Route Swarm: Wireless Network Optimization through Mobility

In this paper, we demonstrate a novel hybrid architecture for coordinating networked robots in sensing and information routing applications. The proposed INformation and Sensing driven PhysIcally REconfigurable robotic network (INSPIRE), consists of a Physical Control Plane (PCP) which commands agent position, and an Information Control Plane (ICP) which regulates information flow towards communication/sensing objectives. We describe an instantiation where a mobile robotic network is dynamically reconfigured to ensure high quality routes between static wireless nodes, which act as source/destination pairs for information flow. The ICP commands the robots towards evenly distributed inter-flow allocations, with intra-flow configurations that maximize route quality. The PCP then guides the robots via potential-based control to reconfigure according to ICP commands. This formulation, deemed Route Swarm, decouples information flow and physical control, generating a feedback between routing and sensing needs and robotic configuration. We demonstrate our propositions through simulation under a realistic wireless network regime.Comment: 9 pages, 4 figures, submitted to the IEEE International Conference on Intelligent Robots and Systems (IROS) 201

Encoding of low-quality DNA profiles as genotype probability matrices for improved profile comparisons, relatedness evaluation and database searches

Many DNA profiles recovered from crime scene samples are of a quality that does not allow them to be searched against, nor entered into, databases. We propose a method for the comparison of profiles arising from two DNA samples, one or both of which can have multiple donors and be affected by low DNA template or degraded DNA. We compute likelihood ratios to evaluate the hypothesis that the two samples have a common DNA donor, and hypotheses specifying the relatedness of two donors. Our method uses a probability distribution for the genotype of the donor of interest in each sample. This distribution can be obtained from a statistical model, or we can exploit the ability of trained human experts to assess genotype probabilities, thus extracting much information that would be discarded by standard interpretation rules. Our method is compatible with established methods in simple settings, but is more widely applicable and can make better use of information than many current methods for the analysis of mixed-source, low-template DNA profiles. It can accommodate uncertainty arising from relatedness instead of or in addition to uncertainty arising from noisy genotyping. We describe a computer program GPMDNA, available under an open source license, to calculate LRs using the method presented in this paper.Comment: 28 pages. Accepted for publication 2-Sep-2016 - Forensic Science International: Genetic

Encoding of low-quality DNA profiles as genotype probability matrices for improved profile comparisons, relatedness evaluation and database searches

Many DNA profiles recovered from crime scene samples are of a quality that does not allow them to be searched against, nor entered into, databases. We propose a method for the comparison of profiles arising from two DNA samples, one or both of which can have multiple donors and be affected by low DNA template or degraded DNA. We compute likelihood ratios to evaluate the hypothesis that the two samples have a common DNA donor, and hypotheses specifying the relatedness of two donors. Our method uses a probability distribution for the genotype of the donor of interest in each sample. This distribution can be obtained from a statistical model, or we can exploit the ability of trained human experts to assess genotype probabilities, thus extracting much information that would be discarded by standard interpretation rules. Our method is compatible with established methods in simple settings, but is more widely applicable and can make better use of information than many current methods for the analysis of mixed-source, low-template DNA profiles. It can accommodate uncertainty arising from relatedness instead of or in addition to uncertainty arising from noisy genotyping. We describe a computer program GPMDNA, available under an open source license, to calculate LRs using the method presented in this paper

Black-Box Hypotheses and Lower Bounds

What sort of code is so difficult to analyze that every potential analyst can discern essentially no information from the code, other than its input-output behavior? In their seminal work on program obfuscation, Barak, Goldreich, Impagliazzo, Rudich, Sahai, Vadhan, and Yang (CRYPTO 2001) proposed the Black-Box Hypothesis, which roughly states that every property of Boolean functions which has an efficient "analyst" and is "code independent" can also be computed by an analyst that only has black-box access to the code. In their formulation of the Black-Box Hypothesis, the "analysts" are arbitrary randomized polynomial-time algorithms, and the "codes" are general (polynomial-size) circuits. If true, the Black-Box Hypothesis would immediately imply NP ? ? BPP. We consider generalized forms of the Black-Box Hypothesis, where the set of "codes" ? and the set of "analysts" ? may correspond to other efficient models of computation, from more restricted models such as AC? to more general models such as nondeterministic circuits. We show how lower bounds of the form ? ? ? ? often imply a corresponding Black-Box Hypothesis for those respective codes and analysts. We investigate the possibility of "complete" problems for the Black-Box Hypothesis: problems in ? such that they are not in ? if and only if their corresponding Black-Box Hypothesis is true. Along the way, we prove an equivalence: for nondeterministic circuit classes ?, the "?-circuit satisfiability problem" is not in ? if and only if the Black-Box Hypothesis is true for analysts in ?