![]() This online quiz system, when it is adopted by more CS instructors worldwide, should technically eliminate manual basic data structure and algorithm questions from typical Computer Science examinations in many Universities. The questions are randomly generated via some rules and students' answers are instantly and automatically graded upon submission to our grading server. The most exciting development is the automated question generator and verifier (the online quiz system) that allows students to test their knowledge of basic data structures and algorithms. VisuAlgo is an ongoing project and more complex visualizations are still being developed. However, we are currently experimenting with a mobile (lite) version of VisuAlgo to be ready by April 2022. The minimum screen resolution for a respectable user experience is 1024x768 and only the landing page is relatively mobile-friendly. VisuAlgo is not designed to work well on small touch screens (e.g., smartphones) from the outset due to the need to cater for many complex algorithm visualizations that require lots of pixels and click-and-drag gestures for interaction. Though specifically designed for National University of Singapore (NUS) students taking various data structure and algorithm classes (e.g., CS1010/equivalent, CS2040/equivalent, CS3230, CS3233, and CS4234), as advocators of online learning, we hope that curious minds around the world will find these visualizations useful too. Today, a few of these advanced algorithms visualization/animation can only be found in VisuAlgo. VisuAlgo contains many advanced algorithms that are discussed in Dr Steven Halim's book ('Competitive Programming', co-authored with his brother Dr Felix Halim and his friend Dr Suhendry Effendy) and beyond. 7 (3) 1663 - 1683, September 2013.VisuAlgo was conceptualised in 2011 by Dr Steven Halim as a tool to help his students better understand data structures and algorithms, by allowing them to learn the basics on their own and at their own pace. "Learning local directed acyclic graphs based on multivariate time series data." Ann. We illustrate the algorithm by analyzing a time course gene expression data related to mouse T-cell activation. In addition, the tsPCD-PCD algorithm outperforms the PCD-PCD algorithm in recovering the local graph structures. Simulation studies show that the CLRTs are valid and perform well even when the sample sizes are small. We present the asymptotic distribution of the CLRT statistic and show that the tsPCD-PCD is guaranteed to recover the true DAG structure when the faithfulness condition holds and the tests correctly reject the null hypotheses. This time series PCD-PCD algorithm (tsPCD-PCD) extends the previous PCD-PCD algorithm to dependent observations and utilizes composite likelihood ratio tests (CLRTs) for testing the conditional independence. Our algorithm is based on learning all parents (P), all children (C) and some descendants (D) (PCD) iteratively, utilizing the time order of the variables to orient the edges. In this paper, we introduce a computationally efficient algorithm to learn directed acyclic graphs (DAGs) based on MTS data, focusing on learning the local structure of a given target variable. These data provide important information about the causal dependency among a set of random variables. Multivariate time series (MTS) data such as time course gene expression data in genomics are often collected to study the dynamic nature of the systems.
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