130 research outputs found
Modulation Classification for MIMO-OFDM Signals via Approximate Bayesian Inference
The problem of modulation classification for a multiple-antenna (MIMO) system
employing orthogonal frequency division multiplexing (OFDM) is investigated
under the assumption of unknown frequency-selective fading channels and
signal-to-noise ratio (SNR). The classification problem is formulated as a
Bayesian inference task, and solutions are proposed based on Gibbs sampling and
mean field variational inference. The proposed methods rely on a selection of
the prior distributions that adopts a latent Dirichlet model for the modulation
type and on the Bayesian network formalism. The Gibbs sampling method converges
to the optimal Bayesian solution and, using numerical results, its accuracy is
seen to improve for small sample sizes when switching to the mean field
variational inference technique after a number of iterations. The speed of
convergence is shown to improve via annealing and random restarts. While most
of the literature on modulation classification assume that the channels are
flat fading, that the number of receive antennas is no less than that of
transmit antennas, and that a large number of observed data symbols are
available, the proposed methods perform well under more general conditions.
Finally, the proposed Bayesian methods are demonstrated to improve over
existing non-Bayesian approaches based on independent component analysis and on
prior Bayesian methods based on the `superconstellation' method.Comment: To be appear in IEEE Trans. Veh. Technolog
Traffic-Aware Backscatter Communications in Wireless-Powered Heterogeneous Networks
With the emerging Internet-of-Things services, massive machine-to-machine
(M2M) communication will be deployed on top of human-to-human (H2H)
communication in the near future. Due to the coexistence of M2M and H2H
communications, the performance of M2M (i.e., secondary) network depends
largely on the H2H (i.e., primary) network. In this paper, we propose ambient
backscatter communication for the M2M network which exploits the energy
(signal) sources of the H2H network, referring to traffic applications and
popularity. In order to maximize the harvesting and transmission opportunities
offered by varying traffic sources of the H2H network, we adopt a Bayesian
nonparametric (BNP) learning algorithm to classify traffic applications
(patterns) for secondary user (SU). We then analyze the performance of SU using
the stochastic geometrical approach, based on a criterion for optimal traffic
pattern selection. Results are presented to validate the performance of the
proposed BNP classification algorithm and the criterion, as well as the impact
of traffic sources and popularity.Comment: 14 pages, 10 figure
Bayesian network marker selection via the thresholded graph Laplacian Gaussian prior
Selecting informative nodes over large-scale networks becomes increasingly
important in many research areas. Most existing methods focus on the local
network structure and incur heavy computational costs for the large-scale
problem. In this work, we propose a novel prior model for Bayesian network
marker selection in the generalized linear model (GLM) framework: the
Thresholded Graph Laplacian Gaussian (TGLG) prior, which adopts the graph
Laplacian matrix to characterize the conditional dependence between neighboring
markers accounting for the global network structure. Under mild conditions, we
show the proposed model enjoys the posterior consistency with a diverging
number of edges and nodes in the network. We also develop a Metropolis-adjusted
Langevin algorithm (MALA) for efficient posterior computation, which is
scalable to large-scale networks. We illustrate the superiorities of the
proposed method compared with existing alternatives via extensive simulation
studies and an analysis of the breast cancer gene expression dataset in the
Cancer Genome Atlas (TCGA)
Bayesian Mixed Effect Sparse Tensor Response Regression Model with Joint Estimation of Activation and Connectivity
Brain activation and connectivity analyses in task-based functional magnetic
resonance imaging (fMRI) experiments with multiple subjects are currently at
the forefront of data-driven neuroscience. In such experiments, interest often
lies in understanding activation of brain voxels due to external stimuli and
strong association or connectivity between the measurements on a set of
pre-specified group of brain voxels, also known as regions of interest (ROI).
This article proposes a joint Bayesian additive mixed modeling framework that
simultaneously assesses brain activation and connectivity patterns from
multiple subjects. In particular, fMRI measurements from each individual
obtained in the form of a multi-dimensional array/tensor at each time are
regressed on functions of the stimuli. We impose a low-rank PARAFAC
decomposition on the tensor regression coefficients corresponding to the
stimuli to achieve parsimony. Multiway stick breaking shrinkage priors are
employed to infer activation patterns and associated uncertainties in each
voxel. Further, the model introduces region specific random effects which are
jointly modeled with a Bayesian Gaussian graphical prior to account for the
connectivity among pairs of ROIs. Empirical investigations under various
simulation studies demonstrate the effectiveness of the method as a tool to
simultaneously assess brain activation and connectivity. The method is then
applied to a multi-subject fMRI dataset from a balloon-analog risk-taking
experiment in order to make inference about how the brain processes risk.Comment: 27 pages, 7 figure
Action recognition in depth videos using nonparametric probabilistic graphical models
Action recognition involves automatically labelling videos that contain human motion with action classes. It has applications in diverse areas such as smart surveillance, human computer interaction and content retrieval. The recent advent of depth sensing technology that produces depth image sequences has offered opportunities to solve the challenging action recognition problem. The depth images facilitate robust estimation of a human skeleton’s 3D joint positions and a high level action can be inferred from a sequence of these joint positions.
A natural way to model a sequence of joint positions is to use a graphical model that describes probabilistic dependencies between the observed joint positions and some hidden state variables. A problem with these models is that the number of hidden states must be fixed a priori even though for many applications this number is not known in advance. This thesis proposes nonparametric variants of graphical models with the number of hidden states automatically inferred from data. The inference is performed in a full Bayesian setting by using the Dirichlet Process as a prior over the model’s infinite dimensional parameter space.
This thesis describes three original constructions of nonparametric graphical models that are applied in the classification of actions in depth videos. Firstly, the action classes are represented by a Hidden Markov Model (HMM) with an unbounded number of hidden states. The formulation enables information sharing and discriminative learning of parameters. Secondly, a hierarchical HMM with an unbounded number of actions and poses is used to represent activities. The construction produces a simplified model for activity classification by using logistic regression to capture the relationship between action states and activity labels. Finally, the action classes are modelled by a Hidden Conditional Random Field (HCRF) with the number of intermediate hidden states learned from data. Tractable inference procedures based on Markov Chain Monte Carlo (MCMC) techniques are derived for all these constructions. Experiments with multiple benchmark datasets confirm the efficacy of the proposed approaches for action recognition
Selection of a Model of Cerebral Activity for fMRI Group Data Analysis
This thesis is dedicated to the statistical analysis of multi-sub ject fMRI
data, with the purpose of identifying bain structures involved in certain
cognitive or sensori-motor tasks, in a reproducible way across sub jects. To
overcome certain limitations of standard voxel-based testing methods, as
implemented in the Statistical Parametric Mapping (SPM) software, we introduce
a Bayesian model selection approach to this problem, meaning that the most
probable model of cerebral activity given the data is selected from a
pre-defined collection of possible models. Based on a parcellation of the brain
volume into functionally homogeneous regions, each model corresponds to a
partition of the regions into those involved in the task under study and those
inactive. This allows to incorporate prior information, and avoids the
dependence of the SPM-like approach on an arbitrary threshold, called the
cluster- forming threshold, to define active regions. By controlling a Bayesian
risk, our approach balances false positive and false negative risk control.
Furthermore, it is based on a generative model that accounts for the spatial
uncertainty on the localization of individual effects, due to spatial
normalization errors. On both simulated and real fMRI datasets, we show that
this new paradigm corrects several biases of the SPM-like approach, which
either swells or misses the different active regions, depending on the choice
of a cluster-forming threshold.Comment: PhD Thesis, 208 pages, Applied Statistics and Neuroimaging,
University of Orsay, Franc
A Bayesian model of microbiome data for simultaneous identification of covariate associations and prediction of phenotypic outcomes
One of the major research questions regarding human microbiome studies is the
feasibility of designing interventions that modulate the composition of the
microbiome to promote health and cure disease. This requires extensive
understanding of the modulating factors of the microbiome, such as dietary
intake, as well as the relation between microbial composition and phenotypic
outcomes, such as body mass index (BMI). Previous efforts have modeled these
data separately, employing two-step approaches that can produce biased
interpretations of the results. Here, we propose a Bayesian joint model that
simultaneously identifies clinical covariates associated with microbial
composition data and predicts a phenotypic response using information contained
in the compositional data. Using spike-and-slab priors, our approach can handle
high-dimensional compositional as well as clinical data. Additionally, we
accommodate the compositional structure of the data via balances and
overdispersion typically found in microbial samples. We apply our model to
understand the relations between dietary intake, microbial samples, and BMI. In
this analysis, we find numerous associations between microbial taxa and dietary
factors that may lead to a microbiome that is generally more hospitable to the
development of chronic diseases, such as obesity. Additionally, we demonstrate
on simulated data how our method outperforms two-step approaches and also
present a sensitivity analysis.Comment: 32 pages, 5 figure
Towards Deeper Understanding in Neuroimaging
Neuroimaging is a growing domain of research, with advances in machine learning having tremendous potential to expand understanding in neuroscience and improve public health. Deep neural networks have recently and rapidly achieved historic success in numerous domains, and as a consequence have completely redefined the landscape of automated learners, giving promise of significant advances in numerous domains of research. Despite recent advances and advantages over traditional machine learning methods, deep neural networks have yet to have permeated significantly into neuroscience studies, particularly as a tool for discovery. This dissertation presents well-established and novel tools for unsupervised learning which aid in feature discovery, with relevant applications to neuroimaging. Through our works within, this dissertation presents strong evidence that deep learning is a viable and important tool for neuroimaging studies
Topical Language Generation using Transformers
Large-scale transformer-based language models (LMs) demonstrate impressive
capabilities in open text generation. However, controlling the generated text's
properties such as the topic, style, and sentiment is challenging and often
requires significant changes to the model architecture or retraining and
fine-tuning the model on new supervised data. This paper presents a novel
approach for Topical Language Generation (TLG) by combining a pre-trained LM
with topic modeling information. We cast the problem using Bayesian probability
formulation with topic probabilities as a prior, LM probabilities as the
likelihood, and topical language generation probability as the posterior. In
learning the model, we derive the topic probability distribution from the
user-provided document's natural structure. Furthermore, we extend our model by
introducing new parameters and functions to influence the quantity of the
topical features presented in the generated text. This feature would allow us
to easily control the topical properties of the generated text. Our
experimental results demonstrate that our model outperforms the
state-of-the-art results on coherency, diversity, and fluency while being
faster in decoding.Comment: Accepted in the Journal of Natural Language Engineerin
- …