A Predictive Model for Human-Unmanned Vehicle Systems : Final Report

Advances in automation are making it possible for a single operator to control multiple unmanned vehicles (UVs). This capability is desirable in order to reduce the operational costs of human-UV systems (HUVS), extend human capabilities, and improve system effectiveness. However, the high complexity of these systems introduces many significant challenges to system designers. To help understand and overcome these challenges, high-fidelity computational models of the HUVS must be developed. These models should have two capabilities. First, they must be able to describe the behavior of the various entities in the team, including both the human operator and the UVs in the team. Second, these models must have the ability to predict how changes in the HUVS and its mission will alter the performance characteristics of the system. In this report, we describe our work toward developing such a model. Via user studies, we show that our model has the ability to describe the behavior of a HUVS consisting of a single human operator and multiple independent UVs with homogeneous capabilities. We also evaluate the model’s ability to predict how changes in the team size, the human-UV interface, the UV’s autonomy levels, and operator strategies affect the system’s performance.Prepared for MIT Lincoln Laborator

Identifying Predictive Metrics for Supervisory Control of Multiple Robots

In recent years, much research has focused on making possible single operator control of multiple robots. In these high workload situations, many questions arise including how many robots should be in the team, which autonomy levels should they employ, and when should these autonomy levels change? To answer these questions, sets of metric classes should be identified that capture these aspects of the human-robot team. Such a set of metric classes should have three properties. First, it should contain the key performance parameters of the system. Second, it should identify the limitations of the agents in the system. Third, it should have predictive power. In this paper, we decompose a human-robot team consisting of a single human and multiple robots in an effort to identify such a set of metric classes. We assess the ability of this set of metric classes to (a) predict the number of robots that should be in the team and (b) predict system effectiveness. We do so by comparing predictions with actual data from a user study, which is also described.This research was funded by MIT Lincoln Laboratory

A Mutagenetic Tree Hidden Markov Model for Longitudinal Clonal HIV Sequence Data

RNA viruses provide prominent examples of measurably evolving populations. In HIV infection, the development of drug resistance is of particular interest, because precise predictions of the outcome of this evolutionary process are a prerequisite for the rational design of antiretroviral treatment protocols. We present a mutagenetic tree hidden Markov model for the analysis of longitudinal clonal sequence data. Using HIV mutation data from clinical trials, we estimate the order and rate of occurrence of seven amino acid changes that are associated with resistance to the reverse transcriptase inhibitor efavirenz.Comment: 20 pages, 6 figure

Fast computation of Bernoulli, Tangent and Secant numbers

We consider the computation of Bernoulli, Tangent (zag), and Secant (zig or Euler) numbers. In particular, we give asymptotically fast algorithms for computing the first n such numbers in O(n^2.(log n)^(2+o(1))) bit-operations. We also give very short in-place algorithms for computing the first n Tangent or Secant numbers in O(n^2) integer operations. These algorithms are extremely simple, and fast for moderate values of n. They are faster and use less space than the algorithms of Atkinson (for Tangent and Secant numbers) and Akiyama and Tanigawa (for Bernoulli numbers).Comment: 16 pages. To appear in Computational and Analytical Mathematics (associated with the May 2011 workshop in honour of Jonathan Borwein's 60th birthday). For further information, see http://maths.anu.edu.au/~brent/pub/pub242.htm

Access regulation and the transition from copper to fiber networks in telecoms

In this paper we study the impact of different forms of access obligations on firms' incentives to migrate from the legacy copper network to ultra-fast broadband infrastructures. We analyze three different kinds of regulatory interventions: geographical regulation of access to copper networks-where access prices are differentiated depending on whether or not an alternative fiber network has been deployed; access obligations on fiber networks and its interplay with wholesale copper prices; and, finally, a mandatory switch-off of the legacy copper network-to foster the transition to the higher quality fiber networks. Trading-off the different static and dynamic goals, the paper provides guidelines and suggestions for policy makers' decision

Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems

Predicting the future location of vehicles is essential for safety-critical applications such as advanced driver assistance systems (ADAS) and autonomous driving. This paper introduces a novel approach to simultaneously predict both the location and scale of target vehicles in the first-person (egocentric) view of an ego-vehicle. We present a multi-stream recurrent neural network (RNN) encoder-decoder model that separately captures both object location and scale and pixel-level observations for future vehicle localization. We show that incorporating dense optical flow improves prediction results significantly since it captures information about motion as well as appearance change. We also find that explicitly modeling future motion of the ego-vehicle improves the prediction accuracy, which could be especially beneficial in intelligent and automated vehicles that have motion planning capability. To evaluate the performance of our approach, we present a new dataset of first-person videos collected from a variety of scenarios at road intersections, which are particularly challenging moments for prediction because vehicle trajectories are diverse and dynamic.Comment: To appear on ICRA 201

Emergency Department Utilization among Victims and Offenders Involved in Non-lethal Violence

The medical literature has focused on violent victimization as a public health concern, examining its correlates and evaluating intervention models. However, the emphasis on victimization in this literature overlooks the strong ties between victimization and offending risks outlined in the criminological literature, which may unnecessarily limit the scope of public health efforts to influence violence in our communities. This study examines whether the similarities observed in the criminological literature are evident in a health care setting. More specifically, do victims and offenders exhibit similar health care utilization patterns? We address this question by comparing the emergency department utilization records, criminal histories, and demographic characteristics of a sample of victims and offenders involved in non-lethal violence in Bernalillo County, New Mexico, USA in 2001. Our results suggest that victims and offenders have similar emergency department utilization patterns, with most visits being for injury. Moreover, most victims seen in the emergency department have criminal records that, in many ways, mirror those of offenders. The results suggest that violence intervention programs in public health settings should target both victims and offenders and capitalize on the overlap across these populations in outlining the long term risks of criminal involvement and motivating individual level change

A differential method for bounding the ground state energy

For a wide class of Hamiltonians, a novel method to obtain lower and upper bounds for the lowest energy is presented. Unlike perturbative or variational techniques, this method does not involve the computation of any integral (a normalisation factor or a matrix element). It just requires the determination of the absolute minimum and maximum in the whole configuration space of the local energy associated with a normalisable trial function (the calculation of the norm is not needed). After a general introduction, the method is applied to three non-integrable systems: the asymmetric annular billiard, the many-body spinless Coulombian problem, the hydrogen atom in a constant and uniform magnetic field. Being more sensitive than the variational methods to any local perturbation of the trial function, this method can used to systematically improve the energy bounds with a local skilled analysis; an algorithm relying on this method can therefore be constructed and an explicit example for a one-dimensional problem is given.Comment: Accepted for publication in Journal of Physics