Regulating Highly Automated Robot Ecologies: Insights from Three User Studies

Highly automated robot ecologies (HARE), or societies of independent autonomous robots or agents, are rapidly becoming an important part of much of the world's critical infrastructure. As with human societies, regulation, wherein a governing body designs rules and processes for the society, plays an important role in ensuring that HARE meet societal objectives. However, to date, a careful study of interactions between a regulator and HARE is lacking. In this paper, we report on three user studies which give insights into how to design systems that allow people, acting as the regulatory authority, to effectively interact with HARE. As in the study of political systems in which governments regulate human societies, our studies analyze how interactions between HARE and regulators are impacted by regulatory power and individual (robot or agent) autonomy. Our results show that regulator power, decision support, and adaptive autonomy can each diminish the social welfare of HARE, and hint at how these seemingly desirable mechanisms can be designed so that they become part of successful HARE.Comment: 10 pages, 7 figures, to appear in the 5th International Conference on Human Agent Interaction (HAI-2017), Bielefeld, German

Alteration and Oxidiation of an Olivine Lamprophyre Dike from Southern Utah, USA: An Analog for Mars

We report on oxidized basaltic dike intrusions on the Colorado Plateau as analog for Martian basalt oxidation

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

Toddler-Inspired Visual Object Learning

Real-world learning systems have practical limitations on the quality and quantity of the training datasets that they can collect and consider. How should a system go about choosing a subset of the possible training examples that still allows for learning accurate, generalizable models? To help address this question, we draw inspiration from a highly efficient practical learning system: the human child. Using head-mounted cameras, eye gaze trackers, and a model of foveated vision, we collected first-person (egocentric) images that represents a highly accurate approximation of the "training data" that toddlers' visual systems collect in everyday, naturalistic learning contexts. We used state-of-the-art computer vision learning models (convolutional neural networks) to help characterize the structure of these data, and found that child data produce significantly better object models than egocentric data experienced by adults in exactly the same environment. By using the CNNs as a modeling tool to investigate the properties of the child data that may enable this rapid learning, we found that child data exhibit a unique combination of quality and diversity, with not only many similar large, high-quality object views but also a greater number and diversity of rare views. This novel methodology of analyzing the visual "training data" used by children may not only reveal insights to improve machine learning, but also may suggest new experimental tools to better understand infant learning in developmental psychology

TORSO DEFORMATION IN FRONTAL SLED TESTS: COMPARISION BETWEEN THOR NT, THOR NT WITH THE CHALMERS SD-1 SHOULDER, AND PMHS

This study compares the thoracic deformation response of the 50th percentile male THOR NT frontal crash dummy and the response of the THOR modified with the SD-1 shoulder (THOR SD-1) relative to the thoracic response of eight 50th percentile male PMHS. The prototype Chalmers University SD-1 shoulder was designed to be more human-like in terms of geometry and range of motion in comparison to the standard THOR NT shoulder. The dummies and PMHS were restrained by a three-point restraint in a driver-side configuration and were subjected to a simulated 40 km/h frontal crash. The most prominent difference between the responses of the dummies and PMHS involved motion of the lower right anterior ribcage measurement site that is the farthest lateral distance from the diagonal shoulder belt. During the impact event, this site moved substantially anteriorly and away from the spine for the PMHS. The PMHS lower right “bulge out” behavior is believed to be caused by inertial loading of the ribcage, underlying organs, and soft tissue overlying the torso. The THOR SD-1 shoulder altered the shoulder belt position relative to the thoracic deflection measurement sites resulting in a different distribution of deformation for the upper measurement sites although the average upper site deformation was similar to that recorded for the standard THOR shoulder

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

A Coalescent Sampler Successfully Detects Biologically Meaningful Population Structure Overlooked by F‐Statistics

Assessing the geographic structure of populations has relied heavily on Sewell Wright\u27s F‐statistics and their numerous analogues for many decades. However, it is well appreciated that, due to their nonlinear relationship with gene flow, F statistics frequently fail to reject the null model of panmixia in species with relatively high levels of gene flow and large population sizes. Coalescent genealogy samplers instead allow a model‐selection approach to the characterization of population structure, thereby providing the opportunity for stronger inference. Here, we validate the use of coalescent samplers in a high gene flow context using simulations of a stepping‐stone model. In an example case study, we then re‐analyze genetic datasets from 41 marine species sampled from throughout the Hawaiian archipelago using coalescent model selection. Due to the archipelago\u27s linear nature, it is expected that most species will conform to some sort of stepping‐stone model (leading to an expected pattern of isolation by distance), but F‐statistics have only supported this inference in ~10% of these datasets. Our simulation analysis shows that a coalescent sampler can make a correct inference of stepping‐stone gene flow in nearly 100% of cases where gene flow is ≤100 migrants per generation (equivalent to FST = 0.002), while F‐statistics had mixed results. Our re‐analysis of empirical datasets found that nearly 70% of datasets with an unambiguous result fit a stepping‐stone model with varying population sizes and rates of gene flow, although 37% of datasets yielded ambiguous results. Together, our results demonstrate that coalescent samplers hold great promise for detecting weak but meaningful population structure, and defining appropriate management units

Evaluation of a Single Nucleotide Polymorphism Baseline for Genetic Stock Identification of Chinook Salmon (Oncorhynchus tshawytscha) in the California Current Large Marine Ecosystem

Chinook Salmon (Oncorhynchus tshawytscha) is an economically and ecologically important species, and populations from the west coast of North America are a major component of fisheries in the North Pacific Ocean. The anadromous life history strategy of this species generates populations (or stocks) that typically are differentiated from neighboring populations. In many cases, it is desirable to discern the stock of origin of an individual fish or the stock composition of a mixed sample to monitor the stock-specific effects of anthropogenic impacts and alter management strategies accordingly. Genetic stock identification (GSI) provides such discrimination, and we describe here a novel GSI baseline composed of genotypes from more than 8000 individual fish from 69 distinct populations at 96 single nucleotide polymorphism (SNP) loci. The populations included in this baseline represent the likely sources for more than 99% of the salmon encountered in ocean fisheries of California and Oregon. This new genetic baseline permits GSI with the use of rapid and cost-effective SNP genotyping, and power analyses indicate that it provides very accurate identification of important stocks of Chinook Salmon. In an ocean fishery sample, GSI assignments of more than 1000 fish, with our baseline, were highly concordant (98.95%) at the reporting unit level with information from the physical tags recovered from the same fish. This SNP baseline represents an important advance in the technologies available to managers and researchers of this species