12,797 research outputs found
Past and present cosmic structure in the SDSS DR7 main sample
We present a chrono-cosmography project, aiming at the inference of the four
dimensional formation history of the observed large scale structure from its
origin to the present epoch. To do so, we perform a full-scale Bayesian
analysis of the northern galactic cap of the Sloan Digital Sky Survey (SDSS)
Data Release 7 main galaxy sample, relying on a fully probabilistic, physical
model of the non-linearly evolved density field. Besides inferring initial
conditions from observations, our methodology naturally and accurately
reconstructs non-linear features at the present epoch, such as walls and
filaments, corresponding to high-order correlation functions generated by
late-time structure formation. Our inference framework self-consistently
accounts for typical observational systematic and statistical uncertainties
such as noise, survey geometry and selection effects. We further account for
luminosity dependent galaxy biases and automatic noise calibration within a
fully Bayesian approach. As a result, this analysis provides highly-detailed
and accurate reconstructions of the present density field on scales larger than
Mpc, constrained by SDSS observations. This approach also leads to
the first quantitative inference of plausible formation histories of the
dynamic large scale structure underlying the observed galaxy distribution. The
results described in this work constitute the first full Bayesian non-linear
analysis of the cosmic large scale structure with the demonstrated capability
of uncertainty quantification. Some of these results will be made publicly
available along with this work. The level of detail of inferred results and the
high degree of control on observational uncertainties pave the path towards
high precision chrono-cosmography, the subject of simultaneously studying the
dynamics and the morphology of the inhomogeneous Universe.Comment: 27 pages, 9 figure
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
Seeing Tree Structure from Vibration
Humans recognize object structure from both their appearance and motion;
often, motion helps to resolve ambiguities in object structure that arise when
we observe object appearance only. There are particular scenarios, however,
where neither appearance nor spatial-temporal motion signals are informative:
occluding twigs may look connected and have almost identical movements, though
they belong to different, possibly disconnected branches. We propose to tackle
this problem through spectrum analysis of motion signals, because vibrations of
disconnected branches, though visually similar, often have distinctive natural
frequencies. We propose a novel formulation of tree structure based on a
physics-based link model, and validate its effectiveness by theoretical
analysis, numerical simulation, and empirical experiments. With this
formulation, we use nonparametric Bayesian inference to reconstruct tree
structure from both spectral vibration signals and appearance cues. Our model
performs well in recognizing hierarchical tree structure from real-world videos
of trees and vessels.Comment: ECCV 2018. The first two authors contributed equally to this work.
Project page: http://tree.csail.mit.edu
Prediction and Tracking of Moving Objects in Image Sequences
We employ a prediction model for moving object velocity and location estimation derived from Bayesian theory. The optical flow of a certain moving object depends on the history of its previous values. A joint optical flow estimation and moving object segmentation algorithm is used for the initialization of the tracking algorithm. The segmentation of the moving objects is determined by appropriately classifying the unlabeled and the occluding regions. Segmentation and optical flow tracking is used for predicting future frames
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