A COMPARATIVE-STUDY OF THE ELBOW JOINTS IN 5 SPECIES OF CHINESE MACAQUES

In this paper, we report the findings of a comparative study of the elbow joints of five species of macaque that inhabit China: Macaca assamensis, M. arctoides, M. mulatta, M. thibetana and M. nemestrina. Results of multivariate analyses of size-related variables and indices of the elbow joint suggested that the breadths of the ventral aspect of the trochlea and of the medial epicondyle of the humerus as well as indices describing the head of the radius are important factors for discriminating these species. The elbow joint of M. arctoides was most similar to that of M. thibetana, no doubt reflecting recency of common ancestry and similarity in terrestrial locomotion. The structures of the elbow joints in M. nemestrina and assamensis seemed more adapted to arboreal quadrupedalism. The elbow joint of M. mulatta, however, appears intermediate between the most terrestrial and the most arboreal forms.

SCALING OF LIMB PROPORTIONS AND LIMB BONE DIAMETERS IN 3 SPECIES OF CHINESE SNUB-NOSED LANGURS (GENUS RHINOPITHECUS)

In this study, aspects of the structural mechanics of the upper and lower limbs of the three Chinese species of Rhinopithecus were examined. Linear regression and reduced major axis (RMA) analyses of natural log-transformed data were used to examine the dimensions of limb bones and other relationships to body size and locomotion. The results of this study suggest that: (1) the allometry exponents of the lengths of long limbs deviate from isometry, being moderately negative, while the shaft diameters (both sagittal and transverse) show significantly positive allometry; (2) the sagittal diameters of the tibia and ulna show extremely significantly positive allometry - the relative enlargement of the sagittal, as opposed to transverse, diameters of these bones suggests that the distal segments of the fore- and hindlimbs of Rhinopithecus experience high bending stresses during locomotion; (3) observations of Rhinopithecus species in the field indicate that all species engage in energetic leaping during arboreal locomotion. The limbs experience rapid and dramatic decelerations upon completion of a leap. We suggest that these occasional decelerations produce high bending stresses in the distal limb segments and so account for the hypertrophy of the sagittal diameters of the ulna and tibia.

Mandibular variation among Chinese macaques

In order to clarify the degree to which mandibular variation among Chinese macaques results from functional adaptation and phylogenetic inertia, 13 mandibular variables were analyzed by bivariate and multivariate techniques. The results indicate, not surprisingly, that the main differences in the mandible are associated with size. The study further implies that the variation between species is not closely associated with differences in functional adaptation even though the dietary and related differences are large compared to the situation in other macaques. The great variety in diet and related factors among Chinese macaques may not have yet resulted in a significant variation in the mandible. This may be because their radiation in Asia, though involving considerably greater differences in habitat, climate, and so on, has occurred more recently than for other macaque species in Southeast Asia. Mandibular variation between these species, therefore, is likely to be more closely tied to their immediate prior phylogenetic history. For example, the two stump-tailed macaques are closely similar and are also closely similar to the Assam species. Function in the mandible in these species is quite different. The results, therefore, seem to support the hypothesis that these three macaque species should be placed in a single species-group (sinica) as proposed by Delson [1980], Pan [1998], and Pan et al. [1998]. (C) 2002 Wiley-Liss, Inc.

The Phylogenetic Relationships of the Chinese and Vietnamese Waterfall frogs of the genus Amolops

Ranid frogs of the genus Amolops occur in Southeast Asia and are typically found near waterfalls. Their phylogenetic relationships have not been resolved. We include 2,213 aligned nucleotide sites of the 12S, 16S and tRNA(val) gene regions of the mitochondrial DNA genome from 43 individuals of Chinese and Vietnamese Amotops, Huia, Hylarana, Meristogenys, Odorrana and Rana. The outgroup species were from the genera Chaparana, Limnonectes, Nanorana, and Paa. The data were analyzed within the framework of a refutationist philosophy using maximum parsimony. Four clades of waterfall frogs were resolved. Meristogenys was not resolved as the sister group to either Huia nor Amolops. The hypothesis Of evolutionary relationships placed Amolops chapaensis and Huia nasica in the genus Odorrana.

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.

A policy dimension required for technology roadmapping: Learning from the emergence of Chinese wind turbine industry

Innovation policies play an important role throughout the development process of emerging industries in China. Existing policy and industry studies view the emergence process as a black-box, and fail to understand the impacts of policy to the process along which it varies. This paper aims to develop a multi-dimensional roadmapping tool to better analyse the dynamics between policy and industrial growth for new industries in China. Through reviewing the emergence process of Chinese wind turbine industry, this paper elaborates how policy and other factors influence the emergence of this industry along this path. Further, this paper generalises some Chinese specifics for the policy-industry dynamics. As a practical output, this study proposes a roadmapping framework that generalises some patterns of policy-industry interactions for the emergence process of new industries in China. This paper will be of interest to policy makers, strategists, investors and industrial experts. Copyright © 2013 Inderscience Enterprises Ltd.

Language model cross adaptation for LVCSR system combination

State-of-the-art large vocabulary continuous speech recognition (LVCSR) systems often combine outputs from multiple sub-systems that may even be developed at different sites. Cross system adaptation, in which model adaptation is performed using the outputs from another sub-system, can be used as an alternative to hypothesis level combination schemes such as ROVER. Normally cross adaptation is only performed on the acoustic models. However, there are many other levels in LVCSR systems' modelling hierarchy where complimentary features may be exploited, for example, the sub-word and the word level, to further improve cross adaptation based system combination. It is thus interesting to also cross adapt language models (LMs) to capture these additional useful features. In this paper cross adaptation is applied to three forms of language models, a multi-level LM that models both syllable and word sequences, a word level neural network LM, and the linear combination of the two. Significant error rate reductions of 4.0-7.1% relative were obtained over ROVER and acoustic model only cross adaptation when combining a range of Chinese LVCSR sub-systems used in the 2010 and 2011 DARPA GALE evaluations. © 2012 Elsevier Ltd. All rights reserved.