An algorithm to compare two‐dimensional footwear outsole images using maximum cliques and speeded‐up robust feature

Footwear examiners are tasked with comparing an outsole impression (Q) left at a crime scene with an impression (K) from a database or from the suspect\u27s shoe. We propose a method for comparing two shoe outsole impressions that relies on robust features (speeded‐up robust feature; SURF) on each impression and aligns them using a maximum clique (MC). After alignment, an algorithm we denote MC‐COMP is used to extract additional features that are then combined into a univariate similarity score using a random forest (RF). We use a database of shoe outsole impressions that includes images from two models of athletic shoes that were purchased new and then worn by study participants for about 6 months. The shoes share class characteristics such as outsole pattern and size, and thus the comparison is challenging. We find that the RF implemented on SURF outperforms other methods recently proposed in the literature in terms of classification precision. In more realistic scenarios where crime scene impressions may be degraded and smudged, the algorithm we propose—denoted MC‐COMP‐SURF—shows the best classification performance by detecting unique features better than other methods. The algorithm can be implemented with the R‐package shoeprintr

Uncertainty and choice: the challenges of pharmaceutical efficacy, safety, and cost

This paper addresses the role of uncertainty in challenges confronting the US pharmaceutical industry and its regulators. It begins with a macroeconomic overview, showing how rising health care costs, including rising ethical drug costs, squeeze the wage and salary share of national income. It addresses the claim that pharmaceutical companies' new drug pipelines are drying up. Taking a stochastic approach, it shows that both the trend of new medical entity approvals and the trend of pharmaceutical company gross margins can be explained as random walks-the latter as a result of sampling from a highly skew distribution. It then focuses on problems of ascertaining efficacy and especially low-probability adverse effects from clinical trial samples of acceptable size. Noting the need for post-NDA surveillance of adverse effects and observing the incentive failures that occur at that stage, it proposes two new approaches to identifying adverse reactions. Copyright © 2007 John Wiley & Sons, Ltd.