11,547 research outputs found
Parallel Adaptive Collapsed Gibbs Sampling
Rao-Blackwellisation is a technique that provably improves the performance of Gibbs sampling by summing-out variables from the PGM. However, collapsing variables is computationally expensive, since it changes the PGM structure introducing factors whose size is dependent upon the Markov blanket of the variable. Therefore, collapsing out several variables jointly is typically intractable in arbitrary PGM structures. This thesis proposes an adaptive approach for Rao-Blackwellisation, where additional parallel Markov chains are defined over different collapsed PGM structures. The collapsed variables are chosen based on their convergence diagnostics. Adding chains requires re-burn-in the chain, thus wasting samples. To address this, new chains are initialized from a mean field approximation for the distribution, that improves over time, thus reducing the burn-in period. The experiments on several UAI benchmarks shows that this approach is more accurate than state-of-the-art inference systems such as Merlin which have previously won the UAI inference challenge
Sensors Fault Diagnosis Trends and Applications
Fault diagnosis has always been a concern for industry. In general, diagnosis in complex systems requires the acquisition of information from sensors and the processing and extracting of required features for the classification or identification of faults. Therefore, fault diagnosis of sensors is clearly important as faulty information from a sensor may lead to misleading conclusions about the whole system. As engineering systems grow in size and complexity, it becomes more and more important to diagnose faulty behavior before it can lead to total failure. In the light of above issues, this book is dedicated to trends and applications in modern-sensor fault diagnosis
Cutset Sampling for Bayesian Networks
The paper presents a new sampling methodology for Bayesian networks that
samples only a subset of variables and applies exact inference to the rest.
Cutset sampling is a network structure-exploiting application of the
Rao-Blackwellisation principle to sampling in Bayesian networks. It improves
convergence by exploiting memory-based inference algorithms. It can also be
viewed as an anytime approximation of the exact cutset-conditioning algorithm
developed by Pearl. Cutset sampling can be implemented efficiently when the
sampled variables constitute a loop-cutset of the Bayesian network and, more
generally, when the induced width of the networks graph conditioned on the
observed sampled variables is bounded by a constant w. We demonstrate
empirically the benefit of this scheme on a range of benchmarks
Fast Markov chain Monte Carlo sampling for sparse Bayesian inference in high-dimensional inverse problems using L1-type priors
Sparsity has become a key concept for solving of high-dimensional inverse
problems using variational regularization techniques. Recently, using similar
sparsity-constraints in the Bayesian framework for inverse problems by encoding
them in the prior distribution has attracted attention. Important questions
about the relation between regularization theory and Bayesian inference still
need to be addressed when using sparsity promoting inversion. A practical
obstacle for these examinations is the lack of fast posterior sampling
algorithms for sparse, high-dimensional Bayesian inversion: Accessing the full
range of Bayesian inference methods requires being able to draw samples from
the posterior probability distribution in a fast and efficient way. This is
usually done using Markov chain Monte Carlo (MCMC) sampling algorithms. In this
article, we develop and examine a new implementation of a single component
Gibbs MCMC sampler for sparse priors relying on L1-norms. We demonstrate that
the efficiency of our Gibbs sampler increases when the level of sparsity or the
dimension of the unknowns is increased. This property is contrary to the
properties of the most commonly applied Metropolis-Hastings (MH) sampling
schemes: We demonstrate that the efficiency of MH schemes for L1-type priors
dramatically decreases when the level of sparsity or the dimension of the
unknowns is increased. Practically, Bayesian inversion for L1-type priors using
MH samplers is not feasible at all. As this is commonly believed to be an
intrinsic feature of MCMC sampling, the performance of our Gibbs sampler also
challenges common beliefs about the applicability of sample based Bayesian
inference.Comment: 33 pages, 14 figure
Islands of linkage in an ocean of pervasive recombination reveals two-speed evolution of human cytomegalovirus genomes
Human cytomegalovirus (HCMV) infects most of the population worldwide, persisting throughout the host's life in a latent state with periodic episodes of reactivation. While typically asymptomatic, HCMV can cause fatal disease among congenitally infected infants and immunocompromised patients. These clinical issues are compounded by the emergence of antiviral resistance and the absence of an effective vaccine, the development of which is likely complicated by the numerous immune evasins encoded by HCMV to counter the host's adaptive immune responses, a feature that facilitates frequent super-infections. Understanding the evolutionary dynamics of HCMV is essential for the development of effective new drugs and vaccines. By comparing viral genomes from uncultivated or low-passaged clinical samples of diverse origins, we observe evidence of frequent homologous recombination events, both recent and ancient, and no structure of HCMV genetic diversity at the whole-genome scale. Analysis of individual gene-scale loci reveals a striking dichotomy: while most of the genome is highly conserved, recombines essentially freely and has evolved under purifying selection, 21 genes display extreme diversity, structured into distinct genotypes that do not recombine with each other. Most of these hyper-variable genes encode glycoproteins involved in cell entry or escape of host immunity. Evidence that half of them have diverged through episodes of intense positive selection suggests that rapid evolution of hyper-variable loci is likely driven by interactions with host immunity. It appears that this process is enabled by recombination unlinking hyper-variable loci from strongly constrained neighboring sites. It is conceivable that viral mechanisms facilitating super-infection have evolved to promote recombination between diverged genotypes, allowing the virus to continuously diversify at key loci to escape immune detection, while maintaining a genome optimally adapted to its asymptomatic infectious lifecycle
Reaction profiling of extracellular protein phosphorylation through a microfluidic reactor coupled with Raman Spectroscopy
Diseases and disorders in the human body are considered abnormalities of proper cellular function. Understanding the signal transduction mechanisms that cause these abnormalities is crucial to developing earlier detection methods, better treatment options and effective cures. While current diagnostic procedures are powerful tools in diagnosing diseases, they are ineffective in informing physicians on the real-time behavior of the signal transduction mechanisms associated with diseases and disorders. Currently, disease progression is monitored over time through routine patient visits and testing by one or more of the above techniques. By developing an approach that can monitor structural and conformational changes of proteins during biochemical reactions we can provide insight towards signal transduction mechanisms. This study successfully monitored the phosphorylation of Casein through catalysis of PKA and phosphate donation from adenosine triphosphate (ATP) in a 3D printed microfluidic device that was integrated with confocal Raman spectroscopy. The development of an adaptive PCA algorithm identified variance between the POx vibrational modes at 1090 cm-1,1329 cm-1, 1411 cm-1, 1506 cm-1 and 1558 cm-1 present in ATP. The protein phosphorylation reaction was monitored in real-time by detecting the change of the POx vibrational mode at 1556 cm-1 that appears in ATP during the reaction progression. This research also enables future use on numerous other biochemical reactions utilizing Raman spectroscopy. Additionally, this work enables translation of this research to analyze biochemical reactions intracellularly
Efficient, concurrent Bayesian analysis of full waveform LaDAR data
Bayesian analysis of full waveform laser detection and ranging (LaDAR)
signals using reversible jump Markov chain Monte Carlo (RJMCMC) algorithms
have shown higher estimation accuracy, resolution and sensitivity to
detect weak signatures for 3D surface profiling, and construct multiple layer
images with varying number of surface returns. However, it is computational
expensive. Although parallel computing has the potential to reduce both the
processing time and the requirement for persistent memory storage, parallelizing
the serial sampling procedure in RJMCMC is a significant challenge
in both statistical and computing domains. While several strategies have been
developed for Markov chain Monte Carlo (MCMC) parallelization, these are
usually restricted to fixed dimensional parameter estimates, and not obviously
applicable to RJMCMC for varying dimensional signal analysis.
In the statistical domain, we propose an effective, concurrent RJMCMC algorithm,
state space decomposition RJMCMC (SSD-RJMCMC), which divides
the entire state space into groups and assign to each an independent
RJMCMC chain with restricted variation of model dimensions. It intrinsically
has a parallel structure, a form of model-level parallelization. Applying
the convergence diagnostic, we can adaptively assess the convergence of the
Markov chain on-the-fly and so dynamically terminate the chain generation.
Evaluations on both synthetic and real data demonstrate that the concurrent
chains have shorter convergence length and hence improved sampling efficiency.
Parallel exploration of the candidate models, in conjunction with an
error detection and correction scheme, improves the reliability of surface detection.
By adaptively generating a complimentary MCMC sequence for the
determined model, it enhances the accuracy for surface profiling.
In the computing domain, we develop a data parallel SSD-RJMCMC (DP
SSD-RJMCMCU) to achieve efficient parallel implementation on a distributed
computer cluster. Adding data-level parallelization on top of the model-level
parallelization, it formalizes a task queue and introduces an automatic scheduler
for dynamic task allocation. These two strategies successfully diminish
the load imbalance that occurred in SSD-RJMCMC. Thanks to the coarse
granularity, the processors communicate at a very low frequency. The MPIbased
implementation on a Beowulf cluster demonstrates that compared with
RJMCMC, DP SSD-RJMCMCU has further reduced problem size and computation
complexity. Therefore, it can achieve a super linear speedup if the
number of data segments and processors are chosen wisely
Phased Array Feed Calibration, Beamforming and Imaging
Phased array feeds (PAFs) for reflector antennas offer the potential for
increased reflector field of view and faster survey speeds. To address some of
the development challenges that remain for scientifically useful PAFs,
including calibration and beamforming algorithms, sensitivity optimization, and
demonstration of wide field of view imaging, we report experimental results
from a 19 element room temperature L-band PAF mounted on the Green Bank
20-Meter Telescope. Formed beams achieved an aperture efficiency of 69% and
system noise temperature of 66 K. Radio camera images of several sky regions
are presented. We investigate the noise performance and sensitivity of the
system as a function of elevation angle with statistically optimal beamforming
and demonstrate cancelation of radio frequency interference sources with
adaptive spatial filtering.Comment: 19 pages, 13 figure
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