Reconstructing Sample-Specific Networks using LIONESS

We recently developed LIONESS, a method to estimate sample-specific networks based on the output of an aggregate network reconstruction approach. In this manuscript, we describe how to apply LIONESS to different network reconstruction algorithms and data types. We highlight how decisions related to data preprocessing may affect the output networks, discuss expected outcomes, and give examples of how to analyze and compare single sample networks.

A stochastic MPEC approach for grid tariff design with demand-side flexibility

As the end-users increasingly can provide flexibility to the power system, it is important to consider how this flexibility can be activated as a resource for the grid. Electricity network tariffs is one option that can be used to activate this flexibility. Therefore, by designing efficient grid tariffs, it might be possible to reduce the total costs in the power system by incentivizing a change in consumption patterns. This paper provides a methodology for optimal grid tariff design under decentralized decision-making and uncertainty in demand, power prices, and renewable generation. A bilevel model is formulated to adequately describe the interaction between the end-users and a distribution system operator. In addition, a centralized decision-making model is provided for benchmarking purposes. The bilevel model is reformulated as a mixed-integer linear problem solvable by branch-and-cut techniques. Results based on both deterministic and stochastic settings are presented and discussed. The findings suggest how electricity grid tariffs should be designed to provide an efficient price signal for reducing aggregate network peaks.

Vulnerability Assessment of Urban Intersections apropos of Incident Impact on Road Network and Identification of Critical Intersections

One of the major challenges in a transportation network management program is responding to traffic incidents such as traffic crashes, disabled vehicles, spilled cargo, road debris, and so forth, at or near intersections. Intersections are vulnerable with respect to their susceptibility to incidents, therefore, it is important to assess their vulnerability to identify critical intersections for preparing traffic incident management strategies. In the present work, vulnerability of an intersection was measured in relation to the incident impact on surrounding road network using average aggregate network delay. Taking the case study of an urban arterial road network in Kolkata city, a methodology was demonstrated to assess the vulnerability of intersections using traffic microsimulation during peak and off-peak periods. A traffic microsimulation model was developed for this purpose and different incident scenarios were simulated to assess the vulnerability of various intersections. The intersections were then ranked in order of their vulnerability. Some key factors governing vulnerability of intersections were identified and an expert opinion survey was also conducted to assess the location-specific relevance of those factors for both peak and off-peak hour conditions using fuzzy analysis. Based on the analysis of expert opinion data, intersections were also ranked as per their vulnerability for comparative purposes. The rankings of intersections as obtained from traffic microsimulation and expert opinion analyses were found to be in agreement in the present context. However, traffic microsimulation as an approach is preferred over expert opinion because of its inherent strengths for vulnerability assessment and identification of critical intersections.

Social dilemmas with manifest and unknown networks

Scholarly consensus that social ties resolve social dilemmas is largely predicated on common knowledge of networks. But what happens when people do not know all relevant social ties? Does network uncertainty translate into worse outcomes? I address these concerns by advancing the notion of a Network Estimation Bayesian Equilibrium to examine cooperative behavior under different epistemic conditions. When networks are common knowledge, I find that all possible outcomes of an original cooperation game can be realized in equilibrium, albeit with a higher likelihood of defection for more connected players. Variable knowledge of the network also has a distributional impact. With incomplete network knowledge, it’s possible to observe reversed equilibrium behavior when more connected players actually cooperate more often than less connected ones. In fact, aggregate network uncertainty in some social contexts incentivizes more mutual cooperation than would be the case with common knowledge of all social ties.

A Local-Global Aggregate Network for Facial Landmark Localization

Facial landmark localization plays a critical role in many face analysis tasks. In this paper, we present a novel local-global aggregate network (LGA-Net) for robust facial landmark localization of faces in the wild. The network consists of two convolutional neural network levels which aggregate local and global information for better prediction accuracy and robustness. Experimental results show our method overcomes typical problems of cascaded networks and outperforms state-of-the-art methods on the 300-W [1] benchmark.