Network science based quantification of resilience demonstrated on the Indian Railways Network

The structure, interdependence, and fragility of systems ranging from power grids and transportation to ecology, climate, biology and even human communities and the Internet, have been examined through network science. While the response to perturbations has been quantified, recovery strategies for perturbed networks have usually been either discussed conceptually or through anecdotal case studies. Here we develop a network science-based quantitative methods framework for measuring, comparing and interpreting hazard responses and as well as recovery strategies. The framework, motivated by the recently proposed temporal resilience paradigm, is demonstrated with the Indian Railways Network. The methods are demonstrated through the resilience of the network to natural or human-induced hazards and electric grid failure. Simulations inspired by the 2004 Indian Ocean Tsunami and the 2012 North Indian blackout as well as a cyber-physical attack scenario. Multiple metrics are used to generate various recovery strategies, which are simply sequences in which system components should be recovered after a disruption. Quantitative evaluation of recovery strategies suggests that faster and more resource-effective recovery is possible through network centrality measures. Case studies based on two historical events, specifically the 2004 Indian Ocean tsunami and the 2012 North Indian blackout, and a simulated cyber-physical attack scenario, provides means for interpreting the relative performance of various recovery strategies. Quantitative evaluation of recovery strategies suggests that faster and more resource-effective restoration is possible through network centrality measures, even though the specific strategy may be different for sub-networks or for the partial recovery

Measuring Voter's Candidate Preference Based on Affective Responses to Election Debates

In this paper we present the first analysis of facial responses to electoral debates measured automatically over the Internet. We show that significantly different responses can be detected from viewers with different political preferences and that similar expressions at significant moments can have very different meanings depending on the actions that appear subsequently. We used an Internet based framework to collect 611 naturalistic and spontaneous facial responses to five video clips from the 3rd presidential debate during the 2012 American presidential election campaign. Using this framework we were able to collect over 60% of these video responses (374 videos) within one day of the live debate and over 80% within three days. No participants were compensated for taking the survey. We present and evaluate a method for predicting independent voter preference based on automatically measured facial responses and self-reported preferences from the viewers. We predict voter preference with an average accuracy of over 73% (AUC 0.779)

A large-scale analysis of sex differences in facial expressions

<div><p>There exists a stereotype that women are more expressive than men; however, research has almost exclusively focused on a single facial behavior, smiling. A large-scale study examines whether women are consistently more expressive than men or whether the effects are dependent on the emotion expressed. Studies of gender differences in expressivity have been somewhat restricted to data collected in lab settings or which required labor-intensive manual coding. In the present study, we analyze gender differences in facial behaviors as over 2,000 viewers watch a set of video advertisements in their home environments. The facial responses were recorded using participants’ own webcams. Using a new automated facial coding technology we coded facial activity. We find that women are not universally more expressive across all facial actions. Nor are they more expressive in all positive valence actions and less expressive in all negative valence actions. It appears that generally women express actions more frequently than men, and in particular express more positive valence actions. However, expressiveness is not greater in women for all negative valence actions and is dependent on the discrete emotional state.</p></div