we first define "transformable to a metric space" and then provide a method to transform the knowledge graph completion problem into the similarity join problem for a model which is "transformable to a metric space". After that, to efficiently process the similarity join problem, we derive formulas using the properties of a metric space. Based on the formulas, we develop a fast knowledge graph completion algorithm. Finally, we experimentally show that our framework can efficiently process the knowledge graph completion problem

The relationship between participation in out-of-class activities and cognitive and social outcomes of Korean college students

In the era of the 4th Industrial Revolution, higher education institutions should change practices of educational programs and services, which are mainly based on traditional classroom-based instructions, to allow students to have more diverse experiences. Since college students spend relatively more time engaged in out-of-class activities than attending regular courses, it is necessary to examine how participating in out-of-class programs is related to cultivation of the competencies that the future demands. This study explores the relationship between out-of-class activity participation and perceived change in cognitive and social outcomes of Korean college students. Five out-of-class activities were examined: learning community, undergraduate research, service learning, internship, and residential college programs. K-NSSE (Korea-National Survey of Student Engagement) data were analyzed using hierarchical linear model analysis. The study findings are consistent with the results of previous research that demonstrated a positive association between participating in out-of-class activities and students\u27 cognitive and social outcomes. (DIPF/Orig.

Neuronal messenger ribonucleoprotein transport follows an aging Lévy walk

Localization of messenger ribonucleoproteins (mRNPs) plays an essential role in the regulation of gene expression for long-term memory formation and neuronal development. Knowledge concerning the nature of neuronal mRNP transport is thus crucial for understanding how mRNPs are delivered to their target synapses. Here, we report experimental and theoretical evidence that the active transport dynamics of neuronal mRNPs, which is distinct from the previously reported motor-driven transport, follows an aging Levy walk. Such nonergodic, transient superdiffusion occurs because of two competing dynamic phases: the motor-involved ballistic run and static localization of mRNPs. Our proposed Levy walk model reproduces the experimentally extracted key dynamic characteristics of mRNPs with quantitative accuracy. Moreover, the aging status of mRNP particles in an experiment is inferred from the model. This study provides a predictive theoretical model for neuronal mRNP transport and offers insight into the active target search mechanism of mRNP particles in vivo.1111sciescopu