In the current study, we explore the factors underlying the well-known difficulty in acoustic classification of front nonsibilant fricatives (Maniwa, Jongman & Wade 2009, McMurray & Jongman 2011) by applying a novel classification method to the production of Greek speakers. The Greek fricative inventory [f v θ ð s z ç ʝ x ɣ] includes voiced and voiceless segments from five distinct places of articulation. Our corpus contains all of the Greek fricatives produced by 29 monolingual speakers, but our focus is on the distinction between the front nonsibilant fricatives [f v θ ð]. For comparison, we also discuss the other places of articulation where relevant. We apply a relatively novel classification method based on cepstral coefficients, previously successful in categorizing English obstruent bursts (Bunnell, Polikoff & McNicholas 2004), English vowels (Ferragne & Pellegrino 2010), Romanian fricatives (Spinu & Lilley 2016), and Russian fricatives (Spinu, Kochetov & Lilley 2018). For this study, fricative boundaries were automatically aligned using Hidden Markov Models (HMMs) and then manually checked. Six Bark-frequency cepstral coefficients (c0–c5) were extracted from 20-millisecond Hann windows. HMMs were used to divide the fricatives and adjacent vowels into three regions of internally minimized variance. A multinomial logistic regression analysis then used the mean cepstral coefficients from each region as predictors for classification by consonant identity. Our method yields highly successful classification rates, exceeding the performance of previous methods. We discuss these results in light of the differences of the phonemic distributions of fricatives between English and Greek.

In this paper, we propose and implement an advanced manipulation framework that enables parametric learning of complex action trajectories along with their haptic feedback profiles. Our framework extends Dynamic Movement Primitives (DMPs) method with a new parametric nonlinear shaping function and a novel force-feedback coupling term. The nonlinear trajectories of the action control variables and the haptic feedback trajectories measured during execution are encoded with parametric temporal probabilistic models, namely parametric hidden Markov models (PHMMs). PHMMs enable autonomous segmentation of a taught skill based on the statistical information extracted from multiple demonstrations, and learning the relations between the model parameters and the properties extracted from the environment. Hidden states with high-variances in observation probabilities are interpreted as parts of the skill that could not be reliably learned and autonomously executed due to possibly uncertain or missing information about the environment. In those parts, our proposed force-feedback coupling term, which computes the deviation of the actual force feedback from the one predicted by the force-feedback PHMM, acts as a compliance term, enabling a human to scaffold the ongoing movement trajectory to accomplish the task. Our method is verified in a number of tasks including a real pick and place task that involves obstacles of different heights. Our robot, Baxter, successfully learned to generate the trajectory taking into the heights of the obstacles, move its end effector stiffly (and accurately) along the generated trajectory while passing through apertures, and allow human–robot collaboration in the autonomously detected segments of the motion, for example, when the gripper picks up the object whose position is not provided to the robot.

At-sea behaviour of central-place foraging fur seals and penguins in the Southern Ocean is understudied during the latter stages of parental care and the subsequent pre-moulting period. This biologically important period is costly to investigate due to the risk (or certainty) of losing tracking instruments when the animals moult. Early in this period, parents must meet the increasing demands of larger, more mobile offspring that are still nutritionally dependent and then the parents must recover lost body condition prior to the onset of their annual moult. This study reports late-season, at-sea movement patterns of macaroni penguins, chinstrap penguins and adult female Antarctic fur seals from the subantarctic island Bouvetøya, in relation to remotely-sensed oceanographic features. Foraging trips differing significantly in direction and distance travelled compared to those performed earlier in the breeding season, coincide with the time when offspring would be expected to become independent. On these trips, macaroni penguins moved towards the Polar Front while chinstrap penguins and Antarctic fur seals moved southward. Individuals from all three species appeared to target submesoscale ocean features once they were presumed to have been released from the constraints of feeding their young and were able to travel greater distances from the colony.

To establish lists of words with unexpected frequencies in long sequences,for instance in a molecular biology context, one needs to quantify the exceptionality of families of word frequencies in random sequences. To this aim, we study large deviation probabilities of multidimensional word counts for Markov and hidden Markov models.More specifically, we compute local Edgeworth expansions of arbitrary degrees for multivariate partial sums of lattice valued functionals of finite Markov chains. This yields sharp approximations of the associated large deviation probabilities. We also provide detailed simulations. These exhibit in particular previously unreported periodic oscillations, for which we providetheoretical explanations.

We consider a Wright-Fisher diffusion (x(t)) whose current state cannot be observed directly. Instead, at times t 1 < t 2 < ..., the observations y(ti) are such that, given the process (x(t)), the random variables (y(ti)) are independent and the conditional distribution of y(ti) only depends on x(ti). When this conditional distribution has a specific form, we prove that the model ((x(ti),y(ti)), i ≥1) is a computable filter in the sense that all distributions involved in filtering, prediction and smoothing are exactly computable. These distributions are expressed as finite mixtures of parametric distributions. Thus, the number of statistics to compute at each iteration is finite, but this number may vary along iterations.

In Francq and Zakoïan [4], we derived stationarity conditions forARMA(p,q) models subject to Markov switching. In this paper, weshow that, under appropriate moment conditions, the powers of thestationary solutions admit weak ARMA representations, which we areable to characterize in terms of p,q, the coefficients of themodel in each regime, and the transition probabilities of theMarkov chain. These representations are potentially useful forstatistical applications.

It is now possible to identify over 30 functional subfamilies among the WD-repeat-containing proteins found in the completed genomes. The majority of these subfamilies have at least one member for which experimental data allow assignment to a cellular pathway or process. Half of the 63 WD-repeat-containing proteins in Saccharomyces cerevisiae, half of the 70 in Caenorhabditis elegans, and a third of the 100 plus predicted in Drosophila can be assigned to 23 of these functional subfamilies. Perhaps indicative of the future, 33 WD-repeat-containing proteins from the partial genome of Arabidopsis thaliana can now be assigned to 18 of these subfamilies. These assignments have been made possible by combining traditional sequence similarity with an implied common beta propeller structural context to obtain measures of protein–protein surface similarity. The beta propeller structural context is represented in the form of a Hidden Markov Model. The procedure is completely automated.

We consider a sequence of observations which is generated by a so-called hidden Markov model. An exponential smoothing procedure applied to such an observation sequence generates an inhomogeneous Markov process as a sequence of smoothed values. If the state sequence of the underlying hidden Markov model is moreover ergodic, then for two classes of smoothing functions the strong ergodicity of the sequence of smoothed values is proved. As a consequence a central limit theorem and a law of large numbers hold true for the smoothed values. The proof uses general results for so-called convergent inhomogeneous Markov processes. The procedure proposed by the author can be applied to some time series discussed in the literature.

The two-point Markov chain boundary-value problem discussed in this paper is a finite-time version of the quasi-stationary behaviour of Markov chains. Specifically, for a Markov chain {Xt:t = 0, 1, ·· ·}, given the time interval (0, n), the interest is in describing the chain at some intermediate time point r conditional on knowing both the behaviour of the chain at the initial time point 0 and that over the interval (0, n) it has avoided some subset B of the state space. The paper considers both ‘real time' estimates for r = n (i.e. the chain has avoided B since 0), and a posteriori estimates for r < n with at least partial knowledge of the behaviour of Xn. Algorithms to evaluate the distribution of Xr can be as small as O(n3) (and, for practical purposes, even O(n2 log n)). The estimates may be stochastically ordered, and the process (and hence, the estimates) may be spatially homogeneous in a certain sense. Maximum likelihood estimates of the sample path are furnished, but by example we note that these ML paths may differ markedly from the path consisting of the expected or average states. The scope for two-point boundary-value problems to have solutions in a Markovian setting is noted.

Several examples are given, together with a discussion and examples of the analogous problem in continuous time. These examples include the basic M/G/k queue and variants that include a finite waiting room, reneging, balking, and Bernoulli feedback, a pure birth process and the Yule process. The queueing examples include Larson's (1990) ‘queue inference engine'.