Graph Neural Networks (GNNs) belong to a class of deep learning methods that are specialized for extracting critical information and making accurate predictions on graph representations. Researchers have been striving adapt neural networks process data over decade. GNNs found practical applications in various fields, including physics simulations, object detection, recommendation systems. Predi...

We argue that people compare values in graphs with a visual routine - attending to data values in an ordered pattern over time. Do these visual routines exist to manage capacity limitations in how many values can be encoded at once, or do they actually affect the relations that are extracted? We measured eye movements while people judged configurations of a two-bar graph based on size only ("[s...

Running SCC graph algorithms on large datasets can be a time-consuming task, and we spent the quarter investigating methods of parallelizing this task using CUDA. For very large graphs, too much time can be wasted by not parallelizing the graph algorithms, and we want some of the insights from our experiments to be used to speed up common graph analysis tasks. We initially started by implementi...

Code completion has become an essential component of integrated development environments. Contemporary code methods rely on the abstract syntax tree (AST) to generate syntactically correct code. However, they cannot fully capture sequential and repetitive patterns writing structural information AST. To alleviate these problems, we propose a new approach named CCAG, which models flattened sequen...

Tasking deadlock is one of the most serious and complex problems concerning the reliability of concurrent systems with Ada. In this paper, tasking deadlocks which may occur in Ada 95 programs are investigated, a representation, named the Task-Wait-For Graph, for task synchronization waiting state of an Ada 95 program is proposed, and a runtime detection method for the tasking deadlocks based on...

This paper presents a survey as well as a systematic empirical comparison of seven graph kernels and two related similarity matrices (simply referred to as graph kernels), namely the exponential diffusion kernel, the Laplacian exponential diffusion kernel, the von Neumann diffusion kernel, the regularized Laplacian kernel, the commute-time kernel, the random-walk-with-restart similarity matrix,...

This paper overviews NTCIR-13 Actionable Knowledge Graph (AKG) task. The task focuses on finding possible actions related to input entities and the relevant properties of such actions. AKG is composed of two subtasks: Action Mining (AM) and Actionable Knowledge Graph Generation (AKGG). Both subtasks are focused on English language. 9 runs have been submitted by 4 teams for the task. In this pap...

We describe the University of Heidelberg (UHD) system for the Cross-Lingual Word Sense Disambiguation SemEval-2010 task (CL-WSD). The system performs CLWSD by applying graph algorithms previously developed for monolingual Word Sense Disambiguation to multilingual cooccurrence graphs. UHD has participated in the BEST and out-of-five (OOF) evaluations and ranked among the most competitive systems...

Uintah is a computational framework for fluid-structure interaction problems using a combination of adaptive mesh refinement(AMR) and MPM particle methods. Uintah uses domain decomposition and a task graph based approach for asynchronous communication and automatic message combination . The original task scheduler for Uintah ran computational tasks in a predefined order. To improve the performa...