Near-optimal irrevocable sample selection for periodic data streams with applications to marine robotics

We consider the task of monitoring spatiotemporal phenomena in real-time by deploying limited sampling resources at locations of interest irrevocably and without knowledge of future observations. This task can be modeled as an instance of the classical secretary problem. Although this problem has been studied extensively in theoretical domains, existing algorithms require that data arrive in random order to provide performance guarantees. These algorithms will perform arbitrarily poorly on data streams such as those encountered in robotics and environmental monitoring domains, which tend to have spatiotemporal structure. We focus on the problem of selecting representative samples from phenomena with periodic structure and introduce a novel sample selection algorithm that recovers a near-optimal sample set according to any monotone submodular utility function. We evaluate our algorithm on a seven-year environmental dataset collected at the Martha's Vineyard Coastal Observatory and show that it selects phytoplankton sample locations that are nearly optimal in an information-theoretic sense for predicting phytoplankton concentrations in locations that were not directly sampled. The proposed periodic secretary algorithm can be used with theoretical performance guarantees in many real-time sensing and robotics applications for streaming, irrevocable sample selection from periodic data streams.Comment: 8 pages, accepted for presentation in IEEE Int. Conf. on Robotics and Automation, ICRA '18, Brisbane, Australia, May 201

Structural Return Maximization for Reinforcement Learning

Batch Reinforcement Learning (RL) algorithms attempt to choose a policy from a designer-provided class of policies given a fixed set of training data. Choosing the policy which maximizes an estimate of return often leads to over-fitting when only limited data is available, due to the size of the policy class in relation to the amount of data available. In this work, we focus on learning policy classes that are appropriately sized to the amount of data available. We accomplish this by using the principle of Structural Risk Minimization, from Statistical Learning Theory, which uses Rademacher complexity to identify a policy class that maximizes a bound on the return of the best policy in the chosen policy class, given the available data. Unlike similar batch RL approaches, our bound on return requires only extremely weak assumptions on the true system

Feature discovery and visualization of robot mission data using convolutional autoencoders and Bayesian nonparametric topic models

The gap between our ability to collect interesting data and our ability to analyze these data is growing at an unprecedented rate. Recent algorithmic attempts to fill this gap have employed unsupervised tools to discover structure in data. Some of the most successful approaches have used probabilistic models to uncover latent thematic structure in discrete data. Despite the success of these models on textual data, they have not generalized as well to image data, in part because of the spatial and temporal structure that may exist in an image stream. We introduce a novel unsupervised machine learning framework that incorporates the ability of convolutional autoencoders to discover features from images that directly encode spatial information, within a Bayesian nonparametric topic model that discovers meaningful latent patterns within discrete data. By using this hybrid framework, we overcome the fundamental dependency of traditional topic models on rigidly hand-coded data representations, while simultaneously encoding spatial dependency in our topics without adding model complexity. We apply this model to the motivating application of high-level scene understanding and mission summarization for exploratory marine robots. Our experiments on a seafloor dataset collected by a marine robot show that the proposed hybrid framework outperforms current state-of-the-art approaches on the task of unsupervised seafloor terrain characterization.Comment: 8 page

Trichet Bonds To Resolve the European Sovereign Debt Problem

We propose the creation of “Trichet Bonds” as a comprehensive solution to the current sovereign debt crisis in the EU area. “Trichet Bonds,” to be named after the ECB president Jean-Claude Trichet, will be similar to “Brady Bonds” that resolved the Latin American debt crisis in the late 1980s and were named after the then Treasury Secretary Nicholas Brady. Like the Brady Bonds, Trichet Bonds will be new long-duration bonds issued by countries in the EU area that will be collateralized by zero-coupon bonds of the same duration issued by the ECB. The zero-coupon bonds will be sold by the ECB to the countries issuing Trichet Bonds, which will be offered in exchange for outstanding sovereign debt of the countries. The exchange is offered at market value, so current debt holders will experience a “haircut” from par value, and thus the exchange does not involve a “bailout.” However, present holders of sovereign debt will be exchanging low quality bonds with limited liquidity, for higher quality bonds with greater liquidity. Debt holders not accepting the exchange will be at risk of a forced restructuring at a later date at terms less favorable. The effect of the exchange offer, if a threshold of approximately 70% approve it, is to replace old debt with a lesser amount of new debt with longer maturities. The creation of Trichet bonds will result in various advantages both in comparison to the present unstable situation and other proposed solutions. First, the long duration of Trichet bonds will eliminate the immediate crisis caused by short term expiration of significant amounts of debt which is looming over Greece, Ireland, Portugal, Spain and possibly other EU countries. Second, the guarantee of the principal with the zero-coupon ECD bond collateral increases the quality of the Trichet Bonds compared to existing sovereign debt. Third, the market for the new Trichet Bonds will be liquid and likely to trade at appreciating prices as refinancing (roll-over) risk is reduced and time is allowed for economic reforms by the issuing countries (a condition of the ECB) to take effect. In addition, the exchange of existing sovereign debt for Trichet bonds will force many European banks holding the sovereign debt to take the write-offs required, thus making their own balance sheets more transparent. Many European banks are thought to have large holdings of sovereign debt from the “peripheral” countries that have not been marked-to-market, and thus represent sizeable potential losses for the banks when the sovereign debt is ultimately restructured, as we believe it must be over the next few years. Most of the sovereign bank debt likely to be exchanged, however, is held by larger German, French and Swiss banks with the capability (if not necessarily the desire) to take the write-offs required. The overhang of such future losses affects the entire European banking system at a time when it too is being restructured. The ECB, and the European central banks need to identify those banks that are impaired by excessive sovereign holdings and assist them in recapitalization – the sooner the better – but they should also push the larger, stronger banks to accept the exchange offers in the interest of bank transparency and restructuring as well as in resolving the sovereign debt problem. Clearly the two problems – sovereign debt and bank restructuring – are connected. The issuance of Trichet Bonds, will help to resolve both problems by recognizing market realities and offering an easier way out than through a forced, cram-down restructuring once the ailing sovereigns exhaust their ability to repay the existing debt. There are significant advantages to Trichet bonds over other discussed solutions to the sovereign debt problem. One such proposed solution is the issuance of “Euro Bonds” guaranteed by the Eurozone countries or the EU itself for the purpose of redeeming sovereign bonds by market purchases, or by lending the proceeds to the countries involved for them to acquire their debt. Apart from the considerable political obstacles to such a program, the undertaking actually makes it less likely that existing self-interested debt-holders will sell in the market. The implication of the program is that either through market interventions that push prices up, or by the assumption that the program will continue to enable the debt to be retired at par on maturity, debt-holders won’t sell unless the price is pushed high enough to constitute a bailout. The ECB’s current efforts to support the prices of distressed sovereign bonds is currently having this effect, which transfers some, if not all of the cost of resolving the problem to European taxpayers, where increasingly it is resented. The alternative approach, that has only been discussed by market participants, is for a Russian or Argentine solution in which the debt-holders are made a take-it-or-leave-it offer to exchange outstanding debt for new, generally illiquid bonds at an arbitrary price that discourages future investment by the market. Such an approach is understood by the sovereign debt market to constitute a de facto default. Such a default would likely have serious adverse consequences for the Euro and the EU, and may be less likely that a bailout of some kind. The great advantage of Trichet Bonds is that they avoid both bailouts and defaults.Trichet bonds, sovereign debt, euro, debt restructuring, Greece, Ireland, Portugal, Spain, Italy, Brady bonds

A molecular superfluid: non-classical rotations in doped para-hydrogen clusters

Clusters of para-hydrogen (pH2) have been predicted to exhibit superfluid behavior, but direct observation of this phenomenon has been elusive. Combining experiments and theoretical simulations, we have determined the size evolution of the superfluid response of pH2 clusters doped with carbon dioxide (CO2). Reduction of the effective inertia is observed when the dopant is surrounded by the pH2 solvent. This marks the onset of molecular superfluidity in pH2. The fractional occupation of solvation rings around CO2 correlates with enhanced superfluid response for certain cluster sizes

Path Integral Ground State with a Fourth-Order Propagator: Application to Condensed Helium

Ground state properties of condensed Helium are calculated using the Path Integral Ground State (PIGS) method. A fourth-order approximation is used as short (imaginary) time propagator. We compare our results with those obtained with other Quantum Monte Carlo techniques and different propagators. For this particular application, we find that the fourth-order propagator performs comparably to the pair product approximation, and is far superior to the primitive approximation. Results obtained for the equation of state of condensed Helium show that PIGS compares favorably to other QMC methods traditionally utilized for this type of calculation

PROBE-GK: Predictive Robust Estimation using Generalized Kernels

Many algorithms in computer vision and robotics make strong assumptions about uncertainty, and rely on the validity of these assumptions to produce accurate and consistent state estimates. In practice, dynamic environments may degrade sensor performance in predictable ways that cannot be captured with static uncertainty parameters. In this paper, we employ fast nonparametric Bayesian inference techniques to more accurately model sensor uncertainty. By setting a prior on observation uncertainty, we derive a predictive robust estimator, and show how our model can be learned from sample images, both with and without knowledge of the motion used to generate the data. We validate our approach through Monte Carlo simulations, and report significant improvements in localization accuracy relative to a fixed noise model in several settings, including on synthetic data, the KITTI dataset, and our own experimental platform.Comment: In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'16), Stockholm, Sweden, May 16-21, 201

Meno's paradox and medicine

This is the final version of the article. Available from Springer Verlag via the DOI in this record.The measurement of diagnostic accuracy is an important aspect of the evaluation of diagnostic tests. Sometimes, medical researchers try to discover the set of observations that are most accurate of all by directly inspecting diseased and not-diseased patients. This method is perhaps intuitively appealing, as it seems a straightforwardempiricalwayofdiscoveringhowtoidentifydiseasedpatients,which amounts to trying to correlate the results of diagnostic tests with disease status. I present three examples of researchers who try to produce definitive diagnostic criteria by directly inspecting diseased and not diseased patients. Despite this method’s intuitive appeal, I will argue that it is impossible to carry out. Before researchers can inspect these patients to discover definitive diagnostic criteria, they must be able to distinguish diseased and not-diseased patients; and they do not know how to do this, because this is what they are trying to discover. I suspect the intuitive appeal of directlyinspectingpatientsmakesthisdifficulttoappreciate.Tocounterthisdifficulty, I present this problem as a manifestation of ‘Meno’s paradox’, which was described in classical antiquity, and of ‘the problem of nomic measurement’, described more recently. Considering these philosophical problems may help researchers address the methodological issues they face when evaluating diagnostic tests.Research and open access publication funded by the University of Exeter