Modelling the local and global cloud formation on HD 189733b

Context. Observations suggest that exoplanets such as HD 189733b form clouds in their atmospheres which have a strong feedback onto their thermodynamical and chemical structure, and overall appearance. Aims. Inspired by mineral cloud modelling efforts for Brown Dwarf atmospheres, we present the first spatially varying kinetic cloud model structures for HD 189733b. Methods. We apply a 2-model approach using results from a 3D global radiation-hydrodynamic simulation of the atmosphere as input for a detailed, kinetic cloud formation model. Sampling the 3D global atmosphere structure with 1D trajectories allows us to model the spatially varying cloud structure on HD 189733b. The resulting cloud properties enable the calculation of the scattering and absorption properties of the clouds. Results. We present local and global cloud structure and property maps for HD 189733b. The calculated cloud properties show variations in composition, size and number density of cloud particles which are strongest between the dayside and nightside. Cloud particles are mainly composed of a mix of materials with silicates being the main component. Cloud properties, and hence the local gas composition, change dramatically where temperature inversions occur locally. The cloud opacity is dominated by absorption in the upper atmosphere and scattering at higher pressures in the model. The calculated 8{\mu}m single scattering Albedo of the cloud particles are consistent with Spitzer bright regions. The cloud particles scattering properties suggest that they would sparkle/reflect a midnight blue colour at optical wavelengths.Comment: Accepted for publication (A&A) - 21/05/2015 (Low Resolution Maps

Systems with Correlations in the Variance: Generating Power-Law Tails in Probability Distributions

We study how the presence of correlations in physical variables contributes to the form of probability distributions. We investigate a process with correlations in the variance generated by (i) a Gaussian or (ii) a truncated L\'{e}vy distribution. For both (i) and (ii), we find that due to the correlations in the variance, the process ``dynamically'' generates power-law tails in the distributions, whose exponents can be controlled through the way the correlations in the variance are introduced. For (ii), we find that the process can extend a truncated distribution {\it beyond the truncation cutoff}, which leads to a crossover between a L\'{e}vy stable power law and the present ``dynamically-generated'' power law. We show that the process can explain the crossover behavior recently observed in the S&P500 stock index.Comment: 7 pages, five figures. To appear in Europhysics Letters (2000

A metal–organic framework/α-alumina composite with a novel geometry for enhanced adsorptive separation

The development of a metal–organic framework/α-alumina composite leads to a novel concept: efficient adsorption occurs within a plurality of radial micro-channels with no loss of the active adsorbents during the process. This composite can effectively remediate arsenic contaminated water producing potable water recovery, whereas the conventional fixed bed requires eight times the amount of active adsorbents to achieve a similar performance

Interaction of topological solitons with defects: using a nontrivial metric

By including potential into the flat metric, we study interaction of sine-Gordon soliton with potentials. We will show numerically that while the soliton-barrier system shows fully classical behaviour, the soliton-well system demonstrates non-classical behaviour. In particular, solitons with low velocities are trapped in the well and emit energy radiation.Comment: 10 pages, 11 figure

On-line adaptive learning of the correlated continuous density hidden Markov models for speech recognition

We extend our previously proposed quasi-Bayes adaptive learning framework to cope with the correlated continuous density hidden Markov models (HMMs) with Gaussian mixture state observation densities in which all mean vectors are assumed to be correlated and have a joint prior distribution. A successive approximation algorithm is proposed to implement the correlated mean vectors' updating. As an example, by applying the method to an on-line speaker adaptation application, the algorithm is experimentally shown to be asymptotically convergent as well as being able to enhance the efficiency and the effectiveness of the Bayes learning by taking into account the correlation information between different model parameters. The technique can be used to cope with the time-varying nature of some acoustic and environmental variabilities, including mismatches caused by changing speakers, channels, transducers, environments, and so on.published_or_final_versio

On adaptive decision rules and decision parameter adaptation for automatic speech recognition

Recent advances in automatic speech recognition are accomplished by designing a plug-in maximum a posteriori decision rule such that the forms of the acoustic and language model distributions are specified and the parameters of the assumed distributions are estimated from a collection of speech and language training corpora. Maximum-likelihood point estimation is by far the most prevailing training method. However, due to the problems of unknown speech distributions, sparse training data, high spectral and temporal variabilities in speech, and possible mismatch between training and testing conditions, a dynamic training strategy is needed. To cope with the changing speakers and speaking conditions in real operational conditions for high-performance speech recognition, such paradigms incorporate a small amount of speaker and environment specific adaptation data into the training process. Bayesian adaptive learning is an optimal way to combine prior knowledge in an existing collection of general models with a new set of condition-specific adaptation data. In this paper, the mathematical framework for Bayesian adaptation of acoustic and language model parameters is first described. Maximum a posteriori point estimation is then developed for hidden Markov models and a number of useful parameters densities commonly used in automatic speech recognition and natural language processing.published_or_final_versio

Antithyroid drug-induced agranulocytosis

Thyrotoxicosis is a common endocrine disorder. Antithyroid drug therapy is the standard treatment for this disease, especially in young women of reproductive age. A serious side effect of antithyroid drug use, however, is agranulocytosis. We report on two patients with antithyroid drug-induced agranulocytosis. Both patients presented with fever and severe neutropenia. The administration of granulocyte colony-stimulating factor resulted in a dramatic improvement in the white blood cell count and symptoms. Antithyroid drug-induced agranulocytosis is a potentially lethal condition but is completely reversible when recognised early and when prompt treatment is offered.published_or_final_versio

On-line adaptive learning of the continuous density hidden Markov model based on approximate recursive Bayes estimate

We present a framework of quasi-Bayes (QB) learning of the parameters of the continuous density hidden Markov model (CDHMM) with Gaussian mixture state observation densities. The QB formulation is based on the theory of recursive Bayesian inference. The QB algorithm is designed to incrementally update the hyperparameters of the approximate posterior distribution and the CDHMM parameters simultaneously. By further introducing a simple forgetting mechanism to adjust the contribution of previously observed sample utterances, the algorithm is adaptive in nature and capable of performing an online adaptive learning using only the current sample utterance. It can, thus, be used to cope with the time-varying nature of some acoustic and environmental variabilities, including mismatches caused by changing speakers, channels, and transducers. As an example, the QB learning framework is applied to on-line speaker adaptation and its viability is confirmed in a series of comparative experiments using a 26-letter English alphabet vocabulary.published_or_final_versio

A study of prior sensitivity for Bayesian predictive classificationbased robust speech recognition

We previously introduced a new Bayesian predictive classification (BPC) approach to robust speech recognition and showed that the BPC is capable of coping with many types of distortions. We also learned that the efficacy of the BPC algorithm is influenced by the appropriateness of the prior distribution for the mismatch being compensated. If the prior distribution fails to characterize the variability reflected in the model parameters, then the BPC will not help much. We show how the knowledge and/or experience of the interaction between the speech signal and the possible mismatch guide us to obtain a better prior distribution which improves the performance of the BPC approach.published_or_final_versio