Uses of underwater videos to assess diversity and abundance of fish are being rapidly adopted by marine biologists. Manual processing of videos for quantification by human analysts is time and labour intensive. Automatic processing of videos can be e
mployed to achieve the objectives in a cost and time-efficient way. The aim is to build an accurate and reliable fish detection and recognition system, which is important for an autonomous robotic platform. However, there are many challenges involved in this task (e.g. complex background, deformation, low resolution and light propagation). Recent advancement in the deep neural network has led to the development of object detection and recognition in real time scenarios. An end-to-end deep learning-based architecture is introduced which outperformed the state of the art methods and first of its kind on fish assessment task. A Region Proposal Network (RPN) introduced by an object detector termed as Faster R-CNN was combined with three classification networks for detection and recognition of fish species obtained from Remote Underwater Video Stations (RUVS). An accuracy of 82.4% (mAP) obtained from the experiments are much higher than previously proposed methods.
In this paper a closed form expression for the number of tilings of an $ntimes n$ square border with $1times 1$ and $2times1$ cuisenaire rods is proved using a transition matrix approach. This problem is then generalised to $mtimes n$ rectangular bor
ders. The number of distinct tilings up to rotational symmetry is considered, and closed form expressions are given, in the case of a square border and in the case of a rectangular border. Finally, the number of distinct tilings up to dihedral symmetry is considered, and a closed form expression is given in the case of a square border.
A common problem in ultra-high energy cosmic ray physics is the comparison of energy spectra. The question is whether the spectra from two experiments or two regions of the sky agree within their statistical and systematic uncertainties. We develop a
method to directly compare energy spectra for ultra-high energy cosmic rays from two different regions of the sky in the same experiment without reliance on agreement with a theoretical model of the energy spectra. The consistency between the two spectra is expressed in terms of a Bayes factor, defined here as the ratio of the likelihood of the two-parent source hypothesis to the likelihood of the one-parent source hypothesis. Unlike other methods, for example chi^2 tests, the Bayes factor allows for the calculation of the posterior odds ratio and correctly accounts for non-Gaussian uncertainties. The latter is particularly important at the highest energies, where the number of events is very small.
Searches for statistically significant correlations between arrival directions of ultra-high energy cosmic rays and classes of astrophysical objects are common in astroparticle physics. We present a method to test potential correlation signals of a p
riori unknown strength and evaluate their statistical significance sequentially, i.e., after each incoming new event in a running experiment. The method can be applied to data taken after the test has concluded, allowing for further monitoring of the signal significance. It adheres to the likelihood principle and rigorously accounts for our ignorance of the signal strength.
Air fluorescence detectors measure the energy of ultra-high energy cosmic rays by collecting fluorescence light emitted from nitrogen molecules along the extensive air shower cascade. To ensure a reliable energy determination, the light signal needs
to be corrected for atmospheric effects, which not only attenuate the signal, but also produce a non-negligible background component due to scattered Cherenkov light and multiple-scattered light. The correction requires regular measurements of the aerosol attenuation length and the aerosol phase function, defined as the probability of light scattered in a given direction. At the Pierre Auger Observatory in Malargue, Argentina, the phase function is measured on an hourly basis using two Aerosol Phase Function (APF) light sources. These sources direct a UV light beam across the field of view of the fluorescence detectors; the phase function can be extracted from the image of the shots in the fluorescence detector cameras. This paper describes the design, current status, standard operation procedure, and performance of the APF system at the Pierre Auger Observatory.
This paper presents a novel method for determining the probability that a supernova candidate belongs to a known supernova type (such as Ia, Ibc, IIL, emph{etc.}), using its photometric information alone. It is validated with Monte Carlo, and both sp
ace- and ground- based data. We examine the application of the method to well-sampled as well as poorly sampled supernova light curves and investigate to what extent the best currently available supernova models can be used for typing supernova candidates. Central to the method is the assumption that a supernova candidate belongs to a group of objects that can be modeled; we therefore discuss possible ways of removing anomalous or less well understood events from the sample. This method is particularly advantageous for analyses where the purity of the supernova sample is of the essence, or for those where it is important to know the number of the supernova candidates of a certain type (emph{e.g.}, in supernova rate studies).
The current measurements of the cosmic ray energy spectrum at ultra-high energies ($text{E}>10^{19}$ eV) are characterized by large systematic errors and poor statistics. In addition, the experimental results of the two experiments with the largest p
ublished data sets, AGASA and HiRes, appear to be inconsistent with each other, with AGASA seeing an unabated continuation of the energy spectrum even at energies beyond the GZK cutoff energy at $10^{19.6}$ eV. Given the importance of the related astrophysical questions regarding the unknown origin of these highly energetic particles, it is crucial that the extent to which these measurements disagree be well understood. Here we evaluate the consistency of the two measurements for the first time with a model-independent method that accounts for the large statistical and systematic errors of current measurements. We further compare the AGASA and HiRes spectra with the recently presented Auger spectrum. The method directly compares two measurements, bypassing the introduction of theoretical models for the shape of the energy spectrum. The inconsistency between the observations is expressed in terms of a Bayes Factor, a standard statistic defined as the ratio of a separate parent source hypothesis to a single parent source hypothesis. Application to the data shows that the two-parent hypothesis is disfavored. We expand the method to allow comparisons between an experimental flux and that predicted by any model.
