Multiscale computational methods bridging models at micro-scale with macro-properties is a problem of practical significance. Probability distribution functions (PDFs) providing a complete representation of microstructural variability in 3D polycrystalline materials using limited information is difficult to obtain since this inverse problem is highly ill-posed. We use the maximum entropy (MaxEn...

Understanding temporal urban growth is very crucial to the interpretation of urban morphology and a key challenge for the study of rapid urbanization in contemporary China. Previous land use change modelling approaches only consider the spatial complexity that may be indicated by spatial dependence and landscape fragmentation, ignoring the temporal complexity inherent in the process of urban gr...

This paper introduces a method to detect lexical stress errors in unit selection synthesis automatically using machine learning algorithms. If unintended stress patterns can be detected following unit selection, based on features available in the unit database, it may be possible to modify the units during waveform synthesis to correct errors and produce an acceptable stress pattern. In this pa...

Automatic detection of disfluencies in spoken language is important for making speech recognition output more readable, and for aiding downstream language processing modules. We compare a generative hidden Markov model (HMM)-based approach and two conditional models — a maximum entropy (Maxent) model and a conditional random field (CRF) — for detecting disfluencies in speech. The conditional mo...

Maximum entropy (MaxEnt) modeling is a popular choice for sequence analysis in applications such as natural language processing, where the sequences are embedded in discrete, tractably-sized spaces. We consider the problem of applying MaxEnt to distributions over paths in continuous spaces of high dimensionality— a problem for which inference is generally intractable. Our main contribution is t...

Sentence boundary detection in speech is important for enriching speech recognition output, making it easier for humans to read and downstream modules to process. In previous work, we have developed hidden Markov model (HMM) and maximum entropy (Maxent) classifiers that integrate textual and prosodic knowledge sources for detecting sentence boundaries. In this paper, we evaluate the use of a co...

We compare and contrast two different models for detecting sentence-like units in continuous speech, using both acoustic and lexical information. The first approach is based on hidden Markov sequence models based on N-grams, uses maximum likelihood estimation, and model interpolation to combine different representations of the data. The second approach models the posterior probabilities of the ...

The ability of the maximum-entropy method (in the program MAXENT) to estimate the distancedistribution function from high-resolution X-ray scattering data is studied. It is demonstrated that a key element for the successful application of M A X E N T is the use of a good prior estimate for the distance-distribution function. For simulated as well as experimental data, the effects of different p...