Strongly connected components (SCC) are an essential property for understanding the structure of directed networks. Given that many real-world networks are significant, it is often computationally efficient to partition the network over many distributed systems and solve for SCC simultaneously over the partitioned network. In this paper, we present an algorithm for identifying SCC on distributed systems. Our algorithm comprises three steps. In the first step, we locally perform SCC over all partitions.
Directed Research Project
We present a new algorithm for computing tensor decomposition on streaming data that achieves up to 102× speedup over the state-of-the-art CP-stream algorithm through lower computational complexity and performance optimization. For each streaming time slice, our algorithm partitions the factor matrix rows into those with and without updates and keeps them in Gram matrix form to significantly reduce the required computation.
We present a numerical framework for modeling the temporal evolution of ground deformation caused by a subsurface, pressurized magma reservoir situated within a viscoelastic medium. The host rock surrounding an oblate, ellipsoidal magma reservoir behaves as a Maxwell material. Temporal evolution due to the viscous effects are encoded as source terms on the static equilibrium equations; the coupled system is solved via high-order FEM and explicit time-stepping. We derive numerically stable time steps and verify convergence at the theoretical rate.
We present our recent work on developing multilingual Natural Language Processing (NLP) systems for different upstream and downstream tasks in NLP.
Defending against attackers with unknown behavior is an important area of research in security games. A well-established approach is to utilize historical attack data to create a behavioral model of the attacker. However, this presents a vulnerability: a clever attacker may change its own behavior during learning, leading to an inaccurate model and ineffective defender strategies. In this paper, we investigate how a wary defender can defend against such deceptive attacker. We provide four main contributions.
As the field of Cyber-Physical Systems continues to advance, new and interesting changes regarding its capability, adaptability, scalability, and usability  have come about. The most notable change has been the aggressive expansion of the variety of entity types that can be deployed in these systems (i.e. the entity eco-system).
Parallelized particle advection algorithms are a key visualization tool for domain scientists. They are also very computationally expensive to run. Machine learning techniques have been widely used in regression settings to predict results based on a set of input features. Our work describes an approach for parallel particle advection optimization; an approach which uses a machine learning algorithm at its core. We specifically investigate how our approach operates when applied to a GPU-based parallel particle advection algorithm.
Computational simulations frequently only save a subset of their time slices, e.g., running for one thousand cycles, but saving only fifty time slices. With this work we consider the problem of temporal upscaling, i.e. inferring visualizations at time slices that were not saved, as applied to ensemble simulations. We contribute a new algorithm, which we call DATUM, which incorporates machine learning techniques, specifically, dotted attention and convolutional networks. To evaluate our approach, we conduct 1327 experiments, on 32x32 pixel renderings of two-dimensional data sets.
Network alignment (NA) consists on finding the optimal node correspondence between distinct networks (graphs). Previous works in this field have had various degrees of success. However, they rely on some strong assumptions of topological and/or attribute consistency among the aligned networks. Simultaneously, Generative Adversarial Networks (GANs), generative models that have achieved remarkable results on continuous data such as images and audio, have recently been successfully applied to tasks with discrete domains, such as text generation.
We present performance results from a new hybridized finite difference method for the spatial discretization of partial differential equations. The method is based on the standard Summation-By-Parts method with weak enforcement of boundary and interface conditions through the Simultaneous-Approximation-Term. We analyze the performance when applying the hybrid method to Poisson's equation which arises in many steady-state physical problems, focusing on an application in Earth science.