Bayesian Best-Arm Identification for Selecting Influenza Mitigation Strategies

Pandemic influenza has the epidemic potential to kill millions of people. While various preventive measures exist (i.a., vaccination and school closures), deciding on strategies that lead to their most effective and efficient use remains challenging. To this end, individual-based epidemiological models are essential to assist decision makers in determining the best strategy to curb epidemic spread. However, individual-based models are computationally intensive and it is therefore pivotal to identify the optimal strategy using a minimal amount of model evaluations. Additionally, as epidemiological modeling experiments need to be planned, a computational budget needs to be specified a priori. Consequently, we present a new sampling technique to optimize the evaluation of preventive strategies using fixed budget best-arm identification algorithms. We use epidemiological modeling theory to derive knowledge about the reward distribution which we exploit using Bayesian best-arm identification algorithms (i.e., Top-two Thompson sampling and BayesGap). We evaluate these algorithms in a realistic experimental setting and demonstrate that it is possible to identify the optimal strategy using only a limited number of model evaluations, i.e., 2-to-3 times faster compared to the uniform sampling method, the predominant technique used for epidemiological decision making in the literature. Finally, we contribute and evaluate a statistic for Top-two Thompson sampling to inform the decision makers about the confidence of an arm recommendation

Bayesian Active Edge Evaluation on Expensive Graphs

Robots operate in environments with varying implicit structure. For instance, a helicopter flying over terrain encounters a very different arrangement of obstacles than a robotic arm manipulating objects on a cluttered table top. State-of-the-art motion planning systems do not exploit this structure, thereby expending valuable planning effort searching for implausible solutions. We are interested in planning algorithms that actively infer the underlying structure of the valid configuration space during planning in order to find solutions with minimal effort. Consider the problem of evaluating edges on a graph to quickly discover collision-free paths. Evaluating edges is expensive, both for robots with complex geometries like robot arms, and for robots with limited onboard computation like UAVs. Until now, this challenge has been addressed via laziness i.e. deferring edge evaluation until absolutely necessary, with the hope that edges turn out to be valid. However, all edges are not alike in value - some have a lot of potentially good paths flowing through them, and some others encode the likelihood of neighbouring edges being valid. This leads to our key insight - instead of passive laziness, we can actively choose edges that reduce the uncertainty about the validity of paths. We show that this is equivalent to the Bayesian active learning paradigm of decision region determination (DRD). However, the DRD problem is not only combinatorially hard, but also requires explicit enumeration of all possible worlds. We propose a novel framework that combines two DRD algorithms, DIRECT and BISECT, to overcome both issues. We show that our approach outperforms several state-of-the-art algorithms on a spectrum of planning problems for mobile robots, manipulators and autonomous helicopters

PerfWeb: How to Violate Web Privacy with Hardware Performance Events

The browser history reveals highly sensitive information about users, such as financial status, health conditions, or political views. Private browsing modes and anonymity networks are consequently important tools to preserve the privacy not only of regular users but in particular of whistleblowers and dissidents. Yet, in this work we show how a malicious application can infer opened websites from Google Chrome in Incognito mode and from Tor Browser by exploiting hardware performance events (HPEs). In particular, we analyze the browsers' microarchitectural footprint with the help of advanced Machine Learning techniques: k-th Nearest Neighbors, Decision Trees, Support Vector Machines, and in contrast to previous literature also Convolutional Neural Networks. We profile 40 different websites, 30 of the top Alexa sites and 10 whistleblowing portals, on two machines featuring an Intel and an ARM processor. By monitoring retired instructions, cache accesses, and bus cycles for at most 5 seconds, we manage to classify the selected websites with a success rate of up to 86.3%. The results show that hardware performance events can clearly undermine the privacy of web users. We therefore propose mitigation strategies that impede our attacks and still allow legitimate use of HPEs

A model for incorporating a clinically-feasible exercise test in paraplegic annual reviews : a tool for stratified cardiopulmonary stress performance classification and monitoring

To identify and characterize an exercise test for use in routine spinal cord injury clinical review, and (ii) to describe levels of, and factors affecting, cardiopulmonary stress performance during exercise in the chronic paraplegic population in Scotland, UK. Cross-sectional study Queen Elizabeth National Spinal Injuries Unit (Glasgow, Scotland) 48 subjects with chronic paraplegia resulting from spinal cord injury at neurological levels T2-L2 Peak oxygen uptake, peak power output, gas exchange threshold and peak heart rate were determined from an incremental arm-cranking exercise test. Using a general linear model, the effects of gender, high (injury level above T6) versus low paraplegia, time since injury, body mass and age on peak oxygen uptake and peak power output were investigated. All 48 subjects completed the arm-cranking exercise test, which was shown to be practical for fitness screening in paraplegia. Men (n=38) had a peak oxygen uptake of 1.302 +/- 0.326 l.min-1 (mean +/- s.d.) and peak power output of 81.6 +/- 23.2W, which was significantly higher than for women (n=10), at 0.832 +/- 0.277 l.min-1 and 50.1 +/- 27.8 W, respectively. There was large intersubject variability in cardiopulmonary performance during arm-cranking exercise testing, but the overall mean for the Scottish population was lower than reference values from other countries. Arm-cranking exercise tests are feasible in the clinical environment. The motivation for their implementation is threefold: (i) to determine cardiopulmonary stress performance of individual paraplegic patients, (ii) to stratify patients into cardiovascular risk categories, and (iii) to monitor the effects of targeted exercise prescription