On realistic target coverage by autonomous drones

Low-cost mini-drones with advanced sensing and maneuverability enable a new class of intelligent sensing systems. To achieve the full potential of such drones, it is necessary to develop new enhanced formulations of both common and emerging sensing scenarios. Namely, several fundamental challenges in visual sensing are yet to be solved including (1) fitting sizable targets in camera frames; (2) positioning cameras at effective viewpoints matching target poses; and (3) accounting for occlusion by elements in the environment, including other targets. In this article, we introduce Argus, an autonomous system that utilizes drones to collect target information incrementally through a two-tier architecture. To tackle the stated challenges, Argus employs a novel geometric model that captures both target shapes and coverage constraints. Recognizing drones as the scarcest resource, Argus aims to minimize the number of drones required to cover a set of targets. We prove this problem is NP-hard, and even hard to approximate, before deriving a best-possible approximation algorithm along with a competitive sampling heuristic which runs up to 100× faster according to large-scale simulations. To test Argus in action, we demonstrate and analyze its performance on a prototype implementation. Finally, we present a number of extensions to accommodate more application requirements and highlight some open problems

Strategy Constancy Amidst Implementation Differences: Interaction-Intensive Versus Memory-Intensive Adaptations To Information Access In Decision-Making

Over the last two decades attempts to quantify decision-making have established that, under a wide range of conditions, people trade-off effectiveness for efficiency in the strategies they adopt. However, as interesting, significant, and influential as this research has been, its scope is limited by three factors; the coarseness of how effort was measured, the confounding of the costs of steps in the decision-making algorithm with the costs of steps in a given task environment, and the static nature of the decision tasks studied. In the current study, we embedded a decision-making task in a dynamic task environment and varied the cost required for the information access step. Across three conditions, small changes in the cost of interactive behavior led to changes in the strategy adopted for decision-making as well as to differences in how a step in the same strategy was implemented

Modeling Interdependent and Periodic Real-World Action Sequences

Mobile health applications, including those that track activities such as exercise, sleep, and diet, are becoming widely used. Accurately predicting human actions is essential for targeted recommendations that could improve our health and for personalization of these applications. However, making such predictions is extremely difficult due to the complexities of human behavior, which consists of a large number of potential actions that vary over time, depend on each other, and are periodic. Previous work has not jointly modeled these dynamics and has largely focused on item consumption patterns instead of broader types of behaviors such as eating, commuting or exercising. In this work, we develop a novel statistical model for Time-varying, Interdependent, and Periodic Action Sequences. Our approach is based on personalized, multivariate temporal point processes that model time-varying action propensities through a mixture of Gaussian intensities. Our model captures short-term and long-term periodic interdependencies between actions through Hawkes process-based self-excitations. We evaluate our approach on two activity logging datasets comprising 12 million actions taken by 20 thousand users over 17 months. We demonstrate that our approach allows us to make successful predictions of future user actions and their timing. Specifically, our model improves predictions of actions, and their timing, over existing methods across multiple datasets by up to 156%, and up to 37%, respectively. Performance improvements are particularly large for relatively rare and periodic actions such as walking and biking, improving over baselines by up to 256%. This demonstrates that explicit modeling of dependencies and periodicities in real-world behavior enables successful predictions of future actions, with implications for modeling human behavior, app personalization, and targeting of health interventions.Comment: Accepted at WWW 201

The ARGUS Vertex Trigger

A fast second level trigger has been developed for the ARGUS experiment which recognizes tracks originating from the interaction region. The processor compares the hits in the ARGUS Micro Vertex Drift Chamber to 245760 masks stored in random access memories. The masks which are fully defined in three dimensions are able to reject tracks originating in the wall of the narrow beampipe of 10.5\,mm radius.Comment: gzipped Postscript, 27 page

Sequence structure emission in The Red Rectangle Bands

We report high resolution (R~37,000) integral field spectroscopy of the central region (r<14arcsec) of the Red Rectangle nebula surrounding HD44179. The observations focus on the 5800A emission feature, the bluest of the yellow/red emission bands in the Red Rectangle. We propose that the emission feature, widely believed to be a molecular emission band, is not a molecular rotation contour, but a vibrational contour caused by overlapping sequence bands from a molecule with an extended chromophore. We model the feature as arising in a Polycyclic Aromatic Hydrocarbon (PAH) with 45-100 carbon atoms.Comment: 13 pages, 9 figures, accepted for publication in ApJ. A version of the paper with full resolution figures is available at: http://www.aao.gov.au/local/www/rgs/Sequence-Structure

Search for B^0-> p p-bar, Lambda Lambda-bar and B^+ -> p Lambda-bar at Belle

We report on a new search for two-body baryonic decays of the B meson. Improved sensitivity compared to previous Belle results is obtained from 414 fb^-1 of data that corresponds to 449 million B B-bar pairs, which were taken on the Upsilon(4S) resonance and collected with the Belle detector at the KEKB e^+e^- collider. No significant signals are observed and we set the 90% confidence level upper limits: Br(B^0-> p pbar) Lambda Lambda-bar) p Lambda-bar) < 3.2X10^-7.Comment: 5 pages, 2 figures, submitted to PR