Producing power-law distributions and damping word frequencies with two-stage language models

Standard statistical models of language fail to capture one of the most striking properties of natural languages: the power-law distribution in the frequencies of word tokens. We present a framework for developing statisticalmodels that can generically produce power laws, breaking generativemodels into two stages. The first stage, the generator, can be any standard probabilistic model, while the second stage, the adaptor, transforms the word frequencies of this model to provide a closer match to natural language. We show that two commonly used Bayesian models, the Dirichlet-multinomial model and the Dirichlet process, can be viewed as special cases of our framework. We discuss two stochastic processes-the Chinese restaurant process and its two-parameter generalization based on the Pitman-Yor process-that can be used as adaptors in our framework to produce power-law distributions over word frequencies. We show that these adaptors justify common estimation procedures based on logarithmic or inverse-power transformations of empirical frequencies. In addition, taking the Pitman-Yor Chinese restaurant process as an adaptor justifies the appearance of type frequencies in formal analyses of natural language and improves the performance of a model for unsupervised learning of morphology.48 page(s

A role for the developing lexicon in phonetic category acquisition

Infants segment words from fluent speech during the same period when they are learning phonetic categories, yet accounts of phonetic category acquisition typically ignore information about the words in which sounds appear. We use a Bayesian model to illustrate how feedback from segmented words might constrain phonetic category learning by providing information about which sounds occur together in words. Simulations demonstrate that word-level information can successfully disambiguate overlapping English vowel categories. Learning patterns in the model are shown to parallel human behavior from artificial language learning tasks. These findings point to a central role for the developing lexicon in phonetic category acquisition and provide a framework for incorporating top-down constraints into models of category learning

Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models

8 page(s

Contextual Dependencies in Unsupervised Word Segmentation

Developing better methods for segmenting continuous text into words is important for improving the processing of Asian languages, and may shed light on how humans learn to segment speech. We propose two new Bayesian word segmentation methods that assume unigram and bigram models of word dependencies respectively. The bigram model greatly outperforms the unigram model (and previous probabilistic models), demonstrating the importance of such dependencies for word segmentation. We also show that previous probabilistic models rely crucially on suboptimal search procedures.

ITI-007 demonstrates brain occupancy at serotonin 5-HT2A and dopamine D2 receptors and serotonin transporters using positron emission tomography in healthy volunteers

© 2015 Springer-Verlag Berlin Heidelberg.Rationale: Central modulation of serotonin and dopamine underlies efficacy for a variety of psychiatric therapeutics. ITI-007 is an investigational new drug in development for treatment of schizophrenia, mood disorders, and other neuropsychiatric disorders. Objectives: The purpose of this study was to determine brain occupancy of ITI-007 at serotonin 5-HT2A receptors, dopamine D2 receptors, and serotonin transporters using positron emission tomography (PET) in 16 healthy volunteers. Methods: Carbon-11-MDL100907, carbon-11-raclopride, and carbon-11-3-amino-4-(2-dimethylaminomethyl-phenylsulfanyl)-benzonitrile) (carbon-11-DASB) were used as the radiotracers for imaging 5-HT2A receptors, D2 receptors, and serotonin transporters, respectively. Brain regions of interest were outlined using magnetic resonance tomography (MRT) with cerebellum as the reference region. Binding potentials were estimated by fitting a simplified reference tissue model to the measured tissue-time activity curves. Target occupancy was expressed as percent change in the binding potentials before and after ITI-007 administration. Results: Oral ITI-007 (10-40 mg) was safe and well tolerated. ITI-007 rapidly entered the brain with long-lasting and dose-related occupancy. ITI-007 (10 mg) demonstrated high occupancy (>80 %) of cortical 5-HT2A receptors and low occupancy of striatal D2 receptors (~12 %). D2 receptor occupancy increased with dose and significantly correlated with plasma concentrations (r 2∈=∈0.68, p∈=∈0.002). ITI-007 (40 mg) resulted in peak occupancy up to 39 % of striatal D2 receptors and 33 % of striatal serotonin transporters. Conclusions: The results provide evidence for a central mechanism of action via dopaminergic and serotonergic pathways for ITI-007 in living human brain and valuable information to aid dose selection for future clinical trials

Perspectives on Implementing a Multidomain Approach to Caring for Older Adults With Heart Failure

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153220/1/jgs16183_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153220/2/jgs16183-sup-0001-supinfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153220/3/jgs16183.pd