Comparing and Evaluating HMM Ensemble Training Algorithms Using Train and Test and Condition Number Criteria

Hidden Markov Models have many applications in signal processing and pattern recognition, but their convergence-based training algorithms are known to suffer from over-sensitivity to the initial random model choice. This paper describes the boundary between regions in which ensemble learning is superior to Rabiner's multiplesequence Baum-Welch training method, and proposes techniques for determining the best method in any arbitrary situation. It also studies the suitability of the training methods using the condition number, a recently proposed diagnostic tool for testing the quality of the model. A new method for training Hidden Markov Models called the Viterbi Path counting algorithm is introduced and is found to produce significantly better performance than current methods in a range of trials

Improved Ensemble Training for Hidden Markov Models using Random Relative Node Permutations

Hidden Markov Models have many applications in signal processing and pattern recognition, but their convergence-based training algorithms are known to suffer from oversensitivity to the initial random model choice. This paper focuses upon the use of model averaging, ensemble thresholding, and random relative model permutations for improving average model performance. A method is described which trains by searching for the best relative permutation set for ensemble averaging. This uses the fit to the training set as an indicator. The work provides a simpler alternative to previous permutation-based ensemble averaging methods

Improved estimation of hidden Markov model parameters from multiple observation sequences

The huge popularity of Hidden Markov models in pattern recognition is due to the ability to 'learn' model parameters from an observation sequence through Baum-Welch and other re-estimation procedures. In the case of HMM parameter estimation from an ensemble of observation sequences, rather than a single sequence, we require techniques for finding the parameters which maximize the likelihood of the estimated model given the entire set of observation sequences. The importance of this study is that HMMs with parameters estimated from multiple observations are shown to be many orders of magnitude more probable than HMM models learned from any single observation sequence - thus the effectiveness of HMM 'learning' is greatly enhanced. In this paper, we present techniques that usually find models significantly more likely than Rabiner's well-known method on both seen and unseen sequences

Effect of Initial HMM Choices in Multiple Sequence Training for Gesture Recognition

We present several ways to initialize and train Hidden Markov Models (HMMs) for gesture recognition. These include using a single initial model for training (reestimation), multiple random initial models, and initial models directly computed from physical considerations. Each of the initial models is trained on multiple observation sequences using both Baum-Welch and the Viterbi Path Counting algorithm on three different model structures: Fully Connected (or ergodic), Left-Right, and Left-Right Banded. After performing many recognition trials on our video database of 780 letter gestures, results show that a) the simpler the structure is, the less the effect of the initial model, b) the direct computation method for designing the initial model is effective and provides insight into HMM learning, and c) Viterbi Path Counting performs best overall and depends much less on the initial model than does Baum-Welch training

Process Control of Activated Sludge Treatment

General feed forward controllers, conforming to standard control modes, have been derived for an activated sludge process. The analysis indicated that the appropriate controllers are proportional control with measurement of substrate flow rate, and derivative control with measurement of inlet substrate concentration, and manipulation of the rate of return sludge by both controllers. The performance of these controllers was tested by computer simulation of five dynamic aerator models with and without sludge storage, and with two settling basin models. In all cases significant reduction of the maximum exit substrate concentration was achieved. Additional improvement resulted from the use of sludge storage. As the aerator model became more linear the control results also improved. The first dynamic results were obtained using a perfect steady state settler model, the remainder assumed that the settler dynamics could be represented by a variable time delay. The addition of the settler dynamics caused the control to degrade somewhat. Finally the generality of the two controllers was proved mathematically for the five biological kinetic models for substrate utilization and bacterial growth

Measuring Individual-Level Resource Specialization

Many apparently generalized species are in fact composed of individual specialists that use a small subset of the population’s resource distribution. Niche variation is usually established by testing the null hypothesis that individuals draw from a common resource distribution. This approach encourages a publication bias in which negative results are rarely reported, and obscures variation in the degree of individual specialization, limiting our ability to carry out comparative studies of the causes or consequences of niche variation. To facilitate studies of the degree of individual specialization, this paper outlines four quantitative indices of intrapopulation variation in resource use. Traditionally, such variation has been measured by partitioning the population’s total niche width into within- and between-individual, sex, or phenotype components. We suggest two alternative measures that quantify the mean resource overlap between an individual and its population, and we discuss the advantages and disadvantages of all four measures. The utility of all indices depends on the quality of the empirical data. If resources are measured in a coarse-grained manner, individuals may falsely appear generalized. Alternatively, specialization may be overestimated by cross-sectional sampling schemes where diet variation can reflect a patchy environment. Isotope ratios, parasites, or diet–morphology correlations can complement cross-sectional data to establish temporal consistency of individual specialization

Use of a cAMP BRET Sensor to Characterize a Novel Regulation of cAMP by the Sphingosine 1-Phosphate/G13 Pathway

Regulation of intracellular cyclic adenosine 3',5'-monophosphate (cAMP) is integral in mediating cell growth, cell differentiation, and immune responses in hematopoietic cells. To facilitate studies of cAMP regulation we developed a BRET (bioluminescence resonance energy transfer) sensor for cAMP, CAMYEL (cAMP sensor using YFP-Epac-RLuc), which can quantitatively and rapidly monitor intracellular concentrations of cAMP in vivo. This sensor was used to characterize three distinct pathways for modulation of cAMP synthesis stimulated by presumed Gs-dependent receptors for isoproterenol and prostaglandin E2. Whereas two ligands, uridine 5'-diphosphate and complement C5a, appear to use known mechanisms for augmentation of cAMP via Gq/calcium and Gi, the action of sphingosine 1-phosphate (S1P) is novel. In these cells, S1P, a biologically active lysophospholipid, greatly enhances increases in intracellular cAMP triggered by the ligands for Gs-coupled receptors while having only a minimal effect by itself. The enhancement of cAMP by S1P is resistant to pertussis toxin and independent of intracellular calcium. Studies with RNAi and chemical perturbations demonstrate that the effect of S1P is mediated by the S1P2 receptor and the heterotrimeric G13 protein. Thus in these macrophage cells, all four major classes of G proteins can regulate intracellular cAMP

The HI Tully-Fisher Relation of Early-Type Galaxies

We study the HI K-band Tully-Fisher relation and the baryonic Tully-Fisher relation for a sample of 16 early-type galaxies, taken from the ATLAS3D sample, which all have very regular HI disks extending well beyond the optical body (> 5 R_eff). We use the kinematics of these disks to estimate the circular velocity at large radii for these galaxies. We find that the Tully-Fisher relation for our early-type galaxies is offset by about 0.5-0.7 magnitudes from the relation for spiral galaxies. The residuals with respect to the spiral Tully-Fisher relation correlate with estimates of the stellar mass-to-light ratio, suggesting that the offset between the relations is mainly driven by differences in stellar populations. We also observe a small offset between our Tully-Fisher relation with the relation derived for the ATLAS3D sample based on CO data representing the galaxies' inner regions (< 1 R_eff). This indicates that the circular velocities at large radii are systematically 10% lower than those near 0.5-1 R_eff, in line with recent determinations of the shape of the mass profile of early-type galaxies. The baryonic Tully-Fisher relation of our sample is distinctly tighter than the standard one, in particular when using mass-to-light ratios based on dynamical models of the stellar kinematics. We find that the early-type galaxies fall on the spiral baryonic Tully-Fisher relation if one assumes M/L_K = 0.54 M_sun/L_sun for the stellar populations of the spirals, a value similar to that found by recent studies of the dynamics of spiral galaxies. Such a mass-to-light ratio for spiral galaxies would imply that their disks are 60-70% of maximal. Our analysis increases the range of galaxy morphologies for which the baryonic Tully-Fisher relations holds, strengthening previous claims that it is a more fundamental scaling relation than the classical Tully-Fisher relation.Comment: Accepted for publication in Astronomy & Astrophysic

The Ecology of Individuals: Incidence and Implications of Individual Specialization

Most empirical and theoretical studies of resource use and population dynamics treat conspecific individuals as ecologically equivalent. This simplification is only justified if interindividual niche variation is rare, weak, or has a trivial effect on ecological processes. This article reviews the incidence, degree, causes, and implications of individual-level niche variation to challenge these simplifications. Evidence for individual specialization is available for 93 species dis- tributed across a broad range of taxonomic groups. Although few studies have quantified the degree to which individuals are specialized relative to their population, between-individual variation can some- times comprise the majority of the population’s niche width. The degree of individual specialization varies widely among species and among populations, reflecting a diverse array of physiological, be- havioral, and ecological mechanisms that can generate intrapopu- lation variation. Finally, individual specialization has potentially im- portant ecological, evolutionary, and conservation implications. Theory suggests that niche variation facilitates frequency-dependent interactions that can profoundly affect the population’s stability, the amount of intraspecific competition, fitness-function shapes, and the population’s capacity to diversify and speciate rapidly. Our collection of case studies suggests that individual specialization is a widespread but underappreciated phenomenon that poses many important but unanswered questions