Exploring Delay Dispersal in US Airport Network

The modeling of delay diffusion in airport networks can potentially help develop strategies to prevent the spread of such delays and disruptions. With this goal, we used the publicly-available historical United States Federal Aviation Administration (FAA) flight data to model the spread of delays in the US airport network. For the major (ASPM-77) airports for January 2017, using a threshold on the volume of flights, we sparsify the network in order to better recognize patterns and cluster structure of the network. We developed a diffusion simulator and greedy optimizer to find the top influential airport nodes that propagate the most delays in the entire network and in each cluster. Currently, our model is based on a popular diffusion model, Independent Cascade Model. We visualize the delay spread in order to better represent how the affected airports can spread these delays to the entire network. In the process, we also study inter-cluster propagation and intra-cluster propagation

MinT-Net: Novel and Scalable Network-enabled Comparative Tools for Stress Studies of Microbiomes in Transition

Community detection is the process of analyzing graphs to distinguish different groups of nodes from one another. A community is defined as a group of nodes that are closely related among each other but loosely related to the other nodes in the network. These communities exist within the species, gene, and protein networks of a microbiome. Many different algorithms have been developed to detect these communities. The project as a whole is intended to track communities in dynamic networks using known community detection algorithms. An initial effort created implementations of different algorithms for community detection to test for community quality with respect to computational time, focusing on the Girvan-Newman algorithm and the Louvain algorithm. Trials were run on assortative planted partition models to test the accuracy of the algorithms with respect to their computational time. After the trials, the Louvain algorithm was identified to not only be more computationally-time efficient, but more accurate when detecting communities in models with less assortativity. The accuracy and efficiency of the Louvain algorithm is promising for its future use in dynamic community detection in networks that model microbiomes in transition. Preliminary detection efforts on dynamic networks with community structure were performed on models using the framework of the Chinese Restaurant stochastic process. These efforts attempted to track community structure over time, utilizing the Jaccard index and Pointwise Mutual Information, or PMI. Leveraging these preliminary results, we plan on developing a set of formal rules to track communities in dynamic graphs

A silicon micromachined photonic band gap cell, characteristics and an application

A novel PBG cell based on micromachining of Silicon using wet anisotropic etching has been considered. Since this is based on etching of the Silicon substrate, it is amenable to fabrication with standard Silicon processes and integration with millimeter wave circuits. We characterize this kind of PBG cell by full wave simulations using a time domain code. For the purpose of characterization, the scenario of a 50 ohm microstrip line placed on a Silicon substrate which is anisotropically etched to create patterns with sloping walls is considered. This is shown to produce the well known PBG response of stop bands in certain frequency bands. We look at the variation in the transmission coefficient (S-21) response as the number of periods, length based average fill factor and depth of micromachining are varied. One application of a low pass filter has been proposed and simulated results are given

Dynamic graphs, community detection, and Riemannian geometry

Abstract A community is a subset of a wider network where the members of that subset are more strongly connected to each other than they are to the rest of the network. In this paper, we consider the problem of identifying and tracking communities in graphs that change over time – dynamic community detection – and present a framework based on Riemannian geometry to aid in this task. Our framework currently supports several important operations such as interpolating between and averaging over graph snapshots. We compare these Riemannian methods with entry-wise linear interpolation and find that the Riemannian methods are generally better suited to dynamic community detection. Next steps with the Riemannian framework include producing a Riemannian least-squares regression method for working with noisy data and developing support methods, such as spectral sparsification, to improve the scalability of our current methods

Deep Reinforcement Learning to Maximize Arterial Usage during Extreme Congestion

Collisions, crashes, and other incidents on road networks, if left unmitigated, can potentially cause cascading failures that can affect large parts of the system. Timely handling such extreme congestion scenarios is imperative to reduce emissions, enhance productivity, and improve the quality of urban living. In this work, we propose a Deep Reinforcement Learning (DRL) approach to reduce traffic congestion on multi-lane freeways during extreme congestion. The agent is trained to learn adaptive detouring strategies for congested freeway traffic such that the freeway lanes along with the local arterial network in proximity are utilized optimally, with rewards being congestion reduction and traffic speed improvement. The experimental setup is a 2.6-mile-long 4-lane freeway stretch in Shoreline, Washington, USA with two exits and associated arterial roads simulated on a microscopic and continuous multi-modal traffic simulator SUMO (Simulation of Urban MObility) while using parameterized traffic profiles generated using real-world traffic data. Our analysis indicates that DRL-based controllers can improve average traffic speed by 21\% when compared to no-action during steep congestion. The study further discusses the trade-offs involved in the choice of reward functions, the impact of human compliance on agent performance, and the feasibility of knowledge transfer from one agent to other to address data sparsity and scaling issues