Positive selection drives population differentiation in the skeletal genes in modern humans

During the course of evolution, the human skeletal system has evolved rapidly leading to an incredible array of phenotypic diversity, including variations in height and bone mineral density. However, the genetic basis of this phenotypic diversity and the relatively rapid tempo of evolution have remained largely undocumented. Here, we discover that skeletal genes exhibit a significantly greater level of population differentiation among humans compared with other genes in the genome. The pattern is exceptionally evident at amino acid-altering sites within these genes. Divergence is greater between Africans and both Europeans and East Asians. In contrast, relatively weak differentiation is observed between Europeans and East Asians. SNPs with higher levels of differentiation have correspondingly higher derived allele frequencies in Europeans and East Asians. Thus, it appears that positive selection has operated on skeletal genes in the non-African populations and this may have been initiated with the human colonization of Eurasia. In conclusion, we provide genetic evidence supporting the rapid evolution of the human skeletal system and the associated diversity of phenotypes.

The role of adaptive thermal comfort in the prediction of the thermal performance of a modern mixed-mode office building in the UK under climate change

Climate change is becoming a serious issue for the construction industry, since the time scales at which climate change takes place can be expected to show a true impact on the thermal performance of buildings and HVAC systems. In predicting this future building performance by means of building simulation, the underlying assumptions regarding thermal comfort conditions and the related heating, ventilating and air conditioning (HVAC) control set points become important. This article studies the thermal performance of a reference office building with mixedmode ventilation in the UK, using static and adaptive thermal approaches, for a series of time horizons (2020, 2050 and 2080). Results demonstrate the importance of the implementation of adaptive thermal comfort models, and underpin the case for its use in climate change impact studies. Adaptive thermal comfort can also be used by building designers to make buildings more resilient towards change. © 2010 International Building Performance Simulation Association (IBPSA).

Size distribution of heavy mineral grains in some modern Nile Delta coastal sands, Egypt

This study includes determination and discussion of the texture and heavy mineral compositions of some modem Nile Delta coastal sands (river, coastal dune, beach-face, and nearshore marine) in order to delineate the process and factors that regulate the size distribution of heavy mineral grains comprising these coastal sands. Textural analysis of unseparated bulk samples indicate that the examined four types of sands differ in their mean grain sizes and degree of sorting. However, analysis of size distribution curves of 10 heavy mineral species or group of species in the four environments having the same general shape and nearly similar in that general order of arrangement. However, these curves vary both in median sizes and sorting. The size distribution of a heavy mineral in the Nile Delta coastal sands appear to depend on: (1) range of grain size fractions in each sample, (2) relative availability of heavy mineral in each size grade of the sample, (3) specific gravity of minerals comprising these sands, and (4) some other unknown factor or factors. Results of size measurement of heavy minerals indicated that increasing specific gravity is accompanied by increasing fineness of the heavy minerals. This study may be useful in search for marine placers and understanding the processes of grain-sorting on the sea beaches.

Cambodia: towards a modern commons

The Community Fisheries organizations in Cambodia possess the basic framework and principles to be considered good examples of a created ‘modern commons’....

Modern challenges facing turbomachinery aeroacoustics

In this review we describe current scientific and technological issues in the quest to reduce aeroengine noise, in the face of predicted rapid increases in the volume of air traffic, on the one hand, and increasingly strict environmental regulation, on the other. Alongside conventional ducted turbofan designs, new open-rotor contra-rotating power plants are currently under development, which present their own noise challenges. The key sources of tonal and broadband noise, and the way in which noise propagates away from the source, are surveyed in both cases. We also consider in detail two key aspects underpinning the flow physics that continue to receive considerable attention, namely the acoustics of swirling flow and unsteady flow-blade interactions. Finally, we describe possible innovations in open-rotor engine design for low noise.

Statistical parametric speech synthesis based on speaker and language factorization

An increasingly common scenario in building speech synthesis and recognition systems is training on inhomogeneous data. This paper proposes a new framework for estimating hidden Markov models on data containing both multiple speakers and multiple languages. The proposed framework, speaker and language factorization, attempts to factorize speaker-/language-specific characteristics in the data and then model them using separate transforms. Language-specific factors in the data are represented by transforms based on cluster mean interpolation with cluster-dependent decision trees. Acoustic variations caused by speaker characteristics are handled by transforms based on constrained maximum-likelihood linear regression. Experimental results on statistical parametric speech synthesis show that the proposed framework enables data from multiple speakers in different languages to be used to: train a synthesis system; synthesize speech in a language using speaker characteristics estimated in a different language; and adapt to a new language. © 2012 IEEE.

Phrase-based statistical language generation using graphical models and active learning

Most previous work on trainable language generation has focused on two paradigms: (a) using a statistical model to rank a set of generated utterances, or (b) using statistics to inform the generation decision process. Both approaches rely on the existence of a handcrafted generator, which limits their scalability to new domains. This paper presents BAGEL, a statistical language generator which uses dynamic Bayesian networks to learn from semantically-aligned data produced by 42 untrained annotators. A human evaluation shows that BAGEL can generate natural and informative utterances from unseen inputs in the information presentation domain. Additionally, generation performance on sparse datasets is improved significantly by using certainty-based active learning, yielding ratings close to the human gold standard with a fraction of the data. © 2010 Association for Computational Linguistics.

Improving LVCSR system combination using neural network language model cross adaptation

State-of-the-art large vocabulary continuous speech recognition (LVCSR) systems often combine outputs from multiple subsystems developed at different sites. Cross system adaptation can be used as an alternative to direct hypothesis level combination schemes such as ROVER. The standard approach involves only cross adapting acoustic models. To fully exploit the complimentary features among sub-systems, language model (LM) cross adaptation techniques can be used. Previous research on multi-level n-gram LM cross adaptation is extended to further include the cross adaptation of neural network LMs in this paper. Using this improved LM cross adaptation framework, significant error rate gains of 4.0%-7.1% relative were obtained over acoustic model only cross adaptation when combining a range of Chinese LVCSR sub-systems used in the 2010 and 2011 DARPA GALE evaluations. Copyright © 2011 ISCA.

Use of contexts in language model interpolation and adaptation

Language models (LMs) are often constructed by building multiple individual component models that are combined using context independent interpolation weights. By tuning these weights, using either perplexity or discriminative approaches, it is possible to adapt LMs to a particular task. This paper investigates the use of context dependent weighting in both interpolation and test-time adaptation of language models. Depending on the previous word contexts, a discrete history weighting function is used to adjust the contribution from each component model. As this dramatically increases the number of parameters to estimate, robust weight estimation schemes are required. Several approaches are described in this paper. The first approach is based on MAP estimation where interpolation weights of lower order contexts are used as smoothing priors. The second approach uses training data to ensure robust estimation of LM interpolation weights. This can also serve as a smoothing prior for MAP adaptation. A normalized perplexity metric is proposed to handle the bias of the standard perplexity criterion to corpus size. A range of schemes to combine weight information obtained from training data and test data hypotheses are also proposed to improve robustness during context dependent LM adaptation. In addition, a minimum Bayes' risk (MBR) based discriminative training scheme is also proposed. An efficient weighted finite state transducer (WFST) decoding algorithm for context dependent interpolation is also presented. The proposed technique was evaluated using a state-of-the-art Mandarin Chinese broadcast speech transcription task. Character error rate (CER) reductions up to 7.3 relative were obtained as well as consistent perplexity improvements. © 2012 Elsevier Ltd. All rights reserved.

Syllable language models for Mandarin speech recognition: exploiting character language models.

Mandarin Chinese is based on characters which are syllabic in nature and morphological in meaning. All spoken languages have syllabiotactic rules which govern the construction of syllables and their allowed sequences. These constraints are not as restrictive as those learned from word sequences, but they can provide additional useful linguistic information. Hence, it is possible to improve speech recognition performance by appropriately combining these two types of constraints. For the Chinese language considered in this paper, character level language models (LMs) can be used as a first level approximation to allowed syllable sequences. To test this idea, word and character level n-gram LMs were trained on 2.8 billion words (equivalent to 4.3 billion characters) of texts from a wide collection of text sources. Both hypothesis and model based combination techniques were investigated to combine word and character level LMs. Significant character error rate reductions up to 7.3% relative were obtained on a state-of-the-art Mandarin Chinese broadcast audio recognition task using an adapted history dependent multi-level LM that performs a log-linearly combination of character and word level LMs. This supports the hypothesis that character or syllable sequence models are useful for improving Mandarin speech recognition performance.