Linked Markov sources: Modeling outcome-dependent social processes

Many social processes are adaptive in the sense that the process changes as a result of previous outcomes. Data on such processes may come in the form of categorical time series. First, the authors propose a class of Markov Source models that embody such adaptation. Second, the authors discuss new methods to evaluate the fit of such models. Third, the authors apply these models and methods to data on a social process that is a preeminent example of an adaptive process: (encoded) conversation as arises in structured interviews. © 2007 Sage Publications

Innovative solutions to novel drug development in mental health

There are many new advances in neuroscience and mental health which should lead to a greater understanding of the neurobiological dysfunction in neuropsychiatric disorders and new developments for early, effective treatments. To do this, a biomarker approach combining genetic, neuroimaging, cognitive and other biological measures is needed. The aim of this article is to highlight novel approaches for pharmacological and non-pharmacological treatment development. This article suggests approaches that can be taken in the future including novel mechanisms with preliminary clinical validation to provide a toolbox for mechanistic studies and also examples of translation and back-translation. The review also emphasizes the need for clinician-scientists to be trained in a novel way in order to equip them with the conceptual and experimental techniques required, and emphasizes the need for private-public partnership and pre-competitive knowledge exchange. This should lead the way for important new holistic treatment developments to improve cognition, functional outcome and well-being of people with neuropsychiatric disorders

MCMC implementation for Bayesian hidden semi-Markov models with illustrative applications

Copyright © Springer 2013. The final publication is available at Springer via http://dx.doi.org/10.1007/s11222-013-9399-zHidden Markov models (HMMs) are flexible, well established models useful in a diverse range of applications. However, one potential limitation of such models lies in their inability to explicitly structure the holding times of each hidden state. Hidden semi-Markov models (HSMMs) are more useful in the latter respect as they incorporate additional temporal structure by explicit modelling of the holding times. However, HSMMs have generally received less attention in the literature, mainly due to their intensive computational requirements. Here a Bayesian implementation of HSMMs is presented. Recursive algorithms are proposed in conjunction with Metropolis-Hastings in such a way as to avoid sampling from the distribution of the hidden state sequence in the MCMC sampler. This provides a computationally tractable estimation framework for HSMMs avoiding the limitations associated with the conventional EM algorithm regarding model flexibility. Performance of the proposed implementation is demonstrated through simulation experiments as well as an illustrative application relating to recurrent failures in a network of underground water pipes where random effects are also included into the HSMM to allow for pipe heterogeneity

Some Aspects of Latent Structure Analysis

Latent structure models involve real, potentially observable variables and latent, unobservable variables. The framework includes various particular types of model, such as factor analysis, latent class analysis, latent trait analysis, latent profile models, mixtures of factor analysers, state-space models and others. The simplest scenario, of a single discrete latent variable, includes finite mixture models, hidden Markov chain models and hidden Markov random field models. The paper gives a brief tutorial of the application of maximum likelihood and Bayesian approaches to the estimation of parameters within these models, emphasising especially the fact that computational complexity varies greatly among the different scenarios. In the case of a single discrete latent variable, the issue of assessing its cardinality is discussed. Techniques such as the EM algorithm, Markov chain Monte Carlo methods and variational approximations are mentioned

Implicit relevance feedback from eye movements

Abstract. We explore the use of eye movements as a source of implicit relevance feedback information. We construct a controlled information retrieval experiment where the relevance of each text is known, and test usefulness of implicit relevance feedback with it. If perceived relevance of a text can be predicted from eye movements, eye movement signal must contain information on the relevance. The result is that relevance can be predicted to a considerable extent with discriminative hidden Markov models, and clearly better than randomly already with simple linear models of time-averaged data.

Cryptanalysis of the rsa subgroup assumption from TCC 2005

Abstract. At TCC 2005, Groth underlined the usefulness of working in small RSA subgroups of hidden order. In assessing the security of the relevant hard problems, however, the best attack considered for a subgroup of size 2 2ℓ had a complexity of O(2 ℓ). Accordingly, ℓ = 100 bits was suggested as a concrete parameter. This paper exhibits an attack with a complexity of roughly 2 ℓ/2 operations, suggesting that Groth’s original choice of parameters was overly aggressive. It also discusses the practicality of this new attack and various implementation issues. Key-words: rsa moduli, hidden order, subgroup, cryptanalysis.