10,640 research outputs found
Mathematical Basis for Physical Inference
While the axiomatic introduction of a probability distribution over a space
is common, its use for making predictions, using physical theories and prior
knowledge, suffers from a lack of formalization. We propose to introduce, in
the space of all probability distributions, two operations, the OR and the AND
operation, that bring to the space the necessary structure for making
inferences on possible values of physical parameters. While physical theories
are often asumed to be analytical, we argue that consistent inference needs to
replace analytical theories by probability distributions over the parameter
space, and we propose a systematic way of obtaining such "theoretical
correlations", using the OR operation on the results of physical experiments.
Predicting the outcome of an experiment or solving "inverse problems" are then
examples of the use of the AND operation. This leads to a simple and complete
mathematical basis for general physical inference.Comment: 24 pages, 4 figure
Astroinformatics of galaxies and quasars: a new general method for photometric redshifts estimation
With the availability of the huge amounts of data produced by current and
future large multi-band photometric surveys, photometric redshifts have become
a crucial tool for extragalactic astronomy and cosmology. In this paper we
present a novel method, called Weak Gated Experts (WGE), which allows to derive
photometric redshifts through a combination of data mining techniques.
\noindent The WGE, like many other machine learning techniques, is based on the
exploitation of a spectroscopic knowledge base composed by sources for which a
spectroscopic value of the redshift is available. This method achieves a
variance \sigma^2(\Delta z)=2.3x10^{-4} (\sigma^2(\Delta z) =0.08), where
\Delta z = z_{phot} - z_{spec}) for the reconstruction of the photometric
redshifts for the optical galaxies from the SDSS and for the optical quasars
respectively, while the Root Mean Square (RMS) of the \Delta z variable
distributions for the two experiments is respectively equal to 0.021 and 0.35.
The WGE provides also a mechanism for the estimation of the accuracy of each
photometric redshift. We also present and discuss the catalogs obtained for the
optical SDSS galaxies, for the optical candidate quasars extracted from the DR7
SDSS photometric dataset {The sample of SDSS sources on which the accuracy of
the reconstruction has been assessed is composed of bright sources, for a
subset of which spectroscopic redshifts have been measured.}, and for optical
SDSS candidate quasars observed by GALEX in the UV range. The WGE method
exploits the new technological paradigm provided by the Virtual Observatory and
the emerging field of Astroinformatics.Comment: 36 pages, 22 figures and 8 table
DroTrack: High-speed Drone-based Object Tracking Under Uncertainty
We present DroTrack, a high-speed visual single-object tracking framework for
drone-captured video sequences. Most of the existing object tracking methods
are designed to tackle well-known challenges, such as occlusion and cluttered
backgrounds. The complex motion of drones, i.e., multiple degrees of freedom in
three-dimensional space, causes high uncertainty. The uncertainty problem leads
to inaccurate location predictions and fuzziness in scale estimations. DroTrack
solves such issues by discovering the dependency between object representation
and motion geometry. We implement an effective object segmentation based on
Fuzzy C Means (FCM). We incorporate the spatial information into the membership
function to cluster the most discriminative segments. We then enhance the
object segmentation by using a pre-trained Convolution Neural Network (CNN)
model. DroTrack also leverages the geometrical angular motion to estimate a
reliable object scale. We discuss the experimental results and performance
evaluation using two datasets of 51,462 drone-captured frames. The combination
of the FCM segmentation and the angular scaling increased DroTrack precision by
up to and decreased the centre location error by pixels on average.
DroTrack outperforms all the high-speed trackers and achieves comparable
results in comparison to deep learning trackers. DroTrack offers high frame
rates up to 1000 frame per second (fps) with the best location precision, more
than a set of state-of-the-art real-time trackers.Comment: 10 pages, 12 figures, FUZZ-IEEE 202
Financial Markets Analysis by Probabilistic Fuzzy Modelling
For successful trading in financial markets, it is important to develop financial models where one can identify different states of the market for modifying one???s actions. In this paper, we propose to use probabilistic fuzzy systems for this purpose. We concentrate on Takagi???Sugeno (TS) probabilistic fuzzy systems that combine interpretability of fuzzy systems with the statistical properties of probabilistic systems. We start by recapitulating the general architecture of TS probabilistic fuzzy rule-based systems and summarize the corresponding reasoning schemes. We mention how probabilities can be estimated from a given data set and how a probability distribution can be approximated by a fuzzy histogram. We apply our methodology for financial time series analysis and demonstrate how a probabilistic TS fuzzy system can be identified, assuming that a linguistic term set is given. We illustrate the interpretability of such a system by inspecting the rule bases of our models.time series analysis;data-driven design;fuzzy reasoning;fuzzy rule base;probabilistic fuzzy systems
Fuzzy-based Propagation of Prior Knowledge to Improve Large-Scale Image Analysis Pipelines
Many automatically analyzable scientific questions are well-posed and offer a
variety of information about the expected outcome a priori. Although often
being neglected, this prior knowledge can be systematically exploited to make
automated analysis operations sensitive to a desired phenomenon or to evaluate
extracted content with respect to this prior knowledge. For instance, the
performance of processing operators can be greatly enhanced by a more focused
detection strategy and the direct information about the ambiguity inherent in
the extracted data. We present a new concept for the estimation and propagation
of uncertainty involved in image analysis operators. This allows using simple
processing operators that are suitable for analyzing large-scale 3D+t
microscopy images without compromising the result quality. On the foundation of
fuzzy set theory, we transform available prior knowledge into a mathematical
representation and extensively use it enhance the result quality of various
processing operators. All presented concepts are illustrated on a typical
bioimage analysis pipeline comprised of seed point detection, segmentation,
multiview fusion and tracking. Furthermore, the functionality of the proposed
approach is validated on a comprehensive simulated 3D+t benchmark data set that
mimics embryonic development and on large-scale light-sheet microscopy data of
a zebrafish embryo. The general concept introduced in this contribution
represents a new approach to efficiently exploit prior knowledge to improve the
result quality of image analysis pipelines. Especially, the automated analysis
of terabyte-scale microscopy data will benefit from sophisticated and efficient
algorithms that enable a quantitative and fast readout. The generality of the
concept, however, makes it also applicable to practically any other field with
processing strategies that are arranged as linear pipelines.Comment: 39 pages, 12 figure
- …