29,942 research outputs found
Rate-Distortion Classification for Self-Tuning IoT Networks
Many future wireless sensor networks and the Internet of Things are expected
to follow a software defined paradigm, where protocol parameters and behaviors
will be dynamically tuned as a function of the signal statistics. New protocols
will be then injected as a software as certain events occur. For instance, new
data compressors could be (re)programmed on-the-fly as the monitored signal
type or its statistical properties change. We consider a lossy compression
scenario, where the application tolerates some distortion of the gathered
signal in return for improved energy efficiency. To reap the full benefits of
this paradigm, we discuss an automatic sensor profiling approach where the
signal class, and in particular the corresponding rate-distortion curve, is
automatically assessed using machine learning tools (namely, support vector
machines and neural networks). We show that this curve can be reliably
estimated on-the-fly through the computation of a small number (from ten to
twenty) of statistical features on time windows of a few hundreds samples
Properties of principal component methods for functional and longitudinal data analysis
The use of principal component methods to analyze functional data is
appropriate in a wide range of different settings. In studies of ``functional
data analysis,'' it has often been assumed that a sample of random functions is
observed precisely, in the continuum and without noise. While this has been the
traditional setting for functional data analysis, in the context of
longitudinal data analysis a random function typically represents a patient, or
subject, who is observed at only a small number of randomly distributed points,
with nonnegligible measurement error. Nevertheless, essentially the same
methods can be used in both these cases, as well as in the vast number of
settings that lie between them. How is performance affected by the sampling
plan? In this paper we answer that question. We show that if there is a sample
of functions, or subjects, then estimation of eigenvalues is a
semiparametric problem, with root- consistent estimators, even if only a few
observations are made of each function, and if each observation is encumbered
by noise. However, estimation of eigenfunctions becomes a nonparametric problem
when observations are sparse. The optimal convergence rates in this case are
those which pertain to more familiar function-estimation settings. We also
describe the effects of sampling at regularly spaced points, as opposed to
random points. In particular, it is shown that there are often advantages in
sampling randomly. However, even in the case of noisy data there is a threshold
sampling rate (depending on the number of functions treated) above which the
rate of sampling (either randomly or regularly) has negligible impact on
estimator performance, no matter whether eigenfunctions or eigenvectors are
being estimated.Comment: Published at http://dx.doi.org/10.1214/009053606000000272 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Deep Descriptor Transforming for Image Co-Localization
Reusable model design becomes desirable with the rapid expansion of machine
learning applications. In this paper, we focus on the reusability of
pre-trained deep convolutional models. Specifically, different from treating
pre-trained models as feature extractors, we reveal more treasures beneath
convolutional layers, i.e., the convolutional activations could act as a
detector for the common object in the image co-localization problem. We propose
a simple but effective method, named Deep Descriptor Transforming (DDT), for
evaluating the correlations of descriptors and then obtaining the
category-consistent regions, which can accurately locate the common object in a
set of images. Empirical studies validate the effectiveness of the proposed DDT
method. On benchmark image co-localization datasets, DDT consistently
outperforms existing state-of-the-art methods by a large margin. Moreover, DDT
also demonstrates good generalization ability for unseen categories and
robustness for dealing with noisy data.Comment: Accepted by IJCAI 201
Bayesian data assimilation in shape registration
In this paper we apply a Bayesian framework to the problem of geodesic curve matching. Given a template curve, the geodesic equations provide a mapping from initial conditions\ud
for the conjugate momentum onto topologically equivalent shapes. Here, we aim to recover the well defined posterior distribution on the initial momentum which gives rise to observed points on the target curve; this is achieved by explicitly including a reparameterisation in the formulation. Appropriate priors are chosen for the functions which together determine this field and the positions of the observation points, the initial momentum p0 and the reparameterisation vector field v, informed by regularity results about the forward model. Having done this, we illustrate how Maximum Likelihood Estimators (MLEs) can be used to find regions of high posterior density, but also how we can apply recently developed MCMC methods on function spaces to characterise the whole of the posterior density. These illustrative examples also include scenarios where the posterior distribution is multimodal and irregular, leading us to the conclusion that knowledge of a state of global maximal posterior density does not always give us the whole picture, and full posterior sampling can give better quantification of likely states and the overall uncertainty inherent in the problem
- …