Home > Uncategorized > Weekly QuEST Discussion Topics and News, 3 June

Weekly QuEST Discussion Topics and News, 3 June

QuEST 3 June 2016

The topic this week is first to cover any discussion items from the group since we haven’t been together for a couple of weeks.

One topic I want to remind people – I’m extremely interested in applying QuEST ideas to social and medical issues – specifically what to do about inner city violence and how to do predictive intelligence (for predicting shock onset) – one article I will post for potential discussion is:

Am J Community Psychol (2009) 44:273–286

DOI 10.1007/s10464-009-9268-2

Researching a Local Heroin Market as a Complex Adaptive


Lee D. Hoffer • Georgiy Bobashev •

Robert J. Morris

Abstract This project applies agent-based modeling (ABM) techniques to better understand the operation, organization, and structure of a local heroin market. The simulation detailed was developed using data from an 18- month ethnographic case study. The original research, collected in Denver, CO during the 1990s, represents the historic account of users and dealers who operated in the Larimer area heroin market. Working together, the authors studied the behaviors of customers, private dealers, streetsellers, brokers, and the police, reflecting the core elements pertaining to how the market operated. After evaluating the logical consistency between the data and agent behaviors, simulations scaled-up interactions to observe their aggregated outcomes. While the concept and findings from this study remain experimental, these methods represent a novel way in which to understand illicit drug markets and the dynamic adaptations and outcomes they generate. Extensions of this research perspective, as well as its strengths and limitations, are discussed.


What I’m in need of here is an ‘action officer’ – someone to take this on and drive it forward by finding related material and pointing me to things to think about and act upon – I can connect that person with an AFOSR contact and a DARPA contact who might be interested in helping us.

We also want to hit the article: Deep residual learning for Image Recognition – from Microsoft research.  For those remote you can find the article at

arXiv:1512.03385v1 [cs.CV] 10 Dec 2015

  • Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously.
  • We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions.
  • We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.
  • On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8 deeper than VGG nets [41] but still having lower complexity.
  • An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers.
  • The depth of representations is of central importance for many visual recognition tasks.Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset.
  • Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the
  • 1st places on the tasks of ImageNet detection,
  • ImageNet localization,
  • COCO detection, and
  • COCO segmentation.

Heroin market complex adaptive system

microsoft residual learning paper

news summary (14)

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