3,567 research outputs found
Photon-Efficient Computational 3D and Reflectivity Imaging with Single-Photon Detectors
Capturing depth and reflectivity images at low light levels from active
illumination of a scene has wide-ranging applications. Conventionally, even
with single-photon detectors, hundreds of photon detections are needed at each
pixel to mitigate Poisson noise. We develop a robust method for estimating
depth and reflectivity using on the order of 1 detected photon per pixel
averaged over the scene. Our computational imager combines physically accurate
single-photon counting statistics with exploitation of the spatial correlations
present in real-world reflectivity and 3D structure. Experiments conducted in
the presence of strong background light demonstrate that our computational
imager is able to accurately recover scene depth and reflectivity, while
traditional maximum-likelihood based imaging methods lead to estimates that are
highly noisy. Our framework increases photon efficiency 100-fold over
traditional processing and also improves, somewhat, upon first-photon imaging
under a total acquisition time constraint in raster-scanned operation. Thus our
new imager will be useful for rapid, low-power, and noise-tolerant active
optical imaging, and its fixed dwell time will facilitate parallelization
through use of a detector array.Comment: 11 pages, 8 figure
Spatio-temporal variation in click production rates of beaked whales : implications for passive acoustic density estimation
T.A.M. was funded under Grant No. N000141010382 from the Office of Naval Research (LATTE project) and thanks support by CEAUL (funded by FCT - Fundação para a Ciência e a Tecnologia, Portugal, through the project UID/MAT/00006/2013). M.P.J. was funded by a Marie Curie Career Integration Grant and M.P.J. and P.L.T. were funded by MASTS (The Marine Alliance for Science and Technology for Scotland, a research pooling initiative funded by the Scottish Funding Council under grant HR09011 and contributing institutions). L.S.H. thanks the BRS Bahamas team that helped collect the Bahamas data, and A. Bocconcelli. D.H. and L.T. were funded by the Office of Naval Research (Award No. N00014-14-1-0394). N.A.S. was funded by an EU-Horizon 2020 Marie Slodowska Curie fellowship (project ECOSOUND). DTAG data in the Canary Islands were collected with funds from the U.S. Office of Naval Research and Fundación Biodiversidad (EU project LIFE INDEMARES) with permit from the Canary Islands and Spanish governments.Passive acoustic monitoring has become an increasingly prevalent tool for estimating density of marine mammals, such as beaked whales, which vocalize often but are difficult to survey visually. Counts of acoustic cues (e.g., vocalizations), when corrected for detection probability, can be translated into animal density estimates by applying an individual cue production rate multiplier. It is essential to understand variation in these rates to avoid biased estimates. The most direct way to measure cue production rate is with animal-mounted acoustic recorders. This study utilized data from sound recording tags deployed on Blainville's (Mesoplodon densirostris, 19 deployments) and Cuvier's (Ziphius cavirostris, 16 deployments) beaked whales, in two locations per species, to explore spatial and temporal variation in click production rates. No spatial or temporal variation was detected within the average click production rate of Blainville's beaked whales when calculated over dive cycles (including silent periods between dives); however, spatial variation was detected when averaged only over vocal periods. Cuvier's beaked whales exhibited significant spatial and temporal variation in click production rates within vocal periods and when silent periods were included. This evidence of variation emphasizes the need to utilize appropriate cue production rates when estimating density from passive acoustic data.PostprintPeer reviewe
Assessing the potential of autonomous submarine gliders for ecosystem monitoring across multiple trophic levels (plankton to cetaceans) and pollutants in shallow shelf seas
A combination of scientific, economic, technological and policy drivers is behind a recent upsurge in the use of marine autonomous systems (and accompanying miniaturized sensors) for environmental mapping and monitoring. Increased spatial–temporal resolution and coverage of data, at reduced cost, is particularly vital for effective spatial management of highly dynamic and heterogeneous shelf environments. This proof-of-concept study involves integration of a novel combination of sensors onto buoyancy-driven submarine gliders, in order to assess their suitability for ecosystem monitoring in shelf waters at a variety of trophic levels. Two shallow-water Slocum gliders were equipped with CTD and fluorometer to measure physical properties and chlorophyll, respectively. One glider was also equipped with a single-frequency echosounder to collect information on zooplankton and fish distribution. The other glider carried a Passive Acoustic Monitoring system to detect and record cetacean vocalizations, and a passive sampler to detect chemical contaminants in the water column. The two gliders were deployed together off southwest UK in autumn 2013, and targeted a known tidal-mixing front west of the Isles of Scilly. The gliders’ mission took about 40 days, with each glider travelling distances of >1000 km and undertaking >2500 dives to depths of up to 100 m. Controlling glider flight and alignment of the two glider trajectories proved to be particularly challenging due to strong tidal flows. However, the gliders continued to collect data in poor weather when an accompanying research vessel was unable to operate. In addition, all glider sensors generated useful data, with particularly interesting initial results relating to subsurface chlorophyll maxima and numerous fish/cetacean detections within the water column. The broader implications of this study for marine ecosystem monitoring with submarine gliders are discussed
R-UCB: a Contextual Bandit Algorithm for Risk-Aware Recommender Systems
Mobile Context-Aware Recommender Systems can be naturally modelled as an
exploration/exploitation trade-off (exr/exp) problem, where the system has to
choose between maximizing its expected rewards dealing with its current
knowledge (exploitation) and learning more about the unknown user's preferences
to improve its knowledge (exploration). This problem has been addressed by the
reinforcement learning community but they do not consider the risk level of the
current user's situation, where it may be dangerous to recommend items the user
may not desire in her current situation if the risk level is high. We introduce
in this paper an algorithm named R-UCB that considers the risk level of the
user's situation to adaptively balance between exr and exp. The detailed
analysis of the experimental results reveals several important discoveries in
the exr/exp behaviour
Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms
Contextual bandit algorithms have become popular for online recommendation
systems such as Digg, Yahoo! Buzz, and news recommendation in general.
\emph{Offline} evaluation of the effectiveness of new algorithms in these
applications is critical for protecting online user experiences but very
challenging due to their "partial-label" nature. Common practice is to create a
simulator which simulates the online environment for the problem at hand and
then run an algorithm against this simulator. However, creating simulator
itself is often difficult and modeling bias is usually unavoidably introduced.
In this paper, we introduce a \emph{replay} methodology for contextual bandit
algorithm evaluation. Different from simulator-based approaches, our method is
completely data-driven and very easy to adapt to different applications. More
importantly, our method can provide provably unbiased evaluations. Our
empirical results on a large-scale news article recommendation dataset
collected from Yahoo! Front Page conform well with our theoretical results.
Furthermore, comparisons between our offline replay and online bucket
evaluation of several contextual bandit algorithms show accuracy and
effectiveness of our offline evaluation method.Comment: 10 pages, 7 figures, revised from the published version at the WSDM
2011 conferenc
Impact of Indoor Mobility Behavior on the Respiratory Infectious Diseases Transmission Trends
The importance of indoor human mobility in the transmission dynamics of
respiratory infectious diseases has been acknowledged. Previous studies have
predominantly addressed a single type of mobility behavior such as queueing and
a series of behaviors under specific scenarios. However, these studies ignore
the abstraction of mobility behavior in various scenes and the critical
examination of how these abstracted behaviors impact disease propagation. To
address these problems, this study considers people's mobility behaviors in a
general scenario, abstracting them into two main categories: crowding behavior,
related to the spatial aspect, and stopping behavior, related to the temporal
aspect. Accordingly, this study investigates their impacts on disease spreading
and the impact of individual spatio-temporal distribution resulting from these
mobility behaviors on epidemic transmission. First, a point of interest (POI)
method is introduced to quantify the crowding-related spatial POI factors
(i.e., the number of crowdings and the distance between crowdings) and
stopping-related temporal POI factors (i.e., the number of stoppings and the
duration of each stopping). Besides, a personal space determined with Voronoi
diagrams is used to construct the individual spatio-temporal distribution
factor. Second, two indicators (i.e., the daily number of new cases and the
average exposure risk of people) are applied to quantify epidemic transmission.
These indicators are derived from a fundamental model which accurately predicts
disease transmission between moving individuals. Third, a set of 200 indoor
scenarios is constructed and simulated to help determine variable values.
Concurrently, the influences and underlying mechanisms of these behavioral
factors on disease transmission are examined using structural equation modeling
and causal inference modeling.....
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