Topics in Social Systems (Fall 2014)
| Location: | HEC-438 |
| Time: | 1 pm - 2:30 pm |
| Date | Presenter | Reading |  |
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| Sep 04 | Jon Whetzler | |||
AbstractThis talk will provide an overview on tools and products developed by Sandia National Laboratories in analyzing physical site security for a variety of government agencies. Sandia's Interactive Simulation & Analysis group applies game development tools and 3D simulation techniques for training, wargamming, and closed-loop Monte Carlo simulations. This talk will cover: the uses of commercial tools, in-house simulation tools, and present research & development efforts. |
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| Sep 11 | Erfan | |||
| Sep 18 | Erfan | |||
| Sep 25 | Erfan | |||
| Oct 02 | Awrad | |||
| Oct 09 | Hamid | (Myers, Leskovec) |
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AbstractIn online social media systems users are not only posting, consuming, and resharing content, but also creating new and destroying existing connections in the underlying social network. While each of these two types of dynamics has individually been studied in the past, much less is known about the connection between the two. How does user information posting and seeking behavior interact with the evolution of the underlying social network structure? Here, we study ways in which network structure reacts to users posting and sharing content. We examine the complete dynamics of the Twitter information network, where users post and reshare information while they also create and destroy connections. We find that the dynamics of network structure can be characterized by steady rates of change, interrupted by sudden bursts. Information diffusion in the form of cascades of post re-sharing often creates such sudden bursts of new connections, which significantly change users’ local network structure. These bursts transform users’ networks of followers to become structurally more cohesive as well as more homogenous in terms of follower interests. We also explore the effect of the information content on the dynamics of the network and find evidence that the appearance of new topics and realworld events can lead to significant changes in edge creations and deletions. Lastly, we develop a model that quantifies the dynamics of the network and the occurrence of these bursts as a function of the information spreading through the network. The model can successfully predict which information diffusion events will lead to bursts in network dynamics. |
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| Oct 16 | Rey | (Engel, Sturm, Cremers) |
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AbstractWe present a complete solution for the visual navigation of a small-scale, low-cost quadrocopter in unknown environments. Our approach relies solely on a monocular camera as the main sensor, and therefore does not need external tracking aids such as GPS or visual markers. Costly computations are carried out on an external laptop that communicates over wireless LAN with the quadrocopter. Our approach consists of three components: a monocular SLAM system, an extended Kalman filter for data fusion, and a PID controller. In this paper, we (1) propose a simple, yet effective method to compensate for large delays in the control loop using an accurate model of the quadrocopter’s flight dynamics, and (2) present a novel, closed-form method to estimate the scale of a monocular SLAM system from additional metric sensors. We extensively evaluated our system in terms of pose estimation accuracy, flight accuracy, and flight agility using an external motion capture system. Furthermore, we compared the convergence and accuracy of our scale estimation method for an ultrasound altimeter and an air pressure sensor with filtering-based approaches. The complete system is available as open-source in ROS. This software can be used directly with a low-cost, off-the-shelf Parrot AR.Drone quadrocopter, and hence serves as an ideal basis for follow-up research projects. |
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| Oct 23 | Astrid | (Lopes, Melo, Montesano) |
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AbstractInverse reinforcement learning addresses the general problem of recovering a reward function from samples of a policy provided by an expert/demonstrator. In this paper, we introduce active learning for inverse reinforcement learning. We propose an algorithm that allows the agent to query the demonstrator for samples at specific states, instead of relying only on samples provided at “arbitrary” states. The purpose of our algorithm is to estimate the reward function with similar accuracy as other methods from the literature while reducing the amount of policy samples required from the expert. We also discuss the use of our algorithm in higher dimensional problems, using both Monte Carlo and gradient methods. We present illustrative results of our algorithm in several simulated examples of different complexities. |
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| Oct 30 | Awrad | |||
| Nov 06 | No meeting | |||
| Nov 13 | Bryan | (Du, Liang, Balcan, Song) |
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AbstractCan we learn the influence of a set of people in a social network from cascades of information diffusion? This question is often addressed by a two-stage approach: first learn a diffusion model, and then calculate the influence based on the learned model. Thus, the success of this approach relies heavily on the correctness of the diffusion model which is hard to verify for real world data. In this paper, we exploit the insight that the influence functions in many diffusion models are coverage functions, and propose a novel parameterization of such functions using a convex combination of random basis functions. Moreover, we propose an efficient maximum likelihood based algorithm to learn such functions directly from cascade data, and hence bypass the need to specify a particular diffusion model in advance. We provide both theoretical and empirical analysis for our approach, showing that the proposed approach can provably learn the influence function with low sample complexity, be robust to the unknown diffusion models, and significantly outperform existing approaches in both synthetic and real world data. |
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| Nov 25 | Rahmat | |||
| Nov 27 | No meeting | |||
Further Reading:
Current work in the area of social media and data mining, providing compelling justification for why these research questions are important.
Contact: