Learning Online Smooth Predictors for Realtime Camera Planning using Recurrent Decision Trees

We study the problem of online prediction for realtime camera planning, where the goal is to predict smooth trajectories that correctly track and frame objects of interest (e.g., players in a basketball game). The conventional approach for training predictors does not directly consider temporal consistency, and often produces undesirable jitter. Although post-hoc smoothing (e.g., via a Kalman filter) can mitigate this issue to some degree, it is not ideal due to overly stringent modeling assumptions (e.g., Gaussian noise). We propose a recurrent decision tree framework that can directly incorporate temporal consistency into a data-driven predictor, as well as a learning algorithm that can efficiently learn such temporally smooth models. Our approach does not require any post-processing, making online smooth predictions much easier to generate when the noise model is unknown. We apply our approach to sports broadcasting: given noisy player detections, we learn where the camera should look based on human demonstrations. Our experiments exhibit significant improvements over conventional baselines and showcase the practicality of our approach

HI and CO in the circumstellar environment of the oxygen-rich AGB star RX Lep

Circumstellar shells around AGB stars are built over long periods of time that may reach several million years. They may therefore be extended over large sizes (~1 pc, possibly more), and different complementary tracers are needed to describe their global properties. In the present work, we combined 21-cm HI and CO rotational line data obtained on an oxygen-rich semi-regular variable, RX Lep, to describe the global properties of its circumstellar environment. With the SEST, we detected the CO(2-1) rotational line from RX Lep. The line profile is parabolic and implies an expansion velocity of ~4.2 km/s and a mass-loss rate ~1.7 10^-7 Msun/yr (d = 137 pc). The HI line at 21 cm was detected with the Nancay Radiotelescope on the star position and at several offset positions. The linear shell size is relatively small, ~0.1 pc, but we detect a trail extending southward to ~0.5 pc. The line profiles are approximately Gaussian with an FWHM ~3.8 km/s and interpreted with a model developed for the detached shell around the carbon-rich AGB star Y CVn. Our HI spectra are well-reproduced by assuming a constant outflow (Mloss = 1.65 10^-7 Msun/yr) of ~4 10^4 years duration, which has been slowed down by the external medium. The spatial offset of the HI source is consistent with the northward direction of the proper motion, lending support to the presence of a trail resulting from the motion of the source through the ISM, as already suggested for Mira, RS Cnc, and other sources detected in HI. The source was also observed in SiO (3 mm) and OH (18 cm), but not detected. The properties of the external parts of circumstellar shells around AGB stars should be dominated by the interaction between stellar outflows and external matter for oxygen-rich, as well as for carbon-rich, sources, and the 21-cm HI line provides a very useful tracer of these regions.Comment: 15 pages, 9 figures, accepted for publication in A&

Mineralization of Alvinella polychaete tubes at hydrothermal vents

© 2014 The Authors. Geobiology Published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License [4.0], which permits use, distribution and reproduction in any medium, provided the original work is properly cited. The attached file is the published version of the article

Earth Science Technology Office (ESTO) New Observing Strategies (NOS) and NOS-Testbed (NOS-T)

No abstract availabl

Testbed Requirements to Enable New Observing Strategies

Emerging capabilities to integrate instruments on smallsats, airborne platforms and in situ devices into an intelligent, distributed observing strategy show great promise for measuring Earth science natural phenomena and physical processes that have not previously been characterized. To reduce the threshold for success in deploying such an intelligent, integrated observing strategy, a ground-based testbed system is proposed. Virtually all of the technologies needed for using such a tool have matured to the point of being used, individually. Virtually none of the technologies have been deployed, working together. The technologies to be deployed should be integrated into a working "breadboard" where the components can be debugged and performance and behavior characterized and tuned-up. A system of this complexity should not be expected to work without full integration and experimental characterization. Further, and perhaps more importantly, in order to successfully propose a space-based element to this strategy, teams must convince the relevant science community that the risk is low enough to warrant the investment. The main benefit of the testbed is to retire the risk of integrating these new technologies and increase the Technology Readiness Level (TRL) of each component as well as the System Readiness Level (SRL) of the integrated system

High angular resolution N-band observation of the silicate carbon star IRAS08002-3803 with the VLTI/MIDI instrument

We present the results of N-band spectro-interferometric observations of the silicate carbon star IRAS08002-3803 with the MID-infrared Interferometric instrument (MIDI) at the Very Large Telescope Interferometer (VLTI) of the European Southern Observatory (ESO). The observations were carried out using two unit telescopes (UT2 and UT3) with projected baseline lengths ranging from 39 to 47 m. Our observations of IRAS08002-3803 have spatially resolved the dusty environment of a silicate carbon star for the first time and revealed an unexpected wavelength dependence of the angular size in the N band: the uniform-disk diameter is found to be constant and ~36 mas (72 Rstar) between 8 and 10 micron, while it steeply increases longward of 10 micron to reach ~53 mas (106 Rstar) at 13 micron. Model calculations with our Monte Carlo radiative transfer code show that neither spherical shell models nor axisymmetric disk models consisting of silicate grains alone can simultaneously explain the observed wavelength dependence of the visibility and the spectral energy distribution (SED). We propose that the circumstellar environment of IRAS08002-3803 may consist of two grain species coexisting in the disk: silicate and a second grain species, for which we consider amorphous carbon, large silicate grains, and metallic iron grains. Comparison of the observed visibilities and SED with our models shows that such disk models can fairly -- though not entirely satisfactorily -- reproduce the observed SED and N-band visibilities. Our MIDI observations and the radiative transfer calculations lend support to the picture where oxygen-rich material around IRAS08002-3803 is stored in a circumbinary disk surrounding the carbon-rich primary star and its putative low-luminosity companion.Comment: 15 pages, 8 figures, accepted for publication in A&

Atomic hydrogen in AGB circumstellar environments. A case study: X Her

We report the detection of the HI line at 21 cm from the circumstellar shell around the AGB star X Her using the position-switching technique with the Nancay Radio Telescope. At the star position the line shows 2 components: (i) a broad one (FWHM ~ 13 km/s) centered at -72.2 km/s, and (ii) a narrow one (FWHM \~ 4 km/s) centered at ~ -70.6 km/s. Our map shows that the source associated to the broad component is asymmetric with material flowing preferentially towards the North-East. This source extends to ~ 10 arcmin. (~ 0.4 pc) from the star in that direction. On the other hand, the narrow component is detected only at the star position and indicates material flowing away from the observer. The total mass of atomic hydrogen is ~ 6.5 10^{-3} solar mass which, within a factor 2, agrees with the estimate obtained from IRAS data at 60 microns.Comment: accepted for publication in MNRA

New Observing Strategy (NOS) for Future Earth Science Missions

One of the new thrusts of the Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST) Program is the New Observing Strategy (NOS) thrust. Its goal is to provide a framework for identifying technology advances needed to exploit newly available observational capabilities, particularly to enable the development of the information technologies needed to support planning, evaluating, implementing, and operating dynamic, multi-element sets of observing assets. In this paper, we will introduce relevant NOS terminology and some key concepts before describing the objectives, driving factors and technology goals of this new thrust

Earth Science Technology Office (ESTO) New Observing Strategies (NOS) and NOS-Testbed (NOS-T)

With the advancement of space hardware technologies such as smaller spacecraft, component and instrument miniaturization and high performance space processors, and with the advancement of software technologies in artificial intelligence, big data analysis and autonomous decision making, Earth Science is looking at novel ways to observe phenomena that previously could not have been studied or would have been too expensive to study with traditional missions. In particular, the New Observing Strategies (NOS) component of the NASA Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST) Program aims at leveraging these novel technologies as well as low cost and easy access to space to acquire multi-temporal or simultaneous multi-angular, multi-locations, multi-resolution and multi-spectral observations that will provide better multi-source measurements and will build a more dynamic and comprehensive picture of Earth Science phenomena that need to be studied and analyzed. For applications such as water resources management, air quality monitoring, biodiversity studies or disaster management, NOS will integrate the use of small instruments, small spacecraft, constellations of spacecraft and networks of sensors to design new missions that will provide the necessary measurements to improve future forecast and science modeling systems.Measurement acquisition will therefore be approached as a system of systems rather than on a mission basis, and a system of this complexity should not be expected to work without full integration and experimental characterization. Although most of the individual technologies enabling to link and coordinate multi-source observations are more or less mature, a few technologies need to be developed and all of them need to be integrated and tested as a system. In order for this validation to occur, the AIST Program is developing the NOS Testbed that includes 3 main goals:1.Validate novel NOS technologies, independently and as a system2.Demonstrate novel distributed operations concepts3.Socialize new Distributed Spacecraft Mission (DSM) and SensorWeb (SW) technologies and concepts to the science community by significantly retiring the risk of integrating these new technologies.The NOS Testbed will consist of multiple sensing nodes, simulated or actual, representing space, air and/or ground measurements, that are interconnected by a communications fabric (infrastructure that permits nodes to transmit and receive data between one another and interact with each other). Each node will be supported by hardware capabilities required to perform nodes monitoring and command & control, as well as intelligent "onboard" computing. The nodes will work together in a collaborative manner to demonstrate optimal science capabilities. The testbed will enable to validate technologies such as inter-node communication models, techniques and protocols; inter-node coordination; real-time data fusion and understanding; planning; sensor re-targeting; etc. Additionally, the testbed will have the capability to interact with various mission design tools, OSSEs and one or several forecast models. More details about the NOS Testbed will be presented at the confererence

Learning Online Smooth Predictors for Realtime Camera Planning using Recurrent Decision Trees

We study the problem of online prediction for realtime camera planning, where the goal is to predict smooth trajectories that correctly track and frame objects of interest (e.g., players in a basketball game). The conventional approach for training predictors does not directly consider temporal consistency, and often produces undesirable jitter. Although post-hoc smoothing (e.g., via a Kalman filter) can mitigate this issue to some degree, it is not ideal due to overly stringent modeling assumptions (e.g., Gaussian noise). We propose a recurrent decision tree framework that can directly incorporate temporal consistency into a data-driven predictor, as well as a learning algorithm that can efficiently learn such temporally smooth models. Our approach does not require any post-processing, making online smooth predictions much easier to generate when the noise model is unknown. We apply our approach to sports broadcasting: given noisy player detections, we learn where the camera should look based on human demonstrations. Our experiments exhibit significant improvements over conventional baselines and showcase the practicality of our approach