19,555 research outputs found
Ensemble of Different Approaches for a Reliable Person Re-identification System
An ensemble of approaches for reliable person re-identification is proposed in this paper. The proposed ensemble is built combining widely used person re-identification systems using different color spaces and some variants of state-of-the-art approaches that are proposed in this paper. Different descriptors are tested, and both texture and color features are extracted from the images; then the different descriptors are compared using different distance measures (e.g., the Euclidean distance, angle, and the Jeffrey distance). To improve performance, a method based on skeleton detection, extracted from the depth map, is also applied when the depth map is available. The proposed ensemble is validated on three widely used datasets (CAVIAR4REID, IAS, and VIPeR), keeping the same parameter set of each approach constant across all tests to avoid overfitting and to demonstrate that the proposed system can be considered a general-purpose person re-identification system. Our experimental results show that the proposed system offers significant improvements over baseline approaches. The source code used for the approaches tested in this paper will be available at https://www.dei.unipd.it/node/2357 and http://robotics.dei.unipd.it/reid/
Information Recovery In Behavioral Networks
In the context of agent based modeling and network theory, we focus on the
problem of recovering behavior-related choice information from
origin-destination type data, a topic also known under the name of network
tomography. As a basis for predicting agents' choices we emphasize the
connection between adaptive intelligent behavior, causal entropy maximization
and self-organized behavior in an open dynamic system. We cast this problem in
the form of binary and weighted networks and suggest information theoretic
entropy-driven methods to recover estimates of the unknown behavioral flow
parameters. Our objective is to recover the unknown behavioral values across
the ensemble analytically, without explicitly sampling the configuration space.
In order to do so, we consider the Cressie-Read family of entropic functionals,
enlarging the set of estimators commonly employed to make optimal use of the
available information. More specifically, we explicitly work out two cases of
particular interest: Shannon functional and the likelihood functional. We then
employ them for the analysis of both univariate and bivariate data sets,
comparing their accuracy in reproducing the observed trends.Comment: 14 pages, 6 figures, 4 table
Structure Learning in Coupled Dynamical Systems and Dynamic Causal Modelling
Identifying a coupled dynamical system out of many plausible candidates, each
of which could serve as the underlying generator of some observed measurements,
is a profoundly ill posed problem that commonly arises when modelling real
world phenomena. In this review, we detail a set of statistical procedures for
inferring the structure of nonlinear coupled dynamical systems (structure
learning), which has proved useful in neuroscience research. A key focus here
is the comparison of competing models of (ie, hypotheses about) network
architectures and implicit coupling functions in terms of their Bayesian model
evidence. These methods are collectively referred to as dynamical casual
modelling (DCM). We focus on a relatively new approach that is proving
remarkably useful; namely, Bayesian model reduction (BMR), which enables rapid
evaluation and comparison of models that differ in their network architecture.
We illustrate the usefulness of these techniques through modelling
neurovascular coupling (cellular pathways linking neuronal and vascular
systems), whose function is an active focus of research in neurobiology and the
imaging of coupled neuronal systems
Controlling instabilities along a 3DVar analysis cycle by assimilating in the unstable subspace: a comparison with the EnKF
A hybrid scheme obtained by combining 3DVar with the Assimilation in the
Unstable Subspace (3DVar-AUS) is tested in a QG model, under perfect model
conditions, with a fixed observational network, with and without observational
noise. The AUS scheme, originally formulated to assimilate adaptive
observations, is used here to assimilate the fixed observations that are found
in the region of local maxima of BDAS vectors (Bred vectors subject to
assimilation), while the remaining observations are assimilated by 3DVar.
The performance of the hybrid scheme is compared with that of 3DVar and of an
EnKF. The improvement gained by 3DVar-AUS and the EnKF with respect to 3DVar
alone is similar in the present model and observational configuration, while
3DVar-AUS outperforms the EnKF during the forecast stage. The 3DVar-AUS
algorithm is easy to implement and the results obtained in the idealized
conditions of this study encourage further investigation toward an
implementation in more realistic contexts
Data assimilation in slow-fast systems using homogenized climate models
A deterministic multiscale toy model is studied in which a chaotic fast
subsystem triggers rare transitions between slow regimes, akin to weather or
climate regimes. Using homogenization techniques, a reduced stochastic
parametrization model is derived for the slow dynamics. The reliability of this
reduced climate model in reproducing the statistics of the slow dynamics of the
full deterministic model for finite values of the time scale separation is
numerically established. The statistics however is sensitive to uncertainties
in the parameters of the stochastic model. It is investigated whether the
stochastic climate model can be beneficial as a forecast model in an ensemble
data assimilation setting, in particular in the realistic setting when
observations are only available for the slow variables. The main result is that
reduced stochastic models can indeed improve the analysis skill, when used as
forecast models instead of the perfect full deterministic model. The stochastic
climate model is far superior at detecting transitions between regimes. The
observation intervals for which skill improvement can be obtained are related
to the characteristic time scales involved. The reason why stochastic climate
models are capable of producing superior skill in an ensemble setting is due to
the finite ensemble size; ensembles obtained from the perfect deterministic
forecast model lacks sufficient spread even for moderate ensemble sizes.
Stochastic climate models provide a natural way to provide sufficient ensemble
spread to detect transitions between regimes. This is corroborated with
numerical simulations. The conclusion is that stochastic parametrizations are
attractive for data assimilation despite their sensitivity to uncertainties in
the parameters.Comment: Accepted for publication in Journal of the Atmospheric Science
Convergence Analysis of Ensemble Kalman Inversion: The Linear, Noisy Case
We present an analysis of ensemble Kalman inversion, based on the continuous
time limit of the algorithm. The analysis of the dynamical behaviour of the
ensemble allows us to establish well-posedness and convergence results for a
fixed ensemble size. We will build on the results presented in [26] and
generalise them to the case of noisy observational data, in particular the
influence of the noise on the convergence will be investigated, both
theoretically and numerically. We focus on linear inverse problems where a very
complete theoretical analysis is possible
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